From 0c49b0dbebee82371446e633b28eb45d52958853 Mon Sep 17 00:00:00 2001 From: gavinsimpson Date: Thu, 21 Nov 2024 20:51:55 +0000 Subject: [PATCH] =?UTF-8?q?Deploying=20to=20gh-pages=20from=20@=20gavinsim?= =?UTF-8?q?pson/gratia@ea44b11b8b80bd8919bda3a3839dfd9a1f2b839a=20?= =?UTF-8?q?=F0=9F=9A=80?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- pkgdown.yml | 2 +- reference/draw.gam-3.png | Bin 303935 -> 304064 bytes reference/draw.smooth_samples-2.png | Bin 168050 -> 175911 bytes reference/draw.smooth_samples-4.png | Bin 321449 -> 320980 bytes reference/link.html | 4 ++-- reference/partial_derivatives-1.png | Bin 157980 -> 158285 bytes reference/partial_derivatives-4.png | Bin 167535 -> 167921 bytes search.json | 2 +- 8 files changed, 4 insertions(+), 4 deletions(-) diff --git a/pkgdown.yml b/pkgdown.yml index baef21292..b53eeaf60 100644 --- a/pkgdown.yml +++ b/pkgdown.yml @@ -6,7 +6,7 @@ articles: data-slices: data-slices.html gratia: gratia.html articles/posterior-simulation: posterior-simulation.html -last_built: 2024-11-21T20:40Z +last_built: 2024-11-21T20:49Z urls: reference: https://gavinsimpson.github.io/gratia/reference article: https://gavinsimpson.github.io/gratia/articles diff --git a/reference/draw.gam-3.png b/reference/draw.gam-3.png index 41127b88802f0ef47e3ba8600d8182a097d9cbd8..985a09a511ed3c08000a5aca9edf86cdba624d7f 100644 GIT binary patch delta 241928 zcmXtf3p`W*|9?tRgd&vtC81$zUZBA4bG8c}E!H6g<=a$7Yl zRxK8jxz#Y2n7jY!^ZWkycz76(Va|D7p0{^v_u)43;dc+F?S5zZ7`$4R;Dlc=uITHS zJg`q(@SJ73q=Jh4FEyuPc}eYGa-$}@WY=Cgc{rQ(WE$>L#^s$eBT4vsZxrEs(z(H$ z7{08>h;NUdEKfUzmgLZ;wKX4gNh4v$9$j35n=`G>{CjfoW4OWRQ!mvi=g&UBc35pP zxkK5?8REE$@5De;&Z*zf5c9?c*im+08j{lP`#)j7RvLxma zg<_YJ{9FDAw-;vnHMI9fxDD%}C$<59Ns^a?%4z<6RETD$$kf? zC|(OKP2(CdW|L;aE{U1zViFuuJ*U+uSXGQ=Ht&u`j`yEaGWyX4c1ZATmPJ6#__0JB zr|J9#k=3y3d7snzB&X(&5|2*K_djx?H0tEZW{yXovhbBPWS-ZvVk& zd)>f)bj>n{%F*Jvl(WOJ;~%iC6mrJMiRN6NH|62WDVX=*iO*S^*V&yWk#lMR$>W$) zv>*w@ES$7q+^lx3Av!T{n+E<#^{VfJj*cyg<~lr#xn7AHdjHfC3fVl8Z8ToQZ%-_X~pK({ysD158 z%C=DM_aVo8+w6i$39ad&ed0&qaC1gpPrdu0W#W)66_eX5rEsg!Wd4za_ST6q#7 zlMikB<}TBDN=PR+Rx$`7MX%!&|J;`>@IG=hB~b4yJM9c^*5TX1tuIPVda-2go_L_m zuhD7X{XuhQ?aQKhv%`bice>H-+&%sjQcF)(nq{`~1^Ww3VVkHp`k>#)vG1&XyR`!Z zMhL8$8}6Am1-j9OK4DSC$=8;icu3!+oL_hJ5{;K{3q^>{J0;BOrmWp47sL4u8+=if zZQM@QkJTyEV9W|3Rln!w3wGRZV)O!~;Y`~2-X=t!CUxy^E!8StZO|*_lwfBH2k+U^ zqmj4E+URrK<`3%P^Qr3pszh$jjnQwnVAzaI^s(RMUn9s(49mgs3@ya*N2Dsj+gtA} z3;Od3R;_!#!sKrf3p%E|(yvzA0ewdak6!%k7tV{M!BTQUiWP~B-Z?jb=5<)fI?kJM zk7QS*d~XCz&hurwxgNZ78$pNJ)&^SX1j%Gf+%?Dg@`YE;{s^v(rj4LYT8YD$V4bup zOVDo5b03#3BRil$uF0O#g8}uqzE;lTiJqNa)orleM$31vf8!&|b6s6sr7NfAkY zb*|DH@4@DR@}p%VjA~JZ6pBE0`AfO=t-5cWp@`i~lW}aasU`gJz2c~lFMiS%IHcly z3!mR->}1imEyBB5CGHzX{Etor_)T8UNYF%zW(88FJjwn4%QxONpBdj1-)sm-OXPgOb zK-%sOa{9H!kzldOrF+NtQWamSU&^C=`>;36=!_9X{2JW6c&Y(Uxm}0tI!b=d(CI7j z>XQzgJMxE&E@$g^#->*LIxjUYVa@Mj!^wcA){hB2bLTOq6@{j{irWVx{_YYr_&i8QnL3ofE?;cyt+KJu@9)NwQ2{5=` zNa%vP7gOyTl~zf~9J!MHV;iz)>3(4u#xMrbg1~&|8u`MraQl&Yu^|s!tks5az;n5M z<++kcaZ|nAqcqr2a#m$!Wki@6qgT_X!{@d^Md`CyeetxtNGvI5b~ZUKR&s89YlvV@ z_&DhdGduk9sOp0zs@nHk|J|1D|4sSCA9;^ZF0dFNtPzQQ=Py8sT%R2s@-z_I6HzSM zb*NaUiD1JpJ787KxC=zAsoCfLJk*gWB8W(yiZ~%~uRQabxv8_*8!atxy>2)?eDa(# zI2%@*(_3vfxu1IU(XW=blS!T;C-$FbQiWbjPZ)oI{Du2gYXND@TOWoT_mjig3WAGz zPb>b`wY8jt^g|611PSv)g_S3LG6+bR%AfSSkdyu(&=Q1qc?N{pIXZ1PIr4bztIx9rPPJ>a3ioz0ZHUSjvE0RemnTM&%4Nh;JG$n*TD#YsJkaw%yer?Fw ze(e`FvVMJ@Fk89tR~st02P~qq3=P5=@-3&l2f5jJc0=Ssd<~=kr*}%#D%;@6dkib{ z=22h!(Z)H0L-!8S44sFUxioAcPQe8- z_3;tzrX*hl@nWjh{<4QvF|%w)J~;U%`j^V_cT!8AaiYGnm)~-^&3eT?_2Ds*#mRtI zrrA>L6I=>O3JUGJY0PKZme!ku=X|{9wmZ;1Pt4Z}I7|*tywU-ESjw+<1db3jSD=`O z1@jDCu-8#Qe?2AG;p%{NS&cn+*n-np9X+dZIXf4L723@qtBXvoxtdLGY`@)jP^V?b zM&y_5{C&xr1#%tjMV0T@Mdg zdG-@PCGJ}A-vktuz3N2W`wy$$8aeyF1<$K(YvUELK+uq)qqFXY9gSV+s=8wRt0B1W zm3I?_ezK-3zH6-E-LjZ8-M1Q;Z*~rVkL&qnU8Nr>g}j4EHBvF@If<&))5(orComL8 zwM3EkC~(Cr`eYruk1`1EW_=S#1ad7Ev{&N&MUpJi9>@~ytY0TYalI+Y(Fh#UtA5x4 zK2ao~j%SPy$0VAzdsR0qH9o<8y4!fc+R8c4711Iegb?iTkR(S8t=#=^x3QeVcYtye zi>uxJv_Rz@s-glMYvPpS1k`E4J*IH&xl7N62*Xnzksur>558 z3nu8MJ}l#}qq+elUPHm@ZTE8i{sX7MAAY8MI7eNALehwk$vcdrzrGVti3KB8((zMk ziJli|J+<!xtn+}g-FI^J+s}}W7 zOTmP_rKdJ#W2!|-oF%Wrf~=2?3Ln|5HBAgq=qc&r+^s9*A8yc%ZsShvCN;w9%x&Uf zE}1U|6Y5_>4lEAm)j*)Ocf_MAQ)^T+9kR>!d*){P9z}`fXyga_B>j&(?fu_igCgiP zjYC%+?=?$JU`%vLxL*;|v%)S8PPv~DJl{!zna?qr9~Ygz$Al-n;qU>gvG;QiAD>WQ zaj+AS2a`+@*Zp-0&r_Pj4pX_#ko-iy3t0rzj{y8TRC3pCa}i#xOZ`c46(GlJ46>Y> z9Kl>W*W2eq4&5I=YHt<>hl}~fd7jH_*>pYF(Y6#LO@HwN^*S}ajhk^(l|kse+o()A zH4YQ-%<~;ST-y#+6a@7Nb+N{LD!Ft`(1B+YxStsMl{Y$+; zu3m7>Z&szs(mW-nO1tSHLKs%(x0~mk;V@LPZ{Jj6W-4?Z3FKhD-HoU!TUAN&lrCzU zx?(NRK;=>6Y9d*i%N?Nz2&I&*Un82}Xswn+I>Db1K6yLTC?hTHk{|`U)Kj8eIVv&f?0*iwL+4i(fK&rLIch0XU!i85h*L!kV5a8ER^UWWD~6 z$_Sqm0Gj{d=(xK^8pcc%fBUq2(Q-`kRMfqei~?Tx6P7Ocrlk|5$mk!gi2kp}XzA87 z5*%$CTB6ceDjOUlv1THPwK>5i=0jmJVqd-=LD0uGwq<^qsdeSIuJ6{q9KAj`6^`rB z$6q2i)i46)SE_lYaP6@(f-%&9`WMLC>r%1jQufnof`((t&GHTrgrBVL+9kis>VoOD zr2fb6cpP}-^Y`%Zc$rl{>8iBTv?C|{>_qz;l8?(#Y7_XuHo{H?C!OK!yJPZDZ7$_n zkaV)@f!{+nfwzrO{)M;?VTg8U1dh8&`3gyAB_4`ebGz^(!E)b0;7_>tT)shYwA|+`K#>J)f2(hd+rxe)4Z5Q(D0P}dF<9gbO|UH zF+-J+)^x&O1Q~|W&v5*wabTmqwEp;q2lu>S-&lX{S4WyH%5_joY^y=J(n?Xs)=5n) zV749ErU4``!?zuGg>KaObQj@~+>DvvnGe~u%znHm!ixF&d z%rc~YT9W2mtZee^+1(&R4lXw{_M=4>10#r6vS;^3#Aa+P*~!JTbl=eL48UR{0xpG$ z%)CUla}76J_@k{ykC;LKZaqq8vJyQ3OKI|WL?5{ASjC07-&fjKZu@rzdt+5Vi~#`w z$MKa%G1VGr$|=YujG@+oYX*gq#%-tzk3iS`YK>jo`QabFT1?TMV;z+)4B`kf{lg1z z5nqqq!Kz499>IA!oH=7lI|Ua_@*Mpklm=0<%I4dTpSs1!!_7804fht202Pd)n?K<_ zf;-B+lv6O8@OCsakoA9%{)NpN}xyr@-rbCV%e=E79RFrDn2cR9R(S z&aHe|;dw6Dv`L=2V$_J@@UWn1g&zc4G4fZ`CHwJmi<;Y%nogLl@y=$jH2FE?(&q}C z>fK!sTDx%c0K`D-V(de}QX$i;y=HQ@1eb+8tcyu&){bk33PNb-)Z-iuFwm0Ry*2+q z6!110%1u`B{b`ZbJM6YZv7VQg*(*9_MNTUJK7TznjGOgx)!@-aBDL_5Eu!gnz*vDl45> z9-~JPys&)%&&77rph5l{JW-4HgYDI@xEGL1-FV-hD-#fzbXE-zNo= zzhD2w=>g&0o);cETefh%*88U|M?Ua0gV)cb?p9NltUV{!RjU+LXLwyXB6flX^V>IN zZp8@Iotybwg3T*cMGh5rDIPbNkdGNRzhdNZWqM^@u72}8-9&It(=et2c{FJo3V2Lx z7~4zd_zpfbm05d8)3rK{^;D*YN3Tuf7vyz8G3haOOs;M*nKYjn-nP4e_y z*G&dNI020VuR!41$2$-2jnn7*DRP6B58~QAig?Y`6N{=?W@=wgPE9HwIS{-pbw=XF zfir<*Z!Y|~EYJapTL0#P`TuqT&t1f`G7P^8f#qSK+404j(f2%P3Qi;`&F|^d1tpOr zFSyc?(JrGd<1q(s_Y^xf8|#i7|7xyNT?KSO(%bue42>_lA*^CPLn zwj1pu-EC0KQ#BSL%Ys4ESyJNnO8U}zj}-PMDPImiwfaidLfJpYeXWj>YxmU2VGcCY zdBF({w8&UgI0#GSM$EJw>~U@`adkUvu7P4`r{oKb%g>jL|Abpt56(3V%|LA%oHVop z9%4J7S8~_3x1OG80S6x^inO_SDYf+6(QeP~c#-&K)ybcO=<%_!u}_WlIAo1m6^OUp zg<3tDS_AfLd%6o}?*9;0>sjqOwq{J&SkMDrXfV3NiUvQO>@s}kKM0xx6V1)tJqb$R ze-TgHD&eO*UDmVU|4s9aFX5N|b^k7HLGQkTYqv4p4GZ(XH;he1-Z28+y$OiTeE-yi z0!mWw+=o{3kIfFGAfMdBTfEa6jLbLJtV&}?uEs*^@0NVDhFkZ4J!6$U#>X`Q&SiA8 zefuWxx%Aw_{p3*XgE{zF)wFES>7&VUU5kHVGv2B%3UD!`7bufg9=~lBURRQQOJM1Ib?P@4t9)i%BI)6G%+*G8Q=Dn)#n*YJ5 zJo@tIWSO0CQ>O4W_@m5Jh*7N2AR&Ph@welC=GbUY4Bge@qK#S6)|GsU_v1f(Q6?%VoU z!l#XYEX?X?Qn4a4OZoTP!)Mg?n!Ak>jXlL!#3`9K4%P(5!L@+c2D{ps?`$*v#bNPX z#shH$Ubr)e7ri9yRUvo6+ElC-FIzJwErmn@?WYnT32GxGdsfL4?IS>M8+M2sUK<$C=+=i@_?X z-s>c#5?%yj^vBr*WG3gXA@e!FiHw(5PW4NUg~NS>i+rE|M?WtL{0EgkoXR(OVN)j0 zoCVP`^lg8QzYWEsx`^RXAz8PD1Jd6maagGeu;`?%5~W?tBR7?!bhNDv}0tRX;$cf*EUw!wrY znm(K=k!Y{7qKSb-#4s?xkZC8y?YIx+x1xEf4<~zrok3!OLmIfHy860Nfub&A1l#-`>~|B=8sQsSr8W}M#=W7Xcw8U z$~XkP0*yC77#NDMr=JN$(84uB;fF6Oc=RNoRY5Jrzq&K(lD+!>8IJz9;{M@X<12z4 zx}AGqw&sX$ViCsmS6sxAgi#uml*C!%SIe?iREa}QHsFITezv&zWtaQcOAX;}0+w^( zO~xm(VO?mMcxX8%GO-A%jW*1Ort_Cgn+7zJ*pllQT|Il%PRQaA;XE@uz2mp5i`wzR z-Xo0OeGG#0TYB*RkzxYsQ@GTwnM)h5avVXfo_7ttmfFTXWUT1WK|qzN;@JABh9`P! zTokJuQi&DNN6tT1wvzo&`%D2#kwnm)5)s|kUBriCoS^KMOLI7|X6-zx2BDa#>uK%4 zuZIST@Pc$~U|A!=K9h9VUVFbOiIT!9(8ZMWPTh2_WeOj+quhsP)E#4y7mUd>1`g>>|^1j6OB3~;QZ`H#YjDC)fP(r z_A$x?puwKt4uy8>#bQcTf1u7vs$OTOb@=&VgR=_R#PKE>!t5wflLqZ;d`}&1z9&@x zDpYkU>5AC|?-tUtfE(Rt-JWew5TkM*jtR55$^Xqjj+DkcNX2^j(tVxZe81DKs+}a8<$gC4F|`fU>N67K zA8R8@WeJry~&-)I|8;~Cy>T^XqM-rcQ*gVdT9Ny(VdG3FZ6Xz z?+6kJH@*O&#Ggn7tHcA_-7;qfJZOjZ84MBeFYZW*r_thHbNFDPF{<-KHQiiuQ~Pln z)<>^KVx5tvm_7V>)2tAs#@XHVXHy^uZAcBE_gYcmBRS=NqXXvbbEzGwY(?X0X3BIx z1wn569L?Kg=%+3V^iCztgBE}mu#VT%#J71(P2N&06-ZwP~fz<5XnY!c7@@k2E$p}yH z4YP2|<$vAqW6JL2WPBHBi%#g_keP-FHd9c3{)GFbMSkJZO_OI(X~;hN6V!#Fc_&wT zS15vrH#j;4ayI}}TwpN{_#b$_7%UZY;-5DxET%T`VZm$gEG}Xv;jyPA=rA};y)0Beu6=OnB)b$X zEkQ&P|Pb08bzk!Ne?hzVnOjpQSZ7~|8okYBM%Z;bK=VGONknOPEL7>kU z{wqR=fI4+K9xZJ}6ENQRn{B71#rz#6Q&w>&wW1t(>^f!d_mlK~wDjQQUYzG><4BYi zWXUieW?RttF|1w*qv#5dF#HN24I^=!-{$!sQyo$X?8!IRUO;U{slrquP$Fe2B4U0> z%dPnt%B5LpZOYeqOkc=e+GG}pc0BMxjKnF+Eq{Aw>~FwK1`-R3ZRE4}MxuH%V)<=p zSH$%Y^ix8jst+c9+#7L~Ja(a4)`|8Xe=(YGE*S%f%*9~7{{Rkb88TlAwFQSy3TtV1 z2}{4B_&hzV!7q>9BunK68PDVXzC6s!<0+Xn<>E>=QZLpC1s<{Q@tOvGulqa7huG`! z^9G5tk?=7(z{phOOZZmwPg#3QZ)fqmBhA(6Kf~juHhczms~uSjNSeZ# zoFClZNwSU9QXmY>Q3!q-2)9z@!yNU1uDWz$$m8%=E zgP9Bd3hxT2eXbf@)FJ@dn%!t+CQ6cAd`r?9Khm1!_8%juI!4`*DEK$xh0;6P!kV7{ zgD+2?mUYkEfE@sdgcBtH#M89mDbb`upcP8cFb$$V9SYTOIM5DV|4eW+z+cMd$Juy| zgTirN{yA};Gd$1o!^C-1MO>w9Xv(&ti>1$o(yk#l1M>0WJQrm&Ku}QU3hqKs(n;N~ zz`H`4SH~!JBgZHbT0im+N$`mCB<8D1@aPNL8LuTm(?#sq!BOn|00|xiApG;v&hm@? zTg68}>dgUmHa6L<+b2QOH87ebwYea-qYbPk1FPl9&(P9qEA_2g&wO|O+U?}FuBNp% zECVL%{TZtxTvGT7hqN)wS#)V#cF7naa36UFQ4~(R0I$u*dMSU#B*^=YNL;aa5~zJJ zNQT{@dq)ydla7@B1T4vT9h=|sa2HpZw4opsDS40@L3Z~(L*mK=XeXt+mM8I89lm-* z&+Ay@5bF1k_zPtWD@)TMuTmf{Z7<%tWcaVDAhP{F4)RGnEykvM))z}0N#iKwEe|aC z73{5#iy89&!8 zIP5ZkY}v+5mvu#Jwco^j3K!eujn=}^V%5U|zng?c;@TKtGUm<$1R0iYzNJ3qyvS;~ zm03{73ynmNCdzAW`)_4loab1ikiBG$Wo;DSrCf(=p)Ky@`_~Rr-pxnngKkm#lJgWO zL}V{*%>zB3PESQs8HPaXLD^7(k;9wo!mcC2yEo-`u~xz@tv_ip-%&uNhPd2la89H% z*Z0#|YPC#qn&W3h_wWt(;o^;aV0An8&qiYC(y7a27#hKa?m0(Vd>Z?1`?h4KmDz9@ z$T_opb@4hDQt5jf)XQ^~oxACz?NH;jnc#9Fuo25$a57z8V3*`;RbneD=Ho?ZHNrbU z8+1P_cUY6We7s_@5q zi#bn2n8adoSJ&P=RPW^xoL+89W3fe^#z%31pC_a#87Yd?C!G&jKR`9z4|IVXY75j8 z+uEK%1f=Ok`Uf|#^8623x#$8gpg5-dVQ{M3S`Ez7prt|kBpg%8)@{I?XPmVd{!SN+ z$zV7B;OI&$$AB;z_+=DdIO%Ca>(ot+s=cT|;oZ}XjAk>geLGXwo7W7fwqa+&888iB zcz?JNWg=b~bI5Fd>`&?}9wZ`QSRxovUX)gYCHhKV)YSaGdIFDfu*MTyQcKG#?)X!> zYi=f2s8;Im%WI#Gx;+ zgWg!uQqkl~FUf9~CQPFJh3wwbf~hraHIofV$dKu#VqJ`Lm6p?6&doO_I!VvLi&55U zg0G%kGdTTstLVfIk8b0?tY+Do)0c1r=xfM-t{FFAt_e9qh!(t&ejO$sq0+T&U)3_ zq)zMFEAIA}{0~w*M~|-M5>Q9ixAWSfO7<*Po@0gw_n{HNp#PV@B*kORBav?`&7;L* zmmeU_W5MgeJ5rzDD9v*j0RMuB**qUG&$l_@zCH14$7EyX^nVj%3fP|7q_l1dxJNJS zEs0+IBo)O3zXj|Ywc8%I>pOc1IKul0d5k;OzNVK<@K)-4DDrZz+r8g6A{jy-(UP|q zVPTtU59+8P%~iTHF#}%@g4BupNd_%3N!7f3m{16QoudqV8jPuiqDZ@#PM|!$8OSxP_0qNxU?E!8&YWB@`qC`p&*(-&Qtul2Fl50B zvgwFs$6s=W=Z4xICRg@?8uJZatn}5 zie4&YjwhRJD*QL$YfUz4byi5sooz0Uab)=1`FpSKg>TI-|9I1{^T;7pk^1^`HUaen z=NW*k_m79lr+b0cb8U2+3^@)2Pt^A>3vnqdU2qq|FuGjb1wo)gKD9K4r&xcJsg0Kb zcN$dC+g<%;KP-4(DX5g!1R?UjMcNXaM0oWp&Mk$2FW}$p8eY6tgj|2=Pp*UI>yK&i zP{IB2^^R??zDid@ABlc=!tog0xCXa%%iXnF3|zl=AiAUm&%OMW!OSPHZ#7|{f_C^0 z0wlggs)4-$7?_99JHb#Tb*-gvHf-BfX6lMKJw(P?Kc8nLfBz;f%K>z)LJ_A0l^m>W zXzg6rQdPwiR@p`%R>gszz>uaC1%J?>v~#1QcP5N26`;0Ph0n@(YHkL{3DX|^AVb`V9hG^o8MaT@(%!jMtu2Git5ee_ ze3ViH%Bh}`mx)gvgI@eOvEv-0j5EyHAG3t_wziVy7ts8YHna=18Ch2xYHrAt0GeCI zSq{dx)GIuXseUbc`KIJXl|wLPy)FTXotzDlk@)93Uypi@!h8*K&7Zb=CH4OuNqPR_ z7nb<7dCQzcJR3BbUeze3nw95X3$>-aA2*LfVx#YRKF=l6KXI>>7X{*WDIH zE3a(F3xDe$iNse{9*l4XUMp=KjFxe?{Nx_Z7lHUw6-fQKH2jPE(Y8D{*Zi^=IZ+tm z)PzGF`>gm9f4in@>@g?y0zGt`p>aTTYOw=te5d%t&XUPahYo4s;I8d0=FU^*)-ZGH z8PfvnAAEo7KN8>Kia_x6bh)00y#{QrP3_toKVcH#&Djv%nHSy_&30iD9WSiD1lCV% zFUSEqStVODeh_hi*o}Ii&}6;LWGp;(D!dpa*yg>z{XQs~RUVjnoc`$!mI%gs!T5Pq zDUuof6>i1&3dyT*U zUg-~e$7NUSO)9<>_Zkei!E8aAS`-4GkopfnHQPCL!9{Ic>6U@1DVjSgpekd6i$8D#Y{l@Y#M5RxfQYJ-lER z-&tTTCVJ?g*AJgZ)~*P0I1&KKW%+GAN^9byz&DD=uSyeA^yj!?y4kdiX*g5KhVoZ)V9zsBm}w zEw_8aY&X6(Q9HvAI>3^ST;nEGu<%2%!Ka9pzE1FgwHkd|P@tQqi;0s`oHKm`jDHJ_ z@tXQ(4?4tC#$3H!Bb$>ta!s2%U}d7VqnH1{@op`jO}1Gki$r=0MmT2ryu~DU_a<1v zr`^{F@`Zrtne7eERC4Te^5yDfzw`YPD_ePO3>ZoDEgOEgsgDNWc_Edr|J0OvJejPfKTC_py#^ir zzP@Z->qe63v)}5)EU?0t1_c?_pK6M5NDU7IC_SBPXA$e2B?)+Q);`cWd%>R?#{zruzb7EgQW?;V&xyPnpz; z+sg2XCa5Z3K0VhMlEOFFB)qfx;Exx`&wYJDXz9Rx5_>(1tPb;jklZ^!XxRRcfD?^E zyM{O8KOOM3zaqA`1N!Xa0g_I~dlZCj0DOxYgq?vS6s!iam@ksRrb&W@hK_~a!6v=1 zVLpEOOVxDo_sf#We;-=gV_Hz#9J|=XobUY1GS7d#LLTTtn}3WW>Ha5a?N&s zIWjrlWkx*2&Ve^fx@2c_if6Ttvw68lsnePUTYFNnc2qWUS|OSV_SvH06aZo__f>F$ zA&T#f?T=;Y#3s0}zi`)IT$Tkk(Z(y)88WLs*zP-#vazebT$oQ>%GGY)VjJyDQZG8f zu_``DYX*UJ?S|<&n&6~>FtcQM8Tpbe=tAWRBW~t+GKwG_D~3mvjzi)ngt8&Cl+8Pg zJlz9m#Lg9-RhVGMJ2U`Y(@AN%BiQlmj!O-sqo+oRnwIuiGDe|U^+9e$1%Lc~80PwJ zj%3yITgM28(dm6dLmn4(+m0Hrncs(uY;XQs!{SQho_CiUq z4VBHDsIWX9&lTmW z0zFE+LH1((XP(xmzy1>`c5f*jLJ2CWo(WK}QmwIZ8~++gf&O($GItXLf6dCuuhTIB z$wa)`=WIO66c7QkV>Bpc6a1}7P{9{1VgR`Z=d!(b*Q zq~n>s>Y|zb^ySx8%Q1HJu#b!Pe!eNg5d(Uj<&UH1ePyZ#LWfi6Z!6T3V$naxwN$`6 z40875wR~Vr0=(_dn_BcoSdEauhES2(t$a$7hthATh3T}jbTqBt#FLOnY`DOGW)Yj+1s zbrI}d$E&xGz--BerGs0B&VD~+ljRS1=bf0^IUgmNq&-DAS_?C`GNZwit<18OHwV)J zlAPt(Lq)A_9?Z9EUkRungfM-nul(nm*LVKh4iS<)j*-M!i6wo7@6{5Ct7q$H`-Ie; zMb#oRbZqy~-n)r;1tRD(Gc)g8HSLcd0v;<2a*g2dLrFXc9a%pK=keK%V&8Y;6g%Lt zeCjK9s`ca7g$JyzLt&3*?Q!Iw zyw$)>qder;kPw1VEw~#(8{!YKrDaYW#o~Sq3FYY>=m9B3;vtHBC-h<#fx`z@!>sx1H6^QA^ zTkCO(RPv9EiXr~y8b}%Ndwp`Lk-rt~cA%LP!(nk_Ioz{=yB`|ZUMiU~;zS6$Z~bxP zSxqTf-CrE}=XF!v6L8LmiZ&JSj(`>9y>OkTq2*4Bo<3!6zl+uTYnB*Z9JAtF3~WbS zgHJF>vy6ZUBj)@;X4`pTqfXGtHQ(N7-Py2=H7;%X%Qg+JW6e^~|yHA=1bm!TD0e?slvdo~<%J=KqtTJt|dT!s3K(oN?# z-aO!Q6hk05U_>(%eu%8?YyTzePMo9Z=@1CH@rvGD%^P`&x?>(y>9eBmb$K=K6o!66 ztj6YTVra$g!BW*{nscMy<$BTp1^U_~$JbN@Tx@cyap2KZ5UVuoY;Sz3N!J3_D27Wt zr<-SdwVE!u=xc+I>YIn}olsH1_#zsDW{2v|FNIdQHtuda>Av=v5!*tIo;=;AN*uw@ zFWEBx+D0#=x%)J_$~OM2blh8dbvyWBxdkr7c3y;Yt_>m)Z zaH|1cxV>6x{5tMUcSQwAD3om-cL&;KEAx8xL{Exd1oo4r=7PC*u-0ytVi8sdox*fi zdokx8Kc_q^`*i2mP$A`GNhG^BkCjv2c1e>7um7at*^UvU14P`@X~`uc2?YJ!shr3d z+nnPtILNBSCLXNOobAP~**>rMqqOv_vcimhP|Vp>tgH9g094R|1hCas{Pdi1ZXQJ! zf$2>kt;>tbrY!2pOYriHeO`OZ99MLg(<18`AGa!H6Z?Nf^YryHj#E#^`<~101i>)X z;N;Z-*mKpBn}dSvbm@lrG}To8(sQ)4%9MgAPAfn~`*>te>CrXae~oO&>Ymb^xj965 zVT_ALkNpMDp=~gCVvQHGGIq_6=hy}CjsU8ut3RiV$xIM2FRf95;(y5bIOUYvN=^O( zPNyZMWb;$W_y*{CV490J!O>+9c&|h1zBZQ|$0s}b50(96brFC2E!GzETYFBrEgUf3 z7^E)N z_|Oe>D`<59>$~p=lWPxuOvFdbHBJdhzW_ED$UOI0Qp|fURo_BofGs+3u#GXYLSWpG zNH-Dm{Fy9qEPmIRTC8sI_qL{}wbGBybC8rW5ocL*J9`oGo53KftB!vWlxVtbSn<>a z1A`0bhq&9iE0Tb)z2g~r`~5qPT1zwZlXHz+H)-;DI2;nygZ5|SK~jPca^&hm)Zg5N z1``lVjp@$GKJWMv8c9=5H&aH2Y`qP<54Sb{CM02=c)sX+vL-+qtEhNham*Qumt{7! ztp~#GuiVs&VK~5#CwZ3s8JZ@&QBQ@G-MK-im)N+Q0A!58$b+T~Km>`+RQ^5qq zOK{9)Oxd$h(2B+jS~|8;riphPtFl$}wtG%c?_M+S`g!K+xhx;S;d0fx1CTPjx{uYx z?BiH4-QXAT)K{cb{Q!xhV5L~fkIZwpzSrJRmO-jFak~Kb0rQ*4!kFW?h&Y+w%`G2R ze~-*)8bvQ8FfSqKUmP#buo8Q7{HdQLOnMhzy3{|HDoFa&cQ<83dghu2B^5tgEhuU$ zr-TgYIM)wbEbY|y={AGI)HEs%=%NUrX!~}`qcsl&st@dO6{vr))qW@BFx#dQMN@DJb9cN z3)Ijh_swnQf4-C(xRw_)<9x6NaB(o*I6E zjA1UF02WU)gHF##ZIAxhxk-nH<(`D$pGA!3s4YJ9^^JeS8a-m8aPc24QRLqp2y8HT zRy$7^S92ChRaXmJ*Q9olxKThWm*B>}X!?(lb_raP(mP_g?Gmj3fQ z(XA&0VIh`u!Tvsq(NLU>mwJUwLOSFOfHsD2g%-n92$LcBu|GUw=_Gp7xg}#T3Xu;`pIVw+R_&cg()=;L3xJ% ztPU)^%n}4=;K|q5mFK-7yrymFqW*8#k{z+3S}(~Y(Ukja2)zk0GXOgXM&oh&NgVHE z`?8fm-)PTnJmBYjTOTo;?RPY^RwoK^6`opJIvWYjcIZLD^Cwb8+f_~JXQas^x+{Bm zufe;P3_%V}aj5t+FU79)dHPi9#C~*k=EU0T16Yq84@$r~=_OvOqQZDNk}74D9fXje zqf@T;&DQY`gc>2S**LZ0sb7(EH(OB@Fb17kstW~@Tam}WY)_a!guNngg8m-Wq#kK7 zbmR?kNG0{xGja%`3?kG@0&f5#>{Z2jX%Gj8w9dmx;_57ZB5JqY>}o@nVq)u$2G8DU z9xNL4k{X7-qNFNcwxlIK=b&BlFL8g?r_ywrg}uf$SN{#!-V(bzrK{}{q&63jREPpG za>k1vl{PvNqc$v$fXB2M@;{v;0{S1HJuxQ3xyKhTKR-x~&C38bS**X2-P7^HvGWP< zQ4|nShIsVNnrwEM%+GfSFFgn4jUV(iP;J}ama(&%A=l(DW*+^wOm5;IJSZ+~s)0nz zRFm)RY%qn}{&Cv5R#0;hot?V$FRouBh@jaE2ep7_CfUT3XxOkAYCOW&*zb5Q=Ix4t zz1XQQOQR>wCqq#+HEtG30{a$;2zQ4|K@|VM2 zx99V5d(!EF-GeI^*wUexx!jIA@X)Q1aRoyXvg5PXc5g_&x>&^gpl@x|dITt(4UW0T6le%vah#sZr{G<_Fw9I;J5G}( z0~J}nqD}q0BRZ3eJH+`XB9)n1I-EN^Qvx;2(mZPdkN}+nM*GKz)eNO2V+6 zZvT>XDed=gk)e_S_7KgU502lz+nvfV2gS|^0vTXsRn@=*$q{mQ&_8}5@(J2jG9ExSr0F<2b4VLd@-J*c z39W+`O{JI{??ptb!rIRMXw_-U-i{;T$7KUzQuT>#69X|{_nOkQmvbqmi$=C=@f->T zp1kvbP3T`}TG8{vTmIcqldykQul-w{THx03 zK_@<&t~@{gt5chmGP?6cumM7<;pg4sKMCNC7lC>~d4$K-0DGg|$}|?2^Jlhj>yVkG zfA>mR)Z~$&V3vtk(_7x7izof#QD0q`zi%`GOy1pS#k620#s>V$6S6mN0y{VHr*WG5 z-_P!ocNG69@hH&s-)$!wrhf@3M%{P+)8cOYvo?CP%vEdi+N&_oPY})<|Kh)1@hrgu zdIJ;@yh1_(Gj*aYLcDR=x-sMj!>yyVBujDft)z;G1?m~~YG*O#udk6F&@++h-K{|u?hW)DI>_}&uv39jw7GDc4$M7%9j zZG#;5f-BtLMO6`mSX$vAvlE^;YJGOXEMp-w4we)JPa(JuG zY;^54K71@PP`jd3Bg7H|u%0iZQqh|BOj&jg^;e7XDi= zi~@+ESDg#gQ;wBCr4;?g=|d?w@j#(XPsg|Z>LxyRy8>4*Vs_rv61>zL`hRlk?1=6Hzpol%Fhs19@BlrU?8i~S?m1Mx zRw!*@+2>%>2mHXX8n#J-IR85DSi<3jC+Q42rz2! zT*iKZ9y)$)ittDUN1CWRHnLBDgF{ylKK-5-U+~&wa8MS+E?6EA-jPGOTu8oh6)H%2 zmyxdos!aM+rOBo%>Vz3&d+`#gaT_*4s7PJ3VLJc20fSbAbNLi{hEoM3t?<;g6oOKo zh1RkgfWz22WRxSNRN)WC4kAFMDBN@#B=W`Hf*<)7VOtxNW=+1gunE$RxD2u5v5)(! zE*R#G^+DrfjKf&J*LpR?LbyRj2*cim<7mhG(huPXJppF;1FscIeK1vSxtQi@5i z+8^P)v_|9ZO0oDo&}JG&9UUZ))fai3E^qHeZS)nIhON8*zU!`kaj4!UFYKf$_}6{s zf24zSa!c%5!&?0tfB~BP3Le?$uU_o0Zde(Z9NAi}z6Rtq9rbG?%qEGGo!~2qI*lAU zCD|&-ZQz1zhPs9`j^5^W^8X&dzR=EKOnm=N5UOiFhZ2rvz!r9Yg;*4ET^RF!1l%m_ z>`yJs8r!htY(aD z*E`$a4P3xQ>f8P_mT1z@)x*SUFtk+=U`u(U%-E^gfeiUL7-E9^LBzuDa<)(-8<%q# zx^V9~f>f05O7%Xj5aJfVgp>}dI413<}N;Q*V3aO^e&$<8~wVc|Jd6seaLv8!_M z2Tejh@#Bpx@I0liS)Y1aVPnivL4HKle$im8Nk`v#R?yYZo{<9U za6rY3`Q4-eb~FV7#B7(vh3svuUb93@N0;cC(g*-%HxF z&#V0OiLlK{t%S(?HlA_nb1;@4hneZU-g)Hb$YCCIb69rz;43sKb?%$FU0maZHF0Hj zD6(L>)29-7V9Kmi$g!hBUI-fR5faATPz|p(!ao=gFo(1t_g!I!ChTIA5rjge@&6)k zz5o>-i2^9lDuynzB84XBOpktI?F_L1mfQB=3LO&-#`6WEaSiioP~bknU4>w!ZZ7Cp zSx6j8df#Tcs@X6d+2A|s#in5<9u)q8@!DD@kHuPIoorOQsAUaOo!W*e2ai39>l!VVd$Rexj4d(6O19HzAm8JDRLqg*H zoj%$Si+4Q`fXn$A0UtTf4bdDtti8vc=mix7T0M19#%FN`3{V!g=|M(55e+Ot3od&K z`nBhETwrs5)H_ejk_90~!=3$fx=iPEvR3t6Tt=0aG}% zqtRJI3=s5)aVTqTEjmHG>6DZjloG1S{s#`miwyOo_CA04d#>U$VfX|x4q|cpWT67I z?p14r0<}V(>C&{VNhckf?QzT4dayQ38bR7;t{eU4!?taN|Jj?J%PGZMeNPOf_W8A$sJC072s^!rHT;*md#$Nw!RvIlj9+BQog#%>D*;9Raf zsvJoJXmsMz<^Rw@Rkv{{CDB9H+~XEmeKR1lP3}E(SVQ+1qDe#JXl_M~Z2>lmbQi`v zn_*7q`_!IclL5PuV>KOgWWG>?`FMxi}P44=+7zC%q)(46{AMIJz_O?}nDLx~AIYy(x3O~bfw-%_0oF4%As+ck3E9T&V~)1)jij1|5wOGxcA58K+pVfK#rfEwn%4~J{ASJsk&P2}@)Nc5 z1>APd;BV4J!|Cba(q%Fcw$M^q7l)G& zvye<~_is3r30s6eJ#4*5FQ%SLGFJ56iIZh`l=@g^g7b+%)olHsHS73p0)T?s6m3WsVWh<19 zE1Q!^6^XsIupWZ9mT%!_s%R+P+hN3}70-GhlI9G(dcOdzQ#w|RAJqEBVnON}KSwBs1dQxHS7DW&Tqf$P@cJ*f6jY*bo&G)Wtq5Ws+an<|S}zj)h_f zpa*yt6wx&K`f6DB$GEx3`FDZ&UfK^?#RHuhHuP#NyzdEI^vmZEOO&04%^nW@e2DxL z+_@apXH-U+l5GaecvMaF42V?HNv*q-^;#x$K`?r>()CzvgX_m|6C%F2bP>454w&lP(%CSlsgj`nJcq9C& zx@ZP$d+WkYCcOtKw6-mL0a1NWj0DP}bnLf?a}dkd$U91@^Ou;vG&(hvuN+k_!Nms5 zfWy}lYF*J8Rel$TcEBPqjz8dKeROfoQe-(iSFR~T1_?5BzTPl?CQ7{-z7y{5pSNl_ zyk-LGos&CL_%zat7YV}GBA6~fdRSLckeT(!4 zE`aVSxSzh2d?hRSYFwMvVKj|9JDx-hGMe3uv?qXkoC>o1p$b_FqF@pPM|s|Mf>=x? z3sk_3ap1)(K)L z6rcwf0W%5vF^UJ}b0`X4=#HEAw5(=L3>zf*Ev^4}oBOXSnB7yQNg;DjNnUrlAZAo}tF;%Tzd6P%y%sC#Y5&SS&Di41Du z#Ep-VY|?=+C_Co?M41M7>0+{P>gqvh{TTpsh3o4CbtvqQn#fMZFA&~z&Q{Emg; zZyPJz+;?%jonfS9wDzk_vC)eYcwzStu15WTSmqI%9Sb~YihmmgSHu;>qCwN!q@nad zD~C$HDl6a9dGv^A^2?(jZ8SiZPCuUB3|80vo65T&U;mZ;t-17d&)W;EfERCml-`Ho z3gwpJS2#qv0)bgfJWav^cM)FV7}7`#kpUh7XOabqy?mq>;sMC>ahT!j#5W6uN#x57 z&hps_H`JUTE#;HwcwCMk+Kvbj?0@gkr_;vJV}a@g6tWd?z0R$qmPRTFJ zlmHQ_p$Iy;n95V90F6 z3t3JlPB1mhQElTjRW(K+b3q9v%vS+dv(+M*okBnC^f9GlHJ9FWIczjR1-`}U5RlKm zgGbD&t%eLz70$hC*kt!FJ?5OaQgdw00Tnv&Dzd0AbU16Gp!FM0F!t49D}OubRAUzS zkgRr14ZHT&QtDg!UQy2hN{D9tXE4tLI>QjOgGlo4MzVMFr|Tf?9K=zdcgOu=Xp<^* zmoeevHz*bA&$u~*f1Y*vap1t_qvrwP7Dr&xYk9MN3tQdw zH&+gWY%jlS5t#7c|JtE|NB^}$Sy=VaNo;cy4dMPyg9xa zTlCAv>9T&SMTN|PYb)^%pI<-x+2EJ|H|v_*-2=4gxrj2*OcIs-2|z;JUG|e}3}R8b z&CG2wydkG6Qy8>#81CS)0n%-*e&;U9Z3{3A+fMI9hWh^X!E+h+Zk?(()Kfvc*JzzA zQ(>RY2D zarxo+W!JsanS2D}R;nZoCrdKB=j9@&c8$Hl&%6)s0tBFbcg;MN8nM+OO>?cYgj%X6 zrNGX8i?GUg@~i^jEg(jvzrVj|y#b`qMf)NkZQm@2wC`stbXqH)hzxd2)>aMN?5&IF z(@`OMRlqq1e_qXr$D%=n{B7^=kiWor7JG}1|LH@}lV9TQ6Ze2_Q^F(eb*!7e&$K|G zGqXscGgZjX4$Y`U-@g7xQOC4m-1n}r3MFCSy(4Gu&VcuZ9{qduF@AjhRegQ^`cmEK z-+=!BK2SA!KwI_Ot6UdniqjT@Pp$<=nt!UiT<+|@#dEZ9qj-!Pg>UTjP}l$Zj~dMX z^&hdy+s1LrMfO(_C7<8LKWMn6blF8_^>F0ElYoxko=MwIz}kWY$mfR<@&`l{cAgDI z6+M@wlCKXPokgVajjriF^|FI1CrXiFAFCAj_=t03H-P;2$GVMeU1Nzm(5%-)*jc38 zdE-zr^ncEm{?&t66oSX8&#w<&TRi`Fa3tjNR7AIyv(()KAjBeKeyW^;JNy0?ZC~(d z-v&a*UZcOO{#&&R*TfG0K>Pe#jIHrouR2qW2iQl$!md&8_|1Tt+#A(a zc$|1?_~^Cs>`;*8fJtLl>L5XD-dEwoIjD3_6mzmCOmC<%7G|%ffE+%6n7DR}fh)JT zYj%Go;ASP0p`1uIsZ8LvgNgIy&|7{Hd~;d<9wQevZ*RZ7-DA`@_;!rrn;Ag87H-WJ zPNiGz_we;i06vGVgkx(!G`aMrA5gm4^>i%y&3tItM50>3SYP4RvgJ>VJD=&#+Y&#& zN)UdVa~Zk+do=3)@6l*hm~7F&tm=Rk(*ybq4Vq79A)SZe7%M`$Z>LXiiZ=YxrM*99 z;)&@wAPT!@O_=+8+vc~s0ec02GQwuh35A_^b|s%|uMk_hC-tiGGE{I}AH?!RjgG1* zfd<@P*Q?f*d=+4w1ukE6U_fHmgSughWgmZo0mS9(_RBhjx2)LNU1OnhXsf2e^Y^ z10EMG-O~&#+#1IUCmlj9M$;D3;ml>>VhyLX!b$?UeTG5g>W9BHf5kNf+IpgwF^dzt z_41%>1^gh<3uZAq@jdc@os0@VZ3b6C>@ZCZ5}`?QqR$m|FSr1e+(+O%mZDOh1hihQ zG)5x&pnb1(vuko_#oURt{OGgvm;&D`Im7_y=4uJ;NNm2sxcU=CK^>%32J8#{z!5?a zNG55FNnP(>y?sYGdbGC+&|(Gp8y@4`zW_&>8#NzX$XjLopMe-QPj|1!Q$cC(wfqM_ z+FL*co*KD@pL;dOn15U%-!VM&UmMEt*ig;m@%dT$UaqWS+*N?oa4*>er%<391ytIH zC9$@^K-Yy%ZEXR9+1m<#I1@-|kRfl;qT8l{=MZ2BLABn{coTzM@R4HoVc1_I_V+8a zqulIK(iV0pzxb>m^4TPyCxl=l0z9*ngs(8-OWUnPqay4cKRcX5ncM9Wzqg2u%y`me z6IL@`8cEPwaMr&1yYFcemmkBAfK)uf6stX1vu#jE?7L5=R^z(s%kMc< zng;QDi4*EfUkLopi{;_BtE=N@HR~24NoHp=QnDMsD&5-=VLDYBcB^P#rxS5p2a)cb z2$IHG)0?Y*jX}W+XSQl}p6NcxtQZ&^JiyX+UAvo&7z#Uz;mFs`C6Rl=#PN0ivT7B` zN(7~(gB3a!?3FIf%vA+kM$p_BEWLW6MFT0;^hC#cTmy!bvSE__TmXfhVP2tAgp>7I z66i-xK8hYqE0gAb2K>)wk!P$R<_)V4OKQU=MRO=#pqrg8yL+Y2kl`FM4iwaP2mtY( z-c$ISA4ty{S$i9P3VfkYre2FWxInnkX7u}c;l?3X_T=-D0>dSCOq1NmTl@1SEj-)W zBDe+P)qwekbPzH>^ppCEpH7d?U7G!Kt{@WC_IygH(+p5|G;aF0zq>`ENB3BK=%~%D z6pQWsJP@8mh`)_@`CU-W`UADxZ3eyIbYiR1iu45a)eVXI{2G(yH8^4VGOn7eE)siv zh&?wK9KNWF)Rwt%NEC|>ARh!J{lMg|&!PDYD(?G+z@eY-Us}ijaH#K7$cu5Lh22`q z)l%&!4l1l1&U{v{kK40x)o?(1MQ%#n#iV$Sey2nZ zFURbT1C-sB@3ObI+hrVU)>Vk7aa|c9B25~Wx5@b3{1+bn`uiHc`5w{+J~*dJG(VP+8Rph705+Z(1oh8^{Q zREqIApo#*nX6S&f!vXoOYA*Hdv3trGcAw5s(oqHdQ-Feq-S!vx^(iRH<92&M(mW~D zl-eFzGTb`O?(FaImm(mY4hyRZo0Cn_vP|D5cN;i#fMh)5$>();uS5x=zg{1J+NJ6`UA^gm zF9C|n3K+Pla8jV1#jp zipD*Mgm*NYENS1#ps1d=4?3VY;pn|@Sf?B$YMdNB&HUfNOzwdFwB@bbn=xFjW@80T zcB9BjN<|0Lrjb(jg?cnenS`BVey!&as>$Gvg^5HI^g8JAyP;6!`I;kNl>xP0IVd)| zrk8y)30}zQs)t*vC)uD-Z@=1qdcm-~m|2%0f|C_oT-D8?fPjIyt4UOxEXXzB4kRR5 z0zZzUvDeSFp@nKOaSxQY)SC6Jrtd!eJ7jIZdpBA9dyk<7k-}}j$Nh&86 zp9z|qIo6R8_0YF|bHn+eGzzVmyM8aj*s%9S5@cM^h}{n0Wy*`IhT~BE4{y$>(K=-GkX) zfM8SjJe62li#Fgv6KjGIy@0jEreLp_*b(0w){&Ekjo4)sRiP3|R3-V{vlBHSC2eU) z+ub+fudLXFW>1wu1@-5cUV~IqnC4OLfS1=yB!k9LBW(4DTG+Ff|K3s;%_604tDz;l zmd(oW-Un#K`O8xqypFrMd1vbX^<4Uzer>*NMN@(0H@sxCB=yBS#&bvPIV}dwBS)rk zFiQLH@?8LAmRx&wG8SN?^FJijG%X6!A}TV>T|lIsIg0ctw8>xBsFH2$$8R~=JO?5NAX0<@c_0iC z?*T3OldD<))WPc$n=!X3AmPTkGO78#`7Ax@Q@~z3NbT$T$8(oJHx}yuUjLnzjsIr+ zDbNvPQCecJ>Q}b)A0Pt`fsjQ(koG>o(a}~QUJFf}u6>QSLOtsIGf3$~LM-IlRJ*h) z;G^N4@PGwa1qFVryd|GgPbGR{JJI$!Ud9wMZwpb+U5vg-&=$? z6?U!WP29mgz#anY2?niL6adx*&w2bzG)FgPE7X1uoUemyt|vaV^zuwK_zeMiT7bQh zbDPV8ZtS=BQK$3L*WF)y-%tOT;lsm?D|QV(_ng{9@d!xbE&rfcBN1Mcu4Vw5K3GTV*G-c)$1`&&{&VK>AY!4`Kx&p}-kh9Jj0B4}Q z1>jskWRE@V>wa@PFsA|BiDHB^@@<0$m8jA15evQbns0YDfUC5XPQFu>QpNLUgG8AfVxYI0OV30 z(AVlPLIePK$C&*czq#P6Svavj{li@3F{FK^7;qY#{;bncL3SrRq@zL;x%&FV`aq#3 z_0{k|8&qm`m7j;h<+4*JTTcnA9p<>SrRA~5&e=ggo3*;C8z^tjJ`qviHQ#}+f+tS~ zf!}9{@bB*cF_8hHzjQKs`Y;HL| zf%z+#|ErV-K@tc{n1L6ucXF*C{{NGk z;Q<9{Ga9?}>@8Iglxv3X;RjhesRV{+`G6t&Vi|qkFcO_JwyqZtkPD0th9Srjs^7S} zCF;afa!W0q^HNKU&khP4)#w7y`U~%PuK^yNTD)d%SI>%z->b5YpiVb`**}9@L+?X9 zY@veBa_HAY*K+819=b_$BH~0aeLdmQ3iZyw?oH=P@~|N=Fo=L4&DIe;px15outT$7 zCMCGt1IONzrX{weYP6x8gvFxDT?OL;ZAL?%B?0W@3Dz=CHPfQSe@fv{-pC<-v&&N~ z0OY9?(_tdaif0{3Z{W{5M3H@faWRl+_%r#tAo5%7L8g{VvH%&D+AD^$l(hU5n);mE z`DY)ETS_KquPTcwH);6uyMbLNoyfqg>VHL9n4KDcFiwPmaxap`jUEMT+;@ZS5$87Z zvl(&I5g-I`*Sx^?913{)|LYJM7C<)fyylKc3((fcxVX&3n|VxsLWwusQcs*=+{Sa3 z&zwi0a-HLw?n`G+R&ldPT|mdS3w}0}V19Qnsv8tw0FMFWgMrRCnRWya=BUpc*&mw7 zJ;OR+8%2;ZR7EP_9$9*>^PwoMTvkuW9av&IDs|A4Yw^!So2T{E4E+B9X>&RtC?E+C zba!VVo|3e~`3^Ei)!ghJjHvfydiML_Br0Nx6Tcq#(;k5TpPbm^puQ+i^qEF-7w z8;@s{YflxXC#K8-i@A!2iJrxPd$sU2(FKzgCR9nBG(Ym;C%N1qW_B%;-VU9l$BF z&#-^@!UADu;;=c|gB7j&+^S5Cp zK1&7&!>Lvmx;A#P z+}4|zepSSHDGQ8PkIG$Z_|L<9u;g5j2!&X)lr zKmT%cl7DCHEgTd|`LIox{3_@=?Hp&LK>Zx}kw7k@;?JN2Pf{%dIN&T3CobrTn@Nr{ zy0e!2pw1LTjt~41D-fue#|FA@5W^L~)G9H);A`>02@@`0_tm;esfl<<-X~BFRlvbK z0x;mw6SGg9TnekF!@ZYt3)(hL=zDPb%_p_PzniUQfXs8F> zZQf}kDVErJ{a~W3hUS6ZnTKXNvPhl%!Z*$}AC#TkaqnD8O`_d#F|!Jul<~&_aoc|DO-zkeoj81&4^wT1G{p_Qi4a|OiM={sNi=B@qyFv`>SFN8(+{dn$&)a)F|9C@w6_cQ|ET=i|Fkssg8uenxER+HDI^aK%eL>UnrcBh^k>HZ^A<%f+97OTG=yQig z9-PW}qWS(??A>5?XnSy)eqt8QN!{G;G}MZ~6&4m}YGwV7?3@n%eluigHJUZw-8~ca z5H$|NLT`E_D07zpoN}o!;Uiq7>!LT}{cE@}8*6&9VC`l)NNm1ZfVf@qns>!h(`UYuk2;Eiu!;@Pqe zx1HEBe~iez_;3<>=pxqZIhCsE0>5seIK4w4rmrH)#y3~1m+ze$J2miW$HsJ0DTrT) z9(5lG-pRk?_Nkvb2mV{m8ZtQbi%OC{9>OXB&AH2-ZM4z1 zz#S$GVLz^az}@%cXhZNLeMGO$!;WLwc^8+TQAP2iy&bO>cO7iwkOY9M#^y#^GXIzrPXxh3j0ZcsnFGSTpYH0Y`C zhvd_6S8`;aqgKIlKObV(YtA@+nLjJvJz)Cb;<%32eQ(u1O(DZFx%W!umm249YaV;A zaTFNEh>%Ks zpNgJN@qo5}gjj%?c+!1gSx3V_eiD`XEXPNIP4>tFx$`~wnyqt_PW#Xbm46q~_+@qf z@!rzr1#2SDVLo(dWIY*bAwAsAlbeW&cC$ws@W zR6?H8JaKO|zE3N=8{EV$dhTvJ-Xqicu^Z*7@(A3`u~TU!|@DDZ?3 z@oCnqP2yF|v3@#8P4`DB%d~0Q5i(N(4=0cethr(ysL##&H74S_qh(MOM%1@_7p-oY%-FR!``I=z9kw zP?vt8dz4o){SC3Y`JSd;)@xwqiMllz)VhYik@Z9o4Evy}FMgbyHIoCH{c>r(w9bk?s3T1LA=WEE8a^5&cBsC7X58NkLOIW z0D~8}J93(oZP}r)z?6xBXGtCTM%5n(l_^2r)ArT-^?v!FV9YvMeqjnhrXHq}iQ+51 zna55bwUeUNgr7}E>>4fXIdRlOa!kKF3tJOV#9GP&b5A-czt@iTDsIHMwnn!`>P#Kf ztsye6Cuy&YRiRK3&zVzYhIaCMR5X*GjInl!)Kt8dSr&^0^fZvw7(2OIVc3Lk7TZ5w zzpDC@oD#u87uaXEJl*-Gm}{hJD>cQ9LTzK}l;h=?S|GU?h5!!#M8ZP-_ss$ZF3VQ+ z%9p;nLIV&In7WUGMOV~So0=j;c2gr4Gv z6aI@Kxs>h1sczZ$cb?^U2sF7&AY5T*QEq!c0XsUiw61O7@#Ly3IF1$Y2h#>PsWNNJ zr{yUDT1_j_ATzW_+}mP&epm&GNh4y=W_AuRk+AT!{k6s~;iBWPBI-E?-Q|(|cPQ!Z z^t!rQ-U4X(=?WcWCDuj%>EF~iVr9FeVf;CiHu`U7uBS9eQB!8i-W1sCq8n!naFd0ZHxN4(|t^?@t5532y?L9*E37jI5ggaz8^3Y zAaux>*}f#d8Mm_$^jwR765TIbXEcYH`fBA%mO93H+@j5%UCcT-c;YBg+fB?7g~(|J ze{L8dY#tjHhOv^j$Ze0G{h}GRFXgV5Oa!S2_a%|o02$N zJV&`p%a{eCMHh|Rf)}vPCFuAnQ(A$e(86}1UH)zK!2K7<>IeZM`BS=ldUok>;er31qpIWOHmb>DF3h8+N3i2YL2kT6%%rn*W$km!}64@;7IvSR)T z`tN=b_E0Daywp9fC#Dq%T2JvV(=cygESy!XZ@MdtaPyo{mqknn}+ zGm0BMiR1IAZ$J4zHIDR5uE%f8WYe)tpr3uz2z#AU~qwM;O)5o_~D zWXpS_xy2BWIm5%n&t=8>o`7K~9&bO1hp(ZNp0fP)0A_F>FIClPz6{CG6TCq=~eKC(fp?S>M_0ul21=@xz zrUn=Q^sz^+q(w(e2YEMO#+PwlLfNGG?#2fon#brt8G%!g-ZQPxWIMg-n?oK@qQUU_ z@iDg3*Bx}~5h~+-a9R?HVle*#$;p6;H1*h5i--0-Zr8$*?(i?oejA7`bo90`bIcSn zoXDjs91O@*BUF0=aU3xH2kOwMuSx)`iC5Kjd=a|Nnb+UGH~s*cgg;jWNJtxfTh%>0 zjkL5_6Xbz;3O0=n=BG790y%6ikd(?hUCQIqi55>W~a4}ZGB zi&G2Rm=%HYj{p`$!nV1_V%X`E21IT#uI*90B6wf$fFRh=o?{p#-}aabFy2&lvip{L zC33$5HifYL`SVdDh(rk(e2vuB z6^W4;IT^?WK63i?g2X-453Bv`jpSo z$?H4VE4sA02L(n1gr`5}i+6x1vA7)o&G!8(*_V1FTazO&&m9P^GY%oCHGz z4i~q9xFs;J#Cv=KZ`Zl@?3;y|8)SeUO6Bwi669Cc)*{xYkRTSyG#6-#Cf)oaPEh{_*@3%`4LruM0+CeWa%dX+v0Q~p#TU+D^iq% zPdVzQ?}}!FsfWagdpF{boMMNE%-vb~ByF*;dXAx{qxG)%{9oXyhHR^Pbxl zFxt??jvi(!42HY^+h8kh`fm*u{zb>kK8l%jWgQ3SERFBi%d_CX62191XbF#PtG z@*&_1;6@Cj^_mTa4l{lbun?{eD!Q~+D2G-VIU#5}fAM9dfC?hEidiMMq3!NB{OL+g zjAzptV4uIGjOTXtmhZK&m2Jz6_~5U5ay%0S+1aNf(gfl<`_WJLs&)9~w1N~cfIXI4 ze7lnfjOcp%%95zS(S^%V)-26$k)d(5a=UcBXxy6*szLG>;mk?H6QR+a&iS?_EZ59C zAo{hy+KvQ9R)-iGgdNDi;Nn1Z*yDze!pM<~+m#407l!-++^GvUNsAGk$h&XfBuER-oX)##;qXYIaWW z+m4>pCv4V&S(8-H@bK{90?nhzKpV*k$op{-B`65$h}u?zd`x(m_q^c(nAv?nMqrR( zjsz0LVg1n1w=2<4Su%wn+%N)k3&8GR6|?ox`uH#2mr>)A5zob_#EZ0xfo*)hE;8t0 zyD^B6ADMr$Y5b#RewwGQ_?@muSWgFl>CzN3VC2mQp87iGdr3@^4pJ}BVJ)p>Ve{aQ z6H2PbL2dFhIF#lsz^gkwbdXI~Yhg*sIh6g(9Pe!Ei?fxPRQPjl&a8h}(O#Y~gd7B7 z9HgV!2R}$Ls2(qwfv>eYs`m>3N4)&>S=}F?7ZZc8tpP)lIbkmXn0wHz&AF~3ZQ~K` zl|Z}lGwNS6G)}XOttQ5o2XDM^duB zrXEmmw}MHP&%F>11=G2kio&KWDcXcTsfGqHWxWe09v1mMr^+Js59cJbEG}tb9ytt> z5Vb^CLOLw_`GN;pW-D5N@F)?;Nqp?Vg`J|DRMD)_nleqt9imFHLbGm|&hv><3Zx+x z9;Ugy=T(6VRwHE({Ly6CLurA~XxBnigEM=jH}NRCbe!-vz3kbQmf2mOv||NHBq&WweD{L9LyPUl^G-x-$LHgL|nP|TAv)9IAwb|>b_ zqlF}0#S}LO+HQbU;LW#lJd<<*aQwaG)gh6}g?sm&pn>S%(J5XQgkqZ{Se~(uwX&ll z|63<87ZS?PZ*~ig02&!rR#TvS6yd$UAz(wu>M&&A&ghZLRRqu4AvNJ0@eNj=EyFL8 ze8I8|y3gA?`R+Bwz%pQ(*)~7_iXb3%q?Yv2w8@hPNZ5&+BULqT_k&KDHj}Vr(`yfS zF$9JB!aDFF$R`dl#D<4I8Xm>~)T6)X^bhw5sb90(veC~h7_v)S81Y?_S{8;(VuGQ4I&O5lD0MutXk0?22K#oI9tC*i0wDtAfkV`QP{w}EEAKQ%(n6E&bxpeW z`bjFaqIZk->flF>kboaQF0i{)-7ld~VwjUBik+{Yc!l4|4t++NOjltEfwll*(K!{& zpg*N@?O7t*zySM*`KqAUc(YSb!Pm&95M84R7Kkc?2*_ELQQhYr!Y~{utKET+aRyOlUhdVO@#9;{Je3pCr2kzU2fgQ7LZeW@O8!rB>&iQcnCrotY>;5E z4w8tje_I2sS|JMn?1&gD6%4OMV!j&c zb{txB@)odX19?ir^WN^=NR7Nj6%&M1?By1xf204rsv6U{oBZ#j4(frSPanKHFRaM> zy1Ie*LsTGKcP#Mg{XMKEEwPJW%40GzB(6wqfS8IMB%J^Bw|dTAVZ`;KcL)j#HH6uA zR~i5A?WuZ3O`*28`x=+LnY=}>FEyC`3MQ}PuG;h&FK%wSr{U3`P!@GzYx&dVEP3n3 zr}t`K&eX1~N{svhdI93;MV;aW7ocButvjI5*0P3~DrtG?uel$`y>p#8r}#_h5wAT| z{8B}Tde7(9Ig$4sH{IDc=(+ha{13jZV$v?AHQ^!boqxk)MWuVnq!-(U_p8YOUYh0y zW}~b{1HDeIYld{o?)2(~wrDkk6yVlg`|DszPGSqcgi*<(p6sEp_oZ;_kFclI!#mTPd^!sL0aK9%`Zh38sSpnhfS@&*2(WLde z?!N_0K~?Y*Jj3bO!Scl17rHbm70hV(y>pw}jjsYDAi4V_=@)=y-(i*c-3Xw2tf731 zu#|5B@SMyxRQiX?yf^K?Y_Biu2i*IYhT^$P`}_CqYt#ICwFNwMz+C411L)c@oQn_x zGgR*y-%<;(@dLXRPlcVMrts)F*Klb)3iDf=M8jAKf%0`G{x)US0UPl>h)t#5mlo)I zP5Z_*zOH<-J^IiQf+&*XTKA?a8GtQd%osw-nBAf=-x37$8jzOdGYN2JXLrp{Y2#rK z7J6v@B5I|h1tp(JnPu3LCNt-<#QmXyfc$10E0_kbFNO_Lqe4+0r3dA{^X6)LqkRnX z3l1&1p6DPyQ5SV>Gg(<6m*vl3-Fj}UJPcFQ3$-BFqki`lzi@LvdC>>Vs)N1*)bd2g z@MXi?38d=#+Q^pLh@~kXY~SX{nrX}i2yH%_IPh-njtH@-_x$GOW*Ty={#juCpG9VF zAz_S9u_kExEbkK)j(d9qRG{qpAZGccW3%18BDvi09J`+)hmtdTQk^@eOH4tN-M-jF zNX`R;I@@~Hjn}sZG~ftuCsA=Xz5tx=>;rxbJKMHIJ?Zpl`0}Fx9XR4+mHWpz#<7Tk z)bzbcL8Ud%aIzm^?+R_p{C{P_ne7!e>`-4H5%)ImaRQb!Lm$vWlm{OStyV_vz67Zj zKK72l%ZjTZE_FWaHQ|CzB7;KshPLekj^Afg5PR~wApGg75DEo^ynxFc4C9`S{q-mG zr@<4hp^=f%l^h^jcR*DglY8StHpsUlU_2mEYbmrku@BjO6Q7M!n5rvv0 zO}56q6jF()R8vh%Wj9$O#u9oLB6}e*La3=GO&K*9OIb(RNfTqqIy51B5`NF=e7?Vb zPF?48t}8R|_v`gs?)!1$t=<|iX$Mp5CD$I$5;Vm%VRo3s0}zykxJ@XCQCjy^>-1?U zuO_gYNojQSsp!kv-{r1P>56|RU>tCkiF=ro3?=7JbbUwbZEHY*(6A1+%$QNh5xM=& z9c^Bu4Ld=paq;!4K1^m!6m~tnNJ20w3xvLQS8~tn2TkEjYaDm=1NM~uXOP=(N8!up zIR_iJ2JfvXLk6s&1PeIKo`LC+_j$wn-#>duIplDAv#LqjmrMgCHV6bi-}-NjQlDPr#6_2+Et*y_92g?R_Y0ozj|nv&#Rr{r%`M<<`oyUo!#BtJv3{sQc@}1^Sk=k&Q$&*r9qq3668NpGTBPz z9hB-{i(<$d#SKV8d$eL~f!CZe#jQ6-$PE+|jp`jVJKeP2j7iDo2G`?J;tpm#3{ae9 zDIURT=r>lpLqk$sW8UsH*W%F-$L4?oVIFV=>cYIs=G9=Fgkf!=kI9AOt5jq5tMmbN z$6J@ciaY{^Fp>NMUS^WGLj+aHLgB1J!lw{K_B6EpKgU%f;*;r$&E6uW@h_N>C~);e zGToCKDHm=ni$2DZ9l)85GP4-di!+aZ=Q&#K32*DqoHgC>Y=oYk**2!BLJbvJS1&0|wfd~in92Bl_ zth!C)RNCC85Lwl*emC0!pZx5fqVVrOrhrv=&1(SyU4B2@?5A?gnomU1%ICNK33IRn zit}#ZrPjy5ku;FI?q@05?}$8J7JcIYe#2p_x=L6j^gJ=Q}yVtJgvhUyA0an1qUvA+}c zC5#MoIDNmohp%<i~!&YvI;nb>sSwCQ=#{{h(Qfff3CO)-?>53H?V^w&7`MX)e15 ztTAM2=F5)NQzD?=ZN<^iPFurx5s6Y_NDqs&gTgC_g{aj&NLo#A+AoUzz48C29Ox^5 zrf-ibvYz|m=309xBg7mYkXkR8Jyg85%GX+U?`BrPZ`yl$dXzYmo-n_9)fOoySycgl z8Ti5T+N0+KPjY-xdt@))2zsa{ECx?5Da1J_aHlHFg)Q$tMb@Vu(jm1#pm@35)k5Ua zVv#Qliz0U+Ipbxj!u+a#SvmiD*FeMT)Xq{;0xAO1(t0xPQ`Y~JT4M=kCD!y_yPNd_ zxOhR;OZy5gLEIppNV)n~5!Cej_>|f`an1u-)ee$tf#JGEwK8%hRB zs9+6~+pyY|WuuwVe{*M4+8WqwJb`(?BI+dEJNxq5Wk3-JYzSeq1c8co-vX9&B%tU>e-n8>}+}frHZhkqAAy6s?Iir#)AZPd_1v8&CsE5 zfo1JQ%~DM}Y1Rrh;y13{S>FTSVNHNjwJ90hiix3&+=~w#yzy!2hFK4(AhyC@y-f2! zB>nr?QvBcz(viSo;dp!WR&N>Zw-cN2NhuZzF5%9`=?;%D$mh`%3s&RNR>r;Tdu`Xu zBn5Jk7QWy>1!AY0x?}%t-a`7-_qNLoJI1;T>qo8f3%nLIeaEAh&%YY9(@9z=-79?l zXHQA}Qkq_+&0%t7=&L=oMZPsH0u}V+`fV?$M+vw@2P5_zq~5ybUe&!KI5(!zf8o~P z1-( zt`an2p4!N^k-XQR%qNl!0+3s!J&2oJWt&^L2=24wwreDtHV5ss}j82K$pAD<%8Z-KKz}0FU5Abve2=+e4Otcoa6(DhyPqW6QCL~ zZO}7i5ctzhR?i1vc>+JSd&NiotP5iC5Lh^!utUbUx5oOe6vOr8T9)S$wV8j4!Ue2N zGnWZ-Ci|er;FHKwEGH3U=9&fCLhjwo9FKcg5iLbM_~({fgdIGbF*!BIp#XdjiifE; zeruMzKf7uMJoOY5XjF=IYpr*xp&ubTqKMJE_D`F{p+14me!NI{@h}2*-j4Pwqvx=& zur~L-wqd6TuJyjdU548!QXz1ZbX!r*3p-o2OTM>|2ru*VzYF0cg7LPdp%LB+Cg>R}Q2rj#Z87m_Ji z0WwFuHSG!-H`yb}3a06kZNMp0a}x?kD}*aqxM`zmTH}$z4vj(9t)DDEKFv(8XZ~cc zT*1#?*Mb+Gi*=2Eh2Qv9?f=~@yVx8a`D`AjedWG@xB`20S^vF zaP`WP>!DS?`viaV{uc(}QV{jL3zVK0!2Sl8QR#f@hZKHz*C3x| z?ALpD8?CFLdvOf$oq{aCv=4t?edyyq&Hk`=1nx<@YqgL{F`qE33CSS0hL2oW^bKVB z{^I-R_!d|R@Vq{s=L72YKe(a{oz4pO0_VYD4>RG4UtJ$RT%OeyI2F6NxVU%eF`cq| z-}5UnG0p4MOf6yO4q8WwEm?uagV|m~F(PPqN()%JolO6PEJ>l3hQ6<(Ru+53+<%T{ z)_i{H)*Gju{`{`n`v|u~Ir%|>{J_=r4Ef4D&oE5YI3a%ttzx zs5=JY@FpF-7p;0=9uzNVZpgF{zot>SHwYS~beyf)7dbAM< zcaL&ov2GRQb~5MHUkw^Yr6FBn`L$DzF=hoMt|pU``>oxSEdr&QkM-YA1C<}x^7^?*X_dU}q`X1`R5!Z%qVD-vO~nWNL(+OL_#11NI{rEa$kA8}p!$mYD`B8Oi2 z6irQdeZB3we*4to0u0^0k+sa?r~4c>8Os&8-y`DqJ!)3X?Hggp3bm=AK$yu0ktxq) z*Pg?bVP3CO_FU8OY>#4CtiyJDv*=xEW8`b(wH!!~AXX2^rQAGYe7LFY7pl z)T1}%Ef*^l%$nx3#inWthKwc0Y?erQTZZZjxkCFNhU*kGh0 z!n`V_aR?v-In;NHw<(X6e=!Jg)N9A-8>}K*Ig#yqM|J7?w{E?>b|QW7*pUhwuMc-n zSO|fwrJwAwTR1RR@21&vrl;#B+Mu8J^Uq5Al!OLRJb4RpOdgcE)2qogPf{i5D3~9Z zTEadvZ@%vvcQ&NDXJ+-{+iv&oHB;M6!KWh`Ti+C;CT0K(C5v#?i_(&ut0XyN`%tc*_k`l$CuTUAZdNZ)3kLRvQ`1C0O z3F-j{F~d&Q%jb|;V9#@^QW8?TXHivVpU6*UtadCYXBwweGy3@KU|3m{%4+5+t&83` z1O*|hfFfV6iWaUoVrehjDpS>?eUvTIc`!@or14n|3|t;y@*393CvGP*%9^d|N=91s zc2Z4nPZGDg>AAbyn_|GnA}DZ5;D3rhAm(MG&CeSlhsEz|#}*YndBuZBDiC%yPQ_}| z1U5Ck^>IwceVlp+Iu5h==;qxnAShG%25T-RutfA<3D{IB36C(7ssf>MhkNYj!qXEc z&L?8>-Ona%%^OMN&d$O{Gpi<|U!Yc9Ev7Z^d=ZGlZ;G2r^!?dt3fLrel@}q*<{KjJ zyQ7+chMEYEhI(_MbZXj2?fHAdYUQcxuZbO$#DiEd*iZwzj4{rNSLaku1woq8S;QR! zI5gFLlQIjZC7kgC;{IW79Pc35n@fhp(tVu3^UGZbeFd4w9@f6E=03l*u&CXI7pe?T z<5j5AR!JU2^sKuM8`xAK_tCXHH`%3`e_C3(ZyW#b^AR_d^4hp3BCtffn0IOI;Z|=~ zm~j`^n-+fWg-sW@X(4CNg>R#{s-yW;elMr>gt%#FFEokJSitwgV$z@~fo~XA3pY=^ z{E~MB*9K1iyifRtnhC3DytfB4EVV>D#SvKXM$OBxNvFl7a1YF;gtfe4*B1kBaZm@; za2IBde%FZ3ZK~g#pnV~!_45>L=`~YncRTOX(O?cSiyvY7{Jmalt2>d>sdcC2&7;PQ z=i)yt!2zDdXROe8@DB>nQe0~NdJet1FiY=vrs@lrJgf(|Gvq94Wg*R?(i>UxOMHcEAF-{ZNRPj+s z5Sn{T{e2+#J{b9mPfh1p&THh2w6BQ`euaRG_~%b_6Uj|{oEKJl{j@#!al-nwL01ZD zsjxd3^L<}ABErtF*TtuP3ui!UOTIPyV2@Nirt#6@Em`n$hsk@r7Xn8m{!Hx?Zy zX5Bg>j$Gi4FqzS>BL&7S5A z1+QMR*Bx7DW+4-Hd<)c)Nfg!F*ChQC_Jt957G39HfEaC)?A$R}74+|;{+08e?pyS+ z5Pz@gn=rrd6yyzxkq|RaDc$>P`%vh*X$Se{c#!1RDwoO3a+ssN;DnVH z?VW@fX{G8(IhkF2JUYyE9mST9GaUJ@Jd0CtRHffmLpNdJiDQE)rDszO9aasG$a8;k zGXG66f0M9X7?9$E{x&REX9xjlfZ(buRCdi&%>Dx2P2pNpgdIRKMs=xtD2X`!sMc#s zRJsKt(hnAiZP!fkNkC^h%U%I}w(2xYsR``kk;axuu;sCJ;-=^^@l}oqh<`1M3C- zYQ@-yazXZ#>X?-}xQC!Yfxj@B0;i^bu*(l!D218lUK`B*+4RpQb2e#EAr|?LcMvBY z%O6}BsqZ)_;ZQh_KO}toxQwAb)7?94|7XjUT4q~dT%vYpOq`90Y4@&H7gWXUU@A2LNo!~Fxgk61)im@wi+W`Fa>c<0&XD)g&?5Ye4Q-u%z z-BXhPja<)GClfBljiK>ITG3tv*DD&Zn-^XMale>)yQ;%JPVn-nZjhiY*r!!(GbOOa zO>-l5YDY91jJj6FKF59^UAWb+5GV>1Q_|u?F~3c8ms}g5<570xwUOvnpRNM&4~p>Pb*1f)pS-M&PZ!&}&*YG5DmL~n^cl=j z*+W-A6&`qN;IT`gI#BA5BA~&i=jBa(=AY@6 zb3quw-o~T8xp{*+ABg1oBU_>qoQ6z}np>}>QZlb?CsUi>8`u>6oT^wf_8&RaH z+@5jrIdD2tP}=Nwe$U;cnGBs~OJ7NBYYp9X_ep&$3?|UEsm9Oqh8H%PK6n#d!1)&w z7Ao(LTt2;Z{(-GhE&Z24T)XSdOtAwH`1MV*w(ZBg+q{B}4mhzYH3qR|W=E2P`|O)y z#TP%wRe@mvFOpxn>2)wFavRh2n=mn1ISub+;m{GioAWgR6E4)@{gHw0L&-?UL_rBuUFw zH|8*DU<2E2xP5410ah<(*%)ky10H4+T^Q}X`a{rZ9TqozN(3Wv>Uv2mTNNwTIOS=* z+!1(7k^0$Q6a4jA+DFb~FofM$+a%w)eYL?|C0vi7j8FDZBwS=*lb%)D2-1=Az=^9X zmyGO^8-}KDX*v48nR=jseQq$IDDmIHGa&k}kJeuUs+*;*A~<8&BpCZBnZF|(lMDq~ zE{B1Zh!Wab4WMBa27B8=WLC}^hjojgEssWb7GycC3gH2m!jPCU4 zCQKUzFDSIgH5LZUDH};(6NRD+#fk8eVXLr)QwTW@Sjh)$}1=M84 zSCMq;PQ_oV<{F34W=e-8ZG+I(I@&8Gu@^`5%uFMZ^&NvQzb*?L2RBPL%0O#-JUX=@0KYv&g{CEIwFHE5ffUdr&Bl)4Vt#!}UIOiNNmP(!~Oy z>eaEGGcJZ^m-a`-HnUSZxmW)DDUb*||qK!MEvD;0L~- zGCZHZLC)(ykMu=j$=;X~9b2Dp8ut(wy<2~$Rh9`5ikALr*H`smL@51$ZYF5mQh4lp zV^`xrP<_@24b5Ras62F!qOj2!RqX0oUA;?B6mQUrVOe_Bdz)3#-PzmgGg)_tmjDYw z5U;d6LbzZ-5u@a_?!68`BSn`Vbf)N?3kd%!TvgU)^iQVhQNJ$=!Xv+KOzTex?Qdyc z|7tKEZ9btt{=l>x6|w&OegJWw7e1e~$5OJHls#4Fxl>vCLXrc@?ABFHm!3Ii>P@#K zb~#gUuQ^PMs5HT>``VGi%-9jvNUF@@0-P8yYf7KHIO+R}YP&o}C|5K_9ynOkc4(j8 zbVEIrV&m&OmkZnjhu#ITKc#l>FWKO~9(8slhrz`lv#w|ERP;|M=;>5&TbRW+z4x4- zA&#-owRl?65t~q&%SD$99<2=cNiAb{HQ4&R^T+|N;Hy1GqF1dnV;x`;tL5W!I9_ow z`f8d;NuEUFMr-Ob#Om^kk3!!Ab|w+y9i^}aNvhA<3lh6MT|1cIengpF5^6{gi$lPM zb0BK5ZG+e7x8e*af`6>-nNn@+ZO~VAJd>EW<3(i7>9(2oX-^A}^B*#?7ECtJN*tfb z(>rIn$IFY-`swbt3FP-3kq@ICOo?6y&!Q^crtrSts*toIUm|BC#}kOfEYDM&$AE8l7V-ZTFVB-k8nriCFa{b~P^bWj zH!{AtDAvUwdT8ISD3{7)i> z?%84gy~fW){wq1QnkJ#d=gYQ5xpA5_HPY>})0riqc2@5oMd3>_vwAjaF??p~y}g-5 z`X00dE6uoJxv{&b@?w0qL&gWKu{rHWb=0xyA=~I`Roj(*cev7a0cw9vUq^qtjgH=9 zZ+P~3ox9#_jPVV);8)hTSNFAe5^|_Uc zw^B?ib<*(u2DlV_O4mdJI$vq`?(TbH6Y0_1pryth^(KwG^$KzaG1i=$lr46(O>c!` zD9X)WT2;CXOze#a7YbqxG_Nxkc3p49xPFE05LgsYK)@4lJ3Cb&i!7}(owyyoNp|eW z6vHhdTd=n27hxTz*!p|dF*~rGYYW1@o=sPc(`q<_lW7Hk2whvDjkIy&#@UW#kNdA8 z)=#B`@0xMu=d2D8S3zA*MF+~Vy(X-;HNP+F86k%Oti2_%SaG;RBJ}VD4KNPOEsAkb ze|$VvU;i4)LuQ5@gUrz__EP4pg4y>}zdiXB+^%*ZP-YE?Hr%!kGmRw^6<4hl$z$c# z%QNL;a_chZGtiq3snkBEy*_2QDWKZ@-a*hw&Uy}b5OMBTQvbsy%M@>BS zDwtMfcA`(MpP{2Xc0^H?+g(@ba}VS@_sk_am_osh6vW1$lx2%h0>*+J6oa$Eoh=Lz zI_iF?c41?QEH^c1X)fd7ob;(glz`EZcm>dId@0A9Nt${uo|YKb4u~w~>c1lq66~ei zT0K1cJ|<)X{5_bV~BM9lWQG!#VmVf;1ciG zPDJ=cw`7=+4|e8U`F#-(@uFMgfSN&7A|k0amKK%|4`x4P8Z_sXOY-;^#x2G~q+YWg;Q z{DgwS*oOab-JXp^j?r6Y-9tTXb)M0RfQ^y*&uhHUwYuzm&;^ny54bv}mXAjYiiIHp z4jnaf6GRzK>7ZX2W`=1bjbGM+C>Mu)4*0a1r|;3c6ze9Ab__CeQzWSI@5PPOer^4m z)Ax>DFEvY|$jI!JGgA*@k3N2-^(4-enNN}hSNhvJaw+;M-8bz+gD#k$(nXx?it^So%3 zd2VcBXx#45k6sAjD^KL*gTBHsTA+TTR|lnRGKkEOs!V=94d37T^7~ono*Qj({iUB| z2IZm!EABDeE-Fh@yx+db$x6a3p1$5h9M|hns6VJVZT$!sZ+9&oD}ZMeC1QM^`m#4}5;Klhb8-^o(^WxWYovZy{+1Sz~nERiFRE1?uR8~nTJ_$dWJ6K`x4 z{in;Pq1RNf{Hh7tiCT}Ax$TUL*`Ln>Ro}6=oeu^NgolTJb2=9Vj$01n;wcsJv=Gr? zSaY-a`+B^@^y1r$wrh87$bNQ@kEe6x1g6@f+g*M3JpXyWjgujs)%Z<8*rZ$z^6sy#$o&1UQ0hz{tG2zAk&|eW8r~g6##&lY8f0jobX#j3kXsF*w%O^xD zGNWXA&E;0i4`vpT?4%n@MW}wITAGoZtCzkir$2GCYC*3C-OiP7_eSxbSh15pKG|kM zd3qluOsK#dfXQFVE|#hN$o+Mnq)w`^Je%HSW?GV;HXST5C+*;<=elu^euhNREGpF; z0?2&{I_?*X#a;dnOc3Cuvg{cz>bfc({Y-g(=xvPKiysICB>f7KMgA67DDH~3Wx`r% zE9RIt;W^qIjNA{ldJ{MXvJUt&TL)9ziw;<+eNf6@m28Mfy2eZ^P^pD(x`{CwF=S_D znL|rWd&1EaiQLt5u_6b^v8>LogJ;>cQT^XsgYToTo;%h}MT1ThVwhvoE+w*3_g>o` z>o$%&Tv-$o1V%h`V{Sw8K?oN)FAj0T4~71o+@-Psa_H3Sy}BA;6Z>*I-yJ)c!uGnt zEaePpo5xqW4#A+>EGe&D0QxJ@3jNFx-SE=5OrAY4Qs-zP=r+z%0A}jCX{ArVNqA=9 zlu2^%y9|N7Ayen0S5axZU+N|y4x=_kSn|D)ii*6r-L(a==1C3%_$6b3%UJ2jkSWZbM)kTpTi4I=RO;pvr;;jR z3I;Ce$P@okJIJU+TvcH1#{xOM0HFHu!G2?E|H6~R<`U^> z`JLHtS0s^E&d6JEvln5G-6y`NQTI=W#lXMaUziQdE+GM?qpJwWG`hNwZ0KF_ZAUhL zTP=Bl#Iio?5*Ky_LVE{w^1MP#v8{fC4`q8N`dPnA8!H}NHpaC{A5a}#SZb+SDOnj$ zkwPY|uY=tI|ArlEbZ_+AZ(5Y7ZM2}``jFc+w{Tad+Fj;nBlVqflQaAeK~t3UNZXTT-sab}c7otcKrwkR>>-q&yUhst5k{zKkL2e?Pl zpXMmlnaA3sDELDP{b0%}mD`!TA|$%&wbvqEQicq82>E+iUBaRg41b?H|GRE3847LY->gq%po}g9%qO4#sj~ok-@uazmS456R0#7aeSd_z?2fi*4ZS( z&=}XIcjp3|1o9)FvK;<$ewUz$D@#Wn1aHx99J{;>QBzAbi>c46F5+-@tuLeAL}c>3 zv+v(}IN?rcp%;HJIT!!bsHPY}4cYAXx`BV-66j(=7vutAqzp=VLxp6XIZkY6IfXO9Q z-SXVHgpM_ff)>(DT_JFhV#L{)yM>x{-%-lcl76wybIv<(JYcZ{VVD+Gb}8;xC$KLU zX0}i1#*(o8zkaR2oi)pgrsw!zD_mLS4D`@adrVKaXJ<=!yQg=M=t(wDgWp?uOcUyef5jT(%n(rP9J z*>jD5cd84AFx&t}RCmJj`V(EpK6lIT&C9dTmKRMo*5WBZb$snr#|n0^MtAd!R}nCpiO{%T)vq1!pT%N zZ#z3xB+TaVym1fXfP>|C8JQ>JBSX4Erj{OE2`Xf*hxWP!%rlA2djcgu1v>&3O%eaf zfC9faQ4F{6AtyY8hj~*onQS$38DGQ3h+tbkP5rbE@0?wlOuu&ACk%CWN z)@#<#WlSx@i@)vHj>~r#PL-4E%rR>b$Jby;QIu=&BqveM;i_CUE7Y+d;h`$Ya9H27FDa=-?}Y?%60TKl{Ze&Uw?cuzP@4ww#|pFb@9#mirc;)7LIuaZkmby zz1h?fbO-81h>^pMg_fZ!K8((+q7@;F?IW{^J{ypk69w(>H7ZOCx&lr zho>8I{*Yn($yfpoxWZ33sjBCpO(ptp-R|cN?Hnb+p<{p*0_cDz{U}3PA zmm8N+*t6Y!{UU#xGhv|4a66dM_~tRe52l-aH@lk*0`tm{&p{X18a9%B1(0>vS+u2* z@{;&tJ2JYZ*Njc#n?hV@9mXeHF`<|O2Nk9=jTWx6}^*L2lMaJ_K$*e5vJ%t5!zt0)#fjA>A3X#$-1g z@;qK^O?w8rSxJU9+@T-FiKojL7_p-Xm7(s*aM+@{at@w_^($`oN!y=7T5YxOgUl z4#xr-nF_hb4wj--YT&?^G{2EQ;9m_i`?a2Z>SgBy%Me%1fu~_MHI8{k>>0w)udg`X z!qR)~k1G8+3)6nYccQEQ+!DPE5~fI%x`_HI8LSp48qu{ALD#iq8)fa}ZQ9N z+QNzz{XG2cjTaVmb^}BF=XR#4S>3ggqg%Et4(~$-2Oqt7wU#6v{Fy%Ri3*!5@ahf) zX!RijZJ4dK!0rEOk-1ZnJkX|5%1z(@u3Ho2b5)=2&vlfNG63Vw?P^%n%pmrECFmdd z=zKAt@{PU;-LKceX46OTxdr((+(clYt1D+Kt;eun4@TyT^dQJJ*bZMTckNpsI8ZS@ryp_y9f>KNmYPr0OgjtQUqy0TG)EWzrOD`^p=>!WU!>sVEYF z)=1y-oEG&u+ngQVFoeUm#GW(@@(v{r;euZK3gP2V4`gP3GwpM3#VFE?A2r#wYtp#6 zmtG5otYDFR+w=UBHdk#nO|pxwcMlcSW|T++6tO?(G?*+m?bO%KUFN52NMTzAD?HeD zkR^2t|H2c+;aINRc*@Jo#^qDRM#;r@9DP_)v}Z>M>|rL{O0;0lU`R1G+aZ8~^S;v4B8iK^)&l~bU5ke?0S;D< z({53y2>FmdFB^S^HesonMu&0X7by|wT5q=^mvT+nyMH**&f0NVX#5+kP)`d&Tan8a zj7S*m?D;)W^M8+aRTGmohczc}vTm6%prdRg|J>iCpPHRv$wUyb-~Ht&s86Amp@-#A zMrmi!b_nWsA@o zTx;8_Hs3Y6eL74MQWC>mugm{(@;Zmz`_1(Q@7ldO%doo}y55Z)*%PJ9{9B>G^svmK zo`6rU#L?)(_yR&WnmfJGd!)w!HCTnn{;Va%{*#r>e_waVul)Ve=yfEO@B97>QoqeT z5IOl)`9-u2VEw`zlec+1F#4`gje#Jq+5h!?w2wCmi)ZXKvE~LzLlsTGMoy$;(=EEb zcn9Uzp6qgb_c=b4!edXC-_j4yb>nmi^&>uk0g_hZ;!GRBiKf=CoswC1g>MKB zYG12%gTq05;&=0IDDX~hVKS;bcl@1y%%u5G52e;Q3s3+HyNVthH-xBFo)vyBCmWpkBHkjm)1@mXNWfI^KR8t#aOLsL-=V2UJ zt-{x>OLWjN+--9C=M6O{!_*k}-n=e%D4PRzmU2gLD94?5p;C`NaOi9|%g=MeS9+E! z2tIJQTM_cAejiTbNIHVbUeO+hP4Fg2uc^u3-tvS!tZ=pUIfR3 z%O?wbVM1Ke{kFqZm9zsH8577T9XD%9%x>F$yCP&TM$n#^1TW$GEN97(aryPI7qa(n zXugJJ&%mRfzlA6tdbJRIwQGsCXU^{-uy>oSGLf}CgrKMXt|{e#d`ZT1 z2+QI9rRHZN%2)i%@!}{m2)N+>`gNa4Y!Xhx!1mDb;%zT$3<2~!z*#W|39RVALMclX z?8`QqsaLiyc(n>Qi)eWB+#e?zi9(|L z@<$6k)<;Ce_P_PM@;A`;`DVE;lXc^y82^&X{77+_xXds1EC=yjbsz#+1`l+@o4=pm z{7M@S4CiN(2hO~@5=2>C{*>`|Jzo|%{v{cPR^6rli~iVJL;v|TZeJ)SG&@EZo49tO zHw)cGb9)d9iX8o$$h5O-IWDH}Z76NiMov38HL@b~b@5SIfa)ewqy!3czx*-O`3R8^ zo&@7I=gM9at}JVTD#Kz;G6 zuBe`8;dlVhT-nnloeuFRcwmX-X5X%Tp1x5$-5hpm^PbVmKZIepl-;&DRqn{WOysGx zgn=6ytct^e?%Mhn$A$#3XZ<}q4qp~ebb!i6`VGs!ou(43cv7%m67o>1%mn28MSpSYbhyQw7 z+TnC={w)h^t7Yu^LTE^cCL>H6Nk9c}co}lgx5&*hNiqA$L+(6Hr~oCByV_F*5Z|O$ z#KX_e??AZ`o8+n~9gk|)2{U>^t%%CxG3ywyH37GwUsrGweS7&eq+jxd6Cgi%Yq6Rw zU0LA-DtK{x83TR!=Exq%1~|SYsAozMOYB(NI1RNwG+T#zcCWy}`gCKLs$%4gnvSi( z7%VVROr;EKM{zVBx!y6v29cDJk?EZ0T_4t^qvRvxm3G(P7@$Ap@78V9Qa2e}AF8|r z2j|#{S^_Im^6n!yVxf&>QI0(p7eSsaU+as*Wm11AAM%y`OnsWO!GdQS{>)~(owc!l z=Eov8YQcJA@6p$kNL%rj*a|tbGnzJjCvJ-yUribMK3g1e{!i&R!nzm@O<<{q3rFG@ zPs9Jgm72$5;6w7Jtt-juSyY$GEr)6`^m= zU-X$79Y%U)Focj{0TQm+*JSJIHjE@i12b+dEOhMx=#|Rr!#whio=@y8U|IhGjMQ3} z&lEdP4?k*opY984H z$b8qx-?l4@TluM&bDgZ>IDQdUaL5ArFVt>_X_NfSb%*PCW~7Uhy=P6f){Pp225+1 z)VFTZ^`wyzAUa|;j*vItr$Hl7GKUa;?(s5H-?VAdTkjYYcHgny)5ryqV>+z5{L<&F z-D_xW&Z(Kb4=Ee3utM_uBOD)(XO8>KB%8jNjJaEiza!l}E#6}9{EqRs@l^D) zaorB8txiVvi7o!|r`u->{oS&9@~m#=Ki~ZWlrj$#oFnV@tCR>V-e1%1vm;(9GY0zx z5`)1br(N#rIz7=rMCga}(v_-4t%k$(YIB8eoZTB4d~oU}hp{`>M4w%uDLh>5a^vKY ziq>}|Q!B0H$k5@259!pa9&+rNw_Q4HisA-3+UQ4sOlu&IEGr#nmy)9eW2Ms)ntxJ^ z2Y31|VO`2>d5>fK1E-TwpY9JDMQa@pn}5kQ+p-ETNT%*oLd28V0lKl6iBW>Yy#gYmTfQ@EbFqRw%fvNK0zcVF!8aBN&0 z&n-diuagiec0YR1(eVT{>m0+9SN(-4c=7|dL&ukLEP#oPe-jJfJ64hY^38FVLT=Rq z`;_^~6|D#({O@1J4hbaWHpT7jj&1!H?Y>e*L1A|_Z>2y~NQKZgAUymadsn<&jUo`Q(n@|(lGWz3<9+{#%9!=LPaP+~M8Eo2J8JSBjW?vt=V4Wrqid%YjBqs>wB$bD18Kwq#nu%~`=X3;^sT;^AbLQ<5)BowtW3z>YDTuy(qK|-DBx*N3n*oe8jdfdm-0j( z>DO|&G>)&OT^}UyosE zs{a|DAeUK1Kk)bcD}SC{>C3;x%m3XJZsxBs|6e?rW%A6N_Lu zd^ypmHpcEO16`}FEqQ{Gzsi;Ds2!WP|JYZsY*76s2R@zjxY00zy0y9e+jm}QK>);L zLJh1P>nX$S7A&?xnRTomWS3kqiyvmX@11lvr)#B(tP1O7sogY@osz}24y-%$dnGVB zFTGBd-9JBC?fR9;mw@CsRW)Xx8!Fu-Xw9MIC}|oU=aA6byrL&MKsm*pdqKWATHfe^T;GQwh3u+@ZAse~ zZ&-|)FsM}OJFFgLNP$0re)-M%a#7PHFb2z`Kq~qEQVgwoDE@QYHuH3+P@}^fpJ_sP zmXh81H;Z5Tx_&J$#&8Dap1WCnhJ}r^_4v=f9xG}+?^WEU*Qjju0|or+@HGQb5|*K& zCVwqKx+D^b+?c8t-kWZ`rFi-ZGT%}kTe^s}JRCeAEcmV}zP)j|{4VF7OL9VnzW=I^ z{CCAj>tZ(U5S{E=Z7J9gRZReGx$w%|Q!VlH)p+M<>7A7_SYt9_V zBLDWd0ONz01bWslKDGkdmZKPNM2`z;aDQ2IA5Q5F@gPD)hJ`X?x@f*P6kc)9Gm&$QFQayf1J zI#A$`FH}y5Hi8RLbo4E+!=3LSiNj7aQr^w=l4~P_b80YHFQj+zT$={P=E={Lc15!- zwN$8(pV?B3Bo3KX$YJl>qDT%#91~aDaE5UvjCWP`G@T- z32#D$Dyapr_t^ZLcocu(UHx~-3tJ)f1_ssZ!q;uPb(z2(9qKJ=sT`w0-uHdAlJ@U6 zyCc?sC(p+cEqI4Ul>E9B6{iKmOLz_>LtQmpz^R=2aK@Q=)O`>(Er#7iP@T7r#SGLL zs(_XRGZRs_oOjmUsP;iS zW*9Z{hIoz&)m?YV7uvZC5@&;YxQ)OO@}FtD;Q#wq@7VZzh{*fUdp6^bIM~jXYgmS} zYC!9I)79~>1zNJmE91xa7y#WEAax9MsHkL^=Z!%mP^aZw%u}>4fpVA(UFL|*+-46IH>W^nC8PrfoN*budfmHQ?+d(l=AI)K{yd;GZ#Bv+WNwF}65l5MRNsML*Y7=foI z?erk5!W1KQzCc)oVttn%}l+ZrnGHq@wx8@qWX>`n3`Ft zMz{6^uua9o1j@oF#AKC?qFF4@;#~iWCna5ni(=<*n6$o&(dQp94eT(mo<#Fy({(iUp?k}aRmO_>bye;T99=|{QWpi~Ke0l>@ z{R1Re+c2~Kx2^=uw4GhnOs)SN)AL(|kC&MOB2;VOBJT)X zXf+)~lO)2GKP_%#WGyji3rmecZ26)=@jvr!bY$P>Ej?2{DRj{ET`5cPl7Ta0PsD6| zkM!lfwU5dMJfy$rdV+LP*aURRs=_}g?TdGMW(H-`@qw-dh_2T(jm;wnglTM8 z{z%(x36ltXH7rkVDqWaRwgu~9KRBq2Btdf#@d$ZDrNZ0E_VO%IOZDm*GiTQ+0OBzj zg?%z^i4(z{jc83Qe(@F?((Vd5$v_0_%*Z4@5^`cvV&*^T5XtFMZt|N7HeUMIN)KV4mGa_VeZ`OG9ExAQ26_CLTIZyMCnDuMp_}L zXPk`W1{B}?ntEF?)-1u^`3&mQJGT(jCwqK@>rii_qn7$uKe!p%o@opHpO_$t`-OGH zG01wL@COOJQ;Lz*i_zF>%#K!755l&owL`I^!GpGi`o7ZqSed(kZ>L_EyP6~uls5Z~3` zC{aZJQ52T-0+#ES_HI-DsEPGb+O)XKyW*p&MGHzt>yNy-7(CnT7-H|7sk}f-P~G>E zFttN|zgFDl10T8Wup9a44(UFAaFwuLy(#^S0JQ@PcIl;&(2omQ|6LbX$(#SXE;#B7 z{VA`Vpb<(U#)=e1w(j^iV%7(;ZfGwc@9^9We+l^H$qJU@X&#w|^-a^}A0D7N3jhy= zmT_Z4RkKl=gVglmSPzMCOjS3lddt<@tT@?Lj0lsKcliyGBf;5;+>p&Dj3l7SJZ1EI zK3HmXlK{^cHpx|VENTBKv4#;Pm_82w=I!|5iUP%0*uSE%zYI3vaGQfzAr-6RF}B}~vJmWhJIZ;my2B}IVTVf!BMd;0yw zOQm(NH@7*BBo5w>dh95J-liYuanG%t8wsNRpR+F&C%gMj_sof#-l2{B{T;WY=s>W% zFmpefst9vmH!CO?E(>q8=ST@KK@|kT;Kqrd@k${de9{q5?SiYgnWLR7t1NAW$XzYy z?45T#@$n!@V=+=1ogV2H0h*@dGHeS*bE}>qOzN@Cd3?!Bk#=Fsu|kW`?&`W}r$jqn z0_z8em_(a2L9dvZTp^RT#ild4l|*&I0oH>z6bcR-*1-L1KrDYF|H) z<-G%+bVf^qaj?h0N=#+#NYdj%_9s#_46!b+ghBA{vO+w=Dsu)*^4##t8%OJDCyQG<}i&i{Y5z$%waJBw?(8%t%NY!B*Z~e7S=GLiBUkDrvpM z>&P)n`$Q*1afa?D&GHni5XC2*S|8znk9Uxon*Wt)lP%l$97>Kv z7_DDhnKIwEZ38%t%(nEQ0K$;r=YxcZO8wkhw#5`zLl5*Y z;A;Wx^u7)Y&W;y{9E%qKkHJm-` zQ8Q{n2A~2v+f!>+w8=C6?d3NSS&|(3Tcb+dtu!cFhYtZ3=0fDn?EZlb30 z@#sumHS*p7R#&a>G^m<&uOg|cSJ2HEATT-qD4Q_2B6Af#blS*soy61s$JM*XGu{7x zz?~z6NXndYzARK^g0Tlle)i>4Yf>CrV^cc0$SgrA5B3^~h$XjWZ4)itkwgnn}xp>?161$3lHc~Jcxan+c1R^rzqY{R7e({7HG1At!|5PUK4aivWxHEkQ3UJ_l3>kNT z8y*m$@RH;Ul}{Mcg~`W&*z1~Bcl(#4lHbY2=eexqt07>3^}DUZw~?|*}-6h5#Mt=`@{S9@3Ynh<0k5ec`9dr z)!|t<;DUBTQU7dwg18&AD|&cf0NrBQSZcNa&2^wHXZ!yeJRJEu@~En<6f75KDNyhh zM2r82mPy)N7tt9My_Bw?%P3?I$r{wLRl0YetS*-ebuAQFU41ebBIH^r?_B99kcVOn z+}{}Kz{1=)?T(CCQh|mYyiedEN%?kZzFEfWQX?oW#_bB0YiS>1Cwo?z%RX6yKh7t+ z40jc{0$?FzB&wrI(xc&bIwK?@Il6 zs$KlA)QAt@?)S34XaPovg7M#ROJ^Sp#DLkt=Q_NHGMvJ28T7htm#SlL=OH&-&gQQz z5skiIA>Cv;E33hF?=eeMHA^wZ%lpxbey1CFr*=> zndm%t5A8$6!Mjg1vIjnEIg5f$YzO+Ri$+=>?=Rf21-~O)OlNNun!Zd^xz%tsQYhfm z4e;*&s;j+v>&sQ>38LqA_EJ{Tl{5vc-ubI9K74xdTP=1dBfoSXNZrf;bJA$=Ebser zc5sXG1*^|~oXA3iY(hc)kr5S|FC81{e4f6$m7~$5yLK(52s7JIBN9v#7%LJVYC{fy zUd?ped@eYV7r(vzUDx%m%CKWH-UIjwgjP;m+wR|)HZ|p7K_M$oA+r-8aGN)ONkNyYfi}qfg=M|MudvT2l)qDpfgOr zntfZ@8)~2=C(%kmY1ih-F>OU3!S@qZDgnvUM>y>DUiRE0^^kCorPMUO6}PjMmKyJw zy$)90aS*+D9l)H|!5DnrLao_pz1?Z=w{0W?TFF=eEz~c)BnGw?rj6zJE%}nm4)Ms(jco9hh9hGO z^7}w-?$4+$&<(Di$96nZZ2>y{VhdvA%}mF{WzFupR%57~s1srAl@H6uyoCH-l^!`S z@V<9Bb2e{_e0# zR{$lGHXz__jN#HdwX`m3qky~u_AkVlGr<2n6FFPF-jI+Razn0@d)ZW8vZ-7#H$@?_o@ccYXd1Ub}GP6uqySD(mq-&^H8dQ|}b zbanA!;=E7%9Z_xd96(+E8>@2Ri-p>o#ZXH?qhe}7GslOKz{ZH?;67MmPiP*Ml}S!C z;DA`oo0ac1dY;$M`CdmL0;o>xor15Ri5vx{FteiGOH>$ZM9~x=X-fK5Q)>dq z42dYW;lbZSl_0y4nm4*qIixjBaQ=HHX-!$aaYpz@x1v|SR{KS8CoXIyZ-M77Jqpsy z;s17Stgm|jgbc*6H2VJHk`t4r>+}~ZiPOs<9k=sP({elHm$ViRGN87S{Ag!NwhoW~ z6lT`E`XgDOvl3mnZIX~!tXhEwrQS)T}h<*-h>ZYu8(ec}-2?$^Gt4_qcacaQ`R*S0p9`%9sg1lBH-r2MkWh~^rX zLMUT~O~G6`pk&0~YC8$=R8+S07XiXF5DK^*5HL7&fb?LC3wY`EN#VHtbGsHTO*r=4 zox_^A9q9J=qX!(mfVctKRgt_({uc#>DktmyVNgd04UXl(HmwZYEyz5(vP%knueo5~ z|FyhZpC=@@^dpS1kyrgrQDE~`JpUCvNb!UomWZ_~xcDrF(D+iNnolNyPv-mJ4lAKZ z!0E}GRq*e$lit-)`r5Yq8YtenPW$MQaZL9&W`^sYWR2dwxAJiI>&GQiK2p~5v5(lY zT_?~EexgdEg?>(3U64j8yNs<|rjC$CK2pzb10Ew_5lAk4d5E$3!FKR*(P2+0dld2f zP-1q)376wJ0(}Sg_uoOmRRTJ65DVpOW1i8q&IR;Tyok5j~$N>k1t|b=G2lJl#6H0-FGXo!-O+L7w zD`(b!T*{;as|N#_X*n~!%(gkGW2GK$zIt+Yex~WIL$rLCDJf($_0!CJ=FhrVw^~u0 zvFHWFteHuWO*+W6nqCE28MHqEA-}b&HU?-5_Wp+()+F->SuQ(zq{h}70Y1kGP~L$N z+&w%E<3t;*J8wx7{(>a-2j!tNUJdgd691M`{4Zf27z`=W`XQZ78}z36EfLS%5T-;3 zx1IrB_hAl!JGaGu|I2aDJ(O;^7rs>YFXh-f^0#MFL?i%Mt~ZrNHiP<@nKgRgc<|ZC ztox^Ms9AalO~>yjU;miQf8DjI;ySiHCrL_pk8Hf3uVyz%-pm@vaK`86-aq*f4IZ#P{PreO{W10D_^_tHw9NS2ds#tg-I_Wtt6V+l3^&|Zkeg@68U70R z+ST*Tm;z-WV0H{!ar=0(R0>q4BuVMDU~;c;jMW9WpmY%AUhfwa#7z}v^gi{YV`P8! z<=iC=d)vYxF^{mOt2V}>#_;rLfZpFbbUcl97#n(Gb3Jg;WbHGoA1j$O9Fhv`&@1bI zDzfJ*kl?a02mfaOBP*RXo@g{pko!9b6jhI-$nm{^{WUWM5@~}R(B138W#9)vWb!ZJQi=(@H^fg?5V1oWbAf zO(xLrj!iDB8^%CEeGdU!dH2%(;jIHQK%CTY(4YPF2!o7EcC%ycJ6cC%FDLNYBdfQbK;$Tc(JmolwR0JmkOw6@%KP} zZ6?rQ>4dX%v+K?!Kk*WteW)SNvttZ#A^qT0<9v`^Ag^GhP)c&CwYOK+csB&Te1N_D zkW%6Uj1y$fKxUuZ{`77ULod`dpreADdlx*`&VvD{JG+EgyFZF(C8huZb*_Q?bhP&2 zwkPg~%~D2cdtV}Uh#!S`b!i}`*Sovn{K>)0DRoehe=7O-ITk%)?mcA8HwcY>gaJ z>GJUEUfbkwHiD(Ull~~{RmU|TaPX}-YkI&l`@r)lwMOGBHtC$x1lInsS)h!qAsT@q zC+7#OI;FP00_E6&U5v#!U3Tro{^>d?u@X`O>7M{9L=!j&2_WgdZ*=WUK`IC7Op~W1 zf1=K@?xT3utqFm44DSl!(@3w)%e|6W09r-US-s~{gs!@EF)2xp<#{P&5WhefR6lyV z6N?48AIfn+X85^EEiSi|Xj{{LGN)5dkOZ%^l>*`_u;E|V($gd9#th)6pG|Y8YjXoV zjzYUkJ>XY|u(QleB>?*PWdFA=x<*73i?pL5G8$s0voAPke@6PfY^Gx+&0wh|Q!1Eh zc5pU1pS?Fa7gQC-k-G1T8a|sk5?3nJv~1>u_7A`%fZ!dRzfXm#B%eb{<}P1zdNSujmhqi7uS>HR-Hmejvuggd>H%H=s}s{e z_7rdl0a+xi8#ZYIR=~b<6h_;#3q=5WrrBUkBvPndC*W;KX_~PaWARWNE4!qxo!15> zaJnfY<}RqBFkBc(AGj*8m?Hj=4ia!l1!ry2{2qY?_6`42WxNXXYN9s6A9W1~Fbnvp zb3$&PoB|;L0X%%T^8~#_`Qmq<7!NC(-QAkh`z>n(?OVI}vXm`;uH_#sKug>*cAH%J z=>C`Y`cfk>+5qoggiH~8%{k0k;X6@I{p3P{yqVuEl=I}8lKv(c%^R_e;(zZ4XE&ys z1lY4WPup@D0>VE8Tqf*?ycu|$3@&DWStyHh)5!-QR(n$g~;5tBwFM{tNcoFn{At4yeK)vPNsv&?jq>6jn-9 zT`l~cuppPQqitFfgTspdCjyS&><)vr##|SdTa4E&-9XVmg7;x(KATqyxqN)|(dyuC zGt;X!i7RP{<23?E;9z-iQP;<51S{#G-0fJNTm*c6mJOd+xO3@Sc7DF@$K{3$ZkyF5 z80C4JgE%pViHNj1pY3)Fhvy_9*|U5AI0R>2nYMD=N0-0X3d`$=r=joOd$?~4MrvED zV#XqgcRlUG;VKPl4&~3Lk6^z%Ry5+M$b#n_hzAFYZF<*7&QCRAg=*6UnPsd)K>iC+ zB^AtxTXZrqUg(*pQK2#e>vVf(MQN#t_TLp3GSPFHNS#g^mhU2++&x@^?uv_e*gkSz z??#yl1?~jW@H7P=8m3qG`tgpF_BM|X8otS-YVb?yRK38rD}h5s=S*^*#~Hqe$1*%t z2PZzCL}w`)TA|9W1D_rJEig68n`t7=9j<>P_jpe-9ZyJmKdtW**Ag>xphT}?6Xki~ z1F%R}h6%l}k5wrXdA7D)_amBsN%r_`WBkS6XyEKnb6vBZ-f@f+`w#!W=W#XPf9U+< z7^TOz4x?;dOs}>a`dmspeqVB8pE}T4_Prgdt=N|$o-Nvya-Lpwe`pHcOq>=XY3_=F z99@-`LIEq)?y~=^xXGVO*Nr|${f#I%MHXl+ z;)BH)^4QZl%wPq|9VhrNx!x<~hFV&s%TImEte822|LWB*Ux=ok+jaK_Xz=VNsm2nn zPi%Uyk#zd01H>MQwf*;!a~=EHr#PEX|3_VOrKz@~73wxGT0)fL$o^1Qg?CrtnmJK5 z!b}Lht?lI%7BJAmjN#qSm($8ei(n=kT)G?XUWF%qa&bqaFt^fE+)(${=LY-uX*I-HUtz-^nab1O(@TiL)}%piV1cGA#sZj( zKiS&)`awLJx%M84UuXp>FUnvAJ{L2Ud(dZ4Elg|`((^-~B zE=AwDl0Ldtu6n0@{j9ARe(mqI(QoW~dDpL9yXKfZnHwj>zxO62-5!Og|e`|~eze;!it<4+SkhC~}mpzY+DF+P^OFe|5WHT}j zMLM5l2!5>*I?0 z+VzOuXCiFP<9<5TXWuVkz1;d>I`V+hpN&q9gvdrrCYRZn5Blz+42b(Lxg13vNI^8p zk@;kyHHhw_qyjr#55IBhV%JSBdR zn;4Br6EVB9kmvF4S(VGr;1A4jxA2Yj_CcH1lUW#;)tyu7F7=Vlfq1zaC|nYR*~`ZK ze;?tBOPY1by*sA_K~467J?{_2N;-?D6sx2Pf!`UyP1}+3wp{xb?n3$m^#kmQ_2p`T z>(Kf+!l4@CL$(CVR@Au7>{5C$$ZnutM%9tY?-A+t@KCPfE?Vl7!(0EAP8Nfaxz(2`O=@@%Z(mCs>I;k4pm(o zhg+&thU=Hl-_mFav*lR>F?U1 z%5g`}7G>S~htU`pl&0au(o#iaieDGKTAqIgRG|C}?j@pkk9you?8-l6y3aSZzhaOv z&s%FyXU@g}+hzr5k0%WI`xWN)lHdgzZCKccI>our0*&#q{SIYS7?0?rJQ^M?S9Dsi z@aNj0pe)}Cz(4^yQkQD!c6SFA+jkLkQve#SWH6EUkp};1dxzu5fq?a0PD!YX;I1!V z>qJ=g4uBp5oU=1agC8(}X(H{D?kWV}#`nCyz}`nAh4zT%977M}GF3l& zTF>1WL7qsJZXmh5Yi&6(&5&7x+}zwIieK+we*iy%03gTx17R!#Gn~4MX`tzP0ftQzfI|uIx8$?%sOGmZju?`y(^lTA| zk>lXqts}CY=c)1DJv|Icli7PaJjat>?L2g7OLl(oVx-DXJ)}*5=}&Mk1d^w{kOBXY`C35j*pbMQKwX()VFJg6 zW24Xgu+~Njyh3}-BJFEX1wR3(-NR47kF*feUrWK(y7#9s^Sc`8qIOq7tGlLOhlb-&{M1})cr8gDMN3`~G;6_Fxi z!I8z%iPIC)i40J%`|j|wi$H@>sJu{K_gT{7a4Lu^!72sRw2+U5sDtdmxTBl@Scy{G zu`qjjzRqd1w4tYf*slB}DD7mc#ty|GP?rjb2IV-vufIbMu*Aw%CaM`{)z=rF=k*%? zR;2b6C9ti&2uKU=buU|{Pbg%p0md-7SkOZ{Ak>7B&Eb%1f{`#29+jz8g0pzz_*}@r zQ^vioMQv>SuyEcGL5lz7c=rP!iXQM1Kvh$`W___JH}Age8q}C1nwL9pSD3-!ZFt}e zo^gsor$np#+!tlP;$9HnbTX+KeVN9J#W#QA5hRQFJgEJWJ({T>Zvy7&%a`!dfBJ%Cd3M0) z=PsJjs}h)Pa0U9zZ2b9Q3=DwJbv}bLWNoR=hcdu5fTtYj_P6LRcT(JTBOM&r)aB5s?fp7oImQB7T~DJutLPl3!vpBo*XL6_m6t{FnanWm;W z!(Qg>k==z6JzgAvqAE_G#?No1Su{tu+7whxx>VQ8eRKCTggpX(mM{6B;@H2wOc=^T zNJpme6wftKpkFJECIhEB*_p-O^izlGj{!>~Rx&m1cJZ7p1NB2=Yx>cxfMbz@fok>xk?*yi zHsAzGJa7Hf*_N_*-vSBF3alw$pXt{O4pU^4bc283nGduQy#o$uw+(s0D6%~#>c|Rl zsdp9U?i63X@znR#5R?=A%Z?J1v4g!vXKlU1H1~snU~+=4g~`1A3QJO;Fz0_f2vgiq z>{42~qj@o8LA{ePU56(EZR+pgXe^H|I0Oe<8B7uMm*_<{TQNI-leV%g){H-FX;fnFv11^SbS2~-Cn0ru+1NK|J%oX>$) znC5;(9+0oBTKA(xKFXvtWU?z=tjr^qbUk`!rS4v zXm=D?$2r52ef953?+I6VNJ6LJ`Va`EJFc8yYuH?>DY+=Cs%xU>u=x zzKOQuY|N}g0~#%5cNsB&70OJiGc8QrR5t6v{0q*JOYd2lz@thP3zVd_`(H{PftarL z&z^K`hrDxx8Y^dK&P& zYOW`dNDCJ#-j2B?+3^|C`m_`X0%kwM)3wR>j(xkuOJOGK-|LjyG#12mlQb3uf(ShC zMBml~)Hgr6z+2Mjoa{uyaYgSyzc0I*q$%3|8u*K{ZEH08EzzgL8=`pJxo3x$SR28p zfv!R2C>xH7w<9#R$`5j1Q;CfA}v}DD!@gsYTPS&;&L#awF|eW(n`veo=L-(^Yn0 zm-%GHj!<&TY}abB+Hi&gluz(0!@y%g1n2a3OB*nC;&0z@Zo@@$wxT+-!YHfg z@QS>0x16+rBdCAIPv=(&A9|GLUVyHDj~N20wAfxs@Dj^ya|BYzF*tRk*!ts*QJqno zPAFB01k1q@GOOx1+~_3*o}(=qC0;rUR7ILT!!d@pSnTBJD306o)nk)N_I`V!IBaPr zxAOTu?Yo6OXoi*+`pSTo0AKtHzUo3xrEuWG$dJm=xee*yndchJo7f=z_Tc-ijg(Nw ztxXAiEj+*tOl%W-U0_Nbn1W7e|H(7>L{>03-Zgn!=2p6ctG+51vmF+25^@RI{keB% z_n*#icn9O$T%TI{<}cY}c0QGMG~46++ux!J^Mz_BtKjjPW(kj1S2m%1(^Z(FY-Zc} zxd(^-KN1Ph9^%GW2>hU27{It{rDc($wR7$I;Evy-CXr#t!%@oByhljyfc`sL5@yssz)hg1}# zisCtEqg}FXL6xG{AlAtC!{N@IvfxqarS&%p^32Af17CsQzpy4?2mzUaxfi1PZe3k# zw5%8evYbAh)k}iT^iyOvQkArkXQHc)`jKcWn23V=!He|I@>4y*RL#WuXmhK~*bmM> z^FokiTIuMfYzbf3p6t4US6*ZAc8FtOxA9ENf&MAc1m0Z`cus2m1FhBN7|ISjq7^IK z{|;O{WuiA;Dm^VGGM~NeY6<2A;fIJ*tA{sxV}u!DQ+d;MfzK}uQ5Im2ge=v*5Obnh z^NT$1TnQQ9YnzUmM5kNi6pz=>ENSU~XgYXNC9U5Q@4(`t%+h4$pinigPMWnxI1MW$ z=2@U2zt5;Bms*=jm?l&kq$wb2eU@@A2Yv@gGy@xZUA{+3%1}u&)}h05%(ogO(MG0S z#Twbrz`fkzi>7iwM5L=t#hcuY-#gNc4e-jv6>vWS0uLH5)+bsgQc>^N;WH&?)wAn7 zPp3br{cHPt8hYYpC3@PJ01d>38^pVz0JJ1JWWTWKFA~Y7v{`rqCy3_};Y$5H!E@rn zh-k2@vvpDrL2=y`una`paLD0I>E-2oDgwWpbW6^XX_}%K_?J^?2zpfOhu)Juk&jVc zsoGPGj%iR2(8(&N?BaUTF+UGeMqy^Y(uS81c*sa-SNRFET5z?1S4Yc?z#RTA*iAMg z<&OSp{uB_&(BKQ4I0=9Al&h3}tk;~*1+B6}_FT7FSq;I= zM(LZVQ-(2ncVAUeNO${Q+f>yYe5_P*ze$8fgSOqDhO0VMDR|ET!^<`YkyoI~T7`n$ zVDD~EbHlCXzJF(Is!F9aO4W1LfBFZZdev&#?dBIZn-Pug>j=bC2)SlMD4UNLcAYDIo=)I_N zuU4t~#{MR@lBchDcY8Mp`gXTE@;S%ql1LEho_CH6ziDdZ?SqdSRcWbrervu?(l3k?^8M2Rd>dMs=$f{$PSp&pQl~)M)ik{UUuF9x)%xAblZ&s zj`;eTHbKJ@%vsa-uQe-N+xj%S=Qg&{$!1XmGOuIJ8ux^Z&9 zUS(R0XMwI`e#Qj8COFXz6=`+r{HCYAxv1Pvjp_fvk&%kivsgojKD_1aOd@@e>-R-|?d7FukMGQ7K*lkvSN+P4HW4>lP z!@w!!d%){qvvzGI!DJ9CZ?-;yfkh`bbJ@2(H!5x6NuZa|&F>%8x5wg3Kw|n4%JCgb zh{Qjwotx_v26gwXZ3FU|isuL=f{hTR>c!z@eT=9qE7J2s_qrrSb}lqUUcYHGsfAT> z^y15bTNxYf88YhKExJ^913h+poB|}8W(^|}5wN6h=_=X{y>+vc*b-uLzrD79{ZIgd z);OJ@p(3B|Hm2C3tk~@v`S`O#jfkrA%qqSG3o2cRVP#DR z=elzzf7cOB7*#Kn6uH+N`S<{y-emWcabs&n30UE79IV}XTsuM6tQqVux0KCL6?==0 zX04R`oaBpqs?4tf^7q~PU`ZwgKL;KbrlmLo!DghcWh@(`9?G`>*J0TJIo6OS*tNg2 zar(x|m4n_=4yAgDB9&>8E64z*2L39xT_f9V(G4XV*-Z9c*czU7j|j}OKZ1V*paa2# zfwlrC@6^z#En= z^KC$ICoY{t_wLY zr?cwTJb21Z^uP)HPBQvwlakz=yLYu+2oRU)td0%5T(w^{2HO*!1Hl%ERxp_bT&f|p)zXGWs8+lnEt9Opmg?^&33 zl%3W!mDlzj|bCrpa!hdR(5 z-jC?;hkbr6-VvTJ-C3ES8T*o+-s-tthky7r+l;5f(`az4-F9BE8Z!(f?U~*uIIv>B zxHC_Nr)ltXy8Gci72>T%|IY7<0bG6UnT%&o3SO#0+22K*rmpb5avPL4+mq>KL#wJG zn)psvM}s$(gVC3ucTXJww-bJP1Ys~)1M%5@{qNIKejU*LFBp^Xd%P3lkP{~U;@6(^ z_L}IH7)`TpK5X}ece}bt;3GuDi9hF6nV8aZDA3T26@q5B1DSRuea;*tCTT$Y7UhfU z(c*xT^JXr;^LL*zzVhXujYFzpm&XpGBL5CD{n&se@X-^d0rt>jI?o0tAPKGl+EOfd zS}ki+iAS#OD(|LGqOs>Zgz4{4e z^;iwze2v(9ds?X0bxy@=M4H0I9&HyNvF^#Fr0eS(gTFf|{-)LH0FeGF<4Y&a)%C#9e>LuTcv<@aeNaBb$L~ax_u8>IRw7PeodJ9}Qb)=ct0r z>OK!usmZ;tCZ`V?e-c7HTx3G>JWuV;1bkw&tPx|hPRa!)9ucmLq_VygXfZz}yYD_H zp8xq0gM6vAS=U@{?o7uc*z?2R*xK2wV;m`tH-o8P4seu)Q~zDqb^;{ZO1xiir*-f1 zAIO|J=<3;}sx}E4)~uJPT>imoy6sd{J2iP|Vh|AXhw&$;Cy4$gDmU!?r%{pa4khY!Y2j{Z~t zxW>TSKg77E+^(xGt8|0qXqE_N01GgS%skM3D zHruQ`-aH}zX5}m0O=>3H)T`9B^4j6k1vb-P&#V){n`iLLj#dQBA$)4oN5Fz*}DI$!bKH#*~`8i`$LLE|Der< z&VR$Ve!_nK5}wZvpZI-WDmtqXLJJMVNVCp&(Hl;^^MJE9hOYnrAK8A~9nVv_A3A2c zj)%UwU3MB~{x6CpCg$BezJzPEWN^K!8EU~=Nb9<{g{61wsm469q6@zX13~#dM^X^A zeQ8p=wi{aT)b4Ab@yKRH%HUdX;>NNAn&8UfLZNBaJhHu;PZiIlw4G-+cn*=#V+h4x zdqIJ8XQfUAzz`j1l`3&MuEQMrQ^w(6`P^)nIJI*luFkR^U4gz!jwX)V?r(sOD{zI~ zUCDQ)d&lLr&YOPrFY2O;3{-uEeMqVEoLbvnoN(pS7JVQudBOkr?*W#zw&H<&q68E| z)v+9oG5bW8Ox0wvy&TM=oo|Zo9b0x!ekPRCI1OY^8~!GYfPV$(kv zodo|3E@dD}qrwxLr|Z{m);^D8F+!}jLnPf_f#Laz3Z-d$gQDn z%^&4GYs(XF!2CgKL0H^>9v#N)40R;@4j#2lhjoLUM$|Yg?12-M{>T|dozSO!1Wa>=OV+205_p-$RXwLp7x>E03JETapR+I%p8tg-yCLW^)zRH=3%-`Llw4>BH zHijs%{c8)y9s(p1PghF>tL8VkjTntUk#2fzNmh1s- zaXB5fUm;a=Jn!`CldnKXOKA;vL)A2VGcexP!o5s7-V~e;~CQWH-ohe%pGiCg4eH3Bx}U+ zFnJipq zo;D(yGYxGjt##*hevZ;qpId*hb(0zJTo31}x(?oja)&1MW;t7ol2_$_cnJ1`QX+t8 z$mpyu3BazJ^cjSqXiLkR! zuJ4;fP9H#RqvKtYYuCg(k|)Ps451;P3liW#6Wrboo&BP@ z>s+%>a6s)c8W@G9Zjdwqf%~28ntN&g@l0g_I_YJ06awGq_%g$-;-24*+XRnEoFd+0 z$2#dAW8GM2S10NyYbA%b!TMsMynLa3m-os4MN{toO2}*Q8Vw>O(ef z|9S5w8}<#IMAaK2u!yj!ZyU9gIj3Ax65V{k7rcIMD3sp|L{=UxCt8<>=>b(CK)4cIIpkr&W zsgC<<8(-jQhmYQg97uV7_}tvo_B$}8Qs%*K_&qCDLyyP&Q-P9hm!tcmoX2gitz$s5 zXWONxT>}j>y$SX?=X&_jiIV~#`8_a4(>Wtqr4h6;_D=|D_j`y=Q$}4TrN<3 zHquJyJKtB`q0NG7{027bYiq+?cTh#}vK!q_J!IokcxUAlJ^NE=;Gi$<^T*2byzv`8 z4_SlS_;Pmhsif0VNxPN|kJQwt_l(*(W)7{yQTn&5X`lA|dD230Uo#1=OH;rfmFq9f zG5=a-SI*uR%Z}Jwu{k3OK{XgUP7^6}_m&dE9kYGgRw4P?I6N$*c0E2`G%u?> zh_Mt(8>o`R9mtsvpmI~%WTngZCxlW`3+30d_}pudf9^)H6$(12DQ@ulTp zGi^sX69LwHg2%0_tOvMxlrPnQrzNJxy_IyQA95m~U+I zPh$>+qWacxwuYA8KAykl+|BHPynEHWvOxQ9_Aje%G_nSCGX?y8)g2)JSsPXiq)35M zWAq=qVIaO`?!BQNg>1PyLar5|f8_L>V~oW_ba`x7#$sj%FE`wk z4CiAaDvs?7VWW?2W>}w~F7y?&dvPl>J;rrKbjR*}xl#DGgXE2OYp-|>^e@Qdyxm%5 zAf;pAzNECE$=}uLrVYrG<_$9jizw>JuCNDw+0Vd=M?XG$xfm{1#>+k}SdIL z4y0+kUY^L?+|5^$S8JNFh2EuoEy7qhivDqk7ZTxBfk1`0RJr(Cc-|((;EjRE3*ZU# zvin}LV@~HLz&{Uxh^lg>3=na7w}BnFidQ&1JWMu{_j`d`o9DibnV)( zMk^xwHO)loCxD0k>;y8V+>*Q343`v?i{0(HC`LiylR6!;XaXo z4tKnSy9Nq2zzVx>fjAWng_rt>cL=K_a=rqMii}LcQ8Gw+q}fX*MV`CuD`81d;|U2- zztB5o77jY2zBg(!Z%v@U<2qmr`;aX&$A0?YcD)n;UYS2F+FH5pV6o%*BToGix|LV} zuWQuWZIy29ue!gLj3&X0U;ncj_G~)Z;MiOc!6d!76Xgqsl5Tlk6xszQJE2{${d;w( zuJwZ{#oA0KqALtEQwL6jj1#AOdxti{pjP8wI4NgwUtKBC-y_2_*EqJzSd;?7{t?J9 zRQhiGnxdk*9z#ieUy5d7nHM;}$P82+|sm@W-x=PdsS#4D?5DQ3Ly}O!La=+t=r5J`s2)Wg=!ZJ<8%!U zUZ}h8;PGRc47F<}+LEzqBDqfr)Xm@af4t)^zDGY~`Ro!3H|*8N)kD**Mcd!ePHqvv zxnz*cL>M~=`rAf9#7%2GQZ@hEr)KmnoBZ$$WazN$qMe-bvbfzTK9VdQ8CWDdI^+0Od@rybA*=Co1bLs3O&Ledf#-FStJRbW2 zlUn|py8mRcD0T=;hxCG)Q9}|alMtyNeEFTS&SsNo_B-&1g<5rhsgXSD2E*& z!TErrdQigJct4P6Ik#F;10(AFKOSNhw z0bI>Mw(K?)a8Oj2+RkzGmymoO8z#^COytc|uS9|SgvHrqEWUi+ zYT4bmP9ATXlKINjYyjBIeO;X>o&_^|!J(jAubMlwneDzqWh;F|F;3rY_@c_;sAT1R ziK%}Np8ul3(<^egn{@NE6ce$qxdAlq+rwGO#fKZ&qrp=6nP-z(uU@I%Yw&QAC`wCc zjp^S$p)Y1{nO1iJb)^~f%XGX9(!&oQvF6!N*$D)DiLEG&CzbMZM&OLB9van6 z@q1NO@{jo{(e;3c^&P-svd;ju*<41`aP6#T+0XCk$O?rxxi)!=LDKf*xEFYn3&AoC zRcA~-WaUqPbFxoqINSO8#udq=&y<~ELI;M9KoVA>K(d5%aIKX>g+YT8dFNhc5noPy z&u;hza^=C*eoE`&#ib1BoLBq&FTpWQ4+1Dm81rJjX!_g0)X)Hv%;q9r&V~Zt5jw_{ z1T#2x?`!P4a%wOf9{gXn=)>!P&aHhy`(u^Y%;j`2&#-q^TW32T8AQivL&pMp?p(MS zJ6*?v)7H`IzLIWyGBKrfzBA;*Nq${WV9+{_GhO%&&EgvfeQP$~h6r;sjk zZQ}AwG+p;snAEl z(BNjSqN41BX`YA#`Pi<3w4`V`;#$z@H#8UQz}pw7d5%GtzGc*Dyq{yKdNH_M>Xp0}uQSGWO=xZY#Qla3Huf(!62w=fP9tx%Pr|MXE2(Vs@5n}&A?zh> zw^0{)3IHEIF)9bGtVr9hPq0Il=~O^OiYi@lk}Y+eI3@NqSk(M~Fy4v(BCk;x2jImP zw!VB#!I;1Z%(;Kr!EfVUHKO}nOR?*qcU((Mpbc{Hv#ss=I)`D( z1Zg}xy3)m3HZ~I4-#rSb!Qc-Eq?U)yy_@Jv^h$R-r^<2Ao%jnR+r?VHgsYv>6 z5CPt&xT9*^;52JdoKKs2!n7{`PLP{{)?t!ECwSj_W`p4Q2l91{r_*2-6Z#~$6Kn>V z17%f{zz~&&?a@q#^aQ~Nx8HqwTHsrh_M5w=vWuU?fC{hInz1hR&ze)>;civv}4hWWOyOXLd@n|#N30iwns=Gih zZ+d)4!kga02|3lCu1W1$fK@$U?>%pIiGhXRs2rTVrGK zg8DoTy0~O_1iMN%BeLiq8rG|{&Lvy103pi*?HJQk=vgq>kaVQP208`~E6FOZ%hb$& zC$dC3j=#L3@#h%uZ`df@PGs$U(@fo*-gMa&bkMO87=?vIGZ?kz^rFeC%COVoRP` zOILmxyV{*v5;1J?zapODEP5Qdqxc|xCexOmrZw{i=~b{hWV08O;#)Eu9GRwlA|(~k zQjw?b0`KJ6e}AfFGLE>J!rQ3qUGjL zIGME(wl@#S-+{UH;}u1YjOGkj8Vm_@Q$W;;b!jQ^a}3v#dmv7TnzQ-v?Ze{;e(+}c zH$nGu5cty}>z$g3ii*rirUvySpfpQH;j>|QX6^b0D{nqT@Q6sG{5^%ZRvnvVEo-(a?>zeA0N}&5Do8QZBiy0uS2DY~tiYtiHk7Ym`I-ZRy zch*P?JYwttf%^X{lK(4^(Z;)>z&YBYA%nk^p4YE5f0V!XbV`YQ=d(1!PRmDU8(EC<`m{q-DD8)o&+C5=(9eD35H)+F&^+w zV8-YgTiDkRk~C|%fhNwT;=5o;2m5yJzH`HC72{H^B6aLYu=%d=*uG4~`H_I*s*kQ6 zVQ>jHcSKEuF{1fthSR%Gn`e^?ee5D1cMN*bK~!GKdG7@no%}KV^VB|B@MtN9Wu<>)E(A%=u)uSD)6N0_ol6{l7ridM5oU+MKrd4WEb}R4%(2 zu5S*2$su`bKe;5FX zx)u9eC~){K@UF(zFmE?H%Vur3Hm@}Zewx?-4tc{W3dd6dB|2W9X5I3+e!XBil7gItgJ#4o)aCKUF<<%?a-{7sD`ach#`+sI> z{RKj_l}I4U{dj%bv80l15gAIs;Ew#i3!S3xJj}lnK#H&4dV!WG``ArM1nQ()#wQh}b>IZ8JcCCxT*7TrJpv(6J!}X%OCkj16yac`n@;-^)S2Cn(H`x zHIL;xqzRo1m5ED8HK+Cd$WSFgFOq+JqeYgxLw0dA695XqddZ{Ug|9w=dV0_W@2+w) zXV;SK$~aKE@#j2+)|5!AZ$nye_8s;PB}~g7D&_L*r8yd52OhNd#GR>5S;mCWMOWxFa$Og@h)USYn6)es z#g}GFUte58heaM$0qxM6XP{h#?xsNa#sz9LMG!W(ViqJWnpvh4nWcp1o9QXoqi8kJJ5`2mV__mLokR&w+Q%4=RIzrrwV7DzPD|V0uH;N& zUh%`DFgsIrAh$`9oZhYm6EK&F9_j}?SEiVSFMEtBFpAXueW#S>jeC#B6|N?u%k%$~ z6t3#V=qDlRK;mU$y>G0J(B)ZYKf=D0;6?UpSl;KQrqG!7QNy-+|9R8@V^`f9r>>;` zw&DqN>4>MpyZ1uO`D7$mDVMczX>-89xnmxcJy^rJ4gAhxJ!vfMw*bx)xz6&93&=@& zl&T4j<7m^CxOzs2M&SJ%f>!rdi@VgUv>3R{+PBlU#d4263IN#J`vVRdbh%-QaYOE7 zO*@qHP~ifhz}$1ch1L4RF{U`LH$_(zbXt<0@Fm>?Py{1*vqS06=#X;Ql{Uy_ldSBr zCmv-E2Breu>WQ*(-dVfeyF)=~tW5tG;LF&>{FhA9DMfQe7c7)l$t2Qz`*8L)YVXj9 z%b4d7INyi=%J&I!*aE+i{YS6++ywFX;KrcebQlVa8?=$%<`@(txYOUCgWb1_5vOM4 zd39clK1D*<l?Y8sLi5+yWLIY!m){(WuCceiO3;$t;Xm)I_OyRUwdv$8iR&wNuvuSEuXXfcH7!I#G;*j7)u%Lezp zd&NjsDb-S%huA)}j3G9Wv(@e(hTPo~21H9;l+ReEcEVr+y7w_PxvFPn2-Jt=moa3R7LO-PGlR`o7D7 zeGZ{0d4qQ=BuSPFwL%%M|7d^U99LMB9&&a$b0y)lThC5;WMvDEA3oz3Grg*hJ$vDQYtxr~ooHb+U zl!S-r^9bYYh4ejSz@f=R&%3vGmzi`^p3um6%`{U@9#8IXPe-1AeDWtK``@3Xop!~B zWHM;SHNO@)*Bs3dvSOMuJA-~ExOm#0trW1%=hB-svtGBcD30D1KV089S${S4_q0v< z^ZsPr#*tvqJZr;hUBl|vwUy=Nn#yIW$!ze$@+ls#pOTO(f#6NttL_DWf$3nwLWLC3 z79RsfBg;}^Dl8=*Hm*HJv|gO&xD<2BfGLwF`2~Ob>#C$Ci@vj4&7zsKpu8kwGy8o} zY}L*2TiNJ^mNV8)O+S2ZW0!)@+5FrKU-f0B^qYXh#ZDyD5MC?PU+CJd|C!}b2sSb5 zCG<$3*!+PlufnE!R?zjwm1}#Z?1oypU%u^>NEmGAw(gs8JqE3c`YHP5j0drN%uh~ zT8e0U3q>m8R@P}P)WlIzi|l47*ya?nbCYlCXJUtIKqEjql<$izyRWWaip`B$WPf|A zGA`ABX3E>*cF`Y#aWaap2OcX(7dqx1&n$uX@?z8uWkzvtp4@l5BHCuMsHD$ED zaUQRKXHZC`+R4FO{ZE+8s9%Hn(a$_J@Dmh|{b5d2+os)Poh`Vos93W`9NYWz_k-ea!IsxyE^x}=Onu30j+ zmluucfAYg9DSrZ)u=mm&cw)p@6UJ{mAT#38#Qq*Hf0 zcesGS$4O^;=M#T?_|gX3xw@jyx5X!WeUi*UN1Rm0PHqYp6=<&KgmFc`l>l^EH&yei z0yHMtEINaJ<&K+gf=fRcrb7BMsD;aNKi%N*gZbR2BFRb%X1WEkju>Sqe-=!ETpgc2 z1`U*c)-s{W4|z096=`byi0_DsJxv*z9Y)rQ>_~kP9;c%C&T5OTKW5Y4PV!La5R=GE z=?k8hYj1Q^dZbju`&SR7VK^y;(EG}|sxF~bq27FRS2@HO07EG>^sioX8%h(4)bV-d z&!=nnj?{K|$s!y%lF)v(J#Xt9EluBk5k3V}G`g#_l|d{JFenpjpv{btMufU$#)uVR zZ^2n>pX6ca?Ssv()G2VtYvc3&_CR9T@BY=7qR-9091V>JZ+un*&4f$c84B<5fU)|gXX+2r z$KZ+8gM*Hny{c1(XyeJl-T;DCpWvS+!OcZbu&Z>v{N1}HNllr}z2Cook5fX8(rR1< zHXj!jJ>;VEI%_32#xZ+7^`BdYnA@Ci8HBY@!Q-;NX2t6+SJ}<232EJ(e871s=}bVd zfGR&M3Vb+mg={XbZMyV~QN?;b#6baaj564T>klDf^KMk2Js9>7&tgcvQ0!Ws-26E1L+jner)R{$@H! z114Ih7tbKLWdL4||x;-j1@nq}LD4O8r8(n!LxZ`|^e_xMrejh(DA$VKL7jE~st zxAX79Usdk*TGw5DqPf(2t%s0~G>`Q@6oA9gueL^2;&M|8>0mZ@e<-Jj0ygN0UF=P6 zJDg%-sx21j-a!G7H{Lltg@f?uCvAQM#H;%3_UU4^D1YTHqZfFA-X2RbNAB`EJPyBp zh{=6#effF11j?vkvnkm zn;(@uG;vCA%Zh7aM@7SmwhZFpBMcxwN1Fn<#-MP*H>br|AJr5l-wCl8PI0X??3o#Lxmw=z1OFHk`o^v(bGdWH)A&- zFjo6(a(W%b6!IP%>6^6%uBBJg-@s$prT$*wVT z>W}l}e#8FOX*t_z-(>yb)VaWA16z9E{k@WstG(Ik!I0Ip<>lp|ho7my2mbfC=6$#k z+}!%rrWrti1=-lG&tbdc9i)@8>t%Te6%W1fU)4Mx?tO(S_?#ouX^ouM~g$*o&P-!u^`PvaI}B z`~0pTu;BQC!PC)H!ho~n9Jr-hrQabI7AUX!XpDb#aV^b289H&krobF%%m$^*E*Bx#M$d8kBh0ik~N6&01|k) ztR@`ZTm0hV*qPp*G%O$Gbqd$C9zIIIkd3?KrK9cCW?@C}{%i@tg`E5_b`fRcPOV*&Sxp9Zvo!C$C#>59^5cLCHO%Z*dupVUmfh`6ZF&e|ai>Ur@z@GI}<;?xXG7@sw{j@JZyyk&-FhmZk3 zHrFn0?un4CO#mF5?;W-FB`dy?c+uLh4Veti?24K<<&{864=>6-yw1Fz~q5$T*G z;B=PK)vLPDP2gFT!MLCpV>5mFf zp%LO$uoKUBV1BuRA06${^w@2kkN-tX`~g_!0WHxb%ZyK7?CzfR@f{-~49AkMUB$cps&~j8ny`al&xpI$7{I|zm-Kn zI>E%i4eIxzS+1518hL;G5CdK{MHOWcqH8CiTI4OYXR^0J2Qt1_6=uTCL=BE$NGv!$ zNU}-D=XioXi4krB)(|RZl29ycQNpPR*+fEYdav{QxNoE!f9ff}i$?-}6D;j=enT9S zXbWB@3hskR&2;7!+A(?6D)+IJC73vU9W%!9t=pmRn79f=W8RgPsfw6k?$?-AoXKCScWqFu03=3N=B37#n z7>op{NKiR!tcRkAW~{W>Q)z?1SKM`<1ye;QL1-QmzxJdm>S?Ie1L6yNZZNOn0xqrr zOkJxS@cOC+Mmg5^pwHM&7IQ9ntk)uh@9gaw1;Ji(j_M6uPNPj z^~B!?Bbay=rW6Pe^MY6}_2qdx!P>V%`1(2?8{dd1qPZ18;*R!a^a$JT6sM^EyEkUu zE*i34eRH?V5`KG}{7gKFz4@o*=KRfnJ2#bs_aHK1Tk(g_ za}Ma%YgY;H25))ZLEq_{QRcrBd^+!{-&$&%7$0}wgjION7x4;#Vj6aM+St)eTlC{( zm<5~zqy%1ZT)pbj=Wd08Dm5*4ju~4Dw>F2@y%5z`!Z4+@LgHi%BP&Ctn$K!`H$!N_ z!#v<>$-X=)qrEhoW#|H!i0z|8s*?n5S`AXHI@KVt3N2}Tudw`SuEf$9_1sKsWpc-; zaGNuzGe}{1CpkAT$v3r@>ytWCe1-m%YF!lmG&_qQdv|Q-`ef;du%g9K4?wMoOtv=z zKy$gok{mDF)mnnzk~Kn}@8Bywp|sW7jfYx43(~srfpyxW%PCY%2M!pA;ysU}Sl;6! zmXy#+#I`^s(0hFShtAJhKnPI_+G0at6$F^KtZ2x@(yKn}4)-rIQTx2T(NFTT!g4Wf5lE9*NZ~bCR**;mREHw$u&v{koFKwepw<*_W~l#O`Xf_ zOoqSvWWoIR7M(N^=Cs0_F~P@`xe(x6S(D9~S$tu5_cApRnQb@Dz&mG=2tWb>HGyPn zo%%xj1L6do(Ds5kr|`MdlB*H2LVfbSM!II~4^8^W$usj*bCs1hTKrC(PP8gBeq`c` zSSFWbd`Mz7!_INVD(f@hpp+z4=0aquHdua}W!jsZ0^1n7A!R*RL9X{PUjoNSD?#xV z*~qX<%=mj3EF}*4({;su&wo_d#bu#?YJO6 zM(Lg%0OL3>)%*l71GZ|(=+Deg0TS!xNWe&6$f830zY|dR+e~v~lj6o^_<67WiGZEW zmh+B#tYYB#H1Gf4#q3=az_S<@8b-2o$?wx%AUJMS`f$b0<@;QgdYxxzdX13`Gs%Z_ zha(G=#M3{g10*4K!}45D>DB91@@0^BTjmUCP1;$dNq&t_=(7my#RCCwFHR{8B>1>9 zBSDP|dqyG3UwzOG|LsW4=8_D(Gl|8BtW;Opxk?= zs`{?;yU%)AU**G2=IxhT+aii3T+bCJJ_C&@EKETr@);n`Oz(u4S$0u0qmReq@AWZI z*py5I&JDWoZR$dsl`FvWUDN_Ac#puB!xZA)CE8QxptqXGiYxS|T*j=WA?Z3w^}Z*# z>(&EI`8ct366W~Qnxa@jzcY@{oIjhz~SgiUBFJ`6PG~a;|lR$%9l<2$Wn3(N(o@sY5@fzPi_JL z2BXNgG6yxDwa3p_YB5Nhp)*CCsN?W@wq7!b-L5V+{&6-U>5<%LoIk(n{Qk1jSChRB z3XfLF8DM?I@dSXTl}}tfSdpyfD9Y`0tcMbG=Bzs!y}{pvXnN0G_UNtA84*-A`u3n7 zEP2UVZ0APJuR;$nXGm=G!AvCMDdD7Kjto#XuN@F3RThVL1Cb`w*gTjR*{@L9oSBfV zMPmRmIux~WyPiUiZ8V@^yRmKM--hGsufhCDF}xCeHNjIz3-7g1M-%qO6FEB7%4kv=Hzd& zlP$r2|H)5kKrJwzz@AD+ssI15zkE6XQ4YQ0&AcN`w(?+r+$ zhLJk75whB*9NlNj=s!l|SENP+e+w0(c;MEi8*}Ji-SNhDP{z=LGgA6&7$D*uhAX$~ zHud`%wxr+bn!0M%RhF<(Q;jjYV(~*q%M*P>!l+%XMlDLuoW(@ri=g`DUyp&r1HXGe zx)yJ;=Y2Hf_X4%@te1~NlO>8(lQ!Fb5dwy|_ANEX`#NwZCw(yL&2%4e_xYJ~{$MRZ zD`98Dz`#Gu$*kJ@k0Zj5phOyoxi<%D+Ffo(2QJIDTGoDQ7e#j5J-lN7(s* zf7mQh$Vv&G)pdo5?T1MPo;PiS#y@I9CpM;5D1lN4vi3OV1l<;O^4Q+3PXD*{177_# z9nrUPeLPi(FII>3bbkS$$ZHn|^t<5J2@ep|@^hbsw&`E+4Xu7`P4kDl>fr4;m8Eqj z75nQM4yw#QrneIkJ<;NZVi#~`4xx@`h0mebOy`y7F0-CV5uFLTk!fiO+8O4QDk|>7 zKT8mtx!e7&Bn6g#ADsBCtvoGI0!BO(=RY13khU!^F#?D;{0C2E7)%k(=-vT?L$yss za6m#`UT6ADI*J+4{M#p}K3+zayimhb^6_mqkn&XUi;g-_h!JKc|+cxxy-st zp`0b+RFw?bPR*Gg`WSZY%dX{@%Eu$Y>oqQYQ!y>VAJKnTYj%274}U!RYbFq~sgWHx zOP38UmR5e5Kxpz*e}WFLdI$7{mv>)I{9YY!t+BgHz6Q4z`C0h#kWi)Doyt*=DWBt< zj|lsjF#KVrIxFGznJ1szG~q+NTmE0uzO_sb|EO;yzl8Mdvm1?ZYN&TK?GvPTye$vZ z?ryMBqMko>;i1Nka8Q$W7cyQ=)R@RY)e=I-EmS+>BcNS!zLDt4QY{u8kfXK1vcn=F z`UeeNPip+_%od4`TTh&Rtk0ZzRjjZ}m^{>^ZB`PCy`*y%-52y8CDrn_&zIAe>K}w* zjpv=$x>KEm19ykg-a|zz8}^?#|DSWg7GMB|fR&Zth226F4<7H&%0y@xbyec}Q(zrB zYa-5T4mUD+W&b{|_TyMXoxlZrnZ@XJBhpu}cNiF(3o*I2i{-jB2QWOd#^awI(4b6V zwN~3in_U;oS-D>)5@<*kBTMeOR@x@F;P9_;<8<|!Z{E|#*n{NNiQVSQp5?W5Rhqu) zM``pDlzix43KyRkr;`Ht`Lr_ovXsv@Ubv%H`Zz-(?x`_jgT+39B?4$eY5#!-K;yTN z;8FiGLhVKnnEBh(_s3hrZ*0*C1pg}i??XQV`=1z;#kNxGBUHl12_)HKDM%Nnt>77W zQ)Yfw!6;G|Bl&?%>0-D!3F?HnA&dT4jF(a)kC-2%iqbugr0SREGgC1mXF|HbhXiI) zpth0KS_UBv0zdd_oD|ca4?dpK=jvV26dxtN1F*V&fW&SxA8&HaBULwL7_fN)JypDO3`Q6dmH? zLAaR9UPE7iYa}|Rr72sZz>-e*ToBeI?Q%<#-NIwcoR_yx{G~oQ!_-1gS?2Tt6Zo~v zG@@)m>`dVJ?pTV>1oT|fOSO$znRYj~JNn60t2HB`dkYKZSxBqv<&f@#cIsfD*Gh9azwlCkaA9Z&omqjeI$g~OmL!4 zYuo?g!yGNg|HgkgU^sp6-#8jbwAuMp1JKbyA%HD04bBL1#edpO;{V?EH3$?hOB%nO zJW~aztGOC_(d|PVk&uL=F7ikkqsD!{h`e{j^#i@htNvep>1(7nS~4h3)~+GXM@2ftY0k!0p%v~n*}x~dfw;Z__bA^n|Q>)`+@c?Fp}$@=GsDrcpWl7iO& zO20gz?5pdj@t~!ThZWg=dgN=*iRLU`P*(I4JVSfX$-BDZfw?stY|V^%JhZx>RV={nO2yDrBm+&0h3VUztGiqCc8q*fxnK>9}tmJA{_bwJsaQebw# zL%w=lKdq{(V8|M(2&LH+)kjm)eZy-j);dJydZ@T8vuTrLGkaraleP}fbH{0Ij!d#f z@v?wj$6`ddS;A1y+x=*rO?PN&DKEr21pA8=z(W<_8JD#brxz=QiR$%*{e7u+$;TXGH z4V(yBqg1uN|KPrWcJWKS3+x{W(oDblsHXJp;I@BU-PArPbhHI>wyjU{^{0KI>po|) z)PwhMC|xB2oA@hS?Nio6JPx+zWfPiuD2=P6DoLrPyl1EdeZ%e?n`FQk&Ywl`x} z4u-hbOLYylJ}b?fiK-K93D{H0^a~PE{37N7Rv)(zWwJzT zdRo!FW%aCdV~Qm$YonGO+3CwUW1akZj8=nP{PdPKYOvpDqNMLrR*26+2#R#FEy;L# zug{7FnOxG2w|8Y!4`9{s`jCgG$K&r}Iq;Q7qk!Mi^7E*BPLp(>(b|PGD$P`yQVN}E z{`k=X0pOto5>*dlyki4#wLq?5hbA>zHF^mT?4VpxVI$LC+iE@BLPE@0X%C}KD|A4j5YG8xp}pO@Y0|1i zvgq^50otdsaU;0yyZ5Q|SF9{+3rnx_$O|wHocDUKpVHd6U+bV&{wbd76NDWB)=5Ee zGb(1I_6k-1+-RZa7yf_jfz~8oUIOfcehQnmoF=W4z7F}?j~FfLhN?%-V3puC?XTMV z@k-~B3nzNt(FsCvdgUQAltb(sr>I5VfOW_?&ZyLg0bw)C^T;x;Y?}DOkGeruA>dW> z!Z78S{G$gvc_1^e$h<9kJ2sUa4rkF6(vJ7w0aHp~&hpIb{*rs}p@uCQ82oL~x6=B? zt6IqmglQbaK0`%oN*9j24};}iHohlqBvzcLQ-$_5)Cw?0bVsoG2&6r}3?HI<+R`+m z@a}x%seJJwP5GStS6BTQHv~2x(Hp4LIPy-uZdJ*;$q*c>+s1FmP>@G1eXZ^-4r;Ps ztO%^>(2&(Myws4PZ-0+HT#X5IuoF;Gew9$MTeKQ{y4v=wzCH{lieL3WYa*`jH81)} zSk8z(Pe;Cf^V+{vUUMB~P%v1y`Vy`We(KI3vlwVg{TUOE9MenD&Ms)Rzol9=>V}V5eIkL2MvcUpz43~p z5zM}wbvt}Na1<0w{M)JD|Gcr#hyM4cuVxOTdJt`UKm`FCOD9X{gSUc5!r#i~vxC=1 zQvHrkfjjlfMvrDM%LLKo{FxUI2ql`1e?DHX3p$v2c3z!#?t6hjJvbm|8jjb&FSXv7 zJ*5iCKK(iZ zN4jj0@m<(I&9=zsg+tHT2OiHkw4=;^)mYZFa3c}ZnNnpWZrN>*U1egES1VP8*uV-_;K z8q;I!h_JqWeT7!cPsz%3@|o;-j4 zp0{#X*AAh4Lxpuu9Nd_8jJ1UBe|(CCg2wCOR8V2QB1eij_G13cuRfbI~GbKMQ%cgsgooUaY;Ke z3Sc*d$ z^BadYBw0AkMedxUqyk`Cw8GVB;04uypXOmp07)h+ z`1hyo`O(w)?w!!*#xZcPx;6k-+2)U`298pZzRjJSe`+}(d=m8Y?Xy5~-K3x0Oe(U% z!+`Ybs*cZsJh{$VQABHML)*v*a0ZmnTWlua@MYv?{_)A^!oYv7Pn%lM3|o-Nr|SaW z2d$~L&|6-OflBq#q?#)0g0mle0*F^7;zHnG0Ggo6!&TH+$A+DDK)C_HtxHSJ!g}%m z;vm-}`SNltGYfc8EU5&VbAD0P39rDrIcp5Vf&y(6qeB}`j17tD)D-vF%S*32C~YR^ zrY}QoWprGJ<_4E~yJvkHis^B(b?9ckj%DEZMh)(1$vuG0a``@lP>qhCsakYYLE@QcjUJK-gB0e5eww2lS> zF_!y@p$)qe?1rHGcktg18Up6ugAy!I!Lk^n!4lQ#-Rh-e)v8%h7Kg$O`?vV2Xr%z% z^d=HEqSlx9)kiEuNPE+*0R_%BlFxkmFb)Yl38KY=r%2}IUH)Zaw3v*7Itdbe!;u9)xO$o14MvejNQlwbDTzO)1I z9eSsk3(U)(*F1D5V{`?AqNV^odiq`1%Dcd}D>k5gHQTAqzpSPv$Y|;dPx18*mah1F zui$O}LjLM!tD(K+kR$z-u#j8q;-CzQy#sKPkB-vIS67FL*K$TntfF1ffrH;bQ6*_VpCt1NuadNMFB9~s^f-p>CdPSKYz7AX=}Vt3~;#gQm;+vi4HB$@prdk z$FBdVqlgp*t~JQ@NPHjHN6jhsHp%elKK!S@k@>+A&C=H094n#EYEAk)O!-UxfWIJZRUdUu=}PXEQ6f@h#6gzNdyn{T zKtN-@1ZIE}wW{?^R_o%1T$BZs&9b!pOpybl8L!qJwA<6CT90JsC7J6=O6o36Q*A>W ziIw^(+JWfEne3$=f`sMM;&eu`qHIYElRR?hMjT#=aCQ&+6zP#5nAFi815{K8ZFP9G{S0AyFP9)rjsptB!;`FEV9 z8h|;KI%u44d7_sjLVh_4o<42AbGf{yLiewtPr}ALsbtE}n+lNDTg zt%itJFX9)URIEB{!`Z=PJsnj1@kQOub(Y$YzBX_7n6o+YnU)FETAr%q;e;Ga+%Iw_ zXgcYp1Z954Y_~K`=8daSmfySWr-Kuy$O)*D3Jv(Y zb z`Lwq_VH~h<7Ka7MOXAukO)+%&h^Ffgrn6&Z#ztz%gjM$}<;d4c;90;G%)>DD>F(}X zO=a%b&L>!8a)BgfwXC7E^dOi7wPx)3A%-L5*$VGfc^GiV{EVsbVkGPGN!%tyLG(Ax zyJNubPu`(*QIJF>VbSo9t@avpmZ+xh;inJ1rFa6JGlf1*{$)+WEenujP!|1_7zSTqN$Ns!TX zB}OO<6t~BC3Uz{vTq;r$ho3u79oT@LC(;YQ|3)vm$1%VG(?|h~vR{wG?%eWv1ZZB6 z_vj^QOF$X=z0gLppI5YcXs4;rH$BwPC-thj~{6j&U%f)r% zgiiQG=&2RN7w?`1M{fV7d~~PpYU{fjyOK70WocW0wvp2z5S-8?$6=Mx-$b&vDB>SWf>I{7*^Cb z&u2#Zk|eGE?eEf|5QbrHoZ?`d(;S_|n90W#ntG zt1>;0iZf$6oitg_$@cx@XQT$(dI+q$hlk0~gyCz_rEO1-}2bf|lWz3e!zf*1EFF`5XokW0G6qhT^{Emtj(3wtZY@KQ^M zlMkEw^?6H@l2OqI9C1zlJrk+bTm6?7-#$n{0gj}QFd_^mKE4$TAa~;$-e1g0b(MkBt zmu%{vLu2fc`QLpz{ls&Gz9<2Pz+s5@t;pNnT#uS^}zi{I>QIe)VoSO?dDNzfP6>vI}G`c@S>R&~`c((0rC zM1n86%?WBD{65p!Byj0A9&b_K*xQ`+@PzHX#7@x?arb4w=sawI&M{`-d7Z&sr;DWi z&TXK#e9G?~@hwpDLRe*edDEK*h1g z=)DxZ4;N>tD7lq_v>Iy;ux@XeSTC!Wc#ckqg7#fJ|Ex~?5z^+jbfpZ-B*3Bc2YY9t;>{ z9!e^Ajqfc<=ncMZ);w=uyM8p23W1elyr7>_gnwxMHb(-49%r~tiq@=D%^~aq`33|pBZQFq=3+xMffb!9?_A9-T z0ukwpo~k8a_rR7;mwXw%o8cL;ZZx|sJFHWdLC*tL056bbqs5#BFsn`r>3RI2yMydczs352&{N%2hap> z`@-Q`euzn0?)!wBj|y+95E2)%PSWEuFD!oznpdRy9C>Q;xSW6YNpWL& zc_eWO42vOK@wW3peZc~!w)bJT%Z(QPfN^i5RmK*UY6w=k1&uRjl=)~cHJkS3&q5p8 z797CwTr&Sra@D}7xnLf)V7@p1li#Io^54n+&htAV4$CfmVSD9=1-3^8A|73(NyfsK zHcm{9r9e3rqZQFPD|kr%){J%l#vl|6x@|B4boE&K>0wSc;4<$Ge&!!M%?w>>>-Atq zIJl{0r^wS$ zLg5oWzoK2iF{1i~?|6{$j8jmPC~Z0wD-kKTDSbzVfXB&VP(0y~7FVwz3x6CEh)Ju% zSNMA{$kJDRU$;Z1C|-R4ZAL4#8dJK4=zZ&|3kkEaJtY^h_HXdkZ>v12w!Qinv~#YL zcfHx#Kvi1_1h9-EMUC{ov)}omOr4G^6vjB_D!s*5S-+#l4tc!0jt=NkB^Y1} zfDP=WG;Vt46JF}Os6CX&e(b~R(lUtSSnqfdm?wZvD#R0B{Piae1{J|b~AF0nC^|SI8Y4?K9z;!<(XeP@u z1t(b3W3~C9lR)@p(PR(!XCm18_ z8@71AB%els>Zhq>rbqh1t|zMRaR*EWEx|uNsuLQ+Uy}KxihgF?9eoQ`Zpua~>q%%)UV9MlMfqt-A;23NKhOVtyDJB7jk+?4yq1Gv%$gY5Il zbfWsX6XIxdBK{$FGUF%0Zyb{sG3b}!lf=HOj}#e)rSMKH3DT2`=8WAn+c$mN-XhQ$* zbZ~EYp##W~b4u)OXF!&xlM{#X+n=eqP`CPwhA}43x_1#M5?xMi@e=fUsZPRXX2?^0 z5fx|3nb7X`?8XUU6?C?ikFX=Kcby8x2omi(q1qEk?3W{F&Y-Ko$0e#ML5Xp|@9aKL z*?6j`*qsZ8Buz3C?uw^1Ixctm@P-My1MbJY@T$|4nwY^-^emP(1UK0RJWU;}X!UV* z_{V5b`q;cPMvAhFk?bC0msWW6u>(wzN;u&U^gnK?rt}v}d}9gOBdh&7 z2u1(SlKsp7@!b+9(!=A$>4kQwO(slECa{Si5U7knhh4T|aP;bEn9^i#opt-(eS_(* z>!rxq@;RmP(EW*V?+*Syzwi8mCf+(!u*}E{;w|oCscH)^>92jC>e`7AVpK-YTtb=2 z@`&z8L`lC3g7P?tcA@eek?dV`12#EHt91Ty@fV>*>vi+qte#ERVJx6jO{pE5{? z%ncV2W8P4l_KPZ%VbT0_EkicL*u*JgAkf46j^`K}3+WohxeL3F-0cI~2w?XIVbY%v zD`DKZ!;MIhyLy4VzZLq$XGx&{L@1p#O5t0sosz)D6HaiDN-$;^IqRh~2d+&IV~A9+ zA#O(9iT%7JiC-ruoDQ4czWvnxR{hK@qHR|#Vwtf8TFUQ&MEl&W2+IyA)das21=QdO zZKw}Nfj6$?2YTeQuLuSW_^WvsMVVoIh5W_?5|fyH)a$edy)(VfkDmUTx9&3I03}_D zlwFnYc|mdZJmR)#TCC1fQQ?)iGW?42KNFnBA=H2_r+Ybt#BQwzM#)S{CHM$`$zDJ?B zztcC3dPO0v{SyM#^B~`8M1Hc#OqgK|HLlL9Q3@)quDnu^xA4=*5{) zf@=tl1QlSz)uQ@$|BjWc@*uKJhtg<_*=(PBv9mAHqFvtD)@UGvzRW)Woy0z+-I!DD6l`%LHAm zC>E!n-bJU|A{IZ$Iv-$@yiY~-8AAj+h#){x@s>(GfL%E#vSd%XR1?-l0~+(0LUL8^7` zcJI_)NKQ}ZONfz#I@jMMZ%(X7-g|nmc(kMpwEE*4ad-!u7-lY)x81=KiO?|Nz~g^e zb&U>8?2V1IrHEejn^@O2xj(AYA4|OuXWn2|5nG6`c;hJ!#Lqaa4)j<$pb|vYtl?xZyr*|pzI&iIY#G7IFuXR z$zMAzZDO{kt)vT|GVF<#uiELrpdf*yd}<-PZ6mgyHD?8FL%Y#z!;#PMIy)?j5@8<9}e zyx!{Q|7)0~Gha&B#GM^Z1wSgN>Q-C&bxp?mOG2z1p4F%C^A8(x&Av_5>#azt;Ex;% zi@b7k@^cqA0<-_^O&tK;Cm3~U-;ZxY#UlGAI;-(b?RuUxcvdTVmZ*>%1znm1?c({X8e zsT}S*;93D3Vu{}aqgv>_$6P{W)3w%-?6zBCT+XHKl5X)Y@QM(4?vB{=PG7e#(~_PS(Adqtgjy0% zzwG${2crP`lMgKT=cP+nTa65;0ty1DLNze%T3>f}_qbenqpHGA=u;F9ti$}F5)9dF za*K|>%E`dj0qE@w#}gad8Q_l6EI)Phqw35-Cpza(ry^k=-tc$r(Z*_^p`W@x^vEn1 zV6Fn#yc%qux3CmI3uPq%lUssSQ)uB9V(9p3#m_j|9Io??EwYB^Thngbo!T|Cx-(HR zj|5Rc2-mXm+tCRsc@N^@BxzSNsK%o3x#T&IDO+AFd7o{pqSp^alda26U6FY;2*LUV z%?2SG=x3+p>ud8(j>{d?f?}DiUaH)x~~WMa!|yxUJJ z96K8e$kSyBP$L;yFF5?|kRCTRI?+LuT2gMhJ^cPysY#0-774s@6ZTg92&EkaW=<~~ zDNz7U&1xN}+1yv91g?-8+qN?I(L!~XRbN`n>TI<%an0&Vj8p+j;h|1M(OmT~R(&Md zN?7O0wcK{ru?981o-z;ccQ3VlWdk8gH&0w^Fss_~U@3eqdo`vZZv(ATTPPMsV%T*p z@Ulv*hj7P)r|#c0s#hAd{V=5TeekklL))3zg&s3r1((2|G7=wKOpAk@ot+mk8B5o@ zyF+s9z@Ony2fF)IA8h#H_T_hh48`WD@Wgd83830wo#1RE*O!Af#Vfx#Zh%eN&w9s) zedv>c!CiLQ=tg#Q#7qp$4|_iG@LzNRvR|0KE6x6O=?K~U73I>Q3rPk4Hpu3eQMM^G z_g}<;1IvQ3W%sydD%wZ={3}bku?p*zGV9@&-*^#_xa0l zbl_;N`@YWe{GGpL;(n)>Yv70-ON5YIKJe~`s?Ihq7vgV3bdGZW7J!Ps!t6?fD~Smi z;a=yKp)`}C)adb_i&s8Vb7n!dPY39T?;yy!N76E88=bYbGjgUcS{elwrWv8Q&CP8_ z&UZRW1Yv-TE#@r?DVico5VhEKm6pfAP9CjN)AG}NqJv>$e2^ntT}zYp;2@RaT@GRF zu_j&#ZCz6eSZrz;vyGyJ-Hcp;^Q6BL6I8j;7v&lgVN=o6_`>UlQlU{vLlGJBh{2HF z7t!-s%*;>>FHG2RDxyX>;Y4j@O(6nl8+EISIo8U@N@kdM!y7On@6Jn~JGHD?n^r1a z@1P+G5zoKqJR6A z<*+`Ro$fbxUT4;n2haN4kI9ubNR>JwIbZsIC%oKbMkUZF9lrq^g7#&DcmEjLcOJKg z%}ZzH?&_f;#ah)A-FI&<6qfwAC!w`fq&(yq>;uJbKeZY2V{q zqCcezTsu|uAmZeBb42?A@m5?h>H=!RlTaoW0>U9I)8)!Hk2q>LcFpr(fN z+P}+(V$qnYz{So3CJfj(DSnE8uewU{np)EK|K5?w@q5=&ya53d_;ah3rCO2LeRTYW zw-NZ$j^$TC+OAi0_w0--owmW7J~}6eLYjz*7+>j7%C1!Zwgs9w_Nq5sxA-e@8Os?I z!?16ASa3Xq{z8K(ux|R9qNZo)`iZrT^Lb#Mb;7MwYEOzgUQkZ9yWV(~h|bN;J1Gc* z6j2!VUI`tXBGGe;08L!5VBS`>nM``_^ER7ayKA9s+bmAH!DF#Tg(Bo;KEgbsq1Riq zy#gfKt^KTnhAzXV2Y&6dryy)wmrhZXA+K&yOuF62+}YPsNOH`kQ>RmAX&v-aHP#bG;rOdGg&1go-&s=nA9fuTow5ml#aYtfOAZL`Af@AS(`A z94r@+R3z+u|9iW*rIXF1r}Gbw9Df^u9A{XaE?O;}Un(^I(2T<48R7{Cz_{DHfehR5 zoP?QkbmX9PWcm`^Hrr#^+XZ{sLY2U2+r{dL?R*DY5l94A&h{}-HSL*K5@7sPfnBgD zGgB<{DvB%uep3iImNbp|&mN^SXD7DZpJW8CC!PZgCz`za5D}(2SY&dy44oH?Mor5v z^NYamz+>UqT-|A?_{Up#fCF9O1A=C<)KIhED}UBKBaEkrzRm~b6g~?1R9@L%)Lc73 zeV+7-R%JK|XtI%G0Y%U|Uh2v^X>n-h-8jv86s^o_$MS07ZIwSWL0eC$ykny5jnf>{L`4ZAI>K$2S4i z8?rH^DmBmBD^BH#D=Y@^yQ)i^lW72*j3zVIQkAOvCX9nC?7dUr2?jg;vNd**PA~D5 z*Qa~T$AF%4eQPMDMBtV=8|I-9s`qAq_F&((>cUOE7im4yLJfm;u6-YT5v6fNa}Lea zGiCYS3^&g@+`A)NWwSD92@$tyn zu?uw5N|@<*^4$q5{&?yBA>d&20*7yqC9nt7>IWfopl46Va zKL%V4b_uC`*47zf-JYNfM0Z`jDt0~Mj$izYjaDn0RoLHO)7==^!AW~E!)ahaAFLE^ zx!d}ze)tcFB4I$SFlH|sCp_drD@;myX3O*YlC^0t9NbsDPRaE=+b7^}(S*PIw9UFo zpCyP4hvM#(By_YN`$|%UN>lAw*2hCs!*w5+AxB5T4TCbx_*^1T!%t8gj+Q+5O5QDQPLtR;a=i#CEq#d=Z-fm?NeQgrj0ieBlClGdF0+5wb` zJ(@AZ%Z&hqD%!s*a@7Yr8gL_wHh+*OL;(1r_GM!m>~M`0k>b#dQ@39IZgCSxyN1DA zSK}ho&xTNap1IxzU&WNQHwYcpzihZ&an8vLeM!rLQ|$lu@csU{*e^+bH#-ySI6M9B z_3alYz`*xUb52d><1Qb3f&F$*OXV9ezVWvZ&O#32gQ)@ov0Tx)q&?H5oS$f@5zU0% z_S2^3Hi!BFfv+(QgncHG`C?#hkCLX8wk)LT27Xe_ZnELDz+ss7->D4z;5$^3Iz?7g z^lZR+NMpn}U(a*25dNtZhBuHD;gpwKdgkYsIz72)Rg_cWcJQno%*iRK+DV*!32Rag z34JZ77oNk;ziE^8F%H&I#=@K&`#+8{Wj0E2>5v;-Bw=~dskdno!6)An`USRFkbjmxCVXq53)$Ii8jp7r)mV+KhZ*MZfbCj?y_s%HGM^uYT*H_U4(iAh^PmUcbu7 ztKkdGO!8>L?;}Nc&CM1&hqY3|OU84fue+;dmUUduVa(epr-V$)%KOXpkBOqK_nhdQ6+8$oDo%xk-qN0 z!1lZmNl*9FNa*gc&(s??;7`z}tP2hfw#yJsJh1rrj8C9Rx=b_!_fEdh#0xhZ7=CZc1H!^fF}ZG4<2^(L;V8P-PS33bop zI#LE)B|WPrv)t5j_`5IHEe)pa#8f^g1`Ku)aB(W0Lc6gBg+W*pPSx*hMsiRB4$){Z z9d>T*+zkv9*K^KC$bm?>XdcjUd@q=Z`ABHh5Jbh%lB4J4|BejR2C4nmgN0iPGr@tA!Hqkz@M`_E4@&O~0S6dKR?q&`EXWJ52 zFD$qOY~=KczaAj^7*9Y$HoiTi<)hNqUo!85cWf0Z>lH9`-&{GOR(gF$;g4M z8T-DJJ|c2_KS`S+hr>ka|XMh zHQ7iwTsd7 z=;wQ_GWTalrDdJITRbY4MJ%dNS);M4{aW`;&Fdi6XRzTR92K_GbcFbOl#~1AO81+C zS2txa65tzUt({^p3c=q!pjJtphL) zqd}3R^(1va5%mhclk^iO^|NAsOwm!_~Q*Q48oS01>TU%2ExxJnH0pF>~J2l1jqFV_%G9gpRii(d00`k8pf+` zI+j|tb6_-tS5f}gDw$ZJycNH!fC8E5%&pZST5{(f0tHL++t)xoPHx!+D4W&J_-?DF z{9p{_h!+TQ59F`18drurl~iW=HEG=e6E{T_Kiz5l75>jOt&10}ntRii{^gpMpi!x0 zCLpyqLvU&oq1DC=e##h$FKx?z{`?ue`U;zk1)i4jvR$;~hNh!TWD&FiFap ztAYRxM(8@eS-b^y^hi$?!ey!&jWv>SVPJPh`nBo9?S?upD6XvNCbU4=1vp(s*>o*z z272p86#a2H`tRpClkI-#9{3`%nFTk=vLBBvrkAn)EwTP%j*e$at>~_W`eWYTe(a3r zYl%IFtrD@jCLFYzQaHJ`6t7@0etlQxVjjygR`tZ5-v)j2QO{-*D}L?yjDM z@*(i|sOFPN+;e!^4gO0QNIw9p8s$2ywT^4B{jGg9}dk}KYV-_gjc+rGKLnXO4D zw~ho34BgG*C>kd`Sg6|zSL7Pez~FKMCmG5F;bebzU9%s_J9syl98+W368-3qLevS) z0>@FKl%MEHUUU8xeoJ;;qOmG70yWtrdIU2~Uho5`k}%UU*>mjuYFOl&eJGs572DaG zdv)hYe>f@e>7RRYR)T5`(@z-5ykH-8V>>J9a1GJ9Ed1J=G*#m=Kkm-0r?xjnx+-<- zjkqU~Yh(E(5ZXl#_t@vfZ78aQFZXXFPcDR`s^8h=y=z(iH=TF!gxA(D$7bmK7<~qX zH-^EG*<$Mn)5Y#K&g^D;wJk%ZU7L5-())w;K?P^+V^%2$cQJRbzcC^~Hahvdm9#hD zm8F&uE-SahX^MKjHyNei6($86+2oetugb955}a_1fVYgjYh3de9_;aQ%keKyM%gDZ zi-j*CdpbI*MFL0o;<~WwS}%=3C>8SQ{eQ|pUYbnWU{|f31T%KmG#xidiJlOrO6=Iz z%zcxm_U}*g!;2XWcJ-50LzIE|c}G_5PUuwrn(PElBrjw5lle7o1zH%^Ace6eRakeX zxrzwIJgYXTvUC@C3}SUE>s9omfKjDcO^lt7X@HYcXcFxABqs}_vu4i{94IRUSgYS%N z`QGC=vpeS2tzJ66W8{{cP=w6$?>@hpU@O>-n@NjEYqIzOxv=es`ZDNDuigCd-j$99 z;lL3%9Vn9XzOO(ZgMs!rX96W}{~$y4b#3(mfs%}IDbm@=yNj=TLJF<1lNCZT|1^_e@%kilJ zh#blE6;igWK%=017!&>|${JB-21Zy;x8T=x`>!(_BSmE&olL|Nom# zpJj9WIn+TVu5c++MI4RQ%{0pMPlj8&hgxdoXBFn+8p5}k5=V&{UQ&m*n7zW68%$ilVoC~&*)00op2Q1Yij}r8PX?gX?B|}&L7m3$`>03r6a|u* z!u=kw)ohCK$AA;dtM`aasAIN7G1An-|h zTQhnNfgP0)NwLem?lWRziejpm*S{|U#VJgQ&LAPIkqNUA^W!zbvS6Md6@EE`OxAb& zF_=znEFK+!IobTMGL_Qcw9&os)ngcGL6w(}aLHe-UK?~+w_tRxMO_SYtOdf~F`HIk7$VnBmmu=u44TO!$Lq%|e)U&m&2(&9MB zJH6T3p~rt4~k_-uHnq*-+Ke=nMQAYb0phdp5bbnwA3noFx$bvV$Lw5)gWNl9Hvu^Uo6K z|0!zH)BO`uN-c4!*1nGS5u^G?!Sy_+Se$q96=dP<-1=3E{sK3bg$-X37b;tNn`C_p z?5ASMN`&1;1-JHfc-m1qo$-fcl50!Xyl?GyqNJ%F30}z~gf4*M_l74FGJK`Q8S;@3 zQ-NUhG15+%f3JN*iNc}UXNhXN-sT*5g;_KCo)5$`cZ3Idse=AC@Cj*+NjGi0w;I2u zCD_#wP?x)WHCg|en&!B~&PhkHx;79o-$Jsyd1&BcV2X9E2|-wDbCk&`7nShesYDTE z(M(rHFTy4E)l4N9412kwMX!)$w&=0<+h;E}=5*IC&Rs0Y0>Ml&R3;O<*;yEG$9%|l zGpjVqEOo9A#I=(MIM$2sliBr#s46v2Yw}NY{ijIZw>2dz{+iRffzMxrC6lU|>Pd+J zoF!~lZUoB!eXR5vQn01MzhRKShVnPc%<{^a_Xo!yylIi?W%OjVCFZRW3%M`)@w7x; z>%xJf@(b1PKd<)bje3EnI}8NQ3>WmTtLBY526-&!!nM2p{O)wzk8EwG_JqZ+!A@kn zf_y6Oy|l*`tbJYpIBaxKZyCA*fwW-m_xrWqoB5`OALm;9HKYQu&ry#u6RcwI62E0C zT3wcUr0f#68t@Dc+{!6}*BY(mhvy%;3`tWZ(NuDYmwzG{o(1(9)XAkO5za5utMbEg zd*|1~w;R=uY!<{p4KT_S$yv5Zzoq3gRnaYOoqj7`k=F^rSd=LOw%QE}Xj`r@O|gKO znmi|Sf5GVPR|q3i&E!&#-iDTOjjkA9CgSvN-71}z2+jN>>^^2^-}n-1xuh3He_RrV zuQgl#0=I^!=ZgC>cwfW5=&rU)U+P6P7IYLQt2lWTG?vyCI?MgDObk;r#EkAHmN-4i zea|?xwO4%uAVwNyE=`4n#9l|h#5*#cd3VA+77#)9C^fF+dZ) zpO(`5eZMY<#;o>Ho(FqZ&b)TN8a<|t-41r0Y$j>#L}o|pT*)m3@FfJ&**J} z-o=?)Q-tzT%2?+2z+d@w?}E6~m0Px%%o2!5;t>Vc6Rqlj7bQ&}QWG8e2Q@H5gwN{> zy;+Nwn*FWRT%ah}ec`c7#0=JJSa#w*FpmQeP%!0kHvwD)QoA6%;{g_ zWg~l#VqZ{~fuPYd2meAX)Q>Ie(CEiAVYjHqyNvR8rGpX~{t$c*IelE-&^I zckd|>Br>I?cy-PwQzQxK2*d&-v*$sa{a%w5#!vlF5#$i8Y8r{;?Y~X=oDzA-`ThJf zqFc`8A8W388GfH&EVP4?)ay$uKEg{;)$nUE1?UAV+3zIPIsSL!C_;0#W(V#=`z0N5 z=$*s#K2jK#Sfnq9R=AS2FVAQy*efMgoDosD;{5@NAua0Ut@F#dSJ2T+T}Ug}>4=&= zNYt_AqGHng=3HCANe&=Tnn z2f$#gMV}HIhOo@t;iW3tJn_n8jZmI#W~hfwdoKrCjOdSF3kHj1AD%*7N;n`d%rjir zD|ugBr@rF!07O^kOBy3!nFXEv%j;YA0LnM2?@zBZ&4T3rWHo($LPUOt7$<3!kC_FM8t<@Po4P} zsKLGv*C3pD3H|`PFQuSR>$W3F7BYC&8dE9pcZI`%Vlm8U?WOT4g?2A5lw(F)4mLznN*w?E(f zHt;S5g}b&xH80Jc1VequJncQYU4D4r3Ep2GF=u&frw2O;o+##yXOWR z1e4)>WH+Il@6!)W| z|N3J)WuSUK7OCPCq9(21viM`TLvFq&qO68_M^$E}*Yj8p0+gFfLcq#Uxoz4yO3=dd zr)i8Z)8j1h&gn^HG4dINx6sKF*LgdvgEpkF!uxkn7PHUDY$TUjLjl4$M6s`JnN@sV zT3f{vw{bil?=%>)v(WmOJcvv=>xn(kg7;nJo^=Wk#9xiL6YxK?9nUxX1=M*fW4{hY zwt=n0id3*}p@6Tp*qJ|50>NlOcw6&Z!OLTh2UcYBh;(xsd48d}?{(ix`~q zO(1z=ELoIEHF$TFtq+Cl!3Q+m!X*2aujiu$HddNA%Hgd8oAM4;8aS_{`vuF@6sj?W zpFPD zU~PV@6e>l=t2F?=LOr+H!2a$ko0%%Dr*)A)N9x)hyr ze^~l=DKehZ@6>lmlxu#E=T1gOBf3N?-~g8V!f_3Z&-*m}UI6)<^x40?A?2kBTkwRc z!WPQjp->^Tx9l4VU4pu#du1_R?kptz+bhi}Z7#vMaP^n?Z3d#>f5%As|7koqgv>%^ z-zRN6pc{H}ImRtA#gdT}?GgGio6pXaC^gDaxWH7{$y{3*K1?4hJO%65Y zA$;T!s}fdB`nU7qfs{;cQr&!1o~5_N-i!C;<|$5LMZ8cWIH|pHUMY+{Qt=_j-y`2Y zTld3WefIBBt^WEV@WY+*Sew@q8L~o;axcWwHTnMw4XS@4<1J!~)lpgguG5D^< zHIvblA?#eI?+Bl30$HUqTGsMUD}%f|?C=IelKy0UaR1FWhQU!$iq9K*KDa%a4`002 zBHneC=SSnVBHn9@Wanh?C|c?gMQlYXRbJChBSbyi2!OY)Sk*Y5l$@6+*6!j_iiPhO z8lFAI!PWe-%6vvk83k$l zYzA#Xtz>0wNR68}NhP?3*+}trn<-_N>n573BxF#gWE7+ZX-{hkoMn@Z4ssr|Y4CnP z{Z`u9V2qj2WY~2>6>Y#Sg?BI!W&d-ogQ=l-gmf1~n*V*JMdT zp?!_7TRidMMJJZ`k`hpWIyK$H&mMK$g{t5)%Y?QP7hm{^c06$bL;cUax-7ykQ3)p^ zBkJUYi0rrM}kv)9P_uV<0ZGu)x_l zmu!tv&FetySy( z&oOrX7rg@$OY0E0i5pyeNGdzL%37K28D{X^AN4bkPnW~!1uvtXDb2mR@`t|bLAT|D zEl8w9j0vLQsaI%{C4=o$g#(MmjGU!4WfH=&YHI0Wa62<{;ig~-`AE&9t_^j#H;abO zeO5x*g_}DH+L){^m!_OuDF+CnnakY$pYg2_69*YX+^Ba_d?B2Issrho;uN*4bJPKV z0tN2?8EYI3u^Q#d(pN73DiWDrWpu z(xGj9*rvhu4|LgZ?Ta>=3~$0Z^EMq`!kCa>#L>^x2Aq?CXG)>q6RpufxNODT8#^|< ze>NHcoa90O?khHeYe@BM{bhdNv$Y3V=5g_vL1U&a3I`2kjfYXa?e4g#5pv_9d_VF{ z>%(CZqPcDJbj@Ui6Cp)fv&zb%|7|Y9ya5NIm9@89db@DBw;wm)B<=KQ!?^V1ud&=N zaLkNY^X`7n2##yv1W&HaFe-Iz(wp?v^5p=ykqZ>|_K~1wsz#XJH*vHlG|nS#{1M*E zSNRoc?N>azCSHf>DtdXX*#kP*RVPe7X)8nBK;SxZD?zaA4PLA1u0ebgA`f>E&3%{q z|5h0G!9JjLFR?2NhC|fBTKu2;FJzLtD&X8Y=# z;q7L+#JlQ0Trk~Q3CIyR@DpWMZJsk6_@cWT1jM1iM3@cs`PBvSjliVpfyZI5zWeag z`7sOg_g}<%Khc_(kxFSasfCcs3Klu-={~SCFC^QU~FA>~!fxH!SH^iz-%fHlr{7 zY~3KV?e9+yEu&Ch6~9+L#(M!dj=_E5RE>bEBx!qKZc8iF%y9s;8-l;Pmr`8!gB&{s zzfPOaJ@Ot1U@jcS1;oM*tSNS_yZb2fv_iEed6M@Plv0Fo!0@6bjanhD%u!%!Hcnnu zARPbx9Yf;_D{d*vQLGQ4eBLOi;^)IhPkkEZt(!$rK{##Y7b$UTjjuz0o#1UCm&qru z^9=@E&%yrpCb+62rz=0JVa!*%{C;hPW1XvqspWpKI{3 zFnmsKuhsMR6wsOcTu|#RF4VEaFi;qXqvzWq8i+{OGM60S`!t1Yv&aK>7Zhu^FG44ns^$6wjD|{G;Lk z3RywZwK977k>9>_L(}}L?uZSxSCD1X$Zw}QCZ+_ z?I^UEPch1q0a{C(3O#(SML^QVL3g96e~>$RZx7u1zzyBZsI*X)(Jo!`PiIgxrf4d4c2T1#tTr5T#56E zp2%o>H)D0f2CoocB>~JqB*K38&M%Xp=dCktK@E70PNym`s=x>eU{@+z1w>*~8WfCX zfVoGQ8F{esLAqjawAKNvE3pkK}+ zVby@L6osLXFWh$tP7EybSzp99LZgr@j)Nq)ZJ)ee%mV|wlqUj8%Xgp`LaQ5atM3F3 zStmPV@g><$JjcI|{oRsifn7HpKtMnlSr*3#ivD7IikyT|s_S?J$ZGGRXa$ z{g)g=3IsRzC8w#2W2*Z=k+!SxIMDYR1h@3;Gq7K(zhQDaGory|G-B33LxUF{#1tU~?3~BHe)LEipzqjL$dI=G zY%(|VL99+6@R5Qh^oYA)+`O60- zY;VbrsN^J#Cc#S+!zB;D1}lSPjZY{7c=NMFqv+$N$O0(gV_&xSRR(J`<8?tcQ{hM8 zi#{n7M(g`B%Aq+78Dck$|16|^y<37c@*;BSxCRz4u5*V<8HJbn(aQwSpcw3-vCZ|3 z=HzcY#k`9~w#)Y(&Em=r=i`-@;(_(|0ipfW1@L!yUIMaj*14D5vN3Ac81G0+giZt) zOAdiuUGq2Ni1P=7U&|;Nz|4gRK%N2{bVU58+G}67Da*YL9*;!5s<5q{Uitx5WjHYQ z)@nsT=fu;RC_ILoYkT(?6weqOrG#uy?0tDUb44<=STB7syqFmBF^>fwZoUUYL2Zh-xts+|1 z0y5kvw?aRf%WYV!-kTnK!QwQ<5!W{6V2olR|3K8M7lAJ0KXwG9_`rU(Qq{-4MNNxZ zVypemdxvNh#Mn59|;3QT-!q0DoEp{64ob{M-TZ#FE>4eKf><1ZfEhLNBpAp zgLEyEfk79&Mr9DhsT@22WmxlM{ruR)m+GPVF(oB3uyQ@E&6!#UpRETp0M3Z8)>MP~-U|moi7PD>*ZaNpz1iZ~yT!$a z2=VNLHKvevw4UsttE}VF;pvmqdB_u&NM%6$78e(}?z>H7XqcaCmw}RbUF0hk7%Pd|&crXAyS@yYQUuN4z|R7k8gdYSh3dk# zpYGmJn8fe-6H5xg@%m))OLN+ukL~Kzq-uJ4I&h zS^d_yX7G!|sZCn3YbQ zmC9JUMvg_A@ZNC>RGPN!7*_0R8RhqcXoS%&KYWMFt)GD!x1ZKWz~83}-$IY2>1IPV zSN72dv%Uj*Y)ZwAa<_Z8p~D(Q=89&BgPE0`?NR4$jZy%36MJc7sN{5i69`_eR|Pc| zHa0lQ=Z8m7{w-Xlryl{g4wt*o{I2X^n1AGOecM3M5+P9X&pN^R9KC;?>juJxxh{ch z7;`54;UAf^f7$T@F_Z-o-QrJ-B-Ko-wQZO*1I0+S+(Yd$sx0z-(S!E2cCABSwnE1J zRZYi%M;o_h)Xe+l`Vtr5f(_ANa(Xd*R|ELEp)>#w#xi+9ZBt5$K$1BW0mzI%V8F>) zYa$#q4{*3no9g!hlnpo$JHPux6jMyIXZnB11elsC;TwM4Cy%tE?MzKRRZV{e@kURK z8NCcKQX*7CyxXNq|A+qI1N&JXXa*MT}W66Fi~N;gwhMN-I($Y z`ugv%zfAEkYMdW$dva_G7@EJYwHy3I#}Y2HM7;kPa}_JtP5YivgNVx5KP5PrLfUm{ zi)tH>G8|v?A8t#TRAT=lq6BH{=D@a?K@F_v0bLgb*xUysX9w|TUn)KEgnPq5T4j$^a~Av>I;j2SQ_7BH0V7_rtTM$j!`%K!vLS z^dmb!$Mi|1bYmeOl(>Z^IUf?p6;C?vW!V2cCPWTg-&Jcgg^=^=`HI0(#5g#tJSmyg zI*X5m$bL1gR5XM%Uc^Ya3vvr-&$mP!v&f=!-i~Ftc}V$1Lo`(Ie3Zg<5f!PfTa+>a zbrZsK8;hZb*XrLv{ZIlWg`g$ymsvf@Wd{IID?dCO$%??%ex>}{REXeLHoA9c1iS_@ zyO4s-^5e$(iL%-(FpK<%QOj7j`lM~swjbDQpUGA5Ea9Q_o+lc#Kf*~YXY>SAL_hGe zl9yBL@_>=x^HS*%W@##`Wmi=Ki9_h}2#IJPQdwq@fQ%UjV(%wT|BT4C3Y`Q7 z6G4FyO=YeSOR=lA5S?_D0(FGeVlD1SYQusVzb^cg=3 z%JrN7Q`~u58Av9`*S;mx-gTaWwTe3XJQ9RHx8jly*n3Tb0tx}t(I?dWxpQf2*?xs{ z24M2-`|U^MIM}ZTpX&lns5bDNP3ZKayb3}9O4oeXq@;bv-Odmwz5oQv*%;0RcEBah z8!aopO@!Wr<`V)+n$%YWhZLi^XOt9e5_$wA$#~(8^4QPne`KEeHV67~T|y&tX|{77 z(}$zZoA=KTKj_kj2i*B%ro8=xYvb)=fZ*ZGHsjgms7KrOrr^7>`=FtqCGQdk=Cau8 zeYmxl03iYnc{Kk~r(2x5L=EImG%z?Q77YrNkmEf}d${&KWZPVh&HrSjSjjeFI$X>! zDBaT_90oR^!M5g&6;R7@KrUOBmTR9txz!Xjs?(-+gAp4aIW;Oz;& zfm>ou17035wp zKY;yWyd_|faF$CsvB++T^+Ez5)`70~)}19*U7f+2M|l+e4SHHhJwWNAjl!lex8W|w zQbb1ZvNyFLPqxJDRl#S%atY+0Wc2imdj4v6QaE>~ltePXKDWSG9tqNQaZ`n+{cQYSF$kja>IewZjaIs4`{y92*+v)PR*tV7C+Uas{sf zfyrjejs215z2sN93b90TwNXq>irw$1}r#k+nIo2vWx0Q)*LYaOP$ za+#EH?RW&&lBR8+JE&Lsvjz|GqOwJ#qJ^x@m(?m@9MiU+UGEf0L!W|~V@u57gj z0hRe_e59z2{nE$HOOb^0>S!+taA<*z%^)BU+dr*BU=6S$W}$YJ$zzw=J`oE8etTpx zj-z;Y6fYDC0w(hJ{Y{1a;UZ*ZZPwZL*%9Zl)!8xPl!!W&Iy)ww4Je%*p*GHrx7)Xf z_@l6z3M9mZ*~VD!6T98?^QIxdSF)q`XnAB+nm54^B5-gv8Q{aA*%W56Zz{Bo{Z>}8 zp2KP{H6>22()FP0=Ttx3A}c|?t513Zb@ffvW#-e1eF<-#KNs)l>R3oSzK|b>wvQlA z82UvAl$lgd+~F>5DitCmPQ!K$efoLofBZ$qSg^9vnQm72w7Nfpkh{kIwMM&}>RzYR zWRb7==?9sZ3R1Sx&FQ<0KifG~DQU=C_Xpgn9fdj$);*Qc4<|+l4akfzJb*F=)nsR2 z^b|439VY{OCR`haQS0N51MEepp5Bi&jR2^vu-f#-n*X3NzWnfSvn0OaQ!+H*ZPL*P z1}{}TLog_^XF#Q>~a$LHsiQ^ zbgyw;%2}Q}nz<3Y8*O!VCueGMeE63qlK8+~$zC^&YN6to z5CQbScGmccU8XmB)KI#}iq6FE;0tdEM83&mw zO|n{_A@^c>R~~mQoNQM*%%}zCq7+}dw;e8h?br-x-w(L_=@5wLsQ+Vosxb`}G>O*5 zU#W^QBuo-s1)dwEk$S_d&77RUK@t1?MYE-qZ6ve(N~fFU*J15P&Fxn^&Q+1}|L=@a zBV$OA?3ToXzkx%y(WZSDsT91^T5hJ3Dsyb^5U_eiwpa%Gu1UohIL={ zqUWR3QrN$+oK>Zz((qBL&IthZJkueZuFT0CvO*g%>J_~IsB`!Gm!jOesTD+bVL{YU zxi?HVk+(4iMc|U&`wlVtydedWnfR~FOWVBer1aw(Q|cK)1!B|hQIVd!e^YUJtaD>x zla3^eH;Ox#*Uke8_LI;3_qMcLNBi!gnZkOw*W;cx5?g0j-YKr12DvVDJ@?G-&Ro+_ z7~5wPVvjJNIau^nw_Uj$&L%NOeQ`5wlT&WbHvS}aFm)yD<;g$E3qU-m>pp#J@a~1C zih%Diku=jpdD}QAfNBm}x7|30vI9f3)t=o6QmUF?F`7xMDMfl}tn`@{eaQ~FvF7jMHM z_p{l>&b13)3+)V^u70Jg@~sH1fAWJ=nJ_BYJIUR?WYYQ&yi?FGa|4F1dm$ynCST5v z?58GEmU=!4&fiUh(VjqACQOZFceuUf!Q0e+6#VPw#~#93!C@?KMi{Z92xUy_FJa(T zNuZ77GKI{nP`(u2ab!_f4UG5r>A^nrl5IcoNrQ;pG#8!l*nwGL1rHxkDi}+GdzCM+ zoo4UR<`$Q16hXGJ;@S6qIzGrs^4xFzLAl#M%?1zc2OBbsxO(&+vh-lQDjAju*Sg2y6f${6TcXH0`q_xUh zh3{i-b|`La(?%_yJ?^FIB&FnetFLPma=V~Kl6aIDeFGn<3WGEF)1^SJrkzM(1?9uy(_&{TG?)EY?PH<=8LRp`S*FSt5SSx zl0ni5lH0#f0MIvKe5>tm>nQCe1WW{{vU$7O@)A31|%Q@a%^H+{WoS|n>a6B4D5 ztE3KSdd&t4O9cO-OV(J1q8?bk=A)9IvaJ3_4-n`b1#dgA<(jPqgRo%T1N2P>GDSUc zncQ7zHHSv@@r(xp+5Xkse#*j6qn-*-yhqa)x9MAPsuOCOD9k=lWTOckx~%op-`@n% zPiHHlALXKw!=aHstDHosM++VdyXl&*n9!Wc210^9efaOB-g`hjt@o6w??scDrQEwf zJP(A#+fD{*a_O}8^E*vJ8Pja-Bo(`X-d;AWM`g^o1=hCS3jMImag9|-{%WgEey+-TvG^lK%x{A9p#{_DQ9Ob&lQ8v!;|5G)y&Tcd7sMWz;W zH^oSRBI`?=)PQ zg8?!Mo5qe-b*b~+k#@Gs`jiHu(^QUOPGS+KQONF-vU;#roc7K_TNvB93|-jRtdm=X`~rV%MX|8wu1 zG_d09q4Um4Ih5OK;<*ci_!9QtGlnX7M-qh`vhSpz3K55UfpMOf{8{J{8gD-{@VBlN zdE(r`GBhx^0<&=bzFHCko7@=psT3rj@7V(S1247ao?mgdbneiX+mD@^bJkyV5CS+f zX*Y+KY;MamuSwRcQ$`d0E@yx8hpfCM>0J2L%Exm*TE*KAiYs*x+Su!UFzNXNHZs87 zdP|-X#RXUa{RyijDINR1pTFs8hSjGaOy~Wma|k+vzb{|wtNBWA$y2Wn=6$+hHjDoc>YP{?dnZ8z78l)?S@FLNnEagyA+XrFuyqe$@w=u3V5v@Eyv8eIhF1UTwV#MJ|%x-V*LfHiAlt~2Ky4jQ`yk}bh z1>)aEn3D+m_d3{At6>Ii_h0dv=UALr(($u0O_@7zxEc^7eRuYTe9uW@^m{%^6K-98X`_Me&S1sTVI z57#CB*pKob1pwCn`3x~*^C+(Ku8bE9`+GbV#&tpMR(nKi z;lPvW3^g{742P_lxz+YNg+V>tlP{G297%ZJF01j5iTUh#q-1v~A=eLX|cw{`PZzrx<+&IoqFQU@Job z-2VFBlZQ`aygS}IL-A6nT8!92G-9TyZK9<%U=f$?5!**8Kr+~Ln`5ar8_P)^zi zE*N(6Tv~m6ZV4QR0IK^_vRCJ>^}03qel`rKpsAh9C`mr0d)p6>fnWLYl?@aMKcD6& zyZ*JyU$~{ZpZ(~vyV&}M^&`QkZd#O8+tQI@0x^ki^QpJ{HHXs0PJLd%OF0Dn3C3We zm5e>H7b2M7QhVY^Re4`-qS@x_>1&c7??HhUiOi*luLhmJ_JjAInJQa2w%76A z=gi&rsE-*rmV$EvJs#7g6cI>?z<_FH?t_8SdoKx#E6TqeA=eCXx&6qm>8i`@c% zu4;W&VGsV7?vq$%O0L^FgYWOSH%yTi+vK8~H5us16rC8}^m4^Y*eCQ{hExU?}@BLd55D5v1L8+lZI+RAb1f(RS zyJ1KvM?$0=U??R9qz@@IbT=a%BaMJG4Bg=O=Dg46dHw)@kh%BXYhCMFuWM`MX>WYX zHY>l+$o^ZzQ3b7i%B;R#w{4Ye?W{H>3&27}5;bp;%9?iTG+}Pt0Cy#c24f{tegtLS zWzdU8rR4Pz4GqIGjfiECr2VDqK>l#Dj`gQ)KVY0l-c9JA<&>sE@@3iS4EoKg_k;8I zxr$k0ViHVoWfrF|^K^(@ni@wWJkjD!7({@!fSE}{Foc;c#tJtNbcxW?EOY0`2t8Z0 z>VfVA+(7m!@DR67=b8tXIYU)|fbYR+vr5?O#_4{*``YG{#PFH^PFo&L2k zJiG&g#H{q)iGdYHrBJ${)>z?u&A7kockoE)O%O34yXdTp|3Kf>cs~RA#yzeg`0(;^ zAp|g{i>+DD)Rx}HPROPo&(!NL$F)hYAm<|60Wrd$OcBD7mDL58#(&;-&J#52jg#R5 zB3^pikpTu@0^RIw2mxVV55<~|o;+Be4I#kCT2yjUFM#F?RL~>1E;6P4y^9V0)bq4# zYflV%Nx1qEJC4-BJd+%*jQvLJ#DwEbd=$c`-%;jC zO*WaVR9=3T*+?EjpOG(9LfuAB}Yv#L_ZHiKD`R^|?I?0j^ zLu&X}J}^SNy=k_PIr`SHn@w~^qq*|D&E1(qsx|c(sMc+ zy~S_(b3U}e^dy0->)Se(rFrc5MU}ajfEa304>GeA5sL}~V43$p<8Jc2kD4JR_SGZh zdwJyECW6QKR)mJ9BH#M8Rg;S;RNy4Fv@0T}^@UL0ZnLlWovHq02u%%5Vtj-OTzdy8 zmkhbP)+6)z>x{KUFN+Fxr^nv21b!XBkTQ7mVLAP79C^WR;KZ>V?vL@R znp(yK{Fi_@8u;-Q6K%bjm@6c`7oedFkDN#r2kn(_*I~D4vKU>O{tmzz_9h7lVh#nv zuyPaGB^UajHB;IzxW`DZp~q~kpO}`?24M}3n^Gv{to1lPNWm@WN2^D@jQ!FC|zXW--N!&AT!y;#wfcw^7o>$O}a+oC-~qx zy$N61ez%O$^DRrY@snRJk?W!c1V*vgE#lp*TvcaMQ~pBslFL_UXfMMl`kJL>UAtz! z9WNenI`jZtp%+gh2%w;N1g5sxUO2A9emlC#v5ejo*!%2y@L-d z`g#?4znnKxpAHk}zqWMR1dI!1)Zqwch(JpDpu{uNruXf4$$1BW_#Xo>e^Tz=lqTSX z1CVfScWD+2=k>r&8nA~glD>W0a&;!dzcBrUwurX*SJcWE=*w3gE(M z?~Nqc*cGXV=fJDvfAV3f^I)w)ni7&Tvf>rSO3- z+;+_;D%-qvcI+%FAtAx3zV0sF9`Kn`=5|g;QxUh^ZWZMh760>fr>%akfyW_L8<-iw z zr5PYhW?t{JprF@3`evUij88O)iT`%4k*FIaI=*4tJlQR8WM~mJI~u%5r7GT5;8sUh z6>|<`TZ4qvXP;p66hz~oBv>>pJK72y0{n%u1|!I^=w)bdRviMNkjV$wreDM6qD@rh zC#o|#3_@$h56_tK`aOmSn`hhh(puxoZLC>a) zhJ0D1B}^bbmHy9P$SjDqjDi%wB*qPBz{%?+X_n~*QdDoLyYVkg1>(1*CB(sB-8=>14icLoD(kEopU-=8uz0i9ETe3gRA<_TqHlsSM(qvL(e}mzvN!N;B`1uJi`Pm@LkBlEsqJ{7 z?h`fQH2+jAB<;>j2rmX55y|rXIasLNO_c25FG1<4_^50Os&^xg*VxR6gfRB5$4h~BTGsBjzs`P z$1>e2-5$&Snx39cXkELqjIlviFP`zi6!8pLGc~3TevV$7$wvG=yP^Y7Aq71W-eN$< zctkyZ5>@2-rt`<}K-2BVodCMllS7uj>*&_sX>PpA*&E@{Upc88PH`Q5J$@7|7dYH` zCgCqk(n0|t`FtO-O;QI(W_C3vC=5Yk%`)QUN11gW$soE2jRaw~1A1Yxds3&8Df(<` zTO&EUY`s6o$%efnu>Kl~`!23AYyL0*5@vKJM27NE0rCunx%?<0MAy7sTYky(B)7u+ zvIv>JYRu#gr#cPirHm(18AEj_El{?yRR37LR2gFsTvFZRNjrA~Nj_UmIfZt<(yH}p zwj?J5vr@UxT(0&HK8i+Z*E-Jf+XwNP>Blm;EP5ghNSdGU10}K93-7b?NW}V>mt?4v z2i7tvKo&Gw1=yo#&0@0rl~q#Q+l19{sB2+M0VV+04%s#;D$1TsQ8OYa+a@``|6xfm zhSmfvt-K`MW3K4wq;WY*Au`q;v({g^Nc`K64uR3qXBnkk3{DZv!4iUkbcq9@qdbC` z{YPT|Zmin63J^5iHSELW!S$0#0dWt?29XX9z@7$S0n0NtagH;d66RwA7XF(oznjZ+ zp~!8uF#KmYYRJoF_z{8#Y2PiSLbS)>8a4}6+1fi5=c zat*5Ve6aa<=!!h_;1v49{A7RM4iqG!vrB@dJi;8jI-~tIhi{O(cPXB>>9lY66MuRg zrF7f`?u3ZBaF;fjf5-%ty#4ED-$eTDK1tiF0HO5C-RT|9BBWKk{QENOe9uK?6|Iz# zH2hIvU_sI=X@*I`3?lEBNDyY33lf_dBf)u@VKrq`RLKgBW@6+IX_yt&tC>&Aaw+XT zk>jG2e2O)kr_MM;DZ20u)r%ANkcZQKx~w)=MR_9t51jui1c1|vA2PENo1|-X;`w0S zB8@Cg`2M(W&BmsXST@8O-`2+hh0D!|;7)lrN9b%La_4GfxDLkrg}8A0;c$EJTVa@Y zG=0?#8#{($^h~VT3fef|CNJJ%fdbho0q@`;+gO-?CaY_%xI2c8Yu?x zdL&^oB;O%2+rjVF#vyFIVbwtv!J2zJZmS=DyQ(p<(&w)amj}0-=8pO27IEg1q_09b zijVK3I_>u4@nz^fTWfu!BchCE`jgOTj_EK^oAl9j=@s}=E)dHbFZ&}x?uDM*q))Ea zt5xaU!?|v$G(VFUAgc)3znnalBTNe8Wao4aY^NIzyFKh<$ot0Nqa+}Fjc5e+z zP*1_wyeZL6k58Bh`TU}uy!IQ>n+NAX&4qA>y$x+SqJ>zanI^LSX?)y0;a}bxuXr-P z+D)tVM3v}0;%d~>Wwy-jU82{F>3lRzFBt#9A(!7+61JIuw(_z4FA5z0k=2V6LM!Qg zfJ!rXi6vzF~)m;3=;F?`cr=a;;3P zz9FKFDKqzc>q-$1GDvjE&p#!wLF&W}V>*CIARI@$w$z*2!C@rKz}g+Lm*abnV6?A^ z#t}IOi_JL1R98gSJVrsxa=;cQ`*vEL3fR9?N0qrQMl`yCp`1TQ<$f4 zG*gH5Ke6o(hC$@1W5>= z2Hwi5YLnz~z^9dlI)tW9lCl#?t#t}@*giw_KhI zYmqSIGVC|?|8q6_&50kwfL$1H+~9V#-zI>DdDOjg z;tzP3!SrYK-h!SpDwX+h-#8%{+ZOYqxDjC%bd;LOr;corL}oiijKv7Sq-P}Mh#sVw zwEO78*Ev4$HD`{30QM^(p#szpoX4<#^DNIn@M`#%v8mO|@&u#{2MXS@UB+52G0dbw zR>{?PDwxtyE*vfsOCvS|xGG_(?1X3a^R?n07EyZkv*9h<)Dy4D{co?(t(JZssvr$l zNFLBa!C5&B5l_NNJKueKsG#+R5=RC^*7|3SQWl7cR2yCaBv3CRC@X)+lylN zEBi@GboW}m{FeFVZc9>12k@Y=J8k9v39MpLTlt5)_Xvn3QlKIG)V?tNPzxmS9CGFo zINZ!pBn8i@bA*b&m<=(RG`|n!Wqo0yH0pKixa1W--`MoQR(3N!+N8g1^GzsVyv!vR zx>45ht#&m3*0p-Dt936W;)ZEHXgGWPZ}bYII#7Es8Rj2+km#k7--M7&?mZXtr0uu= znPG|KPcCwC5Ua^#zORIj)Dz4YMtS*X+<@{((z!ipIB?GgYpDWjQ!If<9Xc9ovIw;&V95oxJ%-c@6XdV)JS{!g%DElUKWF}l+z zln(b2$)YN_%-nghh8bS(DqzFhfq{O~SpIkY$fa19U`gCpNcmoZ#Sa}`4>DepjR>lr z=%j0Q3wR_6D~XQEb}gKx<0!b9Qtw5UJ(7%7p!DoZY#9)&<_ExkWa>lS^;C}&*cnU~ zyf%aIGPblr8P%+Sjv7$#)uZDq>1hQ;Stqk1IzEp7AzM!_gz-j}Z3Bi!z~j2*Z|ocO z9Hb;iuH@wh%gl9#*1q#;43QW=N~&;|||a6>CRDpWw`%;P!eekCCIj4a*3<4J)4eoVRGU^z|R&Sb*rf zu$&~bYC6vLeA)vLnkH{InD7PdqzJ(*(;f=aMwdvN4RLi{^nIN!g$83X7EvvMzh(S) zACTY?=>N`tIDa^>qWqkb(l2~lY$qzJ5^z+)MnQ>F&bI+5Vzcx^=?~{o34cQN@1uI8 zeN(_?Sbp~fQ_{+?IH0%r>De>`@bNzuRO=}ne1s)vr>SjW3lKar!!(FzMI=efv;Peh8v^w_>io4 zA@5eGfnbN)d6RC=jb@@n2+iT25cWup==s&S`Nl9}{v@w?D0I){_HR(0nIJtC2W{n_ zM_So{vIt3^TLrL$IQL5f;6h8|od80MPSo z=Gz=j8EuW+VfdnBm>Zye*XuC;3l%KNT8W;~mZhCj3t=x?*{o{@u&3UhJ^6 zY%|J3HH&eN?cL`aX}|f=j1s4+PxC#MBggIw3J8$4FG8|?kv$X%tgfspc!kezbMb;! zy+HYKdcu^^0Fvp;mbo`bLft?^QdZFEpTxI!AKGrF`wtI5G4 zi;tVPIr$3x&p(0NwBTV+lS+~AuI~V(Ws+ScH0X5GB+;h73}9z$7}0URUf#5SG)IIg{;Ui$`KHi5`ehwCqA>Z#gkF&4YyBwK!AVP0lvU4*C@}SFfL?Z zhb0Il35E0qMI7(Z^mapu zI|7&}jfZ=idu=3-0{Jqt+QjYt-Q%9K{_i9ThRX=Dt6ysXiA1}#LKU$K##LtFRl+UMs7^DH5Bqs0gYIS|74c1!Z>Lik0A{yeP51C zfWr9m!_~D4ciM-&@p$0P3FX=6idL@a#hoI{VuuUE`&Pgvf&YY%ixLR;WtYj`7gNDS>0jXx~c5Mb;?Bt@0Ky-ri8Qw+U6LFra&4-MmZbsK8>yx=l~n zs=yL}x+PFefr=`#TsWgDKdf|l8(;G=N9io@tSDCh421q{#ksqt0_pz0lUG9--OQOQ z)}_}{kdgAklriiT16{T5=&#vWj6I zk_)<90j5WRmloDSxOejtfbjxt|Nd%PlLQSBF|h_Zhr`xvtb$&Fq})6$WlE*=Ie2Di zZ#+c8c!Kghu&`QH{FFvt00#K8L~hfL#tqca92%$3@9lMfAQZ-(6A{4h@N^xD&+C3r2Z2LOIM$=YiviJ4xeF~pgzXE_noN28>2bg z8Xq4?D5=UUX^J+f&+eT)H$;^5Ri?A|yV7?4Ir|x8nMbapG8?q0Vu;A`S1{aBu1fRk z(9;X4VOEhPmvjq8$1rufs*O(+w%ARiYCMw|W{Y7Q3zRwU!n7Ny89M*t^X{^x6Y`*= z*)sq{?yZ&e`F=FZAT&i&4Ee3M?a)EB>1eg%?n!72q|Yka+=CBsuH(8gz>R?@-CdUR=CB7q)cvmZ|fTX%WB9u5);!B|b ztc`A|{(1`B?3Z56%hvJBP;eO=+S}daVP5Y$1$`%OsA{aWk|L*wHX!0joqcX<^Yrts zWN--Ww~2lr3jjT|mYzKHjSi^&-2fLM-1r>qt@Zm4TAE%Gwo?=?a;&nlG4wvXOuxhz z2OxhG-?z$-nQHR{p7TjYrk393M*z{lFqH)7expew02QIb8|hRY_YMnIO6`%CX|r!J zhMb9kfYJJiMELJL1L{0-ASKJIJKsT(yxYqJ#rGO@VuA+a$w~}cf!wGfXq1+f`wLGp z%FAndyypU1QwFQCRH3aG&JWm9X(k%EzpN}>w6m?i5u!0RF#Efb@xu7){|KPNF?{=v z9%P#6Jy~G91z9;X!p6IgIP{$Yx_!(pg*Hg7h|0VoWHXuZ4f8p)r54JMW%1yAS&y-R z!WzP$Ic5YD<0>6XJ@E5U&{ShobT;5}A4;B1Qg$wZ=D7y&MB#s=@;g)z@mp!WXDCdI z*rp*L+}2RH%)=~l7C|?a!F?+c60o+Q^OG$xor{^QlXexj0+}F?wpa2`8luSI?6Gku zozj?yWjrg_9{ZD>$+ips-UhDxu_k6kRnhK;$C6aDSXNw?ED_gNpR%af)Q|xczwTyf z22N0jiApkSE<_&4+4B~Blrn4|vh@$d1VFcQBsQQ~=oLt{Ra5gtzlJ)+03qj;(bsR$ z#VaDr{T8+7Cg&gSTr5*gmI-rPt9LoPgpVF~eQ8olUav!_fj%9Z4BO}91q%%VG!zDv z%*oY1VBntMjQZ!=)!XCb;^OVn)%{Lr7P~JF%0O^y3|>wSKOlqu z@>Irph=yfhd^@HClFt^*>l(4wT~f=|m#;%y^j5?k2V=l_<{*l+XgGB0Y}BPQ`cK-= zR;%8lxLuuxM*1FFNau!RA;Nv03=98U=K#R7>-m7OxMJRGu7r+#D|sH-Rg*lCGE%Eg ztd6D3l4w*h>x|d4H?&;wjHZ$BhTATuB3Z+s+V2S!>H9rl{>ihu@#kIAksgdA#ON4HubRm1e@8LnS%vT^ z6604+TI*l^wG6_3!=O0Khb zolZAo`1Z2ysfT>p1YB2Y9x9r`o!TvMLaV=+P9umu)9BOheCOum@_I6(88tk-V^FgFzU&6+Ks4HMoqHrlcADxb(LeWt7fSlrFm+Oa`*Yyj?y=Rw|*SUd9B^Wb$ z!lRCsE+iTBrl}NCBzmA%-d9BqrKdJ8&D#c0U_XqlxhjvF)e1{$oPl`G^sZ40dite+ z1u0HOmfoFN(rSERvRO+lrHUg2~UWVwn1V7=oT zr{z5sSv*RIpWR62L-9%x|A|6iJMDdivWjfxTcV9ohSR@vpQT2F`C&bLF}S_)zCDD> zV0Rj*XwZwExo6{9$9+i0!ZhGeu%>-sI5vT|*?sFduU(zmONFUVzAm%stskh>^UvM# z_m>o~<-d>&nf=W8X^A;H5-S!-4?y>v3VyD%rKygt*@ySa;I(gBdfX(bqw)*O@F|r9 zmmBHqqGR+BfH<|>=h0vRqCS4V-w>3>w=ch`8~Zdo0LS#BggRf(vKPRk8hJ&Y)27^J@^DeR2cfx{a;Dn#3|4HyUEEThajbfn++zaBdR<1^H`&wZIa zwLE{xtZLRKr8k#vc#hsDJhYaPqEwAdr=pmxyl)i|=*Gz@5RRm`zq;>2Xy@lIx4Mfv zh;-#hgV~UodG^XGwtS`|nr)B)%8Q0`%_YQ_Kung1)KQ&?_MzpsCpc2-UWVrOXE@R`mr3oRF6)COPIyk0Q+)d-n80y*<8J65=m{`>hL3g*Z>QC>#OvT+A8kKMVc}QJvK!lw`f%&gXc~Y%McYgPCiTw zje}Xg!N(+TJHL@ETmOB5(LWUVw^R{4I=HC(koNCwLijEDslM|7oEblIfsBQg!_c22 zczqN=5252(hKotM4N&$w&8}~I<3WP;;fQCj#5#|G=S?wMB4HKD5TW;1Z!c!Q4$M78 z+5m|+U2c)|bdQ1ER$s~4A|L>nX6SG`o>XsE$5+yjoGwPnomS=Pr-{<+$K)Rln{Y~E zgKH>V2)}OLKvl{tz}SW~@!g}$)dCqg^qfCMw3n41O2V*xx#H0)fHPP`NrM?!5h4dz z!UxT5SNs57t;p4&M1y#5JRUBAVlNJ7hGjOl5$OD$ajfs+q0~R?tA)=0NbyzABcB~S z9oXEyqARYoB<D|sPff7x-V5{EMQe}9P19@ zyS=qS*h-~Ow~7l2LIscf`2IJ2$P_UtFHECU8UPOWEb#yM`?uLve#tGx8p)64m-xhnF6(S?#@JlRJ%ozz z1-5AWuPE~srTxE>{q@#n=+vXe32)=Sia}0gsHXB}9;oM@9x>Oc?NI8;m!k3(wdNIC zOtG+wG+)f+*zN+3HXabA{e{bY=n31X(-S|_mww4`qEsaPKhe?M(MbCKv9O)go!L=? z8JWCQyw?lu#_NLKWXCH0_p95iX*{p3PO1*=DvUdkhyEY0$ z)<<)K+mgygnYHSg0jHTP-r3p-IIK=!E$c2$hDC*}ny$Zc=aW@5qlyWyNF-eu`2S{% z)6V^e=?!I@pM$lw(X9JJuNi%{^kO;zaMz+1ck+4}za$K>@^}QvCqpkhrH^R`UDeP+ ztz`^p?7hR4WD3tTYpwlsEyRQ5|P9TWI4bW^U)0F}+Iytkpf zAf`#JIJjbhYI(+fbo`S=jAG#n2(YiJZC!62NgJWe%VM+p$hz&hW`5aWl@p(Ll3i~{ z!KMiR|q&f;(=|8GMCu@%OVyDnro31fzc6&g7t;_5iivBFoB(;~&765E@X90;0 zl@4P&A&+1Mu1=)|Dy*2f3}aEqB1iGl9N|G%)ah}U5DK`rAk`qr<@6lWvci;YUfNL- z*(|41uR%nmDM|COC}&t!A*E0wx7=a{r)u)Hm1WXvQRpvMPOz?n>Ng(1YQ`K;TO6D& zY*2cz0M2EGQH%=bc?+hv6aTN~|8_Jt*tmN4r~UR!u$wZ>DY`e8{4>yAov62P3Z(L^ z_piX1VJfB`8Q~`VA~ z{)2q1=GxuWd8A~cYgX-jmw7OiejM8)n`95&l=5_7&()CEG5yThhbUIF*4n}2EupZZ zTqUjOBr)6+7)r_X=$x_7t6oasQ8XQ7laO=q8bVW!(w(%e1%TZc*7rbcDtk^k7E zK;lVm^P@>m&Zq(P`-D`BeAJiQpJsIBV^!8zjDqQ#DFV0}t6S)qd*^oNQ=r|jrUB5& znQSP}o))>-2PYota9epl=-UR$ZvF#tO08yJ!a+m@=7+7s^Q`EEMCQT1BAg0;=*tA3 zv!uq8(tX>xUjDnNye5bL06K^#3UGF~!Mbvp0gppQTGyA_H-+BM7�laa?JB2@!F^ z`pG_#V6D*H@WELgp^B^_y(xHXke{7;>dCCtoe`c1U&+*fG|~w_Rc4moOW_SM%Bs2K zym+-#78y#a2O%U}-MZ<})8jKLoBfVex*(&<(|p&djI*zyag=I)n+6NmyIIp`Pr*3R z1dM`Fx?t>`#$aO(81eG;toiI|`p&!54_WN0QcJzg|+eN zN|Fmj#N^vRvR}LUbMzGBZMe1%hiE=0!kP6Lu#KL<)r`A4MaYz(qz;!p%WATC0M!KT zW1lmOy}*SkERI@LOB|3?z=N=E^*~~@qw=Q{*4Hz>^1S|I+7n~#;Irv>$q9Vt7}ofs ze;CT(VNL9B$_+&lwJQxuIg0QF9n*aYd!BSht%f)aKUH6>(edj0o;wuvm5W!o+$xG?Ifv6;p_WT*=#WqY(B{&OVf z>ai2i1p>J@Tz!z0xO|>-&HX(qy=Sb4b)HgLvmMm{W66St1zX>QUerTJbRWC44n9yT!c8 zEkx!SZmFSON;I8cw*5nOx1TlCBjp4x+`u8(eE3!`QWa5uL?$fy>ukO)e^Xiar`FAB z*g8~SLABjqtEy17nqOqSfU#75p4#HfZ>thmmquPm7Ei*@^ZsC_j8gM$Y7E{axqZ;? z4;W5J(D9j9o<-te%SNfh=|uf2d+Yv5X{D^>0>&pzXFxjTqIb26+QnQ;#*xVSqxKaO z;8UseZKC<>!cQnUI~ZQ(!oL!6ln?<;Wq#zRr9DY-xOLJvx?+%Fv#h$yGnbDsO5L?^ ze#`WU89UY|j*3D-&+O|T0!`17f|AK%sZR*8hQ+Sja9>!Q4ok~vYDyS znS|UdU$ywX0e|l`bJ&|gq0X2GK~-`CUoSV#Yv?=mldjk?3;+K~gZ_&fKqRm}vWXw} zrELWC35&wj!Ur}FKHls;?Mh~gkxQ-wAvl~%Yu)nM9%D{L`p(nSP+QQ+HxMnB zSEwyJ;dVn)e6+9{xnA;!(_jmIAuq|Fvzw2Ja89+MNBK6Xd~4K00UD>Y3PnSe(KR)| z`#hjcsh}8NR0U-zLtCWhJR>^YxggC|jb|G+ZVRnQ7ou%M_KH9$gGOMkoZjQ%ISpBX zo%OXnq7uK2g0;GJSj)Z91Wh>=UO7E<#{Kc+){%_TB13MnFQ3k6=~!>(p=FZ_B!J+k z&c4c@m|SIl()h$xv~}3%% z6Fd9%C3Hv9Zt>J>mu{U4u%dS#&Mj(=;z#+kGO6%TZI_PfB)eSux3YbOm_skad|JB= z<;b5~lmBjJlBdq6VW--DzQWYxnIMO@ALzd9c#ofCE8@gw6-(WV_9y&b744hR2<^RN z2?~00Z+BxADShV%A+i;LPsde?rWON-ZmGtLNS81k%5{*>@Q zKxM@=U>p?*;Gu$mK61GEm{3gk4IGASgrI(%JOGX&Es^3gES;SW9e#fk^%W7QgP4X7 zw_lM#GB5t4&YaoLzq zyd=YXsQ_91bA+1zDvL)(kyF}fpp+PR(p^(K=wHvET0lW`!>94d@?#W5bc3aqo?Cab zI1m&zo_*4Hc|Kad%{MH-$T7VOJ+*dF^odToM-`vXf%=1wdC)ljIFAf)bZq-0X8|ZT zFvLXVShbvvDglF?v7Hm1(bi(2#(9}n-2Dh~TMib(7i$c@6heSt?ncUajngc zI34u*!HZaFr5=9H!F$0mjE9w$i(jf6iSC_joolm|3qQ3Jg1}9`NtVC~C65hq%x3um zC+n>y|MyFm_x(=>o4e<&fZR=!b-X|K(~w%h-8e-g=h2YBV@WOYoMn}t&ktktOh|wK zcBmf_7X7jQ1G@IbmEP4)kn(tUPPBc_^2(_vyL81GJra}f?#!!pz2B8HQm4HF5^nPi z7*b%N4BTgqotwc=V28LJw%8KNR+i7k#X9-C>@lR;+CBUVoQ5o94!GB-f*DPeSqdq+ zpFBNIdYWr$jxwB$loLO!f~~FQ@q)=qk1?N}0QGowD5WDOqH<_d_(2dsJ_fk|O~&K# z`r^(jpcc}P;s|xlq{pdhT*pi+IVR+$zM&mbn!3;J+I3* z*NOR|4H@h_*WIXQUgo)-o3-gvHzSI=yf{C#-&JMLqZsW#!c!W1y&|x_v18d@$GSi7 zJBWJ5iI{fz-Xv1UsP2Ht82owCbZw|DLH51DpK5X)RO`HbHmlZ)jJRImGv;kHx;`z4 z={t-emcEQADl;qr@Hh^_5smHRft?EXhLNq%KQ$|^jW^sB#TGR7jg-X9hCaX9$|D%z zTy~2-xh_h6orU3}3wx_gP0gdJL9-U2ZPDj*m>PnQuUJK|^5y=}cH>w@!$TMtTh5Pl ztg14Q7pTy9LCZjZ=QF*Fl& zCcLCD;^=9y(&-g0saj9SIba9}wuI96kb0J-h zel=GTVu^z$DHl%7Ogg@Tou|d}=POS1H){>tf z%&?q@hGsm==2Emc+hd>)PDA?jfF9;Uv@pQtTls)mJ6H#_7l98)mMa-4V|#e#TP42h*bEq97< zz@M=Ua~{LHfSU`||9bqFOUK8@rP8)92+<3D0T{nPfHN^$eS9Su85Ow<;*boT5}Ic= zmyj^ZcutmDPb-C8a+Lmpw!kGeip3l8y|mL`TH$da9>uzIBf@sX?xKY6QNY5(nC4G8 zhp&p3YqekdkT$>Y%K849m+ILz*PkPkP)M*49Q~9-k6c|rLy95iCDT4-WcHAD^@;Ng zLJXSh2NmyjvX40_*-jU0y-u3?lIk-7gzH&)WdP5&w^xn@bw`rgMnb3O^yAcS7EzUB@v)g^*10FuHp|T1Qy>toD-#M{T z%k7~rbtowmNNv2U`o5Nnfw5)r(6&Ts9Q*B=(4_cgZdU;$YT+@GHM?j#}c*DU=B6;qi?(JxMIp4-H6A54dEJ{LPXy1Ne-{Yp|2+D-`VQe zViJPXUOK9tWY;-o*v7X5+pqbX20WA!moqskue=b*?P<&z$cYpS&iow!76twE{U!p7gl6Me5xFZo`j*l4 z=7KWwGka9z3aGUAc@?B|6pxxzfp=k{=8AjQHCk10jLVMiah||orPNhpi_6H0G9=>u zWn~!22+)z@CNYD8AUj&{TrZc;lT2uZ%0714JW>A*>Z{~x5ouA2cnn zTc5?Rg8w>iTb@Gy`z0j1#gxXqy>8kL{6Bf|@}S6Ne-x@}AjMP53U~-Aj|X3NK$A9V zephe)s+JrQmHmjMpFVpaeTb;ch#Fu8W2V(}qnKW=^AFpQYJ7_eW`^x0*{{pBeRdye z-jd6r=bkZ2CqC|Z{m1Q*eh=|Ei90wed*e$lvAj0a!KSLMuP23(L10;Hvi_ zL>%>gAU$MQ2%vZG&`iM6q<<0-#;j(!#b{+haZd6JDcM;D*4yG5|7D<*RKSHw=WM*P z33ZD~033M*)4R_SQ?bjIXk5_Wb%90Lhf2>U-f=eg&kMPr3d{mwC*co!6Xeb|ZCII9 zOj4lAV35}^J@k;HS4m0Ya_Irw`U5dEYKlqmS<@MS#)V^_M6D~aJW@#>m2%k6lQWHj z(8y+C#KyST)E7+;=Ef_$O+u?qWnVhu1%Jn|qf%GvE2vAUujoipqB<*7BlJ3c`Nqg; z`l!fvxZo|UG_z@trIQjO(=6|qe&D6R0z+BeK3it4N7nHaJEjX0?jl@kWo)Rm6|pfD z{#^k|Z+ZKQabyF9({J5EufC4B6Mw#|WsiF5GZ&-&CFFyExqG;T8r%9F3u_W13RG=7 z;!pYBEf@kD&tK^;18Y7R5GX*W4!;OqlAQHf8XuP|7GO)j+U45i)25Rb1w#?@LZ9ws zx}1rui#UiKi@>}14YS#GybEv^l~oxzOi5gnR(|fI=49deuLO9h5E3G}cm1>{-(jzD3A+68s6;n!N#l|voeukX?_#*k7 z#L%MD4o%?iVxW&hoB}K~jzdNHrhY%l<+L@l)>Tl zbu(SvnUx$|&pfJ8qSw|t$@ozI{+)rhzRJL6jV~1o6!X%A=yR49$wLvvXUtjI~h4nr-jVm z=O`+9S0|w!M_vCD>v&bNp6{)Et!I>07!^Zsz#k=zPqE&fhEMA8!XVWPL5F*WqbK2N{y>wNNEdxKSu9#NoxG+BJ2 zG@57@K=KO3-2Pru&|YeK$FGikXc@mG?E?=p*I^V-_;(v&9x6bIQ$;Cuj%t9Y{PDBU&a1N#qy4kkY?|LesX{cw1wGL>h1{~S!9akTP zQ-uZ#sR*LFXNUC%53y>0;DI-00qFwB48=%>B7rC^t+%=-(bNsW_kx|~OLY7U-@Clk z;9$xY=?PFW;_CBINAG;xm;F%Y8UEdcJ;mv;vf<4=salLZncsL|*K~h7cNml*iUI;} zL1Qw@JI0gP&R`b~cQ2%9@3lJQ&a#F8{6MOaXJ{eShtOX>w=V*9WbtBV%uB5N*gH~b zil0(+b48^qtG=}vav0aK!w2KcBG+?wzi+-d(9J#RM&`W?7gIa$>7+0$!5gy7u#=9F zaA4g&303f8b9)`Ft7QXyq?!7Ue96o3AnDgQ9-KXX!>*uzjuId01t*5kOHNyq*r8$X zS>wSlX20yhsxa?wi|nUME`Za*KA*-Um;uIYn5<1N;F%Xkz8S54+M_`|h!OyP{W5wP zcZ=W|<^_QaX^I3kdjh3w4H@+ z%mW&q5)0B@eLz<1QRx!g`%-*c@x5$p22PK_Z<4y7$1Vm8w>WGzz}g99_m!#R?Ko`o zv?<41GKYUBYyU9jQuY{*TL)PUTRyCm0EDQ+>l3`;vu`9=v%3HUU5=*PE2x($6RKD> ztMyh`iI@&Zor3n*OWm$25n9F8Cc9mCX#&|Dz&zD4NFJV>YY3Zqu;3R)rmLr*m7J*e zAfB5@rKr=ysgjyti(|Xd zPepLA=X=h)8>?Ln@v(`VkuF!PbXK80Z_HxAXKJNu8K_r^R?LA;WNH;Ih6^uJReM>8 z>Fp7Io__bdT=2`e#n^h_YxwrE=fCDwhb&JyF?-WL>Upwf>`YwY`GT!p3qg9OfOn2E zV_2JklC*uyQQ(iF>1x_ppR9o~xj>mE3lkDN=TuUate-k1z9JaI6jA9@cZK8hXigaO zHdMfF{B-9so=Yp?*(nT6Y-1yZ-SVp@u*C^71TTsRlFrU3_{qw4sB>^MT4`CGET+~E z2N;0b@S?8*>I2|<`|I38>$N6I5v9u zXy-r}qYt6^UD7EQkaOdDMl?H5M=znY-OvB*v5l@uwsMU{)hww3oJ&2@OvcN1=-_-c z5jfrGv%+lP(A2=@UDYPhCCO@2#Vjh>k6Qy+*Vgvc)lhfxX} z6;pBpC)Qwn{AB~l=z#a1Ee+uL%nqvibn6AnGP^v`jACY)JdUfuaIRrq??afJW-?V* zIsc02AVCbBgKxpt7B;g9W+3N^PO*e~R8FZ`iqS;M{AOdDHv%Px=BvI^S(0q(DIN9d z6j3)&I#?BBCF#Xa`eSXQ?!yjCtEWBV&$a+A2-`IY48Tvk`$z&_r?N*$h(9-azI^o3 zbq1v9^8SaZc2n;sb@cD1f=#9B3K7hKt@Z5VU!fz}r8d>cue)PP0(hATy<+Qe6E`{TM8=!xE#7xIwYZb<-hn2nnH1fWUf$06RrvI0J^wtff0 z0&RY8Ivv}n!}`E=u1Mf1V%MIs`B0FeeM$1Z5+mXwR_#r`nMh0{`|4e9e~X<~uCAdf zQrFAKs)P-j{fPp!h*ilqo>OXer((!#Q{t1#obQlD#=71r`L={6GT?>Fa3R}$Bh}!F z;Gh6@UqRT4f?tZ}%_n7k8_KH`Mw^%#`%=gPpb|$hg)J#k7cT_NAc1fQ7`m9?6NXu9 z)d8S!SDW;DKmxvRSXce_ssf+4h2{t^i=qo_k_UYVUPG6?l5HlUQ^yl@W{^2$#dhmU z?ymtNNRwT?Q5@ApGx?4goBzayRzf~UZ0mVC(t*8r>-8K?eqkb4w}@soxh5Sn99V1z zvBev6j5$XcDVOy*P{?NKVwH>BKguW7CX;SHooBg!{ffNX+Aoq0cjg9B`Qf19F__Pb zNLF>M1p5Aa)~l(EP$RA+;rsPhuiz9DD5lCH#kCWHW!KilF=6qZGKD-26|wq% zu55?b`mF{gQyn4lZ+XG552)p;GK~+k;cI3s%zHtMBzrdUowIu*S36b1=aXhPdLPz( zKYZ4dzDbn6DflY{y>k2O(sHxw);hL2YnK_+1BG7j{{3_ZQ25h&Q~S4vEiskvaXrDMcVFT5WtX>Wn|dM(ZQ~WD3p1*dlDpirNfa%#3FGt0z1v z%Mm%81++xq_W~48RDz>&Jgt&tm*nck2j`@)u}6-XW?kF-hPCvXmUw`aD;z*gm&KDZ z;x$O7?z!y5uxu?I;tt(jT_0XWe}xXLc0VWZDNskLj5Cr*7`)}(u^Wim!vkk6N9dhy zK^|bRz`))bU8eJh3<*|@f7jJVeq3Jz)jeYdt(TtDj#eX6afQVML{$=2RnysnyV3dK z5Fl%+q=u%HmGt5QB@MHBTTB7)o4M5Ay9fUKOfO;Gx;XAb6#S~*fay=-C+N(XWI;i} zs$YAK68(=tJG*S(#~5007vaslU;-Q!FtbTxXHnQZ5{%t`(D}KG)ArX{^&nlT@b(Gm zbO1>Z@Qw{V1@zV%+t4SY)84kn$|dAB&AG-r<%`qY@dDXKw16V!Pct-EClICb|9JY! zuqeZ=YXcPoX#}L3p+O`hrBkH4Te_P^kQ@e(kPc~S=?0M)Iz;L2hGBrA`5wB#D zE@tN92b=red#$zC`i=F~3U)rj+5>wmIrZ&2Dc6WL4~W1OE=taPQzwxw&4z#~*(EO0)0 zN4j)-=mmkM1CyeGHz_z`H^=j%0B9a_OxA%QyXkehdHS!^EZdQpM+VH4en;F>{_jIu zZh$BD;29Rpy>#%8`7n#KX+KzArs-LhNCp(v)T|;{|f%m{7aY!L=?~ItsZ$ zN!Q@mKXLBw+Pt=wKmqtyuRV+97oKSdm-}ohwDow-aT)2*wPvU&WOKKTB>2~6;}#mM z(rRvY0(|80F|xQoT_YQzl^>L{^M|U7O}RU_xaoy@fIFngg7A zVpaI|(_*8n7{x*>+IhNYoDrh0LrQN0)7gGw{H5Y z5D?tW8X%*#uY=mDn4?zJ@C0LM=4YE$>ISS z{uY#?Xge;w?)y4}8Uf$F)oq%;AT^!|-2avwo%R4Mq@(@y#%y`t$9A*OXkpl@=7zs~ zeQFIb(&eE*&{{RPR@x9!9IVuJ9hdT~t-(7HUX%vy6cB z!a$wHk&Ig0+Gpz}yL>{ncq}_MAexd%w#ZqkleUcd1Uv7U6m384bOXCC+Ti*O+zH*} zOgvEr{k7{jXkz5%v$u6_6|A-UII-s?q_bZ7)^;Asy=dO#H6gc{OujGCdBMD*(Tu}1ulqi63u{X_*s zn|X~jWbk-mxbp&C438AoT!2La>2dl`hP;!k6>o?iO$uqGiO#*x@WSh5=@AG=en}QI zzMkc09!LW*wI6#-EBr}#nudtwiL`zjk1Qubk``{)PjUpt!IM1G-8Z{x=!Y;7bAWPb1&B8s>u+Gv_DzV#Zv^0MWn4x zwte@f*>~zN?wjZi0KtFp)%fyjP4<6+7Fw2WIdG^%*M*iYv6t@eP?rd*dj#155{%yb zcXuB&WrL>m#`k+bW=YA#WJ7z8*d6>n1-QVuUnwB)M-H@tePHtcuH(uFY6>N^Ma}Qh zZ-zqZV;p(KsXM;yD9{QRCED1{py;kmO}LT(Pjbj_3l`}?wG$~s<586;wdFTC!c;Z% zhq}T|hJNM!@ga_537=7dK@AjdbjgjGNfzJGLcmFI{8jxzAFYb9^G?`4=R zzY7;>Ipu5e6K=ET>$(G`t~xvDY(>&`+6pxR zy{q48eTpMkRR}RTxhCCa-T(M_)<+|Q9RJwZ36@~K>?v*9%|xF1T96b?gG@)Kiesx% z&${lf>I_ED3>3<2r`#iE*m2Zz>W9G}W}>{@2+n#xe4>Ox4A6xC$d|H0+y7$wVN`WK z3npKShyy>DMw)@4|9gT0xrC5_i7X-@%}{Cy1=3co${16Y=rk3EFRxwwWy#^f0-~E2 z+vt)Z{~(5lwTrgV`G8cm!9HY0yIq_D4W0aw2fe(nXT@xN&nsDSNC;ubGGuVqV9_yY zD~KC*C+tdKQt>|B9=DtM7on$}-QuzZHftbw z_#QnxL&FE2^tj}F^61k+q@H)QMdZYLb~VsTc@h;^{#NiRG0(5N%_XR zHWO80{as@VjIVVWd?M(QCIf+?@>mD8R<%iaCEhvt`y4fW>aZGnQbAk4cmdiZe5yxz zsnG3RVN1=$Or0-=V%0~_26!2PLt$C9_+C8CB(t(LM8)GrRf&McOwBwOCsz@ld{ZJ81Ol*=}r2jV_S)?lGK)`JPDTC%d; zyEm6$YkE5&7vpy<;q&;^L2~I3PrS-obvx!HML=Ev5D}D?!DAW5MqoQVr709f9{+~o zL+C8%5tBbxZdZ1shLL7J8lIB&SdU}B=7%pyKCAS`l)O8gP}yn5{*--HoZal4-f^XU z&(d+X*D><%TD+^nXdQ*4Eilgg)3*FiqO>U4Z2E1+VSE-BTi0JV{zJ0hu1LQxRtQj2 zU0V7@Lah*rqn+U0V*7R$!X%#Ulk`djtsAlaJAfAbSjzW1;B;-95vd_^mvq|TrUdwRaPCZl;hd!r;7S zYDL1fNgH#FaJT}2^j8L{%Eq$a;r&nMUxd-Bvkh#8_C+W`(o6*ik~_|q(TSD_k9xdz0VCQyyo6QH&~6Cy=F>MgtaA1*TzKf)+?G`UNG?ZlK>Rg)wMV~ zOL+72G_j;YeuA)E)>F9)^dd7CzS+)Gq-dt$!nl=aP3|3C7BOEk?G5_q;l9GBN~rS0 zHXwXN=;R}!x0ep|j-6N~u>($Z{iQOLW?Cd$WV?6PXTMUfSr%FT2DtGCuoOuQUf+m$md^DQ&EYiHIBf4)d4(BaSVQ6@8_vJ7}${s z4aYtlq>tGN3p&d!96E}*K2gR~lqZ*$aae|4zcZ(Bp~oRD!UK`}uk|E$E~3#70#Zv8t zcsxqiy}n*vjRrVSjD+tT>9D2Im=)DOR%Ym3)(9 z?0g4?jQErxlMhk0cyrPrxIY%@aJxC#XF8yPyaL4Eqp-;46Q{1`>*&xlbbFbYit_t4 z9woO2)BW*@bqj}$$h9NLdWs6TK%~L|!Vs4+f>h6f0xc^|LVjCCcBp2yUjY+lWj$|W zQ9<<%q$qY5@|rR`N)i8`pV!`8vc6PfAp)~^xJF!Dn{z4@;%)Hr+fb(gDoZ$JQ>;mY z8)rfpO<=;4&v7T+fN$qGLQ?el)I9AL)HNx9o|LquAKTB(#d;B+aX1W4oXVx;-npjm zdQ%YrMj#vhYL%Ym;uG>Et`ujoEF)ytF{-U_=$NH&>f$uZRac7-Lk3g%})1f?eg;~rS>NB^9s$`)pjX#AKY--F zY|6eC3`BVW$@UyNS*bopDPj-*LrT!S_woa&!7CRFM20?ph`89t_w_GmloY3GDjU1l zG`UluGhUA;{Z1oXxU}X=_V;2 zT4>>oE~IErz6Vb}*Cs7XINQo31NEIba7Fm6OU_f-(Q)o9!@hn$Eld_C_OdrTmZYv1 zif+z+%Y`c+Il1{2+_sT6#FL;-!c(5Ac90DrL`RcPbN2V|_H7Ha2DjIbL=HbFoLNdo z@CSjkW4io)EbVXlCSH6nUrv>fP`Xpr`2MZr(zusM(5K0?!ac;Kkxmk^#J8EFzNlbf z07w>lc0?K;lw(KILbs3y~%)?XPlRkC6Z4A!FWK%*8Q`8J|nb%IPIEVgP$^S6OK& zwsU&#U)|GQP`1btec2zbZ7td{aOSPw>w;DhBkwJ$1s6mWEb3>v-PCoQPhG4hs9Y2n zUoix3S_f|aI!GF1xZgFtWo!K1otxgV{fb=8( zEul)`B(ROW?q9g}{Ac>}8Yx3km$L(sLCZFEQUU?zLNsX7d>BgU0k4V37WMs0FL3jS`Zo$GBLm$-ta95-ZnvqwX z(rtQu4#XfW`3`>8Q^=cLvZ1E@G;vyH1$T!i%v2Csbl7i-c05KK}R%%Xg;!NMfS zws3g$a7)B8F3y^G?zjzQkNT(K-P>5*-)xC+li)po`;6}FKx{!}eoy(x91@*I2VgJq zUoK%EH(LG76pv?{r1o`#wMWcf7j${+w}J~`>+E4W-iG9|F%Rx$@>h|S&m7c)3@YV| zPQP3MT4gY~F4u^yaiEf^AfK5PDv6PT%HpIHvuY8vY*_d}xS{*Bk~^)!R<1gu#OTY$ zkrVgMw3que#ld+<+4n-%x@FR4;OWt=VbqI7R6Cr%<8oM=@}J@Ht@(f54HgNm zT=L0e254p56U{FZc~#xJ3FH9d>FNBZ&ri%>RTb z&(*M1D@q@o0q>|s;xAGq)zMGrbL5DkX#KVvr#f|-B@a(53mm0O#zw|JN;o)-$=o9a-1<(S|`a_*q!Dsh&H((i${U|O4| zU{gVoMDXz$sT zDe$dgfYU?RHvS7E22CJp&^-M%Yhpqj>_}SOx>HM*ce3oZ^+S3pYBdR))G zW_7nhtByd0TIsV@dWOo~8YEe;wT(W|e2Yy(&nNd!=+8e||5s841l;-ks&oBUoHE~F zIk+X6a#SPV5A$-W+J^mobyT?ZUWU5~hR5?u$ScDCi(Bz*GCl6aQue{p8JE#FQsMjS zS3&$q!S~l>#vF&6qjxi6ga7IbIX~KQv5E>Ta9J{iyb^@6i?!~%0ydpan6I{Gu zQS8U>sV5ur954{60HQFZAJTN|avXePKm2f&zZ!8`pw#6RT7~VvKJ%ujEEURaHBixp z1MWKojSGN8Bc&$2vGcD1ZaL3^E-R>4iVyQ_Q34d162n4kdMLLAs8RjFnY0=xhqInKD_w&6Gxm{ z0qeOG9j=_x#p9o~A>L|l`1clvoW-K+S^^#mt&mc#I9PdGcpF`-6)Rmi!+2p%=B2EsnUcDiy)M)j zf`Q_;%Bo50m;19+S76hbaE1=)ul9eL+F`AYRF79P+t%y7>pS1DK3#~*5mKA1>0=Ga zx7Rua;35Zyk4&HQRkl-eQf_*_hj4qcOZtItF$dgU%h*cREsh;{**I`DH*!NI6+FJy zT!$@h9sSk)V;Hi4BN6QECA-1YAATXd(FR|LW53O#x<&u=Zy@P-V~mQvxZb*(&%V|& z_oh1p2a`+AOAzZK`qxvUgqEDIk-!}=iln+<1J(yQJ8s_Z>FVfs2l`)WJ7E5MP=%cK zEsw!D?yCe(>B0lQkrlE&%TtTzdHqLvW@l9L-5pp%_kI;0Qn}cxQoc_ z$B=hIO5{v_3?-P{$!%X#t=w3mSYexGS+TlyTM}>B86u_RB7c)iT8{XrXDNJI7SQv& zMW5KGR1}XR<#G5iQsV=UWAA4BY1Oond%&fqx_EQJpG^&Y-4=Rpc*sOWpde}w?ZAxd z_mNOS=N3xBEYB8x&dt!c|Ma}bJee$1H9iF&?vk6%#vxmyT0`1J*Bag;-! zhD$*euna34s;Eb8d>k>Lv=1Xd2J#fCwMvT7$TjpjFhjm0GaRe2CRI0{Qqk%a3!x(4 zS{lr%OLaSNAZj!}|&hAw9b4)ee+elpB(buD|?nxTQw;O6}mH z-F-O+JF{_vl7po+M||w(e6?z=!gH%ylzZfW>Z?Sx_0-GB7=`jTdaM~p!pj5op>iZi z`S@-)Vgd|z;=*EP=|lJqXb4F(r@ud+gbccZD%aV)af4IF1g+NBt35bs>n|1)%*1U)wvPDi64gi&R)8{w zEXTyJ9-sNbN_5RB@kVhasilC@#_OjAZyeJ$S8GF$z8=&3`5ik1Z?t3zSw6bI?HF~s z+jKgKy&D%>1#ea$@Bp!f=o7mwx+nb9amjzbnKwyczW$#M4s?S*I@Z1$FkTV63BNyn z*S!FK{T}so+h1RP?$@thILQE2Z{>98zlR?LnZYwE=)O=s(>&tWibJ@!pD?_XS=5Ol zzmBUZY~K8>sTNN*+)g&_MVHE*AtJt=mSJ5TqwLn=O&v--aaHo^p!mdHcI)zLx@u3R zHiM%7x17Hc)+9GMyQqv&`NyHh6a!9uFvh90HJc+?6-jx~z?n!WU>s@4|63Ps^O##2 z?U6bCS8n@~8~NAab1r85Fc^pPV&eA(gMJUu$2z-t%*NvG$+vI^;t(|~!RnnmyN*`c_YatO(hVFyl7V2ZQ$-j_t4 zme=oBUdY(NCg}t2t5K@!q?U2WD_2=kD4CN%56RAT7P5xj648Zwx~L?=w|N*~1Ea|> zV-w}$erksG-A@#r_*;iq4$(47rvh=AKN4&uz+74L&q`+l%wNf3UsEX9Rcl}3jNIN@ z`7I9p#*_G<@j_R1B{LIMgDVFiy*^Z3JtP_(!@EK%d56Ij{qt$=KZ7{<1=zJ`AOFL* zmWcrb@g?{trcz5{0I>|<=h6ik@Ys(1U%;%Z`Q(Avwe>x$1BJSy?6?{17y|A_?=N?c zoT7l1j#I^sZs1Dg9>Yz=eUhyGYL_(-$qyueW9c1s$3;|!oW~Wo-xqU#Vtj|$aaG#! z{HO7?9S|*cR&@90+4N)Zq8g2qUN+Rx(Yer&7e1tgECe9*Ez$~yswQ2V#Hu;|yos}_ zjxp!|q5HRN@KN0FvngP|#{)~H+J6dF3k0ZS+9k>rX&vsBJ@49+RGea+?Ado#S~<_C zTV|AS?n_Bf3(-&d3&Ay@pWeCp+e7CjwB&)_LBr5E?PWl|#Z$qfE*^s0_i2c^xgWG3 zALl;)=uzVxFd_P|?~|GYu}FBmrDjHVc0-JVkS?e%`{+u$6IK;Q=(B(gs=mf$ff6P2 zeENdEG5we->oiv*UWS74SOU5A+mZFe<>?o-3qQe|wouO3IsucRigp}Zq%MxP$bm?L zI*3)|A}Qdna*Sj3G<_t5HnbZqE~d9TBo2d(yw(dpLnJylq4TJQbR!CJCZb5y4lf(Bna*^Od9 zu-SpfnPxoM?d~6DXP{w}AtuSpHh<+Ps1;tn=DfcHM-0T-!wTI&x6G~dvo7+c)wqfU zvZQ2%YNPThjvsRkW!l3T8-99=exrBJ`L?gptUMZ07@jsyV*gi50KC(n+(`p|Hr?>; z;0-P@^>ru84IS<|L#>)znn>jHFZ~1TQsHl}C|PksQ{>R`A05%@Ed`dfA?fK6c$IE* z7e%(~)AjUdKDvg=$gFw&?8gJk2_{}P<2~2oM4toZz2>$l8*Y{7W1L&cOTvy>vpOqiQnvpX;_kr8z^y4Rp(|2xD)xW?oJbW>#+ITZf zPc16>%;`oBWoXpq+XLKI-hcgt<67kfVg*7&9N{jTxC#XRn#ktURD zAQ+Ao7`t?tx%6A;B__;By|5+BcR9WcG79#Dx>3q_@4G(LZ#nQ zA$X#?wp)02XtGUr`=()?<#qAgz1J=;>1_yKXN_gcrIdOB#Df{thOXXDL_ zDy}O+Ztk2A`latesGWw+u%GK9GW~WV3tq)MU8X51{>wQSu}-;E*j-XW@8uF`l9eAn zCNqzNobEIxH_?n@E%1&L@?%CE2&lfCG2l3Vuz6;cxb({a#is}1DVOJ|vTKjh65yp1!TQeP+CYku@_#OEBIL-!5#E^X%}p;Gg-F|1#gqEg&)WG9f9ZyU zzC_!ht;|0rBHYgNWVMCgfCpi#$*Z^Xyk?Hm#1QGioFW4UE*v&(zt4Q6v+GI$vRITp zlKb)sctY5T!_RLY!#cgS=o@J|HxjnY1-*fXfjh~8kzfXv*!sV=dZ^=W>OKu@t>Zh6 ziSHhPa)>*_z%y;&tn+X#J%T&Stj2M3K@>JBcAs&72GRxhn@cxjcMBcYUN_+RC0`cN035A z+c=ZsF-e_*5)pC`E(LWR4j&|B{07sNm)4(W?v!o3}~x=XkA%A+tA)qN~KA zFwQ8yv~Qs;2-Zvm*O2ziN4b^%^zi$1(8$DeNh$o`h+-{5z%2}cuZg<7qn^*ql>9y0 z-(}9{i=th*tozrAN;i7XIxg>r{c7xgi|qif+MB6Jw{t<)gFILNAym5`D|<3Dulp92 zGgC1IHu0KCN<}FcrV7#>i({02UCfMua7!O=mMt3%chnTdHU!oc$Deuass($=9?y|u1CI-tWa0FbodQdf6<#~f!%o;+@=q0bRzj-btT`HxBwzqrg zn(n&ZI)%;BkxP%jEdM}O&j~?uVzw|(TKt7f*_7wX#l%Aq_6fj=!=()!PkCmarxjl~ zv|SjPeTbd-D9!byt06kpWKMyZ`evDv&v55k&xN1uRJzGMZ<3u9YF?jKlYNmm&K+`u z$$T}7fgW=&>fD`$@voj6s^W1o7(^(IxRJ2&p_So;2yE3^>8VLt99@Q$V=ib!XV!F8Kt}>*uf-}VrazqPBu3MNM@7du^`esRg=H4E zUO1*(p{45IL?6%Lzt>d|B&>O0JQ=E;er6!yg>ML(Bm~vu55jD7ixt0MY~8*In(`Ul zTL`K&Iue-U#hQ@HvIM`W-2GL}1lIdD9GSK67xDBD~N=4QFk)^_1F}}$b`U$$u~0%5R&T< zIqu&SD5b7X=}&`EO58b#G=rGc)wG<788uQ{@183qC2aIWkH3h``L1n~5J$wY_`X|U zbb6ezi;#$sGs$*68*OJibuRmntLlFal0|bo;6E>3iax)t09_??j(nk|iAfuYyLrOSz>xZz93JUeyRP?}P>`E3WL-ZhNmPcB*s7=?WnnCI z68)jV^OB)4GPp~I&Jym!70LV>kj-k&Z)G}JJ=nUyJv3Mh7tbWC9V(83IcD@Ao4Lvc zGpZfGyGe>Q30_IDdLDdAiXUxY<?`a0GeEEE;g)yJ5R>#@lJFJ~7~es6gk=S*7{WuV6D6FMmAwF zLxltD@@6<5Xw7F~1@+Gxs6;y>Eh5JBCv`3)=uFy{NgEoP@|p`qhsg0>ouqZ25dkb6 zyCB`=;S<>E>7Dm}(`v@XzdFjkS)uyT;;lSUhFDzr+`*!(MB^$<4(e6xN5(z|E;YdE zB7K%DFOfvv>I${t6u^RMcnyJ%>PT)m(%a4!hnTQawX&gX)JU|S#62mmrm2;;C*;d2 z%X71pi50=Xm+=42ZQil}eb(`Im4Lag^_j45?QO~+>N!qy6VuslEsC8Wd1`8|^(6@N z(RMPDaytH3G@Crkd&0`8HN}Ia5~Yr{N`LhZqd9U14#EwI}J=5RghuRGj4$> z56Q6g+~)i7(W{@^!fFvhp}*hq*XCLBo+vCUI|+UmR8rsCWPzg=geMc1xkgeOJRBDX z`#oq*nkfX_McPpAd6rWuHjp2bv*S4--ypAf%1O$s#gs^Byz5FFJ${`h#;UzaAX3q*WeSnH;rh)H=$wzXf?+i) z{N{u-#^)dGSnLznp#`=wFM*d?6bT+mlL=%2ML#sH!aOrlGCDMuLv?wt!R~P?ZBZ{H zwi}#&%``X5N`n9Pb2l74<(SLrYL_mZsY}PNz3rBsTaj4rDOvsN>%rj6=lin2Yr%V9 ztWAiHt2+V{u_%DfPIPT*LC(}JdfNDfOK)slu#_in#5(y$Y-E6#Gcb-#C_`s1(Bw!F zn$NEEPHq+|j?laU|Ui+A`cR#K}ZAF1ZXChO=Znxrs9>xz%7PTwn~ zzIoJ>*@*kyH99f=SA<|v=d%l`7eUfi-c5$)nbwBd+N7gu=sf1%2Qml=3}Ndty3wd^<%@K!@Jf?Yi!qx@&hyN~9d2%F@oGirQ zDU{aSuBDaa9#aSIeByi>4_l2Ht&0t6ElN372P3Fw*IYblp7C4ve3UdIgZ+GVq?}#?-3&%j9MNyKe*J!zgdlWWo12y<9)J;g)#pCA@<((;cp?{Nsz|>CHhKc({r8s86gv3(JS(nwap$o+2 zD~2Z{esA}tJ?IkBYlMu;KN?lA*poWb;AYA~ee|88$Ve8*392|;sqs`E&CXqws3Kib z=Gk76v8zg$tj$H19OV^AQCE;>szvCw5(O{%6efrZ>x~u;UDyMf$Xp0BXoKLF+!Qe~ zt{hWS(ug2WiCQ@&C*>Z&!;)~`kh!a1hUg>%l{FZw26gD3|S?3GfA0t(cEPTq!0j{z7 zTPYbE_HAiXIRO2fhq6LsR98^_Ue#oclaRX@?wCE_i%Fqo)AcjQ(Wrw1pSjVX{`X3r zX1A+7<%6J6-JEBwgKXAT5k9xP71rhJ5${_P9U&LteOnw^t@IaH>{AW>JkV;3juxpt zL2ZFSp4Nu-LK7(6&dU`AyB84CqBvY(1v1h5P%2{~VjvV3+!IcOt|7#ew(Li<&%N9F zhY#lH%$IWNWLT83f?UM7&O{dVCapOaqYAyPmAfalM&|ag17n(S&0=i5k>0J=Ndq7) z(fvPY<-`9k1*VBK;fehi5s$bfmTjyAy4Pv@kChhAX?Who3Si{*8xeb(Lqh;U-Q*|N zfj_gLaf?07k+EJa(7Jey)EqMQ+x71!dsS#~msRWQBy%xLpu<@!VdwIfp5@a#@J9Is z@7`OJ%Xzb)vj|bcr|5Qv5>3e%Wv|RY~`v=&Q+Ap(x4Z+1_1Wi8527^y(D}DEvqZ$1| zmTv<-SddnZ@L9;bRw9^uiXTbKZ=aUtVK}bw1TxHz>yP!NVhbbLCuSZ{Xf|E`JzNl0 z+T3f?&*_f{*52AmlpZ;BrY{H?PaEF7?k~Kp7*gLBQ5X=C-o<_Fu;j8IgSGZ45p2cE zOQ8pzQ9i$JCbpE8JlqXs|NfckUf`F5=$7-7qx?!rx-w`@6V+1K@X3Epi1P5G3%80=9ejk}F?J!vdc zLuTVvZlK|CfibazN<hgmE1A)Nj^k zo|QlEHd6>m0luK@YlOe1i4?4De>6sq;9i!Lu=8OtO_w$)U$^aG3~aVbEi+dq((q6= zv>;^4DW+hON>fC0a{B}*SjZrE?LQ;k5GD#31@uka-HFo_pvE){zoRBH>J|@zHg~qW zJ6(|2r`+qgFqZfP4!V!JwtHX3pAf1#*BiN2&AHhFerYAXNuhQU==q6WGEN~Rjg5`# zt0(bFq(&JX)ItY?6?;3^54VIljoZcddWNq#JkyTB^yX6iHDPi6g1r^ibBXq)kDfRi zV;_mcD&pfu(1^qGy^^NXO!6&ii}2G4flAkR6Z#XH@bRh&*IK!JeS4!VsBp^lRNYQQg!rHLqS9DOQieW9P4p!wqJ0fIm8QNqPn;mbK93;$pcyV?31rs zN?U7Ov`<>~roe`Tk-rQ&^W;!6!>~nSOvMe69v`f1=sKCm-*Q078Wtt{JO{OIWNdt1 zzH_8tzWAGrY45_EVs@Zr0BO!i-CZls{--&l^9MDLWQcK}U-hs3U0ba2Dn~hF zWP-yUWn?tQD*NbbQh0^zOOfaiCqsbe2}kEfG7urIR<0#yS<$`}@&^_Wt3m);CVUKU zTtQm8$3{7k+^$(^W8DEOdsI}7oJ+^#dCx@oAR?o;gh>%$|LO|$e-}w?uohH%jz~eF z*#)$72&_hb*aWtly!L5Omixm)!<0O9p7QDJ=M{?uLs*uM7aynMNu>k32zMN7Z6L)+ zEaZb6wkhZ+-I#`z?s77k!tyZlNdRYT{qltGW| z5^$M`)qdXvUf-tl6Y(CpKgubW$q8#}{xbKk3yXHEhlE&Yl1xg=ASq%D3kjQ4YbT8@ zZx_4QA2q3J;}KKECz>q9?q3J7@F%9qv~Jjs)aEwCbaBPm_LO==YeA_p-z284=28e^ z6F`9BK|au)Yu$c#_1)HJ5fmq=((4(=x!c3J!2|gSwpTE%o6cmhjo7;j)(1xR4HvlR z+|*JdcN*f88Vn~C2syiGoV#}g&}SjDd)U5PCJa-NKv-ZE|eCYBuXOLGx|DeO_*? zE=|O2>1c;Z+agyC-J%ez=@vQYr;@tCTz;&Q&)S0<(YcgRj;U6h@c8l&rEH{yUW`sd zD-BXaY8X_H76r|Y%SMc8H~iJdu^;XjGdB&uQ=BGUxc&P-+0ML-N$71;GIBWdwxkI}~X9S!$3J%%4G-3SN$JcxC3w2hc|ZxZULrwW`U>NepK(7}hjk9ey-ct69-ZHn$WWZ#b$UJ|N}q>xpV)ujR$f~^fHmm@ zLJd&VKMfa`BB~~sGsR6jod=j)<@lL%07g>#WQ`{-5#l|k5de%U<+u?E*#W~;H;7U6 zA~F^Su~h?;g+FE5AdgbT#tm+b#PV?i3L2LECAP&jz*CsBS85I{h(XoB(q6?C${($1qRKx8dx_Q zp|;1cuU{`v%GoYX?Q{Egv~iwl+|Dg8Vn2hS7vxTC61Zostu_2vL&VYLq5R1n?KGLL zo`=^%XCpbv?`syn)pqJD9>C1x#13~_xn@9khu;@{@(=sp4e}qv900+HJlc?NL$3$C(3*K)Ecs@NN|mAV(M}Ta zSVK)TU+Qf61nf82`|Z+p*+?t6ZB}{&agJy+)2OPs&Un&`KT&ZcoNHZfEj}dLmf4!| zplkC0!EElFe0u(xQmxV^0XFsE#kX3U0SM0@ZZz`-HihF27SEP?>eD#CeU)>c1YC{@ zA_N=GqdX#2Stl*|hyyd{8-JF+V5MPf7mWeU_fqf+xc?{+Opsf0391MQx|UZCOtoW{ z(suQB2g(_^cl~WwgbY`1Jx=Gb*56!$-9BEG_~`>UX*oLbCSdANk(8TH#gac$EF>ze zez1u+Yza!l=hQm$>+u^6O~miA08-1=HfF8`3wlY6GM&ct5OCwR-YfAq+qCMRHp5W| zY0b@oS!QYE%7$j5;>D||{>vH9m+o^(6-TynNm6E5k!+*WWs~*|M;kvLHvga5fOSdx zJ3EmU{_LuR%VOqIXtU$R)d_yVsh_kR_tV*~tY+KqyG{dFn*w3l;5K^@g{=SS&*$Z@ z2deW+$Py>DgOBc}Cn#{hxo_5`dO~W7Lbtp@!)yHkfc`I+;Mn2WJV)(YAyZiD8si~# zk2o~wV;v1EOKYYaH&2+rkz#_Obay_9e1o)v@F$YTU8eBqI(NgUKVY^n_fwFTGbK9# z>qYE9)ZoH%{wqLtSB$@bBt!md7}b%jfGQ_Q(6ZeatVvod;hg66T!|~yT)!FR^@ywD zz#P$jLoL7)<#HhK&NnkG@HZJj`}+OD2F-^=bjpVKX4uYwikj^z48Bles7A zt-1Opm6TF@B*{c30@vw-npwa%3Z@Jk5Sr38{FX13(W|xL6mtI%@+8$rWcKSVWLfg( z^(SC*cM@`<05QsPz@Qn^c$3{GY`(xtJ{vg2!3HNsC-QcXQ7+Ddjkhi~5PqoROO z{>LwPRap+=;}R%isEyV2$*ZQzP42`0!`=~?ZCtF=kbpW45q6PY5Oht@x#CJx|jw4RjlBI zz?c8#FS){A!=#I7r7Z%Mzji4OUz!u5SV1{SyvW3L6K|VSksD%}rs`))^N$wyr!|&l zkTz^Fg?ztlwU)WBz1mSmWr;T*HP#L( z>PB;Q{J3d$dC9qghOp+a9M6$A?THyAZ7#28@%8<@p{8AiDLrrICs*ZP@bo`(MqE#B zeDHSvdxg#Xcs$a?_KW*%BBj&#&Z;Hmb`Y7ePR2e3E|GO%w*7}wKq&~8^jZt~%WUxV zO*2L5k}^)WA1KB_z#)oal*uw*obqs4_C#&*3w1wNhJr+5ZuX+wA{r5c1h`xdPaH>> zs-$UFptZ+n@O8oG;zC|QgjaY9%<1On9e95`MoOVnF)G=LDl=y-5M~d1yZ7o)g(>XY z%CD*O8%GfkgSH9yjr-5MlzFkhbCkn!x2ZgohAx1f5ZRkMiMuA2lKHnIr71kqzOjdu zp97?q{1pRacuMA6Ccb2opZ@W7_>xA#?Rw{&qFyI131S=~3I-)rVz5i>bs8$Nd2}wz z%d>ZMSTAS?pJty|eQ6)76W>AQ z?+?@*R=w`Y^O5iG z+<;%x@dT8tnK`icuc~>P4CHEL7-OQN>U+Drrq@HOB!q_cC4fFYSY+@QNDlkrYQdY*eZ$Z0$6y?aBho z)E4F$fin-h^V*9^{99pYI_>=GebTQwhre(=tAai%g^szG2&a+eRW9AQUygl%B<}Z+ zCJ|~5Fqe%)@exmSB%r(YZ-kg@8H_1pXD=!o#N!%haJ zg90*a7*m*%4-nSCAcLMhnZa|1qhMU?gW_eKKYJk}B|-xJt3E^jEPQFEo|9?huVviZ zKJv2u@663xbNsonN{|ZMd$JIg={Ur5aqUC&e~y|k)qs4gC)zroeLpPY@hh3bxnE#k zpY$tWQ*J=a0U=}O*)s^L-xoyUc&wC*FETehxPj8X*V37W&J{TjgCp3@AFo%A9P?>|C_ zTgP#ga~;LOpU7rV%@aNX$(iA=gF<^nMg^O8$ggZPh-8b(odz6P!o(;gHmMB^CVz?o z+8<_3kB>IM`M9UWAU~wo>V>LW-iC8CEyxQ~()l`MIYGNm=S_pnoP%xxIpgyP2q#*% zgIJ=TW@#yCw=GRFJQWy7qGXp>l|T4ZFbH8i)UW3V1N~qFouKf^pIG~4mdpS$+5YPD zMMj-Wn4)~nt;t1$>IDD9nfU!E3IJzO|7XAu?x2E|O!Y_|B|WC6rWWNkt{)UKcdLuV zhQ`RUf97yWbSRKQqKUHkxyx>lkH#&h>W1QA9%=+vsds&!XMnibzErDDzGg$81?cBY zcu0xHTw0m4E-_Jq{+5ru79pHz=$YCp$f&{n7zzJ8j-Ore0!OYCAioNR!!n>pw`8J+ z*TL@LRIR@l=bGtj$3?5MV_c{*a#Yl!kMox9hCc%1kptW=sWz_VQZlG@)pTd8H zFj?g$Z5(p{178npYfbmLf>IA8ThW=!G~+rBP|era%xe8KkW05yWser9Lk47(T4nKW zIf%Bsix+73KaH6I-u8<@j1s;}jdG8!rrw$xx8#$t?pjUod;W4{`}aWJJi5k~T*<4s z?X<3sifaDRjhnWK@bBiM>=IDcbNFjIa26#(T?zZNZWj+D^{+S5veQdjnr{s__19(o zI3O82>`K`X>angA9vG1yE^Dis<@!@bH1Gc|=nY!H8yp8(t;{j^iB3N75s zd=v)^RadzySf>9xaTV>0N+&5{qa4hmto+w;F-!#NhIKVK6=n;YsoC_KD{TMlOm*@E zUw66A2Y=qh6f82CA!k+`k#cAtMXA`Dcr=-kPG9>Re+bJ~Kpy^cpRj7sw^P$|P1apj zcul2j`DzbrwEk>}&-w6j;4-fUwLE`Yuqy%lvwD4!Y*ag5D;^XC8j2$IY(tl?|Ncds zf`eraqW=%nIwNA9|Bka3PQF$Yo;pv<%)6Yz{ADk=Cd?5l;aA zH;%pZtR1)?#|EQ_%K*mqYdu_f?T*{XJL8EsmgiZM5?M#ygUjo55R=ypVS~1xW%w7W zf2K%GdqRupInvV1y&d{XDp9H<>p*>_Ynk<`LWCIfe$?0VG#A?v3_ zRR{WPgE8f?em7sU->p#2feCCIaLVQz9h_uH?18>e%g^=WU$$<=~IJVlFzc(kvX zZD1&d5PKjbDH7f0FB>8ALt1$TZQg65BWqe~S{quroOU|Af#Fc+xUa*p^lVWnVmKME z|I)Ep4X2#hRqy##@3iCsky(p7B3DgD=cbj-e=u<=JQ7TVE8rEIbW0Zl=akHZlp6k9 zb$B7?eisKvxJy){*@T}0k9V87IrK!lK;*Q7i)*^B7ROJ>sJmIwYR1($0b|ew4iX4? zNwD>tjA*1|;^h_{5?=IHVpFA-xPSYHcO)J=EL1^Di^4fO-Fg*>@(y1M^p$2FwX_*N zw1ak>gW0XKO>e3v8PfFG9Obj@KWQ;#ILWxzEZ%0#Yr58+e}Ie5Is>Qsk%LGC5`QO^ zUD>JLJp?yI#cy3lok~3+kJ-|P-+8XxMT677qwws zq1M|Le~*X22l^bRo%R0lN#IQwN{w2ZNjFyendkeyf#-c6D1@%SIweH9|~??-8! zPU3Svr4~dQI+$b0lgSzebo*iiAzUd@N{dA3OnSiS6f3?d)Z{1MD4aDGefnh5-Dihx zr)f}Pf>%R~mt@s&nmV>FbW}7E)b}-EQL63mLp(5`X+E!FmTn#UsP6b_&wJ2*RA4{d zz^4ALR;nvXuMB?>ndLRJ8?%XjNRV6i(_$i6NA+!t0-(kGu3#Pv>ZR6Ar@Jw%iY&l)qG;^pYO7dAwTH z2xfZPw{3JED$JKEVITJCUg-7kvyH+4SWBedhjY7vMte7Rc?0mO8^#v>yuUniMEi1~ ztw=tr<;vCB)8&q`N}l;9E5Ypi)b1+P`nVd$-}702#p_-2=fzQ1%p~h*9U@rr?HGRRJ*)rl>YHPY4rWY{>C9yw^3buv4 z%7)iK%igpqFnJP6Yh)HVzb4-}?p9wdJ||MFE*Qk?aKXc(>8wr+MGVpoo(&+gL$Nn{FSAzpcK7crBX1C)oW3}-@7 zcV@+y2X?zjR!SN5&R7-~_&-rKm~|EbfsuBNmdEj~opN0AO-OL~Hd@R(QSTiu@Dtn% zM5<xJQa#SQ?%;Tm{aWc-wN!0yp=6-GYa53?feG%&}rz#U3 zCk(087kDy^HHaNk+@u0_@J@*}<)KRtY5A?jQiqsm?76~^+~ zG(B_(a1Vi{Uj}L!7*vVUX2F4y3ZAS|<^_m9naTLcZ?CD7eyw#d>H}nRv*2-aYQvR2 z>S{VXHymZp-pyiz3_s&~D(WtPYP0BWkL|M2vS^qHsFq34Cw>z18=1~DcPDy+D)7?I zvmc^dfAUF3lO~~ew5`wB7HVs2yS~_afiLyn>*K(klA*}`r)EhUpx}eJ)*-B`HheAW z@SIY(SO}%-;JLNZTharqa;j~Ls`@N_VxnSJ{iDRb)%5?`CM^DmsfRh6MYh`CZdCR; zPK0Vq`G>x@X!?75ZU%i5(7W=Om>@busH0@g?$2Jf`R{lzQC)Eu*9MlVTIXmgVZ+s8 zzI=*0e%EMcF{2L!64RJj@P4-?oHmv2Nk~A&a?-+9W@_4aAw$z)kQsjN*L5`)$F7|p zgbDWfl1Cha2Ay?(H1NH;ofqA1+^}K1T@F_oJ(~o>6c+jUaMO2$2aNObw1=kBQa56n zpx6hBZh=;t`v#d4rQeu519zQ(?%}s;CS>R@(n}U(;o9jy-}gQGded4H*XB1iKRWC? z7XMlkx*rK{{YFWn5rRSL886*DJr(I*K|TYP?dM=GjufJ!`|G2JDfdDr9dzbpwabwD zYUAoAnCCKKVfU{11rv|AJ|EnL5cgy2r096zIwOhLwYAuM_un+p>gSNK&y}#83^j`w z@k$=3yHNsH_pgXMG7fG3{$v}GqpAg!lZ)gG>{IJbv*&UDX}?SgNSKkoHId z9q$l#i`-K@=&}Jqh2H#%)D@j-Azu)05+S4WV%M?3t5{unrA4McAh9WdX<2u+kqoVk z^x)hvaXi1%r*4@f;vveS1Aggl23s)n9N^CfY#oqMu}N_|)!Ls3UBS|2X|3Hxkb5cF?7}gGY5BEG(SE=b@7g0ftzwp9GQ}r_6;4_} zx<;Ic=19s~Dyhg0egRe{BofWD5^g1tom9fDt6H2^Wyn*=wfg(_@9?Oy0jdUsYWY+1 z6!4nA-=KfTLXg&4hl;vV4x8kpsFX)eLeGA1u`U<+q`p~>$5x)lVj~xMfeSw%f~9aY zjJGcZ((a1_ies=s9&GW#cV4>5o2CJ5ettGr^nO0>oUAn07v?n7)YN`iI?MFnS86|_ zclG6dUr}ExXwshyJ00 zh^q}Aj7w>uuxLaqQiLFp6EC7_VNjTc8DkPQLnC%){$?kMMX{5u>=2T~r3R3wRcPh& z9K4kzOVki-ZGLS|gdWyC69bnb=|AgY#0Z5U=0?qdJSM!F^`;pR4q=+rx%FCCid8>Adkb{~jB&KG3YC%SGi z21nWVTLIr{=LJ1x$pn@ofVX5PKm-}X0tgoUG8wF&JLo>|NEDoK6)_WfFBIPj?4wAp z($rtaSy@2A+%_j(FAI($<2Q~RQ+FfDWvbk}3v?h9ZXbJVUU*-$t2;VR?|s9yBR83P!j$7|I`utf>mB8 zrupJZYh3W7l)r82(@qrGS(veWR#Y#WB?X>geF=a(;F5CXL-gq;DeqVP{;5=)%#uhB zWS4Trl2ta9xCEH)My$G`raiL+@4 zW2d=>EO7Tz#ZS<8;^Q1;ucfDVca|K@Eyn z!U;d~;>Of}{+{otJgB2M>-9`?nOvSo{lgr&4l?pQuYVZ3awBHjv&U0HI(GUuJV6w7 z!I3?a_40{5CT7%PV_q?}orz#Jqq$`8+M;I42GwGyhqIwZA(yhM@cPo3G=k>HUENR% zyMHWSw4AK}FHuoI6{5G;k_$+BLKB-6Z;lV#{wXrbZ2Ps93_eKv>u?cpUp#u+Ek5zK zt)#+CqS_5X9Ft_MMCRASHnG$w@49%}?p9wa8o?Va&Xvj;2DhX&+`*T7umiWVw58lU zl-Mta=hcY5al-B;VvksbZjsZBvM6OLH_RVw4Qezrt=Cx_vWe{gUjpJaSrmzWP>&YA z73XyspPZo`?S=NjxA_qu3z!Q1WL~#13qjT3^&M7lG0PUc9dBpiQ>-qPjO~Cx`^QjiS~EzZlA5GG`m&bdx0);4XN%t@SGYLn*>5WWmW zf;ozgbh(lr_{D%B)<4Fu3R-)$BTZ!K>>Fr?7H++UV6N3g0QHXQGq+M6<}Z!i#sS+R zjGR~)4@ESr6YQTCP6sggH6`WXfpNiq*$Fnv)KW8q5+>Yz2?;k9q1ua{R7D{Drrkj% z`v^5_x%L~?9j`qV#?rTT{YF!Y_W(6B;WV0d*kKCE>(gr3jnR3t{@4F^(OsIAi8HTX5wPMH+2$k}VgureukdL3MJUj_}f#0D{dn{O+0Z!hY;Zn0g*bD}5IiWoVAAd~xu zt*8nc3o2-{n!v2p0N8e$?z+V+Uv_D!D(R7Qh;cES9SxzJkxR1+R1gUk2snH?ED@++-WNLb4owX2-C_B(Z>Vw^^sE|nFoiq-jURrZ=F=EeFlU>6DLw6qIB2T zRfLWnZ+sDIK&tddS=s1)X#( z<_yg;urLDUbF~!Rv|6p2%JaNkdr0Hl4=+B%n~HIkG`48c`a-t0{x2a+0M^hbhzcuc zbK$C>K*!?N(a|g#Y+Prp6)_s~2R>3gM&0iR4%Gkq>hm!6G;6~?*5{q8OpqXKD!?$C zZ}b$bPB5p999{?OdCvcE9KE@`wAN;O96XE2S$o<5Hg)ABgBUIA= zdEnXCK*I+|!9ze`?gq69n?RZPH^~p@KiSRth#L_?9i7ACRsvWG*2kZ(*xn`FlYqV_^Qt~rS{=432wM^;O?_?dY z-@YoT_jy5ut_jP?jQfFD_0xa9`vhI?Ti{j;DhBLww}EQ**J532LRc3{G}CVtUc6xI zM-@V?h~9|}C(FOQOfsYe4+o5gYC>Y+WtaU=j|X*h3yVKbXRV_Zz^DIJ?RtTSsAF<+ ziNGkNe`h%OVE`B}$x=L|_fxobhaXQoNBf~MR76V3gs4PZj_Aj!G!m{H!5ma8Zpn)s z`kpIh2$%|+^K`Eki#i5{dzJ|?lczemB+~2*W=K!SAC{{6+JXiFf;U7>`v(a znh6%yo(|A2^vnF$VT!EPCfzRxNv&C@3b>rpX!nW9CtWJ^?;{~=8*jP3rj>p1F^O1B z5^m!$_mbQDJV=UuYtA_fVg-fi1@ZFTtdw>6C!SLnjFs-Ht~spHGysinc7|;F*U>1; zNYEBNe>*+3kxewDHHBv6u(o$I$v+XtYQ1^d^DsgR6)M%e?|u>To~Kng|I@fJg9}YM zBogZpiLmY3Jk#gN2xY=#5*0gd8Y+cQx^-N&Fk~o4cjk+Z6x3|`zoVjfLXv@DPrf4z~IvwFqJ=uBInzx`I zs@FC>x${x?Au4d>W-hHZ@CIKi-N}ABWx6Y-F}=d&D}qFAxFd8*pn~bKsyZpe z*TE?0hiqcSL+g2&WY6txr08Zg>%2n)#XBe1RQz)prq9iOd>vx!|}s|^{^TLv0~o*D6o~SH?0{O-OhY;1_m38>*DRrltSMPkVt6f5h^Xi zYl7t4o4nttX)r&Hv&2%6qry1pK+)P93cmT1^|q}5`quvVQHVu;G3}A#8k=2*IZ;d! zmsZqyUrbzFAW-Q+x%kzmZsK0fYaVNJ3X{D*_iNVNlgoDf=e1Hx1iHmX3qzTh)U3gw z-ZVxD8Tu?;&MTvnw%xPD+-xZ|)uJdkZ2~m^#awp5Z(|x2ht6x`1IN&M5ETtXT!PKi)9_4z~L}-oHO-q@UId@`S zJh9}lpM^7A=Q$&)a^CBI5u#VLuRaVOdJbr(##Ids$AB|YUgpZvh_wxz5|%J50fco! z0hdP1*w#iRw3 zHp>XF9PKFlEmsN^H=|kb0^rmHRpBK+W?5`gTr`dpIpZ}tm15yGMi6wAz5Ix!pwION zzSnb0vxB8jLr3_I`eEpcokL2T!SMj}6@yKDmyZal3YVelAoGQw94SNIigdWB=fEEM zhHFfzC~rjs=1x{B6jF)r8^zjfJ9GH2eK`m-71ok42o|RX;0iGX?EPX!80_*W95okD zc}0Z5ogXknWl2hCSQAj24;Ucb>QSpX3MUDwPY5{JVS=36ORb@9BHR}|jQ<|^rN65; z*P~55?Sb-}d&5nN}YFrP; zZKM?4 z|L+?vK{td;*AoH>)cH_inaXh(PsZv$qsrPZq%DA`tDq_M!|8rn#79L3OU7xD<>sDn z<5vfWS$eFZg#*%E@~sz>^sZWPe@VzHz5wf3+mr#`9g2KK?WmSFp46z&mxlxHW5BF-x^3O6^s$Qg>^!JH60`i`*~$GU#}(|F<{aCa3^AcOt?5VNYQH2 z9@cXuTa4N_%Bv4|T-7dCn0XJO6Sx^gHBoVh^>gi^_)+4TODi`A*K~y@mV4-{L#M4H z(kB%w3$Y!ami%}4Zg;zGny=3QFh13_^OEBpXf_7MA>G9>Uez%wbcPO3-(4`?vEF}L z!$k(}dE~F%2gIHB{1XE8%5&=d>;KhZG=sKkJ-w${mOCA2F4Nw|TSHUp^28TUpD#t5 zs$>1BQJ}?TQH*#vEya#YhiMmFLqv)GP(6xyN-i6e=L}|nRK*lywKy+u*0S4F*o?6s z`RlQhbRCY*B$2dwm@nbHNxiC$E%&iht3Wk#N{AYXRn?X+a_TJvNn7QkASdvsE{#pS zFn(>Nse6rk_4v7wD=Bf90>js1kZiS@!#Iyh;hHuZZJPdh51#ld;C4>Ntw0bP=e6iw zU!J+aJqKvfHIG|$bTwe*U`1M6QT7)ZZ@rO-Z=%?u)5Wr8S3{MY?-*c(yyjfC;9cZ{0B>ET{NW{SU|`k2(6`9s%Q9z*)$J^@w!R*=Yp;>K)hzRQZ$3RWmDDhn{mYx7 z!NyR%Hdjds&OzRU#PSOK{eU~n45^PM3qJImoPz+^DTO6{FZF;o6z$XaFQ$9~H@2g~ z&oxdB2r&J0fT&-tOfy3i!Tcl!8|w#LB{!9M(T84M zU7s%T5KlDhJfUX35Q$M4kVA{3fm8BMka+~BM}xy&*^>Br7gC7gcve60>Y|78qK{MN z0}#ZUwR4<*CzXG9y>+?q>ef!?ymcbk!s9f`!^Jnx@_pAbm1~jq{j4?r)2mzW|N8M( ztllIO6aH+ns%BwiMfcx&viLgZimVD~<|lSoz3(i+yI!W{uiyP{Uia)^>Y+UNHyTZb zosO|<$2b=9KZgFgB+C_{bA5s`t}+2c!TH0*F(z(8<-cggSyXS+t^h)pL!gdCYC&Jh zc$T*U!zNN*2~u1Bk0I~{FZ;0+dpwz66*vz}7>O%V`)7XHHXiWD682UCl--)>Sr`do z(b>@uUW28(kxk>N$|*&H8#l)xp;A_h-c^4XZc>giSh=IZKDXMwn|=d}%?DcORWiWg z&`x&%3-g)%U$j1T7Q3O+?cXOwA!>5`Yz(hNew?88-BZq&0t}Fc2UXqG((@pl5bV;PKeDYo1uc^>b4hgLdR zIZ~;Kh%5vJ$T$U*ojifVn;=qKKRH7({p>MP`9W?3)x5t()>biK61;PBY4|Nb1!g%_ zMJbfMv7CU4ZndzOtV(b+B*kzIg5a8Tj1sB zt4+zyHOY%r$}Zn>(sP#om^=G7C}8P4Xz5({zwN?@y39~TSy>tN*w(S9NJa%j3E5 zJ6--ejOlM$`8tFZ`g-VtK}sl#U7iDPvoI$TgPMR~Vo;dLhJF;zu8uy<%aWeLgq5_C z^x^NRBxNYLN-6?qswWXEJ_J*~l+#H{#7ZDdGANz&f_an4khxsnp??P4ZO$X$vzo5r zg0C6t$UtRhG6Gu0eb%Q`mCZnnv0euk{y_OnEb`GRu6c_be@jBVPsZRvaWa?!@~eSf zMjSZxslePiC!2mU%}0lM<#;-HK9sGlMjxL(OckD)^Tdfay_xvbj)*Fs-W7CL2R8si>8m z#t$k;L6**iAkS^DXcQXfNQn4zWnz|zzFmW6VsG%Msh8q}=oN~U);LvE$UnIZtK`8# z(=UF>rC)6Plww_Jyt|t?ShS7_@SdoharaB#2qw1T@8h_j{QRv?P2MuicWaK9Qqe6$ zKc@3J)uK%U2M-6?M$u4ovPDD4o*M#B%a1wv30yZ!$q2e#fsdf7AHI&B_`+sm27W92 zfyg#BRDfcQeDK(M@vCTm?4RRjH&f3F8aF|Zp33E1{>>b0R`#TT@URt_2udg_VwE}0 zoH&@N4gN?Nd=NT|x-}5&d*zuAu9&^2|IL)$`M@CX{LAkS7p`}^U3JF17egvCJxe!j zz$fkCqf~{GJ)f2_+1oYQ&DdAhEWp1qV8puawM6a_n6{O}_^*I*)rRrvS8M*w^_J|! zJ=w*w9H7Qrzx?$gs!&0Fdg=GCgoyh`wO}padriK7SyEuaV`eCSQ>#tUCGib&Z zcIru>0IN=l(#%x$r!_o$vI0uC>7-*7KD(N&h29Vu%coLWtLef z>@I9lK!1pUOD~kqcF_GAoV_Smd`F9Rd`{`lHTY`DeZliEGqOmaTBAV=thY(du*L~om-`a%rjnc-Ab*%Q>;<>dl8>=dRN+PDc}WtLQ|p_G zAEp{yY*rW69Cstzc+BjAx+l=sy@0AZ)smr@lqxQj^F8oJr$tAJ>l4kTT#F@nFpi$Y z;jy5^UFJ@eQZqi>DWBL!TSraVs9FFTt0u9|M~S5M3(n~XMXPPOkqOwp1i`G29|Zpc z%7A~F=VjB}vLM#6yY_md_P`8GdAzO!PHQ{aufSU8=nwyYZX+WwoBr>_ML9^2;>2hN zK%P)f{`GMF!Q4b}I=GGrRm^r^he!JtDimK?aeeMl&3wyG*Ps>k6N$gx*Vt5C+LA$;7DLOqO*oEk_b(r^1~QjMvy z&K5mv(=Qa8&oi_Xu6XdNB!7-(*Q@U zuS950=UYmHZkjoL&Iq0qwmF`+0sD?wS;MyKIqfYo4^1qAB(`lPE!y5D7#4SQ;YH7} zCz$0h-H7i(`SGWTHW}xaPrRP~`;HVJv7 zCuOoh)w>vS-_C)%^W#u(bp?WiQhSkXVo=vz0wJDTck5fg?bhXen&to2@xDevaFj0+MqP5I57O@Nk261W`=Ul{-GQ|GB9kv<SdS)X~HAI-MOf z+x-yVyZXzUvf^0falh0!)Wa5dbVu0aX=9Ij-4Br87lY`CF}@$Cg|Yw}@%>rVDcVtI zD8kDYeyPeS9U7SGoyWQ&jLIo?mXzjpN?7>~IO&$LaalHnfvVkkO zuRz^zY4i5x$_V9Rk{;mjX-#bNs_X7bEhX$8l!bo>=2&tDpMdB60((odz&A#}zPDy| z^CDz^@<(aee0XT~cH`DCJ3T;nT}?B%J7ve{p$+{2yrX7#FuK@w(L19Iqr9xYQ7Ug) zikuvyTM+B1vxkAd({=;vv!@A`sw_&=UY-LvJeIQ^%+Y-wZ85Gf#xj324nlZpKdF7k zBq*cW@%3o5T5KSb+$s=Bf6*Vszx*cMCfCNGu{e3%GBZE=HNp?!C7A;ps|t+j<(wOD zO@#fa*ilQ9m*KFM*z(bNLivi3g-#H%9|9I}v`t>|x^2#;P8sQm8+|z4#aza&qQz?R zbV@=HjYXlfPgOD?9aG3pV1j}fJ#`qp8ozQTOGqR7d8a}m&T))T=izY7kJQGo~6P21aY%4FGiJEWqH7G}D{25EBfvVN#2r<@d zK^G^5g$+T726yQ(<=&Gs)U+^pkOlG92I^*%Yd~CMN$5lkfaFML`vuXcxSMp=`#j@- z+pc3^Et4{~UHe>7-cE#4QtSvlW9eKAJ;p^2;kqBY#$XaMJUB>B3|wCju`@yI4a*k* zbKXg`kNckmAO5|2b@RvQ5{y^I2V<6lLqUn*P6S*PUD`)KeNeH#r$L9)qFp-@-0_}* zH&DT_d0_v+0jum0>qL9L9rb+*?{^2;D-)T!%ZXzU+9}&o7xdp6p7;ks88XK5#^~r; zE#g2kYvQ0R>z<5OGKX_Tdg;#aH2RiS?rR^P_o7%W6M|xz?A0Q+lw>tK@pkF`T$k66 zh}%Yo+EnrI(70?KkAM9lnV^0cr_nrv0R`ynlFfgeyOM`INZ=6H4J%HzHWAieTm~;d zcJs!HXQhL_tpEiIp8B$wo@oBpmc_gk_Cz)}%g(O<^1P(6+;p*~RCVmJQ-x$a^dsb= zycoF0@7ic~qSD}`N(gXZvFks@Rxm4lnD+~p`>_?*o3=bDh$9^I=YINujO9L}1MX!& zeJ@b?>GKoqtL?*%jKuuDb?kt#+$;wwUUtLXE6l*{$7j=xd>o@+va`UU8}g^ve#Qi0 zrY0w73)KSyAow^32ig48vh1pia0|KSB~47B&0OTEc5|(%hr1p5X0iWUYZ@IkZ4 zv>3KgFs`yNCvX%3KZ7cm0Y)blDgwF0Qif4l6^$!H)O1z!jB)x8 zl8?i0q&@2CZfej&0#psH;enK8#*rh#t3|huR5^O3)7*ErAgn^>6e!m|oOh}P53kCZ zn2F#s*!3}(G=KT*ZlmkKM)u4maPxV-pH&cO%WA!f^SJ`oPYLg*Q5YlHK5%z+-gKD_ z?lwpFCD{G%yNbtYPu#jeoss-9O7uVs=M4&D{xtF;GJ*1=NXxOFe#xuR4{H*Rg~lH; zSQz#0=%%m(#C|E}=3%=jBY!85w{8XEx@hc=k@G}s0q$%*(8fA0=1B9EL7DskViUT$ zD%w?#u;z-L=rE>O)}MMc`hZ2jQ!LDGcC2^iyhj7cY)s7YY@moD-y%j4Z_ZU5=7H!r?{*wH`{~xsnekW8aC1!49sK^V)5gTHS5#3 z#;~4py>B@8t(~^CbXDsh%$NQ&|A7!a3@wrguUBQ4LGoN)sfDReBYc1Chq4qq#_rs0 zxYW%hpm7EGU0Dlq4y#wdOFb0&K(81$7Cxq9?rZf)Y_(r09R2PlsKs68kzfftL)Hez zKLfT$#po=iQ#Ys`4U_D|3*vqRO|0C^Mi)l)za7(8v{h1b6#|Wu#g58kgrxY044CHN zT%eX|S_W=^>ia-J_F>!xu!FP;B`P5+AOggmP7GGH?I>~S-<*b(Ae~>GPMig`&4DhH z=+qqLfMt+An9H?kFyh_O6BVVqH^Cp=eq-QxvQeWxy~8X&@S&?XsEALl4BPwWO@0f% zf+y8vE8mTGta@G$!x<#j#Vo}=0+;-TebA<61*g$S3^91Pm|7`7m#6s9yT-Vv`Ee)`>e@)+c1I9zE6HJ}@LktW8mSPKk zm$W3iOkGxhQR$)Qz3%&By0bX=V)3jk&WwYV>(-y6n<|-N$#cSC$H44?({Zvf-zNwksW>5iSKXLd$7WDMA-J8?NLny{*Sm zdkl`ifS0z1{BrJ?bnOZWE-#3}!sMbP9k`p%^Z2n25v*Z>CG`2a>ZPgJmL1o>GYh#c zzG2ElDZGwfp#|v`X&Yg@6VcwOpAm0HhQqdhXo*d9(Rqi*dY(N$siUmOTML) zce(%9OAxt_&a65W#ygrhw%JA%Ga4=cFr&-kEsfr2P)xO-dxETCFENL5iP)qVF}37? zqf}R#>2oosXA4F|5uksHCLX3h+(Oy!XdcqWOFDbCxN!71HOemj!4Cf> znK(la-;JtI;d++;POu9v;v-4z9&F>|w=35|ainc%c=f9)#b^F@=(P`IV`6y8loH)kR z1})iCcO+AyD& z_vDX@u_fSotYbfD)+dPao`xJehLV-NZRu&AXpXmZGR%%C-TfWk9gs*CBBraE_POO- zyl+gJWOJp=lG{f6T6HQrvb1POkidZyO}eGC+Qa!z#M| zv=wx2uS#5BV@83@g6|HcX0WJbP*RUV$4Xs3ybq+&v&RN+LhTB>6?>CDzONcmqSSzN zf!2cP5G02#P6@iNHXS!brSP`i-i?-%VcWwm9$*YYwB>s&@D^B%P3NuxV~?MxqNpZE zn`+Jjc6PUvm+7QGpN1%vV^s%$VbXSLDQrTJPyzofRR)<zYRcCx~ev4XK16gIiQQju$hyM{|0y?(>GREw| z9?W9)dXn*m_@4DLa(BCQm;LG@{M9np&z^XkZatkk&5Dd=ABBW_Bp9oO@E7k&AdoRy zY7KSYIVk~$hw99#sTm)!%8?1YerFQKM#8SSRa~&NqRTXXtnr9i7Q9Sy@lj}hsmoAG z)Dce45tNi?c%|kgqI9=h#7vQBwS6!jat23@qONnYooO5nYQ+haFH)q&>bAax| z2LzSrB(q@2gTaZQLgLMlOei_UBM$GNnxHcv{cfecV-Thq8EP zUUZ`Fm*-aJaj#osw2iD(OSqQUL*R`Or>kLO?hoZ&q-#>^LFdsXAv zo(G)t?q<;Q|D)-uqoQiRt{6BX-O`On3ra~ycY}h2(jnb>rMtTYhE7Rok?zi+yN4c{ z@8bLYp1)?X2G(Nkne&{p&pvw#d6&F?{w_aplHwy~7?%5}kYvjRl?kfvkE$izpjka! zU3V-c2-UoQAO?yws;~$Ixix@7?+%Ez&p4=@(zF{)b|8MoavzZFmezD$53elC+dZjq znkXc)8!W5?)IGYa+&v#rIzyE+@_Uk<(7Djt+s*{U{p81BMTUM9?9YO>w_&&ALi=?# z{!70+WYn2Qrt%5W#ddt%U1X!toQ;FNC4Z0PMRMr+Y_!08@Mh1G*yWY}-I?zF*_Yau z`@3rc4e>?9O&QonJT$#tjMq2Uz!VWkX@&JyJ_}j*MR}c$s@RC;#Hl?tkyxjsT zBcqMDLwm=If&c_>x`0qCz1wg9?T@GAcWpp7_}lksf>#pvuLUn-1YK5}L5IrLxO>LF z=zmD_OWljdAbNPG20D*h9WDouAKzWW7o9<^)4P+K2XLos{`|p}vUc#W@+O-&GI{m@fKkSV(7RuxTv7h2}?mxjjm;|Ln6V>>BP8!aWR0*-Xd z+^gao&8XP2JmrF3WhgF zj*a!KL15TKM$lCr|1xf8Lm@a?XSI2FHS3gfmY3^K97l1A*U?2(yVXp>fE-Q z3Zff&Tj)cLXa@HiH{3;F4xx?|zJcSxa|W@7tydfwO*VDA+N}#kLffXJEF(k(mgqm5 zPN3r1WIMH<&q00~aO)W`p1hLOX{am>%I8DoakaRr=Q3QG(Y<0PzCezq+ck6KF5EE<9RjiE3Gl zcKZH!UEZPVizKA%?phtv`p-J+->d?D+tkw4as?U|jh@$+UK-!;Wgs0U+>PqGnr_}c z0DoG(RS#(Mg?gwd7{Bvh(Vao}@dJZuO{9k~T==apybdpgq;gCF;RvS$Zn)9$8o8KPhy{F%#HcP|Gjr-yKy>poB-1KV#PnntoRk zeI^&?+Wf#SKHFMhu36u@qD29q&f{w4ya=Jn>(y20(8|K?nm83Y5J@bI*dK^_WtB`N z*NrVF$SW?$AIre5hNk;81-@X`$G0uf+JmLn)IR0VPLVEhtuEymBU?dGV{lSXnPDD2(a}R5 z=j`gi2t{lb_d=`kkfC1y`$sO5-_yS?m{ zyV&kKPK!R!RT{RB9|I8eRtQT6@o`e@UZK}~9P)02e} zbd}#zX8L0s?J#cfYRM=dq5Mt0yjJI}MgbG)9+*&y&0UpSg|5>?>sIohCRXXcaKLtfot zsgU4Pq}7*}G#66$G;z(kY&S%pGT8q48Nemrdb(AeaUvG<13(@PD*vk4eZ=a}C>=A2Cz;dQby+nG)H>(hQ;C zKJtK3C8eASNP^nj+#Gk185yLTfBog}w8@ z#U;3TeBV1mhlb!y?e;M0L5=9s^Ec--a@#Ouu30zo1}oyLTsl_PfCdFAIeAnskEU<- z_$4@d!>c#lmrl)MW~eUJPoHW};ds-t60C*L@_JmX3J% zh*Hjsu`ldzLZ+m>1Z?G^H=WIcT757jb4E-ZdzjscJ*%c`I{DQ;JyNF4rxDBLnQe4I z8|&u}T5LjJxIVl%(!YDasQ!&)9=d<9vH!$kaS!qzx?3(mGtJoy56zEv?szT`uQJcG zBKNZ*-KL|)hs4}H1(xFp0=Y>^NeWzcfA}~!>;U@Rvt&*=5NIE;@{CzLYw(Yk8V;eErG1cm@Y`W9v|7At0Ii;KM1Ox2PgYsjezJd2>WmkVH< zQD%~QdC=x31ehEn+<#v)`l z)aXyiEvyfbF>Bzy3R3>CO|rP~9ZMKn#6ZI^QDk~W-(c!owJx(e_b6;3Cg;WA^Jb5l zoL9)MyCCsPLuOunCwiy_u{C;|Y61+5=-GaOC&$kOI6VoaQaj;;nPYfRcJ2k zeH`Fuo4Ld{hwCUeS!wRLM&KUzvggDy5xm@S9di5>W5}~RG2mqbbKumLG3pOP<0&d! z>9HL0Z0XVjl}JH|*{r&L7}W1HN%jXl(uE<#3S=##=o``u;pk{kM;NIwaLZsCP38NQ zBoEmvq$Bvl(5ovi4{crU< zAzu3ui*%sYxouD^HZ87xXy|I7iFt*dRP_2*d*mm)o|jKERN^fArfb(Gb~IZ`M!`jw zZ`biq*6v=IM;kBu)>?-;(DOSpuZ#}VBquoF9_Rlu)|$c{%>(!0?kjDV8@#FO{toQG0MNOP^q*7b z{RR2GPp;|3e9N`d3H@Dr#?k}2+E0GJ3fxQrK}8At{{r&)e*sA^47uCOxUB?trFB;q z?_fY5h(G|hqvxf&7Z~@;f{zZn?xxAz4v+LhA0m7Gljr)Ui`y1(7Fl~Yx#-G(kw)%$ zu`G7zt`bwDz$hFT5?lczZWetyS9XoC`>=~Qz#)=sHoKs_@Kb%d7Ibr!vSAsX>1j*R z!tpf8`HcVS+VN-i70*&y>*bG^QELJ?_>@cOM=##Db(a#G><8MBB|#-=dvFG*xR_1? zc)nLhLp5Xak`1GdE(?EimS!99F5Dy1RFHQujlGA+N(j^PYNq4W*O5`E8x7PN&S_+qU_wR!siwJb{>(vT?3m9)iO zWocnlUu%Sq8)!eO-F0*{5_Sz%mob%g^&opoYwm(4V)89|dXkg-x)?t zDeoG|)W^{4eKvx%fp4`73ynp|;mo$Mnjhi|P~KUXC}@4&h~QFheLUTqZDoFCsi4FB z`jZ5~lyDu9X$`S`^w;B5Cf%&lc>zR9*Xp6AXfG_Ka+@<7mbOZ+Sa7rSi(A6(4r_lS zmZndUPGU!D;3qJ^IcZtjVDkH%YND5;EaTdJv)Q8YfD#x&xMK+3p9l^O-QNr?UK@L^ z(0h7kfWF_q9bpf|uA>JE*v+`Y?YKGVL)(;>0zR~)q&fmHHQu{4-<_46^nu2)?&p;m z>s_9EU6Nz5^a|;RPWK0k9Xwsn$=$6>HqTpbfQ0+Mg1341v5SanPjcWIr1!|c($Sn+ zE;?9`zN_F{!@w(1pfnIH3(J&_kZ^req_n%#iT{(@{Dep9166dDHs$#b5i#$|bqTo|>z| zOO4xvbE*?=sv8(6y1eGwd)@U+rM`njR3cJdlhKP8C03R52pUji5M?sN`wa(^Eyp|F zIVaU!YIn$ZT26nzSj=B3IEBWiX8_&EUNX3-OUBGvL|7HA(vfTTycP~~DwAZ0RA&1P zOSzXr|L(GB4xwZVUsYvyky)b65oPS)=6wi*G5@yUfQDEhW$CNt=ea1wDWxDp>U_NR zLK{}0St9VuY>UM{+S=XL+YWs*cgL6(&tuWf)3dj^lwGW6#d_a&$rJ{}pH!;o1*U#* z6}6Sg*#U7G>}bxM8F(%x!hgtV>r3>w*3rsBJLktYK}W7V#-U!#7KF3@#reZM>rzxX zIQU)kd$pTm(nOSkQ3AC7La0bK68bfHG4)(w*D)&+GPi4A#>qWi>QOueRam=e#PVL{ z7Dr0QY|!dY{Z!_7-UtUCNxx{`ytoTvUc|TM=py6*&kzT|G1%Wf7cybiv3SV`1U%4d z)(Je92|!fo!cPV!_%I$E6TBUpy88}Mc^7JT&|CB1*}D9Yj2>NN+@Ao)1otbtjW($F zYZ*5&GOiEat)MY+_XG87;ew-O6LCZ4xnbO4p;3lJi~h3W z8|AEf|Fj_GAggtj9?cKCgj+|hkKM_lEk-T^Wo&aFU1nGz9&fj>(PY?>K)Tn*jEZUk zMxZ&+D1ey%ypMCCtWx9CLm|Y0cd}6exfF_HE248h*~2*zcDCFNkwd zuIHeiH@A@c*uo8l0&Mg&Jp_{67n|K^<*;Rwl@a?Tr3a~yVjQyDkWbe_;;lBI>~ zFD)sJ;2;=Fc&`3N4Z?03)`_(_7d9J^0EA6mQMEg+c3akF&%k+jc{`83=lsel#0S;* zENf}sF?7+x@)Sxkg>2qInY*Ixrfk#&M}CXU<}K0VY= zv5-M@ZkPzJNtza)4TW#J!883uoH4Q-U9C?PP*F*qOIxeu?clUsT=WcFLCyMkfM;Mv z;&*8dH*ZoW?h{KBn;Lp#g__Tl4sYLmtv<(;ZQ)0wFfKj5y=`>ybtGX@-?!C)^+g9J<`-&B83U0Q+v~sBe>?nBXl?aDo!L2-hxHP+3 zHR+r@7`1K^FJ2&ncqP!%`{0c}ir|O0+zYfI5O+Gwr#Y-gPXrI2SjnwsCdh?GomdQ5 zE-G>{eOGm@ zi-jwo!Ezh2sKDLO*!D}IP^=gl)NFkc{vq^tosx$!pDs%20<2IdpQ>pL*LOR9s?=dA zeOlmCeR}(+2N(%{+j7e%B@GH(F5Q;I)q)nN2(Se>L&UKAj~js3-H!j#d*457s5DZ? zKZED4)zezO*Oz9)d5^PF$DdABrDlMYn>K7Ep8AT}@qH)ZHq5`&f^I?-B1tQy>@)d7 ztnO>e{7-aBrzyTUlPGB=<|!t12?RQkA0|{IyvGix&&|wdDdO|SP^msOVfbVoO^iz* zZSLPJws7n;;1Ox2Ofg;F@{Y~HQHU1VK~Ckjf@mn}HEA1&+5|&iD=9GA=|9aZA4s>f zL%|v~3sX%de8PV5?Ww1lvMl_L!&&)#|PF< zWUD$aCBM{SuBtL55~zj*uTHGowce2Si6BL*+N9>T6TX6h8jwGCW+M3qP%HKcMH-9! zoi{E9-LfK~bYk1Y!U50U1KssQ7a3G!+!|Qq7mwVglwpkd0e+$w5wT=fI2bbXg`((QGhj+{xGKzlIcCyVGQt&;Z#w%-)5p}nDMgW9m(Kaq;wuE3 ze!Ti)xibqB78~=KdVKyVo=4y?IUMen(~CluAuC6gf!w&Wbo6Z68TdF(ltd(Xnsc&d zD5d|z<53)uUX^ItUygyOliGY^g^)ZgpXLFp+E%+kM`1v5#| z$MiW8GG!BMltp|d1vhc<0(Un4urm@E7)o%2G8MKtWi&PdW1LVIF9w32V@01XmoLPs zp<4E->FJ~B@maq~*nj}c>Gy8f`jVBYrMraUPPXoOh$n!GtM=J>2rLWfs-EHXXiA9x^3I#AkfZ57Sk@p!3J{;NFi{v{bFo=oo8L1N zxH|muy5z;|(i+)38Vy~`+U41xe!w4-4@XFF1umWWDaN+fDp&i+Dms%@r!3g1`djA) z6Uj$@T+7E0 z>5<2pWxs$w9l!>e^S9;@%bAC?mXt(b_z$x)TKEVSELLA;OE;aHl5t-?r}f74{=o5? zc4h)CWVHNs2m@yrRtX*`5EcR>F89Fp#iZe4(q-iki8BVPiEKj$T8fwK6&Ad`{fY}q z7@ovOl%o;oPWh9g(owWTXZXBIi?qm_Gl+OkdjNHC)qU;zqAlH1jjX61$t)Vf`0{R4 z2{rl9C1D|J#2ZfrdMiT+)Db2Md0)ntM_=przTh>tmo7Q1wO|Jf}V(_5lT!lZ=FR9X=AR0J4)WinaxY&g`m#c7Sp)Q=hJM+b{t&Sz&-x3lg2xZb}hna}YNg z3MK8eYnNwjM;P^l?X6bejtPRVBFjLD6o`U$J%vkJ2$_b}chP{mGd5coF|0%d?}Vib zC)kB0{lO>q?kW72woKE^N)xqn1Tpi#25t_{Xd?wH!B(4S22Kib${7xj&N zdj~D3C%2qa{5uzDgU3t&6>~7R`%Taq5ziLBH7?tWQcx}zM$BAlg(*!572fs*e!;L- zPZwTNMoBL>`B+1oW5C*cWFz^_E#c9iyxa;p3t1fWby_ngX&-psfhRBNzwGAn9K2y_ zb{Q;>HgkK{kWU37js5pr%GQTl{G)Vox>5To{sjEl-uM|I$>|*7kX}RHE%&#ge=+Gt z?E&)6!YKNxm|`9j$_BjXoBPlW=RIK`_{wADx-C zwIx5(=&wz$gV>K@i#$2qxn_NsxyV79Aj?!H{L{-*;+NR)2msSNdm>m&V+7;LAm3AO zPPy(-mto%+<>1w9>PIyV$r18HibiJfo2e;{6>pfD18Jm8X5(#0 zUU(iqBPSq zYL0%mL>HEhF8&QE_ltd+ceKFa=msta@+;?!)r{aY@O!?N48~GnVQo(JA^v3%R!Qnf z!m5h_ZR@x#dooCu_~yF`#msE5E9$3|5->Z3;LiU1@y@tTPe zRSv~Oa!u{t_va!3lgVltVAVOPb|ilh!zI^cIfD6*Y_7qS0NqYWlMSIPhgq5+-g&@e z(UAS1^HZGHr0hL}CA3YQPK@hoE7c-H+^C6sB!iE#NvExbOm@M#WLdEpUXo))bi*CZ z8c*=COkSlkQvzTZIJ(YA`MW$HpIIVR8BLRNit5quoH9}~2Jcz!>mb(cJ6z#tHJQ=I zKQms|CKp^3BLyzB0~6@<;t;7rFgHMQH_W&dAa^T`3!07lQBwY@41;KranbSQ3ih&Z z0$i`$hB>W@|nS4E|QIWv2eC;jTOhMd#(>bJ!FCAoq{D8a;ZY7C z#UR(4yDb$fD!BO_9lW#L3i}ls|Ex)*(|yoUOB5ZOu{qww1}*B-P4Epp;j3iK(y0g# zQa5hk*Wi73j6x~9Y?F1_B6yt`Gx7MCfoVjusakGAhaPljFQP44f9zy^_$EhSWKA(S zw^qH0PU`P!R|Ko~x(!JXdxc^!(-5G+1j-);pIOPffm}924ffL~XO?|t_f`&BZXo+f>0Hmz^NZ3U(C zIn3@HLWOTD+>7r~4%Zs43F6(^vYgyP?9xJBd(#GcB9A*j3H z9;_PGREz}nh|^AF_^W{MQu06>v*4xW~bOLyof^s=i(=C6of4~8h{PAYY{`eU3Z zXF#>C2*;b$I{O*b>M+<w7iy41eBPA9d0ql>770x~a2resmxT5zpMKk$baQMn{9tdu z%d#jt@yj9oZmHZuV9p~LQ!&|hkQa0&dfHf^UK|pNNu5#(biEb>2P$Ubo2L7c-<0D! zLKrz^vs@Yr37d>RaI#1j(e^xfB3^=vCO3vL?bD|r)iaU>?MB@d<9Wx_m`!!j zfM;Wk+rkp73~t|m1BbynpG7U|#pq#eNUIsYi{GyHz~u}BsAvTR$ytbW+h1Snv{XK< zvU=ixj_d$z@74YvJrDcs1uWr6qgP5VXLCQ&GlZNS#p%o9wICWc{c8-jG^06b2TI?d z#maYw(0Vk`v%d=JfmpU*W5wVRpzX>k$)g~}EO&vP?V9wEoNfZf;EuEKjoor>nZ*Ob z>+l-lz;>ywNFwYQ1y}!sy~!q4UUr(?pSAq@Q`kUO7w${dWUNjQ%GY0vPVuH;eIY>M z`?nz-b9KD!WJWwenQAPS4Sm`%qB4d>dhut%Fdg*;mNB1bbau3p$UNu2$97Wha`L|? zNXPL&H<_7C)?92U5?IrtyrcJ6@2MYk2tRI}V zHlyz>C_6o2|6AD264qrRrfihT#a~F=9mA#KuqXWW)Jt@pES^^S7qNJ1OS88{l!DHb zD8uYG18XubF@_GMq{{ArW&Ypy0oZy5g!3IdIJE3RbnGga%(`p_s>-$%n&nI{{cVo- z-q1=_H~;#!+@B%=`54`pme~MICa=awKnp%jp=DRLW9Ai3(Bx<`^@J#t_Fh1zGmW?S zjw>2%S>Ke5y!m#RULb8x!R@pzV3$oRg|bB-#SG`@*M| zKMv9IcpWxoW?ZvggdeVudSAk-tIqs03r0F4@zOhE=<;HlC(M{!Rk7Bu%!@pH)SZF*ZpeJtSs;m$o0p87JSw+`Sl9E~l*U&w z@JOd`Ee;nThuL)JrgTrCGwM^{A|E5@YQtxLj19gjW(4v z?5=iC$B@A#d6>LVPwcJQ7vloYNGAgHfz$f*1<;f;@MV-J1Ag7IfzrY@tO-L!?V4n3 zu#p7(VoaLL*8QnF(%S}X!#0-Wmebn8;w$QBV_ATAuT0}@ngzv7!w~43^Ep!QHpS<- zm08qd2A4`tE(?%g+Jl-5c$Bi~8U?u04BmDWrFBDoLXeNBNClib>5I&*ZdXQq!{dn+ zU@^-kUxXAJoByWO5$(2F93<%cYM!#ueJ`E27D5dje`;*XrC==*p&CrXI6_A~AOo5_ zg8EcB_HzouCx1_`nTf{`boIvd zqUsAo^2J@#2tEl1Eh;NQKcp1t=zuIfDCToufB&Nw#*yAVm)QIlc?9X=+ytkOm`qRv z7E-LZieb8%fZA!U=C56~P$EM2vT(Q|%NNm()ZsbnPU^vIq2 zA}HfCm~NWN#T($DlAS$NjDMpWG4-L>%F3OaI&9+>N3G*Hf24slxw(b3q7h{Z!l>JD5W^SiDdU_wU+@Z5*L7udr9Ol^vHvR z7pw+SQi>wRihq`HfqsPyHz@@xehgoA_*J)>>GPxaQ2YS`}>*y!#(=>fm&&ith`dVa8z>ao1q}l zK_|DW$wZOWTp%gI2iKcv1T0gVT7D6Y^G0Al&ORd^IP-bLGELq=H5`uemY_6 zY*aUN!+B@m77%pzCMA`sGf>lkN4tMI62M%6dMY^qzT4of14iHIi@~rdb^J|3sgW=B zxpdA7m9V$=q-;D^eI3zeb?HPu>k4?C%zilDRu{$?@GhE^Sxh5i>i?;W(qfI$RA5`3^C-dU|LcvnF5@w^i z@!Eyw#TjMAF0y)Ern?B)8sRq>fVNk^Jj}^lvAf^l$V^-Gx7_r3^fwxXQ{0t~!1&`Z zztO%qw$SvG8ft&ZbUFfk8-em&tKw^xwI+sfR+*zzkunV0kOJ%Iw*zx`e zX&=4u+D6ltrhfQicJbuWY*V*AlOutjv5muRxF=3M{W4VV{F(Z;VfamrfDS_})W2gV zkh71W@EN;r&rk{!+W9rW>pa^E>|e0ANkJwEE5RpDB-42PWAEm96_&H=LjPt{$uTL6 zBl`~>dQzxf3l=6$#L&^_VLokq3hJ>2>yK6DT7m`rd*&I}w1Xep9Nsi1so^C&r)HZV z@--mQykV0yLq-xdEzBMQmc(C*Hj?I=8BqqZuUJIXJ~918!Hge($=x;M^~*}Kq6qE2 zv-w#r-FHMuX!?JJs^!|e=hZ`@ddbomj?Pyff^4QNwf3&?M?Vx?460rwIczpSi79`> zF~?@z4DVK!b3M${+}tkKk9rmFaxSh_^T@}}K3jw_ND7w@80q$G=LOi+8R#&q1jA8T zkXXA_j}X&e|`>}#aC$RBtuEA(n||8N+ZAqluBvq#hTIlIFr%^`RWqW`Q;v5eu?Zq4TRGP zWpyHrI(#@ED@*ytK7}h?ZR}hu-9m+b8yg-LiAw@fHJ@V=Nx0<0*1sxqQsCT}ylOz6 zathUcqAc_@4hmgIQDI>zbd#&2Kn}1#pbp^2CBED&C4cT4nP`jR+gAKA^8-&^)c>Or zMRk`S*?lD;SZQ0Pf;P848L?5A&z3Je$m;t~yskr@liz!_MC&Zy9U)zp;^R>{1EVZp z3+YOLk*AWn_NtHS`m>w0jrVR*xnAnynGrc{ScVzJxQOm92a=DI7X?ixRK-CpisPcY zv{JH!u39t{D)o2x`3}46vRRS}UPpDDq0>A1`oB*~pB;q;RLClien*k#+dX8L4gZ?+ zT2?xWuKT&GnAlZ0O8HN0Z&hao8-TK+zt8*$$unHB9?Wk-LeUFD&##2&%h@o-1?QH| z8pB`b?e4iZtbU&wMx!puratA!Smfpf*8wG{j=k1bkUrM9CQ4Ha*aMg4x3_4ENnFb} zZKJe^Kb$+1*DbyJPf+i^#bpgFk=L|1B-}BFpjb%E8V%J7UO@_Qg}bGYC;+^-Wtbh0 zLpo=-wJ4n(jq~=njPp4=Vv9oDN}Kqn`Hm~$f+ z`MQN%K_fwlHI$~`xPCFUJO{K1Fajwd+9u<6F$sgudqz()W!%Rkk|ebYibD(%w^W?ve*ple`rkb*R?ri0SygyImx}Sa zK(P`)#DUGEh5;^J4*7iM_Ut$kzrtA!VbW2UDBMwBE4e%j`sWyop+hxK?kZM(_m{q=UjPM|i~B+ydQ_W2oGoSp2gCQW zH>)h2E6gRa!$Q2)lD{7Q%mBx~3li>-1n-egJ!58_4TQyp<>(?Gb~& z7Hg|KfT1JTF!uu@N$dgF>NWSuj#xCqsO$wO&M7lxXJ9q{rud^O`6Wd;UOO)3H}Mkm zG@X&_&n#2_s^s7Xv2!P7W~8KUgKn7f)G@xrzZ5S)+pH)bbt(sKBp41~WOvMfHcxK4lD z$A(d?mFhyMDfPwy;kM`gRi&1*t$T@t67)0$UvTqI8ph(g>k`h86V3rAsO5=&o?>h^l3nt5L3d3OD`|b9#?&$Ma`cu*KMk zfl4+`7``_ZINSDd-YBgcjCviUY-X#}yz&d((O}ULbrKt7Sbj7@Rf~!dn2H^A88Z4b zRz-Ttc4?oJ`gj498r5W$#FzzwO3AnUy*~7n`Zg$xNJqUAnZh{r$zc-EmRl#2UUUGh z5U!eAa1VPq4ePX`H#XfF*uGc(~;UBfYo&7Jb~PQ}VuBG-C7&luf#Z`A*@P1xKDl5wQSD8qw*KPuN=4 zCYtA*-TOOe*{O|xD4o5lx0o_;pIxd6?&pQ^4F8wqTx|Xq2YTL!x%jw$w64Ul@%h^6dZBcRf@_;(hb3lrYa2o zm~^bT%mZztge6MC$NTfG--!Qa&T>YZh!x_B%Rr+)l)e?H)oF}dKG>yd@79>N)vR?U z&sS$`-ZYSdVcZOE`^$}i7Z6QFZ{Al9U&%2H)w+-L$((g{w%3N`CB!w?{Gc#hIC=q{ zu3($6J)NHxrwFyhM?Q#jhma)c}EarKV^%WM392!Bix@SySh{-QtaNk!DlRM zs^r?(Pzm}L$2ckbUaA+J#fckWQ2JV%6h4#IU8LF1lq~oy&H#X&16(5Vc(PEP@~N~b z(C9R_>)C2{LP+hQ!cm2GeM<%x*E29XjVA&j_Okdu0_(LmX^O?+QM|2a{H5?9N(w*G z^=DG?F&lVg7TbUSeIfe$`Fj5UE73;6Ou)T4Gx;uW^UK?qtk}N+LV~C?(sor^)>WE~ z+nIxpQgV%9rxZ(Tn1A-K3$lQNqb$+D?wVaCX)V=VY0A$V{*}3g8FZ>iF2i{Lb<2@BOnqABXqG+s_3nR?^qLjT}1m13&*6AQ1g<_?R0^YmTVPgAGoHz z{2?UcXQ0f3OGo=yLQU$M&b)#2d{wH2)3}_B8ZHTQo*Q4Mq!sffj0pVvrKNg5th#f( zlfii`KUoxk5i6Ab3LN~nH;6EHC`H>e&mXOwhK|#K@=;mX6-ck!pWbbGeP z#ntv0;vskP2Sz9%1E@@)dJGu}KKh9h)??Re>*XG|6aL)&MRVwGm7wYrx^{r#i;bVw zhnDT1f#Dda~R?!p28t=KKtk30>+sd^EWRr~;q);) z^~yFbEGRko4A&ryZ;vR=BPEGDGT1)VdBY%CZDftU%fEp{xmQYDK6sL(*5j=BG4(*A zD%O{qdEbeJf);n%2{AfNm?u3q7)}zmF6K|;yJSbtn2K=UweKt4znHxA$9zm55=f`5|mrbf0?aN+Jc85_aCCh2Y&=Hm3T1@G89ohcAMioo{ z))hs+N$i-WuL7U0C7lglOKPn{)>rm#wXM?0ip$+~ZMJh2AAb=vdD zm6@n^Q5xoS+u8RQH4m33n{T+Pf%DdE8iUy5;B$W)6s43~$ytNxZ(pkggj=Yd`C>lK zQ3*T}=CL7VM;1O*F+6&yKEcN)JJFym^Xs#&nRl*=kNl{JgH@P3w(R8!P9eMa95Bpr zPP#C&{qFI%!_P!>+>voI(eTF)$l$-z_CHjEgn_nem$vV1!=a5lT5Rs&x*?8!Drf^h zYO%;cR9=-LT9Eixtcq<+0tke9kWP}%z}2S_*V<&ck`U_604`sD0O95Xf8 zP>Gp~fm)}xLKRko#q=B-lkBEB7ik!R!khdN5fNC*74wsTs+gB*bnZU);=v*N&m6rk zT<-R+y*n4a9v&fe0;#Z|@ z8ZtHY0w12UX^1zKZ3o;f3>@mB^ScLM$uY)J~wtl%++|M1^_pJD0DlTV9#Tkf@+jSJv4tzncO zjG7pKonhfLEwWlpeX`@7R8#wX$muk@jr6fm37$)G`5U{6+VU$_e@V#C>Zbze5NIkV zssgbg1H&OZcKR3fJ$=!amp{ezvk{>zTB(oy)qgp`<=5GbZI`LsR%5&uuHv0Tixq$BokN76T!J%oMl(z%559y1K zb37wsXz|*x!@9F%F*PdRKVjh(O?hun3bVe}mR)YtHj7ZKXfN4;T-a>HVqQ0gJA_lN zZ+nI(s@qsze%MJhsaCsdd*)S&MHq-b_9u!wYta8RW$zJy^CE&Qt#K4_D)xeM$)_cu zLA^*SXsJn}rBmv)IkSjhq~T@Ba&Av=fCt0@*W6)__nP|X*OW1mx>;jm=6xE$B z5{G1$k26lDokBeVB7fkwzhQWxc(wm~cIGL4tUwwxjM^>S3UU3Z*>OR!0Dq%MVV<;31Fse|y;rgDabr;j`1W?hd1~e92xD{sv-% z1QatiR<&aj`1awPnkxY3`gWT{`{w~-pEH!3{I?bt=b}bIYUE=|qvhpHq$xAMiQ~P4 zG?~d*%)sYlTd^Dh;_5jbU&IgUWcNi{l_824M4`UiAt}HGuysfDnn6(+ThM;XL+Bf4 zQT>_~f%%=|QKQ|V%rQHC7Ul|*stgV0Sl?d{=_F4QN%pbv%HLD(&u*&_o`l?vq-~tX zAQ2S)U7h(JWU9iy&+08c#lQ~ob?MZ}0~;_3cKq88;!R0f68|tlq4WJyozrPs=xzsODYv+6 zHBi%9&fSo@s(rMz4?vl)q;hb^hJ70OAtJ+ru@#VW=F;o^)V_r}W;)cKo~4~F`|sbhA}N}`lmd!jgwmix%r=DPTL+V>;L}6=%+eldGM@v zf1BsIG<{YKyNdel77!in@WqZuoT$T?z?mLi$Dcl(#`|8)>m3~YkBtb9ET$;iNnU+x z2D1N_3!6)4^PuCzxwb4;zf+y8*cGo*4qXtOMWiMP>T_CrTUc9avU?hF=d#?{75Fm9 z<~USfNrh@N!>O}0I(pOuT`&CJSxOd8A5pzAa)x*Z<`T8e%fmv(sQ~N2b`}U_B_D~Y zjx_?zaaUgTZ^|)5GtbQMbs9Vex0>nzzrRFx=Y4z&7PYvW%5?#p4g+wmTGX~bj!mvg zfd>IL=}3yW5$*GQzL=nbmnUB;sQ-0lb~ckm1U}q=HXeMc0pZ`1!G*;p4~`Q9^e=uX zOBbg-j}nHk^4f_&)>7Fw_74thNUUS1J2k#?PAaQS(3eR-yl|s&adzTl-T@gMHwlw& z^m;N3LF^!&xHToF&p_)vb9+Ub%@8|U%v$=o7lkWK6#i%!E0md`kjR(9S>gjgpe$Wg ztsuIXiZeEhlN*J3<8q5E7!Hl?;b|iBiBu7l^mEQmbicnOJeWEnVL;A3_(ALzT%Tz& z`C9*lz{kN0Qv=@p^6vTcN5CVBM^d66Rgv;^S6V-v&>5fY7JfbJJT4VBN3q}z4^JFzdYxgr!6th;xGKD|!Kr9I(3~+WTu~6h zg;#g``hu`EK!Zu;c*eZ41jTi;j>oZhH~25^)^qQ_TOWKUxiA7Shzs zkQVf=X#5h)1=`udwJbodf zb||=l?PajIc`D)pQ>T9=ZLWQdZgMw4yIC00o7L_TaFV{m0cmWwO`qRq4p@z}>xj`F~3!7Lh@$@z41?m${yoCSx8J%r zQvvJMK<**eZiW~=3R&vx_o}Q0x1E^XK^ok5*;9q?cS-gVgl;#}-$KP;J4ceKn28nM z?5hD}y7tqqj`DegMzAHL2jTDPgJL(}%F<}U z*+s}Iwf}O)OQy&0f3N6)C|lR+k{e28=}{gDb&uSH^4Pc8cI}a`zdSi8`14Xx zqs=rmG4-AacANS=1%;ft!Dp4cfto_7ziTp#jN$3-XjXMr3W>DTq@h#^Ev()>#*JVD zvFKo!=)GQ(F|BSlxIJLH#Lb(F8N{RXs-wOpa=BJNAR?-$ChMjbeS%dC;Np>QYhRTNE~4oJ^EM19J#Nc+D@jpIIts$G@iCyf#gSupe7C(4HJu9j9_I zh(d3i)8ToM%IO?=g0}pC3}}q)sf_wpCUmu%TQ0QiPm5;Carpn z-QmOO^dRH=$#>=1f_s?C!^O1-mXTDPw(ag|Ql5pomlmk`T~7!qE6*jBYNrLUY$)Qy zCB-0QXPF|r#(=k7`_@NE-_dU4?=$uJgb{pyIp<$Cjzl<3{PoR|Q#X5`b?uA(fJ(>bFh9)sq7un}w+#G*Y)Z zqN77E{|=Q>dt6mKf+{dNa1gQ7MtcapMGf~5#L^R@$bZ?^Fc91a>H^0?Hk|$bn(Prf zh(;}hcrxk-2~(eSf3rUwYyW2L8(Xe9MkuC&t5r?Avq!Wbhi<`9!|5BlVLBlEHC7n1 zAOMJ+qp8Gq5I>_d)pdNgnOe7xjPA2J%@-SMQuHb$ zemDNi^_lQb`_=|sPqCTkQu4%9{nW?G(m-(G`mt8DbA#ZpT=@AbEX7Cqvat?6%teSH z^rO>sG^{Fyk=#_`r{s}Ce-nrCGts2-jl~N2qWetrBkP^13i%~G%llJL8dmLj*~6*1 zxVYx#<`k#iOyJCu~Zht-uTl5Pih4LdwCu>kUu z-^sy7zt{LO2@u(mDsP-ScI{iH8vzNdFDY`e#*qz~xN2*EGr1V=Nn|tbI1aG!dbZSk zJ58@BRFsa)U*nOq@K)aEgm9(em$d!VmKTTi=8(Xbub;JLbXdepaFr; zyN$4I_HUPW#XnOrt2ahot>3or1H$J|YmAR=>{Bhvw$b^%D}4(-zD>n2jVxTU=4JD3 z&m^BAM{g%&=))6{J!A`L_#QdYKAd*3j{u^+^Ec>VwQmKrs*Ix*m{^Z42?Vxp>jZXR zMpB^Z;GJGSF>G)-h4BEo=><-Sg;X!Y5GMki7`^QJva+kb=_JCEx!?Xq066u@zv_&>s_n6o?2#&TS=QI2lg7%E z`4Nzjp}C5e{|=ym@8twW{kP?qNM*&(A+e7)ZyCqR_%#_&%msGGwHTg(QgQUS({t-g zo>n;ADuX1~+e}-Uz4;>d-9;`R>hM0FsHF80d*d<2B){;RXxmj$% zqb9RiiHIlp#hrfLf*2C@J(+Pd8@|}sMR-M{qv_c%HUa~LJ~XhGsH;xSmF<49BcVT^ z7vkcrkwbeNPPro79?(9p{l}`||9HB}uqdOgtF%aW3Ih@&-5}{8;Q#_dNlP~p((wv{ zq?98Hj3C`ecS=el-I9ZJBmKQz@BPlNd6@adiM{vQYpoq4FI!r|1&dadDRttiM69BK zgI>Y9)^sM$;!e8jjM_^P5|~S!zSawsQ9aec0q3cs>uJ&P4~@pf6fH(3rOx#1=m>#9 z+VU<9s>IJ^0_-K~(h^?6Czq&664q`lC7r|}dGqbGn zeU$aRuHV6JOznCkp%g9S7Z5P8v%>=cur}*+IKf#ZFC5UoS*objCN8W5^x_d2lxRHr zOr7dV9FK5wb$tTDR4d!>ur&-=)q;3rq;=WefuNJPn5(PL$+lpZL1eiljc=FPSGzP> zz{}R6*56BkA|X9Z)?JLeeJ0qmDQUi;(_h_dK4SFXP4)UnU7h5FXA>m*sTc70XIc#N zI;<+Jd)SKusXMk~KiGPWQ|sDFh-qM`dZ3L(fk0F_T(m!!Jgfbo^@`WT$h4}C87o#G z(H3qVy^0#{LQHTu&R_lBHgLMHI{uZt+k3{Ma9xH=EH|9V7lAibe%({uY_xvxiM>9E zpXhT&(tN)&v#RtDKSX5KXA%JKVisUQW?Z~`zVfdQ+k5~2b=XXO7L)yHZp==7fUq>f zI&`^PYshnccp~krQ{@YGK1vXhLk+ZEO%GVUzu7f$Q_r-JRn0=3&-$Kp%%R-YPla7* z*x?R&vYByHR23`R!V)vzhjpH7CN9`YdSk7d&4thFr5{aaE&g!leFzvK10R0d{WN3-z`?FtR4I1%O2pReyua}sj3+uDjht#aXlkVyBvK#Gte3Tp5n~3(y7-h?osf={ zw{tVXg#=U<)CL56q&mUM0vvSq6LK2GVIG6Bf0A=5p!1%56W2(S#Ml6q_ zMy_)NAFM#)v4Ny7l`F4sZZ}f(r{!P1@2LYB`lg3|<;g-D{*M;7-TEoA%>DB20ravCT&j z-ICvWNm5MVkMpF9uKk-HrH77SObp3jr$HA&k4?>ZNnY+z=aTpGS&E^yCM!t@$*`ik zPubd``DI4KEfJJ$b_axZ8`^_h>7YXy*5N^BG@t{p@we$lM8_$Edvt4rU2<(BC`wVx zt6-HxMx+VnA`_~1 za{})@>$XeEHaE}5^fM>ERDQYlT27_cv`d&zA(5j_Z5t|= zB&(TqIx?;j>G`&rt?hjlNsj~s$rAd8$uA=fjI+n72Nq$~otXs2R`;`@F7r2?%5PCD zBa)Hhr!q&U4jNd`f1ZXcael?Z;UHd|1c#Z!F zL$Aym>}|w~MF>ViB*lsxitFm9%IzFx>RHs=Z5BNZKSmFdTXYXBJ=LOok%`Vfh5$ba z$*s&@j4!6O(ra1c-(y=VB*jsLn)lZ3S4a1%KY<4!6aEDAuUcfKNM)WCC6g6D*voy~ zZFv>AS?l;nH(OyoN;6m&H1XcPxXXr}J+jDM0?ASir_Fq*h4=acM|S@@W=-BmBowDI zb-w?4)wU4hQkA`OJb8tmY#Dv)d;@R~`dyfHgiVhWR%MhX`dZ2A+iJnf^oys8YQ0Kj zuC}=`Oc7Mr?c&Ra+{tvs(7s@u*+A{Nrp!kwf#++CUA7mlmdSJa{vRDD9A>Jm+&xaL z)5j&k+=(puGlvWu$|eM3SaozX6WO{`7;m1?THxJv%i)9{+i-sH<3c>kN&wE5FH|~c z&{i#RcswLbQTG+`g5nZ#j3K%XPj{LNX~~zbih3pHCH2$T?jr?j`5UTDrQV@_2xN?H zJzpFOEywN++;CKnzNF6?y*ru4Z{@+~xS$}82NBST3M5g1eC*N{kq|ekQA%Eq{~(4P z%k)@v{AKU@$PcSxoTvcZ}=e7d?Vo?-oh(>b+<^j;?Ar@@^_hO;L{H!McHmXq=EUs3bg~% zdbB|?1su-Klpob?{$||0Pehn!r zr95z8`K>*G5qjrprzjp4n zZwjoo-F0{w-ASji_(j>{Xjy_WEGUDs>>CBa4Jq;`XZAr`=62ttFFXQ8rr0v1f4i`I zKbsIvAM8G4y^j$m1o4i{frLiW`$k@z?=8H??4<%WG9CtIT2R6eMb&Rw-!p1?VF`{X zg~Xu=7O7^7(!wjN?SDWaIGw{^NZ!OnGO+50OUTz=yr|xzR>VU>dn}j5`@8p}wH|*F z`t30ycbpYV5dI%%hWm>9e>FA}?AbG|fa_p#DE6Kx7hOEdlRxYxNukym7;L+Ln+e;$43YnB|;mC1Df=LIe(*1}X&A!--CpR;*{hi?F% z`OaS}q3&fY9TOQ29g&Rf(a={p58~X=>gZN>5|LqFz8~rps)@omQ!wgDiHCKD}yHAoMtFj@_k+6OK#t4w4`eSolLjxM>`64UpF ze^}=_#Xo;AqrvT^#{c=QIWzCkw#~(0Ggfs`nT{GTH5BT_WIeRrKb6Kmf%sOl>YODz zCwMY%MBsJyvY%Fr5DT3*N$)t$@9!(vmB!Ku&T2%h_hST>BVAmH7k`kp-9A1eT zzKq;Fue}H1oEczf`3l@m@Uro$fYC)k3piqyUImp?&ge#8E^4m-zF9?qNF1;IYfaLe zAk>Y~z2E;0v7!GBvECm+cBPGFuj$yAOI^4_s)uDD)Gbw|z9d!}@-(@;bwmG`OFQ?) zbAbf6b^DXCCq-FV!Z=VxcBWLkk<2uONib@z0_*v$mYhX76A4i{utHf`4^QKdwQ*Bt zm}2vCP9lgNi573ruxfuR$J&@uJ!Eq_1O*oBpN-3hAfXSB0+VlRz79vu&^>Q|+I!R- zm{;75q(Rw?>f#CBHcD$!uImTMbUc-n2%Ij-YyR+u7%dI%az5CqpNPdWvJf_`SQZCx zPP{$)udm}k*$}At+r5R65^&mCjnz${^UOH@9%I;b&}<5QZ~0StHMslTWRlWplVNAR zX62{yqt&V_J-bY|ENInpW)-pAj$z;|i^Q(Rp@4d>WDyhYP++YV8NcNHl$y$k8$`+p zYF*_XDd>O8)1wbgPFvFgG8PwEKvQxk7Lo_t?1FL6!3DK%1V&$MOzy;HGPtqVM$j-w z3}oBE4J&z4@yaDQi~yEs%r^C-(-1Iw;)O^0hC;XeYMhSKWa4t7A`z58g{>+Mq*?!A zyE~@^IxjrKOlpAz2`f1FlIebvrikPjQFt$y$WN$pv-@xNlm55)iDoGA6U@=oaA1=*M+~+^URgu*pAP&f0!{c_~{Bo8k~O(t0O0*=+(g7yXk2A6gPxqszToF zI5lOo^%ZbQE@O35ZFlZ59~TsOOz_max6yDMFj&<=pe9*26o+Uq1Ri4r*AHLS+uIiT zh&nIxwtLYrrsVj$nwrBaEY9gEbmk!;FXy#YA%{b=m=XQ_k9+H7x<#?-N6HG|q9@z+ z&C-ObgXByd7OSWv|6k$+ATFz;6EM2st1KJ+8n~7-*5j*pPpMzN@a*P~>8OQ$X{kU7 zO*Tl^G+=&kAJb6?@C=C=bghv61!G?8_iVbb?>@F6g$)Z6w)2-Rf`_RlHe`6yKYGD0 z&}s7OaO)d595ibASyB5WbBJ6sA7;wlx2Qg@O3~xo;2&yO)axtu^aTahO$%}3pJ?ZP z0yCv`wZHg%FIM8O2SngQ5@@KRqkj2UZ$c`N#2%wKpw4{+9V>OB`0`3Pvx$<^6rv#T<}w-S9n>7!rJr zY578ob*p;Gd_K^tF3L2)%i=j{rNS9Zd)IV^tNX253+80d~y z(;l&S%BTh&gV9o_UcWH#`^hlYTMu^-v^T1*_)t^%ZkbpSn-f}V?=T~{?9bd0g2aGe zh53R&{4a|Re`ebk*`zQRI_VZ?gGlO}jk6EA!t2l8?M>zgC->^PIGbA7F<;wAH|5fBm5#d~7L z^WjK*FMmu*+m-EkGCe}JXr=vE1m$&@0aZeRYb6bCUs-oRcl6%(DzknXAk;)m5+KSEW1cFE?rwXA^!Y=G^c%#EN}d{CIg< z0m16rj3fw2mrt|c$kMh*B_Yh}Y1_?deui|kCw(1X5ucJxzr*$6xF_wJ{5T66_sRDJ>X zl7a7tjdOpI>@{CJn#>n^Y))31h_Wpl1pKO|kY_9B`+4iC)IAL=Y01gL!>D!1TT-{d z3i^Fk)0ViyM#tt`Bap2J_K7{Ox8H>7Mpoy8C3|xHMLL!V1nbkMPqr4~4~SGpLx}I* zrI=ViAD;#ciN_!np6KhCTD3mP2H|cIAa^vVc}oM%n*FdtPC3by=EbXOP_2TT7)yho z|HOVpS~oR`f0`H|y@M#w8`69p(JU!IcwHyVmyYaup)y{{XMGW9m!-{@m@6Gc@(q8A z_?dvgoc6<(Zepq|YSo?Fi&t9O(-|PP3&yVxw z1?_)3j@K?;Cdu&o-CZ;WltVZvqqNeav(p;M#NWFNn&%fFAXl^FM`#*G zA%ffamthal{Z@v6_Uff}^7n)zhVH+4|HhL4cDCjfdM%N=EwpFA^=m0U516}-;6`gZ zZbe4rTsU4H+l;^u`*N&CwYLLu=s6UbXpH50pV|qlNl>rq1I0@WPZHb}br)aZ6eGy; z9||ZQbHQHX1r>1bt?FmNq4t*>l=kF(sGZg9_6BN=QyIngAi8s1$nbeRM#Q$F0NqLa9D)0on z$7HgnS-)<7>HoPQg_+M1!r;O5;m$0zzfq<|r6<4zx63WV+x~y`R@(P}y>$OvOXij* z5I?&DYuRX{J)*f?;ujWqLq3@hta?{Vm!P;`lZ7f>zi9olxVetQ2q(~={IkD0+T$?M z!pwCV`K_dzi>D1j#6-=u#CUHLug||eRVs|G#&$gpN6*2|29=e6nnhw*Md_LR1Z)-^ z?jj);-Ex}a;kay`Z;wzgYk2UEwRD4xyW$IZV+w5@A)A{E)ufIUQ)`09spfA25Y!|ad*vO-fY z!bmaU7HX(6QRz;G_sa327%$(#W~-kIZ(n^*0Z#po3ZEq@ ztk&Fq1!J>5p91&Dh=;324^QM?_8%rW7}6zZJUrKa?dw3a{mJBJdJH3^?VtN24m`a*-9A1OrS;ER-28f>44r z-7z#6c*DKsy$J>S>82!8i}kYeLVaio+8Do_bV=&ec?ESE)Yw!(y5od8?z)>b-D9Ig z(m7?Z!K6}z-JH0jvtG*;E^za+-k(#E3WPo zp+6%lII+U!+ac3~xnl|A>aEFnvdVzG}h`xoJ{PR`IC$tl&%WcMbiE zvzjlj`~PQ5;QTdht|F-gz{#XYxxn_SCUM+MsIF9%A#;bNs;tQL?_6sm!1hA~FTvRw zU;>BZ7uyyD>zpUQy!nj>$-A#&?7why!jsqM#;ZBIBXNT1bp65zlxA>P_4OAlmjXnr zEBuJbA+$H9ZSjbK&~w&7H3~k$uK`Lxt*a%2F9I%Hm^*?2cbGYOw|H}4dUQbBM`43@ ze4Z32#T-UHBAGmZy-xU6Gz~r&aZ_p`zWWSnUYLxq6HMA*Xm?is%18cN?5>8lw7zH zW-wrtC3+@6aQJ@t_+Oqs)bZ~rxC;IKENRs_6+S2Rml zsqiYWA(P)ab%Fsy21|GLzGxw()6oL7s49b#o1yLUjSs}on}#Fudw<_eD}lWFq8HNY z+#E}OwtCLUbo5Mm>*ONpsYnM6UfZbu!-hE;`C}WKbz%}AV0$hk1t;=w?NSI!-G%)6 zcWcWv`ZhF&wm9KdxDhHF(fH<4hQyV@1a7^>cxK0m8#J57rPV>VhVjxCF#gya-Wj zsG|9#MdT-dH840uV}@mM!^QMJ!S%%-hl+n#CO`^{etpefQ<58v4AznzCdQ=RI*Cdn zhk?DP5ue`g8SlINZnOM%AcMHk2w8=jT}E9lUJz~rp=@Qb$M~7n0-g*#mepzl3WgR# zomSAHn4(Geu)Rax{Q;NP#~(6zxs&`of$d25kr`o|2h{7D$9LzfZU$#BrfNvdSx_tV zl}Po8lj124%$nRFB>AVplguMZE$cANI{`8lxG*fSFs|428vjZ1Dnzm87OuoD|^fNUS+5sG@qr&JN-=UoW!WL7&)ZN45 z8T{V)cR6LvL~pUBrc0Q9<12d9pX9f+5;noW?QDF-pVggil5j}I<|k9GuI6qvRs|i_ zv`31*}*L22KOhx>p{E9b?HZ4RsB#49KpdMNuk4ICH~V zV_Y4$lkbuEcHF%$=EgE}BA0C5Cs84#E`Xi$C_q0UvGFn%o1*HSG1;$4& zd9l?GW6Z0bw1~*8^T`d;x+Sy9CAKyHYgQ@!?}2+$!ZbC%yYWsk$cswg`L*04F$fr3 zd?cgKe+XqS63|v?^JvM$@2t>A3b1wkaIG`ZYHG{B(EAGT+^<9;Jw4|xXyReR+SngQ zic;|J&M`>hP_J)bSl8?^+}~FnEF>|i6uCE*hQq#SQPtMnVdv6;gd$%WQ&tX%7s~!jj#;iyA5}tnizYFpqYebf``lh$33t zi$@7vUZ=J%$t|9Vqo;N}!EQVA9We>f*_{djVFcVfIqH53mVrZbwd#k=0uw4U%3WLS z{ZG@tJ3MGrug8MN*G5vFH#;;5Ukm69a<21+xquFk;^hmyHCSfngDUix4;J2#K!&SZk(63q zC|0@$Px+NCG!&jS)+ySHpu1!q+RRO{B4O0y+r7mio-|1ptXf`jvu;$0?(8^9hYt+D z0j1+wZRdO{V1tQt?hVsCgC>L$LH}BFv0LoFF>L!v{IqABpofWJiL0bs-HuT;1u!iO8XAmo(vl$0#HyW$M#WxQ3PNf3iLd8o@32 z-e}vC0MnrR>y$?S`l-FKkvig)4t@(56olzb!RP;huT^d_Ju zum!!o4rVd7vmjmLy@<7E*yFI4CbqTyhC(iq*dZM}ccV@fqD5I19UX0*PLOZI8L2e; z3`z!L@m3#3A7tRe4#K}IHyE(gyfV&NLh*7-`j8D(LE9v7*@ibRh`;9Lo2|-0Ie9qJqxAJzObGOe~Cww8W(R=v$hy7YJ9?mKZ8Xd2^$IA zkAg$N3$EkDS)>33AGYj=f9s&B^hy>rX~d%ma4_b0sWY%k{ityKvfn5T_|4HhkZ-d{ zSk#-~bacZZ+R>4ort&+4!iYk>Q@fC|#nFJB$pzvztzER0YL=XbfBCfa1meb!Ten^0 zyYOrWwt6ZIDxzn~zh1RQNQe(Pi7{h+et*>tB0KMB$ORNOV^^Qq7k~C=&KWW+|KQ#g z>b+-FdC|>F;m1{2ou1DHbp2TB-)A!7LCN0nCKO8N$aGj{@!vd!_2Xj%iGse?sI$L5Eob1^Bz)w?)5V-skqUb=7&3M zkpG&>-8K4o7k;G`ai9r;hj>d>pz-kdE`4#LQDqnMwao*8fx(^V zI5NZW zDgs~chH4`V@0$bDB-ch)>>()Q)@szsy+E=#mktkH#Ou!m3OTrMS=yGm$&)1ZUQ9DN z@Z0}84(|UA&$9gO;v>YYU7wYfcNsy)C_8%3OX=Ia+Y3p(Ptx#v;2gbH+fd^8zJcVN z5)HKK#6UrIX=42&S`IPEc(}Y7p_B3T>k$HgX@&RG*)XBw=BCM?CV4KO7}^bqqzD5U z9y>7GN&arDtMkN;P%Jm*I$!CYgc1}l=qNspC8fY}R$Dz}R`U5uhaJj2Ak8qR{2fXt z*DM3tFZQE`5(8^Fb95Q)IP5b$^aLcy@q=B?%(JuU%uoSgkYdOShzA|TvSeqKz=6a2 zu0*oMlNoO*;hCB{*+d7t37|qB^{!(-)tz$eQ94mXNJ10CKAR(|^ z(fUGOv#}*{B4xmT$gWfN_J)`!jByk!i52x#G!sL9NjxCF<5*3d_L|XmZ)$$-CnIt6 zXt>PngAkI^E@sE}^<^?cinG46Yv7)knq#W#1{`Xq=WyCU@2ICCGC&^mhp3p)qk+Av z=XYsl0exi>E*=8zEk+uwWT}?w32t;&`)wMwAyjjw5uoGy3N~wG zxuCw^h2y@5=ZhVkeO$PGuI%(g`N|viNLuOT)x)9ff6*TO0&C&#Cng#u3>j|_XPmqb zu}#5eqrAOLyUp@qy}jvUGqAh(0Twp4g^?bHqZ6^6P35J?&@ZOWT8dr2EAUJtY}ohi zK?a?l6qm_rbfMh=Rfyol8{N$akg`q@oKtz6X*0H|5OfA{veQJod?SG69~~!@t)rb| zsYA`zMY)Qio!iDSX(1W7tD)bw*G>NR$gw#n&kpP^*Fj#I{9yRv|CM*3u+RSy)TV@( zgJ{0h?sKU@`p1s2{&f1^D&4End!mK0RFgdZvfg`1IT6)B0BZ43$jgTL1jMqpI6ndA zYIHdp++j{wbDVN(An}AN6#0$R!m?#{#EWr7c%bf^{!4s$o$(^E6u)#YKi4OC4;*+D z=|yinpP1bbf#z-bSa%$ha&!w1B~LYOr-n|~H8j*&dHKB;eHT^eb#~@Dn3TRZz2khh z_G*n9nH)O}yeGHPY4RSr4~6#Gn|LPYnLsrQlRaZXG$LI$HikOQVelfJ+KaVUAvE^} zrMKb+B?L}63OwKQ$ll6nX-;C*n2RqYq(;2Kb7rZJaH~>~ooa?YLLxvMvwU9aN7Ddw zIV>haU(=~XqbqfVxz=mO_snGf8s$?a!I6=&|6v6f^eL*m?z8-Y%7b^Oq@fe2ohGA4(iT%$s%3Q ze{w`p-8x3U=S^b5cYFKG%8=sncjmK#q28u(IUlqBC?n-mlRlFh^y;ap!TJ@fAy$aN zyl>q5y1Jvt#4UNHbdl2}E%6IEc1=V116r(wfXNEU(^`^R1F=~6EMjn5UAjRF#?`5F za^9Ot)yyqn(*v48-E`5El$7vy#km3O*-w>&VANv+*iH>#kMcJCZVV0U|=ImXf#DI~%5b~Hv=l8l_a2`8t)6s!V@{X$mm#F_?aDrY7`Kqn<^xM*T3 zBa4CF9Pg}*ql;>){8#Pl2&32QFlkM?teQy~9@zMne0XSh1cQO{Da2DrVY zc^-Cj+BvPC!kHqXFwc^m3>a#Cec@zHT}%?4YlzGb_i0vK@4=2OHgIgJ_nHlSe^|j! zSi7YBnl7Rvy@0}dAb*xgrWevJ7jyR{e(Q<75cP6r>3_K-vLgT32m6?rCq@OU z@>%|hf09uaG>Xhei#ONQyrK@<(`FBc1n$D2C0>77991{dd0|6syHjP7sp6=|$92Oz zce|U>;soNXbu&fzH{Y=hB-+!N4nAZK<1>*<*i4!2Gzs8e`&o0>pXU}Iiq567kG7nF$<=$rSos}~qa5BC@ zvV|;?0ofEYG%&mko19UQEYsNdu;R+VGV^sOw}~C9qsdO z*6#Lr<<;dw*7+c~rT(Be5d*Q9ZL#;!DWB?S2Ou6}c|ID;m2Ff>w$dZ4HiXl-BF-O! zN&QJr-T4DG+vV1o>Wa7LJbuV81ra!$+i7e_JCnvCjo`f)^(SR0?M2LJS(6qmrxWKE zkB^S#7&rm%TEfx_G(rYV0n2K=oix*<$oQwq>qq5l%al_5T~|}(P4fxb)IwYhVhm~f zft-$Y%A7w>rQ^v2c)55HSM}-N=>67DTcs+MErvyLA>nN+7oG=KUCO$zZl`*KWe>}~ z75!aTcP8F=Sy@@F5v!(Jt@?DG48-@+Wi@H>))N%&;b8mAM$hlWWFe z-?{E4pGk=kbU(=1pz@9Y+k!I_POOWhaB*(RoS@6LRQ5cb!bkmTtH4}G4Rnwhgbk+3 zdH$!X~t;WqZ!C4|oN$qhCWI6m1 ziAE1SHDe+wX;8p>60fgV<9Mmv>bKz=si~brKd@NaO0c2T zPaLV@nVoMR$1AmEaQ;Zx5H)l(D-(8>TC(+dJ-)B6Q0eRw8hcRUqS=P#UAFX=)Ox&G z6jSE=^gYQ&pkA8Z;-Pm-<(ss70BIpf^G9YeNGKz7mz~nTUB69 z1o|*$|Fr!eaF*M!Ke?(d84GxZXyau^v}LZ2-m-@;r1v|q`h(jIZn*5H6S1XUa1BM0 z?0+XyDQ5G~5mHON&bMgD->VWgR^C?UNy*80#MR$)f8%&0kYa`A(>a^EbdK*lcJnY3 zEW*=XB8Hrrx`FuakrF_W-6p9dDH%DfwHDbywl7^Redpkut(`cAzAEz509UJiz5z@N(ubzeUnhU;{UXH{r6QAWneO~Y{3aJJuKqDM}4@4=(9Rbc1x_D^8PKXVz4q?K?h<=9Z^H-bBR}Mc$&HZ;%^g zPw3qoekBd};;G51J+&Z>6@Q|B-8!%;J`hw;GM#!t2mQ7Y7N6o8+@Fbma-{(SRm&T{ zKc=iQHWKTIw&xW)Obys!NWQ{9iQ}ebiOnz;mR=mYEUWtN2BIU--irtlV zr%TriCZ*^fiF-q_r_?et3sb1(J({k2*%UHtma5<^?ANz4+>1XT*|@SPgO9_IiMO(8 zw|vUS^J$h;HAWT zfyFHw+a~no6hvF=eoG;r^Q#LX;2@p9knpkJg21+s|Dlch;>f&_Jia%8K?Z%6CE6D! zaK{)BtX3kSdV2Hqo?qd`xlu5%cKU7pmK^QW5f@^%`&hNkza_AKk3uTYQ(22FlO^zQ zrc+6#g6lsE(EZ;NH0V4^0$Yt^5xZnH;6frpXr`IeEhmPNlps&SjS0f70`mFE0+=vH zrC3SS!>Z)032#q^0D*p?S)l%5c6Mm8O=1o=XWIQAf&4H$QJuC`%&?MN-a|GysTKKO zreaF4$g6AfdtNINco*U*$4Jy!Zj4zn6M>ZEYt}I5jsmv*MukUDiUkFWC5~Cwx6wKX z1e#Xsx6dNZsj>PO8?U9KZOnP`FO>IaJ#I?%J?Ys0a`T_}L%xPI0uB?p=lwZ7b$KmD zDK1f(k@Mt{F`vo8W-TOeTjB8XA6jd)vqNKIbNO5*wY%!v&RgB5zvX`odos+L*<}G^ z;-6q(@vZ3K9K)mNaM*uCa@`5g!SHzb_#kXV-I#leP3_jx5;O*=iWBR0aK=N?qZ~aP zy;xF5Y^Ahe&?v*?7QhbRJ7es=&<+k6PFhb0*xBlEdGKhjze$lN_g7iuieh*5?-q#a} zf7HKqAQBJP+ASH@4@y&9)^7mCN3#SUK7I2WgbI8iQU&HoL$dO`yxIyX?wt9w6NrXN_4h)NSQ}p?y{rjq%U2{7b-bnn^O}M|@ItF?bfHPQhQ4hpVUg z@WW`xbxyqbOShFq-^a*!2`jtS8enj(f#@u%| zc@{Wg+LgmeA=uR8lWD%U0uaRoCd1SD9`PZsn1yNF=V6IvZv6{K*0YUM>moK^x`Z42 zAXmAn72WpNw)B3Vt>3xk^i*(ouq25Pd@!fUT`RlkaQ>Zk&w@bj)xygPAi8F z;8!srIF|-U>zJ?|yk>mV;sP!0Cw7yJpr33H_U;id1U0-{y9`vF)v9fIOTO z@(}v0U?`dpPMO%=I5BJY4HIr}6yc$N`;uluS`;qgX8(y3`!@(%&ax;I#Okf||BH61~q61iYI(6YF=KYK!GEE}gBy%J6Q;K_GeHN3mo6jqvX zP5heIZ2ovgd$E<*tuBvAU{YZ<90&{tVKtAIXQ-bU@l$D!!LN5j9k+<7dj|c8a*1BR zexyy?)kEC@!Az)Ng}7ULn3-n_qd~pv`o+`;al0`ItyV_qJKw>3k+;#RT(TtpfJV`x#p?0^DCJRLbALPJ^M7zVIff|9VVI%d|W;ZO}SH7g{*6Gz>r;^{PM-- zG*wMULn@=#CGgzm`g@)2dvF%l(M=e3KqEz7OBX{5y>)YR0VMbo;^ zyGmiloxMMSM++4#b?Hv(46}i>C~W0?&fefg&Jp+sk+hYL^z>M2*WFB>_FMb>07urTTCs212rn<*lm*5XlQwK%k3d`wJ9(aLkKyX zJxNVzk4JuelxWoh!Fgx5&U7275cYa&o|Ig1&r` zjqHirS--8I)`!9Nfh>wF$G3xpj}0wZeVVot!>f_73P8u2_rlHsvEIWeZR%LX%mj{X*_fP4zh|&;3ia{(yqgZFc;n@IN8Nj`&hY_`EdtNe zs5M>J0b27q<8*d#Cswopj4vZft} z(WT>l>XX&|ALJheO(c%~RvvYH&{zUiLux_@A4Ih3k#Z`vkx7#R6Jd9I(3aQas)zij zV^37Dbwj$d=-5D?lP)O#m}k2KmRrE}3X}I9DApqb)2I3Esa@U&I?p(>^JVRBBF%1^ z_+~R}?JTz?5d_*m3S;*{^N=3>+dTTN(-2Xr4{Jc)k^9K24r+PBd;$S)bG#uUq6W6? z@GZlcvs=rl+Z2j6bu=q!}f4T+|7M+*I+Mc#3{A-Ca$S+W_?gH^-Hw}Zg_8jvI)H2p>rU3#ao?)6!*XcxuQTro?UP7c0`nKF=ZQvKc3~) z)!n^rg3)M-;F^YH3rXgTuYVq94OXrLEsIBsKwSp!E^btkCSa-PE_AAGqqk>qT#;FN z(fRV!;mFS7j{}+c!wzLIKN_miB{)(IPooyq^HH=GKKU#(aN}&}Yb`P^)6g=} zV)ldKk!>xxxh0Cd#8Xo6=z4GxNhTHs+QhI<(0I;Xrgax^Pcf;x*q2R|UPr&%03OLN zj(W`t*6!Tf1nNN|`MbhGu4v=NmLn-}_M6N-$JBT$i6ik5PY#evw)cf^;>Gp^I9=A; z%U#YvO%wSI-|OzW&$5gAfRvhJwc*0m+NU+e*f(|z=T5+beKxFfA;?w@3ktWtDJvxo zPHtg@>H@xg^MQ=JZ7)a{6DUV)FhJT~*XzG#g7OdoI!+@#5kF+|pmW0lXFO1EN6nR- zyV>sL>lpbW?tYe1jq=n&)eBFeL(^x1oR+V%)LLoi;}aP=@8W*9F@6WdznV%% z>KoN;sipsY^*?@0OZe}VCMO3ea!M$7BjfGFoE0Z7fq?x=QJrVAgao6<4HyfC9Cm>9 zPNo^?gW2aKk8yLb^$K*PoHQpCD4iWM5*A6>O!@_3i(4k16ekc^s50y8XC^_#@T{zs zDzbL`{QUl&sMQm@e$4rxgoMXKG&V!&^i#TErf&x_ z4)(IDelq6E_42DgUV>|c^Wxs0U^Kwo#r8E|;CK2*bHk7OaLH%ZgdY0*gJ8SGVpjWX zftU&7QcK17qvIx7c5~WMHIw3uvmE@{br1R({D<@C{*ASIynb@Q#S%E{T>6^-*Suh> zs-0S@70vYu4o4`7%ODRIbE-tkr#3q>CYD#8lF2z{$vJC#96@DZsJ?x0TLlQ`r}-53 zm1jAq_a7Y>JaLIyJXQ94oIuUy<0?DF)f+i;f95oNg3h7BCT+Cl*HKH&s6Oew8Zo*L zG-hHH;4T39yff>yWgLZ~vG2YHZXRfz{ObpaOFR=aFk9C_*}4IDe~_jV zN|8|0z_TJ=?m)-uMUOT7{;x(lCzbdW2k1~pj2n2ZstOE_std_*@2Wtt4e}e_IXDyQ zAnEd?S(m+8`DpNM0QXlp_J^v^v-1jY041H1p!}V z*>ACyis>Ow;jYw(SAA;&^{&>2=v6)f;F7Fp!qPxU;&wZ(?sw5H|01-u-qs!#&M=#L zcF77TI%H8i{e%YrtH{}$oc!j!(mcCV+T8ffq{JL#f>a+>cBhn@`du`#=L#0R087QEAC(68?y^yMQ_z+^nkf28hF_SLXS0QTTO zv^?=X^IVexy}Wfr1+jM)XG?$GjX{>dJ9D7Ur%Pt^`RSl0k`{jb`NH{_2z1+#@fQ2wjA@d6@2Jw2vYs_3k7@@4)jpH@yV4Yku=i!*jx;4KGN|s= zrwPY6P74sNs#H*p7{kdYFiWLkDjw9RQkaVV?cGWx@|cTXL1DqD)h5u|oFHo>z8(lh zx~vH14GghD6+nMvbhrSCe8(!=^!nyj^M+00OCM(6mbo`n`1yp7x#BKjGq+y+d%rb{ z?(Y4?z`)1eOd~D+jP2)7)H$OvbOiBpXm@(Y9}gtPuQ|8RMp@+zb?M;!x|sJX8i~EI zxO9cVpTt{v?JlH&im6Ly;MZ8}>(S`5AS4<5nVKny?b_t!T^6E z?QwKsY%>lFuLP7|KAgAwYz>;%L9tRE<@R#VmdkOSH!LdMVPe@+XUsf$^Oey?v{P_@ zLpYkC!UDyr_^fwMBTWZ)F_J|HnY1)tW_237#nCmrWbsSz+mDtHK$v0!)7*gO+CIJ7Z*cFR5X%dSa%n&vwqq-Mab#yJwU0$9ywN-?Vc(V zz_icaRsSwx?k7KT!2r9R7#L1&jbGvrd@ZKEIA21RB*yAcP+mofqeLI>T(LSPIDPX0A_{l65r$HpF%er6oKPDGmI)nD4+CGXO zZ5YDhol!LFZxRwf$<8a>o7ogb>XXEIG3Puu0GXO#<9!N(4e?3te)=Tl=G#h&=-NE4 zYMle)w{osB)oMB@5qeVNSHOl^xZ%mO$b)Gr(`eUw8qMx5bQY^))!VO5uT~}jFzo`{ zCt%B8G`ZiJB9vuF$ayApr20_6gGXOeR)ezRS5iGI z%uv;Y`RC!n)WZnKuaKu)C)=gUR)E`_RC=^A-8?!417YVle&?RpX^4*js-}Ja)Q}bskQxmtol|M)9MU1p;5|Oi^YRz` z$7g5fcg}s?*Y&;n7|QS`Hn5Cc607ZH)yoofiRVJ|-h7j?9He2Ex!6++`7}oaSf-3D z@k1S$nu;3&TKSwkL%}j|Dr0DDpD+GY=SnZ>pf#vWjUl;)=$?xh54tgx37&)eM@F|< zx3dG31~~A=2{JN6=c(tJCEkS&6;H6^hR-C~G4jTDtG=sg$uH$RvVrZV@=bci592^=V|ie2bKx=XayaSnt&Z9SwUl%)2!^e4dF| zQViJOuQWCrdmX5`CNdhIqV56v9xV8DZL%868oP z6Q7vN*xpat)#vDNnBQIVb$09fCEE@WQC)5IHfgDA1_QJ6_bIcZS^UMRNb>~zHWyCw ze!yu+zs&1UR=46MnNcyEy`}s{n?i04`%1;)UGntka4N_vc-Qvbfcno}U5|!y%PWgl zR206Dw?AL&aHOucr@wwPa`m8zt*JxYO^ohhsxxTaP0F%zeL4Z~cVXY$ey5LLaL7>PmV+W}%p$v!vA`O#?^L&Ni2nZEt z3%|G!b(igYMtV4Ys_0+jsx@q-`!e!ocM;gl@jzooK2P8nI=xT9hWo9Weo2UCpzj7uXUF4!>G==mQnVrMH zG+w73DSmsWISZJS-+zLr$x6ASdPW$3u?||>XD`pP%0Rs?yyR}E_?l<`I|ViV|76V3 zG!+^Vf?Sb)X-|X{E6tvCzqc=s-txtN*;>r|aASt2B?LnZXJs+s8&WBqVJ=?tn00HO zmwGwK9lB(xr)QlW#Qi?^vHPO~L3M+*gx3#)a4sE6XgRi6;N$XisOZ?B9CzlWw#O}U zNyb|&ig@z21TQoPl=U#*(cl;l`}F*Ot)kCaDatQozgbzCb>9g*3ahNo`8f zl;fCixd%rr@RH%WywmIR1$`TUJkr)VlS2LOIicb{a$DO-C;k2gYdZi0*v26AW(EJ; zTZpI=l+#`l_cm~cxd&tD_hHbeYz)B^1X4s*ww2|O1)P9Nps&JlbdPX#u_{Fj)ALgm z9O66yoNsS{IbX$O)_l)>K%~i;AWhW_Z*A-FQw+l~hyu<$eJPDqIWu01t+Q7LbP;xc_pyWpGU^v zkBrKMSEu^#^xl3HhIRH5boe zj$Sw5y7|ndYv`^%gIG`OEuh}z;T^vnb<{8;*&vo>NRoKGh*>uqd+}NwAJHty5-Rul zYHb^j2s1oK?}i~ZHvh02k?jg~NJTcmYuG`-+ToiV1$jCNbJ!;Tyj>X@{->Ww!)%+U zdE;uj$_lq5icM*y7jSEb-I{0JylXHk7AhI71iki0bAm`7=Fnro(`Xjdv?%#$N+L%u zP!Wq?O3JJh`gWM6oTV>Fv2lCq8w|D6RalP2$O0)%ALAEj$(*AagiF~$?{x_m7Avy&B z;AD`fmJ#O{lD)UT2kz2W-FXvDWJ&#mf>rlPd3k}emUSk(>s6)0L~QsWYFVsIAoC^A0h z$*D!U15+0q$5Q11S&?{Pddg!gW_wS^A%NBIQh5qMvz7k&b*vU1+UNKES#w7b#AeuT_Ql*m+8q*~v z5DSD>BN+-slo2{>TDaJaUBxAWeR=oH9N~Wo&3-!gm@vlIV?JZI39!6GR!0LTS8Zj* z{vKW+0hp&HMk_&}xI2G)rm+hqXA~z8-MJ%b?J>kJ8IO4@KvOY6Na$;E*a!tj@^O0; z3NRN3)Yr5ft(H}=fBb3m<5}=(8GCW4eza8GxIs^ki$AJ)p735Nvd13<{IMSZpM=)T zJBc*_yviu2n#qhQuVE2DyQ9=xWytFFI+!vWx-nLpRw~#Ca;V0 zVs21+6HW&UWAq7hu2nxK3*f0NJ>9}_Aa-y?_?zRFv)!|mk|-Em%}fOQHCP^`F1Lk_ z)PGP+;F5K49D88_aU^M5YdaoUxZRE_lU)u^=hrGCOYCJy``U^JX?S0Nb=Msc z#o=E+L(|UsCSeq!W<{)!JV6O6!#iGSja%S(w^;v`f}rHn)0LsDUATf;?Q7Nd5jMBF z^X&{jUxu25tdrdfqDdT-!EcgPpjLl>Wn+#?Y-8$g$IQ-sBwzTt9Be{b%eSMKLG z@BWk8QA=faB^OC1;tsA)epK{-THh^Z4~-;bc-n1`*;IB0C1tNB>CKK2vdZ{H-mN6J zL5z*FQ)Z78!t?oaaLL-26ulRY_iRyD+4ufhKt2hmv1e{%V#<2@2nm+*$brlbG$8U! z;g|aO^S z7~w~u$|-wibY3c+;@ZE2YnIFiXpk=8k~lOwSnk4O(3c4xs-V$Qg}JK*_r-CGGaFKS zx`JO}_=t^v7#N&u*c zN)4}ymoEuq39>f4`SKeh{kt{3%6Z1jM2be3tJpq^z;sH-2a|GlpT{s=wqrzHcgc=O z;d48@Ns0CykH?(2T)I&W58r!!ea+)}GSF8^1-I$C<2MME-VWCD%*}iAJAi^8Do3k+ zum>!H6rKPuJR)taHW>xAM67&<@HQ8k14UK0M}aPPvp>n%+<%}J+ti^rgP)r=j3G`* zi+L>v7+A3(#3gt204-cG+feQ4bJwU2wsUx<7sDsm>;bvL3S-k-^Mrcpk~#h}Rj1~= z%zGWU-wb(kLs-M`oFA?U@vJGUx(DUzzVS+zgQCB_g|aSR|B}6-rqm_rB9V%Df#@_$ z$0Wi#EC018fo|%mhW~y^f*w!ayuD+0ElRnfPCLr-b5)%_qxctY)fuiW+Myg?j8Qkb zt|_gu^NCMg8tFOpjYyulBK>O*SXS(QttqL+$vhyWXh31&L<`JmvASf2r(;_*9*&%! zit|eRFW>$U?}Z)CykU%F6SU%oCXJ^2hQ#o&!6)rj? zmK(1zc21wLC!+w`vQ;#k0G4aaOhw&PUGA6e>a%?vR-4Z>06aNXF}{HE^>@;1l0TEJ zTt{2K*RK~zoOuRq&uh%}?P7OWj1pN_=++`zml{0+GPvS@5aP5H_* z56XrgF3#e;4B0Hj>3IMk%~{eEu!44yeo2)FMlReVwlRB<&REcJBXAq^-xlufb4U#qT5Xv(a+kmk3wdgN6Eg5iU!@v1F6lIkYWxXKk|e!=t2G$)=_& zR%`If>1Blvp8+c)Ge`IgvBH{%aa{!?QZCQR12M)gIQj^VSV1@_CmCnu`9+!mubTiU zZ#Bx|{l~$a7L!pJS}Fo*l|?V0ys(1|CE6<)r>*RmcEm`lfs@pBXuyol!@F!0h0XI=%D0_I=$!RjF3Odkb!~63`RnM^mZ^@(!uvv4Jt!rN@!YmmiG~CS}rdqz*dS*THs7x4sEGF21 ztMU8}-adyeNc~PZj|892#*M41jpxQ*BsZvY- z$WIS?y**E`?{bR%;s?uFvwfulp__fAwu2-XjcDy%Lq(E>#FTJwOgBn*Ei2jL&-1&} z^wt&RztY?A&EM)P!qz>|%>INC_S{kZ<-S%6-VZCw{6QvKyAQ`HB3tWqo6xrh0|qn; zAKzdD7kmS&3=3HGOTooB(y3B}l`M%O(8-SVh*=vy-QB~)`E~4#J7S^~h+13C3d9R# zw2nyce;(w{6q%zlz6oqE-Br~$T*i<@QWcg(hiIBgmkC`^6WQH5Tk7~pQ6eSLA1g}p zv|^LCw`mtl;pVJwcYR`>N0+!nZqeVK-JUbrTTQzlO-xLf)+s7QR>gjJ(}R@(xAe#I z=#sMLrlxaItxKUKDDy;Zz^y>YF@S2{;c%&1WDT>fo7YXP?mRpJzI_5-JY!-@bNF~g z$uF~0GA3ToezC2iOF{1}VxSsHZRbRL3-m)UH#0N%6gC>r`PzbVFIOFt0D%uWjEO3f z{);Gn8oey_w(?uLh~dn4hEuhPRWvc=ba>Z62W-v$gs-Nji%D1QDT_k1T5(3n9x9w@ z+T-0%3LDRYGxenkIqG+{_rW+DE(x@f8ixmqATZALoYm2#d_M*}NU{0pOEaFcAQqM^ z0UWJJ+`WW%99hZRFY2mo2=hI5`cI!>cKp)%cSY*~ylBse6Ais8Hf}q^S4<#GN+mJk zvPEa+`gWl)D(_Xek|e&<(8`a4XI#p;cJ*CKX<8;CkG| zxu;)muTXHHlcl2bhP+S&m^qzQr+^4bclOYH;X52SMYL3Dza1dP!OIw3hX_}j@Lvxd z+q|7(ovkL+^pPTabm0T0pV#EaE+y+KO9rh_fQvqps&yHR<~x0!w_lGsqvH zjmvB3gBbW=fwdT3^wBYEB);JCXo3Xb`yf~w@;hOwhfhCHx7pDc)fTW-1MoI6$Zetl z*Jtd{7q$^n+oUdCnq>sfX*G0s;!K(Q%Lycg@Qy=y%Guipf}K1MtsO;*L*>4)J_yMp zu<=YC*{5q^4)D4sSX>OtA*qrr(C~RK&Q@@yBwJ8Nk~;bZxdX(Rm?RMD|n#_3!z>7@T(owfe{Z+M4pygZT-HGCxMO4n2JmtC(;cBl#TM zBQ`=+dA`2&z)|+Lz&pn_hj3gu3SK~NSFiH)sjSAh&7oc+qpN_?p{?<1m*DtTLD@8& zaSPlcz~hyn>`3JC+h*4Zvv&G&oRyhAU^6eQp>gbS$Q$}$cqirSAGF7gYCJ|R#g5Pn z7f}9dUoD>ITt*4z+3zF}p13>(^FX9XHXoa{x+4H4RiNzW%7;GL8@x(dVE3&zRfMWV zsn_dUE2Z9#rVO46sVn!RV z)W9B|K!-wbMFpgYtj4f+@%F~oXO?HuC?^}9HC0~D72dIoS^nbFh;Ovr8ozoNemZUW zN!f8^Vz$w#Q8M6Q&ZN`MWBgrW_5ZyD!9$Q2@6pyhi%F+$AEfu9WNK&NW(%zX5G~UA zLqC^4Pda+pLq@oq%n~z1tbDPU0Z6+m+nd^gUJ7=Y&RDbb;iuSho&8p#8hO%3|qZ}6+2o}GN6fZX8>bmH}adcNMzD{p4QP7#EMr4-9}%Z!}k-og>#9t?-c zF%dy6D(Dp`H+PO{b^!jLy`-WWAd(rqvVPR8FoTtn@ewsQ_3t%@OZ@)?h)Bh%Bx4S3 z#aUUN{&^w|bD3U?$#(OJ_nm^!c@B*n_RkK@+cg3`=pYh<%;O+9Tj;s{+lq#j@iV}# zb4@7Brpy)fJbk6`)t}4hpQ-nsS9mIdeW9pNqcL_zWr?&Ocs)+%zlcc&m4po)`uw~S3f7w?$Z0{e@B%%+&q$5H0I9qZFos;M zM(IuIrQJVKeRs|vWfmLi`Mx0SvPwQ9H900`eq~6yq0Jv2C*5hc;2%n4;?!tjW|k;@ z2!or1v{HlD3iBKsc``Cygc9}F;#=Ws>RDB?4|xpMifz-V#5SNF1zaJ3f=t_18~k8E zOjU6N^r?uh;I@AE<8Ir=LI+Xb*Wvm5;`C=!j!kWshccrun%}MI{6KTn-q&WEI(+bb zQ-1}(V$*SL8=9K@y6zp7_AjFA2q3Njf2#5Pm5cE&FmpFbz)J@dbvdkU28tSea4oeQ zPGLMw_-%Nw zG3;6|Svg9RnVn(@lY1Z^%jd`ogb=_T(5BWMS8CxiT}i`=;t4cdaH^IJi=AAcSM{A4 z<2A{v(jHTK1-2~s-&M3S!f>m+i1*>6(s=hQ1_D1j^eAG{fqMAy7kfp+HDT1bOQ&dN z!}arI)aw@-2=5xBTYePh_Tx&{mJ8oTg z5FfUDc>0vGgXMt|gmmq#{zhH)mw)zWTwMR&c}K@A-P-|y)gx~apzEy`OQC1isN2ii z&C(>(F>Xq8cf7?CK$N%h$tIv|4Ie8j~cRaZ&w55{TAv01)u!fk>2 zi%pi<5+fWCJ6<05k3A05$W3>Rz3cX;9ea5$zsc3Vr2wGQ z=6(;Pz{*^K^$(-`QM6`DBj)iSfsM9k-x$ey7mkOL?j*s4@bSLylzs{;OOzVmsJgtA zp4zYeVT*Skc81WK`XNl4r3LUO7{2y}{*zh(*fUapwZl|{rwB?yB%A)o&vZy&?o;>-+ zdJk--b}h_0qQQ$`q!ntpn#%NgrY{aCW3>z1`}NpkFj`$`>~2)srzi=3H|ef>w;1M~ zhwScE}J*;{0=)i|MwU%wk7|^ z7W~{LiMp%DWe7gOc!8cEH*)aUXAWmiAe(lE?1+jerweRRqcJmO7k?a!pFAuVL{FcZ z?D^VP4%Edq1G(3N1{UUX?eyzL__e~tJEQqq4S4kMTr{T|ntN3?x}_f&9f9SpYq?}W zEn$*gr**&RdV=JF3RmoNFU`wC%ln5yI`$p*z>Y$;#7eRO=W+-eV2{yq+I=`Kh!SeuO^+E;2WG+)Z@Q`~ z_d51afb6xZtiCGd7U-?KNH{zesvSZ`YUHrhVp}&~JG&V7*bfg1jxH!F2l{s82P3Ly zv|x4B`D{nUSJ#CB*D!*1-J`hy5?$Nmx{$@W| zP45L64A6}GQ6a^tLw|0m^3b0Kgu0LpK&Z%Dtk&Vlg82@f&8QO)1lCJFys<>B?}@P2 zc9?Fl*ctPzJG|JHOQRj0Npfhnb3-_r)>c%g)_IsmOHuZFPYP+IUosqu6?Z#OmBr+2 zkw;(T;YjI1>3+c?l(35A!GHOX*e?M>ZdDy>3~`;HrU z_tA3~ziM?5%Q0#R`oOC$Izl?S^+FElMey#Lm>^g9qd$SJMW&Mc#j>p}-eMNZwxcbm zGE1WhIL{m5SCFidd`^mdt<2a0PBFSiF>llD^d~TVuiyxdDj73(F%!M#0(lEDT_3~b4=%Rda8+u_*hrE!9R8`>i0zz&Fc4D{I6fjS7+v!0Cbk2paH zdILHTijf+}8u*9tP5#?;+tGj3mECM2zlKQ@Gu~&jR{kqzi?WuZA?Iru1@H#n{RMa6 zXY^Bh!HkpN*$pP{^A$T3447a0R+DPW8OZz+N=X5Yro^GY(?~1b2AIq}0t3T-Py-Ox z7adN|$*h$}`SEucxN{!9bS#4y*ZQ;A-q_a`Fmb8(Rw>(U)EWPII46~&W(||S48HHH z`y^|gz3QWz4+l;{jfdC-i8Woszh#x)O3#$|2k7RqZ|k@N&&670I|Ly-uf|6 zN@A#@m!{Snlz){3L@*}Uy_1BD{8Mb~Qwz9j(KdJC_l%w{?9vi_k0WJFpvO{UOEtm9 zXc`>2TzU0weBs6Cu6l4o1;^Aq@U;iInJ((SZq-ZIPhZx ze33#&l?r4~uD?kwkUYiKXY`%)cLT1e}lgrDeokZW)y%j?V9ZS{C%j_(T8?lCd z`ISlZE%z&%BhPA6s$dY$UXTLxslfyQwC0Z|d{Bp)?`(1TJf<;)_?SIAqOCe=R6quAak-n^k$uF^gHA^OoC6dRiLP8@2#hJPKx9E>5 zuCJCjLUKW9sYNQvh);uqs;2d__nA@R!&-gmMPw+2Kih$P|A^zC+BlvE{|)cj0Q|SC zmV)10GG>gF%Lee=SsF1U{jBCl$qlk(n+3%wvmWs4w?E{9ViJ~fR+RePD5;v4=@qdF zWc>xpw3ZBSv%vM6=0XNfj_m^(CTyz8vgn*AsD4`v|Y~4Pe8EAG$yMg+k%;qB)Vm2S?+oY8ha;rK_d@5<>&~ zu7Vle#T3PBU0vujtXEt;cIa&g;RB39hI9K&vRZ6l-?T4Kynlj?{f@U zT>ImFmZgYFB&J6D^_1=Xbi>QE?*C86caX&_o&$ma9@6_6M zlBE=f(i+bR6YaQBXt001A3DAnppZczaf>`VYh(&-B2Lr8C+yWIT0%8JU@7IDb3@OM9|-IY)8!`JD_iA)eFo9Dlqn?;F?hY* zWjggIZ1Uo`s*F+RO-cR6{1xHa49Ub&4ttN<^EJS8y=D83w|u{2bXUq#g5iBBo*kOK zRo|iaBj;fBh)+bo#tz`h&7Qw+n68&fzFp=T$Q?v& z^0-Lxpv~3B8DF}UY6K5Lug9$h{XI_gzM<|(@Fm#>n*V$BNM)QT zTF|}8IqOdCuZx{8v1l!GC`auvqT$jDdB;!L;Z2+(qv;6|q!CpSsr`cQh+;nYY~k8A zHmROvPvzNMzSyW{+QZUUmN%sUe6vFs7x(sx*{Z=RMt-0P31AeqY{oq-S=T76!DpUmc}Y&_Ub++yx03BE^RKSZXo*>%*n?$GvI@IC|3`0lqY0 znj~B;%v*2kG(1)PkZS4cE?+m#i{)yR)7#5krAXVMrG_Yt>grn*Z%|GBxdw|DYYhe7 zi(xc)Up?I~ZVq=Qs{4nc+djD*y)-{+Yi0w;#{w+SxO^TScVO*c{1nXS}ev|La8&mHR55Qjp#j z2R#rA%7aK*aFo;+pnV3R$4PrM+?N!1T^3-wi(VUUX=6 zaw=#P*f%z_rm1A_I6Fz^?Az5Su}OuH(SSw33#4!9?tyXitAG#tb)3F0P#f$V`E_|C zc*$1(xfCb2da=$R(Y40GDIvL@X0tpfDw_@Nv-wn%DOBZk92oMkO@VS-Zs*^DL;b&F zqLX+5x5eTWGX2%AVBjyRt?~fI3_2sVR^pi zQ6KHo%jp$E^pyFf9dEjn{GixRhqOIbo;n<{(kmuc!>)?Oa(s} z>HOoDHiSUg{md{`w*bEHU*i4>ktZ|c^) znk;E>wrr1gK881jHj*BHA8X`UmXcI@5+*8cXkZn+Kb)W$MJ4ss2v|-I6H>+(|5US4 zW0uL1q>XxC@Qoeh(*hA?#?d;6|FpoePi}p`$flW0YAb4$H5JYg=GOV~x}Ccje0IZ$ z(AqLH?}n%*mvTi>?#VEHJyoGx<% zZ=dzj&$@Ny;ASOcx8!Chx;1aw#aE44pckMQ9ZMyVFtJ??JNrE0s3r~>AN4)HgE&LeloMWFJWpGYhrHt1Npc&-+VB)sM|kc zGTfbR#@ysX=L?O1yQ8CGl6oR%)~N_$g;%w7=yWXPHEwIkfr3Yxs+BO+Gc53hw&z~x(<^ai4!0ZxWx z!2mwp*OuXdwF-RDysMl1Qps4-XKmt9%3|JrPKfHpdLBh&Qr(R9 zbMi6aWlXX>4H22LyQ^*08De|ev8uYNY&)x#+riQj!NGgw>jifnDwEb}2|3GPoHjMl zo;qn~cLpM_kEW*l_NRCGw9Ta`OB~kxn!;1!~td><2G*$8@_VnujEH86ORUQIRTgCmweBMObQOkU(@5~P)p zBX#Q&8<`6X4L`-zf6h{jMe-xhv|0IG8N^=LM{F5QY=;|}v_`M{x+wL1Iqb{)wi>v= zD_)q|jG%i-CxNWBc!}6Z*68NcBqZ0(Dkl3)$Qh_c>a0k=|C3UlkoWxbN~w!Djs)vD z%5dVVX45jZ+@#61c2eL!PmlHL(I@|GTicAV+u)l+BVA!KeO2i1-Z4ZT6qb4>GAu*n z{weN(Vg*8qh6dIfRb$U1Tg~tE%?QkXxf&XIrzZf!g&6HPwxgo_?^E0v9-5P2nO`u^ z%>YS<4DR`g=HAk$DQ}3g@O_!Pr3t9{$YFjiG2yH3#jswyqWQ+6c})H6EbLp8HlLeI z=Mr4vfSDf$HX8oSZpumJV%qESIt*)G9Z9d2z*8+$pxv0>?0Ug@a?~ur8kk#m@og9O z<4D%SeE+Mgfdt2z?j4_-&OGSgwRFbqKVX~pZ9sTLhPN?l;K8ZkSD#k9*)y^2{-Lb$ zg~Yc9JuLwm8e{!sj+r~dE^v7gEJj&Zcr6|&XzZ2v{D)c zmRay8^Y@6G(8u!Bb`kmyXi2_FON|71@%e{q@R=@jIz`|5Ek4C~1e&TY`l#iuhS@$a zuQn%%$*S4~2^P-HcTiwh`@Y4~AOEJYdwlffU1GGDs&=B+w>bI*1`u-+#21&H5pH}I zcz)bb-o~t=rod-uUk}1o0PFu?iowZOFL?OT68FjDH>%OZ1d*ROTG-KM4?T|&4`fK% zyJi_oh1t?bb=b&|`vS!*>{=3?s+J^b#ZSR2yP-d>Y%Xv|mou<-6qQ_SFn49W*&MVD z@2Qi|CF9LC_-#`@u5IMZB~9UIQG=f!VK@kM{5F=`5Z6B3Ln)}l>c;Kj{crzjHIU(d zzbe-~HKPLYfRmWl_zpX}fuUe5X_weM*Fn(8UyxS>^GMQx1_}*BTeQ|rXsdtrAh7| ztcwsmre!pP?8(PZEP*0a+-dTuwAXGd*b%8DhI4SZ<~>fmrQR{!>-1`6$A86ll+Az7 zx)m`r5N6}#3IRluXum1VbhG4&*-2AmzG0n84iKFKiqT3913rD5o>8H`2pb^<)nY z{Z_^a#%HC0K@k*u795oG{u6s>f5Z?bMQ`p_4OoqossBQvEjwH5Zo%x&oYf5|#%-F- z-VqX6v30*q-WsqfYG6K>!(TQFhF~Y0F<`nsJWsdrqXfwu#2!}fbMQf7oy>Ee~nvur@V%ZO2r^;>epQ!P*kFB%Mg~M6n#-RKL-~u~AJoGvC=tyKL z{|N&#`ID=T=_KH-#4t2f%^9@3w}`F4o!R2DMRbUj7P%tE>MnIvu^x6C9Z1}&D$0w%omcT!Jqzw#o+qL_zC{D9C zxTPE~-7Up56G@DKo~#dGbza|WTJ&1Yws_8)XI*!S>V|wjNP63|msNxiQu=SPSm_xE zPoNefI!UISQTf}yRa6nwwN4jx@Gc3|egx^Hze40r7CI&qBL?h+4RoZ$UVV0c64WX4 zGeM}eq*;6s%%cpI<>FWqH_6#g8MY61FUU*yLSj{0#+B8wVcCe$YdmbIqw8dMlniyN zAlp^9j6Gix$b`Awfo`wcjT#MzZU5zoL2{1m#h4Z zqK<*^EaMC8(=6>?rea`i_vVXywk2Cw#vvj({ONS{ z@2TD?zMbv)SAF7p21nSC!Jf?$5K1_|BfLI6%tA7L2ii)m)RAb1eaO&sW>;CO`(C>8 zGXn}Ih)9%>IOzXo0vSIUhIpqf)P3hs@s=PZu7N5C`|`X+8NSe0oem$PL);PWqbX{f zWt?QG7ihj3jF@}imYBCRP`ku_?kBYzAV(Wkhp>Xn5-C{grNwkXuuro^NDstg>(<8! zV#a(rRVz^DH!<8+KhtkpBm|8k5_C&QKNPO%Cl%rru?{8@UfG2fG_EOTfwxt~shXVqg2i3uw z++Z>N!;3)nP!s$b^h@?Kf*^);sipl$IJ;4(vGSis3{mE4%uJe5WLU1MUO|h2nV{ph zg7=TgIR9lSrKK6SjdU9QW4cDf5rsI9@f#1F9Cin__Pk#|yai{R%HD26s%9zdq$#g> zoTpX;UybQo+sP|Bj`-+#I!O+)lyXxDta&tQ5bj+D2fY{b#?-cjmiN!Vg~>Bo=NY%u zhGRK|8qM7YM)Gcbq3Gl)#C(Ygbij2=_1XOANT>Cqo}=%l>Sh2ccdIYk`vqa@L}dU_ zX7kJ4+W19dw<#K{)WSN;F5F?|avYek%=rN0Mf4WlU7nI22cA6c(-bkr^WDq0-Uy2v zioTZ{YG7?^am>p@mri(L|Lu85`7o8BmD=m}^p3Fd@);+j*uJ=lxgLZ-0nT7TbIHKW z_SY{f_mo-=K{ zro+j}b%plE3RnZy3E#t#m?^LHlo!A*zW4NPhWM70A61(&0XIm?brL(%~xDBBwvr%%lSy_%SBO0)giqMJ#sGwPIp+e?c`$K)_fPI!E%$ zZo63`*Zv9^UJPyR`;sb<%Aa#G+7sbBSrMi`0sKfRWNJ}SY(LXBra|5DDyY>k9re&s z1An!@uZtw%V5s%s@IAfr7iNzYK&Om6QbQF77y&&U13aoupLY120N z7>lbr#l2!d4bzC-40(xsHke^yOjg@W8@IV3sJ5KhFJf1#h)Uaz7|5}?2?a3#rn8cd zi`LgY9>QxR*#~40>WRg=Jjo%VjZU5d^Hyk^Sw1QTg(K#?AT>_5`c6sfZdC-NNY&_lU zDP6I)pe8`tU`_*AnK9YbMn+(T$k1wRq*m1eZNaVL**^O@5lCiEEVJ@>cyd0}bFbh3XEz&n>yIr3YP!m|$(EzFcNc%~1EGOKP7f-Zyf|E=6(v+y{ z=vk6(e(kl{*{-KHN8$8NqVCd0QnU1mEuk(`8ndg<`y-ptr59mDYL)WbE^84F)@5v# zp*9`H{m%`f#4}1~Y98~m6opV_+)d$DwIeSv!M^yk04LRu|3k4aoA;vZSR~8h-vO2O zzXfq8v4Np>+Wk>j0pUx#i-SFqO0v3!?sLO0oYDA={1O~YXEsa(N3u>WHtEENaa?f15QJ$rT9R_*5L(y9RT z<{bef%;!v*AI=GGo^y^A0LmLTtke-ao%`)}$aH{Mue(yzDeim59QuVS?wA364~Bfu zCQItb10-)8Xxx1=(<|eEB5A-60lTU9M2YwP_Ossfx3zmJz?{a0^3*V^ncvlh@|^t? zi3%4>^u{ZDOU$W}PwnRDr#OREZ>JOspNkx``U*UcW~ zU-7J~P6D>X0hPyg79QYZnvzUsJsn$W0rs4;9WRXD3N^=elZ#}yLvKru4p4mCPvDJk zuPM{1*6@21+rn*Mo@@nigC#A)t;AwhhrCIxindiny`P?h6~5q#N(d%ec}LxND|k09;Jq zQjXXjN9^35k%XZAyp{di%vc@{CckM_#=Q#)uVT%yV;av!vAZ3aAkuU#fnXHUhE8H@ z_t8a9VA?tWbqgOOW|q&!cV0mHpHwDVvNc6*N+-I|b$>8V=eoX=w?b5;i*4ubVkAQu zH3(2D^J6w50w$;6AZB^#>yBukXd!*mHm5-jJ00`FFqk(>HFWtuyUNwe#hkylO?4V2 zWuBBQqwbS1{RghABX&UL{Fq;W8eM!$Vnw|I2%scaAdO2D_C?AXw+v~Vg?V&gAZqoKZ-|y+ z8ENd4!_!G99kGz#kT2Eu;GD?5G!rZl;uFv`jiF0AD>gq^A44O-uWcjHU z``NgS3eBk1b-tP%Pc^q?RE^<&U_HUaK%8|%5;VV8qhL8NIB(9SjG!(`9X@*do_oQ% z5gumgex=1o`6?x2^HbZ4M76Cj%PB20nX(Mi^QSU!4Bz}zgzfXv)Xl4S_ngl@1JhD; zj{uBkDLWKDI!%5uvb|+^P6nuDinU(Nj>b+Fqg#zfg-19UOQ^pHF z@1BX16HO^KLj!eeCFt;^hj)|ZDFr2~GQ4h`{hU(eV06z$5B1o0O8FN|OlHFW zrySWqNrUv4gja*PM?j~QaYt;f(Z6t-in{+!S4Baq{`&mgOXC{sR?T-ruigMA^?)`R zA}i}whmXGGnx-D`=GpcZ@bIF7t?>S-x~K&tmhM^njKode?YX&A%->BZV2Wy9-U0yi zz?-%cqdQdx_O{d{EC6&JB=+-^wN+E|u;GLa>bN&uYw$1?Z=#-BH$&gVsB%8a@YH&I^Y7!f`-sLp%8r;jaz)YTLRld((Hz6#8ZT z%6A8TM%kDS9s)o|VsuqNh_)_=+rK^RnE-Z)#TUsTpH4c`N&=3d&%1Y1C_!LwdG!Cd zI?J%A*0+l*f`Za55>m=Y4j?&z(hMRpfQpooA`KEFu|<(?a7aN(MWnlt&Y?rPl^VKX z-p6y!|I7Q$>(a{)u%Er3d);gO7MdB>@ke>4WWX1OxnEh35_|n(_(I1DpCyw=(kJ8U zPdgI5#%vBdrxnB+Ru7V+b;-uAb#wqP^ZM%|)-R&J%p$rp9TU#4AEKMcRZhWz8}bvl zL-~%Dph3f3Jj&#zHNB5WhT>}xIA6gn`8(-7tbCd$Oy72Ym>b`Dgnvr?VuM&aSd<85 znO=1B)T-_s%a(I^1e4*9#n}I+-1(`(zO6>RV3KrUsoHsM&Q4g>?U&7CbrKfCang-} zj`n0UN@$DEKJq}pNf4ugIN#jL(3gx9<##1K+<|c65E0$T0Ud6TuSa>Ow1M}g1CS+X zhS9%X%p8xV6?0+9;`@l{59|We)c`EWAVtd0%49^x{#N#V;s$T+0UM?u1SghsQgrJ@ zT^e7ZG!-e>u+Vk7?4x|PMgh4vEY~>mPZr^s-V6)W8V{Yt8ruc7|C7>=J>ehEyp_=; z^l*1Rpu~>KJ>#a}jfBc{cV*c%{U%A3ahH(!0Gi-@>*CC8YcY+T)P|%O>L*X07#JE- znJ$3Vo(x*}Wxp@-=GR9?Mw;T>@#km!eKkCwliid_#iUx_-O{oX(H&#%?H|fFyOTt3hvVlqQMu!#yK8 z*1$-V*~-K6g2rx|zAKcSM?-pn4A;?6k%yC>P1>dts^0{b>AO#Q~+eV1sEYLIJU!er(W8&0sG zN8~kskAI>S9)#qV#@v?4rEy2=$8EFjKP$MSOW7>R4K*XJz3cuOAi5ak0}d%8P|u4w z{Mv-{ELOh;jqoBT{reJt*cGE=@adVKc>KRvnH zFfRCIMeza_(2_$ozO-JRmC`5_27d2Am+R5f(YlXxXXB|%w)#sUYC?!ayZrX}hp^yX zcZFVjHw&>?Io5k1qcGA(Cs{RlK8&+A#rT9$Za~D zmI0+OWl92e?#v+FplcE~#$18ldFJlKwh}pGnDadhUW;G46##hqxNj`w| zzN|RYzu7FedS1$zu2@jzF0?<(Pr0U=Pt%WBs~E=vaVCpGf)g#9{A|hbGJF2A zs*g9bkVPwWx{On;hzY0}x6?w-d46V7mVaDULI9Q$VyWYVO9V-7fd%Nyd(#;)Hv6$W zKMDq#5d7N57q<&J1d$2rtS+@-8klGkF2BvsmxlF&!OTjavV$P+Qqa^IBHh~9!XTrc z+}#;W%bL6IGcraZy|0MF7%T|()afKtPYI7*a)nm3#;8v=9|tzjyjNfdS2`p1)fqhp zwQV`I<@}ewb5D5Txqp$S!xQzcrtD)){m%#?cXfq5Ku0<0A_Fh*$jg z10Walza>Ii%tB+;H8JOAQGI#hPFh=#1>Jru@W=T`bjXWUMc!aI_Bu$lW1$}1GfonA zF#dxRTWS8P_F*7G#3nK5_)0VC!Ej#~AQn=zwqfVOBQoD8USz4jtJhJERANGSc`u z+P2u`!286+Q)$J@)(V^F7;SOid&J3Iar$)>NogqM7Dq)0*}PcgRtB*WWDSsfL3oX5 z>F%ZF&QlT~+dZ7)jcI=Ip{u;yDkWW0B2fJ{APvxG1R{$!1g4#--<;g+yBY=1Js7Z&hCm0r3sMWyXX|9G$1CnF}v{X{@@AsuWil!P0m$G0g7! zcyz|xg|t2Dv7CB(b+d>m#h^d!1Vg|1rykNaH(z>a>?6Q5;czpE^y3rz`3f#t;tGiY z9Ipu(t6WL>EOB4-_8S;d9V1G?CG%zHvTn6uB=oO(VYzi&YxR!_?sS$GeXby^{KFae z{a>H8AwtE0EPg$^(5%dAR1E9Jlo11Mz6eA@tk|4B@!q zVX>Nj1DZ#iv0AJiy1V?8@(;+Lw!u&fUYs{@x?ASX09STKxOk4*^(`)s6l;P0@Zl>h zW2mJRr6lhpAhUz@Nk{d}oPiy^i9yG9*lm-msufkbbNiN&mVOA2sbV~o<p=41c1%E!F+YIUuua*NNk{sur($pj^i%p_Al#+^bhp>N^m(A*zQswTJb zXLx)tBj+uV@F2rRs5g3<2z`G1>8);2gP!^btCd)lK8N62H}X%5;KZ*9PEW5I^mGQL zPEnes%dxfvQJZ&Bx6U%#CG%t_6xs=45x0UlB{&uaI4UBp%2&!=li+?=mL9{(Peuc} zSDrEF!x2@_*M`^^q8L~j(;_eRb*C?Ag_rBbnnqE**DrhpvBCO0_*HEqIw&VYbc}87 zk%tw{SQePX11}$eGVzh8K^LWq-7(uWL+$^Bkqey?#g9k&{!w+U8&B$Sz}rF&v^ksX zt3T-A+)HClUJ|os!l8$-NbwfcaSCvaat`j)Mgm{7%}N3X-a8T);D!no!G-?g+*vaN z^iw-`OV;6~Gz!W1!;bE?*R^3ma=>!MmFX`W6$NpqsI?1W7b^6ady7o-X>|1>REy8b z6&VMKdZcfi=*@(}9bftv6_wR4Mmu@VuliP-aR@Le%_ZtuMETdE{Uu z3~bj>NU+%%9;$wff+uo zdf6TE+!B;+@?<#l^c~2^BgGQ=_hblg2#u%KM~frIR+{|le|(ED)u7Fmop1>)fAzkz zV*|L?1KyGANwBoQ)X?m~fSyR!HK}f4ePowp{SMP<1`mlu3Ukb4tw!0#InuL~G z==Y+{KQ0o2<#67Xv;W|AXgf*mAA>yjQs@6WweiaNngV@RUpUQb<*H+kuZ}#>AoVOH zn8&&C#cgaM*2L`FCwo5HBhD*QE$wp(^>6$!QJs?*$kQF07dwulz9VN|f0cc7B3dM; z#H7wjqL>BT=%D36PaMINM~E1ZAKxnW>TsJU157vs=kk0V5^dUc+hSEnx!?z{t48-X z9LelX?9q#a>sj#=W)!Rg2$7H?^CXsNm$HdEdzK%x%GT!;9=IqjmC~mpxjTFIp29xa z`qeR`z4JHx9`K8e#ujjjgeoMxO~?3gM7N7xx|>eY`+)4%z(AVf_7s7z@K3zAciyZI zA@xxnbm#Mhm^@a> zV%BeWb(5nPiOg$i=GV^R+&JtX>l~sve4D#-V_KG)-;Y&O;4}sLNbhZi`TLg6fJY~$ z;9xm7`BMd~`3eiztu>gYzi|xdjP(`1{%fi0Y%8puWLcNjG`O@`@`>bDTtGbPo+`9s zw$K>cNX;KscPT_aSmg=B(Hj_EWiuew}e`}>slG#_a~&b!Gion6f-=_a%4=3LOP%1 z#b=T%Tc8Xk&W4{=zWDjHal=HDn95pY30(=Z7`Jg zbik}h)+jXl^F;VN0y{cT4B{dej za8hsh)s5=~{JiTe@#~p$@C-g$^4jcFXlLEt30#mDlR~4FZ*wp))D7dl zwd`*znLN=4Fs0*-iq(D57(t zd)B1~WE|=y5$I5eOcmA+?J%42c}-8>2)@u;%&(*sAy?$Cj%@$_B=2Q{9~*QU**N<) z#tEH2YY~xvtHp<%H51k0&u}U0q+-J+wt)2(V_wlzLQB+q1#H~7b2Z7#bxlLKv2^Bo z!q~t3zo#Z*tTf_vuYD=SiC&4x3X0830!w47kJazSyLTR3xB%U}pr&|Vhopx}JN?t8 z77H__52Xhn6N$cWG_0Q9v-P&UXRP*~1GzX^F!8BkX&(Qb2~nZDtx6ZV?TqakG&*au zyWbORj`-?a);GRc(f}~h5&EXFk$~y<((4$yT)I>o)uW;ZylCJ57}ih$P0yV zW&z%hg+S@u#4Em^C>*CmuwjfW$UDN;xBxS_vg)4((l)m?XTP~t$*;JVmC16|HFL-A zKabkn@&tH-l^6k;C4|O~oQJD5bJhW&MRx>siwVwtqS@af%AZ)kpHJYhXp6%Q6P1zh zzbN8M6>xO{bKrD!gg;u8l&hZFc~zG2#?1gW*NBSSe^vM8^z(wcTHxcW%Z+IpC^n8~ zQ*2tB@J3PR$zsw0PtMI(8EqnsnS5h*8NVj5@NUB!^q6itkyIuPBs zhD)CQI8$Thop{UEp{=&uT=>!%B5Xi$+-0rwM(JLx%Y>Z&x&^3aHZTN$Z7s}Vw-=@S z%%JG9!35b5r@%pYd=}_H8)o#zpG;3ntbIf^IUD^(geE=AN@VoUa=*P~#FpDuxb`qt z;ND0nW3(1+bN8xzKwhfqx;Bj|RIkAaS4uZ9flt*u$zuGIz-c-nw69KINcrwd3gZV8 z0hORP3PzK4!eHM>i4rEP2q-?QvN6`s!WEb+8ObKE^QNbd@{RnQ5GT!|${-`Yzst%s z`by!kt=7^2;yIY=mLeH3PYKz%IxNIDei;LEM(M9K=Q3u_&gCo}ddL;RB&Pb&Iw%Bh zUj6o0)ZHZ%|6&u?qrPy7d^+#qH`0z|^+6QJ8x>OWZ){T7Kw9R0vvADde~dGFJ-O?D z-yF413I7nOYst}C=7#Y<86iU_7YW)+8)TuE9A5b}Uq9H2+H-SflMo~Gk`wNxffJ_d zEDWP`iK;ZtjG*ei%jTqZ3HOugawbJyFQ1~qQu+TFB!f#h!1*u7*PT`28}Y{{auIs( zQBkRF_O%Ar*m*kN*)alCIdl8Bls|@_b5dqIPF@Pw>X`;b!bpO`mv2@xL}c2{rDns2 zF0(qE1UZPi&YId>j?LqI?_Y{zpfYzA-#K2{dWj%s=`o_fzbTlb6n-si~3{LHE_``*n> zc89wmUHw#rO^X{uCd=x-CXJ6X4DY1B8_6?{rsK995s_922~3SK8%8}L+N*fP+g@cl zW@td|D-V_Ox7yv@<3x2d&)PZ*2pAQN+kM>#q&tdBH@bB%gTX@e95rrjUhaM-qsEkw zyV$))(p7xgrMEe0SGFc7sD^Z$=Dd#AY67o~u##&5T|Bb?eByMv;te+h}IRL82gpUve*Ya2Iz*uwM82jHEt!8?C=^_s#bgOj^rpE-*T&&ihs^5i@DLFSpscLwmoz z@`tE{L_-f9rZh2WHh#2{^5coAJz1OBPn|IirYexs+;ZS=mIx4i3sD*;(6_b!=yhb- zb@rnGQCU-YWT190fO5)KkhH^2Rf}>W#BHPcnL?^fXfaX8<8qIsgT5`_*igCZU3X(f zuWdVo+SAlx-&!TQ?Wq7i^a?wPuMl7CPiWrsBf}cWm~@Y?0wZ2M8oL@SgA(9)W+9SL+9*fu z(Mxs-ZPA{?p3yYxp87|s(z{-R2EvESZTpKqSrfle9BH%4lwltn0GTH{BTCZ|w|zI) z1W(zt>96a?JA1v#AD&pXu|1i+B|8f&P|Nw#!(Bb>AD%mfadcv)d8y&Ia!RVbOKyw% znGW!;|CW!q-rSsP@npo!;A{NdL69NE0+HL+aPp4hHMoo zmMtCMsFds|ahG@uG70q-tvht)^nI=`C}6mJ7W(xtsmp-9Nki6Nm%MAB@PVP4n$ggp z?vaL~i06ak<qiA%!*bLMCAt=pnufbv(ee@o;97>oIq)A@M)9L<

uPVhYYH-RHdMl@N;hPwL zy1vl#nZBdTR&`0(`}&-6l<6;$9i^zb28-ShBN1C@^DvF zkMbL@VC~AX`O0PQ0%h@KM)`zbp8ZM`egmbC#*>EhNiM$lk;)cEA0~JZth#rpD60Lv*>U*2iWI zwO9%tZ$?L1fGN%&N3fW0F&Cpe-6~W_?u(O#L~}O1y@#Up!wCqE>JV|oO+cQ`NsrY# z5UmL(a-|33$9xN=K>Jije*~G=c*`=0+|{5n8uGzXfUJoG_b2`sZB*g(Aag=82li{{ zTyQWSr97Wrx13M5L2h@mOJzAr^LzCVb!0K9H8<(e^-a_?Y|Dd0_ApU3u2ra|*Nx>o z`Da5bNH)_dHS`{o7bQYkFeQxT0=nTEX+G8@`F8G9?c!^%P&h~|e2(D&ZtdOEN&E@^ zd2xfqQ8$qGK^yJYY$lttuZ;)V{hz`}yCdVM6VJF!Jn=XAGNQiNGM@M`5)AFn-QPmp z3GKg(yH%Yiv_EidL>?J)x_e*<+C56k3tH*N^{Eu(Z{&!TLto5U_0tQ~&3@+jP^w06 znP;RxOgg2+W%uvQA?7CYt8db4>5o%?w6&AW{_m1Z|G)94(Zio|4xCW-;>U;O#KJ4@ z%@^lI`^aSAO8a|f6~Qep&$q(uUydigh{JFc^O?HQQ|5)Mggg%R5aiB8D_a8^Z{s55 z-6|@bkekMKS>V7U-2n3$#EVPsNH$^634i=9e!uH-HuXb9gWR{g{b=ww3+nT@J@M+t z@8ihQRW&Z!2ulpuoj8?wVXo~~F#b3?cftyNe6Zs|HS$tPZ-GrBP|krTqxB;%HxMzU zbNY(U+SU+o?hw?P7@dn&{ zKS}^hg8)=4&I|RIuo#XRK zgA;_Fn($O-;hs$(IE7m`=C+Hl-%~6zU+9@@;r87&JlMIEY8JcYP6baPgf`Adrcy40 ztnb8GcD?L<%H^M;&$olzjWa1f?*vtVr~&Jz*t($LgR&3z%;`{;iXu^SQGurqXF~h) zG2j2ah)iJrO=)zBn`J|LFs?!aKdA=Pc)-Y>U0E({if zsA)X?lT`_9NTZ^zfZcFqxtz(|m@_xwiS0j`=eE-xI#muVpKc^X?WjzAADo|#es2NP z)&0Z-Tir?GY9unJ59?~?=S-jKY2xA4^Eea>bqN_B*?#`0%-g-jjNWqCigqS9-_;0d zR3~9~@RI0t;cpb8tTx6tUenOS0F<^}9bWHX1EC2P$}v%=Y2_ar8GoOUQ?CgcN18N! z3LksgIc(6I6c(~hl4!0&f#5mO>Duv4R4JUB^_P+-4(SFqE1bn{R=`a)5}f5b=X9tX z(S~aAE=(|nmC>tnearW1-?|diiHp;ad$Ko?}&-<`kfXfNSAs9FK(ZX+mhg;a%sxl1y7e{lt zImx>OgbarwvvWuxYkdvTgI?P|JM^Ul_!Bn4d`C;-|xd=puA z*7AV?Aj|Rjnny=AM`n{~n1uLrz%M|h?e<%(!*iPUL9RaH&G$$z?ld(D+VaaIClFCj zC|W59#nTE&_udgS&9G>D{OrE;!Zn?i3iTzVVS!sq&gjvBq$-TxqW5+#C?QQhjGliB zZXJL7Hy%Hhz4BjNFcc_Ex>^pesuDdX!mX;nTNZ|MbXS8#%1=33x_W62?7e+n_6=m| z&F&$6O8nG|s$d%W+j?yD3{zfO=Z9`jz_HXa zN{?j1KwxF=H}P>xh8!=|@lw(5K*mca!NG}JBwcE0-eVsA#Tg0>38csqyhvTlLbB~rcGGegfdC@xv&CGCdKl#vS zhJ~HbDN$}5eWTh`f`xIM^JcG@0$^}PqJM7=1c-1S?aw`-Lx0dA#yH9-hg`H>`w=oF z_%0@(fhI|Xn6hIcsgd>!r4!p9HT%6QLH}Q^dONhpTjXyd^QeU<7(mr$YnE3WGz_0S zHN|WT;j92t?gdnqH>Ts(*_Z==YR#BM;^A778S~peWAEIgE~ZRlhx+65o@Qft;!>C9 z0;}KOx3zqpSbsoKvk@G>7d%8F1rEk{wqJBhXb$Q&l;!}9*4PZhXiwV@@+v0mHewoo zR9hynG>Xoi7VbXfRZAYb@?}ezzRg5S0Wdud5v$wxPeiC z@iN#JWcb4U5ZG_4Z`udF0ay)N(f_V={pG{kF-Dm36E5%p9USK$cMjW7zwV#>1y^x+ zZ{my+^lH|8u?FS|lOf%4{J*dC^YNz@{hC6e=X_WPECDu4E zUrfPnEAK=9OU`Z_;~QEXpLYn3B$YOIo~9T0qJ6I*Cs66xN7RzHgK) zGaI}U+G|mvT*GS&lwyq{N9)QCQ{?*1MugH&4@Ne*7ebWYM|aG`+_aFTB@lPUbDS&|HPjRj zVI!^%5(dQ4cOaSfUbyVwY1-IO@IALlQ2>OoAS3X(mTbq^p7WyY+o+D59Fyr{C-7#D zuL3kqU@gvmW15i@jp?%$1CAeA-p4PhCQW{1@JVMnf`k|?wUuy3b~=xD!ta$D%VeJ?R1(vIj^{Q@&QlY8|rS#oliTmEgIpEvW%6HssVjMvpg z$&lV)%`GL}({6F(?`Y;Q1FekCC_FD4pIlgOVf=HF44NP>)ShfqSt{`^pdThLi_3qd zBuMdIlJN|ackl6Ns0w(ORT>WOPYMGO0Ew{9LRa?u7U8ORLeVVFJ&S96Bh}qb#bO3E zM}HeWe`h3V-C3wuaAKSik{aFEaAT_TWxTW_%5Uz(N3kLwL(w7&5yQTT>6>isc9TRe zH+}9QO1dZ>>MpKr=SI6nZz^JgiC*NM;!iCvqxAw-pX^mNhqbCRHY471|km)IPxL@qVzuUbrxqsx2i2*-*5WPz|kLic#FNzM!Q+{ z*hY?xN-9GeN)!}%Dr^sz@|3<81WeXrq&+D)9ZQD=trYyiYOl8jn0No(>+>25{P%?- z(_WBlRtT$7c}&KDZu#CRgH%8E{(M8FGB@jwl=lNAcYOu6g#vt12!-XV@CfYGuYj7_x-!g|h3(lDUA1ib9Piti7+5ThimlkBhFLM(? z*dS8Bv$S^lW9c^}7+~3gZd}PnMNRz52(hDctAqKyNVlSQbOUiFYOai!L6^{&sDVF< z$;*GwpU1R(z(F7R?6MMFTamLkLhD_5KnKaUg*2SP-(Hj_po=y0#ZAGhHb#`tz})$# z<-j;SU*pzB_H{Lx4Vhhuf9v9_et>; z-Cvvd(K{Af@Qgh}G4AruiRpkUu-W-U^eULr1lF=6f~uqWMIn-P!!6lb02x8(k_o93jmQ-4GSvltxUA@!w%Ly-5@G6}nhP0K;u$wk%TZW7<; zOjqvByua;N?+V@jaqSc_P|{63wE0tVb)2SqOLbao)W&h_YC)4)aVw`?LLmGRPGIBg zD2E5Grz}e<;n>|<;_H^-0PiomU7gtey}NtVmlF6_pt&ri8De&-IRgV%*$Ku==gcIL zDU%g2za1Q!!Rw2VSSyy(n@P@E=l!6cjq`nAcOppljdZ%*H$z0JzLcLB*x)raSNE?@@KLkpxV`qT9V&yPDL5%gFf`q(XXNoW zh}OmStB;1E7KYn@AhNwmn#0b`=o~^9q~)$!dclX4w^p{JC^>pG#I-IgRU=N}bt&#! zPB;b|e81;U2EG@d5qw88|BUO+yPo{S$@k+t_bS_RNT-(6+b7kPLSlQc*Ge@7yLmqz z;9)fK>3^hJtbSI#rR1x~chS&Wz|wm_(Qn2oy#DNa&YeNkUc?ud=_55k9-Tgx(AgKI z3I?Xi+SYCnn83paiF(HWudjmaX577=zi;|=a8wBY zDH>XAi#-P%#jhKaSNKx#-&S89hXn_#6U_D82os`@W{99RuVJUbP!gb!OIDDXLvqf< z+Y8G-so$sZE9pLZeAkq^JmTds>-fQfrWr-JonxoRGUKt|s<{2{D!<8Q-gJIDc^6-; zI8?8gisw5X(J{^wS2(;Y+0lr(9?5$uF`GzlOFAa>$X3wKeD5KJ#>A!S@hNqUM+s=g z_64W)qcQlyFfP)KwK>=M;)P$(RSgLh(wn4qQnM{2=Juc^_ytv#=>_%(q}<9jnIJP> z__B+-1R1;IbXIS-yKymYd7h2YW0RE)LE^Z&6u>S}pv-h0+i`RSer-oIHGOV-?|_)(oUi8&f8Q%d@38q0u>^Fo{kAh3C!waBlyK;! z)-rnu)+w+i&6NaimcpqC+EO(bY=un0`KLwmdjcrWO=NPbt(UpIW5rd>4*9?o>mbDu zF~|@rS4H_Sg_H3VF_-xQ_Dnta(eXxn&bCXT|aa7A75ebQFW20?OtM|$^< z-33Y|3rpULyX11=!8cEPBCYv4%#34r56qKVy|8Dud-5`9?+5JE;VN>m@q=^R6o|h! z&%g6m?bZL$=*D!+eTIYhih@Oi`BIe)A1_pxmrYKT+dq*N1jdZV%cNbc!5N+bmRe`y zG;W^`s%4lfDr;{Q?ei@NID_FrG(-YNDZja5f=>*vja!Zvx1Vx&#WaC1{h@?Z8*vfM zqM7oTIz8m#z zOQa|g7rs#s@%biARdb0DraXb*Z{;Q`%@Jx|XY`}JB;TRWu`~E7?gUdH$x(72={5Dl z|AvQ&gV_-IUXNE2^-iyF(nH@PgM?O&0N&`dBB0mf6q!BAR5K_$C|k(Q?OVf%Ga@_q zcKA8v?=;DbxbBLm7C}q6fAIsJ?{iW%{<&-jaui{3#>R7A^(l3T8@9=`$j7oW@g-(0 z{oyo2myVhT5S&DRU3kew+5=9AYmvHWeZszH&IO%-Lfc==Vy@-6BZ-A8gUSd18U-25 z{h{yrCTY%LbtlvKM1Y(Qj>NBG@i^(rD$5p?Q0+1W{*VHY1I2H26gDj_YFr#&zJ>&}7Nf!m-sp!1qvt>w!b#lwT+XWdv5m>M6!ZYzO(h!2`6kbQzmTdrw|+t3*b zP0UIW+$`+UC@}PWDM#T8M!$P;Dr+%Y|@vP88QBN-X z!8+B&g(~1SjX&}GCOzv%{)z5)HNj1y@RZB%Qq;H#-{5nG$WuaNh`nzeA1x)&BGVrD zNwoG7SDZ?N9uiiq{WFi)!OK>B-WzI=casi7u>R4d)I?PhP*&g~^RL8{<2m_1#G0nO zb%`Q8#}jU|5|zv5?t%D{&V{wR1}J_BrKWY0o+3oA*r?^r7;jY{01DMLU3p?LU8V88 z-Nw!CYt@D$eWm=*th2K#cPin2ldhfk(-b_-x4X9Ek93DZ4~N12%YM3cai#l)4Tc8f zo+N-qd`mDcD=@gT(>UWbS^d-C7Zp9r3}z6MDO@bgHX$)*_Jq+tvdhAaxdG7Z7>HUM z>BKtw4sr|}>{>ijY9=Q;uAaxC6BzwjQ5=G?&Rk%(`lQG1G&TG8!;uH`4n<`7A)l%U zMES;YV3&)ND9E8_yk|;i)q)`(X zxwosHSt{@-DF1BD+0VgP~H4+CM(ulc)MnrYHs_W@8=Y|8G<4wXm_-IV$#{&`+Z5q;+jy`jv}VcE$N17->;cZRgT1oe63iKsqUM6?1xIlMf+3ker}#g zRvtJpl6$9xBjuDl(O(|;@ms%sEaXC_?O{e9tniVM#9P@){jqKP3O{jy+3!lUqb4ue z!Nm6qzvv&FSvl5S4!?2bwHEc|D9fZ~QAIyV;R_efcs*YXeadnRGx*Sd{SL4F zl4NthYFL-)B^dv^9|EfB=HxB(J(54A@~BXz9sKeE=6LOJXI4U0tF{B^ooXN#;Z&&l zNp^&)Z7(`ZK+LyTHtt{N`w{*B0M8%o0}9(Bk&$}pav|mU0hg38k@o!K8cMpfnz-Nb zU6!*FP=@dDit?lLyY6ZNHpo9=n?uDctpkB}jG%h7S!t$QMBPx=VB>&s$K~t~t^7^j z&GN>_#D#o~^xQy!Gq=L8{XV(KO^07dIln$W88#!sPU!oTgS>Q{1=J(d+Ft)2bjnbj zD|={uh@d_+4F{kex?kv zhLn3*SDL#RF{Ja$E&8WA+i(1Jm~4gRM`+%wywt$Z{pM?>-3xv6$%HH8(CklQ1ht7l zDRevHor;xu+;qzK-z*Ipe{1C4ff8(TeVnYC&i$*Py)$_pru=g(CkZ9LLb|T}prY$T z3J9lgQU+Ji6T@ykZE^%GiI?~8q_;*Givlni+T%!W}aDHzWaTjb-Y4o#7=n9`Gn`kpYi=QS5~`@Zn1He#PyB7`Ro-ku};R5J;&!3G)IflE}0$2QGHD8!uXT$%J0TVWMGA~Fa?>s>VpymKc)OI zI}_F$29QSFo3;JB4I1h;N+mUJpl~sUzsGKdK(D&)Ach}$RC;fMbJW}5=e|-jb)RPX zj9o71`uu5T{P^Mazw8Kx|8Q+Np3$W9igFU;2N4!wF`BArmW=nfX}+beUkdv{CX;#^AA< zaKxWqez~@iMrY+vpjwr>~>#^Qk`po$z~{vt^0L z6iCC>IXuZcN}b=C#5E1@Tl}UY&v&+!&B&+VWHLZxDEw)b0tRz{?YvESlbRCHK()jww_k4cbYk<@_S0LrgWwyMe&{k~8cuZ0ce47x(l-?eR{xFz=e=+;-?`x#(-)&rur*?Xb1oI8B-CVz%?&nxR%Iu*G`M7 z8t#X(x;OVqK8GGDaa>Iph+J~7i7ESnak|UCS(GNpx(NiW^r>0~I$KO+Eccd!36sBm zB)OiJNnz5yd(8qHPT;R$M?oiiX`@yFsZ>*yoyG=ulk{w8+g5f(uL-sR+>9RJg$~hp8vfV;s4A+i%KAbyu$_}aSL^z zl&uFSrC9ta_9(dS@Y;EE-64aV{^p_N$$8BDGd*FBWi&@dGq)8hWkW3?*!cl4a4rs1 z0*%=@mMgLSZjCJS=Y+jOZ5IWH*NFiEF6?$#7N(Sq67@N~32JRVDgAK=qyU2_8*}S3 z*R3kO9Vw@@2Q5ND@;B*M7zbANgxPY~30)9ii9cK-v(F_LHS84XJ{;2?x+~|2Pw4Et zcKjm!4k+}7W5dqQU$hobzT+$Eva@gvn@ckx-Mn_4FsUM6xHju%cE_f{N8=C?d8O#KW({;Q3@Zm>W3CyrUa@bc&4%v$#k-RcvFWXL&&2x$e#eT9PG?+Jm7UUC5Yqcvly zrFiuI%V_oo%DZ7-D&$>nXFrkSvoo@dKz-Tq`Br1Gq5MPaxQT>&yb5fJc*n!4cJR|0MZ1_AJ$Y&mn+hFYC94I zLP9Q01xhPxn76#X@@)j{cVDbfA~`=EnSF5oSNWa3x@tTR{)k**9HgF2ULilkqdT3q~Op(pCQ`SsJx>{<-~Cl{?rD@s&hz}tyG#-CHb73D{%OgUP7Jx$KjBhrZ>@HX$yhm^kTM*u_1u1sN>NLP2@Kn5rzg@|Pb z7f`@pUN&nV;5J?|dWcGw#`OXk?>zYvLk{xTThYAv8wHdgFEZmlv5e)2HdU}x`52i!cM3eDVO{Ndso|xGK95KZImyc23SJ{vNQG`|iVHnJU-;rFMv zLDfgusXu;`4d4*YSFx2iP03ti7IvehQS=wT-N9GbG07>`TuHIpnPZWw8K148I84%z zdr`Wb?}x|3w+@d)D& zEhKdWc$9f)p#y($&`lAe_=`D(>)4cCxu#AQK>%ury45MY z%;nPATD-#C&|>p@UXrLcFZk-OfgjBhzyCnsdNa}V+2*Cj{3c9#4D<*&yL8>0Ut_wc zL=t7S(v3RQ8M*&Z!D+HhtCN62?R--y_MXm;q>Tn71`BT#D za^25YtYCG;1h!Yr9>Ysje1B+()!h~E(;&nNEn>JtP9d3{C1Z&L!Kw2PBMBZ06SbT@ zB0ZA6z2YOgUg_xp-nV+lVSF1OOale0TmRh@*INAdD4s3@ErEo66N^8RTQ85==2Fi>xU^%db4E|@ z4HTaq?(>6s6&Lg>Gw-)i$hM4`e1ukN*#S{FZ3`1tAE8(S0f2mBxUe+VuBwKrFFea{ z)j6bLvDw=(B!y^>kuwWe=ej(DIpM+bZ~S>)vac-$|b47DO2AP!n+Ba65Ip%mM0*9HZd3-qUj)BNWJm}&OT18b9M4jA|O0CcQ*;dBVRXg-Z} z7A^?lw*@aA=Hd71yi2{H4#%_MKgT|~xJXp~x9`&G_)s{2m_5|!EU@y<(2@;tPmg4t z^0Is&PNZdgsWo$P6f_?@R!nQh5}_?%+abXqv?h$L00Tp%>{Jjh+W+9~4O^$Hn4dX@y><_vhP;K!>Det+%oz|W`1cvxWXG7zhl7YC$r z1iX8ckD`l7ZFPC66giM4ua6l@YyFV5|1@L&$?BwwPVpOrtJOz=A2(52)sOEh^51li zg@S1HZpK{YU}xuwKUte*i&m7kh~+(5FS$~m=D38))){++OUSD* z_i3u^e%HyHs&Q$eIvdP0mfMq=BmNj0d+_!AM{^W8mm$*JeUh2)BeKK1vhpmP?cwvb zh*@E&*S4kWuPKvYhp*AqP9A4}YPVSP3^J=+$`b?B1+)diKbCC{788KKL1y&x6GNm= zOb=6XpSC|${AOm5xfpA8#ALnKeuFQTZtdg7U0yZ6h5gIsM4jeDZ&*<4*V9|(ZQ`yV z#P!k(*-S57=%tq9zgYY|GgG%Z)7R7JS3Y;g2_{W%*T zZ`6IKEi!7j6}UynKA*KNUHWw=nF;G_xf5 zEKLhI9;u!Aa%!E#lB7b;@#2qjuaKt}H&XLw&)dyEy?t1Ca3bJqhQ*@HiN1`~NaaS$ zauMlphdeWf<~&56|CL4M0+*m>Na@#hA8)_AZgh%Y6y2qfDik#$^sITFhwTZku3nCO zs_D#+fWCr}nV1hWmPV)834;%f_Xjric{hhWEh&=KeO;1`XEflh8rcUBKPzOMskK313; zF1>Os{yeV9%WSyXe<6YHm)^3zyY?}6Du+?+==Bp3+9zV|#ShPxs&eBS-OhG07#{su zX}auh+SQfL+SPPG^VGgw-1~MdS4LOr&e%==WzC& zJhO_huQiS#wMvQvifpQpQqhI@0_WhD2?dsi@xx=!(!&)wn^T-5q~g1DeP*)o8rPCF zUf0_%ruAGSCX!So9ZJ9x@fQ}uKJJKV=8kEOa=wZz-PLSNH4eVfS^t@N+s0*$jhhDE$i8VXHC2k^Or$x zGh?)J!NJ`~b9_L&pRUli{5qiH-qpsSPE%@M#k?u+n=jTWlf8{f=bXW3Z9vToMK6muqqBy@>Qi{DU)%*Z*fcg-k6 zzts7XV>*LrIEg%Z`Y;9~5y`PaKZbELz~ORI9p81R#OZ2;*WXq?^~`{w$1g0Qtz0)- zKY82kLl%|vZ_m`bQ9A_?4y{m;uYfqUsJm8hS+9|ES))fHr^I z*|gbqVE`eAm(s%V>6&LUuI2sNOpUNKcyzyR)~Cv#Bvd|yaeGkMgyNu3Blq-ruK<;$?E3$4^)=(B}yN}>qhVg`J*QCEh-q4 zud;V+$(@K!P)=iI4kcL_nwC}7R<@q-U?d-j#GSTGUF2gM6sjAEFcv%$g->|YjGUDc zuG$hiq>;{5H#T?Xz~tHn*mTzxp+JxNBQgQQx|f}9I>jjyE^_?vu>3CD?Os~Qf1NYj zs9j&Bn!ha%M}c5sT=Acsf2q5ps=3*sSr>;($jr#jRtyO=|Ym~KPEHDw&ing>t{Qa}&rz5WM?t9W? zd6@lEg+*?9IF zBSc#$&mpyvDyP`#1|s9eJky}ZhSOIU-){mF1Z`o#OR(Pk>8ayWRPT9eye&O;I(;P5^2n@i*TeG4x?;haGE zO%n{}V(Qr1Rpyr@+m+`5S~V0E=I?{gn@N5Z!XDC7aiTW(?aUm{XR53HS2L@trKhSN zd<`S%WJ_H9ymaR94?8njfB~m^!&t5~M>L5RF-)i5s4cT8?@ZS#Ln@;Qm*w&I-5BEldnnwCpX_V5-wFFo?aK z|9p`~N!OPYOlmt#7L8Ilb#T|ir~({dkt}$)I%VpT_O^S+m;C|=D#%>hF|w%TvY$(t zPirxJO%aoK5W&UQU%BCxu;fJ2T(C{kyqBeqOT~1lO1O5abS@%TqNw|3E%0ey^sUlw zF1{T(?95id5sArm+bqP1@WqBF2l2;QY8^z`1)5e%ZazK4j|rSse7pOliE7PHdVewB z)cG$?`kEKrTDZSYHT5oM+s%)+vrGB7b+0ac?|ZgTEjvRAd9|$)W@)1o0_kN@w}XWf;aw_Z!p*mN z!Pn%A-51y>*WZ72Ryff<|FQr4)@D;n!tAOa zYYV0xc}ro!&@x}Yev0*9U+6Jd?G0Gy(!-~wgk8*}GzeOlJZ-v8uFav~GOLegm-hzr z5e3L2P;0fk4KjFrqL#)#b)J3f*ss{DV3;qc-lQ!1sNrj7#$aC6s_nra%io;imBom9 zf7O5G72><$Z4;tWNQ+h9(ORiW&~#aN$=17!Xd3Rrw~guW9O&FAzbJN}T$?8TlzF4M zNx5RcwR>zK^VO>o6|%O8e-Ie%gGVfsk!X@8ebxnl9k2E`v&AaYyp@HibSH^yeg0_a zQNbAgX8bVSQ$@%Y14rziKek%yk-q~3NfWAB(;QyuE-}OvIt*S^>Nu1Bq63GRvla-Z z*0s;utLL%5`;+_Cxjy9`mC|{7wSh9}9vtKBG0lYPD&Iz$+QX0tYn2G=>~kt^Sw)^2 zBLr^7pRRV>oOrn8nyEMFvjuKuqYc{_3Ku$Q*?}@rQy27yUt060MMg}=*c(LO+fpMb zW$~pTO}gSPlRvCHWceeIH59L!UM7*jmfpYA(6 zMa2bTJB#+=4%_R^=H zZ5WCPl;}g3O5brbw3RJR?`N8izH~@SlwC(SR;5jIQGa%|QrK7!DPFpzo!3K=q)2fv zH&1_Diko}f#v%L7nADoDo-%m%GUY7O^7_WS>p(=vaGGMZW|E(#&%vKp&nUHfB${68 zA@1~`?4}*%kE)xCRd%n8zI^OhctYY9&Jg*FMd6o7t^z%UA8AdvHRDUpMi+=TrbiZ< z3i!oDMcu4dr7t|@QboR8I0u@NBmE{8`?r!D)v?Rf88( zbhctX|F{B~Lay}^T~ffHK%P%8R~sG(=w7>x^+N$at*=nAWEcsk*cWoYA?#=tG*5I_ zbM^COStNf1s!7hs54w?6y^mJ#tuDIls>^x5CB`O+blQwMIi=LahnM7OqH1;Hv1T?x z+*IcWwDQ|AniA39b}8++dcH4)-M&#vlh>2V#rVp@J@moOh&yp<_i9ZaowgHMOMZFu zg6G!8bDvf3u+)|#&)Sre{DCdDuY`3)PQ|yr7)~vIFUI_j%><)9dp_oiC@F(tGiUkkBw-v`|K`QZZ2t9K_xt<%Z^v|;d?0&#nVmI%pv2KE z9^t`YK5JY>7Xqf}HkDH{8GOXy)4K<|i=Do_Lk=yma7yaetc?hXOvft|B2`WqLG!e( z*QAdX4Is?h3CI&}y&ob@iXj_*=rY=MXSc&w&EDY(3^T6WbgiU z%FEMKhN{b_dx`#;sXFz!41-o}iBk1~SEQ5FlQqf}Kk~q?4w6Bphe+N0W|h)FnKd-icxz2H`-xVo&rr+~q5$fC*a*YP&_UtS{r z{*V8k{S?9cF9Z5-|K!KFSuqk3I7+&*q0aYd_-{~?%yxVly z*yO8FL0%UHr&r5!V^a$~W--|HNxhm5cqCt_@t=W`&u<=Xn7qxpMdC?C?>2x`X`mY* zN5mZ>u{x+-*(JKdj$VpuOYxF4{bu`FTXjrBPEIa@@2*x?Pmg$#qItvPGcICInI;t; z&Qe=3my4`_TNHyKo%jsb`*l404_~^lR$wrfNlZu1tH`YOA#GTtlZs*d=!MT{rHz+T z!=7QIqN5MNaCFGUuL0kDhO{?@xc7N!S{@S->02AF)=a20*jC#@3Et>GIryi zl2z*NTCAklm3+0+rpKFupsi0S9b!F*gB`04=Q4H^P448`-3~%#2iFx}a zERYq$c$3=jquT7inWyU!u8`} zdcI@o&&Q#pvqY^cBsf9?v>Yrb;sm9Lr2UlW{}>CQHoUG@XS16ZeguJw{-TpIi+Ww652lT!L1bJqi{ zzsz+tHD68TR;Ymiv=kH`sE{LX9R>~@clOr3fb}V}>-fZgm5K9Kq%z}5f2Vu@Hr9Fx zQz(a#SR}!<^+EwZ-;@U|DHRn6+xEZOGgZ989&l7?##WT-)5X(}UcHPMh&n6F``0B3YP znm+=2XY7?~r8v~(BdDPi!Ob5bS5xq*SAw=%4%}J$IfG9vw(Ifu=LJ1(vdH={Uo=wp zA6)7ze`;_ptY>(0!xP}c>sPO2gLbgAHb0jTof+DR33U7v#EO}HotdeO?@U)u%H|VV zq7R$li!7U?9oR}V#FCvJxqm$GFu-$Wy*YQ7-c~-Cl~F-St=ElW2Ck4*q0(j3}~$A2_H z&|#_p)?mC3gp;_7*Jlzu164(mq+<2 zH4qC%=6h;3bRam$#XY8!iR!4J?R#vQP2zl(UwJ)R zE_a+lensw}cjd-%8)7@wnoz)h{qzeb*>utuL${xw(ZC!&h6d(8DU!gQ0FH5R9z6D_ zd*_WoVTWW}*)MLSb9RiL!+vwBPmW&FbscM^%`y>E!le!IMPO>dCqcP4F zu8)e>G+r;Quh;mDT&rp z%@Zd3LQ(b0w2@;K0j+E$HS4&Cb%Xw^ixc@IPBP50{q~KxYuHy}cHQHXS$Wae1i#rv z_OmxHTh-vc-AK+=_`7~2x27;i&3zQXr?QFMDKcW|c~!?ke}Z5z)@Eb6np>D%iMS+Q z8=d^A%WcI0>pp`{b$i(8Mc?{x zmL;IBAs4PE2c#)Yy!9CU7>iF;2t5Sgw!~m}&k3tX@+Vf7kjH0#g91RA&b8&aVvELoIR=GSeV3-YJ|)YESvDO= zaPKxq*tq7W_d74ia*9xhg@XJD*=_SKc-+E|Iu^T@I5EQ#hXkU-U?wbRQ_kHn)#)9>a7k(@TA&YK*(4Sy1 zH@6FYI`i>2GCM|pUc1|EtJMF7!eRaY1p)mFIR9_Q!aKeGtN8zEd$fSjw*?4>^6V&3GBB%d8hAdVLLfOu;6jbLSA0pc|chpXdTQl2AXHTpQ%pYOr?$;_-G@3}il9kU}y^Da+V=0FD^i15#OXp5n4jxTbb(lr5`gHMey z^-*GxS$l(>fB^1%eIwFq17Q>?p60kkD;Qn8MyGET%8_gSWtF zn9niEsY@?ggi;cHr!Tw%85Z;MuU~8X=@yIByhiAz%;wshLgpXvi$M9%?Ju>g+d)_1 zsDc}a*~J}RU^4(F=;-L+Qvn9p%zkUE=idWkVTX6QZ52U%@e`8U-*1T0SiGp;Pbb`P zwF#9J9)pGSSbX6-N-928{raFc=AA(5Zm( zXVn1DC|{Y(;#QJbpHLUW^%oslQcsjLa2@>^fkg!gh)KJqzyE6<9gp*85tSrC*tCjufv^O=fHdtaN@P#t*slXnGk8y{HQ1(VSg@& zb^MPz=#Vi0xFN>jO6Y~qLXbCd8^dyhm&AD zqnN}bYPgK4)eGmVS*z76lY9dDZ!U{iwRS?H=~^q5f#)3++jeED$)sV|zpx-rEe#Bd ztZ#%u%IW?&LlpxIN6^d=l-iELEPoaB_?2A|W~mT)*%`0OE*fMhdd+6_5VGk%=tX_v z1CUhI`Rn^>p&u{fguD6^!iNv`B~A$#5v`k}1>$6o>VWwaE}PnW+k}}?2G@x=N*bg# z^#5_fmXAyWmMvq`VrTL}g->y&HrPyc=Lk4}d|g3w9M0vw9(@fjz4~2b4{`*aS4O90 zq$hTO(=Gr3A^HrUQXBBc$ z!w1&P9q96ii) zFRui-fiy1?%8L1Pa__I$<9QwwBt;e#^ZId9YUcfZ$Vh;yR$vVuX`A!|t+t>LSC;5z zofnziKF>wt7VV8z(OyG=u!;XdSNUk19aae`8T2rExX5?rO#An~!Uc5i)-&un>@`+z zSU->JPhFUTT|fLNmPk+Z$=rH35c;vuN&-F34dy-BuE^B|FpQE+|8g1h^#TvzH9$P7 z>OYz*0b>3HiuOVf*V<)<9n6s=YQ(L>Fvjv9?{*ivO>~UXFGO5JzOD7*AkBkJ@yjy~ z^67qaw+C?u1*)Lal(<$5UYtmMhE*yqF3y<_Cs7TF_OXIBYc5B9zvn}j7mblSZHYPH zDiTwrAf$#K66m-v_96u&O1{HPlkfMdcM2iP*Zm>1*p?c*gGxlq7vxn73F7v>PsxuF zGS|kBhxpW%cyX%%I%pj+*aVM$8@RCd>mtGthtey*l&w){o;N<@CKw55>*!bxoZ<|h zs@=iW8FD)DHS}Nd%}JAb1uIk*78Wkjipc18A6?2GHeq>m$$^N)u~zh#I3}UB8kB0# zbM#;viEVycfn{@%Z5QtzE@|T<=k2Yho7qF;i*D6zU;oNlgN!@%_QmGgQv_G;;bQqs zE1{Qnt&RIW&eruTB%jBtPuRtj@kPE%4kqqhIyrYPd)5OJ;8p@!Yn#4kR^?NSwds9x z3!W5aN#4M$cW6(oDljTWySCa0-_hcDzIG;IK> z$hX~Fe%a_=te9Oi^o}eIpzyhr8KdR;kdSFMob;>3bAen`!Ykfc_M!_MlXF5+t;TldWU=^_pg8d7zw@ zLjQFd^p>9FbtvHT=`!m|B?B&@SexmAGPV*vAtA>^%k@ukEH+RM*hQ_M3RZj*P&&1_HgIN zSP{U<=qiG=YT^G8l^-Bek`1g9xRqAMWHvJt5l(M;6b*+-&R&DL$a4ApxY9a&oy;^) z8?e|4ZMqDj>E-b0C+2`40-N*DEa?M9`&TuFD#o^i48FM)w6R*VX>>Zl4KJIl;kvW0 z=w%PoA|rbQstTHkGqZ9#`mB<2WHy)EP)CX&zq|e5aMq=xy(eB36ljXv`S=M<^@Qs& z#JQLnJ@BG5P1;g_P zs4!@$b9H)ceK;8H?|?!-es>3_1}q$O5c_Rw;vLGBPF{Wd36#4O0Mqgiy*YQba!H)j z$HUe&BnPxjTi~83)^q#vfOV2Gyy4;6HiW8-rPF?4T_()TWKW2Epr#WaMcV8Omwwz` zg~&pa5DM}k0A#E|7c0P3$c39hNYDe8D&j<}!hk5HN%<~{!V|(Fkq6ED`GVXA~Dj<67YjTYN1ohfPq%!yQHky&{v_l0{ac7&Chocq3^)lwG(qW`R{`F z2-HeN&6SgLI|NmTHsn+5izPKtZDV8rUs*6>@ixI?oKe0$|I~A+IGq5Na-lH{k*NW# zjF%vTrJ*7?!*-?*7mZ3knY%wUMhF0PEHEP$gctgkNhbP%pDbX*s5w;g!& zh!OP}d@8_$7yZsZ{iKy9KizW&J>uTB-M?BM(6_ZIRJf`||28=}nN5Krnm$OvWeSi_ zIAAbtr+?c=f%VDN_{}Pp5oM(gkgc;o%vA>k17~WDsIJ`Ee#gj9I_c^Yb^>2sr6yOv z&s%f$Um(kgjrGr7h}QbK9w1;`s)3(>mb4}xBL7N@$|13zn4Fwkx0>MAp@i?KbuwaR z@=RBWeFZMqMDr*qQzw752HoaPHx6*vh=-j$<6Xrk>|N7D4CcVT-?8yZAL?kT8xq*x zsi%jBv12Vay*9u}p3Rx7#)&Fv`gl0$jksnfeg{0#1GEncUl+&X=wuy>3`l+CckV~q zizEP*QVJZNDHBk?vj*&klFa$9p+dwX)5^mfH9NTcG=3HCO(-(xrce*kGW-^+1T4$Z z6ZRt_jDM$}lFYn3G{su2k0BfB*a^Lw6`fGde30&*VNHB##bBTCINuUo9i54lsXXXO z4MsT+fC{4uKAr0Ey|-PGDjKD1v${wt|6b6HRRW=S>5{g&RRi#3*Ew0I9ZNerPSl;9 z2SEkFr(SR!EwYVybEgGiH*swdGc0oi7~+g?ici1o>b%$$c^-eG>44qbbIT)?bVk$cv(CTDV`F zh1r6#m;ulfQxJE-HBF!~+QL^daWpTD5{FcuQctueQD+XVLj0qx6USm4xhIH4x_6Ck zA09h_c7HrfOiZ#2PpigRTra3un{5_+Qfxy`8(rNWA|I1j13N(^byZbW<09@Cne)B&@Ue0iFM5*KHaXC@DZvGtOGR?fIMZ>J{t|P(8J!Xgr8i$}5 z9EWWccNum}oS{bjLZ51OQ14&`H1B~^E)2M~ z+4L9L00oPH?%Iv-E{0ySji=qw1RbSk+71|1>$?jQDA)K;5pSp0T{RG!;dDl5f@MN& zL0Nl|jaI01OFe*jv`BJ4Q$ke=K~}EE+h+d?1Rfr+?-!Hv;UJ5;L&M4z+FL82=7pN5 z6}nGx(&C#jm@TR3mmGBeiVk}9>Q0b+q-5KE?&tZ8oc*oPF$%@1OoD);V`)jy&Eegk zI8unHyC|gG07R7-$ofGB_I-`O$F0|Yr#B1y`~T>? p|68Y_{2x93e+}Z~zkKq_rl%`+U*Fdwzcw!^DX7Wk%HF*9KLC!KGkE|2 delta 241818 zcmXtf3p`W*|9^@|2)Rs=Yc92zn#?^R*Abd=|9Hy2Kds2%^@{Xbn7K#U zPI9-Hkf_l0mIN`cz2f=N;|HR}4iD{V+7moxSo2?kK$MDhdr%$P-(`89I=_;pzkaJC@U)jj-bz1)m5e;~)pZ2Q+Dq7`1K2L^XZCjS(tot`AG7M>IM0>jzSnkPNO zW4gMi83}(BBoB2%;r|76_XXLp5|D&ik}Zf=-Y=u^kBrC?Rm*ko{#q!hLo0H_He>9M zry`uBg^{JL88K$#W&>>{EMT}8rzGT*Mi)UHXO+&oE0=L&`m~&C_;Uvo@R+YcAm;@L z^+ZmC8MmHQyN-OsY0Ar}npWni$)HcmA70X$_|tY?p{NLbq$oS@(wmjdcNquR$N0YJ zMzVA>!0aOuUAN-M=v%o`dQX=A!-PqNwm`yJ8Q0iylCM5N;a9>;$+PdIOL2sE;K3JGW}+a@j;E_&dy#0DMyJW8k6VmGyMpc`@XmQ?`um2d(S6lvshlpi-k;Ds<%>(jk6KQmHi&Zx}RrS^gu z2cN1sJGXdXj?Rf2Pf8%9^({AB#r zc`}igp*s6i#!X%M12{Q3`QWH}Av=tS3WONoV%Qt^z!}t8U2(5(X~47Hf_h&4(EJ8? zEl|$@%?0x~XQa)6_zc2dU_CQPb9(&mvc^$RPE5w!9PZ#dk4sw$wTD|3#u_r>Ip+?X zdco%be9#T^jPD#Ro|EbG4gz1{Z$w;i1>zZ3=S_JmGjisy^TcIr&g<^&%E~yifa0NN zKD2J78MLH(0$B?jEzQ{@tdFInJ^0`6fV<&3{)D|GZmFh z!p2cbP)92LB0)9i+zNTf9`9YwvgP9RN{8&5jvn(oljQi_AKq&9#-SUQbIqmgzP?Da zbk*#zJG1OARqJm*yRsgl_am8mB^C!(;R)s8O%lMgOnzCEZj6j-9cP7AXdX;}z* z-s1_($A;O|flI?byxNtsuvu*EOuc;F$Au|4Hi%o6S=gjKJk}>Nmu6a4LfRel=YIQ$ zv&|lyE237*Wm2HtZlGb>oWLcPb8;iVoLn3I@OZ6!^R?{FIX*t#VH8mPD%i zu9;j@Za025Fdizw?y`+THV@9;#QtdDpcbPCHGQ)t!*|-Vin=oWD$Hf}kG2KT-+PQp z+3qPmHhwO0#&_fUlFQ#ULU7v&M7)Lj|VKo6X$bETj-wFREcyB&487Oz7< zEQrEbNXZ8z2u5m!;QVUbzE&22AGAQcDqOA;Cfrr}{(E@U$jL@iQ&XhFTw_JSbW@RJ z+FfGmOuy_Lk#MLM8;IB+<$xy!%Uf@Kfp*_@+qaKF7HEB1Uw1U?x|~%C@Qltq#Ef@oxv&7pknJo zF=u?>{yo!?SNm4e!hYSMC)G&KD#gA~7yMd(bB136n3)6E@B&yO^Um4L?`zP2ZWYza zh}ewDkhj02gY&77DBr|NO-r(46&12Z#4Y*5ES&mCQidwjtzb$7V-7z#hCF9O<+ zU|huF`6B2QE+l(>Jh}rJfw2a)3q_=6dTGtn@>_C78fCK-1WMl|VPKk>)SgiS*loDU zENbM^g%cqhJ;9|A!zcSJ(k?wa-Zefwytg5rkneM_tW1gO?@srrzScdO!umdYbkafF z7pK1_xWa<<2FQ7twD(oM-S{2Ss&uZK>y6Elo>_ce2*!Z9PY16#>o_}WFYjZoBn>Ch zxJ-To^U`HW1VekaOBuoVgh7m|7rj0~ZV1QOzCY#1IB)f-++$|lgt?Utg};Bx!2Kkm zwZC1RT)H$TK;evVJA9+pDVDO9%Vr&cHOjX4vRfoLPXlOHc(fS}OA+4Qd1XaINR^C(SXj zlij3KH1gB)PBUb8tVg81#POguh$62UeI#V)%id!eD$b4R9Ilc z;LHvGnT(JY{|ny1_ukYYL2cG8-`A?lG(xK#AO|CyU1z$OqczvwuLiVK$U!D#eV0)Aqu9I|FD9NQ+QlZd6(G>Hz(`%}qf;6L1B9r;bt*nIXV|0mO@uh|3zf%!&O zhogp0R<&fi>G0$J!5~DDg#4GlUxr!)@^R|Hua!4)e7K-TIqHOipUyVO5mBS=9)`BQ zkjCGUoF8EI7mG%pj_$K zbA&l(lCiQmif=Z;DO=?4TUv;exA)NCbrNvrtQ+-1ZqjSn`X9Q(^Z#L&R~zpQn>sp|8(?lZawjJ%9-QH4(e1pkX$4^MUji#kRPP!31XRn(rN5OgF#m573fC@c;VnQn!~XZ#23u z_O}BqL$2*o7x@+=pZKnS4*NhzA`Yok)aGxbdF3R&t9{>tgaRCj;Qsb1uy+mzESse| zO+Q6*H;p3%g<6||MbsUbjPBMxg7;A@E@JDGJSaurgG3Zg$R>TvUiBRdR^)oYRU(+g z_2Bhrg*xC@2hD9NnvCzPk^Vsp`6K_=Jra;0l|M6O*g+5U{9gGE3}iK@3UqPyXcG$- zd2%DlQI?vM8~|qqSt)9t%4mOJL8m@;DesW`X5W_%vv=g4&+!(DaA2Gbj(mh>Se9@? z^Bo!s$lzF*q509cg|R;rq(pcB^8hG^rqT?ZH#}rg%RI0Za9*SO-qnJxIFeiMKBh@d znv4Zj3?orhS6^6GRu{4TxnjB=S6|kNKPc3CgW)-NQsrCu$*Nt9Fg2k3R@mHY{Zm3e~5$`75Cj>}{iaIJN#+L_z-KNCBwmd$l-RWk(`syJ(^GacL8- z%z2wgc142xI?&tq&ua(br&VH_z@_Mx@H~!ct{H+5DZMM69*K zPucD3j3s^JpZ=!*8hp?bsbHX>O&m70JDid2_7UCnIl#T>IU-KWW9vw<1kPYGMW5XD zpi#*U>;lFebaMa-vlN$~nmc~QBL9+)x))H113%%sQbkHZt25F;^S@?7V;} zZb1Q73gt)g>=YC}H2bZar+@ehV+8~qa@J?MHf@&b+l(TbB z-HXwB_|TvwNChmFY*3JkB7n1Oi!w8PzWn@YyO$2^i8##j610{y@Z-(M7w`RuDE*V) z6H$8^#Ih69^*1Zry%=X(A)=QhSrpzfG$TTU*wNnJZteJzH0k#X3(Vg`83sAAaO&bv z3E$9UqNw=GHpSjy2_ni1LNyM~m3{D|Ab3{v{P&piUKDbk*j+T^Lc*WMHpSq42has* zx8cFjK85=XvU)LApk;n0Vk3Y+e0d^SFASS~ix6jR=R$-_JYMI91xQ%*`I1)tQHWc2 zmr%Qvy#N9$=V3uE;*TR3r;{*JSAITM(F~@kq;jp+17#6 z6l_iDWUEpLe4mDI)-{ktyw&591Tek$8i668*yoO@7`*FfBi61>M!(ARlAZ8^Y76fH zuc2pjF-ECfRO8k|dk4s-xFtPV^Ch#$bd}(I{>wl$VL)O1?B1Vm&@vrjv`SGM@eAed z*hG5!9h;<>s<0YHH{WG0YNq@Q{BQ)*+x1t!H-~HN?EE8O@UXFIH@6ugBqTIubg8HO zu>}`1zW{I| zjCqffoHrj)kd<*AWZ5HZ>LE{E!~DhywJLO-{*1Zaz|h9+qnJp~cNdqQ@)9rVj|O)i z`v~`Oe99qb`b43wRrR?!XIeyMrmMgOUd4jDZslhC)rFrtlk%4(zuM6MKLV8Sryk$^ zGp-CQ>LmMHQ_J(?3-t%-(D7ugCTmb!LIf)5cTLDiJ>PjsxsWUK&^&f~kH&GEuV$Z_ zI$bMAgktL7_T4=jj}OvN$>#v9=m0fA3C(yv=#FHFB>zlO5b$_bD7??v^)%dn4b%+I^9X&K-YXjrZw9 zNwt8x>W|-ij5G&b!1~n-2N?Ml4Vepkx?7{1qy%OZf=^b?%w2DWSDY1yzWPJa^10Xf zgqS;AS$bh|=HlDetIvTJMnucRBaaDH*co=H4*13z2Nh*5w>CrJOLcb)YUlnGRO7r^ zb($fL`9{Q6NRP#ndsDKq9f@zc<0{K;4Ty9-ryfw0L=Mwbv2t(57dMe@=%Ula|f`;dLf+bZ3$Ge=Q-R z!8a;gM)NV5%p&%+q(NLI*s3?BzAXE*7-StN)8_=O5%fNCqEpgr;2FzccaJHhOTdi% zxOME2R512$rBfacy%tVkh{pafg|WFJ)V29gdF=ybr#mpfL+c6X;#lv27Eu~83XAmB z9P-X49)|1-TwhdYyJ0+DzZtvB$|I4U?h^_i0 zTe3?f-w1w>IkVxE*pZ)!~J$n@PSeu((epboj#G{_Y z$HU{rSC`ZNhMMKa!y}`@f5#+2V=nRXec5<*!9N2xvG3}_{Bnt(0ue2c(DHqdC81<$ zRw)k;L~O(Z;AenfgZjTEX_Yn2!{g0E&a&Uj1L1j+h26{JjSUHC*TSwuqba%kB)_mg zpIlXEZ-BS3e@;)4)iCDtAiuK)2JGp{)xN7F9i zxTRa<$3FdSnA5&Igc3Om65A>86wP@5Fxx7Bax4TF^y=pzhv%Wn)7PJ5`13^O`ATBi*^Y~cu$_O5Yp?q>=x$mwk#5SIB_!K9Ay@pk^-0}UTP z4$L!6wy}O>gK(;k?An454qh7+caLmfM>CvG(Sc_q{}iM_N(lraDyQr~E9Aakyahf@ zBNN$Bmtqel3RQFu8)Ws?ioB2g=Cr0deE1FaK! zK(@nTDEk}|v>%UBcy>&?MJNFn-K z>~p!BWFJn+7VtT!N}W>j6$00UQ57f zPWa>A^wV=JLs>@4p}nIp#U!OoO34Y^QJAr;R%`ZS>{~;on3^_9Tz)Y>6}V}IDLlK; zzyRvF>5V_!d?d&Pe7dsTT_qv3C}I1N-B0??@E7(yLL3n#5FyWirc#22xxjP|ez?&( z|5=HC=jhT{lOF@OyU|ZwXL@d@cdhUtCA?%*oisKnzFE>mm=vD_)5Mo3w^8Lul8-CE zJKrWYB#{4zE^hwD2*m86N1PG+Q$Izmw6j}Uz8p$>-ekanD7Ar$hJvQu$nBwxEs&RG zZh;n265=VYFH%SQ(;BZGJDG0Sw1^^{iCHQvn$?#L3LJNU+d5`K(NwRr^t=a8jTe<( zAA_hr4BJk1+j+^BkBb_-Yx(nokQo+)eZLq{ppN@eIn(n37v6??UL?~ENuM;JY)+rF z>+%9wex{Ug!epuARb95d?Rz^g^WpMZDKcwbRcJ>1Cm0PFftv6!9dpCQJL^;9seVN* z=a3Nly^50Od+3;#64TZG{^^2`^>&{SlARtCYFHKAvWuxrBmXOmX@z{)e`=*bo#uB( zOWwl_%zL>k&f_Ip`bMx7ijkY_Z@59~8n|oxVI{(n_Dd+@kni(5 zESMLgC+19i38qM0u%7G#DwnT49AnH<{7$JPO|0Npoq-MBvKK~Gkag}(5*v!;K|QoQk&wpZ z_Gnlg(+I0{Xmg>flF* z$?=J#?we-fYBgl$=@i%O0Kdgbl_#wJsDVS4d_#Bk0u>rN^hDXYG+y|tVc_uo3GfhA zQ!B9$PoB+gJIVh*blu^>+==2ozaV#XV799JTRceDyc-Ep{~Er#(~~j8((7F_@{CxK zv~YYO#DaL63YdCG_|MJ29$+vFW53;KP}g#x<93(Fc~%a3*2HGNFX!)!z24CO-bHgE zT%NJL{2behM|&Ie%#1zrI_5(5BVyVfcp$tdK8dmK59ScX=u7WUZFiRatL70?4aI+V ze=I0Y+t==dPKWmI;PvH*0tJCSw64n)T@$N;o_9WFWrZhf+Ekv}C_ZlcU}s2I?#uFP zq$*|Uwg(u6n`FDyIov2dQ4Dkz73+lnQ*A{>v5O2y}C`u2`x5WFjIbzM9 zs~kFU;IxppW`43#Jt=qTWsXI%g1``4JNz)IYxtOxvD$bmZkPLl!(Uc*!O8-E%p~5A z@oCL)6M4=GbAN(zkEE=us@N0x%SJ<0@qNL4VTh2oF_zi8=Ms&487{0|3C*qHd}fo_ z>BWC!XGisTEt51&&Sp9R8tI~82X-xDi*9o~25@Tx7sCOh1(pxVYx=UXAW1W)ljFl4 zg5uUGR45M68(wC5MPQIv(1jFM^X-U*A7k4xai-6Ugv=b$I9BP)R}u-C(MnqP z5e1MW%;1v96^-M7H59IE^1H+}kR71NyK(p6BUXLe!uI)O(P%*Fs+ZDEQJ28$7+lX# z3DGLP=dTi%QcRZy#c{4gZBi3Y$8}Pr*V`1yd4VnO>KwT!*EM} zl-*Vs1gD(O&g;gkx@Ua^f11x9!$ll*&w9C$Z;3_ds+9`|3=W4KGZhWLU-5w*IzT+G z=P&0k%il$ws*l1vAA)@yHKRd=0}$S>Qb^F^rmGKVKZ@`mAi%=eS;cyBjAQ1E-(nW* zWM*l342K@0L7lEfzhn!n$}j;ZNxIz~lw*Xji|jGS*fE2~GG5#{D$BSD5ne>#CJmOc z2nW$v^M#r33*V${7YC)V)TP8in(!ZprIFv+tC<$o%%EEG6C}uvX60k>5Yh3V zT1B9zCX>36zwf*i;2vUrTPBK-*xe%Z*|_Tke}W(G=Egw;7PgkouLDqP6m~X$vir#- z7sZMMiSAcK5k5XE-EsOFQA+s8lAt8AU{)G3o5wWPEF8J0arwmlRczjHm~oW+MctJz zUV4fu5zGt~?V&S?zOWlr+`8$yCWaU&O@A%JoNK;p=PEUrXU5J5F^C)snp z%X2&F`}W-#lhgG?(w$9G zG0CPyoi`-}B4R;yd3p*T%u%&o*q_J#Z8LG zi6JG7I#1Dq6<^J?h7&N%yKB~Ea-3kKO z1JO^BAW^TD=Io^xXzmTyVQ)tpMbDE54IlfsS{ov*=g(zj{d zQ{?4HoKm9qm1Mp*KNnpELj3=ztbGo`zK-zx86POyBs@`jB!n}0kDy9}7WTk8sE!hR z4c+_qwcn1wtk2l_Yd-KtWQEtXlH~wTdtW}xk(wx0OizEGtbu{k{d`p-958ee6 ziSL)k`>-~SIc{OLcfJEO0bv z`$TcLLGCms8*;9&MH(&W6p4+z#DvgRwVnvfgU_3Xcrg_{2Ky z-tlQE;=jA`qS0hF1R~J7hXzR~oa%=a$#s0HK*S;2OjrXPEH^TcZIUsC)BObPHhVcx zcc;zp+zq55^1Yb=rq-CN_8=MP6ar=gL{kk52@9$!cM~xhvhyjY;WvlW=S$1QulViH z`k(ZYy}pip2?+k(^yeW*PzjuZf}uk*e7OfYj5v3zz{wbp$Y@VTf{*LGIfe=mvZK|~ zw@a}XL7zh`-RRX1(XC#XYmk*~z&Ja^=b_6NU#Si_!zfuG+E7DQOHnE-yHPWUbUn*X zP7YfGatpttrGdbL^D`mVbNHQX)nq@QC#aw{Kk-$11G>m0HydxkTvPMj4~Vabf`W50 z|1mz3(VALX_l<%)AL(XM`AA(#wh34kh`H3~R|Awfn4r);ceIqU+N#Ax z&2R1bS*_N7gC2m&-S0i6jpmx3kuZkRF?uu@&-H6k6i(BR>XC|l75^tgzTy=9BiBmB zhA<1>_leScbLUn|6Mdh6k(Vih7X5{-c3Sfen_*6#%=8W(M}myJwCM_?uLI~^Fq z+Vmczfe7||O2Sb-7aG*jSu%oN>Hf~)!tEKd+jPB>{V4yqvW+qZC!_p>P_ZzC7c#o+CaIg;?O?=<(eWFq6^<< z6U90(7j4oe4@VBu0^|T?!*Bm$gM#g0BC014tNBKd>i-^{fe}w(SrI(u+b6UBu3wKD zHpm%q>GE+n?fo^)_Z|&60GglGf(Y-Ut{4X+b0+JGP%SDK$PIEBiIL5v0CNFr24LY( zZ&$0>UdnX(fY9|VKJgeu2#yT?waxtv^Z@mw;El!OXvV1A7Za^6aXKbpbR9K`ZE#XA zo{x|(RzV{UmjroZ?Pxb-Ki5f7&Q<~agZ&LUYDhRT9K_Mw;ENsGgpEs@k?pC#U{@b z1O!?W44I?#bB+riOYdOaJCp^Yt4PKCCQ~Q&uzoE@Fy+lB{MiShA5qwotks0`N4hHC zza4((Ge$WCc=KISR;57x2e7vVlv*JWk$osar3-jI0@34-)8k)$rmnjz_s^`d1U&{z z_mAlO&A|8vRPzm2b#<+U4uRr6g;qb$Yoj>mE_X&qRb$z;e|>EVHfTW`;HBV`_{t3}-(oawwaq?PO^b@vpFc=zaH+1`n859%6F+239{nd!8(*?w?<{E8ix zc{v-OC~t1g^`EFaB>pLUPD=`~qr7SuKUVjdiq4z~QX3!-W}!4YD@)|$yT}!OM;YV; ztxvH46GhNt%b)QE0;0%S_sAVR%;JdL;N3=Axv^jF`KP(?5*Tr?f@rH!a+Ldt-wk9Lb5 zCK*(-uN6teBA2Vt<$>;5DscXsw$3hT&+aTUDIOi3*I6M_JnEu|qkx47KfGA@Vj%f4 zYAZ6ULW<`C^rTSh^=fxfUSl}zbkYGu-c`!{37pbr)aaWmt^RRfONg)UfD;5w5m%w; zm~2%|&0q45vrbC$=nGMexhaqoQ3tkv*kl$)nnw}4_H}7@rTE9Ke94AEocz`>{}#7t z*MKpLXYR~0CmeQ{9X41dJ1jLL-Y2ZiAj(o6|0(Vyc4u{=ryB9*y4$c$JfN8A#MaTC$gIKFY$j4}wI18HgL^14U*U&E78kJv69?XIoY(|qh$ zR@i)wDD4r4pIZsh_Pu6**p+-E&BsOe?C_6jt<>!p-!UZ}A*=N4vQ9Bt=g+&gbh7$! zjBM(|v`Tlxdw1}CIqnS65@~gKKh(#X>@wqh`mPr3zZH-0LltkY;!IkhkW))fSHc8~ zSA8Df^?di51Y(&hi>d1|@tQ|Fw2`3Gt5~`7-}IgWZJJ|IlEKD~{W1yllt(;W?i^?S zkao1Ort1i3i|_yQr78JU0^qwc1LlwM4w2dFU(?C6ppH88UW_aNN#NqqM>WxkybOeW zqqM!6B1HHw-9Mq_sRd!sz@;#=u#CN}zrQHmCGPQLLz|8I+)fW?t0!;wlFcA@t-wY(fCl|9;0Y?H%^Le)a)o`qvOT6rBLJ&kc7UumO{M_h33qCH zB(?6f{1F-ydw?VM8+yo_p!(VIcdpKDS5*+io@Kx!mXZ4X$YVZ$Y;=jF*~D~-En{`~J03-SO{@!14}&Tx8AJ?JEH+!^8Rb{3$R zmz_iM3=D>nNWpS6h_WU zaT01G*$Nrz4^T-_O83i0Ud-gf>vZVYy)t92v|+x5Mtr&*q3gX}r!8azNUq!4m?vl| zGv_5|(r_cS`OCY1GD%4^g?$@J;CI9yXKv30r8zCm-oxf{!Y2)a^E=A1+jn+$IN06% z-5t*E1edvM{P&mSt35g}>m!G-!HX->S|0Pd*d<+{Qp<0Bl7H?#cW-y~-o~#Q%F64t zoxZ6C90~nJDmkO8HXo>*%LwdQ@n&EGO}Nj=Xqn9&d~;aAzD4#qW{`gm2H}s{=+BFW z7EAazUX*Cnac8L@ldlcvOJc&w|IwwvOdc4*kiXw4vC$g zx5O@6ZmoFku8iz9t&bk00EK2D3%ilE#c0|-s^#%XheNw))$xTLPcpicGY^4cW=Cswi#hPVoNXM~6N^RxzeYRdoJ68ttFH?6^#q6_RW=DQ0#{dSk&N zK*FF1tAXEM$#*UKx0>|+MVaO-G}?i7V6s=XyGywB>AeWH+t!q&$!Lekjy85d8oTgo zXpP@wou^ri(-R?ZNRmU6ryR@G*(5vKIF9K#yY`Y#(V*W>&Enyr`(quyc~ykt57n~% z$|8SqRv*%!7}f)QJb*(< z?8u?HEVrb*Yi_3O#}QIyhPOWC!+GV$5_pIOuZw5-incpdubxWb3FDsftNICq<>nS ziPl(@gFAa0h~QY?u-X@#EiO3=wVtbY8R3Q!lm3B?j(@@7x-0;%FsX26)56gv%)=a$ ztyYtU_X$N(Ar z8RJDDW_107jBS;t0A+Vp|7-nFhc(QdbXCRLZjq3%LlQfGKcHGs>tH! z)Y@$^*|FE z=~V-?C4=d*d|TQ~{|X(d6L2Ymr~#1Jj5&=b6Y%%w+?Cr;P=qmyPuSh%-E_`_X)N)A zm32B1Ov^;du1~+odI&V0>hR_2iEB5i?IluV;-XLl^?+R{wpazHlbpDi?{!AQp~1Gi zhDG^AVADBrTiQbSR8qXYj1~!@6ot)ub3Ajg{xKU^ixoO6_9-eSyi7xsnN+sGjQMA)m zG~7{fT_Jh`e(`dGlAtlj{BZPCj5zc)((ANh+pX{BXKq$@KU{lP#bID~M=m=v89^LM z1ZOs9DJpJ`bg9JNIj^{9ZEZWEsTKCLO`AZ3$8<-Zqm$XEFCJzDXun%J@G%6RUMxeJ zlne2js<&r4#I!!nVSQuvfBSbS-i!*md2`}&x(d%+Lak+mM$m1-#J8R|8R|*V4%kiF zo~^#r?Y`13AeW2XqONju?+uMjbmwl-Otuf-i(Jaxc*0zNa(F3>X9sAB&$J>Jk}Zu_ z*IB7cb3sj9SvRb9=AadQPz)^8+D4WeAS(-+`E~RUnIDR9z)wCz$IJ7ok=;G_TX?)J z2{GtMGRYNURD$ojuU$K^Kp?(C>H9cdmt$4tKEJL^AYl;+2f#Sixt8Lgdr=#e2=2BgVtKV!_( zB*V3uKcK;M1G;ZNSvcU5vUP7F-KiDg@=j@hXuMRf18;SjxHJB5&fmni4(H|M=soMs zyZ|?#{Pt>k5r$WKPh0=yYrL=d(9ZCo(dPE10 z>oF$l`zXuBS_=kEO9tUnX7c(AE5_V@>LSo@rGeQ85ZSXe02Zz7+?-1z`@Og$vQ;2& zM=Ar`eyK_!v5BQ0fDpJ07=Mf+@4oSY29J9;$?edr>VA)=;*nuFc4^{LBexG(<3)US z0uYwsmbE18+dbjOK4USz^|QAc+6IN=kl%ykLh`*QHWEA91ALMwq8@4IZ2-CqF!r`P zQ9()ci%?<4hx=YI<+vixs32jj?ln<5q_0_flre zOL@)-!2NA>w!(kD5fY{TNFiZ4A{Bg+Z7M7uR=}o0g~44aSbHa_DD15Q02aaQ-=h!G z175RliYwD3))IPK|Hg)-r~#?pdUPHF$lAq18|cjP7@@OpGe!V!J$6<}!2VU)$**N? zhNgR@EyNJC(&?mnlxkFs`j;iyj-0P^`1Dw%E(sq~+QSsqBOezDpF|XBX6~2?Z8Gw& z+EO-!Z4lw*0f3$`tL+YgK{j_Z|C?M3I+sa@3IkEU!UPyDO@faRx!zNHHOhxE6=xc8 zdF*lF_X4eoTS+(mgx&}lxOdj^QiO@mciTnidS#-^AMVaK_7!JIvnJ!ai#h);{x9J9 zo^Ritf1TKWJEJvBf{VTH8Cx~41@s0iPcV}^6g2vPYPj_60z(7W<_WN!`$*)H#%P&P>k6yy${1>xFHlhjznovY8D9ycDOU`G(?pjbY+?^tr z4&yrTs(^bzh%VTJ&^UBW&wEKNtZr&$nGHZx%4`O1hL%?_o)TNzJo^&K)oFr0qsm@$cs-5+aTuYs`Qb7 zFKT(Qz0u^H2i9-O#&$iz)70}%Uv;*~UCi%DBhL!31Q<|j!v5Cp74Qtwy+6M+jjy|X zy>eA&`#TMqF*Oh<=U`YFc54^MeI5i(kA zo%Zm)b$Z{Rt5VnL5D)CdpE?0F1%(%XjzrxZ<{sNTaK_QS0|~lt!%?;S%xICB1YaEH z#C^AQV+u#^&mPL0z+M5i`$7gtrbd$szf!3#YZ!kJD7`5Dn=2m%fh`7{3Nr*GiQCmm zDPCpupFX@rAMl_-W0AE|IsDPeHQ$}9fyTqu z-CG`gbIJwh|L(zWA6HYnvxi3nzoS&?qPTUA&F`}^%ST79(%$(?cH(^wz&G~P?A)4g zpZvF=-dvXo_Pu%2-lxue?|t^Kciu|@Ko-kq4`nipGD-2|0BDmFW&Nd zXJgX!9rlWv=2|_y5l3&F3+OP8aI0+GA1Ow^r`yX5D zLVa9CEq`40sPY^}yCr=*C)&Ie&g*zST_lMYC=gl$}J$b+=C{ zyAIl)&+>$z3C{``l*5NLQxnKYJmD_>Nw^XTx~he0exHFZR#Uck(F3vRlEnJDisR9(#T~=Ndu)tu1VOk z*MPxa<p0nR+y=toJ2&`$kY|8SGx1M z;H_QlT}vUtDo5fXt=>LUNz5DH+d(ePB~avlg-`_RiFqjbB_nAB{r!WCoBsA0$Dz)x zWU$2e$kv~?>Vjp&i}dcqknaJAS6KG`q_+x5qk$JuvK@y_nz|*p$+B<1EIwDUM&-r< zIrZTcpO7O@54MV8X19KE9;Dq{&7#Q`q^tZ`+a9}5+21j-?IfW$asp70K^?Pec~@mz z@lIECB1Nwwp6*+V6tfUBmAKV_fB0;Eij^wgmnvkJR5<+0R%>a>+u=D#IuNUip#OO8 zM1h_8do9_FjBGvAKXb;vbe)n~RtAN))--LS^TQXvn01A(Ru%9KTSksq&ataEDU*Md zBA#G+!%%=HdvEZ_W9dhKj12t*_9je+Gn#)-7>SMaE25 zqbnLrMNJ<8j)J7!NF<~C+y;Tp_Btt=6K0j3pne|jdpW@RxgD3xk;6$lO6HI_f9cf$ z0tuz2e~$x+Xb5VsP4(QGqB_xama7kQp~xjL!+*7%TD7(D3q7@;h^(x+_Oiy(!1+xX zk+UD%YECP$Ix+~k2z^rc_B}4A;v!uqL5Xlz;VbrQ|LVn&Wz?#LWBNE>vc;alhqoFF z!Cno=LLAexBnK!F0U%S+=Z5 z_kt6)dUr6tQ1(VnyH_HL@EVA5vV-&E0jJl|3KYi>>Q3%yGdzfG1x|JFp)biF^#;sf z9LtXMYjm;XyN3h|mT%4b`$nBd`&vZ-8J)<=2HP$~$kQNCRnkao8L|6DM=#O-+z`Ps$VIMkvwbeVwx#XwPSxK6^vGN9Ao)nSA89wpD}9g zEdjE2Hi;cPk>iW|R=3@O-Lj0_UBedCd>lWuHF0leBC&j_A~izjQ31EEz#t%Z6bqa1 zmgj>0D&`?G6&+;UyRx$N7HLZJU&CYX{9oGDA&=eqcyGfQfUrOiob>0lW$lo;BM|Ou zx&IhA;Y^0R;w3D!jeJjK+P_;B!^kSyC^uT<`(N;|6L#BpX31%oq1j>^g16l<&MB(f4#*QN<1IvZZNn#!^SR(4_8FEI&) zF7f)a2fkm@rZ>K8;Dn_^0Xu4dY*)}%{Xc!p3oX!+ z93CX7@b&ad^%qExRr-yA)Y16(v{NdzF+tK zd_GRkjSo-$E5Qq0i2rJ)j)#*$zrT2GO1=NXecu5fR$HU>?gaVUzYFrSzp_JT*ztE~ zwS_!VtpdM#0BR<+`j_&Er>*E1Vj;@rZkWy8uX`G`7T4MDvqeL`o95HFpWCiQuUQ_E zB13au3i@Mhg5GeA8Mh`6`5s)44!cLtk3E>2cUElv!H#I6l3l7Kit}uL!;6@T*UfIS zHl1$Z9WKdNxZ|c}J_0HWaC$(`d<)V&*03TMrb<>5OmRJUMk$(_4Ir1m%HLUYR$n8I z)n5~;yGON7+>ee9lGs5c)Hka||?z731Py&hiofS;)= znUBNVpX_x*nzQ9PX5}Kb*L@PLKRU2G$o6$vnUb~LH-Fpb_q4}NA{9}bE`P(A5pUWm z{tcmV=ms+LIbgw*&I*K`9P)?a&tNX{-H%+nM@%RQt}jv9ekZxgAeNV`nu}LY!OKsj zytUe6nd$o|hEAT@cU3}A7+dEfXT`mJ>ZjA==gs9OGL&F*TKtiFJPGBD>Sfw)fhQk$@ZK^m(FN|1UT9o5L#t zA z_LrtkudE5gR_?wI!~x8B?bt_2eC&WO5jS@a>4;Mh^8*6cNns)JCvg&GfAZ_wHGlDYH1=qw5ABjImhtC`h^A>OJ;!k|u zOpr@BNZ=@Wm|xLop__evts@wqy~qZu7WrBaq+l&XnyV+bHI)v4wQyQF6_bvwe# zRW=eoTs1Om4w4JJT4tbAR`1`tVNPAVumI;oFPV44k400RT44V>K_V0XgZ=gbSFq^a z5nk{^0kL>1bU}rfl=Hhe$H2cFlrRAqOTicALw?x~Z49j}3;`g|u`{Fm!2v5_;uq85 z9My$o(}bd?M`XliB9vKejy2AoQ^~#k{@v#lxg@B9*eC>MAv)(myx5%Hy2*0K?bNwB;o9ZI*6rPrTET;-3i#Ok?>K2I zltr<+E@V-Ss>M8{cY>prAqPReU`G&w+KJaLEjJjxahB!pr+k(tZPljh7!Dl>ZkA9l}K+T1$T9c^X#m6)6%u|}%|8+B!`_gy(+Ya{n5qN;y`asC4ExG&0ry{gZ z>BYVxp6vnZ)K-eR7g$INPO(`BwDt^PIu!7BB#MpxYmZZ|vgS-iJv~#Sc@qSqu>EBs z;#GyZmkJ!L#rXOeFJQaj=tdvMqK&4mHoB)o;G%4&=q!m2{yv#ebu%%ufPlbGC^TlP zGl3vMq{%y4@JJAreg3Jk=`d=TICX^PfJW!zzYIRmq=F*4{*qfOyM1>5kqYi5zq+fV zL3}aw1sxn37kDE8@(V#ampIyWRh! zkxK1{@^8KGsCWw9#nLYE++EjIK#hjCi#$0C~^4ln(V z78As-Ru-te)aCUknXl7dG}9cMZS6Wh4Ch|n8(r+)ENFPxt}@#DP6q?pQ+$i@^ynYi zp(m%ZbRTJJ&-b+I0n)#rc?l+13$b^$*1~fIb($1T^9%#`4J0Jm{(6PT_O7MowK?YO z({JTh>?ySfVrupNN44`OORjO?pt^?@f}pXWgs>Gv{1fCUTR;N0-aU;as*h@y%7X$|db!oA(R?w=WT@>o8Gp2`pP@h2R;nI;*@D$2>}q(FtkkJPVm2&2 z*kC$!{xG_GnZ54zhM>vfrWNPwq$k`*XtnJGrQ zSo7$BAlO}T6dPqV2Go{g(-VW%`OT4Y)8W7}6>lmF^wg&@m)tw*{2fy%cu=}~zh#d3?j!E(fh%{O4^Rl= zYxK8nzfc|Pa6(oHBept0BFGnjr6Z-NKgMjq{H~OyCH$- zuZ8U9rrzx{-Q50TZD_lFCYd@tJxx|s2dDcRVG_0bhTZ9+8ANi8S~M`l+d=@#3KBg! zKI^7<>By|Om*1qsXi!@=pPPe#8P=z!XkT*EjkH5Zr>;-!={uZkAsAR{6Ww53?#nS0 z8aVz*l#+oy?E5m}m>dDiR&4GmL3-_tqf)>3yQqJ^Tyg2e(UiHG{v8)nF5v2YH4Wr} zI~Ndvwog*k=Wp)jSGc!-snRXTI`g@9t&+oTwXs8|PbtoakP#Hsvw%=RqrrVZ!GlH* zo1+9Yc^NJk>b1+Xv_rc>hPyur#~{6*TU7YW?qllK6EFNS6yWJN9#L2Nir?sU-m5$- zD%0p48GyW;*b}$U|+v7b=t4q_Sg5<)1S?-$nnV}U-UyY(q~*< z)pXg!T`Zk~mS|cIt(gwZ+kc!MFvq*wQdT@toWLGg`_up8vhP^pez?DB5;WNrE5S$< zioi>}x_AK>&doBI=LyvLo{S*vP<<}~=f8bD|p z1)s~^iAMM6YK|e4nsrE2@{N76#O^jt4Z+qdIZ)2yF9!s8Jw9{C3LM{1XcBJ5J^@Qs#!I9EY zc@Lp+2?QmD^=vu}th~yEqLF~^_}$4D+~fTWCeb< zWv@KPw@@?8I9cOic}6ZQZEd#6HDfEET`kN^+jo5IcQ{gSilua;D*djiHRxNC;PpBv zSgQd$)P=M&g@^}dM9e|K#UBi3N#x5P7i7!C0~>wZBu_uIwaDHJ#doL+0aEwCe2HdE z%_75YD#2Cau^lMGZthzTO_Lpqj>hlv_^|$?^Hu7p_g-DzcC!UkyZhs4(Kn8Kfbr6w z#h4_H9}wXBeiT7>1B=Ji5`>TT+$_CAc+gNj!XHaa#Hpd|M)|k=SaX-nY)^`r!LjCI zW^|aT?Xe4Jb1}+d$Ay-DonM>jW9fjO!S4&Jw@harN2UM~atXWYze3M`cWKRuEdad~ zm%P}bzodplmwkOWhPb@fo1<%Yxs2?&c>BuK4pUY;tp*{aqE#lYIJXpW;q#lf#QAEk zthy&&`_09Hnq;-8f_5;<)sxfUqg6c5)e-00u64@HisdI^vIueuA4BmPTD>Xvxd=XU zfqSA!U_ttKT0(4o-&D3VJNXQwkdXv+mS!!K^;~|A*B$>gG#>hN3&j@5OM`Wm02CWB zbR=I@&|dKvl^Ql#4zfGciP75ryr?atl|oQs*FOfyV41h>H9G9AeM@LnkM;KS;C`Fg zxbH2Q=)R*Bd2h@LcuOPZDu3%79%dpT^{Uaa&@&LE4e8oTx(VhYh`8wJ=qOpM9z*yB zdm%HS5Zry*4+}cHzSK=^t$)0{bRej{BB&Gw5Jo_w9+o+b;qe^ z1k%y=*vD>B)pOEccnMnzar9{M#eXWCYZD};ZC`_a<=(nsIr;fRLuvKB10dsX`FQYL z(an~##IEsd}=uf&c$qT*GCzU6FGRCzSQ zqQlVdl;V{@KGWjBj4QYoPb-F;7=O64nn>)}d*bi1=igxISs`^v;<`3@(IgbFIJ11iHTme^>hVWETKuyhTR;F~)^qRizB(mSI5&_pY4+~`3VX$M<~A?d1Hp#;Xg zp?}Q@>)Nrg0(p=dN2u#d>>UGV#8uAO&hxnV{rPD20@YuS0ucJg?cRGC! zz=AcZgWHxCXn%jV`oN&`h>hy@3IR~LT1q4#8df$)w zb8fiH4SvJ{AHDd4A?$;#3)>YdfggDGyJz3#OX``ujegkRTn@1t!2p%<6Eu=zt@HvK zCC&7?B4Td~>BDGgqaM_%mn9ycD}kI`pCqLy9^fv0u2$w{bL3b|4M(24sSOD2Br3LQ}#Ls+*??(nxe zD#UUJZY$neXxlN?O3_xYHg>$@=~Dqfo5(hTWu6S;kAIIcGtHg^pFDpFOP$Zw<}5$II>fI~66MeU=U~)9px@<$CdBsxqJKAs|F&lg|Gu{EpS~(&cqK$d z%Q*fZVPmA&IhD8s*v_Lz_fKey{;Sd3c8uMTCvbqKIAqwFWCyQD2z^4C+I~aC$Bf|UlB3mq*kjJZae%m`wPYx*FE*46HRMw_G*@*iFogihgbMdwON!mgLlcv_{ z8?AuI9cg+4i>5y>qMqFY_WMcJF*l@c9fPIJ-_ zvi09QnMeX1?4!z6;$d$taXhsfF4yWWYCt?_mhq*zo_ut240>uIe6Q2U*3T`${}EJj zLddjh<;LJ#^~YByNSh`a>^?Z>WuCA*%TwZv1 zw>R5%muONX=F?#3uOO{^&CYKEH+1P_AVxPeMhe2Dh`Ry8!P|Zn#HcW`23%Bf)dl_0 z-8rQv=R$ovGsvm}bpo~!(Q*(=sJrZ2j(B2R*rY>;+DVN^6?Zc<=|!2BlvZz;rQl$# zlN3qBSKj5E#3;2-df`RcjNz@#G62Q$u z^q9Q)L0w42eyj^DE`u-{Jfyhj3TXt@kDcD?{REgLx3(&RdGkAHs_6MEZ={A==t@F) zQ&qFGPR!&o0;izntnEwcjs0@D_|h#(n3%c!+xK*-LfMpT5ew9&IqT z+@1l!ArA=H#YQ+wz*1IY>T?MNEd`MC=fClOo!vPIFaOz_cb8kgH)!pJ*Wx>+JnC_Q z+6&)ElNR(?94AfG`>NN(bs#y&C$8tO4NMv}9_=)^^*Q5~R`#7A1sH*BVtRTIa-rqj zV#dEps{GnZQp4=5@R^FtkJU@470T4+aRI=y^ymun93(@rF@)nHSa!M3*v<%ND;^{; z&^}XVP2xGrZ`I=W@Qt3iDZjFN)H3P(w;js@d0A_%UTfdNf+S-1nPlF0rTPS7Y%i1%U2;zO9N4+`73@2OJLTfILyd~@( zUP*-FLw+v_jRht77mL!N2Q>rcw>SV(DRnTRfvP)_= zAJMu0ke)rT(8mlJV*tdDe0 zgTKDJHLqy?(- z8@7w})-DvsVF-=ggPyt0B!ch*`+llsv{DLAu4T09$K`Io?XjMaoABHdJjGOMZwF+~ zTP_io4w7m^a;kHD6(up%Z!&^0Dd!;ROxpdi{FiLhgXgODNxSg-b}r0nkto1jJb0&{ zir)vYJ#KGf^l1?0_4$~s?2%aB2-=)R$$pDczi2~;Nlu64X=0=}GBK+@%;0cyOcKN6 z+&2<~yURcn%P!H}ZAt1lyT6+;v(FEWJn^-{R&od0*0P5$Pixs1C2FMoZYS z!NN+69*fhCef+UmvE0Wm6^$(EC>_2;8VAY~fBP&LOnO||*f+e| zpm%$rS*7^;*?&lf?*F>qw)8%P(Z=Yi+Ot#?NHX%V!a(4JDlNMtVz<9A>y1EIeZcxu zT@xaH((9%1{pvZ)%uM3*T{v5-AbOISJqSLr!lHpZL5y$jGHGwz0fEcgzv^UN21Z-% zaXd->vL{@K7nwb__J>#V?ulJ!bl+ef#=Uk(t@-$j!)N%@dZm+M2P}^+7?>xyNjKtK z-s8XOYsJ*$7_E2^;q`ApPwWqkfSy@FKM#2Sh};x6sEi#>9M`A0?on1tadzPZ^7Z@3 zf-cnO;b4}^>v-{c2og*MSRg}N7l28H{=8{7FnMwB{Gce!KOp=}RET*;t9 z1uc(z9LDW%HIz0=bVdvh3Yr%29}T)dg8&0TJ1; zJY4dmOmLakQ6XLsJQWX$bn;6 zZT!j~oDp#E@y>rP4)xcVZ}4f$H{Qe>tu@;$q<7wpD84(c@hzw$*~X|j;m`Mkh28Jx zcmD<)>1q^v_VC@&-IX&0FJ209U|1#lcGzC0+xLyvl8s0A2T!Wz?*Wmie$>Y?mvKg$QDxTF!KQgF0 zs6z;;(qk!HAhj4SXZk>ZZlbQM)By$#jknpRY=5Xv8+iKBVTP0YI zpG;n*^KAd;whv326H@T8-O*VVK(EI=<@7Y&1Y1>2%_`VoDb=N=^)lE z^Epr=a^A*{!RxVlaL2}Soy0-7kSvEx^YCjowL6vsSC0MK{6fQu$AqI4m>>i zbcXQNTOj6Iuf?Yf8- zuImc=8D4B>ugs*34WO?F=mA}ErO$)$4lU4VD2H%~o4NZtbm#L_?Uuc;=$E9^mHh`% zcGBh-u%-~@_3DF{!Ue{F|3H-#aAW6s+xR>v<~KR-Bb^1TejfgFJbCSA_4?=H1+W93 z8gH_U#|d7+`?pNV_k!C(8Smi)>79RodpYyU@A@kSuiN0s6W(eAK+GzTDjXxV)UeNy(k- zK9s7~k+2~`q!wk(( zZ$z25UD_U`B9TgwBh^-ca}1m#c+y+fOD1giuMnhp@dZ#)a)bF!L0ixFz>!HfkT@o5 zF3SSCqcGQ-A__Y%pmF8rWXy>fav^f{bSe$=%7hGc{vLb*3H~vJaDHOS&ne2sh+4f37YCSGv&@cW}3avaO;643wt0T5Y$LMDzH#ch^b#Fz9pW zrRtr*RxC3;RIN8M(cKLX)SJ>1a;d>~_IxqhPez9apF7Er>6We*tD4L0O7KgiwCS`Z*k}# z7=@)RkSBm;F_Z?MY~FZ0E)(I*@;A)RSLm&VwdRkv@f)XsI|!fRv-m~x=I{{5dfXl@ zIlNL7TjhF0<{l9vItkx-jF%jSr`|~JgEj{PMVkK`QUr(Pot%+~tx{y=E? zKoTcqQV4*5UkPLd0$&(#qk|A#)SezF!1aMJFvTsx$8m4*vC7{)Wj}dE*;UoF778FK zV#%tM&O5_#OOzs1(#KZ^gWLmG28z1_6Habn)QaE zcL~b|5m#s|Sd@6~dB_eNrsu6N&w*w^B9k)8oDJE{%ia!z;Qk>eF~#Mw8a|LFxhq~ecnOWW^f*7{_Kj^i~j&^W!Tx>*VDmM=jcoLHt#ff}A3-g*-TqOKf4kkoiPvg!aLb6TzfjH~EY<pv!X_=bFBWn|O}Z%71g&GQg-n~5JoMDh8w-FREe81#6mz|5h;)MCE&l9khZ zMO$5bwaN$;{w>*0Q5_T7@tMEWJSo0&dLP~r&L(~QXxXMDAo8vOmI{f zUl#7;yPAddz#O7+M~26fm?8kU!k~fVul;ga-{o*1<^Rbmx24ahg}Nx%MF+|VglQww z9AA6iAnDf<52vkzk8QPKzb9@vZXxe6S)J&L>uPkSs7_Uz+samgc2 zX;&>CNr;LIA93MJJ`nvFXh)k4wVak%I@))%G{53TR%hfK(FM)+vr(XUP~4fp6Pq8P zRfR*BxL7oIwes&uP(-e|xFCV;!Vy_sH+XVQ4WB{=(P_Y1-gq~3U~WkXh?~cKn|;ec zj(U5BUbkk3vouKR-a?JU?qy|l?|TVrEjiDT*ErY7na%_?`F&4FO) z2E8iR;kUluc`4340m30{a;DDPjW>f0+I~WI`Ggxnn?ss4n_Q0{f{p&5YhH9h6Q!OY z<0qNYDz_1}(p~=CjA2mpI4zR?#&GYLuY;D^7LvPd;!G{25@QtR%7}dXbpP@a*xjOGj7X;8@TEITQ+wAHRNQwYPBA;S8n7+|n(|jAsjw z4Vrq9`p%#GCGlD5gZGOD3a>mmUKa#yaEAW~T%Nz~`0f9HlPT#-D)L1u$vYfj6>Avf zxcL|1(_fO#{JQawQlRpvf;iS5$a!N$xdrZ$K* zl72eyjd3{cGC<9~o$b?5Fd1V0R%EXS2jpxs1jS><(p1l!K0$nH%F3+oH+5k7r4(*8 zDZL%0eFBbfpQP80;buOu6*MKyLK3~GiJl3>5$oCYyRviZv=4}-hTb*SI&=yc|I#QO zGbf6Za8ut)xDII)%MpHuFI6qhz#o4_OD!6(VY;D}b=shIi6+M~_)`(AT-aH_FX?7% z&olhsM?C&Vt`R8i$W!|Y(H0zCSU^BJD!+|lSLE*bBUFqJg0qCszB5HQGKN#2!ltT6 z8-PI@hgdSjL)GoCE*FdWX)+jCV)G!UuiX7vANhlu=iALuOtgU7@Tkq7fl5Suy8uU-vxRz~AAD@eoL*9awaOZT zQP|6vxq4X_%jqfRy)F`iz_JrR9-(oc73?dnz7uKCs+#}ddv}G@tHFW#l?Qu>ytr&A zsD29i)6NkaOiftuyxzdDdv~(^`93_P;dj|&T)VhRJAYGG35@%W9?N z0igqV+mXy4z+svs0vi<$5! zwql-(P+msceAqT)0gy6=lpLt4u(4yi*3-yj>OPa2+1Yk;5gUz|FofkxY7%Yci(=$X z3IWU&{IP2?xK7(gHkGd0R0?S{G}2J))BzL!d}aOA$cExWi`gsli3xYz3OnyEBNgDWbkZSJ9!T!heyPR!Fp$S(8EbUX)?5+vk{4#& z63YmeBWUJqIAr`+qz(#==gd0pn(tHskGif0NM}j7+d-jPC&DQNpodu8 zz7da4g9MeN19HJF=v2zy48aLr&(>Oh&mLIa3J@tB0V$~gq|lI|t`s(n~=SM>{m6~ zWO(HXt^_}ZqS$Pf+(q|ZbN~+ht$V)aw((+jLg*8P^;`uo0MYZBrQGj;oJ}yiaVsTg z_uL|K{*7m(nNpB$q753{`bzqHj0Ik1kUslbvKcEx%DrVn96HfmCi$v~cDj*INe6=v z5a`u&L70N;29q?jugV;XQ1Ijlx>#tRZsm?x2@<7JTilMz+4U#$HTpE1OFdgdpE=S? zp+$jo)0Kn^tzY~>V*=jZs}cr1rZ>g-94MA4lW>1w(*!0Bm|<2v@>i5x>NwD;6Wff} zB?<%eiE~;*I=X4|&_|Tf%N#ST-Y5SD_%!!WbBc33Lwae-C@NAWV$|WqhNW!frnc^Z zy(qN(1^@6lIY~`Tj{*A(27s?3a`SKQ|3Nv0hV;Prw=Yh8>vhk$2tgxlKl5K~&?B6< zH1N{qW`ju*WqNLIkNH@Up!<)54||2K5n-v^`LaQuZCXO6bQp&ZpvN2`g!Q34fSt3w zV8u#$U>1AdCZ3!9y)IZ9ypNudbXj*$rUE-34wC@q1#vt{2xydxW2dpsHvKIWT0C6i zp%^T{OEzN{5O^)tOQ)qy%Al|b52C2iGl*V>sdiS+;E{=p$O;KX@|v39^+&zWxdoNTCrS=a>n@gRPMI= zY;+jhof}3mgIMW1LM++BT8LzO7oJK&mps3rJ`q#joa+=FHWqK*wN&3@5@;lk%M?wz|1#%W)Q@Ce3I&5X9yE!ITQwklo_Lho<5 zys`pDH#j`K3a0Y}#dCLjU;kM!$`1q^70cA%l)~8#m#Yr;dfA#a1ys5?q4NPHUiSbR zo7G3d?}MOCSz1@DWFofjw0^TQ-J0j&Jy>fm=L((ZsAc;wjN9AG2%ijU%V(WU{~9a< zJ|SEN3DP%^fmk*$HWB)4rdPosq#FAuFRjm0RzUWPH%ONDL%@-S;hOQlu@VorQ>J!Bq2ah@ska8zi=@G&74l8(E}HL ze_SdgHF%ah;BEl7p7AeLlT%0itZuz|qVM&N^sV_8;_(dZ@bs3DU>wX@E7S5DjC4<} zTK`dammL_wHa*PMDKk|{w2+tl-@SNWn$iqNOh>(Z8a3|4eF_&5f2+V`kWzb_Xou;9Rq2JIy}vLxZV~(;)b)e5OmcJp=IXl_wN&M7%)bMlR~9Aw1H8g?+chG z@Pn2TV~FL3TXw#(V8A3KoKEZXWCzGalgXK-5}>>$f(#bRB)dX`Naq8syJZ{8CGMla zK!cZpShm8R%5X7o?|HU@iCb{gWchwQKxVe;_+)SQh^TuP|6uanw4|hE($^sKgc2q# zD)}Mk501!A`6TY$5o9}pST6X?0P$^QWsMqTHx(aVHOc_Q9Eue?N@RxNFi?E$D^8gY z5u3UN=M%{sMg4RJ{w38{Nb8so)2<;ymaQD6^n+?(cU4zN=6Qge{>y6q)qZy8oX0Wsi9=GNhfAaD8l!J4nNFcsn1f0}Z z(P2@oio!_~U{kybxhg{GxN9{Pv`UE(w!PuvF7T12)nrX4% zeSL#+hL>WxK@)oij8I$&F&NInUm|;NjfGgMT)J@M6btT#(Y;8-4%`1U1au81Ui2*Y zfL@(8?P1Mr89)Q#NYC-ibjn?cpcL1>i6ig7vBXS3W)Kdu=d^h80fY|XpXw*Jk;%>8 z_i-6oVzq~*ts2cU2PxcvwgVY^gY{vPH-~Oh%m(WO6w^B0g@k~bWa!Ty={FJ22q$)4 z)!79$P%m*gd4rI_q!efz5}l8_2nk0-1DbU!V&wA;WC{)_{?C1idto~4?~oGCW%gu` z3U)2YVK=TC`=il~4D8CoJBNajKxo@mitN8@Xmc}U==X$PPX8>g!s@p70IUyj<_2I> z+3)`>RsQNZd-u;`&agl=Kf5dkpk|Xqd*YIP=l^e!zF`y1loAbI06s*valdiJ{q5+N zX({1gtXV=y7@f=X?`>86GDTW;cqIyM+m#04fq}eB!Mg<&uVTmhg*VeVm-LP?Z)T>= zhhSnA^~dU>YP+lVBCYo|J_%~})HOb@R!G%N!HGvGTFSnvI&;{X_=N2bU_gLSewZin&tKujPa}`x4%(sLj?Q z?y*)xmqTxoA^oLw}DcOaf4I zERiju?pY>x;a%IzV+&}K`REUW=6}Uu?R8Sb_#Y!%NH=^*`pa|G3s@xS#RVLgw`S#D zx;y0E^hPM;Y3UOXu2dnPDlYup>tj+z`9DPI3glh!Iw?)_F8yt?V~E!eYixU4(nLF>U3YsRiuLSK zNl(8Hs>qZz!DU_p*QmA-nDm8_V(LbijR#e){k}i42pcWeTIwe|mtS(;cZftg;s@qR z9(+=AY4No~s1t?zSiO;<%1$9_n5bIuDu8(C+1;xZR)AE|%>Z+5oFTSPlqvdm49dW4 zqo=Fctsh0_<;Ap@ia-ap*cThBlZ(F;;VgT#yy%8K5O95IT*8dT_+u61de1|)Chdff zXv38k*77I zAxWiiXlN+;O2M^z^iAG1ZK3JGs#buRkf>kp42g@E%^k1R{um%9b)sT&S^Ms}$1H6j z*viVTyvKCa*Rb7ayQvN5qUlrMqJT}r>``#*+`H$kArA98Ffc*Yadcfi#sBd2SA@$z z2l)mzQL4}Co6h}GvQ+QaDk@7Gkpudd%VkcWpP)NTXI8HIFMjroGTwFj&)VJ&A<}W1 zrhfdUzWv4?l4xDcU`1dbhf@b{kE{PU%HWsUapJ^@Y4Am_T2go+F0F``h zG3;>#eau3Pj~^Us{K0o3Gm!>Qi`rr_4kWc7*z@uPL|s&JL8D zqU|&0%SNj`F0=gs$@m+HMz$2D^1Dzx-$g@Gk=Y=BzSH=x!H^oem^OU#ibq**YFYc* zjg2jRgJvoeZ#x8Kmkob;Mat>TQR>8Ku2_-dUxHB;Ebqwy zF;r@r^)*#$7(QOnF@Egvr2gm);I*Be9lGrz8J2?cQh!v5`vnNwgU24_KMYMx z80)>AefI5@B+67GMoK%n!WMEQqZ1@>QE4`YD%(KF+gvc4Dgk$VB~T|xU}lJr6ff52lF?{`iv=13h$S(CTHb&VMw1iP;^N##WPdW>uqjBGXPH6bk_sQ3X z5U-dpONonx%EP~je-p-sD+m_PCH?J_^|H}D&iV(|2*PYQ`DSm z#-Rf4`IP#G<6tl~2=ixTsr-2GZiXtNx0f20ix)DD9C5qS`XDuehGaqDX#LVNfgJT- z!i%+JsbTmrl$|T)OXAAhusjGMDg+bGt%b0h^@;&{ZGcDjE$*`D(9dM3V!X)$JZ}zQ z8!upj+rZp10fUKrV&5yz?i>|N;)b?>A{hP3syyI4cSK?FGWr|OW%TcxRbyV9)R;ms zDV}SKJ2tl!s=5%%YrPxp*HiE>x%)a7L5e4U*@lNzG@=hGdA+AC0175o6=0t?rGMp5 z+i^tDo_2f{Gxso(~dd6wA*b=sa`6>wCAFYyDGKNlb2{1~T?J8_3MgJLPh2U23@HUMp2#t^3# z3bN?u&`+6owN?qFEvVS6io2_ww(L10ltO{jqEuu zrJTFBB(2j1dm2dFTIyml0dw5gt!l92gY-2;J`({rwvnv@nky0%lze{20e7@soj0u0 zy{aT`;<%wUm_3;|*>^oCm)dyOA%mg_GV`Z?wd}M}i>5C>o9+-g$E^@Ss!r0M-|h!< zv!TGt{g622Mi*&js`z<;222+RF6-~dv$;mMoc%hFZ}q8aK`E>^!gV0A&jllOf9^`b zCMcXI7WGewons94CtWoF3mCa&4|V6qvH?3vrG9aK03u2ASyL}|G#Cm2<8^>A>Ce4W z_SpQHOC&~B50BUI3ZOK}vhU;7O-6|CwBotImK#m}x^^G(zbC}wm{W0ZYfo)+?YS*Y zdz9zxO7u><>7EM8qW!RyWpB^j9HYM0*EIFt&3vd}#C9NVSOA>>mhS-M6mj^MCC5j= zUL#tmanX>E2dQeF^j4YUD!sF`FV^`UH2T~zNvxij$vL(pmh+Wpa|oQmZMS;89D$@2 zxDIfb?CVwDHTQ=n{LiMn7igi9RnH0yBG6suA-G@fv;?E+1DT>=swBR@yXTZ`_fnz# zXSE5lK)8e_jg}8Zea2+Z-Es{~ z-3q9+I+VO;#}KUb+%e%s9|mpy1_*5-H1|9K8UGWzch+x48nI( ziB9wDHdo2~q#4jpp>2=&Wl>>xrcX;LkCmhV!EA=_Z|0S28|1FR0cQA+MO>SHuh%ekfn)j$}hrJ ziDe5R6YXjl5(Tt{1H?f2y(XU2fHA}eIxGOtFJ(rIAIzcKS1(h- z{lhyznjBDr0X;60)_p9FpK z6u7^&g)(G#Q^v$1H9+D1Wq2#CN4n9+=gW%xzb9uQZ_FO+u-JNZ#G2z4`3i=Xop+>H z3=*b1Pjmhsn$83s>iqxX9STLtI;Py3B2A8r`wF=#WSOFjLX;TyeNB$YT}ZA7HEOC6 zYB1z15<-)PCbx_u#@Ue6|2^&RzmG@zXt&*VcIG>u&--}2p08Ig%}*oZcns5s*KM=f z-{c&>-_?zMo#%37qeIY5bi1<=TUck;I#^o<@Uslb#@*p#veJnL9`yZ&&hNKLafD=5 z`i<|*n4b6&HsWw7c5il808*BjIEChWqsS8&6x17VUt1z3ghGZCEM^#9{ln@qz0TAh zHW~gilvdr6r81(szoAIWQ?~yqr~7Kji;6v^?zO41$q0n;Xn0bSSbJXy>S$uIy5B-e zIS!nYsM8cu$uqfPMQMe^l*Xrzcah!3L-tUoeL4zd%rO{f(1(UoM0fQl2C<@9k!(F(7^hRC(In*=H;y{GSukr4^<=D}xcSrQ#TB zc5%Q9#z1xg58aG0G({dRBfIYUg$K-paV{*)Wb}a2knjXR1_0Tf%Fqcm`tPllkG;B_ zV8%Z0j~H;-7(JSm5dGJc4q5TaIcHS+)bK>2Q*LYM% z&|7&3nWH!1IWaGNoOsFs?C@q-wJUDTA5!wZhX2-p$M7zh_qKM3KlW-+P|(~fyB5Wr zs|Z5P0IdMjNsVL_bUkjl|Gcdt&9Z^Kf!At#bzK4tZjuP>+7lLT1ia%&4N3G`P3uc@ zXaukEtwHi3$bO2EaCrdE^>|k=(a;L$o^!+Yz!TEaN;)le)+}Ag2H1(;H|k;pjU12X zfLX^ZeWYJ?US;228S1p{KyK?BD-KvH^aLEsbyd9oA^XhLY=RM;FRvSRIRbGLcBcv$ z&e=yw(#l=%7pn`>)N&lw%C$*BM!9buntf>WMhZ}Ubk5LS32qQz`Q*eo8(uvwZ*4xk z>~}NRzsF;NyT(bW&<{Vp4*QaUJJIPAAoy#pKxMk0y~BD4rNU)? zK$BXDLZEt9fR`rZCcN>9W`sV@)r=DtUuvwEz+hgSNva?|p85Iyrk9yGU&Jt&md$dH zb4MSH{G1D)>S43=7l{V`hF5*wtBm_l*__NC)~1pVOR<2luT9AXgB6K7kGB$VT?X)k zmAXa%R*C0-eFn+mZ|#h+eQ^DUm-`htUFSDB`SdnL zeldFF*3_k}Lm;LJ6h(CyWBQc5%`xRT&LGXV>IY9+E`5i!=U=?_8yVfE^9?^SpxxQ> zS2xa9$C%evpz!PLUt*JTfU&0l8l^4l4z}C!_k^XxsbTgFq$&Mh@eRFE(rm~Z$6KV? zwv*I)CVO78?ETv0Y1SMC(%&Iu+g81Esz|eh$(gP`IsY{1tc}aFU9%U!x3(_qAen8E z)8(4>i7#Qo?rnf5Wu3=pAAF6{@vZP+>#cAVjfCjxk0`BdH%l*`zB-(0`^@71#ROm6 zD8|&6&|<}sPb{hT_=_1?f#Kv+BHaGs?y z?0LxFvlBXVRe5LXt~U5+CXNjMV*Y9M^~it4s3#H?PiQsz>t>8z?^bV7TsWw=bdW7r zQ~xnE>I}N(`f;uLn2S}OV(v87rt{o5x$f6eM;#0YI7iqZX*OP5p~eKx244a8v>MOf zuF`@wkCuGE)P#Noylse90ukVb9&N3=qfN+$Fi)IJu(uSTSMmuI#lVY6dsPjiTqKlZL$*0yK_R+w8H}l{2 zX&CO0_aoijduZyEn>*kIu2E76*KGm}M>b03ExDrH4%lFH&IQe$uidsxVKy+Hkh_-i zQ5_4~f8P8s;1Z>cUM%~nDV>@}SU1(jxmf)>xg)i9&(+tK&K>F5(hBdEMO%(90&WuB zw(C2f=UGuv>rR}j`&TXRHL^8V_hsjYy301dl)+CNj8tP>@v{x=hm0syMq$4vyg6r# zy|l12SlrjiUPkozr?h({GS;EqF^S|7GPtq#>d?FEM5t5&J5I>pR`VP>iTPwrZ9c@` z{v+Gna;&J(gJY276a*cB}ZdboaHqoh@v@O4};eVLnC_J)p!k{3Fi;!jpDtrgV3X!lb0}B?m;e ze&pWUSB|*xmx%174t(p6FqJRt4xhv;=QaEpmG~XSrjyCccSvmXw`~btd@H>E@m~F# zst9_O8Pk>BHJBF6Yq4wMnpW8xp>ZcPcW>K91NKZV;gQ5nwE5eL@5_gZ3Jt?B=G(!o zoQ}ZcJE-)bc~f1K@+`PN3)WtA=eX$JM1MZwb4rK~o1X}$i}%Cnzdi7^;V$$a7gtPg z%|#w5pA+~n+O+kiNx$#9Q$JbnqI}_2%)Z7gEhmOqfQ%D`&3>nx{aBbTPT3+#(22+C zpKU)QeCb9=^&2eNo(tpT&*GxR_(11R6sM{Cxy0pmUnkb}sIS#_( zEC-!8j4YPOdJpmr$GMbn=_BF3 zL22U}#$2#-wj~>-BROP(e*wY%aBeM$2bXToRl@Q!ql??bJ+aL?WcYI)n%^I+d$B(F zlU4$VxHw1K@J*3FMi|vI+wKkC-$8AUg+v9a0F)Jl^XiVO_w9ciHWl9r#+m`U%L792 zwd)g9{6K^PaCrGF4O0{6c89SeqrE)_4_;}EVCwAU+*KC~s)6)Z;NR*bZca zRA+y4*uXz4T?;)J3m({?k^T67qA0Lk+;VRtr?1h_Ct)z)cHuFMp6p82MB1Eh(D#29 zl__%9g!&WBO9)xX2L`=4ePl-5?(SvV&*i7COL0(0RI>9$>w>C6vl+_H5eU*KC2MG3 z9C>--GW)X5m(cj5&n`8u!jiOJ0>rUa0^;0lk7slHuHSUTPp#GCAC8?`T7b%mh8?n* zhJ6@6O!r#m=v6hKahe>yuN8D_e66B3=#lNseI1H`fDl^NP|#+HQX6Hswt3KyM^8D_ zM8wmB9E|qu>K?3XcZXn?N9?evqGv?qd&2=@Fux*PK1~ug;0o_{4ho7Fz7!lKTmXjo zf?9mL|M3ByTk2$;xYdATAq5f_TlE!7=hTDUt=1}f0`3oZv^tePV7ATF!(!#fj~}2c zq*r8jF*;osMxN0aiUyFq$LVxAQcq@vH#`UVWReo4ZTeF-)AQ`6~- zP!=xZ>Ogt-o)-1pmsftxDf0X?7rWmu5nud_?pf2DeiXW*U+~x|vEjOGG&snuX!So{ z)iHFKqlLex3oU+ivTtT+W8)kbzA51M-(ZK@4A%wz(VIOR%&diEv4hOl-;R0JyokIH z4^{Y2YT+c787f>B(&Y|mKx~H(q>4TNJD0vV{PQzm4&GQrJWmXpJf25$01CMnz2L7? zgY}brCi_SpWw)TP{3a41cJc(*(yGk@$$kG8teUqy@Im1|zc<+D4!D3TK>f^miA~z; z^sLcW?7QJz56h!g05GzxFCrrca3O}do?&G$wi!(dLS+e)eReF3UQhe`D_=u?S$I- zJ!AHE7Q4Iufu)2w&A=06>#!o*3wzVlKREgsnXMTcu5O|a`}I66JGHyvKB16k+jpyg z5tz7ZTa+{1`C92Lsob9s+6>+07n_vAhF={G z6Y$Z|%D;*7$l_sP$AH{N1xVZ`r9I!+pt3e6e+AzV<$((LUdrDw!O5i){#Zt9ipWI4 zX{*e258QUKlL=VI*5;>SD@db?%|lJF87>__=} z9v@65K*EURt{nJoDp=%}tDf2dyaX%b^2KNIRac`b|sSbrK`yxH{iHhgIX@nPS^6MCkEk+!JDMsHdVf1)b_bPC19?#}ws>j$U<$wd;TW49_~ zdjew67U2FZ0{p{`aZ5$s<$i`BX1~#6TtrjU<>O*3d<^pN9>drzMY31ic5S~#NYKl1 z5+^olKfoOT=o|W&|NhC~xwY5B8#+S}-KY0rkfF=ch+K=uXDdWZpSGN+pD6yh3Zkx9 zHmx*56q^ZM$!W%P_$l}afBVgYv5f&b;AyK4Sw0LYLsxVQ^#XmW+z!A=QXR1&RON*q z+z4X)z@;NYfTKZEt4YpUPNa4H8KBD0OfeF8lz~ao)pqk~Gr*%v3k2>F_&(Xh9B9hK~ z4vu}EyZE&}BJZ1TO~8(eh&brPKce5l21(NS`Hpe;Y-e-v#VC{7xYr=g3>LlAeHZM_ zLbY1ozg9;vcgPYGU8ky3yF65#DcR%IZBJ)5>JM5fzpr>8cj}AZxwGQHO`s0szx}`q z*rvT4*4i`%1XnKn`m?Ub(ZqpB#p3A%DKjVg;kH%s^6$I$x^U=KsY=iIYkp&!OUmk& zc9~Q0&>?}_lZ}!U*@$I(CfNt)Q26hFvM{k0#d%?`P-@95VMC-zSy#fQ`O3 z8QOsQ%-?ZSbA-JdOubd>6-KA8RpB?(K)t~X`$BfX;|tl__ir))pH-cMte$p7o^Wtg z((8V_DvOxw|M3elM1JC1@aya|cI&%c>=nlydpj5i)HGNkjCdl_+;X{!lBJaG3@}+} zbHBMwM2Y(0T?e&?+v>dD*Ad*{i<`nVrv72yFr_rg5%Ia``ds;(9_Wuj5@eSpq7{Ah zuGK?96AmFzo?z}4qtwb)TW*~2UR#)=lpnXpFb8+4n&Sbi1%3~w$hv>+X#uX({TojO z37#XGduB8m{!l!*#;-YV^srj4ZCp+!^Holt+&QJAzvq`U^D1H|^pas6haQMNdB}vU z8uRs5wfZO`5!~)f$`p~ZxBz_vg#^8|hEQDqKWw6qAhSK5*9Gt@q1q>xF0EoE5jXmBDAJ z?UFonQzc@!>^V=TpN_{R6#(MfKLbZTercK%>FzovmhQ!%L*~3C+o-Nk{rvnljK9UG%Nc18ed9M5K>hpNU>L9wiVgr37C zO6q!ex0GcH7`m{xG`BRg$~4NqJW4IsTCRc5_;JNOKt9-)vt-c}g z-&dYdZbqr^Qrq_H3i-s~Qo#u;0oy`h|G~@U{cjb|5MOyMHQvC90BPXc`^LUmQz{x_ zRDW&V!uA1$5NNN9W&d&#k}my{X5YVdS-86EW!D=(vDf|kd-s+#;2*Dxv+H`jtlyxn zXm|15ELi(n^}z1a19bb8Vz7D|H7SAy)%kdiwyOU3%b{P4U@$#;dQ z)0y6{nose6bw5x?CG%qG`LjAR)$VR6tb+_0x03XoU+VwQ9Y7zsYL|_DED})_(qfxX6!@Q@Z~}*@T^11T7`ZLu33q- zMov=KqCB)1bjiQ7s*qi?|X?gsor%YDVmJ4LkS7XC4+@Q*P~40 z2L_M9?tU-d%3Y*DKUQ0eIvTz4?;u|ZQI0}_J}LlnAF;Od(tB^@iaI1QR>(f4)d&7% zQ)Rj6e2$lr;>dC-BFY6|3*`bO9@<`-Soz+&R8*|XwP1}wQNtgM)R&{*`OOc#?vUdF zqw@W?`Ri9IxQkDH%zV9C5fmstzovy>K$2#&Fo-dVe3n~kbJ;8`BC9-y9d$)sV=7Ul z#G|pr#|yi%K}d&D(&A~UVcm3!hy>^Dt?Dqu@UkuG9Ms+;VKhSSlG%d#&JPwPxQjqD z=^7fWRWE4kQI1=l2B=NZ6FDGr(p6y@M?MB9fv+?*I+>l6OBBb;E;Q>{!3<0y)aKh- zNqcoe%G{ov(I1o%{(#Gp-gW+pTiiIM|F><315IVr*XW`e8rSU+7_k!}+NN*Ckz158 z3H)Pz6MzVNFRu&x=8bYiPdu9Dp5Dhu0&b zM@y1SEDpffoEj!`8G+#%-O?B!b~s~tip9F^kF6qo*4`Mk>O)gVgELWo8WcT# zuYC0K3`M+jO80OrP&VNV76^$vIiffe3TeTA&e2r#?MoadS*ZGwSx+Gt0@N41=m_ik_4bn>zqO`uWV9pt1KwZGAh_9J0#J=iN&x)Byo=ou3;pJjikh zuK#2ED+snI%z13HtN}&zVgCzZx2pdpzKn#h?bTlhY|EwVTR0SJ08Wz^?f|5U^B9V3wp>3)5>ImxMB83 zkNi_i${yU{aULCmTdzj6H5Tz}RmXO>kq0iymwy(_W5JdHGq`t6_p=63nJ+rWX7&3n z8;sSHnV(1B#XptY=#X z6@i6&o;magrzHwZ3<$5)@{yRP>?x9EXi8kny6IvE5lM`7L>%5s)I>cs6*h1ysHz+YHff(3Qf76Gk!xY7OgM_xkg~;PHTRxMqFh z+r3Np$&GsEhIow^y9n<4uWqup5|BE3ms1Z##u?H-t&w5^uZKm=_P-1;r+u=G`|u-U zGk*2wg$m^1ZhoPp@w)~jXU79<_m=1WHPZ4~>twNx#&RBLN_*1u{P~izc9>g_hs|r& zP0u9Nu`P@8W@Fjf-Bvb&n0@{@d<(oZymgiubZC>tXb3|u~_>IEr&mYY@j9xNzOvmf=9ad%x+3mnbZBX(6?!3CT$|UP5M@E>k4iDIV zuvt%UpJ)BZ?sn}@P4Qh*!z%n~%mwzkvgJubJv>wukd1^9oc?o_Y3_uy>s}hZ)VX{+ z2NSVV;!jFf+z!($+Yw$wLf^jK9O-v%y6kLf{wg*peY?&$OW(NFc-xz}yGr~SwoxQo zdy!=Ss9MLUz1#lG%@)-@J+3^to>ZN5tJAscGHh9 zCFSCddUZIgn5OfXa`jTtD#aqgZ*9z9;}tJF(Ocl6Y!|++>9f2kL19MTuAU7U9c;en z_4?Hx7JIBPC=$gYx2}TM$m~}79pvnD5_m@P z`z8l=r6j8@VS9)b59LmK(Ca@PdHA=c$vT9==ukJHV8hV`&ZC7o@D)68x|J>s(v7+F za}Vbi6fCVRI$Ky;nx#+Vh}@qWL#yWT!nej&-cJ5r-S3~X;lrGIrU?1ql?AEcwZcE6 zD=V>GuF9%+l+BypE?FFx?x=Vu+sgmlZ)|?QJi}i-sPA`W`O!*HDltQB z-eybHhTWds0I2dlY&_0xBl(leDj4N{Xgs_cxJy)iNYp?;2G+ED#h!lShriv`f39Ok zZgEA5GubD6R2_!D`(k73EggjN^GSAIs_^F{_}RoBmYsaFU)KPsIa{}qSRc9voRo%Y zJ+Fi>O-5c~XBL^6R%R;l6uvKm9grfIDK!>t=4EbwEjNqcrey9XU@vyh@djGw4=`za zR8`z^Q|A|U6OAD6gez)u!l3t%Y5E(da!4b+@`Vf=%gtX*uke`F&=}ZUQ;aq6E-?yh z?5-EGGuiHSONi9ZXk4+ZbgGkuMB&2Ps3Ck3!7EczX%c#4D-*GEu7>G{C3$0$c2G!Z zDN>~xe&x353}=Cp5nZn$&tLo5e{GJ!Dw~D5M%()0;z0{>-HFAL#fA(MzJdIL9(0ga zTlZJXGt>!oVAoc*?dc#MX3S-;C-$X${anq{GQ!T{+QJSueGgVB35mM2%05H*Z$}b* z*o?=ohyJ6;EhiqlbO##@nXR`E?Vr{V`QdNS!|j86Qcs#Ksa@Ou_N+N|VY`GL*Db=~ zGg?ZuY$THbh&?#Zt2%|9dtWipTVkRexpFI>Phtr_HEJks!Pk-|u8Zuhh+)~Ck=~L) zoqZ-dN0DdZ|7?T|Uu25lbx(|JXa~=!h-<6YukH-d0=Ix$u6v1uaS%45w9978N0jsH zq1&$eL1BJUt@X$=ns(@~Y<06A^j0&2{D&(BFYM)6(a#+b98qbEnm1N;4d((s1?ae* zb@0J=*6#E(*V%PhG0_`KV+vb?Gzo)=-OJ#h4l+KG2OT(su<=B>pwEg=XEm3ORH;-y zwD}q!hD26q>nZ3KO7VolYZ_bS3?19S9 zK;l&6844fgJ<|fTFi7L((x;P=)bH_jj1 z;renXFWnbVStl!^kz<+OxOun@7>e4uSNzr}m`z|RM`{38_;Ya-n zH$(x7z_^s-69Ivi_`yFLYzxX?83O)S8~(dU6)(ugk$=P3*^}@y1q_Bm!-eNR{`_Y1 zD_tvVUOBT9d~0|oZ^f}eLFYjn1t$Aq*=vOFy_NWIT{8?OxFBpie!Qcf`<#OGnE|Qy z|F0!*?^!l3I)xEJbQd%7OcNtA-Q-h&#OMIElmP!?q#okYl7|AEoxekhc+qNBAewun9T@157af+ zN~u&3Q%fyqBh7N9w_P8TEf#j_3@rFMh_S1%mzS>-=WWS8)#LXS@>i2GivJOCx#^ij zU47&<_0xLetiXOHu9NukS*(G9T5|hy2S+-yS|}*G3~2@t8J>u2LdaDXM6y7hS3p-- zf0mIQ#s-rTkkE(&0;zKl&4X!&5X+Uq()Jx{kCf8CnXsqjP7kgj^bcjuxIEDp3}0K?Wy^`es>si1qp6wL}B9 zaId^U7wwm9$lbCqlJ9Mo#U1Ta1Ub=hYQtX@62DK{ne`R=stjLgh;x)ax@%QH%qNRT ze76ifX4_aW6b%k*0gH5FE4?f3nuz1Tc@ti|8^t2`FnVrK{s#6fX{&=Mm;*fPXf`{f zLT4z&PegdA52iU>tj6>f2i*o#F_=3i>5IxbpP3JjyH5UIisB2u@s~TsPHErzH#bnY ze&OcPOUvNa_<46fjg7!uUhl>iT>7(miF2gm((mJJ>y5%-n{dP0TfaZUcpuTosrblS z@Xz5H?99O*ct0i&U&EqELlfH=H@sHOzF9r<{tJu)b=qyUpkvRvy<+!?aWg!C72fx<3&6bL`rapPv4T0$^wM&0Ql7krO=Q3Nw ziYriQ29qjE5?WFwS4GSV^JxmzdumZiBNI0`Bf!MD72bZLK{1juxc6-rolsO#vLjE=}afNwBg?BslJXi-SQ0qUc8X%XlZhbP3tw0KZ z37FyuL>$M}TqB*Mt(kq*ITelyp$~)&@Mq5p&iD4-by-jYHMchNSIi{#P);g|BpTq$ zeJPeS3yJr30?y)#Tjhu723$(2dPfr@N<%PKFc=?Zd7>s}DXAopOc$RghMqwY>y~I_ ztvT_!KToRk%v}9p22SzcPe5t9A3H*zLPEGP9L9Y4ZHs+6NnwoP6*Q)}L!3uUXa60T`|I_-4QSjyhO%3;3bE=fdGCpZN}&AFk;!?%(CgC{T; z3kz|bL;iXR?8w)XT2~kAudXK)u5pX&hQ9;#o8H3RvhbVuzh13zkztTlrKY8=!|+(2 zojtiUuJ?PKy(^FW*1F0LA!JMBCKP^;DXir+tc$SSUX_EMFiiZrPoJwAp1c}4c~#zi zW&MuC`o7;A4S(|V)?)NV*rDI+Y-Y<<{zUdOGavoGgT$zxv7=Qf7f-mq(ZaTLb&S;x zukQ&dScX+^+GXFVY{JcPb(Bz&yPuy|G@LQ@dVzeyp4-o8nTYsbEe|9V;S!3>efk{FjQ(Y}a}1P}cr2w&K~@ zQbas?wg=?;csce!R5xE6Qn=Jp4ebC~{QDEgDkaUWp%xD))EgT={^iNZP0Dc(tc0@Z zMv;ltg!}2@je3(iE;EEj*`i@(4ZnBBl{?m^oV}9M$a2ed?PT3Ofe{2fenndt;~8Qm zOgsQnIpnebBTrkol6lO*fCK_DKfmDe)TFLcGhPuC6eqKopL3po$b^vLdL%VsUC^#i zC63gvPT*;=6)pN@zWW5<(KCd{UEs4XutBSr^JW%on!>eUCM1M?2q+!CXLJE zZ7^cO{<*HtPSs4*A4%HQg62ytt=^3^Yp%dOlbMX7kfzGk=eOQ=ak;clae2sy7BCdK zfrB4&S3@`95z%vC&l5S-#3P1$<`%TmF8zFTY3v?snf64^v6n=VOjabbW(W@-{iOW= z$4|xHuM~*eeN&5yUdPw-Mc!scvTCyl?En%(oBLziDBolu^@1hsB4Fg4^C$CZ&Bkjc zr)7=jr5$lw0XJCNI$GTkr!z5nh2}4J+B#asH#pDiM88Ug$c@!dbIjdap4w#;f@AjK z0L-0-M=DU2;l=A0?1VO!;D9L;JafoQnx`%QlX~iaXBEtU%DY8}YOcpfRts{j1=#!F zFF2Nxh2)NN6mamrk1{LkOgPrTKd9)I2lW;AHoUgDUbd6ezeHj)JH^2D^$_fl_)^oj z!Z-=nj#*I_`oNvms z6>!uLia}!zOM4j{onMB#K+9O>>tqOEszFp#OtdbkkE7u3lqUPMk-4KsJ_rRl zaHNhyAQaHG&6l6JTsYY)gjFLw)yC=P-A}j!X-)b@#dQf#&c!^n0<)^n!a192mME0G%vLCZwBoTWkH4yp@ZJZiiq(~S}`K{1yJ!}JM7ygi2g1sv$_FVnKbFP>hNy$}lN|Wy| zkc}|sUShRe?#WGroEqkXv&St*wjz3JhgL-{71a?IKR^-`8 zc3Rh&`dG;BKyqr_|Myhv{I2<}-jrg|WASKRQ8k=mHTk%>C-B&253|xbX#ev>REbFI zbL|&}U5)|<-z5-1<9MwXGxI}`@D}G-G3Z$?@3R2ikCgz3;(BVna*soz$YJg@2_0!O z{*R~<;cYZOfW||)(c2P_YW5bG!3OE2vK3?6p)0po4SzPBw~HAS<=f{4P$emQ?C!Kz z@E|(&dX3dl(J9a@!UiO-iB~5b2_oa&KJz)&AWXuwZ6;n!1GJS zKXFTUoXXg;ZegftPKC$qtj$e!97>{tziCCX8)B`%1hJFV{F)vhme-ZF8kppQ;5h1~ zu*RPGGxAAlTSO(E&!3d+NQzz!HOxegBeqFE<}Nhehe~I8BlNsqUU+@mbhlZ$?Xi1+ z2P?3HILl$acNMX2$>kDce8Bp)VH~*1NyO-tr5)|Htv;gKH0Re2RK~jQrC<{*1{Q1x zd39Y~fN=OzB^8cRNFu@`_I!gqkW7F|fn|gvVh6~p70q|keYjiRzAD!X>Ou^TT)wcw zE_ZDGx3%=sz0uMlc~7S)x~*t!hSOOE4#Q`3n-CPyL+MG)E-3;r|JOu2Q-vNs+@;e56z;BX4S8LeHH)x z&(sv2i;9+gnh1{QGtv*;E}#PdOjfEU0|GuJ&L(L8T_pc~)l z1_f=@%F3d1_%I_yZK%53`BU|y9XBAKeMdJ-Lq}&6;XX)ujSuP2&x5 zP+Zxx9-H6O?FVS8jMHCgKkxs7K2|zwO}0u#rX2c~h?e{bvV_AX-ffG$*Y#wkD6KIi z2j3HmNqOZ@zWD3PcLYZ4N4gp36KvxaZSrFwH<1LmDx_N?q+b1=qnyJ-yl(10WS=u@ zC#?;jokgeA$@UxXBNCTJ)~3!4|B(}=x0cWC7XIMs$!BE6C7e{g)j9u#Lt(1Mw5hX7 zEb^bY-9jB!`F*8{cZ*4^^_RqA&?384Jes}mOF3r+)VLEGd8@hgObyLQ@6r?@ujttj zsDb%S-t-F5eF&m9_-iw{+UELk6>^T1#s=HKXN3B$ygNnh5l&=mE(>LTZ6>rX^$>S& zfc0VDo6~VYha=S>4#!^1%t!bu@=xU7J86;)MP)xYB4@L{?Z#VT)y{Tv)qp2z)_I!Z zmb((6$Ni*4Ij9PUp*_xcfXVt+zq=ZhR=ypMYyrFmj^+~{9)7f0f0*S_brBd$T5y@}@Cd0qDr79ezj1vACHM2`AW0c!S@kVee!c&Vj2rO8S3%*^ z+uQqE?+?N80c5+Nja?>;68@!e^&ULgcHBPM;HzTm1yKnJ=IIU{+N0dqdssIp3^tx! z!BR~%eOzK{32q=JDl81+3b3|T6U>Tu)$;LW=Mnj$`hQ0TRWM(TmEwL|SR1+VKRHH4 zcKDKger0YsV#ZH)N%8CsyY>h#5$Blr_8RxOd%LSro&6{SXU~0mzPkz+2~6eP4y^znic&0TBfPGB>kyUMqXbux72v+BmVjwR-! zPES*^MiQ9nc3EwR0;xl%mCsP6?NJU>i#iFYj@S;tbQDBdL1^g}yoRab%69Cq)G9wX zL+aX($w0MT}gEKBu;&~ zILr+Ae{k*pGqdN9@arrsSO9FXXTc*wIwfS~HB(Y)S8#;hgU9b3R{GE>4lDk+6o&c+ zo-_p3D4QL{&j(2VsFs%6guI8vLx5r1x)7?ao6-Vd5E!n*ybf4tIQ<>)xL*2p`$_?N=ie)+j)}#{~@_z)Sf_ zC|=IM(@Z}9lY?ikJX_+yVuF`fX^x0WNu%L|?#F+AT6s@qPs}a<*+y&1{&1gUI`!k9 zN7XSm9P29V=?`D>q$4ScW#?}dpi>rgB-+qLg&*1v?c=qS{<5<-(3Xf#{&Xo)S0$l0 z>MMCX@LoQIuFEF!gdn_+&EEHmbk!!|$_I=gx)rN|5Z-l{(|$eZ`Hs_C43GEY)V3>j zQN_tlbuJfWEx%e;Ad5`-vDDPN+_XV2&NEV7nQq@TKHP1?O=+52Q_0KJL>vuS3)BKIUfp7Xt;Ln_j zAi8eN)5;W;q`C+1mLrBXjm*`|>5i4UhZJ_%|M~fAHm3Vql#Iza0imY{QEmH|mv173 z@g=t`{MAYEAp|9NnNUEQfAQR!@U3_8ZqiLB%^N85_KyN)|Gx0Jhj&!RNAge!OwNz$ z`hC2=MnV^B|A`lyRs6~{>iX|8qF?zhOVeRrYn|RbW+XE#oH=@sLTL3FEVtLgz3brb z4uB@I|408P7n%81pEFr56CoBf?kh%w1vW6se3N_eMApZQos4Ge~2l3 zQ{R4GzzFhJqJ8u=enfy{;+JIOs&?*^-g~HyDN5u~mw5y2xx;rKmY4T_2+1QH3BVpa z%@6-0u@6vfsdMzXEU1cK1RH$4+3Q1^)g-u#_W!8&`QoyT8KKWslT~Cp2?jDuv%dK~ z>Y|XC%EX{;%m6t2v3(v!9j*X9_^$N%^7YI3b3%t;0L-CF2&uOA6E6CAspP}0Y0Y$n znV~=^T^>K3{8DF6ML&LO^{mcz62eKRB%6m*8@frUUx z>0z1DzIN1Zko5emoF|PlUCcz=GD>Q<_Tz(Upk)tHc(!z+;P?z*(d>nv4&U=gcr5yy zS$YrpKFPf;c_qxZFqiPk>kDP@*?xq#j>K|8{#CQA#qdIODumE<1itWE&~Z2~=wer4 zYhVAu{er4br~wwk^ZPBs>+enAI_dhqqu?ksXwDyqsLE_#$6AuniViTktK|njWj>eP zm73a`K6igh9%Bb-7C+x{hZkGwdBDQL;zx8wn3>GtQ(3~!ON=*z%3$zjggkUhoTZc{ zH`W?veS3X2lD=`*C8wUrl4g>SVYP$OP1zgjMqUW(-z6?~kZ(8)7@g0vDoM(R8a6Y# zzNqq!Iz{Ov9K_P*ln*kMEh)qDN3HH56ADHRs=m6wV(dEp)k(mJ`@)SadkTp}V+R?M z8$*@H{GrZvL}VI8K00BKS&bYWmxIu*m2E~f7jD=Hy1Yl$94oyOTik2tqmBxT1U2_04P`hYS`%up}hyqoXJE8VYcYB z!gB|myuBdxb-iVu0nF~O^wD);M*Q}E(&+;xpD-cM8|f^aVlm;{Wfx9Dj8wATjArcA*}hsY zS9NyIo$|7$%m06y5{o4}j^hO|-#`Je9ja&}^hy}xPO&U32qXVd`Zl{u3Vh$F4ptW- z&@?@nG#y-EnT~|_1xM$svA((Yll6G!Q;xmnR6sdUND2WOuW4{RgBGyEcUaP$F>)6% zn5vK9H|f=IbjWgvmXtJa3yuTj{I&Q9T42UorzWTC#Z?{nhY^AwYKn=nM6kM@cd#_4 zJLMdniONZVxxoc_AF+ECa;)Mif}1TN#AUg87KrlJYNq42kR1yOwy5?bD1KkyW-nAd zN1cW)aIemJ-luuFat;ix$Q&ro^B=x(OJrzf$HDN^j%H(?;;wa#cq;wjHDY4@$J;JB z=&l#|W3v~8mn$zL;2V7VYDtoM_wE_8zJH0cy|o}u%R}5naZw5EddO--Lt#Ag^gp#5 z7PJ88)|J2{#s))3`waD~Nx7sMf|BYcQNI>^4~CecMsKV-18a*&c~9&HZg@^*1_IFo zL48FhL&rU6_Tl@o^hp!iE5`9Z3P6fe`lPdu*L%^HXYs>{sQ~71M4h(fRIvH4+N-hDru@ET%?|MhYyUOtIuK1>aj{zkJ$K26R55Iex- zE{r-uPGRgxULz|2Kp>B`i|e?);)2iu0cJtJiGzSZh`x?dH7qhnv%v+mdLPawEDq%! z2%71x8u1YeVW)X!;VuQwfj9vXcanbWi3|4V0{$~5fYe_6jF>%19S*B9;%__In+0L_ zsI=P;x6)QiCmj#@ymXlg6M8CWE^{{1$1}1_y$NQ=eF30ZWbUX4U-aQ?QPd1lnfr+Q z?+A4DZ2hl!s*`5Z7=L_;_v|>{o|KO^FGnT8b^dI*c&_mThQ?9f(4C3H=Vtxb`60g& zUbS?`Eq4bs-8bAenEPU$|7gj0(#g>9U}?}b(gVV(JoR`?E8s8Tuz>t2^4#l^An?i@ zRQztX#`!}mA$Bk9R1k6fmJ@PACSWD^!xAZn6P?4mwPbDqOeXzdaD6<(av(oG@Djy^ zY;{t!^h(Ii7W@su^fiicE+Z2YVS zS>Wk$U@*xz6Z%WW$yKXHk&dQH>8q6~`-)3QB0n8RUX35r?hYeHjo^yiG%KX7EOw)` zqTGZxN3&rV^gRz>-tc;-Djnqazyn6!cG)4Nc?EA*Bzvm4M~16KYd^~lLQZ5)e^HjM zR64fwb)^m_&FM&LB;l|#1#pOf#R=f_Yx8XKt+{}uq!i1_5>HXlX!FWK;cn^J%e8~) z4Dhf^X>LuAEe4mP`Cg9xA9M11KLr?N5o|E+vtD&DSpf-MNP#1L&u1I%443_zJ@}Gq zJN2vIgte~St2M>|XyKq&KpX2B~&f?XVE)l|G`GZ&XCcd|9T#c z|MfiQW3f4#1D&6fAmoEyL5Q>Oqlz`|2Ld}-phP(_7%v;&kbM;>XFDOY^nAY}-S+;K zj_M2){rvup3h3(b;`fs>Vkok^5OJH`FnV$FJ!yWZaSLc3U=qpGa=CIqGPcpD^=ljU z{7+PeH&$4WYyMM8P+qK~dX}Edk-WLU2H6)EPcCc+1fI{leoHmd99ToBZjk*!5@B2c z2m-Ezw6;(098~lc%^NQf%nI?_2c8Qd{S&l6kMV4LsEDK8;Y4)naaiFZ)9A?|WEPAM zr{cx!G!@ima$9?^!(`qjKxa~#vBTu)p)wuy*7EzOjoy>zer(uazFqdY20b&exreu& ziQiP1As6AkD4D=28&rkm@GY%4Aoea*YnQ)O{@%CyT8&y$>q0!t7Yx|K3Ebt%BqvSBBK}- z_{xV?8f78T7#-W#PcW-15hN1+75Fw4Dj=5lY^3_f1lJzV?~lXmU~EN{h5q^kek|yC zJ!33P-mc)EpSoUkKMoWC>b0{#s>83M4g%#*4wt+;4%ss5szaY_77l+hH|+bA9j8CC z;ctEOB*3LAjvLY5K~xAnodC4w@l-#TELb-*Eo}=bS8^#b@kw^v&#B?YC)ITv^k2Z3 z?N_xxr0$k7xXsux`*)t9t4s0sG2v=*t#qs$0QBRJoSV`5=bp@lb~d@x33>TNc~+T* zV6Rxhu*%UMu{{&XZFb-w@Ajm(V_zffE#F=xzSl9NI<_RU&8oKM55LoM7-t4`7f@SLe_P<93>)TMs_4nlX0pd3oJGg%+q)Ss{_+yFN zjI4g79>vn8HJjR}+xr_&orCZ{g}Cn=Y&VmpW+;I(m@0a@hZA8mbVM)k@&h1G7Rm4! zLT;^cjp?N;u1u2qhJ5#D#bn{7p$Zi9^up^SS(jfsFgwzL>6<8BEDOtcq}fdp>^L6991k1%y{!Hay=L^nFF!XfWV-nl5SqzG8sB1c zJ66lw4zPEtGr0se}bsf%zmRAfyc_;$f~5KrpkZqwm_-zj2mqI2#&U4NC)zu zM-rIvj))&DrX4&}YCJ?D#lNY7ZG$`1vsyZT-|F3m$BBu3y}dVSpJY!#3OYE2V8p$! zrhwObv25NXVARSN9@iwYToMl(x3e~ab35;#6htsoF5wcy#Vh;iU7^1^4 z$!Bp!$jA{-Ronkva`mcot@)XYl$`X4Clr?K|E~)0jM-Rzh-RM+euqFSXHiLZsT~}n z%sbo=$Y=yBJ1~Io?>|S3<5Sdr)I8rnooh2L6weA;FDqb-Qihb(ZY*w=pBd;0AQFpl z7m;?&J|1wTvo>Siz;gdcV(+7>q=#TEY4e#!UWegvII+riOmR@NiO7$2VW*gFw;v5!IW?_iiE0Sa{V7@OXS7*Pgr5r;>tJ zn9^MAWLo#B;IxcwuFdp6{;r!aNGo!aOC3M^Sp^R9wIPirFW8PFjWDlHFUMw}4_c&` z9~j}LRZNAAsq3%w2J4|n-Su}}LUOeV8$68(EU9MMl&;vW<)_>%^38XfXCYgRy3f-)rvA z{XM@wI>$MkIyLiNUeD`!U5^W~l?^nb<^IYgj6`QjzEoKeG?%~y&-} z^OvWqmrZ}bGhH|oh5yJ5W|jMwi0VVY)U=P12_FZ+dgSyMODP#o6$JZU1HWNykrPqj zB{+(f5{0R~efw*_5TH9%0T#vbY~s{h_6pKuKmpXQZf>R#2+Ni$`yDxxb8gfwLgd7Y%<@R4j=_ zv7S4%;#1>eF>nrx8vQ6gmXQCp^(T2wxJ8a?P=)tyZk+W5^(PqKlz?z1bV*6U^e*iQ ziiQ7<%yxL0r9Gs|q|FU9bhp~J%Fp$dU`o<8wO>$+V41+K5Ukc~t>-=3FTp6I8@P9 zk@z3ttQO51d+8nx`#EYHf8A77B-urXY0yB_WI77bnEZs)*OWVLw z6EH9TZ0C+H{$Z2u44Qe})x)|A_-PlV&O@>t;%wOw6!q`nF@lEEAy2e}QOJL!JK#_% z`p0zz9^O(p1zv(e@`uTh`On8P`w0W5!p-^SR(W#MOu7yqXIv3G#m0t3zwbP)xoTH- zR8)P8Q#gvM`7BZLZ2MOiAyfJB5+&J&J?Q3K*Rg*lRms%FzZ+ zdcZvcW5;oyYDakuekN9eB@{-gnGA9AF{x~y_P<_?q0s|urH?Zq4S6wltz6FUe2;(T z_RyG+_}Fg+9}jjm-^*`W3=Bpig?uMng^pvKO{r$?e2@EfM~iodUd^5gv7Nx0$X-C0 zJGWk8z`32db-D`e4ldWgwrjtX7&9Hl;!cJqUVFOsQT7Pjv8ZwQeygG=q|a6^lyo1b zJN4&n9*74ECUr{=)K~h*<^Nm6x5_0(RaoQ{3*V!YKU3FvRqx6OaM`^={T{4a zh~t5QLjwGwu4!xm_YI;v!^W+ZHn5%dRJv&jR#_P+C^El`eZ8RfFD0!tGH7oB0>`JqL?-^~|^v|4v0&5FvXPj*aRnz$~zzU3toPWtU z6bSln^s`YjR$YFqx&EIQ0rcRZXEk+N$)x=yt<;D$1P2?2u(0lH0F6KQ)Vm-So@RfZ z)?osi?Hsv3cE;n1JQlH&RYYXzNF&<)E+TwdCGp#~8!38ANhsba+a3A4NMoW-yj{v| zoOHG--5qE)?0c~Q4*w|F8n!!I3}xv^IXof!D$HMhY(__)op-Ftcl@D^*ACR$vkTDyFu1i*{hK8yL{t6gzx2=tC z6rmpjaG%$O=L>7~@&m2f&)>vzk6-EICAwGC0pfzg`#^dYp`YmMeDx)6B1^+`L5|5<*ma*%6?h? zd-i7q18t1`&sq9^&cev{o+IugC4jO8uTBkb_e{r3Q4HKJ)b>;P2PxoFs|jFA5s5>g zLk@Ppbv>=tWxH_gxjb>85a@uyf^^TIqArBRYXx{q-$R?hn=2vUEjYVK*CHnopN(-p z&wVG&G&}GbS4(i!3jXc3XbMevMB^b}-I;3DIqDZbrw487u}e>CR93A(za8}O#ly!+Pwcc@7~sM0oRJ#!x4`|d&K}K2ea%}yYe8ini5*B zAkw{2)H({r@0&QeL{Q_UV0ssk_gh?_4g0>}vE*;y=}Zyk(A^!eYU>7sB<^&5Q;s)E zVV67EV8ky{8#Vq+8~NOAqr#UMTKw}dWF`))PM6x8E4fMI>^1Sgur%bQUm?%_#-T1c z6H|Du;5*s!4S@tXPE@yH_acNr+IV@hFO5V^XBb}VGX!SGAls_4s zTE=?yC^A~`QYLe<{+5u<2?~5XyrwElAS$sZpQ{TI?t0>}Jwq3E4l`J#B!u1DI8_F+*&yO+`*^ob+|a3COOtv88cS z8Jpxh+jhTwWacpMZ4JknN6Q2IOm7BA=Qms`sC|qeZUTB@A`$>#?JD5d0M-Lz#9k4U z3GG)y(^U8eHHxrnw13nBX&v0q1ig);pX7i3_)!-cvI`A$|2r}ojhK~x%l#L#-O`iy zWFZ};5>+DEd-t4GihCv}`HZh)z{Ll55wL;&ox0#iqQ6ai9aSyDpGtSm34JGvj~4<8 zIp-VtZ_3Wxw0*(=`&-m%!`+=l#=Jvc=fZuPp9pV^YA(&|sKBiaqPj4%Ny4M#P2vO=4_{ z`XgtKgT01Ev%3Zr*$Njc;JRY3gnPEiOz%#F$W}#K5-L+K9{I_WdJrdMCu)=UQqHx# zIN=S6(db(ER`TS01ZJ77wAoM8nAmj9$_t#NexGLZoG4h*_^~=>(i(davg1sE$mZ=Q zq}3h3sVi^FQBb^SzFi5ox?R%#q(W5R@unE4TC__4t)@*<0%lTf+F;_3)9UkHDJbB` zPWj+u^vnVIlA=Koh};w^^BoVCMixVhPc%$Wks$VOqv!j?fQ0$X?US9=Z3k|E2mgRR z7%4-*Q%Av*ozIZUZ%wMuc`4>jmivATdQ}y6zE)$pM9M8I`qris;d)DJL-Vz3SN+eI z?=4ERjQq-ITzw%6;_m!MJGk8?SFPHkU8*0GT37BD7WTvL?||LfPwxmQl{swUWK7b= zdCoU0PmI9pf@1r>gN4hSt-xjac%?aQ+pVX=mD0eTFg780FE-bB^G&QCuFM=cs%Q%f1HW3BEwWn)I8q@lc0uxs&C)fLrsP+wol&tDNIuuc1lxnY|A$O`$M zOi?H%giWakn)_4x>jHow4E=Z&M=oz{s`{0{r<-0H_H@MaQoiuyOaP;ti#ML_?!;jg>F1f4)v9 zA3u=;@Nn_@Uy~O?%suhV7^&#VoSih1kJ-I}U;t0I#@bg{O0{cjy~p|9a=`M_8kvjH zg;{sEA+kMiK+XWuHNG6!iqLS-IHjxX&GYli4GWw&z%q78r_ECNDP)slssrbbzLYD&Dr5O!=IDvAwoyXn6aYZ>0#{tW!fTHaFsF z`}V*02_Q z(mF#|3tHz`bp*TydloGUDWRPb8@p~*bv_sMQ$)ESFJlKQV74L#es#E>GH-GQDi@v} zWYRS?O+SayRQr)o;>h0C>fYJ$Fqtg6RDIVNR1AdVX3jsPl*dZbLv2?&~p$mg( z^?}e0%-Y){e-8tNRz>4~#~jx+Z4cc`5B9SSv0X%=yh=Q9Zt6@q3zkw`8PTe3rnld8 z<=uC%`lV_b_=*|237!e~^4KYEg2J{1TieVgbIU8+Sknc>_}fxoAuoro3jy)ynSjbp zy~WnSv;J^*&tLtD&Z8?_$Ef;aC^AG1iMg324SzWO)#=cm$h=wqPKrK%IojbV5RAqw zq_D`65|;dnK|-H0P&G-HGDEyC364G(X-b$;YCl+=yb+&&Y~pmp#A%t4xuS^0q8%zY zliD!fz#_k;iF60X>j8)VO`fd7)7Cl?NsxDTX7^mqF8B-3^*9C4^k8*%U&^3K0 zNe?g}#nptmr4aBT&-&@J#QM)4DB-HO+5k;yxgjsxk8&kuBsC!C-S6QJ`S9Jc$Ny$R zboINRLTHbI+I!C&f*-*90$wY5Z09*;i&Cw(${=w@!h~%+90x-3#NVT(dVG=yNqc2C zVO!l%>&mAkea6@B_p26N*+y=Av_0sBq6TX&SFGE-wt8(P7LhVr(D}Wdh~!O5 z1T3We{8(L{@{vrPz?PhsX$Dr2wfw=7X{ zDJl8lAXF%q*kvUC6^)8o1(CiY0>Sk%G9TNBmcBIW$a7G^D) zi6~~xuK{g+RB+#kRce}}bd&Gj!GjvlUZZ9_G{X1N>~>z*O#lQ~HI>~OF!^@{mPoz& zM*rLRk2&l$=q+{HC62bt+SF{umw)(vHWR`Wa$P%??hfCUsf~1BVU`%gfzLl z2`c%m<~QH)GBpDzTXrd7=ciSGMVQ*LAmzTF>)@YQfnQ;WdMX~Tp8}-XorR&RQ^Aq$ z!yIigQGIen)?EVEUtVgMpT-8w?}hRvhDxoMNwMQG;`qhR^;|Rs0Bgy)>kpwAhru_` zz;^&2x<6Mr#x>oA4QH2MZ7rjlwmrbWFT&^VTw9L+)gyX(Q66vc-e09_>h1Mkmm=Y8EPqu_y%+b(9Hr@c67F+3xf=4gn28moYc?#!=TcI= zlz?7h7XSFZ=ls`|!^45iYoujbLD5N7W6q%U8dpA9?3xc+P*44kC~aU$0;ng{WO#&PU?_#~HM{M{Mi@y=#bugIU=P=A%{Sud<^po!mYyLPRcgK z#H?yv3hra^I*t9WC6^cd)U@n4gG^S#lWgfquqgM;`$N})nuX%de*o@HWVHkm?yx}X zcQ>H^n?El|s?~fA``y5DLNMCH<~s&{X^*SxcT6mNNG1ISL1~`v(1cJW9Wl{Zbug2l z(y@u~%u-_jt3C{XLM24Cw1lsqp)h@%g~5A|y@z%pYgi+8CG+>$?wK?W!J;zF8{zRn zV%_Bu4;~>l;y{b`<5%}@X@I~5tY`N8OJ_kW@=?uoqTSN{pXI*RNgA+TMqZrnc52?$ zJWU0{MB@?v(R>x@FaGGjBX;=@VBud_vPWZgM||WPNG#jf|49nW zAjC;tQJ)Ru%MPDW4=Uh!0yfy-W~V}5=Yyc_pj|)=6^r;wgmS1YDBhm`o>etB2JFGr zG{+0PxyU4C0Ch3Dgq*ftpw$7*B_H76S!gM9zf^b~12?3)@_?|fv2lGxg{gs3F^Y1- zmOX?b!qt!8At-o8LT->^EtpI0+cP5w4Kq+2iJOCa>e#dtJDMMEXb$qVFSeXhN!6L+s&F)OQ-G7Rg! z#GsRj0XE0I2Hds=_KWMCmb9&$q@X7Ux1L}Ss({=lPU43;MA!arcVQY zY*48x{}aK5!q#&1p?Nn(I==jRU7M7%4N=unx=cC&REa;ot{>+-WDe1;$!Jc=5D0m2d-%`1jp33n6%Q!7RV zy>Uy-dnF3(0SX46PNV>AdHj$U5)3VJiC(!K>PTfxU?b5cKS=)+b#-b51giis&%$bI zx9K8#@aweOkVsI?z+BN>?%uoP6zK0yyV{6~@-U~GpR-3Bke#|{3u1@=XxqEt*M7FO zxA7Hk*{RJ(6L8UY4ePHp$AH9muDyz7P=Pct-b-1{ORCdh+}m&Wmzis zvU`2sR|E`Zgi4yoMy}^lghy2mCijoqjKBZz0qNw46R{E&_O0XMAfZWmIyX(9^Y6^B zKOIK^S60F_=ro>JZIO{|Dwj@q^uVP)_{EhEpMYZe`FqhU{?ovD?e1-jMU)@}6LTF& zZBf2Y&#A`>CzSv6dwNxfqJji&h_rZJHWpgVbZ29B!4Pe>7g8yJs3-nUheT-IklgK# zCso3m;ftur)rqMyZ^8u!A4`6#klz1MCSLf#{o!-&KcrgZ)z`+q)&2^g6Ng!J>@SGU zJ=APK*tjOQwwm6SMM-(KD3nP<bkg2@hF&fW zux+`!I)u0ewtkiqibm|CJ*74&(l?V*%$as9a5c`P?>fDQ#*$B=|1%Q=jlL$v)`Hc@ zA~0<|Oykkc63PMS(l6I4Pjg&dFTO#z1M^*WA!syZvCYhGIbCS;XvC+AnH<{zm`eZO zO1kndTQJM&{+)-dCI>WjyPfxUepNTLQSl3Ttib>VM1AKl&RuS%Y8xkY&DYZ*gCqqD zUC82tr5X+T)Z!BVHxzAxII!`UsneFZjxWCV;?cumuQT$Z&plg|9qE#P|LU5J4QP8q zjM>C^UVij&^2PDYFnLWz#&M<9W&KSio-8z_Cz=nwuqJ&Hssc7PE=QCzSZ*4e$wb|Z zb%!oQmvLF!#_b0(kL_)BdsDbq19DiPC!BEd`@sFyN~06R9G=R>Xl6@g?j*B#H52#F zXIp-eng*|2Z3-jdLh2va)7rB}a;gtMb8+O_yih`ZjqZ|U>P-{AOkbigUQ?&{oCRn- zYH^lgth8+7N|tTMhgI%ym@CA>->RUDOC^5c^}mPteQ{wq*`1^vz<7iB4CU|^68&R1 z)Sh|ay1^}p>o7+ztAUvi?NJ4F!Jy0^KD?)kg&SN8B=xO0xY|`2{PwJPqHji?Uc`Vb zw#|DlzklQvRz1&tv8CZ3pHSc6z1UqvUClLEa`1s&H95BB819NUwB%NO?Uz^e?(b2c zlb$9kq8!Wy7;VP$AH48d*olCyv%H}CYUl>4>$66Xp8}m(ZnfH&WO}KpskLv1GZ1u& z^{tm)WQY!@pJgD_t=54}_*yNRbjx~XybH*Ad>K}ND&M@P#J5|Piv(UfARbTJf%yreFeV`5J8AH;2U1O^m zK)L*`5{@$vjB#KK&UqSKR+p^4IZJFn{#MDbX(q0TUk#?%|De=w^Bo)CAKRA6QBh@m z06;R(pQ>?+g4VXb77hT$rE{-^{;~h}!L#$9MJp&4XsjfpaKp%34{o(|IABlM5T~)q zc4ME?ARPn5WJ>p&x6gZv73N>QIkt>Pdv$NxA58tRymzk6_$IrUP^My>Td$14l#Pu` ziTecy79*X--%w-W?I-Nmh9a_hwqa`xy9TjP97MCUl+pXb3@5;@9x+-xIP-|@8{Jx1 zk_tl*vm>D)uD}P%4zTGKI1L`*_0|)+GQQl9Lnf`(!7Aw3dY=ZaK?TuB;)ZoTSmxFP zQcB3Txqy{f-40P$$Hoa<*_sVAEXtMvz+6yd?#xJc?!@UdkEL3^Xm`0QHKJr}lN}d`bSwgFUos{exo{0P#*y zF^A3^-2Ae@QNhin%D*kkX3-a z#5u)}N3TSvNYd|_N_&_)PbQlEru@&fXcXFK+@5pw$61BzSNX<;w`>xv2ny7&c@j00 zK1ZEYcfj}&F*gtB)>PK~v4i1Kn>_O{^M;Ka%98G|Y{Xguv!fA2RXk~vys=(X-^`qu zg3Y+PYpG5#s|~?hy5TXH3)S5+jmPbHx{swED{(hh-_GI;Hn~}a_2Qms6PoBSgO6{> zNdH^}U`D)ne70^1I6R=qATH#Fv+OSblpd&(rUnT$^*Pv<;>VbXmo>K!U9CjdawrAW;L!H@jbSd&=a9Xb zSHkghC-QVtRyR8YsRL}?rM#^&M=OZafje2aW2`E4kB3cX^^Kggy>yqZ- z!X?dJ@bm5rJBG9zb@}^6lh=8w$OOu1bEQCn_1AEVbbzX-oCZ!;`gJ3`Bf{tVj6(gmiqY%V zuf~1!)eu|B1r8nbJS-m~jt!}}c*Qs@BvGKC&FWdI%m9x0|JDldA5QYGbd#urtb;z; zzii>-8-QjpYh*0*%??bUjwB^2Iw@>LF;5HX;s;3NRVvKJwX+X^kEusQK-JYc`K|Pz z6DnP$k}iV&-I%_`c@s0Ja_Eo~2^3Zk0+(O>B!9v8E~|%)w?w?ie=o=IvE)_VcPp86 zZrRy)`l`~WlbW9k1aBC6!3e@gr{~-tu^`T{Exj?V(uV*o#B8`TrW`hsJ+CcvP4CfY z`xxIeFUVhh*#B{-i5a>stEL$Y9fgN=hq_K*SfMZEfRVtTnl}E7;0f)_sgmj|5u!UV z+;$>Wrha{LcAj*@iAN8pCgKL1wdFcC1o6%kDZG55{x`4MbgcVsX5hD1z}UotDAeNHDD9~W4*&aBP?Lek7Ro)CA^xq!t%vYlv5$; z!0A8;?Ah$lA5)UPz#?|W_u#R}YtSbu#hD10Jan{*`Q-Mu8-rUK`6}8Hy6IlN)<4`yt(1 zY4T4L*Tm}TD=H;xh6+6Tci#VRdT{2q5 z8^!W<%?f8nz&r}MMKH!M5>D=K`iW{FM^yduUnhmg2d6u(Y1o z2a5n_DwyvklS6dJ7;)1QHAt3q!j z&AC5*-wcL-T>92XrDMlhnhS)-ZrufDT;ID2aB%myjW7jQ8Tah!3ilHhLNO#p zXVX3mBNn~fSfoGk(o*IPSiRZY@Bf+Lkux8<6LCF7N(NCimR3yHf2;PRFF9>htK zuRFjmkdMhRB+N<*(_MOYSv1b;hB@&O$thk#53rdb@>7&4$9b4}evz7)L4Mo%2x9Kh zW?uUAfuLgs^QKP7sXbogE|puQ1@4)hRIOch0Iv65T^hOyKv%M>)IPC;U*3OzNq0(# z_kQ>)`b+L`~ z-aTpATVx~mG&6Gt-b*1?S4vpNw?Rgxki%x25XhUL;!v+CvO4ys zV$5%8_@C|Bt5z(rbr{P-9 z?j`G#<@{XxTc}Ci%GJOZ+_yKfQSQI@BiJ|@d4bFp^;Cu4+jnei{h(oaYa*SLRQ+diruZ>wIpBHdCg&ECBQ#61NgPn_s5*KSjAR6f4K zsWGSM#^^eRW6cA?HZOv(EXD)Jwfl}-?v@Y11qb=A6_s?lm$xbCr-(EerlPD& zszteRtSt(1iNAN2TbBd7l4Kc}>Xfy75k5Yv?c*C3ta|(#{72-Y zbxe$}M*wbC>}V>>JCytCHty(y`>t&)03Ric`=HHOh7S<|aO^(S&}#uC|sO#B$_a25+#CNz1;HsN9;Aa8Q- z&FozBbtyJYo;?pd@U%@5$L~+7G2v%Zz~@e2z*nkhglRAbZ@)B)SAgknWcM)pjB?ea znj3$+QY11kcgpyWREd@*DqoHgJ0Q`K_EHs#?{%wK9kmdDXIQV(nAu) z$1@AHOm~p5-6kN?B2XsnA-nQN$0wjtP}*-sKrGpY$l^G-OyQJg`1~fJT2plcrTX@! z3Tu-Qp-%b!hSJZ$4-#ffu8v-}VDr1Z@>&NgLGl4n0@?YZX=W;D=!S*G=X;;@paZ+s zFaA{zF^Ji+c@-wzj^c{m-*|jC9#c$LK!e+Q<@Zj; z5a5BhTZLCIq@IO_m@oCX5~sr7T}<-cYIow0OrG&1VH>omm3YP09vZv>C(}3-ym-;YZw1TshW?cH_1dAnt!sH!nu7E< zhA||H6Rzxxt>mL8pTWY%$!aE~Ea;ETO%h@dgOKr7**sbD&ZPoYv;=@l=rkex+5DXA zbc7m2-B7u{ISi9XvJC#WLAGa3!_M~V0p zES%-9m*O+{l&Xv`&!{`kZ4v;swF%JR-#p)x%HyfeL|?rfH;?@sBr||@AM_%)_mL-9 z=2`X9tXox(a!5Dj0Ef#6!z?nd91?7(2;0)1wwm5T{LimTs+NBQdN-;XIxQ|V za*p>N0=i*CL8{+3TmjOeP47%%D z5H=^q!P~uD3RlLWuKzw49FkCt`{-Se=*99(o56f#d8PIfITKsL5{?r% zm#OqRS`h$IY^?K@ke5QzO<$h^{?=Ii2}NM!_4loR)#eztcAI{?-S=j3!hI5mym5#v zWVQ$T-%RJDx5AV5_ zpU2#i`WW}QaT;J6^B>nhHu6xXF&7}3Jl^inQuXsr7u&ZS1op`Q!|^Tfpc3-%<8dDj zq}W~!m^R~D07)h#{S<-O?}4?WXOoc2atw@!t8ogZ0T`pMo+9g^wFdt{epZ9x;ztV? zn(hz-o?qx#e`YEXU!t=KooMOVKHmxILG@S@W|mzw8ku}Kz%7`yoDR!m-P1%lUqN27 zQC8j07Z;AZv~)I`oOo!bkT_>LbES%TfxR8Ao6P{l`ez1K?>T` znNZt?a!1pAd4@hnWWfx?o=lk|BZ@fykEU`6+$k}q$rsN+PvOL5lxm1=dF=FG^2_O1 zvdV3VAs1D(y}M6?PWRH8NzTaG*H_jxE~_n~nl?0m!c{31tu17tYB4%mlK6XnWxV)` z9(exPBnio)4?Gbz(@*%iPG-zVlxOGv_}EELP>^R}SCq`eqGSw_C-YjwOnUyQ$P?+l zg2{3!4@iK>AzvF};3V;4P)LaSeaEa^O(pCf@j~S`j!Om0M=~MjaR}gWeem-AEl&J7 zl#}@*iIB{cm6cM2g)w_AwyT0aENN@fT&PAM5%fMZoi4BY7C2%xQGY{$ugG$*M^u2y z4dXlUA2%70#!sc`a#ADqcyanpS1^WvM|LjW5g|KqUiu8|N8cM zhNwET;8Z`*LA7osxECqTVA(4Bd<2vyZJelKPvGB>FD0v;{v?7$!FH={aCaaJplrZN zATd8Bj=dt;w5_!|VEH8f+Iu3~Z&oWdmcN{_~LQmd0 z+sPZmxPsX7$KT^m;CwBmyJfkcx81q9%FT&W z*9qKql1e<$ps;GSo@*A0hCszi_mkx|g{*8q4Fq#yPS?QWT_ zTW4LS8D@}N*zc^3Q5CZM>~mTbJ{|fU?RqC=+9db=#S7`nOY7mh3X*&1(NWiV+@QL4 zX!eQHKXm`yx<7*2sq=G8yqR0U2XRBdPSG`Gi(y4>OdT(QtCug!Pxm+pZHHtjPf1@i z!uiIlKwzqZXRaOWWXP@lE|D9i6)gVxMv9Bq@`>ub=GAYii}Mp#u-L6M=$>7|ql6IN=7x6_$# zWFMmAvmIEz+d9=hu(#TK``6>*m&9-kNg4@hvZ;yA!THzQAG!pMO*be!ZP7Ja7_J=U zI0bZwboZ&yG2y<>3d3JjZ*0epogmTdZ{`Kie;9u0qK4k|#pa5uzVna!{;BCBO|;j$ zmu&q5_*rk855X;A0|}VYPnkf0J_)HE%Iw&l7@u7TzVYyW;@Ut+9gqjA7|rN@e4B@M zb~4J*ZdpjK@%PbW)d092PcRq`NHy(M8%~u{C-GN)?=x+r*=sZeSpRX1k1CU4H7!bl zHp&vK6>F1fJpyu5DdmwSG+=eO8lI0<5c6sSH6U{XTHxhx#BPYHZcBx_c&w8er z5^3!VZ<1%NHj%O4P}gl*#cb|;`m}?(rVc~c`kEe(7G&(V%JI|hd-X+#w%CX9Y)dsa ziMIDFQY!>GyOZ(D%Zw1+hNmDQ=n_NU2a z0Ph-N`{jX*yq({8&B{za$>ltTI)p_9AtejJ?Bd$3>6q$L3BQp!muArxO1Y*R8!4~~ zuA6p4W(>v|1{o`>+HcGaS2DH$mrnkzwv7U(vI@fLx zgHAn@hLLQ~tFBtE2F}K8ccjUB^j7iCiR^pw=nZ3>ptr( z%l1pp&w-%R%MM=2!uF3GE2ri`69jRqYxL|&)N&v`u(q&7@|nQ5QQiU&S|0Uc)p^(d zeToUP0pqvR1poo>!$|wFxVZ^a(Shta@qNJ;K^mXz0{E_!7P>hJuHP!ie#DEr@jXd# zsn4;JzxR<(LHRRK?J5y1r;ld+7xoe;JsaVmaY5=zPY^3_zAHBk=C4n_nT(Dy!&Doa zqCvcr>mB^GW?w4-bbNjYm-o(N?=IgX?!?4^oC$!FVmQ&BJg`3dm39nzfcXdIE|q%9KoaBL zzUFdO^(BH=wE|el&Lr@cbr1 zJo|WXWwO~%A@NUOm%18Gv2JZ4x$xlX=>pCv4}>cKN2EdtHk&?Q;pIZ13WE(XF8>uM zKq5z%c-cOjsFF$$r)Q0_&+>|EX2JP_SU%1dSk5ie+x*Ie>ATw-vs+by(NX^0yWc@F zJExYswU?OCUk*NFkOFn&Rs$A^(Mg&Y^ZUV2gGpo7L7xI6!-lqz?E?%fTbRb-=G%Dy zzB2NVJj)){>I+Y4d-kXdlvk`9U12!s2dJ_s7OQnkfW0uI$Cm?cGmPLyj+g9qFPSp3f^r;; zMM59H;!amR_uGmyAAWw1uMjnD&vFtC{iRv_u^gPuG=@{2qrO%4T^iJ>R6q^;XzjkU z+CH#91#l9GPRMC|9{KSG9I~`_Ca?BMUD9_dRp>HX;zZPa~Nirnh<`3kWvA}sTL zd$HTxz2uADZ}UG7dYYGO;D&2-6=64kt;)ra`_D~>?~_liy}pUK)*>JukTWYJNEa3W zo1dP~v#w=0lz;Q7UfPHt5a&cIp>O4x-M1l@@n)fNI@y_-nPf)1Ua@JdCL8o^y}5}VNfAZ{y+{_duz;;l`Q{H4 zY)Eg;FqxSZNf=1dssaLzNv?*VnniAqT|WX4xP|roCbxdbF-)nTK)KsH(VEkz_uS>f zzlw@Zh$c>}oP)w&66h1}kAjU_#e-6?==Z2Dxk*saAv7a+pXEILls0_s)o>t;g)q_JTb8G)3PD0BCH7h z7+^1auT|FAle>aHi+KF+M)%MC4;=k%$viv1lK=dl zmjEY7FSPFHS0$5@0jd#B=6%~vek}RV?>*$!lihol7>xYcLUc8H5+cTVR-nN@RZLeU zz)J)q`|-l6(>i?$@OV)<7{&_I?GVlY>kXF$^(P8=OJUbHcWRMQ^VkpW(P*-Us%2_x z?@X8Z^vtnJF+KNDg^XxT=^KsB?+3R|5ah-)dyXvQn%$efDKIReL}U%Xj6`JA0FOM4 zdkJk({*>kfZs5X^m4=8OwO*b2^$vGml8uZ9rSKU#cf0BB9^d8Al~&h6=EM37b|D_jG@y9H=;=m7Y=?DoRm;aKU0nZYpT)Ym=< zux__X@5M;3RjE#SGfRj#n;NCQ`#ZCzECuv9x`KsvuO4kW2&|CZLvJZAXASJNwLW#= zcKxG09z?aIKe#QCY%sMeXeJ3v!C&9K0v8eRZxR`;acy+Q@?=I4s zF?m*B1mGx^XFg@v>U{&7*8#O`m9u*+1Ob9jnSZM_B*uH}ChbRUScj#dXS* zqrpPEsoY4499Z3p0l7cg?GNvkPZ$weAq49COB9?xV5a>H5XbK^I&fQOB)MOp)#m|* z%!c{38T*&vO@U&LtGzA>^EONUcOSFq0XA>_{I7%=R~SXdY@SRfP%EfY8cZ@L0l+hk`UP!W0y;GV`JF zwCMmM`|r(m6{K6VE(vo0jqmea06nFq+zup8j5F2o#|>aDv@O`RDryHxD&!anG;A2PSC<(x#6{2Va_AZ-&%2>?Iqa+hchzpI-Z`gg@s?f8kfG(B zq+h3y9(~i^>Q!-(AwDe}vs3i@A}K>pMb$+UO>dHdf<;>|4&pDjN!x$wHU)Dn7 zlsj1KREd*V)qK`Lp?>SiKf_|EZ6_h$vcLXP0xY&CT$(Kb_l*~_rv2GESa?u0-8k{FOP2W1DibY9J>qLwGs zx?WG48YEL2eR?+?`Oij2qcs z&!%#7MziYWg+1e@<<>L8HxN9>&~QL(+^+6aI1kPQ`Q+G?Hcd1TeiqAUI3ifSsE)~< zNOzwM<})zp`^lOI{^LAQ*j#vS-S%kcdn7Emj9v4@Pjxh1AR<1JJ0dlZmr$$|SSDY#=|LsH zmzvf>V?}{uU_OKgO}s6kKNZZ@m|;DSCfh&cbhd(dkwSB?gSLti^l`engwYxRBvfNU zK!l#kpxgsey zx$vRplRtvK<=2UdQh1kSsWa0V;t(?V(jX>kez)JE4OpXR=?Uck+~FYhbul`UpGM>F zQYepR$dgaq!`r~KWXrvOEH$-8ia!2|7#`H(wrrf_!8I}8XcmuHrmPAZx0a9m&Dd*b z3CzA)3V)!4rm$Z)1G6zpKWCBt%*+yV@>1C9C9TfA4%guIvD2=&LO$FFXx>SOa2@&v zsoO^Xe-+`xzl)NSV#s`nYGb1QhW@4k1Fr-Sa6#kN&9#?rL>i@yT3RviJv6S|C=+vS zUM2VDM8`jvMD~cK#=pu;OkH!Qa?||aebUZWthC3G8&7`zDNRTt+?Yq4*oR$Gj@Gun z&>n>xQPGA73Y()>mdSStbcg|)9?zf83caA#aEfn}`&pdRAYSBloTOQu z_naFbXl!Gzp6`gHIE*xJT}4iJ`F<9iY_H^N3tE=}3=RAv$!8ah=_>-6`pVtG=mvJJ z1-Es}Gt#Y$Z?9SyKGj|CY zs@x1`enVGhm76-J>c?$adf(@p?bWLmp>8PD+tZK#FW) z@(ujO19<&^*!ncezFeJ^ck5bqRjM@lm4iBMCVGp4)Cqf|Dxd ziMNPbZz8y&(!`6uz{$4?Z#B$MhVq#iq+MW*dMAE)z5D~EZDwfz0oEvV8l1j~Xfn|K z)45W=?U(n=?@Mv@R+)+d)#IL`uGKPq6sm9;<2<>~r_b?Ihw>0$1(rs4fjJ>KE9JEa zWMNTo5fp!1di|VrQCyh*Bsd*)d?$ufxvoc*O;J1Gu=%B7do&~Ti#Pk*Pedo>w z-qN2f{Q=>bcWPQEAt56SqNS-xc(}f}5GqkRojCeRhYpU&JlS!K&`BYV8L~%Fhfa0M=$MggCk{?ohs=y* zB|CM$^}B!f?NN`1zpV2<@6UBzuh;c_op~(rFXw!#QFhzte1M)n=hnJzu`Wfbtjd?i z^i7lMM`buW)j5rRUQwSBu7eFNIBD4bjzd!Ns<|au%JCJ;ETaqWEF;%!msU&EJP7Uk zWfpdUDmtC>FFodeW2hhnEOi-E&-=GF$*GU8X4E1O3k+YySitl8wfnjH6eHf4c&D^^ zEte@94F!H`^p5}F zFz{or<2*w&c6m^;07TyGBg+*H4GwcL87JG!uWK z8`jEPb`-BQxrc2|T+n(YV8KsjjJ^ODEpR*8kkXpbO792)Z-rw-`upF0Vj0FHlxhSi z3dcpGbGp2V=-Yz*@XbN@FHuVPbGyTt^WoU9b8{;Pe+&iu(jggXel!^TyjEN;@qM5O zj|rE{?pwQ*@fdPU+Y4R4#(^I8=X1%nDz(IihiyQS+i#*xk`>4un^Axh7#3TABv)3P z$!_vi=@pnXr^2c+dWYj7U4y(Al9YRwzCK&hwie0o)zBm+o{f3)TjTzUrY6xM99!K^ zI%7!%hr)RB$^e_?L3Ni63}v>A`KW1{BJ9%WT%rF#8BUO3#wVnR6_kd3W!abHwKzNb zKgS_=ulp$YbN&&CuZTTW03M?1rz@d=d*etm)d*%%`7ev_NT@l(yu9TQw5cdyQ_cULPhE_ChSu?+&0w4Fu11w8-xO@yTKy8(QyE1)S^T2blf2>az};S?K<U)T&#Ux0+iZ5GqA!r=6rmj-jq_W&t<*pL_R>6;eP=e0WXGXt;1 z8@#nlKYzdA>2gfV9KoteHe<89rJejJu~MS<_V;?1N06Md@k|%xt6$VQX<$J{F)BG% z!%T_69k?HX>YAvTO#+;pf)WDP1F=JyGJ-=MQ5r`#BjZ-*xK^V9&-ik?rS{Uc&@>+1 zIIuR15fQ<5dNjZ{qW)fe`kLVVTe&_s-B2#vy>AOFYW$F9SCYg@+!uSI*Nt-@vm-9+ zcinu)V`WT@GTl9QU?>|a!eqG|84avlAa)=uIY7sxcD&IG0BY|mL#9^S0x6pTQN=48 zTwtwjRy<~L$>fTlDGU?q>rh)1ioXsY=75q7u)${xe`yY9-gAm`AKkrsXVTb z(c-H*%K&iunPhz|S0OwKt1vbP{;;C6tCe>!C2r=A{8SX!hBsA`_5!z>!T~gLOiljG zQM;(0{3-O)QRBdjQ@;#5=hqljKanQtpLVq(g;F12NRcA54JLp1Yv3@>@r!piubshBTO1`QP}{Kk!8@OZ}!dtMB615l@+_0*MJ{ zG_I@a;)#*1rp1eM_Pj=B6%$iJV|A*^9GdZZx;%6iWozx3D&7twq$`393{8nrH)hXI z>^Ho>6Y-oE?F0^`@9@z=Yj~0Z8jJe-mk{-)DtPBT{mK1aWhMp{E~dwVIR-5sjL*rH zR0`9ZCik3`V$G9LvA(G4)uxaq$S9fUV(R=c{*jl*Op>Yc6LGo=kmCqDM*F1Eu;Hy9daT9Gnw<1=vS>}CYzGlkwBBU)Sk_bD^<5qTnU{IV z$trn}Z3Sj$Z`Hc;uG#n=m7NoAhAcDAmxURV;^=vWy@<84=jvy5>#LhRYT<9P= zehh35&~4cW+n-Zuw_M86dOCl z%Mi}d1-U9A7s8f779l89z7jM4*+dCl4L8rK$kJS+J^mY{xuVh>Fphm`T>T`UAMei7 zuF`bjdc7mJv{zg#dhA~Ha)37>4*xyI`W|{zr!qJ6O{u(k8HMqixBo#!wm+>eVR;Yi zj|egq<_{^$zQpNLyp~vV@BBGN-tgM4k8#hf^eZPYA!vuAP41^YY%`4@O@tpZRLPd0 zy&P(H+cf%v>3dbJVOYM2w_TeBC0%btPLa2~9yJ_FcbCVR7k!&Url4;GW}h9Nh~#H| ztiN^1lgbgnPaCIFsX;xz==`r-xk7(0gD zUtgy!Nth2x_?7t56oYQ3#=AK12Nt~g6g*LM`-1SvQ@u8NvN2dr;1Q3t$gXRw>)s#B zseHBFu6$7d`Btqk{o9^?v_O5-lr}2nB1$7s;-AYB z4-U3`Cdd-Nzi7%yCQ1c~n<; zQR(u2G~-!{YRYVyrzo{M2H;p6L#O9fJRGoXpE0dox*bSvwWqg)NwaBqS#y!uI|rT! zFxf!zCJhg%*s-h*vw7cr#Rr$XVlxPlN_5R5bO;J+0vGftuV;rlxp*$GypUd^k9qp~ zLrEB_^Q{k|(pbaNveV!*M|hFVJjy_4G$im)oUrs{Es(=I&-BqkA{}0@EI!!w47Snp zO|KsKD1$4lZ~yxBYn)-u#Gq*5dKSi61^o8iiH$r~#S+n>{`px615FD+@2V1e0(6k@ za~72{mrFD|0(~MW?-jMm(OD{N^_D}@ijI(^oAwY(y_OItQ&F=8=MVh0Tw$0F#lR_+ zAXi@SDe>D+v2_`l_S zNny7aiQTpK8^1lA{r?WY?nBoq;n^o@xKYKkW9sH(COW!b=+?4b1n5Mur!>uUJfmu& z0!3yO_@vlCE^SoQnS-3V5l=>BmQe4Ru|N@(LMwrVL@z*>IWK%lFttNoQUpYn6E1sH z2~{oek}JL3`37LTCISvBB1wYm4>gS4V$nzt9$AE`V99cKJqnmo7JPHOAJ%ZtMDl&n zLq{VC#Qq{O9x+t00>G!5muFiYPkJhM*@Fs~%2s18C!r+?8Dy@Of_S!*G~ly7y!rN zqyw=CYAgLwLnMVNW$=>DK%UFVyP20fZ-=p8)o|5Z4_z1*1rGm9>ETUFUbVsNA5`I$ z(1p`ioWzoDR@bdpEBxFqO2k|*`gt7i03a3*ag89Onm3ZxX6a+a}`?b#TBOrgXPp6FL{g23K2vJ0XfLg-N?d~7P%gGN2ezdxhEN_1|Y zhh>?K<**T0rVcM&$aL=x&~yuXz>Xac)t7qUqyC&I_gQZkOiv-!F(6wU zKl%--p+>j^)9^>x#p4_7WC`_aAbmGv)Y5CQe)PnWLUl=dp1}6Y<-wHs*(Alm)T~1D1vA4-|IXY@=-L)TgJHIvs#2bfVC7ndxq3VZuV^`Fb6Beelz}hE*R_hzSXuE+b2|ajHGC7J7hXHsahh)KCWB&f zK<^DtTdWxQwz{-UTjSy&;iDyaQG!j5-RMSLYGNgXtXfoC+Z}cd%9`qKZorFCIb8}e zzlkZ=H34E%1yk546JW=ZL$f~HvMFI z%FO>j9#^Tu{3avFmZjxeccxfUS0XnR_8)>#oWVsT!wn!TZu>>8WbuUi0z!_Z6!ln; zj-o4NL+K@sK-|y^9p3wUZ9XWQU4%~iw3HjNV^dd`;vmcxEJ%~6aHs`N6l*oM=pEB+ z7ZEy{@Dx#?;ecs?-B*mcu3rN1^gGn;0js&<3;PccI z6lR32_Dyum&(FYGReRg@&jVv7%p6U2Dw%&y2JdCvVS_C9N?mE-o|o}Z%?0K&6Xfl1Z^NvGu$0!Y5x%Oc2u7M}LPn-0 zJTTi5^(H?Ks8y#^LK0fbx}gP~ZZHn-h=R{4zFg9>RmuQn-1j>94eeN8@;!naARsvL zT1wkMX{HFoPsr13Pfe|PFnbJlSH6m0N{UM~K08xA~05c6_^X_5Ogv?Z;Q)5W3w0F(DjhQQ=& zfiQWNV1>*QTs?+(Ke%Svk_5qleF|i{H6oe7_c_p{=($W>t21nMydffu56ej20<#R6 zoS9nDF1sPr<8x>cmj=1WOE&`n z#3L{rA;^KDIa-)d@8UamX7rt~sfqz-+I%}P>dT?i!jD3x+Po7fO8O z2>aqbp=FIQ>+^Z+4q!`1vJp|1%qnj0hJBGg<7CHSo)=<+c4ssnJ3NRZ&W4|K$#l-0 zM-+zQN|c4!K^>3ee7Y=*cM&6VBTyN5AwmgYqI>;2F(T zf0m3$ulmhutAnp-e*Sh%o9dOF{U+;6gGmrLKuJL5yEgA@lH+GVv|M*p$?4xhyX;Ze zP;nBZrE=n>wsb7#r~7FTbACp~npr^F`?#a$=(+b!DF%SSXEbkWimd89yk_D86H(2z zOnApI9GK0Yr+X4?O%`xrRg75%+8O%YPrTuFt=Vx+Z#!Uc0$^O!8lLm*6;?7yh5dmp z#e7q-^gRGjYl1yPkzpg+tlyQ49A|Cd$C}+r_VX~!<9sM?E;7AGpLymy6TopNNDey3 zLA%*Iye(3TS87=X#JW|{+!+GmKXrx8Y~4gCog0zRgzx4s2xz0{rKji zyRrp7`E{(2J~SvO5y=aNiFm(z4B<}yT|@aT|BaeWFyTid5CB;Q0z#_`F4P2`8=T@T zllIr~jpbxxUR6(r02uux?kbs&${a%{%UMowwx6gHgTT+5mik&h^KRxWbPZB11Vz

SCxBA*)o^sQa(PRpzLUiDf>sM6Ze`O^dZ^VzGg*+T zLfY}lg@&9!AW$|4M9q~`ANqgwZK1xH@rV7(H)`6L%Mgq+&`KRm^BTas)>-RGpZ;KG z>z;IO)t;(+X~s?b?v3PQ{ti;hvnm7vK;2-`S&}gz$XK_tr9z>j6&&5S73zS1J_!4k zV3BQJRj?5dzo1=9O7_hS5-9EQQqI|E3|s$S37%~Wl1&UZ->FnWLl*);$kV0) z_dVMZsQirPIrw=H^jq=TCTNr2-P}5)f}L6GlrI@VvG`e1yTtg_?}Ic}ED0Qu75un7 zYdX|`+Bb`W0dB^oNJs0mgjFfmeHom!8{=?bN}>yT6KYKuo zN98*cP9^|mBVw1jaE1!s_q0_D8l4+SL!Py(7j1ccIt|nsBHGKPk5r2P zD_(vC;(3t(U88+VJIT`0NSd5j*FRn&qZ$X&LqRyedEwoWEc3k3_Ou~0Nf>;EVekx1 ziOiki3NVkODPcS)YOSy9`@{~J2D-2O!tr9=5=2$;k^iy%RSJKdw{%k@JTr(n(TE_fiv{HGMf>;>K>FVwEOk3w~go{0mi_BC*^W^gsWfpT6CKXtLX+< z!0od+{y1gvLHrERHDt)R4XAnyKab7q%^bCC`2AH&*)j@#BkafgG2o2zBHRzZWIO%tKLqMr#8%qV31D4oGX`e(enjp$w3V%CR2CT_B_`7qTti>4&1jS>f&r3H71LC*8l#HBX#%ZL^J zK|oi=V0|1r?Ri;w;SZL_8v5RVR=+gIl*J9{u^1kKoykjk?ss8LZ#9AozH^|_a1rEi zTW6->g=Pl=Cbvj>|5c)7{9z)&tAXNaiv={`4bk4NFi9r)F653Z=?8|M1}6M7`JiAL zWI&xI0_fC*4)|S}=)O9gT&o;79uzTZ*jm-uQrhH+jP4lxLZMlhV-5nm}vYsQL{JGp4hmcw>BNkEINh#7|Yf; z#i%p|mKo0<;eAgv8nMf7e@2ZR$;K>UH|rf%>TT|4tbSxNw#EclCr71lYlWCwMbVg^M;V zukdWTnJnQyl?kD<&SJGBw{L06O1nzmQ-7%M{E{%H^)+SpYB;Y5El*%K4~&PJO3*Zk z@&(P^uV38t{n&~he%CV;Q{Y;+Z2h3x@X@)xNw=y}M?)?y?qT3P2XEogCoIKQ{* zAuJ%<1Kv$eFY!t{cVaRWsWg1Dw`V*A>XiCvEJ~y#9fSY+MF)F>#Xzu=!Me?BYE9>q z8r)s;S8m^Z;#9HwQ&c;7N{*B$5f_-``X;3Hn@KbgZKmJk`4~#>2K$sGJ12ioq!Vrl z3`}XiN{$O^xiR)dT2$j10J7l-#)!}K6WbG8IsZNWGDt$_nHG66cNl?=oA5&8J*QA7 z;JSv+tQH+=iJNnSuniL~YY~@Gb(}=#c~MJm_u{HEfm|lX5eRYl+tTZ-(Is+%aS5}U zyAN6v@~O0m(34kg-O65-qZ7f-oEN%d#q}a|U4MJTwQ;vy2|o&K?p>0p>qq||Vs{_v z{$6h#vA_|DH-myQXnqa8latMVw%%<1*=a653bX#*f9jBPXA=8&(9=OB^}0^{?5B?J zVCaCURV%)A2YmF2VJ5WFY)`NqIhXjd+yVFu?BxdM;SH=GV>1!qw|u|AIFMPV3Y{E5 z0uVt|<~JCmwoTJ{37nKKEMJCaxp$O`mrjFwGq;a!@RfYnV~DInhAfE9IS{4)`@JBa zM(5kj#zdtPnI13ZDPbH$24xXB*_u8DNK8cblcW|$3u_>a%M_9Rbt5J))(V~i4>vscnrKB90XoMlTmb^dFZWLF1`s6T!aFjUH~<3yhvf#a_l z+})9;RNJ$&r4shQ>ijKjMxPcnRH;AGzfdLn9UE8ac()gpEJoHo9=7&mus4SDzAm|( ze7m@|fu}RDt11ax&BvSf*N-<72&0XS?cXc}WN~zGkV`vcpzQALozXosq`-v+NLC7U zKGxoD((xt1xbRd?b#d9=bF`NQ?>>_+F= zGH8P^7~rlyN{s~eu-!Ik{gxwg!P(lP3y0JTC}Z!H!<`a=_lx%G4P|8uQqM#GXJ}Yn z%@HpzxvR=it>$Z^z}GWIz?Qt0e}*@>b$X5z?a^F&?%jP7ffI3}x8-ECUUR_QIR$vf z5MoBzS#v#GF~dWPRRJ$oTFo)Ys&YMBIJoYd6_2Cq7Jbwsyx`SDtHf(cJXR^wAtM## z=UVHc6RiMu8T=8bAthh)yl3lA(o;>W3vb9iiS4CjAnflb7GL6{U7zv5ul zB*a`>;pZ9_p}(9c#-8ig6i*;@rGKu=br8IxNZ;9Zp0_-krJL-u{*tbT8_9&e>%}g_ zZbKWmo2*UkzM13ack=~mwF+VmtnUSQ)T7#H%7@QsS%=%l59Tku;*<`4p>wMA$rDTJ znfuNSvo95Z>C1s1kCkglcfBqkgn9H-R_^o5Hv%Zoro2dGf0cn5IEl(o)yR$GqUxIE zdkWiOqMqNI=@2&RH1_izc_DwfQ;>Xtcnl2X=m%BGK9k7R8shb*?2oS$ZR5`TRT}df zUBOz=f*QBU+%lEMUq64iT+-?588wl+&6eG9`B-F$ki@ByYaVm=S`cw z*FCg-jKM|0IIp~v3xXm6ui~dS`{Y{f>`BqPyDsYL>a~CW9PEt^H+9rRl50fJh2GxYzb7US zCz_gRq~6rOyQ#DFiaTE;l)x^2b1MK0`x8apV(Sr}f}mk?)cfDc_9#f*Mz-fRw!?>Z zRZR`)#A(d+Ai8FdkM^0PQlj`4Ca{xqvXrj$U*0z`Hwx3#e9L*-M@JA| z0znFA94maOL=GB?dq7{NcS9kL7~~gaX`PiBWB`BEP@CwDVWeYc7DXVis<|M%5;P|$ zl`7EKvJJxAr^0u?{u$v|Wc7KS`cN>=G~J_N%u7(aXco$s63Yo39Pd!omRTxr*J*++ zUo3%ms$kn6$?sh%p5+euA(zbHRRu(gDqMHi<;TI5?nK;LJ+FIrF#q&Qcb_k4XnC$7 zT7p)dA1r7V;I)t{Hek?_`p7MTEt?efb5JVWTEaNd#fx1pl*=HXs+7P=AOInd@NRz$ zL<_Yp?tjY2-d3hl#gaGu)E|!X^~2z^K8eT7;>%tv=fU9CnZz4}WiVG$w&Pvz`B|XM z9sp&gO<=KLB1+lGg-b)sla|BFB92c!0ey!xZ%hmC0E;Ef)S4GW8WJuKLRyq9gDECd z%*RoNUgdsxh9)a#NsF?A8xr)*-w6=PZJ3eSm(Mm)*p5Xl1IP(0W!uI2r;ZW%G}5=G zgH`$|jk~iN+fn+0?7MZv2U=funszpxo{EaUaq#X&!~7&e*eKbFDL86>Xo!CSkU<}9 z9Z4AlEngc!^yk7*8wn~xlVXv8!5xg%fEttud4}$ZP0R+e<3PZNZ?8`jEwAzzQ=zRe zcI+p5*4rp~fIn9kKF(>X;*cSPBZ!$7dRq*`hD?pX^J-uGq?&oU;I5Nhk|#e{TmbCh zE>%Mo(CL|dSZpTi0b-#`tup? z$W>$K;pg^k;WF|&X6YUb*2a0?*aWcU_;SGYTpp_p@|+&>!TH*E{p21Fqg~Bptnqzy zeqArG?j0En+r5a6W@q_YnLwCGXqeJ|*uN9nRAs>PCqm#JSoZcYWP%Fd9t z+w7XgM`0XpNGpV0P_$jUs)Ft$&5q$G6pZpfT+-0QG3wXjtl|uTB3!Xt)LsSS2{Fk> z5EXP@h~MH~6xKr;Es`O1qUOSEzKZQH@ZCz6wA~R`5$AAsw}vf?JVCU_`_ic#7bJ6s z)i{d`p~uS?lo;k2IhqA4T?E@eN9N9r|IVb^25&&>T|lfsI^>b3Y@lF&-|Ou>!gReT z&cnrCy@XFDN_c8?%2GS*&Ea@`z%<>G?*8H^niO7?5oS4i5x<8Sw?n&iu#F zD?Wo^0Owa{(-(3L5eo(wcyzGD&3A`CUpn^{7MdxTx-V*b&vKG+^1^k*y$0_--Kmd=)SWVd6Nv2{h4n6`4abH z-dC+&-{p(U&(OiSFU<3-hqrL|WfahP=SBC*q=;M)V#LU7KQrw6%PY7QL{l4K5{sD}4~1x)v_IdJBNVN*1xVEo$)ReW;Fe8T=*& z$S>?s#nS-+UX9>KOj(>fBdt2JN&QE4c zjng5PWMJr-(d^hH&gInjl6}-A*^SgSl%{L-TL)NLRl${}nH2W(^yCvLTZi}ZL=uQ; z_vd7|p3>`#4GZ8>L)vm~N)*{`W{Sm#<-ikW<0HRbxG3$IPl}zbWsfW6A+zdnfQ5&~ zrf7FsCXJpWD0;Zshn)&}eJe*`m)G#g5>x9t3=Uf8nW%p0Qh8n+Ak0%ak&g}xy*=X` zYP!aFuIL{Buu?PGbUR=>ml7#;?C)Oj-|*h%n%+`usy=IQ$(S-5`#(qW(cX+ea~JLFHVGw3xv~3E0Q_tE0Jzy*M^Dqo zez_04Xd!mr#t>x3wUCK;_NZnaHMPPe=+wvOWMk_#XHbY;Z&imIV`=Hngsgct+uF%< zCM{!Omuy1$+CW`BJH1}7E8^F1nb~P!)yLsK-aW!ynkq0A_g^X?cI9#w&RPtsiO{sK zebzk*@c3i=NWcL(77ZnXHLy<6vegr;&k(5dY#$YSS~u%ue{61r-UP4{mb8+br}HkBL0Ez4P6wE%wbv z+{}CFWVaFQCGW}LF2muwa8y&I&^>T11$|Sc(fR17cGe9cwZR{+e)#>#dq-$8-GHOp zt}5~GpQE}8q^cd)UynW&K+)S-?>9PCSofz1oULDA5?WMrzHe`H0bEzb`!9-TqQJfO z$mtrXnI5$@Qp#z<<&Qm}aT%_I=%>74Y~#F|U|dUkqQD*~&rnB<+;#A%&aP=bT(Ntv zaCD3*@_>!%8ntkzdfC)49TG=>mPV3!$xT)jN{t)I0`{N8-DA{C${d9bRe8nec2|A%Kmlb<|BX}7p zLFO|#ZGnB`Y|SMyNgb|FXhWkum*wr%Yt$1wjxQ92h*Gu!7iLz~i3@=T=Zg$POu~o{ zm%|fB034?_Y)LgdYx-3@5x7JN&I5iaoQM9lrk-?S_qr;ohDH@Svk*yfGI$rm3 zEbC>sY?J6pJ(o2fqtUQX7*=%IdxE@S)iSi>DAMVnxn5UY*Y2e}M+!QlI;Y>hzi_7E%e~19OZ_Mq<^4hTEr2R&ja&n>PUi7ufR1 zH8ALqc0U0}P<}U!SY!#H3c4b75Qvt~7!mT#sX72gbq}FGhFy7_rdQ#_TdH}QNP6WyIqa&IR-AAFD z9QIizSvq)4B6+`~V`dFb4OW3UGCu6=fx4KyZTEXt@Q1LhR?#fqN~mw zMq+QdSj^PX>sq}3xh!n!<&EAWHsTw{?XMAQ6FW820bR>C{@z)wUsh~dp*1Bli-z5M zb9d|Icwpxly9qonHsq%s`B&-^!lt5`YZr^|JeUn^wJ&+{@8KlFPWa+-0pDhn7~ia zBmhB-UVFM)-sd>1W*~?;Y4AL;K$Gm#`4lRjN6H|^qZ$HUtoCh3eOJY#KHw}|-c}xX z`-h4H|v?y^AYSdZbJ7h|4Y19e(W%XZySy(})!+E94c!Ms`aMf)SZ{DrP z^GKb@7-jEN7dpjkQDYv@gtSh3?yzj1?Oyk?rpmP#NV-{}75u&9CtjoFNSMk%#j*rW zl>QGw&?|!XedNV-JW#%W!i>^CC^y_M`17F?(}#`AxC%11D6N~iKcVykSNqP8J`4&; zS3(~!^cJCKaV1lym|z$q&OT6U8)>Au<<=iXIl{keFw^T!UI=lB<-F$3IM{MOwM6C; zNUO|3^{vUqyZX|uW-($AN_TCj*#c*EXu4Ujt?l(KTgdo&rZ*co5IV(iLppQK|fIvOHjR z);++G(GKw3ZwgaEL4<7z&RzSnx;Ed!e0J{WAiT*>y8{vt?}(X;Sri@%61;v27tgY!L{dlgyxmh{MlZ4-p6qWGSR>bs&V*SZ|vEB}3<|goZx(TQ8F%n%%DTPmxv7FB~?+1ebFu$lIJfZMO*vFNV zvpHZT^!?!m9sWg?y72l|rxItoY!+H^A`7adjNxe2JWV%`wjSmHa=oTGKb?|ecg}WH zcJ#u;`nf0C)AnBcI>V3txm_r>mo|5q_+G&GDD*VadOo*@ zS!W;?%h_M~dBBf`3Qi)t0P_pY15q-;x!*vnVc#{T&$BDo%-74GIUXf5%bkixTF6 z;~P-h2DiJx?8VFTJ<#fs7ZHd=ff1zO)!rL)eD3;N<0QZEGT=EWRQ3=otZiU0$x`7c zger9<(ph{jfJb24qvdC2eluc3pHGhrDVojOBoNVp^#)G7@{AOZRguqXD{Z(q)TtSW zyKs!Lxgi2WXFy|xAUobEL=L;_j;|<^6VqkP!5-a4K&{0u+j*0 zh#dx=75V@{u5{%?cBsQU=!<)o*eOBVXBrx1WM1Obu%Yo$9k;JvNe9pQ#&@7T_Hqb- zo`&0S`7W>k_Xc8uuUR;GsObB=C7;ER{kZ zuGr%%7FJh$oiBEhT?k~kCv&j5ppHS{+&#(<9PQemhEOSqsabD#r zLm-SWSNz0Y$XUz;m=;N01EYd5aqPM_G4)%)VNT-dtkbWsn%vS;Q}0(wRnN};udYv` zfetpp3i1^hG%>p&G- z?i|bF5ic~fef4{!!q;(FibWFW#^I^|$;T{~Fmkq`FAH`yFgTHw1AG$S4S1m|UOU9r zqsza0JRiZbgZ(`nHxyMEVN3&Fgv_}9Re=c2}{11;At&g26PGg}fU7HGn#CLW7bo`Y5_c9FtOHV)*g;&$nYC+)6@?5bu3#MNBB&yA z(waAyK)h6ygeL|v(|c4M1G^|8@d02fFAt<+razM{y6eiW3_unloyLKN2>%8vEFbhG z&B}MRDAX|Eo7LvU^1`-UP^ZtqJ_*`eTj!8$o2qH-Oa>wIM|2Q zk|%)HVNHi%K2{Pl7WQVeB$IjW2VCYpw-}dVY{%Y5I#Z(jL{1)jYnW35{_eXM-gxQ> z7vjfGdYVAG2=IkdBozp zL=xCZ8;#K{5SM4oniZ#PcD|I5KIsu(f0{k**R1=&7^yd4Wa0SP9tGh6F>`JZ7alZt7pbd&Rp6#hOF)v7C+ICS7=N_O}@J=$S_WU3-|s%<7RW z6T8?*xa0tY)7RwJ2d0zY3A%a25dL?1LFR}!CA=>v3_N7(mWd6-J5e;oQCq(7=n1Xh zn@%E&~W24fuM(4!53+81f~PP zfiK&_P^TwejDiNg^UCqMN(7=xjU&g>6PRqB6+g3zzlCS?b|+;49>~H?VnbN8Qct=V z&dXRpU@GFuYc^m9VQMmyWWZJ_gRmQRl#yYcj(Ly|)orRS%+u{wD2;>)tW@p@orr2@ z07@)OP&v1^AH&$$l-_FpMGf&qdHp2+elWZg>P;Z%2S)G6+)Ra9fGrRY>m(f|XqE?G zB@_(bve3%HyEz@n3fGII3|R(Yl1i*YowLu$GN9qU?l?AH!jDxbS{5gB$N4!s%ySH% z8nYFU6*q1TR&Q5@gRGR^pR2+}jDStCTz5T!>4hZ~!fy24l^>y}0K`b}JlAARpt74R zv8oMWV)31G$PO>q$7-lZ=TjLb7V#WQQorTPhEtTn5!3};XJwI<+v-ZCz7A3j4#w6O z^B`;$m2wu?K}{*RO+lqI%t`0xMkp#93XDbguF4n*`H2t{m7+Dt?{Uv$gxHyPTh*>1 z&ar#6fynaHwZm!OiXbZCdCqzKAZ_+X9R?WV77mQ9-|Q~{`d9(t)A&>BX+Z|{g`b! zaEezO|WRE1EVeFxRQh2vT*fiUXF7O50 znEpuYN}&1; z+oW(_g1`UidgA(~2sYD}QzDYmH3Kc>Ff;X(r-z&s{&o zhP+UGs+gAd^Wsy4pEvL+IR;e{J+i6hbmCZ1l{HPq;S^-SYrT--JB_XpN?DOG?)9PGv9SeD6De)4K0)k@d+VnG6y z51hnV?Nh$c4@({hOL`^aWopb@HW!`*?as+cSU%Wonyd>tg~oDzV)-cKZmlem8mh+z z+9=+*Pg3V0<}QsPVLoQG`z5!)5O(#z`Vw!*Ul?36q5k0Q+CkXYfQ?r(cSKu)<6QDa zQmQJOwV?JFC=J)2g70)J?_3lX|BO(5(RMBq5nE|A_bT=Y0mLdEL*h;Bb3{$>w6(3Gn`q0e7f7)xGE=fs#Gza;U}cPG3i{x@x1hgMhZ^8CWI4K*!T+Ymli>r2PHkov_6J(kUxVoh z|G(*p9|&gnR?#Us%Zy!T4*w>9qx&dLSHbjdYHLUZiA_N9m7^3{^$8E^%HVOJMAZa= z`+p-BONop-g3vcGe9iC~c9`6^y}Ois7rg}5wkqoC!qs}4rCv$RzJ&m2lTJk^sjHG$ z&K8`~uB!wHVbsrd9_0Fz-g&pnc^>SCrB|%q=f1V%A*+rSSXKa^;I8oV6IA7AeD~i? zvykg&1lPthfM_FbY-EjCv=GT8lsM-}VUdqA__T@+KWnP zF$!7spf#_8Am;l*?i^P5u#nj|>zvKKupS*rffaa`yZ3i>E1lD>9x5GI}=$!q>` zC+ho|9#CkFga|P6#B$-$z}f%kSzn>k+(c9Xe_BJyt&}RA^qYJbR0asF&HKUqIP?!` z;lRfV?}j#9s#`Qms(R~l8f$_8lo?$LFhEZ3SYHDWmp~DL31xno9GMkI_z&T}U_X?y zMw;wX%z~g_EQd(rN(R;Mo5Exwoc8PoB=S7a(e;IwDlO&hg$G1x$3eNqhe14D>KI3e z02h-D+nSeG5K zH~NN7JAzWC0vM-(`v^Og>>nw4I#)}Ev;SyL(X`BO;GBCr^|v7q8B$CpMinr{`X7A%+r;@W*<@J<@Y#Qh7dh) zWF~`TUZx{CXSCJlb+fBLFM^fv@K&7_y3))s{BAqp8)m}UYrnD9K4C#oz@GyyX=OvW z^-lhvI+uy7N?F3{DwzCWpkqVNqz!Ye8$TE1w&m;Vn^ree#*Z24f$C0Rz`v9@+&1 zlfV)&R@{Hj&oi$W2Z&`^6=-r=*R!#gnSWPT(~8TGud zSx|Ljp@aG4cthxqF6`q+j~)pfwc2Y?sk%N#%X7e|D7^42?7pE3U7En>1tz0w$|6g|%p_YxpJ@kBI@>A7kTNKXv{Yld>MXoqrii42>R zn)t(LpLg{kH!J@!9CjYa*PLgkpWIm*OKDp6S6Ii=X{L}jy3g+ujjrHIGh8jH^{V_A zSprL^g3f?!>?gl^v2UvB0>cx=*`jx)rfuclMT98+&|rzuDdWmb_E(_U-zp8&{YB zzdn5qzWZOsEErY2w?C)In47*#vV?j(bOWFn5dg)PLWPCM~PPI_L2gEzdNCq%;tX z0A1Qw$J6?TwbfQXi+KD@JXrJJEP%n4SONeA@Qbk^Q|e53($$+El6hW`TuVC=bSbi% z1(nc*42F#!Z!I{FNfEX*(~!j=B-!f~J^@{zICr}_#eFo`H&k(GiBn{_JrLzXtFvWC zTo$+7e9d437A+JaQY7mPlWcDp!g)wh;aYj_G!SZ!;)&Ybpu1Hn{8jBOMK<<*qe#R+g|3Es`EQd%LXXN(Kj3PAB6x>P|ez#tj17+;a`iJb@7VN`kfo%^dDSL~f zCnzbY*Uz1xU{maLb({hlFTV+%&%-CU+b7L#b%u{T4$`|eZSe)hi+~GvW}Ak9olNeB znHZxlTAf_tJ0w zLV*HeP2}(2NmnT91-_daA>v=pSqpNTiHBRSN_smh?h4P{Gn9M|VFB!*m@5>F&@(qd zQ8SfM1SHThOA3e$ci!Wn7I~!SMDM=7zO+&Z+4d+*Vb-{*7cwJK^>p5nMu0fH4k*~1 zDZ!%iK;=k#2OJF|iXMJzDoLj;VA1cd|HgOr1bRf*tkuicWz6fsSMK z9)fqE<+QO1*ZQ1{=yu@`k{ks_O;*0 z=YkK1J&oshg|iXA6OB^j-`;!#{mxcU)b+s~C@Kp6>>pbt&cyVi?hfzxBFC_z@;F)e z{17b4@@FWYgw`}X?J>@nGgru79>i8sjt!6bVQtr%))*(m^OQRo3#9|+r>g*{Z_HdY z2tflN+0j{4k!tK;W*o|F>%>;&lSra93~Z9<>(ue2LEYJ?9c}jAAChODq_P1-ilb?Q zd$&g_>EH}b9d*St#o~4^PwP^OV)?VBdhmTCk!(Z>Ie8ZQ0Z$ArH#6}4NgGb(>oB)f zuh$m!WnC$XFbpzH9+N8`46%_jb`k-$d6nv-tmWGd^XrRwq>&M{?l9JCVo-0+dZEs|dNRq9&q);0MI)Z(>} z-aQr?Gy8q4vhL+(DWqRu;GM@ekP4AQJgS5nn;bpT#XHR+>_59}?nmds=iY#i+;ec5 zpjb2Dr);=@RpQ|x{zU#?4A3tkP@<6!d&sNcd`Ue7GLXG`j_q^*3OK$4ahvuDc$S_; zwdkRgf1Bxl+2?*s|H7o&0eM!AYxqEqeK(|r>3beRyp=YJq+ylFr|)LF*=YBHO;3}U zN}>VzpViO8k5QsbP&7~zlVe@v1vAn8cdZ^C2>pY@Iv1s(a6*M25~?u{WTO!1FCW%y z-3_PKwj^8Ui;hH|S&OUBOoJL-H8WdC3@SuBEj#_=wtC(EyNEZBB7W^?POo;prR--e z=u4Km6iW&jy4rO^B4K4*kbbuIXIDgR!G*&}eLAco5|tp3=IC1BGci}nQg5pnm`^^75{t zt*O;pb06h?Dc8f-q(1lMXnYe0V#C^0&(yThLU3*L6o55AzCEVhx$z}|&L97GVb{QT z$FT(EkSf*n%M5s6u#Tk4OXtro!(eU-;E)l7D(`Odry}E?vIA0*;Vo&S*sSmEVnm&d&i%+vv#`t1jo$d zsr<)B!i!re|8Bref4KJzXf9;Tra#(VL9Gj-n!!ZSsDAr<@!D1~3gC@)7qVh@f#bp| zU|KLBh`g6UuG$uu6>r~C1nXHL0B6=ukj`DaSVa0M{t@{Ub(LQe5XmoCm%uHHKt1xi zXx<@Jb)4b+ao3nq_P5mY_^r2!Y(ToKW_0F!&K^cbkVZ95$ggDmhBdqE_JUaAK>i=K zZ$0DV@`W2gj#dUTVj1&HyRLr*VH!O{vMsR81m3!4?*JA}=IIev3sqv5Ymo7z@7+L@ z4J!r&fqSDG(v{5?{oNv~M6UQY!F9_m@m~ZePS!UDK{_yO#BNaWRv_Elg`&nC_Ak#M zf-`WYb7zeDEw038e$R1~i{q<*hd)S`)Qz8vecTev&8Ni_x|JeNoTg%Yx-(jkkcmi1 z|KStDb=J-DKyR)ItUolFX1GV4KoH_uaMdea5jE7XyPKtU+!sE?_9I3iW%j4kK)vN= zS@T$$hc5ovcV4>d(|q27mt_EdSrA&!HF--qD~+qGTy!iwYv&d<4|oTF7v$HDPECPh z7ZzzrZ=F)!G9Hl)P;(rpJLbj?cs-!S$QEdgoS)t^!K&8F+B#fy7;f=6Ma{^VII!6_43^<|cimI1-df*CUWxFl zT#N(pj#hbM1$Q0Dwf;uO0k_mM}#l(i7A-()9MnjKaO$y(ua^B3#mcdsl!LyZ7^6r77jpy`QL_-yctbUuQCP}W0G0d-p z2gU{PpJAVzM~)rJW;Y$Wzc3@1Y-%xeLn*aFj4H2O{8~GD+or|+HBASLp39_wgsk zzp~&ERqlEc%6maybd2L9)tdceSe5>%_0+dg`03feI$<~i$BfPmbT9E4Xa(Ma!Af-E zMZ@v$b;T^A7vbQMM3K8GM&RArL1O*9tH1=>DE0WZTCV2u225-b3ICDB%^iMw9rNsc zb`nj#1Wg-iljw6+YbiIk%6j0spcG^_QX_1GZqOQ_ zbwdyz+{PPU8c%OO7;W(E7S=tL!&$w;PE7hJsw4|Z_qr>9NZ3}!d@1jR&l3@&TZ*3& z?ok6~D(BWBNU{|a6x^*3GnS@v5mztMp~df@!ZWG+rFzXsdx}Pubw6kPFG}l{>#8Oe zt~#yB&Uq{16<=*rI1hXN2)IKHB0Or~|LnD2sV#oW-k!Eob8SdBXth~~fj`Q?fWKYJ965s&bmzO zff?g2r}gCL1YinX&usnm$|X&Vmn`<-rrBl8-7F88B^7o#Wwpm6+rKhPY6Mml;)GP7 z-9GUo-<63D5h_i>7GI4^agA_LssI3zg*LtKnmHps{JG%ezIA0h(r@UOnJc!#)R~r! z65ME2)M)IYsk3W&DXOdZyI5ylmB&XCJjvEHtlEr=f#D|XD4siyzQ(uTR z`;QGV5Y}A1ax8nX<&G(9h_!~_o!B%)N{ICjA$}hNP@baTAeXxd#vxA>MB8M-1Ilst z?V}&jsJ>N~YnN%mI8HB7t5R3zgwt-R{zwl_%9!v z$ zZDx>UUY+yVe%LiOr|%PB7JwO$^}3f;jbO|5hDR2ihLDYB@Zs1v_PY+65)&`tFPeeD z<;f%`Jf!>A06*y$(l5cXnjU}aicT;HB=bvJxRsfqC_0aaOXY4&S`1SL)6kEe*mTh= zx*dqzxN?!(T_;m2bJ?*S*`tLj*P$f3OX_}Vn5f$Odz=g6PsALF*96_IQoXiKxMReW9yj`p-=uR;Har05r|J4gv zZkOd12t@=TA*c?_21Vs!>PS(GM_Ci>EOv3yYKlTZ3OUtyUwBi5>M>NUe*8fazpi1{ zR%A!<1p>&QWf#<0qklUnD|KUfrJ}(r#6hWX8?PjIF}djei{Ht5Zy-K7$YzEorkBw3 zr2R8AHVvKkl=gmZ`A$Qtt^fDyp*DHiVS1tP3kQMqT(9aD)7|UmyPdF>2iwi0J5aS@ z`lx=|&wb6`3qAj~-_%v!d|5RSIIYmg&2L8t!Z^LC;Z&^^eYc^Igyn+w#JN)BL+8?- zMn8w-ifn|}1T^5;MlGZHc_RWm3Y znE%VnaClHYFt$*9Jd8m=pV@F&8l^mL0bQLCRIAS(arzUMA*8ueoI8|Kzc}I~@CQ>A;tLhJyJ^79A;roYxSyc!VMX%N2kd&Z;$xTfwa}2aGM?s1WTJ>Nb5;6;} z^j$8bLbf)`%l~t_XXb$c=Sd{Fb6=lA=$Yz z?tBRxN^#VWW8>FEL8fYNAb_J_)gQ1C&1c})XmVA|t-Q~Ra@F~9uSz1 zVDjF9J4O=%dA3*8XSgMG7k_UIc|bZbcRR!ICo2c&oT7yTw43_`(t-Uv*a_=2d6++` z5m#R+qk=3S-Hben*{Iw90(+e+^iJZ^#J_*7S|uWC9~{=KtgrV3Mwo*T)d8dBDFa!e zl)h|q2E4Pi%phPW>1Oz;MBatW1@|OL<%yMpP=riT!H|D^wl4A@_PW6onQ;F-}O1 zgJv%so*&b19ZmHgg82Xwl|=fh&*a#4MlJhW-huPgBXW!{-^JCFH32gQ?c}z~>aJ=v zh>GfOUF$*aQxHxGrL*Lx1vp~E$SM~yqQdE@kAMX7w7Qk+`m*EOw zYx%lReI82XaIpSf&Y0!5nCMUWbo+hnjQ_dg0AlIiFEJj_Z-VP(lccb)ItVX*_mX^kE|vltuFl!a`pN9m1YnQe6k%M1E8 zEeez;y#YTIE5rz@&80f-eTJWJ?kZRkK_Pydbd$yptESe!HGkIXwGoqb%7!bVCc|mr zOo>h;$><)_zD&FDi^6ZI=$t=r#cFxsJJd}ut)7heR_jJE@q2O3Xx!kTv`d;v8xL0o z1ewNvcewU(##Kkua9Iy+iuF_Tp#jGh(+t35f1%>Yy&8?!U`E7hv0JB$Rz8*jqKN z!ynYb23pMv`@FIQB>QZu5YCmNI7J{O2Q=e3mdx>}S-4PtJAJd-lI1^h4mL;3<0bcz zKWV|o^C$1BIB1M&?MxCi+4n*GuGOP2NVlnb4NhhW1|fkvFVygCLfJpMBB~v)gvM&> zCO5Ytb*5q5;mB2zh#{|6U)kp(t^Fc`-N4K2Mi1(i>_f##{T=3C*$_QPlw_qtB;;(MnTlE-W{WWI#LC&Ck81+ zWJx-c?2ar6%@l#QKhL$T65YmMlT4_sZ~O?v{6*BDm9>DKJJr|$dk)E5JEye`hQ(!K zR*8uUyu<5o-Az;r4D4WZ-}RdHmjh1nFVMX%QplopNOzgf$M6vw#g?~iY927^K{PH2n`eXhRi3-e-aa@Mtw4&iVo=}_ET&^qRg zCh+S|l9mOetP*m((cRdB-3n(v9eo){_JzQCmo$D9#BMY?b5efF0AzBfLp)VV!AW&C zLp`xIMtHl8Ss)R?n_au@FM(t1l5+kjj?(bMUtjg_=^3gtew%P4-ax?n$&;tFn>jBl zvon4dvyF{^o=+>uuS)F4ikV`0BRG$*J~3lWv|WZQ7}NF`XdN~ZYtVyIvCg+<|VKrFZy9T!qk z^e_2J{>C_3D7DfSq03DwgL1uU|L?;(5cBxA?2ZOqGsevNgMe;-?^f9_{t#v6{j>G= zS=H&|bVYEnMdT|ughVB3Kg0Lig9<3kEpN0z3Te<=((IffAv4X@K0maGi`tKDzz8(& znXu%W*2N=ppOXdJq`uLvsMmFU6i;p?{3&%XAB}8}0qALKr+hvoOuNMcdk$Fp z?f8c<2xX7VoOAELq0v!B8q7j&_!P<6NzR{Za;hx`YXXEBMyjdixmt_+{DD7E9sG-B zy#;0<3@E%iW6jU4qQw^kw8Q(@oEYEr6&~uKe?EM9EkJZC;joi18*&UYxNMpdTl6S7 zX7Y&a!|BI|%VkgkATh=>0@ z%b6FANQDsSgZNB@aH#%GO-&!a^-131p_JQ16e!>=4Q)AIv8$sSMwyKaq0r3-33f4i*iUB{*DZX9U4oB$5ahamOMLkj9qx zsn}jxsZmq%&?VeH@d1~SmGq|S{gj`XGjKf!|Al-}Yx9Rf3bZW9Hx*zw7M(GgL9zQ-V_v%y%*zjt=?gW!dt19%#~Xpu35(d8ZU9*Do}sX`yrXNhZ1!+D~f(zVg|^VGy8av)!EW;v8(T**k^1S}CP?j*~ zu<>;MaDy5WljFEIvl%5{{K_&CX#2AmhD&?3`Y%#`M!ByIE*ZFWFpY|>^N+J3ICex+9US!E@>baOe$O%v80WTHMnZc zzA+Q#s6V)@czG9^xn+^!O7c}sc2yLsNL{#+i%=9imt4Mwh#LZ2e-VIxkoI`(Ks_V;(t09d6l{njX@cHK`ZRX$fp1jHDo2+;N4 zvj>ggfP44L_l$~nay%hwmC8B;QgT%DwRgvnmd)Ii3|p928A1`%L9ea7s^ad_29hdc zo==m{A+QvM-#X)-)-*A=RxO|^BbhreEPBb4E7R}+DamM%C0guWLc;?K`Q-T5(wl3( zZQOXZ<=qV;V52rM*bp~pk`Y(!c&hCI$5znINYiJ%i9fzRh+FULx|ly<@*Lx8d07HE z`gCy!ZcM8ZKT3(6aFcE02J z;s)h1hs)B?c0?@_k}_Ilzv|^g2L{(Plz%Ou6T;zts5FP^fh>vPd_UYkhC`E?X?J4{ z=BYRD!M?CRWwqhu*fkB;J|pxuv|kr(`{BjKeyJ()P0M(1&DuwvH_$?>e-6$RTGv5W zhr!gbccntxQ9OG|NxKol@}~ zk-W(UDef6~+tA>%e5%agm$ysx6W2(Hi;s_|LT+3QZCVpt+_Utp!u+t4RA~FqL$b|0G!r5(a6sZn3P`MBRx(K_vE-E_4KT_9Tfp5<=u75{!*>$ z^8AR6tJh!$=Pk+Y$gd;5FIGsO3B(5!bAK<;x0zZ^(ncdJnQh94q`Wnm&UxwllG%`0 z%74@Ojhwvi#CjWNeLN)|QzoPha7z9N$R>s{9=!2Q#OzwAiOzXky*&`TRiK4;y@!Ef<_Oz^lo~H-9!d-ja8j-0K4;gQ?4Wrj_ z9ZGIt1`aO6u7E$R4hLu~`IV&|InB*6ET`kjj3~2!=G!vtXd*u@f3s7iGT>b+@-2dT zJ#S7ZTEbMZ2sFg7Rq7(BzK{FQ#o8|~f2TL*!=k*S0?Bc8SqochJofq-*29F=iO)juso+u&g2JgPV=r~cFPFgLDn#)!xt{msQl_R( ze1GkebYq9EP%Kx;PjBx48I5)%Z#YFC?2i2lLLf=Bx@>In03>{?5L2^6A|eJ~x)(J* zu-CO39NDf-UmPovLVRR7`u(gCjs}&25?w*xSy?oD=$)mrlr71TV_@C_NMlT4O~Hwf ztD&V0i7$|QuFeE>wIg-Vq*hqj;%zBV(sZlz*$6xNb-pMZb-I6UT3 zc)4Tn;(ld@Izj5|HGy-eTtqWNd!7FTSY0HX)3s#cgXY-w4zYwLcS(!k)weku>qO$U zjSm{q&uyt4)<}o0NwxL;|4W3MU?(#%H=wIN>?s#$Jb8Oak@hAHnLPIp+Aa0XRV6&( zx=ZrvW9R}?^PLu}JL`}zsR%g1;ae)JU>!=R-R-CmuIu+I&6+0OQQrkC_Lc%AG5tf| zU`Q;(Ha27`x_Jp~VVqosyf55Y@J-H$Ty$)bf|d`pIwK)evX~y)zeTx=^AOz@ z2KLvak%B%3e0ofvU24+vrAscYST04IQCO6@?LRU7cL&|vg450gTdp`R_&ST_rJz(S zgeN{eyO1a=*Vy1*J-1iQO>MzTynfLil-aO33IOiaXrAnyx!C|Uh1}4)>}`s~b|4+D z^)m})v(Lak8Y6!QM?2Tu$6=9>usj5C43h~??>2+S)MYjSrMJNK+%wmz)ceEu_$x7% zbni(&gam`G-Id27-v!?lry$FrY-+|aqeXvG&~VL?#}>Hvz&C^?Tr@-IZ1}Y{*JKt6 zV+qnmrc5C``ttYLg|h($ZrP*dD@!PBWoC_dPMH}@YxX=SU(ZwdT0gSaH8ci@J7e__ z{hy$>bPRUY9r*EU$4mM3^4NVy$FFWiTJ-$#cvX(AwHa#`qy$1mKyAHKRf=(h>=2?c zIE>>@5rH>s;?~IjOdi-oDafazci$ib8d<$GN36p-#p}kM5>eVhW=o}4*p403@1-au z-6<9JsFwIy;`Qaw!AWe{B_j!@(5kiL* z2gkl)IB$d+H>eOZQjn0V5OrnsP?$IHBrnd`fs!X)G9p& zI%9d_j6(zs5BKJ{>D>vIt`0=&caDTUFLX4&INB6_y)I$n#(^NW`1hm6>aWj@H@N|f zM!G^LUT9UL`Lz+G-j({G$Ju!o+3QpLeq ze057cTJ}@+Uxt?W#t!?FV`|LtCkd{fhzXy`-|{1{2vgARGM>4)WFw761eXNqRGo>f0HwINKmKL8jSCr5g!gw7Lg$XeNg5W+`b z#+&*6UNQw9{2&{W-C?D3yT?FA#FL6Ngh81kK8Fe6AXbah9Xm7bS7$^Wt+cC#S~F4t(qwY-#Nx%pWe4K zoG(H-Uf$=N5F^oKP=KTV9#43QhiSsq(nvwC{tbTZOn<))IQp!9)X-IFu+YCptwBl# zB=2xnnA}F2g7{j%H625+GBD8kX>Sotf&)7FkooO}??V8V($7Zcl3Z+_7(MTM*V;?( zMXh%E5M9gR?r#R)ohAQX4-Q8HQz#oA^^pyP&>>R5sSF9Vx6BRea|c7LGe}?M8{!7g*L>{i#$4k zDa@)wN!=8f-UZUkM0@eP>60i|-iAhc-W=qYd@Tn&v6~ft8Ia8TJTEFIpeEHXsK_tB zXWt1ApctN|{cjm4`9Edg-Cnb@i8-(VH>!wo@mO$*lr7ud^U=N9Hb2eoKuC$K`Hjw5 z)>zTd@dcRsw?VJ5%cHFP`Fu_8&t;tYJu1;H-29fqacvi@SDUZE=c&2upm?6j6qvSc%XOyJjh-*6Sit;~BQxM3PFG);z_ac@|`C*>oJb$ZI9y*sVqqB<{mpvah zl{%s^$<@oNW3uMhmu7TxF1$F5tkzQu{I?vG>!$Z$iP*K~$*0W0Hs(NgQf%)GS$T56rUR|$xEeOxS5gT%<^}J%K zGzp)0Z~8mt0&!ziAqM00D!r@t90}gjH5R{HIDG{a?G9xi%Ji>ZPmlGS#y|L-7o=n( zY;#?U#fxlaThpEoWi1f4U>MRe2_8Yc0wZr5PEu5-!eu2X7Q!5?(5w6_$JXdp7Y}qvs;b? z*dPhYWa+3#7AM)eSq~grMtxc2&Jqge*bWq9!r7Fwj|Q8#*!OgqP?X!UQ6W zcacGhN=M*9cTP{v#@|<*qCcLb-$ms&?29NK@7e^cQlZ{GKAwF%$A(&CBfexJJ8t_L z+nAm1=uC^~PJ8kD3AF_yGyIOjv3D6sLWr6(&TQ8nFiV>)CnnbOQUD{2hdI#Z=ubZW zli-uoY7MKQrtJokQnD|YltR?&w+@+cH#D<=M-`N%*-``y->O103IseXMcO|2J1(*H zNGoe_wwqMU8*)b^`gS>Iy6IicFSU}7fZX@IZ0tRrBHKRg@D!;W{#PDrjOe2hE)uX% zZ@b2F{d8JxbS-_1)b2uNE}xg$?@Y6_W)G=XA9pXWL*-JC^J~e|E4E)P3lCGPy)1Ea zEhHBFuz5~S%XdeVJTV(FSL=Kb=ZfJvKLd};o{+noN(S#>c=Nk@t`XLAnvjoO3|ygs znFShg$$aREUHLvBWf(27ay~@?=O0C!&t`TXf8~R{2W_LmXN-4TJd)B)OHYrTmvtmO zCEUtE^mRF%l)15+6h4w6FLh+?#4Bp)KD1REmj0oxtycw{!K)qb8Y>GJ0Ptp4jtESqP?zUgxp>K_0{{jCt;yP&q-i1}4*Vn5 z_KK}3X58sp9;(Ls&=^qjx-hXaew>=nQc>DP)>63&Kzas(&2QJLv00)nJ$m>Nhn^o5 zvi(xHBMoSWjCqfq(&ulT{;f?1A*AZF`Z{UP&g%Qwto`=z@7&)LO!MEDwEGwkxwUa$ z=$2iS^WN$EVxiOsJL_m^RjGF+^q)fb{_2L`s**n{aA{XH2yWRrF4M+ve}gFZq|R!? zMrj)uQCd%BwT6lOTF~RI$0zSk!))e19{>5c>wL2OaIcW+zhZ)h@x=i0OC4}Pkd-2X z|84Uw^m!=g4m0i^PlVgi1R|(sitct}L>+LMi|12{auMcvIiE$Z`N;k%=RnylLI_}+ zk2XqSIPW=3S1<{59)GY;3?KeL@NQaWHmcY!P<7;djm=WA z2PwD`&Oe>m5Awyx^lIZMkB5#pZk)eWIcel>{SIFyOgw9ms%Jee53x{f`fl)N)%!ql z?>416!Y^OmsZe^c_DP*sayhSd{}Hh;F4D3)*z0b5v|amjyArM&)43-S3Onw zG(`Yd99AuoPL-Q?0n3h|W+8}y=v(8O=_r?y!>>C%9@3*0gyNpuT#CXv>Ld2`&aDFk zR$c@m1)LOYMG}d&rDlaA4!0_cv>7bYqYuPaE!c=V&`1iLyF}fDCiF^nYuAmm3eny^ zGs78KfF#EP81#ep{JAG2LqIl;;(dg}&9_hO#hn0YU9M{+}u4c_xFcO+mKNG#{ z{`jvtwI60n_+NnXH>}b&G@cscmVrK>xR(x$B8Qat3lub71VDH!>NN+oPnz{Q)s(ux_wB7>YIwL3O1F=k!`=5 zz{R%rDyilO-q`+1}O z>D$}&bW7)q`fuo6N65$Y!=i%Qc|$%e-|jTF%Iy+bWF@+GOfwuj$Q^0o{j*#l@i)=k z3I=TxzEmnkYt0UpH6yG=_J#u&L#-!ed&G;sf3$%XF}BXaCG{>N*e$Nit~ynE)qWpx z=uC0fFNF%gA_i|Q9OX%MQe3D}{Ua9fGkm^-O$BXV+=bRcKSzk-?fNRAq- z`q_H*$tiHk<2+@iGjjaZk-&Ar^T3`Idb0|x-FPer7B zjfex*aTE*;mmBGwnCpmbrB!I;?Irpdw~@|};T?&vz^64iovuOL?ey0{p0@|Y00r9T<~)y=lI6D zrY3EPZYuth16@@}`n9vBu+x)-EZzt@IX*d}KPIF0ZlB;A{L)c-F(=cg$H4rshdP-h z?#4L#YYj@l(ceW%^AXhjDcVYK0wi zTTja`GcwJ-Pn=NY<{lo8&w45Mw+L4%CXWNtxEVd6YKI0R0+7{fkE{e|I*lf<7!l&` zodFFnYZxR~62XjmAbl}4GxICKb30iJ5m7xAzm@15P(7FWv1spEUADt-f4vog!N5f| zqbb=-`ja1h>rOQ;)Y($Dpm86MK1embw8^%;MZrS{B#YBR+%$d|K7mWYv}$kfUu=Pu zID$6uF-^hF;jZVD!^cw}lk?B*?~aj(k7Yg;UrgJDQRY*7oP_1M6Fah{&x{ExW?Yb$ zJe>E}iD2)%ef*1xj)94i@4WiF&g%=pD4FqZ{zL)M2@@4cM@ksjYWETtk#b_iEH}=vXF;dgr%|( zGf45xm@m+~uWuP+5GH8d@a%VWW}$EBDWSOz3-eV>K@<)`y25ZoLuJ5Z>Yjj-4RjBp-twj+^+^M^Pb8HTOa)>{(d+LZ1 z93|UCBLGEt@d^4xGC=3<(XGkTr@0-D~e=m=6W^fXG1x+uK4ojorndyyqeQiEsp3wJoLkMwE-YEP`;5Vb

+(J_O_}d%2c=j=64upLMJHM68j0!ysDrjGU+I4By>^XKum6m5Y4s!UuVE za|%W=PwT{}x0_=nL#-}?Mo;*@tSN-d5TJ8jAv5$0-qjhNN)%X?@61!XNU1`-&ZlxQ z#nLoBckTP(6B{wL!AT9}t4ei^sC-;32Jtl&VBYH(yQZoyb;;Y7<%1n7AC2PfKKtg6 zwxUmYaw*D3FFvs{Q1O^vMaB{3ap)#??O|v_Us|CPTMGvM+}x<#|EE7~I+enOpkoDD zkTdpq;Pudbl!SiEo#8Fxy^i{Mg^n-12a`VH)GKxKms&uyNDQyI(p_$PY*#Vyl1<_S?jx{mRb8~zAmNR9j!l;RF|^SZh9ywenfQqR-Ys|H=LV1 zg#{E|wmh}6U2Fu#Hk5ov0GGYa&6_tLQzeNI<%ut>qMNbRP-b%PT6eoAR{R*^)_( zjGdi*iL}YQBmZD?F!-LdbVkXi!9pI$r6&(#UJ5^mtntkAVLy*z-!5t++&yzsk8R+q zwV$?$!mZ4yMsM$H?d-Q|;;Ipd8cv0mXAK=d0IVox=}!A@SX-^1(5V}e(cQA_2Yuud zUBa|R7%ZxYwpN|yM%X4=}EI@U!Z#9rtt1kXayP{@W+!u?XG6$+;oszDJYI+-;ZQQ+d{;s z!_~&FeS1Z;uai{tA3Hi2`SE6IK_iT-f@@2IXkXyQ?&Lj{M7k24&Bgrk*IPM2c)D^@ z3M$zVRN+X>LzC^QwYhHKRh{+V)h*Ex9tfngTQri7>q&Soe7}fQc=4zULf{)#g zhj&@~cPLZG(Q1j)vi+0|3{M}k&TSAH_HLdp{I-~AY$xb7aa3_M?=m)cY{oOFI z$tC;JX)%&5`!h)63=b0i0=zK(hB0d(sb|5t-f z^No6mJ$9Qv2Z)^GqMtLU3Y;XG6x=#M|9=n7`fI1p(l1Bn9D)^x6bS4^Yw$?6b8~cb zylmptzEVE7l^W^0lRI!{(p$>%{rl0eSVltJdMkT3@y; zo(~pzvQRYT$IlkVUPLFfPKB|&@e>*{yJe)S?+Ln&7x&Im*oazB_HIYmCNfZgty8H; zzFgA#igaE&zh=*~pM_S#gE|-sanRc>?lXdTedaNVx11u$6ZiVJr2O%Jcx&8Kr7oGW z@#~_94Hb=xw=ZRgKo|d5BO&Th-!*uOE_nMuVuPr3h3_igfrKWwcM0F8_eid=j7f+S zqWdVC@%ih^h|H(3#Qwqc-+MC$oKp-XB3+1P-qnxYM><*^9e2nr{Ej zGo;%r96QwK7O`R3@q(`BrSjD`<(faSFHLeTtjLL1>Rl}N$B*)&u3w;6I+Qb*_)&fI z&})S+Bv1MB*c7`#aJ%bF@5NHRmhw`5ZWOmaFU(%{O~Kl|&z<=b z_Wp?iv%Q$n_=5_LT$G(16kyExzX6F5n_Vt2!DLBkT{NNfo&&jl zrYVYfuD2eGmgw*T^C)v0bL`VXZaPXmz5AwxkIpvbribh&du7DT?=h=bZlc65C_Ub@Fx{5=u-je0tn+pqqk?@gt3NS0`Z%7cJC7qib`*s%4u!U;?s zU>Az%-8%z*{O_&0!C?(f>baYO;nzWIGrIomqxWtf_omq&BETj7mkW)gMh=v>iu27xf>GYvt)s zq<3dmFTY}H3w0ds=V0*i2Z52iR?gx%ab<*n+#wB~H3jmgjYl!p>kPP)(`qW7%?RlK zC@MhsZaJKr2w)dVMOQD%ntmA$QRhLO)nuN8$5{?C?Q%*a$qI*;3}M9V4}ZY;@y@?ravy#R7-iNlr8LO zKNqh$;%ce49yNHQVeSP=X8`u;d^DM>{*eS2Zf#83e*`BjMT_E!v z5mT&@ZG149?!g-lk$lt?aq7aJ2MNIG8JB={gnFEdoL&Fj5T<`O(*S6@w*1a#n>@DA z>3ON}DcTkog;S*sG~|iERc^EHCs`HH@K1pxGeLURgHi)C+cCHk0q>~Xost`J4wyV} zMs-JiJWPP4@#^1Gc)-?=q$Qn2v3e=dX7r^Wr0s&hD4IqI)Lxf)nS;ceA*J15C9k;u zy{hY@oOwW4FxT#JNNR=S%iq=aG&Gs5PSThSFg|NQ*=$s5<9Qsan4u`bF${vGn&CX% zNv`rOcCTllIS?g9F4E8axm0KB7LZ%K3)+tR0py(ka{DbX^E;twJPlw_`k!hR1ApjVDLnaFQEtWAEQ=nRwa6$ZgY4SdFc*SZXOlLU#|=rHMP?eji1{ zb1}a5aC0&GL|#oEFbKmGQTv^AO8QF7Y4FUZFRR;lm5r88uo!OJF@x8V1ovgsd@v+t zA}1E>&gYmb&^vs!PpDS>UMi|%ynsq~aC1|OQJp7a(Mk;#l{Iw`d6B6J|2Tc^n#6s2 z8g#J-#KTHa-SjZH`efgPh-8;ad>^)1loj z`gd5vMEjD}n7G`U6xl9axD+_Ar}yxz`>-Kc4%WZwF)`tj%oh!K!0Cj_mts@v$SKgw zdM6)FeFXJ|(@^GNvmDIZdogdX!@1Xo9AiW{5=~uN-UJw5JL1ohEb!zfV5Zst&iq(@ zN8XbO$amvyL;ulZ9ew{wEG@OU;5RLikeBoIWoM2mLYIBFKo2PUbrRb}LBJ**ne~e^ zW4Y2ZUmH53{>;!o7^?c{=t}fwGDv8Sv#^Scfn^91tT+WT3Dib2hts}}U z=H5Lxk586PkBwMTyHm!Uv`rHp0_W|OkbJOu zJp?~TDdZ3S?cD)a{65_t>KF`Uwv9jfFxTf&;h^p3i075UYrRn~HlOF*Ads z7m9oIsCSnCOmLvJ@vf!MUw%R5W&0W3wB-n`zYjSNXnBjb=~~J|M#pLEj0pO`v%kpX z#py`AURmeGOGgo~Xji=3F$$e01U;@l4nz&JnFkyl$)1oaiq{m9h=(28$Mg3ej|)HY zE{Y^qe^n-W%MW})a?A_&Z&$gP$Bs!~IxJ&;DzO^+MSpj+(u2NKgV(CarC!!_^=WKr zCzF-vjnjYs`>cuAdxX-rw|prtQH_cC*7cprYJb^f5GG+ckj6In{r@oamT^&qTl=sg z3L*_s0+Iqs3)0;nARy8?bPwHIxnspu+5A=PZDoWCWAD^unquX%M z{n;7`cR0ZMgxGVxopS((i z-f}=`JD}_bwhZ@tUP%$!=wfMkolyB2K~s+_e=Y+~tHJ#nECkO&CEtC9k>f4A833OaZWX1}AK#bO5YVtiB0Id!MK zpU_sAsgjjB6cD+`B7a=QBd}eRKlLOUQ!eN0_?mxa#;V(Tw>(p5``4@^&|2VBG3tRSqURXsE6jg9>whk25O#$x$7r&@1xX`si zYnBlaEQ(W*TKbF)Llz4m{EL0U)ZYWhYt5a}tbd^g2CoCA4Nj6SG886U(P$-STg;o^ zO%uJ8!!PfNdxz&X0`@(*ZuF}1UowAJeSAnx<+AODF>0+}MlmDxnHpdl&h>Ql9cuzk z1lBCX#KgO@fFp4pmIVs3Tg*K?T|YNTB0dl{Qkd#h2=di`pe7Ea*0u&; z5^+#CDdQRAhV#!m^KWymp$gOI1%luuXRJBSU&O(9J_~!bx8B3=sZm4E`%O#3S-Q+5 z9>nSFruaGv3pLIzgY2g!5%KljdwfPQ26U&)7qSKt22Ftbp9hHLKi)w%ptsZ_XfM@O z4S=4&_dejc-fh8$qgHy@L6Y#2?)mxrLCD=MIQdNtG9QA6J>b0rRtw`sQ;jcGwj!qg zhLCHb~q(hy!Pq+ZsC{S&-{x13i$EQE|%rgW=3@N$xRd|BQC4^^mWilm6vrJK<11gS$t|R8oD~EWQ(Kno+5{iUzST zvnyo8aBXDqDdXkit6$&z7#CPnG;LQ)MKPog#)bdW(BM|rQ|vN98*-U);Q$?y8;X9$ z2ijb2z3GKR8@lJ*_CaOGbmVm!a`@HM$v0K-muve%xU_mxjDSJyR3Qn-M4<%6533VP z^o}3)&o@_7t?LgE=fwtaXAVZ!3EUmx+<*H&^B&MB7b)=SR^op{Tt#yHfw>v-iS^$7 z^PB5J%I<-2sITwcYU_Jd0daLfcP}FxVvltD8eJP#i_+f8Q<*=1wg0jo-rrv7fUxFC zW|K+sYa%d>INu^A1%ztSN)yp)>;IJZyZ&<_?%X-yE*qX|($r(K0x@E7uXllvIc9bgTNi^4kRkdxUVZlJsTT zI16oKAkLge66$5MmBQzGy@DFdlS)A>6;__e+yZ3e7Clo`sRf%1u%*X;=3t^S0TC2b z(yA9qSB=aSlb2iM11Iwg$y$smnHOy`yzU77qrC=0F12T;wwl&_3*T>6ox5gwx8G{F zxvZwIyoxd+MER5Nb$eK&ICV0A8Eo`-H3CsKRb&YU4aHXyjPXjkkN;@Y2jRsR+WDGh z08a#ZnSPm{(a6o}8dHQoO=G)|QeNdy$7{pVYMY(tBCE|SCIxkLzXOn<)W$g+hD^37 zABxRc!drR#Tc#BmVsAgc@+N8^7BZD;bCKEkpSgIQik)Ch$LHxC0LuOr3^6>AD z*r$?NWA?|tAM1)6yu{g^+^ACtZZh>RvVK*<&8%#IUpVgDNDE(}1Uzi+Vlt&}Vs5sA zepgmjGTf&hL~tb9&iif~kw@YFmKVt^SxL^lYz@S{-%-OcHd}XsWbmgyAnpEJo>J;O zKaCbmVi(*+&xB$Y+`%pCZLz`0)%A-w#)c&fP|2Oh-7omYr+pdsKNKN-tU!Ay@=l|# zpqc}gx5ogkpG;y;DmBdMRJfk2i+^L_2$+n_O`hFa%59AC8D5*{jpIMHxbEd(4(fEG z$Y}#6hvsYfdI^{lbF8Kbxuc!7jw$hEi7W>=qZpv)q`#T0U0oDZYI&nOzvDx=8|4W? z!hJ{5SjMh{Zek*Lc->(VYVXDbzEHg@J<>&U1Jc%nfTxx!wpQqR-6zXi?P^BN+0k>f@qeagGvU$B3dvgrW|mELdCGEh-IrbaE6 zQc9$AzO8y|#IFRpg8&tgi^HT7=chi(xS*+!0+_CF?I>sc$Atn#qz%VAxIA~~3hj9l zoMga)%x+K)cqPOCV^olt;U@MFTwa|2EIxjlbdH-;or}T+oM|;>vd)^W%P@BnKNdz? z7q|ki>j6bGKevaDE}4eCw!sm{v8{UM1B>E(T;S5F>4h!B8Zzj6AZ0OK!D}viWLo?i zj+PChQqz$XeI_EyKAOYlK|yGVdDBY{(DBlymBpw$YFESB`9;R!G^_6j3>&4q|#VVTZcUKW?ubfo<&aFj`7l!wDe&|=x9t_pi zng#aSxO{UI*PYcCA6nCMgwz&h{VM37;@9umOs^;|z950l(M5#$M14M%fx}~SIg(8J zw^wb&1V(WPQ;6DvUPb3m;Ku2-48P-bw)wO{gxa%OH}kFdYNDywyrai1w~Kri(?#%d zyCVotBVxDEtzYqsWFYhdtu4=5hK71jZKgYcIJ?E5SuHK*li*Ruv*oppKpJW>S@`Ij z01SH2Srhu|`U8CAFSpM^cD1y8*p4UYd3_q~%)7vW+kJwb6WRd#S%e`XZ)cPHS$XQ+ z7Y0&a61`VD?s=*1yvJw>!gkk7eh8T@lu|4p9a%naXZU){xP%^Eyxm6b2P>$K%r2ZF zgNQ9u`b)m5jFC>MQCsOy9)iRSYGeqqKeTKo-#K11J$x}>_xx+|etACrxo3s;2p5YA z7t3%fa}+pd3M%%D2mS?Scl$;%=fXV6wGQOt80n@XK5NH-E{lZz@q(t`g$+G`i-B}m z>YkNv%oroNe~h7d-j2k(dL-7rW}}PtGz*T@*N^B`(TSiv1y(<2qq#6bZwmD?8mGW?EYL>z7mL>-HvhJPTd@QzOTnF~ z3?QK7-V2$Wy4CRJE+$w)J5<-7s%Z(S?lbJ#m|gVt3O3)}hORqB9}N)r)0vj*`f^R# zXRwj{6)i`Myi+8R;@w|m-M_}pC-i8cGGIPP3%p4CHb!lbZcLupVoe%ibfxPcbVEfK zxl%lVCf-y0Jg*U4GM3&NM~^18>k*I-f1mjy03MH1yw>9X-K8Fz9i`P^*v{p87gKb? zW^)Ia4%Xh5Ny!2~jg6j*Y2#EVAXgmDxAU6K>EL{h3dQ{fr)KcDvgoo8pe(Bp)RPgG zJDMm=!u^Rt0(83vNPtjO``3ckn|}{iQOXV)($<+&<@*X&ntnP2wvj|QcP0#y2bEN$ zvbMP~r6lUj-!bmD1mXHKM^CF`sgK77KAsU6nqZDbj|b0UXJUf$yzN|eSf zR3o1PvYD+LVCms9`E5Aji43F>U2*IUw5ei!18+(IWT;_+~|nyuqjwmlruNznLtxP2=iL5PaZgf3|BxZP46MfPlr zvHe`opMj`zUmX1pr$T6lVdlSIg{IRmK!TClGtXvFY45%Agu{2XZ7(&L9i`00Id6_R zELTQi)S1f%s1}@tqQ)M?N-qUSY1(4W{_uKgF%?VlJ|{-+)om;&?&#~m^5*B>txSHJ zu@M*e80yekm)#w!kELmS;C%G}sTOVhb&yyLVq&=JOeNo+)E+>JZOHQReRTs~A}Zd5 zJpt3f(EVlvcDG?0No5O%P6fS*wcTGnW2vOX!jEJc%|S4)^4;{l{IOscPQlcBvVep7 zNWW~xcX@=UEkG`pt=TN2kio zBBce2_BT=~w8)h6c{7yBgP51UF=Cz#35T{XYK1l0gWg<Ojwvm^0Oyrbyg+FesW$D@nBj=Pi%i>3RfCS%{U?m`aDFyCFgjAKT zOH-q5^$0vEBbe8}G5LHqw}6lY$I_*w?KrZLzflowXxbXP#^s;LeI}I%w}}X^*!r1D zMBOFKZ|3;1NDL%W7538)v8YfO5a>;n>z6SVVNCq1+b7guj}B&x-aIB3k(&YRYIj|q zdStA$R!bs5m?6>T!>hc6Cif$hA47kr&+h^;#+G zmwZCvofkDbSkXJ*@|a*d6}qI`6~-F7pwGB>Z2a1bc6Px{)JacK>!5@AJ^ypT%!ZN? z8A7umZxV6&inVaxss|yq8|_cnlY-C)aH5xY%rUux9nD=}{+kEe+vMaY11JX%y%uqj zEewSMPgy_hSj6`@Jbump@Nqx9)j25@hT`#9DCuPx3KQU~r=)^y+Pii9JL1IJh~(Mi z4maq=$&T4OtW<_ff~NH`vQ1k5y(F_{iRfDH%k~Px^%oolhX4?JjHA04b+$hV_{yk+ z9`Kb;@sm79^fM+9yU^=;OQJ)7`ls{=(^Ta*VjpJw@)Q|-z>9t9D3;Kf&}HY?v#-}4 z@>RuQ9r{1&PJS(v`HJW|S)ZXYKwk>#%EcYZzC|a=>W4If7jR0rP;r&7t->h z!e8tYH%s<`6aGB28o@P0;3bwY@+d)-CeQFwiawTedP-j{67N`Wiavs1yC&ZbAp7ER z)@7cFl+sGv?Tcd!hpmma(`B#{y+EdARwBQ5ssy^Zy6&{Z(Pe{(a2PhNR~&m~YLUbUj>ONb2ckIimpv%OaFxI-O(BKS)uSOgVuu&Jx_5TI zGWGp|rt1VPCx>K*_!Sh9$^ckjSL!ar-0eI4cP_!zUWs`IPG)Y?L;3jsihMiCv%xH` zBZ1@sBNE*G_wP!po#g_NNr{6W6=teQaXxglMiHVgiN;(ZZ6=N?b~oR>%z{HrxQNa( zOiy7e?9m`}e~!@v8j_?G$}%yfMn~vzlX@`M!TM>!fS&kGvE-KFbSeDNVm}uZNC`Fe z6V`u4V%FgWv<`X|yfnh!WDsZJ1sbcpgODYD>}Xm%@v&dAH5!rdIbMwrXs63+^TAR# z)uGTSIOs>SW7>r_vg;M1_q3x{XNrcljy*5>^}ED$(~Ic9WTHipQ0nhO%Nz9lp&S=I z;!^GvR9^jCy(In$ls728nMuYKD4ak=?+a^8b*+&PFQv5ZH#=Q)Z`kR=)s}Zxq^kzJ z`Pa+*9-fWCv;WmtqtY(pGK^o?(C-J98}ubf=+wBOXhNGLUe`wyQQ0>94f-6rLHNT^ z^e36j*sX8N-S`X6lOpvp0&rfwpjqfJFr)KR7Kx06Qw6m->BWsO0K9~Z*QCq**o6_8 z)SaL2G(In$Y8J#*abff>hsNt1SN`MWj1GyIMHyx|^>I}XSdoWAC3o<;?~iV{CdXXD z<8Lv(HnPJy zJ^bL4-;v9JPsNkGjD`$5yBn`NcG&89F}P>ez|@B9oob6O2rz!~vC)+Qox43tI3@)) zDFbhz@%Rf)_UL5t){~9lIeQiYF7!50xzrdw4f@au*`rqcsrViKI#|HiN@-4UHBr7y z1ua1PlAH-d<_tWs4HoO0__e-O{aLN(dQ1(nJweJwKx0=k!a+}t9CVieHHe4DI|nh8 zGOp?p|AxKIiLV@L($y46RP(S-szW^- z)J>zjE&ld4T17|>l_8fwS)CVV#>UbIPfGCNmqe;!ES%#s7>{$&h8s7)Oi2pXu3$U3 zsMHx79FesOlF_u#Jd?jyLknfqaF;nRktDfl6nWtO0{Kjdi|?nK4q5k~lYn73y^zxj z*OYEX{Wp8g|!1Q)iS9FiKfJEl+@hcGz7wVSoy+rtIjc3V3a@UQ4 z&nb@4B)LHL(%oPy2$#-ClmazDv42@KBd;3lz2gG^MA08T?89CXO9gE-q_F^%?_#Gl z73R-$312>9J6t;&x73W`fz8$rQo<) zY$qU|E`rXC>!C~M5mF!Dr>J`-6b^cgzj`}HgVog<}R9@pR5B`5-Joz=iso1e&x%M)@<%^fEC=R;D9XH@?;%ioBqNL zTfx1cR0S}CRm$rbEqz|ke`o@cwoA8NcghN%ZP6Wh*1kl=skq?B$o%&oV-jp*4(d(p zL`}GBQlnERo`B>SL}J}!+!g-S*><+vqhEol?_5){{!Gtn3AzSAlCoRZY{1KfDe8j= zTnaVAOYvj%b;%;yiD<3zqr6DQ;(A`tA2Pt_7vrom<(Kq!k`6ht_;9@ONr#di5MU9f zWQd!lm3p~^)Dw0NfWV-2?~~Z-P?S+bN|L_Tp_l{6Crh%+T={$29}rj(T`07^f-RHU z+ePh~VkCuizCDe>ixNq4xNuaXNXi0D&_5QoL!EEJe)dzrT5y?&m(pYK3{BV|iUaJ& zOviugrn5J3KXSReACvMFmeN`d=74(gnrm&X>@RFb6I&0gCQK1YqpnzB+p`sXCk16J zn{SV1o7W)G8xl1^!{fk})`Qm7-@2$7&^jw%8qUqj&d_TP#hkAZdQu2J z93pqHf?8$0=7MYI;ezBpcPg4?y&_nl4Zn7N87k^0ZNSN=(}|U9m$t%lj@U3%NUbDKOv@b;=H}iDe|&vm*UibEZ3C&Ngj@sR!$s`W{V_ zEw7|cTGlf}Pfhv4ayx{D+s>POK1<4HiHIheIsF= zQT);)qw+!V6gOxVKq@l}%Y|7RYTr`lgFJ}99fEF-{lHOl#vrn1-Us`kFGbLQg;U5gWzgjP(T?DDJ(z1w zW$Io43`@mAH}(=U>M8m+m=1k|B{IPB;NkpVXz@k%&dXHe%z&A9!J{9$Oh zLmpqQac(U7`W-TXJGutj=RWr-br0RF6q!>wOV~{`b2KG)(H|FbeP-3TkwWz+o?M}~ z3*Fhu&W4e>9GJO(psVQ|PgK#th!x-HqY2}X_%0GJ&vEmMKU6Ot>2pr6n&}y}H=%Ql2g%elD z3TR&M%u#>WS7564S>tI$F6oh8Or;e3t@3kk9HS#t` z6U}%-0)aMURM?TY1Ub{?QXctaPH;kEP3gMp78i_~=gWV2fu>{8G-Ez=5vosi_OQ7v zBDqV5BF^t9k{p^);sK8nzVQ6al^+k4mNNCfkPgb`_hs*9Q@w=i{E4GGYL@(c{*;0a zH$5c{g$lZqz-1wsF65l_c ztBgIMAnZ1$B$koaXLibID-2_Ofw|YxHo_2*o7=aCgloPJ6Fl!!m1Saw3s2L(2{f!u z>TCwvHeq@rlH=nwg&f`jYn@ZyiHF4p^kWJg37^kelB#~#jp8^>KHV^}i58qU+F37z z(4VpYNGN8{HDaX!9NU%y7*K<7S3Q(c3b;TttT6vJV`D{FEOy83;o6)e-n{#M*>bE01#+YaK(k1IKeuKr(2eYKw0@C)e?B3cD>&1hWDRcCj?C)FT zx1WsQvG_9ln^3wsNLUl4jPBGQK1Uib7zH%OON=^rii{DP;QH*+VeI1RkQtq@swJ(( zO$WW%eX9QhW9myky=!@010d6~7u?)r{T4L3t=mW`TiZSRG9Mh?2;Y&v=_nj)KPjyq$+tgJ=l(N}EXh%XKqu|gTcRk|7 zgot(}(VSfbB3z83;Ge366h*S&c!8ZRbg>(;T}!2S#0GEGntHoE$_kxkAUc0LmzJD* zz{~n<)$#@j28`l$ZYsb>v#uU9yERDrEI^oLu3EaauV-{$i-!t8u(uoT?q|HlKk3YN zGG7-JZvU#E)b=`f-y~5xs`R2X3pdJ;wp@UbhRp&PVH2|)f0iP7lP`k#79Ah;IR_V6 z03(LKB}YJBDrR07?%|H%&36mm=a*IaAx-0aUS+@F<~P27`L8j(gbzs1gbP`Do7lW2 zVE;asYFn2zWM0?XgZZz~kOVT>@zZk13kfmxL0;`rxys0*qRp!ev0vtI4wWKl-1HMM zeUzJl`A zYAglxRzKmA`(!nD#{ayjJb|t}4|qkmMtYex1s!{)BGM%S zKqi2Sip~6QD!0#6MbC?Mzza>1lhWxo7+}j7A4roNpY#%X<*4yQE0;rlDUBZ2pD`|w zIAKyTZRm9WKM$0G7Rho*Oce7RZV4RsAF9^?1sRb^T5qDok}~?mCYX;4D5;J)O4At(<;_Q}z0r1OcmsZv z*q=8&JYk+RcEZ5yHcbWZAxAmL9;02;SMkzfT#P?CedQ9vMX*>ENiqe0Dp4JUDV#j$ zKoj#1y%YL&C?V8KCH4)6qKM%hV}T?Ml>$K#Gc_SftCK{!6eFrQYRvQ6LZcFi&_xbv zO#l@Foek(#?gyP=gs!qo_B`fGbT&o=s9Err2g(I;QuNFB2>sB1q~j;YGQ?4RwME3s z_5M*x1Mo^xqjPN~=KrmF{L$wldQAqgd?r;0S1m_GY`5ENCq>~tt|f9ZA>5}i5nY&a{aNG}XWRGEAs|oVBW|o2UoT&@wl&tIcFy4qpwr48 zr)eGYbY2vF**q<2w|OCKoT_u;O?g@+S24XF*sumfs^##)`6BiOK2I4(7U+_Hq&c8J zYU55l&jfFnAMWyR{M?L=LM>#pgE~3U-yNH)klGRCV}6vsaaR@hrpO!7Cm@$P`) z;!}FIZa@VLknya}u?5IL_-39l2M#h_i4!IwOT5qQP8s96lk3E1)M~e8gFTyHrK*IA zE;dVO^&H1Fl_(7!2chvz=6-WS8f2qI?{9GYS}Mu6uCd@=92ml5rnO1(?(5x$MUDjj=2Lms!+@}`u#G{wL#2-&Kh8PC z@fP&FZ>TX<;~7vI{9_ZE8%i?_lfquNT(uM@>pqpIFpmos{{}ynwCQj$841o+9IVke z4d5rrZ4Lpy)SH#PWnD8?gvceso#o3x>s~dotRqTF5r(wA){wy z*Vq(uFpF^<2-M1mV>osX+mW)cwnU5K^YuZ&XX;%JVO`XJ6}_FPIG$Z95G`!+Odzp= zY0&q(n&8Hqjx7r|kln_Z%Mm{{9q|+?_G(tl$Q8Ew2{o^r4#79XCLv&vA{JZNn>;0+ zOiylSHS24rZ!)DyClvJwz2ZehTiFw>6!x;WX`5QCz*L@P%Gse-l)I)p;sy1JWN1yLxm+0H9f(B$Z zUlg_nffVLprlReO_vQ%aszD-aEHT`NhQJqy{PK+pfd%!h(X_83tF*~AzZMe15kjm{ z54uKir?AI`vUl=D;HWu|stueCMa4KwMq}%hGMaf6!qs}{y2zp^`A^ve$06>fN4j;| zoBYdV=ic`Zjex?^)dGhyc-kLzdyqE4fC}guH@Fl)KJaO=B=)=>OdV?sMQp60%8oA3ewjG)MWNO~X0T%g0ygtpU^1!2FHdwR8+4}o-6W!5~ ze;t~P&w6Azi{;HsFU98?ZvpmCQ&{p?yA*RmPzp0kc}CRK!9*@Oxw7B*i(GMZM#2V* zY37wPRHh%*zgO9U1AI2jI$Y4a1!&W;Z|Mbp?{Zf*Nt*^5+k<8dSwwj;-1T<+Rb&SlQtw?ANa;k+~VM=1b$Sd5)Gu*QmL>-OBuxYY6W#Dh@Y|u zng|CihzBVXq2b#Ckza;)N%Tzx`q{#!+pL^I-TubXh+E>_ILjnL85{U$K% z!M_bJ7>|<-=xP#F(E3bIjZK;GLhsuXFO=NOowK`e9*Dl)VEW3xmo=@1%zc{CAGrUq zH@GCFvV^3T(Bvc%(2x~xT@$U5`{5F* zNM@iDm5Xjtp0ECLT{)ieK=&XT3!=2{PF-gwG=YX<0z$rZ%xI!k+xa*TeQrdkCX=KGrQfXbO;-hA<=sBfIZ)a8wZ|%O@@I{d z@N(#BOz3Z=ir;UH4JEBvaw&3Ko#^doB_^=4gwvwHBR30vZ8Z`VqS*~%w7=-H znp4UH&_6cm7<*pnSuK()?Hmc+Zc+zp5Oh6pyK+2GJJQ%y1@^lHHJSoia}KrC={+(= zaWEj#=Mci6JUPcA$b`a4b_bJIj(S=;@k0rtOLv7jARZvIsaBi z*R77JqO?I#)Iim->OEd1yf`x^`=-k`rs^C30>LocJwbJb&vrsj{8(`AE_^DwdHYO* zu;ePc(DD^IZsFE1z$1X2PU(G(qQY1eVN#?O$-sotP&~pqfBFLp9IcLu|ER8l5*-%` zud$2OQt<7YZs+YrqdrFhnN*l^6=}S3u~7fY;1*li3i8<1X{yfsjE%k(Ag}GyKMLR+ zdHW{h!1o2UxN0&dX3C^1Du}x#NoobDJVDqh;^xy`Zp9k}aGuUmNy<75{b<4=sd_W{ z4CP3*rFEX`zm0COQXBM26<{jwtg9D6*7!9_xBHaj@t8c zm@RXMVy9;JXu-Mz0zdpKnDh=hS4f!s96K$izroQheN-uE^k7HXlF;@7Vz*bSB}N*; zXniyk1Dj)Sa*i&=lrT9vRnjtL-hr7Tn9jnTiHX^fnDEif_Ic+gslIJ|F`S7y6Ej2I z%!z+fInF09PbGy9D`^zyJZOFibmjCYP2A0j#Y$3pFb|W<6mkIwSm9Lo?e>x23NJ=j zf0_%T(hjtZFyRnc@S)uATW9UN;U0?X+etRMev1cA<#)%&rq!y<8VZMx2M=u{%L8Mn zhMmsN&wdt#e+ukWHID4ky#6@JlVB>9xOS=+QtqnJL=;DttER3eJ>e6?K5JM-9X%}4 z2#p(YeHGcV90+X4w2?${uLr(6m&=PBm5AXpbyR;R)vO1pf@^mC)~jr|rv`&byPtO% z$5^$<=zm_FsMOkmL}ly0E*EN0W~4Ez-4_%t$Sy?Kg{CpQTR+u{`f{6$Vo!zoht=&u zFs%}mh7KKmKI-k;tih^>2IKcT=0E;}_WCeh7>IO zBKMb0=l2hWAtJYh_ov^xxj8tXmC2|`u)_#b_^r!oaN$(o11v=~IYnRhbMG`{BkaOV z0Z9@m^rg-3Id(FSSI+L1C1XcACmfKF5{OnUb1F9J^CD&`(g>(sB>6XL&OXgr3YW6z z6D=dcv$PWnr>8P&(UQ_^c|Uf!`138%6l2p^K76rk4@o7n6_R6Q292=*m_y{o`y5h#llu=b;c>GyzaUCK}-Y!zKJ2{9>y4%`jsOXN2IBH>i*!qOKaGW zuLYhX>FDg~iQk~&K=C_y<;m3}#9A*4SucmWiGprYQi%kNov|cK@W;W(Nz#&=b zObNE-2Wv?Z$LrEs4*A@xjBIO;N!%bj4;9t4xP|K~Ok2g;d3@>7Ur-`Wxudp`njT(y zL&Bh8nm7&>@^M?9PC3`Jqawdg@W9wSOR~iN;`_ndpSyc|&4(M_+Jbk|{O)CS(;Ku$ z3-{N+hF8mm*YC5X>eK&($gTtScW>^G4ek^kvO;4wl44HlfqP-l5Ck5U2|Og1oBOVt zUnFNHBHUT?jB#uM=y zImwtmlR5n3`%|ezRU$sLh$Cs+`k4w{`^=Br@HtM{&XxeM>*7bxJ>JH+(W>HoEv zb0up4Ofq+wasm(zb+&wSHMpqcpZU;;O*Y=zzJ3=WfQ5%D87oQ};IVqq{F-7g;6pcT zpN8VSSCms#M=!vL3!&I4$nR4tqVZz2J>v;#u=P0TxNf@sRxh; z;#+HWi+31;-rrF#+}yhT+96`12NqXeui)JH#T=E_F68tnfc8KOHITzee9+vGSGNxGkZv5q7=Bo2+F!HkuYW~7&x zN^6Mvvf>1{4F0H==ESykjfiRwsJ@fF^TH#ipg2k%_I_r~a)0>xP6GS~4)NU$-(QN{ z9Esd@UjlmzcY819tCYdL+X5&a8D_g3V>{Zj2VUh94)LWFED`%I5r0!Xe%E^Z?j{~M zRvwxkZoQkjzjAln?gcXMP76i)jKFqs?$aBz{{x!3z(r;a80z^4iDIN29#&bgXwjT) zCs0JgpqK&B-V{p~g6M_mgtNdXMT(svfcWQ);0rfnetUuxd2`msC|QerD@(Mr`R%Av z#XVP2U_)r=nqXgjLulUc6WlPpaNLs_h~VS#ZS#ovM@bqyEaXc?=pXQwA&NeIj1fYO z+3m$BueJq?z-OM4^Nbd2rtt8K*mBE!k_K$_A!elr zlaF+eV3_Hqz}R~le3-u*_6E5MImAWOewIUcyLWhuiV7DcmrI4*KvOp<@#Un&m6<}L z7WFtGwRn>g3HM?eeW{trS|rsE?~h4q($ZW_S=9Ftz!BSs_Q}*eaAmOZ5I-IR+=VH7 z`EDC_h=B(Aj0Rd0$PJp{%2H%VgK9R#GBBEM}O(#PFH{-yS z@@4n^#p_Jc$I$Fz0K@^RSIm6hJz!-&3_s;ca48T3qAy~O%Vrdu%8$O|e%_=Zs?%R# z$2a#E4E|FAOq}J9{`9S+L*`GLTIVVlIaTqpO0u`m!qXXvQE>!*Hih6!>XcNIPq0{# zfb%F^?Swxc3;yeo?M8QKx8ca1+|447}cST^CsK#|4 z7o}?|OW~p>Sbj`~=Q{B>rL?hv0WZNlx8TyCoeqGn)YrnmKScgjj-E*nN3HpQ3RnDstb{!p=dBAICRDla> zdtlxtprQo!&%7(5-D1$Q8DveKW?9ar)Yv_eNz2=PJ(rWVr}B9s-$0ETRZ1l%#Bb0_ zirWBJSbJ!b+(nN7LoAV)ZY#nIcqS&Q|D~I+!9#`SbuWhNy(O=xrLd++@w@6+h;bLvKXq22MK*LkB2hwsk5lQIgX#Ls%qbITmCBsf!NS zt=f2{s8Eg4zP(}6;-hV#Qt()&&Pdg&IHNg4o^-MK;(Y~9W9~-7x;IYv!2aOg*7vFq zNC5%x%$3(S0l?wMjPr+!1}E~J5H=1@z1R8v`7eEuj>g+6KB#Xe& zytYZyZ(#)iA(!!}WjlOGIz-*grZK~X$HD6hKI({d5Ee>WWs_H)hm*ZDQ8%iXDB&|k z$mMOvOP4ZP8ZlsLKdGkAy@)$en?9&gRw=mQ4uSv{pfQ-9H8u6}A?W2y+ zM2`-soQI!((XktL1Xh*kI9zW$s0<}MhfOcetQ1nO4<=gai5U7H@=oy#bFKrG+sQ9s9tSG&D>u9xG_RR@ z9EUgVmK%egTZT~ zLLYXx!MRm7aDd2Zzv+m>uJ!1dear2KEz+S02ByAa%|%_dn9w{rOo_A;l2XvL!0H2S zX7h#j1Q*a>{$2tUttcuY{stNaIz{V~jx|xS$@gs^Vd}-84F}mxmNyNEq0S3EbytRhTq-&(a@mjuxi> z08*boa2Ne(n)uJS19Gn>-OiIW;ygplvZ{Q*O-eHtqE6irs+EBU;=w(dlN9Q2pG&qi zZ1Gz0|I`6lbe8ivJkHu``5dnso2jDckkQE{2;arul%-R(W?j`y9D@WPOhpb%Lc%l) zxe>6}FIG(gU7{65KAAqR?IWi~Y4NEOd(~Y)>JUjILHK|!=5u&Sx&E3CARf#b{E@Un z{`j#9qwIL#TSeZv522#p7-D#4tRXX1LMJp*pS%`Ol@aKJD5~-qMR`07r8)Vd-)4|ZzGz0z(gat_X*q3NxYt~70kU6{@|Krf32xC+$z|cy~e1`3+-{9 zFkP?X*v;w!F12_duBP>3+U%DNX7Nz9T&9df-d@=;@_9FtO43IX3u87Kxs|gX4K;6i z71q(*dWyRsD{ezMA;9BWf_W*D?0L-`vnaUM6F*V-bCo#!Udy1d@x|NKMWG0{1s~$C zF+RHxb2yzh=u^e;sP#mk^<*I99JI@<{i^aAR`ouU*`Ry8jR>Yj|Jd3M5($40~T7O#uhZ+y& zQ!(7ED9{*gmVcl5?H9%VhApOX6Jn6ornlxUSnMgf$W(#4!aT`$79JV1ppPrOoD(ko zM?bdQivPpBC|Um~me(L|V+H4i(D?_f*LGwj+;p5K2rR}O#<9_`8_}C40XEXeYG_yp_Vm~jO}EjOIdo$d!WTl;ZnF+1=p6rjod#MoTB42E*qGxdD1-A$w@~gsufWg zt(5^5$*S^GPiOv2DRL=PMt<9oN&NozJVL}fV4st_^(=u@fiwtRHb1Wizlgaxmd70) zjdHEUGmaCQgKNzUPf^%&YcAEYui-tIUI1QERQhU2zwC~awEU8aWyB~OAGpaR>1NGx z9s_c-b--lGb3e~mBoUL8R`P$b6N!K+6_BFX}jb1UY zN-eI9(tcy-j=DrLgwt^j%0`4&0lYexsl2H=Yh4RM`?hFvFZ#BlX#eQ@-c?`F+%?TN zy5W3P_C1a_w>uI2x_9l%Y;+TvIj%Q##B&Eir*2iE41+x|moSMb?__PSbVo+~ zK@Yrv8eV;xHB9f=0;?dzRXl<-_zwwK%6dZIDs;D3iTk$byVAyJVuusX6**Rwm!wj@gRjM^N`KapjFk_UIZhwv z?d0!fR-S&xSqH%Rb5Allzy+h73OtShBK-TD(v(>S0lsFR3Z8H2j=c@~fPIi1N*_i} zG4DZhX;|H*=>|I?l!h*7N0dhw#;QfSki%+AQliD8P^xer&$`zP!sZlpu+6$$g#Q)N z_;XZF3*j;4Mu{;6xnSdgVh%cVRt?VgHSumB5%dodSpK{u5KXTYH&#WVh~UiB(_`e< zF4cs6ao?z~Zk0IbyOL<`uRs37tCFH5?Fo^DW zKW-bD{Mpj2_2O}-r4si71Is(fFG6qjT@l>2%|;flOyY1Ea#I_MYfG>X)R@m0&9Cz& z$Ztq8N2d^@FWFG#&PElM_1~dfNT3myY1h?K0Q#B<5~SfTx=(4U){RKMo=M&@;&zAqFl{N%+X-i$9Nf*fuB5vmm-goy(WyZn?VIhgySF08 zv?A@mZQ1?zVV`}chf&>kyZm9V^YOnp-LuLc-x>4yW^LHrE9hoSpYdNA0nV>ji{iRU z(q>(Uv(!@7YO?pAJu8J`|4vb)UoMpVADLi$lu7X|EeeRPMRuo-2!@4;ixEa+H%Urc zK=5d&h?bx&Mdd{K(kg%XVnPUL>=;q}4GCk}-X416DfbkPhjPX6PE}8ipFWq@|^#OIjKPL|{Nb2C1Pt z-oyRh`<^c_KfW=~+0Wi^T9UF8eBd@SyXMp zKLf>oq`F`RGlTL(55}3?YLL|EaR5fsg-RopA&-zvkj!1rx6`xUVto$y^!*$^tG4wx zQ!Vw!fWnZLU8i#9(XAf?Aw@G@R$-5q23r!~{I(8B;K}reJ$s1V=uA8m8MrX{(-5Hv zpfO1uk0;6G^vz<6=be64>HMM->lmx2ghmz?0y2)Hi$2j-2ANPqOh6)s!n2ziD&0^* z*F=USM7NlK$!1=Ov;F(IcwLAr`D*){xq>9Gz^iBO%RI+4W{Grb2sSkWGg-rG+yb2R z!g2nOtJ(v8<9AngOMde`-mo6)CKr_ioO5W$w#B#yHNFTZ?fQOBI1j#VLMWLEbT20Y z0>3{m*MP;5$bTRJ$vf2D^@!uL4=S5j(vXAndIkTMZuw?uc@%WP2o0?UU_<|#?GGs8 z-1AxVV|a_>;&2&3=j6LGh6T+2xGM45Y`*f8(}}ROP?*p`|Z+Hs_(e4 z#Csk54yTP42SX3KynS(V_43q4ILKS9gcHuN%C=f*_>@7CbZO)WCi}zKJ%kPqOP@%~ znrHhPE78MJSY*yb8b+}Ft8fXa9dpvGI@VfSkes0c6XqX*;efz*$FtyoYlEr`xb?fX zc1cN6ugasJH&WGjuW>dJ)(g*sP~KOjt{XMO?+7C3f9NGbAx~-$JnKrq{?X>`PVas$ z37LKUkWJ$JhF%XL9Fb`ayK%7?np6GkB&w77YN5rdd8ziJVf{C@%7n>t{nUP3Wh!{e z+YN2GY4?%x#PckxvoW8nhRCO5XF@!GI7b8|W2?6Rwd632kCP-6a5KRzE(5#thATr1 z_3SVD8v}DspuT;REy8d|0yCf(K6LK9AqDUQpWQVIQ}@uv-6480lMTMVL(?>)^R~ar z1L2fDIZ0N+avCx`oJGi;e6B)7X!UvXvryA-UQ$dJE#Hcr{H~qynQ})Jr?c8J%<7As zDHk~}=@W_43;lmGlLKve$-FSuNBq0<(hdt-!>Kl?j2PSI&nM;KJQlkBIP~N#lS?nx z4q9V@O+6v$GjVJ3$hu5{niI_f7h+NG+`h&b$&->>lFqBvPQO*u+1`?cL@oq1M)RQK z_M|$u8IT1&g_-Y92dasp&aY9D<6PI@x%N}<4nJ(50{2ikzYzze>H*PSNS}xgR}CN5 z8Uvx*jrYZARbXxAR-O-_ty~M5jS4&%824y*G=}1(cd4C%04h%|g)kgLjgX^F)K4E( z+B9rsOp9JN^0<20vH@%PeR{cmnt76PQpTdrluok7)8fkEm)<+#*5Lz8=tbtNq!NwG~ot_)|Ft5&bhPXGFKh$wLKAbxabbE0^3){j?ikHGK zdHv?Yg^|fcL;K>A@v2x2AICxphl@>1>_RasU^pV^Jp(B+tSLwSG;+IAqeyX7sN`t> zh6BJGH!aL`pK-8a82Us8YWGU7g1YtcnOGj|IZ}om$#w;%=dvHNFwi9hx7a}wtOB<# z?6BoXq&OeRpjV=+wX2^6R1aFoF5}x;cwyOh3;m;Gl%b1}xePQV|9KQ+(_HrGB5Qa1 z+nP&*b$e<0sB$p(`obAp&yh-&n^VBq4@(BfqaPLAqgzXJoP*TQyP-_)M*KAh!z0;b z@So5wZA2aDp>TR!NUqhWwuPZ{87H)B9K1@jPGni`5IJ6H!x)eF7ZlFzCp?GwtG73A zUN5m-%QF;KC_B+me{F2^DzkN&NuJVu;ZoIhhiY#RdB-Bln`cicL-^-9EtxZaU^yo9 zgwl#1P+u*4!_>f(p_VLBnW|Z@MH1Oi)Lmde!j7gh%R6kUJrA)1`fi{f+{Dau;#tA3 z_YPX4RO|0OewydBv? zy^)+adqQ8}YR-wHG0TXM(eo3_Gw;D!I+IoYzI$viJ`0N`}Uou z_V?Te$WHQJlN6`4azCPaV-d)_+o1T%{Ck~M9#K;t@>pyhtg0LQDMUYrH{4P1>#m=# zqiuMQSd@a7Wk!5o;+1iPD=od44~J3_6AoQ0Q0NM^qWPh4JHN2kq_l+InbHYBk{0~e zwhS9a8NW4mhKDHkaN>Yb3$o`M2uDsTz1I@er1yd8hu-sTxYcw*5pJWW23`nH{qL`3 zqQDJ0U{Pr^<0&ii^@Am5t9_520{LTrK>5VR5tYlf`onx^1j}|!Z}ydT3azt6b_os1 z5AxIUEPd#$dzG&}O*Z9b1dpc_MT{3MJ!UCva4OA*M{P$BWGQj;=cJ?yK+m#eKv zLEjVaxBaRH286y8@BaEm(|AVO=6Xh2m;af=4JeR+MNDe6N=~4f1z2e{&2fpEEF)Wf z&VxzGt%~YgESRqA12_20(qDl@Zc#*N#S{usg+|^(9Z~t44daUeiTtWVwXiBj0!;=P z>A7#cu5SKS8O7bUZ%LFf2v^z**nSZEwA!WAh=~-UMXX!1e$73k|2|;;Dyz;RRe^s& z0I8qI53(TKCoi&{eJfkm?KhP#W-|_=K`*(&_o4{|(ct8Cr`_G7m?(+ceky0czcpa_$glIv?|M@5QbFC}=pO!l;JK)pnX@CD61lZ>EBl^Eqh~n_m!VSN) zN9cebTm0fW8c_aG1tc0u8}?)b6%;eVWf=!tw9_q)LBFcU)k!GAkb6(o~|-i%x*Su zrq<>8>L=@3BJPOEif=$~w#v;AHDII+Ep~kRvw;+=0L$YGbFz!RdWd z(*TlhJgU4Ak1k1rvL$IUFb;9!$)ZROf%xQv)InCG5@`ZD#tS{O@!mB|X+RZ=Z}IBQ zGVyTQ$ww^O!+fb{Pf#3oDdv?n?FM|Aqxw5&;-MszA?Cv|@yPY2ht3F;)Z9<^(Ub>+ zHgQjv%t#6Qc!mp@@2QeYqcx6T(-FQi^(Lh-9KyUVif7Q)@d#{Ufw<`IF!`ir2|iGped4Ps9WmJOCUf>rFW?sdb2%{f27as2vm-0&i@KCD5p-vPpo4#DaXfd zpkxE@<-+~@j&MLL2Lo2RD0&R(1VyACY<0KH$?kEVDTjxoX$4~(vzk2h`wNRIHHs%2 zKGBdsj-!tvwL<1JUx)3U&2vFchhdxGLp{kS$hhH~WF8^W{#O8C5StH?>!$k@;wCo6 zF^ku~k_9wSK3N(}hoN<6KrU>-^y6))x<~^feY{!Z(W7QWTW1w6#`*c}n$?^3DJC~} z3slYeE$6^ly`~r;=8mqg9mhX}j9tjGxGvG#AN+w^Dn=*;GM5GW!}q)|XSn>U;MAM+I1P<8{a1-aWNh@dt08G zm3py7jwBq@+`W`5Zm-UW6I>4HXx@D^)tYO&IUQ)e*~qjJac{RFxeQrp;1Bdgk++bF zA0uLJv>I`HY&jIS4c^ZtCf7kxuXE^d`oGt+eYf?X1_5U!0roHRl{5lYW&DuAS1)6; zT>)QQUzjy<9GtkJ-PhUi967vyc8gqIV}%~F0$~*gl7~a;fr*`WXLl0%DF>C#0WqDI zx_9i#U?k8$Yt=d3^+3<@uLt95^n|yhh%7H+hORRh``N=2j};s&0)mdJ}B_9jVaJ|vmE#?_Wei7 z^RBtnH@F zT@E%7W4UDVi#&CGjO-`G% z7y5@yQ53b=06*;=XBR2De0Qf-<1-ZU>vEcj<_0hz35oX2AvoPzzDvo;v<)+i-JP3NB+41I9G z_qU?L;gis)QZ2j#$?$`KFdR^3jJZK81RNUe+q??@?ljep>JLviB`7Ip{f(TnOuW>0 zBj)q&6P}x_TR+`hxbAnpe*B*k6nFD0?%+O+Do*@%;Pw}24X}?xsoxmezcSb+7VZSD ztzB%q}! zvDn~*F=*u|dds%Ow1s@Oo;l`--U%JFYiCSJZ1v+ijma8}%RkU(wgN(xY0kD&wD|}g z*ijS8({77}KzZFmx`hTRc_nq%!zL>;Op>ye>YBckd{t!n&Ejn;N^RyKTk>i}qkmPl zS8P1(FRsRU*Q--^i^r$+mW{pLM-r#~#*d4*r6XU5SD|skq`)^nF}S)a z*bk7zQSmK~dV-IFaBy%U0eOE&!0G9!NrvB?aKuuwrg%(I*EnU->Oj6=K_^f(wnUVAlB$;0OL=76Yv>tO5+H|eu}efg{`m`v1aX#JP{u}J~o z9biEuawis8ERf^KYs`HNqwW9R#55drlnAZ=qDIlz>*L*|Ike*OM=mhna>qt;xUuub z@pfMF%H|$L+GsyRuC)R)-^Cx$tr<{?0yi1uw#8sk!r82lB{Z^q`0jT3F7qB2xaV?s z@2A7J)BOIc#JTs-J6`VHHR>V=e)-^+m3%zKi@n-fnNs?|tiyj<6rMy%&? ztw(^l{*R5j^4p1?1Ie)N$$!b?9|OAf7Om*O$pLN)Q5TGUzaM?x>*O?<7SsQ%(9o0F~#|2PDQg&=w8Sglw3 z93_5F3-(HDYZl^8zF4K$sO)i0j^|^-vw-*PZc6*bj^3}40z44> zs%EUlh*K#sjbXgn9ZAcKgF$-418Q=3f*=+`CCR6aW)UHUk5Zwne`jX5EcrDEX1X07x4}d{|qUF)GC6tc4?Of>>`Q;zm)Q?JWa6 zSFL{a;L)m3h_1by;asnsy#A!0d3B$6juk{~gQ7^rp*BSdg!PR3$kDONq@Ob@L5b_}L>>jEC6IZf zXzg=3wN%yqy1sSsZ=Leep=^Fdkw(T3@>=`7iFO={Cz=$sy8~AHV= z0)C{mfB>5D!Qy;o_P-s{Rw4!zwN*y^VB|MlTYE37sCY#mYA+Z42=8ish0Kg(xA$`L z49vgDv#^QOt4S0xDd{mA$?9w*4w6d1aygT)uz~$s4td?bZS2qqgE|A}CTNo-Pqkv9 zteu=BTLolC`$det$41(v^yJASAnEg;!Lu;Bw|u{WIB~zS!=1misq|46i9-DfT#{SG zNGE;HRO-q`A^RxHPzbI4GoPgC@yDlUXCI>X@e1tEHuvL4KMIJ91A3G<6*U~X3de8a zcrx5Npmy{IINeZxXAqE&Tl%ksw2sDRrVv4*EvKO~vJs=}OLQa_UR8t^fdSzsp%oSI z@#-^phn;OBRj`z4n^Zqo!~ep;aK_7hLBP13c{FAqaE?-S`R4$|hO|M>`3=*{c%^8K zCpH6fYN3SbkvOuEb)IAwbj6wtagk@HOQSJtgk0>QUVvc5eD$$w9s2kE;t>mHEDveX zN!Ue84a`$NIVE)a)&55dy9jKI)MeZXUBiqHUAWWWgK@v%XPHhgPN6SPe(=-BGH-uV zNaul^eEy;S0xOtKXsl>AS=h68kPXnBPVZag#mZ5<@$Gi|{$Acz+svAk6_4ai7csxG?- zQo`64k8sL5vJcuV#oQHA*@=i^7krr_DLCBV8xREmcX z=32`hbj1j7sO|h~fsLr)ef*`aJSsiMZ+fVC8dTeF0wQJH5x}RXkNp!3kc~f)qAM3_~3d-oXRaO}s>No`_rz*hpbz?NsS)vm`)S5|k+DdiU_PIhL z`t4v|1^e?W@rWB*%l=D>`95VZXEpMjwg7ZZh~S!9@^gJL+uK8b&t3e0I7rHnZzzI1 zX`JE6dFNxGlBP1A@$fIue8)vlhbXVR!Vh0kpRjnuX!LDU$&xW+ZSd#l`Nb_n zC$U>3>&QVTS9C_=9a;Fc#ei*09fotV%}@G+y@|2;iQLFOF!S>p?;oQAP)qelN*4)k zgSRRPPSnEnk9znMtRS>vLO~KK+?0z|YWb21nwH;?C@`#7_tQDDcQKL$> zCXTPyFPza`fw;k5LZW$yVh5eZa-Wtp_{h3qDcvZ(!Xkg|ZBL4CdwXNWo5Cf}VNJxQc-H^)=`Z7m97A3-lLV_8AKw0& zj3oxN%#*?OlRlnHQi9o-Cg^yX_-$Cr;IEv}VH87{R8Gpdp(URO`oZa41h(GS7|PBrA4S3^9nzYL0X1*^%RnA&Iu-!#l2O2g4d>U z7#PNyd%1L;NyvHvT};k*OCWELhgXFZaCC#J4t?`6@Im1JnZ-qm_YyS!s@TiAB4CX8 zR${N<^!$}GSI%Cc|8oN{(#XN}CC~z~@gX$VS+2|#iU4oP7h^ur*VjalZL#!O+moyF zj}DbU-U=d-+`{D#ga`0{G@f%080UsB8?7`sVNF=7^S8(1d~mP?_?YUrH~sjYRI z(3hX77|J0WCwN0A3)NY?`7}i-s0wlY{oT3WsP^=-SF;AE4aoj|ntO0{PTKkvc=p+> za7CY~073IwSPUzUang~> zY>nMm47Mf?Y_<{0fh)O0RAju>VxWp@(#?};SY_41{44yA?E%lBr*@L~WZw0|Bs;fC zVfbk`dCa75nUSRCdGaq0#%vGRYXU7pC@GLQHYXEfv? zk(yx)!e$Bvy*~MO4X!%ON}J+P>}lt8rr%g?E9S;5Rl~A&BSl^vXt;`x^S`kc36qs?DdE31CA~1($6LcpH*Iy=FTt~h5}6=0 z(>T_a_M{|LL&-r)9r)0oz%-Y}g?Pl273TU-X6qI%eA05Ro^Y4j8H4%9y@OiFp^m)AIvm-EG# zE|g>OuYid648uMrP?Y`q|NFC=DaR`bLECZh3RVn;R5UE^#8o`?P$*2VSo`(NK}TS_ zHtlFsBJc)ty=D@z(%FbW;oz00>0BI4LX$$3g&8atj&y^G zPa_~R2t;~djuZpPi=_l}!0GeuW8fy?m79XHwi>ZTy@>MMEag*z{MMz%OpK#!8mBII za&u4WV^LBe1T#rIzMRy|#51>a&zTgdh%3RfRWi|Tl(XSsK@eDK<|q$O*>EE9Jt8Dz z6>$sx+Kr^2b$nX14Q$fm`HivNqqOp3T|ZaXF6)0<0{Pq0FSmm=a|VP&)PB(61yEys zK@mtH+?DhTF{@xBA~cQM*wj4`ZYKn(dhR&z=ht-HS|y|KFn>`<3w!foMefGni*ff| zB%cZUkZ(hOOPAAft%(361#Q=MmUHt*ahjihbjytAt=P1{U8wzq#S~wEgu?CXUWDSv z-nL7$b+eZdjA_chJ&=E+IT$)_adhF}ufl|h8+1lkIi72e7Q+OKW4n(<>RZiPR9a2Vi}ccgjOGeY2k)se%a8M5ZkohBq&{d;WJ)u;E-yJgm`P9{tifZL>Tm&kaF7#c<*6K@ym zL5u1^<#xQYLFJ=mDzd=6__v9l9Uk}r6Kbb4Ect?jt6=?Y?g*YShaNs57)E2B;n!7 z3uRjVf*toS)%1<&VUEg>=-85asY<_DTAKe#DQ+AnAN0IOR#a_pgf3P2(IYCT8OfzP za#)N}kYb5{lH}}Z*0_n&DZQ5#abo zUb{n7i(f2p$9zMP&YFHNO7(0O#=d1FRbNs%8OZgKOutO zEJ$a@D-)_I>rDTw(0#TrdYL%_MHzms!^^h98(0fj0~}n$<8Edx)4MH&r-kw}ua@(` z%*A$v2F^U{{?1~db&G!fW%+)ocf4P2k(Aiuz!|UH>XjjH|*2DbI9S zt=^^=^0&5ai9ubWZcS-+Z7H%CLn9HvDt7Dz`pFj|Zx^!hg~-@wqXyV`!%kdDt8>Bo zAHhC#?30>PhR}$Uhh$>P${CCAP!yTGl2mux7hUQRaIb+NRV&*P+=8Dpgzba;oU*?5 z|4Lvp)!Ouu^u3CaxHpkfUkaHDWxd0%u7%WgE)$SIG^4A+wTnb8n&ozJPwz#z*fjrz zk#!0YY_VcZD0H5=qI7>PLwy11&FEn3n|H9JFzWb z$HoeEP?I~BNfJ4IO&w<7h9|ce+`Cwq_IQ;(1TKG4)Or1lxj(WlWzW0N-tb4u_jS}7 zSz&x{T8A=6@0sF!#s1h7%~>xwk6e(D@Jc%b77zn|8(k(ZL3DHgmQjo@qxT;8Ma!cd zduvO@FWi(WchFRD9yfoa{>7@-2sUeKFjrIAIfL5u!=! zbW2Oojx@(HE%oRv{TH{P2HG~kpNI>8#OUmtDRxHCfk@_KZ) zEP3@PU8k{AP$gw)ouU$jdNTk8RpZB-F1$CYvL4?yOY??qI>jYT)M1S4~?h+y7v8(pZf%Gjz=+8V3GyqX|h{f(4X@|pG06Z&5jl*r|s z%^yJ?M~-KW9QSV0{oX|0Pk(wDv0T{eSI_;jTiw_Q=fFo;`S`VEL!iJscvuVAGaN2U zt!OLPAWaBlR=zG?53q^#kP=r6EXUg17nb{g%nlWCOYES^`fjITN@rP+og&j+~ zjG6W;S{W(!sOxMa5E5{;A+j)ad050(HbY6zG(9o7=T&m%FJnakSSP=KW>shHYt4c2 z3H;I=4~IT29A1is#w#b7OFi(>PttuR*KYo#Q=RZe;DEA#<9joC!Tk`>Y5u=zpLQji zmdeoI+j^4_u)wF1!aF`#&(f>75Lb^}eY5^?NsPQS-X?>A?|7|iO9l1`KY@Tju6QWt zYn*{n#?*u=vE=S^1V)A%shcql-fUixxz8A)n_Vec_)@f(TG;It4G1hnl`TJ>+vG z0M&t5s#+7ymZUIstkkUdCFC!MEZSLFnkr(1Q94gF*M}I6A>1EH^A{#-l~ESftSE4O zU@dgm2WS_3eq3LNJgnTX4OMyR^gD>N?ta$%8cIH2p>zGmTp*_7CX__MAJ(QX*tW!v zXf~^VPPU;koL>fpKn7;6D#4I9#(y`d`W8lgIrBuLIMZiInx!A~@K63^9t=IYg?DYY zp>FoN#?f9z4wT_`z<9_j=p}0e#?mi_){OmW8@PTv`QQ)%&N5j!8qYp`D#d)EZI0D} z5F2l9Ym>$49?koEbLCaXCm>h&p5H^Y-2lj5fYu48SmEkAPZ;nWi{DHnY041)5& z9`*E-_P5UA8L3(VgqRA*yKCZzo`*>k^+N<0zk7nn8Ibz3nNKIYXvU_60Pj5L#@i?T z9gLbV_W|)~?v>)86>%*Uwq&zI3@x2?t0+jyrkJ8M;G1o-NVT+29qk)7hF8pX;K|VM z(+3~J)7MD&+lQ{|+7meZ$mjjxPZk*|DXB?@+={Ut@SL;kwB`gXShS-)PA@jsl*eT4 zd9PefFa${&IgKIDXXMnhHlS_xamPtTKhRIuBxFyG0q+_987{$iFf$8ipMIpd$-qpa ziR&uJ$xR6Od>BaGy!TkS0A+RLqozgq&BI40RP;G9kZYi_7^eTpC{?&MurQmWD8slX z*Swx(oWI?J#71^+K6ZQl{YATscVtw6;FPF>pa`<@QTEAud5At4)Pent(BIwXyF2-T zhENGqT)O`Xx8CnbH+?c$BKncPhRVvYDe8eJ&-!(PDH35GmeBPWxwjz3q^EV$nj=n| zGWMYln6Z+FHu(lzv-FmUAO3k$SO*_&DfDMLTwPl}pcfNDMo4Ab*rscdg*#>S9kmSV zr3zau0k_@F`o<_kNg5V=Kq8!0AQ)rd#i5GnfL(r%CYo*$leh;%&vAP z4!hs1?S;3MiDvSP@|oyE!5;!#<2Mpi(H5KE)ecP=94mRXAKC8lCLwxL_aq0UU zNqWDZ^+4@Oyk}l!9o+*;Z|nYV7=6m6773s%6cvBnmKI+kXXPYf+>(lNIZo1f*_oNi z$|)sJf$4KniJn`QAi zZ;M@Vu+D`6qsq#}$uV*|7J!uUin@Gl6(Wd&i}}eb$t!FmRV-!pJ+-qvzMm!)WOAq` zB!J~uUfl3&{-p@^xm7jW*!c~9hvN2CKTurn2^euDC*HNBFc$jD&qp?Hm7$sp)w0g; zNawrg3Ups|KQgLEG|B`z(`2&SXl2y-e9vXT#KDY9hihd(!gc><{Ylqc%}l1txZ1MoLhsg4=G|(t^du`bNA91oU=FW4 zXbcCBXH~|Z|GCWDzCdz{fheyCS~8+^QhuMYGX9?zJ4B~EHE8Urg&>CML2|V!LxnsW zSIAKmBR6xCQ-l^CW`$eNx>))5ubj^~3x%oDga?}VGp3-nEa-og7KgVL_TF%>$ckZV z`p;tm1-jJO!(~~i=(qF}n&uXtGdYr;8olmITB>Wjn!w+=iW(DnQpugpP~5vNwq)r_ z@1mXh-sg2{pOr7^sCs&rCJrxibH&?l+P4)`yv)qXqczk%o_Edf%}EoIKBY16uw?Mw zaqRVmNhRAu0V*Rs&4R-J6Z$8CUK$Ko@t*aW_3+3%;0Wuoq|i=JGS81mSR6TEAM<-Y zkaJV>*Swt}mnG{=Wm?vl)H1f1O5Sq-o@VNoOlU#ZE9HbYU(GgpX|j)mKJ7~@9_${~uMuJ7dXmwSr(f-iHBu$+ zwup|jBmF1|*9UkX6Xyr}V~Hr{fu5XP=q0fn52~LsiLr3?YpWTxNIww1(tf@n3?Q{_ zb-e@xn)$$`K9QxFQRf^HkHEzg;e7|(E7P*k9-{LQ0VRrDRXs#Y4dv9pYrf6pJ8l_el`>$Xjy_fz5% zMN4H(^3hD~KK#s#;ghEe2PWhWkpSyqEUB53a@0y*(-TS* z44QpGHiJ6yk94X9{?skZxxG5p^NDv_5A^F~TFL#rE!b!JJ%6*=VGaT67c@jg=|@t* znJ@s`^HQMU;zg5ZDgW%431tW@n3AH?IMS~dm1r#T2ag@`6jheJn52z2ezNudlXBt( zP6-Ny#P%FW@zAO%+ps#i;hv0T)YkvpkWY$btl?583C#UEi3E(2?XA{c4#H|VuC+!E zv++dWBJ>y_a)aN4CQ!}nCyNq&G|WmdA* zTp%HtEHZcwT%1Z^p5sS16A36RPfE#!9D?8$C))+alaFBy#6!M6%2robqua8o(Fmfo ziMrNimZHJF8LC?6_#x3wr-5G{M~mO7%kxy^P2RsB-O|Vt^oGWHJujUJ$?teAyf#7D zcJ;fpW?xwm_xnrt{qXzw2pEqj{%<_`y1%L@oa)r45W0XMN{krpk?6A>ZH9mS1`<9c z-foc{a6#E59WkxsS8d9_wd2TCfASN7ur%N?7(f2>AOkE{OmgFWr&^1jumQ&oiNPM}76B{8GZ6~8 z^A%Kpdp>GNt)jYW3=WPC{Eo%FXlhZOS#ntml0Zlt|9N1M5F#jCP)^;>qa%8L~y&_c46$Lqqo(luPGs4Ax zddAln%iog*ju+OUlhEn^S1-a+tqHyFyIfKe>H>l0$sM@3M?&rk_wXSz-q?#LZyN(5 zVPlMH01@Pcg-m0hmz%Zf?f`u)F)vgfLi7$q821?{u_sN&rUbgZzgW@>28g|b)PUid z+_28h<}WEjMLFK+kIbjve}f$6)+2^&dc_?W6mk(nM- z)>=;&YrFQd7)?1+l{e-FQzBV7X9Axu@PuIgn*8C}uE!{E4w zQ%hohc*heAl?3q?o0@;RXwO+1912RB9Go5|)(+0ozzY$B_11NlP{&$3=&K_l-?+p> z=>(BpsRb|UR>mG7x+0))-7Yh4T}o^>z5Qojj23CBD)_i1y1Iv5h^UyQf?tVi&m?3s zm?1PR`zL7eb8)jf>45Lr8};jU|8z%qBqrE(hhpF%ng+sqGR%ES6X0jLC7Lnw_+qjw zHD4J3%KU!OC!0e9Ts=I;WE2bEH%+4oX|TgP7=;E*UvWQe`%NAkH)vyB#C6?qo}-(Z zwI(p;bB}%&U38%N?+*ZJAep6s#tW=n?#btCyF}=?^eh{9xM1ds89q0yaYFOn2p2T3 zU)pdzCY}1-c!W1P@xa?{Axgn%Yi`Eq(qri~l)Tgp8j=_c&`4&hB>@pb8;ifxv%8d) zl`EsbQ>w)6uYP5P&Xq7c=;gvPlYa@*Jf21Rw7XMYn+=B?Rj6rTpuQZzL#t=UU!*vSWg~Gf1|^2IO7*?FGg}ud=m7=-AGh*vX{aMGpbhcd6{9B3x9yqT=C|MkoABM1Jn&Qajg-dv_4&rLzu^28^Y#tjM%1rUe(e+X zr*okMieL~4r3{|Yhh>)lNQef7ZmMqDbj=PF5bJ?l#*Q{dUl?#XA=PW9LmN|S^|NUT z8srtb`Bje|VHX`B0n*D#f08E7i`UmI@WTLk8nCdyWvjD|jY_b3>@41mO~7>|p~k~| zOk=S;S<#pqH6?=+T~;hgOMV5=}{twSB1Z0}`-%lmq&$=N`UrA7OmOiQQSfn3q73&fLu6?$hr&5$st5`n`#k_+S* zvRz|~S0=lxtmrUB?3*QMm7utfQNJ1(J*o)1$NOuR$|G(*Wu#P2>133b;{IS==&D)Oj(w#M-1x4l=_W+o zBuslif7l2;>!Hc3sQ{An);3(ED0-}#1Ko`JARw1>o4~*b6dbzYpTjFr6nb*xCr0PZ zroa+4U%92lF#I%_K!rXpI(mf~jr^d6U{Yb$^Arp3@BIOvB=WbYS*!Q#+7UI zKb##DVDE_QHQSb|-f7-5=ncn7ayWwx8@t7Tpy2&QwHNw3wVY|aIh4KW5;0|o@aZ3K zB~1b!`lTp(70A6+%BHGi#j$GEx&B%T;Nz1%i(nQRTqQdiWQ0CP8_Emx(#{VyWy_va z17fY6q6;(CqLfjj?vKh0Y-^`i1_Q!od00xtaIoVEChBuqc4c~^%GlZ)F*!v=9l8D% zZvM{IMT(+9J~{gv$60iM3TM2{*6Y<(`$VFhtAqf_cYi60CL`mrM&)1nZVTZN%fCcc z+1G1c@5;TgBbnPBbWKyVqg@~gZyB8b*_Tw6i+%yZKzm|Wok1QhWbP_Oa!~R@E6+bb zFQe{`P`MAAemfn|6PQayX5Ey2swh=nXI)L0r376kDZl8{-D=6NvWE|(?0P~5JpQ!- z5-+DJueJa`Iqg8s!#NM}xNVUiW}nOUyew}87(%6#geBem3gfwYKamiDnLMW7&Aogc zA>O}UAxMT75SPsoZr1e5#P2Es3ev9@gsnxM!8t_lF8;Qc@Cz%yyngymnFrH+KhAPH z`2E(pRN#34V7M*AE)%Jk;4+){{(HnHFF3ha`LLj(u;lyt9x0QV&OilChHxomLi(BP zw5Tw2(i~e#NB@5%GyiKKN6+aFs~o!5t<4m;xt?;#UFR1;Z&1L3){F3uywsNoGj>ro zT#$sHVSjA8+eostWIv^?QML>bnFBAIzNGYno0ZuHC*&;0{%9vFLDP-v*sEs=S5982 zxs=}c3C;Z6W=nu7DgQna=Cm|e#!X(-FF(Hr(4!J@&R zUVyI&&XF*oW}Ye>{IZr0<@~3SHEfPGrFbrNtL}bcSmnS#|L5uul*5rur~09e*~0(q zLz^myVb)I*!{=U~=x zl7+UF+EUsVoARi655{&6J#-XLULn)CwQi*j#5GML7%8GNkMd#($$t80LxwL_B8ldj zVBup&kPgG7{y?`n-Nxc?&>VBRK@I1q(Yc4)8iq?hF0uLQlB=a4toL3o1vRiU&nUoS zLD{h?RGm0p{H`|BCU*dobiE#ei?pqXscT<}d#=^$c6hOqffF<9EYAAxM!L1z!|g`} z9suvQX37I#DNjvv&>d8YVAqB+$(KmKSA__2mSIa8XHJQ6@MtZpG1ahM=lTM5rUzmJ zlnGCHE1Q2;m6l(vfuY4i07^cu%cgz(s=4jgSg_N=)QPy`LZTv!-psAXy1}~jMojDE zk-_$S`$cs22p0fy1NPQ{Xbv%z`=YXs+!o(&t2nO=3f|6KBS)G?V~E1fH-lanPyt+9 z85Lr-F&KUOYnlchnD@qfYNuuAX!I)^-{Y@8fIf3i$*Y6nEqiwPoa>ND8Gl9UMf~< zIVAygdS8=g)~Duk$7vFvB(sl`vqU^~buv10BDFc%(MbKdDF)2vtxi%eb>6#AZ(UNHV-ZWbUXzMNlpv@2LzyZn&ttYX;`!H0fP z;e(lVQ^|gM^l{^P3yO+GVD^o|%Q)WQi!nW+r%M)j`QT2#LS^h@yV-HhT42Nwq;~pb zHQR;~yR58OzY;9zDru8$=I0{^O914O7oU%_w>5COt^K40eOpeu%z$S5sDZXk;p3<; zM7j<+LNUb!jnvF&V@o(34Qu-T@AwTp4K)AyH5R>QRJj|X; zT~$?VV`uOJLfAdBn7d557nSqLrH`{)(Lq(E^Kt|0gy)n`=13# zdj4RbF^5!Slv4)QZ24s%J-z6zg)>da$p;Plu>)c3F%s95HRf8+8suXrmvm*Nh`_elh1mDCDz&BY`7G% zKaF0b(_-iy(FyF)rl zw#?s$~$7Rg0HKuKw7=>}=(W(mm!mgfKP{`|f(|7V67hM65^_MGROJFfeB zeHwSDCz%|M_VZjKV=s^!K;%L-*@Q?wgBd)jU$gtS^fz|I@+FN8M4wDEi}kfUpDdcG zV|;98O|ShvdzV4uOs!7w7hTp*cJoWPjF`RAh&c92wXW{)Cn`Fw2jBD)} z34EhqJ377f+@W}c)@2@UKN!FXRZ`FBq;*0az91ChZf%~L)9Y~sz zpx+RQ7lF?@2&HZwUO)a_@wM1Ky2^rFF&N1>{?A8Z^We@PH1#d`%{oQ$E|@f}Q8X2Q zniiOUiH9$K9&7B)&XH=Nuh264X4-xrVU1NY_OC5CIx!Sn2lfX|-bLe!pQ_0!Y6RZ# zFm;REmzqc%U`U446I-*W>E{qR0R`hqWyf9};xR(%tmj4-R?R(mgLt1KQ2$dUR{g(* zP(^&Z*qm8whViG|7%s5V3hlm@6^4H#7Ugz*xf1ZZ!AA${=2eod5f*9LukWhmDq*-$ zN;0`?1g%!>3o|RSbYtf6`gEvl1@649712g7cOm9A@tr)CR~wCh@-v)Hr9z^xF+G|$ zyQ@^6Wj>_mrmiljq+c1PnCPuH!y2RAq zJ}qEu;GMKQHkQNxmilXH-v_9cec7K?GANE4Q(F5ESMia@EGPz{3l-Io9#CYU6v_gL z7G6zYtOL3)M@<8ncqr>4t1Z?|i<0SMO*{+*Hyq)s_6WM3)p|K=vaFK&zm>hS4J%NL zkJDs@tcMtL6Mw4PX3dH|u28Wle9Md(KFP~-(DAX}N$|fq=joMXl%`)T>yDSu*U)%7 zoZWwo*0rMCo8$Apg;A+OeL3)xyNMp4r{`n)zYf3I>J+c&=sT*j zCrOVqF);JV%B5nY?B;sIZO*!S4dhl-GhZG{&X+w5L{{%v38i`c?805e7N zbmnm$+HZ`)qVxOVpizznAxG8BUXOT0jshsGW|7u0Z{?}i1?PvRH#U98Jjng#ZsX5g zWYa7+WuBomc~q|DEU5HEKH<+Zg0a!FVvt;^NvlJJV|IRm5^ zeLkTj98-w<09QLKck94S!cG1C`SJxK_kKtUw5!PmuAT2W@3)q3vREu@fdSwW;Jy!Q zyT5J&T!y4>I;D=d@4DMAtlSO2Wx3@zym?Sygy||`B#}N*k!&8)QdW+19eEr0aq3PJ zgAx3`bI+|W@s4?u@zs`6eju2%m!GgnE4ePQ(Dl`VnrN@8E|1piGH)0}YZ+S8B^x`! z9}gCm=?dqwH^hYNQEJnYf%-l2e4@pTpF?m>mG(L=u%ZOj24S@}-L1LQ99CJ)Qr*zz z)(Rs!g>t0gxnuAAAogBi9>r5;xQ6ChGdR(CtbXI-*`F1Itvf%gOJIWQ*jrxh(fPqM z&1XQ4Q|-0!XCo(J?-QrCR5MDjt*9MnqBK|)TetVOmcUc{U92>~t{fY+xHKVvJz>$% z{A)QZ%aY7_xz)1PfcN!883VttsHJ|uOz!jN2`TTtN&F)V#}SW{qj?o+I7XzU@fNqq zFjp1C!%O_3L8fYl+_ARlv&&n#^^{vWBy11;Z1j;enFfXMEdG0MN1{(+a+>NZSco)j zo2xA@eWk@kK%a7;PxnIq!XHkw+qCR4a(~-)?|rw~cJnzjL1*n|UFweE9vrCe1FqEX zqkxMDz=y~BRuYU?;M9~Uw~2YxRswf6o|tUEd$Ay!H+UXYbi|J6!ZX*Kc~MU~*+8YiF2; zF~aiLV|D6%o9Gm3QdyZAB+re?c1PD(WYxfb(8&IMl%V3MGJlRN&|w4o8{^X#s$i`c%-mDPJRKqYNlUIDhdrsC(p^9 zm11;My)zeYTU9zoXe4q~x?XGE^R)B#HiPrB$&%~U6`sgH+@ijx?VH`*v-^bd(#K)( zBVa~M$Plq8xNnwFoz~3CtNoa5wbT-D2jV!G@uN;)SPR_U@eC6piJPL?zPKCO(E7Ia z_$QVv^xQ8`s$?1}_J~HK@wIeLdpq?j|IRB(}`3N>N7M*xI^f zdRqd>V|)a_sSAnwRjC{F-NvolyPez%&b`Fka0C%G| zO`v;1Yl$c)INoB?y4+Ox@Y1z;b?xM`Bmg_yP24TFMd09wR=OI$fua`78|(GRSSngZ z+EF&vBujYHCj(6i=#0XgnT&EjRQP$+w}vmpR4nhKJv#fK^Rm&CqXJEZa06|aNNX0Q zFw5#hSY-11k#T60*r<)0ERbTa6t(9>JA=sy{}tRIp!So~y-qt^#PWP8Vr8d%)_^EW-{>sc^=G?+}1lPGd=yH?A;T}hGv{#Eg@oH)G59#{-=u%UU zUe(fOOItCr%zvu!)Do~dud^he2&Q`9FU=fj{z6u6!$S214gL$%hHY9mMvuIWCrVv1 zVjsCl@hIpX;|B+bB)PlMYC3G_eF=ypKnn|H3|*JQ2&H^(dh}0%e{oL-iS&|u>wUPB zF;y?I*kWC`#}X%Cu(w|fXwMW+oi&eM->~h)7w@DWE;Gd@oBab`jDH^T@)Cs@y%FNP z?82uOjCa4A3gvKb51rEP5rc!kgmOm>m9Lbje6+9MCo{IVph%k@r@3z)>t;Le4NnDq zIXENgHNCrDJ+Aa@Fbn)8_fpH30v2=KAB6)Y5klTC*LpDG7FWf!3 zzP=7PNy*)aF}FN!_xj(;CqPs&yl7DZ`nV`djJ56Lc@j^+OS`kE8S71N@{Fh{&s)cdQ*hOgv^!9VO6GuKQ4IKf)!D^KjR-49kRUuHR6Mv~O57*l4Wj>J!H zR!Et}O}hZ$AgAp0DP2>vmENF#y)ZvOEC=?>8Q7l*+V9!(HTiFygGi~G86F;P$_`}4 zH(nJA9c@{BG&$)HhyJ91`Hv=QM-K6nSbdmLbGtQdYVa+6z7x@}@K-(<&fA(MU39jr zY{{gn{yx+Wnel(!<%Er9As3^s$nc!r*(1crWGGHXnkAA3it4fbUl*5 zWxIhJ{FNx&A@VHh%d2(|P2u^ngDzaom-a5^`IO$EnZCQ85aUM+;7V_LgF{tMm=irK zlNIAHC*QaT8nt%^1LEp8z~aLN`TS@Wt_FH|lI z>1hH+{fjT-g)*z0Z0F*q3G^Odt4&Sr|LzXR3>zsU1_g0(ar7Nk4)*u);If3lmGM_f zN1h{e*BQ$3eT6E%i1(8q*7qlA@oc z7QH5?H({VPgAJYkX;N|AEPXRFPI$<9@p7INOlcp3Wb4t89fxo{6ibk4TwEphLppMK zb}^7E@0H2h8co(RjNnJ3;)XWZd67=Fa>&~Z^YUo;wGYsmAQ+B7*~$^T&@?d=I-f=J zW$aF+jjogq)yCZ8XvOQJrH|CQ;Y4;M!9z&K+x?#({F^cHk87l6wpv2AA|0s^i=gKr z=wEk(@?gAqIYx6kR?(lSpr8PwQTXU>0X`dHj0q7>RXdR1#k-5`UQH6QZ@&J&J#q_$*d}ab2+R`RFaw( zYRUWhC3AYkbw#>Yto6tpds>_)sX}iZ)fa*DjsDHdC{g%d0Zz-sar=-;;xW>^SII@Z z@|i};f1W>yN)0w)>!AIzFHXD{jgJ0&+A>Q*zxk)}!sva~FqZa_I z@^VH?Z%9G9$U6fhi;CH&v#_Cyno~s3i4_{C--d0E$33;OTN42ohgPu(V=Nb?XXD)B z1b^6Z=&@_C&?*PPY5%gf+!)~N*%Ef?-l8P?o|u|^7|%KqTOE(r_S+72_J&wQ3DzfIkUjikjx9pD&1K{GLL$<)7|MK(S55dbVbbnnpXjAuM z)AY{r?g;ljvB~@J_3i?H#qGZ+C^_7s_{|@rDkBZ=eE12k=h|uGp?3PQp zagb*&f#EepXM5xOotZ8(r7iKi6|2_sQg<7Bbh>>MI$k`&l;Q(A-Ho{;$I@Ul=jXb@l}@fZOiP?d3&#z>L$(JkKcB%hcyZh>iWY>DE!Kz zp7=UBcu_ue$V_`}@5V*!lj9CC_*7bwS-hV#u6RKmI8(`GzgwQKmAR<=5$N9%u*FYd z;>{{HOYVGce9U6n0wjzP!?DZ~#~R~j8H;QU?`A%?s#IVd0*6hbjs|sch5O%k!)=qQ zjVi|Gjy3{hG8rkkz|%7(f+04~ogd&H3=l#Kw+eW0yb?9yWILh61-;J2uf3JlBWIVm zO3pGp_48^J&Z0dV=%89B5Qdk0AO-1&l9G}-@Akv(Z6J`h?S^t&y_TK-!+x&N%-Xss zYYqW9$$QsgFlKQU_1k|T(QV_z2bOS%i$oxPPQyf;Dq?t|pRK1B8@`(h=aH01TFEY8 z6GqjLAuS?_{=;C}DdGg_SZDqfmq{ZTfbn|c4Nh z=;l6&0zjIDI%q)>(CZz5-gai#2Aceh%zw89-`Z}=o0^3GuIE2Wyj_BNa1lLSn~7@M zIq9E|j%e|c)jA-mY+O6x&6u*xJG0ov@urg0>;6l_$S#VsKJ>aX=4}@Z5Mt6JsdpLmSk;=HQ|Q0icmABVDGAujst=r#S|rU)yh`oLXF)B%765 zYES=yMjZuuF<%zcW%RsAfyh#Hem;;|SzW~}2p?J$h}nTCb%5x_bSS2Yzt8Vmbli*$ zvcsU>7S;WA(gLsq&n&MnQ8($Rnx0KOAu&pE;&x5F$C09ZT)5DozUE6UjvxyJmQ%j5 z9kTOvyzA4!2}vw27d7G>PPXA74(1zH6toZQT|aA6&Wh3w35~*J^N7z zyx9I(aN-I%!dhaPC2PoKx)RXi_*1O-bTu)~5xaN~BQKqiJF>mDdejPo2te*DBYcX*!x6oSs-_rznR5Eie&cfG4T0v*J zz$aZ3-!Sl%UAvY9h1a72Tl_a*YKgo=-rp@F0zremhp?=l)UrCdiUf>GP3d91ij+Qe_=GYdNiv}K?I_7PY2rdG5;VzBDSn;9cEoP} zr{_gcvP(<}FpGc$B=9e`Bp||B>uv*=DY9#)jW({_eOkTQ>dVH%t88trU^&WnsTiCA zxxPuRKj+>EV#{aZ8SzQ^O0xZ&K3;L@&!57y2AvSr4HO|6$vrP7=oK;ARlU6~Qz{nV z?;17=#G7;ndL)ReKxx0Vp z=}B$qyw}RV%CH=s=;J_KsmRwO(xl;Ct}@Am!nvDQD678Iu9;(=oKsfvk{OblsoH_+ z%eBBp3fY)ouV)`EWA6T!qwV>p`BRAnyU)k_LA&B8#QyNIf;xp>M0$J3Riu*BV`q zkFn(q7oIBfPt^Nc~}o_=*C?>hnm{6Swy%hyH3Yv5c4* zM54;1NnNr6GJ0xCdBUNufCugnOu(!u`uEF%R~R(roD<3)dBdS4B1YvHA?-{s=Pppq9(Z5gY<~^(BgA5i_A`A5Y<5z9bQ;+EBcwBDm4?EZlC<|7!TfFl8Id zn*=|CcxY}kgB|`}5x5>Db(KMR`fC=oq6q?T-5Jk?wtH=%62VpgD}Dc8cH|S+WKBY} z>dTSsr~a`b!+(m=?Pp8VK+;HJdm=E8B+c)SX0zq(CYb@2EbtOXy~c`rSq0U0FHy)s z&9$nNR97i)4rrwON(M!`tKO1Kr-qIO1p8O1U~i8rp0tDm9^_V zRLtVKNhCG!lC61DIQ-`^TR@=Ly$iHL(3Y}NtE(uq(Zp6~4;zNQ*JueVf^Qugz`r-b z%4b;3j{?XdDJU?$ z+=PPq6Ikn$rI2MrYlV-!l$~NXDAVZSQ;&euD^rfycUB!Jp}4XWOzRCc?nf2SCded{ z=&hVV(ye)?#e^J3+Y9LuC0pTNYNIamN zcQo`Yv|XB7Q!!c_9H(6`>Je&@VH@_yOfgB^!RkCzrBHSI$S;8xY+zf%OX7rZg*fBA zn-F;q2r)qJu)5TRlT%vd?Bg1Kf4yN;rc9C>Q^q+w7|H&*|04^hjA&X6^xn7DGHLby zeA|*^p9>mSX_e7bnTdhzlmiMh?|Jo7Whn&S0 z_;`OwZJxbh>~w05f|4>}ZzKhtPQo!HAwaB`B>4-MhH=Dh6WwdZ=)OEQ6Cm}4!h9B& z2?-xY(hTWi-jmu8_k-!|hMB0!(V%;&_*&7J&!$0SAGiZ|RIROMsh^httDvi^bvfL% z^)jn1WBF!ydE@T(dK3x;GrUnL?gN7P|L&4!J$lsCI63=Sb)W}pz_KD6R9jfUQME$A zzj|k0bHjLsGpmqU{XI~{8LUUD6wOGUS*}fW`15?IPP;{jvQ%ZDu1Ndpo0dCmxJ?9o zd^)^e>vg}hlA7LI&Jly(`TS|+c1f*L0`FpEn3ad@s;wr@ziGH|uKn7io3GvZ`b2xfB%@^z5wvgvNc`6^xfyug-=<5emkI2HqpIWu>hU^)s z$go9jUPAlIgHHp`S=d}@ZF9UQ6GxN6B{gXK){oRy@6s6)30*_^!aJ)9mFQ7;LxNZQ(fJ$oqNRbd#w{_G)SthN3k|T{}46C|7RM|eEAB4N?$L1D#KH>=(ug!IL8S_irczS_E3>CudjxGqH~&T+^X?E3fgV4tua0 zWhJ$5FMIvSMH&Q=ot_M`U{62VEiPnN4Op#i13pJ2064bu;mxdgGCUct^(%B7gecbQ z=6(N9OaQWNxM8_Qx^T)(D`@TPA5Tt&m&GHrD9V)G-+ZP0LNw4|@FXIFH350g#XoO{ z5qO&Tp)cb6j=QRpDmYDE1tJaX?ruS|eD~!x??Y-=%85Eo;HDQC%Dr|64u0^0HBsQ+ z{!uGvgm*pr+%MML`gpO)J2&gU>=F21_PF8r`+bX51Dh-H`PRfl#;1|Wxm1oBCQS^l z!qL%?OHYuvP=w{ZWgR2&mGD=754cIx5+VTb&xGQ`@7 z98CJ8YrY|SdYHK)M>{pr6fuXssN*c+ZZDTD0?fS$6W*Lfo6ItN)WuWg{gpeB{?4$d zO8DcUui8Xp29`$8ickLIdCMb6!e!EWBO#UN%mhV9uS?Ln3*HfeF%Qrt$4z(>T?U;G z7&Z4!rVIPxCRsKv(9(f~thu;gs#hb7w-N}Hlxfj&G`rv!Q#qO}<`uzkUo}LkK$M;n z`$6|S+P^BNqcC+C6RK`=dEm0Ow^SaXxKvadCcd z*-|fAJa_c*-%{a8&p0f`0kKp~4`W%F6}atkIaa+##_F(*XZW&))iY;p3;r17W`pOM zJ|)-d&}DeonOi$3qx$i3jgd*&J@yUjRJ94GhKhMvLHdXjP=iCqNKZvmreFeT)uC!8 z0i&e*ZJJw#{~&1zz|H!DKcdLlomKVf=b)X>k1h;>J!j+jpthqh?(E2#<#Wg7bE8QC zKE9LpM>k~^U_MGl)_`wZ@qPDc0agkmP{rX$B0)cC!s}tU>vZ-qmYZF6`d@ck2_?>` zyR{h5AO!6#-brj7J>_*JdHT@@R=`>|TSMh}e_ADJi8Y{C#riI@=ik;dfB6B9m;^T< z!u2B_tD}alQBM$Y*?5gg?w$-$t2!+@T|M9s6~y|K<*-G$XRtc^vlW?lbSg)quPbt_ zaCNott5!HGsodflO!5NXw?dGG$r-Qs_~T|^)tgM&)z%eBx{UbCsd=F0elX&80DXS& z`uV{y=7~s$m`_bP=sUM|_rCkKJ2S*HYz{w)HXt{LesgIg^s^Upa}EDyK@J`M^@ZK+bGxp}nY4xq3rb9B-*GQxB;O+w44$ z(efiORQmVbNVa&B_`hN6E{)LC6IP0uP9&OgCSLgCR=iZt;gAqnYHG^yv)fNdB%`Lv z7J>{spQfxp;!L5f;apul$4lZECfDSjfc0nbYyZesRR$HgZi4padR)4)>$ME%{~VJG z$*%pc{~aFkXF#L!(^;c4hPVD5{Nn|d7e$#&DENPUE5L&*a9_0kMoL_nUMA68 zz5rV@7c@ng1=`x(MUrQ1k{9i`C`Y>YyQZ5It*aE}wS^C1knp+y;^B64AMVC91l_%J z=%Mt@{Au!Fut}*1dVLoONaKC*?iOt=DGn5txT|ze>E=}?)%5PHbTph^+WWerhaVEnx$Jd2vpj)I^^savPSXV(eh=PX4?8r= zZr6+Pe;+@Cc(st4cim-`6QVs|=^&o`COOpkMif8#>ob!nwrNLoRmP}!0yaZ zyq~e2lg8>LSY?`uP7du3)c8`XI8XxV%x2d(j*{>yyIBD$X&@6HWwd>y=V$!RX953+$l5K2$S;@be@B&?9#d3Lbphu(lrlb8wSiy-E z*Nxu!679DU=x@=VCoin{cWUY{qtl@*oy9i|74qM3dajvrcnrMxSzR4co3@m~p=;3JKa}Ktyx;JMK&U8C^VRt;9WS1)Xjp$JouB#{d`3 zgMlaA*NI*+!4^mlvETbS{dJ#We!rGp4Ii>B74_rsPba|T`fTn`Em5G99KH=4WW6hA zny}~U8o#+unOr=Nis|f*?tU+CqGnTz7ViGn!&$93lE<=AFC;Q!s9rsI>UNVwa_`QL zY+83#=v7&H-EE1&s~X*fppuGTw=d&$bxAAErtIo<(S>=yA~P3=p_CN^br}MdA~}zHAe$u0Q-pxVJr=bv9gM~B^LKl5jk)ZNVwgND8ZbBq+Zn(Lke={_PlNFYvY{&Elmqhg@vwzXTl=@gaC0IWxiOIQjcETYUVZDstsuI|8X(*h=Sr7Lgtm*r^oWY+9?Hjjt zrry4m94BGH?G;DtuQ{eM+m!v-OKY^Pvx1v^5>QrJORwA8Zlt@e-xJGre`_f}FCaHY zYjtoK_EtMsv$I9;nQWddpkzYG*gb^`7w&Xj+RK36hLVOZFx1cUd`*0#&6LuAY7?7~ zh;JUQ%rk{txs=lgy-#O#24$ZyLYW~ExHh}t<<73tU#UVh)RGI;K(64$6}b}3_g~MK zNtX&D_y*-5ZC5&W@nO8%_vIkbQ!qjf_I(_7WXN}fazwR!4C)^@Uz)D|2an!hk?Ow% z0ndLTB)f3$R&Y-q+|w!42y|Bt7MlX@5V`-Q@Bh`<7vPeL+S<)nf^YY)IiI)d7aKK*BbPY5n);;tnLg`5Xpfr&>>o5RXt?$vnr;vdiJY5J#EKqA--L$chr5W9A zCp%wD>UeZ(j&zVohEPpB`9t%AZrXraBH(u?I__^#&>=W6xv*THgIMu3zhbib-`QCv zABRse+G!qv0Dc640-b9%*i~zt1_Yl6Ckdz z95yh+IX_W8JM1Pcl=0IWUQl$P_-K<1XVvj-ehAW_cllcg@>=rOD7tCmoW!}<-}(8b zv%pq;9AT>a84#n@GG%F$!FuF-2HdCBH^wGqt#`uUw3b@TFMw-4iu1$Ar$bRs#T*pa+DOCWiqXNtj1|>=cRZ`?ahqVO@^bu77!}M zQE4?ezHc7u=|f#)^t-2#4h2P#P>ET&tIEmMRq5t$Rloz(u@?b}7J&%!BUO57l*W19 zX?TW?_v(Y;osFM-2}h59rU_k`IyHKYim8fTD_bcz(|ZBLDkYf*EX$I4vmc&`xjNyQ zni@L&v2u|)fTXn;b^wM5IQakjJ6 z-~NkAbm}sG_m9M__~?vHh2H!Xi{7HVF$8 z+NQK&yRg*c7IAOqNltMKlsP zNYfjs*0VA$^Z(j+oEO!4^$Jw1vWbp6O)f;|BVpn4!v-!r&zI z-kZ4(M@g-{0;)<9gRhfv*t&dSinLvLJ6}%O%+6hl%UN)kG-$_qs+Og6&iWjQ`C-L(KpkH;g5YYc}CalC&*>k(!1#9L~sVublB!h%(IzNQ3T{P@zL=M zI1>h*Mld9dj2N{jhr)4ve^0xbR7KW0k^0N#sX1d=7Jc^*t4y0Eh&y}q?R0inYua;g zwa_Y!>6_x(uS#NY_E>a{3u}yO%f|N_&?mLG6$|47!S9-2K@q;P05X4&_ga(vWCYu?Srr_Zi3 zl$P?ub$Dg+U_QQk^>IwdlVZD0@uf>HW_1ff^mBOghHuooue1?WS5Ddj%jbk!2)aa&Rl$*<96e@LOyxGG7AO+1d_V@Y@{#o zpb3YLNt!V37;7ffJ*}Kw!!bldVwxN>DvSD9rzg}l9%%0x`mYkx*LTJjgouC8S*%>N zj+XX~IS#uS<44fBQsU$R$&~`e;PAG1cc^|+n;|4b8vi&mA?@L%PW6vXBN?SY;7U#M2zt0Nn548Mskg+I~fRX)*x z9z9#+w=WosJvfeOC}b(&$wq#osoi!7ayFifq?n@&&z^WmBs2ntzCU-jYv|%Y(>E}h z4Nu-l^7ojnQCen6K9O-fVa6x`x%CD=+^h2`3fS^o@P0TyD!|sB32_gVaTxxK7Ksv- zDw$Q7uXQE2a@I_Dd_f;k>3+b<;(LAAY$p!t3>MEl{P6yS9ug?h`BL(7kMrC6ixY&= zX^>XwmG_PBj{#2}LFe(q=vD=zR~=`@YdCGa&VvG%S>ny-$_w_cC`f!}vat82G8v)z zX-s=lyIl>27NUq8DryOz-+QKk*lZ{+@jDKYTtBC)jJWi;h~-=ZP8!{D$^?2y2i#MexD*h56bQDWYk$1X><5{WOAZ4g9&U~Yi)K4D-3Sq3cSITOvnR=%y|(ohD4!ii z^MF#eEF4R0&B%6aD=bq{y-HRJF9hK-V!M_OlTjSh(x~dE2}W3koT5j-*U1xw(kvJ8 zdxCArVHS-l%QTr^f80T7Oc!73c&g_=rJJP^Rm#s#4;R4d&;Q_GjWvqRi>|Sf(zn9s zD~0`J*F+1sD|gxgrDyBQ`=DSu@H)x)1rR$8O*oaPV*SPYk>r(O3|gLhsM9EgQSY4o zskzhm8%z|ozco}`lKyAjB?9eZe_7bF;zOYQ#l=yUuc+l^1;Xl5Jyqw=NbP6a+MhZK zhk;~nHopR!Oq_ky&_jj-Ml6(Z#vUofUul|&h0nCLv~;GXDVNF*cdlS9Iij|1fx!Db z-lzG)KMH;4?NdEejQt^9DW`$z69Tez71M9C@NpA%PUA3e9uIkM)j0OPucRI3OJ<~t zrQdBVEM~W$Ri?3*gZolTlj7mwg+3na4(1pIFW_D(d78-l>lwQSNVJ#;P}PnM|BDKD zYW-?W*lXeYu{s{SVoAIEXuli^00w6^1xko#zPu8jSZ-?LtBK3}O2m!{LdG&Z_@vJGrL#eoKI7URf?$s-{+{;_t=ZVv1^oQq;(UB=OlgBm4?LX92bO%b z9>)d9sr0)W0MV!c8w@nQ1ETJq9D@$rbJvkUhscXgsT*117_^BUAQd-Y<;g=bym@;F z+-FE#gJI@&+gDNibn~c6(Srj(=26SazpVaa&Y|)2w`*G!n@LfeIjvr z73v5J4W%SG4+B8UOHsd3IaFeIBPfIi(I~Ng)eXSCqY; z;b7ju-x^RAGE|+e>{gfl0zFxNYTUGvY{YZbmq(Q$Rj@Q`H9h$iOu*UB4@w(Qs5~Ul zBKmj^De+CXizOT0p=}lUC=mwGSBb%0MqZ#lPD)L6tOo>5XY^F3yws{wPotEu961IH z-)m0|ji07I;s~R~VFLRY+Ebw)7v?CBd<*vc)Ao`*^^dn*lothTP-QTqIV`Qkn3Ugd zO+4EnYxw(UA`i68CN2?-7$maAX9)Xkpa?2ci>-w+c(PtEoqGQff{b<|nx7+#_s8Vs zpcSz^egGavLF%L@bjEwAP+d4RMLP0_-8e^nIs0xSb1eN;dA_}2bpLF|kPJpJxLw3! z$3oQw7zt%)!{jJBeYL`RNCW7OZ|5^^UKa9)ruOlCaW;IrY+fV%^s%i;;#T+el0*k6 z_xgMa4LfK`*)zmY#$%DQjmH{og&UZ^aSYj=1B$v9&4Wb>N~X8#))-J-oym_GpCuqIy%ViOn-ht=KNiH0uhNPnQ~)AZBbHa2~@J_>td_e zeT>Vgx4EG#R@@VDLE@Oxky6EY3w*;aUF~LTG@>L)Y;oIdhE`!1Cvoi`ZwN$ zX(H_jzTsq&yg-e`8cGgbN{-JQx)hm(y$DLaG@CN;`02KjUlbxW2jUm%tp%h&)!U+R zDBfF7=)C7-57@QWz~f_cvziDNQ2sSiB8q|1!TM#-ufsh%*LWt0`B^)5FaNLn{0W2n zE7+^2KJWmc4xVp^{#QYIobSroN!`t2L-A0|>)`T=8ghh+4%08!M3&g?#NjrXnf(?j zgv(32A3!qrx&>YlP+Ry9OGc%GM+^yBL^U|`a10T&!h0LX5c@v3UT@oDWXwTla*ruc z>qY1bnLfJ0eCLD{52AhxFRL>(&4`YruWK+4di4GTudhUumxB?&eId?MMC68La=DMT7E% z?nZi5bE|F&`YO+OPkr}aODn$A*VX-TO|!61CKQ<_tG*DXMyhpR9_9DsK!MA2jsbN= zoV;O7^Y5?BVF|9p=A5H*`pvt9+RG{7?b8cJ%ZT&>UNGbo8>IRSljDO#Ri1wvn>AFX z4LCmurN_~jd$;(L5|X;6U``hQqO}*I`nR9pfO|UiBZO&ATzF%>!=*xnUU~LRASZ8v zeCJvO6vAM|H){PVZl{U^aKF7vDn?nj#UCxCRIXV(=VUFpo{Flb5&3aPYo9LZxV1+m_d@7 z9|tD9GND8qS%HK}0c8;p5yp6iV9w!}^L5?&s+onlu!UxJ&8HXu*_Q##h)(~t>UpFD z=hXRy=Pc?{v?R{zPUQfY7;HUSl6&c@Q50wwz zo}>NcyZs`{dXTBRUKF%jba8ihA9ObXT&Y8Rjy{Nc9~k*H$!i?tgNK%S3$^?Un!BTj zsP**Ri>TZc)4M^S|Ka+BHJ~DysgD$w-f06mr32xRn5bNr>PuK}X`h~hijcM}v7!Z> zqald;PfuRl;%;U@_8XOH$B3Xcy0c#mX`QP%1$iB`dVWg{=3J`ISUFV|mzQJDT}iRx z&T1sJn#=v6l+o5+M5ZLY*gKD!4V?zGx?UX4<>bX})=q_cP9)Qa;(ikHkq$kQ07-~s zJq9}3s`x7B(f{O7`KK$(=U1~M9#?a;i$zo($bBzwgo$7zCPbxAH|Dc`LR8?c;${>& z7pz}Zz4esUpFwTc5Gor}i|KWHANDR8UOI@aObLd!&~6FY++-2vC};Q*eds&B zj4!EHwFW8Qo@uZrP?m=m+Ja6;vjISlg&QKpKpHQR`5D+QH0LnD?I?4PcdPyin&}jp z)f(X|yf7nNEj3S8pIvT~#S2B17Km)Kwjcs&aM$sS5{B8F%vv?m4m$QYxHyq(7^}|> z3U(UfSzo8CF%zcj#O1`mKiRc>TjBuS) zo_fls5*Z@7J2C9B11J9U3Tj+=Y@NAM_RC)mj4ZwO=>LD23`K18!*5|C@Qb+R3+zYW ziP8%Hft(PKa2>rncfLQbJ-7~`xmi)h&Klk_sF!dPt>LXPPUw_iI*@z~>jL#46I9&K zyto4(J+v#5!z(<%r<&b>&w3RRHsP-%uV>15I~ySCSf>!yybp|c9J@H?xsvx~`eE}O$x ztA{qKP3<%#v}e5GKLul)uOywC)^ zV3RXB!}r|Z2rpW{?E8Jgs|i%Bm^6H|f4QOGGcuBW84%8!*8RR8EI)!oArXj|xA))q zMW$A?F;Geso%hb#n*^l&fN0?8VXI-kmcj*xH^tVH%bNwCO{giGU+I6Mq^86Rrvp84 zC#Nzh0R?S+d{i*ugo$Rbhk_qY$Z3(VLQsWbxj4Cys>8+q9qjWiFE&j>Hb_3UI@;_- zx9;^7@dxvCAOawxicjf4Rf@i%;U}=9vv;P?Zy|CmvzCi!de%WY;l0gI{_%?M73$-{ z!@5==R)4n=({6mKhe4#lN&_Ur5&=S+&vW!u&1=RH07uE?<=`4_Heg$byjG zz~{DO;k%yN{p;iAl@b|vDxZhc?{cCIQ01fbD|xzq3(@E=+cpVf-zMJ;t|Cq ztDbDi>xmrkqs|>(($?qJRQJD9Sg}w)5~kMn#MEynQO9Y|;bDkwU`m}MlnIjo1-ijT zI?Uj*4#TOlP?$)JK~t({>>pCijK9M!l|Tm!aGUfV8LQU&%&IdDBPK) zdnTWDT$C05eg9b~X?co(4B2oYaPX^bAu*H=LBGBJj}_-iXc+p2bWK2Pk5`$X zgRxt@=<)dUddq;1Z!Oc=2ykV@{(OXBkH?+ z)MpmpuH%ZEI#+$G%T`)@#=|R81WyKBbp15CP!YsL{_vEfF3A}rRYL~g&8Y?sEeE)R zqVCTw@58#s1to0LlD~=CX08t6O(Fj+H7JsXbLiL?(lZV}_bU?-fiNm-cCdDURm$nf zGg`5cE%)T+SGnRlD@oA8F8z2-iD|u9H~}iyIMn;){`KA3|7i*SIGT9t#=QA{g{VGv zb`O}6GWKs^(+4VMMZku4g;3Hl@ zTc@x*5)1QwtLVSmajFrdWUYEt#Tc2}N^=g4u`^tIAAa`a>Uq4P>DSq!hOSD<_@;L! z4iEJ!i;exR|An%B?d^{r`TJWpxtaLwFg*fsXxxC&UhDmK!W{-_=w@}=CzP8k0O5R5 zwtU6edh)4)Cu{hk(D{nFb+4&4=3#*HrXUTYe8#@Gc<#!CG~8Jkwab&s@vT9AO;3Ya zgE!$*T}*6|c!pm#H0UKn&}EH!D%gQ~cJxD9B+)c+(_qs}iNJ9h>l64F zz`;M7kumD^bLp>yU@6zpcQx9f1P~P5M}KH!)qONN`mN>mEd|wKju;QrL%Hx^B1ILmlI&OH7`BU zifna^@%vcxdp+K?7H*9!`uo5l2p>a@!R~*=Au1h+eiRr^2Zpy4c?n6L zo6BH?lD2-nuGdp?9~rB~4xBM7`?`xI^Hk0?cp*7AU~#*(m)!lJ6|&q*0um8Q$ufU0 z68E}3JI@2@y760SqZx8Tn57yXo8jf-eLNi`DArU8p5N;j)Sh>g{%+ds+85-vP?u^$ zracrglalr<2z|h!36}-XV)Dc}^4GiwvFUYdFR44{ApdnM{2|tSeDN^D1QS08!_=Gp zdm4z(?+|p{3-9}b<>QaI0Uv-9^}GHC<3Eq@cBF2LZXT$!b*;!@P!M%9P;oOrR04P{ zNQc?yI#RS~mI@Jpvpn{3?%?3C1}V=o)JsvPF7s~?wgS@~?DfyJOm}dkuTqZ)LU*Tc6#Is_7F)O3`FQj;Z z9+muoo_&9H>=Citulna^gvXwTzPQt~bd6?wKs&W1?5QSxH?KS|ok;Y%XTf*{el?mK z^3s&O0??4@-M%EqjT&X{K6@Z+%%IYQYdbTu)seU74e}{jsGPD)rn*wqO-4NQWtfDW zo^_cw`_{YkK1yZ|t}evuo@M4WR)eo4s&d;S5E6s4&cE(?L;-Zl(W0%~ZyaNG3@69V z0G8knztUe=q45Rm9qRkSs;jFJI-hy@9v-)s|I@~Xeb0C3LeS$=lXxV2{weu<+92qJ z1xN<#^rrWFKn|Fx1RC}h*bYctpx=LZ5Zew}@k2)Q%kA&>fZK|jHSRn5djxQ&ZW4H= z4JJAX5?T`6^u`rFHF{MGz*{~0D3mDpb&YXvW9c?Z)Oh&i#*PAe0z(*W6oXDjk(2oN z#m?S4^sR?c8_j*#a1tQ0e+75xc*?KB1a^1*9stu+L$jLD%~} zRv(a*?z->(8#1c@C2?#-*)`|ubQk*h~ z|GCI@WVe$mp@@}fX~5j8E*Ia{W=wE9VObadN66xgzCrndUI5;zY0wzijF&X;$gZ0v ze5ukv$m8B_b1Sv;Hx>>(y1xQ`f_6Ay<^RUaD(t0{VjOHAN9Cb0mTYe8Xl%`*urGyd z@3D^}#y8XRI@6%`X?&Bv0t?vhy!QA^uv{>Ch$tI5<#H$M6+$Dka64JChv zOELKCzazMWaGl`gbF(435%IbeGzbuFa~2i78jZQI2KLJ4 zuBQKy`ChL2X7rit94UozIJM4zKdP&Vh4u3}hqUD$90j~@vh9WL1O^MUiWJyt(TwHa zH?%Z{Qc5fi0>`WzlIa=6c7M$7;v1Q#cX6v*nlL`8$)DJDg@|o~!Y7*8J?C#Nv87)# zH~Du&T_oW>QvAjIM#6OHbB9hIVV7p|UvpF#3dxXG44ipq#z<-(?$+dn3Dx^a3W*xoVd zOH5H%oD0r&;y}88TPcN=?rnYPiix&z4d`b9WYy*5BToW=pUKbsk}hM8NQU zs(M|q=dm?0$xPx&Qca5bcYKIzi$i^L+=)*@CgzhZg77R- zvkkrA`FySm+_ZtXM@ZgdR&BB5zvr?#31uGqi>6(o@xc4Ao7mlM+?A3&sAC}qXi0@G z{|%!<_ZQUnpZ~I3Uf!Q%+>P_LTHGz+0-$vJjmaHYpoQMww%wn%JVgGDvWxRe4zCTG zKV_R-Fu^+8Q+t9zWhYBP8#6m>`x$p|k*`xQx}2em8!8O4d#A0NZULDD;J=8e4MWElu({3II1pG}Z|>hhM#IBm8U zR}uX_7M4uQo8o&CY1A02VQhtMh?=_av*FF{W`m6v&rtg^VMsYUDFoEot+&w$^h2^1 z)s@<4CGxi%>rv=HMl zQddrTX}zaF{fPKFSOPJA7HyI5tOnt>q#j7Cns{{j%!5fYV$1D9(fZ7`bB$@_JbEwl ztz18D>wQ{P)$_v2k2OC=bvUEx5pxe285?RgW%#msN1c5=`h9`#djhK*78o3=tNe3W zD<`Y4-BaluRxZGH@bVJWB_T@HE-aLm4->_q*}1}bjXj($EMYBt8(hOWSFx80{Ye6H zw;D?q!t*w1ol;F~l@YbSBM^v!{d)G)wX+rh!v!>s$HAqYg1t^Y*R~f>`J>8@7P`TO zUM}r|jYb&m=epsI(y3*{Y}pV837IMc9Ucoj-6$eF6R@>#BhP^*O$C!b5gn_;ma1td znn*CTmJhRQ%S0eaLU2r+70q6pJ4}u5vptex=HMc>dZM{x2+gH{Ip_3yOS#f+&kX zQCVx_c-i&ZSHnjK=J&f9J}Vm@r2BX0_je1IAiVtxc>_m!ukaj{-OiRB$^a)adU|@^ z*|*+SSavz&6g$_CG)vyebJRcM^{mttsw#s{M-Ia^U{cakP5t+MT=k5@zX(o54m_UH z$}+eq^{$VU3s;kraOh;hx}rdD5)L3T9gn_9@B|$h{uyN*m<4mNc8?z`dN%o$Qa9o!bN5yj=*iN$b7|%E-8otUX@M3JpTI`n)AxFqmmv=o zn5yX#=KZmiFQVDWlZxDZdAk=0q#;nNNwr4iz)@0J4W#C-NQk;=XBS&TzgQ=*c z*?`mhkCV{f3SuL!!W>V+t=?8>WmV?&#&zcl>Q(W?k!r{|BF8+|AjH_b041e zf<*ZC6EdJxA&?}DBa8YIZcTh3!BdWdB+;ij5M?6n7nso!K6&?|xm^)M`|}8xiG0FB zVppl<42905YnaE(%ja=F18_;dhTzaLsqF7W^&}&yK+mC-*tV(8In4r_o>KD7v^JuH z9@yreY73g19MQh0R;w?fiFBMvU$Ux$4GnguT@wKJ%j*{kM5fWI;l$rI28W1f86ZrF zuF=B5th9`7wkrBs1Kt9y9FLEFElR8oMz9XCj|i%Shr#*J_Hx)RN6m*zeKMjZ(^#15 zPD?EzyS+=bzJ%G_d6QW13a$4^mBuyAh|fB!i4lXWaA9wS(@e`5B(5y2{jQpPzf;AB zHc_B_Zm<71;Bo{UacV8mGhef+Q1JK`46oky2G=WNv`}meSx*UI-Y#y!SgXL zUrmOw6W)!7$#Hy*6LK-SjS(E|IKp9pt$?|hb!6P0rlIjsTBQzMuH@71B>WXVx>;e5 zhOSlAS*`HKp60g){53ARJ*~MvH0!Qxzwjgp^`5fkz3wl%_UW9}s`jXA`gk)kj^xGK zdq^v`y=50XdRo8l_eimWgd_Z15V5C2!R7Mpxy9F0SE(E?H7Dr5q~oTv#9!k z_f%}H{VVa%BI%i16hr(+RJ^y>+yCLQZgU=;(Rhcpj|crehQg&&`EpW%s!mKqHnVM?n*z8meU{m*DE5coFyWk-=-%1 zqmJ6g6w~D6?8as{y%%P)3-|X$&QCU;5eFbMib;IFyVJWG6<{hDaeAXiQVj^1cv(zS z=tOG#Zjh3fbLS|x79^5_N6e+052BX&HU;9b48#xZiy?5S_1mw3c#`KFd1$~?F06|n z>~M*ubjNG9bmin^6>eKtxZ2&#s6SPy!?u5uCbL0;*1L=|^iUD$n!aIEhj=B`IoOe2 zMs;so0s5DnT?@Y)GC9ovUn<WFi$uZE*`a1M{? zN>r$d)f3x%lRdYlB7bZ9F^%;9;)VyO2$c~6ycdAX(8B#SKu!Djey{D8*Y~U{L+N=> z@}Fwa+bxyD`QsI8ki9v)2lkQoU;d8wZ~iWCNA((Dy8|n{zMp|RaNM8WIk)FF*mmuH zA1!*BAlkt5pcLul3o=HJ|IYFs+ha*L{E;VU>fY>0DJpGW_uX$qeyT%Phh zpeSHKNDN2JJ;W;}1_$dO^$Ewn1Dz5w`m3PR8I?fcpUOh{H`&h~J&t$Phpv-0<(W*) z3u%R$Q|;Ic5tH|aJwhU>?|Avk*P=3A%AT8ir06Z+@K9M~%$0oA&39$}SOIKWHX0cCwGu>`mAoHGy;Utr^k`pF4i=*GB}nzr(%QJst*Q zP^a1}`Za&B zJl|a{_EK)^Y?v)qc7X@W7L@z4aQ2Clwd`CziNxzvU7&4?6JhL@4S#mn{-O43M58<9 zIWJ>m^qgkfhw~!C#gIe*)DE+SZ`Bf?T(d%yGe2p(kJRs+=0&dZnf-U{%N=DD=~xw zGbE-kEbu*bh_1_e=LZ;QG9y7P7bb}IgP)pR-Wb+(*`BvI<08D1E%H!|EXouZW7VS) z$l2_#&Vy4U6)2@+V&Zmj^d-OPhX2A7&mosCo%ZuDk5@CyTe(u5y{*&t(*dciMsb3>~R0cd@zRvpovG}xWtAi~-$XEB>KY0!p zd4Z#&du*0$tv}sa*ZOB%6d}tYL`Y94)@lSHG}ZD!k#p|{&;92^|8hw)?z*45nc4d2 zO^^M}ll!}cd#}5{Gthrs`tovs;r*%M2J&vMZE)dI5ZEBMCWOkL*A^StmzGRf-+_Uq z?%z9c?+*anOHgIb~~ zI%?CArl6*u7>1muWD#p#Aih?>mlA`?_P*JLbFv2pjB9PEywadn7p z1*rK1wN(kv@0sKvqdLze>gznfZYRW#<7&Q5Wm4x&D!4dSBp84oq3>k>5a}^?Fq~i8 zxj>{pHR?_&rLANY;Bd_A?sV+tVjE!?z3C;sgR3MT2G*ssW>BTB(Dxw3Q_TaL2 z15-jz3Ybs>B0{`S)R0eL6+d)?fBMivjrn#-a0GKSk}AbpY$Wm@i!Y@V3@HQ@8_<7Y z%9RZ_0+ai{=rRBNzw(p;4u}66YWVKd`RhQ}?UaKqdd z#O2l0%9#s@x$}#IO`Jgk< z_S*!Hxd-B?02HyONh?6ur)6U)&t=qIiMrW&F=18)PtTl_-}lRw1=nixDvlxNPO4wH zOg@gEFpxqxKgU1H7iBv~~V&PoDF>&tyfQTUL8YwqSH3 ze35dFWK%*Mfnn?HQu8r1z^Gn#Wu$&5hVk2TKuLMZ)?)Z|xs=D+>fG*`_i1-VRyC7V z#8)&I-VZj8L&xOa?bvanPsX0lXV^72*uG0tH9J9I*XkHtr_NqWo}_0A#!gvFe|cs% z)2k4S_8w9;&dX{$j+gx)ZmBnV6!qukfsdEXdo(?-@5m?Nr+#9_K}IeQ|C}_v$Z4Li zrU23%DlcE8Cs7yG{+I98_1V#yIl+@bV@Cm+x7$uRHyS8~2W8{GMSmNo3ya@9!;ToJ z{4oIGQhHiK=#i)zdW}}@^E?nQNzl%)%t=9ibvGqC!PJMICIV7n%S3JY1rR{d5m!9P z?xjZP=s=vZRTtg?hx+CR{u0PdKxTLmMTCJ3hm}}7mOP4!URtj3PkA5f3)i>uizbo> z2l_YA@a#5$4ti&cR8kwW%_r^~`~s1_WWP+?G;)6Iu8X%1uG`km#Qr$a3&3 zO3Jq0gW8rzO7k7Yi0u`M@-uwe$HU4hBRWP^k)V8R$lVXlkz!5E$s$H6;I7`^&{chm zl_NJhy+pWED z%rPi6#8egDGmzGP5zK!5eE%_L+59@JSZVcEWAYp)#0y;C30(<=@8%T8VodEGW_O-ri$en6Gur|rO_UHNWD6dC0Rio& z&|bf(k4ep92wj~k$g}3u;4Vsip@EOg-&(jS|A%VVCG#(S`->?)-n#Oy-GLwvH_XhO zP=fIwP6~I8Q%;);opJLyzgPzg8M^u|*MG#YlZm%qg53teWO;G83e)18JIeJu=$NGb zEmd=)l|u^|lg!;c?t%<3Zu;)a!3;8erZFi)t= zUn^q2rtL})`t53GiP?Ku`c_`$ zsjQ;>V0!<|+tt^<5>-63-^-d)Sw)3Un7?Hr?n+=%CtcVpm?JpP7Sz-xVKO|}+JdO- ztFh%XxlJkAF5LP8Ceu1ou3*31?>S^#QNOb_&kubG8^aawGi=q|jz(=bmy-|6L~bGg zB2fWj`5Y3CJe^X0o7Wsn^sf~Z6PLQ?Z0Ej$_s06HzSVrewcVx8 zpNCYchGr0R`xH{kOgbmlwD2t8#o((qx_Hem%7|rYLFBq_AznO3PYyO#-5Ci zJ`j%}V&bI1jkV+S(*nw)Ol~Rd1a)qpr}cAC`pO~?Eu+BTGo7*K3qo;uMFRuWX>s{1 z(6))B357vU{$WAYRz>Y$amW9W2{>XKQH+>5)?tF9&iS%PmW^N6{B`-wsu?j7R!@Jh?Li`_G5`+V5o{ebsq;l!8+o zBbha3>R-Sz(^cOW|87-j+_4ooeU2iPS7FvT?J9C$ys+#|)74k#jY)iI9|!wRE_219 zR}&o-Pms*SOMRDt2I9edCwjG%u6EjOPKqzo;jQ3kEY>`{s(Xk>D~~Wa@gzIH&khi# zl@^a&U3U`(I`u8UfrWyxA{e#o96svMKmPPOZ@7s~YibRZ>MY4)cF&l}l<|b; zVC6e<*2(&A=1)8^zwP)R(t}_;2N2O=>#xLZN=h{TST{F9b#`LQiA_Qfe^#E)zbnfG zdXMcAEtJ3#BBro~SJ*#LJMf`I+AWs1e4j(gLGWWvZx<yQF4gwrxE!JK-OBwrRj`=161>OI5N;6A^sg$R}f(k8zM+?I%`DXIaUVi`u|J#iF z8@;`YE_B*3S`du;Kvo)z+eK2E&dVp`AUw7z`(^3Y?>(vZ>8W^cj?=VCMj7;@loD3@ zU3mw$U|=k>NsPoAT?h6>=8GONgEEg3TY%_quHocdcq>x!D&SvpZjUOCKItm;Wy9sO z$w@kwR&`2B(##8dEmymb2po!F|8apfp49;|B}@9RN-+enmOkbq7p;em0;Lk7V^M~6 z;AMa%XTmurb6YZpKV|GT`s?$SvpDTq?};;_7SWlHpHW=-AJv?(iJ5Y~Oa&?|JtN~c zr_zQv7;9}+zUdC|YTYou={#1C;!c4_B^Z8{OxDH7)_&ieeePBH;{OhvEjvf%Qy(RsSgTzO}*S+F!)FrPwd3WAKy*KAsh%{ zqt;Mk@P9arAaKD~%Jr{UP7l-=Kd5#*IiF7vj;2aArQX{^jN{dB1+bk`Z*a`Zj0@uE z4_B7g1>4HYCkCyJ2)^7A3HRg8>$=R`sN9le*cOtYj+qN;FLjUX4O7gY2(hFTq~46d zL4a`VSM-NpxLx4mf&l&EP_?oCdKYZkMOXv!rUZBg<-8Kxk(N)bU|G=1G>TfkEHuON zKjf=tF8Z4E;z^_qq}9EWZagKA+Ck=PReXGclvJ|rWP}x&)`b}TMN>GRK==Z&JOtDO zDw@DR-j3Rr#!<^{vITyqNpJpnJ(oTh;T+kI2gUQI2^6c*D%UH87#

=)tY|wm6Y? zAAkyxhO@b6U?;*ahdG<~HZ`*Q>ZC^`uLrr(>MVhH@&X5u=*6#GtBR-Y!5UBOv9nm8s(2{Vq` zq!TRrnUZU*>IxFwv8MD2>oCZNLDZLnK-h{{WL$+k#3l^#(l``76TS6C9@o%;x9dnQ ztdE608l_ZtaV{rf%zcxwdMM1Os|m*l19r*vTj{^&7MY3*#AUE=JobLKvXvR~;2Pzp1~ZFCoMg$KTZ?jkQFrD86!ojC$RF$2|jl%8``Ub~KDy zUoLA_Ds+qr(+2ZoTv389*-K2Q=}kgOTmdaJm@#kUg@e~;TUA8^&1`}__BR)P?U}IU zoR?YzXBSjLcM@R`j>x-h{-}7ZYz{Y!tTpoBNO^g<24`~!TPG6{yx9wlhse8QTuzx4PrpqP2dhbMEsc%2)`7>Tpi`(F{3oZRsF|H`on zZE5{PEcjtj1SV7-tP~vRIZ&H%BeORyx1MU7iCpZ}El3+HcEPAzQ=8+LclJaV%|~PW z0Yd|xn=Lo+!8$Q%w|JYpRrJ;ficaJq&B>?3>l^E*`6m(mPu;C>>Xe6c9O;$GD~+TA z+i&Jqa`XCpIO98>p1f}&=|z|#p)44KGg+P^b=M?$8rKw?@McOd3{HZ>>Gtcx?Mc8n zVtrBKS#xhpbYUw$jID}aJKfoXPsS2ZLZ_m>sgdGF%G~flSv3;ii>(rTbD zjnjmxpb!{T68ZMekud07@EG*G82ce}SKEcRnaV0r1-6Is*q{&5#FHuRlMrDj zd3g7g>a`Do`I{<>axCw56CdZKbcgq1kO`ci(BcTxxDZj28sqxRh;w_AH--B*w?$Kl)Le2S+I~wmLD>so25a zr);7`iocS}BM{^T{xp5V^j9n*_nn(kg}&29Az39?u5J^m2AP5pnol;O=2vdGHaESB zQ16k)+{5A5Xw>ju-sm|nYVX+D)bFq?7VB`Q4_=3l2DV?WDnOQksixfDpT>h+7b^}m zbbi!G+&A3sP{Es|m^jXhCRu8a&7c-};rN{eY&V{4u8aU+88ACPCjvi;2QTWbmkp_i zVgzxXp9epLD`)I6vV6_S>4Yjq|7BEcc;*b0MHy*>c!C`9^c)gbT-x)=A;E*fBPRZf z1?T_QIWj}#b6^%F7kB6BnNlfvt(a0~oZDG6arJ*FM|4bLKBt<^<3Q1!vg5?rfnW!7 z^52(!_>YQC(Z)Gl;LbBAdR)TE@zaGL98cg^Bs1=I7@0vIv?*Tq1<}Nbglhc!QDD@B zq@3G?{2BsEa~_AxSLjWyA}@$8^W1F^>BI;Wh`%#^nB36eSnBl)!{+uVDm7MZ^|~eo zQFZaqF0KN@-zY{`zkaEyg0#^N;QDaV%u!Vx){<>~)`e%`rW;&}!n9#qqXm6EHU5B0 zw8lCAp+#Lr(QMMzrL_A_JC7fXZa#*j%Afn<LQCvwzn0-WvSsNI44@X(YQH|xI_f5q#R{S1_>{w=w&{Da2V6Odv0D=U zW{toYjmg1N&Fv;2EZrfwCh{3xPIX6_U6;K*_-{{kXoZB@%iACF0{GxD} zB(h^VElcOm$YXF>lU4reOswX2T_M?39_=8LaM$1pgjfL@dEzV4grNB%Vnrd;fp%0$ zS4K2_bjW%~@WQ<;78ioiHF|t)v|m{7%l=5W?5%81CL>m7ju{>eEKlT;%GX7VuHMLs z6Z5S+5Vq(UK;Acis@=dB+>Sbr9qIGA-yY|N{F*2!xLmlFRQTWBKGt6yZ@o(T+xOzE zNXL-hPCVc)U^pUD7qjD>E$wY^#QC)FCYQUokU|s-#M6%y6DV&B;AjFH+N+7CaRA;| z*7il=xy?~|-$EIZqC>Ej}jSEHPK!z7QS>`H^0UTY_!Dk1q6xV_!RoNI4f)UhFqY&?Rs z*C-)*QnLQ>_|#0io0a?8tXAN#R`eha?oD3q@m5uXkJn+^zF>Q;Mrt4vxs@Ue44y)Y zs?3vqDQEt`i-cD!7>rS!U5qM)KcJoFrJVg3Y4=^A*2^${CM~P*X@~Q)UEg3hjDaS! zgVRM#D|t%cNb{!-gz$9x@mGRUWF8);6pV|Y$2cOM<#k5Rvsih$9EM=OG55L^6Z8a( zrMRI*_o&!-?~0!3N21D~*FHeG02*u~mq`83NWq@chY%Y7H>BD%KQ$tX+{#LbayA|J z{4-~=B3qpHgB``t!%FhR_~00SnM~jjO#a7dN{&fsdQD)W*p)OQt%3+cyuQg%>|zKl zvp@cg5Xz`;`E+!baq(h{(k>&te9uai-k4?wYmRU1+QnO-TRW}_U^yOeIhP=TGNWp{+K#U+}M_6I#X+@8zbHqP=fD}#Y2ksyCmpqF(x2nC z?sv2IiKbC*^tR56gJrGaFN5%$pbun&Sga&Lu}nuh&r%YUCLqEYCl&L3qEXSrgu8Ms z7Vim#LGyQ4|L*>o8YcTnLb~%D=4o!$Yd0FkSb3$-bK&@LWfr}U^#I!r0_Ja>juziy z@pHKh&~H8+`&jfgEevv4N^{+2b|fa`WqR$ksBpY-L1}=zlkKt|=9sU#M;zQ)W~kQ> zce8PA*K{i&=>c)#P4UmaVvRt}^xRm1&Q(;ompv;x=nvnwN=k)M>-0D_Lv3VDRcpo8 zxoI(eK93O|3Oo1*EVzgMukw*pwK_6twV9X;!hOa_Xmy5c1;I#`!L%{6lWb8O{Zyv! z#L2D4KJFrV*gNs!cxuK6{(01-CVnf`*MBSkD^_hPwC_G@#o!_u9Egw(hPa~5 ztiPhQh~Z?K#wGSzS4cZ^f8&6*rsAG{m)E$oi`{DMqmAoe;-)7iTq07xSUVO~Mdi%6 zXc@#KrvgUZ`v-YeB(UHd^>HMik(f=QQPoaeSIoRKUv;(5@Rnzk_Z|hiG)Tj5_+tZ9 zs7t65{lMLNd@CypSxW1?e)gaQn*x!;iA$er?BNt#0lMWJt`MZZ%w?ZkHjVbRb@X4UCS!be|UrqJcvb59mwCNwAq|#9^uzKUe%)(w9L+fS>>Uct*&sd$av;lCgL?g zk;#rI(5R>bB$$FYQb{fwX601y*d^Zd?irV@jHsfBJ|y3-j>Sn?1G6&QEo%p@(dc{M zG5ONI0!VY81jbHLZWn(H*iXG{$RBg3MC>KDBFj*lK~6N=R|2WacB*+?UkXR~vz1jV z-sA??A2T81=x?=InTt?4o70wM4SIn{T6PR4x^ zNhovwee%5K{|%BvR6#9SDR44ubB?BQs&u|^H$oHi9kgJ)!G0}fK2Js~ZLmwubDhW$ zEXP5^0Ln^yMWCpgp*xbgL^zh|vN@PXRctupULq*kY6@?%F+Fd5ZHnO+Y@+V*+ zl}DhVkP|Od=ImQ;;0;^azoyIU>TX^0UceX6F(|nJ;FHz2?XTgyU`5LD7(Yy%12vU{ zDaH3Y8_TT48)tXtYuoZUUK?DrGo@ZW&zuzRFFhng>K%op+LLNONW@o*jum*CP((gq zSXU&|2Fm^&Z})Br535XloLz1)2OOf`3?I%@qwMGg8;}ky$~3o;Hv4?#qd3zuFsMZA zzmt!nBp$nJ&7QL(MeDsffi>PaD6iu^LJTF=T9O1WZJ(X9aIQ-ATh+93gziGRNMa;v z#I_J2Il_bx!aC-)GFcA%pXktegcYIqcNo)S(UyH)o78+5NL-UZIPg&b=)Me$d2j8L z89_kZGwc(JOzBv#lWX|zC!!}8tuMMm3GmfnNzF8`tmY9VnE%zeoEZ%NgEXHySL!YL zd}vy5_HAISBbA4UN1k;0siq{X1td%?OHglPm0Nn&D&anYM?d_NnL*|{OiU=wP^%vU zT`nwn1y;3M09#6;gxL@0>zth%j>kQC=pthmaaD!it5Y6hWt%VH+Ndp8Np%CB&!hHnEeve8y(lj)&!IQIUgtmgK2$>Yf>fS`@8U$VNA~ zJYUZTbabx~@nTI2$&f2y#dZ3sOew?1tE$X5b%W97d-()XZo$5KhT_SC>e1<}?}bEt z?gha9r=Ky42tJi-lP0%KfxtJ@**|G7o=x!vj>Z{h>6I+VX48Dh(@V$u{DKF&R1gmo z1~OS#kqO~REG~X$W>EZh6ak)w{(s!AU*11uAs)VsJr+s{l$&$Ee7A63ELKmmppNhDTRzwp!?gU)zGE3AjvNJ}&oTNDqTmap;{ zZ%Rm;dxQLZ#L=5}o}}S=+pBX>spD;gJY!`9tFtpN>Nxy?-6kJv8+o#9ztXzL{4ofJ~5Ta)?vJIrgL(*5{Rv zEt0R4rcQ6RZvt|#r9tN{nz}?OecFs^z3AR<=8aaj()G!BpGOFB zqljd%BZg{fr|jfvz0rpnk+*I1sGz_>E~o64JijA3M9LUTt?B|>_jDdx&8yBDLcC)w zF#<+9Cc#>-a|@I5iPYCW@?ZxN!_)4Mk$F?-U9^i92P-cNhm>wjDVtqrmbG)<6zKN?tTCrR@SIRlbh7j@oLXlzMsbfZ(@w`(r+^mVQ_@ z8oo=}JYUznDE#@HAh}gT1e4=gG>*ET5NG`;=Mxq9)WdZA<$Bx4RKy3-v|~{T&gYj| zT|r`TKonMX#!r1(b{*Q~)ose=-V~&r7}BIGC=`FY$B&;JBR99*|-gNwj7I+zR1iyv|pJ<4xn6#SB%`N?$@ms82Tqd zqQX+L{ccV9Y+#t>vh7`SlaYPKN{I03)ZEwrij{}?swtHahFivPCcAewy)!w8EEK!q>aml#7YbETllQ^J)>I)A>Q_#xAbcRVHx4-dq<%Ez?-rjV&B8 zu~jmxug=}H7hG%0yptjXOy>(lNAInng8#`X)>`GiwsXP-W8ZA>+&aGBc-4kI1yC#! zID`qRU)kfpBGyU}So#WyYtuLWnU?2^S2$Cy)0OE4gRdUGejxs#2zBl(epDIJ5~fU> zE8Dg(Ld_#<%S`lQ>g>66h~VzSSRMKr+90u$1kYu;p(R+W2(|g?|GwM0fS7anPk*76 zbvcXNk?tmHH>ViS`)bj;XM-d1e+{ZZRl6Vrp|Q3 zyREWNhJcux#j4>?XO|mbmJW@}v*d59XNbF)pCP^SPxL^zf}`c~nf+0n1YIwiaxZW_ zMSo!_XlbUZ%KZvRZ-sD3i(|z&O<8e6o{6Jxp4X$VrI~D^fd==yf2uEk*HeD;df$%N zJqI5|DQ%i7=Ud~rNXcopkxRu6VTN0E+?yhWQ|ar-ScX97EiMKH&nCe< zdLA=lxBL6ov!OFv9#N8SDF4)4Ka?ZV=2C|Z6dp)!Dge)(etiJtJOAU{c*}ZwD5lMO zUV#T;{4?@g?Yp);WLtFqQ&h+E?EJCU)FF6}3b<*XKqLD^4}`BB+hb6~Un+vDB`Tzt z+>yP)jwvJRQW*YtZ=jHBRu1)RE3Tez`8& z{8}IZ=tPT)i(RkrsyNZy9jRBD7bqrlAeNtY$Wi%n5Anun*wl&|BT?MXbL0adTb4dY|@p9SHWXX?=TCm9g!s|}p2h=nKT zrFXZ~79jIm&O6&Hc1ZH#KwcMMUS)L&+-(5n`kdtMTu%tj(k)F1Mv|(Z`w8s4K$zBjp-LN6I1z~J^wQqWgC zahf$foePe2XGm+~JD(Tg0Lwl|iX}lazPWe+uFY6dJmsjk68qyLtIkW|R;Rdf(f!zf zmT}Llm8WN4(tXhAV&P#;@&uSNEyi&!%T; zt#s1nNy=VKX9Rm>rWjc&Ks`xpW9L;^*zPk!1y$Wo283b#~? zP8K!MzvKJi|D1vo_)k%KOU>sdW4h)9VX$IE$~gNNvq)3BpvUmuVb}ieiUN#ck9*EZ z_NGvKP$_S{5B->uFqlwBjcmv<>BsNu+tQNE%0zu$AnS|q$=zt{#;+UFY)i~zCN$(X z`cEgrn7ks=da{uT<|hv`@cHkXIo-a%VA9Tb&NsaZ#-5@c#oRHaZDsx%7$tAuA)5LP zB7x3KZg0_(=ayuT1DZs8WT$X{tNZ)VV3cT99n79OOCyI^2__Hf4CfE+nsWKR4hZKI zSo^j$0H!2QYIi0g#9c%qkFor+J~8aigGBZ!gG0oU zMT7P@?5=2krqzC9OI1meDjYm@>QSK|x5O1|@i=ED>%^uU-x5pmR8*y<_69};_1x2d zAWPbGS6l$oN#1!>59`6P$1^}k!Q-!)tS6Ese_yTwxnJ0(ZYm~(%!=||qL*8#CWs7~g$shf^sH#&-u0QXkEdL=Q zliu{Au0~3sfALXE15j4{k4peZzJ2ek1<}$(E#ggd2k(bPr=v?3Y+vdU#u;LiC2PfQe#Kzf7pPuCi# z8P>A<;RYWz31#db+Lu))8V8v#Oh8!3ZAy=80cq_f-w`pw1=G&oXS`jU z+dJR2v*)LX`K>NB{>zbIo*xYE<6O^7R=2me6Vi2+)Iek7kWT+Sw`)n7A%15e3JXMH zCDD!TveKG@@RORYiUxdC4x`3|kmtSoa|vii-lCaFS z-FLsSO@+%yM8mj4+ZE?1Hj7C##$Vx-AOk1FMG0bik3cx3wANm+LuGIYfMVurzoXP^GP5($Hi7ul}i#zEuZ{FU3BErGc( z(r*BLu;dJxg9@sF-vF8NO4sU*`ON;tN5uOJk{L>h`)SID4~<1??M`HAwZ0OSfJW8t ztJXS4Qa_7t+}8NXEoO-EWqrfL@cuhB-O0@~9Gky_rXQLJj%@Ut;UJ{|?Hnd{V+d+EHs; zZ(&`1)kqgQ8eYqz2zPE0t<>g@8JH9g-Bo@un`buho0)N@)VdjW7w1K(yVrK5B!quK zWn3~e&K<#;{5Ai}Mw%Qg-CFOlw`dUTD2@L%Zbuumx;{)W#xpRckm<2Brg(C9dd&^^ zOdW$naNc4LNp8#$9qWNY<2r;R9|dznfZOSzmVaTPcF9x@K|@PJrX|@gZM<`Mh2S^l zf%pk4C4C^})tjEap;IJS!$alYs|SsF!eQT->`H+b7h+8m1XiA0dEK3qmAlPPdXmmp4Dv@4XFeOwBE75T*N$f0Gdoc+E$(6KX(Y-rJ^Ou!XP_O6m@rj+ z(zhC!@W$@Duuzl&F7cP*2S0Er$M~P8(M#is)4`-gOa>>VxhS6SyZBowq&UF-pTr73{raUwWd2=RWN7wC##LeLn{APQ5y5jg>K58|; zBeS=|1P>q;n09}?y0*X^(9C0gKGV%QK!Q)dOrN2Ip2Ks@HKIbuC`w1sjTa`Kg(3Kw zJi{F={J8ZAgWNW7P8aWMfamK*xi$V3mBvS>Aa&HC;mhuIoRP!sbr>GXqqq7b^R^m# zwcLn2N%r1X1kxr8Kj_0x3{G@O6$r=*WN@;xGnjBb)8_2DQ+Ees-GkB z))&fn2y~piwr9?xITXSx>7EV+E;qV!$yM62-aEQ>dFS3LC!?9MRfZ;xILB)Z(TuTrcK#6qF(S-)5?VPHI zL;W6+m}C_LT=*4qE>2IH{PXYb=c;@B zN1sjC(-tW6$HN>c8X6jw*4IIMwuX6X?HblR&t`a|zN44(XVzB(c|M*tTLspxH8XJ6 zplw-EtiVzfF>8s5V$0b?vns}qhoiAOv2W_?1nt~`W3KBaZ|w&V!dxC^lZJ+tZqt52 z6HzjK)jI3o(ZlN@>}=54JqVgYKtImae-KxtO-GeihtyZ$iXbWab03<-_>C zMZ5Gyr{QZOE#Tsxkn<72{N~Cb!nG`+&~af-k|`zoS1!AGb;1cT*()wyO77D}5g2MP z!)c^du+p>a zWTieQJUM^MpnNVa2@iQ}c~JO$ftndHl)V7IygpKF>3mP9ZPA=@&f)tmn$*OY?8V6{ zp7XndA&})F36;`5lA*rqiTYZ9&&6ORdh_2~kC_n&3;3x6YBwSclYRF_(eUcZ>K5?Uc#DRA^QVkWj7!_#Cu*#NVdqwU{Y4o8+Ab-7TiwhrbQ_VH ziT$WJw_=v8lbgidb7lT`0fR$8%1@n?R%0hSx6X9raBV^Q>Yi2Z#oAGTD}tS0CDIWd z%N<-<-USQgaP-;kZ^$lK*k}$W;ZGMRtn9KnE=;YZeo{U9Uj4Fgyp5SfN|hapzGy;? zmJn`gkdSS+9g`R+d_J?YV{K<|5AKIdaZ9Owuj$bdyl*-HwODVb!FsCs8;cb;|Kt8qv9|MB#d0a5MU*GhNC&;k;pbdQwMA|ZpI zbV&*$p)?#ty1@~NAtWW0knWTmkZz;~=@5qIJ@?+{|DLbsLQI?o>M z3}26f<@2IJd`|D^aut0Vo4c4`siX>XtF3ii`$;Dg%OV0vSobKqi6qvj4yJlJ{86bj zX-B>Kcug#>MPh_J*sYaraNEuk*J|_e!bkYsuHc8mi`TSY=*FerF#p$x`M(yx0ruMe z`S?aBc&?sSD!z4|+{yX#TSS*yF~hfdE42E$QP_M_Kw}L_EZTI6yN*WizWVEhP4r`C zyRhO~8(5qu=38G!iq{mp?-0>5R=#2i>8ws>voGi!VF<0hC!)y}a!1h>mWN_$!f;&B zy8QO8DN2BgsiLitA&r<-yGg)pkM$#EC(`cyq=VC|sWBeygB_LOh7}od+8(Rr3YVWR z5wUDx@eyF^R)@i?tH<`~#o&8jCzk)qDHz(ZdtJ|<^c13OMdo7W?jhsGtTt10nU5c+ zL^M;MEf0#8g66}pD`9d2V*%lYkzSuof(2#Rbxjv{*SBz}h`{+qC!m3+yLIRH&x5fM zsW|+6P#u2p9vrhRjth^~HXD^9Z!xp5QByWjV0h#)v`wiFR$Gp#(u^f+aq6C!@DEW@ zMpd**meJtb+*k3kW$|NhI|KfC+Z%5lon#C_EDklz#IJ;0X`6IXACGry)jchAUE;cuJr$}KTZPq`D^O2?x*9ls^%<>~x|&UhA` zL}*fVUrD3(ONTCQq2i+|8+sZla=2Oce`t#CC-v3_T>+JvE#_QALrx_?~ zrp#Y!=uKT{>2TnTuD+-tPcwMpr~oK9ni;1SEe#)=zBpEq(|~zw5Y80k27V#hG)eFP z@;UfId&>o*?2m6j@wR-0BDP4eM)(U20Z(z9t;_}VWGQHSMt%pptQ4Gvz=8-F1TaL8K{ClZ^01U40YElzZM6YMH9A(m8s&v6rk zaN$sj9@>`0x$H>h_n<|MR4TYNJ~PfT&R=USS;OS_McCM|hU5P8DvrWV7k?>~*s)~QfI$E8M0|DjeG0i~u#3_K`El|-p z%b87r53O0e{>cmOXG411;|d6aO{B2Xnq{`9h;j+ofDOv6C(OXJj_yxaaWYKj# zt}`@4d_~dywMO1g(SZ7g_wo8JBe;IVm6^kqJ_{K)(^_p4kBVTJ7l~&?zf`58 zRa-LSdZ!ri_d@>tG9y%%{(XnMX=e>&8yriml6mLqtjt~wj&~(#n~YA-`!Fk9RL>T@ ze3Jo@MNsgi7PT_V>*i4=Ad>k>JBqHa@fMQB^fmaQ7BM0sfF`wmcSw!xvp^C!NiMRJ zF_@Esdw!Lv$$lK^aO|x(4&zi5{{m){fIG>@jK>a+e~JxR$A<&U|xb0gZmf3_bPbI>+oD499QS>9zcFgolP-V;$9 zjpH6&9a=7aS!oUi>7^AHOOuuk!B8F$8}@(+!MOSh8Fu-9SOBRB8n(9{`=tt zNufk_QE%Jtdih@xai~V9_vNrv5rMwcx6E(C;Zz=`dP9z}POkFR&KZNQjBu_W=^;yw ztP&>a0&uZ24Kq0g+>#8BfQuuTQ~zu3DpEY9heJzB zooA=cp1bo}{?K)$hF(Hc9Gavt^Wx%^brl6eJ)kIvE#q)q_$bcL1MLs8BQZrtX9_ef z9Q<;-msdMgGF|rEn=B>TaFa43##RpmegIn(FUn&N+rTf<9e>mMz#}8Ql%pGXAfXNB zvTS?GR!u-`G+^@$mNwB~KV`UeT&e0|fomf$@#1_}r6`RN{YQq3PBkJR&vHL53#kT; zp=7tUXr9p5Tt$kU2MnEKJuUcMp3YFA{G>iGzC#hW=}VA9U(OBdkD&o9g7sgL?ceiXfXW;N`=Y|KT+DR{!g-jEoN1M?e@+@X%JUxCpSEHT-FhcZ&yKGveYj=<|f4 z47!;^#Mm8@ZzN)nss3~r{)-{BZP2HFYt!wSidMvek4k8taUZ41qf9OZHK*bOQEChR z#{1FB61Bq1O4rW@j0V_wDKYK}$I-L{ArBqNlm;*stC!gw(;@H1Hya}CZ^@t@l)c8H z&NJ%E98v*hC?2Ouy_IF{{73luq}OL*yT3X^gC8WV9}>McjI1vj{>EBBwXBe_hQ1S= zb?-^h;gQ^vTqJ*<>#mzZ=|t+FoyQPXJ}dXGcO2#Wm4lxWu~o(Npc%+H3vr_+G_~56 zRXsKgxA^!x;-~Ci(1>Kl^U7(io#VGqcN>bVL*^;a{4`$bFPo}?mEx$^3D0-im{O(;dmgf>I9j(23#n-(mKLw}Ec6uqW{s=-}X zCDb)E^7Lk&B7}x*-^RM`=f-1Nrg8M2p?kE69*rQ@Pw~y-8)X~P=zqP?(vx>114xmj z!`0Tn+)b>QE|w3YlI;6NFDEKegnaccMpNTI@Z|d5|2?tAxkwEdxBhkZ?pzJr_^5>Y z=t;tn>94l|+r*r|koan7oG=!<5wFMur1@J)ENSFf{sal2%L%&l!nf_a%|qbw~&_HD)Q!fhUQ%*Q{w8} zP7I)c-c7-dUbf(e7>-T2GV{VSu*b?S^7)-ha%t5{@n@Kwh$0a*$zM+1^WKAgPu1h} zPC#Z4Jfb=QXwS;PyGzVD)2Z}5qTdzSLCxakvYVTsX2k4Og8rgtG(h`kAhX6;|2Y`$ z5qZ4XlX~u4n#WHP+rxbzmvYg{Jj4=B?LZf;ZPiQk=h(?2%e$$fY z75h_TeaTnwXtExFyZ*a_4Yg9DHCTtkp?$upO*2$umjapRv{%;*KJ1MeoLSC~niigx zR(qTvd~KID<&JQ?S=Zvl+q65EemV+i>(6An9H_((!hWCATCoxZrq=M?g|ocTgOAAMvUlSkc%lpnMqb%m5zMnfZ+>32On%Xf)QLwWIIO)IU&n~ z9bX)J{kQr<|F`&+WGkJ89Lc=uTEz5h6A-Yr6>4c3#k}%hW(y0v+uhkkNKF+o@}hM5 zEpAv~cGdc{Ls0d-jdc4OMX)N&U`x!w&Y=hbXEXPqM>X30-?l=@{^r=tD?Jx=F!_x0P>A zk^n5QYrG5{(VIAiRdWu<#2Oe>6xvT7?>JhzXPn+)=f(`I;`UfsnmsQm+Dv1gR0c<8 zw+J`ueMiz~$LJ?(WJBHVcGGWOrUPrJx`my(dA4NKKcNei+;85*rd@$lJk%b9<)6#u zD@IWT<%3F(_g8^v&xU}z!G;){zY9mN8rDBs0D0dTOz3p2*_sJB@eTu+ru+djQ}ZuB zz9`abA-HNqIG)cwHIUpo4lQXOSW21*9qhsn8yV$@+kRc9=Q{+`6}9dM5|2-Uj=Mfn zCoA%KxslF5=0rq}<>M1Ht%_5#iwX@{z@a4!M=sf8pl~KvN|T%OnKt^H(wo6=@3GgfvDyJ6p8}prxdM2Y9a>lKbhJLflM6!=LEc3K(SPD){GsbNl z+>V!-^xjW$rlshoWhpv>X>juHwy^13R_{4z_J2zYb2W{g(4;5G;MI_{&6doff&qZ?;$N8ybkV09>$ zC#oyv-y)SaVp`^O=col(JVLL$7kXp>i8gdjO*An0Wr#n&@j{wj4eB1ch%GQ$`f)o4 zgww1>CrW_LJ$vK&quZFP;*@eFB9d1rs2(5Py!Eyeo5@N=lq@n~gZc|MpgDcac-MH-(uHI%%;P~ib4`^?|0*lA8%EQSdNF6l*aDzroB1_`7O#}_kXoY}WR9ne^JSvFUbNzTb{`26J^GPZM8S~WEg#n3950^UhMM)Hsgb_Xk~ ziuIt;w?3qXntSCHRYFA4(}6g&_OZot{Ckpg#-it~fyu546)yJ{N|_A}-1fEynJ7Ab zZr}ooY}3cwO-IiR^;4&L8eRJAYmT6F#(ECN{F;wQ!bQus)Mwh^kN}HwsWVXgZ80GU z;}bi3tkvO&s?<&U^|YycYU{}`U6K+SQw$ZHRBnZIfeqIEsKiJ!Ksp}2++yZpBuzg= z$cuUW?%vjPo%tsViN&+Hc#n+vcN}NuAxMnv(G{jXFbm{~tGDb41*+g+E-l!ykBw9E z?2~ZTHa3E`G6$}fsl5I~L|NZY6bfqg{JPz81xWge&hOzGLU|Bn-(cw!f6c<+%upUq z0;Rs_j#l4F+aG{=PF4Oj8LS7u%ahpJ>8h(j#NLXBH~Gf8YyO>&aNI!W`4HzxbQ>4t z2Z)YhpK#~-mdouJ1$!H`25Lkm9Msy&>^`l}GB#USjEC`1v|MrM)J%@0-7gyKx;QGR zje~$?9^A2SoIvqdMOv>a4YT*eB!ipR_wR?~&=%(U65zWPK9l=jeiN=wyxl5qxZAWh zvfGh^e%Pabk9)iB5cW8~;@=flg>*-N^rkeK45J3@OD|nWUg>=ysryy%&SbEpqA1wC zUSzkfTDP8eZ;*w*(ANJXtV4ctKS8`Q`HUDRo^JV(;m%S2ZR>=;U9}*&GjFB{~p&*C{vlp5@&*54u_(hk5!pMYzb-x-V#zr`1Sy8y= zC9#kSA{IBqzyW4|8mnNLYi;t}RAStIwm>n;FwsH^jFz|F6Q7p>H`E3WoL4i)v9In9 z+KC)Pl!y|qI1dze_>`!@1G=tRPy;y1M9*w}IM7tqojzgk!|~fu?Z)8d{qv!{g5x$P zUfOXgsPhAdtU>>eVXV9YUg)ezemwYm)8T86y1Kc&KWqO$(=cmt(!eH4AS5KjK^!qJ z;x4n@yJ|F7+S26B7yK}wo$eIL6*kLKzw+LZRn(5**VXFoO{v*yhe?kSAl)n0pCR^> z)Yor!Ut&ii9`m&*l>%92C* z`sY;1gB?2^mEGA3kBk2~$EW3d_&0>;c5ECK%`5_pW1oBYhPF)`b7>t?I%)s@0t0zz z{vU=*_aBuG{FddxJtHTXexfgVVYTY(wd4u3GAjoRv4B1UG2945W!J@;&juA0{^0{!%%FH7BN8F1j$KyCG8DA>M4b{h0s7+-A-4L8LzsXk zYSxAXj9gG2B=W^o{i~;qU(TAo@4wO$m5Dq0jiMJQSJO8-dMS%T)x}b9)9mJRU`V*| z!NUlckTj4S*3K9t#cdG#`yQ2BI8+PbB zs@i!ri1P9<@oe!9+ylh4=fZwvXhFXeU{ zlG|1WJ`njVK05sSJa$Q~-fbxOljchIv|GdZn&LhOGX2adn}hew$Z|b$)fNG0lm-fz$=-jXNplzQGWr(Hz=c+v0!-QnoLsMp~6=JU-@gK_J zja98M77m9ysOr;~^iAR&>8cYwGJCPPe3bF+H8?uq5ix^61BDz8TRqF};R$Ba>ZK*A z>(`gi!l&?nzt@AXkGJ#88K+yktfRNFekT|5V5G=GUYfNUG~`Fp=%3DO*VY&P1)s5K=k?rkxYe%*{hI?iBBRtzkK868I`YDsX zJc+vjQsdAs@?~W0JwV0JZ5S$iVZ>QT1tA&j3YHa_NrhYo__5T z3HsT_7INp?Q*01j!po@~mxu?H%fXH>=jQL^wv)3}BQl-E3xbjO3DdVPP4gq;6IqY0 z1QJk+T=DC8N^;Cpm-o4U)WuoFzoshy%X|RDQUiJay9<5`Ye|FpVGXMRZZ|AvasE2! zey12cHeL`xoRW#AuPo9$A1RkT#v&%dv=oLLBBJv11r^P*zN_g&I+iYTw#^v1Bji2@ zhWy;Xna%H1aCN;0O;8%(+M13zm(+v98G*nwQJ%6m`G&ww)$hJTS8o?QSOGNx%z=V? zov#t(f++#+{js4^RnYO8Z0=>_h+^3u_-?}ZipB|&*_sd# zqEU^=_Z_c+o?||1qOmlXLPs2~KF}G*ZQG!#;j_m~uVk4>u)4SMwz+hu z^Y?*}+85s}E>ToL72l(WK%XraiG>TkwO5pG3YN%o=%Lr%cVoBmBCy8XP_G}+gGfe| zuRP?fba4`zVfGUedg9~xvdJ*W&_vnHArz#CS>Iaqu2$MSM@3EK!;Wf2;<721gpRxx zCVHBI7%JM|tczKB{uO1fyUJK<<;inVwWg&U>EDvYz`5u(_Po2Zc8*#=xMXO{mAk6m zi%>V~RMr$NgCyrts~=7Npk8TYLQ+|_LXmwMF`>I_i;=q;28-t5=De&;S*z51*y~9@ z#B0Rq)p=@h@e{b_PeuZz$coMe;=OsszGWa_cBylcb>JtA_VQve7S+Y~S~2t?8MQ&n zrLpLVJc1co#C)cOiWj^OkJJi?D<vw+-Dj#dfm(U?2-m_;JV}q8Z(j=tTZ;l(UH|7+Q~(a0D630 zd*q)3o4CS^1uv*~q8D8;`FZJqC~(_-D1Manb_Lmq;J>OtIqUyja5x~Nz3ZtiJxAPF z8Sb`8cpjL=PcrchIi5p_n9h5*cqclPe)Axqh%k6W)LM9$cY$rX5YK5*x2 zeZku*$_nVVVuLGb9u)a`9ll=?8S&GV!LTvstp}7nDY^Dk+S|Y_Xrs1X%`VLh|E)<< z?b0<^z9OLmTHhDTeroZmf;Toy(O7q0Jk$hjlO`Y{=!V0z`gY)q9%}1}@kf}Y&#w$o z+q`Trubg)_e{&ibw~CtH!?IEko;UZvdJr`$>+1x!YkSM55t%Vx65uqWrlzMn;5coS zZ7Qq5afJ*9mE+`uygtMQi!zXF{IZ8xEsMGu_U{BeV*YSXb9qn_UJR2y;e@Spvwxwh|7!OfQCi;dbBrUU9(N|G z@;gdgR3;HS&j49b=L*gL!kAXS?J+%PiYNbq(DqE- z5nJ=IR|s7r22+HT2JACdX79{?(j%Zj{mHR4o-!GA9=+PD36~FGlkodzkqf)3?7x`P zOugOsgyN?U*w|mpnzD@V=WUaV=)lv;%Ii7b>IgaD{PO8;x@z1BircTwcQYRlhz3!R zHVqhKT37fI>Z7y7=TEiBdJfCJ#vSU_3}s{!+|I3uQb2PxRnzF3&o$c_{|V z^ZPv>!;RQZ=(AL$nhAN9oUNkSF_ozSZDwY)>Wf@r?(U#a{G0n$DYD^f`ZTd~pHr;1 z+o(Y36IGprCEnZ#x?>WPZ$q=6kz1s}@LHN zRmxC3XQhTG%!LDG&o`E1Y(M>na{!P2_Y#7w;J~iy%bI^wSCuw<(a4}3C)FCZvz_zM zdV!~!G}fqs-;qWyo@s&yyx2+EN>KALT$7*YA6lS?B9Gp2GXY+XFp(+_eS6{2^3&o6R=Y~hOqwVef5*Uvw1KsVND(SFmHhDkhA%z5mKHw7ITD}m}{@1vsn)nkXV zo`4+?>IBQ4ks`QA)3;#s7`P`g{Q zT2LDQ^SC%iPlSCia;hkQ@kU1{<+5)!F+MWeOg!C_h|{%49)NZaG8msR9$E8s2GFc!#yW^Rlm^_4z zm_jk*=YUU7-}Q?RdjD;Q%(VZg#?$xUZ4d|%?UtkQ+WXoUq&@q6cJB9-?~zt+7ML$6&QHLZ?<)M56hs7WQ^qS6=fcO!E;DDlV9A`Weq`zAx9@f*UiR z&I)SlVovOnBuGG!(`s8aKW;9ixZl!hHxN8xhHnrWQ22VDuL#>BQT! z4q_BQ2jzKdkZ2L^ajFLig(c>@%=)=8W(nLcst4>E)89pdJ_&o!R0f{$?U(a=g`1K2 zK9|!q%lVG6y%E%z_}$Er%U3_D7M$1pbnaiDVF6c}?d0wRh1&E7aUsDATI#MF z)9U5xVVw&XV{j1wt)h}3m)qeKzt55h-k}O7o+nqRnLK%b-j1Kia=BgfFubyrt_v#GbEzcq@;Kyk421!bMoPygQHeOX z(VR;*!6;~Hwir)wy|-xD9n8Rzo%H^Bhy%sDKI1^e3<%2ph+IWG=XiUF{PvON`o=2m zTSs%Vb^XUMj{q6B4Fa8x;Bk`Dy9zHU<~1xH@b3B47zI?-B7R@A`JpAWt9Di{4LR|^ z0aGMRPse*J$Q^s6pdMhnk{?l2_TqJQi3K#*KL)~mF(2E!1u1YG3K+!EhZGgrHBHZv zHqAznD($~`B|7u{tcH}t0uDA+Ljs!G(ZCw*?$r|$?K%n@GH@p1IH69xo9#xF09BW1xj{{X6cZ1>4%Ibg+11;Rz zrEOdo;O^9L-8Bk(GZay-sQ2TQ^Vu0;YGgfcEq%v9(qAn+6<693ZDg_FdE1G$-WMbY zWFmLgAMd*8P%FNn8;o@F2oXDBC2VUNooK)Y?O_I@24U3m!I`XHLpFIk7Q7#&eAN$W z`UJaAij^Pd7B6NNh3E(=gaZuJo7Nq$a~Y%z6#M)TNy z1-;9Q_)emde*aaUkj{zluDkPnu{%iq{^CwAwZZ-+pc@ZR0dbdkG8_&A06EzHPc5c6UA0ZTrm|>1Y-`U@7$e1th327a#_jxvx2BAzZ$m^wk>XGuB@(*&EQFB5EJ#tFQ+}vp+)->%S$T*pNrEG49Y>s_*4Il_57`MJ=FF;EZ@EF=ut4pl3*+|YBhq&88`4;! ze}Kl?dp`rftpdt;krNU-noUC9Qwch>&0oI(SPa# z4oE2m&cngACf+`|%Gx1dbs;f+^B#i@-t)T1S z_~KD7nvS-C2fA`VoZ5Y-SM0=}up@-uCY+lVh20j0CekO>%p?<7MCtY4; z2%~uVoimh+Tb%o2BwvPK;Pz&s_VJsKTOMu-!zZ-BikwGXAxCXY9u@cl+mlqy;fMmN ztrTQ+rFFFI2S1d_r-+W(UUR^L_tmqJN-Q6+>{mbCeM==jfBqay1x9-``99HGeUTQB zko5bFoVy%rgo==m;xD$ZDoJb6E3F5vA0{HDU*EJ#Xh*MTBq?6i!uwS3i^gh={&jAZ zjl~64WL({Rj#s68^eS2c(Gs!tlcf)d%%A1tcK%Yk(vplUKj|Iz_Hc_pCVttIVVjlx z^!`_9#X{n9dey57=CMp=!wHSNh@)VtsHjN>`g;D`9|VhK<%3!Ls(KmKcVT}&^-;~K zKnm7sKeah)I-6TX>WsU4X;~Qjnp#iO&3`UOCI%IFsdAFL+sTxYp;rr*<*uLiKHWy-fqUdStH1lG-6{|*u@rBk~f+N-Ca4@jNw zOt~}jW*a&SNlO@WQq@vzOD)bHCgxSBY4XW zVF1m%f)nE`1i~PX@0J+GDXa@Z1<5U-%}MxJ+_%kNA0mSn)X%F;W;)^4G0RT+ZhD_} z>q9L%ysx$BPJS=dn}BwlDYkF=fofg+#;`KX)Jatb4M=5B<#5*|Rio>s#-5=!L=BsT ze3BAunapM@=H*ReZbfzfHk|(6a4OfbR|H3`M-6r^9RSC~lOiM4j`MZ@b%koeMvA)z3 z%bm}YdiP?am^^@bQa~HHt~xrkSJw!6nhKHARJeK_w87RlC)NJXa)S#F0>rdsB_$=7 zB}BJ{1a;m;*USEn-{RHvPrM~#MbUXN8c-YcqTpKExh!?B|-8tax~aX4mEG-M)_>|V!VIxF0Fzw%Fshlb9u*Lw-|df1&jPN)9g-dy}gcUsv@TN_dWA=3eB;S3a%Tz zn_Lz+o#G03*Ka-U=Kfq1PI8sb_>dLX(VPF%2iSzsFfJwpiOdV#_n;C zrYb914+Wa@@Vw0s{gi=qv0OYJ^YLUtQIndLKHsy%LWdUdK(%~TueA!iQ|9W25IVt_amoAve zYbj7r2aYoN+PWS?MAhJ0KN$_#_<15Ysci1_dsI!-`tbH=AiQNUPk|ZKhxQ(7CCifA z3_S1rv;QQ++X>^O2%zKX;*+xKv+TV`)=W5jJWw@IgRjl~wlc3I?OQ$sls5ep>`9|- zL+2vn;Z_yVC0|9*jqDb*g%WGKBMzY|eZ3e< zG+Brd=79u6d4bPA0@8Uuy}f(9eZ!t5y=it#NmQ98bMeqBZ+s4a739^qk?^ev`u8Ik zMPV4^|grWDZ3cP#Hq)5KesDwE#dh@~v{LbR+|8IK`an{yPC4w#G@xTzZQm1C` z;Z$Qmfkop@%C)X zzpfsPaxwRqaMY@7U~C8aB>YMy;;PHT9KpE>&{+e{muS=>0u6ODX5Z4?PP$GJ>2^I{ zCztBqXrRD=-VPOVeEnjn_4y^{SouT%G&Nvk3&ZwT_?43Tl=~xs>AV!C3y3(mJ}LEe zrRivzF3wTeaG{R~7iOq%R%E_6)!s#CQp#60-syi*U!6DY%rS4bAuE>lag9S^%SSI! zC@KF^hK%(@I+qSqbtUG`APS{rE7X~{Tf!i6J67o7bO)?ppF?nbu!x8!pD7>-A7eQz z^6{~^xXpRj$mruINw?I!>uu~({%z^wTTv6gX%U3q1vl}coXpPx;%PkmUT#u={)vrK zchJ&W^Q-%#5C8V>50%Ub+;{use)Rr1a~wRn-{;O6CC8ZEAv3(apF+ASYC>59PDWHH zq8c-_MY*%~>EG|ha^jsAU~6l;ru&X6NZV}W8PAdWlRq&Nmls#y+`;1TF>XYs%+To) z_1)-+V5+B@dJOo}q_pD}?P0CQ*BY~wWCggKogUtPTs+^^MxSBRI-!Eh5m$vBVXfnc zj_PaI_uGva*Ufi4$jg-U{4_B>@l-DHC~j$gh#hwJOCF1TSsdFHATo!aAj(7X>MLfO zwMyvX-1~H7TfUO?rLc^Hc&TFnC8v;p;6`zN?oYCn?cF2{bKcL=FW=7})q=5+Q?4IeY&gV9xcgVTFAnYoRTObtnx)y2nT2`9CUe0xLivA2E>EBAztVt?uv1p`$ln@A zcnQn>9TKf7$QvnU;M>ElKKU@P#M>@s-Cxk*Xe$u78G&df?ypWiO+bi*2DEsrkm#J)wvji89 zi+?!`mK9Fd2&PCPeNOa+z0lFT;*x*l=Cm{)fw5@wtgtt@#*?Pc*5)5JLTKkD+(u8l z_Ow6s`U$NeYdvTb!;puF**GxE*Y*T)Yxaqjg2;n7!^07R`vUiZ#45=?+9%ITlX^1( zg!{qRjQ(}2cf28%&p$URR=?>`(O=bN7k`HgGBf+_Z@K76H<4(6qVq~@DAp=n^{9bws$%3%7p)X^yy?Qito@AZm*xRmkTE(4AYUo@Mql3Wleie_a0tSzo-UkdI6Q-!AU1hd-v>Sobdce~5uQr{`S&$6L@58b6&uWZs=9ZW9mx@s=3!M4LD3cHH^wpP;Z)TMPgA^b;?ht!NE6C|Y)D{|tpHwMpW7MVHmj0G99!UDmGKdu^n z=x#_ToFtVCBN4sb@P3Z@+V{)IrgCy|uFB(sWmi^?B{=?;|(yv%<>4M#|;|!+3H6y}9B-W_d zHl>O5_SJxm564qYg@(ZzOV8NFg+*7HbRzx=0p)uk*bJ|Ev5i+5Y_Elcub*^N!OL#mEv#7%%?==Q2LnI zf8W&9oHID9JgrPYjSAC+Wc^^p@ugN{Gmc$}_BTG7;{=)eUk zx1leClun6gS_E@%WP!3$9y$EgoE-IKob1G62Y?BTO-g%>N%ISIoQU!u#g4X$<^3EJ zGGDAS8>;jl6=A3KXy8+gBfsrOabF5M*U>hp z6+n6yRI6UtJRc_FeCWTpH~QZdp-A$o^`9#CkPqYP%v6xla&yR71-B1TR_V1R2I?eJ z(^ew%XZ6?5%=%9xgQ@;pbj6*oTVt0n272FTWx9RVCl|}gyZ6_vpJ+ztOS=Zh9M(jo z<_rAHsQo%=Ozqzu_OKhxu^%`fGN{4n-B1jx#^oZ3)K%KldUcJxkiWV5e1CH}@Ov_o zs-1+ijqY2|V+A!uS(0K4GP@S3OprEi;r%C!f$s1OYx=571RZ?pW99M5{-v6J9Bg!Z zFDc*NP+xv!0-xcBsvWFazD_bORF+e%+i}^chFg4b&tDtr<{<+P|5?Xa)imAsl=KB% z9pDu(yds}PJA_{si2aohm=ydln~_!a3V}e>*4MXBbzR7!#gF7xKDMp{Oh!rKakMU8 z%gf7$3uo3bT?oP1omekv!m0_-{;$ZD(K3UNLV9vww9~F(ZO|gn2=SK)1m71=Ft;s~ z;uUEt0`Ir`w9Y-LCoYr_?e22CBs@=LgbViU&X4xJLxvAg(`Xf>sbUj;{dzjikp4M& z`$HRoQSa`MeIshK#n!0-dEorRh#<~Z_?1wQ3Bjkl zrSEI&fp`2TY?HHF8R0+n_zDs$W@{LCg-vfnK}D zbWp9Bu~ZL`;k5mk64Rl0C7~n`Go5Uy&w%Wf8)oB&79K^rov_xwcL861pIJAhTLyN} z?ax=f(#BTE+;oOKP^AvVGc5MonVJ^9UdOZ#3$LAA#R_{N>o zoOt>fA6VI*0=s$bw|iHh)I{+g)+S#_Z}S57`ob!AhaeX@7U!Rliv*jlz1JsnDS3rO z9J{N>;2;<7f*|eya$?mebVL#13MaaanXABAPC4>pLh&AO9@^F9I5Ouu8ea#}K)|Q^ zVDG@<$;b1Q$$Eh&^(UQjy5E@bYEE6^s(=D+uxMn&erU=r`P!7_66a7_F6DOu3m{dv z@smxbzrFa^wHbvZTA=R8PNjlpyTr2iRX80v5N#H)D<0=Mgtu|#s%boqOsIP;inx2u z?8pMww5RXTFhry%-O0Okx2l&42RRmf^_yqi~@caobXn<7X_7GobyRzARMYIy{OD2`tRaar2M}l-0c&0blZmgHPeE13}-L{ z=JMmLTu%?@lD-C~Ps6NOEO$fxZ}*xR;8aPq^~%7y@@gdw;jnR4}ISh%+;`fBeu;Z5vCO5CER@&7aYa9zWJf==DK@ z-zMlPKf=W(?udk%|z^;@0m_Wc^+HYe?T$&W{9ij=gW zO^T!RpyH@}-r%3*J3cLn)>UenkKRO3;k-5a?&6fXc|j4Cx(=kcF&AbyI%-0DE1mU> z2gJJ=y`5T+D}OoT+5O!l!5f9(Vt;2 zHfSefdNI6@a%cSc(%gW|-&>UJ^CnfIFQsHJ-l<0(_M^7GsTSGtC#BhMi|@SLV^vN0 zG8R|3r?%%hssa2xf|q@wu@&IGzdME>$nF*p!5|(}++jtYjxe z=R$Qji>!KPL7y`hl6>LmI7kp77P`TvX%dXoaEb4uT6a7c*(5VC2_8(2C3M!ysRXBg zRtNF@*X?<`A4akl-q#?O7Wy!ADVGRW(Rl0{3&9mt(E`j9UCNqwqXO-SlL)i1{p1c` zK<2Cxr|?FmKK8%+o1=?)|6dF&qK<{&n90Y2OK>n{@ja48kmqVUV}2;?p%IxD3@4Y4&d3w9dzGRA1!weit~8_vy*V_XUyygjL#~3lUtx;?W1VTrt zm$GLmd323UQvx4uLY?(8bAyFQOGYFBgU4opi>{0eW}4B&$L%pxd30?TuQzoAW05qI zlaq}N&33G6MHP)KPo>bYqX(T!pTA!Yrb#b0)d}aRu*6`mr?aH<)*wGdbO}b3G=u4a z_byJ!uWMF7?+PRfygLy@!kj z6pKMV8_D&fb+D}@nsO@s2E0b2CLVmW_sq)qXnNA^I|D0cSK377czIA2swK0B_hKfJ zh+ZD=ww>M0Z8v0DN5pmBQF;5?A#I4HT~3se541!Uyfe((GfNCTKD@|t9zJ_T&)QE; zq8L~Er3=mdKzJ`Bts=ok4s=}sd>r(QB8Eud(JG3xHV;vmA1>t=R*}gUxTAjv6s!9O z^_u)Mf-6*>XCE=A1B)J>V7~}2 ziujPk%8NEGY8i?FyGF0drB=T0X&79sdalS=RvK=`@?@lgTb8cTd+md? z;8Yqp+sd%pgYCygs^iXj-HGs*?4a6B4^d5bjKwN7e6e}JmK=6cMtnaQ1Sm7ryGJub z&?+?glZo`lxCW2NG74q6s=8M3xdiuOv=bpXIvQ1QG)2@i-kw3)zNx&CfM)2OPoP#f z-ql7o87SyFW04C4B{xr9gubs4oJF2gB1xe1ZQ7@Wd;{G1CDh9w_hRq<>?8jtHHoMD zVKPCGiE)3;#V;xK5TK|6}tx`Ms>+F99iL@r~rNNWv;Gk;t>%A<3GS zh#sok^Yu(4xO)6^oSd7Yt=jQx>iWWpvzKf(rNitfIc|@e5kes34qD*(Ecxd&g0ui4 zg-&~l(fT#X(uqkG^ObFxGZhY+=g&W5CFmKxt40hbEm8>HIH_m|<-p^UP{1xJh~z-?y||WPLxyV--++RymYm%C_@;S% zhw?(Eio{PGhh(3E_H1v-zH{82d**W3kc#*F^RgqZJ8ct(_8TC9B%Bszk-|U??8=aU z?zTxy4D+mvZ^PI?XOTc7N4I-S-=CU^YcE(A=1(=1`YQNur``){j{&Z?-#+Q4pVW!G zmKEhMnyH+(+y6O{Z8yC*XL#(tl5F_rDYZeOULXk%TTx2IUcJvPRej_gH+@5U5=kOjn7_5G2HF|Qv&r*BW$Ffr8k zf|;^!|9EQADTQQ8E50{LozE6_2pP#d*b63ju%B_`^F(c_fXtSVLw>_ZtOwauTKXr(C(J1 za8P?U8q7aVo9E&@c{Zk%l>NqnjW+~kH~f2EG+B8+@r`3&?7O+EEa7a$z#6h<;guZ8 zM3skYQHQaO-hZp$aL0c)WADra_qdQ#rRd!`EV#OSmA*=NNPf{*pKjl3-K`44jQQ+W z)6^Vv*(Vh?&MD7e@~pKuiO9iEc?04Agq4R|%KUe?{|uY}b%(7|L+a*h&635JO!EfN z&+bU(S;t{<&t^9n3C56THWuiW%=#LEXFBQd3jr!#aJ@6)jujUcvE-e;^?rYzFB#tN zlq}v6Wad7HhS6!jrR{KBI5_xQEFrlxrPh5ymsZ13`%0QmH8guKQ^Ccq(R_94o{x~W3Zu~V$Ag#!+tFQ+ zo%ATR{%x*+`2X;;@i>cwu-!3I!8CyepLes>qpRLD{n&|(IB!XiY*gmX^+?0;=ff-} zNCI&_Hj`4Oqi|bwBhg1z#^KKGJq`l+;*yY_;;fe+cX*u}eOS`a10q62FAr!xI(-1C zVmYb35m^IH`CROZobrzsxQOlDaFk+lensH`P>Jw>=$^s$FFKF?H2O2G6WIBhDx|LS zn4mLWE7ino?lQOIs`BNw^5)A10Zc#=tCI#F#6DtL%C4&OCRlA3&+j;IOCzlBqV@I5Bi>gB?rInzvtf zVl=5sTZy=9EDx@&93Ndi3J3t32tNBVsn4oUMX4QD#ZEQ`bM_NK8I1yP#r0*!mpP;P z_cBTfO;au9ec^!t@f(uAn$F^slFuMAm|A=PchXi_LA8AR_xE$-t%?pyr3|z1@c=Y_ z2%8|X~=7K zhYz|<=zl&8S$ro#4hE_tKk{YtRD_4kufEw(4+mE3s`R$W?|@kPb%rCQ9P^u!$rN}7 zQy&3J$WkI0KX2{Zz4g)%rzd$tF5?INi?@LY0!Hqmt7IHnuxd!lNhMgAs?0g7>Nvqq z0>QdefLzwVFk#WoSRLEH71A7hurEA+*mJl+Zp$JMg()hI2dlbdB{W|xu# zbmOObzx$a<5KxAh>k}!Pr#ico|44K=iQ2XDd7Hu$le@4U!Nr~HmW-qeLOaVx$CC1Q zt(akCO{os*VbGU&c&KE{3YpaJqk`G!O|MT}Pr)UQw_kJ$J6khybN=<25{1pEVP6VE zbMXvo0K+%e0QkF8wqNtxv=LH0jPH~=fTe=Fg*)xOm-L|YzTV*U1iM!dh8=7nf)sy= zXKc0CwaC$G`}ejH4gRlGU}un*9kZy-Lxv5ge}oR`EWFnd4?bIpkqn=VdOs0}SIay*fD9l8L-mavWQHDjRV;+S%$!5sanl3zs^+}Z_us*zU)^z} zrNOi;WO^M&gz=3#c@q2hs@TIQ?b&B;Q@}UyWC{fH+qss#X<`ympHDWsm)t_OigJ56 zXZcxmqMcMzv=y`gAxg+3F1@#CSz)H@{G?{5{eCG~px^wdi^Q-$E+1+{2b59sRkP?X zazxT=YT}!81c`Snu#RC>Ld=}61S z!Q58qzJ8u+HiLVPM*gKIMJwj}>g%|}mv(7#P_^}$eSw9fv9TR#W+OE97vMgni81hj z-$nSEZ{dgQhF5zID;6sc4(vv6SPI&i4~LK7`=&nQ=&%s3t+lz!|9Ss6b5yN4@c@o;mG9tiki$Ai#}aEm0x5SI_qd%(oy+tV?$m^p{r+CkYio+~|LO?Mg zNA&Tx`10H(IcQcf<2bn}&Y2aBW28iNrf`;| z{~(&yVR-dFgWqm$F|Jawf5y=eCE#Ob{tlQ1X0{@slnEPg-k!2aF9)b^0oLdIcc zr4#twv2JW&yyN!fWbw-NN5O!TaX4fjRFFJ-(A8URE8j;~Kp_pXa8fR|MMg`($$aAT z`h0mF#fzfK=9QWlCQzvDm(Tn)Ea(H)H{ykdUS~CT_mu=4{^23Z8sT)-X9;=^UrO}v z2xUQMeldzjgX>;a%*WQMFg#=9q-V5}thL;Y7l0AgAO7bkejKov9H5|k$y#8Gdma+B znTM$3&!`gtd&Q8Vfx`avG`ArqOTqjZeD$?KI|azHo&1l{t*{@Hi=Pk#A%8TUI^jCn zQHlhiGg4E9yT?O}NWqnxjqd3sA&BBIEZGCs6q=fj0?+s}bn>l!l2&4yrJ3JcBkh4J z>f7sE#=CEY?^+@G~%X$(rZYQF+O}gj1W9qhYY-;QfabAuTsJ zU0Vkiz}3c~IsXIs9!c^xea^9Ot8z?1OAZaKRLPhHx}cYQnTkV{^5)`~abAd$oJgR#Fil9N)| zv}**1u)s*@75QTxGar&7A4PeZ`-@e*vnrh3KDp0iSW}PQC#8@n8PQ+biOIjQ!+xvu zIUyD#pqaaoyj$;51-MZSPQ@ zG`^Z=t9(xN$hqSu-v&T}$6)#T;*{sCk7PXY0-IkjM@kXX^u(_?fw;`Oj;D*ypMLIk zGHP^+Z1A`^?;x#jZeE^0;l`M@tF_Ft+zR}HJ%q9Ett)ZK`oVTw6WpdX1hte9YlygaELu>dduJD_(6QU8}G1|7Vs}XLsjvh>0hKDY_yZp@0DX~HFSCo zv;R560}EKSv1`Um{oC4K4MuAdCt}&q{)Z&N($+jKy@k3DqI-uev2QG2=fE3n%XByu zIax7_M?sQNoNfsZ@j79~I`7EvTq*-l3@u9J=bN zlSKByjpdo3n^qpICSDbUWhBG(BxBUs-F`UuspEH>TwG7%HQ`)xj3x3 zVvp?qQX^#Wz+JdP3ABhs0)!aTwxZa7`i{dJil*({^b8D~gs8y&w4sFLbL*ij-C=FZ zbv&>A!t2P@FjnNgA1su)pD#baX)ohvwzVL*%Nape?S{ZJIJ%50!z+rIcy)`f1^j+b z(R8&s0uqZg;S#mjp>Y`d!Z7*JhWwcwrXn^27TWlq8~iYei&Qs&C=)ZK%`-m>%zrv+ zpydm4I>yzm#!$GEYVX=pDYDEnhnO|2ZX^0NHcjkb3?lPs>?+gQIzN$kJv<~THCpQO z5C)-anzI>I9+{b|p)87@GQ2eEIw{@IBIuDgU^a8I&;n&$P@s|-p)>XIx z<$|xsF*!aT`|<#9ny;~DHdwTeP3-^ttq)iDXYf+4f8UE{K`0AJ_K_EUgFBaJr2a79 zAHJ44lXzJ8c=%|jK}kv3=`ov5w^i4hC!pV3zA8yj6*6uH4)(4slZy7r*-%QZNDcZo0jkN)s_=lUlg z)0T&VCp8d32g)Hq_@lu?LLNWW?CN?Su&A9k{@z)CUI71I)L^_xu|WOA#;yNLb@MhK zmAz#YOP5a7q}~tje%HqKhr@#>p3h0@yqrG6m9?&(S>ZmMUCr2}Pu*SrLE&`FHTo;+&`FYr`n!vlk^Z1 zBa5hJme$#CNTjq4tvUDg|6GC+8^0P(%pkWXS@)$kqF*6c*x?};1Kz;*e;@i)LFNA| zfGl)G9$&o9nuaP8?7HJnflAGFk1SB`Zr<%2wR4nFO+V(mUBh@gf@##^`S;N>;=xf5 z>U3U#UTYCz|`C2hlM0bT^bH6Q0-q`rE^_h$XCWa@PJf3)wl z+UYmeegc4IB5JzTCimx=kL&Ml(OE3wgjcRw=+sf#zxW|a$X7kzTDV=fO+ZV$%9Rx_ zE*CPHqNHCgn!)EHtf{REa0TzTt#-=t1wnNR_sN~*p!IdrEX{N3{f+3U>0{50A2N`( zPS^hR_4_k~nca+asWIXr7s)k~AK`E*h#zx5dlxXm#XYG5g7wT{l_f?U?=E**Yxm{U z5H>ZsU3mY1sZ|^9d|*ynPZ%YyijAvain&vPIq5~lxMNg#VE;Gj{nfOBm}cuol?4lX zRB5_A{n4K9cU!CU?I(z4J(9>L(dc{pvryF=R84%7P^HF@a-;#ZUsB%h z2BJ5MTS01uWx3Qf4R&PPYJNuX>^w#DX^<2|arBGrn*&a003$?;Jde}(q6y7DaL;GQ zalEcBIE02l+{$^>FmzDGQRKgatM=|Mzb0NV0B;SoWCqy$zVNf#oUx@48G5KLuV+`w zRLJit3^$(<8Q*F|lQNUs0PqA3!Qth9SnMKuGWiJ0OQYn+2J49P;mUz??wz6)WvkIA z^g$wY1bD*9+oPme^usB!@8a``n%Qnv_8ej|C-2ikcpYaby1EYe1D@4 zCR*^OIhe>0e1PjCo19E9Hv6UD~xeUI{gqa^Hw>`@O&O7XF4?u))#T6|g8Z z(Lq35VuS@pwj$5^mX32!MSe}nlzMbbp2Od-mc{PJ9dBkceBSnCL0YM>@75c{4bPeTxH zx51?t&RStxWag_Hd(ca;Yj-ra1E^3op0W-oLYJ?_IEJuU4>pcgh89!Twr_qXyf0@E zf=0kn!k8^6=!aP3!TgOauZ3JsB#=F)`oPRdh(%4rhDL_LY+hA~^A=>uar4u~2Ip=I zl$0(yUEfxs&5@3jSPAPjy(rQ2#?-SM++Zw;rq+T%9+0| zYRG2l6YOsZ0G-*SLQXP#c4ll6`9hfcL3e~^oy-Z4K9p<@R_w66O3xck+cGxK=YPfr z*tIp(@TtgT55-ps$gBYKkB3*&Nu%>EL%B}bef*Rvh!b;o0b|k-G8$%Vqq1hEG%@pO zn~dsgAJ@phf%)zg*5v-4cI^P~*J#=B>E75_13IWi;7gvbO>e$lX=kopRooy}Vx>HN zG8EG1!C=3e&zBcJ*9~-RPA85=b#2^)NUFJWnd;AR%V2P!dAt{umMt5SKXU$H$9@IB_r~2p12&$@Z46rMx)w7Xhpc%FyH>L@?e3% zE3g>H^LN*vKDJ0$gSg~?w+aORJMp8opo+DY>s69I>WQrz3z+GKmgE}&3$k0a2`Opy zryzUR8&IFekM|BvyI_>4fdX@fJUT{I+1n_YdBhOg%FS>e?F|)=ODDUs9<>U1ew%}F zWLtlo!~i}`8wMgxs|J9#%FSmn+w`2>1}C|t`RmN@>st+|3+LRDm#4_#yTlyN8=6sD z4w8_z(*4EjTD zUB$dza`CEzOGGLQEhZ}~6`Kxux8L@+R~J5p8P5X zIg(9ER&HU3LrTek6WUeT`b|!5k-%IX8`DA#W_QyQ{+oOoQM0H3GKd3yP`-Dg4;N*QD|N}sU&zO~0FsUO}Lt1vm4gG%7! z*l;w7C#zd2H0ER%M#7K$099%H@UUpka41r7Tlmwl&33H5zHmzXC2Xx;tfVr-sA2*g z+zn*}aRB`_jZ)QtTdt)T*vg12Q=wgjRcVQeo*NjtcXra=D0m3mPv+oK>O3*RcW9xy z1HwQB%{e7Sf_sM0`$(nsm+kgu z|5jQ5Ep~g4kP?wpTgx@FWt0m?svP3oX9|8^c6f(bC6rUNC;H%+%kktew0$|HqNvX6 zcJDdKoK8PbD7?HH%h-h6Z?UG1*I>$fBtlaebpQSSvo6z~x2gn%2x;qx+9$_7-W%WZ zzFK$Sh7#b3!}eM6V8%gIf;a)Bq!v+Qf!PbO@x*48*R+{v3;OCeKE&M1qV?a0e|}GW zkewNVid8GbnO)ngc#|AnFS^ndxwIj56(353VMlcc9LxTRY?@(P*`%OPHvxxzx5)8C z*>wC3BS-ezGL5sp_)ZJY|4$^Fi{{6AWCfB(!N>ZK<@Y_sLp1FdL{sw5Ysyn(pd<*| zfx1Kehm&;ixi3Gac%1K``262ybg$=2zmm!)&T;uud7oxH1&s_;(k%1Ny|V-8&io?g zY1KsHtLL&}t!W<|(>jn#zs{M~xNmkNz{!_dBq=ACVTQdLAd1DQBge{QVax3ezSGl=7cE5J ze|VxJ!KvEGe3y98yuA^dH#& z_V%mrp|3?-jYnpc*)*>{1{xWn4VU#=brgQg#Lg3P9LvNJ-`CcD?aCs+nK_?ru(XjZ zN!O1xhRa+R2OHgCb(H#C$;siqwi6?7arR@lI!ZGcYEf6?$|v zDZKKj`_f(E&kx-133?ygScN>Xz*E82;`N0ItIMbfm^tl3XlQntSGJ|y?|^3{qmn`4 z>BE9akWD=~6o9vf{ao9ctUC{JXzhq5gvo<`0F*L;j$W!s-g`H{Tt20nH=Ih&&o#T6 zU{m6P2D@1$sT3~`Gedem_7$}W%D4%@CUGuyz9>SC3S!_HUwL(l%q4CKFPi7-p>J5ux*cUkM{FKz%x6^0qGnHH|zm5S>Px0~}6uq$>bR$FaF&%!z?o?2ch&KkYHEpWWnV)f@F61~ff|N9_ovwg`<_OB_a@xskN<)j z=&=c6C4Vp; zH*Bc^+CtFgbL7|?u{_oP>?Da(I|vOruUIN+u4%wepAUg%v_hO~%-OP{;iNe;+&Hk{ z&Iq3!S=7td-?^0ZV~mZ#@pLE0Nx}K^WX1Vey(Y3ue6dUtg0E8gMPT}Cus77uFh{I75P@i6p}AH7dvn1^G;3B+6Z zWi+RV1`{qaC|43=VLyjBhqR6L9uRq{ZjvR zaB0s+3{?(gx+z@;k8$R4?=Ju}!wFv>pqQc36?7Jr^Dth}OxqjnVuwl|KWr6J;uB<> z$&=@l2E!6448Mtd&DWFnch^x#b@Sz%khj49Qdi;IG>x+TTe=mF?Fl$g2E@zIt zIs(4K`H~rCAafb1q@?ccdds(ec$22`^1NND(6Po#D2>lqI9C!#APabhS*X5& z=K)~W<-2W&QG0%&9CY+l*hLd2f+u`1*?SC5TS=(%1?>0WdY41`NJwV!SaB4EQSL)Z zea(!z9;^_>0P=_$!N{XsSA6&J6)qlg$7iz#-r`3>N|YiuLxsZGi08vj9W#tVlqv(` zvXG3hesq*YsgKlqtu-^3xbBPCMg8*!Lo>DLbt)i)ubZU9eaeVxC%l%hk4Q|MW^bTL zRr}3m6ecg>e0b(VP!YAYn?9T%Ab}M^A(%VC6nAR`)Hl6+HPTGi8BC!VGXNaI<~1Lx4n}dhQKr*Kc$w@#^*$Zj%*|Stl0$kbV>@ zWKnA+(#?J)DITu(Ot&PmKnm{?3>u83Y@qU^;Z)#i?dwCUu92nYcY&rIhaLH>y`caj z+hmZKD5!e0?|bYG(R&uDr=K~^#eHepE?VmOq<|16x0Rj0>j~Dwge3eNoYYdi9{}T4 zLw=z(NdMAVBsP$MP)5ms!m`18j857aIT7YN9^qqaQhsB#>9L?Sb8|r<#nFqsYn8|q zl%z~6-Jh2yCRmS6!ohSX<=vPfps4p~7`1pOHFpz9D1kIq>@u$f4lN*#ZyCJeb0r-T zNE3RV6>`m*+M6Jky+p$p>6Y-8XLYsA z0~br1fqq3V?SQ@7{;)iULTc%T68f{A+pERfUcTLP``aVj8m0DiA9SLlFrbuy+Bp?8 zmZy9YL?Kg(=MPesyFd~YXy>i*OTVpKM*&#_#_I4zT%BHu&g`;I+wgL^eCp*i20@9i z$-o9HrqIUWYy^S#B4@*Nu9+74Y-tA-#6=3sz=Lk)O8k8 zCJF(rLt*miMq4bfTkeNd|FJcn|Lr!9c#YD`OPGn1*&Fv3HGtY>|BPmLDy5 zHfX&ddYQ_4&!Q&GlRtBIRkMM6E)U*xK!M! zN4x1k)(Dz}#D@38Kx^*0!?jW3aR^7C5or=C1aXIU#^C)37ex)S5q-iH>D6@6^Lv6o z;tR8aIOOUS2^?P9-t9Zg! zdu~=Ru74P>U^%#U%_JAqzsUI$j)xJq&R0uRwk|p?b!D-dAG7RixTV{qB5Q&N@vp2a zf8nlx?bqaUMk*{TUk7iEpl{RNF-<(S;}R%kmpU=c{i!e_72-%}x5I%Z`ksdeIhHL4 zf{v_ukr8)oA4h}D4KO=fPG9Ppbamr}A`*Jdc3U+aK_uiu8Egc_B!c8lGO5o&#@Zsm zOL?H3fRGMHoE+wWDY%^5g-~dl{k#6}I|lQr{<&{k?p9WNH3>Ml2*HT>_gPt0JMz-! z`6COS?zfLUY4N^ET(6<9Z1k(>u@7ssIAW&P3Q?Z|)SvRhPmioRhC74G_+DhGdV&+aPpdI$+6Z2b#G(DO)Xcgdn^78CHpK^ zf`FcZQ(beHXSW^Tcd=5>u0-G6&Ydgi1l@DFy}>pP?;urEfw;&+$KW$91f!l{%I8F8 zPf-e;dD^@q&Qkl+bkGO`}Jtc%nk8K{FOFDz2)iryTh6Q7m zL%(3H=$8rtC~2ZtuYd zng>xHxzjB7qed=9k7`li#Y~|)H$m7W6TDvkB2V0$@gl8oy9-=)^mB}mwE;s#Pwal; zytq@bu@V%z^Y``uT(tcDWj_C+Taq%cqhkF70q1p+IAbPH?qB!@e?2h64jY<69r#D+olAHJ%t}T_n zulBz(zaGYi-`%*GS_+fmKL0sY*sPm<%)~})KZezx{=T7wji~Fhho95s{V6|OKMru} zcNS$Dq`(+9fhkud%-cB1Nu^SFcxQ)5=z`ZFP z@L9>0%n%+#;CFO|0vx3>i@Y9zKO*|^ltJg#HkO*q@i zM3b%WomOvB^FMXnNm7~O6O5Un2+-)@_xH~QPu{^P7%1CRFUWL0-LT)DiPl`l;SeYA zz?aC8qQR2~vO*tdb3yb8^u>q;RryB+Vd0iyrre_}{Ait|RJ6uH%;vL&)6Hz!!n9zz ztTOK?C@~a1b}md1C-zcF9^d{&_ySwMsKb*MB139AkFZ_bvqE~gPkrR?ik1=-mr6Qy zTlivrNc~Pr;u@9unYhnu75tME7j?2$YoeXu73lQ?&}Z`|`Ma2JGWzdTVT*K$bWFRm z=Q4uZ=*1+I6~tf{)7H(F+6*UjjxA(Q4&f4ODuL&kz-} zLKOshfEC*$UDAV08L5Z~Q6@k~7NS`FW}R_V7Y3nuTCTaZGg$+n9npR?gSrb`a<{?z)&B+*#MA2u3 zGG@fTzqJ__VrS2Rfu^*4xrm;X8*6g0WDMSngq2z^79HJxNtJMb^}oB0q?E_Lw z--O^Um+HkF+@s3n$$laa{5$daTk*ZI)}e@3n;bBCh@;zwizpB)Nhw;JKDGQ&&soi% z$M`b4T8u98IrQ#@FqWF7AL)(4wbI)7$GLNZt?n56<6+OkWUdEpn;O;}*}WnmAT z6U8A-xPO4v4pSCdNfEuh%(Rso8Q5D!CH=7)VUo-F^lKHU;|JZMiZ87FC*)qfr1Z28 z5E?2}^xtvX9vVZ?kDF-So6wst9#mH_w>OF%_*sMl-n?)%&`O|t`_+!nA{4Fw<#2wX zm=H(u3*K;R5CDdlvT(l3;A?@g;u9;tXU5SU4glxq;3hNR4yOeNidxRLK~d$U>#9L{ z`WDv>(R;MS+D?8GWTQ%$`+fncWH7VM?1p&VS9$)S`(XCV!%cWfJOnx1AjR&MW-I;PnFjEcya}u-jlX z30`8`XQ=L1T~G3Jbrj_kY1NcD#684q-qm(g#Y-VYS7P9jiYJk;*8y zeF^p-&y*)LaZtkSY6A}}GU=lC{# z8d23QD+up`cV!mbvHt$`D9sj7A4RZPE7T4-*??q{HRHxMufxliacjxT)2C?-enOmj zO*HBk7T_(3%x}Bw`L!&jubBj+zk?Fi7mX$xbR8d=0Q9n^bXUg>)f9)R^*kK0do#lk zv4gH)SnsLx&mltX_cXaQXJVc3b0BF;jzpsLkPjGdZX$y!U19wqnQ4A(|jm${~U_?Y`lq{%3Qj zdob1E1jnxwqIgdX*0$x7= zK&g}9#Jwl8MD$R}+@a{H-K-C4FQ)y|3jmHs z%sD+yvv%$yRuo=KwNX=5*S=antiggog|Lcei+0x#rSg1)Q!Vqyq6)*t!o#-XLkClJ zfYs7zX!L|$N6uAALjC&c6i$nIsU)FE6n-4;lp!fDCzbVOKokhs?GD*i?=IX1vzDN3 z-K&pK_hy;WPAD(^@aR&D^eCHY%NC21eTR+GSPJakrPW*tgNxcD15qn(q(5S6-QUjo(lTkav>3o@FM`$%&7?ts=>x-(+>P~p&wNh6Z2nbv>b95g~C4s3l z#IBo6DV}>Lh%$*VsQdu^&#NifJbcjL4>O8r?iwRm^+T7g@qm52j6QH1lwn!u`|UQMjGB&Zr?ZlwVjkw|k>1V{FcqTz?&^V0UxX z0;mflI-l(DaKq$%8bv;C1xlH|N@Pn2sxdeM&2rU67m6D_z7*T7gK0LSk@U!h2F5)B z5n@dVqW=!3=_Oeln7VZ%NBR7Y5-*-e?0#trk)nO+k)}d-xu_&>`Oad>aWei zX~Z)T%akn+ix$uQ)7eizl6{-< zS)m~6HpAi_rb-#T_y-NG1-_`i>r1VS?*HHfoUh*qP_+=uf|C1~@(}P5mf*?|L|cE- z0vQCGK`}n0<&%ITyqsPX>;4RXm@9PPv1MK~>c#pdguK3G#%y_Be5^Q` zjreJ)1ezYLN}B&!ITGHN&|kwoRt#1MZ!M&LiaL6EAR-?gG9v(6Nf;5)9sc!O5*gJ& z+WT5UFjB9Y#ecnN=QCLKc4D<)*||;iH&j32!(6(>`hX4+jU24-dXHyfOe!NnmEay= zu%K^9mGraa2o&{{wbb4DvM&(WY*F=u_GzNIHtWybo0XN`w!C>CTn11;<0-&;4+0|) zXXt-@dWq#qLb6H1Cj~rT5R80F{aYDj;ig6x4`y$t%j8=zbb({nb?Q@iSS2Y_ywR3` zO$+uI@Ca1Mkn2yZ4Q`m7_lZYq;Q+oS%67mjPwH7KUfu~rq7$R~vi zEqd?gync;=d?W@h#xn1ZCkuGj)C~YzTaehna#ycSx<$>N@|FKsC_D4zhbn1EFPCl_ zZjvZ_*ztFKb%p?>b}ff=2{O{e(^ZZn?7;Mgg}Npf2xl_YJ~N{uvQ#w4DFMV-gpUtw z_|d(l#;|SS>*X}Dt4J25&HGaLDgco-n?X11UJLLY8Lhj3`<3}iw1!J3m|uDv%UKft zKAWU(OZyWTX>m%DxF*&64m~|1E-(8Xgn#p`M!F2!IWa3mefONRtyP#zn7$#0nd8hr z5YtV7E^lS!nyohSO(?zvU?Ki4v;;Ak^e`KG;~YR!%DdUry5&!r;1-H_9KViku+RMT znyk}22!X*HHL#1%>luwvhz+l7N3c0PXn6_h)o_d*=|we|&`_+mFOTQNYx?8|>V*a) zh5hhpi971BlT7&qzOk><^;n!_7(ULfb-z7tl20!k{m+sISnT;f_68c87cn~w5qh@@ z^ar^mTIMBvXY3lrkXAE2TO#Xc||D$}CR(Gs1eGqbfEa z3BqCc4h*!$18pa0x`x+5r&r0>rpe9tZbpum^3a}$H#{F5v&UKSP6`4HsgdT|feBM8R%*qxuX+)e=>8$>D^y`m~lBNcer zT?_pK3J%N(tHFb z+KIA#<$^^`I|O?e+VpqC(1wR<{>!!2kRSgX)(Jj)6jg>J(ZeE*U9g!LDCc`n5yir zva#ioQUm!Ay~hRVom*s@nvGn2NST_09O0kn@qn$03`{3p%p+Ll~EIB`uMDf7em*wH?T7*XDmO_CaQR ztgz!q}f=7pq?!>Zlk&aLo0(hGlg`&EBntZSNa18)Sha zEfVr**3|O2X8xP~XCg2)P;%eTd6nkDb%oyY!!x2Fns_?CFsJdOdRdM4S;vp{xkT40 zo24O-wwBSF*iS@jBzaeh@6(m-v;cjNokxY}u>Us8#dF;k8+zbm4*%X%ceZwZ9u0%$ z!rUqBMRJ(rQ@U0qRvh#K^zw+sA$kzK>iO5i9<^CBT)8hgqz^0UvEP%noK@3!AMOc1 z>HKvV5KU%QnYI0}Vr)qw7Zk1RCJg4&+JA_&RjHmI+i~kDtssl@(FPXEARy0o6+8`_ z{C}Q&$64psY7G(-98?^|uUc*I+(QL7v0eMKWs1})sc~XSOt8GoF%e(RZF>a2Ww$^F>*sd99_4g78v;i?qKlU#HQUk#{M=-OCz__FJ<;CL$4Fp7I6c)C&7IuF(SGH6Sk@`l~u~YXo;Ns$fl6I6m;vOyQ=g-<| z#(jY`!uZh)X6z_E9Sar_f1HqI_m$}krXq3@zq1pRtfZEMP|X*tt=*diTX^`Whks>& zQ%2DkxmktOW~05qb$?_8uw4F!O}_=Wxd|jGz1%x$Q&t$Aev45$Le@j6&J+Bw!YQ-z zMg^iyZ}!9kkj`*AG$E3Wqesx5b*KY^XC%^00WT_wa%$@iC6fdK5ST`I|D+=;(Hp0X zOlEz%j$OTchCDLcnfQx-8>Iigy2g@8*Erv2q+mZ*|83)mXjnxxBQhKD4#E);5 zTe1D~d%u*7Zk~c-g<6A(N1%$p;Uh3Gd3bx(eQT_13T{eMK~@yb+wP}~peEM%GPwPK z@g1ssXjNhf-5ygzJ!AVJlr2BXLRx);te|9d8ppKDq5isD8zCCkZ~G}W)~-gevR$CN zd>0y)HlJ^<9*=^xd{$xoJ+NsZ<73i1=JROHV{d zL~(3Lcid+R#Q<%g1p6vhj*gCoJPNE#Nj(B$C(v`B#37(e<@QkY<{`*mTWm3DX{oDL z)J|B_?pk(;h$T;6H=WrASe!#?*>UbIXxCh>Ct2FEYX{omCN+u4%Bg7%tjek>^J0{y zKUXZr9*ekc8i2Snyw$ScAD>>`xmwvx>-M|3@J&SxOId={nzZ5%J?~YuHc*f33sO%Y zd85xinfio|{~2x_rg!B7H`j%n&T4*mxp)w}pN%m@_p~b^X;4NWJ2k{8gc~15>vC!c zp9KdxdzMR>-$i#U#7QPeCl?wmK%ahpS>=*=CpgnHU}EzFOUpk60xS{#NA7qf<>mAf zvB!G~x2Mw!USZKr+wy2g$syfxiDv9;uKG*&t%~x#?3PMuuU)m1HW=Z*Eq%gr%|%cw z1(gd2VvN!XL(}qK*3a2SA6-g5pvDMl2XTqAq46j8rn;%}7~yt3;nHPwZW4O(A3)6S zVuC}Furc3*Qw;?0PE%9N$H9#*+wbl`mM;9wqVTGSv#y}8Z436Ct=FA7Dk(DnLJhKf(r)bO69}wm9a0(P zemaN#euV2j1{Q41e0xSS%soQmc_wsn$k%@Q-=Ape7g;7y-dP6Kij|e~AfQCW0&08M z(}Q@p3CJIImeJ%D^HOf?7#>n>YnaNMrm7qnyIL>F>&62oIa zyjOap zgm^ek@cRqxQaUu7W3@lA1?3WH`@UXAt=ZeiKY=(MRD;@Y2z!iMQL}Wg>wBlW@?i*O zb=QV)dRtVG*vHpB`8|r&ZEcBDpRv2+3Ow_fgyIJ)skN-t_%)C;nP2Z*#uid8aV`V3 z(v#|{7)tz(_q|qXXBo+pJ#dWI_XrqodHwiV9c(9GFFw)!KihQSo~wn|{{n4iDq~OY z6qNvfsAsj<%U7&AR&c!x-d3uU?aNI=ch>?7^^vFx9ajG2mx_fGgJ96Ubc2GOQ7Kjq zVw1{%OS%`vi>?)R9d$?BjmQzU-tFOxFFqG^TKN#7w~s9W1_;v@VnEdRh`9EhoJTsK zomW>*v)VvyO)P8}h+W^)=GO*+Awu?18UinNp+A!E8d%;>oSsC8e=KDze};Tpg{sUO zwTWxUI=Ah-yufUqc{_JsVi_+#6H$7E6t+NE;Hv06kL^4s{QiWINbZG=s;f(qB2zHW zy~^Ot*F`fnqfhNxMbGI19R3E=O4l8FQbX|J4Oof31LcLbr6tzo`1qevGXtfZJhGtF z>sNV-_!4ZSJD_%(x-ufKZEW+f%-fK*h``#>AxLy?qx z!Q!r%RIv&VAX>nDGLw&{d&2ZP`Tvk5ZgLjDErfX7GV)|V@ig@*6OSls#$nrFQc#ak z2fk)SdizwIq$BCebWj9nX7mK-V~qlmYTUkcE04lEMv#QPrHu)tw3Rl3SxQ&scET< zH(M+-7-c~aB7SbF8MjJBYG1NoGnZ7d>4dejCzihE}?Kz49+<>sd2+ibbcK6rg$T8YxDrLw#i>#B=uUM8+qPL zs8h9{T8L4?YQV0nG?#cq8kYgZ)p@B|w>3d$dLZRrLG&B6)W z-yGNU!lFxUDk>`_@#+JK7*Wn#VFOLv1RJ$&qx#4cU$>rt+-&gQAGJ6b7gcNyo|92J z%~%Lo2+`da!I6-%k`3NA9lHZ+-M*-`TnlNyftCca#3tv<{eADFqWS4sRGfgk+lsxki?0 zBvfa|XEKPA6X93)ycjKD`%t#X3^`n+_b(pAwln-vkL-vY4J=b6KvB_Ex^b+@fx=Sg zkS%T4^q|>>=QZa^3)t2xuX+2#X4OD<0oD%@V_wzzD&p{zMP|x=_X4N;@Y?4o7rA8_ z1+}3tFQt;PX_67+Fl8yZG3)kF%$+xBh#lJp>Ay@DdyXho9)9K_qMz=L>aP*1^hWIr zl;f2VP3mU+j2{O9`Sy;}JDA-EaAj4BAF_74kV`82V+Q+EFa^v8;Ha7YsQy5UW=)Z3 zYmWCeB(d5#&T76R7lcBLD5>cbrn%o+y_F>JV}3K_?4P0}9y)TQ;_-Lsu|$;Fw$B*D z^Db#&%|2~iZrQS$By7J62uoEejiv6j1MKO3L`oXIF>8s}F|GE~`(WPy{t_T`gb1HJ zY!!qFXZUmjr;cect4G|rt*tqChlQm5&drMuZnvl~F*y~@RUl-%khRe068c!YE+ZT? zh!==)_rwB)aQ@FiQ|m&o$%y(gE+xjXhncK`F3~ZSOIju#3hbd{`x)or{!R?* zbSeks1J!N&iDCU*{!tE40+3d+IT*WMJ(V>HGe??djKOOQg2M7|F>$NhWpc~jnY&9shZCB`bwIb8b@S5!WZOsP?eTDJ$z`s6H4@> zEHY5_b-8_cY1l-?6Ys9EK@)%y;cD9-*}ncW;ZBcE<)M7h*c3$Sezn?Zg~Ot#&J;Si zFu*t(XptQRP_dlvN@yY2O=1NJG4}h?YC{O;PwvY++I00`X+_cQuR+{@uKgQEQq7wF z=dI;e;%ixh^B=?K#&AvY37O)mSEv}m>4Iz@utt7p%B`gP97Z)6qluLdp0;}a?vqBq zih0`UI}h~+G$-V}C0}o}cKdxrO+^cwU^Jz<2(g{};(vaYFJC6A`|2Y8XG$`loxbT^ z2fQtUOzCgbHQ(lfIwW-LdLwY1ST?B!SM$h@dw*k;L+a|G;DB5JPJObIqfi8lH7|k_ zT%0lItsBZ~f0 z5|G2BQ#V^#8@@fZpXM1uU$X^-{K&rE$#L0o=-PJr4FH@ zs8?El-SCT|tqWI$!Ky?O=I<0yyG;Tw9J9{aCUL`9Ht2M6)m+Dg0daPCCIYXX*29-b@=mTMU0`Tv9%|9?Z9_6fv92lUyA7-|*uS3yn>(nx^@ z*~Z&AH~RP_Q|980o)lj6Og<5^5~V)E^MZo$+OTjjLT{_|Y@yM^yJpfm?&S##M+>ih z8HEJx&{&nmluba!Iu*`b_MikLvf>I7SD(wq>8ktf(VW92>z?Pd$;#i>D7aXk{y7d{ zQV+cY9hz;lDK8MS-OEZ(X*1>RmbhbijbHuK;h#V3(bZ)i!@_pzl4q>k?)gYxCrGq-rjG2vQZ9c`SZMhRaw7 zV=6XY6Gr4f7Yztae5G61u&sdVT#DgTxej8`r1tnFQ%?o!z#j&Z4PDae&m_LF74PdG3Fa`GH(nQXp#Bwn@ZcJMs5OPGA(v2a9dT;2ep~T|@rypfiP-6KF z0kq$2w2u0ckha0g;R)fM=}2%!nalVmvq`?5+)%W%HW2yRjorCeB<;R(ecMg`?d@q6 zKM86NXlrdOJ`wKOd-WoMN<=oBvbEe(0l2YjdL|jS@sRcgCA_6?b&*bA?nVZGh2i?~ ztICRb3UTz2!fu=m&bI~xU4`9(OhT<9e^kqw;XbUwR&b&!%=v<8A{&&r%HUDS6 zt#ydq<8iR0jq^5R5lYjZDib)+u!LO5`q%mwykf0=($^ zso#qkw7XlIL(v(u_WQ19tJ}36YD^=6K=9{&aTHP|U^0vk<$`1}0LT{^;}&bl!yGPb zwbGvi=<6GYrCNT_3p(Bxni#}2GY0?@o=MIW$9le-swdJ-vgUVsJdj5-h~HIBp|2xs zxw-S;&2hQBZ z4t|;LWmC$MzcuNNM53v1U>U3|2el^L&e}MyY@ZOMDa7R9OVFB zYHu2DB5TU!=G|=T!^QI2w|>7h{x$;?u>4^|l-|1zyOHXo6y{TqMjT`-Ifi{jnMS5i z&r-CP6Y`T-(IlH*66Tu--v1{~e*y|t90NPDZ=#E$LdX_`TK~G?{+`$LhG;{aN6_DM zoC_?&b_cl$?mn*ODgq=18-4XNN$MZl-Xr{6*G5fD)GU`h>=*hhb}~{7kowalg>`M2G~p(*>QH5drNw6WyOjjz>NsVnon9E&WI5H3fKAR3_kLxF>v}Wl$QT`olp;hUM{Y zE29t{p*$?^Cavv_tdlM5_NbxW4tD19Vyx$l3Fojm02pY;D(NYyz4Ro`0eQO9!IxT3 zT3+wzsl~GHivR#%5UcqDkHfx_9G-*vH}i#kV?LCWVz4{i*8nh=^T><~y|I?n(s=7} z*~pSexoB1+y+%g_eceEA-oB2pANUpYTh$ahp4y;-qwb`4y?5jAfMT+EBWGNM{R*Ta zZ}#f|7fuV{(Y~L)5=$C&_@;4;lPOO<$*Yr<4?kl!bagm&Vb{G^G@tV*=`P%)L-e6w zlZaGErzgRUBvHN6n-;1?#6Nfc+dAyS>}Rn15ip z-YDq!PB_DI*;u#8U1Icv7$dsB5!9EG#te2k@)Rb12!^hYo`E73?B2*vrx6+JCyf)2 zz>XIg>3Ou~Tlm@!5UK$SZ~3F7qcsf}>@}ljtoKQ$g^U;tuXL(hY@^RFkQMe4w0E+ZhGjHq;ii~hBItq>7D>yDVs$uxRFP^DPE6@+SaA??<4Cjfy}9%vdG0zbc*_VgqW?_Q`l z^)yet6rFdx{OX?YQqd@U?x^naJiO5WCL@CJ?->By%ay?nZXvG5TL*IhP_nhScI~}Q zC=a!a+GO=(zvG7?gJMs3ZcOr%s!MmUQY=h)IEO^%3GuqzflTU5nQk*tod&D1FFU6F z5Lg^Kf5aEkO~}M56%{H!QP+7mM0UWStF^eaFjgz67}D*?+2z$}Pop;ekyv{Bxo_@M z(;*5ov2L7LDiI?FpykOBOQu@8EaaW1gTP&m=rh+&ozPyz-zrS~(|PTp4?a8m zeQ$)yL>c@dJCJf%UK@3Eo{GWT^p}->_lY!nNV3Ht_l2DYf z<7;X9RLZr7Yl(dvis@N`E${=Lzc=b+q7Ey5mS1;?6aR#r z6&7-~+*v~G3#b&a6<^I?w8C3Bx-;Uh~8$7OBu041RocT7h zSIhG8Tums&TFmWFp7FZ9#d?#dnY;4k=Nr^DP_U|qGZ)2J7d>ZJQ4V&XSpvu+&%<2mHZKhzBY!Qz0Wn|VXFR24Mzf%Pe09^~L(2b-N& zjcpwCOiFrN2KQWQ)27bT4ad_ZAG)2NKG9a|cM&aeYWm8q2%3wWHD4y*E{UlYj-6^| zea>z=;ka=FF3y~;M>qWxGe1vWwHW}dp z@%~=laUgNrxl(%8Q(fe~UQJCZyAo(Z!SngsGOZv{GNnG0n3(Jlg0ZDEF@@-EBpBUK z;)B8q$eI_%O1|Glk6&Uplj{`U{6RkwSg#7OToFWm0L>6_B9lMa+jH(nIi(33EKDk^ z+1N$M-41l+Yh3`n%Gvh|hy*KS48E7qWRN)b7{tL-is%sL1h>R0eIH!*P2z=TyRfIo z4+Ifm&P|0n%;lq5ifdqNo)G@!WQFoA0hd3SEZ(S~>8rO=_&Z(~bN(7S)EbL}S|(>` zI=@fkB%;4u_rsf;I&Ap15kQ7p-|dF!$$4_WV_07Io3($Hg*PL;s@vFcqf+FNe;?a+ z%V$4UMWq#84W{kzw_h_EVkGby9bqD3rTOKNcK?pQ=6~V^)FA+hFp6ygOJcLDvHl`O zv6~sogQaBd-XH|%HT!p&6M1Laqi~XWiFvrXrZ$7|Ci1Adc9dm_Io0gxcT3?jmV%p8 z+));>T{Lh-x(K#}yzlEbC;4}eE{|CtH>YOrqUW^jrFm+nr9rQkg<4INI-Df&&=(Dt zJZ8}V-~x?g-mvPf=@~9%w4J|;3UPEJ#M*Rk(82K@VAu<-PimM+Ou+oH{>sDAMDXcU zf5hYK@oIV-?_8X{TKyBWrSvJlkPUK_!$V-wC1z>O_zvuA^ScW>O!k6L=pybUW>&iC20s-|sv<6BL4m+PlalCl~j%mVS$Hb`E|)ENZIgf0>cy zZ)^n&*lq74Q8Qg$LL9zM(KnmS2E$zMIaC+gxCvfc+3^ zQWdiJ4{v2|smrIg3wXfEgE4{Tk`L41$_oQTect%{y*x*9%PG$j>GX&1-G{lIi4ac` zz>EZ3F&L+NDgi^WD;CBC0*}m~v{J^Po|%9U7(#X|h78BZI{Ja!LmXrG*CbQ&_zX#Q zQHPx;8~7w$?DOX?ZlMR;;=&B1>1Sg+G;;{>kX)zqFuygwmc z2_(Vb08VjL_kbD&`uU?~*EkOZ@q3Vm5|SjqyDwvOytU`6qAB6q-Yw8-S(4V}^t0m9 z#G@`3Eos|11kDvAUcrDW=k5}^0F-j^ISCuAk1bU@xIak>v9e-Sx>Hs`oVt>uw!G~c zQ&Y^)+1(!VRW8h z-R4qiWCUK3!d28?;HK`fD9Y;JSFjw*?(=J#x-X!S5vuQ5Yj=R99>`hfne)0!B3WAN z*qLfMh4i*i-|X!A;mBv6;;d7vZM}5Cu!1o|=9VL(2KYnj@qhxmAf}f#TnuDf>HX|B zJ7BMM%S61!o-<9lT@{*<*|s+crS-5bs3GUMUtIt06Rpm2fecNR^n)`RJRg2)L#@-3 zOz62;s6A~Oa}Ue(ozC0FBqb%BYGcc>-~R+xfVVJUawIMkMvcLo059qe@x%`63BWMfCZ#LA3^PHTnp zL(M?brD`=Ixt?ZPH}!H+2)lVBm;BX;95inGM*UTr(?w29OkrDFDDs(`H=W!I6{Tnk zgr!4C#D15cvc}T9d2T3{R>A!!B`I+cM%MHnYp23S=`^%6HQ4(DJ}&1Pi1GC z1gjCm*G2+y^hbCJaO$yU)MdROwWU3@afnTJ-tO6N;{GEKYZnjEr3&<^-M&|Uu}2O9 z9VcJIORT$byI0#rQNOfh4}O0tRBaYfEqA~Fh{5{@i{I~2WP#PLtt=FR7lYXGejq3p z()o$QY=iBQq6dse(AV{A>I1PTD6U$BW2lK(p6Q;LK|J&(qCx)w-5QS!GgwiquZ&K7j#Z>94O zzeB{DKK-88$vQrVLe9m-XgdkV%u=UY8a{;M?`>q>V&6`E7R#I*C^JpW9REdN{;UIE zA{Ij?^V~JUuw3I6;1+^QmH z{lm;@P2DW+2li(Y_IwXJUvjZa5KQxwuuX~(n}gUI6L|-fvH|JA0j90Dl@g0l5L*BqkUkC2pjVy|yanuS)DMW=%dYa+sT(cY1GY}8o95$`%#cg{h1u!iXnfc@lxk}ipL+GG(cH{XfxcG zPfruTTl%oxc@g*>f&vvcuWyzwK#h*} zMKNy%_*t8UZ4VKowy0p9)RKA+*XhGUY&rrzi+oyGa)*ml4tk2ATgtqykW29N(brZ7 zk~iDIjG#Hw`TM!dH*-mBPLB$ zy)DxnE9cDe!Bisk?tWKNy1Ipw`?zGBVVu;p9X~5d{;eWr1lI+M{+-T+ZDWJv(;j(* zBZJQVPt0LN`>4`w1rir*f{JsQNEgkgk^qNvSizt?0K00 z_`WZMCObSb#(+1Whf684BxW(*>t#9aj}YHV4naEFuzuIQZ~%5sj4f=-V$i;**>jG8 zr>;K>FoNEoEZ~Y%CT;u3%Dgz`O|~D|r^rW~Zzm#_-%GBV1y`y1f=5#`RVa#CsYN)f zP@kegiJGQ}K(Xc!ZDpwu-Dxt%&cpPViVHQZ-frkL>G_&Q(SV>B$MzZLzkQ_-i-+Zz z6n68Du~~iN--_$I@1ByVJx!NZv+0u^NRC*9G{gIsr$Pket01WoVSX%DH_TD4#0jNOCBxN~qu&vx-kTMpw!l!LrWk{vrkv=G$vu(Y9ku%cDy3AE z1`ggg9{c>xVF$#~hvm?ytr)wLN92wqSUf1nqLhp-2J zi1&?t{Q-_eEcs<$Eht9Z1r{={GA+_O2z)?~GThp4vi}getqbBaMc0zZM+;kGiR5I? z`rlTaO;-rVKtb#tSSodbE~!tjckeF-^2Q922yky=)M=PxK|El+)yEe}-*@N!_3=e{ z-ATdBsZr*0=@7zbn~}ouf4c;`sQ=D~8`1E+HDgunMF1=Mn~ZrRgW|lFH|y=Aq1f(S z)m-&^?!9R#VJ;cdjp`$0W$Y{tcIq$!61_dw)ZAOP2Nr(~8SflQVLyqm zVCYcw60!Z5i+#`9IaKU(j!9$6>PnyktTuRL^D=~ic3M_z|y^j<+Fk0TA@`aA@W?sY{vFk2XQ!3uJVbQhQV26Kg7ODcGv{tbs z3bW|$&Ekj8w6kh4-H2iL!&FmNhqAl9!&0$yugcFjhb@$ISW#ZH;wVZ~kZ^hiw=4ba z*ge4^XVo~A$OtMv!wov9(QpS;=l6czKX zi1mi*43gXZ1;Lu$Z)*DiD9Dv>$wf}hj^}BkIamP9Pcesh=%$3TKFT@Do3dMLQ#cA6&D;?6-KP2QWxHh|PtRekaYgU?3jP18x+WT*#B1UGK9Nw4o+>p5cP9jkAdn4QMqc$!k~aODwed#--Rik8DsB3P>=n3K33Vb3|A z!-3gsL>>mL(O^FP`}sTK^mAj}B=$DY@DUMqZM@4G6#4@hr6_hPh5#C^eeoYYqAnJuB)p@zTcI z_`b|cU)h}j!Py#daqGS?_Px}MAKQ^Hez`xdsgh7^7Cij38T{Vh;2VND|6TTnK%v`! z0~c|wf5>fetkCdYaaZq$i^nu3#bj$DaWnvS$4xv*W}427z{FKjKDcKOO%eDl3}T(p zBTl_nZb@>iAa8>1nZi%pBlF$3{?1YJUTX+*o4~ah3z&6#Z}eq$2|N z1yZepkA0ch{t+Xzyv5epu>ZTS)c}eoO8eAEFO>!XoGslMdF6tbMQ>$sf<S|{%Q|;QIawzkz&ZE6TV@DJ*bBnp>WfkK` z=?SC+UWUV!>tb%kc-BXhgO$J0eEJg{YInGhMYW=Rbdi5-K6O*X8z6KE_c(OB>j<=RJyZ{l<&GM~9yB>P0(2GE_GW`md{6!Ee^xQOx(D%zLj)E%ad zWj|zbm)j%E;*?i#yxUhrblu=efhl2CJk=zI^Wwk6!O7y!Lxvid6zrp_L zuZFu{Ci5~$C*+wRA5`DQtB<*@I|3#|>jHtSxTJ|VTQuABW$Q#BvAr$H2?Bz@wsKJ5 zmSgu{JatQ5_m~v?2MHg5hFLB%K!9A z=Ava$9+00n+~DGB4_%w~`ptZrTp5wEU0z;(|K3*h9G(e}`&F3QFfFgqEhgZ?M#|4$jyv02oQ>!<`k1vTE0e6uWZo{eW7lQT=Mk@%aZD za#a2>TpOn*wjjyRqJ?&pcN;gTG0j(h7B@uRt+niHiMoR`bU3f2wf(2JN%VKsX3gOB z^!UJN<;NqaWcr0g`G(n^$F>45l=5JlwrbN#OuJy8*ltf4)DS z*=gA2GI?3%H$Y5!-4}Jp+!0xdNM&)2mhl zFjjL+ucDJXRIo7A%#b^jS}Jlk{r;K{#r_veLHS;PQHRr_5Svg7UY+mVeRaMUckPJe z#`7$Y4*>Vq=|uBM7zil5qQuT#UV9q2xL%zkIKWEM|g8Q!-xueFqP;U zLB^0A4NZ>)k3wiHMlgX>NWtYb_D>oeu&7^pOPJ(LaCM0+ve?{~|KnF>e47Wc`cNof8|N0G!&@Dht!KSe5V8tnac z`tRzQ^X8wRqlE%BY;SYhAl@oLmPY1xgJv=)U6+aOaIDMHKC>TS`1btvtbW}Q=tePA zC+aZbj-F_Y&RMtOwL(j7@lsBEyl4#CCuF*w|M;*jcr?Hr<-WgsCo`kIn{QYr4e?%I z>1)4W>I%JoP6oh0mWUWc_>q2(o5awiSgPHxkulx9JW&QG8SgiI~vcMDB;W)s01A za1%EvtQaGMgLz2NNA)K1S@JxOiKN8d1oOUT`1#gMjYu$OMrA;KB>pI3VMzD%uw0NA zJ@SJmHpS*^0P82$s=9I#qc8nu$UD^z*6PZ$_Io4N-a_26$h@zp0IjO2OlrB$=?s!j!0*ik34z-iM4 zF<#DqwsS*HYxCvyWGjq^h_i83`YSr!ox``BSLoRzpx3`>svr5NfuBNPwDI!Lw<FU1}5HrMUb#igQI(_r(q_e1RSs}%AY2dTA zk~zn@pqglup-49$Tmoe_vnV9|gtN7XSo=$v3~??9p@-$Hv#5ZmZ?i!1KAp*5Yi{G5 zM68*$VNE6L(`T6Ds>|~_a7QSoI;p!jyR0>_e{Y+y?s2gbdHJyqjJGKqO4bI`WWT&1 z+r4Vg7CI?1Q%%I0!sxu|9fTmwy5CbFR~9Oey0B@Y0j8$r3ThKZ1FzZTTPB+^m{YvX zUHK8ZK1FpcR&NxK3-cqz!s<6G9s#A#tyF5JX3mg*!9o%c$4(2?*UV_D26~(KMw|ePnMOZuvW<- z4))sy8Av2*J^YDdD(t>i-nCun)%NLmbOpWx)U#ti+RQmZ>um0f$p_C`q$b_|2TnX z_lvcZ$QnB+8#5jQc6nLrT}8#cjnbV(WTvGs*rW@ne>-Gik`6Fx@IZTe)V&1LvbrXy zTNEdlss26TOm8!hT9T2SEu2qlGHO$HhQ2%zkJcF|y`!l)6=Y{Kv3Izsn>SruZ!W~- z77>W!HDUyT5L!ZcdG(p5;>eY-5~|pBPw(f4nLHFGeB&7v?KHKK4pi;dF0CX;jF7vU zy@;Ms5uF#h0~cdn?K(IymHTrxGCnidVTFlx&rwKDvV+xSX`_WJJ7nv9H$S1k#Ffs` ziboQSG2ICO%iUci(3MR zSHAv~PWD$4cwe5<7)x7SC;Y(12~JRMDIE!nR+FP786>)!w)BC$H*)USzTK4y>_1fN z!^SOU#k7*)*uKRZkk}c}o+X!0k9r`7A%FJhDtKbXJI5*Yk#n+(DlksTUsqxzny&Li zFFHbZr0Y!It*745-Az?K{Wnp4$@l%wo~3;v+pFkMF%xrLPk{3rRNOzu^Kl~F#sQV> zw(2t^P#v!4PS>Ljfz$+|MRoELv$w>f#OE$BLJdkU53zXo;wAQjVqEnrw8k3Hw|${rt6y8 zJcKwZPR2!3Q$lsND6q-_D9lujc(HQ5H27d5XJLCN;s1dNrqRWjEHc_gMFdQ-g1%tM zIuY=$==-=eWA};kKB!$cJ@II*;GxL9GhIO&_^aLV3=&zGB4{A>HIx>%+77pHYi*4$ zJ+l3LrHRWU{I(A-b%*I_p{xb2`UrQ4-109$jo|263q+~u5CAy(5-SDdB~l(0_uHdu z@W0vA4<=I_S?!cNNKmpeO+$1*QJ!G=K*QsH(7xxUt|;(g!u(t z-bli%7)!~oe;WI5{Ehzq&nkdDlrD=bzT{71(Ctvcm!{Mw!-Z2C8Zx_oB2ev~6cOr* zZ*y(0FByJ{TWrWcY~dNIOgKOyNnpJ_<__1pJ}pu-sM);a9m!)jlKcXghw&nXt^MzH z@>;7E)|J1GJw!*;GQNcGs$Q%n7ffy9P2ZK22x6UoQ#IuvNVhT(TL9Vw>(|$uUfMkE z+HYC~r8||(T=>Pq_euAQ1z}|q^b_Z}idJ}KGA)JK^03r2;}zk`QmCTh(8hesn(K#a zm2ShPSCdoAc2)ZE6>YVsa9pp6dtans;Rt+;D35e%7=ewH!4MI}Pgt(nIPux7_a9z% zg&DPTd)rnwtFq?1%7e%e%X?8CH@{(1@Q!j+R-3^ag2}=|RAXyz!I)aiJg&jq5T$-6 z=M^E-B$LvhM($oY6z#t8>tO4%G;`KUMmw-sOtRvT`S{&s^$kf~F-t+%0qag4nY(P0IdfTAp zG&@zn7U>(wQ6r$k@}(j0w@XzsmDCIfP{Tre&%JN{?maTqcKOWqztlANZpe)M7tky@ zfDCy0_b1faplva88I#sUQw3vN45E42({GBx3by0XW)_N|*MpItkHBP$H?W$|iglDZ1I&82nPN|{J2a}G zvi{mTB%86G^vvvo7$RkNg<7wJA62L6X^5{?Ec?Cp{F*d`Ods%U1$+$cY2!z|m= z$bI^{>boVR8vz#ZocWI#B7~3oxm%*ls0a9?F-Mp?7TN)b7~%zw)){T~&K@;}eg3<~ z_s~v-M9V`|QX{3u`=9*s?|>-4Inb^6fK~jM)@KBTS%QTXe4WC!ea^E1m52)m(Ztgg z6HkVhfmu{WbB1&xyq&wnaZR}Ergoi73E3zN$_2+)t*Ehe1TWBr+dD(%gW<*1*$(?n z2aGeoAM}*VPI`HkexUa|f|=`)@?rem5C3u)+ygJ`fZ3LDCbbA@=|<^v4}DTG1Q%SM zf^%ow**5KQeR1Lb8h^Y&1eh?=V~@U*(e*@@r#yccp6gmSn&O|;ipG&_OY|+{CW3%y zdm2ndXSB6z5}6p#d?%kX(rjMQ2Ap9bh8WR9sy_7bMKRfLe|I5QKo)qOKo|c?{``cI z;vX~3GyMNQFB!$)#)6d9XXCcNg#r;ukIhkLU|MUa5HT$1xEL zH_nx@QkgmW>+-u#ZZj|$6eEe`3b(v8`cSwp20y7P~R z)T3zpe$@C=p^|LrU(+Yv)w997lYTG(fmVu7QV*rHwiWOuN14KLcRPcE>Q%sPQ3JbQ zCpT%|ph^$dMYQ)mu-MAmZG3VoxKs#qjCT^@JuNB{f*IWMi;uP8~tQxzj~y~igxNqooyArh=z^>q2E-W003MTl!NMzoV61H z7-d-ACIm#QQX3(CP?Hi2l;G}*dEY4)k5*g}G~X!)+2t_!be0PBmvDl!GszB3H%E+{O;fDF89f5Fk-=@Er7E^sOwLPX^D{JYs@Yah^(P}O9 z)o687_kxg|_BTX}sn7##A^vrf9pz16EQgve$c<3(7LiY>8%(9QsS+NUx~6_OXq;aj zx&b49#`tyIYT5R_GS2BI$s#IIBf6LF1!r~rT~j1Bw->pHVV~j5H;CX#orl+KS9a6` z+q2U(w!#X9B&p9tW&n0%viTdxpoZms>Ma zI+fUp!*f{cfo?1Icb3g67B$A%$yTBhTW^=W7V1>r)6@4}RDZ^)U?xS3cr=Y{(9B0G zq;b`+Bw^c7|03l3`RXw-RVH*I`=g*GYAmy}J5LoPMZjWibo=S{_lJJJh0q0-V5^8Q z)t%vb8U}8UXzTSr6a2j{V6j^$Z!&44Y!W{s1#;&=X;@?1L?clCf3-yEh9ZEu6Eyd1 zUlL2*_oL@|V65E6wycV;5CUH2Z2F498Ji#!YKg+*=uymh0%-zQ=wMxT?>e$ks7&t> zd>$z*M{*tMH0Z3Nskz}I{NCWD<`b`(qPp^Xolb zUzWDL@lW&8?Swlj4H!5oC(^9}sQ*J}Z;7e9LuZ!NPcx6*zv~*o*=2_c*R-ZZFv-Ae zTwHtVok$=_&|IPIe(812Iv2noaQL-a`d#N_GF~QyKx7)(6`y{JtL!$vFpj)TtlAa+ z#Gt*y{ybQZR&pl|6l{Lmzot7FYDGy5C-IeP`skM0yQv`NI4UbZGUdm6!s&2C54W_g zNUuA+c{Z3*v}ZuV*Td2=YS+JR>Q_dK(YMeWje%hsRaQNb}U0C!->8}K0sF}#9 zr;WFLDDl4=+*7XBFs-n_st?!?K?I=l=(KA4#KJkek0$;8)%Gl#V98xq$n6uqkfK6b z_pU;!IEC>bcD<41*Hfu+WEuyIb((CIFlp34Z)b`~oSQ(&T0!wvI+-m>ZMgrBsBR+5myBr+e^pJWCS}@MP>&wq zuu|D=o7OB7%AY0u=n?t_r}gwa6WigcRuwxqoD1Id?pkU5l%I|eJP>UZ`D|0F%*2iL zIOO909QnRcUhG5{*HGp4yF8aBt!~8zIq~(NzlG@Heu#?QQ+m{!Az@B# z;vqdsW45pBbazU6hfv*Cfwysn&+fwC>2iM4&@Hz@u4hwtUSt76O5u4qsm#bJhOAi3 zZ$vfB8wkc8H+cN(^jNa`7e7HYLl@}Fc;ai9XUUgN7YB70BLbI~jYQ%*+n48c7{l`% zsQK@oWVranZlves`BAdXOtPb1of%va5Z$Xq^-RK+50-HEaCV!7S`RGS9r?#Sd0zaD z2S6U#Y#lW}OUlO=!x!VtsMuO8E8Bl~1qupG%&_G_%3(^{ac5nfAvHw&DxHybGpPvUS8-)M z1u-F0E}e7sDk)9?9FLufWiA3OnYNI}B~B#pwx!b#Pu~sTQgAK~oW9@qp zJdW>Z2uDWP)7_licUOrnSXvy?wmpo)He8gf{m)lGjx{(Z=Mf;MbFJ&1U{TwG%wrjhM!DKj0`0~V!1d^$tG)Z>Tp`$ZbZrzjd z^PJE5@p_qA^i=S#mmK_qa1Az&K%V%76SPK)pJ!fP|800aWH{ws!=ZGs$8|~i@$%I0 za@}I$D3fl)E-8<3<&*)2n^;*{^k;^u(YtDHu$V4!`4!DlV~`w8`^hEXkgxpc@VHz^ z7WFVkKcTdBv>}m^A;wL4lm3uhCUU(uggH~rzmy|KF7meOW`QEKnLk{${^RX|+eZIG zeVylNscyNxK{^sUkI7+$671yCp#3=>J6$LKAlR?#emwW*_JAX++@H979^(7m8=}IW zRZ%Bm@6M9?EOqX6+&SFJ8JhMgQM~_o%|JU#p8dzi894XstFjPB|7%w_r**$i4S6J% zJhDV6rhie+Fmu->f{MMwlHsce_-ZWQ1r4^B<_;&lqrya~^+fDakxtjO$EB}D@+wc? zs#p12U#)HBCL_3hUoJZ*_cIfWjNoZ8-4DA0uklh%ef?Z{!8G~Cb*7{jIi;mq2Yp{F zHTmssGMkA-7^)fMnewKLk3VZ(2u}5^%ME5D4G}owxkuT^O$HI%AdhTD$QS?QAtuNk zA(2}NV)?KhH0?E*>)}f8AZ>TBHKkhi;G1V z)H4K*tQGUoIT{a#da0XU%E(kyzS4Ra|03(%5yQ~XP)?p$g5}Y#Or4aM^5jcPA%1+K z$|k#A91!N)Omb0co)Hzkzhp>@aY(Z+!)Ifq%mKv-mqbj3WkMmn+$!B%+Q_=5jEsMT z;5^>J`ix->_K(`p*3^<6tDxAV>BTWgl;wvn%1utTaqr5Xo_K22n};*N38XRv22>|s zU@ll1dok2pe5=-anI)~fYT7BTHwtB?#q2&ixt5~5Jt6sJd$;ZX75ANC zQFL3nZNLQT_bEXUFaVOHAQ=oOHj*R?LZcESNRk{26MiOInj|@cWJwJYMI=aura?eJ zK_q9I(4@Nx&zU)Q?m08hbDz0)`d4*zRqegkUg2Hu+P#a3Ka?TkP3W%gib^mKGMvE$u&_nPGi-fK(29EeR%ek!R((A4Op z_hP{L`(NKs%5h~LV=@D)YUyiCkHfBE-HDF`g0y)vwWa&ag?}!DD<_e{Zoi|=m)^Is zabVAST4n=(TChN(N|L5>lIDRGfz{0Ru5WB}ikiOY3}Yes94da}%hLgO zj?M3B7#0x-(>;|J8!^OmcJ?4frg@ZCeBGZ>YTm6&#zB2 zth;l~!X@l*ooj0HtEQ@Nh2vh&B+gFuJef3Yz`u0eQ&qRy%zr2!q0U*?T8=jKT#=7w zrCVXx|C9mGRp4rzSZp(2RCl`XKt1#Kvw=L-N3p@7S8|u;f<6)2iJIbF$>GmwXquwP2e zJV&y^tiWx$Vod29Rq2aOZ;0|DbB>q1en`?yG2V)S7{?ND)b^5-;XWNkux})vrmkSE zOMbQo{>c27-qs_W!OFaBuX*X8a~tXkZr$lIDU>VSjPQ7p~h#n65@E>z^2SOVb^&ga; zPBNi{VY(LUb4X!Ns|fqULX2@pQEc8{eV-Pe4NF=cUy@H6S~GR?BGj^X6Io<Mw3-kT#?ov!o(EXJaZ|fbEo0X|Hy;FzW4x3Nj?Q|~6`DhN^02+R|h|*F-Id1n@-9Jfo(ls3N+ zu6C=plXGqz3Adhpii?fUd8;{=&r%!ECB{Ivo1X5ck1FFWlQ&ayZ*%#tuZ@ozcqyc6 z8-TB8tJ0{L?lXv_m5(X349N>L`SsS!%zUlJWKKn4fMsy*$@; zc$YoWH`j*$A9P6+5MqZvRlW?;t1}6oH;~tv>P}I-Y<(!ZD}CO!kN!X?&#|-iRxcF{ z1&{m5f~_4D;fxO>GO>|*TXj#r4U6loIjJU#i&;18GffG6&~7PJ&;KAalgm!zt?q@* zTsGy^b!D!h2b0?7Hze^qcJKK~%Ab($ljN!=cj`_c+#L ze=vQEzu#0d=|#fjN?#D%Qs-pg@Pe85^mzP@)lzwy$CZK%krByCawR(TcKQd%O2I0e zX1zqDw9a^Sb)zftAe2b0Q7wKm5Wb*~pz& zbn>#Z?p#c0RMIce`Nib3cU`wRtUPIO&Q1G(}M zZ_qW4eAgKqCjm>db4OTms*`jk*jcZGw!VJqix)4{vS&0H4k*#@+iez6nH+Nw{K0|U zKj)mHtj$8rkIg@87^dtEQu}PSBC0TQkQ6kw(6Bh{+Bf%BK2_1%P!1Q9VL$A1I*+|- z7O5Mem>Gz(G}E!4yWDl)i={=>onu#%IWO;c)aNFPBP`qf5Ei(J!9?MGnJz>yBXa{e zCQ8jV@on!CRYt?rzq)QuMgY5#)z7*BshNQSYrW>R7o4Ms%8avAf$PI&X39zJJ!L5d z0oQKs$ky|_!yT-ao|T15bus|c(Y}G`>c;arHazkAGg#>p8riyg{Q@WnRNLF(Y!HLU zwRh7}GaGtvX984s8DHKr?<5|%>5|T^C;W$oWYGSzthE8PV#9^}S3Xsq_pC3z$iY(2 z;)qw;TDMAB>g=%4(+MVg$yY%xFa*5!A0=yfud&3{&(9x{gV*<@aSLgkVMDs2%ft^P z?L4r1muORC3JusBcuSq2;Plec3{zzH+}g%CC|D^jdwgPo;bnEWeAD;CyXnD(^!0ff za)VR~`)zkljfkjHCmDUm3QRo&q}Z0d2G8(Sy{WaHi_jDF$XGY59Bzfkk zW1}1!gn`?_qv5y?4199gztglP*K`{_SG)x8$4)AzWZD&cHE#+RCnRdANhNI$vF=Gi zSa-A!LRK2M!H6_R$;=W%EpWld!w?!=TGjllZ3i+`9XnJi|am_ zz4mswtWTZY34ODd*oni~h|*mS*tcnLDejGzn)x0#en#sq`F`1!02T@FaHOb61RJ+Gi#d{oFhg^fqHUd`~^2d$oXe4o6v?|ee6l% z-GQ-RIR&#XI1=R=%_v}yINT7LZZ}X~{QVKFseXvdR8LZ`U4>DqwsL|JUN=VG8!?zH zx!)}+H~Vpy^xWs8&LmX{2B*o#-G%AbkK>Y-N>uiG?Vw@SHC9ho%URrUgDoaEgoCEO zgeOXccf2E{IA}13!&tFLA@@sJ$8E>*+z0L_HZ}fmT@t(X*B{4|EqPg{lTC}yHk>@r z^Tw6yjqI*|r|Ca3&e3Evc898^Fl1XBBdV+AV~uO7R^?qEWn}_`hCg@Y% z(SBT@UamX+F_%ABV)3K@)dWs`x`u z+6)F$h+2(plXt(~-xm{9Jv-d>?So^1McbK7g97Ce*O|`mm5;?O+c~0~JN5S#D6dz( zf>iJK?Zu+oP1mO zYioSmqJl#483VEW!vf3(T@1Ix#Be8fIDM6@64nhV-uKbwQ1;))gR$Rt`a8|<&S65& z{EI{6zr5oA?F9(tU-#|5v1L(ZBh2@!{zH+xmRC|IKK?L+?7(9(ru6;@6-fJO6XNuZ5Lk-Vh$4M#p>GHMdBYL|esIygvom=FV zuMyxzLnEiQE1X&TY{gus4a+vE6NE(Ljd49@X(x;4d^*-lg`FDv0lHLmdC%-VzTZ5R391iIa& z?|+Ck3%?3Z65MHj!a(eAz4X2Q=eFO6umAKAfz$5SjW8INw6yee)YQrrnq_+O?&0?w zX3LmCw$`MFUY#=yH^;afh;fS1dZa{QGqy&2`&pE~3(BREs6AzSU%$24g8-Q%a=Q(4>>Q%zDbqH5hm z9Em~7lhtGTC(mbHnI4k#hn`SYM`9-WTnEVY!F(uO2ZS{)QL)-|5#Qp;j`{e*-s>-IG-gkuF`22l+ZDHgC7kWGPK+z8WHTu98N- zn&535PIR4FcAqI*c4`nP%rElZa!gBbv)hfq0H~q=2HGm-xpcpqn3<}TV%3vxIls0+ z9Z%18TAM1g9;$hb>b7}Hbf4uAWBOTobrJutJI9zM5ZlPJDsN*G@bU)0u+6*4Dsvq1 zK$}?ZbF~J<;4z+>;xt#yG{Q3Sf-kGJV!fe)ey~^ZPv@G6a`us(UF@zrHFNW;ULKpP zBaZzQjw5PD9%D%m-`SP?8$Yl@hlN$OpmoyM91~}lHEU9`Dk_~r9)|rVc3|Eh7Z2kw z-0xuqaAQseDN!!r=8bRTE}S}5CESssezMPfBuZ3OD_u9Uy*Wxo#^$#?YeZl%4avF7 zC`H7i=A9Cr4KDG!Q#Z404T?&oWFrDt59(OTtti%!x!I-4gVICJ0|AZ?Cjv@>2HgaV z%MA%m%~B(ExfzkYh=afbe4x6#ewUGJR1N-Qc7K^CAN>Ia)8E~zEIEVWK2__m_|35e ztJ%5vC{E*@C9w^(tB>cm98>+f|N4_XoDCT$_og|)6qTJ`K3!sERF_3x+u*<=V32=} z@HXnD?p*&qjIZ4vvpX;B(9zT5W5Zu3?4djM#uo;enfS`^Kb3ZH*|- z13D9zT#_VlUYQm61MlL%P@%rpd)2-H7A(my^3s0k+~XOPxE+3 zr%tJ$nGYW+bw<)_Zse{m>*^N_ghZ0MLRr()2S(O@RL?(3>~q@@>r42OnAoY%U%n-@ zk7!dk!Q}V&c;QX)I5+9Fx1jteyzyL`Z2S2oP~5ccO-Aq2OQ(&T$vVt(9{Id{d;p~( zk)TYM%#0&j%SZv@GG)-`*dmu9*-t~i#xg$o_dcL*h>!?o4?^M}aWQ;(YI_Pdz@1&# z;a(QjU}a)lc)xwjiB!>BvU8>K)vb(h4cRF7MwZ$(@Ee(j$p;?(;t%A0ibova%unFtIQ_ z5sc2O^9@3Iwt41F%-D9Gl*snKHI#$z-uy9L7r^dZbx?xd)TRAbQv|2kxRT{RKA$xk z@^mI+uNwq0%l5=Wc+y|iZ9g6^U&Ua&9)rU^06T}MM$76G^6h_oVq8{AxKV~IwqGYC z&UnvwMv21;0J7InBx;AO^}0MO@m!uJ zo8aY=h00gryW_=jZmOWpk*sGdfR$eT8A#4HuJHHi<&M2;^zzWswh>oL?x{4LuLS9~fe$WyKEFB}ATKW}T_3(7gwdSo||l%gKWA#n5kh05FS$p%G? zAGG)xNfof`uEfRwERFGJ$2M)Yx;2S+jpmcotcgN1uYIeU{|)e#CJ6%Uzw)1Bb=J*m zfYrAxv|85Of9R*-&E&bw4IN!wWcG_~=HvOggAW(1WV%vVM$XKAbEJj=7 zMCmqNz6H|{vHZQ??xp=669U*Ud}SfSw6fvLZ~R>?ksEdjv-i|JXTi^7%x8D9euB52 zqn`u*$4f8lZGFtN{SAXLd;yC1`-2_={@wokhtvPx&LaP@L;td6eW;XReJYGY%*F;b zrv?oAmQquq31<4hgW~0>7F;?oaE$Nv;PhFYJ8pQ4#ytyCIZIh2sZTuTimof}3lX@5 zLJ7Vw6|43{Y`B!8IhP)C0PbQbf2GNUDGK;|@`S-~pRaXrs-$5W1%MYE6h@Y@XpK!? z3;dGkcZfCZ^$CeI2>R5)mqPSVW6=QxurGA*yOY81{HJfP@V2O>z~B@%#?(>R&M}se z*Q|Laz@m67Ej_Ef=Jg3q?DbGV&Tjye5sdV42xb_J0mFf(`Jm@m)Uh~MGw59-;5^1S z<<@7b2#GQ?rCRJ)UvcY0@B{GodkSrs&) zeO;douD1UgzgMVr$0FokEdCH1q(&kWx^SMEV?=w&44Ynrm z?LBC3da8x#~q}#qCK*W_b-&lEw)~6+T2=SXbF9H1g^y#5n%Q`6n6zU zbo)gJY*vkLMF@$26yBw!DHHNdzZ_AGl*|cALYvryUi!Btw6(Mnut?xpECCg2=B+}j z$*(zt#4mwd5>Ly}=SnmEmdvoEzhKe#oq$^!#8d_Ja}}Y6C{(sc*!j@+Zq8dY1?nam zjccNPtUC1j7Rtw6hb_k1lL(1G@1#N2KoGTI#<~5V!M2BsTRSZ@NrYyASH^rt?5{wS z!T3gQE)`ac0bC8?*jP+aAP4bih4(1{E&&XBo!|ttge4qB;UF$y>?KVC#AA2eG(8B1 z@3OPIVlO_<2UKy8S?thWw}5*}rv-yD043z53h?M&p2M zia;H8Ld4I=E~ln-m?;yQ<=5^50XfxM=t?!|B}P%)ty=VC*+PRhKEP5nY3gW#xZ^LC zOv4YN6DA`)Gj;6vj%p_348Rmd{>V`Ns$8NbkKEd1o;2t*#4iP!#}^N>NNO+{+COA- z|M{m)ckXa(bj%Q50e3Ib;p)pZ6ofQINT@^L=t7~$ViCIRrr>V%i$e;@92-9lBX7*~ z4c_0rnAgi%+Xjh;_=BId~Ol% zw}ERzzpI9eVTCHzb5%%<)UPEjQ|)g>O!DEMdFF+7eZ@LJ{E*|XyyU>fcrS(d(H$o& zFE1e-TI!aNMtaPD;kqSj3sBJDeueXduH5FzkO$;T;_maNXcB?5+pJjXA!eG2cAxny zPInoQb&Q%EMOzMJCOuQSFwb@Z<9o?u+j;oLuMVBu;;wZFFV{>BDqk6--56C;AOwZU zop@QTY-l#-CL3+uqCTVtL<2%PucM=*UkE&VQ%%p-2ciwiI~329uW^_?YF}L#xtICl zt&V5|jgF0_#DuhrkZ6TU zW5Jn9&D7d7004~Nv1`vLI0kb@pzf~&xe1e}A+~pqXR@UgfE16x=Uns$o z(7WPSW(^@<99Srzj)~5bc3UF?U6b9NOCofa85kIfAtQDMC~=Ko3pmDQN)PHE$)MWf zu#9Vq3rmgJT8+tAv-$e|qIFkRFlbSMRWK@MNFt8=orZ#3w4CQSV5tsBnA5=P6O9I{ zHgP_yW>eojXcZ3lc;Uu2U|6J~uu^%rqXI!zz2qSpX>cdP$TT3IvZY3ArE2%% z`}IX4+m zUA+#;TONJI&hAZ;sGV?Qk)9$4lg9m35i_L5HVwBt9^8?SS(4@*|xnw|`a^6;T$Z=u9cuRVw2g0`)q@^&3 zQ771#)biJ~LoI99;HL5VAd|=UhkL{Oh@j!99XXC5&16>BZ*ddZ73I}$x6PY|2;6Ua zfpyn-R>`!cj!tl~>rDUT=6$sPoe;aloklgVnqtg0%ZT6Dd zWllO!(BmIQqP2j!p6t#mxKrh4zQzx+l213IwKeW?fo)%La(M=zOPFhtkk8ypmuFF| z#~TobNP^JUkfu*hX*tavEqY!0T62EyP%XZtcmQYx4y=spbcc?f5&7<(#Y(q{%t8(< z6i%jFH~I9kf;bKWn+I#y1g5vTTYQpXY*w@1E2+H$o5|X|Nw69MoZ02Am0AU{d(DyO zPny&R@emR#HYwSxX@7#V*k*?ju%&b6@Oowx$3c|^7Kng1B0+b=?=`as7!`|Ik5VB; zGz8v8Yo@HSpftfDI zjI!bUEzme?y}qX`>Do|qT?F=2g3?+xT|CiKfEaswmu*7C9}Br<8^F;NEhjVK0!)-@ zUdBm}(bJTPE_IN0t68W~GWbzTh~unT-js1NArT}zTxJI~BZwvZSbe6ydds~_0K0LK z#0oqpRtHa%) z-sl+zYJ{MCDeZ)ko1Ry-n$4rfS4_ zvp!dt6Y9(svS}CP)Q)|jct-WK>Cht_;3qrc29TdZF)bxr~4D_RUUzSL|!b0OY zM}JE87B$;O9tSipCl(fI-6*l%jfp+>+e^|nJ&cQoS~Bpzz|zO>Wpld#G-4>u!}8^Z zZSmMyR122BIpXD4e;$PpQ}A|w>DH^;Sky(Lk!y^Eq)Y(RBf+e{LlbOOIZxN=Z{5?y zv{LY%Ghr1V^-37|Z^lBUGLPI}wKfx~oRse{bQQ(;RIq?p;9Bc}vz7C4cmGhn);D9- znW1lxYx*3jKV-4IbnG3*wd9YWz*ZaI7ZseiS{b2JdE~0##197E=~89 zaPmw{_FXLl&0P>VVGTTjafua-^XBhy27QLSft_a|V0DA(Z14L!kA2p92YCP3hvr9Q zAk`wwL#6!_6wd@WHg0aLO@Q6OazgU6lRoeEp7hdy^^t8T4eql+9gY+*oXnBFEKmDx zxTi%-6=C&WY@CL5y#+A`0s#@!zE1mT2=qM?GWDXEB>NT@6hWcH4ZBWQq z00d0(@f4zsHG~y3L~&qY!}#T19TTM*G_sQ}+3#K2>%v|oyol1BghcQq1s0+2t;oDa zj!ohQ<%rjZazJb5d0r4@Gf`tt-8%y?7ZU59jjyjqJ^~|@*FSJ?v2=tnh?qJKFB8fH8nHS=ElU6 z{YP1_&7sCV?iwZl&Em#3wRxR2T8yFI&YWIcTuin(VU1RV7Cc@pO-opHa4QiO0Ii-c z*?29{Z$Xi=T4xrw3ewWk3)KU0**J1Y0WsU)fzC`PB352IWa;A1x4aGCxV*0sDRRwspVR?qeO`^w)u&lQkFL^+3L zYwX2v5fi@%AG%df5-KUmcY=q3xb!z#anfwe<7wl+5?fidffF<$e}5y6V}`3(F5 z=+*=TvJQ2Gs)gpU$(Cukxtd40)H)!VW~nP|bO)k*(=ztB3edJSIQXWYbCBm6>JcnQd^0WLyInm1$I>>-ej&4e%O-QStjP`__~e*)Ky`gF^6=ZFdCVx5*ANG%ZNsZB*Bz0t{5-PP{Rk$ zaqhzmC`3&X+$$OM>KvVk8It-E^Uco}(r8#00{)_?eJr24IF?XfuZ&K$e#|e;2P{fT zZ0?Eh1JSmR7ys;V%}yHdE>0MQ=V*f%BY(?q)VCCXU-n-4@eCUL0whB|U`!z5y`Pj| zd!|orM(N3F>OC zJGRvrgP~_ZzpQ`wbGym^gU0)(XVVY!i9&IX9jkaz`yV;Kf4ul_{NMi+{f9yR$6Ef~ gFzdE349B;21w1?U)g)r65j}yuqNbR2>Gq@l1%;giA^-pY diff --git a/reference/draw.smooth_samples-2.png b/reference/draw.smooth_samples-2.png index e26155a15e52962fab82151acadfa59a205f9b97..f5630e935ecba63b06f9225a25f7ea82708410f2 100644 GIT binary patch literal 175911 zcmeFZX*iZ``!{-#DJerlG9^Qq2`ObLDN-cKkhzp8RAwO>NK%AKn%qK2$P_Y%q>_1- zlFTF_)B8Kz&+~uZZEfqz`mnyNZMAv2pOWi3ujAa0eg93z8KQUOz*Ux7dVv)bjL@dXNn_d59>Re1m8X$nPv zqNSm3;Q4T*%j@Lb{w39=mXY#TL0f|ZsRe_3uW{$6pEl0F+m^9A(WYN^_>}j@mgt6^ zhKX{A8zi=GSAL|^a6~HK`Ruu^0%B5muc`%=%XfW{DgUrIAH+XHPtAUMqm}ums`9FD z-Q&ZLO7qj_FIlwAM=%5m1O)%juXdi~>@{i{|MP3+I+eNLe}6qwyz&44X=O3~Ut0-# zR$5xxSGl>@^^+@xno`ot9*V4|p<4Vq9fZHQvU+{084tMtd%DytHdfZ7J_4*X>aW~4 zCoSGrbmI0~nmd!1mnX1e2PfmzGO_-xQc~~Ug>B04nK}1x=LI^E$km%SZ}wc6tdg~P z$22=XzvHh_qVD5&w>R&jQ10Bh!=+$vaEK$8)Pl+7?BP_+c-EaecOKN%E=xER!?J#T zP^74?;Fc}>Iz{B<*!Ept=k#0F_>8ciVCEjC3H_|l@bCkVRW4B|%ZuawlVxk9jS_go zPrR&Hn47Z7Fyij~kw5UL{My=0+-CRrm0Sd@ZEU`N`xf;5`>EBLUQ<81 zWb@WgVs|*dD`!@{dE>?nO+*nwlJX+Fp0`G8R3jaDdJp6jislYZzO>ggAKR(>w@-jRutn!d_ zQDc$~Gw&{Tb8~Y_z>aRuRkm6AUTbv8mXRD&Ps*ZJQ&XeZetZ~mJjL+q&-(a-hYkgq zr#t_0{Xag2ec6@Ot;PPT6ai)BO>w*2?3<=Gg=yx!dGoBWkcTo+y(Qdoyu0YO_VC+U zqqn|0H%J%>d=wQD64<)+=gWcFU$xSce}~goQP%M&1x$Xc+Hq_!1UD^7!KmP)_YSrY zjfZa^v#+HLr{a=wu1I<&w|)QqJ(#1vG>G0b;aQ=_xVdbecx25hx8Y{*paROok635H z-MdddOdosizT?yGshdhgXX~HMOG}VN%YH-u^kX}>(aN3XC3hMt8>+O&W9($gU8|Z1 z;lAG}t}!K-s~Uo(2ly`ueM~paeR(wBQ`v3kig}SY73JBpXSbuGny#>|$IBgc@MalG zO}p%7h%)XdrAU`rXY^KttT%dPNkzxZ$8_Svi6jHDi$6b;qA$O>ar;Ewj-KAX)5Cek zG{do?Ar0}GVQ;?eJS?()@7}#n48)!qh)urRu0=v)rdMoRh^X=6cf{a zxLnS^AFJHcJXD>FVnL4=5yf8E}V%V``2PI(dQ?>p3_osDQO>61u9>_h{Wtm|_F-SOZ zPsVC*P>28D!9>sDv|I{hY-}vgZFmC@4^PGPiFEV*2M-1-Sr{15oH;X4V16u|Z_n*} z_ZTQfS+;9!KRvqCa>?4-TITF$0m^Y^I_yM`uIpqMj_LZm$Wqk2@V%Gz-{t3UEOjVn zWMpL7u%Y?qjbCvm^IX&)^6l~dZ6^Mn@;Td~#l-CUBk!SYr_P*dDR9!!xoLEiE}*)) zdKX9kgBC2*CXPSKuW$58{rU5( z>zg-GYBswUe>YgS$VS#&$*`$R#rB|BaT+`@Xz$L)5VSU z=VI^PZA`lK@ZrNJdRr^zK79C~`8b?h4|jS|ldX)X=rFDl$R&- z1Lvj&LWdSC4G|6V1|1-LZB4PH1z3LY4D!r)k5&z|Riy*VX7pCr%<7ph+DYk3EA5`US;^*gg zeUP@9ypM_I|K)vJ(M>7`(~FnhXvNPre+X%A?aWnZis^71ZO_g=e~(wim+RK8TWk5^ zcfMOAA6pmJopUVlNZ-YMUajQilI+?|^FLJW4KOX;qcq;?(V*ebyO{5B0F}R zc=%^##`(7&2M0&rYm>Bdzx~KtHhPYxeQa-E*&_DKT-d_js=^Pbt5cDAxO-{#oAnV{ z%2qM4vi58TS^LJVc?AVFxC?9f5^c|=_x1J7ONF15N^c5dqS)3wXiDO-RiHOKq@`u| zd#7{P8{s28hXQtpiXM3Qa;M%V(lHd${}`Nca$4ya+5SuuYzAT+kNp1HjdT}n!ie3t zabs)N8D?|e$^F%*nt`-5_bv zCLm_);8Q)z(m+}+8Owk?2@#R!AKewiYp2tSWBdB7Z*JOg&9yxxCe?uD36q{iNqC~gSKejR-91Tz=D9XOKcu?)plnq;r-d&* z^t#STqvi4CMOD|oDd}(b8XFr2bub3%IT-x7zx|A&cW=mpG{5W2{C++0GAr|F%1#{X zT1M9Oj+13Ti{^0ygI@mSS$g!@u{UX0Ry2glxd-{@Qu6cjOIunv)s8Q9kPa1d8q-h7 zZ$XLj1ohNf=#{0Ub~QcSIW0O*S7q*0+GRn%@fy*`SM-xswow*>f*y*HHY?%eag9SBlHrCoL;y!%A;)T(zoDaU?!mW$*QMyfRMh@;tje zS%Ao_Kgy`H=;`YRqT@WXDE32XQ&WL}X?&4OnhhbFrwsB9To#FNZc%Ok1rCH1}7u>0$cG26?`7a?f?{e@PlO zJ9*M&qPLU|^Ru_V|0=-LDuP#r>U<0jv(V@~kk?Axs`~e5e6~qr^NH>U`E@GG3m15H zT}SA|<&XqUXD*(jw ze*IeC`^O}0!zM+?lI|iORt^r@`1;`6w^?)^DzSc2UEb~eXT+=JLbDJ9wXd)5?K^j< zJUl#rAa)F0*i7#8bGvZ2=9z9E=H;cC;&Jc7zeW<#bfy!TWR9FNI`_P_e)JmidxzKo zBU6_@BgPN+_-s&X3a%LWp-F~;`^tdR7^PiR*eHk_wC29fwZK5?RO767?UiMSx2 z%@UN7d$Kk%wja2}W5`p zx1{oHWTJ~*1R&?ZYdZeR!H3*A?oABw7@4@#l^E~Zm^B7KJGYYwef76 z5A~|ToJ8Se)wYff@{w3*Vi){VKsoBq>Y13>U!Ypoo%_Q4ecRB5o7i9DCvyHScC@v% zjW|Ev`;?{FKkmqx3sim~8q~nf1_lPL-)GY6&sI?UU0q#6;|@s0mw3o+=0yG5eeoeN&82VJ&oX(%b9oA~7O8;ai z>s8Zm%t4{Pi_^jXw}y75jdf_`%2^Yr2eX+7&n9|$(tL~cx@?dm`ZO17|YH#pI#g=Qz=&P-Nu#e*rEdIK;(~R#tSy8z=Vtdwc6J zTJAMMd4f82sn?b3p|Kj$G#+O?P+qG|IVSZusf9Z{JQ#P3;3! z>m3-NPtb33tP&NpPiI`SMi$+SA&^2(Proox&fF{oRfv_9VhOAseDh|-{CF|5g@uI! zXX1WzPYn%b9(9)6F)`P#T%kEx;J$W;bGHWGB{JM0!>Gc5F;GB_g$CPr(gL*Z{Q2|H zd{#XXD?az>(VqH5UH;$0EzA%-4oj_q@U(@2I$FY5@cEBdl@}H8v3^_by7X6G(W#y0 z>fXy3Onsr!Z_z%o-7_BWlGXdI_IY@k%-z0k-&_a32!9_K=yBs06&1ZW*13_7kI%)! z<9dAjmYbSe-oJlOz)e}%UUp7SGEgAW$Z&^Q-_Aw50l-ENAoyQZokbv~w({b-}#Dk%vl`p+i}I`9}*p<9c=|xd}9syz~8xZ|8n& za`kIWIyx~ip{A~Wnd4BC99J(Ct(0vN5*m(<5+CD~u~b)-bN>GN^=mI=oi}d7bXZSM zAD{TVJT9_)`uc3-Uv=aEz+V!uThw;=;2-d(?!q#gw)@uK&X-{26JaUU@Pj=r^`vw91l8v3R_sd7G+ z<@f%%xp3iEZJ$8f=-QQXd;y(nW&T0h!<9I?DC5d-wGfFIE?>Uv;^Lxq?wk~X;EVG! z-=LtNR-GDmCg^V?mHG20gO867ICFLXdGHjJP*xjLd_65cv8aAy&VC|K^)lXJ=V0Cl>}jPW=9T<@$Ace46n=TPX(W z-`;z0n{MB~A9C|16Ir>w%20x%3{&=>J}pe6uIBEpNDv!juc!XYOP)(}1G0{-GD!w6 ze3l-9%17koDQO%y@a^&P!ho62K>#n->}#2sa=uV?taWvuHGFuiTHM9_sE#6yZ!xH| zxQw_>kx1Ti=aX^s2#z#G@xq zGCi@^dkQ93QCV49RYia7xlZZq1tuZ3ki*NPCn9V6M{`pA_Pt2Y-Ge_G7+B8z>aYIz@tTI<<=EIw-ChH{ z&@OY@i+W7bu582(dS-j<=+Vm~BaSPpgWZW6BfzjXtuSxhx^)m!nCY0f0>E zN)LhQ{d;X8QBj+w7sp$3oHku!=D!6JEvTf_`a|rFMx$SvaTXn25EV+Wc-*FuEd~2BSor4cX`z=i&l9D+Vby6yKaJ(lvotM&RpI!Gy<#x>}#pOk1 zB_*Q*21A=OXXurcl^rN1@yHZqC6!r40RQ^fw!L8rj+!%c8 z*1HoHm#%_kk{${iR#Q_`{pr&I)FD(;(s{02yC(PfkcJa9krtMX8$A zmxTssnXbORp7P>RL-(Z_Guga?i-Y=(oq4Rv?jve5V_j*+jT_3zE@(=@i-&}Sz$Ms= zR(P)K%~fy~lMb|$v)LwC(Z-G;L)_%9F5Z_fU*4`wIk$5|C@nq^Y!0_S$*HMVFniJW zq5SoZj4(lnz%^^^-%pkFzNsU_=76WScUf)inmA>TYhVSt+=fmy6>3B9EXh1w>oPrb z+;^tSBhTe4HR_8D4TqMOPhY)a!38leF;)B?Y^cvVqni>H75vlhzK)3R@A?BEpOx4* zu@_U(MJ(Un-8TIa)!kRJRZ=}cUtRDHCLpld$`;X2X4g*8=h177#-@tvi0Gq|r~9t=4h^+ipN6mF z`^V`p!3L}WFQg>Ntnu~T&Bn%d`{BdR-msoia+{^4b$J|9cZHj}rF4LBZ&6a>di3ZK zu=H*11l;W^+*%?t1A0Yi)8?OC7g|Y;CXX(Iv3pkjf$|THFM}5kYH6u!Yipc^wHG`mIr*s(j2kwK}c5~!_mbe$h{ zvCxAk&vhTKla@|56yL;p^q5oo>(!XsMwzFyBK=MG?cK}5#`dN6FZz%O@A8qm+6hKW z*S=6^pV3ZW!`H63k3Xn+}fg=JM?e}BIsXD>jlUP@C^QqtM_n548(sHCm0?IM+yVL}N&Kfpem zMU&33u4KfROE$QHDlM(6TMPImX~>s<@>~=<7g^`FS2u1jo-FiaMdjytPWJJN=Ytd- zIdTL(+BL(Jt0rlh0ANq_lc7=fj*Z=N{r1kP^_A78UpPfsrZ2=e=nAA&n}SxqN3r+ zb7#(6$JIUcnHl@pU8IcV`V1v>!`i>vLTop=WT_#1l<2B1ULnfQlP44a$UOH^?p+>Z zHzD1^4WL>tqEqs{CbIJSVZmqgZJ^0il=QcM?c>~dvNtHD8L*BB4vU~1(&)T9 zhb!SdxWc&G)wK)?KxV+5EeD=?jCZ?fZalt~-Q~iCAdHiR#l_*fhX9eIqXl7)-M@by z)4y!ABNxki)Y(D0$UGeQRY*uExE5Rj|N86rcz8en1?v9mYPB_(FrI@6F+?|UE?(Sl z?%cU*=th|RsPf+7;n1!E4|4VQc3u#nTgTHna#}#peznJNdt3J#KWw)8pnepzQMP>) zR|&7&DF&iMP)cTc{KKJ5g$|>AzPdE;p7Q*+gWXEe1mpItOgRXFc!#0XnA&~@M#jBB z;H=!-jMCE5nAlb=FU`;*Uu384eOiKTB?~oAR_l_X;f9Y7cLt$ITH5$1WWYh5X4=%U zH$nNoSpdaUt$6nnxkBz(XiH1WG&fXpp4XH}L}cWh2M=!i{(bS3t!>#UInugT1TXc( zhOdx>@-7ZWCmbSFKJig6j?%I_rmXm#zt$D6nZt*J95wYj=>94?t z)_u!S;wRt)^7#L4c-#y6Cov!_Xn#)JF!ij988JaW#;pNpbnGt7=Qy+>+Kbm~uzO_`<@%=HelBMJV?8 zdTka+5F7)PxI9@6J8To_IrN3(@Ver;mHMV6`%7#NCV#)W|+WRkXbV~Va9 zJyH?Ox&_9km13;xSm(}%yWAK#rB1SZGQKbmU*B?e;_FNocN1Jy=+*~ybTrPNmm_2O z=FOWve3QA}GE~_nYl)Y6+>Q_M{NgTNe1S?-^uE#&2`Vbu1BC=4;APBVj6GsWV}Jha zD%gR_ADe%JEyLjN?@xda8A+Cw0uU}qC1RYaB{o=14>bjcgp>k9p8(_Y+<4lwxEhPYT*XQg%zX>vf4H8eH%rZnw+ zs@G_r!Leb(HLTAQ2#9Xyhr7ez>tkgw>aZVZIt#s~9%(ANU%Yq)%pEK6r2Z_rJiY{? zv=Lv>Gd5lgY=X}KK8KPj18C*lb&)dRzr}7CBoCcRTzm~W)$Q7T7kBp!^fAf+y_6CF zbwF8Sca@cwj}Mx3b#_jS6^!>KKK3hHTAW7#Q$dSeSnx$`LoGpnGguK;wPM9kf4`eh zc~I>$^4ybX*<1GP;ZlESPWTg>-cyWz`Y>)xykS3NJq+E36 zX?~OQj)jKGyU3g;RT5VX@ClT089GU&wYsq}(SD8^8eUt;e#qh9m!Yoh?CcPfCGpO1=)Y8Fuiiosg*6TJ6MBpVP<4;)XH%q`f>!09s= z!!$I~k`Kj5%}%#iq?s`hGI|CQP;;vBmUR!)^phuMikAs@tpM=Na33{sbs5L#NHxt} z2YlKraX!T+U`7S{f>lS3lhTDBOpsVu!PdyE$G)4Hrug+$td_8NyKt*R!n}|R@Vl@_ zaQ#u`Q}Wzo@fKizx&7+7hHeNrmlQc~$U;K|>f(i8Yk>XjN55eLZ9De-2?*%cZQDxm zd4ax-ogZAsT_hR>m!c!rU2S2QPZdYCH(ATwcUk=F_E6QIZ{XA8^qIUSd=C>3O`Yt* zi^I)paoKgz9Y@jr0kgp-{rvp;&WBb8HZ~fZ%s)>HpP&Nc95oEwbQBuXmoNJN{PT|* z8e)0*Dh$WZgBOEu+$aOd+zevM#uf`QU!f6qZKxxi!m9o*>ipH@GlRZnUVd->KWI5^-% zPY~Q}D;QQFw%N`2Ow7n5z2tbE+C6|GXno@*4!6TTEoI#Z8XxD0mnTaBMO@)-?#;6?MwsRTn1qm!iE;tDispj70;wy*Z_)erSQjyl zvw!g}KT1!RUP3;~2K*Vl`3j~Wur2VL1l`wki{e}U@M-TUdq^EqBfp|6t7frHck7U?-`e_Anc5}gtqy;+@`(r&lHC6tr zzxM0!utVB26DXH6KT*6_F^FGTmCIcCq2rnlDPjZ6w~gCv1;oS{;R$Ec$=av-@ZJp$ zc0AzcrveRB$o+8g4^%of6-Zg$M-^NUDbSCPRV^I6;EYW)9@C|wtmE6g^@rRGtpxqA zU$67O18*6o39GZ+E+#Jiv|+HKGZ#%!$$x1#bdlQ;5&Gi_$hx33N$&tOcaA-@gY-&p z{=SSVzH)$llJpZ25rOTgo@_wW1vCe)o#%xx=ZR~$<2~e4xT&RZ!h3)GxQ6An`Cfge zPrzC2xXBHCPF~f;-|PR3v_bWK!q3J=4Wx}G%@7#y*zfP^ZQHgHS9UDFihoEX5e?GmA*3pop zUd?w3!T=#+0aUAQ-@SVUd}0d#Aw>@_jQD|ofBkLu3^w)BvHG^ni(B<&;2sNmrtZdWp{4nLk7YZ#sy$uG<>}YNeg#tjlAH)Umzt2CY z`&X)0V{JarzhRn*uu828pya(>Zer2W7PpFgW(dJ#8j~`4&453=oKXS{@cA=R3L%I# z&2SAC0;Qg(Af3=8Q{ux+5Z}36jP7KQSmXk2AnFhm6UuVqDbhRrm!=+@1U*eQ5Q7Q6 zI$Y&%Qmc2)1Ee9)_Fn-*nUq?pvwTK70QMnz9OZ?{*XWoSKx`feJTPZ~G^zm^K%a~H zS!t+1TW|yRy1L4ux`$KKq5K$#T?O1EjNaKf9Evk+^@AVcz>KfqJ<0%`Fv^fja?VnJ zkDg5SDd`a)XLs)2rJg6pERuadM3%9h{=%)>jkZwEjP73s=rrkPVY#MY8eKyRln2x!7w-*9rMNOorgCsv8t|%-5 z#!SwEDE$6;6y||6S|XKR)7k_$Y20CSuzny{?El}045JLCSPnQwa%AT4#WZolz-lWl zu0qkD&o4Ceru<7E0Pgp)Tv1-fl-y+hw8`FevTwiZ{el8zcrdK&>?HUJwhlmb(>XZ<2zfykBA#I?~uR`Qt z*=k{Sl1tTZ_cf-C)S#cKC-d3h0Rbn&b1Q>4K$3l!z)#@F@cS>wF8zur?tOZlFSC7m zhrF}6r|zt3j-M{EC7#V6gZi?<%ru9<%n**^WsrswnUGyE6(IJTE zLC?$wgG>>USLN(INE7CMbN#!*{%Kzm;*u!p@)b2mB+>sp(yyb5jF$lF+10a~w=Qw_vqF%)#}%h4hbHw(&ZCBsFb05{y*;mn8jb$s|Lakjz{ zFuGQ%AZX7C8fiMx3P1|+l7&XyvBK&Qpe9L#HrbDRtDbX)i#z+Q^Q`<0>32>cXdDD3 zuUV5u(gB7kUweCzYO=a(o}PO?#N=^k=$a3<7t+qGHN`gs)^V9Z(@orA?IU{UAW`n1 zgiYr6sq2G;FssZ`z}qM<-o1H%D|`EQNE{vkL30oou&M7MipS?OBJ0`{5y3XnRltcX zR$!^6nyu~gR`Dhn)T zG?9qbr1w_H5_+1c4fV`sVh zL=X7qA0e>h^~9{FfRC8F?ZmZh3+XW)y-?JLso0t;i#Q7~c@X zV>>2CHasCCJxhOfKm`CmvBn%ExKYaD?Mgh>9i{s4a^m~IAq2aM`1 zu*7R)q^mI4b$ziseTRwwf}x;tUik6B?)TzKTspY=ftZ-sEBu6P-Y~$x33D%6asr_; zl(Vw;9Nb%T&#$zyd8P&iLUiuPkCS#E5fO;3PB_f1NuW6?-z2{d`1XYC+2$H2&R>c8G*5#E+zD5d|pL z_)x6NpH3|INa6$sSWJdPixln`ins(y9}5lSB(5+`e38tvr)LSoZ1i$i=m-9zI;yIw zLMynwh#k8h_+R$?_k)z~S)BOEgFSHMzQy{I8kpjrRx^_hk(ZK2Q&Lka z@Np|MbVf!-NK5iX67t}|gKC)D-_P&F%_0d(qJP7RCrA|G?8W;}g%mQUbkT1%ye6daX2*f0-XLa{O`Adi zze7uJ&o?zb|2fiZPzm)CXK+Lc6se9&?oBDU9}^wjJz06c=f~LCI>2i9RrBq*OZ-^2 zN(e8Nr!kMW+kPmS_*!1sYXMS-C7*bM*02QMN9aYKp0 zcweysA_H7N4Y&~!?)eUf8Lv{qmqv3bv~iKfErI5vsi}D_PXgsh23S!)zWok`?mE<% zY$DBq?tIVGSny6sN5`h|xARgTAMFVxfx$kH&qY2SWXxTUj6Am8+{vs^)WFrnCE%pg zful$7kZ4(&t1iJpS^SZFjym<9&)<7inlo;yQ(wZqcI7u_?HhFJEE5oPzJWx-lT0Jw zYI3fJxQ_PrFSt76K+H)n0a^7#Lrc!27u(S^;xSL*3n7kE4zByT*x%oE6UJkP72`4a zRwd}(6g6{mKFo=tE{l{4zDO)94CgOGT-mhAG^InHHQTHy-+jlqP9t#^IT#t_Xh4R4Hl@TIr%ot70{~29m(NesUl~bY*OBnav-o(2uG`L?d6!9U1W;FPd1+DB zx`F}Lt%{4Y-6db&iBfug@|}|U_ybgw$;nC2@uC?S=kDFJfB!bOccZ0YPrZxORS6-7 zvIxEpc+q?Ztq86&8de3i;Up5uy>TAhmr+iJ>E>(Urz3?#UYWpIE-sR=g_U$efMFIK zwW9YWPWf#Av^xkujtG(n-vH=?)R%xCBck^T>2Q0c2EsARaV=V%;#M*ac0bUxoIZfC0CBrhnneI3rl`+a>2^FCl7Z9ntz z3P?9LEC%6q5cg7??!kK_V}d=A1SSE+1!xEcG2jA1gBbDa&YU?zk^ zEZfbvaiAEI7&IhDwLf&j*QgWbME#1p1b1Nq@}Es#MF{-I^{{F!5f*@zN#-Vakj#A@ zqe4$vVyG7`{^^*7E&+Y^7E%%t;B4S3jx#$GNjF0~Yt6qPO5}l$x*)|UodX1g>gz-H zug7*$+PU*9hGRB$0X_((<$im6F`^how~^Ki?R`M`R$P;L%?3%6*VXM;&x(U*A{~P( zB7*RM`pNVb8(2W7HHb~;va$nev6mkxI8Z3W`FXHAVqwu;I9()4yHY&{=5?KW9g=O3 zSDAHMt~Ebj(ZwlhI_oV%;lB|&SGeD?8k)jH+nL89UB4watRaphk)^?UTXUTuC9na- z-2rGpDXYPe;r?EanFZGe@ln)|HK%WpxyAVM0mP?Bi0S(EZ)E-xd!{97fFFZ&_m6TtQ%C*-ripC+CiL4fht8BDx_=F7)fj)5{IV!q+!ry zxLJ~%D&dkp%Yyz#A5=<#HGm=|k|97>-KDug(S>D{_&@sF##f)s_u1>l$dJiBoffs(WGCIws5jsRT^ z_A}81)g>e)pWABC4%9ly%W!?B7XQ{;YQxZa705vB9s<>iI0PDf8qKUol=oj{~y zIM!ikgCTbA`1IsZqFuvt1*LOAzBRJc#8Djwo@7ugWurMNa;l1uye@qts*)6@GQTYN71; zN_I36Qix1VIo{>>;=-)Ar>7+jVgSE=Uv($248w}wcY^Be(q9MI9#j+}UV;BC^h^6E z&nQP86k-5kGBV=&Gu9P~42mGM9TIjqeE90Wp)#Bp^S8pXk$nw$AJs8|)vDK|#tDxN{I+GDxKDo~&4}>($awTz%I$UoyF%#BuBiSm zbct^WV5g| zVfNwF5^7UP;K52X_FAPiac_|QuCz{$Qq9@*=^QM$uOlN8IZEf<+^(YY{u>=xh2v`o zN9NkgoKx7CQ*+l9p!f@n7C^}l?Duhr)q_sO{mZOJhaCZT^1w@EHVJ7hY`mD*}^%&r&`Q?)&c-|?D6p7KNSWe z!vM;HQ%BBbumN-^SXD%^KoslPc&~$6IhFYnv9C(M0?2W&Ve+dMydOyJ z>a7p9(fV1G0v;kOTn=>-X@@mQNbTz}LBGY>Sb6{(@X0Nk zH&dw{PZ_{rKAY;>n`g|LA9(5}l~JTu6-s7=r`ZJL_TwmN$BZPM=f>XC0Wk=Z(0Hc9 zdL>#+D)VvGeO+%aZ2}Mn_eY$@+GPilZAdthkq&7r<9z>nlbI1Bbl*So$WDfC#)oMl zCt^PNx1s7yadyVo5BwL3xq6-Wm?qi{16r&z1VlyY(M51r6+B%8Fax45lC7e2{Uls$ zU~mPFD-@$f#@QO=)a1FLtE0HUr;H?)`F+mtaUP#uULJ_EqNBTgoSnu%&7f0%dxK7$ za9zU5^M~ssjvYHDUaOHl=*D01v6 z^$|yC)E2!Br1@u#Mrv`fDl%_RP+vE~&uVQ;qmeGbQgif6-V3^&+wGmALHh+Ca`oD^ z$S&`lQwEaFaQOyt_?Kzp)T zfnugamUE8^npOHj5puj60hx=TayXctb3f4jy9SGeM0H1E5OXzZ3B#Yw#E0Uw0!;-^UmuJt0972IVPe6Ue)VBM@^Qo8lRHAnHhTf^q=cL1k+2Ci|TXNLZEjr5i&79vaQ+cHgKuB`7R>6^AOfc0g>fjl0H3LmdmGfGxrSdr21wPpB+~&aV<% zhP4hI+9E9-{_L3`4$JP3(2cJrei8N*3Q1U2_6{Z;3|-Lllf{0##?g>tpCY#N+OaL- zXuKv|Z#_*9r-Hovd|9xcM_xZR;y@3vF)(3iKV0sHT{s7i@#mL>HM)DI4eonSWSx2Z!cVqAwJ})WowKF7%dAQHb&e zp9*U%6>7SkK}=Zi90vmp^<_1IS!aZ~R-{jE|5Um;Pc4dtpM5-h|;f zT4xgzZiux5==BegC3(C39soP`5md{AM~?i!XT<>&sA9qOXAPob$o~hQ5YmEh^vp0N z%D4=PbF^gH`#Na0`bFMu$Z~B2n}*XUq40P#^B*}e&*cz_+K0QnB4CmkPbN#g`}pw| zoZZjpy!MSru2tU-nU=}mkTGurfE(^}@Iz1e(wOC35EqxB{jY17GX&c<6k zx68@h+kR#p=pT@WE4+6UMntFE`SXHXw+3J5^A7gt_Bt?kZV6o57x8%Q%@?vw>Ohgm zlMeXKevn`<>etb@kcXklgtWotXuR_3?&n8n^;+GMue(5^Clc|Y*3sZ184Y9a(R__81KI0 z`y+&)#vXwSLz)9n!~hDfI#UNQrg-VEI3$gD)bQ@958J_XqqPbkguvUUgKpFZUIg11 z#qa?D))S<4+{YKNv!=Gz1}_JcN9LwUnr(NX*A0boNPQF%CD)9CwHH`5^^KQ zK7RZ0;~m~U38!;+b|3D4a+0kOZ8-06e^aXQ_S1i6QDz=#fudS$@L5nXhO>S9s4qWR zz=r(>qaMEU4YUE}vH1G4@cf@*H^L0k%ROf%q}7LoK&otB@wn(caX&g5nyo`Uc*+TI zIkxX+Fc1(P^ayeBP+$Wzny3UJfRUTr3O5h}OzAG@?OD&xUU7HZF%ldFgePZ;5kbO_ z;7x&0O|tFD5rU*+iMN_M;u{8u3P8{;bUK8ZGacJ9*@V4>wX6{{LnaN`l^V3j;IOd$ zX@;bgVN-J=NvB1Q*np`gBqaqy*a#6yS94kOKO1`MPA@MnsBBjeV?|c(pt<=Y{ho?b za`mt6j&*i*P0q{=K#_QgOM#zHh(qWuS{y(W zlc99+_0`}(IQdcdt%T=^W*9eVW8lAf#eSE-QYlcpuwgPGw{^S~uhjko3xXWCbsz6O z3^)b-Mk|6ORgiYUtJghH&^mI&(2xE{ze^C^TH+piOgp4H8qkJb^8t;0hhwuJ@Eiv3 z4z^MFkqzkXE{QznIipfmVS}2&cOba>AX?BF{-dKs>Vv2w5%)dZ+udvH~q#DsN)MQ5U=$h03 zoYUE&dGm?xW0jH4{75)ZTUT91l+guYDs1$RIFzo2MNey9mu6Ut%`u0kckFZ2Ou{Y;2-s!gTZkvYc573 z*~bg~Ad=*uK&n|mG=Mee7U~b%u5Wbou(mcMPK)A(tTDX?A$$^aQEczVxFaNYY6mj3 zv*&1keA!k{+SAwdCZgBvEj4vzFRT=+#UlqmkSfP{czNwwUh-nsDnU`o6;73tlbc0a zuMJ5CC&oGiU{E}~nORvo5hg?5Jz3;^{HTLgga%U6An#8BI*<>GMmIBlnCL#*PIfBN zZ{l5-=JcC53YUd2z(FOmAgc@4NNcfihh0PX;WLiok&4S69-j6Vu32O2a|c4n7ihop ze6hH(xCvU?+EC1bQ1GnaD>zGz6XLsnf8GaMQ@5@7A8qq`(R-|n<{kPIjPL3G@5Y7Q zHcyx&$Xq+NzYalOF!KI=Ehx6nn%yp3*s^U~2x4}8mld@o(PFJncGlIn$E4o5fw&#w?4 zfS}F9qjVe8^6pS3oBCYe&z}dOxemhXh5Ajvv6mDA*X79N*fl1x!q?2zes&_INe-8b z(~M=HM9|uy#sxzr>6-Bd!_$dX41?~sZQw?DIEro8KZ%JK00@aC=(oJ+p|;hFHgJCP z{|t-|U_-{a=Xmk#1=xt>EE>GR=+oHk*G$qxFhY=IfXx8Gl*C@}Vp)!DB!v3>VG3lL zn>;&z-LmmeuSt?&?8an;DnKDJQ>>!Xn}H=DslHn2Vj2NO#T=a*T?QOj-is`Z-M@}a zJWouV!sN&V4iBw(DWtU z*Vv3al_eq~;#1(cT8WOwUUkWQxzu2nt?XmX`AQ%P7_6=W5z%c0YX~UnT`tlWjAodS~I0dEaSH1 zW~~Ue;f-PMIt=~Ws9~FAn>aOmQXxmrchZUmz!}bz==h$M-ek`)dDzP8XCEn?|VF$jWUbr_p>v9YR`-+EH^0Y^p~q1AMBlMJCP5=;r_8jkAe5zVg}9hEYrJc#EK@k2+w9h4$#oOL zT6-geVhcTHi;IgTY85vYwEpTTp6xDmUtX&GBcJ0rp1ZW@G&y&jetw=aJh^zbZw=Th z6_j z*8`w4a$Jw_m&#knhCgevM}J`^;nAJSba#3cL_N*S%<#A{SeTDNZ`g1;v!fsbO=b7| zNS4R(m}O23Q$pUMqR7l^7tN0qAQaR!(~1H4>QoJzj!5LM^jYU^T5N|O$bW?e_I?Sv zi9CSG8_62+zckyy;7wPXv*t#xEemS#W?(k&7|XpvpFYvc!BBcgT@G!!Aoa<5r_MatPkUx~Kuhv7Rn<}q)8NV9WK)f%Eep!MnRk4f0S~1o z)V}G(Hr2;Uu4Q6QYz#F0x_kFhW3u!kUb&%zAH5JW>0%X?Kejf`BJ|=!gJaLzIJn5h zLVS>#CxNVx)x^0;ZuzrnfZ-@l8vteGn$3?+?NU~r!ci?2F0N{X9p-T2SK?(8&i-L* zh!qCX1bCHnB3$vtebn3Z*$lEeQ^Xew714P4LX;<0B0H9^r9TYVZ#35aJ9GA-A!yK)>N zr9Q(WU(+%N3tWeTEJL@4Q>!08klwO>!v;L2awVKM@+?6_9bV7I zK`KJu`Um9>juIIYbEaQcL+}39BfoL1_9}1}0%|YWZIlt-#V$?+`XEIJu~YSPHV!Y} zN5TYvP-M@ZI0Qez)vJ+al4u4M#k4|Z2;6u=zM z1$&Rc|4W|Xv9XT`WN1U+sr_r{7|0men|3sw4n}8ymq@Q)&==G7k~wE+}X6l}lvQ?EV8=@4M7|I(Z&*vmH%Lu6yc<_D*}0h=YGI|eWA!VE4)>V>2` zCAv3a=~K;%9$+E2cDy*A@($;0U>20!vn zR1^+U(L@M=Rge~w=RGZ!DRBWyvqsn%#si+6LFoO!AIAT4HD4f7h`jQ9Olil>Irben zl9>|gSy|Ojp5)1t_=eyy_T2uAUwEhv=6V^-;V7F}1&4Qlhhqir^(K0fWmb11ro8p? zFDUGXAv28SF9WzlJtkW@CFd}R{aWv$M=*3A!SxV}W{eObPjhNWI=W?PY2KOKX1ws2 zr!U|fFfEctIQ$Wkm9?E5zab}-Iy&Za9Gtq|;JIN8H#Orki=f0{>7vq1bI;?EJ)t&) zA7J6NU-O(0$m4(}MxLOCE&d)r1<0Eo6;_@30dc-koQOp_U=>PW{*y{P5?RQfN0HQ4 zP$>~X$xTm(jZ+-M>)qGiUx_gnMdBEni_IMP&P?%$D>-ZI>V(~r1qN8fcu2-T;V?!Y7ZJ|NN9 z_R1>G1YKdfXR|i4?-s6{h8uk^%UA0@3U+l z#>Lg+v46t}CrmgRP8-|8w}wM_84t3JKwtP@Y`u3}&-?qwpX_iXl|3RPS!F9DvxrJN zBTm{}XjBFqJL9a+X;)X?NSPuoI>0W&-2k_c?9;}u=PNrMI8aJaAZ3~d$Ff4*&e zwBtoFWunh!b-b9lW!!G3d!3KiMLg|!Yap&(<0bw_k1jsgTAvT!cF)a`N((NGU_vu~ z+qZl7Yb7exzM51aaGSk18e{?T%5fibuVF=C6`grc@J+_d9c*jud9%?NT(FLcv{7fs zTs7kO4Vw`BUAw!CPBo0!xZ2&_`sI@QZLd9e5IIxlvi9x?TanhH`G_!2EY7i><3uGA z>G6=de+onWR%oZ`2^z`$V9 zcH3PvG_(j1!1KobVXg-AUf65G_cM z+c+f|2fszRQ-tCo!qjKr*c{ZP-e?x6^)015vz7@}A`^u3{sA5d;TR!e8>3z9F=`p) zE&Zsq>fd0rgL(Yo`mWS}z{e!dcq>w6PAZfS8U_ZlViuz~CKV<7f@Pl@GZs?=X@VZJ zVe4wgHfiAB{Qpk)PjV(Ld2y8juA`%}?)!%coPuUyIdoXtspDp?&d_DdU?(}b4baoe zHf#r&fs3L5MlV$P!YunBK$cy$WX8bz^?sC!g`$7}&`WN+E_7~rVUVBZosvU?H{0y3 z$|_FmalB^tzJ0v_EWy`>!`$`mR(AG5u$IE2qVb%!o}A}$ria+Zo}2Iv?~EvPRV_8f z@a_)ld+H8$c@mwN2V!EAv?juPSrkGoFqU`pju39Q9qZ1S9M#2%4SYIf)#wq&r`Zhh z%g{aKGj&nFl_}Vv*q<_{slRhuvBJ!!O3kbE%XXc+xLAE??-fhjISyi*{PkCAX$%7< zYJ+iyUqo7lsk_7-!D^ZwUQIE517I}9SCd`}$Nni=>7eP#8?L5(XkFVvZN*iS>k&^U zXn8~cIsFITgW$i6-%<K9D{@z_jI^qYG-O%d;X%AdCeAO)G@t0`_pN6ONHn08Ntl>;#YL7p`ncxZ1;`9=lb>swT~vB^8#kyR)#t3TE9dA>75Zy;r&Y zL07f1R@$w7M%TAb@zVOW01EJdfQC%|F)=alS|}gGI-WNV6`)Ixjb{ zJ+{F7H_Zl-4~VQjC^@_yP{&Q)Q{PW*73>n0?%~itEV9eH)DKol@C~FqHP(82d^LA2 zD5#tF@#MHgd0sf_^4C|~cWtfAdvl-}5PgLErT)KqfvvJwZ}evNxw;Rt!<2%iN7pVeHM8v6ZE0NRf2mNI z(FsD2PT2BnNNcH-NbgVdwU5^-^)mAP=S6HUIsEM+0w9rRuYKQxfT`2fsq8&eS-s;% zM9xkz<|(Lt%^%m&;5xlbpKr!J4ixmAgg^_uqFCZ!J@|ED%ZeSXX8J34hJ-3BNJ+>K zx-mcaF0VyhXFld+rl+l z3Jn4*Mv=4naTlfxY{HS&SH1HXz=9wqPN`GsUV8Mdzkht5LJ9YG>QUF|O-3Jmk1p4@ zR&FG7<}k{>mC~bq<^y8734|8b0FwBo>&z$AM& zEHg`)okh2=^JF);k0gG2s-r^}nrU=(S$#00%#9b2Hv*P2{KgALOO^g`7rX^n91*GWu zv@k+O!K9GsUMqUH7cUNDAj9W|Rt z`Of?xY6-z37VY+(WK3qy_nFrSMgs&DuWhIigBOs)uU9I7h9O z%9UAtVT)Vw*w$v+{>C0KGRXL#kM1m>V8+3@(g9- z3reeV7hv8WMN8qEH)3<|d2lC-_bt*g0)0ch)Ya79WGsHs%VyriGu_sfDTVJE+-v&k z5y#7GV{g=k4XP>Z)4@?^-mAhu`(0xtXVR$bwGW5NMIHO+IWG_dS+(<0S5Wy(wP?+;cCD$2^Iw0!U(2O{j|fP{DawN-Y<%i4d-?`ufMH$n0czCTT{11w{q<`bY9m&|q3iam6XnkQu_OEqWFIc|#O{ zn>~k+-@JK_j&w#n!AcYg(DBUUa*Lwk;uV}Zd_P&8$WVG5dDyCncK82rwx*9L9}aYX z5_OX~glSPU_y8a#Kz=ZfBD)Y6Y~I=dyMkDMcQP|6*>Xf8uh~jJB0Le7UOb`FCNDKv z-cs9Qm944i7^X)mc>0FQ`|$I~CKZ|^XkZ$*P4ZOStPpFBaH}mQyi3m`A1#zE4}x`; za*sM^>$22e6Mt=>0a}*)b>hwoE?@G~PV&mMjONkai}D1ZUHpKP_H~YXo=<3!nEv|D& z3A*e6GiVVt`#5sps@bt1$Lzfds6)k)Zq50lGh~oKNP~_FphlO%S5S$rgQ$CAOIh^t zQt0YYEZ}3x|6z?=d~OPH6yxaK?W#hB(lfCxq1U1 zK}eJlOb%_jkC#0)NlNdE)vG52IC1t|XNd5h$9Oll=hVREbH0b`+(nrkyC}P-ll|n$ zO>j#&lMwQ!rKqwFv)aRaa`4!(sU9AQH%b|KZvmfa?|G9nrfiNJ56m^hRqO!>>fO34 z=FRyxeAI*shBCC*T)w?|yQ`cqxX0T99LS^wU+NdK!;%k07-ta#g+kXpeWvuj$5kRX z*htdip#&}9@Z(Sy@{Yo2M5hp5))atx>kj*df5=}8RSE|yzq^jk)SVM3Z#<_UeAPY$ zM2qfd(P2n52@SxIsPRxv(3UY#0u%b8b(?m+J6QsVn5EICxMWuRXwYMfZ=W4uwE!@F z4hfVDdbJ?n9G5sBi|VK6oafKi%9+mWv)bNm(sK9Cm+Vdg7&#^)h8dj{t5JM$g6$l(t^8wGR@8E8IGFYVcCi7CYv7rJCIoF%Jk zhR;Yflo=8b#?oTZ283lr(n*7vSq)wkbsJ2m_mn-$@{$*=^tMY6pY8rW+Wax(;+WI#72U-Iaug}6aK>NciDo}Hu zsN}0b6LzU4slwCSFH4y~MVL~trcZO88vTT=>Z(gUV8s2qE#v^R>TPG?+jPf8!|M@F zTL<@Jj87NKUQElpQ3ORhgA+HD_6Q+ zyd8w`mR}TjxO}Pc;lfX!!d25UZ{H5lo$zC6Uh*CMwd8AFx_JNDvqSVdLbva#5i*)V zMGRm8P7L1SjSI{14Gi1?RCfRV9>|c#7oDbvKsU%T(O)9ZZ>)O`mIHEXzBNQA6atpL zu06WH?Zu+hJE!dXc%K};@}|pYl_pCL1C670^g8;?mA1;`jdz<6`-FZDo$qmlk66TE z^v-^IhTVvH(mj8C81I!D{3$=J%YgptRgw`bHI1n4%?PEzX+Mp6H9;fJc)Yh! z86ap>W|oX38&h9hTeWzFq#S=7Jhf}|SWX-KqHxT%A@l0juitK4R{c7Ugj(2b{*#<4%z^^XVM*5s%$9A7Nb4E8-sier2Gi2e*OTCdB#YKB5yov3j zT-_SbQB)Zi|M>|^Ud6Y-bC=;j8sKgw0zEYLojr?MNir7?8Q@TEAQEc0d50n4d7 z(yBMty?S-#*~fZI?SI=RqhyQJef<047$AzjEOUa8No|G|m}wi| zqGVkseUAa$lCiBkrO)iWq5@b~nsN(7N};0C4OrsRvi#cAXE>SO@_gUWLm&k|h|?De zi7q+4uE!c04e_%6qJQd}S3jZWvAfOwD3G*|pig3^kLjKFFmx=-GJV& z<}ep?>{tY?BraV-{?S?OL1V@g$FfJ)0RWDT4N&zekb}5z9KKZ02Tda@0DBbm(e5`UUnnc56?E^U3+?`tfrJx8bNE zL&#g=b7##uph;?%nEWp4^x2pM+9c5_>aDXao6db?I#IB5f%w%n+-UjabwILO8->CqtBIo|HZmE&4O!!Eyu>>=CU0(HGMS-^^H(%U4VQ>^YeIQC% z4zxR2S?^e|^AYEuzOX61*-)|1|LIhm_AoZ)r5wenIn8KVSdI&^Zj*rs{bE*p-3Yp@ z2_;Fl%`y`Piy4?R*6Cg;@G9+LJDd$I+7Hr7Sl3#A7(A!+m9SAtfrlc00amZ9BmAfM z9SxRk>>|an{=LvEgGBq$)@#2)7wbtl;}^TWv8PUL0;TO!n+$x9FZVE5QEr7DoM>l!3|kJX$y(pjN&LF-~4(vahD(h6R_+#Td@1=%c*^#$L8)LjK015W9E{C$Vy9 ze>BJ#+B?~!sdlIT>xKhFn20vSsaQvJYlnxe2=aEolXzV8;wAUmAzZ*GG?sH6Eiki! zS*S%Oy1LOk=G)M<0Hh8555i`CCfZI#s}7yIjLs*jqGnz;|K-W4Y~^}%jH&x*Mr5P!t`Q)R~!QqXU=_{0*)N zvL+zXDS!>qrH?gOw`ONg3nq54J2F-=rZS|Z!$z@`aIbITu*(je(Ti{?*HJ$U>SOsM zj?oE~t=xOVv9LJ*${16Z9f_7AtW_Lqy8?_!PRnL}qWN5Un9p|&tm3?}OhJML2iRe_6j(;t9&+wo7d+@-88p7_H~q_QVYUhs5q=Em zdWhLpRMC29lSaNAVCA%}@v?TPey^j3e@`?lE96O-^*%hXsO0@Nh#XGR97!XJT=FV$ z#hE6)4YzpR;pE4Z7D_un+=>KvQjU2%z#e`1R6t#DZe(9+5pGvJ5-KO_tiY%RqM&x8eLbd$EuSY@j>ko zlTMcuHvT2Im5CV|Acbs{2rbVI8#Y9&zonS5wfq0Dho=bO{F&bR#Q&`TOoK}K(N)9G z4E~}eATtzN6=~C0+R)?2cOsPb)ucma;(8J_=OpO0-KB-6Rd=abbkg3vB_R+&2G8iZ z%I73nEIgF1#p;#@qVo$0SIQGT5Nc?rc)MV88D8b$p6T98R=AZg0B*AV^_2(o7@nqC zoYe})q4YZJo8q7R2c(`1{CI5^kfkG{B_Ly2(7Rq_qbOl1PXMwG|Juq=+c2Y#YO?<< zEJ!>Kfq&o?khJR3r_WCDz zr;Y{bn=_w>aZE7j|INoxU?S%M3QWr8;CCXn;THZ6VkiM3?xSgM0u0@&*R7tdlPzau zgq;8l2-i*fU?Aj#oH~S1zTv&N=bonxV3f_K$<$psE2rRJu=6XgevehV>z0jtY8GqX z-DMWwB!C>dRMq<{czcO#@GErX7%FS+ArZk&zPE@FFfE z8XnhHI`4>iHd0|d-n?^XC*AuD4kMujLC^d)do%n9*e3l2OYP}zEn~I+Xm|_zKQz4g z;5-I}0WDQKb?gZK67sDnq0Lh`f)^p$LU@4!pQKyS%i)M1w&^%K(>}TXm#4iT>KV}) z+&GJgMHJf%Rs2chtfL4}k$rkl@Fl^Im&~hkE4tl^b!hfJU^kV`LfbObg)Z1yVRao5Q{Z^Nn_5*Dv>*kIOH4C!;!@>v&aLQfHxlhtr zIxv#L%DHh2S$w<6Q>SW@lEGtvjpsrC1#eT#3@`~7+CTqaeUhO^?yIzJ&=!m zSv4YTIW`}_4QWNun=)ds3eM7u^w9p%Epa6J?GY9|8*+K!JKW{(5D#a@vjb*t_?}bM z(;Nz(CP46?O~V)IZLIdVvg-X7<_z;O??D%0qSq^sWXF$z+x5H%tcMUlsIrHI^N zQ}`DmkjT!Sq2&>JL@7_Vvfwol>MG;qf|3(eE^&7Bv{QICcTYJfj?K|TSWYX(fqQ1j zM_>q6aPF*KJzr-+WcwHOOXrf@@pOgtRu0YrVOh?L-IXC;PUzQh0Zwv-T93=G&Y5%& zu-MJ*;>BeQ!EL#7mH%2>unyZ-uv~t|R~OQgln8y#SOWw>muXBghNQ2-pL*Kly9iWeFE^Lx!No!o?Y79J_rW(!sn}v@%+{Yu%E~5Nl-rDyhiuQT6%%o>i z<1VKQ`gYRZx@9Q8BR*SHb&Ue+)=0(-e^cwxSH!{)OJ=|7Sgf8OLeGO~$Q&yU6X)QQ zFg}!Tu__X6ClNXqJ-#}!>fwLEG$@jOr74<5(k|Dl-^k)aHzwGPn#|Aa3HoNG-SRHx zyQ^2P4qNrleshCg;QeQLpDk7I*?mk_d{syNL!2@9I3pHC#W6+Zuh!G6%P2`N>=OXvH$2M;_WQmREP;2R{ zw)MI(`H0~vPQu=_fM!?}ujK}oVL8lWx)8SPh*O`A9yyW;-H1tBu6GtQ z8OjCY;0wAJUfW|#p}sY2xNP90y&dQB7T8)MiDlyb%1umX9zBelHInvdR?vb1CJ|?p z_T0%ODkBhS(OU7u6uYwPg_8(t$x>Ateuu`!az=)|+S{iWOW@@h1v?n|qJ3D`b@P`0 zPelzlxy3rbi7=mEwN<75l~GIt96Rq2Q6bx4p3DTGA)7V%MvT;u(OxIO2IkumeG7po z;aJiJK{!<=$79NLZ5VYSy#I5ma`8Kyd>2!z>&wBx{=hLRj0c>11=>UzDJisfSxjXK_Hnb6K@Sj8KamW;KIeVDyomrdHpGs%Y7T}0> zkB^FjCA9fp7=HtGosRYDPB-1Ceb3#C4e#yr7>f6{)?fl2ryL>2pW#DM9RPS49h@2_Y!E{&v4eVtPKoA|@ zBpx8iO@CAB%uP*C+33n%r6wJrLcbn*xlhP%zsnP@@vwg}SJe}hDB2;hFst9q6+60? zrg0`DgmK()cTtPyM9>k60mY#U^i|;;&qjYL2Aw+O$9QOrixIMg zIt-1x-ZY0UT2g<(5V>{bz5dd{&Ws!e9Ou`wvZk9Y-fa8g5-VOD*c?#Mq0oDf4gWEb zLB5G&OdApjG89w*z>I{<@$hEsK}bDj67J1756Xes;%8)OXSC>eN9lZu0BE7o(bKdb zgb4$%Gu^ukWvpBwD12I8U^RH*qHz7z|7u&8e|XYcj@w<9 z^@ZGmqDRU~_Q+jUua1&0tp#eCtMoDc&&T;{g3AfG1b&#TJiV!EW^RxMmIX$qUEAXa zY3+IibdP8qO71rC+(D||m;D)6xbNx#`LCWoUM#r#J=uMn0Fi2Uf->go#w>k3LbzsE zBp}kW_5XSbw)v>cvh?pLfhK~l0QhE#wqOB-EjX1zz}*$G7cbg?cASI|_1#L0~H@-(4#zp}~uJZ+qpmnU+q_uI9Vt_GcUu^VJjk{FjfjEn805zETj z$KIkH!fn;MOBb(O`9{90ZUrV@BI|3cuO}=E>6_@!yIb02DcOL zl{f{9_`yq_?`QmY`~LkM0mNbObZ+|M-XfS4=YiUrZJI_n(Jx|pLs(T6W&Kyv%m7}Z z)9#r8el=bOlTDl4-+9bo$RDJ)Hj{$ zpGnL{{EO0zh|>D@vpQQ;A6pz}cU0SA#*)*LeGZZ#dqH*yFseWv{u|h#k~zV#8ymg4 zw==~2Om0x(V1Yfw6@iQzuMt&#l-PgaU1j$p+dl}B#ja<4)k7ZDneMZAd1bBE z7d@>^nRur6tpLAK{)3nHT?~IK7JRx@x#XeJAvF0Iknp&FBu=MokXoMN94_0;^zi^ZWg z)yEs|*nd0q{xPCj?yjEFddeM_Jfd0(59nPkIkxifoqMbE-d)KnrR$WQ+Vb}F^VuR5 z7le}%I)w`sPyC$rW%o-CfuhT&KOta;tUf$=alqN$t=%oV)yvcGRaQ4C4#Qr1twT?D zoyk3X;c~{)Ek*q*=@iX}H{G=^4T*?|HyYMP@@PoN_;KRhs&+N7d;?H%Be-|#hWWA5ns>k7B+nck6iy# z3?1`1AZZweW{pB(*p^TWc_UJ2ii%7_&3nYXERt!OU=HR*Kjvvs76DQcv*ARXJZ8$+ zCk;Hu*8Y{gR>S<^o{ULjZQCcP_cF9=wRTw5Y1lg+PjO4|?W&w25PmAJ+S@vcdux6}=(KJd_ z?%*Fa1KdW}f#tip6wi&!E#2txv+nM?n4RsCIXym%`vz^+`^&Y_ez`5vlUfulYne>M zZ{>m?S?rfOt^#o*vyz5tgkcC6Ylnkf{qQO)|C- z8=aw(@*Fj<{m`ZN2obqSOTP8dLP?szV;7Q9$|8|OT#@84=e*n6a#UAr$)Lb!_T|RB z9?{x9<83=9sJG$uux};IO%@h2RZ3~Q`;6!!Tqn`<+Bvl(e6(jD==JCke0%QoQ+po$v~Fqb+~`J6-4dz!*XoFRereI0Vsz>iTA5E@k_H=VhhJ)6Rv19R&@klK<+u}H) zo%CP^|MD>|XV4zzJDS~&0L!O3m*!s9lO`c2=X>M^j7;qr#l_F@Auh@~s@}*n5@O&dlO{GnIQ&*E#xBbPEp2$h* zcfaPY%;HvxyPP38ySG8I%Dq)|IXUi4ZB}kCMf-%h71~dbLCIgm$9Z+hkk;+m8NqJ> zOEcWdrDG(S4<`mxwltT`G(5LSs~<=~%Iswaw>m}t6}Ym0Al;YG{D~Gu$*E4Bxpp1h zwkAO+orVzOI~dI#qp?kS$fWE+VAL51FV5W@!U*Z3C}Z+o}T^o75zpBv}vw$$O?!a;eJIdrhP8SKgAwl1#1sB;ET7d5A% zd&|dI>LlMunr35TGKI^4XJathOPiJ22KXIMb^M*wYjs=f9BPhTjB`5SP}AXJLP9SC zgV#L|jeySuB9$RXb}P*-WR&i_o6W&2|3+tnd|ZS&5S;*=l0yDe6y6sTJ1v|QotEA_%I~7U`JGlcp&E!Dt8yKa;hWTA8dlutKqDdM1} z7!Bk8UW-uk;>}e(ws{OT8Kml}K$8Kw(JOGU#?dH2X?dDMN zW_#7Fj!WV9q7M?}j)B*7pBJ(W;m&Z6ggM2tK<2?I4oSK&UUkn!{iS`4kDMc>ER!YZ zfx`*Z#kYaGx3b~$W6Y8~{cZXgui?Iss&_o;!SdTt)#FyITsgz?tx&WMM{}Fy@NENrQt7!^E|&hl zf8{fW0i-uZS7|#3Fx)dD$>jt)F{ILKJKktflVk2VM;s z5|#3UQ|pO>+{C$_IZRNNjny82+Lo)sabNj1epcgikQ%2 zXd(jA*>YHbjcF$KVRfBj*UI$4;*`5a9A>=$Ety1{D{@HC4&-qc9FSNS@&)t-^=TxT zL@X~@%mvw&*eI9kKG(Nu|4?lqg@~b|VffaPHnTl8qfjb2tj3kBSzha($pztBPbzeF z@QuV)g_J}hyU9P%&MHMVZIbebg;rntO}}ZQZD$h-Ywp zkRLjMZiqvkFq*fN$a(t=0R*A*?_-(W z7-Qx5pFCOX;ym6Mw3*n{9a7p2fU2aOTAlT-^N3YD7#j{EuDszWNkQcON&m=#PG|WM zXOUD`F~i)PN>L1M%XeW^K+$uWf+%uZI?L~)z(L|)I6VTNVu*XSB+rCqLT!A~io(C| zmlX8nDPZFsa&&9LsnTsAV89cRFTa0&b%@6w;=1d`Rf@PUB02K_&BW{r8C-UJx4g(~UOPvX6=cl8qTNCg#4*DxU7bL7>w%I=A|`()FI7 zeKP3xw6gMY?Qa|Mrr35#Q1|9E#`5Gg$CYc=kSP4F-{s@nTjB1YgPUhZae!KWx*1SZ zvJUmrx%+BO{1>-mB!$sh36M%)@}o-@V!c@UkBe1!0Z|ZI_C35^1Ix)4m}45QU{z zeB-r9JQB9w$SCH{)pS%6A6}l)y47~#nXi#C0YT=U>dpQ+$2$Y=o*PleIy*a`=(ryE zuI`qe2As{D=jS~qk!R+>es5U&0No`ptGZ2*6N(!hiV~Q#9)eQCSEZwS)XTeS*0Ek5 zbxOiV!akE!dx%=_CNhgta{c!lYtIwz*mvN@Wz9Z%bO8UK__%Iu;FekGuKab2c*2sn z$D?D{(X^fy!g}MtIqH))St};16r@zR8!nBU8U2?Qv&7;45qGH+?7zdmn}fJ;AJZnj zejpJ)YS%YVo!Sj>*tB*wI}t03?TPfGHyky(cOSQ5NDbGs$Q6kRBV$%2v&oTmVZB)$KBq2-S5uwnQb?CX|A{qqJca_ zO@X@A2wX>iO?cwB^|REqFVy&uC{dbvxAoc2+2^wiwkCl9GO7jA(S_U^WO9uFGveGb z986;izi5saaReE4NWD1#{*2h{HWG^4!YVPsw|p^DV_V;U_;7To;uJH?9wgK>BA~$Y z4hxCW3$X}Q0httABob4Jl^2?warGD1xzNtM;V(%b>uDODO@pKJfP=srvUmvOZ*mP6 zGrpK1_*r5aVD2O`&-#;|=JhjY!uQ#8NH_UfAc`6fk73ke#qhzJZoO2C8m(EzZKaeq z{vHuZA&M^S1qO)rLzdiTXbRrAN6Wm2?$(5^2`&Am-05v_aekv^Z?rPoC3B8BLqxbF zapF|obMMfplL=}ls>g>IJa;Qm{;NIbI;ph9YqxU(>AHWJIk-a(A&d}%gU40u8Jbss z#@1%#q{wbLQkb#fe7HfaF|DW=tB4ns%r_8|0H17gqVD?l1OV$h-Djm2*7&OQa#RY?hi(KQ%bZ+dgEjv3+;tA^OcY16yP$&Q5La$-{d&SQ0# zsu?!X=xicJbl6);{sD5@K;#^b*L1>Tb;B`(Yamd70U40@&q+}4=}s$Xp=%h9_*FB% z$7)#xJ2#3_j_-$|8a+aoQ#aA0qLxi8JS5AXC9d5>v5YRbH4!~B^#$DG=Ewb%^2)tc zzhDQM77i$V8M5%G(8nb>#IBVYAV#+da<+o#rdMRU0FXT!gn;BPm;+px79q5Ni zQs$Y;M6+fKX;?g?G3r2hc6>n=bF z>kp=`v)@D(fYSTIj+adPRDEvV-Hw7JZtYWt0S*J&ifn`5muFB;4?ShMwQ7=irvei) znJAIeI09FG`Ht$O;WHI~D@T`^=W-k@lg5lm@6_xgOtgx+`c2+Yj%;^)r<+K04C%O^ zd8x;M0cCru=u%qZ*s!b@KzC7&d?c0%o^uX%nXVd6CF0{yAq0(=cx0wkt`I6IuTD|Vc|G$B{f>B0-g)Pr731QW}5SJ zwYMo7`k7FPp@7Q4#8;5#AW}tOfO?CM6L>v|Kt~Z;Bg#KRe9R$?UZSg^7us`)biz?^m*Fp%D>RGMlH8XpOo}!hC%2mi1 z5yZ@;i}DQUTbEJ;PcdxmH!X+bpMN~HU-k$ZofAcZ)*MVuj7Iu5*#1i?!l?V~WDAF2 zgo9#V(6Nu7+HgJm+!X%ZEH2Hk$*Ui%H9f8dk4qtb2~A;=;wzk~2WbsVFS|KW?g00h zx)F$`^LQ?EuXv|@#ILsHo@EhvL$0&vge?KG7o!k8x6ZfJrmwmeTFJUuf|E-X-#rhY zzEHQc6bH#NI=4u>lUUUN7HGXLbPI!d83Yi;j~&2if%0I+RfAUXkxz;W8d7^s0YwM~ zP`QNLxF({ua_HMRhkh<-l!1oQEXp>6uxL-C`_1hi%Va`$E1#mG(p&!f#YqTzrGck<~GmV*%w}&E7Coh%Yb#eR>Q{z6yiG zN+R@G*pg(+lJL(+^lg^YSyrYMy@3M8KsX~~7G&Cg;iANyn0bvv(njzkid3_LjVz_(y%MWH z#zYM%fk*%;^%g7sLW7&XLOn*=P(PePc^Cn0=w~+T3ihAkE8@oVmgWwVcHUpxQUac+ z0pb&f>|NLSF5N7pRyJ{%(@OB4b6$0|$W-H(kI-DQcek`HJ1+JH+ydNX?cD;s5I_xs zv9zdg06c7f0xm4`N3Q_025K+f5pbedvfMyEG&5$IUGO`iP|BzLMPpQNVfMKeI4w?b z#JC^xpAj|N;CpOyzX`Ty7PUXQzI>;WGsLxYysUs`OT3UxpPPO%((NNxS(G7d_B^sN}7n`TIqu;MSnfwVAWtthnL6J$s7J>>Kr#)~-@t($*M+C8AGz^?i>oPu(l6kECwZ;eF%ASvc=k~Sif3OpboTx68eb&7yXOyanb z#ASEL$7sjnQ%@RrEIVn2!lHWQgH5+rSE;PIJfOAHeI9v*>g0;b%JanNHjKxxsL9{y zhP^3Tz2y-!3cTiGexPUV8uFx%EeLq{S@Kd)Ylg;i&fLcV!}WDaAXZkFJ;bE~>T`Je zmoq$tT_k*wUO8xKZre@Z0EXknp?%>&aDNHFJ7{y5>L4LBz-=o1pIJZ#5!;AXLCZR_ zqr?5hU*5gDMH68)IYlLY%*_Z+sPQixha4qQs6O4lm@P0q%SC#m$*^C~GtyFh_Xh^v z<`Lc|Y82bguFRfu44ghXb?61Sq zGNz$ZvJ-NVYO9PsmB!}0v&!%hBXntK)dsXO-0{|N*|IE9uu$`0CAk3x)YJa;dhN-K zPVMwPqJQVUN?A7)DZ_`qpQp%?L4lP^sK$`L!Vh#}=YF_(SlPYXf9H?fhXREpuqM$u zNP+gyy{c(joI++NdHC-i_h5Hej`&+7^Pu*K7LBl(Ou5p`F!vA-fCSzdD7xmZKL5Tn zVNUw5-@nymDNN?zZ`!QcjJrGN5+C6CpXFfsgENjm+sQS32hp-&iV!U=Dj;Iu_bROi z{2+GG#=&6-jWk$VNR2q;LD(Dzw3QoZu&KqlR8cI5YMu2@jk@8EsyBGHXa+1#-+}uU z7~9C`a^Xrk;CldifO){76)0QrPz_I>SSYwWi@d*IW1Ld5vOWMHGD>hw1?(f4%sCK+ zSa>9)!qOC<${u+zy?UKrUqv&a$ieN>XgTyM$~v(l!*c3-x=ZQ@INvPI4Q+S~#pFbo zn(j9~&ZWcmL9!TmwW3ofAZ4ahJ3JGpP$C=1OKxx8c1sl*TPJ9WwOfS-JyIPSgj*i`M~j0F1M` zTD5J9@A#@?j0BA$;;3&kLtBDZ)Y@NpE!}d1Vd&Aq_J%HMPm9rufYRs(n!d?NhC3by zQNZ|0xg%Qvl@Ix)Df2!w#4RmWIDScQq{bYqz|Vh-YwBOpYQOh~gm^NF{ViQ0S}hHd zj355)*}2odJ%{mm;LpRjfnry_n-+KBF2fYqLm?^MTno(88kaqY6X+JSt0Wx#pNhXQlf zE$mr}vDy%bUW~+OY`~aBa);CD#EV0QoBr)O9^Zt|LpYY7xtM}f+wL@o^`_Kxnx&xGI<8XyFQP8)1^&< za!{fGa&i^v&pD<~Z{6xOCyA*)7B<}fRw=q#O%iu3U+0km1J+{onX3{#so@o$MN-bh zznt+yfuWg^|CsR`S*)qmA3=cQ>v88Kdc%dX+`ko+}qKa-kH`PZ%`I^0vYiZrz_3;X8pH-_JIsA zw%lS;ykNBf%-@V|ijhZ@nsyl#O|s($amVt zn)2p;_FRI%q408cjAKI)VgIO5eY=r2wO#y@t}PL4a)1;qZqusOf%8A@-~(|CCmy*R z&$5*urk9n3aX=51yqdmLdk09c0-h(Pr)2F0K;I|MFSncbNwwFimZesScSSmjuF?sL4KG5ZJuiVV+lOR`LU+qQAFz*oc4 zGg?Xi4TL%-*%Gny{&`GyK>Qi=#`9Puu3V&_q>BPKz`zNb#GPgRJp0ZIoxCKxlM@b8tw4d_3DrIm0Jxo}ZfAxL1 zvZ3l}rbUir#y-KmB4^n<>`$O+Bk%%sgJH0-|C6bEfQhKu)gw#3rX1(cL}`4E&OyYi zfGEi4pO{?ARUP`jJ>x69r1>l?DYgG-T#wT`?Om*mKv;gb3x< zZ_UgsFi{mYtm^tfL=C0%tn$XXPH!srgt{*#_clW!pw57=51%wN}#sGqfDx*`y&%6g5 zWQfxXNJwyYpLu1~PP%Ln#?!^jU2UST`0oNy?ZTvwa|=khTUuRhQu~hxHxO12JSw-N zoL|SR_umu;7D}!eOJxzXByUKtx6lXqA!ABhy7auUI;0 zBdHYO_6gHm_Xz>G1clr}d2-H?pkO=ds?zG#{ZmUa>grL>v6s`61LGlWADpXNHmH40 z`LFFYOL~X7jcyHPs&vfqcSusr$PCKkQTqvVDH`||%w63{KgKUy)W>Haqx{CI&qLF% zIBK{r>YOfU6AeM>;*y(i-ExbJbL?T7HnY_Ap2%;2(z_y$5?m{;?>{3m0#t9g8-Dqq zK6fM?+O8$}%rmU2K_&>`I!st|>~SXQKH`fZOI(*W=k(^}X&m1B0dcv;KX?0_WC8;2 zpSA0`3MeJ`VZFsQzPX1_#$W%#@r3)h9qK`QDY%c!60`bzTr~p4RasdXz8=mbOeVrt zNd_sy2UWE()N8D^?~%a3g)0k(Fk->!1He|!+VRuu?Uyf+H^U|jze${!wx#n5UG=I~ z6Lzk9J~sDro5x4ay~sT^;1FEY`RveI&?mihKbENFen%G#PlI8&`cu zGT2y6OueAaur~0hn^B~M_74|cMU)S>GQXq3FKPPa2ZfK<9J=Q4;ls3H)(zc-WFaLl z*wFKS(T1|o@p*MK<9~addS>6|8tvH87G-&nTKSDdy^5y@?5_S(ML&)MkO#diPeh9+ z+2QDeBp&BIT!q|Y%SWgNP!!gKPBkyq&W&ML62)n==?k_~laO-C5RP;P@b!ep+ooOX z+_ywruBm(M81wv7-Bx-n4WFBwl%j1wdS_X?rGJ&*Yj6+~&v)+St^ev}2Ng z0}t1S*YZO3+BSEY=)#^moLphumRE=5r2FvU?PwJKyJRh$euZnoPCffrQC$Z&sfT2Q zyUm;1EZ8SLjJPCX4q%f_^_sdo4Zi~lNLs)>>#qOM?0NdcLi22<{VyJ#M`Mvl7b>=X z{s<$mpW%BihV>`mDc`<*tI~G(rltF*eI+y6>hXE=Zn?R+PpUw^Z}hBxjN{R-|C|#~ zo;>MxJjKpVtwx~IE4Wt-j40y4A}XrOA|KFFCvr^Q0Q_6OVnlLg()N6LY4NkY^bGZBHpXD?&dh2i zsYob6UR8~lO|!+77WH@_;K3^q;?uSVOJk?DhgW;?*|pf=yT|?YS(qnLN3m?6H{4+!zpfu{;+ZgoE0VQ z#r&A||GraIdO@Paz)^{rHCEFd(zZEh4O<&)De5L~hLqMgFho^zK}%zae|5tqUpH|t z(KJy74{AzBk@;*X8?U-DtWn(F2D9=2jUFZ*TCo?yBzYO^3~Q&JrCaL8FkRjdZ_=+XLYg) z5Y9k96SEuS6G|_!`rvEa&Ju3G870jZ5Yd-)bqQ&;ND+qEt3G7}wXL_L&(hMBi`LhT z)@fR%^i=_*wCP`em4NRlpw2Csa!_IUKMyr5KX)pCnH6YM0jP+8$f*!L3j9{65zV+7 zOmW|3o{30jY(@u4@{o*amxkkP7Gt_Yr{v#+4yUgFW%v(hu-`PYe1m7*Zc)DU@yg|PZ|W>7e)WpBY<>uA z0Yg2yZSz>puQ(yTG&jv2-Qw9>y(8SJmVF$0R_=LDt&OkEYrHr*OFb>>``Qhm>uOLr ziA;zdrI51v52tq?{&gs3w?<>gNfJrW3T3T*+0m}V6ot!-ic*i zHM`1d=9qgYxR$D^z128;-&yDupmT0k9y&B({UVk0wWrh2rVZ}%7C^Bezpi#@S=!;G zgvRbflkbljU>^zSBGvLS+4Ae%krI4|z2YtB9-_d( zN81#D4msfQm57B2dCN39uSVmfb8&;jgt5;CMNHoJM^nt~WHfc^rOWM>aI-i1t#rEv z!xPeD`f_(CV8)C2E*i8_mhUwC_B9E%zt{ubmlp#}GUd00g6s>>9MMLije%aYFy^MX z9SCe_a)sj(dVqNTW(0&Z{{4xUYPC6qw@;NQF{$lQ_IOJFN4yAqSIR8V#YHtG zN0jT;RKQ9QBsLvK;?bGW%~ zEZz)$S#P#R?PelzHj_YO_NP^!6Z`;PNM^t-5nLODNv9)`ON{W8&UYNE>2=OuY7KG> zBYCPk4qAc+;SIA9k{pkE?NwXRMfl3}BU79qC5K$jo!n&ZD=MAL<7+=5bCFA|MRH0k z`8b1Ar18KU=!8W3{-NJGh($`;heofDbNs8cZ5z^0cWG|ofp=97NZCHDv=x%rusVyA zOwv5~(rQkAnuV=^%nZ^jCsq!Cg`DtW-{&cysK0b}y8RAw6He(|2{0~a3cT={b|{>ZW6s=6pxw4C#B5*0tWp$AJLNc>@!VqW_eOKB&wJGGKBd{!&a} zcyy!*bEy&&XY}Nws8kii2hhd^+6+fb*W03J3c9iJo zI1~rL(xZ=p%HI(dw%F)oymIjDn0Ey=H1IxW%_ZQy*@lybMLv7?I-@7}kixwBN?-4K z`jzYRd8)YORif49ilF99J~Cs%eTUhm&^41-JhpFX%}5R&B77D+I94w_)(zvs@uK?%E%ML=ta5vleY;-jOu>#qjWpp6{?$h4EF58rUsjb_TKu=IP^T*GwK8|I5 z8&vo2HE-#WZ9qQ{)4b~R)!JqpNu3KUxoL3X04)BD(8C#QDL2~Z&(QXb9Xl53#+$vj zs$uB_JaC9`8^I_}bLID^+k^M+(YW&a_h48h@9aey+dLY`bzvOQqAgRPbCqya8Pnl3 zP?^RO2}3Q}Htqb3Uyu1aEz((d5)j0*ayOVp11Q#BR+;$?yUauVW!=j^0H_dI+ppB2 z|ca!x3RA&EXMY16r%Win$yaY>?b-Tt!+mEZQG zc#2t9DG1=^Fk_CxppulKa^a0QZQ~{HpN0=JS`!*EtBhHgkw;aRT}x`@F3Gnd8i~h( z?#lc0i+atH`lA!5G3*HTwZ5s6#`Sy=>p%loyAn;61+{I;{H-8XRk-4_8}spd&-N@< z`y&tMpRRj0k5dE(ctrWn*@Tv(FAd!l0A1nFMpm^tB;Be;=59DoWv)9F7#N)7@`}Pn zN6wvoW`E6$#d~Stqb;($fRM>Z>;5dxP=Ax-$&7WRRUD4%n%RJLd~tqLe0z=4X&C>- zRgRs1%^)f*Xz53`*$MVj%z~`WtQ&+j^-l5Mv{UMB+8i3$Sr2U%?L_FiORld1zwoIKV*=!e4*Q$kS}sv6y<8mi2*7UrjqBdZb-URCK?;(z zjJMSKl{glu_gUE>@_N8YmKrIB-b!M9qlgqW{X(o+tmlQ;Hkw0EE)^wK->XBYVRXUe zaaFu1ECE15U6#&StFuk{?E^{5K(79z;Wpy``Aj^PjWyq`Ej3$*hb{wQ34YuUlJH$` z7tdaDE^43hKXGCjMcr;LQtc?kdw+`lzV?86aFAW*l@`bUPYXbF9lU%m=@2NOz%7GU z+~F`~_!B1)Bpw%ICSjM*YM@e}_IBAcHYz7}&Khc3vtt@af9%K;_{*k#J&F4L`u2uc zZQ=xl!KHBLP;4b=_?MCJtW7qN)fkjY~!Wzop^5?n0c$~n6?Sf56LQnrkD7>xP4&WMx$oS zajx$slS0du3jlkLjoNSoDLeJNo=yEUnm3uusE}otWf&F0m-;l*=zN0lGtq=rq05GE z2aLTQl8;5Ik8jj#ipcYL*A5P|GGB4MoCv8&saQ)6&qf)IE5na$WCh8sI&rVba)r_I zk9tk9wCM3OcMbZZy5xg~P39Id=A2VZU~J~`iYs*L)Tt+O_@+Ul$+?KDK^%7xb57Ei zH0DgPuw>Oqk)i-z$miX>bGh-iRI2f|QXCk2<(z-cu@ImhW~GcWk{GY#a34Lx_(WNI#H?Ce8cI)b?uwh*Lz7FzQ{w4gV&y3LF2O$ znYDuWnmD^9ndIF-v}u_nljOL)#uOlF?(08864)dzKdq$C(f2pQ*l3$cC|({Wu9(N+ zxt+e5$KD+T9A$Pi@CAf(#ib2cPCOxwa(>Vi(c)kESWEQ^d^eo@wNuopO{>p*slAY(6{Awx%=VWq}D_j6b$pqEzyOpX2jAK17iiDgn!^Pk$Gd|Q_7-S-J z>A&(KYt)?x+mham*62JDofR{h^<`JGk5DN%q;^b1!vCMuTzHq^%L_JLu_0~7k?452 zIiynGdE&%Ibc3Esmr~H<{(4zl@-79Vi$v<3;q|x7wPvMRvBMXTSWfUrRBc zBw9>Sq>iquKFA{s`8YrJg(z1UTBHWEEM&xmP%{1*?(MPawZXuJ0}=C|11Eu&J9Tz? z@d@U$7lg)xEp$hPL?F+fse!-Q*jTw1Ukg_sRwu=i*EnRK!H?%#Ph3o_kCF=s1ss^Z z0;u_aCzSqYjIH9o25+Y1@Eafy)NXiTxkC7#WjG|?(c zsQ?{b`G@TMXkGpVDcYORSxH004ot(pfJuQ{0?YZDY=RBN&y_Qcq^{D^EM`lQ!*Zz^ zhaxON;>eAqKnvXQXv`qQIwp-=LS5*3K*G3^QOcB2?Pc8yI(zz{dSQoBGK}D9&>;H`Ta4Ehx?xKsn7fUdYyBf z>s;qL)kXMW-6PzVsYkVbarSf0p^z?BSrC0<4IDAq{I-J8k>$jIJaGe}KsZkd+GA@Z zA^;<^Z>Hk^&|{3E^w}wv!goEs`>mqlF0Lb+u@CnyI8lNZKL8dtjV7vs+U@QPSy8Ju+W1$e!4;r#-jT&JMS~GuX4n+>VX5lNC9lCV7`G&i8-%TOlkWmKZi9 z2x3Oh*ATRP=*UQoQGjTtvpsvdNfk`)(48|!{o|q`9l3HFZts9$^^NUliE$7IU3d0u z1N&>6ADpi`E#ec0s^UAp=OPjPpnj0;-1cwCQUlUMd~pV)Qr`s};`%iEvWwLyG77pO zfo%|7mTqJtBOt+S!S>)+(l4@dIP~o7rNhh8ZXM`-{hiEi{e~Nu&dfU2JLm>%J|lZJ zw9k!NJ}}nQ%URYw6>~QBH^IHi%ny6Pz@XjtKt%Y)J}4 zxCx_ZbdT_0!Q6O$bLqip_+nikUkfuugWdC1?qz9Fbe<>f-p1)V(c_?R|$VHebHx469^h^_yW zU|xK4yW}4^e3*)9A6Teh{mZ~P-4Hn`PF=HRkVub8=QWCMMxrXX^~!DN zj;g==XhhE$q+r{(seOx76b>atS!Xvc01Fw;X-E0FlzG`h5nRb`X%h}Q!@L~@xkv1~ zh9U~KRV#g0+e_V6VqC$#zMPUQ(15+K936p8Qf0rAW2&1hSN;y7numFuPeFcCj6>Z^?WL~XnpdU(ND|Sf zRLis*K`ShiIA$MJ8&G5{F>`1|b*$sBv7r1yF8OQ^ zOkgfR6z=|-)Q$Ka_x#8UlE`?Zw(j258$nS?g{!0O%9LrXobU$C+S>0s*{7w8!3eOg zxHxhE7B5@Y8UJj+8L9PnWwEwdjwPMsiZ+Kp2`uD#r}O0(kkosvGb#-8Rjc|(!j?>9 z?jX?bvfj>9XZAaV@`-&s#YG0QAie-|oJa4NySLC`xi4Fe^!R9FV`D6CL?B9dV;bsG z{-|#IO2Se-N75$sY;qBAQ=9jtz}imq4yP_$=%J~1&U|E*Yw9vz6{X#V!+mN~-@ugb zy;Gjzw<(D}!im~Sp25!Y+(tm~I|#uMHJMvjRD}Qf!Tmk>&p)c)BS`OQGpD77o>{N?py4g9{)TA>wx)0JEw`3J623GA8X=n3NxdLQf_L0?zxFiVW21F= zL(o9|Aj?#8Q9K550Y@XHzvrlru`j!OMf^&}gGli5+iM3EC?+spT1-|CS>{_lv}#$i zX&ssd+>k-~bm#g+G!6w$VdNR+=a(&8#%<6UOdX5UoosksvY=+QV)IFdXMI@JV&|}-L&cAb zKtQiLz(7@|zX)@bokvlXUc~{izLGpt8c&lH5&ovD;Xb~;m3;i<7ZWD83tU(;H*24s zhiu)EId;9kPOV$F1~R0z)`;yB&LZynG)}SC>%)A0N{sI5@mu?GIyJWDxZSFUt!Ct` zaekc1WQVG=pu(9hH(7#AH15=`+ku6_B)9#TTg`SdH^^K+`czTMCMJ;38j0H!Kx&7cf*V51KVsoS*VB(Ed>DmcK9hy$-pp2Gd zEvyQj&8<$)Z;_vUegpnba4uvLu~9q!)O^jFzoVlkaI*t>-(I^5UHasQj&xfB>9G zt49yDhFY2nsakHmC!K&ryMhR-=7M6ytQp4pY8B=?DZhIUBCIKTrkR8h^9SUV_wL+j z6u9u9cEIgt&paTTQXAc6Dgv!m>dx<(Z=n)e2hL`mwJT)X)OSZ-)5|?cS%UPcB0~>R zng~U?oyHedv4*uU-CrMJzPsJ@6<2Q0pN2Tmx0mO*5hGU8Od^_ie~fbJ)ADY{%2lfp zG2~dFs)UeGh5ou(c#8_xbHJ1(%Gnj54jG^5QhHGFs6n=|4W2;Qf*;pq}GM}M9tTkDQZ-wj6R?A@e$K=wlMT_ ziE%MVGW6-YmX^M|vtSjH+HZOglF}2P%b3-`hAI9mHX}vq7xa>^w`s#(XblTCg}B|b zH*_E4taygVVe9a>hb&CNnFA6n8p9Zxnr^1f{3;CFD*ajt_034JIkJMl$6UnN?s%*wGg>eNXMU6!zMIQD^Dzfj*L`VY zIBx9Nu_A)5xRYKv)Y+k{L4nrwf|-E}S6W)m#DtJu*fss2yInSQX8l_0g5HkGtZ4Iq z72E&hzbPDpYcgSb@R()#@HetDpn)E~U zfk!zb>IH`L;P5QKp+;lprEBp+ppEjE=<6@i?SD>vx>0eYqMVH5j3P<5NER6E|9ORn z2|{VIg$4;NqpLS=%m`d46d}rh<*JVY`m{8f?$xUN&Fj|>rrmV@3F9nPtZbfDn%j#m zCUD<*!(WEypDte4^ov%3$y6sOii0(_OyV`lU!bW!hgt~5^S~iPK3;v{=m|(1Jki_v zLV&89Z&VN)H7BjWb%$0`9yQ!1T2fGODd8Y2Schq7o&Mxp(`{Wh`c$Xz^ykl&bUj=h z>nJh-3++?V^l4x31a`Sz>dC@*x=#5E%%sr)pMUvT+4^{w4C`+Wb)&XEE(i>1&{EZJ zrlVT&?dJxcy<`2&!kt(5tcX>6HORB-^p8tSyHs-lPxQLY{_{Z1f_lN77*nD6U>qV9 zdkfJnDNq_;K*=+(@I%D)>mR}*-&xSIal=XB^@aYYkftL|m14?_N{dWAyuJF3o)Lz<*5+EJu)o zRZagx$PvzzIQg$d)Nk{&XNvFqj~_lHnyhd{wYxLGw)A=MbStaNyZ26nc12OV{K5x{ z90ZgO_V(}_6{E9UI25hgwDGj=fSP6KtZ#f1!)a86()>q9(L{hE=o&wRNeZIjSKRN? ze#(zMB>xOF4eff3$2t#Z2p3t+(zBuxgemp?ZMrnd1}(5n9DD4KjWxt#o8!Jn(`yD|cgCf85> zX-iYe*MZIRrQm#Hu30X z?C(XQT7;A8bl!o88FG=nbL##Z6|F6xeiu8{@$$ewy%;fO*a8VsI|X#fXud-3J^jpc z1KFf~Y>Qa3V7(i5c6f6~_X^ij)%VK&F97dGX|kIN$fnWjTBn2jBnC5YXJz>z00n3; zf;B75c$JmaQCKq2(@NlHzbN&VMt(n3mg=FanHpd~hsW^Z-PP3p(mkcJ*3wF_sLWSp znZQf4T}J?MN&)0Wydqv%7bTa-xgqF*sbGc6u7<&v>e@IN&N0;zs54Dl?&gcG1mD#(LQSD*M9Ef z8ncgT{o*A{ynbgb?HVi_Wt*EB`6*#Nkt|^B2a*^EsBz%J2fT}dv!4+e!{6H-{B;@6 z8TwT>V@?08{>8%sx)f%_DuKLs_6NZw)Kf`N>ostF1xqKBpybffZn0qk>dU*hG>)LS zUOjx+X3?TW46S13ZGLQb1t)x-k=o3}>$hLLI3e0AhP?NotGP-+uN{u);!oyJEg~PK zfA!T08SOtnS2#F$V(*=%sLupwY1TB;EaKwGRhI@0bsXg~RZad&Iy$ob+3{!9o#)Rz z`P8lOCkjJ{4dWTx1ZcVhe&w5QBOCMyzvD+|cK5awPxzHz#n^>Q(s^Z(;V8kxXbxNs zVulv9#qOH_DKLpMdjyKbbZ8}^6lof(2ygT3#Fe=bMggr!lWHS$oVn!Ed^Ndj%5oaQ zR^M)y-ZFUy0Poy??LIZxK_pWe^!6ZZsTKaEe=2(KhkBiwPP;e*xvK0UB*NOj2xuqe zVml}<7s6b~nl1)e(2tPau3ouvn{P;!{3m%!yZRZAb#oWxT(WJ5W^bm>gI?u$&%ER4 zH4goRs6q%?BKpD?9Qx_^&P>SDl6GNqOd6`48+5SRAvg|X{E9=#4VUHcoU!u=<GBYPQ z_CvilJF+l^3Bf)Z?U`>QB8}|5h|_d&C#l$PlHF07bA+k~)UaY40Wgq*xrY*6*j7#_ z>I8MFGr*tpdt{~qKym2PI4$c^Mlhfi04ynuvKc}_G*$6X$#mBl&GvOKDs1?%Q&?rM z?e!AVth<~`?7#<8FhHHap@Md^_Ef<(RX@Jn}Q-2dtS;Tuw@21I)eS+qR~yI_7fhmb^zDCo^ik{hRHXs zUBhr(qy?PCVL>m8Uxup)<#^4>)U+4!W*^OAvg8^xRqxX_i-OF2<;q5EnsErI>X&(t zX-_be@_LhOsbP$ZCaY5=UpkSK)Z<8ViY88Dfkl^0U87B%Q)Q_pbxpI1HJhY=0e0*phiUb|*N4X}9W(i4@L zUh`(&Xl3cx^4EYF2@yNtL+DWA`g)t8><6r`{&UP2q2oy;uFzEcwCa1;)6d*U|EUU_ zr|PALPw$8kSF?^TGV(cjk{W$Fv`0W6M_Ys5Z^N9g-M9g}vJX(0I>k$IEg&K2e%ix_ zo@FikS&Mv>ejOO2UG(gw`1dRBZw!xwJfb^dYdA__(XtYp{YiKpj6N<5o^k%E0c}2h z1>wA}-n@DF(;T&Kl=l@w-=co*cRkeKjt_xg->#5o2y5_Gx*anq%KY)kbw*kZT3n!+g4GRL?AGhKrADYip&~?(VJ80B~jD8(o-t zH1G0{uEKFfxNJh{F_nGNT_%ZpMwOYxIfx^8AB~pBR9ZevNim1GDzIPTQvQnIhYaN` zIauD#s8OSo6q^BFacNUwZ8y=Oy|U)TPmB1uJ1>9SJ`wk&be_=?6gDUm26Hj8vP?H^ z-i%Zlv%6LgeaE?x(ks(65`@8Khz#0_HDs&Suh%7T2vy4kaIo4N<5C_QTCUWV%1~p+ zb8X#(Zu^|!ob$NuB9;UGP;F{xIa19-!u`o-ck(}3OD7Tc@<#UhTbKseJF;Mm=YWG) zVQgurckE-mdcoFT&q}`yWmz)n>^|1_ieSem_J({L0dKG`HON3!MvP_IA3X&LJaoXG z;4CME-NNA25b%VU;byUnT$lsXWDUzyv-#P)JQ_TvpW-29hIF$U^G6#_ow=oD5~P&u zK!rBFR6OVMO%D0R+BY4iULAD;6uOf$@S1Rw?53hoIeohj7ppm#vb>ThEt+UzYA|Jr zvaZLKn>Pzd;%CXThyNJiB5JmWKGW&jb5uz;aw2RJJ`8|x?qu70N`?oIQ$8|O88 zM>Tb#q}s(@X8Gv_{X^$FEhDdUr%qAM4O94Qv~NjRl4@vwc&n_sL(J3%;ntdjwJJYF$_QZCK zmoaKiF%OL~S(o1i)ENB^l(rsoYY^_{jeL9jpB7;KRr_B&D4FFFtq3I<76nFUwCgx3 zLQ5&eJT94Ewd|0duI{n5!|8;EzOt2dYLw1cm5gy&33|Z(% z5$@&H1@O#1#f}|1Oa(O8@Dn7~6RPOnG*QxfV8BE=;VI;z#Aa+nvAG`sA3=04yc zw{$B$uSnHath+In3X%RTFrJo@Oyenc&te)(8A*$`H;u`4FdsgT)sd9IKBc9}Kx<;+ z`4k7n#Jqyu1Cm7`6%(_|=jaf@^uxHdxp2I)K7urdUzZL7m&Q}!)_kxV0Pq4Aq%kf= z*?bG!4H+FhB(Ud?+7fOHNxtvIi9K-hB~lBy2bp9VyYrY7_W6N|EJ z!VT3zzeRMbtyyBBM}&hSId?y`!H@vhq05g^6+k3 zO+qs{03fYkMn{oA&{F>vty)_XrC49*@0C!O;hFP2*f3;+W1T^~bA!iEor18Rbu;&` z)n*YrZ|Va`a%M&35G6$f4l}{MC>a!(Jv_K)&z?@z-YD>?Kj{@I+}M!m%2w*YxkbpE zlA-T3ihem_UxsdT`mt{`Ve*F@R^a$|8JT{|C@}S%lEvOT23VRC5-S>zJ&qYuz}wc}d9z`DfPrDDkCEl+2j62ckI(#B zw&HYn?~3@)iiGfx5K_-IhCKMS(OXk_^18&B;O5PnV*jvl<3`#tvPMKS0O(a9aixe-jc0@GqRpK6IYVo0j`861yK56-y^5+=SavB39ogs|4a~Whr%Yi3v(4LC z7jLmNk<`|Ua)c3+ehsX%9xCETo-{y9`|^uyTIJ|`j4a~&=z7R9A=5iqwC+%xc@9Z1 zK;zJMNcV=xBAG?0LCvXvztah z40&Alf@IT2Lt&YROQYrIpQRNgjf>PDQ4ER!CWmixRm=Xm~Hg0_C7#e*2>*k zi`3G_Hny2|?b^a%Lt%C7`;V&7vGFuoy7ZJ^&v6OnOQx}1J^&caV%0NTbJ=)-Zmbc8 zjPJD?YSR0+J|W^FnC-^){m&8I7hTA*_ZT3Kz%*DDaGzL-x@qQxh-`>hN+a$1O*i=@ z0M=x?R;94e`F(adINb7c3%^AOm=T(pY9A2VbJCQYQenVSW zha3;e9;A5)c`-bz)D1i`IK4&rc{#Z86!Zyyx-OqcjxON5p&HSJ+LpCBVR&Hf_QmYv|ZNO}nd|o5y9kzbI$z5~CugQZD}wC@Y!BB6<(qaT;(euJj7^ z{J))=#3GN&7drF%m?N_i(GH(^tlJGXWyTj@qvR=bo!UJv*-2%t9dE~AU`0b($&4^# zewl*TM>2Kq1b?hanj5OfdT(k^w7slQ9-ENvuJVfAwMo>JG~<~+O(%_j9}B4crNII8 z=LWGclE*;7as_aRHz@dpu0z4DjMN^Mi+!GNh{zgLQ2ijJ=b}X!YSpj9Rl8(f zG!kglS`I&4L10R|vz)JuY-2zR+lr+7_pedYN1reL@L^Hm)4Rl`yVn6k;s9hHQp1O@ z2Z{xJzd-mACuZIen=`IPL`X$Ob{7>PPC-W#5}c1Y`uh6H>S*@B47FZ`WPlJ#YbXEF zxSUH@N|l-_fam*YSQ_aPBn*Dcx;36U*m3pGkioq@%YS2PB}Fjx5CV?Nn2?;*7s@#L{7nV*|ZZW@M}z?X`Q*-4AG) zWa^;TWCz3H&lb3&<`5;nHyxPq;>+2TavPB zhW!f0NOPPUmYYG;jHhQf^YUg~*!YbVaQeK19c+kYg$>}R$geVI zc$*hD)-RIrMG&DZS;rS(*s`1WfXt&H#5#h&Kt9G|Tu4<4b(RpE)U>~5+J9pc+>V^C|4 zpfnq(yvSn!Jod00b1`~mcjL$-|FW#?1M?Ec(_@6#V0P!pL_8F@E;7rc;CK48XyQX0 z-~WD~`B1#?p_?^#|8R$9+wmn!V4-4|Xtog4J_O^yBciZ6LSaY?DUG%r6Q2%OH)oRV z#U8+Y+|Pbv#&qGp6LR#KyE5zhakb`1!5JIeamP-ipdt&dNOuFkYU5>(LL;cl=o#+!`-n#;;vItRb zzrGhb?Lvkihg=hz;DXvaATKeax0vD8r=|9oRcqGV7xY5~Lqp~~4NO`+p-&t*CabB^ zgf2x#AoS+p!%ZtH?$Z#DONnQ0bSK&~aoGk<(-iCp5D`0STZ+yDm_TvM9@gNUV% zZmiF!A9KKJ#*C)0?w;O|e0u1*<~a5y7vuiO+1L~7TS`;9%PH5BjL;zUn!|tWZn9eJ zqDYzEKX?8P;OWW92gI8~NogOoB2o@`sG!*jPq26tnw|!6I|jv=q9oXfEQ`^Ql>XL= z4hR}1V7-Jh{;OB7WQRPR;M=fVTu+;s%Pk`M{|uYi7IzP*LRNI`LyBr0`?dL?LF`}{ zP>xA?z{^tR%o8&+Bd+PwbPZU@tOjpU#tkVHhCDmU|CXx{Xn+3v#*n(=-y@Q|BS((Z-hvLnZd^>-QhF}0Wiv|M#@wac*PZcgu_+Ya zGC#kE6h*IN>@?^^=^gtOiW_^~cU$(F?>DpyU6%=xw;i zbQN2+YL!Pd*)L~(muk&#%QkP`T*Jkb3PI*6eaCKAtGb0UjtTjT|6cD5nAIfd`HkP| zLY>E?ht)4_Zz>7Y}X=(T%jhKNhdgV3aOA`nx^Kp3Gc0}mg> zxR*k-!inU^Rz8bhl_{L%PobP;8j;6>t%xRG>QQFMp6|KF@YhfYAVGqL8eYAvi(WdV zH&4GK{a}jM;^)B`{VNN)N>zZT+p^!ui|_7~KelG(W-?jJJ)9a^TB;d~(y%Ls{vD)& zrA5e-l$D4pD5h9A*rcNIZIlMig_R{FoR<2J@7{F*9y5~F@;1(T^Qle`^?vj!V0sHu z(Gs32P(a#gvZWVi8d`COM5gX|%dYG(tnC&Xn|{=&OzmK{b{9s<^vg@*-UppFeuZ;e zLX#SV)1Mh#G6DTmdoF<(kQ^*K2o|aoMFLHG23d?g4K+S8YdYjJGtOA-bk$u!u{|BM z=Y~Pa?lK2h9{B+b(r|XY_9lVZqga-SHXI@sNiNhDJzjTKc~ZNti0=kD>t(opNRN75 zNRQqHQZiRTgUdDn8nQzdZTUtvp&zuwOnLkk^Q$#28`e{M;APslL95JaNk!Y4Iy%>h z3--~-6<|E4nLSQ#-K%TYzqJFVvH+W6$U^HS&rkc?a~y*O|Byyx={x~P3|5nF9wIby z$;5b(l$Z#0AtOEfYxNz*DNvvs$vbQZDbJgZ5HboaCH=&6+B+t6sC(fd_HRr^%96>~ zLH(es1Eg1-ex?9c3wdxv%HF9dyHrbADhdV5WYMf+FDq3FyY?F3$wocKveBI@RF~xx zah-_@kiM+f=nBk{LK;$##fn3$Uq)K5fa+FNzT8TCjIrgy(Mo>LG&Fw^s8erbgK#kq z+#4$aECWM&=j<-Sinx?ETe%Ylf^tK zWmy`(ezzg+r|@OvPvV)&^BK%IJUVX3crMv)s z`50I>jz#Q%Cj_fIT@6ntg&bf;q=E_U6n!mRUGLF?tp7G^4ClvbW+_uwPk>hoRdVPh zQS41f+DmSuzvvzm3$r&NsdXpMWn!6DP83_ot=UhBAu*DX=lXb23D|xJB|cX)VtIQnIlgd=G}h z(&Riu7isiw6eavyb?dii8h(p=xBmll^{2C5&8W|B{Jy@WmyG?0#dW)9H#v3iFLfKY zjN{orbto`zrR`JsrJipD%x!`==sq7z>DIKO$9IO@(q_J-=A!L;4ph(HMDL%Af8Vty z$caU1#`~l7gB{c!e8QNg^jr;Ld%Lc_=9}Njv$e9DPB`_ zF>o~UQLu*d5MM0!ZZ|rQ8JbvsOfcoR};LcK!Q3>rgJH#w6dVW@z9431zYwqmh z_9`MWG8wxl4oD^KT#L<{AAwM#wiO+XjpL3`XNtk9e#5*D+&W`!9j$ivNvYHnl=#LK z-6toUsT!2;|5ockJ*|Bo_Jq;fpZczC2CVgu#naw67iRaStOFAc|2Bbe=_Lc>X3C@2 zx%({|H+b*?aW|^p1YawS1nqIJ-(MJBHu>V0vgx*Q^)4@$XXorK`Pq=W{${K6YcBqK zov%eiSR#fK;Jk-1($B|&dvL5!>8u3-h=nFVN8G`wT{{|$(rk-FggQJqqk@e|<4JbOMP)r&tRG*G?mTi9T+%jY7v zzkD_CjAr!nah}v@*g6aO(11>GBwzck{mXoV258wDuSJ!J$sT$;g!4`Cw9?8A!cpeKL644IdkTO=8p>5 zdE&&0lP6CarWtNSw*cbByKXDj$>86XOXImR;vS22Q#2w9X^P82HwP+S26&E8 zYL3asUGmz5?Zq8H@lwE2n-9>{4mNyWT&&v7GOhh5k@}EJw39ShV<0M)Ps@iXuFE@)teV1O1tAtF_0K%AfB$|>y>i`zWoL|NdT-md4Y(YV zJ#VDu>9KkC<>NQs1#97rB(r;@YGTeTYR)-8{<@!-Q^^Kt`PB(oHEbYHW477sqFa7h znHx1ZC!D)*fZ+ZW|5M3|$$EBGqYY>H(uMo>yn2c8InjcZZ-VlmQrZW!-A3zwfUd{3 zH-(kuN?ns9 zv-YA0;!%nNjBF|bc2fOA0O%~U`u5gyrW{)2SuVd$eE9dUaa6Cylafj2th zQ(RW};vmrk&+_LbI{qbE{wG=u-1W@YT6njIEDBs$rh(p&F2hnMwe=`W#V9#2uwd2a zjQ~=-jGEZD&1YCAzi!I#-@{&Kceg|QkKsWhk``f>_!qBv?bXAFhXHN)l`b!q#&qDq zC7btl=Mu#Gq(6Ez?B=;mw&v^CFA3Ybl*4ZmG!_P9c2aH$D}l|etS}avf2`;Jl3d-l z`-=824Vx8Upjq*gxm8)$LP5~PoFgK$i0|Jod@oXcET}vI?!0Dxv*uKEmgvuT%nwTA zIACHtPX&(_H_EoOE9wpKbKb}0`Tcoo240P0f4?znC|d`5 z1Bx7Ah=LZV3@{J_35_UP0NHN-{wo4TxAGXk2HqsXiOdC1sByT30N1J&0+Zn-J_FM2 zK8nnLD2Vv4kum^OTKA5m)qxm_Ps_2{eSoBPhL;vj;Rnpw?-a?}KDfj3#cxfL&)&U0 zJ17H(T2=l1BgTzeD@>x}`OY0XE>JD|0s_WbPve`t2P7dBr^U~4G0=G)j@({!yyMENg;#kspuCAQu-~62aH%Y7I|41CIevQOQ6kNm9&}g0Hyy1GMrDgAM<07#~TzNPH+X z3!X;75EXDKWgO~VI55V`#4HcNZ0G15&8De(p|S6B#+fU`HBOnS%X|S@xGyK)Eh5f1 z)RNA6#f@~(F#V)lq4nc-MaF8vfD5;WEQi?S*>g25Zqz!f@QvNSP-QwOri0!{*lzd@ zM&@9BdJ54AI`@on0^1d5$BbiKUmg})Mo=u0N+{*0+{w!7m7*TCqwryOFtI)qITC}x zu?Q$MU9cO^>_Y8dOHT`L-IxtVnNb{M0#Rkz9nmc5nYT<&nfbpgCeRB&7@8e9S zx7=_T7>;A~AWi5Xs{KNSISGTata6!5OM)vT6{=>|%Xmay4vHeyBes;)5f_P?L5x@J%FbxI+M!dlP_q8Nvz5n5k-;cYgDPGt z3`JlGsm&U9loBaS799HbDS6*!!5CDJ+S8WPxDkhn)~!EKiy+aVmeSj>VMD;7{OQmB zPYbYp2AnxPKFV}AdT$7l&vBmjKWoh#ISE=^ zf>r!e;Q*?lS#31nS~reb>Jb*+&_c{dfRbNWc#X1OTogJTItTYL0*ekQzht`CD}K2Jh$;6hzIxyFMCC?~zG*CeUF3)25RtD?s4>kj~BIw!N|*O|C8`~Vj* zI!|t!xn>U*#ayp@(#}xr!uV8SptspM=Z5-Xhf(y)> zI#GjYcn3vY68&DY_dhXU5}-;;abeK>-k^v>Knvk42p!_EgsO{y>r|Rta)rQ;!oD1S zPifKNj6J|v_5tvhK_*7U5i~9lp@VK%X||Op)bFkD$&(DL=ob@|=IuB2miaWzVIBjv=G|?EDoZ2M zxdws=gn8yQkEWGDvD0IO8(?Z4*#R_VyjzBb#4QH$LkFoJPW8;n^R5^CyXV5J^LO67 z>A{s_9BUdy_d3jtL^DCMOgeMjIJ5xx1}hLV`|DUL6xlcBy0f)Q`SXJc;RaT?bUY?0 z4b`Z|MxRdEx2uuq>2H1g?OwuEKex9VsB3|O&1U5|1i|m@i+ZDjE2yLPk^vlsf4;u@ zB`08Ckc_-B%q_-yZJIY>t)zHL417EQ(W{uMH*MbhJxm1%6nQ_b`)e#Yf)DziAxIvr z67kleB1daFl^~WheXayVA9tvH+7lx6yC)9J$CaE)1{R96kc0x zZ3`gJWG0tm?L~SKqO4(5k0bkU0&EdB()8SWH@?YF=^Qb0drbDCU4wyR7--vRbQ4%dzV?i%&)hB?1u z*O|Fa?o|k#ZJyqsor?HKnhL3@ih#y`e;HF`F1@))m)j&`FQ9W_jDA&@$;v%GkoL`f z?Y=Gc4b=5$a;xV2b{?bfG2#`);QD^of4h+QgaoXw`!#`b@tA0bkBGXB$J(j=5-^2{ z(eT^qx}DJ6t)#!gMxsLuRVKfif3XK*v%7T429L8M_K8<@;D#p%4aFimzkq+?4R-V{ z(d4GQinYHU`>CKHi651p?h2k1)_Zj(O-Q1cHB}7@1dbAls3EB?WFZut8qcUl9QJ6~ zSkzdQJq!@-q?HHQz_tHtPD8yn7aJnID+zEmL_a}yGN-NcxRd(F{;TVIgQ(;;7RrU@ z*MoR!(6vCK1 zJ9grdhv%QhhZuvmmWqn#-fliY-sJ+nB5SS?!Kc<=;E;K0dnuM*2nx!;QU-C~GN@xs zbc*d#&YQp$-G};~WAthu#fC6!HEtY^Wg)R~3U*(zWJ?pOWLC9@8H>?ujV%`)_q`wC z`l|(41oV?NLdbpC-r7z6^tm{~LV5Y$rN~AX_=1H`- zM#AWzDbabHgr#q(V$9_tpZ}hfPi^fjGIKu=4kYk|i&)nk2@4wrc9ifvx`*z(d0tH- z;eAlOeLZ%45V(a%#y#ws>i?E?w|sMIX>||1yam!X2X`RALJt)Gq)iV6DiHdBbE!1B z_(R_M3XMJs6v4rQ-aIks0i97c%pT-WKIlJ9btZJA25i?B*BRdHx|HZLMiqeH_jU=Jt>>Mh)4C)7&~k6w9*Sy z%ib^D7VQZRI8n-)4+sP(#jj`;#Wt|hOMH4Jv%>|5cI^1^y*vcwj)Ys{t^aRy5iBYV zDKaBn;D^+UYO}sh>HC|TQ=NqEA#z=LmNY|QC8D4B=mp+|5uyzRzrdiWdqxg)fKIz! z3qvz`f{b9f0>23Jw=tO%iJ!H#MvL8*Eo$<+S^kRL7Kc)nwqhQ*+vd$bE~pLgYnlXF zQ?u2ZCh>Y^U!!9aEFhr+Y3=>igp<6_EevM7?3j*Cq5lT9v6I3S_(o5um-HqStu@lt zzT0^-6$F)13(W7j1h-K%AKl}@qeqUPh77NfZZp^Suh`~4881mrO#E;)^5upIu{seM z1@r{$;Z%|dx?pCnMbQanpJO|JAj)%qEa|+~@O$+o&ZWD~iNK(sH2Wi=J>h#vjSz%h zj%r?aA3vV&^v`jY=MD?%y>WC8G6mR(jr?F}M1PldFrQ;MU~O^b{2R-t(xJ3;_va+@ zX+Vlpdg(GJjO7?$5NX$Mtvy|`ii=BOC}y1>GWKGqfhyd&I0#atXAs|||8d|za{o%d zhEcbdncg1Dr5C9yRy@jRaz!rJz zLRMC1_+|zLWO4CqsIA~x;aVEt&w{_43SK80gk~~U;PtkCj;7Uqm8(rsJK-1FO3H~QTN%{D~;Gwo##!Z}f>q$|rs2Gg4u)AC~zz8tLTZf(? zY#b+P3j!Y05^#$_hD8H@<0#fLE=3ZaVqg4{?W7cGQv2+1ZnAPTOQw@KhMTKn-^ z3`3p(gp*yS(yYWq6I)x>5fulw*csaMKT>=5f1=_FCWJt<)40XJ3u=I_4!6%5s-Xgd zT~lgRa*8orszg|j^7fsX$KipcMK=#8A&aIR60X^b*Ok+MV4QFG4|vK;6>4b5%VfTm*^U~tVC2M1&SzU1|D5w z9BfF2&4t%XNJxkZQfeBIN^SBE5l~H$%ZgLYtk)>+@a)~YcR>!paQSvm|CJQTJQ_;* z_W4HBdrx-Q9(*BMY*Z;n_9FWOCjt)=ng5niyGBi#Yi2&)^V_;%FbZH^X*&JrxnyZ3 zvsRJ^q??6p;{fi3vyyEfa{&aYQkZeirYz3liPv{X?&7DJlWGNUByj z@sFq=Wz47f-hfxfZguV2RW@D$)43y+om!s(&>0RSMQwzs+tjWDlp)wgf!FCV>1ne` zPoQKkpFFOVngpPgEne=``?k{=Lg8kZ^_}Z2EvS?^HY*rs!LXgixplPP_5Yp+{0i~% z0WadOSeNCjp}mux9rx-RqI_Lg#@ATUDGs5ADE9b0s99Q4q zwe8uz>mAMpdM-us{i)Hq$7}10HC|@F@m%%sYu#?migmuG(G~Tk zy5XhAtG`9R^PlaQ=<2E{rNZUt!c+0bFZ_b<{!-laoc^?T%t#e3lW{K7DJ)wqb zyhG99+uPfV{AFL3_CM3s-X5@%MIN55+U=g=zwn0x;YUyBwN<2g?}EokPb{k*(}*P_1&icDPg%Nsj~lo35?x7#5o6X4~ah(%&9EdbF5XlC+2{qsNX#c_yp6 zqe?I(E$t&`T15&hp2=^u=FY1)6SDK1KFT2bHq8x?i(@4uT%{kWpOLbfD! zw_S32elq#VQQ5I-fx=j;tQ77=)W-|HSKfYBQd0U8NyLVY8%h6s0og;cr8F$^yXEn} zn|@?j^WVqK|D?2Q)vQ@Fi4QUz9h9NFXi;Znw*wI7!_z`DH_&s7%5`bIm=P85`JjOV z3l8g*&+M$Y|KjD##val5pYWV!4!i!u2k#f=AgD}acRM+M8l&K0VPVVX-)`0Sp7~hw zPO-u{zD%eeqri621rt-LyFOfw`O-MIh1QQoAWnMxfr)LlP~lfQ-hS^Fhq5HXgN1`!)ay~lB_WM-oigjf7o1w8Wvdq~$)}yaC73w?x?2Ll{ zGd0P%|(7dqnuRrPLig(?8bqI5NvVoHI7uyY%qO zt6W6(y$rN;B(U|JH0hUTy85tT1<^%kKH!U!_~EO9819Or zZoQuwFdPY@&z7@vUT#cWHhlp^0~=XtG~10E>pSn`pX0`i88Nlp5Jx?F0$j3f+bW0X zxVM=Zw{`9xiuk234eNR!ajV&j2KoUM-(?Es5lih+0qu&7%>=3OmKRT$5@s6hL6!M5 zE9+r@H_f{gbFVN>4KVnJfj27#E54wi-R2suakLfeOkDiq>th!G4BMMsqj~ex+(CwF zUGlck6aiM`mSwzFxK?M$|3LhIW_9maA7g zatr!&=%b{V_s!vJ&F<6z9?XWJRCuu30^OaoAt=7CyAJ8l(!k8@vDgz*(ukMxj+i>n z^y^E*fJ2N*&LgmZjRw!lMTZ*rd_kWM2A2o*o{w-oz+iUwp=10w@C7k*mM!ZUU~tjD zf5|TTGC%6-t@@W8*qd@y<*~I&lVa}&-CI0tY3BQ=tL-(#Jv6#Jg%m4ms4vsox_!?w z>o_mC!g&2j54chX->fe?(tG;Wts}H*9_Zh?R7y7|S3EYrBAPY%$ETm0Z#HjUTNHt= z71em@%=gJ^+x+)H074F+t4s0+Se|3hKV?qUlSqE{@rhk=JN@|xE#j}#@SQ>AZ?LhsHuU`q~R&#TPBDG5h(4ueBN&P7CcA%D4srD(>) zr)m@1oNy{Nw;Oc)%bDPmhWSOSbG9f2t4ubv!#%gbJ}~<3@M$u6Xo;l%$DO@!8th7u@2{;OQvWJD+!KL-8R!S=U2{NGu;u zZ24JI|7`WhA-ZhcF{LY_V`1@riMJC8ut+m?=bwzIT+A$4X|GvQyQI5fwAb~(jE~Gu z)%Bj`Us_x5pYG`F-1cYpKl|_0*HKkE7ay;jkc01yUPA43S)7IklRJFK$gS^Zq^73s zl&!dQu(X(ANq2}FHf$KXtUS#BOo9Da9KU-44WC=LCdzsvH8r*0K^1%W36TL>I^{p> zoRsbJJMwLt-=;qiMc&YNFt{8ZE^83Mp!-r&^-i5SMWddhRl!Qas<+)1cE6+Ef9JGW z<*6;re*bZjUs=akg|=Xw*SY@@9>v6B*=d|p#vt&4H!%=1sh5&m-g&ka_$J`S>6 zcE&aNLmtdsYFJ1}$-i~8n2~2#XW<%xk1vv^)7yIIJRaEN?~QFa^t1l_12#J_Vfx2y zT@THiaJ68|_`kDNjVTuhHdc`_$(=K&FWJ32*2dz)?(ZYW|6_eW$A%>&zx-Tja`@b> zTT8nivxrzKeQw4$-r8)4xRs-`;W6!HhIdq*{l{-TXYwLuWny!eJAL5xwt7EUB#_U! zzR1}ovF*6>a5i@Q{OJIjx2s^xuS7`u%fB;E)Q6*!95iPiNL(qdono81dx}a#M8s%9 z9^VxW;v=z`IDZ7&zc!*S%{{TqDvvkWR5l&V^&5YTOFUz_6I7N`mo86Vytpss#zZ#B zuo@a}D)r?DYO8{@3Mf{9W5}}}8ksClf;aqiO?U4z^Ad9RjD19UsQp?ZrO4jL_d06z zVBYWY_D)gdkn@_4){G)Cg&B*X6v~^TQyVW&u&n4u%p!Bp_Zgz522_6kZ>#0_&C-+h z?0Ack-^OBY%f64NCJfryT2V3T%dOHM2+m?ApFsQo5%qV{+{C;si-WUoVz+4JmVEDC zd6>21A-Ue0-;VtaKHFV2sV!a6@E)3``Na6D+F~88lB-iZ(hA7wr);`JHd)+cVu7>asOCLzR-0c(BTU(n9 znz{F=`Z0KEhCyybL_B@-CPeeHaVV88mP^;EY9NbPTeHb6++@WHeY`2qYS5A@{&$QS zHKbI^kaKJ4KC^ZHOl7fY-+g~P{yFT1-Yz6;(*Ucg%-LrI_q@f%%JpcCfvs&UN1;!YmlvMzQ%`rEcBCbOn$S92WL@#AT3- zOsCKx#8sr?ta?oUeI1%CPZ+Q>&P;>S%c%b7!Gn2Z@HLEBNXHcMnc6pVG4^KSOn2?w zUZ48eCeWXE7`pw;sZ)yEcmMUrCSVIfNEeTLkJHnOcu1da`Vmd2Z`abJOm#eFQ7^i- zs9k|t-_&Ig&+kA)%o%!gDb+bWwazu%Q}9U8K&O;i0BcSC;uSbxWQ;ZCWD+InH0XawWOP zO_Bpj?c?6w$&6C{F~R2F!%wz<+{x8l8(aHP-x&gO+VkZ2y3f|zl6%|v zO~0{x=0wH8;=Q+E(W3JYZ`|6DsdmYDqqik*&;Cnv(Yj5W4A*SK`QYr$o5mBC`T6^& zPj5eYwE3EeN9eJJd~a`bdeXYChnih~o#pnl%0K1P+eF*0pq#K;r(4H2wJ|#Fwkk36 zW3SGChr~~jX^btLR++45R1PD~^ErIc_NR>91Mx-it{{zi1_MVUFJOAclT~3J&@G+A zUvdarGb7SRyz9GrZTKTK`FG{gykAWAV^}z`)=o-FT977cm|2JaxPvo53Y|g{r3^YU z0llAssbIo}h*O+mIgZrx$bX!?SM4Ew!f;3@2Ju|=_~1=gqt(>Z_Fl)M@Pr!$xD|my)|IVvCIag{v>sAB`oQS zq+SZ6A1Tqlx4UhlUE6lGGai4c+ok{00tEf+)y6h4uC8NZo%715NR@o&MnAuA6SXvb z(6finIu!b>6&!N@d&l@+*=5pvC@Cqqc=2K`TWmf|%Bg9mKr?kR?w4y&t^U|Og}-*Y zv%cmXlnoBXWkm+Rdd*HSYB|Ed(9rM-Op*wH;c=d`u{+cfEuE>&gMp^@1zyaooN;k! zT+iXhAJpuWuP>;L?ho9Stn>y5ZoC}*H?Z2yXQdNvKk}Hl%NM`$p2V}3Howo<&8Iw< z6ZoB;YqWZoa&L)3H`6WCo{McuSZzFQ@Ji)H&aQs151aPrJg;bPb#S~UzG=!jolCag zI{w(q;nqawt)=T7g$KZ)#H2nx@9&-`N3Zv|R?CpZk4M%{iPQ_8mgvu-iNvBY<2E*# zxTft2HboCQ9&BP!Htg8uKiw@#HePGHx~Jdcc=PcF!G?+PJY3sJRWIXnX}A_NfS3+s ze3a4~OIpqxZmgP+ z^Y(0l$9;*jRo)~wF$TfzEgAGapHbcCj+cx>2lCgELW!so8_MZ~X@Z*Uxu;3Gc*wve zy_5G*XYc~Nh5Hrf1Z4Q}@n`6IUd1FM6@P{MtH=5Z1Z676942OP?|%pE>u|2DYK}`a zSR%FTscXda)$Ht)n+sQ$YPXr<@{KQTfBCw#Sb3IoKhdFE z?(4PwlRti7>}c&DD@`aJqm|y4RjUsiG_iHl#sV0}pPsK!QBkoNXL?+ve$+YDe~(P~ z>gHTK{o`4NDW#Of+tD6-01<&cdHPor7U(SxB_hdh8nT{%Gg1Hd%=yvW%gYP)=*f#0 ze`i}25j@!2vkhM1%*emv#}BG{nR4Z?H=Rbd$}RXZx?OxZR|^`-u}T zG&ai;i{^W4Yj|Kw<_1awGY# zCQx%Ce->7Ze`3>&d?}D>IVrN8G5`;qwD`2uBc=KzMAsCKOE}0FCoyVw)l(fF=?up( zqzY&X6X~zvSl4}FpxwR>7OEa0MW_8XjxuDwb8QbUFhzi{bNo1lyb6xAu@!{?t{M?YHgff1jg_Rc?=D6sFP2izzN0f)y` zEyPYtfBN($Q~cYL>w#&MwBh!Ane+2=2eC-zAr;kpzwviyA^aYygMr`z0Z46^8HX9G zAAEuAM~>o-o^1@5E=?sZ-h-RZ8a86n)x{?#Y#TH#YfBSmzlD`z+p%j%h#DjNBfHII zkgva3kcq?;R?p)(Fk;$_PO(k)Fy)7Df=rW(v@MI?|8nJOhq%-MtGg}VzQB3k&Nmd+ zBj=2}Z5LnJn@)j%pv=t7)Vko{;H<65z4aQ~p{0qXHckCNb{@s)Es41;(0$rm04nCk zvhpg{Ok?KEnF6`_0(Q%Guu65)+nYy#D!tmZz^Q4_xa{qnY5czD*$+nEeC^su7!j%` ztY<2pFVg=i9>K9EE_KrWCm{91*K*3HE5J%gMD*B(dxtJ_>B4B{NN6c$pwguYHbp57H`3DD| zIU5&u8m-_6hPs_x>#N9rC{%u53ZBAWQ}^&qiu8h}2QJ)-1bQ1Y2IpRu{&n=}1LvO^ z{+_mCy@Ze8_c^tKQ_Z~?+7u?&(j|ixhlgl=*{O%0T^JlCOHW>>J5da;Mw}>IIUTTV zq=9lqKUZE|%wdKgDJd<*egz&oAun00{_B;?apV1dhH3wEy<3~+L)6tjP$R-lAHRE7 zoA*HNylkW~^fx0*bbFY)Hiy|lSFwH0jhm5?>VwN9B4EiZRgX{_elh}jYu|BQX1 z&&96;2M^xHU?OmJutRbbD+BHKR;fv6QDn3 zIK<5Gbo^6BrBANz#ZA$~ZZ7+UBWwiEt@k(EQCN6|H~b7AZhPXf@rt?EjG@;gD%k}` z`0{jm+hyIsjmh&@JC!L9HJoeaym|Y!(GRZc+3_6I(6qG(AEvdj( zE_QOGj7=SZA%>C9!`+fPo9?}1W$EI=5+j(15?f=QshT}VV@}IY#kKVHGQ9_^D zD>l34(<&sYo5sGiU*_x<{S+Y@}t*2S(O*{-c?-;sF7&g-4OBY&6`vll%(4LbNtl16xk6&Pq&o`aTLu358gZO2ZGG$bT=h$8O<4(al+2>KB&1Y>5Yv){rF zVYSwKy@q>u-p~7avk04_`=gozD8vZCS&+as!xSu;wf^PylfV_Eh@%t~&c($^gM~5+ zpsudY=*hP6(qhUFQ}3BFqR^W|Vz0l(%4&kpfThs^=A`-rVRDZU-ypYJmzHsK7F^1I zgm#5WfIuIO)T-yDUGh)CFZ*Py;tt1=8Tp(}VqN~4HdVMFZe7y1SI49Tav_F@Q-ie8 zXEg8@Xbs!AwbgculCPA4CttLw;OcP~ zA7p2bM0m02nnkQX92TLChR|+^q2l|R?;-pIrBJ;?977^BvRjBo+_=R2Z{Tu!< zF4QZ5bJb09q~i`_IzZ*j(r#DOe8wA&ig855`a5BgOx+hbB9VD#l3|gHDZT=G1h|Ar zstS$6A$I)-MlatdoS-VTUSzgJ1|qC^^~yvLe;ggB!xh0rVPZ&x?tH9o^|pW%UdN9= zS~0fwt768cuKr%~`twz?Mj>?gQ%6Iz<4|9!U)|j_ClgeQ`efIV3J7BExzQkI#580&tfxn)K8ODH;K>rML~_f@>UaA2m(sg1r6kHRAvU_PR$Gu%8o4o80m2u^Bs5X!ZbG`w#TtOZb|mqudz~8Z-s(lv%BdF~D&$ zv+3yR(*tl7V!jJb-EG1@r+e0VUD~vSXNkVFxh_0 zdl6j0P9owJSSUMTFCg?w7qSko-Ors}`&`G$jB0z?%c|IW_p{QY4 zSs$vjfRl?ZY5mdVfOp^&e>ccm8(YE(s>F(l)!>|4+3qFS8YlP9=!ON;WW=2G%jeEX zy`A=YH|3ZUXU{&*_WX?_GM~``6^mv!{2>B3=afLm*9#-kc9H-Kl$I+Gd@rJz(UEcc zC^Oc>suM-ce6ve8Zupu<|Y==9Oi(fQHz(S_DW+8P@ zYppGg%OgKng2?KKu)4jC%n1s_l=O9cIS2wqRi#;h!`aN-j4$_xd#sIqwtbom-5D=F zJ7DEr(!c(Oto{|&7(D$dl@&3_2K(;bY<8h%GGnesomkmn|6D=OgZwJa4n{KF3ZlIj zJix!b;ivPkVmJ?wlMtI$tM4)1ILeQ9VbGgE5%E$a^7bs;qqRncmNNpi49=Sf?8RZA zS%Y2sqsJj4fglzO18|ZO5qrCZZ2EJ|otNVWd2~V^g6a+c$vNVI@2(5XD%H}*bEPIw zOJp8axa~lVLK9kB(Z_rBpYz=@CvYYNCPIBN z5ki9amI4R?)$n$@LeYq7h^DsoUEs};6kSe1H4$RxXoycpi1k>QK3qV|qATNL=?bDp zzdsT@|A_vB>J`L7U#{#9Vq&yU+mpLc45+{qh-5`(w)2^=u&|Iz1667on}EDF?wjS8 zaUkvWKQFh#knt(-_;LN&R*{=V?XETl8y0j@-O+1u$ZXC`pYC+H<{jk93P5JP=H@K> zn|G$({OsoJx){GCeHIRegh>??8SYrb3Ow~QIkt_&MlktsNuqL12iGaKjq%li*@qx3 zf2>Ec%#j{@2MkR-B^GI1K|f=Mb+d0QJzTSd=*-twjYcdzBilas6asd!M%eXF4yx(b zI7kAjhHrE+$CSKVhFXZQwk*MoqBpBw6<@z`LyLToPbCX7fpd~qQ1IF+KEd^2HCdYQ z{UV8mC31Du7$^cO$>Kq=^y$fwEGkoDc?dkPasAJO_~9_lU%GUFctt>&OSAe)-Vwd} zp&>io^115t--ZA#=`G%;cV_CEU7tHB-TZdpOFi6MIy|<_Rs(sO*KG~ML3e(LKNQ&X zelzb)>>z3%F#mLYl}{}#26j@dFSeEv*vUiQ8B7DMS7>&6Y9Yxas^T&><;x6ha7#5= zM|ns@aX&dLq9{YHh(o_Io)Rm1EJFNSK;a`!Nc#6tWmpA83!f#msiE9N_FtG4iXz0F zKZ8Sz^tdBmv!I7kC*H7eqvxqp5&}2wD6N5?g^0)V=gzqW+bk`EBO=_^jz5ftlIa12 z1VJ6{RM|}J@O(TCJACB|g7GF({Hc%#O*+QVTK&_Z-{P?(Mm>|4|C#hEN#L)iHlNje zw_23QS6jp{G+Qhzb&~?xpn@{O_P5!?^%2S*#E2ndCXx#MbN3lUz~v^w z@m~L(v52=f`Wf9r(5>;(*m>RB6IWo`#K{-c6Km^<#I|o>tZ5Fx-0MGI`CB9$IW)-W@SGvHXpbOS4N6Lq@lj8ukt8HE zbf`=Z2=*$2NdBEIJbSiV1nTBzX!V=;mv9!^+|i>)2`ZIZ>zs0O)TUrV!JLwkeejAZ zEwudkB*;{iu$w)O&AMJtg>zaec}r{jD;t%)?s8#}fpAWCiNAH+|JS^s^Un9A4)Sak z9*T+sWuFAsE7gWd)4guNwYVM|i?SZoru4Ultv+yO6XXUEcG)i((Up@@v0u(%VN@e-)+3{=EwkPNkcU~JHe7&H?e2GK>6N`yk9>`W$TuzEElW}cVd zoML!2Nm!>BMAkwbpeeui(2m^$mIvp(ppktP9t&hrgOe$&?Wj?jY7uGxkVJP1P*1E< zGB+XG>;nf~#NUWE09%5h%o{a$yas@c{HcMGw`!5K``tZbpm0&G{Rzx0>Au=W{nD9T zN9D|HRb*iv1!gaTwll}CedIrDhoIoh$*9Wv_GU{i^gt_5)fV+Gl0V% zQ1OJJYSYTB6e(6kMLXFeJ_uEw0(_L&L+DPaidv3lYo@7)cPatjxJojf?X|)?Q+-Pjy zu29FOZ+YxbUL!m>Y-1645a?PFH6x|xb-&Ij8H`jAv+#xrtIw_8mxn{u4W~^xJ#+1N zg(JmTzm=Tuxpwv0R?*|`?Y)3oAg(XEXP_P!s_^_jf8x_zbb$$Sq!t?a<(q9J@8x(Ta&5lMkc&dgk{$*7O~kNj)pr@`}}~br6fgRZ#^8(61DUr=N~}cTea#wNu}^unVFen7&cr&W%neYI|b!ZfL*-TG&JkDTLeuCC;GeSn46D=T8To;m9zr7Q`EJD2cmZh zb>uoHb_jM|D-}dDeb@r8tDdK0&TDiA8YrKB}2iVg%fbR_^Cq_hQ) z!!Q=_CR~e)rtH5d>&yc{ss2PK1Ki3dJ=erYUHG_I$xv7;6X`^XBt8k}h;0PKDs}I8_@(&xQ@jo4hM=5E!;YeDu@Wo$liTABVN_i!YN8M#7|oCs80#Se zeOz90)%el$${FlXSu+6w99CEn>PAng{*r5&Zrw^X>FihY_HFl$i`lc{azw;3$&kbn zizH*q*bN`_dd07i^%?kmhSyU!Ie$(e?n4xisMh}dCwsi_fQmZ2?e?0D3 z&mM=cTNj~bVHgQ(wwRf{`@5qhHDto*^-!t_DJ0)J2=jcpmZ9{Vpo7Y&9vIn|q{27= zo$~he{UR8-kC#ntBkMPO4H$@^+XI6OE*_ookfeUR897_b(9yDVktX>O;SUu;IyBuf zH_eaGB^nXT#T7H_dV`X^!svCSEHK~Y(2NaRCJi8jrZDN=;6M_l+j0X3Rg|6BG30i7s&-s|*Wq+>zqNM-DCiGEr;!&f&}sVVRNbZ?vx4WDl4f3(h~&njEvzl@R_|5eD?qNzC#5{Cuq;4 zo^4+M_6zke7;fi5`OAD)e-kJ__MG|&w(A;VOtctNyIeYZky6K_?W%hMSHLtxQ~<=?^?vxuPT_Tr9KoP8@|ZWF_-yf?c1skE7&l> zc2ogqjn5x{lSfsk7S1NJCF75bEC?-JJJNa zfQcY^au(A+TE3q^W{4jE?ufSJU%t0d@d@;NW4$4FYGR3VZeiTWUh${JrJ@ss zgkcH&W=J_CpEd2rQe>&2iIpPv=+K&UR^ z~V*RRL-ji9rZ z7!!Fg0h>_$5Y%jTqt>jkt~Ku$RT2ux7z0Fe*28bY?y3(HHv90rKa=igR1}(SzGxk+ zNG?*-&@ue}{i83RJHLI}5rWX3AA@b0cTY3!{iox{Lsc#R=%twcp0-V@rT+V1_3D;; zQ*Ztw8Uv_tnOa&_z@jOmOqOQy_x9IrQ>~z55Ln=G0j+HN2Tb$6v42_&42+d4W~Krd zimHnMW>-`>3JEg;N6o=kuy{x*&RA{GD z3^N#hEty1m=BDVnlXt7#7FbnSsd!dTDW<-dXI6Lh%3PW6E(hE-?e2Vjlhm5!{a%#q zykW3R=k^CTx}>fh->oX!`OP=0S&h;#jnzJN!OP4$yqCrV5d~`M>xwX`7+L1`KUDejdZd{?ed~Wb# zD)rufT}?F^I%SG(JjE%xVh-h-rEZGmFP8T&`hN4zpqk>}$SLo9FKVRjPff#jZiuEw zaE1Rb-@HH@dnUXO-9$7kDKH>|#nB~A+o8h2J{rGffwv6wIZapUFsM(xdDi_~a z85}r)`SQfp`Ifzoof%7Il0d9KN#-6ci+`%>UVU}|kHp3cdSO(Qp4ZJDhvtV)L(5R7M|g3yWlD?+PHDc3FmK?nohElG{7m_=)GN# zzY<9bM~v9Cc**R%-5D29#BXftwm^5O}4Y!tU7DBL*eqt{j#t<<#;MOuNa0RlsrVtw?p@u!xm?<0sp zUz~C~0mOT|#;ZvSL>rp4*{{%c=yEgESVTAs-*XRlTWFUPy)Fj?b2&7kVJ-z6)>w=N zCdn4fTXRnpcc9WCBCv|2L42~eDugBhoXFusFUQ1i?Km(xX0>Y0b~i6wzD#qmD3zg+ zLM%>yQgGg9A1wv3WQW!MY^|I2eB~`zpjx`R_Xr@s+g`$g+e`;=(}W{`y`3F!B9ZX# z2OQ49*B=eP`#?C4&?+wFX!i|>iHq663#sL2*iU?GPfJUj|UGh#i>^&1G z@7>ozO6wxrqT30W4CbnIw}Z2`rL?9y9V;s?h)}4{;?wYM48xIyPPtyJ zga#LyIKRU=*v+)g)y2K%rHdHIx8{JIE{)wn%>fS*EF)V*MU{r43m5jm*j=fq`46f3 zig1g+!C6L+UJ~ynJEL!Se&Om&@#%NRkNnM8e0t{{26V6p2KYA^Uy^Aun{e|&$b>|y z*535O-~}UooAK;*y8z=T4;9UmX|K;;yXHksM?@b=cz^kF`Sn6Th(*X~_^sZx>&N%b zYs8^!OFgnR_?XrX1pcMq`9X#oD3!u`3s|0AX_7hhR?LgpL=!v>MNwAMWC+kfC_o)a zN|#r~lTA_?pFlCCtnc8#N-^V*ou7QQ*!_m2;D76+L?r@Y0 z7p)7XRt-VTl7M1N-zBpbD73Dc*}vCs^Fx!rKb;8t!PMmTp-PG!BUtqlo}M|E?%pPj zK>(pi_H^rtZ#nVrl8mNY-z2-QXT=+@CF4Z?+9Z3h^(E6a>+PjlFZI9w$#QoEjtb*+ zCzkY>Lx74HPD&|CV6diuyLtQe`{x2Isst>L|6Rk`L$zMJzC+tKxO72ARqEY^24Q#O zsM*!F_U_7GzJ;3Pp+GREFELA=?IP!oqwcL8?_f~YAcq% zbE|npK~<7Pgzhne2=@W&D+=6I@n|4YQUv0*jxIBz5{ZbV_=zjQ)~=g#n-{x!)}2wj zXIgdSt5d*w*Bi1c`d%!mraZ~u!sYR@D`F#NZd>G|o?m9&yT>7Dz`=F;A0t}QqV55E zQs8AShT=r`zy~esR|KX_bW<*LT3*-k^t-i9LZhpx^1*pSaiQFHaEj>XQ2}OwJH{!W z{oq`^)udbdwgf9ZyBvV%@@?16xru9z-8lQWBr5gp-Jab#t_W`m@R&KWC@LUx*2Sq` z&1a@MTx&WF6NKVDDeaKEYlY_|54dsT>lKgG{dLoV3#xqV98}L5e^l!B=JlCpTkn-w zfvQp5fDhZvzj^1I%`4Kb-29MIpSA-5yB;{C>hA8Y1D4pz#TV~8E9W@5$hG*U%fHr@pqT&Mb+vlIuV1MUK_~+)9S$H zL+Gget=FFg3o4`R_SPwsw7eUKb;`qWmd)A&A__XVBe5iXL0^Im4DqY&)4cyOc{3gC zi{%7~#s{BxK*$%w`2hw47K6GXMRgB5`J}*cCZQ++Y@V5|pEMB1h!dmCT;XH-XC5r5 zn)zC~sHo_(Q!?*sr*D5aFrwy}xU@|E|b9Ru!fzGB^Y zF;%+D0n;s8hSHm!vAJnO(ce_%SS0!-{=8WFab(ZVwbJV=zPe0S0~-d`**fKMULLx3 z9DbW4In{Rc-&&-;H}C4!|4Lf?VpnQc z1Xpb3^5B}Fw0I6&;K$czh>N%=+pdI%SNSwIu2soe=$>y7@inQwv{NBhLivwm|ti z=J+?+CmXLoy?c3ZuYlzRubsz!X;D4-)jYUxGXOI&0Bj8?ELP7p`YGpiOZJXwwf4XM z)2-LP*72tuJ3LqYm&g5O*OV8-KoGY0Upv=vl;meD`|A(y4w3)mpMJeaI_P?n?4Gwf z&FbRI>Nu=moKhj$VdJWZ{4r={md8@(L9A-MB%;igxrurPDbLTl0&qfPDbr(g71bqP z4l~lHaacB(Ve3Unwpv5?EXk+-*YK(%KiB%A#X`+*&&x+P=4Kr?zcKg>w%sZXCXYFolyO zMeTKq-iGg4$`qTHjyPepW}1xd6YH@01`fk4YwV?JO3luu{I9}R-yGhTHTl!$uqJY>Y zz4hD^VJY3AZJ_z!z3tibdoNnjo(C*iqy1gp9d`#ybr`>%gc6LMwNa8=bLZC9cxMFjdZH$u6pJi@y0gdTEW#XCSx4OMm(&@$!{`vAU(QhGaF1{r4n5A znn6T<)?}&BhUw-D5o6g{uKe=lOP_?ke)iozr&by6F#cn}0E11X_R8NAql_w!29iRQ z+lF6LPEd5I|7tsK+O$<(8)A!+PpA0VMkY3@=q26E+_civGz3vB!?OeRM!G0HyOH>% zp3x}N1G2On4VS$yI9jx0W3xqUQRZu{U9L&_Q9cEm<0iVq7FCyj@5P5%r08l|yw7xw zxTfq|5GC=V57omh(P8xOH>3})bJ&>iNnh}&=FQtD7fO$h6KW61GJhnkqq^n<5G*ed zECP$TmmQTBwYQ9`zEM<5x4>dA0QKRCB_GNTzu3~Lhs-0Oad`;jGVdqR>gKE=UOvFf5_ojOLi@EYGzVVbFpj2BrP9Nrwi{x4zNzf86 z(SD7ZUV6qK|C|tWzhQjqc^z#&oBSnxG!Lk{1wBp`t2c+~(}C~#qI$cb`^!F3KYzWr zsPL)n&)@FxxNxiMuRor3w5`&w-+oi*lJx7h|Lw)s#G1_L{LL2vQxnhO6@*C7iV-g@`y^;&#Sk~L~BXXPq+v0r|A{lUF746oo%Pu!h6^f*buI}U|c z+=yFKX_Ht)jh6$GKvf}GwukEBD&4gi_wS3@Q*sgl!j))~CGpiNsqr#W&RdTBa4E;f z*uC@FS|~3WFSjrDLh`Pv%+%21zkHR025tV=zwEvwS*c>Bi4#|%&RjqXvA3t^d4hT~ z1_30%ec2HGEGhTKjP>)L8760=KPN<2x1n+hTTE9??H~eI`N#rwA22E8_6#BGXFvzR zrUTV0P{n~Oa`SDR#w`0?9}IVlsr+3vBfqie<#toh2Ooo}Lx;|_f9@Fk{p8JCHQnxL zY}#}g3H^TC;5Q+3p$R1q0ktb8D#Uwhs(3Zck9!)uWnrTJL45) ze;@bIC@}Hepd}6MvaJf5Z$6r?aRtEm(xAuv9G=6ps%6gu>s#crkA#I~eP6g{&zHQr zhEGa%yqx;sX?FgD_3;*tzBAls$A^F1o^~_REKBk8_xj%UA}a9 zlc;XE(fInj_&Jq7?eZ$5qPBos4n#C%N9yXk9pY-N^#ayQTwdruFDxZ*98VDEHgx_$ zX{_U=vlo>B@|)Hj7yuP;>02R9Wbe_$!I8Z(A0}1cskT zvnu%_V=wDsrC0jGZMTtTmYMXf=9#HR@nP$}wZsHYcW8PF*cJ*^%4lbG402lDNJMY^ zdzCiGEy_(2s>;xRbwgUWrX|MUt>43iNX^W{mi@!uu~|izE>8N6qC~Z&LbKTKmYD}@Lu9>;>>te-sKT!WoH?jn*Z(x~i8Kgy;6GBhcyV8^t(%_ND zQeRzjXnQI+Ywxj{zO|jsmPIrr)1WQM#W9TjvGj4jZ#UkZSyS6VVa2kC8%t#SIlsmn{=#iesFIky?g+*6nx?jp=7qI*t3f zDtEAh@6@mMtdtP?lnCRcXy|wIR; zbh?g^=M_RjeIkX;o6S*xLHzEtGXu4gKi?aO;<};F`niAD2e#Asxqfa<$p zd60P9_+h}1@WNKpt{R`s@nrR4P#~y#n=@aud&E~E)FDJgX`Wxl*(fwZ4|Y%cW8f@ z`&0~0pra%6*yhhNrXebp&L0$2T2EVL({3gM%FcE+v`aL?@`q~;q+90V|5z`p*@=o` zy;PnzobgP2((P<32?bva+A8z2P^v;IGl)HLD_5N50I=?jIjs?`X9qQBiob0voY5x2l2xB_BRW_K=;S zTdsoKz;QVv>&(f6wzRIzhvyaYCteX;Zb9>6-1i&ESm|qN{sGC0chVOIt(T^Og zT>ACxYeu2MD+r>hZur=Swxt^X-H@yP9a!RtA-C#o>6~@nC^>s&W1-LV@VgZ$mwz1} z40dl&FbM;EpF3t(ym@0v+nHAop4RE-lEhmF4m@}s+;F~F7Q)C{nuQXB;^-_lv2!mg z3F_`sk(q6@FwLv^Rfm0m>!|4pimz`*!wd*8cx6Y)!kUQ%enz zk}R03y)nGeK>KW>yNiz4=(A<+uUC9{H$&AkR(Fcn=*FXSQogS4c4vIj4coX9*y168 zcz)jvfkNxBu%jnUs!gi!Raz4{YUSk+`URZIeDn+p?R3^03bU?r6q5|a-Sj$oGc(LI z@|t)NI&bB#AqEgLhuwB4y1wNi9#|hC$Uc#W(ybS*d2zHg1o)6NV)4QUABAdZ=v()@ z>(;F+i(Inig)H!B0>S;mG>X)zp=%$sO#>T`r1{`Rp1G3EAnl5pr9b1F8wpVh%i>>u z)0-@=l4Im7OwY~KfTotzH&^})bXj4wIa2X^ea!6p=Bp1bLYmSTC_NWXDApE1+i(!& z0n@ghLn}frB5;__Pt)0YkTgFmc%7QL62J`U?ATWt;LHEBYej{r~)L(4wEuG zo;tqUouau?Qa@G@(K90@v{fV6wmd5!909ZZ{hDF{QsQYMh=J8*70C1*9p0tW@V0x7 z^?OszUp5OZZ2IpDsA7yZw0v{l^IB20xN(PuwG*kxt(5T#+ezIj;{vYjYd>(3={YoA z6-kVEJ~%#)ch{F}j+}wUAMF)ctIw0G4cU_)X|&qDc*i07Mj~l=sic)l1dg$H_|)6I zyGu#=rDtX75+$+_M3c63Tp6LgP!Unj6y0}C^`(At%K~Q^;V)8x=Xc;CvaI3sn{mOt zPF`rU$8v3ZC8?j$P0hhS`R!h5@J%?D=v9kGfKPlk z-o-mjXZmQVZ@ommDA9rlRY!+1l)<-;SIo{AT)A>5*xh+l!NwiBa~@;+4u1Wpx*s&s zWtP$G=AD>K3R$xKPIs>$E^;Ml;5dhVyz!^ak?HzvZUt(*=pc1c6as1vZYHtRLXI%B zLbk^t28SvEctRG)449z@&Y7ozmGzc`_coc*`R!^Z20k@pu;=m7&qcEP228X#&H`hX?k9HOeEBg=(A=M zVy_#=1z*0bJHwgx`!-we%@wH^D?}=-{@d@r7tnDpFTqti!O@hLz!P}F7RfV2V3`(s zn!R(|x^?GI8BVn*{8Di^Fdv0k2oarO=qk9KqsauX@u;Ey&#a_@7 zLph}ne)Kma7^_8zGUj1HpHjnRis}hUu%-MndK|i_GL#cFHf$bBmSgPS9q_XDC%Rg*eW3iY1fzj;3FRd;-FdE)?i1NaZtkB!0v|r_FMg`Cjn0FAH`aR#k)9LckMa_d8m}PY z?9)@D6;{ybD&MDMJ3}zn_lZnIEyg;2!qko8?x=^EWp$Bi{)_zng(^JRa>GsZ%pYJp zN)XQ_@gjM%MM(tw5E*v=3`7$30Vtd_H?$RH%q^Z~L&`dCMUy5{oW=!XSNz$h?uMzcmeGkSF{4Lg}-BhNbAja}kOT9S=T)@$c5r07!0+U+dnIHsH-;%i~*3>bX#R+W%c?psT?;f4sPfJTM zu3wT%owyEGp*f^MuOJ3+mB0|P*u8?c|D=?kDb{V3YuA!Couqn>Cms!@OJa$axE+~i zrq?9dLa;IKyP1(lA|^Z=n}yh)7+#AsuHkln;(;7$;^FN1_+K}O@pLv`H1r^*T?O~wkKCk!8^%Ed%PN`2I=J{mB^&S)Kh zD3r(uN}_LR{?eox&vZ(%w2{fsEYz1RyA%hgwMX50kvJ=RP#OIB>%~RsfBrYnuKyE< z^z#G%pK!GQ|Gx9~W2DrhFiB)0X!%6O-U*#@XFSx$kzmTgWEZ_;>27tnuir*$&+Mze z@Wwzqg(=sGQ>IA<%Vy8KRzhQ;DTbo(@AZd}-n@X9mI$~Jr!7}`Eo0nD<~w*FKYkA4 zI`QK5+-*^e)Ix6kxEyNUVa$BCaQvN*`qa&`?24AA@)UXbg){8lKO&=vVe*VP<52nw zxIs3w>4_kd`A~t(dJyv8I$=gQ6REbpcQB4ChBGB@fg~5-WecCrX+xrLc|sLYJ!&wh z2+AdTxGt|$lh8c#1e?kW`0r30Lt%Z^P63)} z`1aZ(pUDbcBbRWF=d4|Kv}0T`o?JK1pe5`ludL#2k`VRAdsI0Fc21dEpx4 zRxG=(Kc5%`22>>!HV?5qAj9IAu&)h8Rz>h=i(t$k`8P;5#3g* z=PCk{ag!ZBtsQHWpzTEq+y&S%j$4ap#dv~tY7_(+4IJZ^167b5aHV!CPPHLH7uwGN zwk4wPSDZy_{G?3}r6svYnNHgN;?}!PVyitqZ^|<0Lv!zWl<_vsIy>7%Oa;|_X)X?1 zXPMp(FDHQiX&@XSF9BM54E&oUqnV1HS7_gUb*k-*P!W7Wj> z^^GdKk}6s)w&5DRY}k%16ae}}t`OVyVlCVDkR~{jakJ^uHFgcM5-(l2V8Ka`*lk6+ zp#>=NuMkd5ZVN%Zit~@S)vs`Md#b)0WEpG)9xngF$1BCn!n;WA-QQM;Er~(7X6EO+ zy%@3QPj?a!?gaqi5bjn2ZHTZgnwGGC5uY|~;EyKzHP%+Hd^v@viQr;%V#&C~l1NZ> zUB#?6?K+-}>OWz_;!Bq9x_t?Yn3Z+fBRV>IbWC!Y}gG_ReEm`yBkE&(TCxG!_M{9`vPjwLWbuA$T=) z#fCdW5HK*Ufm}s6_7Lip>Q> zPp`)V8S`1c562=Pio6wXgxVzT(L4SFVpo|9^u55l!vho;j`noS2lZT(gpDc-5c4TC zT&}L3@FKvK;9p}hpV~KerD3tp(ef7&-C9jSyAIE#rcgGvHrPi%yBom|QGTH(vdl(> ze|%1yxCj)29hVB7uuHR8>DW;o$FsEt+i}zT6(d{S-C1nB}^M?U$t;pJ=2c@163ZaR!#0%Mk^TZH(@e!iSlW6IbT%}7(W1vN1 z^$qouDYQU*83&FOc*?dHV9#WIk(b z+CIOuq~Er}>k_Z)h_7lLnacAS4%e`HLLc^9U{5dY^2J3N;}Vx5`~;j}q*FOXzI7qn z40mYv6vOkdjK95Nu&oKuq}+=8sPrxdrvY|x&EVMbG(yF9DBSvLPfA&}-I{i6F?%=~1(Qj{3XN=jnioG}H2>(|t9t$d3Atldtb(;o`Vfi4QgG=)A*Hj&W4 z(laxCtFqZxi0abLvYX^y{g%q)Dv1%0ctQO7ZnxSR zEQ(&G9f7#iCzsw1?U;5{&1m@vSf#~YUnD z7^tsZ@ga$t%1x$(>A!0s6^jy!oxV+73!blt{E%GWqB@phLVHs%Dw|UC<;%meM~@zv zrFO!!UF)EQEiw5UZs$_YNDjIm7wFTm*8aqkPiAPGdh9|D>z zqv{+`l=E?t6}GmSnx;B!sJxSBGwmoMbx(irAO}1)vtqF08_x-gx-y5l2~AzLpzkMwF8t9IlCd7@!9K4VlexG487VDC zT7!nPK&vO|n3r*rEw?im=#sIBrNxFE>(Zf9rz+IM9$_4}HJ4&HvsXuLxMujIrk_s$dbn(`fX3I&h^45$zolW;ZFL=uGVMj zfO=-pJ=4w{;*X#6&(xVfXYoy+J$j<)UiM(mtvP%9`B(w;ozB?UbS8omQEo+jvfG^y zn}H;tn3gnmp+GKS)caTU=szJS&uy5q61J5s z&H=fTmmsX3D^Sjk-3Mr^oK077*YiR$cXs^sZ9G@+&jmaeVmFD}SuJx5(4?0yNkrgZ%LJMkP&@2{SHNBDnz2zLu|bfJtHqrp?snKUNmsxCfE2RlhmW? zsaLrRly`y3+O&4Q1g;Wyfh)8 zOJ>uhMkHnD^3anK}M@MR~ zTHk*_frwI1UDoQCu1fc-NtFBM#`#19*SNL{M-ep8BJTXd3R=4?va0f#aUY={efjnc zj*6(|P5dKp!bbJbzZ&a5qe)$+zI?f_+)Ok+ozT@>5)m!jj`ameLP+5G6112aNUN3N zo|9nfuq_hd+P6Kw3vebCS>`c4T#kKQ!oQ=VyNBm120zxfQ0-94znArx-SWfQ`a5_)Se`jgvHi2~PN7M!EU-s{&!86;H`-Ig!;8sU zX}Q_*z)N1#7X?%-i^MA6wAtg!Zo=FmDj>n+>S|~_>QE$>+0p(L2}UJ~FN)U5DCiow zG!~W036KRif6uVt!=dof}CAMaoDd%o=jXgPQcf!^w{#^1)z@()`FSY$aB=90& zQ7!ce9nrKb=WjSAR$O^Q5@8RpOPdq>gNybIsI2#%kE-Yt0Zh?usazGM?b(6%yr`|d&VNX#TD5~ihq50(lPNiLI;wvK!L~;xvV8t z8Do#Sx@yBzqLDFFNOGQL(Gbx;$6?TUY7FK8!l+$tP5eia>V{AC@QKmnF1}q z0h3IRPq(rshA?i$a`{Sgmfi0Be84$eU-_5gL+7U(5>3r&$aZPI$ne8-faGb@;)=gA zJfoybY(->F&(z^<=3p1Rl17cCy*vG3Cxg%Fb1xCr6&Zuu$ii|wc|XwbMm zP1={oFc+`f>T~yJzjm?xlrBk==TLN^1R^IXb9c(j${H6l>(}3Ea9}ahauN=ZV$F9v z{1PuB)h*9^3YeB(bJkwGwR*l#@nhHXkR3ZmHQ@f4lsvv9;X35J<8mc*bv*CZ>(>u- zzpe3T04jmzGzU0gPU^^2-;wFgv<#T>A#OffS~E|;%Ho&VFdl9(O%wnGV+bc!QJ2Ug z?d^jS6*_DN2+E#-(eVDiT(RB`<|nUS9p8QMEJIpF>g^PMzLS*MG_Z9=Z*oUY8!mtp zT5KVz2cm)iSRZ?bTOBgt&UkN*G=#%y4Sq7rwXM>g8tC_?WK9Ms&dKPH8kVbwWEx<| z2Bu&*H*Ao5PmFnk$N&+8&}5IFoSVD9_n<)^s*m#~;|5TTxQw;gjqH;F=chx#vK1@z z5LM%s4dQHO%xoSoe(fGCW~X69&mn{GeAJhzFjV2a3g`u~As;t?bZ#!IT-#cK^HJN| zZx2SNmIlL0vNv67B63uV6lTsef^LSLOM<=IKWXiH=lLVrzdPY>O0OT z4A>$Jp#S-<89ZY@zIE{7!<}n(@Ka)n&Tc+XoNQ)AE0A3QNptp~zxS9ZsE!X* z_oAUrB;uop=ooecZN?jJF#pKuN9a;G=f6M|qW}XT8M9(f5G<%8N2|K*Z;^{uUjIxC zoY@tIJI`c*yg<)T^^L+5NxY~O#6ap1pcO>UJYEbMK2~WFKcF#-=a4F5axvYqVc=(q zGiI!2FX(jgB_!5fwMya6_=mP*`yOLY`TFWjDfwnTasjH2T697ELOp>}E}>yF-cOdr z&Ve5jCJ1c;>SO=T6UC`34tUu}SR(-QoIP#Y)l#?coQG!$@dU+hV}{;`6ZoWeU|ry1>1nts!Knty4DngCCKqQkOv;c-f`gFX! z_+Ku75wV+vL%$CN%=;De0s-^EbB#gHf8s~|>!zOYFE{mdT5n>KBs-4u=tx_oo_mBV zL}6eQ=0D}fmt**+hw=8by+CCXtdqKrrE3VmQ5HLm8d((bFXD)CptN!flb{@-@RCa` z?CtMw?*3(&M3O^2Qj|=WVeVPUujQ*dN(~tzKw#AGu7B?ksa@Mu2-h}zWwkW}uxec0W0K}L8>J%lmoMy1Rs%pDLAzIE2T@HYtNtLxXXP7V~oJ&U& zye}kbnBt%y#G|Z;pzXP#TgV-#$(_+jYRIQulq$LYc}!^yhw(a!_=odBHJEdF0-o=6 z!N{Q6n~({fH?uBpczr~=Ufa%QG6#c*~CXDY1Zq6*f zy)ksvB}^8%6kLjfY(s=NcGcB6UXx7j9jv&U@MC%=#m>nf3II;-AL>4 z=kRzlqUF|%T5P2tt>R6?uU^$4RBS*a`#ST7sO&BgK8+mob@lc(Qgfu%wO8sX#kx;s zvLYe)QWkyUu|!y{f)l}4ZZ(suZ#*3Hp>N9{HCzExdV{?Mhe{({`W)=dB+GqHcqZTY<@hT(36F07 z%x+0m-A-le#s)7!<#)u5j{!{b!TzixUqZkI0jv~QhNx#88_=J0P(!x2!(J(&<8I1b z7G(;eIrF~uQSs#ilA)#xPeejGF4WW_nZBcCXQpW~oVI#l$&gf&zXec*1go#{?mC@? zix&B>oYH$m!Eq`WLMONZ0HCDKj#`65>f;Wagy3RxM~;k~1ko;@wEq3G;q-9I^zB=I z&CdM7l5z11#8(tzogB+3?eq2^Do$iRlPMDgOxtlg!;^rtcuMeN=Z?;{dYv3^m_7-%C2u$ z#hf)!%TIWJEyM8h6t?p>%IsdwqhI-6ij(r^&O>%R)}R$DSf}Fb%2gs$9RH$$?@++S zOW&2XDSFhQX_9Chuz-Of`fkG|N;XV+olhOqn-s|vV|32r@`#ZeayhgFGQ{Q9qLp)p_G?yCfJ{KDq4(VgI9saDTCa@(>lq(3Q zgujn~n_8qx0C*LNpmob1Ji>L%g#Uo2vU~Q`f6|r+PhC`yB4zZUTp+&FUA5eIfA2K7OEmFuY;*nm%uLuRS-o<=nY*S{+>UMm2jCJZ}3j zTye;rT>z1Vv6g`-L=Opl>t6`W`4)<*s3kmnWg$qLXl?rSwLeB!ZU+6}-_la=-g1PO z>^t&Rg1zu6@-Zc^Ri6isNv-+hd+A$0Rj;tg`!dIOTXt4efAx*60&BG?-CeH}Kh}jl z!(b|QYec&XDaFHAd)G-DKt~_s{B50hIj;VzRZweozpvk?mDq`uKzal^@=$v)WL%*t zTEGh+5}7`4FC+k-(8C+n6#eihZIJH}8$Sg67|h`keI$5SLON(VJ_3PD{&c%*o0XT~c0Dy@{4AT$2v@s(?{E z?sAY*jvYI8Ld2cdioe?)vuw^MD_7ihF)l8VxtX>fiuHkV5<@(1L8BCHKIyw>jU5}I zzC%ZiFWTRn{P}zM2>WeSkTP;eCPaO2Hyu;ugT0}BJP}-Ff&9QP88o`qU`$_7c=F2j zTlWTk^Z1C;{hl$O4jej!dl}E={amW%)!0RKvJny;xB5C+M*~GdAW38NiDJg#BgoJ@hydWq#YHs_#^Nd#(CwCUGGSXdPBCG-**A%C*Sajx4 z-FXYZ_JnFnva~#CnY^%&CejDRFe`$^P&_>O580Ran&Li&zO7JLG3$E}p_6GZZBt7WMP|-um|BE1%xMF<^kLyurWuLTOH0*d$7n-QC>h^L z>BS;ZHp8YDCh0HM&^Rup3laIJ8&<$=(jzWR>T@CJg62X2>ewP=gM(a7&BJeSiu z+2UIC2Ht*jX-S-Zn~c#giD~ss0NVwyyC$R72Yw%%SAzx57m8su#`HA6hgemf6a!e#dL;`168kT$DbObX-91FCV zE~(WDN}ni4GI8!oQ)B&3Wp(19Udq_yUZuNg+&J!+2)js+1v*E7E`ac$3o~>)L>B!~ph*i);~JCcg5?>}{ntje3n(`UuANWopEZKNo2HzNu0^OM8?s zF3V}djLFL|NpjimUwic(XK0$W-u&-XqDs;K;JW9vZxHhm*S>fF^Nw}Bpc`L#lJ4SC z`*giW<=<(8R&p|8m`m6H;CWy}fvXFTS&F98F|+K|^l-v>YWJmB$#H)G#DP7H`WBzR z_wUzIlfO@9Oz|H(BpbIsq{gq;-pdlc7S&QC*uG%88a!OG%vh{M$7`RY z-W<_91KU>3y|3-m@ceh5>zzr|lF`F1&zwe9EFhoJY(Cb5So0A?kXK)47`EKJZ>`+n z84Voxif*#m*6J4B-{|4rn?J9E4_{NHWQ_-WA10H6v#l zb+5Q~GT@}B5Oz(bXgeCl$67ta5rf5eCcZk0z--?*Qe?3ETZVK zMWGM4dJvn+Z|pYog&0vvYG;r6V6_^0f7R6vgd{5OxlPlpbo{b=_`zf9C3G_(lrs-wMU%h z6@ohfnDFbAeL($YA^`*>z2pwvdFk1F`7%aEo8z{qvHA2c$f`gZUO^zrz*^bciUdMl zRH(L=X4=y#wqXL(MzqhIVsp9M7N+6UF>LN50t z(#AHsOHKq)uZ*5n-XnE-$F_$Ot#tn{s=fmp>%EWrCWP$OvPp%IWUI`yv`h9%PT4X` zc1VdFNm8M53L%*{LL>>P>`g_Pk&)H=`8nr#-|Ky@uID=Ec}n;F|Nnm9@fqLm)1ji1 zi?wNJ;Z0!?BK`>CuD*NsydxFi0CH_G2cMz7g_-9wDwYk~1@<9{0PoCzG-?Z;EbQBa zs}$dghLZrf*k6(kIa+*>+%?|8QS?AGvsI5y`0S70Q|xgdATi%(vi;<75SaUq?fK^L ze2&`GLhm|ewCI^2j;{4i%c0YFDc=;;!)cu;$z{X4?#oOVK$Z`w1wc;xUxe3b(}a|D zFY2}#L(R{;YxhH~MQ#qdQB~jhmw*`ws=>^B5ov(@M&xUE_aPbXx8d^iX3WJn%zH}5 z_JRKLxxZy%zx-4l9$Kzrmk~P6V>kkYdjKAGr{yK=FhsD1>wO$J8DwO2=5k)g$=8R{ zozI6|P~Ffr37vG2ZBiVqEPiag@z#lBy^qT_78IuKUh7rzr)h`3WX-#G9}$P&2P;TO z=z&@IMo;+RT`EF05MYvU1L$80Fda&BSJgzKxFEuID7l*Fo`SFQABT|@-z^b7$y9tI z0z&{wQ}v#F^e8z{|6jcMeW4E&cVn@;zwIY748(fF^FOZQPU+twDrn^3XLt%lU}T%1 z*3Wzce$FI02(n4G2O7sW-C;_@D z?ybjA&8<)pLIz0vC!xT5zwpgqm?OlzHbG4`>3$eopGm_fF|S(VaVx!POfNSE9Pb!G zmA|q(c@`2l+n&plU-r4_IH6SL6%Y`JN@-bFzOmIC%g@)B*D`jO)tN4N)8TPabOKs@ z)lc@qLz<3(@0wGF5EQ0UX&RQm$3P)IGEOwyGMGr*}_?j*T_8hLj!uP8+X(43qEwb z^7BJx`&;tYlP6Ckk^FWq{fdz({BmpR+t9Qp6hUfEkIN1O#L4roK3-gzq1M5Y=mPcSh)635F5Y_9zP&iC2 zbU>rhc2i7EYuoFNZQl*Xv?-VvW8iHPIndhX7gH0A>5{1<)iD@uazrxL&d$!ceHRAE zpbB*UJ$aV02iGJjDvA=VPlw5P*i})`S+d5kfwp6&HwJmbSy(|IW@mF^ zPG6!6)M%J&rh8bH01rdXljfJv|S64MKtq zrEMcv*0V53Y+z&(5)#sXe9WW&kUnPnd`mC!7`=NuaSzgs!KIN%E#I^Y*bc^QsB4rE zl#`Uy_q>PO_>>dIrDAYiyM6xI>2AoGqiVl;=4MiLF{l{N5l>__284}7wqZaR8DY=4 zapUkC=ER$snX;FJg1tr<(sxiW2wiyL)M-JgIR1) zFEzO1bl`C<8i z^QSD!T=-#9;S>_;d5)&IMNCO)Eeg-DYuB{EGIyAiFo)2hYpDR)0T{!Jwtu@tP6ijz9^PBZ181lc@#6r=RC zCJsq(zqj21iaG?@i=XI)sI}tc=x)U8mMc7Y%1Kw39{YO?PmLpie*nxJ*RnOPmP|n+ z-%g3gXEn<@fArP!i(L2`!J++p0r^lW9;!b%94m#ZP^XhNj3-lY@jF+y-ydHOQ;2rm zm^U%92M&4@+(~+KU3K;9($Z2&`cVeBsUN7-x=A8%k%??skTPS|8_uu)pf{%f0FPq8 zJ*x(R)wyzg`$CTN?C;z*TIl0B+Fm7pRimouQppD@x_h__^%IinHS+QAyUvm)V8$?=5iIuC=noCdaYaIQ zLEq~^+w<+m61Lt&{rz_^@WnW)kCVhNzO`$Co~*-9)xp3)bk8P68_7H582$8=Hwn5L zvi5Alnu_5*Z$Wf`G#8d*_ir*jRE$ryPTnRA&xr}bBRnPsT3XtNCnQCp^~`=FUW4V4MHL z{(^F3(Ei6-h+89fSpp|)K`)b;;*{p`J*aj?q77e_;t;N?cEPncd+@FT6Z*K*K&HP-rP9RDL>?Ib&U!{#9lh}(!v#3;rM@K{U zm#3-(H2Qq&xKa8_tJkC3&*{|^tPtP_-iBQW6o+;F%(pYCj3_vNzo6N#; zwY)ON3-dFh5g_?20d2}O{Al`qu@zfs$)fP&jbtT7KhST@2fKVtjJV0U^s64PE1s^(=dEin>KUG`D&T5nOP)`K5j8(kByCZ?Lq?1BA|YOyi98ONm|*4 zskvJ7V>1tnsb$R42c%gf7#h{6yR+M)D86->J6sZ3TROi*{07zYz}{rboU>(z90SU!eF(RtKCHIa>+ zJK)3dDJ^e6X$&{6FG1HZy$vZV$5{F3mbu@*%k30YGtSN0@G#Tk7iGBMtF6i~p(okB z?yvn)peqrR-L0GYDcdD4Doa<;%lCCulZmsLgUE^M5Hn{J~aR-i^JV_sakm3HIrDB51X!}mGNoo$f zkm;DM2(Dc4s_Bg9K+6lG(lr_LiNeH zM;sLs=sjJV-*grKNsMnEe|5xs)&0RX9|^6L+?-?o^!RmbKOELjGHq|}w6GijV@OVy zX)p+#H8C}GF*TL-|ETuo1#8qRYva0|W6Qd7G}uexpk%C;WUT7Q!8z=fngD;f{haQR zBOJ#2e&dUdXB~eXk7uZO2lL+zmXcnotiSa8*8j8sYzS)uU`o8IGN;fTOkMaBo6KYk2`dY|o( z#e)W7YYmhV^Za!XiGWPE`$`-4ILfUXD>@gWFFw?fFAF8&D*So$+}Yi^d3k)OJwVp$ z=;+WT{Ooi5;;O>!O)8(@uKc3O)~`=n`QK}ebeO0(PM-+wq_)3t@ITpG`ss|clD0Tk zwr>GfV}?`z2JCojQ+U<0H0GIKyae@jt)t&qr^?`3eGW$pAbRhaM7Y9R12>@AcjT1Q z2uFYyQ&rvI?;G=^WjGs5?1|H-sX6M1-4zk!rf(LFW(2#uSU>=R&9`XTfsr}Cb>e~BQz4aBhn*cMm`j<$`*kTM7EUzDVudZlN7coPqYjTDx(2Q%zFdqE-E@MR^5_vDUFDnCmY_*yslX&g&=TkXQ8kR#a zg)2*sN;m$XZ5+)4rkXL;>+VNbDPZ_v%ybwZj}c_!9E6}hv6~LjyWDa9J9yFw$gTRp z$X0^03BuaT)Rv1SAF%MPO_9roiF{ubuj^V6^GSH9h=}yv&KE+uqJp}xP5rFHF=m4} zfEA*vEE4tB3JS876$xLZ@^MZn0E04QL)mimpahZ?<|o2;vj@HYr8T5DD&`^|2^ZWN z3MMi(J`5r;MvM&haACw8{?->~*T4?XOSZdKEiWx3?%LTS@#_jim0m+hq3ygranfhB zv{vdhWy;MnHlxru+Quy{Ejf8FG)sLH(D3=Zbth*q6~mcHL0P-%mcF?3B~L7M_`7HJ z4`)P`7q(qNOL!gY6KGEb-J6T-kHW>r(rY+O$%jbYHJl_R2IVtdQxc|2)-v{H9S)oN#df2;+Vb#+392(*PU!J}2J3{q5Ti z^$0uyYkYV_I{s?Srzf;TqfyS?)40S(xE1Pwl_$#b!#KR&-%$Du!s2!ahe&C72m3-% z+O*E#b6TNoTY1uGG1dZjasFYYjn9i`jum}d@g;5Zo%`WLf7^I~lu(`l$U;CIMkYto zzqI=7KRT=zBCM*W-Y`JUpOmT_e<(yvlxBU}!^XOu++=?tCl?nj1#fx+(nP%S_=9AZ z8e;O-Z_MNzIiWyL&f(M5RGM4=P z?`IKsZB5q=kzY=&;$VLrpuTf)<5(fSa{eA-!yDiWTfelX^N3MlRaThiTz@0hTLu-7 z7u19zd=WJMg^4t2YG0umaXaUYFBcrA=_}QE1_uFkAS2NeT!vZt?fdt^4;~10T$%oH z5*tc50ba-hZ4Luh#AKV(wl%l&;Lzi$Rvcf(WNWYMWeKsdRWh=MS-rRO{ZMzeeZ^z} z<;N1Iv5n*?cHR(Dgp`X=^+uu^bB)!JIB34OAyQ)IeN73?XwUx?#=DqYK+agK1MJkCh*hj9T zmamt|dH)70946rKqotq@!V>J!dUa2|U5gXi*n`>SOvMOU2)Ra|&~MhxV<}Vh|MewgGwOiLmoHN& z7~v~zRS}D+)dH9W7}{tTd_t1GVQQpgsJlcBjw;qdld+des~H$7FkkfXIFMTz91d7sA>CgF4w?I+a%2jh}W|$o0Z?;`sdS^xWLs9(*Es9g)3W%lfwr#Aq%2qp4`LUSFA!l6z*+q9SQI!`N&NG+Y+}ymGoB3xe zD+MMgjB5`ZF*fxcnIs>)e5zP@LGkdq{GyE_v(NKfHd65Lu@O=2vJh8z<+f;({t@86 zapSoHc=WJ*-GscALIL3#2rdDWS6|yHBqI+3oAT-p-<);%x2wl1Y>iew>$*M;1aAcn^#5ic73%km-_ZE~Mb>-o&&R+Exl9~&AA%SSd zPDqvv1d#=<6QGb@+sFqDCs=lcs8h|)%;r^j0<>*)!RS8?I!;mU~ z4g_Dl+K(p=>4-hWn)`iXz)CTZS8K$>%p4NP`U_KGdl^7FU}!Nd=5{;x=jn?F9(zh> zk$6M2QuEe9i6H!tUoPxf%!~DR-?5gZ$Pa~d=NB>mS4$WR9}Ww;v{D|7_`r=Rgz19T zBdyseNp-NZ0*rTMUJJI#An0L-!(RMP@LVLFN)s?jI3cNPV!{vgmq=W#3sQZvaa*Y| zv;&On?5_se99#?UF)eDTd#tJ$`}Z~3yLh0AP`B*-;rL$_(R63%dfQ}%n3!oe!nK36 zX%P>&SZYQ`>7Qk?n4>gC%5^ukVv^m;ScZWW)P4_9n|e-red%=<^c?8j8GrB&wunPL zKx62~134qg)2o2yeHQ&c9Ip>v#t_FlJ1>wmdN{rK;4VO&Db0F{va;-;hYi}4RJ>}+ zrF7PEsCb2p268+LYC&&YAHrW;KcL6fJyYP^M~k1GeQDMa-Fimd01!M1>#1p8 z+cb)qK2#}zf8F|uQp-KWi^9i@9&w9|416IriWEwX@lgd-{~3Iza$N1C zPv4K2n%)2_io}S>C?+reaoeeLp4fwT)YO!fV&EpoHFgfAipTSF#@&uc&O=0C)qJX)OLANEQA=U^{TFb<8?T!m>#YU?rt8~@~-zWi`mM5`OAB4 z4Qipvdug*4zuhVNnm%EO?952ApoEa7?pD24ZSALFO@h&;?86SI}oHXy(qq@Zs>7&4-hndP?8E zdsjiwKI5zF@zw)}?7d7j*H5W~T!}ye!@)?dQ`|^uJBYGjH7%ZsY+;cEr5A$nJd`Zv z5^Q25+jW0JAY+9|kA@KB^;U^FiS6Fq zb!uy`f}rdgG7msa024v1>MPY)%EjBIx#(%Zz`e#8Ea$K9c*%x#9NfLGu9E`OvH!Nb zFnK6n9O8g@HmT9h8IqcQ7;tj9@6$BK+sUSRV%~|gUeeR2Ot{PtKxhIOKogghC7P1!+Z~&Tz!|FfjFmr6x%q4kvNs2N1nyoDk6)z^O3#a~`o1cFXa0c*O?~|ha z;#~MwlGpl_IPmp z$9ydPVkUNd>XJEj)}efU`{WtMld%a@K&#$vvKxp!cEt4j_0-{E7{g%S^{}f6!00*( ze2fPM2dhm=P_?tQrQ%&*xv~pSx&5GaWJJVkG)2+a*ot=TnjObFp2j_#tSkRT(N$26 zB{1vG8sxpV_4No|=c%mUf$6q>1zVlT=cN+)8V5QXYB>xu?K;-W%Engu-Q(QO+Kg1S zpX##8(W~ahEyg?^T=Mo-!N~x0gt8WQ53KN2us`DB;uoK_WKGY0j&ZfaCb8`h^^<_F z-hiEstAP!*7P@Vw^rHK&!iQ|cH0RKf!$S{z%jTEWc7%T7<_y=Ns1N6ivjs2zUW;oO zJo|f`#djFm`U%)gUt48cz>olQq$*GiQgM>*40;E{7dA{}4=S6+b6}7tuN8Kcc4tM)w&>ooPhK_D7Xw3PHU0i&IT+(-(geJ`_j z`Oat6;xBx@r7?r?kTfSVs12D*){G?oVEz8fpqw^ESffG_p|whGlsqEx$}E~`$~jS%LG$cCQ)D+1kzTvoGg z2!>&T7YZU|K5GNzzybjXtmGhGmp0C0!7xo)jMfpPj>4z1X4mg8++-El9B%dN{zGFt z_~sa@CVl1w9Z;0)9j~!*#{xdzxla$4gVAb#yc0n}*h{o>3-}{3re=8PZrWXp36BLS zV1@3Tirb6}TKg5NlRe#b2B%=F7>dkLy%ft9NE!D?^NDTRROK7uEdu(JWV%)|39q;fw!aSfi6zQca=+lOJPzugT-)~JZ zyU`Hf8Vvw?wd>F=vr|Md~XD5FVD1aljxbX+(g;;rHr+JRYI*sZc?4(g*0g9H`U; z>~I9Ph1Vec@Gg8V!o-o*i15va4(Vdnpup$>yec$rq%Ol{fqDD_mM6o@mzMSu;FlGi z{2eSaC7Zc(eyqGM0)BucIvb}}^g;U^yRK3~9Vt&IkZ-#t6wl{w0Ls40_-*h|C(157 zQtH-d#kHDPFF<`ac^xJ4+`l}zj~KG^FIsWp ziBvq<@hgL@_(;F7qf`GOl*|;1#3y5H_;}#HqCN7%tE^HtY_zYQ$DvJr{+tbeKYpR{ z1<4*}YKo8<78#kxeWhg6?S4mj$3(L4o~$w?)FniMWFMz7qdwN80(K?g3ae{_V0I(B z`2g_>w1=)#U!?BC2(Cc1e3p3j*seuup~0C0mYBpl;6(cS_WgX@^1~6C#45Ysb!MW{ zhxtcnvv_TmU+9a|HcSOx{$uPH_xRXT;goqi1k_bu7J7;{yEZ#Lq+U4=V3U=Rk?cFA zr1k!_&{z`v;iHd_0n%fl`9p3dHnullVO*dh zA>!Nw8iFvz_zkz~@_sSc$j6`7c%YWFj);axBv^n=2QUhq@v0rYrj1 zy-v%xoz*VbNLK+jbtpIvOPI8_Y}-a};a-_(m`(d<*6&X!Iy+L%1+HZv&}2ge07g96 z<}Ksxxod7>&cz|8Ke(^iwV3c8cKm`Eu>kcBfLLgTCzWy@RQyIn)y z0}y*|HN^tODvH|m#OZ_Qh2L%o-^m=#Q;F^TCX!(d$*FVW?UCk6p4K`x7Pc84+$sdI zo-xI2!|Px%Q%8*f@rrZxLA$CA1OponU0|;tL0z`2#W|n)qT2nn&m>$7Ag%Ib>=oa0TL6 zZP92_B6t>OLUY}Rm{AEyCAPo&R^N2fsQ+<>=ED1{4iiluK9qH5Lee#WY7}o%_sp5~ z#A3HOxFajP6dQ7Z44|iIY>^Pm4+TO7KM^Sb!KaH!N;0DzbT2G(T!BE;w=IDEUd}gsg2|I5;?ZC#p-nUNJ4L-iW}E4fP6k zBKG4s5S#~yh=r%(V=O@Ixw5d0RC^v>=uq}`Zc^n%=)%TwTff!x^Z+lhp`M9Bua1LmeDq33Ny2v6>EFVW~imI_W zIEVe?@4sFE1_kOKYa~5(7n;;pcH?2Ezhd59dqDexN@)P@t@UYo|wHM+rr zdiKa+5|RbQCTPnrTW$jJtPeA5w0wOvuFO(<+8kc(%CSed!p28u%Mdyj&l4w4_Q9C( zzH61?+S#mapEV0s;sAk!(2Svyfd|qbV_|#YY|}v*16dl8ROleioX#{GlZhiqRZhLc z32ELRz%H7&Lx)%~Vxyz;O`hGIL+{1gMPq6{@7_5Bo&^jH z=K34+3$%mr+Mu~#95e^XX+j0SiqRJp=&)OtP;7AfI{dMJLP_>jj?{+kz7bkWB2@y| zl)K{;U;%z*XA0=8JrHHep;rT$@*dUGl^RkE-~?!()wtfoQKV3k{bM5FXs|`t?O>k8%r!^zF}>`MU>P ztKZc%Y><^v;g#~2Ai#sxB6q3Up4@E{SgAr5|DL}6!x_@MM775|4jvcUZbb(>$trB; z1J?z8IX|daa9W{l_IlW+j)#gG{IYS{1%QbtNHo7GZ{=bB#`fua?nKltp*YK0*N@=j zBT~_oL-9ySKYlwzx$dY$j3!>te8FnN#K_S@XFYlD-*Nk1<11C|f-*|kdr}-5*>;%} zxc^TJ03D5Gz9~vVcNLQK#*3F{|U*ng?`YVgy-fB*bZaESz{Ow zp*{p8Wv>ZgqO3ri>4T>bl7JAiOe{+*vfk@8%+g)|A;+Dk(1h$6f0UX^RA&G?fEbOe z%V;`Jb`z086wNX279%Tbi9@y$f-id2YSdlc+4J@4`1hZjn@Byr>F6*`~m=DE|`f{`W?VaAp2k2D~d@= zvp@-Dv1(u7&_Nt6A}2c(O6QLT$;hPc1|2;;QSXLgNA_X{?K3-e112nTXAy!FPMxQ(c0rccMU@S$W(as9kbT+i z>u{I|K8SAvY{v?|5`6;$_z;I2G_IhyxbL^@O*Ur(B`ZfX!_sOC#f0bw4{pN9bRE?u zz(PH|gT&t_NK_3NnAB~?k$-(RVf_x<0@s$xKcReQ+ys@3ZAKy10%~J>yihsvHHh@+ z`}ez_u05U8c;@wwZG41!fAd5ardkfj1GW&J7FYd-=`1XE79St&Q}UHIvbn)`CPlZ| zE_!ED-)Ka-w|0*Y4hm7^jXDmu{YHy{0LwSREe)~WGBe5_qwHvs)=30_~fnc zZ#18IMVJ6GZlXGuY8}1v99BMpHZ7+!H@-c71BcM3_Od+HpJ0MU~%P?RGp)E%fd?cY|%$9UwL>dWVFyRU&ta1{W z01k-2B>x!S|GtB2OF>gKW1;6`-Ddu`P-?a+L^a>&Ly^`t4W#9{OoZ?~15HgDr1RS4 zQ)x|T-PjV}>0jiJ)(^&zOH1HL;M_r$yg@37hE5VM13D_qg4#gxB>`b^Ukj)m{<{^XbCdQ-0S6~OgduZJotql zbyW+mmL-hb0X8!nHa4>##$6Gh=$uvumHUG}O9Bf^YG_&6S6?rnqJ|_BQ3z-vV(&|% z(QP?4yv+2-KB2J1C8IJYvSd^m#mSpX1TOpfp1G1e+G=|Ico6C=Yuuhu!CFk8ibi#c z;e(x6vW03t8-py+{G=Rm>cp5>!jpkl@~j;;U^8=_xD}M|7HKRH0MWOT$3&4yFTwS9 zi`wSI?-csHh04Z79tT}FjI=~^G>2xGVn`ni4XQOR=N&oAK(+nu%85&R{*sR;?OvY# z8^Ac*@lOD3Qwj^$qlSXeh=hN5uBj1sqsk8at0-h9MDn z+7^y#Z-uNW_TI4qa`r-Irh{8Y`aHmhT^|Duk^Iv#;2PkGVmJLLB9{Wjxr9Pt|1FT> zPrP>myZ%9&T$|+16`Vr4K(N#!cSmqittNWQ@tNJM&sI+3%UL1r5=|vkwS4%RW1ED$~eG1^CJ6JJBJwUj@dyLR?r=k|eNn=MQS%tVr!4_>&g+ma%qi=RT z{vuqDb?!JL?=2sF`rht7XXNyt#>=USIBX|IN^2WboF=B3en&4ne~I+0 zkWN_1xRDrAz$c#6R2 z-7}4{YK@;1!6yNc^!+707+iVaXDx&OpS8q{9p4l~J6A(X<%Q)I2yK;P2mYfkcp8Nj ziZzlUHJjDqWc6fdm@07n%FKPawzjt8hDX7!XPXJJ}1e0ll;czIwT*D>%H8n11-RsU;fDgnJXI- zv21v4_*TT_0-MNon3;)!9_c_AL1g1aEEYyO^LYPFnGFhR zFwppge^77Gh5`p33)ez^Y@`RN{5%vJ=L@`kiV|iNH4}la;XWlX4<&p==)|K|8nk+J z%aMXqNGRa`LtzsM1d*0PgacRpAR~}J3WYHyDzHFL3k-L3mme}YxpDtWu62Nv-o1O5 zVz@gF&+~0Jp-)22tH* zl41QbZ$}c*u>FK4XjPW#@zTY~1L{-ncfe%jua^=4{}J*pBXEFXj8R83EB1erqi0;d zYjIckC5_MdMSQ1~y>9ZW1j6Sc5t_&{08M}+9p^Qo_CLnOv@+iy{y~SI zFnZ<6m32GM^T6#7GOlIgF1Cg#(ja5N)lE2$s15+oNQHxW-`ZB)Z%Hn9o$#qHm^N5B z{A_0Vo-%pga74593QL;4GbQhM0eQ_LxVrc2Atd5Nlt&z+7RvXZB1NXMb1&uDvu7ms zVYma>SDW z*+DS=PJ%mp9K-mHl-9$pqA_M8ZNz;7n7;TxPXr}oqLCiSOL<>j)YyPz__)@{= z`dkBM{gwy)TGxLB`|_@dx|uN!Du|n~1GW|(hPG)t?2+FtN~o_zgNHVg)Euy3kk%Aa z(E^ad2%id4jOaN60|SqoJ{<~o35CqoRX;y30_fDm6W4jk`iIhBhJ;v zbeB|xN!p==jHmoD-6I2~KfI=-7jkxXPUZd#6U}zam{bQ*L%fVcDg)_D-V$kJ-M+C9 z5{a}rU?y=UUtGEmwF;)#f{PGB&4a-k44|87vg&Wcm@fs{XL-L89)jtz`oDNHiW1l$G0+IO!?q>imdBU>Ej+X3qB$X5E7@nCcLt*V;bcZ#$-s9Zp{d2ZaRV8I)5{Ma!h zzKgH9>2W29wgM*fu5&R1$}8QX#33!vbPg>#|E0z5osOf7T4SaqD#-zKW;9yxzhp5L z161=uw~`HO&5RE;HUwrA@>|S4{jJD*IN+nb;))y=2Z_eew3QH~W?0>gju;_bu?kV& z3eM>}8kam$-4Z&t5gW{!s4}TaiFi8H@rmJ5bY_&%YlRF!G$=br=p|%4n0C3Jvj$fN zahW>8O2^(Wlxz&h*YLVu4b2)F!~k?A@;_&7Z#rZfK5VqJkdME!sx{AU>V}RG!UbwBsA{J?J1E6`xRro{FP6o%l@|kED#K>O*R%7?2NCluXSs9l*HB2m()| zWBw;0?IK(E<%j7{pK1cQy5JAgCjgSw%QJ1~P>Cbx1KrpI#}2ADXQKGQw4*>S?|t|y z*9r%m4mFma>Ss5oRhU6NMo-2x@OPYL>oMWrea4Ke(+fmeW%U-}Wc05q+_z%>q!^ zASXvbSc2vu>7RcXafd;#qDBJLLO?vyipIx(ANz<)8%Fhqeq95s6}mD`0Rab3jfN?h zSYC2pargrXYOc!#+WffLOC=K*Y8R$|an~TBii*;Lp9B>}YvEgpOJ;{?WpIZeGcL{F zCQDH34X5dfa&ixWEI_J1a^gg#{P`-Y?da93_)9U8La=aMJRE8SYs2LI6^3TZmNM7@ z;th=vT+sl5S-yh(>8mgYkF-G9+}xzZgXokXnivUajTZahKQN(dBITeE+3r1Q15Jz9 z;$R4=chyYBdYAAsqM&78#tvg$L&J4gAy)UC?BobtD>)D5O5jvVzBZfLU=oO3uJ8%W z%E16M|3pr}yk&q_!9k36(jIYn7{H}ONVP!YI>FpX|G!o`Rn=>p4yur=R~ghmP|)?%EH|BZr$)2qC(I5ruO1&I z!usRKUsrY`Bi>WjLns%@P2zeaytAitX# z7Y{O7P$QVnA%|^DK$AWt-8}GVz)=WIH2};C0L6rAKsUZ4RS&Qr-}Wf$S! zGVP;5#bXd0)oa(V6RrPuG}wTc-TrInq| z7^_O4>rQA?9iR1?9q;cM4fTsaEKM@iBeqm;(h z@X}*X|6bbl^n~P0u>jGKeHL?pgwU7bdlA|LdbTI!~y=|E4n|p|kW5<3O{q$d~R|INO-g|xlHtG?v4AN8&x)}4hWyPz`-K&@$&Lg(sv6Zg%X$I-}@gY zNL>`vQaOSlaG74PuRxbeN13a0x)Ox<2}Q*pnrau+fY9KbJ9D1Dx(_@=C8Q3WXYxLY zkiOra&P+tHrdwu$7D0{etn&kUyI-=03%QxnOyk=x!(Sd0W))@6G z%&%ZNJ$aHBj82=VBhu68wNk~tk24Kc*{w{as3u32l%SsT7DValA=8729qx0~Q5FO> z3{?#TOnZ-r9z9Ui0Hz^2RLFTpd==skw&pttLBV#!*!Y6y18@td>Q^DJmnI!=Pgwc5 zBG-+bh(n*t%apS1RNqP@N5=v67C%lZN=n+u0S?4PwBr5vsuw-I4RrNaze6S65A5bD zX!dFsqFW)WAo$fhSkxO{_w)#XOR_qoT@T? z?u~d~h&XqKE`dK*qbbm6obrLH8wfOz-!HC(i39o5yF;}40?%DM2W13+llv;ZXrLod z>?)3@!aBfx8ERE@*9eICZ{sb6xoM-B+!g~!L3a*bH=D|UaA-iOf@@dQ?q1))mF zSAGBx9ZI$+aGod&^S#-TZ9p+Z-@SVcSg`+qGUQ7*ll`C$b%564k$K$*5p@*~bZF$? z+!SgEmUYs5dO}{4T38sr&3>~ufJ&HIj#q9wTRwF@QD%?C4j7g;dQ z1Jn9g5OpZ&H~I-=3%9`bwPhF;!hxy=jW*?!$OyaN+$unUohNn@IRzuk1*%erRfH!p z$2|t%2FZVyWzt^BSWqmqgtZ12_yM)3N1I1~!*Nhmn)t4eRU9=kx(2!()oRU{A{u=F zd>((`zlXK}Cgp`5MQf(4YdtulgmWRlEg&fB=<|n=2&2_(E%jByZD_Y=M+4{MqY9@9 zalpaT0P`2<95!r8$f0IvC6yuN5x2dfS}0QP&^n2jr(N{B6K_Cc7CcN;utz`-qd$bx zC;{9d1kM1QEwI4W@p=7kMwe#?jvwd7Sf857+uB-MgwrI%HI6*s4lfW;aJjpXCZ;=~ zW(8NG9MK8lNG)XAnH=EETED1q6eM)HRAtXL3Shh-gH-FWDsXm5KIU+x@EP&PvO6Z!foRoMc7tLBP5F9^x^27?`uF(#=>|1)SBNLYs50yvr@HxS}(9zb6M!Qav@ZsqFkKdPPKvJ;2 zG3m$_zyfsNdjZJ2FLzMbkeFE?p%XrFhE~i3u=fD8OBN@9r^73xdAvATOo9L!`IZO%!W!|4}+Z4 z4tQ?)uXk7hzk{ulSj-0hHH|r~&#*>?Vs7rVd9mF}NVR>u7L{IKJ{}vvJg|u3Kw>5U zkAuBA0#xt&d(no~PUUX<(md~3XQv>a-E#Zu8p8z(kls4xAzJ0ZBbY6P=oG4}R$%p0 zG0`9t)deA$;9(lw^@ofV0Gf(aR*8l_8w1_>)xAE9QBk51`UJ)eGNsrcqb>FbgY4M} zW|z+P4X9)OE+3YF-v~mgw)MHd-jz;&5x}$ZLOwI|>Z2!L-QC@tl3wpmng}w^Bq7a> zRcqIgv9_GmesiydfCEAli$QMqha+dsFqNr>ndF}5C0;drzMKUNmF7mX;|W_@2!Y~h zxV%DI#be4A%rS>T9sSGANRpV-nqCxyHb?ffx3{wiu&YFB+u3bFB`xZVyL#`%-04fS zPM5$bfr!nK?tylKWT0skP!MFWK`Cx3xGqrO9)X$rA6t2U#c@UV?h%o%;ueqtd>4yD zC`1y#EsxR~DGdY^k+vd5_d-qbA8$Eo(t)WSZ=*zrPZpxC?=Ou^=~wcOd*^xWwR>$$sbB5PYM1q5sV{#H^4_Oz|Tf2jv&uFD!_?npIiPCAUZM&Y+vA8>g-J zVEm}>_~2A`gD=37VX#7vUxl`8)|n4*g|S`Qywv32Elg&53rl;|xCI0(=_ZB0tZ(c7 zPYZAhvtu8?XBb}X3th-ra6_xC3j4nb(`WJ&3)4G^Gt@-Oc%AKRr|Oq?T>ABQuZB>$ zMHKu}2!7C0@UZrbcr551eu6gb^*i-{{=BKGGId@2Y+ByIyKBYTaKy++@{v=uPpRO| zy^KAP+g++ZvOQaDI-d)65*feZZa&1j(_Z#NvkTDCu>&~Z-*tIiiP_;TJY1qs)Ih4r z0ugQa&6|U$8KD2kI{rJ5NTQ+X$5;=aib4@3P#s)vLK7D`m;>9tWMGPCX%I|29`UQXKUyhDXFx+^=C!(qX;sw>3p6;j= zf$bY^XjMK_=4S&oyx!wztrA^Oc@|60vuBc8NgU8_p*Lze|G2dDKJM2PrXJp621>yLJD*0r(O!tBKg9peVv# zpN{+$ED(hk)SOz5j?b@KZsH2Jp~yX?8yE2G6bbx-3Um_V2)1Kyz08hsFh`r0Ww6{M z;KidJOif12kd-#fF_iAaYRKfiz%M@lvQ1#G$MKUVt1z&gF+>ZZ90HrVVI**1xiB<0 zgS(dpc9RlydqQ2haXyrSfA(0Y==|h252^~-V(9D{sofTGjBOo97x7Gg8QBYN+*pe>1Htc4@q|EBisThC zu}xNX^E#Gm-yEJzxGVE(Ii}V5rJs^QBqc6Muoif9xER+r?A#yL)7sj4K+p-E3(Roc zjC~T8NaPrfp9T5n&t0caotlJzAQoAKw9;1=2ZY>aM$V8YjZj7bn4R?KQ82PObo=j7 zxI7w))fc~Y#ioq08#ND}Ml(eYlW!p+3OkjC=_l^%w|J^s8yOnD1!>oL&N^0K^#wLH z85tY9s3ziqfGI$A`aV%D|5d2Ock7F)0>tzH?Kc@-Wg(}J6bqpyc(jx)%)~PZGUF>6 zN=!|i1h&5lkb;h}@kTNh>Yql5Xnd4_@ax8#Zr!g&#(|v1V5eD7xo~c7uGZW$+5+6B z+Q-v4^{9#f;#538opbHer%&razG8!gu$Y+k-}{I5ef~}KKS+oS6$jM*>CaC}(!5@1 zJ(Y)NeN?g1M&2^lD7#~nWD?+BdMP5<_oCobTVG%L=$nI4#%&;UJ3uP)v#mxU2-GDE z-TcF)k{$@tF+o;*>sI1s!g!Ka(3vPfDCn;OVQ&iulbH><8BB+gy5z3?TD3}Vf~;`u zAIAoUuN&D7&e!qyi~PH;ex8~$Z7$|X(b zqbKl1kprk?q@SPW2?3%dav4pM)cIxA#Cn4cCWas_mT~MkS#(?#K!G8_^!6om>l!nv zMUtZ+gr#jV=QA4y*Iej+V*6VWvjD;v93gKdyuNx{l9->r`C7=-$|^sCXNa zzi3p32&emD|HCC(%Twh z?caF$yKKr7&eYb`eTB$+&w}g5X$xq`Cw^b1B;FPJ=Nv;ya&qM{&Kf1Lg!E~Ce3Hff zwXYr)6x?ZUK816rm~aE|f=0i**V$|z$NUXK3CM12KjsU?uFi03nfnKC?Fs=RMI;z z-MRL;Q0J$&5*P4+9yJ(?sUV3Yq_eIo2i~LOoYcHMWQ_iuYNAZP#OJsmo??w^2s7uv ze7}Pi9xoX}Z=h|JFxGxfIk%We_a#8yWP%o@%M|oouBgpLywwau0j_xC9%7^?;j7ZI z;&)^mER{E=n(Q+$7|U;Q+pb?OMK7ygzA;?W^%1k#`urKzV-quir>RJ}j`oi+rz$F5s)f`2BI@(df>33M}C0GI5&plC=WdjU+q^>9_EmY^I2{7otR;a z_~Tjjw@6IjY36SI2l;xgBa-w^Ld5X}Isb<%d?#58R2e=9g#MWNjk=5iR6n_P$F*C} zqXYBLA^a1q7_l%TW+XS-!HlSmJrIjn!Z|wLH?1hP9J=${7;WL%p!_N5OCbAoPKD9q+FYrr%(Y~T{0`l1J)j4n ztvZ0l|3Y_jb2FcW(+xfAI}?no08gb zvIfS7ETfEmpIo}i{L<-+D~E%l|KIcS*}0~h=6Dd%UtGo;r@-F^UAss5!UN14T_n4; zX1{$9za;}O401p-g}weWH5Hif{PAYWEfxjzmY5fP+Ura7UgBUy-2$qw_A3BwOYMQD-HWY(&@H{_e zo8R$sVJR_hVA@9o>J~B}nQE5$2_gdN7gw5&vEyK^?ef&b-KxusY>wb3QxUWvEQIYq z=Z3|}d)TQ>7*xzgvsXk?fD+3J`jN${-G6rcS#0>jFX4}BM90;eWnKBD+HLt^dwydA zO+QMnW%}_yfLX7-8ZQ`#G!sbB`N)f+<_W}zn4e4?md`=zfhx<%eLn9GR^J|!*cO3jq%P={0{Ya?1rgzB4 z`1pzxCE+Yk4@Nx|ILd=z#8>6|(v~|;9_m0g2L1p9G12jWzW&hPKNE02;H1y5)c^+t z4|^@Nd{uL}w!tGvY>$hJ?^(!gxtm|u<%o&ThBJ4;b)V051y+iog7ST9x~#BR3VsTjuHyo0G4$0UsT;HC52wrT-qN9x>06)x2T@b7;Q88g#Y4UCM#hzo>b)6SJ5(-)fZ=sTFBclHS1(RKZH$!0p zxruv!4D&-ExUyHA!nCoUnSbUpEzguh2b%o&=pKGG5-FA}xFB>(&-i8z#$MM#Tmq!U z7dbiJuvmi}t@!Ht)BMl0IX~7qt1n|%`vBhB*w1!HjR5IJ;R6c((v2M9wVk55IziWs z7Z}bYC(TA}+E8&o;n(MEcfVMvwkm=b0SahQwZr1fz4C=&TMM zI+XZh&f|I?lo?UQKVBgLUB7-kOS&hruH(N_12d@=!h&X&Y!R~@^AiwU0EKN|#{w7w zC2jQ~r7rRQ@NT`^cKF*xeY-c*WT#0tepqdoaSAi~t1xa9(oWc=B2bKMwf|m=5l>$D zf@H9z)N^AjMq#EQ9(H!53Xu zRDZt}1^8`v$LN!yqPr-xIFWi#CS!flIf!{69CkrEW&elUhuqi4au)JlCy8@q%IVLH zQ&-VAwuV$;RA5J~SkZjc`r^0-Y=NvsDOL_km;Ve)9+o`@k?ty@N_2;p*sp^_N@pjY zVMq=%Ck}n=~GhBCZRafojddp%K zU%o~@NQo;)SafI5Y27y&B@yqD9c&n!#%(6dnE!2KMaYAz?CIe-em|o93od5Hr>~vq z<_UFITZUOLu{~(m{Ni%c!5*xXL0$35P61_MV=)w{*P!gj<3O542S=hZQfP9`qw0Yq zJROvRNMfj%APGdHkz-rek3%o9bLX^tciWWM4zIHePXDVkc6hD6*GrGh_r1{74I-G2 zPR(77o>>65}h~@fr`@F2{J?Q6l zz|AksSL>Z~rOD{Ez=hxUk<0WsPtF&0jb;5Dnx4;k!FDPw7VdKdgMIq?9P5kECu<)% zbYo5c=Cl~_u64GAbCx!G37O3GS|8a4UGho9{=IxW#Y@2rgWR>J5$})|Sp-ynC9B=o zUlAQRJ876>%j$>Ts|{TPvSG%l1CEX|P*~%{>-`_P{sgY)b#42{OOzobLxrf6F-a&S zk_;I$6j2FfC}c>bQj{rF3bl+8t&mEQq**93PazE&WJ;5iCcpQ^-uL}HumAtqueFD@ zE&6^xpX)k@<2cUa>}ai4Z@8TW8|jtxwLJAWM$^7I>uZH{3dlm$JBU}uXRl>}$^Y|7{yvlNqLxXv@88u5{9{IjepS25JylIDKs5C8 zmmrLDn$5oM^y)6{v&(ewZu3ZuF=OHv-V^qeN$^4&Eww&(}6cUnEeD|QzL=V@1WUmEPGc)>pu6;ej z-zsKxhQ5t`mGhowYt=`MI`Z)0!`6NWjN4ofy<3219=WHj&tyt<^y+AAa!yttwk%t?@I_e%*R;ZUl#&FO(;E-^`Prcy zJ>9m7MKehJd(}IvUth}5hf#yDh=_QhWS*5ZbWybR4{ygKbTMW0B-uF zm>5wffc#Bt`Zfwa>}JJ{yxV7%5dqaJWY=a43zQvAk!xaS7rvxdc<>rZ0R2YywL!yr z&24nIl$zQhHH50ux}Xv&}U*hqFI&RM z!^}6~uq>vq5-sUuVx7JdKn9{!S-X7h&j-U|gzTxh0q^upSRDVHb#~aS(b@8y$=k-o zI(imiD~h)M_%y#C8?UwMbC@JfhC~Z_dHq_#jl6FEN#)VsjfTz7!J8aX=7OMDq*I#m0`ja%D=^6Z(K=&q>5+Llu>e)=YtcfZtuY z0^YB=bkL*~jAbeG{QW?_bDZ&%zmfEqMsEl#&r^Llm9DbdPqaMU@X#!KY6!*X*?OjL&t_k({Qg;NpB;dGi}C zKi5^1Ew24l<(5*sdv{LRAJ=?(D)itRQ-7^)Wl@@ydS8T#CKpuO#TY-ILzOX|!?r}5 zK$}e#Z``~nD6;d(NB6qVn~<@*wMKzyazn@+z@N5lBsr&ocm^}K`&CoM=dHdL+9G7c zFr}Tu#61ClU~q^J?dds2)RmvGGKRd-Ror}hNEm#JcG7pt@t z+e`b<#t9>cL`x2<5JhH{a4~vJ%_y&hzI=dXBcQ7u5~Gz^yI8tc#3N{@ckBDM{X(@s z%lJQ!Kx$v6Frww0)6`>qL<8PjwQth5%5x^sE3qV)vwHPFfqS7`AVe0`xlv}}w6=v< zxi#0yrJA^Siq;Ps2BFiSbVgVu9)M^qX=$4Nww4zHK(%{q)7U4g^t^0`7~R4VfnT1U zFm=7-hQ)juWy08c-9A)B4$+ANzv+(oy7*Y?mokrd&m9x{a}593s@RO~Z8CGFsIxzQ zT#bDp{b#p4(>}c-mD0Jjsw8g^0O%)0y^Vc^IW8`fvPwE_qs;T%9ijgMe{>^Vs^z_UBTBo%4HA&TKkktJvMr!-Dga z=@@bA2VCsD9I%Pb)~a9-_9HvlUs{*r{PoM1@vlBFA}FWyUbF3e?z9?v&APEY);aZX zllDo{gvB40gVnkNy@r{i?t=sMr5zfrJb zfV2c^e)Zu~dn*T6xur~0E0l^dA2!L$ty1yL@~j>dfQnFUOjMj8roMRjQuyq{t_>P7 zqB42%^$YcHkq!e$5kI=gZj0BPulBh!5|T-c&jQ5jK?pn)SWxigWOgQo5%-s`nbf4j zA5o4K))1k@7-oK@h)_)BBjy}Y;ruG7PaRZ0V8_m|!A#YOU^Fnl(7zBl@|Xv+eb*^3mS{$3NR z?{v2+Sgms$b>CM#|A;~ROHW{XKGnlI@W3nkib~ZVeWsn?oZkKnh8=T@72f6O;j(LP ziK2HbzM4x`$DnIkuX|A1fv3U^dH(+WA>zvX9^M&e*S`Mul|9|0Yl`*zje3Yx#i-hE zqB2mt4$Yrn;Lvg;e!uk<)7fmZJ_o1Pw03W(UMpQ<-F z%kc2!C>_c$dF{ETkEED!Au(7mtOo;NWL)Xdc#x>bf}=U8RS$%rKc(^Zpx>(GUaK8~ z?K?t&Lpk>l=>k{tUWIds(|F)Y_!fT(10l{6^hGT{!1sPNjo4)13!q%DJM&82qyn<0 zkQ@+Iu0&vC_4=P$*9jCRmRbrRkqLQov=!URHTBww^SSAgCGD@voaR;wPL4m6K~fEk z6cR+QFAK^#-StdUff?5U@&cPk78;)t%Q8%KCE^jhl{~g_@F;n+PB!OM)!-|M1Wqme*K-Y zEs_;E7FbkHc5-r>6fj%fm-?)R?R7$X+6o$Vgr4r1#ga!y8;OSxiG2->mOjGOO}r8@ za$ZDvJw`a|aS&UYXGVJvAjqfT0O$Zt5>G*3AB^C52ySO=U;5aQ=S+KOtk*AJz5C*} zRMb>-DEl%eQ$dIB_^HGbLTbLRe{0n~td|-uKN9RsNWjqkFWnNY&YiLK{yt*RxBC?^ z&qryTOOl_II432I`FB=^t&VSO-N#zlMf3q&{ItSC$K%sle4nlDF{od%wc`@+ITx7L z5xx);^dz!c|#_YynnA?d`39y!#O+X~@eaAp0SW9)N~57U4C>89qSp!!{C+XAv) z6i)fP6|ax6CQ+;F>ZMyY%g^XoAbA^)1|w}nTVN!fxIu9vG7-<(lZBf|KUjp$=7h6O ztYz%;Y&AvMIEEleiF=&5ZE=YwE-ZhxcJ%u#Im02o_k^~5Tf@aX@NVUo1=qpB(Mhp& zNI1rVKJBJG1L|9x*4VS_get>`dkR60)8_XOR|uUN?K|rx_8+q3*7A?`H?)Lg@&G9eR4u>Zmx^N>=2`@Taj8JkuUBiL@Jw6&=u@IT@+b3Cm~Nc z&^wFw_fNI`0992X)rMvs6y0f$km(@9p zUv;`qi^`9B&?UYg1i~n2@%$b}=MKx=0U5aVR&$`D{pzcrAtlvUY2uhB7lJI;qFpk0 z?*=z(Y6SKBIMaXGtEER_h03FR=w+qSX<8e1P_Y?5KS@s{+AZ;KvalGI76LZ%tCiIp zntIQ)<=KLLf|8TOU1z%H; zyq%Y#t9J4irzZ9MlXjI5TJCmA*~dIyGj?I2xwVgu?TnE6JJ($uc^Kl4ivjB0u#@!e@}>BS^BJ?RDpQ(DCtKBAcx68|;uC#Y(8-gtC=j`S zgAaIvZgKI^eo@>{RZb)VsJf%H&}o%Qs3Y(zocL&Z*X=CW@Ze;SPYnf79<6f@orNH3JY zM;h9DwmL|gjYt%tUD?}bBMymk9uL!yHwjRkHCk5ULUe1j-4-aFAAtJ3=6M z_U_#S6fF$w9M}A9Hn-N`p9szXwFg%`8vq_b<>%{R!+Lb2N*3p9&Er!a5O0>F zS?-b74^CQ~%o7gLD`@IKp>k@L(Fuipu6f=f zGwKSqYrQVpfRn`cBR0z-URYJn$rh@eS+fpizXOHEP;eJ0UTFPD`@q&=E%D1cDyJP8 zcw~s0k30jn88)91o_Jy2gw!cW!P<2fL+{kXgT6dC{%AFnFe7bhT3TYA4r~k(7FY~n zk%7EOxbJIyPSp1-U=PZHcTaL(zP!qWfSBNd?4>_lnf5+*;zp#*EK)?^L(k?NHkhz^ zwxuqBY@lD6e8lTlh*yS|9-jjOkl%1{WCMr4TRL?_up^%dGlAO2sVz7 zAz@*3v>8+en``j0axCs(V6^QX9G&Z@_Zv6zKl@?OTAcB0{M;{3ylz=z!AhjHeoc!* zOEIzK$3o#+ZlkRz1_Oy{*bjt>*@#u~OZgMm;1ey#ZAw>O{C1KaSQPdq^DQj?97q(f zl=KldZkWGpH0!X(hzqj-bpp&F(!;wp+BU#RZ`X?k8=X94^KC#mlUcKb7h~kBcL9}0 zjn$O0*B%`<&OT)(o#Gz^f(eWdWN5RHda^=TP8H$9HKW5zyxax_-#eW{3;uXOz=SQo zeEcOu0}is@o5a!i`n;k-#2OpZT7G)=J8ryX1%13Qt`j?vZWG0L$pi-E2>?kx8HR&< z+_sM|-jZmXu=VuZEWvY#dePbWr&*Hi>ysN#RT+*GhFd^!7VTIN?)BVtA0)3`-*Nk@Du%V3GO*gq` zj4!~*M?%Y*54ZQ^dv<{?sS`62FloM8j(v33D>4D4Feq{nGNEkP&Icl4VbSBZK1pGg zqprolfB=O-gRu&VUK{8&gsz$}PdfBN!2oy_c7N{Hty_(CE&hri_OFj)6j4k#OORTR z&RRtA2~~0iN9O&7(^ceX&wNb>8}sY@-W37J?AB0*0T{a2U+pC;El6R(@Q{Fn66a4n zSh&63z!YBsFKn>dCDkC#ZSjTVTWVU*F*gK@ ze#)H@C7>*_3B|9~uH%n=_}T%a3yTJW)O<&ef{9+!Oa{Fys(CLpYx*KdsqEr!I}9m_ ziKqxF4%W!hXH>$6wit}|E$#E{vr}`C%p?`~4RB0Tb92UE_N+~n-ha;uxyJeqZ)ttR z049=h4+cq5QI89okbZ2o3PxieGSwC)F#I@mK2uzGHYOZQe{7@8a9uiw^f)u}+)FCS z1@q^tekVE$goT{_2YO^efPg{LGNEBYcRpHI_dxHBkl~zvXQlK=$<^5Dz_!Zv9=)G@ z!0hG&6ne&0k`)NXNvAcNrLzYmS8zK?>8c zX%U`}Ad+C#p!9w&%3WlrHNhvTKS6Mz!{C;2D8f0ZlhUbEr)Cpd|B;x>eb>_ZetFLO z!!y(|UMy0U;IY;`ozitA>=E6~B&Y4607~Ce>a95k(AcR(KMBTe1*0~@`lPrGoP4ZX zhRxN*k9Ys*$s|Q-R6>Ce&=`5|px0f4ftiBGsdwAP$UoT)lrewiosw$Hs@->Av3pq7 zGtJA;?zW&%*~hd6@u6ye)NcG^wt@HV-8*n1df*Wg%h2P;P3TDR?GnmK^4Wp!pUYRq zFTHzz=)TO6Dmv#VE8e7eO`?@66F>LqUL#em-bE;ba-k(*huENr6VIql*$Z_f2UH;N zWIFw@T_+%E5SEdqwpPUF7S}gnUV1hzPW}&efhwopjtah^2#)}E%=kSuxzT^3#}Zrn z$e0Qs5pK%xbHp8n;&AbKpN6Z1LRqT34JTZvrnqGO@x{ z{*vA&*V|L%m9k|E3Z|^CdLRf;_g2}0tpIv>6a-Q{zd))HjhFb!E~T5sjyHM99Tu|% z3=pTqcGyV6%=EvI781F(Knnzxv7zAhotRr`xQ>NQI5pG*joA3pHlvQuMltwYPhbBe zRc5yl)8uu07O$?dj#5I1+eLy$*5JkNZpkXZg>A>H&uaPfVJIJ$rRR1A%N^V0*q?1* zairN_CT*Cnjdr$F*9nGnV1OpY55EZ@V(#3>?fUmYnQ?bzH@{l74%^vL&)n+7!kwLt z@&@~RHmHgW^R*7lSTyfzNAq-1F7a!rNtdrI8YPL+U(>t`k~1C9b)K!L=dU(<&a`iH|E_8~$4XFsp?bD%j?F2r70Vew>hsp6DkvymdLQ>`^JXm!QV?#1 zfRXGkPnA0mhFt$X`h>TnZLDp2w2XP&Gxf`FT1JpR&DCbBj&7Y_?*$sRzYtPEbtgmH zT1gKd8%}+^Y=FM=G4F)_2!-;;7g=AN^~(^@tF>%-YrD`VSwf}b^tMO27|fxr`~Vm5 z0odicok3`iWQEm)xaP*(wADxpX!7`?tj+sCQfs^yJ{-~Z&s|&2cCA_nIf46=FgiXoZB=M{>-ckT zxV<8X5?#-;LN%wuE&3Js)tKB_L<3Sanj{Ir@k}HTFm7R;w$P!vw%D69+R>YO&g#1q zk6aO>Ss{1DDeB~-X)9N%fw7X|oE%I}A$@?9BnQGL14ow8_X80AOb)o8oo$05kSGPv zi?AVcds9V%P_sX^KACXgg0V+`9y2LHtVIzr>B8oLE^zDv^k)Qn>z$ghe_50KU&cMW z=$Lo?^8l+|hC6WwiF2-43e=IgkyKv#@gsVlaX^U?J1B64)Eg=iXO|Zh&pYsP5rPS~ zoEsbZG}TvTZA^H9gw6bM?D;C8w2xm(=>xuR)Nta_>e$~&PhBRlHFI|JWv_?}7xouE zIkx{)!ZhbUsy<3mB=~|@@j#!QZ~c92y7j%OA^=q}C>#_T`h-5Chx5t2ijVZfYI5zX zgcd7KUw&4G`wHRFMtf->^oL)RQs4sBavF`0G`^L2uuIGJT#JgJFh$KNQ6Ycz)~()< zde$)!G)_pNf_;?yrUjlpfouwtB4pP^X5C_PM?p@{uOEgmi&IG(yW2En3B?_P9|9y$ ziyzk##ubXVQ|c3NP(Z?pI2=N*HkJVu-dhUR;aqoM?p1O^0%i%J=u0f5tCDZ_9N z1V{bwp)C?{WMcazhG=O;XASbDt3_$QmBI|rz!0V?qKUf>ym^-;=`*m)X12OSc(enF zqD2yJ65~&YopI%K2}LZ~yCSgnan88&;l7`}^%j)8PBrRCWEL8S@S>FFRg1HvZ2Vr|QTuubKsUAFvR zeM7|c>sSu_TNSyyFKAxpdDe8N>GIAglYjJz76>!$Z+a80I~HV5G_~&F zy|@g^K5a{8{z80pLwjzIu2%6oyD{+Ixa!x2;c(fzOj%lQpls{kQU2iF-d}%b01fRBz z!N?y`un^dhKs~7s#kvfB1nYmgwPAEvu7Ce0qTd*S;54Vp?eHSMZz|)0n25=W+Oelc za>_mte|gqhv$Uqp7amL^`XO;xkzf&JzVYSLYyQ?QgkCZdN zn!Vk@HY*xLNRqdS%weIWrL1sy6|hzVER{-dqgYZUU_-?5d&sefYw-}05(^Mobbp>P zin^939A#HgUm7fsna&%(hG~gdh7NH&$lg)QB5t?XmYMIC+#@+~$Jykdwj-|R+!Rs> zX3b)+=ag-MxW&Q+#R=17=wR-Btvg_7`skxFnV!^R)N!$y*)z7vgcnt;Bl?iv?5Lc% zU?j$I9u3-rDY6JuCNhvMiS66A33J;0PC^ToyV{k%Gx|oMhNt^u+TDLft?9xaxw7W> zoFB)E%+OtuFXo$?rt?`dgFmht-COXDE~&N3BD7Q?a)Q?vV}F8GJf4i$qLe};<;q91 zzqj#{_u_j|JCJh!&pWIY?+`XnAetXJ)+-W@&mPEbmM0*WLfl12JRRqGZpTFYQN;!r z7Qx-RQqAELxB>nGVj7fGejz^I?8kbE+Yc@i%H}`T)YA)r6$v_VVkRvC?bnryk#r4BTMBx=jg9P)yu%P{Loov@k_nv~yRD47t2Cso-^WzO zvv2k+Y$x^IT7K_+AK)@Nuv&B+Gpd?epVT%z(&#|ZA)y%HMqw3^+%2zxmy#%)ua;~hZ&OC#mKk>WOe&$3z9Uw<#?f_5=NM*6?$G{GgniZLM@{O!c3(llxp<}x6-7a>ucb4l)33^Xl{AUV9uQg&Q`6` zi#}kiGzBCVn>QPVFiFkw{rk_uY5Z`i^6!+F4>7CzH23-96sLrksV;|WUuXMUaSNoF zZh9bG;e?(7P)z2Nj;XX*9Nxg8J@j_{zx-=iPPs+}LbEXl9E9G2ty4wyO@M@@rKK=! zZz_D4braWdLAw#xE0<&9`Ura|1@i%#JvxdNIddb<-?$MBIER`Sks6ni3$}LjfsILD zT+k>$2e30xAaQV~eC7Z=P#_$ZF8|_b6;&Il)l=Zuw1I;=lKglNcpTj3bc-7Rtk-SK zYW>$rFFrf5g^!*XOl9Ts-m=nwLO{chRu3RY(*;8D zi<#Ue%C4Z8T+yTibVG(*a&wa^r~n-Go6vuC1b?*s_Ypc1dxpq^2&Qhh6gAxS5 z6LE)?7{#<#++CQsq#oBfjC|_(&nNdxr3nND-aqbI*kGep=N?FtLe#t-t={@iRmDG& zef#(KnqRZ{ztwP8VKc_Ll921XS!IH0mpw+s`!Ho05v<8CAq%P1z7oSXxL&=BF(7%^+3L~h_N6<0IzZ3Bx*hOjI6dv$*m(`b9m!p9CRxfK!sO|o*aTSpghqZGo?3=WRGbrp}@L8!? zwDjw(uhFZRMv}#=hfx-S9MJrCoqxg6)Fw>|rAx6i@4pB0h8|NpxOW+oN!h%c+Ikah z`?EghBU{9dYNb?~DHgo?(=f9}?h(q{TSucI9i$&zuHwS>^jiKvnD^e&{3Q%=9weldfBA8)-El*ebfCs8tp1f= zTW)}PK_{`%k@P4^p3dHj@0L_ofwPhW8|TIhqAKCMfe}sS>p)pkXU~5;+3MfEveKDt zmQdT`fgbTKxxUplDjt;=mN>C`*31>vJ@t+YQV8aVbvJA%| zW+lCy-BF$w>yzl#|JvKWfs-E0n%c48vCvTPRp!r~b4+J&tlT;ss*$Ug5&*QK+C{DC(Q4e|gY`2G&K+Tv2Cw zVWu2;QBb@d6w;h600WV%2lv32@eq9;96B&Ad{^&2ePH$=bp&cveR}rHnZ^H}+^9BU zM9g#@=O=b5$gjO7b?Vf~B5_N2!<@@v)z1g2)0NAyF6U^=l19prcducQNxoZ`|5t$P zpAm_sdZfg?o%S405DWUiK`#EX7?sp`SZ2XaY3R2f#F2oWGW=dxS+C7t;x z{IEaLvN^Ng@#j8*t~fq^n|;8T%ij*{8tQ6BApDitzg74g?Q*Ub-dWGqmO?oc>qox$ zd=piakoWlxI`hf=wBG;gjOmwA>>ogCQ)PpF;iC#oZaB88G~$SYN#+}U$1*I`}-pt zYPo(ZLp_Gv%>OQGh*Nz+FL^6`x?XHLHGjJE(Ny~v<*4+mO%y-RQWJxU?JC26d%OJu z$8o?g5qxU2+b__i;cd#8vHG6cv{8cAazH>8PM88d-lzOt$l?$;3SRzx zI%`=*Zg@9(o9a~G|7rm!si#p1ia~MWpxR*5b}^4upO20f#(^kv0g8^!K@oxY3*1(; z5-1QkK`;Tvpe+CxT$}0!9!M2T;Qoip8B_UE%_Kgz6}T8nC+7NuM$7%_MAWVyAD?!A zR%aVy&Sl-smUj%BOF_uN_;k2&VlWSkhN_eR%bpo|+hPt|Fwzl$bsfs~mA{6hA$=|4;p-cuAy?@rexe+CF52CgK zGQzcHmb3E>xm09Q$%B3EW0%}%F1GZTxO~Zd!B-PgAQZy}-A%lBU+#;;moM=yud0TR z8)xxQXaZKJ+g8EbMQB>yl>4JWM$s>Yn-pV<9iL8I=f&(1LeBSOtKuuI%YQd67Ib;t zlk^xv=3B-BkCveZ5R8*y)7#*}=8t+pS)-6L{CLK&QLZtsWecKtc|h^P-xMZya2s_x z@36t>lK5OS*YabAjM%-@e{$55@L|xO{ zrDPo+7aQBEGY6lPpNA!diQaZ%A@lsgx(SKu@Sn?RbSVWeGp41U9&sxbU>wf|oJ_^* zn|dn0yHc`((fl5~zTH*BnW7{K{naE;v#%JL3QAJe_oZeb0$ag&5Lgkb8}ur4IhCNR zr?=4B8uvTD!N1QwD7tm)R;}*#R5|)HD_xISCMIAM)=nv<1u8+SawEt2xG>9)Hg)7= zONe^mKzXx5NQ%&4%nk|YqwlOq{H=Typ0K#jf~wa32g1ab2f+d&3nXm|lRlu}gZ9z? zstE))P~BBu9?X`}UVdLWvnU^VV4#ZNo=5^vb23p79zONurwZC9Ns5M7_B|CSEkG|Q z#w@o!8bcBeW}e~4*9Dh`nN%4i?Ox6>87!j2JB^s7X|r1%(jFy#JmG*6s)VbkuB&?^ zXT$%aZmUf1kvtTTnyJ8QacyO-dct}#8+QkaAhURRNX}hADlFsV2Gu;-VM!;<2-2ax z(vP{nq!g2fMW4`yE=y6jn6-V>&j-dftKl^TXpq9CGX~^ux%UjQICFdT0 znU6%U!`sNm?y&vj)Yji`^lL8;j=uieE7Gk}y0=`Q^5kxZcdp$u)wX-c&1u)>1e@p_ zFAXu7z4B$LZNR9x!S{82eUIxM(c2ufeN^{R_ij42zbIMnH^;E8#nCx-U;q7_;_0=i zvCD=5YECaYZ<|!^YIO16FTI==8`Lj+vZJ4?yL+BlYt1<4aPQoyp#02KdV|MeX!gj;2}S0gzI+0QFkeAkX}^C`FWVo|tvNXLTT9jzyhT-0ua1QS@rFbhP%Ax$n^{Il=p0l+?bC)>)I= zjp&)YsH#in3?aIDoUSs@od;HIxO(^qh1T z#V=DkjX&Lmo=!b8K_JK;V5SQHKYz4+ z*Gg1$;G(J;{TcsyhZN7aShTJ0qAGq=yUM5X$-UU7glq%Vpg%TiksGkd1AP>OscxEj zuIy9BT#5+ZIPBMZ7q3cO6!5p{2`@5t_zr1_ii!$~8qorQqzKbS#_?=pmPQSVBOvI@wI%Hm*-m* z|FSZEv|TS>V;e$V3_CkH^y-H5K|yb})M8dHQtAH;)# z8g=%p1h^_VhgbtOH^OITxSB<3aQ%&N&zODtWOzC%JvwH@Y4Xg)lDh2d8B}%p8YMN( ztZ2}U_ZEYfi6Szx z*qkgt!00m27*;T1=_Zk>EylLLC)ds$>N>ey<)&WAqc?8Tx^C=Ka#o}FT3_jSZUyqS?|a5+lcgDA1VLJB{gpcxlSj?s7F zx9YTM?!^GuA$&iMZ0W+vm^Q5S`s`#rClBf8lHffjPxhH$U~q2HP)3omlUYO9pGk6r zdqybP1MIXc0dIFw55j~#`?8!mgLt{Vpex^3HuFd0PWG4{SD~@n{Kg7W)~Recg=5UYT4yD`K{quZw0Z)vRW)L$BoJ`ef$7%P)>^~8E)~TO7;Wl2(L*5_#TZjTnQ+T9x}U>qZxtt5szC4W%_Dqn zI-zczxTT%&{)h0nZ?KdNDBL+L$N5X>g%NSf?EHJ6Du#{5qGVCk_%8oBUJV_sI}twx zd&FcDLj5 zm5cpL&Mvq#+;6*g1q5LLhc&NkrP2RH;=Y4?SGBqMn3;fv6-A zANru>I^^-5|1CvordKWHUNbFykFpTEZHb-9^-@w&!uASnQdI8tky)Er@P$rM7s7DE z!aOr|`4zNbLc9r>VYR^3IaG8r!X=WrnHE~?@ZNs<+1l&fQuA}hy&oJ$&Y7{-5wRaZ z_0*Xtw^84^C4nHHJdxH*^8M@A&UsDUc>>kdpJTsQ(#S@|U6N_jGx_L>cA@PYnNf!k z3&31X!YXGbZFgD`OF6ISw2DCa_XdAekBy4z4Cj28R3PDGRhyEWjHXV4%_fK`#pX!$ z?$Q(glwLiu4FC7){~F}V1hq7$Tw18>$@W)n{Az3z>j_cb-3m8#aC97GkwW3Fo9Dxd z8w_kAkg)1WI+JPmX+y!gf&%~I;*|@YpL6fL^2mK#4Uf*!QY4U~@87$0?b=mGAx`DU zeK{p1liiMxC}(Xi{%kirD;j&zX{}4!Zu0WKuPw`P?%P#w_t77|YyR#|z4+j&_Y?1W zN3W(n%yG)#1k4M^j+q!8)T!C<+~03}sYv8LaFnnLh*k}u*@AnH(AS&jQvDu?-6qC!9x)vcW zF>-Tr3$_1xdC^c9HcTvJXw!7_xDh5%*6tb>`C2Wf6WGefc9g^43gEmV$H8X}yp32$ z;?tM<65f5H<;DRw_86&(UNg=ki$Hv7k$uFqZ_11PtT1Jdwob8MSR*7t5K3P|n(Jz6 z;^fnW2|SpBq-n-i?TxR)-xN%m@*$?k01m7lChl9L>Z_|KL(j7mqQY7Be`DW7lhLe+ zXE^MTaQ`I;$Y$=evtaqak);lfslNusoS2ble|Umom08vEo7*;>_0HY=rxFv1WNF)& zcK#*8q@I?KMRM=I)KMvmd&HG!R9}rjDxEcU;$j+q%kOI>Q*_=vz`fLTbhemYbE_ER zSn=(fF^U!t5+UM$V7Qc`2Ef5`jYTlqnf4?lxd*6)*jWhOlx>55AHByb&I z6}!86@H9v1RxbJNx^@k)uD9!UjHTOR-wOT=b7oE^D$xI^vV)ij*CwCY12e%aweg~t zu!j~~yyo{%8Cb!0O!*UzMKr_iAv0+bTlUkR*|POnDZ zXzoWEIIY&%svQ}V#T2YD$y<-AQEX#2dmS^it=^b17GtD-)YkTSxT5RODdT-|YmQD= zm+!WDdg2(5heatYZ=m8eIb+H#TSXnr=9u`eo7YN@cvKXwlswueWQ2tw(1=($ycwAx zUn8i49v+)yGnKJp6|omJ4-KyEylwBwUb|Dy@;f$eQy=YZXSNbU> z8;G??2*HG))Vxr^*BA^(zyw7V75NdXw)EXc51*vcIwC?OZHFvbEMU|D6KL$ES?=@7} zi37c;69H|5g7yxW+bx+@FxR)wEHJRUb>+&pf9GjZju9~qh=C~&N3`VrN`vi|;3**H z%7W$(J~p!&m(*vS`tLD1Co8dcMHMQ@F4QD6PXJ~TS(~|#RPSmrO=zpDv5$D>pkAb2E^git9BEC@z6gXNPS~W-jZf)72|Wm?gE{Ci(Q0 z)9i>BleI!JZFN{#J4LhCkqUH4V&4(Of19(BL#*p%3yX@1PTm`Q^uxY|GeHW61McCu?(^Ppc;mvfgQ?~qw#Su-$7p`4<5nY|{_rv__ zlfb7pUu}OX-`U?Mc)z=GIfbK&@zfPetAY1;isT#rS$@o|pO8V5fu zFE1~=Zrwjq`#*a+DR-48zWy+SweC}Xf`%n0CsPR?13M zzLz9x9c;%U!#_(IF%khaHH~})s%Tu(h)*`||1@Ryu3njR8L#*r9j%H}_1-weC`SNb z+;Te~Gsp5eg+td0Cgm7mJkJHooxt=lP_*JvEoLnR?dYv!R1S{k36);kME7x^)5-sQU7aj$=IXE zj;Vs%A&$BF>mehZV)Y5nNzuT~DW<9Sm)h5W?m_v>EG^$hy38MXOzb7#27{xK4G@TT zOV)d^JlIa`X9VyOtGQV_J|*tHm7{5fetXcfBQY&4EvLr@edxNpts}4_9%56pS|ueV zk^HR<4gFRp1DNpL?|HvJ^;Q2tzP&O!N&Lp~qnc^RvneUcOcQZRXb#OS4vE1RX1E7M zyjr-eEp6Gn|KI9tyiwKCPv5@XK?!vgw(Ml!UR;C@(c>Y z=9&SE4#lxx1*%!5PN&Ru0`=8eo;rKCBa-8o$V)WCzBEZFN)Xz56d98VLh@k6f)d#V-eIMIRpR-*Nk+iqVG>vaqTB;JlaBW4Z1nj z)ZzzWy{3e-E5k( z3#iwlE<<_&&eOJSTPl_?N;-Za;m26sGeG;IMMq!ETwA9w)Ky0E`0Q!kofMRwU)|Hy zJAuU(q{`CO1}pEhN5J>;Lw_AkL%CmE%zsZqemmjgv8R)Qn2+SnpqT50&IQQ=e*u+J zd9zOd-9^T*nB?ap5=-q(Gjb+n=pQY9jkQy9&4XbE)*;}-2gM}yjs7=IP1r6^1$gaO zE2ZgGzIkhxSJFdWl?g0=Tck>x_ST&UW>g-xN$u<1+5mg5K#BX$_-#X%1Xtv?qM6== zAsY{;&b1OVJY&di*7wTNyB0iLraylCCcu3)k3N0+NS@7-@60DeMm(JpLoh^bVY*uF$&;=kEMus>jLos|4U-+#`Z)Yo3!owgufkykQRIIJn?cgF*go>fAXKMR zap>kqmS8c|(Gib&vF(<_+8@-1Tqy>)1;W}Ob`_G;3SN7*Ya>bi8_7x(uJ?HcqM)+R z_&oL&&B7Gr*d=%Y%8JfLPfr1aQ81$tMVPb+1lF_l*BVXhWJTU^!f6S1#0nP^{7xm< zkAuEQOnPQ#CZQxE1bqno-8=~H*bfM?D(p@Wn^@>X1rkH+Fc+5sazG!*0G&fu7&h5SN=Aj}hN`?+c z^8o(mN0@FDZW-t_4mpf4z_ag35V`_;OpKJE7(+X7w$tKp5!+yCqtY}XAL z2zoo*B*hL`VX_~21u#{RKmvlDII$n32O52)Uc(+Vdb1J-!caVKe##$ydg@dVraRoD zE9RrXT}8+@%)J~?RkewEr=C>h5C`A}Vh=bX!YKb|V7oX_(9TJIS~_x7UhQ0n-8wHH-P>=i>4(duwo3EmH%g<)sZ=R3nr=@n`vV6^6lIEN#W6L zcI|YEC|kC@W3Zh`?M!gI({p7&cG#54kTPv9l}JrftftH;MOhn*Ubt=g7^`Jq&T!ge zV9RtiL8-`9Z8?6eELFy7cv^C7>uH17JBrciru&zT^UuUp2;#Wv`K%Fduh^nPS?lO1 z;!IIl*&aB=`lclSSUl%l?DS)zLwan@@8XO9^!#+o(IU7_?Z}PS?o{C17&7vtv6`yJ z_y*TX=?3M5E+12J%-DM)QFOhghh~o2-WT5SJX8$J_y)_ z+T`d=4P090{PHJNi@QW_=VkBa4l!kLmCYMSKf0Yca|Q{D?xYA-5JAAQlaG7JOf3WHu2!3wjr#yX%z*s#x9~Ee$LPJbZyITXFP@yx`FhHX>Mdn%a z$P8`S4i}++7(eR3Mm97?YtGC}1FG}#b{Tw!7$5b9tpXB7xQ~2^jpoi6qlu$Qgcxhw zp-GzPCjxd(`ORY;t;+$V=`Xdl1uw{c<0(_7NPR9l|H_`ZgPnkY^s6kC3wlyfkshZ> zS0?sI5`K&Z9uasFjW8T1j&s+q_h52VR5m?2w&UXN+ZMcTUTh@HFr2qoYsHk9D9O1l zhws~(URq=xLl4f9WkV(+LA+=8>%|O0cQH`J#0-6ZXMzLxeq`b~#-lg_+y>N~dvJz1 zPmabU1CS2b{*%k_kEz<^fSjXi*~FG5b`Fy6KA@?Z%B~$T=)q2dzR->d%__C$JUf=X z7OplI=EG{SCRKNuU+=Qg`Sa=SC;2kdi~1uLb`eWVOxA>;7lC3Wu4$2wJ)Q$4QGiSIBmGQ}$6-WJUTHTuW9$2oj z-|VH2L+##rb~tiwuFLlHknpz#lYh51ws6ACNfMS+>~;31T*>~@56z~%$#WG4w~*UU62DLh*mo>9f2b zomlmS0&uCpqFCGYp=fptb2B6U@{~x>d(&n)r_6OJ3^_mXw||e9DH)rz@0kPqIMm()kzf)7X;VPw?SgULyPq^9)&e!nkSLb(%)aV4V)K0xx5o0?ZioF*+C1K#jk(h_pL zdm{kuh;q=7M*sY?^!2)#Fus~O#bRvJ{K}G%pUOwd3CdO8cg7CAi6j3xoxQ`uqwkH% zJUecVc!-ETiOC1qiV~5P%SOowKb}sJ5bq_tu=_^4il_?Bog|L7bd3rYvdT=rCviTguZ^N}M!1^nx=r4F=Y$&8G*L21HhQ{(___%jOwn>yU|$;_%je^fv*7fxqTN%-V6f;UMEA+t zlCTQag=cg1bhftdI_dyyvy*U587ZWn zVnNnuoAWo-r>Jg>xAp%KY?i;n_rj>)?v1~Gxe^Hq zoJb%-jpx+@_a~Qw&yQH_!~R~hy#-Md+T4l`2$oq&YIhm-MB=b)_Bm6ZaZu=@A`mOL zaR|qtqK#q5r+i|(C}Joh#k#J7f`Wwip}5G_XMfK%+`9r~^AMV=57a_pM<+=048Pad z7_bgUve2WpGite!js+6{YR7)MqI}SFaNC?X=g3LJdL0aexJRSEsqa4KB2Wwoo(&fk z^W)D5yU=!mn3E6{9(pCQwNOMTFmXCDVMkg1Bs5`&%sC!?>hN;0n&1%fEhGt)``_w5 z=;p+gwm9k>@4KaQ>Db3Zl#;a;hV9aEm8IGjOC7+fBq4RQ9!|oQ`K)qbo8iw4XN?Bg zx^660IB;gd&}TNNn8cFHv7XIicSgSp#T+w(eJy?<#7sppnLusB38#32OY9u#QRAC4 zCgxVRrD40gGYPgeI4p7SfsC8{7*kHnz8ubo?EQS2PQ^ihs<;P>y}jU}q83pz{i^g%z84=t0aEhxJT zM!Ko5%ZrR+G&mUs>NhM-Wkv~raJ8bYLh=(K2viT{#HnH=3`4xLiPvXDOd#Hb;p46?A(;#)#ff_)*`r^@IgHRkOZRgS`=}c`f6{dQoAv`8rFJq@supY^BZIWV9DTd03GdHwFnbfDgU5MAJG_EnT{ z`lo`{;cR|@7Vw8_*JNeIy_3-ECD%TwL} z(s;%=8mP%mpiC56rk0lL%^o9N`+%%Z%$M$rj<DR)7b_!;^Ylc^LGm=$SO0C%CV^ z#aF|*M#Lt7-1m&>3GIY*{RNp08B5)x&xgr&*!WF$-3Z0KsFi47t0uK z+?8oVj}_BTZ&|u^3`;HpXWYrDX^W@b%Lmv^BwKz#qDZ5$*Le6acZ6;x&P$LARtDed z*tQMr3wOQXScHv5@aPjL4H7Ire){~m1JVEaU|zo*iOd*GlbVdy0p};GS1Mqrjq&>W zo3Z>LV6j?ce7N~q$NhI#@^{u+TW`Ft-x)2h?0=F z7xig>ST>=cvWR}ypT-}>q!h#&|Df!q(%*OIj~w=NTVb5#tOmsd!bFPolbnAN0+9=4>#k{rc8lRZFj zU@QeSW0Alb{883E**vUga^|e6rSu_~+DvE8fICm)@+4bQVgBLY(B!R~=$+nQoDQ^b#8JQH)zcGw1I!5b20+R)sgCow!{Dxv7< zom+O|%I3cN;K@9g%y<*;pkU!v7!rE^hu??iwi^I8m$0C;Lu=WVaRS{!Ha70Z&6LR} zuRI&*aQ%Mo4RQApe77t=c_p9W*-VPu`OB7dk`Tj3pnu-Xx`?)jkd;nPdM@`Xxht1s)#wS`^s#HoA#g2_|9}g-0Q_a zGe5uQAH=%X;?W$7;JXwUn+kW-tE#Epj?Oit)<$<#p zQ^)rbt<7JdSfPW@_4IT@Q5{2Dvo~$@uN_^>@#oiYH5=MfID&uKmET3nskhdXO>)Ux zbG$u;RXjn06G1sCysx~st@$7QX-IeRR=-S6UKL|+_(3Y- zpFzA=55fuDP%C5rm79pz{XD8B$GN3j$i7l}+po%^s!RCII<(Ah*k*y+@&E$_OgHqR zmSM%RTfn2-Mm5*Uq{`G~{ahI5#=z4&Eo;vI%7{?oe22dw82+p*3}E!gFv{G=&SFbT zf7&+;vGzktzB9OX1I$qX6*x0(&?&P6hkkTo5A{XRoC*KP?tAlUz3zFIoxH4MYh>Cw z-BfyYSas2>69-n{Dky}=FMdjp4e3_#+Ubdvqx3H+XC$rBLtY8;HD=f zL81TUdTiG*dwq&puuJhrUK4mS7LjWKvZ0A{bo+MrFIGIFaGWNG$7M|KFkfCs8r7`&1Is{{N{=YNq}b zm;sd1pk*KE1NucB1+S=S5!aSxJNEx;?md92O1G`itso}AoKVC7wu%ImBuY>b-C#hF zoD4{moHHgcA&PV@knV{#6r6Ek86E1aZ3HY)CF7}kQKjH0@v9&{mJldu$#D~}qK$M@P9G>D48*(371n}Db$ zyX<&ChJ#SQf=Oy%UfzD0gzqCC-O=E5^v8VieiyE=CSnSBOo?*=3UIjN@l34KwzM7p z;ea^%8(XbJ^pa}J#^$W>;(D_+nWgGQE(~$>yj9z2a1pvQ?nuhRG73)Fc?IEfSAP8Ul^2^hu$vrTaSv&&1UFBF12SG6Q`y`I zEJg1DbZ2A?JQv_oRAG`QPcA+iD0-W0Q-`0lRGjMzzsGOqMJV$b6wk!QhxkeQe+eO0 zIC@QAKbI5>#F2yg4?r?S{TG-Z5*`2+;ocO@gumj3;zClc-;TzIg$D9M9ojm z@Y6nI-}3P)nh5eRBH(mT!hNY7#2|ta`Pq|`M4xy%`+=XCOnQ)#lA?4)`HYfLL_XWV za*`cf{3zE*F-64IQ=GUHzn8hg8T$c(_aNLBt&mMy+hvx0X+HYfZXxF`1c(fNn+%A& zLDtd(UFy_>d>#Zt)`AHlf1{@E%NDgpH)a}y9lvVk|)zadM6Pqcj9W1#$1}DZOYvq?wC@Ch5JLQ zZn_S50SU~&djBi(xWB@HaRn=z$zTDuxZFvIm7H9gAg2$ab?*|!{tCRpw0R2dtKFS1<9V`hUAW(MuslKhAV!<)@7eJ zvF@qC!9wUGGsZ|Y0C5EpC9>>AG#S~%&Ky|V-PM(g^d!4W7Su78+@l72&VT8~lanWL zh}Sx{bar}>(E{Y?0CbZPqU2a%NUgwo{ChiWh9uTjBoP?iRv=@7+#_ZxtOCLqAo%v1 z@d2o*$ova}VPP+gIj|FNNA6gf5AHHtI?M9;H%ho1A@mqv<7I3l7r_)$w>ZaHSl`~c z&bff(zXV$?aXI%6BKLv&=10YYPS72&W{}CaVt0O*&C4R>G2-?D08<_muDB~9%x5%q zeR#3|3e!Y}BS@B^j>qvU*hQtXQ=D=s#6ZKkHLz7R$3={^5c8M?4#V=eQZKbOCl#FTk6C3I){B?=pedM4>`~ljI_!{-OBKZK~0{x#yur zzd1KKhP$0Eq7#GQK{BO`417$3v-b{;E<9>%gv&fG^A|t!O;i=ci#7fuu?%g72f(R5 z?n56b@h&RGY23vyleGx-O6{S$r;iiGH@S2x5A7Ug614Z$vZD&7d7V!lTz{)Y+3K&AA$;{=tNf zFp!0+9rK!)^lwef^VW9;H2FBKhgZOUg9dKl@3QfiRUqHneaqqg2{979clLE+k&81N zae&~z4SJ+=32ZX{Atn8%7aHu~XCU7e{RP+2OaXN`S6|@9rH#Py5Q{a?6@eo_2r1m6 z+n{UU>qW1VtK^sxsJ`ox7gML2|sG)SS6>xWAV)mgTx`fG7tc9<9P}u<3 zL(G7KY@v_LZ3LEuT~QO-SgZr}k8EDXk%XGiCf2kDU1&T*LLuBimQtI9eXkboXIkTK z`B>Ess(?M)J4j&BOl!%OctjjpYuU%Y|K5hyoe0k0tnzLv@}MsX1q@>H_>SGX^(Cj^ z$?^}B)@8}(55Ic{evHPl#pvQ8U3nNF!fMe9=kYNBYYh3_O8T@J#NEXn7qz1j%ly&A z>mySrU-j@ItJYaxqI1!Dk@s4DgfffDM0hiYRM7zrqA5Ywn4JU~NN-;Gx1%avcDfXs)zl9=%Ns)(%jtxR<-xg#hr>AeiOuVB?O1_@k z9^lDhRv47r)~{*(NMFPdfTin(maMok@M!QEOX};tDJcV!M&1mI69HG7nA~DsqzdD5 zaTPZ7{#dh3dCOHK#CpWdTSZt29gO$gz?xyQvERi5;G`kqfan$so#iG(1PZ&AuqH9o z(d1TVf~&OaiF{2|$a$M0BJc|EA7qH)*@>^iO#caHAFc<$hpV6PwQWb~WT-S8JJ6=+ zzw{7l$&oaW$+=wid5 zC!*XtIw45`A#eBEV|#r@sRN_# z5%yiUZnEGP;`c|LsnU2Bg~9*ub#5oSY_g^?>RWH#Z6?9&l_} zHS_Y#x4?w@*v}9379V#w+N>_J?$e!cV}KKI~e>$_~3%5fTbkbu!M>V_s6 zMAcu*`&sk(A(7sckK|lXYv$f6A|NF69bhycEiP{{E(MgfeeM+dUO_tvOz|hWy9u?T2lM}D!Bh?0hB#l)V#&-w=UdR~u6qfR(#q~D8w_E; zuXRgm1zTnkQ+!ruEM7p;|A{RQnORxHD`2sLnC38B91=G(h@{o4zppGevNsbZT@3&n z&?ZsKv~7J&>osrrun5C{IFL%rY;8T{BdrR@D<0$Luzp7%Cle1b_**xT2lPG!=p1{R zL0VMQGQRBbm(@}S0W64o0jMkI%uQEcRktu2fm4z?;ul69a(DQRB*E_j(v|(31|9hJ=A>Vt^U%d?B-9*tTUEt zp-Epoubx;;sF%k)96`{w_+3O%O6rr(GE3nG$H;2}yk?fkC9CoL8X7W3!wd1z0Ok?P zJ%*7HLKX^vlSJY^BgLAddwo{F;|9B%5+C7o6YSwBA+xcLVY6z7g7oPlRM<<9Heh5rlhJcmC@-T&xH&>avpX^$+_`G6Qh%ISavLjM!1 z2yoz*t@Su1L}o>;3%ZDtp8EG|#6^?@IPMm~GQlj1-7T5oD;Z6}&q1TLETYZ83xf6 z%%i~Vhd56xk2@ZJ{aFco|G^So!~wvxP|PNEG9^~#bc=Sk8+*{#ps08oWMC_%{E+TP zv>C%(3_~yzI-juR*gv^(oUVTNqHLm&>>452okFs^?nXQgfYcbV6V739cd?x2FnWrK2uI5cf_<$My)5?g(q=IMb+eBE&$Qk5HIV~-dWboBk5YCQxb~80KHUHB^u+9q* z_sMuXYy?-d-;X{w^od@EehV_D_-$zq#vGJd+1Omhvue}b@vP__$|>^75{(spHiR-a z-gJAkqyAdZwg%M&IMi=x<~vFC4q67-ch>hwdMK|QX`>*Eq1$E%vw_gg$i#FRP<9-( zoX5x_4}xt|KVh2=3Y!nJGgF0QNR-(7U;sRnZ!&flWgq4vyu=F(WJG!h(VPL29zbG2 ztdbEIQ$|r!z}<^Qu8toLm;mdzh&*H{N>Ga6K<@<732DV30tV@A<6&strr*$W<|LJRw`3Ik4#bU$ z`?S$_hT%Dknfh+`XXZ6h-ZHZ(kAJivg2uy1rR32C$ zve5|vfOImUi5F-X1D_RJ%XT>M%!_&T6ZwHh}1+wb)))ozYS6SV~(sF`$e@@k|}C}f8LVlPnJbwHiN zr|S zM9ewm2XQc)z-Gs2=T^{`Y5#^voS5G^bQL>lP4pp%E zZ^JNgn@Q4^y@KL$1-+!GZ={iC$ro4KT727FhN&O$5~JUjQUDOd*8A3AZ({EY^g7u; z8T0|EM*u4LzEALqFHYh+xlHT+13@-V5HMM>!Kh;T-(hwj+&(Zp5U&)v_t#Fj5T4@k z<4a@a^=ohu(6(L_(v~L>_EFT{qWzKkg z2Qy+noOiQO*3~s#QIi5m?Z?O!k80sWouk8?cBbwyw}J8iS47YPne+l>Rfl6s0bW@0 zx4yu!Sfwyn`S<$V6L38kP@;3}EBpO#eFn$Ou81`UEb7MmlTf+b`ufe7g>y_&l0t?^ zfK~uiiF3O;T|z}wZoa|6)|9jeiCFksTp2np4wf;#tZ^aTb+OYC*U7QYF*=7??2svX z+A;p{onG120I9p^z9r5%@YIRXU0Jmg=sJY^31bSE!yunj`x9nQ5awihjDfkInAxyk zuFio&5mx>RjPW?BnjIy&Lucr3U_(8%P@ZT6ZD$B@2eJrb%0DO*u9oWs36h7L?fHJLl;HrQuA?M zdH*9Qc}h_NP$v0%BQyS2YUM%ZE7jAn@v57A$1)>+q-{=;m% z!3L7e@?zc)WLLC%O7FZo_~VTBmv?oWUoX*u4k0d;#hx7>zqtoEkMy3BlaCrBhF6Gy zcVW8IO*~8BWG6V-QGGLr21PH`0$~e_<3?Bnjw9s5Uy9b>{mzqCa85(ofoTJlm_s@Z zqiCr3(++I9=Bo0V%=K{^={FGnT1msh{#ya{bEf^Jtzl99GhR7*yBt3=Em(bQ$(M&r zi&m9?e!1_m;2r%~>lX2A_fwUOOpUX?+;pr?F`?~^tx3^JPD)lw=2cd`x=KJ=>1geS ztMp^5KWCTNxVW|n7chE1y6Stnw0FX8{E^NsJ#ov@7yT2(j-^iLggrOzKL&u3(R^e1 zQ*ZBz&`(tx2G*}#`z9nr5^fd2vM*vz<8STv9p2!P511u@!Lt~&uZcZnLY)<7s8oZ5 z&@qRsI&}GfJQOVMFY<|BmAhr;G$ZPhq_P`8;mnB z_qpUzf>_ag!K%{`I>v zQ2t2q!Lr>44_5MdZCKLBVu!JyielLMjmvmK>s7?*!G9c|ks-Rt9bUxUWI@5D{VFR5 zY?>IZ@~_0lsSTdFtjA*K40<;S#2>l35|?Aa&!4}6o$VY{AB4$g8Mgg#(A+*26cm}H zlE3+xu?4rV+sfav9X)dxSE8_&k5Bp1rBH%;doswM2M={sS2s#K$K^|L@dac!j}AfX zRK<6V7l2L7Qd~F~gfk19z^Ny@&Q!Q9V88&$J${;LZmPZAx z=grOEq0QTRa@fG~1?C!7;tXc|Sj#dT8+tr#?QDl_V$gsR12~^k8B{Hs+p8z= zMenf7bQ(DcSf|zlr8z1x@;tWc3SvNGds~}6;Hb-#sDa)xyRjd&VHf~#905_FGad+T z=xIPeG(Q@C5%msnJtzf%<$zb)Z)&jGCy-m#fl=f zzHjf2V}MyDzO^;>SgdsWCrK}gMH)Vd9%brm zE6_Z$pZPdGvl1=KF;K(=<

q?#bLB)s@Ok?5bMMIB{riD@Ju9%rEd-!W)*A(Y6>ubDc$4I|W|lIBh@Ag= zwMCdR^(@{uikg4Fg%?OsMdb~8g;0P;V(8=ffz9mf##-YCw{JfRN zaFBR{YAOBSuaN^79>*~-x!l>PcjW*RE6)Azzf8xoO~tTC68^_<9Q&Y=>|P)OB@>f4 zEX}L%QBoXN!b60eFBWZeYE@Rsp}TR2bb^mP|{msCo$2s zMr?W@fA8+yatO>|ai0bS#X;jdi!JZ|wfY1=y7<|Os4TI|pOUOSPojmA)01!O7B)Rx zCiP^?5=gt%Fu%Nnd>bfgEdw*lJx@nYU9Me*RsqWqt}TtW?ny@EBoiK&9nXLSagyM-aJ+-YcfXbD#n75BL=C}sm3Yo z`VUQ({I5V+Bj65=0Iu}dwb!g!BY5cp;~9T}J)BwW_=2E-CY=4US#DV_x6XCMIMA8# z0*M;XQMkBWctemu8g1&0*#r%r5;>Dvf%7|ZUweATwq&Md3ruMuaw!}BM4QzE?&os3 ztG&uVB+%eE!Q+IX|AM^yOMIMg#&ZL_H-gCVo*WWd$Yp1Ei&_V}z@;bD_p$B2EcJ}V zu3P=Mhnuk361$~;{(4_JRRgmVk9M{O`ZGn0f=i2wt8m)m!L|6)E0!tsJ0N1mswK<7 zeOstH@u)<%^27$saBN61g38Qu*0}u~V`i89u}uR=jN}Qby}NuHSJh_o-sjPy5R4A( z#uAarc$X1w%SZNTco47~Xm+b&+s&A%*(+JA^yfjI%H6EBApU}@?g;k@K9BtJu6)GtrqX+ji~xG%c0(>eVL; z)2HmL9ys+E)YXmB&FW23xrKz3i2-sJfj5YM>p%!< z{QxCU?Ub*qt{x351nj-%kPDHpuL+!t?@#-ydhs-Sx=LgExC67w$q)x6YKM`QH`kPq zHvOlSC{JzTiIXP-7UZn+YV=N?s=2uJy=XM7Yt-w3hjS8pzVxf3BV7Qw@=TzpBD_ge zDDa9YiLYKsW8_geIE}Uu2kR#L&PB#Q66^ag-TNzCN5#Ej@ESC1aYZGRjMofTbs9KS z;n=Yo=vpZASNp)>)L$Pyxn>O(WzaeDO;Q8AR-I%ZFGyg~XO^CZkF+BCFAWb7oE6V{( zNs41i_?g+o94=rYjUT)&+u~`_;?9%Lr=lfR4!o3IdLmaH;TuL88F2RJ?d{Vuwc?S$ zo<4pYO7_-}v=5&n7qy2LA8hizFU8GCZH+!&lqpGjUL}*K1Y>X#A=MpbA|%~vT30KQ zh=uEa$RT0!(+e4c-~fQSSD5S+5gz`$@`LrT`BJ;`tBi-6tPF4jEc+QWZ8)h!HKKmd~h{s~9DVVDl> z=`oxcel4MhkrBu&B_ZgbmIiM2L1P3aTo4#l)MvA1;6waBA8&khQ)=Bcp^+c83Rp1| z!JFQ;I$eX8Jg}`^C*3b~p>c&ID}vGxQKz_08Q*Vf)A3{zlmYjnC?j(ZIaCqbH6v$d zK4QCZG8Wl5BZ1=W?18H|Y)^jO4Yv)NGH)?zugUH>c)c3f^%PZA_aM##d~3Q)w3Bb0 zjKNbpni&_ok*G~EU3sXeWu6OL08f-v8EV>vITWK@thf6NzP zwJy^>(U(s<4k#qz)hhs*cW1ng=HBP!dcL3^*E)hO9)ge*u+|jE0}W`^z#Cc6b7jt+ ztwD;u9JI;`3uJn*1qJo+XwAX z{zsq*lsq^%Mr&XrB|&K&s=}nC3mA%>0sC4z7BcX!Eq~^LrJul8ZTwEt` z_O9NEW};9q35UGuWGHhlSWNs^lbFR71HjvzQg2d8-f-Z4#NFN7w}&dS2vR5%(Puc3 zjoAO?FYcT{(ir?2r_k=;9gZj83qn&FFNzg>yxf2N^}11EXA(S(FA31YZy5pmkn^2A z{Y!S})=SWK2A~tLb+n4yP|T;o^JGf?RC?o}9nbEKdL*OIbO`5^qdC?J9&=z$zf|&E z@FU;+uz3$HA|9tt#@i%&LKi@ab6fJF<$42ZQ%~f$gO}!Q%6yT>ZO&w>EOIjyt(Tkx zmB5)v!$IPtlDmn3YF&O}!yaTh1loh0C7nBr282ri$4i^rnYFmbVqfGHZOT+~rPp2P zX9INKe%xIjmK}1A08ffr&2YqojO#GT`UoNxYAn94qnP)vfb&mqtr4E~y@wB#P%rV^ z%U}LqUVw=$$lXZ<<0CJD*;mF>av1yRT;o@uIl+u9vV56z?qSro?VX*yXd8nK-Uo?; zKvJ9{KzJ{iGiR!CPljvMO{v~!AR)m0xu;Uvj^^ai>lI%-4-mX2m0MrEP%CmaO$f6o zfCUWEmhBymIAVZOl1;=c1V*SEqr3dvXb%59NYPVMs@`KsI?G0WNu<8>4h|Wp?TZgO z0MRGGE>GYXP9%b5M+WmNxN=xH#5HIdmY%?buVKMKF!oE`uy(CJOg_+npMdO8Q}dtj z|Eu}Lv(;U0wv8Fq4yX^UG2<&x+*u9k3L@A&FE1MW*MitDK&?QHWlN5-w^cria2F!L zl2e*_h){hD5&sIxsoYGN@-u_ZnCQ+Xp#KO<_jbIlj6~xD?i)AC8`PCPSg_Q5o4)FI z;SldLr9`rHbdIU}7d`L_0&5XmUeR;qwWld&woxG_Ufer7S_{q;N*Xnhhu zpPeQ~`hvDmKGk#j+&LOH`o;CTjJ;P&H3<}l=8pFBbMiCP-58kv6-!sFsuh*HLp`=G zqk3Ndv>!2OF4#IL~`b_uk*>m37IvG7s1dh+2URl{EoLSK2$T#Ec2Bsw?GlRUs zTZ}45mROqn%WviUi?>>Dz^3}3-DV`cO29fR6awtn^@~S>p&;J%eKi0Khi;~>smpRq zh3irrr%e`V<(J(N&;b>Mz(Lz6c!zgq6m)=^da;hP3J}K{ooWarg{^~cGBVUagMKAp z6^1kLF;?T>9bbTS`~BkoR6lg{*+IM5+3aMotZ2O0AZ~n~JPAYP9GQ?%F)f`!>m@H@ z5=;rm1!!Ud|3|&PH|GSa2WAhdX7ofwTNQFW`^?WAC}RGhp$r|e(&VAxn=R~20+{34 zMOrq#I9;M6qLMlEoRKII;a2py;A)nI9V^FGc_|eAo8a9V;d*D|zvxB=mPMJ;voqR# z)%Q&>r6&SweSLArk|5!s65(E7b(8Z}R!L~*kA!F>4=T(Povg26T~2cF1IdV(Pp(Vg zVlIX2ufGTr*KO5vpD+6>z*;nhk#%7hSp^>W)?B^C6w0ws*SXmYXd2eY{5)z&r!c`A zH~0YaaZpI*C^-Z$AQy#EBeTNs?cCh564Na!tm}x&;%6A(GT{fC%D z=*1`Cbyr4pO!mXNQMSDW5{Zd$X{`BbwE+f&} z-NfN)k~LBjqJ4EUg~E6ekqu_-*Z`9ZDrVP^f};4~Wnqv!V4#g4HWSc}eTuvRCF{LK zohxsg^*azDJI2L6)wI@(3H%!Fn+m8b66NtDh#PT;dnN21N?KY$;gj*w8n9!B?%P12 zSdkU!)rcu_&#v#<=pKrBctBIww(`Q-5mLhhPZWfVVpA_ixSImJ&ULMzNYZG4LxkXR zn4Rbp1Z5;$X-JOvIkAzaTb+l`8^d92p@2dE3;3p^0KefFc(CbblUY7XMw_>6aXmVZ zuDRT{L?O}xF!@Gi5rxu~fTFD=eOzaFm7d%TO~a-@ROdSP5yB!MtNGwEFRyWAdnl6o}^(Cu5#w4a@x&A1Z;n545gry zC&@v6CcF}iN9f))6iSHfVe6i!oO=WXlWXI5zIy#yHp;cKoeva|?*)#%Q@pv~UVvLh zttI5>c#^N8#@k(cO>%vo@8Ph|W7Eb$>XI}|y=t>5b!+xhHO@b;%{eGJ|I~Xpr=+#` zy2DRG%mRlXmcBv~25BY-X4^X)eF?pU2poYGVvn$}GX7aOXONva@7vsQ!F7vp!@l5j z!HmHs;gUH|QGU4&r9?2k!r|jO6{Q$iBIt~two&(4BUQl-EC;gMhEF#8A z!-4fedAEnh%zD;+`}WP!Wn-<+qAOE$iz1$k?pFbrSNhPzCOCj}UhPOut9I_m+E>97u0oF|xcG0BcMGO>*=FR+;uZqf=5ulaMqi?7jJatJu zNR(lCa}~)eJ%TCZ2V7kjn=$d-xD1C%RBWsdrzQ$zl-x_qPvCB8DoaT@2lNMcr0!wq z%<28CycX0H2`PA!00mDlj7t2Rq~~?5c$(9L zi#wW6HtTC64VHLU%ON29XEh3IYi~m{6W%A_Af62a$h{-rgdih! zP&X=hKYDaAs8LH*)u8ZEG$x*aFi=2ddLz3u1G7bmamb-m_e;+!Cse42 zGKU0Df@HGam*fqeVb6gB^5A5)N0T@`iKU5zvsXj!*;jbx_e8p!c7Z52g3b>^^k9KH zR0Q8d-zt3IK*f*8FV5=Sr!CZvN5}Ic4SioX+Kq)H)DM176sNZ8A^9Vw_=0UiZWg?l z>*jXEMXz~^Q)F#$%`LV3gRa2eT(s+^-Zs(Vw!!XtCsc0pZS3xv_G!7}9Q=-6&|;=y z!G7zQQK?8bIAP96Gy~R7Sf2|zvWF@PlX$(N2VVo)ug(c#`do?M!2*j`3FNsPLf37` z8NUtu6OJK3hQQ@WHN4Mc$<83ctHE_npo!C(=`CF!cy=S1HG8#~i4bIrC_-pN93$^= z%qm#aK6t!jS=l=-rJxvbxi!fiYq+&zIKi1zaoxHgC3P#JK!RJsHga&&`6cs|erM)6 zyhey>VD7Eq)I-q}WKE`Xp7Q?mkiEWxjm`Z%!A$Va@l0&T%hiJ-Xf{4RTIYb(!;qB7DB9y|dc7M8WBI}U!c zdEe+e9fhfjFx;MQTRDQ} zUeBkskD7tuZ1qXk^7)71qTU1gZiA-PW3of#gC>RY^%5re_=`EFpywtMLV*zu2FgS{ z6xdd(P;LuiWLzV14%-2pQ*Vm;E-XRmCTE(>=5nH*pIK2^xfiYO{5OLzC56{@)pEMu z7tI-ap@^2vhrJ4}6k1>0=#CowYVdon`o5I5kITX*J57@9r`di^DLKzzWl9mw(XaHW zhmPLX1+%5s(8%L};Tj{DiM|_}E7s9I!L7QWp%Fu%4oVUf=St8C!*bM<4SmSMBYlv> zlhyGq)mEheCAz^?8mU#wYK%whN|0E7S{DpO9)@W2?UH-zP2`P6r=gM(@mc6;U)!oyB6!AP4egtL=YGeG*~#8y*xj7r7U@Xs zu<37##aOXk%mt|g$3k8f*~M9mnM;-cOQALSxkMSPY88js5U+M)rg1M;`$RWkk3_8I zdnLe-8U%7dh=))EB9H6Q+1$&&CkZG8!~xhbN`uY9j5wHOM*?Yp}e#SND99sCBcW29LOf-kOW2N6&{E_ zXcq5q)O}eXgoyr0UK<3X+i7*V+nR4{S65ZNITtK0YBxa2ZvotYxo3>?pXSC}3P^&} z3Z>*0AW0aE@^ZimKAgx+AXsmweaPL8PXN9#fMbIQkYGM}-v=55Etvm+l}8Bo?}|-O zJ%E~%m`IY0qed`8)zwAJIKQxJN6VHOFZ})EN@GdFs4a8BGFcf2i9A*9jLffZSuQv6?6xd-s0PNU85rLNo&tqNaI8 zRn-rr-!6~*OS5N?Co{E7!`N8zU2IGkPSRYr}e9OJ%$(75q)2}7Qx z1=PG3@&u|OTwY1dx2hBq6?~n}sCVfjb{$4psBdxJQ3G}K`$w0j7Qga!?nnc`-v&My zj~$|Ql97gyh^~NO971)fLGb`B&88zo+)^VNHU&Th@J`58kgaQ5%u9rIZ#ifTRd>~A zvlKjVZ&kzX0tx^#B-)Iw9NZK4+{%Z)YL>3C`Bq}r`fz7`r-WfKa4$! z;5|E!lv*dxis5#GRRNDg=3)tHfQ z)-ko&b*vv*7745)16y1SD${B-Ot+k!d0*5n51Mjfx-6zVmAzCjPEb&n(L%$>P%I_c z6|FZkuqObIR@Q=~js?pEvNm9`GENB!9;mp7wy@&kO^$jwJ6 zWiI4A$!Iizqu>iaGxfkTOWn`S7U{)I&suHX<9Y6y_boKNk;eIf zz>Y(YqYC>RK0m&h+R4JNi>SA`X?fEiQ`tM3ysvcfmoX07W9di+dH7x-!= zrJ6Q}2lwvDV=8SJn~{-G2^++&@X6~{%3%eq`;CTY=&geSYOk~B|6Thyo0Rs-y0`eD zM&j=AINR0wZ^x>;jIuR&mBZL%Wo0o{VNCRwLskMNb z^5T6~6f;tc7ft^{Sid<+2R7rK_!J#a7H#fqw$hlvVsff6VdyNYN4|zv_GD=ThqIuN z(=1-SK|xJWfZ)Y}Di8{EJ#m=zs=FLxr)KFIp4s#XttULyc5x$JQ|j6`yBue;-+kyE z@xO8`-x)<;gGNgK=)rWqj~K*BYrpN^%m5bN zKjvLaib+XY$L52_4#iCb2+2Mu35;8U`@U`-8# zRiQ1FPIIH(UzE9|X@rJ?IEAMfG$hhl|Ce*YBlm*uW}>ANhDo*8EJOo|?oXu_9`drB z?V+pV_a{GJ+|ucE^s%WJzHeV2J-<(+6VDpcuzxP*Veb^J$XVPgLi3$%+&~y__%og_ zFjXixglHjHCLe2M>q)vz^m17pA&ZO;#}IknXsV#$n4J#^>gKI+evR-Z z4VmJ~HF%$)Vphu{5fWfFHR_WkPiJwbyt49ZybR<|*|^r|Z*<4IUqXj54aQ>KXRXoE zEhNw(w1|pk6y_YVtVLB-4P-;~8}R;n4<9C1h4J?kSq-H+bW)mZAPh(XtgavZ8 z`$g4S(^IS!1Se--rCZlu@C0Lq1JThBn2N7fm$Vh{5{ZYQ#*J0|;^WN>V5W+66T7pg zem-e58I$#7NlnJF8k_xQR{XZUPcQZ)_ItmYdB@YL=4iBNO)eED(Z?tMJOAeRuP)z=aLNFqqr>v{{D!i1IWBFqFX;1iHCZTkZlE2@n^Dvvx zwdY(@V{r%Kl2cv}dKwYU7JU^UaGrwLl;z7xl6_#*=`j1iXynQ#z_}4@FEhgYsf-^9 zMONVTBBKFg=peA5cF1}&t=JlKpEqOo-|MzN$MleREMlR0!|d>RH1#>54G5a%+yg8O z5WB+s#1dZ=F5T$1@a7~i24)su=)7T&cmDNrSPj0+Tu`!?ILC;+fO8KX7OV&4j{xCc z8;xBdHvEW*g>CKkdn-?)=RhEJ&N#L46=b#BX*(DzVE{KanIrWf`t-?v)HY~G2wdmQ5<1lxjj@uirnYQr3rFkskz{i6jYU#Q!5~6{awB78@ym9CS>B~tu(f| zgiY?3Ze^hAnExeNKWI)AIr8iO;2WK|l|i|6*1#ZgzCKV-J&Rg~$Uv`7`@LMcX4mHy zvs>3{ug5H>{~B`uQmclPPHrDz9PuX+(dAJ+5z`+^;+Wc;8Q2~LOq(*-Kbu1tZ|S2T z@oQ{!tUySIzJk>?UJvL<-6TCiaV+qVAtQj1oVyku$>+z>P;g7=+~zQ3$xA0wDeSNCTkGVXl{yJ8seXo&<@FUzY`P66{9(`d1JZ7X*%vby{-hIy15PkM+ z;0txWK4br!F%T;m(}~x$6=z=v!%a-#8rTfN3k01sKr3cAn2I;SH{8@lcZHo63l!o= z_q6Mg6G_|Trc_U+Y@c#5yJaXAe8j#2-N=I25NT3XT`id5=`3LfNm7T*wju2ao;PkR zyaK_Ge8ON?n3s)y`j)X(r-oxfOp}Esm5i2u%rJ>G6=eQ%mpi~xpp5z4I;aoc81%j` zqOeY4EhZhj!gM);CLvBJUb=Ki?q6R6(;daog3pFYIu65K+dcBPL&OHShMdxsGuxZP zNu8Ud<)}^nU1_3sYFUzGzg}Oh`;V!-RZ%0;DCAFh6ZRJT(60XcYz{kON<6+oEF94~ z3f^Uu7ehrBhkovS)TMe9hXQ9~{XOeO|7^v}b1Rq}Xeq6V2K(Wh0L$=I&O) z@5LSI;^KFa=kO!z7WGiIGVq%3CdMI4x~TKky*l_)FYw)~xZM|eYx$Y8z~p-C@N#G_ z{@3G;J^0gz{DHk|d;PPMdwY8dTygrKxC5C6az;XXIn&gcP{jL$GgdoCT)&7_M{`{1 zO^s##gu!#Ky;h>FE;XBsdhQ)d4kDgjSVc&Y3^@otTy*insU@5aN?%je)b$!@qF2sn zD(Jt`J;_%k(eGhPOZSGsfO?3#n~J|BLDrK z*3I8jWzCuKbGb(QiKL(H+#>jq_E{wGto@N7UG2-L;+0o##)a^n2hGnVbecv4=(HXk+c1C zdseL)31|?uJ*B{=#goFL?D6CCeDGOUJaTQLJGnK+O_{178o+wHRQ+&9((BU>d_{@qh#ojagkCKrU0L74VE5PTCT^yxpayVg8|9jPFE;2-O?bk;7FDZ~JXc z{u#GAGDel@d*993+Wh&!7)RZ4iw=W_=`V5U(vLVj*$)f-c{xL9mifEv(NF4s*fvgE zJe^QJ;ggf)E};!&{`K=`vv}kO@B*?4wF0dOhm-b=&XjuoBOm3d(Pn>;d{i12%`vys zjt4pd5tqCPrwSSGKl#*~DL*7@ZksN$5JTGsIdhoE6Um#=wS$;7T@1XJ!%lHMA^pB) zi1o4L;N&Eh+OeBEpZD6sDh*)=)Rz9>-o(xisC~>EPHrntyn&2^#KWV~Tc_}Mu}EQG-m4d9UvoyLrKw;Go(}|a+~t55Yn?D3DFb@@2;%>wb1nOX zdXm<*l$>~KX;G2FZ%Xu?l5inp1b+$FY~obc|L_Y$1|bn8rfSPLsR+vQu=UhUB@NCc z+fJn>_1ju)PUv5Ja$CWyJK^xC6+Ed872jvRjpd(|t^ao(Uw^0X9Gy-4M)~`c6jPsX zbNP1_9hCzvZInK>b<=iYLn{e9oLX29&-idXeO3t|T(2<&1K>!L3AopqHRq1B(5~7J zo0dDwHqGKKsy^rPYYlZI_*$}nZ7mLE2zqLUI^ldkj*I^20AoND61FhlfXSeXe&Fb} zIp0xd*K5a-&X=@(Ljo(L&QjJ{?&O>_fG;ZzX-}t|g{&<)m z(xhD-_jlMQz;k$l>DJg<{nVX(X$$dMH{d%(`9X)|FBk``)=+%yg22Bic z^VkXvBUm|M-Nh}#t~wt(7p4cHMz*Oir}wcye(wdrS_4l)G6t9ugB|K%D72Lb;cuej zGGqV@O~p=mHG z{BdXOIe1XvnWl|j_Eckk4=f^z;4$75p&WI)4R*i);y(nnN^`Me&3>| z#4$P0|DfK(YeMR7(F@&DuH0jyNS*@zAp%-mn)#JCvZPd2BYL;k?09P^F9XG23f1vI zG>m&Qppa>VNsruw+8Z?^J~s)y!9cx!V>x&S+eaLZtL(=I#2CVSL|jnhEx=bQ?6WEA z9f?9O4?(tpwoL|2V~Z!LlS3~Lxd+Q3%zlN9ZXpGJH~7pz#6vA9txd|t8X_o7@~F(oM%tu_cbMNz zqJzH#2kaIlMdX%@Fed7aOu%enGV6%7Ref5l6vkzE@~|!7r}m<;BN0XVEKYqlSzP&K z?66hWBa@sN8#r2tTU%m0Z$;gR9cssebAeBvR>fV#r{=2NXiStL-0ws`nA{Y{LzI

8$1})Rk2PHH(8%Tv(0mDx!~sfEnNDYxP8;9&FGK=vc!UMCZnYBZS0fS`Jjq)ysiy z7!tIMVs)oPUj#0YfjLkr81rBNslS837Gl*yxJtFR*NRsrdJjYwnE%6IsP;~`#S8Iu z`)rSDbX1hui!a;ZFeR|`>NCxnP=^KERz82Zfbx|SSqi4)uyR70+_bs<9%>!&oD0I} zNXP(WzFY5cp>oGbN)uu4FhZ-NcX&&_M=$Ja9Zpx^ zlhJK$@Wx5+fzOfDv`<0t!(6!fC%I%(TylyM3Xcg>uY%Up_JwY+^5Y1ilqT{g0s^?3 zBuIwjCZgy4-rc+BQ9))81@`0a3B7*+TP{&6iP;*xS4o&(f{xY2gQ~dP5bmLqWp!$p z8lQ}HeLJW!=guKWSb02KN|_}gO}`C`E^fl)yNBw4@w-*8&v8&kwC&a5*5Wk4i#>^# zzp;&Oid%qxQUJGrL{9EFF>#B0BxilKXkDTV_BmS)qiNF+##p=!hHIH{Z=U}GrS zFcFiZ!p<$AOy4^Q+S5B6B?37y-d1QNK|g?&;V&1^Y%hrKi<;5lZ*&1n_je0TUfQ9# zD_alZqm!i3#}eG}p;6s5OA!Hyt6|aaz@W3y{byP$E7UO=Z``)pYeQ_SR)u1!3v_Ja z6emw?`~V?v7M9S(deLzZhSq5@l5#|?qwsjh!9=qp=?X&=dKGCXBVHy{ZJ@w3a)WnL zk`^ln;J+uoB%gn0{(rz@{eO|y|JO9}{|kWO|IZ&hBjF;-D!iLvu8KSr>WQ;bamOy+ G_`d)X>Q2@G literal 168050 zcmd?Rhd-D7|2OXh|9hA)Ablk|dE4Lbi~-vno`Qq>@$1CM!ExAyKk-l)XvT z^*ri4f4|>-UHA3<1Ma(r^Q?%^=Xf8l_v`gs?;}vi}b$r>nZnKR*;@|i!|gNSzNh)=~La~Ta;DOyip4y<#+gMPR`Ws zi}(>yFZyGNCWh-a$DV(G#jKYX=-T){{ytLX86NQOFB5P55+#{`fALEl@7e$FH|}}z zV&~*$#(#e)tI#F<2X!g+g7Uw?nJYC@V(-IFQHO~g_Kk%BK{aZPv`WX^A`%xC0Z z>~NVVY$^7T=;-KB(bc7oS3P-j%*4#BOk#D4ewU~tL$~Y9#xtB-jA|cy*^hkdU0s@x zmy!997GnQ<_Iii&NHWRu;lr&-8tM9PpC}VI%0RE6GD$9$(ca#k>)^ps-wiwNMMMN% zcy%^9BSZDwIYmX6=h@$J$6nu2GrEm$zHj#i`3KU&|U6BFD`U+>{mbg zT7Y)`%~I2>P0mrtS~-2s9J-k1^~87YCcAw3GR^w+r2{qLyTfbb-{cXm%I{aHJUl!|CFk6yw`d%Gbl*bs&r7_+OVE<;-@otpGunT$%`A2I zi4)r{rCjmSNiFW}|GQ$k8ZlmSU!R;Xn;Zz&B`2Ahne~p0h`bE3;%Dw19}iARIZ7%S z%jpg-^QS+>IkKi*p~O>ot25t0DV#_9-L30iUT$36>4B@B{2qFaoD{t4q<2%|r4W@7 zV#}EvzPs?)p39r#bbIrK7_y$nVF3YE&#kF>f*!wnCw%=yfnKS%4HFl;xT^-O!@!M` z;;X;<$;z}zEqS&F2J0dZXpf79S#T|M*oM4aspYG`;$iUTr`C4T8GK7G`Ex5)^h%WW z4#tLvKgf0%Z8?6_!f|!+NBmeNmgvofOElh|g)R$<;cL_3#dL|=Q&UrUUY?8EL^^%$ zoV>Df-Nv_%iN;XTSa-~bb;GVqG>ukz>H^BdrrV(h&gn0VcWR{Rk#%%-Myn_G4h;DI z`sLW+v9{v4G?sH-Ma47m;;iFXR&(6gwnOSy=8h}>scNz?MXE4G->Bv92g*#1_4Q-k z*86Q_F>f>AXTJaZ`BCkzY>W1LmCNHpmcG8er^YErA)?a(Z{2==j=nl}iYUEitBkvs zD>qjr;m`A4CmhGFC@8E~Pq`9!=u!&Z$SS+Xa(dm6PNAlzW>0S~8A*jZ?AT5^fvhrW z)$`}Q($bD;B&(Ce2DW`my3F@;CCX*|r0B}*7ZT~*xpNQ0!b+>F72d71TNRhzW#Wsg zNls3-wzp@UKB@dncw2dSImt7p%XveH`0tmjq#21Io<9eNp3N*vGHSHzp6bx z^YZE#Is?|jC?n{<*MlsT)n&fbIG(Ed>9c23=pL~d8Jp1_)DySi!?}>Hsj2DL?~&B0 zFHx&oH*MNP!FFix-cxPk&-NeNrIl@d|12wC#2Jk&(`~gznRRY$?d=xoD{cc}ml#-B z+9x!^!onJFnwb1dYyZ4Zw15BpvbM_daw^4m)pu94wZ%p&_hMMY^hTPa{675c1bQWA zm8)~99t!Q(uU~J;wqPNrpomuE-c3VJDdi~_>s4GVLEO7=U3aFDZ^sOwQ!#K!Gi}R5>LS-c`&O8^@~@2tp}o9d4DF@74nIAK5wNh zYjW_D?>3;@zi*#R+n_lOap4WeTPuy%%pg z9ZIvJzcx23wPXFuy6W|goo;6iGHO{OZb!qUa`y2NjeerrnM@m$nEg;s)d_m}l5@1X zm>oqBYI>VY(#-78{8+m`I!DQuFI(1a+O>05$j!_pD=SOe!DTD0Z{2l6QkvV+{MDvo zBl&5X*RKzyv1cYFooOq2-+f5|-28%S&aVZBdKLbu-bq8Gq18G>&rHj`9^~7MuNis_e~XUT}ovSX47r8vS z;_kkBFQm1zGsxSUd}z9_{EmNT*UXQ4`rj?Yz2tZ1oPKPdWJ7cw{>9@x@rkP3VJ(F& zf`gx)9nubuh-jT~cUxCO(JB5gJ6m8MC#PaK;D+)pk(+eAy}cyQ4LgqNp{ZXI^V3YO zC~%sItC=WX!wW8TM(ikiR@&axWqBbdD0BCpp-?ZwoEg0T54;(rpwMA{)Y77H-Q~rX zR)C=RF>IxP7@<)Etz>ocmNY5u1nVEq9QrV_eHG)S6%}u*8H_bu(k;KgS3bo#>D6;* z#Wweqx#s9EL)3+-T(<=hc_oOY)^UuPNa~JP3fEcSuKja`H#k4y3jg+2_MMY$2YpIC z*Ou$oWG-Cza&G}O{;PX!MR({%w`NYEsHi9dGc)arSF0BG_H$|N)5|mU{T~9ikVx$8 z?5@9h{Ts=*MMp;q@Yx8Ox9DZ6d~mR6Nh9M9qb2F3Ygn(XER+EaId%(nIZ_M_4K)rF z^-iidw{IY|qm9kG|9LyEq4u@H>VH8BJvvKESD_0r2?6qUR99EOPD~6n^$Uy%n4iBH z_wwZ)ad9?`6cQ;&&fo%dbShekYMc1Y(U!C}0<`I_PKP%Rq!zC-12hRNSlH_BmOfv8#y3?K|j)3MHI-8Z5seP`t4AA@*cAVt+maHY%V}Vhn zs+|fH**81%<&v1Xi;IZ#sZ;S@zYuG@kyU*l!^eq+%yq)Eibk>ek*@u?^40yZoF+y?+2e|rx>LBV|o4$up$ zP?Lb@l5`50N%x*V7uV~|)QREJ@2?1Ilw1rJ8BdEiFQVeI^i9BJaUgsOmA27QSBfFc zs#{!iZF%ODcxqjwAi$(SOYc#V>+hjgjq@IB3m)_?5(L|MClKmXOFR2;D@LKf+Hb#n z$#XqRolCRYLKesV>wWF#7ZB*1>@7R3s3`61EX>W#{i4bAxJ4WP;VZce4xRQp9`WdI z@|8aRJv}2MBd#gXV{NIse@F4Ja?b^qUf&&YPja3=f6jI2kWXmnmwT0KlV$YrzppX} zQ4{bpCuiZsf|aFZg~&vK4}j%fF)>!ma6Y5jlHYS9QzaDi9KyoRRh6SkTV-$Ey7dAh z9zX1Lax&|;5ow|>1&w%vFE5O?`gE_(=bU1*;!sbt|IAeI_s#F1F5vImym|AzurR71 zIiGLe)L%CZPgynwC@3kFp{0CH~I=OEXbxbfll- z;}!DkZv3B;nJ(u$Oi+_3sj2&1x|h0ya{P$zN!jsdZt-6}))UVt=rV7faJDT)JKyi( zUh_Rdwu5w^o}Q$5VJxfve2|!R{5F4j7G433WZTmGn9r4?@)s^_=>p-Ivi!7hi!38% z<~mZx&3232vF!GWJXzv0?PCAzyqIdw6aNni`|1DFWcOf2J{kYKu&}VDiH8)!3-v@Y z5}=)2tj)eBCwjTU6srt*chdPyS$@n_;o7Rg<=iC=K6!Y1jZlt>s|i2z<2s|qb%TGt zoY=%bNk;nI*(rwG$j%!|k55Yv~r-=oY!+bJjO+vP~H@){V(kCS8hc zFp0Bp87=;PnQRr~H{ZzHMbhJE zzM7?3nI~&-F1Dd%?8id-?@{jXn%IzxvWTAQ|W;P4BXr zMt4^^s>HK-_3mJIXl(3WLtZAC^KWa4wZJ*nty^bjXV;=#QtF=gL+PZ;$}ZwkRzK=^ ztZF8Iv+Q2N<;>1ZjE=s|_ta&e6cQ0B(>vBOP~TlzDnf z`2PI*&%#Pl>0b#(FfKlx3I*)Xb=&&JjrSN1f?di)r^#UXM|=7C4nqQcT}Lyif^Ems zefS#lxqn}%dF*?2XjM&3Sw#>_W&T0pg>MG_BgJU%cy`wRl8zHqqI$BlzJ8yhpg(qB z)zN?So|uv8kB3qv6V&?TC4MtfO6{B&WB@8^-T#luV^^1vnF#{y6}7hfHE!Ssl%*DLM6FRfo5w0@$(y5Pz;XGbe*AO^8QU9FQG&3THQC z(#p>*beju=w56x|5qCJ~b(fVqAtc1a?Af|)TlBkk&N9X{5ob7e(oqv1L2~$be1;R7 z`l*l&`Sv?hXazA`(($ULt*w`TziKc+f9M?@r8{uoKyH418L)I$R~HQpjijC)2k?=G z(t`jKhQ-Cj(+UcuU^+d+!@^y4yNU7xVts#yrzl`$X(1LhAmT9o0foCxUcPe5vMs=X zP!|Q0va+@T7!wOJJw5%oqeJw&IL0-WesX@^FFZV4&63!=fHSBq{6OHLGRQn}9v3fO z{5&#uEV2$Cs!Z!*8)$q=Kl7XxU=A`dHI+u^CzUibFcOr*)|O>9(kxm1&g7)^Er0*L z!oth|=$VODd4U1c2D}kz4ih@J{QUIihMNTKlB%jsFE16ZEqUft58OcYc7C2pl}l`r zW}pm+p()gnFvaZ|78BD-ZEn{Nq(=8_G9@En)qsPZYVprMRNRkpM4e!p8Jvw)k&IU* z`{^VB9MInplaAHY%hJ5c&-~}B@u~{fgz-=RX>RqUrDW*A9Yzts@eqXwm~mQJS@zns z{ghIYr%qAKI+s)zog)g!3%0IMxi@h|JjFb~#xcw?7~zV0_&2 zr=2i9B5y|48G(DnzI;gyJOQRRIXQWcdWrO+dE|b0xPL)`sOwbu4%`hiijvXR%;UzN zc2QDM+4%7=w#L(o{~}pf{85Rlez_gnvxgk*!l$dNrN!}7WifM*+}m}Ai(SFYB=xmZ=U|?elW`|xkIX%tXblgumdT01%4x&4nXw(|<6|Kx` zL2%h~;J`WnXlObs;yoL@JwJa|ITIz$dj9&Ow- z8=UtwljX3I%PT9r{rwa`{*KPh?>jqLQCtG-ZDf+KzVtT6E9E*)?ZLi{Yb}rA0vatb z880-SYL-{&7fAhJOmT zkwjwiSe|mvnWOdbCb}{|S8PK7m>37N&eqmepHDZim*nj1_@OHM4t;s0oAvTMI`OAx zBK}XG?z)1#_vV!x(7+yE-pv?o&+S|@tEn!0-5^gs<(jVQ2;Yc-QC z1|iDBna36t9tN0f=EWX2HS-JGq->hgubxA$7IW|9^1+A&Gv-9)YH%r-FA*C{D-m}=-DjLgj3O_dhr z<{t#RU400GhFeY{o}!xY0XiqNy*m+n#=(v+Tuv0m@52;Iw;xeAG&b)4*$@ldLGhVy z>F|*w8`0?lf`ZB+*QQkV7mDFT$d`VyU+yAwAR_#+@VxB~RR{F_!p5@Akt%E+9T zv9;w>R8l&B@gmew&9C3SDVCa{@ZY_A_u|XY=cc9;dQKEl3~@|3++m7OY!Wf*@S(;X zU)eZhN5Bw2bQww3v@VLP9OB zEe_DQo}RQlhNk%8!-tNo&yuek4gVlZUd^K;=o_p*j878?4g$?n zaW`hG;uQfpYKq&pZ{w{qGBE}A^yn=w|B&#Pi)EVN3|>!kg#Oc8WwEIg?R7S6*no)y ze3O{Cft8gN!jq)2@sG4i?;h0*s47tTN|QyLp-yb#_D%EV!1_vYhL$!r?wz086r{qRQg|yqy)^>Yv&le&zaJ_&CGqbA~h2d%X7(*UZ8~<=zlcZ^RJhg<$ z2&hBxGa|j2MXr?vca|&cqnY?N_Y7-mYfm2^3et<%*#0*gt|=RVH3NqH93Ez#7mwlE zBO$@g#KcrpU7fxdIl2;QYGs9?=C8&r#Xxy5DGW>OI0Pt#3K2SaQVOUdK~2%id}8zh z2`KWR5gAKRg}cS@7|jlp{|U2idgxK7{L%&n283+-D2sj%A0k#dHkJbQ2m0*CbsPFV zBvt?X{QTK`^4aCF4;2-Ajvn2Fn*^G`JbN64F+4m>^b4q?{3lK*@C9Sf6V>0^y56Vs zn2C0GQGEjd)~8ROz|#C)y*hx+j&<3ntE>BKaj~?iDNC2Z{MgQWPoCWNSx4V?_$@Wb zcCfaTf?m`Uqa+rz1RVzR9xBHBOS*2?W*h+Hi9&|5;;ee4qCz~H;ALBBWsAERU5;;e z^9~Ph4qy+pv=kIJDrBaor^n0(`gr;B<$0H<1S;^pb7un@yZ_3-lp&~=F(b&=weTp| zJ3;*OquSx`y9j!qpN@NAP!P#GhzS+f`n1saSlbpL=%FE0j6X07!TK3x8Kc~PAKbJ{ zh=iIByGFBNgAaZ>WNn|463Iw6Tx)!NZtTel@LU2XX7$tiL3P(J5#ilA$=gGfa-;`f zp*-E7(i-Mzg5{yle7U3Aly!Q?Bomw71FvZ&t8*t~-{G1Ww{7!HxbRAU_-g_UJ-xqr zBI{&&lKP>eS8_eK95~w(X;mbJJps5E&s+qE?afhwWjCVVg~-6)ECAj!-b3bk)-25< zFO?(xu)GF?wGlM5wB8&vC^Dk7O-;Gc&|rVWOMW%smriWbNB0NP^J+DhyLxpuEiElI zt7wfOuZ+CB!SC6j$(fl`(BIy?d84AO9fbXOoW2Ij1=r{cE7hShoqDQu)i5Hj7D(v{e?9WADk6mo*C@PhFR@K+oPvO9g|7pof1^B1vq+ zz48wq^eXN$Jr;KpCKSeYYW*xt!ZpO01=4G~RpmCfJra9O9^e_{9L!Ci=e=nm@Hqgr zlF}VzR^52j^$}j!NVf>iH*e)N?|e7+hVStK^7-{*L^;Oauk>`7 zJ=T*o;eLl$D`J@sn3kCFMohvX+Cw~6-yq`+t-b~1Z+Pw6ZV?fe&-4c|iV5>}`(X`OpY`rJEJ1P)A3l5mP5BKKItGy6 zZ9hM10JF9Z-NdFn92_JF$_I+pRwnl1&rk$bKJK$~aEOhIlhM4J{N~Namt)VKJrkfv z{PgK80p|cgFmwq7h}prwz(C^R;USQBUEM}YOH05_FU{nlmrZT5=WjKb$e>1^Jb3~F zc^AevmJ&pl8D^tE{1MU%G$|ktj8qB|Xv$S=f5++S12(Llzz`7`{qa}FF|8~?5XR50IW2C24e!eh+^do#rTOC_c8WZUjfJ*-SmOKX@4 zB-fYD2WzY zQ?mhWzmePs;1y0DB{?}c%F>q*3bt>L$;g;~lVu}&{-Npou$?f-Z;%`X$lH;-FV(mS z!gpBv-Bv#QK1J2`J3}*>Am#{ooL5!##t(oIzVY`i0yhE~LLdG?A&~oZJ`a4H@oJ&j6hcv(wX35odVcMN3Eie1iC2SD=7T z4(1=>s)G8`z4&oh0b3YDgj52vFI~UgwuDEG^|OUE1+h^N6dP8^Rf2l^itWL^H+;@7=xo zYL%eJ`K=&NcR-v7(*5FvUJ1QHVWslv(^tfU|ufgsGDg;hgUS5v#&B0tqGilg! z82zP6&=rkmdD#_O-=>o%Prgn~WuJ5hivxpv-;rm_$j)w+S=DT4x4vW!^Mo_X`H#!mQbC+C`V7g#bJHSo16^wD((Rsn&={jnPXjM!8eJvO9u!k}<4~EWXJ-Bp z>aOJ77!2ttMeZaV%I3yMERW^Q`0p(N(LXh>LUuHrUvVyLJmB4d_Tz7jcCNw zi0&FQF}Cb6>j;XE4M2zuKy0K~kqSi94E)F@s*n-#AhRMS-LJk4%ji(@+S;t7)2z2c zLPGfO*p+%y3D_GKyWB0xv|Bf#;ZpVm7|Q_b+^lxafddkRkljy)v(JR7U`gC5_lEci z{2`sJehLgNSzY&p(9N4S9~pJY$;347lUJ$0oo&KYWlmtd&g%G}%0@s?CRmzjWym zW_}3-M7?7@aL|7ZC#J?(=jY`y0nMNZKq<3{ZY_YJ zL_!K_7`M*oRTG2%dpBP&+T_T`%R7l6my&S^SV?Shvhr{$QB6ej(D=^w?c4j1johMo z;X=F{ATuF@0lh4Nj=W#T?&=9>SFrkf1d$ZO5~WZIimv;mp3UOqI_J0EkBSOGCGX7Et;$pdrfyYJE19{Q^&-_P;U%Ol|^I-jLnls zphD6^PX)aus+u4GNKTrNh9@R?!p;+8H7ZKC_+CR@okf-BLjJ6?7-pIZ7yyAl9zLWS zS&&aq+r!00BPAt;H~P>>=}t@lFj?=fss6?xkwR=AL|gA-?N~8lPl;E|{eqp8xZrT$ z;KB8{RRHsZ0tTTGQ8P3!fP(vv9|s`Zs>m+D%*;HAdlU2at(x#RxI4GU+H*pxCO(}0 zkE_1Xv3T{j8BwC);+LqYsTIwhl1c#Sre?pKQ@Z2ffh57{vuCqUE`3EaJSRFuiLpI3 z^eSo(41CG@AK92deTe_ey`Ep`-!{ozvCJ44z)6DM`}QqAB#zjmBs%#2=}eT61o{!) ztK@N?+HhUKyxVPNdfFNo5xaW!(EiU#32OS_P62oBoVs$QBj9VuHd#i9pJ;MI;%m#c z0Dy|VKfRtaRkE?MJ?6c3n?HBRLJ4B?%d^k8SMtA6wfiXn(+db}2R|S=n;wK66CD>v zi9}!H$V#VkF(zVse7r#EfzLBib@lc1Q0P7n*`aR{q8exhP!fX0s;a8Tpci-8RI=io zu!Ockbs|J2=wVPS-y>mV1K#cE=!lk14hffl3Lw!K2So7$9+2QgwPYI8cRBrF-6i3^ zi$qNACr_9G-7oxBL5PWv$(SH7)Qd0CMY&D~+G#@vH#IelvlbK*+9|QrT2C{|&IxOvt>sFOW&gT33JD2`B(0qBmtuU=ayHbdtbKGSd8UF; zTWX^5JO~Z7K{^)h2|-=nzTFBw4VsA6BSdDnepCRdfbj%MOGsExG=0pqPf-#QKS%xv zzSce$W5ITpMcm5LnIhsR;*e~_YCtHW8(v;RwDHHCpt?7lO4kwB#~HnjT}o>Bt5Ni zlYQ!XSWlzai2^{@h`_{Xz34e;D?mw7`ucm^p+19&T%dggu%R9KB@|cYd+@@`4(oh* zTG;)6xD5ZakdMQxH8p`}{(R!k{{+9#p*c*2cr2G0i9K+d8H_F{*oAvaa{lzx?fZTC zdrzO<9UV0XBkafUB^b1H^u13>&rX39a30-9>@YTdRVNO)8FHvb zuJvvL&oDB2RaI3nMm`hKB%6>#RRhVsNlZMCR3OSw?b4-K7_Og-PXWV1b%!xB%Th8| zB!G@bvvH#@8aYT;&yOGLBqWNj`cE`(o}4#@#tsTdEC+n%>z=^yjGH%mBk8pnFceA+ zW(^UxH?fUk6}hPgFTBh-sR1(EIzkcPp9U6T0~YDDI{pW?hm%dzZJyLTyq!SXV8M;{ zC=3~QcX7N+vf1TMIXSsLM0QA?Fj2tTAXE z3M3$@2e}P`$Dy4|(4ODw&uVf;01`RO^|_ge{GKKm*F#X=&_IQ(dpy5hCW(3M#Zpp# zwq^Fu%;ZDKPGv@|(2;q_z3VOAuE7iyJ%m117tz4O7!%; zK7E3gg6(v#XliMl*VaBlm5)~i93mh*a*+f8-p$W%_x0?l`!xfN>6NygUlDY~eTeix zAtT)+i=7HB1nMFjuEd#_{m^q2G?G3ieAhx^Ti3EP&-RI^&k$K##({ICgGkWSr?=#kgY&^ z25xh|^wDr7_Ut;NnOn3cK#2&?3Zfo@Um4vNC~pYc_k`DoNr)m7a+=-;_sQtOg`9=5 z=)Xx$f{&p?X$^b&C8B|$2&?Mr<)x&^fId=wKgrI{R(!PpZCP;*jN2N#&tTwVSmT?R zSP*mtvi>^ZJMAX?F>vC*451aAx_MIoh|9OJQ57kKNRE$4fy4Co@tZE>Ge@p2Ub7s# z4DvQuWHYPjwr$&baYmwoP?-5BzCsJZX_t^GP@DIFNnraKi|Wo}ZCQ?JByoBcq6D)N z?_K*%i6jxXS3_1&YU78$;u%3@3E+>t(w_i$!Bg~W4=qfQY%vfeY~>PQGh)dfJ=%T- zk$v1{h_!%T(D_hii1-rpE=fJHZX!cHvHZ~ytu)i7H||?(aV#dTZ1cvwKKq$Dbpok< zNszth@JoX54by~S`*uI5kq}{Zo0HnQmYA+NNVw;`A9)4Ho}x6Ot6dX;-dXp?)gMvSo`8 z_=EutRS>GVPOAK+OU)PC3HyVv!*K{8{HS(D1eGWQQ1*4Qw+Ms~;|3WONgJE=x)qGq zben#;!#V}I-GUDduI^b^P>2<`nf-ZwmzeX{!IfImEa}(=LUbk^b?Bxj{@)w@sH8kq z6CQrFCm1sz5hhCo2o=m*N9D2O5oZWdeF{K3HalDWoerdLVjK|^>HBxXWn>{ATHd*R z`~7%le(GYn966>aAp$g3L92B}KG~rd$K?Jg*lm;Fnw$GujibW0pSzIQM5vfkg!Bt= z2=Nfj5c3Eg0^yHA^N7pzV_{~7r)f_96_+ZyJXr=7m^*C1*AuVc|A#iY%&aRw&Okvy z0jP6a&@mF$9>M9(L<(%QDwv`sG6JAO=x$O7iu(DrQNDytp;6>|^3hRkT1?j=XK24< z6u7DY7?Imi;5bK>wSKrriGh;nrQF<#!eia2R&Nf7Wh6kb^+&be8D^fFxZ4T!AXq7_ z-HeyApzyBTUc%-4HF*_2X-mh;CR3YsiyVawQDKmjF^X}N2jJ0{gNEXp0j$b^z(6T! zX>w#xQxe#00V#Fd7q7YE*owfb6@O%TCb|lNAS(W_qVBI9aY|9!ykca$NH;0RD$+g*w+vI%Vun>O)d6!OM{N_!p!e3MQ{jVIY#LQ2>Ii?9E zMsWeBw&amOCafxS2r^@LI7qcmPHe#OI`GW*X-eGCZ}sb+i*a#tXI!sDh%nu0#)zPQ zjifsCV!-FObrX~SiElb2QFKAXD}eXW*!>iiZ)7A3k(P1SV4%b?0{zNhRv|>lKRp-Q z0I31(0|(21DI{OOt@+l3C$8V*7pugOE{8-3Pg_DLHebNTX6EMXlW@~EL z0yxhFw}cu39p}cwMd+rq#A2$eKdPI9-_VkFl>)^eZkeUNj+cp|s|Zxn`se4}PsLoa zyKN)+j5iUE8RV?@O-ER3Z@0|fi1~IZyG%d5 zC)H!+P?!6XMPzFq5|0qA))Az8LEqTec)4#!v80AZOOV*&RsH`vn|C-&(FJw0;P-Ii zv4}H-A^r2GCsr6hI8JpV)CJV%5*j9t8Nfrzj7gCVaN8NvE9fa-?)foU;)HRKcm3ZwslrbUpMoj8Z4MVxUX z&Z-=F;*fQ78HW;OjEr8+YNDX8+Z1*!>_uE1`o>;P&Oy27^J)J{fK?}(AOxr%5^GH0 zauA8Itb~Y$eToYnI@0!!>J7*z_*7uD4C(8_?v z;38>_qM7a3TH4h(nL`g;{42AvwMnahsLGk-yKx=BL} zS&cDa7ecIrKP7qg?AhYxkI(>d(yHviekDJ;8Tws9dKZdme_npfBPx1@c@i!!PG?G# zA{%HmdtG*k^s0iT{>b^V{co!=xBhq|;LC7qWLLt)8 z(IJE_G)Nrqb;slVwCaEJIU_|6OC<5}YuB zdy?owjbpnB@;q=!P@E)an>efVP>Z6!Z|Tt9FN(kS)Yw~YGnvJ zeD`5b4-tqBk0m_B37)uZ+llXVwK@>y2{)HmxJatE2!C1@uiQit>oB`^)s7zN8EXOo z0T4u&B3uz9&om9|%E~Crz9p4H=Eh07bsfd{0@`2MqYpYk|7HPTn0N9pAbwg7V}+Q6 z+9!Tm;EZ>w``jTMwyGVpUznYh(bMAqfPy#&cxFSGC5F#`kqCrVVx~*RQ<-ew^M9D~ z!A8VV%ujTOV72Md3gA{FK_YKr!iB27nyx{R(Y!0cL?^}WLb(vB1$Z++k3fZlT0`K7 zGVbewyHL?I>pmL%MG_+7WD^+Zlf;41q@>!x-%NZ)JzXvn2(I0-dxq4_jT^i~$AHqW zB!OVF7o^YS`J7_bNi9iOYFDn@g=WaX$Cs(QNSy9P-jFz}uJO+9;9a?X&sSaaA58f7 zoIJ^dlit3{7V3D5XetK8ah>jF8NF08D<15;yLah!oxDM)Py+ScFx$apa#!Zt5nmSV zSp`DEFGA{tLSkWQi^U}-8SJUVqp!n3x4V}I1yv<6)nTrHq!M9IDTYZO_GJ=?9*dW8 z2m3$REVx2^KKTD!TJ;h$q_Ha{RvFpdX7A6g4uetx86Xw*A}j0S6Ut1-DSbku>ULjB z=9hy;U-tI;7vhwR0X&pu?cl3Ot5#Oc(K#8y1t)Zhqapl3EHbgOLMqfJCr6Mt!QKHVz9PH<3xh}m!9}nz zD{YAHdtvSR!RzmPU&X2ApL4%=Q|RDd5jhpzwcp`&6L&di2!$ityp^1tJ<7~k*^+vm zZIWEf?H=j?DBQZIgbXp+)(!*!)AY+}p^RC6)~t=;>I38|hkRi@)VM#g9p_@L>%|s& z@KQ{6o?lv91I1kCd#dj5ZLEKR02<+q1_pXbN0*e*OWcK)&FEN>{il9mH*)RD(GmGS zeKF=gSPIJYg}7I*l=}AqED_m_W5*cQq6+!w?+y*Gt(kor6=7_w;touHt)B2Y`Ayqv zZIi^9x38&#G!q|*n62)=(u%#61>E?GU988bE~PzONSyd(EMB2}ttY9BB$iPIUV zSg3?ZM^<3snol!17e2?Pz|#uYU?HB-`C^*&*1YurF~b46{Vj|4DWII59?{Bv+;WP< zf4%V*B3ELnR4P(U($OlDxcd#S&+k2|Q&5Fqq>$Theic`@uwV1QI*hw^ovC|r!b+sb zedYJpRNW|ea*%j9Hxz;i;P}}+xVR0ex)Py>=Qf+0n?DA+fn9Id7^i@P>UtIy$}m4Y zRu>OI!U56ogH!~Qqh@Ubu9!0*_aX-a4JCQ$7sWu|zFX_JoT>N%W>3d1{uFjj;qC=8FuWb1XnwGb95(gflp}(UQ@{R*D)fK0RK48fV5@j&MI6t zA#MRFJxFFrxsvxDna=A?Z?<7!%e%T(vkltcUN6H@&jE9tLx&Eb7o7B16@_DMu=0Bj zE*oqv^_MlzD7{}XG3L-xl6y&VFmyyf-~$Iu^I8K+9wdC_yKusiK)8C#A5xz`I?MqCPHQ8-r* z3Y4Q&K4)8KInR$eMW%&?hL$1Ay*N`Z;nhd3aVb@&yt+D&`Gl#SnOO~doq?nToI2RV zCRzo8Xr*-)0#Y4|RYB*4G<*pz(zi1)f$xFJRHO@0>K+`YoI! zjs&)X(&x~gWoThhhbgHHC4F%;vz|C95v>Bz^bGKxC>e<1o|IEwJ!A|viC8+6qt`)M zS=nVo9gw$;z#)&;>C#W1f`Rb9BwRRvsyvGT3`izQ&lhESQb7qj2+#94?lk>fgfoSP z?YZJq&yg{$c_A>S>F%}FaA2@UQi-+edP}tgXz05#7PJm?ap7S)-bfr)f1p2l?ASdA z2$I3-)HujNkJOV#b!{!>?IWy~o$q{s%^y8_BqS`%dvjC+tMWam2d+}rO?LeB#w@c- z!_BE{DlwO?(|5t?%(m?OsOzzEE#n-L;zO;O2aagI%{6<}9C~}Q=g9mQJ8NqWF|n0w zZ`b(hswx66E<EyxFU*gf1zb0!f1UKtKs>3l$Bb?6WTrqwoY`>G=gaKUMVYwDj z49+u;OL&TxrO~a3vjtme0U;M5-|Rf5S7LCdAm$vGoa3Z;AzQ&7%Vg1yUs%%ZrW9>w z^ltTeWhutemQBmv|i$`F?0X;ppXwSa?@FDRpApd2grhtg+x-agBA|ZGA z**6M2A?JJgIj4CKx!9@friyJ+@iYameK(rl zeD`+QOWBnnMN@E1w>FDAx0*Gb3-!|6Q_a0xl3=;*PJK>ZKl>Qkvur1rd zjQ5`F69)1LpA7~3!JjjLJ&{q8KYKQ~+6+&Qh{mnqH*c8?es2cV&a62lY{f15S40O6 zQ42eayEYfi;545RY~+ptrwh-ubKbuF4l2=yE$MOi{-xRVJ2agh8k1c@ z%tB)6W4?;{MVSP(k5p^5$rI{aTc=B)x<0C={1l1<`Em(rPp*~9hbiu_Ou=R_I(P$t zvb%Dzm(NbVZZgfQKYI8uzmiw?`}ckKQ&$l61EH-fa$AC+GXO^h;~qHbOqt)t2muRb z$P1YH!9gs7H|B;fVmu)Vi6?JFt8h6E#fK9DK7_{s>zfNNV}83UJov8fzD)sl$)WH3 z8^#T68+T7W+9=&~B(l^MIxuz#EA&PGKRqb=)H<{)`^w=6IE$`$4opX`&8e=z(b2;^ zvg?3Ujq!JczR|>}p&wtHW&>r0(4>;8Qz*YZ2WL}oTw52GV2FaJ`L%1ZXb*^i?1h^h zGTU{IR=UT{(JSW`2A2Eczz%n~t?e>WIMIWCD~ASTl`pIaeWNu;M)u*uV;BnPcLRiu}cJJ9U z?N;RkYkXngF%FrRjmlU8FavoZ|8%3V(&xtT*MoRmhAcE2JnRIx2}kr2wDSevjof9I zc#eri=%ZkGO3=t~jL9TR^YG!rkofhg?(Lo(X-;ia&k{a)vYOn(sbVKQ2As&d;j)(R zZt3gMr_FV6&t3+aT7Q5+A`%i>()Q&`sFV*ij7PWj)d%|s4kcjO!R1iF9d;XH3Grqw z)6C4z8+{01gvk^_^gkIpz-v6&G=xf3ki)MtcSnurMF~S|s5KR3GjnHP&AaXGam}^9Ldr3~l?dek|Mkx*Dpd=s+ zQFwSK6lkB%@azfnJR5{p+S?0*#qZZWB5-(dksvNJ9*(6 zuOrnq|EiuNEHcy+vBqY2v{a^P6E_CgZmTaQ{IfqBd{Ca9HVFvI9R+>p$8#_q5jH@> zn~R4KZ!^5#)ZHBg_jGDyp}R((X{&M5&55pOSOC!sZE5ss40U7l>Knc)IJvk_0dhbC zIs=Sa_MyTkwLdR9xyplyMNCF^_g}r)Ki=X|2^g(Cc#nn3xj@>7Aw>%2b%CeXV09;) z#a_n65xV{~-pt41i1_&Zpb|KKCZ^^k)o@_zUS7w#Lq3;Ojt9_nO`kHHeBw~NpVv{A zI<=~28&knTP0x{obcHC1MNNgXXE~1?3Bn{cz#i*-XO9S`d}{NS?b|CbQLAfeVsTOl zxCQ~x>+RV`px~r#?*INu%qK^2eL*~Xii75(mDfwOXBf$?S~)1Y?fv5QF=vsFP|JqGps^oAb_dZ6%I((y0QLj;5o%5XcMw zxHFoeJ?w|m=__4IsG|GUD?CWc%sln#ON3n``UtfCu7>emDaZpvfg#({E%3{%BlkE# zcoag3V@jdn;R~murs9aXy2DY4D{mFgA+c$<%?H4l9q)vdF#OnD0tZ(PsVCjzt0im8 zv8o3vf5yK)Rw5I?N5DSX`9L z!Z@tOZuO#r?BVX!9WaQOjg6)3*|d><8+JN zL--9i6*haU%R)Z4YF2*`R zBZ-2=F`sGh!aHWV9KY87d+FXwtSy%z`KG%?GgRZ;#r2ey50F~J6Kuk5NaN{_6Z~iyS5SJ1Y%u1m;~Xq0}`RXspQ|ZK+nUS!!9Kr zNhD#Xg(?G|CQgiGfBXq>HapyOhg10pjg68@0>;1a&C%0nTrGHPq{Q+h1)=Oc_<&^T zFMP8T;s;JMb%OZe(Wq=NkPKv1p_W$H*Bc{VLijvDSRDeTjkz`knD~Ub2_@<rGf>qaGW$b{JxqLZ-IcE648Rg3s%fc;D@|vzs>4yx~Pn z)kaBlfB2FDl@@F8u}I9J5402ywUnN3N7UmQHpy2!BoR;~5a>Mg$rDB7d$3>`x$T(0 zL|P0Dp6$)E2m6R~6Q~rU2S@12ZxU4$hixa_vQ6zEAk)u|JU)z;YZ$}?vO%vwQ~=pg zdK^=Y0e*jL)%_d?$&iiwgib+Fc$KGOJAq7)hP8`4b`fKRU{**i$eNoU#_lD~6e&Dt z!2`9xSMSQ>EF;aXYC~M+GyDfWW}`2ef!mRCvynA~@0bZ=DDG2uzY`)_l|{(%*kwtXxox zW|k>Ior&oP^cZ^ozEqZJ(`z8k`T2ReT|)Oz6yj>_s6ng(YA_7ABeGy{cvuk&=L9~$ zX7YKwuW!%oq`*C91FD8&Qg~1ho^s}m2d722uAMOb+Ji|&j1^QRI8tkoTmJGpkUq?X z0t2}fjJXJeUJf2Sh%HChX%LNY0swvyWG5zG!+?IzjcI>)(AhPtdG(rc#A&#fmc+t%x#4N<-<+o^i z2n|)=%4$tz?C#yWhA_QwD>{8Gm}Z8}J#cip(d7G@&hGVJ}THtyheF!h7xG!;hAfo((33XcLm@ z54xaGxQw&2v3-Kpv!MibtRo&s_OUSN+=h_I>+p4OFUS#$eyH^5Q>dqUqHMe>vM${D z2gAJOVx?jm{ufc_9oO^T|NrcLjFLT~l4M00Wko2nPD4d9A|aAgl9icts3e(XG^C7F z+K7{oQ9`MtY!yj}`rV(L>-YWRx^CCG4)yuG->=tmJRXnd*DYu~)8~2@Rcm#edK+4oPYGovuTAo^ZdJ_wZr6qth+J zI^E%J0N1typq{^ALFw*O)UfMoJ`TgLqFM9i!9*?|@VH}vV;JzJFBs}@KegnC+sc(i z@vn~w-}za~3xz0YfP$6OECH90Sj@k@P8<25rr~MRzCRlJ^!a(acTke*_&Cj;#gBbX z>RaBT4bSK_>D7}BVP=}du!RHNTnu*G-|_4kVi8fw{KeL-ivrS`rGH9LH3Ip+?PR<) zB*(EdXZy&b_m)`<;s!$X2I{$;Ph2(rfU2$MM;&+{0cvnNV8Cc2fD{A(CL363Y)4i@ zKIE+cM}7i^yGUD3QrE1x-2L!@_B&@L58h@PwoW55_*|&Aaw}R zo^<;=30>elaLML4jYT@-b=S&pLtM0kUa({{++Tt+%jr9H32kCTV`0-6|I?QXn0|Ho-S{6 z+7(1<*mZQUj&bI%9-VD1@-S)N$a0^T zO3EU$e7>08jt?th0pM~bMV&uB(@}^|S>xgoLZd8IJ$1%|b2Sa;J+K?C-0F8FYdHS9 zidN@4o?5-NI&U%L`wi-`Ya6lvCe>A<4)yn_<;2$j#m_zO>U;EPf15#wg>M@~0U~h5 zA2>)R!ZG(?pQ__SkFD9YjgqYXvclw}Akmq16eyLuyN)s-;68!cI~2F+`&5qJcOb9oK(&|v9tK4^*H+SrtGrNzBLiw3zsPcowq@4w2U+ zmacxeJL-j+xaOkNztGv(vEO`i-*#~p^)4^n0|4U(}PFb+qh>o zc2Rbpa_|ffxr59U=#LtO^md=~nl;I(?9)_*VRV0kQSiV0+Q*Ez7!pOVnbc+>i3WLfO|82 zlLadwArV}bA)ph(_YUdU5{V|_0y2}#$zCQ)XwL|n>i(=zf#%CJ8v_D^2i}~tsBz1U zT$`bjff8hm;kWFg5DBFApaDWH93MV7YndNePRulRsW17;z^TkTc+gGV@>`-aZIvA}t>H zfW7V5Y3>!xTNi{Er+E(i@bRP2E&$!Llh;`}y@&YRxp(gwYBy9z2S|t}CT+nUo9OE5 ze*5v`_N6a$%b-Rp|A3m&F)^n>GDu%25{zcd2ncXKI@;?n2~hAx?V~fpZZ~ahTlu-D z|F|2GrMrOR$82d5<+0EHj%(Kj*StGyX;S{EEG-Bq8?tGdMH>%K&mjQ*;89IePZ{}& z)TddqW;|Vn@_|_mBP~K6y`|-T^24p`zW-?fo;?#PjdufDCs6Wz(A0cR%8 z`DNn+sxKHy+rE9$>2)SE6nlBcE*o^KWxDM1WJ_rEuuhmKjUoL=Cew>IK!7oV0 zRC2Vt!Su&Zo;)#oyrf0L(!D!u8vX9D%%E}`Nl{K6osq{8r50}5s`F3 zna6Bf+wRzXs37Ak_{tcy$XL0r^1`29w8x9DL3;97nqJu5pXZCqVwUELG6Bh#?o+1~ zE3$+W>=%`|dF$5nmq*TzwNnPo9YXJomrEw~CoL8iSWKwMw!+q)HgzWDGh=i+)FcGo z^hQ2?=A*=LUbSjI;ggchOw<2Ep*=sD76)+$Bka3qJXi6T8voUGGI77(_d9={O;fm2 zZ-PGd1Y_sV4^E7Pf^6Acf0nXC?25vBphY5I$LNUag-cE;cyXEylsEve@2==k4$dy) za!&GSh(b`_{}vpCX7JDNwJG(dh~p`H`~sqwp(j~5prR}Hm_2b~W2P8XF<;TJ{cD(e zg`M~T(Ep;L6gBF_xTSn*&Uq&ay>98x`DDMl&SovsGna9%-rhgy-DXXr&D%-$V(5n` zbYS$l@2j4UAByVJ=m61yndWVzG?2)t403+h2?z1!Sxi3)Q;vYL#=Wxss7Oznntdv_P z&~_8iiWkyF0XS;f*NsX|8`|F@=17)XSI%P{%yr|nbH<>*MAb9kflS7TXm+P zxWjS$R#Tp0?;HyjOu{~1`>Uq#aUk8rE}C)~xAI-QanGLLLglFBAK?|ll$v%|MA|Jq zH@`~suUrt04U>UXYSf-kD|dmrI_kSe8=c(BTSvB-2Asic+QlgUG`kc0CiFT2)loz4 z-MY0A6=5;KTl8V8PB!6w8~(3T*E~#>BYZrOp>#P#4Rut;&RKnC99sy|L%Qi?bUc0^)1ae7)@u7YwpYGc!{X?2RzY z6%8@d{Pg9^Dd6$goe4|;N!cikw^Cg+KqckM>UY^j3l*9-wBKQHvWN=%1q6azZ(2?X z7!yNO7z3vkfRFnAHE%_{N2NMKhyCSGA$RzNEhRNsTX%*I0wCd0!)#-p$SHx`~y}l;y59k6URc1 z;DZPIsc)SM5{-ySOalwGTUwe<-4?NKuua-86N@zsjB&E)F=Db4oCEL8AS}^E5!2Lx zbzW3_J`TqSKtxpf&uKTF6MlxQD*3m@Cjsw2IikvLb7s#zG;~S3f`Wpsqt_iGHEw25 z0zveB$Cz(5HIFVWo}uWBIcWgEtsGuy=B)B_9Aj3OiSuylfvZn@2h zq^&Tni2Yx@7{>u0=9E9^Q`P{hzZ#?P&f;-^?yApRmOJx6f5vpEGYmr>95VBR@S2OH zkauBuT|zav@_A}Uno#spsPwIG%n0cAlOII z^Tv~bC$M9Nw?U=03hN-ODu%BrX$tBn-2>pyi6>6>pGz{feRRCNZN(G!NNv(VynhBh7AEK0^o1C+Av6{xx@ZG5i06^c%=|uB& z8U#1Av9bAH4r=FblhClf%Sk#NG~q5#7#=nfa`oJj%P&X;DwZvFgA|J1zZZqg1IHKy zo#GtDr#Ij^5a#I!A0~TwHg4HbAr;8<9&d1lqht8bV}Jzc429=fq||7$G0q|0D5@{= zHmJcc2h;n5oZ**XL}c()pEjQW1uWG@yYN zzgs(2f6Ti@dz$%=%6R@gZPMqKc}5;rLDVUqSr zh!Mxf=ZwhevP6fuqVCcfBd~%oKa{wb=kVrv!Z2R7y=O2#|0K^Ga(o}zCmQ!951Xk| zck6%GCd0!h3|!ooYbR;|2&Jcq9z&qb;Ti}>JLy%3c*~w%+$t0YvQ~)PJ*q}PhVm9Z zqfdVba~p3LGj8%9{TaZ@tew!Pe%gWur;KFLR2sN1fWS2e-KvaZ{D=rYCuA$E7+PBMMWDi*_K`oVZzj7`hh^It6tu01umht=7ZQD z@Dr5W+^UU7yKE|2Ga+B|x0LHd;=lLqE#f&*AxtcIMT8}p0`!#dG+*P^)SIG`_dxio z7k%ME+U?uTc~yS^$kaNuWe{`9kkgPbnx%;iOQ(RETU>o(Y^2_EWv<$awMn+Md5<1l z`q^2%jea^(6KT4w^3B7{$nVcB^ZiwKk^m$$c-R1#V=0HD@L^_EM0|!n8x0*5e-nPA z^<{@md_4>YblOUC0rKidx@}*4e(cPxQq%tY_KAfZjrc4~0i$%n;$|;Ur8MER2&G0n zHUg{zk}63p!ki%YnlXP=euF#j5d`NyAlBBa%KUpA{qtaYA#VKQ&+F@tGaviEI_0)E zvIy;Y*BUB1$5J=RR3pEUA&E?IlZ@N9i@`v-q0L|g_w3noB;F>{S_hrMrsB8-uz8TE8G5FGzqP76{NCrbpIBp#B1ly&^MI7*fA z+gk1)(ghvjx2z4d*NIcZp=rn}vlG_r;jj|Lg^DAD@__<)-WSOQ0E-%0S|uDML1LwC z_{$7t>;IE9x7+<8aTrP(d|{yo1ps7T<2@vrD8`&1&$tf3KnFyB5-aF}gj+hR6Ye^5 z$VzW=B06;5b4Gi{w3*|~I&F%DHeGdM9inQesiU4{DSU?S<)giR_$|%T^g%C29lhAp z>pQa{B1HpXsf0Au|Drv1Q_=LFQE^z8b{w%vbx&uVtFN6WhU`a4A+t$V`ThIexI&UA zJhmRoLnRMT$>5|&jliVh3_Pmm?D(@DFdJe$b=6Q z+*Sal$GN^&W{E}-N$j%2gUzJ4F<%#-K6Og}@h3cF&u=-O`<@%M_Ji)Iom3&g4;(OU zjQVg!U&*bV+x6?$mpqAnF=`(+Me}i9l2HJSVo)|5ka5nXJ7J_7A{;^#6~*AsXXM1 zJ8BRhUZj@st%=~2Bw|o&e!ei4QZ>>73eW_Jn$o$*$O~7lH1=6=_;W>I|Gw`m+Yfpj zr_-lPE!$me(|-_k!n*yk3~c<;2a`0&^dJAI9Rmk!OMaqOIyre=7F za{E3%V5{zztS2^&l36UInD%5eFfuw}a7l3r&T%x+^heo>I?j-bQnJ7%#?dfk{jZX^ zb3^fTpuZs@+`0JgVa7hY2Kg=CjXY9(5Hi`_v2hrzv9H$`n zrt1C-b$L4v@cGMxCK+nX#NP8eWLX#+1U8c#KhY&xu5tMd#DpI3o29*uA(zjmqN(}38@IX!XhXX$VT~uw zBw+8~r1(PY94%hHeA#=(EIRN(OqkSI4`_bO){KG9K+~stG*nPj^IR{mtB4ANhq*mt z_h%_xor|~ptOKWf3r}o6G-fJiOujd-0cPAQ=wZ|Y2bKpFL;7&!Fb$_1rUhmkja$mFgo(-ij1vV%qAy?e3#cEspm9T` z#r3VMt%D!Ey`$|Ne`*GElYM&jloQaTOP4N~+enlFhengbaL5ECcO{EA^5YR`Yjd}; zxuYnY0XaxBe`J*BK`;vwayGoR15h_oSIS0oIS{e*u^n-ICL4#}a5=5&TWs`J@iz7n zy=xNQR{dC&u_)zlbz}Sa=ba-cF(Geh+ZQl?x3~?Hnv!+_gk)X{t%B&z-1_TR_w;-H zt|M{e`_p-Qe+3wyrgGm*!n^c7;l`|28C`Dqsi7%8|H}kLftvR4ex1qRsz%y<6&wek zLh-0NIsI(J<@PO6t)l9iG2L%-@qC8`H+QS3t8c>qf!W;2v^qF|jJ@D=%1qNF%#6Rq zi7b5rq-y~qRT%qE1mi-_o^?*LU2!lsnE|%00diN(H0@QAdwG1>5phHe@8UpUM+K~R zMN&zQr=mC8)W6>t9&Jf#F*3UNm!k#w-k7Lw+ta-x>M0rPLk`x!r$G<^)VJhfCtz*x zJU_#EVCN)uBDV^)n}0vgGiT4zzf2*U(%EpJaPKjUw2t`(PvOBax4-0u(Dci1$bR}! zA11NDDxN%jO1>En4j@bi&7e+OQ*JFiwj?M4)D|z^ZX4A{Tl)Z_916S!N=-tT50*V5 zAPy(oXC_0eg6A(kn7Oo(c>|Z-HszB5*ESVz3LP<^?sq3!WJ}#6R#B@^w0PtJyIunX zv%-Cdd{#mt5I!G@hhw6lsR_;}xWk1D+^RMl5$0pQv0qexu_GD%2F(rMZ8&}jOzt-j zd{)Ia0&Hc>3QGxq*~Y-U2N%yiu57$6g;rvXWz@w3@9uyvC6(83{0dyvlT|Z|oxc^n zj4*g*W^bC4RcJRz{O_h8AKgl6*X7oxGy7Aall*Vz95uG=)S$v&+-wNqYVaB7UwwQ)4c`{ z-cNJx(aQyBfD@DXt8*#=csEu%0*R&k`q~Ay9iuzx+ClFIM9t>B&BM|`CYiv+>WS&o z=wDy{W=CglI-P!R&Xw;SVcVc?SN!u9uasj8_C0($bH>$fTYu|_?YF8LNz50HGDmPDji1kR9)3QRyD5t8xE~c=_SO?5ETC64xiEgqvKQAw#b+n@Qa&NgQS2 z{GpvqB60xI_5ieJq-US_W_1rCORRz_DJ^J>4(|OO$Tj!0n+<%Zy=u0xKoM;l*gaTxU zQ})y&!}o*6pO}@Xb>Urif$Ykii8C3zt9k)MA~s?hUtHf_=vGGnqi#8)mwe`dgT$xI%U-AZ zdX$l)F0HfmyRib6f6?nSo3-uGm|I^DzBpe(Rc;fBFgI0J!tvd4gpDYLY>+OVG2n7Nmdb z;h{TeA?l^KP6C`q-6fu-)G3qBK5E;o+cZXS*7g7K?OQZ&Lp=RdGtRZl%W>417;@oZ z%OCx^piXSk{MDC)fHNh54ud-0ar&oab-5x_IlIxMi42D9B79j_VcCo;G{`Aa z)U1!Rr@C3G>p-*9e}LrfYRx%M~6dJsW&&+{@9t6m)7$(eJX0e1Vs(vGMH% z-B7obG}&bvH7u`Km$OJ)$2aVk&^K1BSA|urD@@t2fkG5*im2MagAfijNG0OXdHEhX z^lz}?F6eU1>JH$`Xd4yKq>?H|%lh)a=YA?rLG{Q$+xmEm7w2vdA35@NnqZqxN2Qz` zKK#$j+;gq;q$VE~Q3a8U{sNRH#r-3z%#QaE4&&-o?fEx$E%od1X1aGpc6m3z8>Amq4 z4h(Xc`)ss*as0`D6P5~a98O*C1ol>2??p}HR+Za7{yPn)=H=y^`|M+4HE>6Q0O#%9 z6crUYz1y^=QuhpA|I=M&i>bi-YHG4|VlQeY&={9LAVPhvMZd3%W5r|vc)q>tRch-Sz+jk zg0756lj>){kRxRi?If2;lKG`9(>D5@dpRYIIiM;A<){LTr3Xa-*1;JHIj^WLF1kw1QKDlxkU#otIK8@5Lqmo<`k>wy$OiS^*bC;i0F`R7p~a$bgP^ew z-pl^LKB|?Km3LRQTtLy5W;ZCaVS5B39;F7{j`)Sv#{g&;KzV$sb>){YQg>mq??Xkx zqaifj1jBf-GD;?ayTvO;z~4K!08 zymD_Ni)sa+p~c$-n3#%F6D6Gu$JMl&-HNH^W#C;o2EDyA0;Y+Z?7= zT*ipCJN!&1;DqsB9e6d>i35AMe~Zl||2&t0 zqh2f>+WGEhy?<0QmTjnen3VuVrTyzKuw^(wQ7EY^F6IICZUmXQ(zR#}^>A_CJzm{J2v|eixGx z$(5Aq&CUEH zyJP7?NQLU~f9}`(*5k*@G>j*PdtW*?AsG}*dBE(+DBUYI_-X*dA)w_9F{(sWjbKVl zvlv&(_lChCviNe<0*r#nw$LA@r*B16vU=p4c(3W^jIJB)HNQRNCfbEn^WONBJTNXF z)!4$qLTD-YYqQ(UEX~1od|gOps~v0YS+&R#xJK49W#7f&{Xyva*&DU%A!XNs?j zxG-`)pjY5i03thDt)4ms?`*%mpywA?O}DO?(5v{d9ughqEOg+&gQ><#d%;RWbyldZ zL@0weqUWvzzyW5G4^SC#RRwQwj5*BtB?B5(s!BDvi!Q8nrs@%-NImIWUXMtsQWN^#$26KDK;q*9nbRJe_H-?as1iD7mZp z4RZ0Kpd=_CabNU%1gr!5Sej|avT0_c#MK8#ip5E3BzF=CKJ_?V%PhvlIGeUlt!LEl zsPQ5Rnn4RilWlL^)pTLS=%akQT{~>*X5^1zoP|rGWtpJ9Kh0+S zj+P>yiX=1XU5bN~R4Y zbFHQiFz`9Jt7XAiJ@43us68j>6we1d+PPy#5jYmRU0Jzu3o(r`Jxr#SyE0>uGut;T zpmDD_UiWiSZ7VPkWC8i3n(QSTiVbJ?b)IY23OC6p?!S~Fg87jSiTnow1C78Vq)!Km z)5*whUw_45gQE8JqZkd0nn8ykg!xZk_p#0`XGU1`M{cH4POWr}i3DcbnS$6u(VUr8 zz-eR(XATA9{Ni2D&RGpHsO#Zb-L0E>{HS$5JxzoH^Ltq}j=e&|T>U%vx0F5h(K%&}YpWNvC&T2pUpH&qGWl4fUzl!5|w2ZF#88qH}O&HxwD z;sjs@j9IIgzshVV$#Zv?VS7<)`|>Yv;+V{r05DvHN|sR2E?TG0w5d}UqI+5W=KdzC zf@LM)(`A$~4OquRqbd;TtzYHi!)l!p%_nKQf2^^dsO_%iZK>_PVzhVeGTlp11Kg3#>GM?x z(li3x{+wmA;9}C}v&A@wW^2B7pzKFuJ`ti4Q6TcEc`)8Orss{c-OI~b7C5fBsJhzPfyjSoxN|SGaVamV#iu8+@}Azi=NUM^@b-n0WLRlt03hEtsgzN1 zuo<{retQG6gu=!icNYy|%iu{O9{`O>k|HqLJ?2;^1HJOG8ZH}_BE18sK1`Yj4ya9; zyQ+u#(o5z`=CQtnQAb%)%CN6O-YG9|!6T%v#uNKH4hf8H&I9=R5jYqHiTLhu{*5M2 zJ^{}gkz;IS)rn1g$Ss%yn1C_>LZ78Hl$$UCa1pdTqD_IjjXJo<@!Du}&yBGhzjrUe zdWb14Oct|=R|F=m-!CGS2OS&gl>cp8;iVOYqp5>J2XtA1k`u68OnRmLqmN@SL`)Ys zSM= z@G)QtEszZi7|YCz{SJnVBYNr@Q z0FdphNDCboOA{p!m3Yo5{E(XL{F17A&$E}`i7O>{u746bdaU|5K06 zm!kF)6JvOyd3?D;cNbB{13Mvn4)1dU$dO}{eQub_mQW{D&>$@$b*KJfgKnSEqmOYd zc*)oK02CXo@I}D|oh??^m=suY|3p2Z5-HS0)3gzwX9#MH+N!7xp9DbF4ip^Rh+Jj#41fz{yODVq+Fd}a&d5alm3e`GPG)K)mpjnbjutzhwRX{ zv z(0l4jg<0VLKKbJ(1}Lw%!O1Va(j~UwI+%DT080Rd4*+AJ{LMKbrC}T z!M|iPMpV+*!`AOk8WbXgc^9SpeRgm;cFCKoGVO6$nflV4V`(TOB;JF6^mqaghd9UY;6W+U zU4y8A>dp*N*%D_Xn>&yXg=S$-oI%WG*nPX&l!Kui_Q8XTaT&${qNKyNn#YDqmXe`K zx%!o*xzn|YyD>h*JR0VaqW>N-B9I&hpmGS49EmZ925suG;pcwB*-LavT&34p$DuX0 zi`s;OKK%4z1xN;umwSfE=aZ}l!a%1ZX%eL$vymk<#e8|Vis>9I7NyN>RoigFG+&R1 zHU>@S^}x*ixYyTCRGod))C`m6Mdmo-IZYv0j1ZWjfDa+4I(PLFVk6_bYs(YLN3&(P zebvOxZE{XO8{)nK&zaE7=Z+aF&trZk>b)%sfVTM(*?PMeAd{K5VY7>ccm>W6uWj{w zL)|X{w3%!67sljDT?)n}>>fPhl*`kh2=M&-xxSir?*9^HMD3A(uz*1nE1os-kwkHu zG(=*MFu0&RGAYU;ok)u*Xt}@)Q`6F3xYbOZJlPKVhf^03<@(`C+e=UHQ0(U9!styP zX4QS^sODW=cBd$*ZD6cL>kMkY18GYObF~ko_3M;)AkAikH!~qYpfBrxewsx21oLG& zrc1Z3U0ELpkgh;F7Vk_kOXOapGq!!|UK5x(Obcg64#?=)DbdZXanz|UO--ht@(|{{ z>XZw%F@ve9f|{58)kSwVQPrY(+2}9#$K#Rent%`>fAvaBiaA)qE+`QZ$kYQ=OhaXr z)1@EyugJx~=aJD$1xh3(z{{`zgI%8!ypZ@0LeE{6Qn$V;MqI-vOU0Y6Dh3dZ8OAG` z-B0q~iDu2HW#&`h$qvKWiSGNs1~>~Ku&q8^!3+a)oPz}C%71(B6FH+~7M-~FFJFqb z-{ZrDTaL?DTIT<9oVL-euJ)J2BwSi@znpAp@{+6#gLHoFqAnaCit>UGpM18$JAul)gsN#{|F}HRY1+7)-9_{%0TkJVt5#M&dxh_m{hIip-Iiq@n5O>s z>Amm1-T;D$9JOTMl>F4M)ukY5Mn*g549Gcu&t)^)1OPT$Gx&USnXWJQfd~mz;NAJY zPUk%`qE1-_lIfE^-=s@4j!O6G!$=p|UYb4z00i+MdBeFr^e+?}2$g-HDER#vKG$H9 zrMDP9{M*%Ai;#&r3|$kS-zxm|k(nm-gLVJ$SXvc&`|~7R1b*d5->M7%r~sE>%SM8p zTSm;(oH@6$8;v|VJ*l?uz=0`FzL5z>(J~U7xJ8FY`#CQDEj;CLeufIfK}LSJF)I#n zWd@#s2NIh!I2Zw{FxQYJ%3O^bytf>eZL~RjM{;V)y)PHSn%V~V`#&u$Pk!KF7Bweg z)n3Y)Y}1}OVUHWS8&>UCC_ie_UNMBFtY{V^*0@)L8UT0QAO`8!>o z;QN?vpfI7Mte>7P3Q&%j)E|6#qNt_g?ouK-942*du=V@c#KhU%@UD14S;$>~K>t$M zEuf+M$9}2(!nZeckJ!uqre~<1NL!k#5QSO>v&;E>f6;Dm%y6Rxu!L=(T^tVp&`EEN z7NSMAtZk>6P^4Hv_z_&zhkS~b0ZHcHXN|QdZ>%o+tKwbwuWl<0c&;X&b>;eenro=*xpW4%y48Nkk0^7Kh=tKWa9J z|4$6Z>*)AXJ{S)K-c{<5IH1UBo3n&os3xIkuhsQbBy?AFI_ctfEE7%NLjwt(`=8opT zYO>dW(w1gu%cV<~o`v3ZjJXK@z=>QxtlWWCM)pfEtcd$F>SvHYy5|PF(yz&gIr6ru zmS)+;+AG?V>KtRKMr2E?HZxJQTsgKam|7RaA>L&%%1{F{Gr!LHqd*Zzw|q>BHf-jn zY)%Nah%m2L0DgN6Bba6zDe;+jxo2GU>3j|YoK?<-c2jp;b>PYgUjD9!^;5?9#uEST3on!ovC=A1|kv2!=#vDTvB^jb&P_drPwwox7 zv(^3E%jlL@_46jOE*^kd(q3fXLE<}Dsa1@l`RTQ@sR>{28CGzDBuEQ}oChpLMEFEH z;SH)TSw89Ur{RkEn~s%WAageB*sX&E3`ju^LqftM+69@IWIo4%PRQ=PidfzeDyM+g z*hN^5f*A)Iw+u6iY7=7;*{p(a81;MaF-}GPbPSsiN&|dFC3rEbn0-szMAA{qEj~KTi9YLEs7x@&rC1E5 z$lnSCQ8IFrQmDDoR#`wYYE(UZTGJh4AhUNHpI#|N8RsE;)d;(YOg%6%Mp!llYl!^` zd59}H1GJgui+w;LhbQ)PZ1LaI*oR*QZ*>tpGOHnE!%Vn~0c#`RZ5uRhS;S;wDU`V7 z`#0)pYO+HYYj{1^xnKhJw9bOYPS-J2{2D=eDP*%))hA@tD-P8&IXT}$EtLZh{vmEl zsOb1v1T!o0K`wTl+~KG1on(-k4kLS23$TQRxc9W|qC0iY-r4)(J#q1h>9U&JP3LT? z*Qf8Jt3u4?-9zi8uMNDkF^H1S@LmL^MjHXNq$L**|d;$20ISR*dC#EZL9js6k(P?tOnSY_#zjF~vZ z8YJgHX{Uw{pY`h*=aXqxi%TBu4PWf+b4+we;3=>b6WJ8Wtg-s2QAZ*0!;e2IF6;9r zy{zF8NIpViDlcE=+xV&V^1kQNDDtU9|0I5jRkM#CJ9baTl*#NlrxbuufNO8U{CHhA zA7PMf5u=`J^Pp?n9ysm_34wCSQ_ZzIbrHxwxH$#G1xMDR8=rMXv%V%d8e|Wd*JBS&+ z4BK)X0Zulu*0vJHA~drK5eWl0qq+Z<${hpO{!a_A;+QLghnz6cIRMbGU;HJty&r<5 zQ2N~$S37Kn&=Zzo&e!)$JMymYA(#~*dcC~DTPMbO4|jRBdu;$_G22k;u<|t5v9muX zxjT_tX#RJVbmJ?LrdP!y59FRXzC8?7_K8I)Euy{7j0SCi@VoS|q!tZLFWMJ5lsDE{8N~FQr;kp0GAjKcz4-avFKxVnD38oQCdtTxCE0~%$+A2 z40tVD8`aN-%?Rse$+c#y!DZ*C;F(w0k(#ArGNwHQ>2K1tTx8m9YLN-`7 zu86YKX!{J6Ept3kc8T^;0dN)8z`}70i(N-;Yu)4g=sMI~i25y#4|BM`ktFqrGAd%q z`+iPW(I4$4fe4b0w&+u3We6n|7gV+^C;$3tQ%g%}`Dn7?wEcf-j3Mmbo6b&5;S1UB zQ3THcLL%}3#~87cr}n&P+RL9x3~UX`;k;@8pDDjSu8@dB%bb(Fa!cF-y2$FVv&T$( z)U6p&T7@QKwwDxkmKlSaIiK~Th@lCDx>Z1q(u)noD^`>jF1LZ+A(+Mi9@`~^bZp-~ zoqrNX47-R=)ksm{vBDNLsQK|jTUt3QL$Hp0N`~{On?(e&k;0i2J#)#Db$qUzp3xZ# z=Tm$5U(`Z_BYRU3jH*+tSMh)ca#zfq~BS}cY6yHC#t!E4)hsUQoiv6M+L*8%MMxy^_FPVnq&-zt4h z2+5_U>iu>qVFe7N-d+6izQ1c8XN@t>Cd;h;L6j1j+@O82I&H0hDg!<;2OymaV&I=n zdD84;9gk|fneDfMpy2=_lGaF_!5Yo?>3?ntP6;T|;L5VgaCHP!bFT)920N^$V#3;~ z(@s1d?3T_VE_t-(H4aPe)2D$jtO{5Sj3;u*8f~ESce-LwPEhqf zcxCyM=yC5(?Xb>tjQI?9C!JpEnim~9bqdq{7Jo2({ybhbYApp6$qbq33j!9TyZqYx z3-oHVag=1B81-ykf8+7Y9oA)5Qa+qXBN$of0z%*Vo%;{bNnzpmmuMmBMrSq2y= z^IIu-`=*}_f!YV3YQ?l;>cOf6s(m^$j3IjK>K>$rEyCB1mWy=|eFhAe-v4CpvH^eU z)bIkZ5G&SZNFRcbNm5V#eO|p{Rtd~<5bm!!^~UZ< z*vTxC?v*pGgAE`L9DtrF+(m!z`vwks$86)=R(|!)&5l0+1eSZ_p1hd;ZEGsvYz2^p4%TuYaz$wf&|d9!k9{>SFsxO`_)hhm6fgGvtZLjadsx%g0Fp4dl=?5Y zj0&?+)d31UO7`+Z2`x)Qn9>N2i#x<;`^Vqkkfzh=$-4JWCf!~)?9=D0UUF8RO6B8l zw*;5RW9Sxh&aLy*->@Xc8p23pzaW<Y2Q7y)-y&`De+eQ)tq7{*H3i2d3@UL|B(q{S@fOm*9)JD?*Qz9h1Y1|E{~1M zl!hX89+SAtAe?-5ihYuM;4C^1by#_J+v>knQo!|OE?Ez9Z; zPfQ}?9|QD@7>@Fh+$4{k4HDsL$CtjXS}Qms?<)%^0MwYHQ$g(`BMnr^0?XoAagwb z*-ir=N`?j0Kr3Qtz0>@Y7ZZdgQFps`>()u%eGr9vYDPvg@3a%T_x%;3e*G#d{8-O6 z-FjEn8{jMuJ@iD|Af5sLnRf~6v(R~|=E1Lp;Xa**+0~4>m|J=l!+FRz)G_O;W8gbw za;?e(OifMI(>)ecT2gY# zcG>IKuk%VvIxg$v1qfumc5~=m1aH|!rYwYeK1X$P|LD@PGQh{Za|+&s_IuqBWSY*- zXzJ94wNY1`49}E_)6~wLCSx)wgvW90YgZl@888G8vkfTTXDV%l((JyjVjPnAyG)9+ zXjw`FK@HZvIT_w4+Tj< z2pYr%%h6GjE&@g|th71kIGl1Lg4lWMuhG4hx?vaXe?EhX=q`50n*Q3Wot;e@tUyMw zrbCIDQX?dc2y#ArZj2ALY{dd+g~^Ijv3%xZO#9SYEup;*b&jv7v8irkDa^)? z=N|#zVT=?We)5+Zn1649`@#HOUmjuMCE7!K=mE9>Vp7ctKV^dF7_8%iDG_#+sbge#rSBrU42 zh1GLAwy?sY8?3{x*OB3>zX^vtZTG0V$#+?ALwI?2TN9ZH&jMtKsRnVGAjLLij8T{0 zX3v%Ozqm%cwwM9^mJsi~u)-L%!I89xh_7x+#RJwL30H2g8ajN(;G_C7CrFWq6;5JUA zRFE|C{zfyE+F7nS=aci6E}T0f{m@5`4|XlqJb9S~w3E=iCT8EqPm5=s)ZLxLSwgSR zyzPE0VTcawDuirh=@>(-Hz*(wX~6@a)4xU5M~)Bx<$((5Ty2UF8Zy(cGAAphuC9H| zx{pTb-#%8k)y3A;<$M~Q6J`{dmspPxhY{&uZQgU1;U z%}#Q;hS6y%VuGem_HLk`8oKmx%V^i7$A&IBFmkVeOnr{I7tg4Ei;{{y-W`!uPL^|Y zBmcVUlsegOHZkP4-r}fU_0`wgu1+-`DsqbAMYEIE`a?F#mM5;}2)D8$hs|wf&Ds&H z0e*@DV-}id540R6N?Uo_;vox~d7>O+NcH|Xb?NC9%C?j{R%8<8wMk6yAwnj&T8(}bO)}hY%IoJ<@j>f3R_1n_M z)ruh;3EiBQYv9R@Zo!3YW#|~?DVWD@paz9#fJtP_)d7yjny?swsa5f-BH=X=^)lF2 zpAjQEf#-4cvOp|wVvTw@xViJN(Az)$Aym_F66vp$mzJYlHw{R`R+L2#!zE;h2vy zQ+anee0H}I`QRQ;UuvoX#lxutjLlPww1{Zz-d}mwoZ7ngt4Ch8j-0di*Rv1PeKQsY zjA;<^VAg_^8Ym=?_Jfo>Om9xc!-u@fTLFFO+ppi>mW_nNq;lzn+vG*#`_wU&XH6wq!XJ+T6y_%wO#d=Ya&^!C_u_ zVoRHAe*pm@?7)FXK^Dphp!qJB5-RjyL)f(!U}triH5N)+`y5|)fDTwr5*Lu(TEi!J z(JuCYVLGr|Pfvq8NZ<61A3>U_#2^10Vkjc33tmgQX$ zah7q~a};T?vZGkWUJrHzMwOPX^SI6GdY}0h&gm zlKUQQ)OkNc%!a1Z!$zzhM2ou}rDs2XV^H}^Yd^8NUHlfacWoo@t%5BHdbRNJro`)#xfaZxd8g=p(SGybQV}Lo zb!#(%#?_O6^&2ZOzK(pFdQkY>m?k`Gs4IhfSs{p_>3Q9AsNUz1UFaL;y467cU!Xy% z`i{|QDwqE|6YLO%lQ5L**l|ai-AwOh;OCND&Bt_9*%G~gGOUDFsPVFX{Ux{Q&T7GW zF!IW~T%fNtk22$FPDKsTl->4piTs}&HUogZ?X7z(!q(Pp&Dh^Mw3r(JWaKM;*7_pK z2fiY$F?>Q1f&vhX`GYz#86+xcM6x1xKzm8XntD3uJs)8qi&Vw-?BbfOaep$Stt-6!UGB;7-awE>tzdv3@m1CIe*iy@81lnE{6R?oE}m04&y@_w^BB#ho-e{W-@H@M#~*38fcQZU|}6z zQ1giYETDJY)x4m)i$pm`Cc#iXw$rM3p2sFx415eqrrc zxrQo^!eD~2iZ%>+Kcwt5P80=j2$&rcgjjTph1r2K2Y6<;#0}6l?qIjMURC}hJAB4a zMT!xOm>Iv7m+15lFL8rNE;e+?$L<82xM*8pr3`&jD~fdTO+k_#@Btg)=RDsY5fNb> z8;gQXKvmNjRD+I_L1VH%4+l{cs~xz8v3xJ;==#e}uVvsl#Af|*;aN#HJZf*G5ev0t z;S=fKwZ{98?qrdGyP$v4I`H!5&+kQ05loI%q9hAvRQJ7mH!w7DU|S4AR~5EEQ4fXZ z{;NR!Gzhu?Vd9m>lT;@>4~ZxnrjL7rb#B@8t8f(M-$rlUSeKa}IsB~*-z|BlO3SeN zdbzChl0DDaujz*7qe!BInipe@4>O|_j$=f@Qr{timsdH4^X zP`vLKLIF87n8k_D6EBTE3@&-TZV(4Mgch06dSsTInA2Dl5Mx)B<&SV31G6G!0b%TM zk;9hIgz!N{+ zmk6(^g)VLA;a*e^5O~1vZ`+9J88N#E9OYKfX1az5iw3bcn9C4Gg?s zdg^MEWaP#V_B+q-z zD9^+t5s`7dXy=y`873IZx)wPqq~4bx<@CN6_!qVeX1zbASnI>Y4xP^2`?x-mOZyHO za0XORI%cq{=btW^w;$0*>i4f$3U1UVBy=QRmX5y5DX>e)0lHeYxp`!Yp;n=V}< zbSJyGxGbOsU}xaWZm38mg**t&V(XtM=P!9BB__tLDs9f~?uCl(=ybG-r-?BT@M1;7 z_^!p`4cn0Ov~m!raQR=SavV|q9-4XvEl+3X<#q`JYP%OU!vWAcZDYp5ifUMGuioQsXV}dck8Eo>e9Z|0w;fPNAHp9zHhHoTjq)J z0jdEIO$AvVNH|5KW8OJ}UIwE#ujTaMz&1^yf9+N>d_goNL_r3KWJiCIvyB$`2m?%(& zUiG~m-j3%;-cNj4^K;0bN4X3UnzRvcp%$0c96fS`fPM`Fd5Pr0F@oM9zuBI#kohZb z5>WMgwi+=!TQVcRV z4{AC(?F~&ptcE(=SHPi!qD}n&9EMtacPI1ZkK}R!)15xpLnb1z%CA?1ihxwmF#7rM zImzq3FE1>T+LW-w!YMo5pr?dnA$*4otNz-!mn2W;T~z-9e2DUxQ`m=#2I+&B4VH6i z?BVDs4xiKwYEmAJ_Ui1Lv4zk9P*YT7N70V_UNoxU|5H|4?$GR6&sL>2h#ODO!OjfU3W=x_J%rQJ!r$fVuzs*d^g&`l zAPgJ;91#u@8d<0;_aWHMgLeE6hzhmO1b#(yfRJ!Mg`Ohw`r})VQoWnLHMCbdX1kJOL_$2I;L3$dfyAz-(y?cU##M=8()0y@C^%{oJ{85h0&&iUmw1U95PYB$MhiOXS{(CQ6j%;#4WK1<}zJSHAX_ z7x&2$T&+%2uA~))^d|#IlPu}tB$!f3q(i{d0yjXaRRV`3bR(54;xD4JXgQIBLZ!W* zkkMD=-9_sjMg0d3JR2AHZAM))=LnGNn_SqO&f)M!3+4TVUARkgEgDF>%o3fTyGRmE zXUz)L?e54hE08A)bJ)G1nQi%C8+eF}vzlIVCCXch`a}V!RRv_T)suHKQ%LWzbHYXB z!$5jtPA~^hSRORp^rKIgCskfG_)V$5yNW!8V=xi3BJt3s!vd0(B9ZV%FK>rj3hl@t zGO_qrbLnY`Zl$Hwqe2qd0_Y01S1&1E|2a2DC45|Diwb1BwUx)~WJZyS9f2fZ_*?nL z{dnDzSYAbP-xwK)?(y$h(}N%3kH1}+czpl=v;b_SsAn@VfDxoIV~$gaR5g6eG!|8N zAm~(fCsY?B?|_^`(1&G>!Pf?34e)0)IWgydfFe01n0W_lV6Liwa|wzzd;(uk;t_MU zAtBCv`s#>V@88;+xFGWKAZ#XO?2xNPUc3olLXjow!Vw+q!h#WJ;Mc&blEIlb<(Z3G z>-+al;9y57{|Dg#$?sUYcCx{fJ=X1u?q+4ZUa_I3i@?9gO5UyVPzk@rdzJb}jQ zT`PCfsO02S%}9ymE+L%nz_bk&7!j6G%NfjGE{WsoS5!zoVECF{w$szU6Mcu!8w%ua zE4*qJUFt+m#>6Yw36y1IjMeMN=1rUS)Yfi?;|_olZxKwz8SR%4vX8ww=uGCX6gX?>@L=OYu{bR+AhA z@Q>`n3Q?qHLMX^phmFvV$El-(AN(v-VGeUYNRZ_Ij)+L+uKU!8{}}G+dMPxBlPGM- zQjPj-Qqku5>oji zgrrbblI*0+L}XMV8Ig<<2~jdDDp@I|2rZjrq(MD4$;|5ixp@D_@x1TxK3;U+zu)(J zjq^OO^OBu<1+(W0^g;VYM&NLnSC>nbDe;%Ei*mJYDVm9A{3kX2Qc+u}jz%qeJF9r| zfQ4Q+e+&bFK;Snq{=t)*=My(mP*7I_ygzvwr0_NO_`QqKd}bo4fJKcB&qG9^4d3q( zyzCBSF8U_{mcfmL2b0Q_Y6}JgMbFZ(a<^tUdv&eEDxA69}7Js5u#$s^skBV@Y8qTghx^mE58YK8MnC{8Nk*<8XP6wg4}CvC)2c5G-$`4tfKqi z@)bSs?S(LgmM(L74|eWN=%gYhIj0d3J$amQzSrNfX>2%!$Q}^EZnQeK4ett=M&|t| zJKIf>F1#GVTE-3Zdi2nwrsk0YFge3vP+6QAt?U(W{z<`J#RpW+hX-Dyfla({mA;n=s)VQzDA)_2Y~zp%3URO&4XBY)&9 z#Si{mz43QMC@>XYDKV)OhsUZLeM(~bw0=`?d>DPd$TF~Zu|9Thdt~HjJPSoe0quV9 zzySp!ewrxMHnkaFO6oVLiRJw?Bv0w*%*J00t#`n_^4;T2r;?MCe|*PX?ZmS~n#(4^ zFN^Ailv$UeuB*!xRTR&w#(A~C%XcILm|XHhX=$y9OOR?v-YKw9Y_j9;FjDgIy|iej z&aRhPs&$j?~vILKd6m+mFeaP9-E1jGo`?Wtcv7?9^!2_N? z@C}S;_gDS5Y!n0=@)KAu*$l$v=gG=|3ilHTa~mm2Av^#YzzWDi9!?U`NS)`bERK!Y%b$S_M2aCk~C5~WBFAG!vQzuJ{b>AocuT{*yVUH40A z2)yixrO=balkv$#Wq_4*S45~RQ4q1*6l+1V53b?P>D&-Ez*{59GL z6=<36(9)k<_%!Z8>#yj*MYT-=q5JobqM-tFBqQHpu1MGK`wSk6 zQ@bzL0qUzyAX|#)HUH?qi|4uxf(Hlcb4(YNQ3M^WwJ32nrl}cN767 zquw23R1|a#YSNM?4=^faD>6GKoq6)q7k4vO*Tgm3AV~XLz$zmquTB=6{)x&<;Vmk9 z&jHAP7&(7?E}}AkDM17anadYqxV7&`F(`xKz=6dJTMl&W^S1_xsZS*;ntLiOJCELA zhO$Ng_R@!7PLl_Dc#7qfw8~lH`gPSM`tV_8h^PVIiiUu1${Z4I#BMvfx3zj_KY#Xa zR=buIWEU?uQ{zlJ-XRc6TDx>CRr?<%cDM6<}Ef4Z=~D$(1aA z;ERCeUsJ7c@6&x9joCLO(zA$|$*qSsUHvcVy7f3-kO#v!8c_zy(Ld{QXV%JuFQu4b z9I6gTUG0aC>7(IhM7?YnNb;gSKgvP+-y8?6SOCtON!^60MdSPR<((m`(!lk;d=!Xi zpdUe4>}abg(MBPeC@37ZrP!(J0E(Bp_929eviaaS+fkz`j)Xgjc$K89I2Un+H&Jo=)<9R#Suj`r-buU! z8uY*O&_Kp#CsS$5qzv3ObKT9mIxBdUJLfyKbxRS!W>L!NAKxFpzf4yrKIkV4y1)5U z^N{(_n4sSuYZ9N1>K!(7-0(FH!}a|2^+v7OZkisbH%i%RyZ(W1YL`_UDmSaC1vxZ1 zJbbLWNw`rj0}EAURVB+&di7onxDfF+vZPIQ#mdl@7Pdcbe)?GXF@(2#-5_GXlHwkp zA}_9DN7=@Ga}ub*8#M2J*u6sshv6zCoW*v!dLqvS_Q42$<6A4!so(#U)cjoS`NxSU z4BT8kb_79_2-berWVN+=ssIYC3!bjGdgu!}MkPjvx9|J*6ju_&(>_gad}7*d@4nTN zQqj@!TH53^c9L~iP2mMF^(jc0Xo~ zfek9=o!m4zWQeebGEl(URXp6lFEB!6+0dp{vz4PYV!LiPp>$v$=kbD502=yXsS*eo%4m;AWJ^j5rLyDPoTON z6@a)Z7Zo;D9;_bMv`3E~tUl`a%?2qG6wd@jvw_2OGEMaf*Ok?AogG#^sC|cb*GV-I8 zQnt(-b{30uLN-l2XXYK$8;QQ?2get@%Hh#1%D0E&YZErrtZK0u0g2;V7Y5-JY3gbho5P?!N6u8 zc6Y?Mk6h@`$jCkE`HfF-*=BJV?w^?SDJ3xis-FA8aLCY518yv`5+zMY*^7#|-`Ft4isv+sB>wI26<7A5XXx?+I{z_jxs zVq~qm2%^FF=+WpgT{(K9uiymGM`GrbiI^M*MiM9hWL93D?M8PuY+vVq;6aVIx4G7nFIj~-q>hi?LP~!*#Q**G->p?o>6gSYZSLHd zRz<=9Bfx<)05RR&ck#hyH?(*|k+#(ReZAhdVpI;wyb4iyH+EUcz7B2=`s+H*SA(=g zO@TN1fC*;!zzXys29l&;b%1EnkW7m5{7OMD>Q*hu_hsspo1roPkcs;52hV6wDt@Jc zyak&d1JRQxMo%S%?x#*f>Zw|WNi#&>DGTNRnyMCKdJcPc9SmRgS<$`!!Vb17uWe4v z&CxbC!Z~3#Mt_7yA795Teh6)2v+gr zeU-V8kFb(b`|;!Hn-B6v@5fIGN2msgZO!bOxL6`8XE5S@O1>MwH1W!X3JL=76d;pO zBM97IVl!<1#FN9v!5vp#;9KWl$z<7kr{J;$Pb(;Y&oJ-hh&}EEs_nm>Q zE$hz0y?O=VfGLcfBz&?fYn2=Nr=*yHMT?Ay0JH-OF^;p@(w8A=W_`;))J2m+;k;{g zA*Z+MQ-Wo5NT2o|l-DU|Pn&B+Hgh~VHs0ja*|TBymh29_oKbW4*Pjl}wwTPHrCXYg z2Ufc|oy^~#cYbVO<3hY7=MVX_617-Y^jX=R>#v*)Ttk>cr*-I# zapU4awv#&P^sbndd$j0lt)stsbgeiy4#{MW4~EKDDN$T+d->xA^L z;XeM!&OVvO<<`WxLMCdZ3$6|5;_1`RWkeqg6aJK}_~_!Q{;U;ote1T5brT}o_3Kq_+C*yY?qt5iI(Fr+ z$eaJ%f(KUh;_~?PczW0Fywj0MqpgoRn2t()7g96QIcQxkmHbbmK7N~idwERXjMX-w z0&~Ie=|jh(Hmdew4Xy=&gsjzqG$geHq!Czf#W$QtIf6r&(PFN!N;VLp5!>wUJqhnV zRIcl=pvQNx%$5D11OstZ2KX$ZctaQQ5oy@CLqi+Nc1}S2 zy5%0Jju)0Mpzd)D9i=_e;!@}XZcBC~ENgAOZ}GavfTqU$N^5UqG;mI*nmv5YUm+3u z_Dz+0PqDy-A=8ZNoP$Ss`F$ZMJ9T;SvX>#w)!3TWsy5k3k)4+^bk=dbPGf0Bw%-bkg&_|4X?Vov zL!TZ!h8()H^YNE9FFp==z1D8D6k9#!Y-?52t49x4jx2V92d&m#Q1J=UTdm_xwNIpy zV|D(#7$Hz(lW@JCozSGP8|*z2D}-f`zs=@HrX9Ldd1qLg zv1pJrOr9g>oQVuU!C4X{mRgri6jq@+dKM-rMj5aCC1ZQytuKBRWSUx~ICdXE4Tga1 zPV|tv0)fNML9(KLfs8%zqQy*ySO+|Gci|tM=d&7|>4w;e^EyaUMHg2^2+vdCCW^*A zeLNz~Bdopq9WC-GnA?-UO$i5}MuXF||1rIEN2rPX)D=8A?Og^ofajH*l_j*Z9LEat z3lvf38PsR&kg{hO7&Cb>?9+cu=c083EPhUjDg8vw8&MBY;YyiE6`;5J)O}h97^YE= zdO4`C_lyoP9saE0*~y(&n+;eH7Wrbp<;`8ZI;5SjNh}P{Ic3D=8WCEnsf7k(T7y&B zPK}7i7jYNr?NQvo0(sH54<+HBa#%v1uk@4#5FY8u>g#Lf&Y!=T+hfyt!P^_?LvGkP;FHSmS~|iemW5m5w0E{V{C10Q{L^K9BKPJ(ysZV$B$iR z&mQXL_FD7S6n2FGTGu*&@Q+?xle>iC4f}=IU!Phs3{C&*_x^{}m#AnPQh32y;GJ=R zr>~H*7ECtx_nKqr9|qV=D$-P&Z81UlXtBpMP#);n{;kM$(#puLIrZ!;R9Criu z+5+88j=zQ&gML-ETQdM+$-m9#)!jPxL5g?o`UO=n=20Nyl~+Hb^D@4r8p7BhW0lVsHeh%eol;vNl!G^LURKN zT*1*6sP7c@twIcU!UW0J@_CA-3j?-?_qow^+FdlTk=2U7AfD)S!}+U;29%^k59DwH&;kK8+j>oZ z&7_>D-(s951~K#qEVNQa)n(lMNfARH!bXTcDYM(}OES7-INsfHM0s#bE7Jx3$(5cO zAG_LD4-c*Vx)Vk8ysPo{ZZR6pZ&< z^d5yFWxVI6uucLd1y?+|Kg!{xPF{`}SoC3hjYO zl^>?zkOd>DxC z^lDH*bgV+~fYUO^STW7&>C>mp`z@XO{}1$#ow4_e{a)V-GyVX;Kc`QEzXvDKBvHZ4 zG?u+nps)~jcgYS6hkd_Z!Llf+X!*JT8~;(N$a^4h9AZ5Sy&Wn>+H?(VZHYs2j+yE9 zNoZIcXL`qDl{ug(hc_EKD(`}+#1B~`gBtG?U_VL;xCNF`?gA*_1E41hUGil_s6)qZ zY^si?*nxg;&T=maShNZ_Xqk8t$G^yKN!VvxXZKJr=O!{w%U05z8c+63RbQWXY-+7E zo?e%8EoaUEOuMx-ojUO1+_3fCNzmC;dJ|^Wvmw!g8m!-^nQR~%EO|d;FRnRy^eFcK zqphrzvGix91Y54j>H(IQFdz9gb%e7D_qogmluWQ%6ep^g99By*rr4t2x;5tJVzra7 z+h88wr}mhCVcDB&a7_}JI&|1fPy>F~m1W809%p(oWDU)Nn4po=HGCQ{^poW+rsQt& zJ5J?LX)N>F%)-G&GL$~f;N<*SvjS*$XV0FE#sr(pw3G=V-w|6M)RY_c>}gJAOOLmX zAgPfS`D z6*b*wg0;7r=qYf2sv3O+5J%oF<#@Qa8Ibc;R#!6z%_I&jE0tbjfD+C=;3}FMZGYa( zrduIW_WXx?y)Acz%g`3F62X=Y3aSbaFELX@?4r-mjZgv9Qv*I39l-0o?T`)XSS}FA z12=7wJ)RfVg2CtKjJw~O&#)y+;VGhLaz_ZhWVF13{FLMOMV17-4LdHwxf|U}A^IDfA0f z9gY4bjuAAgWYz90$}(#`gwGO7$Y-m)Q0mm`NIaP{CT7|q<6}YP51yL8f0CD2;Qhw zAVxtb3dJ|&$LD#~tw7tE0_CwgJ^G_Zx;UBF$vUgXKGwl=*re;tV*BwkCNVlDRk;u~ z(#D5FG1hMKD_nyG_Slr$Z@btq3k#c)*xag zn@|C!Fg#$LSQ&O0s02kts1Su5aRJTn^F4W>Q!+p(u{nR(Em)@)zdMPg3fzipfkg*Q z4(l!S7w)2>_daNni0(1|#xw${!nz~;^( zsMe~4wRV>o;u2{sKd&l@g9_{zqOp2ngT{?t0G-N!AC9H0q7lHzK5XsSySVF!*#?MT zo8sbP!^w^1Xn8w|FBL!xoss8<;sNjk7IZU8qTJvWhzgQ#Q^i&eD1f3ly8GO*f8cK7 zUloAopYFhwIrvrNjp&kY@Z!LPSOqm&O%RVSo~k!=+9FKnsMid<%?MW z49k|w0#V{fNQecWQbDf@j!z<>Ilgk^Mnf1Z%jnFaA{kJ)i|rxTqTJboc^tqTS$%xf z^FkloxWGG0D=Jj{of79D3gG-%I9aBpn!}+iE2f`dhi2?GRX~+Vv!-Fu<98!SsG#rWVnvob2`5egDsjX zo;se-W9Wc*E=SPuSD7J6l)b3J8*@dmJ5yCvHST_Ig1X$!^>M=!3zs0u+1AQTe|7Sk zFJb^kUA7_`v)mwx-DTgtzbBDZzz`lRSH$TCo2{Z_uU;fn6SYvY2|f}DYLO{8aEEU`==XG+-Cf4g$LrLq>6f$6`wi5Nz2|i zR+m6(2=PX%xyyUf|EO*``h{;cUoYyE^(a2sdwz9(}|4^@!1&f%QR$7*#yu z{wNgj6x}NMP^PD#F(DUZG;rccXC+`Dj8(+EPIjlwG0oyL!MTQDQzw~W>(;P7Q;(Bp zwx^1dJO!GPP|V`HGjm6?asNvR!+5TQfC`2v=-_JQq%0k0Z(sh|uiySgbm|bKfGiHj z9UJoGWOXbsdeHR;orb?##K;m+bMww)_Obu(2T1(d8H}Ps2sd(h`J@YE4L(4(MWq(k zubYniK2&|ZTFm8@Lu8!|eS59yLsMOH2UlpTtNXKz6h?(Isu)u@=?BvGtu0MpRE?=H zZly5Vp!_r=RIm|XV)Sk2SJw=rxUX}7GjT-2U69xHIwI!;EBc>JiO81eYxD-KUM1^~ zP~-d8)K48a|Btv`VIUzZ;owHWX=ET8BfiPyX+jVD{;`YzBmUr6IC1lbnNR&wW0f&p zXsvs1zEB&lMVYXO)!BMAKj{bdW6B7~#HzsIq3cH<8}fG#lt1pUz}a{L$#6eU&*a4e z_RmAI(nk(On3mMD0asd9JV0GA<7!uaRy=q47yXpw^Y6z^=u(d)}ZWlGns$%esv_A zlDSsZy3?$n7LbJ-PoS37Zc(oWqO=+bKF6bP3sDa)ibRuHNBiwJE|Ywip_oa#MMPf= z8bxb&ofI?T+?}wUvEL-@nYObDJ-_N_Z>Ff&t-TMW0i7PzH4Qd%LlDPkKKX0vtQ$P~ z=Y;eX%iJEEu;I9iYhpq|Le;W1=H8hMh^aPc&{5++C-Lqgw#|E*dUoHpi;O&LNFu|h zuBzJ+dmExR;u4FbPyAfnW0!FfT=i`l7ekTBw;#1|NH7YQfxucW+4YytO>oCvV4_=L zXVAW84Twh1UcU|kYXx$UC&-e@%`_rR=nlpmn^KwtD!kLC`wb+=1{4sOfoe@&6g`DV zX4SKzDtzf^_D}vRO2k2n=Pzcvw;YBFy+>1qPPHgwbrb~`n-Mb+scm*ImMxIj1ytNu zm*38rfGB|J%^?Q$!fXR9qb*_`i20*wQ1Muk zsYl}7UVnduk6kZ-04P3n>{O_Ole*XMJ^2mp6x^)Ny_+2`eDf&TqYXXFu5a-&fH{k* z*`>!KxX4K)4B4B+Z>)kG?#{q5A*$U6y?JVCRh8l~3dtbvnhJknaY5XoKYh;Q!Dz)t zfZB%S`NW`sI1BUtOXp^4YCHOgo_!DLWwrN1`=LEq`SsBp-n(o_;a&6NRm7*2rg|ed zpyDJV1{mkaSA$RTGDQ4`kZwCaf|!OI(=X`ZPUrGu+PVRwy%fc*{RMA&_ipRv%_Hia zJ@485i}Vkc*5OS2BH@#12+@XIG2x7maA8@Ai3c4_Hy>_uitTUUOq6nRnJJ97Opb@Q zCfy0qV_;Ceeeg6H-VhrA@Wcx3z5(E&+)c4)WmdOpF|8Ry=}5YltLqJTej|8(*Vts6 z?&~%C=Du8vf)K&~M?W|N1QS4}#`R_&FRyRi@ z->G2gY0UOV7mr@M|5KU`ZKMsN;bbcah!xutc1_1zRE{-h0E8^XC^^L}I5juQ$|a6ZB9{QE_(kOyh-8lHyM^uKOTW zSWbJMDyvm=Mp$0NNSVpu&ki5DJe*}8vlq-B=*mNOvd`_w%~*@O8>BQOC}{UcSS6N$ zIE(zB4c*MWfN-!+RlnCd>Cs}Pry&*NU&||Pgd8iHuUz#;LM(sMM2;eH;*KcqD zxs9Gvj4PSyrANDT^X4`{CT1_L=)Y)M{(L`jF?Q7nvINZ6gY2d;-(yz0zg?PkHcy=y zFnuY0Ef3<}1GgjJ85P56Hdao}qD=Exe9syBu1$kfpKJ3ohU>_;X8AW zOf%BdQXg^_zFb-Pog?jDa&iCd+qdk zql@fC2Pld1F?M>!K=ieKue*abWm35DHViSO|{mb~2O$CI(XejA&i8m?>#NT_2|*zR32;NHc7!xHK4cp z$V*U_O@V<3!9OLYqA( zY}toe6B{Oa4xb9uCdZY`aG~whPIpj>>Tqts`ecZZID=B zddGcmwm6<9OwZ`trcaBlByU+Yjj<;7EXF^!(D=)0b!LAu@m)Z$FXBM@6WuA1XV3QP zlKxo(nZiLT*)jVq%YbHu&j;6OYPN9JEb%CN#k{-Q*&mZ4x&Y!>p}Om^d}e1e8N=KB zbfg+W%k|Je=da|M<}9AlLO4pg4+X~`AXJkhSUpr`)gn0EwW|BTjU}_YiA$Wx*au(> zUW6a7m=8CMKJ$3`9_oE(SJ!dH1;%|~!GepdF5Tb#qhxJ*`YN9Z(M7QZZaZpP?Ogiv z*vLJX>Ab-=s2qV)6<#vYpVNmlp3JmhmFLB+&9G%6ZIU7Xl`UklPOLWfAX;`sxb3EZfCrk znNOTorTC<*NkU13;ZBW1;RFCs_O3CM_Wa$u8-y+J9~TbpnfZm=Py%T8Urod4>BA1) z|HL2~dfM>uLV;VBV(O-@p>dg#={yH|yM_Dg+0ri}_kur`G0w!o+SNP!3~Vgp9bFeJ z*vcFNJtE*Q9B6H=Y4f;~!T?Zu9HJUP1oUmsoCmL$_G3=`Dz}7`Ie2>^sxhI-InBn) z2gwbXr=K}Rh4t^+Pre`W-?%Z`b-M3lcqQF0I}KhXz0!*;a<*FOmhc65n37B6=&avqYp>FIOjjd@n-6xdQPJ>V$)2roC{TCTME1HkGrB84|`;Lw`3%850S)EY`l$aGl~BX^L2$Hz;*6Be^Ac;Ffj{4#9=7lRk{}P1>>FU6 z0c?8Zj%r#nVf@Qv`s1BO@s3jrVy1YjOuRIEo=U_9m~Nq!JzW6n0MSMiGg9Tqb{7<4$hCOpK1}lwSq~wI~ zhv2}ynoaTo>@olH(=<-37SoQ~m8sOJBii%oZ!wO?8Bz3HCq~KN;+?1(rjR z2v1VR&Saco^3S~I<*v1B%3x<6A(0AM%n`{uT94k2h;^t!`7h=NVvNRyR|07ToVj1z zn+&R0jI`vhA+8A6#(H4}0(Wzo;vZH6xu7{f4>bE!k1*&u!7L8HTG~$}6p+jGH&^ej zz)mleK8eab)PP9~=##;Tg|1zjQMjOnp;6psdxVwQ3>@YkPrMk!If6VzskrN5x-q>y zbveVq)h=fK^{E=}Vy+4m+&*!p2Jd`~wrOSr8|sV(Z=>R%Q>mo z5r(e2K#hBpIohkmUN3I8mZ1QCfu9b}yp9E)d#=2HDo^JXyrV1fPpNGDF}V)o2)Q84 zkHV`2S*$)*JKDftK*X;f#E&4A)O`F=TwIKw%|7Z3#Oc={c%`8g+5fS2TN6%}sry z3~byVpV>r@&z}JCfD(W zTbE`AJ)1Rvl|ev7ZI<=;v8cO1jfav`nq#xXNO0$*=Bc3uO<)u`67}%bqvofR85Rjq zca;-nD~}6&nH<-U;1 zlc&C(8Yh}$oi5MRywqkXEGqdfxcN^FB zLQsAcWu~rK_ZCzaJb@GdD6)jiCCHv#!_Yae=4rd1+7%jVKYNy=K|r5L`8hdVUyXWz z2gMbm`QS>SHl6!-emJv}q2X17Px0aR2SlIhz`yqDb@qOKDlY(c1=MSNb~FJ8kmp(7 z?ABsy$EoK-sFoFQ4U`a~|E}@=O(#ox*g2~D%>Qcv>TwnYxJEfa{7F7{2EN(BYXhU$ZUB*W*uKNDgZ9S@V4*40r*=FxcYOQzQ>uH6EpS`5 z$vhkOfG$`%C%&s^!c^E8N5`hr`4slKN=0kO?+t15xzqXS{BagOtY_*GH3lDAhVcPf zoV<^oFtCyBnc$-&4H>wlGi}yj!?3SUVbCRJVtpYobA0C*miJ*JHfFzLj44Sotk2sK zqeiWYORFBg3DSc03{I-rqhKdWfSiwK@4j_K#YpxYtCT+8y+7gAHFn*kN9|*0(tomtW;0bi zHDnfe1KU6D4{}rrOhDCg)>sl_ z00w!;f+g9<@=T~DC;-a`YqRylz=nrC7Ak^5N}}(iNo=th#**zShvTDlhaI~o2para z%HrpB7*p6e=cQ>_o{Q3yFk5Sqmp2B3@*k>v^;gVr#4I%(>Hm$;QdPrlfzs_ShKCxg zzQdD=qWMM+C-!6_E7`GPm+We1gTXXfB2C$6Pl5@nc~@3;339TcWPwor16O``^@zhb zvJcJzuof;{R_ODgNW}6E|I1!YL=Lo%2KS%BNjIg*UY2RmnTq@fZU!2Fn<~WJm^7kT z4?goVlAqK;a5{8{Ldv2sp-4U1Gs4ki_X+e<8Dcm>zyCqB1m{kOaB8mXc}0>W zV-agxgQ3(@RrQ`3=_6I|VGxk|vt4Uh_s%tKRo~c@f&|oIwM=aghK?zYZQxBB0 z7gk`DLkl z)w*SCD3*m75qHV*W7CmyTz>lW_ol)REKi}1tzC651pP8S$s>k|Nw_PYZ5cmKPY|Nr zLI2<22>Y)1gctut=NB%ofu3d%I#COrf{B_349N7?l?=opseqd={c`JcMXD zbj&Z3oizUuT?NS}L3;AeS5I}L)ZCVs*ae7KmPZ{Wi&b4KMkH{nCJQDn#)1<@IrZQ6 z>DL9OM%dtwdv%KDwC{C*kf-(z(~68BIh9;Y&*@l!D;|b0Cdc-Rnv6o}y{5-|6lOa= z)pI=VxRnLmTKWBr1@Jv8dz4x+Masxu2*Q2@u+4kBPfYqmrI-)=D4vO^KWRlUvPx^e z(2)W_8VPaZgn2}s#gify$&@6L(nFl3vcKYx@p~9oBHGDd6UCiqvL~fdh{4m)?D9}J z*BIe#xHI=kUY;5qE(9Cc*^b&55W4TksC(o3b)mNeW4m|nQ=2zq)pI)Sj{e!xXI<;o zty$2K#heA03;Th~TYX0I>v_|ggA^wR={Iz2!$wQ{?N93|Y@X*Z9^$0;#yYz5I_!&? z7UPY=jDpTpxauTE&BO)Q?EDjCzpw}C?f-N}pex>a$fl@9M2Z1BV4MEuZ`IfY0R_6c z8|=S8%_NA4`0_ohecCd*6oK;`m=+*nO|B6Y2a=FqnsvuZwxe;RxorTd58|Cu#k88$R1id;H)=_Vw#;Ha(r4eb4k)AM{wGzJ5!zGY&cf zPC+StjWPqTPkQl9P(d;~^r0)LE{GGc41P+0RP-~9KJ5HZ4@!|PVcohR+ovWY8IlE7 z{1pSg{vjFX&@89;{q9HPECndK%AS%BUNQ7J)IYn{r7)E~rYPb_VNV_!f>tonH z+5gKr!gb&sxsAVbBBX)mDeiK6V%l9Ef55%zJDUbrjx|Ck4~bDREx?m%eKzAUbojv5 zec6--3;T?wmweA%F@13h6*@b|8v=TPCgF*FNX9q4y`4Qgg6(5D2g7t#mojN%zk$6D z6qiQ;JFOkJ4G*f-V;xcaYrTL$0NJ93p7bxMPiIPj3xabky;9)cL(f zOvjmq8U21#H|WUO?uKB#Mp0g=JqOkJ`1l-I{;qv&SEXWZ?&+Di8Vbn2@@-YD3z9lr zf&M)ceGKYnuiZ*~^a)?JoBn|zkVP}hDLV{&0BRz0%)wWBC43x z+q+VayAR?@8sXCm(a7h0{^re;$v5I|GgagM%j4uR10-^PD=M4FtqW|4ofQ5&#X*aE z#KjFiVxK#BIK}Xa6)S*j-cFi4Sp$bt{uYryf$399&FI?9|JRzvzP`So|Kxk(IF?QS z7QP{P;KCxjt*&WUr?%VK$&DIt=67>K$KejErKZC7p~8|`J%C05t=qMGzUj;TxjjAO z#AlR}akI)1c!;Q!cPeXIkGmmDlKB}m-#;Y=5xXk(MDQUhng(|0q83mKaDx+3)6Hwy z_;T`NcBJj;U>Ow|ddTGLn?dJ_x_9~5O>Iyh?bSBgidq6RabPOt8T* zxtFR|bDi0d**981UAAo_Te}%zBX-uR&bWZgbWlOZBCd?>MI{94$f$PTjXnPtB?iMu5}Ejp2qD`Nl6m|sH!TXelS=zp@E36V2hwEb}s*9+De++=u^O=&!N~QzCiG?oVH$h zNn;8kf(bRZ9Td6@um0QIxMayE{abD-5j$8hl3Qq8%92}Q&v{bdh2mb!;Fwwa>O&*| zx55NjOwCRF{_|(V=TNDebjHu9m6qwc#GgdP{^)$32-Akc5(}Tde(hFeHpDcJhB9UN zB5||jwqwm$x4cV}{TLjNQ-N!GhSYLrczqH@5Ix(j(N{qrm7_yV)5`V=9(QTo8IO`b z+xzdFGI=t}RV^8tLKwpA6*9fsRAIxMo&Y`~ITX7^E-ti_e*gY0{lV?GQk(BB-&QVQ zyQQ_EjRw(Otf=HWvDi;Pv4fTxn56jE0S_pcZp`iBUO(~V31AgaFd}SD#55f_5KJ$8 z7qf}VzHz>*e|>m2esZtl6GLZ>NH{fX(z&UbD(>s*>Fl#>aP;cbg`w>$+8UHOg*N;C zOx^8$FJ*648$FAc*XkMVc8R;xsB~?_)R4^`pP24Ck@|Srf|5NYZGPN1Y1v`YuH{~5 z?*9B@zQW+$&6|GzUBABPbku+)jX);>7&i8QfBw)Dohc=Lx4>YroOXq5-;D!%VC$M) zfyPZYSglRIcu|+iaa~rK1-KlB`1*c&V~-c0VsX^7?5D$6&1D|1x~g*Ojm(A${MWvJ zNnHK(Lt`@`YT}L`S6ceC`{xTKzqcj7BK%l~L`4OIZ+<_anV=*~0sJc0wJF8Fq=&*8 z5w9^jP)Tg<5`0k{iiGRVg9mkzpKPTx^;!M}yrF(_k>`k!$qS5UTnG!?aL~?pJwwv& zMdzC^!&t9H%a#>3@rG0cJml{;y)Lff0zBvKU1KSET&CsEjw9LdfZH&h21CaFogbLtaSQt*+4(2t+i$v>bIhv8 z5L9eykB03J@OsO(^?#p91tha<`Lzpmw6sc*b}!5DNMGDC{jY?RyP@$Hn z%_FK>?`~zKB@|-Aqfs-_oxKpK7JO3=G!?R}1t%sn95!e3eru(rJ<+T6U zck!&4zyLh4@O3f$$~6N=ef@)FVqOf#kxj^&pvV+75G2_tV`@4R+29rR9LA5o^lB+f zWFs?~MD8?Zx8_2ou)Tl2OTKge{`#+9m*dcMFg3NsC1-W9{KR6dc=y7H(1!~R4@Mu1 zjjczwb`=idBvQ7Xy?Y;II{(Scc?###k7Qr_iHS+I7ACipft`%Q_vZGrJ6@njO=tZ&gVRVN#bKBmd*92nIYa9=gRGRw18w{ufA z4Y!H5Q{^gWk(A}Z+%G8L7#@g?wMp>xPq;M_dQhq=CSKQ2I=XP+z4-`b-+ua@pbM?L z#zKL;cuZ{U_BP*Eq%TUDN_~Jah`O?pUq;51F-4?3nMa4A5wdgRtfynFcFxtx-Mygb z{Mh%8_CW2BSTD~$oqG!obN}vM^P5t>*2yRdUd&@_P_IEwDNMVv05KNvrE_3?`%PLd8Ojs-Mfo8Vb7Qm+F%?c zH%X>|7*@>4KE=R=F4F>mKYpy}*{9E6YgeYvF$>!GuI@^njd3yWi{u~7?Rdrz3yVIi zjI}K1&+o7|BBEkOGr}d+5k|O1jW3Csn)x0Ta|jWh_|4ZprIa#Rw(y}{C_#WncA z&A{bJ;g8=*l)G{Jkf>kf?Z0QW`Qwj@vxtYr77Mdug&AKSOp%&XVF9|%2y~L_j=;dV z4_h$g*g5s3z9>U^>E2}lI`|yEb{Z4{A)!sc>Lk{Wip-KfU;8kjc{ICx2J9<7D4A%C z8!7rOeX0KE)1aCjN1q(*DNp2q)z~Z4U|<1jsP4AHU#%jov*sA{JFYIh6jV{xU7M}H_iFuu#s6TFSX=hZpD{-8SFz^Ydlj8^}#Gu>;KN>a6f-P2b!Y+u%vOl~(=E;q_6+ zk;PS5mR+7Q>~fTco7>u~GXDa%tDpj4q7AUfmOE2bp1Y{u{_iO2d`F%zGF*#o0biE` zp`_sYEWgm2e|YE=WMsOx52nbyl5dY}3hq!)Tj4NMHj@V+tsn?scN9546v^7?^Ci;lO}U< z;(}DG?ByMrSYs8}7p1-2ji4Yk;_{c53pxbp^PN!rC?VznK4W6GDPN1Kk^F!1k$X%+ z!ZoH{OWvI&;t--)X0XP>;!hK0B}u*>9ve$O3}Xb8QoZhuT!Y4U5EpMk0rf2H)6F5c zrTf0tXkw_x4Jp$(b+NN*hq-4FC!Io`pcly4AV93)K-X@_~T=%o`Gt*tkot= zxVVMEb=M6(KeaQeLbH^X4eFpDxc&HX5P9nS(}k3Ktcf2MsjxqEN7f)lRC&c{)cNJN27I&7vWABKZ1jph1R;=tIh%lEKK`~7o-5#lY&Ii(3(Lq)~pielPxhC3D5XY=m@I46E0^Q9lU_lCPwBYjkh7n(H4bWVx&S{mFlI~RNtP?eG<)E5RgI0Zg&=0Y!c<+iT zU){QiY)X_1yU^=_RI1Fb^7{q<+;PFGr~cV9XL1HCWg1a6v9RT0GY%$P^g#+a4xdy5 zB)#$%jOUY1DSGHwi04X6S4BVI6mQH@8mum!T6_1Zfu8f_wry*4COP@g>C>8gAykSo zJy(7>p}@@=&(xd!KbT`>(j04imSzlCzA11+^Q)R^89xdVQyae49o<6nrF*_oX1ic> zjV}+42fEFl|B)feOBJ4j5!_H-ZfM}e5MZ$G5Fnf$&tU-Zq)nj?F7-FwnrEaU0syWzM=ud3Z*t5N4!L#(Xw z#+qOHiH)W8EVJG_HFp>Xc_Xr|s5cUbViBCzX&s9Hv05~OWqao*Tq>#g!iVV<`V*NlwWoAYpCYd_h-EG8$6oSrvFNka9ggt8=S?E6+dC=~o62b9CgMwOnvaX1RQIsfeUQ_yeSwpVgjo%Qv%7 zS4R2Jj-HOJ#%eB}GpMK}E&3AHVVmPtRNdlsultvI2_&nEADnb?D}{Z#L>YLX!1T}9 zsKCHyfoBJryvBksi-M-~qc`qe;_TjW@|oeUPBO~1VZTfkz(rC!4JH8!5Uac6*s3WA z4E+ehW4HZFsikHvHpH2Ru}+rfvAaTp;5lU8JoR=8@q8qZT+7(=9mtx~S?Hm3+U zP&6VL=~v+_^cvk*^#hvQ8u#|ljK0U?9ABpq}6DRj#C@P%(=_76r=d?O^-;=s3w<`u%YVGHaD}`z) zhqUtNZ<)O0I^f`(@HZ7@VY0qMnhaTrsH$A+FORl)`mbjv66rUZOM()UB=RC4d0z=< zP3|0RS?@{T(5IgHohCAWN6(yuN*+~XOO`k6iUK#yrpj46W?;24RXyF<-$y}b=5j4k zHzCcb3-gl!wA8f->lin@S@sBkN8IMvt7c947Tm~$(=DdTNsOc1FD%S@@u{trTCR-? zhza!t;hd1qqrlzU+gz7_&YU}kP9xLptP9GC0V}rZXlu^^ACaF69slt7zQDj0r4w!+ z4n%R)f^x~))wLb39FyxLW^a2tat2)9N_z(qU;3%KlBTx|er|WaWo}W4$n?KoYV7C# z`0?cX4;~y$PjAgPGw}QQq@?O*OIz=MMTD`bsU+|_w{QDXuDS9mW8&i08V4y-HP$lc zmB~#z;CQ?n>wsiiB%iHSa-YG}kae*58a8W|A-KB)L+7p!UCoXcn0~vxpFl{&zc8Rn zK@_WWd&|!A=cuYkAyrAWmOkwTyy~1ZF(fp!*5<2s-<{r;U%@g$Yd1Hy;9C|PEh3k! z-I5(@)|yS4?AO*z)AFLGL-cNroNXOB1F*rB|A|g9=x#J!Tti<{>-pDl$192cN-tY@ zmT}R|=O29F+_h9{lJ^&XF63c5I66MVI3=5Fp&op4(=(>C>oOZ^)|nxE0+ozWf=2xv zeabznJlKM7XWF5|M^eXv5)p71jOL~KZ^skeT zWC47%=+-%n`cNe)C4A>A1Xgr5-{|gvJdVi+@VL{cNjh znY;`IFLy#XYVJX0^-Ahan%?x{=-sGV+4mqD!6Cla6u50&F-Zc%CFL$x$8#B^)x36X zsOnmOa1e2xQc+Q{-NlwjV)9wF)s$`uMa$mqnRr12lci2yJO{!s!O6!^x_yOM z2Cu{VUwrkuZQV0`9@=PHA&E6@RcldlN(V1z18%y?lLRJ%*<3fpQo}0H$qnSyXiWCm z02({0*}20rdI{QxB4KGu)3#G79i^SVq@r^mCPuZOpg`J5NZep*-RC4bHW~WoSA=HY zYFK9-G0yYDg0Wf^PMKT-Vk^}Un}ou4q-%KxG5^&)5h6%k2M*B4i^Q*Uz8$Y>ZV=0n#w`ou)pcOvv%dE55@dORT>e58AHJC zXz#ntxU5W3u99rf?SHke90`!2+YcW$TWm(?<27QpE=3T%8Saf&rvItZkr`01AfAK8 zoH@t#-BwB6FpKw3JO}O|LIE7|WB6})m1e!%wA_17xw2x+v3smMQ5RAQ0HmAWukX6g zzax;f)+AQ+VMsO$-GYDzBq2FdK%7{^IP~jD8QkR6(TIhXbeOf)C?qtw@_z~rJ83$T zkd<_MS~VYPeh=a_{;CSknCTfl^bE6XQgEV~7on&VBee4_06z&GV7*`o?)l$ovwS0#cb2m_CT3Yguh0IvKGo(87_FxCH(b4baUc*l0M z_AC&k)0Ne$vdDHr=O#_AO61hpzkk0sG&GGoseT(feE4Ql7hl!>-z5Qq6$1p>2;-kn zuTVB9_;^NNl&}9aIYV47H0`ZsFv7}}x3jmer6BM9eY!}lTlHJ2y8Oa4Cr5Qc&A;9`j;U6jdRO__{+6B~9_{ZDLYSki$pr6jWj~b!|&?JiBYr#OVzh z)P~k;po*oH)&2TL-X@*7V~savt)W=Way!dHzSh)1Chol>{^pCVb0zGf>I~vw%SWeR zxP%z(=a3Nd8l%$LN)@G>Pnl1X%8HP{gR0^A0em)TduRuo z5+}S5=rC#nBrOsuW;rQrct z=pShN3gk$7bDrbA(Z|e3yaii1x^ymWd%H;1I&bY$9LT6tD~rPLQxw)~uDXov+x=h} z^Nur+Tu6C}LQ9P=TvYTITO~ZnFe4v*#Yl|@{NjKA&GvcL`#^_zaIl-0WkSB+7HW=e zC3Itx;X8ASETh$tf(Ahj&Y;x?-<1w$_2Db=w_X5laFF?@>XW_31aws8RtY=rpPkt8 zFR(fI{f*=HvCd~Fvzi?^kMcn3`l+6|hC+?4!syuvq@x-GzS|7Fh+@F4H1_M(k6u>>efccEuWVg zxT*)4$QX8ag1RyY_Sb%wb_2Hons8cvVMwKXyg)7D#s>Z@Ng0_W6e0i3Kjar5OIpL= z>EIB&sDJ4U6u0clii#55;9-B>Ex*9MD_hi8mg%8jQ6qy^dYe}n%qgo^WM`}#W~(Aa zgLl9p`wvsuCf9y>r>6C(ou~#7x(Mo%QPP+M+I|V}799%jHwnC&9${nD7^?pnwENw@ zQ0p8xdbAEfmA(TZyfo2Bvce?#2mV=25JgRK85Ep!CjPdGW@&3}0yt4L?wZPwTtov+YCsnn$-Z zF`8G^18Wg55Z)-aSyCm3-5+Z(US=gFTYlDb8c44x*=%4s$y}gJq_Og3UouV5+?W|_ z!(EQX$2Sz1*ZjdmaxNuu!o?qMqpd6`1g=O5z}C*Y=Jz)qffH+e1mbPw5t_QVZ!2Jp z@xj^X&69jS&BkP>x%8YoET7H3TFo2OVP{NqOiav^=YAP&=-Riho;-Q708RuBd0elS zHu^r`NSS(_Pb-q2bZWnBfNo>|g!KuaZhGn=*?D21x%RhanB>Co1A4GElj+v=G;q?- z1K?FzE#>x86Y&!SLgEDJUt=$*KjsQ$koehjIEtlVQCVfpDtnXP8fug6ucyJZlOhSk z>o}-Th2qEkp4%FC&x@ZU`2G56VAPc8%^T&sB(eE4r-#2_2}2 z1!u%BHrZU_SXY!UX9*hziGn-H_uIlWc5zX^2A+Rs8fF`5yg^kY~;vRv`NqM1D%SXWZk zC!=~)a5e%Cyn-4dY62(Ffn>;rcFdl+_cEXcsQHD4W2x~r(3r!G_C8S7)PP=+67C@b z)ex~8sPQr>eh(e0O^&Tvk6T`q)mAFqu|$XW^E{GwmR^wH%T+Am#`pqqm9U-uk7Khn9;I7k8fcTWA#u>9O2096D8 z8i*PM1CTx}JilPh7JH8psZqhPD5D=-rIzSfPJ|t+t*U&IGpVo`H%=1*<^Vm} z!%+8VSOEriK3`f(z#pMVGk$ennDKl0k*tHti!e1+2=hMDXk1i2SK=85y-5^a(E@bb ze_nt3AO-4<*642Bf?xD%F)Dd|;%8T41S|)j31IfZj80ASLv%wgfB2TK8JRM*Gv$ML zXY=v6{X<5KsK0mbUgzW|RF0bURU-cHAUCq?)Z<`=1W*C7M>Kla`{oa^>$8~s+AG%8anN|O-LAR?6^X%HDR zm8n8RnG20%Ooc|u&|nOiiOLW{5|Wuj$(%X7=hA&Y&$FNBc=xgYc=td1*d4#&zAJpb z*XO#{wbr@Lb)Gc1jV+Y+=|tJ$??Ypx`pC#WlU!$8*$TlfvSZ2Vt@l~7CZp9OKdt7=GZK9?aNtXm zP(Z%RW=^l_w6V8L-E&xrYwn(x(9IdRS8U?2y#cBRXqO9`c8jYjB+T*s3?hy6dCh2b z>l`#c$OZt_!Y*WKwsJ-!X-mnfkcOp#e)}{K`xoh5>%~%hzTT_6xh2I7p?JQ?0 zQqYJ_I@#}6#k7QJ8ico2q4yMrflWa0T0d@%<|30u4j#hCyLb+8?IJ0)8J8HU{QeK! zA*_M8BsH9v}kXKdAB{ zO@#M_oE~WKH9#52yKuz5P1ae6(aFYiNKm=kF|-Qsw&9^U2W1Ybwdn|KOXGZ2qn>-+ z`%`?nLuUP+Og+U@({#N@EQe+hbrjgI)SSxl>CI_cyD|uQGd!0)d1< z=*1!lfe`(tc@+^60hOJb{=@|`4o zlAl0&sYu_{dn?R}CsazhhlV($l(E{jNW-LZK=ZbUb69um1Q#&lQT~PL7HGXOLW9=ofqoOj3_1Do8Hj3FAcKLT};MNbI zY+HwDC^Eb3EZexwAe`rijS3Q|uZnYxPcPeOB`}cZU%4)K^`;FTA%E&WNxV%~<~j!2 z%@C(XO7c}rCVI@j1VBvA zzqCyTprvcf?KZ50>JGqxyj)($p$(8O4Fksn%maBM32IR*O9*;Q+qmb8zKXRS2z7k$ zNJZAm_CV9{u1%8sP(6 zdGqniEiv3Dc&y#47|N`Gux|zh`Jf>`lKk5ap9JY*t6nTJo2|b$O;b z(Xq(w$6p8#*7w0g6oa8>-!$_Nnqx{S_4ip?ELy%z-E(bvd znr1`}vtu4s^v<0|yR5OgmkM1$^N=fJ7lUW>&_?St@0#SA1>kSt3I{ZRc1I{cV&uIP zevfHuH(95Ci?b>=*&@j9Svybcg68bgUiuqXLq*;TBQ&dl$>12ESnVEMp5ERoY;1Jcj^8Q9lnO@$iR`j?AZ-;zE~Ev5>QSTnQ8#g#VA9^IK_9@1|w`8+yW z4nF`L3^cB5I=hA>Kw_dZk01CO~I)!%v zvT$}HwkWaMq1Q&fFu%Mp-z}@0|IZG&O;9m%ubJ3|RH0oQvlY}$Qw6RvD+dWV9uNEG z&eAlr(YmS@FGYmHWCRGz@6gF1mm}Om(22ZqLkAz;WBGb}TIKeK_z2)cFCjx(MkB1| z-d^L-o8v7Xde%s`?fVlq?pqtYcPQf$htB7+wyQ~6;Tl%WD6~7-Meq!Y^FUs%)|%nw4DU@0h8+7u|$pq_o05i^AD7(Ht7GW(Dk$NRZ&SXqONW<&2=irEK;^Fz6IU;uGe1Rdr}iU} zyPRX%u?yDb?Ox@IG!^+60Pf(@z?TEZ!UldP>uq^ZjKvxMI?-dMq(AU=_fM}=pUpCJ zzi4Edh~A@(Q(t*_ZY%n~Af*61oAH``!%rJTilQ3eYN7RdoP|~5xHgflNja;F;kHv>OQj+g9s0ris%Th}#(aL7momHRShw{CqV0ktU-fiJy$) z4@wHLJ_tofaRrkOnN#2B+p2P`Ioid~D}GvFX>p6PipT@CL`Hx*<(v;hY2LQ4VzB4jmoFLuED*s{$W!)EM{R^F?Vv`P*P`y~U!_PX{p{)aUY-f_ij{&_{4j|kN zSKNodPto~ANBX&H#4bZtITmcYHdbtj%Wsaivm35j>{{A}@9}EfuTVtAi`#ehRPqt0 z?S;qO{vf)P6X6{^AD~6JmO%Bv7>2+6P zwv?!23(B7!1ColT{Ox!#hx1*L)$>ZT@#rnQxzEdg00RLTq2z_0r|L;Q-PlK)@)T^Y zgA0C0tnMmoK3(~g33J0vMZZ;|%}gOguPuyo8`lCxVidsXfU{-__?U@9iywl`o;Zz zCMHxY!;3|Y3~Y^B1?vKJE;pGBheuJc{rgLP&fn1X_urPV{=F&awg9uw+z;9w=Gf)JoB@aNP0+r{83-EGqyrzx> zsvY`=@*j&q(*>_5pMm!Rwgav;2Ptd{B!ryv3w`$!RRIx`(iTGmER0p@4c2t{IlJX^ zGGBAdK^=fDajXb_e`K4V!JSG(MQ|0XI3d)T!-;tK+0-V7v%+-S2a_$b%3hj2pBnrO zn2@$H9v$8gyL0iJ-KhO*R-MUWbz3zDMJIz&Qp>sWWZ*{wZQRY!{1}$bC^EsRCfH!1(-g=2Xh_ZeY`hb#%;*Q z>cMF^Y{mMb1gJ%w!EZD_XI;t6vrjVA<=>ZhY^8|2zoN}y8+rT8^%Vp$y*d7J-y@cd zd6zyf>wR7|Ye}f;(-6MBvCys5yX{k+P+>OX#g@^CD&E{L4UU`sq*XSUXJ?=@vjjvNl4YNYOar2eZ&6I?XPU| zp&|x#c0!#)vgsxJOHrDQq*XLhMt;gE#8##aAslQ=VwX?(eu^GNy|FJ|_2dpJIhsX2r5N^DJomjtm32 z$*eTHnCR^vRW-WY&~ku_K^&rn91oSwULH0lF+IfLNaqm`ubMF#QwI6VagG6NpKfj&>k04{95JYLZ=5)&re@l)iI0 z^z8TiwwDZ9HOD|k=E`t?@T88;>kFymwUK927w2_@*HiA^&J_!LF*w3*_v_1Q5G38- z96tj0IG8*%1F#XwYQRda=p8^uo+VINU+6W2&dR>XN_c7p*!4ty+}x| z7t?Mr?foNZQu%lx=wfa{TjgCdtgrbodhG!g&%) zzZF=wGZ>&IG_)l@G~E{Y*xo(gLG0$mg3(p4@9=k*?i!oV=y}wAVK%r7&7x8Bnoojn zH(YG#e1!lt^p9{uC~N)C4{~nn)CbXvD&3N#>$ZJeVVd+Ab=NkRsN9HX=J}Im(!L)~ zD6+~CS%12#d&>vZIuzv?X?eG9r>gf#?~2GcyF4pjmN@V!b$LWv-4SoI6qSe! zIprL*Ceg@{ULgJ7cE}yUlSzjDVyvYJPdLlZhRbRt6ifW^zG^1DG~?Z!It#bbwiyf* zsd73d&|qP>l6R8v)HXO8@!?&5SQDpeA=iKYAm0_@)6t&7bQ5~OvS%{4zMB7i*g$u8 z<}!~n)tdQo4M*Z1R{N;cACWA*ZgB9O{}SWk?HxU0OJc?!8lAaodgjbb1~k2XOWf86 zwUzNWElonG!^ij_>pc}?XKKah`Ue*n{j^C1uuVE1dohWKKG$ZnK8W1|!B8%wF`t{4 z6*Sw-f7mf~`W5|$pt3)cp&4f&7u>eB<8sdSyDgqJ(*{iVl8g;ka$J-tu>WQ|R`N=VdLjmb>=i475cXFE?`w!3S+V+G^2_mSCH6H?=ruKci(Tw->(&x5KAx7w`962~FHdzRxu!D@(QX<8a{S7{Me#xk z=KuZG@V8AQ89LN?MBD#z!KR3@4;)B+r8O;R@`mYJow0k3E1!Y@<@(xEfLXecX;1sm zv>hL9@3% z{qOs|GlZelg$&}3I_h4xdDFA`|N4H{)VJ0)*b2sK_p%g?mnL<5FwhyhwY6&=SIQIy+hukh2yfv!-K<;BbD?J%gUDf5 zOdDScDa`+GJFHo)q|Ab@hn0#!C(YCOg@cS&MsF9$?ss{mFTOahv*wUw(N0$8 zCit;UV_*>0{78p^?oH1BwtaF!9~zeVwAu(r+>tE(VymW?Q7Cbrg5Ts3iVO#P7DDlY zoSJ4cF#I^#-kXu%@+Q6e^*jbF820G=6SZk45h<1w$J%s=Ph@Ai;JO_iEa|}T4no(!Y zyfiZH_{*_d1A8vy_T69H1vVIiq2ttV4q4^ zSbS~GT&C`SS&>h%MWPgx!czdw_{vu)1F~ z>&lbT*5{K3${ezWQ%6KoZ>eicUiVPeU&y_b1OQYsJ7n6zGwDU`q%4Dv=|Wbl%rA(3%WB zFqMlb#hD`)8~Ylo_Uq9`AzEUva10?~LF41#I}mjfTX8-lqgQ^8R+yn{CukV*-azX+ zGA)UZDZekIV9QI6|1PJ$kM1_QQiroK(}T$uOia8(2T@r^NAhvC=L@^I9kW8>gtd5b zG@^=}AR!2z#vuu9nR_;utQ{j0-tq9fADaAcC(b~(r0o;!^@)(o$!gm}f^mW!=T6hM zYT1O0<+#26SoM|vTBuvipF*<#AC&a}ciHw|@bqt@{(q=a`JX>71HLW#!ZJ_EZdPeLMHED+X1_5PcZjU0+aN-k`dSt%86sW-T_Zvi#~un-kSB`{{2}i zrD-V_xRjkrSi-Vk1MPTaF-Q$StXPE6h`%=cL&=4iK{^?|zyfA4xKS*ku^yUlCiCQG zZAwXLsdR@EXix<%!B8Ol7>B_X3@L2ByIlaR)Tut(0417|y*W!6u5Ce13$rH2yIX4- z35I|&$eQMk3F*PEostm}os+{(CZe|wb7d-aOTC&cyZ1)?w0V3Xd_P;#eoY8!+N$N~ zp5OG?v;A^pjG0#4Dt4x;tK7G4oWA~+1NplPQkh5i#^VT^%cqHvI5#PqA^%<(t8q4BnZx284_!h;>yUkm# zQ&u(%q>3A`UVHVq8w@)oBcpF}zL4qLY|_dNN@9E)xa*sf5cIU)D9GU_=jf}gJ;}!M z;fdS=r}6po7r@IxmPE6T0cX^FjKrNLEzY{Q+uG$ zWqehLf~#}0sWq*f?9|HTcG_-n{Cx_rIA&HsmJ5O#4W@Jt9yziYExA8kc3JOQt^FPT zKp4ln^JI|2Q`O+QTOeGSn@eMR_&c%Cw^Rw$J@cOr7k(!ryc30S7p6bie)Q-N>9RTW zeKRfNOcm_+crQE61toT7_TwkUNduyjh+1y)CW+;AA&F`d33;=h~rmaP|)El z=W$Z^6mld*`jL0EjoYFi#kK}~M!}(xecoKTpov^mt6IgYQvEq(kF4z3^txIw zIze)p_VU6=pXgF!S2*S%N?*@4cw>=FFR5@eW-|;4n)vL_k<3Ue7^=p`q z&7QvL-(MH*FlYSxRlK~r6loLv`->-hdhEYnWm840i8booDGqge~DiX$fX0`Ui-mj zQ;$qkEa^DA1LEhxHOZD6U=fNr%X5v{(@KJ*=Iurh{816fff)*ekB|4jkJsPn7p~iK z*v~Ma53>hmb^Btlmt|!mt|wco^YP~eLHY_)gb65BlFX~$V)&5!8NvHUo74?Dc3WWR zNGxuQzaNdU^2eriALe1R5C3!qN@AE>n@;>Ggn}-9@ouO!rF_3{R+#AmZw$-KfvI_K zBT@g3yRcaxCnjXA;+R-t_YA*WHno4n90X2o8YI7{6K}6tcT2-7ch~suqcG34ufF*A zhjApHx~Vt>>L+6{Og4eQXRavD#f=dJU_6li+vi+LWBSv`gN_Exm9B4~{m=bPK-?@2 zYL;ct(8O5je||$ey~?@`8xk;7%OId${l^)Lb-8(YeIPU>fDi;$0%qS}6o7Jv#9yF^ znD-hpeBU8fBv6$C`$y;)&SD&VxPt{f3%}v1yW?DhObHi+^jF%|zHylx(?_=-(u->_ z#e>||$nN6CE*+|+XmtY>>-CcE; zevy`^_p&W{U+SSNVmzdQON+Nz8nF+pR*KF@J((_!{$9<`?*S7DQlGDZ=X|B<^FQP3Oo;iYJF=oW>b)Jo9mKt<+ zSxs&NQ(+5I>1`G(1^D=m!(SYdU;$q@Y}u0VIU)-~H?!#Bhs1?U&>qUOMcd*ODz{YM zXgId0M^+4rZ`(Ey(Ly(x-@FmCl4D1XWMlgI-qzDgAWbRaJc;&+GG=P6y`MZ(LoO)& zTvL_JTEH=~O}vmit`R zfz8WBjpiUvnZ9b*t{^gZ&`H=N)C#noo;mjW%o zOi*lR-hFS&8PLj#kpdis?r5RZ`l%@2JNy2dj{Fr1-QrNAbVPEQfwi7zi+_%7*Tgk} zk=xA=JSXfPLUHRrfZ#NbQBSrFg%Gf&=ho1zm4ZJV3j`tXRWZCsBn27IQ(<+RL9Uq5*?zc@Y}1L2(!3c9~PGwYK>qa&kNx z%9)>1tPCL!bn;aspYYVr+7`^5;ruJsWjr!&9B~Mau?JwRsfd~sbUlr&>IJe1M!~TH zy*2eFpc!oD282`vDQE3#d7GAz7_xWHYq)!m*d?+pW^WEo&Qs zf(#D)IIt)euq`sWMd-kKiI@#zv@aB9Sm5LZI_do9!$cwAQJ6$DVvq>{rg~&x2Z~i& zg{XqL$d^##oyYDs9DQq^mQL}{jP8v7h0(_n`3KU`KkI{5ikkgFSL|=N2jpYe*)^nQ z%z5<32Kd?uNSXkOja$jedS0!Uh!P6be>R@n1ojbRk)NVHmH`DB%(=e5%XR_J4G!`X zYu|9%r(xfZUaBh1&CkCb6jXw7QWy?i7TL*wh)X|S2L$ZNnH6GyS?xR4IXS^YA?9r_ ztVHjmdCxDpaw_XzEEO$ei3~3G8RU2hf6arMnwr=0nJ}7hCr%f!Jd$gw zAu3eDV_kXR^w(D};&!aa#>LfOoge$}18KlM^Baz;F;tqclSs}4mB$C9^lrHZ(>xrj z>LGmrS^UTkB8nx)p?Mg2RuGG8j4d4QEQUF1=An5-#l^;r*_TM!m$4OEzL=6p-78h) z9BH&D8I2&l0sE;9WVKEFDF3m^^O4A$sM0+I3A zpq5f+{|8jpjpvefZPuwaStWQ zJ@5+kZa`%?a(P^FeUY)0vpA4wLv)VwN~uYYKmiINJA=&RfmVjpwzRgklCC?^CU>Kt zsRq`HBdoDPTMNAC0|RHu&F4@o>+Y?1IG~8p!hk~QqAjQQh(}`gxE#5pQ3zQXp*&ly zEi+{MhO7(O6BF(@FjhA^MioC4zl|@_v(0F}m!6wj z_xw2@y4ucrf?q`{XJ{_Z2EZzi!JL5a@2<+NU#J!P85iDx@mdU97jcLF6Ew3soS8Tw|~q^A2bC7>>r(0Fx9jQP(h_?P`FL-mMT6uI;_E&nkm~B>v@V^UE<(r8!L@wq?jLBmR{5j*_^Fhr5DLEc0CTVuvJ`m#~ z#|7x$i{?84-Sh559_TM{9cAJc2yV2rp*w#GQcEghn4}8;e!rYgF-N$vMi5s3E)y~t zVj%War=Ht@oyV^ss6pIu(WHG|oXS61s6<$G*#pXpW!!w{0i)r!(`#+?dl-jI5mCx`T>=H;7-f!v!TpoZ)%y0_!jNi?h zN4^_k>V5O5BT{x>{8!&P3Ve3RD4MKoM(0J)|Lo-Udgwu%BRMW^r(YsaR#r9*h8a`f z&l|I#jCKkN@;iYt^%@WDgb6T2`)58zLXyX8K?|#rYHCoz{1QMRGDy|$oIs=ki)_m5 zg>25On>ud2Jh+ga!DyU5IBE#~stHK70iphNtH21ZFi`QGeH`$$@t~BzciZ$-B%R)} zBiEN(5TPK=C<9PV=EP{n1x&^UdZ#~r{J3_~-!8WWVl!XAOn@4p7h_22#J57k?PH&E zJ<@SwlW=h>f7j)ygw!QuSq9mz1vaCh zO*3x2-p@ys{8Jk!8=?jx0}jDEE)0{oQQ%4!w8`WrUp{`k3PXm95Mg$WKiQ*q14IjK z)Q1PUT5Qu1{WfAZu0iFF(Z7I>Zdxo}jb~54Mdf05$$^B2)(NTFLx#>8V{YjC%|$#% zI&=Ql7e6GFu(H{Q{7T5F-k?wmSJNL)<_wh|$Z_Rx&%sX!q)4o4=1P@tvfMxc!Du9B zS13a1`6x_fSbL}T_h(kqvQ_?F(vu^L7yx%Dfi5ajjiJ7WE09|&T-Q$GTof-}@J(H4 zDo24f`Ntr+kPm)^7Rf`9n}zqOibKcbJ+9$PA#68n9z39<-mOAgk9XTO=8R?*{=)9T5|UZ28oL5Mv;P&A@&D$+p*8R!tZg1mYZi zBXgyqKLQ0fiPOPnx>VHKhd(YdO$5q*sm|ir%a=I&KhWo2N%0MW})JS2hx zW@i{{hFVPh!tlatu$^48WV-9SCdfgQ8n#8~rYGg+1^g^HDlV`as21E&d9Gnt#45dx zd#8qt>cixd#>RdR9&~h`jnJ{T-vRe*hVw}Mg2f2fNKEviDjNSOKl7JE` z3Vn(L?n0(cz~Elv1iZ0L`v=T63CO*q2;xcgEhQPcc9l?kjvrgQx(unQEL z=U?m;z_@rWKm_m(SdchuGZ78g2QNqHie)tp0d6$O^k-wOXh z##CmAuUH89{c2HbGo>kj&|$_y9>aRhBgW8xB!#8BTTiQ`=5M`skP@PllpW+-kEJt7 zxQ7=7A~lT~6ZVNZ_9PA@cZLL=uZ>UTD(PuuO&_E2*L+?g6@q^#q9t*fL9_l@6Y+N_f9eL7lq9{)w+ z6c!)=-II3N#W2kCN^R`L{sIcHFdNe;ny1@3E2AG+IlH}6KHWla$@|=YKlnRD6hq@- z9Pz+ZB^ZG)aIG4KKT0-=Z4mg;IRzWCJ^S}3Af;SLxrcDt7cE3_21J97hd%-co4H$A zS$xNiluvmCE&6$u^O{Y)?`;S15_(+ZYU5csj`#QXLl2u{6H}lDq!7@3pWTSWEA`ie z^!V?kRCO0!_+XmYhxON0lMGbpJBGHGq;1eoA$ZUs>RP%fw<_+MM2U!rSYi2=l~{OG z?osNI$ucjj3DZ3{Z|>aQBH>Vv$px6=aumN6q0u+8XyL-aqVz*Z{np^U0`Hh zW4_(@&jQ8zVpactz|a>bfT6e^NZ!w<_02j}M?S>KBP5h;&L_%^-_H)7oCS`mYFrV|NPqvLiSg0dqhgQS8n%?W;Q9#n z)#9EQH}_CTB~v4GduRry+8mQ-p8$s;iB!Be17euG6*9eUNzJ=5jImOAgB2IDaUhYS zKKk~}XmqH%6tMajv#%NjS>#c0ceCF)1)?w?#gQmH4>pnWaMNrE$P^;&#Ft{5fMbF0 z1(cnvH&D8AO4`4SGfl!Ku-CV$xrBR4?|d+7kjKwCb;`8NEs`I=mH8#+oMFjY3?-yM zYnPGbFjzwoDKu(t#dAUlU%G15DiQ?K!_+e}u@tl+U`E~hMLcrp?E*6Y0P2T0;JEW4 zKL$Ku7uzbRlC3H?}y-z(Ji1DW1vj9o|qB72u{}w?N7fDGFxStIfYt^643A;8+L+8095Fxp- zjvz>)xRNhg`wFDJXzMIpzPt#U7cr;SzrP1;)!7~hSVg-SatAlf8v#i1;Z_gf!|)VHE6qN0B~pG z8yy^_paIEi^cl~gI6{=PNUmu=WGH$M%>vBN0}27aggGM&A*1qQMVv7}F(f(ijcj1@ zYXaAz>`0~_LA5#7^Y;Dwij+otISHVY@w>BT$Rl4^tF{xq$avv{sd@pBBZEE5@)7Uy zA4aIM$f*O6g{VQ!9Hew%wyM~RDxD*iVy9U|K%;Vn$~}2eJ5P+9Bn5d8Ofk>1#SELv zyJFQQcF*3`UEmQSmH_gkItDoxA!7vQI8S;%#+FG&utVc&ZZ>LChc_))G!k`loi+pM z<`?Wbv}wJu(&gy1(3tv&jui*4ygnpkNYCfP%+doqdhQw+Ws%-FK7|Pak+Z|REPU_b zg+=GkX?~b|#~+Kv_7DEql7JCYB)V^KmZ@t4+ft0d z7>M3OUuYFv9|%drVZ^lQK5XG9PoCh3;ZBFpn`6Em>Li^Rec3qds-m=Hu;>o>t5WK<1F9v=a^2dwoof;E@M_8D68JsI=(Z%nqwyT@ zr8pR_U`xSNZ=>3rLA6{5Cuir^RqA7dPjT+nNDt%>S@d*gB=R?;M?@m@Hh!9CRjCSV zj=Z%`kr!rNSn#{8HgWjea@Ioz{j!TJ5~|G~hVqZ>tB{i|{0$&T6i7bgqLt+3uPeXW zBq)gEaUI8rljnJ8NXV(`_;t{vyn6lmtw)cF0fdme3zW)WllorsIi^b>x1O&2dy*6z?m=q~VmK#|kB4(xRz@v@dgQm6`H-P7JWs8KC>Z8=J@N-HG493CE)091?W*E+;a$WfJH4AXYpn1IanzuUCZ7 zMkV_i!~f%# zZTh~FqmL+CT(ROH{ECxhQo~RcZb8!;&F%xXwn@q<-X0zyz&qd-YlYR^jBnLa*-?Zx zI?X=NLal(ML3&*YFf?!HOc>sfZ(kU|hoFqDv5=b(H4zy@B{8Q(JsD$pS?LvU4*Q$T z4yD&*K!X*#V|UpZAHaHgKF@ywe1t%exYJ-1-Sewgug-hKjY}dl*%6`!HHL-<2EM&` z$mYTRMBufYw3O6%d~Q1k*4JQw?Zm1f2!51ajY;b~cSu)6Q z4n3PKc3rj1F4Jg}HZMRYn0FV6*Li#Bj5bP2No#t=gctre< zhVbc@GtmZeF+c;se|yloCX$%MB|}772w2D*KfVf6z0C-b-vRL;7ruhEM1~TMlpopu_q?Ei$02p~`$aPV6!=Ke#+| z-YK*I*Xi9u;C64TfN>xwb-tL#NB~Yuj!i8^zmOpHAP*#W5#n7T$H<6(Pl+pa3#;Im3XKc6=UWPHFrCgM`PT6#3T*2za8dC+Q~KOwfQvHwE}7 zu8tN`|EGg%_{%c6hTao8`-nc6x%1Lq5E2@C8uolx*zFdUf-iuo9K0urF<|>k*b)@_ z(E>t9M+@JIw5@UXBfUE6?=qdzUq$lWq~&mDSu@<5&$Azq27kI+xyTLG${mnRMbHbS zswUK*w9wo)JkQo%fINj%pRzHUEMzaacI{O-$|OKjj1Dv``E)B*d-wvJq6QrVk^O+@ z;PwS20IBXLG{AzKM^8(fZ{Lm7nvCVpS6H=fU2A;HtXZ=ly2jDm(((hl>_t8djVYC( zcXJvZlSrbme1_W}kj)qfCMIAv-LIx?wX4V^fu56~Hk4!tKC~Nlmt8i8;}1z!&gb0h zif1fG!63X(5MQy)k2#~l6$&X_>H2~{I)L!g-$Y#G?GsS~99a_Ar8&7W2Ho9^EdWBqk^mUs=@MN0jnJ)wbMM41V)S-tL26?uTj*0{o-` zi07w_Z)bPLZ9<1mvaiJ(VA<$dZT=Lk6ly)ri${xgd50a$Xzo*j0ngbbOO|+L5L^-_ zik07k&}59yL!0~kdj)bihECs)&o9c^Hke^Da(CI!j~v=JpHQn7z=>&3uEOrHMQ!zI*Vg>A6k>Y)Ed@V5x2*&qqp+REhw5xEBSWLy7a!z`z}UDMaoQ zNrYp)?W=_a&=k@FB#m*Ft)nI2=EebB$A;KN#{~I1G`HHOPV7jj3!=f2SqJ@$NYfe9(7%|AX&$fF>8LPaE!+>f0+GJ~FL*T{1d>X` z({^wdQu7yqJfFAR(g-h>noubChzPZ3&osj1kaCcy{952js9vqHTLT2O_M$}6f}Rq# z5(+~iEPTRYKwa7WLR!vW|U+A30*BY|BtP<>cr_KJX{IvO)Q?EiHx&Zzz;~n}?6T6S1l+tf}Oq>pY&r(QS zmyH2@y-(I98mhIM3e2}}xiEZ|pvPJBIgkHwMg{c;8)W-2QS#0Ly2OllpKMX*Wsr?* zBo%D%!o-(TD5fy;{P}C~aCDo&tfd^(j+Qt`0Z-SN z?b`!@6jL4q1i8pY2Wdj~x}~Xqi{}fe%R~`@B!mJZKjcGMq^v(V9yCXs`b!~|3?!rd zg!-XWz?mv||Gvj`1&SVAO;+dy$5b=$Ig5MB{!5Z;<7UbxGi>4R5r02tyQcBv;P9Y;7G|o{YS;O_HFPB4KN7?iC z&$XhWw@_}tXRN#PNCck#YOiqxwvF&8m+DciXmvCzbMXLtf`Y$occ>MbZ(qvBHj}l^ zPo>uOeK68>gT!NTHJyYe&YV4aDbPe#w&y|v#sd`|RQrG7RVy%v`-t`~0SAcKS{e2t zB!S|gw@)KV^9{7ze^e)IpiB%c6g?l}14Qh^ZLS512zuLTA=CAHX9oZl(tnj@VdYYe zfu(B{jvIqrU{Ioi!G+|N8?=WIjt&N1N}Wqw#4= z>B;&defR4uk{iYsc*Ium#AdG5ojMOA9H}aZ_ecULsv|3-9s@Q6juc`YMq9B>%Rqd< zCDUG?nXc5f5AUy8JgqA07J4{y8a>#L-SG0-Y9a{fvw&2x7H72ABzLRH^KH|$un?l= zAF?mf2Lz@IBT|$HTSuk#$;#3jrXH+sF0#pakU`y>={P{SsR{Ba_+H#a3)vb->N0^t zZg^Xw_Yk?;fGcbe5h*jYq9(Gc7%ILKZ?DeHK$YXgDH8Je@--?>kn~9od_IaHhr;)5 zcrUUZFS507U?%xAs{tx*jjk3%VB};>jY#}58hf$h(Y~F3BqHmDf)?Zp-@`DD+%b(r zS+D~&HSd-&hoQ=l?EkO@f&YC=9NtkN1PeH?eZ!YhU4j@0@;V3dZs4p0ff;^Jeb>}T zTo!msa{DUL%D;$6!~yINGN+)Uj^MJL8*{^G(*z=M zyNiL#4;^OG6nutW%ne zTyV!R10@EbLmGC|R9Y?)r$*fRDz0NZK9o>Vx`xht3nulqvZVPO;0)z~fL{zZ7Fno* zq3${u9P{~B`M+F%U61hYHlJ@l0t~<|Ob6x1r|{qCNnX9m)H2TosYtIde~;cJq!9HNhjfXmUou)#a3x~qK5S;Mx_%vt^ zV`39(^XA)$C&K7TX<6v_yS*uK7`ufg6&x@P4YD_cEc~fj$He^|;F%CBdaj?f3keLU z9_YQ^-EwjvZ5|-pdY;<&bU8IxKVT}M2yB$!pe`VJUYz^G&u&8)EFQRN+W8-Ev4_!U ze8g^qdaHNM`s9F}gFDeA?0uht4Tr_2$v4DOZgC|W_9aW`lq1hnbM7K)FGBDwXm|k} zMmPoKjBwkA*B2c?f0Mf3K6~a&7IyYotec((+`fJEyEB|mH&r;F02W9N&}bdwgBR0r z{P1C|m{=h88L{!QCI(^!j-`Cm2GUHu_MJOx2xM($Uw-5>e@?5l1MpYl#ADb7irwMD?N2BUN#JNnJ^bI#y+d6^F0wPvddtV`MZ=Vu3aYg#Ios4o; z8?f7~589D^nUQHI_#o#vW?ZjcUmCsbo^)GGZg9=x=XIv={tXXavuahgz`(qLMz$Bz za3>exg7FqRl~8>U?J9r{Pb})vYF#GBD@@q;0R+8=KwxsNtf&-CaA*Pi{#h{(P&NJl z{Z}et0j*LC2{h2Q+dNZ3Rg&TK9q=1}Fl}N4`v>_q(>=O=;p^Ec{}=&5#(O}Dw!8`G z&pG*+=qCd)aMd>Q@nxHD2X=d^{OJOqW|DpH*D%eV?X>fQ3T+NuyE@}Pl7PsNv+TyJ z#~p_6!r2gkIWyc$dFa@*N1vUF6Cewjiol2l46*{Lp;{SqoxSoY>%xZ3fxFaO@Gt=i z=y}oTkfa=NaGqq8x&TJBe^y=wlDinaBovfHrp@4}T{nOEb}!`1GaBLSgM+kRL4iRA zreF?(JpdS#%%(ChHFIp;F?_>syr6<0S2M&GB2MU5%lcY-K~&Uum%Z=T`1SyEfFa`d zTpE@Li$CqRjfF>D(rF08t+)(vQt;WS!-uQ&+`9#0_B(StMeiM?mx7^NBH04wMkY)S zx%iDEn9{|SvmXNjZKc6&$Z!ZCBdoLHX&ohE_J^%}C6)N91Cwy-xp(>|=j7*;B{u+R z08jJa!!ygnIv>=rc+(30SqoRZhfI!Y#NuDV=o+B~<6O=B0T&c#U%x{JH>?Yuqz>%m z%Nd;BcHlpujVU!Mp{>i7%zQP(Ib-{SV$U*rl&J{xBdz`V@d0oSmFyGise~Fm=486W zV;^c|d`HU9y{yVebXjr*Xf#@Sn#n(K{QBMEUrKw{N%D@!Y|8!oT8Rjn+*+XOd8TYDUX>iE-;EOa23K z<~wFo`s2LgFP&WR>%)tbCMH$?G*yJl65$xSE^;lXsW3RW|bmqc>gDmi295?7^+ltgMOUe9jA^ z3c6haRsN(_+%C-$do9_Y726?edJ{2@E5Ytb`xRSM`O6>#j3no`_4(GL8FOkOj6XLU zdERLhy+}?%A=-iD<_q7$mAJuR@8J$mYY8`ZSL=K1x}4jRscB9|Mq#~8MNMZv4^LMD zbdPB$9%L&-{6MwBMV1CX)&iGBgvaU411i%Fs{4y&KHHT4X>Aj;&&*)&p`~bV13*Fh z>Yt`Lr#{zrp!uzV<+!VW(D=SCQP;o<=Y`zC4W+hSJW>KhJ**r?Tl*~&k9ljQqnr;v zrmG%+RG~%cDgO%)9(+durDf(!iDsB&kRC5P*rBGtY`aClPrwFXu5}q^1~fg zm+@ub3jwfOy<-O_I#zWSZ9t`oTUx(>Qs$i56~GHYGF~ugKC_gB`?Go=WfKoZS zoxn-6>!AbwYBPR@>_ect~(QiCO3MB z(;mY=CB3f8uV3|KZo;AR@^UH1ATW2x{{zhc83YF)K2TJ@PM{$umiE7EIga+J2SU$~ zx$dVmxRBI6g8iFSs*b#67`l8gYlNIW0utW6JSt!ZxQ=+&`&R5xRlUztcMGUL+;D_5KI~)^a$5jbJ=EFa|>aoo=wH{;F$8O(BPL)`((6?kba*96jTBT4s z^7=DOKh%XlHnOfe=yr34lz>1{J5LsBc1ln&E}&_u56U0H8KUiTk&BK8l1}xYzrtr) z1|Y00(7{aYwVj%VY1vtuVC{>KaLKLH-b&5WC^?zG*s?gNJk3`q%?3#7eQ!6A0g9?1 z9yyA~N0y@^06e&Wb(?c(Q4zsQI6KP)#?y3!5KRjxA$oPnFK`V$Crj@|EFLB(dcmRG zf~jnDK};mo!FygL{U;lw#5YJE#m~3j(fzbr$6?+AFHUKunI>DvrdaSITg_D2O}>87bgmME|h4%#?ab12~S(f`tW216QE3|uFYeNW-XIEhI4EUiuFz`rL?;TsErkT2 zP}EMwE5R7b7WN2nyRL^Kp2@fukTzJlfoZwTt54mJ7bu_C3h`U$tp|ZFNSPlna@UY+ zKgrtz^K|(DRd4wE4i!#Et9+5oTzB*P2Ry6^A11cZuT_!JvG6=Q~YW0U*O|nI1qk6KCZdxOzD`-p} zvqc{a53OH{OBmEGBvM_ynsO1IpdH@>3WNY!VHyrfj)vs9_kml1O+!1D`W0MTPvNZ3 zrqply1Nf++!M+wFI~drbz=*NGeW)KYSAu}?c~zmdTp=jkgp&bV0>nvpKjwV^jn34F z(J25D*bdADx4}I6As4!Ol}lke#|)ID)kE5pH)Y^Q0gH1K5SkrWKIeS`1P_zjdmWupNAu^bfli&`ZW5G z#Og&C-a3D!-C{O2W61#E_P8gFi32{P`NOfILu|dq;5QIY z3xRyPoTu*qYBP*D*^9?V*wma!hMMIew4eawL8TTui@;HIdeMMI^*~c|bcx%h;OM@? zc~8yw-=hWlxE-#d{e2Y#OksJNaH`4pbR}63F;}dqV#Oew z2#KbHL=M+G^TC6&e+vz4U|_^Q!&N}wk3ZMDR)BC>lP)}xLtIB6vFT?? z@0xV%9Qt6E728wQrjPCBkEZc|UD2SxEC` z_Lm^UX=A8uOpJNUIv~N29Q;NLG}aw51*w)M48#8*Adwql*s9FJV7N-Xgw-AA z2QCGr14+AoaIyqYyx+ES^ibkAbuP{*G)?@O*x(E7z+vZA{?H7yFauOTpaF!NRmEUZ z!<{}H<_drn0GkCTJHdI}5fvKX`!xjUCzd!MpN;S|J=&<7VILP)hu&jRm%epwb54%z zx48fUgi0x5ZEd>!^L06phgF)9bZ zT2j_QNaWz&ageSTxIUw*g!5S4-CWQtwLtnrf0D8~a%bVb5h{p4PGv}7pauq+h4${< zi-r~hR4Yi^X0A}Kh}RUv(m`qL%!i~Q4jd>Ne*vB`-pBqMU^Sp`{ny>q=G$?yr$LKm zpgm-NuDHC-;|#TLKet)dKO}$)(GdLz71O3f+}QD$@J6#s5zHyP?QMPYaR5{4RZk#I z$+LAZ+3kO@4wlKKHlnL=n+~)Ma;BLoxs($hJou zn>ce|*JO|&sWP9%q#8x$y)rUj?{NZi+_7R|i_9A&P}E!)A8I%W?IWFLoX-Cen-Bx)>O}OJ{ zN3&zJW@TcufRMVT?#yZP4y|IHCc|>%3Tw;LgG;9G=lfBxw)|vCx9`ZKge@CC|2XFo zV;1x9!@Ya88*YD)JQqBc8jNK-f&R#!Ux}cVG+kM=i%3>Cz~fs5ffwu4+I*I!d*`g4 z$hYJQ-zFy3U!zf;RV;p@<%0g+%hw<$i~4-T$nOYW4$y>QSw+60q9Pd`ofW7AXIy#1 z2rvfyVzr+__U8Ezm4#lTvWm)O+=A6rO-uI9nJc>0VqMNSY8DX;s-r2mb4maO_<#Qx zOkgN$qQglodHm}I&W)?DE^c=Dv;NKUrAyyg>K__#>u1eA(lw-TxeCApEN_aHY&2e| zrl7{Yu)^PE@eNrLAO{Hpy3|%bKCMF-=-I>zrG2z97NytuV8I=tbeUFPz4=kLMSZX2 zK3>1$fWyH(6YIg0*c#kR4JS8qzQoN!aOg1P2p~by`A%Uj9%QmfY!lc2qU+7WvEIKm z;K!6%GDH&*5z?^RB_%RMR3wpks-0>Zqhy|k6se>M$<}06rp$AtXrwZegoH|n^4?45 zocI0bJy+Mh_H}-{@_e7~XMNVX*FAug5QWFa#6TpHg;$)%?VC4)I>n%J)wW+z(|X`i zEiMe(4U3tS1TJBk01y9s%!+T~I7(HhB?yHxu&_7-rz7+skNVsY!UnF{h7B9kmh)5m zFc8G(ERB^QUrRANw!MG(G6>|rZX9>`U=auoI^WtuPc9A(9($6jU}k29B&sn$_PqcQ zpAU^w8wUnvxn~!Lj;sI&D+Ntr-NOb)X>1s~fV0?`E<~3Bc$6F&*J4`_5fwL8V77N0 zsBjA9ce^ZshnD|BBWFGK1Kp`yxe9{2V`?-bvY_{ztwfVHx$Tx)(NZW@3u02fsuC{0 zVKFZnBN(~|0s1Qwkz-htcrL(S1R3IXJ-w*;E2Qy>b}=7mGsC+;+19wQ3O&apQSlH? zzKLD~&yQCQXQny3VPc~-Zpqgt@^7*2MuZijG9Qgw%XFZtJWYZGQfeZ%{>%CflA6qo zZ*V26r%v>CPiFN9w`;^frsAd_SR$|IVYpqx`ob7v{c znbJF95gUZN!X8@?PXNGEtpw%6g{F+-3V)1)v$HnP3Txlx0+HG19L?4hQW~%tq#_tz z;Rq>l;qm0aEqrgVctsJXj8~Ye`Ychvak;@S+uMv~6TLr_$qh+5_my4q!fc-xpL8w( zM;+G46bOdLlR4lDy;30d_Y1+T*ZTKg1tG4`@aTYZU*K*-K1Q^0_M0f~lt35TS-<+x z51cYPd>%w$egB^iIk`JXgh2!&dN*b!a7`9sf{z7>2>a`fO^2m$z( zjQSn{%uOk^Z!(ElOp!pq)!6X!1y+o`tXs>26M|!o7Y?DNL1_5Iwe>h8qz+7eD~$6V zVF|!6dBU3nX^a3p$*bqoYVa^A|CW;0h*7kr@E^b#KsmM|CT0@8nWwR@ddcZe@2&QS zA;!ZC=g$XWomAzp;U@BcL=-I;4Y@$Oi1IR-csPXh8UoYfw;w)mK$Hw{Lx1_whV5sUZ$j^Of_pI zAQqm9f%Jvah4F|>j6k56*hG|O;`E)Kt@$Wg67i$G(Mh(VziT|R0V-)@@WwkuxelmP zqqTzIdQjLN6bPpUKfd88bytz_!!q6;R8BH6UMla19fte|_GK-S@PvSKK$wnHP_11ceqb27!rfF; zaywE#7*vxT93979A5fL)2kgNp{LC)WAELPFH^<)?1g+m*V!e%qQo-y2$1KPoqYxyc zFAnT%L5?Ku)owJ{b1B_^={l&wtc%MtyAY^__`@#tJFmkpvfjtJ=ZjagbThSBjmf8e zFWbUMO}Y+l6c&miy$vM?p%b{mQK-TV?}qHT&BCB|9056ZV^+|ms4(N$_9Smp*U!-t z^_z1BqK;g}^FZROd%<*#-MZQ0@2s&ptYNmmT9{}VfGjR8Gqc5LS+RDQmuEJdlQg^r z{~>M^9qEbN$e)Ja1!cOw1?dT$ARBs@4U5s|@u8PnAurF3`5&^Ne+&wC3Px($m~NEP zcY%BU@4wf){>`qGJTt@1%KV%4@SSb5DC|7dlj$#C$BTw_*XzJSWyLHD{+Z%<%d6)k zr`4rHImk0qur6C#4oEEC_d3kGyKq%70+8QbD^tEzy=?$1)jn$*giZsjSO=wyxVEq- zaHVJr8TOc!Kp(^dLM=xt5T>@g0_q@)g3%by5o2=$bjC8iEBxv@0bl!F<>POZfPEiR zfNTZEQt|Jl?TJhbl!H)U4&#O8$;p-Ws^%##)T9I`Bo)yzb0E@nrOJ_*$jI$j>4K$z z828iIx+cv;Zw<8-1)L9qx(bI4Kb4ne6(f5394axQRv|)SQk@xPnU$EuDv0VomQ(@Q zB~wSceZ1{BBnDvKP5y96)=V}~OEzFIzI**Tq#weDP%_?2ht{;Rs~mWIaJ}E9bOcXy za%=2FrKz!V)oDB`9926b3Xm?Pw6uL#RYOYP)PLjE!v2{p{ym2f_s|9fI2OOpg`Lw{ zhQ`47VPQ}(A}Bff;o1@P77yJyVZZpHNesc8U`?-)x|Ud6eO_9RsIdsFtU8)^v$S*> zjy95d08EgHJX)A+5Z%dxa=T^1b`FV}zi=rm2CIdEK)jKh@-KiX7?Wl1-^Sx6!4Zs~ zKZ%k29BJ&x1Pj+awDQ$DMzy+*U%8CJ0fa^&M zVP-lLgHmX5J7?s)ACO|=+;w;|qtbo9+;tTKv2sfc3};6<#x9dY9z7mosjx7hy36Yi zosLQ2vH;JYE=Gy?hRU@(xKPmfP9D)g#t1q+6j~zZcogD5GoLGuF-O#clmI8A3u~zM z4pO!-GXPjL%deYC!IULRE^*XO*<$IAKc5SPnBulZd;@q%VEI!I(qu3I1XchDQpBnN zm5V(tE>2?eo6QH%GT9-L^#IZkzdRQdZ24arBKGr-6QQ~xyA zQ=j`XwVQ1O1mzr$A!9}b7|A*C(JI@dds1aDG&z&-bz>$YN~@?i{*yhoPP&P6;Q&() zj*($nDYxPTE`c8ObG10iObp~{o@?ks8KbU)Zm2YqH3Sm_&I&%D@WZu+9);HOGv_!F zaeo=^1iTsbz}cCU>RcN4mthTtmud9p*tsE@I9&)L$&LimF{NBXkk44Ue;Ny)w_p!b z*GA>teS8Z|7CoCdI|!J8Bie95meSt|Lsys?2rLzgisIMbf^(&4QCKlCNWxA`uD(n6 zcU{m;rQm>3^iCj+u^=r2Gd%98Wy_W!S3Kg(8BO?;QX(=aThq!1apN;lr;3tR56%RI zeM2eG1~-y(I#Q_76^Nn)`|Wgkdb*-a+{8#{3@waA;Xy*Gt*qK1eRRNKJd1)PG{P7z z*Uzc?|K5stC^#J8T@Ji@b#c5^1AYc9@nu)9?#qfgecElHuQkhBRr&5x2A(8+UOXp+ zDw6=8LS7GC3jJd|PxcO2T~P2i$T+u~kK#-6dEzWk0;IXWfSq@7jq}9oC9h7oo`z>; z6Wfl6a9{Mcicx9f!>ybCyRG~16(%c+6Ng$wP%@SSCdLRTpl%leMFSHP2w#-Iv|I!q zxR4zwV;+y_B~ZI@#vBSEt5bc#L;OnICzX8%0j1S)K1f(@yKT-@&mKiuIuMIxw+svK5`DuQD!#zcLIV zNAXAQ<4_pL#StnKfPs#StU|NYX(Cg@MCJvwqZ?OGEnW)Ru_LAnMU1VW&5avFR>yG9 zF_V(QMqXjP&ot+O`rmF9IGlLRsj;(H9OMdXY6&tX-m_ZgG4n zyFI`Wo;d*ti;DPi2{95GB5o*Kfji80a=%yPG@g1dLIrBz^2f+IaHg(q2Y~k*d3kZp zFZjj9W6<+5(OSg2ZOw(pqfjC}lrOna zx%wgr7)=!M#g{#a*PF>NfbT^Xf_X`JTK*|PM@k((Q!Joe>pM?c{L#_jNU~#AAo4zq z16X4+GbnQuHH|6?&~a^ilPD%WOV?qRGU&o-2gXKm$Bd=>S}^}9dVBXjiKGSP`VAt; zTO}Sqw766B_vtLZm6ScZ=K(TIlw{Hpab_K zxw$oT{Ki(IrvUH4=NE64a61=5B2;W)jm$e7O?y0fG<+Md;$PeRI?q zrS&m}imCqnca10}@_||~`=f*FXU1auYd|;Cj?k1u5adlzvv51GRj-9Ly*dDXRCMGU zci$KgLC;$A7qe}f?u=mdS~`2UL68K^QSl-_B{%nc*68XrU^AFsYxo6BYYub)`)Xd- zfdYwj>tgTu{9;tE^${q?5CzVA6?6_$8*Na(Wsy-1pb{&HKwNVBxcN|ro$9mw&i{>I zD>aZ05H^LrgZcub1L0M|8_t;B?IH&9LY5!y66*6Zi)P=ABUgx$-(%4pfS7=65M-Gm z00EavPewsU5XftAfD6QQHgr{N8V}kN7vt!2jrkI5)--8ocfoX|E4IGRjE50@zZox> z*wp6VyU>y;#;4@ujGZp^UN~^f$aT}LBFeKZ`jK!$ zE`1>+55$0kp8Yx^ziQM4HPOtup{z#?n{Mx5!AVZ=dV`)^$d$yNH1jzZlW3=VhiFc4%vR zf6mF;kE=U4@OE8#K+3auEoS)9Uu1EuI&et~j`P-iX>X1K5%jH{Xg)U3*KoEK|A&5F z#Z}aA_13M|XH~z~c%L*kUqUvlG>RxEG#*OYk+L02XOz0em;_&m_3H&zURWhnpV8qn z^3?ag<4dE1Aj;cd}h70PKOgtVuQ4QEYlOH>f<;s8V z2OcuT@xKWQ3|v4igJ=lajl(}OxM7ab(8 z(BRntUbGnq&+whLI(eq})4$F|rz2C|fA zcyIEDK&W~k*5pDI75|^%s>Mr|yg;*!8Y>AH1qBAuef7P27huyvj6~~jAhR_wY=eby ztVv()`K+;gSX1CGD=)9Hd-qwra$Ce>;=(AqC&a*IIGN7}p%GcW`SQ7@V4p?88H$|9 z#Q&a4-or z24OdI;CGwkF_j|Bu%l2)s!7ILjOCEF1*zP3T79wBHL)0&+TjwlKU<+eX01k~+Hz#& z7JgReC93}z*d0}NIRR+-YHO)&!U%_ru7~QEw@li97ptzxt<6=S6CflaQhjj!7|UUF zOlUt**l`(Qinay1bMEm$lX1d=6&iky=Xjqsko}-K6Z&4#J|Jbiwyef9Ys=`SRYJ$STu zb?!oosPt1RmGZGVlWelr{oT=P`2WJSUb^TdFSzEO1F7+&pz~865al3bPOZaNi!81X zjNB;tYk63ZZ;L(8B9Oj9z(xk+1TI;*^4pp*1wUPr`TQN-Pw}FX(w?g-^*F6`M-78E6$+I2y}H>Ad%KgSr<4qE*I@$S zE}!|zjnL)*{J&=(ijlmx!9zDyfbeddT`}0IFIDjpLKCA2hK%!ue|r!&BTWty)_tk? zp3wC^wiFlL_^&9{UaW4_S6)cY$yoqu52f%zyp5EG8-{nYu>sz7Z6hz%&U5fiL=wkq zS9ytqbv(g1wKtJUGxP6yTm#tfpL#Xq{ST;O0e(}MHnnR!o;z)W^`R%=0VCLMBt#zRAW&>8uM~{+$CGHWIX%aHmj-u zPGoA|ns~^Rwi)DeMKc|~(7F}QxmYP`85=LeD}i;NTP^Dd{6D4PJGOicE*pg~vCHQVxTeoIMBmHu#R0KgqKgA4{%|2oD&IGyNX;%k>^gjy)}r`UDWIPwc_w&Mq3sW2UOx#i{FBIIm5&GU})8Y1I#pnU? zo2P2xcdgVwZwpSds8G3lzdex4<Dq0xmFu0PKkPs&4e|}8T;twkZOFwuadPKm z^Xu1zZMT6$;C1SfH>kB+2NICQ`Ledh5-;zgJ%s2`h|qOZ?Qjr+zP^e~yLT@eN>UKU z$gLPhCzZC4QX!~9-Xuh0=_7bHQYI07@`Hv4NszgGzIq|G<8$o5^|ocS>%>OVdnSEV zOZckiKh0xj-(z8jcVv<^3)ArGf;RQTZN8u3TTn%ShnX1{z+o(?V|79xPL^D`avtys zyBtyQEo#9vJMypj@!tM}XfDixOX*Rd}KcSQB1at6VCl!Bcl(7rPq-D0jHTsrS;4V zSOr!B=_K8bPO2dyk1>=$t8D|l(kEmS8{VzKN1;C1s{=2uChOP2IDTsZaPF{tKu^i2 z@4o}gEd|Abu(4IE2F_PF18$=Uaa$QOozUrE6QqVG0|-jnH+xn!P%VIh<*|Rj@4)Vt zwz|v}dmZ2@6M(VY1{5H22cO-Fd7abBN`I6&`mbB0B_$>*ZdBOn;Aix+U7YgbQ@nf- z6102J#xVjAkfmTzm|fqBF}oPOp-WMtQ$aP-j5ljMo!fi{tv1j9KIsXz9jH|Acs2EX zj)1odG!c~_^9MR<9Qf}I<=+g(5yL`Pwi+--pw4)i@J};yb3}npK1qa%opM%gHZR6| zF3IfCrW>IOR0@|E980uMoY=^yKa)iF)y0c99y|~NHlY~z8Z`)L^<3UVJ!d?~f#?cA zJ4#sS6x=)UM7rYGv163&079B5_%sR)JtP|ejEFv&oZxxHw_Uf&!Z)BZB~>{jP%)dd zSel<>GSF@LhVa9tKXT<$ts6G1L1&A-tSD4awXjwQ!N3Lfv2+gj*N6(=^Wt0Wo7P1R?;M4}fU%Q8FVnnX#q=sWL@RRcEkLPsNiZC#Li4O|g>X_Iic4-lH!I-xSba z$ z+}?@6pb$X*G6c177M1%3c#Sf@4&L+}I{P~YV#V{0&$mJ&;=P#vmD)RR>iF8FJ;g!H z1s!hzUI_Gm7~W~`P2xTA9fN+AUw4@9-ye#tga2nbp~0{`p{)O1)m=&YP@R{jBOi1#n#nL!j)oZgX5BrBh%$j-Yt^JTq4kDFN`q8$C0Qs|7e3b%gMA(m%hTOnvTesqSxHvfzn* zi_M5|vM#3VP8-vNz;nXM?Kthj&oMclP`8b?X*@Hw_CXdCbS)4&5-2H83}i z!r=gM2=pOXEO3EMo4m>iqfbP*XPSzsZ2tD%=qt9D12+5NqJtCy)Jq%SVfeG(ymW-RY<(%k%qjhEUjiyjPQ;cnxMFBG1z~Qr*5twmf)9Vqp~3HKS?dk7Zr6~~*(v;4BGvbu&5 z58=PDm!AOj7c?^0Y`udWzCH46f* z2s|0Xtg_gv*Phc&1uj6s4E%T!LR?TchT+|0K_zAEA2noKL;rC8w0}FRH`|)C=A4{M z@iDRRqUHubXc)~(eqS&I%?Ct!24k-6XjkQ@H)-+lH^(#1pBJFl34Z@64dXo3HsAx% z8KiquXc<=88==f-68O3@YqkcQ3mYsl7y-CODA}olFOWZrL~_uNphLyDA{thSzt?x! z!Xi39IGJMP8iBhG*Ng-|$8(HM(6ItR!%CSlDk0~h>^`4m>v5>(-*8QHMiP09N zYRg9_o5l`9`G*Dh%;VEGIGvQ?Mrpwc4L8fm60^SiYV!XbjJJh}p{hvBnR{tzz@q0@ zxEdY?Sraf4!MO_r1qCk(JE0PLVu&Ktkv=b=*$*NQV2V$ZB2>^}&|A&xv+FdxD}7sT7A7;TT~o>K%Ii zXg?KW7>4X2H5Psbc<~|Q6OsiD^h$Vs(9fY#ohASsSK*(z=-LYup{=G16yqDF`k(!~ zG2B9WV|+H8x3;(Y8zJ!-r&4sw2?a*ILJF8T4pn$sfRHpc;Q}eIs1=#HN-_N)~ZX-p~STOdG>C zP|2a@dfvS4@6O&>$eY;PcGDJ-YUH~EJ|*5LY|_{H{&WYN__D>;OLJ|WLl=qw{F_)Q z5d!ZN7Qn#s(X;ZyeXxApQ2?=^W#OL3ABSJb#%$`V7vIs+SXaMS=y7 z%)oJ@eGxhYx|1J1{FyuP%3%ZOH~k`xL)iURtW;Ssx5>At%K%^CEsRugF}0`Ws^B() zRZ}IV{pCVjrOPtS=kNrxY+t8RYoe}f=ST*;Id0ATRn5#~v! z2Cwp;Q?uzNhU3cJ_`cynCA!&7ilH5J|G|Tc)`c1O?mdy7>I6d(TkhBq%OwjJel+a9 z21UX1EOt!Ym%&7bYBmz~K$p@P^CXOe;tCYsrYY^y9X$|6r9Ms#fnA)q*h&?}gcCzApH%G3$Pd+~u_9Un?uOfX_SkS*S+_HDSg`6^|SG zJ&Y0!^|7%_Xzs$Yl%J?1@J|M-kod@!mS-{RKn$>5VPP*=Jfd(Y#H`dm$8z)Jz#S?P zkYcF^tsZudObkqQz?Cq_Y)2yjf@8yX|ESt3nP4XE(0@^%J6}pbUX<0k42KxHCUPAG zuv!7wE(&W<78j{vIMGESsLTc=dNNEv!J@5bjR>|lpjcR2wr)0Uo$86)$QiIgB*i!_ z*+@ozr-q?pZCTWhbWudNfS$sB`t;vO}_sw8z?s(bcO~xeiPmx&IFI zYCx=HErm}5EEF{`*ner9ZeR6^K~vOxGJQ8PF|h}IkGM~`n=lE{H3HL5o3=t%1jZ@C zj0e8?6Mm8R4815IyPdsozOIAod)sVc+^xvM=lteQC92G3I`|ki^ z*#gIUH2|ti1n`QlF$JC88Bo5*6-5k)paSFZghHai!~iu2l)5^TDYq!I=_v5T<`DYf{8q}i zfO>`F&xnRPq~!L2gWheB$WinvAOaBhNZ$th--O{Yn1-$Eg7eqY2>rl&bDdNTcBWJA_C=&UYKYq4FhX$ic>u|!rR<1W=}Z(qC!gxVn){6gRWXuc=0TUm#+G(sllHgc|i0U}j* z5(31F;U2Ifnmx2Dz-K21JJ2~RM#hAN&7+bOK3{wu11sYH0ptNQkR@q|dh!}Rb_N(R zk(7slzl@C8N83+!jybNPl^rH12^h=~aS3nwFUifjBi6hdK7mE~r}3YPkKf^t$2%tM z>Hk{#?4NHA|MFC;-8GVgi5qTp{-|2~vJnl-%Fb&YhWO_X#gwQ)`}SSXwzC(VCl`0R z9Jj@{Nb@h={RcC5tsMHgc*Vk~cCq;K(KOZ(9H*)6Zh$QCo<&16?&N)v{Ixsbz<(of z{a6t`nqR2Jr9)F97cgkPs*hcuPur1Eyg`&Us{b`VFE1H)BXBbCtgzJZL}J+}o}q?Z z+|M~ymJ{hvB8q|Nqf#e>w+`QU0mhBgR5rj{bbH{s$IEH)566SNyfNY2fD|u9s8q7P z?%uN}OjB~~XAT}Il|FzEb%`q86tm$cA?=i0gy9c1K;js4Q#IFcdV<2MUuLDav-OmD zR!i}WzBgBVIxjc32H*g^ETUn7I#NBevxOlUf5^cv96-R;Kx3#X__+O|ZmQnPOT{ib z%@mI`jCZ8AoKA!cft)*tI?JG1PVHT}+WwO)eNb!pA%ueggylhto%DZOux!~3$R5WY ze~Xcv7S=iASGf!^ThP3YL1nM8ab5Y&mX;O`#rSHBf%x{YudK)J(gl=YCT@kbKK%)b5Ta%@v8h{RwqEEXs252E7&aj}@KZ*padTpcRn~K`Q9AXfq5VVff;(@%dk+w zw#T=&WxskRY!koax?FyI5H#}b_~^v5Y-{^`{;9|B*)gfio0s&SD*F$YJX_0y+tAvw zVgDO9YjB7rYlh!^f8&w)zH~8Aj>Tu|r12bGs;jFBG#n~E7Nu5F{lBEY1d;y+2j*A* zALZ}Se$4MZxAk%qxUo4x8HU7iT6sf{Eqpu3*e0kPq%xc?)F=QwL}DN{8i*2@yn#lp z0G$O)*|~@Ih?j;uU1@S!sC2_bg@J;g`7 zliE-zDNlt;xmX=~OyGplMTaM)0T7gdp=mMt5>3cVAYKp0aJtt+yc<6+DlYod-&NAi zt@xwM+*;eEx3DoY&6Yh~J?XImuMEJb%*+3IbS&fw^|{$RZ3rj z47BYhcQ(6mwl;RMAMbo`LsU_GGk()%-uu0Vc(^50rhvxUpr0#fo!WE;;1GKF)|TW2 zYYL&Aq5cIdS3j;@ILg@5qszj9$7v)3trD!jw+#fNg{|>L3(GNPf({!gF~I6r*1|>| zO)*-Jo0y=Shh3nii=U?E7lQ8^pr{-b>wZy8q&_X2i+xo%(Q@FSz{_+IFsVv(d<_$m zCHN|cA_02_PActa-7WWAjP0t-u&>&mbfQejI_uTI)`xd4V+k%IIONkU@SxpN23v`m z<0p51eE;4o-u~r)J1VzWWchKAjN@Ba`$HV@jDZ1whSe^{KDMmKqYhIMt{0ZB15Eo?Uf7O_6ee~O-?lK+C#L!l zG{i&o2Fo)vTiI?*-|y=OhRz9;*HQs;?8hyUOduPy0_N|1O_DGjWpt zNqwVZ_>+-9Y^=)g3;$9!zEFZTf!IDsC@S*dPGiF{9LDuR z_%F=_*HyfmQ~e8HIJgh=$^#z4=6V}Z>$uMD=)E)vJPCFaz`!;9}Gt~b*mUS^Q6VYtb;_vBMp{YJzieWJK^^0NHuUzwqhU&z=5hwZn+5; zqBG*Dc5iLoe*0DO-MgH`E}g)D?<1UZTLAQ+2YPE-zm!2>=_4PW_UqTVE*hEsf0>I9 zZU}pw)ImBeW zDE!;=-_EjSFnKHN8OEbZn)KMijBjAuqR-d)zmz<)EF9V5FC3|fD|p8D`kJ<~#@6pl z2^O>tfRdXSKz~@%vTN4#PfuQok1v+$z-R0NAjVWoTfdgWlKqlV${_ ziT_!nV-B_h^-2kfJw|=xWze`;A$%l$IZ>CcF6cjGk?N?Yrx)|s{EL^q!p4;gfxcd) ztHh+=@r!&5`z5_*x2Rh{`=Dze4HYhaOq*)Rh)>Gy^ZM?^Rlr!(GOzN~*t-5^j<%fV zotujm{{Ipu?=}tO4YUP6PCygL(EeLbTRXhoFIsgDn#44Z2KUc2k4?w6yCQOEv~iXg z^``#LnUaHRFpZ7E55XQ>eqvw@jsIawf<}h+APCDYV9_AeXWh8h)8n<1X;ddk)?Y@5 z3qu+@obDHQ?SBL98I4rI{Dp45H=;xzrwuw2h&Wo3J@GU39hMX22}?73+w0|O&*QJw z+uY@v_v&ELwzI!ClwnC^mvdQv@^q~oc5OI0_-DLtV<*HF_%{-vI28m#8s_D+wZ1)U zT5e&K#w~SKA*{A}7H^0cS=Bf`wpw1kSH$VKq65>?H+lcJl|Qf+EfWfrw6vv(8|YNf zd~?B34jJH5*mM#4z@W#C1@mA3wm%f6RtjutS*8;#u(;QAyQKGAS;nPkh<9GFx&KBG zJ=I@res>qw)`wfinT_P)zN#E6QeWKv$85BI;hK0m|X zvx;X(CaG$RvNA%EX!n6#E8&m;jC51a&t^Xgr9{cHpWu-(Lx<<$t$)L&d5*MsNOT@J z_Kc;$eT~5ZHm^H6qM?B#^$Lb8EDLb^V>7Nmq!{b){{2X@(Ep0gsL+?6p99oV43cDL zPR3|hvjkPzU}DDRpOvbBhzL6l9Qw{~I8O0hJ-sQ9s|(~XDrmmfYJX{R_KAE#@1zOpSJO?PhjH;+c^RKvS1 z7wYL`%-YJ9on(nNl>y9r@3h+=hG){~o1=Mm?%pl>>|1D6fyJp$&xF^W*EtGN*w+ce z1U$?*&Ewr|VB=UFw}IQ;^8Ml9vk66Ov;|O2o!I#{|G|SoqprbmFF=Qd<2C7O%Rjvw z$txDiwisgVFUxD(Y1;ogud%au=+xk<%7*c-J~L&P*2YM)e&hKVCz!m0Breuxw)JnU z*n9b7Br|WU&W8)88GidJ(~RVD5b{CoHrOWUtSTvM`)kl~e5Is6L1*wGs}-Vl-t&=7 z5KUD|$3EsY6j3jg)%Up%=>i z%}h8Mi_hL_IDsuIRCV4jp%nc(W(7P4h{lL%LwntYER#m=TZrkb~SEPq7R^#%u3aW^Pl&Xeh07jLaxgh57EA8S=% z9@d`3Lwe|Ixd-n+C)89!x)Gj(t_i17pl1e0lI5X5e08ou($qb!Vs_1`BnEJhO{&ah9&TF!Y20T0G`7M0NvM>vy3Y8~W;r+e{NL3$$x76BD<& ztWsXQO;u4p*@`*F$v44JRtOLy2J%nm)BCn%%^~r<=P&muS}?aP;WQ6a>&i7)JU28B z8SWjLDQ|x@E9>jTJDhWDt;y(5OMF7ias=1UF=oG5gIe6-^KCcK+f$o!yVN+XCWrwC zd;qL$EH%iO^vbR6 zsomNM=UpH@}<%pW{B{6bqIlp!DD`Nls5cQ+^#wx7>*ZT%njJ-S2)a zZDY>IJ^r@j)L+!y#Seid4nFa@Yu8#GZ??P|u{=?npdX|xbEJr?^;kxJsToi8=rBKs zIk@ukF%$vttjJZ+lw5#U&;=PqWiK!3NgWr_KYIlBs|CJCw-jh~W$u#|a{85YqbVa< z)r2x%^`}oB8*S?z0eC8$O>V1q=uA0yo!nbzfS=hoL>8L$^Yl!ub1|mZ4j@|ATfK{ z%vU|yf=~oJ^Z70<1MlfJI`tLQYG0S zg2Z&>Oajo4Fx?j~m>^RIj}UDQf^oW`P+_<78jyyJ@((K1QrTITq0m5mB_ja)2#1sb-3Sdxu8%6Z*ef1=|=rs2o) z4PR<<6=wfZ3Djf7O$DwaM`)GPh025rwI+ineg5_>z+kWz&iGjVFReh^gt3B#TKMU) zR_sk^*gpZHrX=`&ByNCD2OfVUG9r30%rO|jl>vYb2`&XDT8rGnhck{Cd+F;vdhOaZ z%lx7mG`~^if`Jx!j{W%@NAs>)d*bE#>VLa2E)Oopm2iJ-vtoL>AX3vOxA~OqhJNn& zlJW46)e<$8S%M`YlWAbPcVkjQ2gD4!0mk+hdU1tGnv#{UT?hgY(5s5r($3plF5lNu zY%ik=rXMwkq7%8As)prESl)vNqvA8b=v#*!G4%!0Bg70_GN_q;TP&Vmzjxt2=A}_P zTvcv5yZ_*A$hDLUPgtX}U&Q)L&vnfcCq34+9D4`xGt!SUq{9qkOYX6aDX)o^iEnsO z;B1XkL2?Gb(_pgmHYTb>p~ku&8Nyc_cwsiCbRya*<>|z1XUg9ALx1b#+HX;GJ!$uH z`F}Fk<}n$*3*n6qZLs{o=UNC&b z|5uBDGRUdujOswufFaWhEEB-{=^^(|5_w;~%n{!}b`$6h(yB5vB_+O?Z7mjcUvJO1Q*_+8BbjH{oeI5V_h3kSD|^QR z`cJLp(b)3=eF^uE=d!`{)Rr{u)8T11zk#fXuV3srj|z}R~ZiG!_CTS4cJ*B___rv&G^5eoWn{3c|F*+ zf!_&O6@`tu;6;nx4Y=YIVg?377`?>#f4tRS2lB^acvZg|BP=9zE+W5UhYN%InsM6r z@n$Z&443kZ24k$D`=`#GX})PNPu{32)!<>otAQHu`YOL|#b1pp8|xVK7s_wO;{+b! z&$oq&A3(w?)PNUlZ9K*8e2`FJVz5O_25l0^_;BmbPbuG@Yz|dVHh6e;?Gcfh=4gK7 zGYDI9S=w?pDyp;yD{Mnh%O6zt!)gd7EZz-c?jjQY!VJ?$;nUyuYh?Wn8#!#~@b5yZ zZIU}WKB2T!#ZB}0Qf*Dg_y#Sf>1`j~mAl70D_obZYj5L9?0-CjyC&1R zJQ@%wlxRTLCEx3&8lpZ)c3?9laBVNRPk2{ibu=Y~Lb5ic+^^%kvTH}k`}~_3BaYi2 z3_qUDbm8HtjSCKvoxfaab|SF3lL^qv)WpOuf1#*arm1DDLnXRwR$Qr3G-5&W@}cZp ztUK^sc2}c|fkDP;wcyA^)6xn89qdy;Ivn*XrKy^D=3t6d+pML?uu3sSdOY)F)q5pv z7kOzH3PuCMJtQ_mJpX#5~pUe&kTzi>n{W8*R#JJW28&9vadSr3yfDEkc2Byob^@+ z*25kjB-f}Ql?_iAXiq3AwjM70A>e7etb9K--UI%iDEKwxvkG7wn+cV-)u|}1PZ*k) zv)j^{Vn%Xpt+kJ>{h@~kwh=TxjEwjNe%gopkrOGsonOf$I!MrsA zmX)*wI%8V2yv8qNFja#X#${`T%p1?~g^B zkFd-(e!i(Uu&P}GWID2Rpd~eJ!EvPEzD-98+G09Vkhsx~W&_sx!73J9_)2+}Ls!JO zaqHH{u+4>_3o-&oQT=(-#*GUx;%)NjIGT6D%feDiF)j#)R?E|a3D?m~+ z*t?erAU}o(Xsn3nh)N2*H<*IXo3qDnPb$U44h0}ND;U0G{Iesozf=L>F2pm&J%n>cJOp~iu=(nS;uAJ-hS}jb zgt8ZzQ0a(Q+|Z|v`5;_<5H~Xu<7CIF!_O!1A20_a{=?1@BMbChdok8Q4{5Z2e=<5S z%U8Mi>cbg|0chYPUt?pVy1-=8OMZLIAyZXX<42jp4&Qt&Pu8ekI)p1N65K+Vdn_zR zlml5>!zF;sS^!RK!RiF}(_`*v)TCDfnz#tT<>I|D9%KRz@;k@_F?;Cb=Rw*3vQW8d zX#6K#qE1)dJ-+3e)TG?VQLp-Wk00tDv)q$7yp?fto)%p^MyTE zUEJda;2d%!76)PP(C7wyZMS4ZbYn1*P-H^5DrWOZtlvrUnbi^nl!=(E0OaOG%NQUf zwr$(?zm*JJuQrHAXxUFXTa1g7DkCmRaFpa_P{#J$V^fa7W7C_2gDfe!hu{B=q5_uw zxw=E}LcvX<`Cwdl0ymszVtHzp496w)zvQ-OkCzT_5wR2fc&X}N?VgNXE4lBb?Spz8 zJtB(kJn$W|0bS6(5RC=t#nBQk@Pa)wP9>;S^hl8b$D=m%BmgKfUO#ekK%kF`lijDG ztHY~M7T|Q6-VNJ>KdgL7{%NlIvKY>5%NG`SIGkjQ?)8!mg7RxYxQq5pm)3MUXnI4{ zndW4@qUYuX3+b*+W?mEeawl~LaMgWju{--mC!R&6*y2O{p!r21lSU97)>g3Q=1-j@vSg$fjxLheYULUXJBLT(#oc?+9yZ+6sQzv|$6-x;Ma&!@?yZALgeA!+%Ki@4x)&fv{8P zZuknNz4@uNjHBmYZS{<~_0{~JU#AlVgoQ)NCk+O~w7wVvg$nQ1`SSQ$yFi?XT`Rwt zpKiz2f_g)0_Raju)zgEsJ%omyn}8HECm7=c(>}2uyqMw=@x*@gv%f+*@$z6PHO*OB zrtWGUcwq9qpmxryfe^G}pcEE=_mzx|n1P;WtU>#jj9sYu_V${Kek|4DW?e$k zHmCmKsXk}1F{VEF;lqakCHiYoB2fn+g+JSGBzi`-w*@zIcOc ziT{NWaJ@}~7vETX!<&>tdf18t1fMPjKjI7AseZ@^A=>F(44;(MjP7q)uYk zd+l*Jl##i?=g}h6K2qcz+<(3TQk1cl^54RU%_2jW_s6D(ttN_X+HDrvY7~g7E z3mUNH;CtdvSbrrw;hM2S#oq_C(lQf~`kO{x7sw2%$*RMiMMqCh1KtK?mt$n)-r(CV z+nk)WfVIP>*)iyfmJ5zSU74M0YrRix@5hfH6=#zsxk}U%-Sx*j1gB3|INm?qs(jYB zpiJnrM$rl3kB(|XT36IIn86XMRN%$SzKP+XO(oz>s&UD_4~P)Z0id+k+FJh$x5=#$ z;6_aTp;up0a(ETkxo6n6_!q2p2+$~*?1_qqisJs^vMQlw!2RaE zdtCVbfPUane;?{nh}9v3Eokd8TH_uB?euvscc{R@c^d7?V9L+HPLGsjWM2cQ8z`48 zc=o6qI|`Ivvb&WF>1OkLEuTF7pg_Pev|m{uf;GPPY=}8tltZ>V`EOy&LxTqkHoW|M zZm)R3gnkDEJ=(N9J}9Za#~kjd*Y_Nb`DUbnJMIi&iKNws!~1E72rrZ=#ILd5fvF1C z1=00l-aPnHDXPcZoGHUGJs1i=@Y5naATd1 z#)4Mfn0gkNqJqQ%!~m2NX<4V9{KIj|INHcT6(y`h%~@(QQo;f)B2 zWSlAKH(+;mC|*9VI5@sJqcT+4lcnC~#3)zZ+h?q%_#olcfwyk0goEDsx51eeu5G(( z%_ZJvta#6rx6C)Lp^wfDI4x+x@n%A;1mg`fJMPMs;Z=@@IS&OE-0oM5s_l{a&V}oh zG`BEk0XK{tO)Y3$AjU87fda?Ls{~6b9BJtw=Nop*eg+3`I^e#+dHk77+5Ys9ESt|u z_Tskfb}I^+dG+gBzFkh1ES${%$Ii=YY5h`^e+_-JQA$l<6@GHVz)h^l%+4(+!J;5t z;jL7PUcvsD1S=>W=gGnK|J}OP19ul~XzH>` zto3g&XR)I1Am0e&I7EUwYvmsU={!jUuoez7%lI<>>!gSr{?>S|63g)BjD%pWlE9R5 z?vkMCycCDvC4FzW%n$!Q4B@6JS9>gfYqM`_tYCbvQnrHkzS!o8G?#0KOo1?gEQLC7 z4*v2%|DqBc%~QS+#3k(M?e_&v&m;`t6W{i^zYebR z&LZr|@|Sa;QfBoncgKS0#c!va0@H5?G&jjsuT98wWi9Z~M%%{OBrkM&Nm=uRYow-t z&r!>4S)9{gOBxsSjI+<=Z-Ru4CUKHPq~|K|QNwI&l!%>7>94X#RDLS~mm!c!+@|OJ0C9(>HrX4U@h2ntWAk2yoXkFIMl9CehI%VjB z1cuJR*Aq?2`Bl3)6%`uJD-0zrUz+lst0qXc?Z+_`Uhxs$oXh>EayvV2W;iG)MTfqP zj&_~!&_KWoALnT?1&T1{>@ zR~9_VN4%EL@AJ699|=Sbg5@3XAO7@+@O;wI*wM+?kPv#v6!jP%67Ag$OeO8d>fkm6 z0adyAvUA?tgI%e79hINEQdva?yLgwjyN}X8wKAfz>}KxBP-uNe;X-)46C+kBS+r1&2x6EN-)e{%!MKxlxQ`w`W_s?mUa+X_}MIq+}LVsYbjo_mapj> z$Sds6-yduOPK0bbd|Mdx-$D_O^p3!AF)xmjZLZqoCwcb*GfC|q-KxJhbZX-QLCtqd zw8YUsSECo_J{$N@c4+9%uuit4xAhzp8e}Uvx5hWdX{$JzN4A+@b{ePU)r*`*3Bp~C zoC`LHM(6B^v}?>Wj9Yts_USqx=78*BE^2OC3Qvip^?|)g$c7Rp>v7Qa$^2dPY@PX+ ztds-cSX@d@e(dir_{#*A`54^LN3t>?ahq9M27ce?X0E@uv?7c@J>#~l^7*kt-Su!y zZ+?&~mI|G03z>>6I*#>zaYii?p(qXf-)69XdvN{Nf}?XM<$jb!sh2P*w*7uj9Uk2p z1H=R`5$<2y6XjoOK??@mt`=My6v`cqrl3=h7!;LBC(Oy-KBe=#GH`ep*JCilMq1cB-+;Y9bZtnG;$g=J%$%N)H*6D?eB8T{l1o*c9FD+iHGvDqL$ zJREg^PUNqLuF?No&B1L$N1UKxFmHohPNC(i;{S)J_kinpfB*mAijX9G9$Q9auTaU(9-*jg zS!I_}iR@!bNR%y-l}ZaCNrR}Pt)xhbrsnT{<@@>nzqj+f-OlHngWm7g^Yy&0>#?q9 zJCEjAEA@{llmJJORIvBX`oDQ!C#JQwAhGiG$Kdwcv^x#A>_B3O6F%6jCI=dSaOgcI z^VCe;I^mnZjJLk|XfZ@aqI>%%dV&jN2(K?D91K9>5ofn--@vT_i~V>086*mO(oiyG z84*FF&3-H`wL?HGm=CwWuFMh5+|r9}fwmg%d8VIXA~tXFMUm_CS6R%Zj_qNSB_Pob|A*NLNwALewf-Z1YERkJBEQ>QvbQaYeuwyp*+>hqtKq z&@ak<;_`l|v%m6K=i+T2%5~#UY)nc{PQIZ&Yv0n>^$g6ze;l!FRBF_x<(kA~cg=N5 zqhlnEZsybCsX0$vq(?e|qBb`J8)xuf#m)9@JPisO2 z_eq%QoXUtKHjo!f|1+gDPCVM-AR;d%i(rMZiHTu{5RO}3v@!00mvCkhTQ<>o1 z3B;6C)m)iXKCsF#eFGU{oYGBPe@mKN8|S>T6z1XTh_zE8@f8~TjRx?#N#=c+@d4&2@trsnGMt&nn}l;cgRO_6!9 zGE!y2^5dzmK5rDUxWo*x|K{A)-=muV$Wjjo4nPDT={Bs_-~kE2RZY;Uc(GYQn%B;E zYke>m{FdUC!bwe5!1!rL(JYA3Y|x+rocThSF&yy*Vb<-QwqT)UC2|hAizQsYDsXPE z;AxINi+a1ufqMqOJLS5)IsTY~8d?qRJ-5F$=UeH8&L32#Mk@DvKrf!*-!l4O$Rcak zM-Thv_DnoKO%tjdODSqNj^_KrCr^((h69nbv~^vJs ztMqGrq`dTNv!GXtJvu>^6U!Xtm!CvZAORy7HCxeW&PF7}t=a4oi2bbj^Y=k3HX#pA z(|w%X7Nx9F@6cBVKTgvR+tfj_6PX2?``u0dZ>U=oD2r0$194CgW4b?B49%ay|VgN&A(QJuR00!Y{JFFBI27U} zZYpU7QY`Izbo?8(Xk#-1L`GfV?@*<~GNps*c3E#)hBh*1xaI1h&38WTaRTf;wq-z}C-z)E8XUmP zzSv0cebC>X`+ZmrEw&?tqWy5oAM-xPCso-0j_7HXqiOtP>zY7K%NC9{7bgJ-5!d{= z*p^d*V0R#P(L0O$)IpP)lxXxmt-faM+UM9qcR{tX7g7VB5G0wGZ*;k>=) zKZvfO(smH@oqXgPZp$3t>yjQhJ|lfvt}&oYIuK6Ak1Nybz8M>u-YEl$gXA3H6@XU z(;h{biDP` zijW<1-A#2`-mIGQB%HDi6I9P*+h$Gwfph219cQ9;rO1&#uz&%v4k*1y6|sc#>P|f4 zZacc;hlGT%%wMulNEFV}D%4cx*Gyc$e*L}uX{(V4ia<~P6~OH#$dZj6_5*XG8Et`> zSZ*i)c%j4v^B(_L;oW>y7uVU^u6wd~fc~6Pu^vNK-3fm>3#IOqSO#9iS63VKXDtW0 zp+~_a((i|N9EToMeD6Y#)mr~3v?6UK=8l(v(}h$_O&%f|u2PE&EevlC*7!c(FuN?P zzvDsk%8b^^!&-mhTwK5YYGzPzu>Hfn$?d&IEHggS;BiRC24Wiiow$7R-S?Mgl`9c{ zC%$H`t^R<)vJ);NhehnYB-23L3aAE#|44~*12vF*lQtJdcWe@Ymu{#1I`fO!xX<=6 zgOG(%Uw_@;Wz^NFQyI9fYdc6A(7wK;aq@qPM-y9dU=;;h41jkw51=y9VJUVA2wIj# zWcaRQ_jzqt;seh30`Hp6&Lc0EJ+pEsY!9LckkY$XMes+XvY*4M16mq9RPiWIH(#)z z_3_e%L->jYh-(?9`9WV06#kO^I7`V@9bRR2Z4S=dv724SHMH&VXxllk%L(uqT3c+Z(?F{ci!WSL^zFw&(Sy(&pb1MS zGFnRP2z5MJw7r1160a$Y669hn+c(=l~|Vov``_r3qs2g!iqD@t5jH*Io9*T`*id%j-Metv_r?s0xBahD(@BU{v@ites%%w3!V_P=sE9MU%mUjuNsI)kl|hS+MI0*7ekbum^g5IpnHpV zA@v@`ejFH!#u`x+pMfnR+if&js?YmB?*5yO_*_Bp1Nsjc*KXwI6+ZQo=Js-$*!2XJ zx#Lr&pxT^D)C>Y8a~MF6_^X3@{l(g?L5kq};qxk$MXiUh>o6@^Az*i4tck%{A?M5!BMsI($NpAw@%Kw^VdDhfoJG&a<59sn)EnXDKO#U5=VP+t&BMHK|0GCmbHY znir3wMm`C$pH1TBcg%TbxzhR1ok7 zcT$l0ESLiW+xs+OC%sSGZz!_QS_m8~d*9_6WSRUDGPWFIMs-kU!d2FHO}^-q*#BCt zIREH>00mX|?%t}@O6TyLUdksx83h#-vGJzD`K*%3+kUF0GH;GR29a4ey}X|DElFsE z0L0MU2J`2~?}~`b*wImtbihNQBtoxSV0;j31$J-jm=dVMCfp88TRi0MC&&6EAZ%`o zzeoS}xc&fvu*8rQuoaSY|H;Q{{5b3T7}`y|uRi6o$jn9yA$u!F|9;&~(kLWu6k`+n z60o_UL7?pOn-Yn-oHi3EKsIjxI{wBaq@TT*|R=xg~BA}9PkpfU$=MaAzG_Z zv4wsc-clr#wTUp^BNrN2zZO|JfkYsk_sa+cQc{E-EGAg^-RjK63=P9JBs$|?mMCC! z1pAQLd|dld5>NSTM4N8+5Bh`7{i$@h#1;n?y`LKV*xWE*{JeAkit4D3JPNwKPH#PD z_Zl;?kIORIg~O2}m3O}PZINZ%evqJzXB@E-Bd zsOIi7CjGuerp3m{f%p36whrXhQ>Pg~3W4g50UOe@78Dc$m)qNY$je_*v%l=<$VDNT zwftN(qcTs3K@22Xun%k@=-Q>7?&U>hguS8KO;vt)(G2k+BQp~os0`-hkZX1;))cWA20p&+?rKpV@%7sm`d zojYQi65YF`W04%Pl$P}B&z8=qqT*vS2o8e=zb&fB$sz-@^R^qY{!zo@pIJ}v<~IAA zQzw~^3W`==kt<)kc$82BVjnyz`?fH2yplG=I5Ij~Zp%g4E|cesUaEgO(;n+ zQu=srna6NrJJvkkXU|^f*R}FtSTf9pE4zPmgj&p9>K;z6qh%%VGXjNDY)U(Hzn z_Wk0s8}sd{h^=w$E=DgJ7r6bOw@hM3o-RUNt9d|@KY)>6ZxN-_{JV=keJ))|VmPa+ z?HyJIqC=tylHg(>Vi0y`!JLqA4et=?T{rdTPruuJUnZy)jS15%igueE%F z-hZ1o9$G1{d6s@tn|f4XQC5Jc%IHfo3E$dflQMmDZDh`enM07Z&>WL-5)MSTN8mcb z=U)6tTA*abNfqblPPEK^TV)ra_pux-|zKwRP6V7CqwfF zvP%d<)`d&ElIHfbWCK&E#wIHj5gkmem-?(*fhw|tu0`cfmedhlrTD4>&Ze2yOMP50 zy2;^}yV-1&QP6U2LVF?x4*tgj+=m}W@0n}u1H7hW)#L1Mnb~|{5(lfYLsvTllr~(;4*29CwI4G|-FVt@k6vErh zast7oNQPV{oeQ`}PK1iiPm^TXF5tYmp;rxLWZ}H%&9xmfG%( zuU1Q&iiWf@%u2ir?BU)%_lM@Z^bIj+$R#L}dP8mDJ7pDv=!p;jF1b-UuJ%tK@3cVR z8F97$ObL|70D;u?E6;#Jo>x2}lMO z#tZUf$do3Jp5{)aeHJK^5hyzUA%=QcWDdAcix~5pu~&k^D&`JUhju>$>dWkeAkMJe zSl@mHVshu+wKj3S3{l)V!n1Eds;Y@!$p!ssPwLW%)0Nl0&t%Fu#N6*Y)j8?r&0by8 zC$e9pYu9B@5BU3aMaMGZ#=}XC4eIwX7^WNh-S$VM#jWby$hq(dL}sa$%qs72Yo6i! zixBE+CQjxXi&m{(Ed)V+fE*sWimK~v7tNG`8Gwtm&KLs^!=}1b&y394EUq?!Nw8aS z-{iaWdh6s2KNk5QPg~)vgq*|Wtfk+m&c0e<1IB(^G}xoAnPEQUyCXOM03sqY3Rn}j ziweqwCjbn|Pup$Hai}Qe>$kG0$I<@<4|gI*CGB;+eM?DZh%lOxc6})fM#ajuZ?ZNI zn%$ALZ%{BwR0q~HqO|;FBY_fZ*aBI)a{W5V>3fHCx$WcAGb(-O2`J7sYRvaE6C$L* zE0K^}FQ#zC9Uh*_9;YE=n*ZL*&NGNHd9eWeN-QF_nyUsW?H)yk|Bu76K=2X}C)xZ* zmIB{`654YF{yp!vqeO;rSsN_tl=#GkMV`MZ(rPXq_%j^sl6;{4D?4vNX)L1(OvYI7 z2>G$Eyqmmy2<@ZCv(Fo4r8yU-WzV8icHNbI^l=z^^L!reX(7PmEJ6tZ-&w~sYRhGh zdiO;RDm+-yl>ObVs|^Dv3OMke>#SO-mXBUY)JIqyx;WO0OFp@*sWwe)EqCUqHe6PG zLlWOE+Y*VGUFvI+6tEMaAHot;eq=^RX6+99oO8PQM3xbBJ#l=xh8+Mu#0e8%(?j*1 z&G&D->p;MUrN*tu<={dBBmi?^PWVeZOaxIQeZsEzYI`-_NMfa6$DWc}QsrEXi4KZ( zITwWg_Pln?l9-kUtp9I-SkgtSB<{k^cjUo>74+WKlid|8)6O`vseSGDh-llX>+#~# z6R&j1uHzOJpQmz8x#hm44E^ttlRG?U+IaV#xd%3@4h05BlsH+Z%z0${SlgefEn9D) z6v=WoqF)9rX#PE(+9zpLxbvTNSf-DV1Y*0#2^n6$SZSXPN|e-q1pn!=BUKtb_j>De z`BRZ5nGfTKjBta|nobL_ouQ;4Xi!JjMxHqvzG&Uz+Gn%%#d)Up^548oN7fay`LM5OxXD}wvjN$w6H%{KgX>SV#6c~g;gD}Vj?r7n zR0riX4%h{OD@qky0Qa|knit7hF2Uc7Bo_Sxr$s}C($f7Cdeb105PrJDoa*||d6m>Ue z_Q|#Fbl9??m9QUv=11$2hs8F?N_1IOP|_7ysAj!+Y^~jY%({M>{cKD1mNqp}!ntN& zv{To=ec)n5sIVW%OT zyFFia?^}N*AAK}(y5%|DPTp^%xH9pG0HvZ62WeDzN5))qC|`ptoD8mgLGcT|Fie$Y zcB(nR!B$T@T-&)d+3r-UdquVRzuT{+&5nzc#*I_v`jBeKL*ss~2-fb`??{7SdcZ>V^`~ z?wh-$l}*LY>la*I6PDK5b|t*`Y5iZX+FKn@p2_?TYwtc|KkFsb-dqEZG^dElZhDBC ziZRiJqhT}YF85On9&|W_YWC$a>}Q1gyuWkg{TB2rtjrjQaX1r>dvAE=ScW!;y#Zi1 zl41liLXRdBJCg5EPx>yt-@UXIAJ@{^c4w;E$Gm@sSPMzdJEsA>rtn?_P`kHTACSpp z`_dHY3gs;p>#OH*maD)smwPm`4cu+usSgr{7D@5w&}aBZ##M33Bc!WzUUo5i6J({x z${>GLV%K_Uyh!DS0R!FpA^&(Y8;euE8qPX4b&xo5St4x(>&r+5`FhEUr+>i%x2_AV z6)buFOs{7Wc%ow1l1BB)uk1;1r9BRUo%zydOymQJBA0RQe&YIJwL!WdVqnVZ3Xq5% zmq#m-S%JGSWM_c+K*N86Jo9$9`JrxRxY1KR_37CE(?fy3$3IJIMSb|cth&6fC7di2 zMZb2nuvH#tm9?WIj8yHZCnYxUTPeJa-m~F&7y3q)*z|Spa(Z0?8qt+ZA1_;1RGRi4 z`x0l}icp*&&99;VOn8|gHYJgrWbXir;fXU90F;hKs+tlVNy~$zp-XSezd=QcT}gTB z5eCMdv)~px_DlNbCWn5`@vR@WZn)R2!z9M}@@xLyao;ww)ut<87jGo6B0Y{EOVFoc z%uY`JmoxsE5O;|%sE0<3hs@nBYD|QDH`ysWx%=-$S?nErJ@8bf|M3gmi9Nd4H_0c) z&dFdzejV@TW4r8Q<;75y9$OpCi&A}b(T_e>;k+V6^FyAtt5)}QM2<|XTU~AYQ+)&Yz8QC|Qk$>8;a06j!T&M%iC2V#1zbNEK1 ztVE-;es#QGjmHR)o8RSyddX16@UaG_={z-798UUcPYbYZ4sg$k-OsH<%O?1hPM2!G zZ>{IFda2d@xHc}uFoIu(6dqzs%DZq@yt)i?+(4VyR42KGve!z%g)Ik)3}Qv>d2?N4 zY7$MOXldib0LG--useTx2PuJ8y%N0VrRQCEs+F!)M4kxm!QKy}hq-Z;(uZlDx|P-_ z*nXf@%cJ*sP%jBemQ|*2FOF%?(W3#Dhvef^>Na>&hx(|5nZhf}GLK0Nx5S76#6nQq zoabfMyfLPhf8Hutg?^h~h+y%gaxQXB<&rTi-r(M0P#X7BtA#2vF1M!0)em&Lf@|<; z*HaPq@VxAV)`#`$q)-44oE^5I9jcfA)6oRs5D;`Kr2M;<;wd#(R(xI1wQr{=C=IRo zr+-LUSJ0!B=(%{QFg>g|74tDPRyhj%V;{K3tuiuY4jGDjhxpDs|xmQJZgM%Rl)lwzE&P>sgC^Z|NWj;F3;?= zF!ZW-9DQIz(qrF9x4akwD@S)f+h8-=5oQ3_FJ-A-kJXwqnZ)^V-LkXx;j|5^di89o zXHh-+2~p=Nn#BOm3bDr5S~@uyi}h(IrLX~li7+q9u&D5!dK#AdC@ZeGGeq-B+YZ>^ zZ(H04G7AQa)nzlw=Qi22E{mUGd8WDSGkh zRaeZnYr?uUww$Ldd z!SEu8(~A?cR8*;u_0>Bl6ek06f7uXrC>tpOh11oK^JVUS%9R9?UVG?A$KYJkaMcdq z1hlG*tXu4bO0wwYkVp+>?TSKbE*c84k`8cGQ@v(BE_c2>7Paj+W82$Up3c--A?oCc zmp{RIAjMm>Y$<6cAkC4hSFRKl6}dWxeZ`EW3|v-Ob-OH7nSxKiRzn z@r$fZgiCDHS*JNFErG+va>?0TWaSGBX&7`_ClpTSw|mTJc`4f=DneLV#%6bgg5)=d z)$VSa7&)Y@b4nEoQBk9PeLdl!r0qi@v8R3VJ%_rgJ(k2iagh^sIy%nS(;AB>)%L1^ zRTCaHDd&^GgLcI~$)w^sjFj#54ry~)q=}Xsm#gawqwC-fEdS;dfmmO4JXObV*7-Ys zhMmi4%!w2A^16!lKb)7H^2KN+-7UGhCRPL0F+_?@*{d6FcHroxuO5l0yI@cHxL1R{n(H1}HtvX$Cf#Ftyv3=Zi7So_GtOOe$~QV=dtg$-V5Mgow&T{M zzA2RbCrGRq@~N|U8J4uU9#%f_$~zebsr+PZKdt@o`42HcnYfZ{;Zx!Ukgwoe6s0|p{R?F1GxCgLp zBSKz=$wpz)e=T1yC#eb6jIk^cNShFikV?P~76{gjwH-U&A`wn}A0k$h+|s$byB1Uf zYvtWN$+>JAo`BZOvdpRJi;=Z3&OZhB=cwqSypfmyu0&>Ht}d_#5CvYB0Xu$wS<>VL zXQ#X|a~)Y1&ycV%|Knaj3e@OmCh8&;;Jz~}IbIvtdBH9*4MKIsG5^z~?}tTH2`&HV zv%6~ftX{z?e>+lxZzG2{w=FCz{8gUo(~Ym5$2yTi*wR+nnU#8oSFg`-KLqQ@>g01H zc5d0cnLT~qTTfVtV;u&B8%agM>LYP80Ucp^BW>x#u;Y0>dDo&|zqTlC6Zzoktmrpy zzUOw?+rsHdVuqb!Rh#Q+i$B8X3@3)-^_RX(45<2U#4SY@8?5uW*`TuW$e2{uvad}J zjlX=nl0^k^KDrh52lHz}CwEs3*gdVRvdNj4YSe9vGWRS~bnt!>{uB0jBN}9}(5xqa z#7R`_)Ztf8w&f}mqpW|9XUq4ulJ=?%E|oqj@XPPBp{BHcX8x|fm!W4-#}I>jfErOT zHrwLDotI!{*O+rSKIdB9RxA`q@Q=cQ3iZGOT5^;oEM4kpetHikX6dAuofY1WG2-W{ zHyTW5^0oI6V4gE(4YC=9YxPhvgpg{+yb#RK=(t}+gp zHdnt@xgJ$dKq^Uu@77J3xwOi#;GegY--+o_Z{8SiwBU-yjvae%|KOVqzlCm)s)IUsR32by*hr*>;I8_xx45ow}`ZZmemn^kpl# zY)NCg!V)cxScwR!QYR@6kaUtUzNnO}+el!B5DQwj`sFK8BYGCfbLp;`a^VUn5k zoHHBp?w&oQPTz(1b}_U%=4YR%n_s$4gOI`AT3T_d$~x-qEW6|5$;!1cV}hqXPpGKK zZKOQ%S<>qBcZe7NvvT9-Mq^xTMr}KH-OEdNX2Hk~>ZvxFjr+E0#6w2qgQ1zWWfyvR z%T8BH^`GH=ZFJ*|1#O4JpLRFBzWXIv4s-LZ>*JT|tM@D3eY1|c^3Tp z-h=!F1+xUc>b)q@=|ZF79?t1G>N47~bYlxwkT6HB`kURU8m2yv27Ts_(w(0_l*VUm z{u8w>HZ3C_91s9}0-Xj!Du?ovn_DFGe9Y}T>~q8PGZAU1=FS~yQ_wlK?U3?CRTJM;@%p{x`S^a?n)>h&b)J~p$+rh~bJoas+hW4!qoaO$ zczYi~#upnOKarM#P$FtM7y3wIK>-l~2@Wl?4A_f8X+LWcXnWou(2QoejCRGgfgVLS z{EKnoRI>g0=jMz&095+cUC6r&S##)K=ww8HPnz+U;de{@PT8@a_Q|D)70Xy}iaq)W z0dNl;s%$r0|3%{KCk!Hxt|GMLUH)V9h`k#pE?k&~8b=~0jf{Ah;$xLfV|)#!^G5`x zSVC7YL@Yy!da@vIqCE?2SQ652mgR;G#fN*iU(SYpoPTGb=QfAZ?K?})Sa2=o4a%HH zR{6Pa#kLZ6%K{!f`f_A2{HF-7kSk;Pf!S`$3LB=2j!ZFwWk0!>*8{nDn>Rlvn;D0X zJ75`So-v=bH2}2S1yr)*d@mPw?NT6(ibkV|LhCCAqm_`>*+u?%Zx}WQL1+P-07`x4 z;`#3B%?6pz$GBSQ&SkT!$>jvMUddoi6h+xL#=E6o{=KNnq04km<7vqknC9zCGP9kq#+yG zl~(jRv(KT+Q74OSM(dhB@dsqZ4mA}d(aUSuFt>qi)F_esO+fD5z(+kkrbJi_SvI6y z)q|tQo-K)Wu&JLhs5t*QE!FlwyGJT3&0DrymhF*$h7DJWmJz2nhEc3aXuF4$prqiBD57&H{T1iGV!s;U4S)3h2IEm#oFU1)$1 zfqw1JjBPWs{C%yLFPJc!O%Euqju5pjF~W>G`pcGGjt|ZG5Cay6uTM}#9h2Hy!c#QD;hyAf|VR{6u<}FtU>}}qW9(GtJv;N zSnK64oi3&Cjb5^mQt%@+ z7;Z!aA%`x0kBf(!mhY!>3Wcr%E5!@70rUKse< zj$2T^Fq~XZxhqEEUO03aEtA8HhUV0$-B9{49n`t*{revy{|&N3aw0N+k)g6#HjnCopHb(x2D7y&D6~%(xM@>^{=TX)riWPEL6Db_>q5Rp*wEPk7d5p&_+{ zi{@OaJ9^BN|9x}oxLr|pE7Di%ReV}Ab#7ZV6*-Wi_6!K%OQXO#c7H~V<>2*rmZaA_ z$c!_`)xG6q%LN)58v1M2+$tG0A9ROO0#QmUh8uBvqGNu3xVL@p?PKE9(x`ddCKN!^ zllQpEY4wRxcniSuiRdE+ULpL_7c~#5t;1^z*t+1jJo|UJUiAsR>R-pwzNJ+x8Z`ET z8G9a}1%s6o4ns>JrQ6W zKYm=F?}g$anKG3)FRr5X$JR90+&t!CjBX^`HoE#g8Xp?pDI=b1K;d2U^xKQ@aAkIb zzR%2TDplgtr}e8+gLW+5k>w&1bq@X%H;a>(F2x{nj#%kx`U=NP{q^VWIZdA3f=hYI zv}xUk5BHlIH!Pow8n^9^H{x!YHgWH{kyFu@6#xw9oay4pJEB8w#&oyYo1@MI{h8e} zCcn0EhnV@SW`(DMem&eZthvE~IpT#yREULrDGbN~fa+*8_iz*MrB8Mf z={~DBw~*i>MueZirDkik_a5Km_pkxKG&ju*Uq4K_X84}GiVu%QbrLyQ|KOtcYpe#h zJ2IM`^0pD#B6iUWx>^3(OPtFEIbIRz&{j`wryU6U3sUhG<$#9ow^d ztrtuAhKOLZiD zJtVbmm*}aE2Nnen{x@cOy6xJNk(s}WY!Fu)FL^xIHMaCos_n&)n|^79A+Il) zGx=wi_FPo;r)s}baYgI4Z6mz>Qs4L{m__1{69|Wh6C)$8u$g5=aWexl4r%$btBPy0 z-j`9a=4mxFU?iW&wi*hpiOCzT8{ex|EXLYyej{!Qw>9NQaMHyXgenspt6Ily$q)5; z<4|@+^P`3gRX7ywvR@-sY+dvQ-8`AM^*~JzUaJ9Pd0PcpA zuSJ~z4EzH}8lJ6(tR>SPS~clNyR5KxC3kqvEi)=m!esK+ie>}O3wNW%5LtglyV|?= zLHI)RCUYBfQMuaQ(qGU-(SEKwqQQAUmiF$<# z9$)kOsd5wM_3|URY54HG+q(PxU?F%)+fLz0i{CDK@8vh$UB`t`lEJAzy}KnhC%wS8 zyNx)m`@LJxVlSE+ww6|mKjog@gvEO<_tNcnOT$2I+M&zBu_e){s@Mk682OI#8{y%6Git)rVBQ#E8dI z7I@31_7ASr?=0*qgpXfR6hhmwVjssSh70@PwgyFwK#wcpD<0E)ZjpB zb9M+e>K{ZW+BG)dbP&@e+_OfD7UR*u(HW#Ot7KtfN;tERRbMg|&BEXk`MJ@i8RfRg zI$SI*CM;!u72A!8djy?D#~ojqq8gyf7YpSdYlse%LYE{KJK|qP!Y5hE*{H>aqkvgy69uUcTd7q{d%M2Q;@!=$=c8qwllEj*%^mIr=w;E9_G&6luKRTL2RR(NeT?T zKOwFG6gFcBKziEjVt-4&MHQob&%Hi(w>3Rk`2*0ypP@AlLeywSITCLBhoHchdz$FJ zhK!H4`8~IR`P>h>&35odPoMgVl_V$lJI{n%+(z&`=`}j7 zuWt7*_c94z#S6D(%a&uKo6zO<20&@UEek$s5vj?o-G+RKLzfE>vz!GE>exQ)Titr~ zh;YP&m-8_@uMraklR_O2HU#xw*9y&I#>NpWXe=$BWov3<;~#(G(f#|g-J-Jk!yTcm zm8?h8*c%DC3@mk##4-RSW|a^wbsuXy{f7dx(@n28h%6cGq0ct+E&VpHDK?jNAhM(_ zr*e!vP3aV{$&|)AO{7p9nTY&i=a|!G)@vi`kc56Evd!{k%WQw#k8ye79ehP*&GiP^ zrdc0Z$73B~VU?W9u4I-?7H_mQwXo>L*r{*`$ln*eZ|?TMl>V9Vsma#GM*OTdy~>aH zM|IknkQone1eWnkTD8TUSiVIEEaR>3DgUY?df|6dGl%6YKEc}%pBIC)3(h^KB)n>v z{VJ7wM#fvF)u_0)#`iY_PjRfuHD`Y~-tYMSj;AZnd3gn)OS$P~IDPt&`7>wuBn*aF zp-aHVLzS$sxysYWeG!SJ<5txAzJ2<1?bYi5wE+BxTs}dFP(VK9%VSrkA}1#&9kO@~ z5Jq8E!QK)54ldd&P*^{jX8dTisRifG-7+<27+`X+mNbiyvW!NdZvS;Hze~%J5@H{? z{Zo=rg`T_Zs+2`(k*glcR-57<>Eka621AoN+sK6yoVSIp_ywGE9sjTA*Im^;%9Ybp zEZ^X9Ll0f{@an_8)_K^l`L`e1u@73hOnI5u>@l7%A$DXp8#J__EMcx;tuxwpx1^Km zQaKK7iKYC>xJ9Jjb z86^dm^tL9)W)!t#0FxF`fnRD(N-x#20)VKtR8H3c+L z7Fy8?ny#`)^Xli~7p5wA`{h;8c8^c}^p+hu>`W}5Ni~o>UH8bL!DEYV)!FLc;E=C& zGP>P#=Gv9`6+}SA6+({%Tr`9wH?iuIV?HuG?kM@RI>z=A!%$*=?qf-eXX44+sxxxO z@2ihH$YH=Eq_`Vg0*!Z*XC3m4GD-C-D7;j2feC}j5VhN9x?@sfXu&3+H}eTDX}3Ml zUFJui(-Vv>9)+c$j~|~&vJ9Ml+pV{=Pm!y?Q3F0{$|OGNk@n#{gx9b{#&`#re)U&J zRgpn*^5Vs4W*td{4E#~EH)oCg*9N`Ej4@p|4gSaXXb6bnJEbf-tA4X|eKksM{d3rE z4%4k)^5)3lclEHZQ?&pgWHCACVr%rv$3{l1nYxgtTm6I9L-vE`TnNtHsx#ZDC7NJ} z5TGk5Yt-$(FUE|XG*hcD?${@ObD=1~1nIV;P&Mv~ZmR*9QOVapf3X>T`9t&s$d~(!#FfUhSpIFa9z-^iN~?zB}zKsKeYV zT;gba*-T#BeTPJfZ`uq-qp8qufpUwu0XSYthi~h)NA+ zQYzojb7~jog8IN}0tA_y^5xqFfr@zZM)rG5nEhz;Xu_I4^U7ucENpVs+z|RF$_nWA z_%XlnWe=-0YR~WG)mRy{k>LpdFF_q>wjU*dbiY6`8G*S<#1uP67=G?wK5e%(*l6O) z{LRyL!DX#=l}#wpyJOXpwX2VEWnogun>Iw=?%-Z-OA;ot4j-@Ivz+ObO5!C=(`jEi zZN>cXZ{DVXXF!V>dpJaYD=Lz2>K#Ude^CdRH{sB|@Ix5-bwOyk%bD0Q4wWNNWoQl_ ze2S5&#RShQSN2dVq+B0j`Vnu~hG8o}()iFzKYAHu2Zu5=<$#p&IJ?;H2U~!Nm1aa7 zV}u{9^Fvd241dJGdNqGyOxKn*dY4i@ZP=!a7Y7rw<_0zjfA#7&lbe#e@7ucpU)u>t z`aL(-DLVQkQ`ES8^%AvLNB?QG^~@adopd`!Jq19EfR5P}J;Mc3{Qn!#P}Dx9N9;A! z(pls3CGmubo0}_*ivw+UZ(wt#;eXoJkL9%pp2YwQk9}yLmB|5X=sW!`9C*GmuS;qZXiDUc9?TIeb_xMsi0Bk&Yhp`@78t%55_0xw<;VE zFdKfeW%nglx{Vr*zea}+Z*hfy=UnyKuLXh8LHbQJw5K4jXWY0F6m$WWN5#$gxYPf% z08Ki+F*LSKn~p;L7b`WHgckN6aG%$#)jx zZ^Sk2)alseUdhC9hjjGf>)*mlt@kHlCUtfRKc2t_CMJh2_fqRP`fQ|j;J-d0<%gjC zK*GzyI#oXfKm3zlQ*DKcbq9CWnhhIXe%tyR)pP-r)kZJ_;f`{rH+S#|4fp22I_DVk zGQ4F#Mt&x6D?jyB7q?_YXkxU2*B4yQ^n0o5Cfydezs#Fle)8lzy@|=9MGgPL-n5D4 zO>!?F?sVY2e9t0OIr&>G50rp;{07{0t%uHXB+uO{`<9%xnqpUASSyIbYXUVlo*HvT87o+9og<_c0W>N z0co#8=Fwf}wwQ3cOWrS=9A&+J!L&2hk*h=Xemj)Z39YWIC|unsy2dXJkak5xGu@iX zEu!QhDU|jEbtaN>ELm>D!n#n^1h^u;g5&i{W)AM`IPGKBe2ZHm`}tWk9$|OBvZOTH z%F+DXA03U1`0D425-<0Z33b5M=xsEo1%YXn6K2D=UI#UHi~5@Tx#$T~b>7vfK*8Fs zk>TCG+cT|l3xQ&QGMKey9Gf*uBlgqNBul>voAJT@VXNU!ptGcxU9!Yuz7BgZ|GE7! zaa~Lbs5{EeKS%^O>9{8rQF3$0X&)T(Tcg6BEkq$;Bdt z1}ugrWr$HScE|H2Fo<++dwAcO@3<7|ixVG*mVoPRG4N%HVcNHQ0h7@TURx0M#aLLt zu5T{#D_o!SIYy;C+tK4i?TIfqLUNzwlW&@G)#B2wO)mS38NiMK5Q%6C6T;6qp$iV= z8`N-GM}I!t6CLj{X~22VBl7a7^Wx;8r!EOfEzj9T`E>%@oNs{&BBjo1>6+fn$$d3zFJv5%$_fLD4l(M!& zXF*1Jrs{>KPxTd{7lPLhX!B^wa`>&Dp-z|xnX?A1dEcET5m|uA-Av}v^`9QpV$C`+Df6D&%F?`h6bZ0+Chm;~^d15foWWFBu)(f`9AP z6zY*CCml7LcTeXRFC62W92NMXkxpnYRybrAk3df)8I}n7Y>b~rV7eI-DS2r%^0VwUXI?a#Yo4j*ou45TcD=t0s6(jX8P zngRqp%rfg%C$O#q*K!7`SNu{!L~@+?6RuK}uzG=3z64H>0&^F%-}loLoEievVZ#_; znLL7=hMC*9KOPd=Z1u+lne&-84Aj)%FOr!lumZcH51un?mO`Js2Y_(er0aY+zwWN& zSEt?Tf2v@n?vw(|Vsip3>2|j@Jn32Z?{_SsZvclBKtU6H>b?=!az^Q>p#gTE_qHZh z<5-bR%9#AO%gis-%b6|>IZ_lRZ!}0W)JVesP(e4j86c|L!a0f(srI$LT%Ug^k_EFJ zSuH_qXDnIL8L>3Q1LOBd!aA^=K?!aolN{Y5L2Q?OeO)+XD2I7NGu}__pN0SV28-B1 zzt=r%!7Ys4ml(_CL)x{)q9fW1*jrRXTc0MJY?*!C=T+_P*k&P9y+Dwd@K$EMaoNdP z(_g*iu{7cArM*c1Rk){9rT(J5U7cNL>Ncepe6MY8`|a9|;22trJEkQUgL4|qGg`^8 zy^};;R9A)nEk9Olv)KH`oj)}^_y^1+G6kbg&MBSl@UrIDl-+(_UWS1?pCmqxH7+wF z{pObq5&Yw=8t2`aG5se6?YvkhFN92E4joN?H_H$A$)1M9VlZXmYfqB%K?ms5uchy& z^>9d&+>A$aZc&ZC4{rZ)611zZj&ON&7*I(-NeK7E#e9EZg9Z&)wQ&)Va+CiO#xm5@ z%x^&HWyn&e+8FbQk z)J3{UpU%BJVK&`89l)H03vV8QCSEsqAF7Lk$Bqe~DoUd>5r3Sj+x?yR93x-978r$Z z>^OiC%Bi|r<8M0BPZ<+wNAX_5;5(@8*fPy$c5C0|JhX36Y-@jM?FSyOm=9Hj`8W38 z__=L<1-PRN&KB*29qTHqS2uMuGBPH=jy!SQ%*-Qg2JYsV~*3Y137X>m{~2La$I)XF-i)}w~tTi_k^oj8pblV#W%?b^{M z{*TF?JNHVDUAR8cFeEN-ucks5La)RX$iOqJMLk9;*jDLBRk?6X-fmv|qgjUz({T0i z^$L&NwJU9*rjE{e`a$4J-&K-wBCX>vH@jaSo2M;sJW`7X3>?5}`I9m%LU-QhdPR_` zpekosIaainSsuC6;;ru{oXja+KIc*9=v!X4@%z?izdtw}R06L293bjF@=qkW<h?RU-KfYn3V`PQ}KYNIirT|l+rYaEWcrVABkY1lrp zDuyp9EtWY&Bcl>-k%&buE>B>8MAB>STk(U)%*Hz^{T>M#Cd&-?FocS=PhfS zt1Mf&y|d_!HitjU>dyN%IpBhv2L08A4*+dD+UvST5+S7}t)Q0|n%u|chmRhOZ+G>U zm*Li}j~?rdbcy@6BGzLpFJ$wM9q+nqBV&zK-){$onU;bfDjW>yZvs;9YvUG|La`7AMZtY@k zU1&4f(ypsC|FJ^5I+(GfgyF#&p^kE@Qo7j=J zSz6QvMxL`mXzuvu=*mg9>c%e}yGI2K$%*WzRS!RJ9D(h{L@8ZwkN&anD?{sg z-@NI;Zxnec?%RhqgJoydjGip9qN9cd*cGjOBt3lzWG!Z{Z0OyIvs=*2NSEZ#x*%Fa zopl|$+wO-Kxb1h+vifXt;qQyR3RhQGwkykheCbJ4b#=S4!pE1s)l@W^Fx#IABY?z@ zPXvYJht`C|*H5R{kq(r@ecsLT$w3O>rILZ=xsR=HI2E-k+o@t@-FC8-;mlmE!8QxL zOS1?(Ji!K^g` zfIQ{OO=*o;C$KHiNW0o&c&Vwq5%jE=SIOGs_yuYoGUxM-J>HN6P+vTJ*?ZtSQs{0@ zzd35|kMB)_!Z#?mmRJ$ba6uEnZS1G_(@)pRNAHS8Esg?}wk!HcAm@U2nn(IBx0qg% zlFlV3g_}}aVe^8^sl468PjA^;^p-|YR-=tTON#*zr4gQptj%j&i*1AFho!6^76%K& z5&O1v?O+RKWo1N=r1M-N+TUYvfa}SV2F!L;AN0#`j0NuPu7R0iz>_-lyixxTcL}bc z>t$A(M*G9Y6HmGTBcpFQIc!%GSs!+B9d~y0HQ=&lOd$dAD4+Rm&~HOvlep}uBjg@6 zIB;?GfHr55O-dsT`nojg{7@4mN48Cfr3Xy3Ro{hJe$O16q-DE%X0sr{zFx*1(!C7H z$*bNGfMU&>Iyy0(`y3KWkqxjl{{S>b=oxmjX-Cch+P*NG4BY9f_^cA2>w{V`;RFkT2pv4t3j zHnW#D7c&92Jk^7T4!u7guw!+fgIjw31#!z8F?{fVWulUGn3zoxR^4jVqQ$IPv!2p7 z1aQBF3|-Z->M-9<>bKbEHPDAt)@e}dTnk`kC(oZ(eTo1i3<3j%m5PpAEMl|$2ujV> zjT>|sugA^AWi^n#bUDT_)+{VvB8x_lh6U*4&ivc)Id9viKS{BqcCN9q z%I1$ZdEZ*6`9qaGW7kDzY?uZ2$I=yEs=OLb)WCK}677SF?ShBecy8I}bGcXIa?X(V zyxDekqIFL65B7Bkj!I?fLc13qKOY*MycoUQIrokq*Wk-0U2O@SGbd2iGvw91Jz{Vz zpV#1BnoVJEf+JURU$XLIw$`Lytc`xSJX{^QtK?a8NQ-}2Ot5Cqyu84k05s8@)&dwG z*0x`@awS<+uisyr>YI3gRvl%?>x6Nw;6IM>aYZ_*9q^@@z!SJAKCv=^+(M8BHMI{X zufvt{T>I5oN2gUw%7B=08B<65S_Lo8zm#GL;AF9F6niB~N;mk94t{uGX4NR)E2X!R znrUe4^75jJP}2BqnBkc)z(x2o{~{&a+Z%@EJHpId@%LXi>fra52s}8q_*jue^Aru| z%{$%QUYMFLDhgh5J}6lDN}ii6jiS$F(4n6a>P9rm^q1?j-j1*7-Vkt^9&86ORCQSQ zAk-@D4cvB_fU`nL5;nw$P$?1&^}E8DZlQUk?OL&NrAWINLq(2DAi<^hSvemx8^xac zQ51dLHz|+z1L3K!9#?8)*87uBZ)k3vIsJf_m+h;Gdk$UR*=@)7;7~6wnlOuphk~as z32Ln7JztBam{b&$-RywnTv5NsiXp@`DQ^{!f^?L8ORRl)MhiE>p|Wg^+iTHfur&Za zFd=IjPC(oW(<^Az(*K0Et!>Dj-=OCCqew&|uZf!KE19D2Gm=<#^c!)>z-`}a1!vw) z4|2@Wx!RJeDBX5g)@E_8gTNB*K)pj>@&~8ar~AwIZ$Ew>das{faz^gbMo8xrG+f+K z%Mi!#=-`JWqms!{gp5E3c_CaSB*4Wt{}=q+FUcG6(_kx_BYH`o6Pi!nhF*s*yR0tI zJi;ocO(Qy(%qjg4{#*0JA`=s;6w2MD^q+&8al_MP#l^-xh5zQ3N4 zYvcmW?Lw)vcqpsd6rxy!ZO9f!*|Pfzi@~b7 zT`N!jJ&q47EAwv~O%1ZG@%9V3JNz>#bB2o-UpP}hv}&3TN$KcbS;2>WZ|a5(-e+va zKyM8HneRUK$Ho!xPh2y6ty5;qh;R04;+5HMJCa$}3l}E9j#X%t^yXc_e;R4~?;^lY zW{<|R&Pw*vh1C;2=V+mdk?sh7*>HFO3uoa*XFk_(<9zTJe{aqIhAB0!;APD2U7;9>O#sO z5}VaxFW3|xt?j7oeK`+O-%p=@!By@QeeN|VD4uDiW63$CYMWjpTzG<(9Y6aHU=WUD zHv4p?a5qRpPM7gG$uh24FnLjvY5^09(`uAp$QVzd40a#Zs-^vYIWYgPx-So>I{)83 ztur;IrqU#pN<<4KOW8U~#mQE-LMUV>agLB}rkSEdjwDKC&7O{Ztr6O6;mBUd+94rD zhu{6yeCPN3KF>eT^<2N-b6t;Xu4|gPbk1jazhCdy>%Q;T{Yp+v?LkVi5UTF0c!=0a z!0`#r1v$(ZTKVi4^H!}~NmBKadLB`Hyjk=TAP9j=qg{}RFHKL6bt1tQBANsfi^(HQmfzfJx7@V4t*<%`@xF9axj+jCF&pzKR7B? zmCka{5ugvRK7OzAMq$KSbDWqEo~_5BhL1)LaB1<+=qDl;Z>XKBjW4v5-UmRB2;oFm zfV9O^k&c^=sR3+K%U=PEY&j5Uq!H7TBUVU-@RH|8N?w9694uEXkSmn0I7Q~pB?N|e z6vgPFO;dZ+Xe<0Zg+qA<={Wk6uUr!^AQl)7Jb?-)67VNIB*9t-i{F6Xk-Ot8Tu%~D}cOCcffE^4L^ad0P0VNwSzzJ zZF`Iqob>(^?=VXijt{IYP^6}oBSbM`Mb^25*+L-Xr@?9B?Xb|M0E=IAif*pW2f(}) zEp6Ko0f6ZkE?EQ}1G*wq)zy1|rz74*vjoolEq_Lg2bJKALrxOH2{88s0g!xDA!>KgL}BqOki{91JSJ>irH;` zV0JJbsT#1(JRntFkOrJ?FJKS`edD*cKfHEpQu$Zw*dP7I9nG@s8pnANB#0Id$pRU} z>a-7w;iyNA`cE*V8UYMbfQV~4(oe-bIUPtYaNgo3Oqa(uq1na3 z2D0~tTi>Qu&E*+@h^9P5i^ahuwFYD6o37r(sGkSvf`pMf7y%@45XXhib)PJVRQpz} zA<6ZhYYj6e1>a3^8Fo#5J+uP;sNIKnZ&<)#kw_p z0$L%A1D|L0I5$K=A%6+FqeA+Ht@~KSgFqi31-PRd0onG>yRp{l$ZW&oeu6L## zKqllZf@s#nc}?al(1+C~A)6-bB(hg5YOg(|-HMJ22+YvkH5^wz;Q885b7hhD1Cdol z;5tYzZ&8r~F>-~^0BSn<;k-)_A;SW*3jBr!G~-BPaWlbSfwa6ot-|qj(c)AwX+A2B z9q&^zAPu~W&?=R^a3l$J6Asn4cm#rQFG1FiR0`h=8Uh6i+ONA&yTskUk2%plVzELB zV~OJyxV^c|9j!a42`}tAVa!;K^L}Mfj1eI2K2_2nJhMJ`u2Fc#yRBtI<`5XUFHe?x zKL#t7l$I8sl(eM3?ZQY)_BEV?Bxg`pKXm-&F#pt~%#Bq=+A#;{g`2Yy?=-grLH&5$0FSafQ1kC(}_AO$qtBe;3 zOb;puu_x20O2{b$%abq@k8Nm5LKCk`y~bWxCch%hM5xF}*^P4!Tf;o_q-U}F+{TFs z6TSKuS<%QB$P60z^2NcQ#utpj%%sn37rv z)D6&?{p72kh{AKxm9hhnYXeKP1Z3;wJXnGlDRBeBlK_0uM`XM#1PSbSw9$|jU*wd? zT6|`wI|(CC=HQ+(f6_~)4>HPjEE`J`nvwL>=eEkUp7HK30yLW75al1NM`H6D;9;J7&VT7P? zChi1XUDkGX-Ix~;6CeL4(v7sWM$qFy6m>~|)^^j!YO_z3>A{KBZ(k44(IP&_uzMGB zOynY~M_kz*HKkPV&syB!>FK^w>bYsW**EBSAbK9sVuNZBNj}IREp*vpKT<#fqqVkxVcRSM!)`^;iTT%-$N`x;A@!2ev5a6P!5V<=E#C_ z)Gp+SA_0J<%6Pd`IYr;pw9`Cj!-^I6HTFRD{;{w(8WOz?8#JKFj&l5^SqHc|k~d0p zetz?=J3En6p%(>c!JH!c*s&WqQ%}t@znqQD1mXeiC_B5cNqK+)-475EN^M8SaM*Pj z<%0(Sz8IQk-RpHNBx5nZ0RAG}Rah&60$?ZzCay}sZ6QS<+zJ!bcDCaie( zH-%8UKyE1Yt;qwJj>gN6s0J}@J%j7C7|g(m6;GhyTYO6S7P4;jt@XaG=_7wx?6HXy zbdWI$qQ`z1LRE?AuC?KO1|^=dBsAdm6XpP_8YF?{>i#)N!AmkQ1c8;xp39kUCzOc`!i4>`D`skQF5@PP(G?^Czqo=~-6-v&A0h2#LDsUiGzJ_(#l-+S$hfAGEQWEkdeHyi3P^u<{Xl zm_H(X$E(zGJ+r-Y|8#p_bKh4l?r`tdwU>jt{FpBX=tt!yS|m9}N*dRcFQ2jKnWoCi z8ztC$e$eC%TX-9g#~wYwDp?>C|13Mh=`1xR95Mrt2#73$mb~+^5G}`opRff7lAM;t zIqp@fgU&Q9^k}HE816Oq6kAwUK_)(21{b4P9+o_RBd|ZJroh)x2c%~`HdNM`vc(t< zs*iVV-#m{(*$FxX=o=gJAF|xXEYnPi4FO)>q;EGhczF6Z_%FnxM6!XA*tS3!?y;?I!Bs7=%gS(^|cf* zQqZOrF}S<#cbr_JEB&pZT#b;l3dG~Gm$@_bnUew@?fE+=03w5_+>eGKQBfJtJ)Xpu z=$${GiBHF`oe~d&_#y-9LS5!K%1^7dTgcR+xu}X6kg(Z7El_}5GQyz}`k0M&n9E>T ztKZ+BdD=>T@47Q%F@fY*54A0|9MMVdz>ga)-czZA1-9~pI{<4ixw#Gv=~JjD^= zuv2D=K3h#fC=?1EwQCMx_qp)8V56rr8HvnL2_{kuP-nWS`%vzdi_=(&uX>&H=}5tcP#?X9Iw17U z9lQ2i03Ld}y8T4TGy(lt!tNKt*A(4WoPg*Kz(75YTQ!Hy5@VbSQ_#fdojR2Ysb@*? zPZTBoq58xi=n%O|5h8@kr8?FW0u!Vq zUVVWKBTZo4j|+RozM6Gdv6@$U1m`4NRB7e7h9`tS>>CV>$>_9XNf#b-#Mqjd@RlF6 zdrMp^X6YL(KG}t77SE^;UmBijfqjz{{Kqx!$Y|j5XJau8D*68X1I%&2T63ELHZX}zCiVw-#)B+C0HABj-+Lq<2@#a zBL{!c-Yz1NShal1#Yc3$SyF8kpuJfS*MVX&rM3|7i_?~`g9_m_mQ~BUgBkSgO{z~# z*Zo=cE-$xPfY&XUjD&;fmo|fuU9C8I>V0poEhO`8l_7(>e^J(FCrMEYmY>@hton2+e}_+ubwmQO+kx)K7G?qk z=#puj$b)O;5E9F4?jTgNt~U7J2n{{UU2;}1DpB4=E)ovFRi8Dgc_E(js6pKo&>EvG zC;fwZy?xFyi>nFUQAsYFLMl7at+vtHa`21%`en`bIXAuPvsj2o#6=l!4+HR517<3% z=2?7}?=mTsG34<8j0Nj}8Z}}F>um?rd^=S3@NC)a@_0ATkSx=G{;sYUQX0iD!1y%H zMR4Wc=M;^@fNZ!#IVn6m0W!q90S#Hz1zatksSrJHITCw^ImM*|Bbc#hQ zZMreyAeJEvSPVhnf7YT2&)7e+d4?d@-Nj%YDNX?>$^so9T8N=h1+|K*X4UYqcQ z=C1wyis7xCoSfbt7tUwr?+$_P>G=RX86`#IeR>O=Fr|T)6k`C@6oOXrG0`}ueW_?^ z7yvxj0I)>bYigX8b039>ejqQ;VzjxgnS28pU)%Y9^KgFbp4a+lz+SvuAQcJFHk|Mv z+OT|Hekm?SnCudsx*j4qYnKfEgh@xb`)g^WGf)=VYv6|Rt5ve{3L&b!zE<)G4(ayK zzher=Vlsl+kpjOEu)uxsQVBc&l4A zBI2-l7R{fBqjCU}JR0L9zFe^aJu;2G64N}hB#QT1)jk&c#(mClEX?Si$@@@RmVw6auLB`zrWr+B-3GwK}kf&F_KBoDFStz zT-zV-wzxhGwTbWm0tSNnCJs1Y5Q#l01GtFrSC%aDIm~giU*2W-C@Wur!_E#`#O^Z( zue8W$=%7)g4uV8F{<0mk^t=~vP#v8ZZa2a*4THgC3=l1&e3~flYuNkUkNValGKJ!n z*(JK_3YKItdKqEEo*t*;!|&FpcOrhDAnxE59Zjk)ELk?#wuYHzfBya?I!@uB{z|bKZ!U@JZRc zjUVd0;wJfA{&rV2j<4+LGkY_FD z0#(Bs_gMIOBe{#3$mvHd4kT6Eck2?kyCxNKS%gdKCa!FRH3E82gqWj^TUzpJ1a&<9n+j z<#UZIF?LK3+VSh|o$#^BMv?4x8K=h$CxIZ5#?3;!7eC^D$&XS**SaFV6okY91Z?IR zJ~@x^2e4LqKlZf;t06R0xU@3^fmK>YX8ZbOJNE890UaFx!4M(rC5Y5aklgAaO%XDn zUMx1j3Z^j|13O1ffMdrQ)2J!5G8q2_*oT#4XKRb()0*3w z!adi@{>f=ZwQS#ml9CROfX#M4XUb~ zljKp}AC+M&ab#rql`eS{Tn4+g+<2fYtM;*ht(v#?e_iGKtB&NQc9GU1>@XImAS_QU`;csg%-$#yuGZDx4gY@+D zaz69x4BSO-_G)uAu3PWL;*t^*R08O@C`iNCI#k$Bzha0{siw$?@MfF~+anE|BGT7p zva_eArk16x9W`vKUOyz+lnwC^c09IFN_x5`rnS5rGNf0jP89rlT|%xzqbVR^$S}jC z*dn);fg-{&KmBl2#+D&NyRl5005CU$?S;8Q*hVz|-KTwh>BJuZm)H+zgkv~u7J04y ztHPRPS0HafGX7kq#%1(bXRBO|u%r((?#US$SOLG4eC=H~G$FFXXJlDlzSF*)kRKt9 z&UU{yWb?HUuX{J2u8GM#DAC0e>zljW#ZA6mtamwY_w`~CJ0Qoo0Iv?PAu*~tYiw6w z`P9z%+WxH_Ukh6~`l7s#rgFfWD-Ri}<)4>(KZ1k-Z;m}gm8|IBmzAAfZYa4jKfQdg zGO%|9c@96?t{n5;EuN6#MK1Z|^f6RYS(@qZoKEhK(!zh@0b_eDU3`fxh(lHn_5CqX zp@9ZL;YiHLS@Rj1MF2*(nufAJJ73Fju?*hqp?9dX^Bi}bLu=odfrxI|?o?LX5}28?_=-)=z2b`<*;2OK0;P7PltxKJ zMgQ2>meuB2oPOARvbt&O#bs4@%KuZ15?C`7WPHTcXhyP}9e13GDFcEnts({kkExI@Ke|thO)*0> zHHjlXP1i~9mY0{e4A7`7P@G%7@9{g_VAZ(hi1U^KOg!qe$x|w9Y5Y4C7V#N&_m4fm zqNwLv@SE>y^LlBqDHmOOsxSRK!Ys-~_u>vL)jHX=idUo**Cf`vi}S};S?XXRYH9`| zy=+yeDc|99j-#-qYD>uZd)I!w8pC}vK5;?DM=N&>ts5kHkX~*mU&9dg(PF!&Z~1yL z>oF9xdd9{MtKG~a`?|tQuN=HDGd79aFn-<{>ynST>h0K768G}S3YK@_aMWi6yne3Z zGZqh9eL|{c0C(LO`mH!#ddu3c7ll?H7h0LQw8*N0FLH=u&}5dj*8fyfM8%u%Z;En> z9)dZ@QRjYqr4G-X0^~nl^DZ*^{(mW|I=pVc4#>|YjpJ_@egEUX{j#op@&ct#hzvq; z?MR<6R4oaoGSa3xXz36nW0f{VLuiHABrPQ+Dk7qSU2^%1sJwh@fd&(pDRPF0_FuWO zY=`d~hh(<)xNcEiW-G_>0ttrB`KLHnGt*qFJ;e}7z=<)--aQ-+vI@X58K4{Hh9@K` zZK^esugi<*U~3293t-USYhGVhG=cO?PLnh*^wup@A8_YZWyWxEm5-`w}+SvVgd+n9goWG=x^ zh_xG(d#H~^3fl$3`0fNUgjNoWd)gORzTtdkO~F9W)q20Hh4GMjz(7_yK|3&G;&=7> z1~lq3&>t!6U~K0gP;PFl)(}w_!p??8hAlV;w)x6X^G%Y%pSa^lcC=AR-xO+hsZo4U zQxn#Z@1iOf+1FC|d18F*6nJ*D2@BykB(35EplINJx=`)n?|&W%h6ikhbd8PEAavIR zK>6y~x8lT9J5H5N%uox;j0rSq0GhBSwFmmHJf0S?*+W*w^4XRE-4CI&lZiYr0U-4N z!0r=T2T<o$es=AbU3!Rd+{6ek3H&aJv*|{a5UDh}3fnMcQ%*I?3KYCtj^sJ;ty*lHU@B0r)dUv<& z-+%r;-J*j)P|^&ukD|}!H-siqA{%>6G)73-FxM|rdC{V2acZAf2lvB-$3&gVMjN1r zbBG9*4=O3VlR;D#bx_J1d43@97vQ>_mKhj+=Do04Uw-jhx5VXF+#bIR)GG%!699=L z=L!_K0OdkjXZZAUESw^f48rLWxP^#!vGt`PYJs>Q<0Z$XLq#x{^?Q*tD>&CO$L%xY z#3z0KuTknf=^Ipj+pA1)Dgd8;JY^3ZTLF6#Y?M^zrC-W-^*HA%G|Ec?fRe(NKyAd2XHkum#$mx={irgjD%Z+OY+w#9#^$t)l+^9ZXKoWbfb<%VSQ;3F=fiS3E;sU7o|B>ARCC{0I7SWb$#rbE z6`*>z@mwiOIKyYT>6hKP_RVIBouohbc2H-QO^AWMz9sw_#RYb)NKt0n5Mc{kMnr)fAYkD;`gtYL?O9rP>2TD zK+L2)!?e_!83%AwfWHV;n?hMGNIP^umn??8=R$AK z4kRZ=&2IZ>$rn{4GBCbw*DY&7bUt8EtD)yu1?&4=*VW*ZhU@$-p>_bXX#&$|YdcBZ zsOX^!?O~(*NKq_`=&C4cX`|PT+8m1jAC<}*`BCeW`uiYCByK6pIG9_-G@6G`5BQJm zU+I5$bx&IOb!>d$!VGz4?$KAX^v3#{Szb3ubz}eyQ&H=ry;cFW8{T{N#P=B0%(dsc zOtk7K#84yKU~a5L5E>^6(3dBL(G`R+0P29(0PRl`@qgiT6dM6(IF2ZgXnw$~vXo{1 ztW7IWcYF+%E1PKiz&}tQnhTV*X`Q<}8LZlqy6Ie%J+4cv*{l6tk)GL1V&OgSnZ(C; z&OCag`LjEHGtyW0o5>M)CQ77AT2|7L$yN zb5U{FW9nA&E#GKySWEpF*ydLDIRe95s1+W0P@KiE z$MX+EZarx$$EX{|$H%CPI(T$Wo!UtQH*}JvwI)K&xyMh)26|bVoqX{5cUczWE>g4( zL@}HKu_(I5eLNU5U;EUUXg+I>cim&Wz0W}HU*8TsX&Vi=*^st#S=&g*zen6X({d&x z;*QMS_xy90Vb}3U<{NZZ8Rh~kkU_N90K)HO<*Zt}MpKPW-K)XGT1UJy}Sto>mod*1wT!DTA1IE43u>M8|fC+4=+DgOL*4 zMPdPlLYVLX$dV`@&rv3_qjYEiz#jra+}fM)mar5Wd)wonw5}}Xq$~W&3G*8b+WH*& zV|^IrqTe~wRpdE@5QdlsHUs`kruCu1FF%5tI!Y{Wg=wIElcpv|I-A3;&o(uWo^u$! z@co2*mR*`e++CS*uu6etTZITLl-idQgFIw@sB{;HxB0k3_r^DSl^N;+1=UF7F0QRJOo=ZdgKUe{B zYojZ8_~-z9CD!U>%ftR{0Gn^=!uf-(xfvv88$+VYFKw%vm|+>f`X_}_rBly!6dAlu zqN>^Mpji%!n5x;i)VIoy>;H<)#XJTpGWTP1 zb^flu{}{JzY0=+*ypbUKpFrM!^DjTr1~(gR<$p+U3e500o_9YEfO=?*CSaGN<6UZs zdgV2^JUW!TL#Rd2)2GhJ!<9WdjWyZ=>ofTEr$UD5g%u!ozUvP(%+sF>hxJ2iUJI5- zPg|Qgcx+zuVVQcVD%~IEQTk=Qp_8&kBeH|Q30>VdP_l%NrTHzzhgzqDn+B+Z#1wi1 zLPb|usyXRWYR}u(fkR$`#YJovab|!^!0#)i023dFHEsfcRzDCRluhZ-hK&<{G01z^ zY@T4%LzmAyz^B43O0ToTE3Nw+j58nr_{$Fk$BiU-565(JhcC1gU(6*?&ax|@TL1)- z>=h?vy__o$ZxTP3#xC-GL#mV;bmSc9@C>f$3lZYNc8XLNI_p>Ca-4ENx1E6T-mEaq z5DHQZMu_b4qeRP=Jo$-ogYb{0;$dnC-ws>TAkQM$C+Ope&RK5f2Jdua3pkWJr?@CQ zH#jc9=Y@-e%i1+8SgvrI`z^@lTWmdtq7-us6}~DH`9(7Pi7^6k0D+Z#ZC453SRTY$ zgHYOmczX?j@HlKsOLc1YI`^m!^#P*P%*Gx2zG8m3tH%U9l^Rte+jmzpcLBx!AYg^l z#6X`8of(aZYA+Tfu2dnFcc~4OM0=n}SYZ47x+FfCF$lZ3;59 z?HH$5{*1hqZOmX6Fk>&YS|>kv@G$4sX(Vkmj4c$p8o@fMAmdLnEj3}%adMENn;8e* zbr8)NAwpt?S*oyNrzN%^ia-i=15yC6s96xOd|9}RT=9yLyNv*@rYFF2BlosJ=Qm** zg$9TW0ouJN-xKpG#(i!dGAjktP|o=d$7@CH-!3lBCiDiekO5j;fHVm!g>*VVz~>S< zCAqg=ih){w5PC0wB5Z6Svi5yX;63`@6Tc^Ghqr=ewQT_9A`%#dW$$W{U>lJP4fbXJ z9@{9j!ZNIZ7mFy5XC*asI?+=P()FA{E^y*OtS~I}L8ptC&sf9Wh8ztbmGCR2nUmu9 zf&w8E*_6(abBrBV2vrQNY%TU|0q-t~(0n5>e`bKHVQnmaDWj<&-# z{NzTOGlN8jK^8>6>s7fyFJio>UH*UQ#?Yn8MWLG~EdeU7edVFFNJR;$4)tI|2KGaS0KE zNzKvO>C#y{&R%^~Z^G4SvdD{w2`&eI1<3MSpq?x@#z#KYP;h2{26>s~9e7Cayxkf~ z*Kde1(QE6aq%jE5z1RfsXfbTf@eB`AP}|!;>0=AE7m53(Po(Y!0K| z2%`^bb(mb_NYh>e@)?Jixzh6+c>=&#UJOmxqU((WG0-b_dloJy3%#}xTN5hnUMQOl#V*U%{%jRe!?vF|*rOZ+nh zWG5cUeLrNlO@xd^BKNF&ROp-Zd?l-ghe=c>KXKbgNdNxx|NhkepUVgSfBfW`vC1)ti^#%>g2TcqszR=M)%H6^jI6RgK+JDpH)!;U-CqT)!JTueA0)AtgVN=(i z*`F!YZbHgw89raJx#{;j-pMn_%gT!8Fi0|O=hMi;fR6x(xHf*4s}v~(ha!9P{_FjT zIr#f`??h?mz&~h-hC{e;<`@DAH~$bI6=<0O=XLxUTSY0 z9v!Zye7ssjK+0E}ubJ_e<}Y{CEy>pB`vW!IHqWcmiSB?xofdXcGKP|n%DeX$0aoHw zOyt~fWxW}|uE@-~0PA`$rAB!zmQss^U*Njb3gPi&Xk(}fc{6o0yihkUYF;)?7@e*C zLQU@${3oqMDHe%|#vEJGMOKtl0RZZ1e{LX4BnSc_WVaQ~5f7&qi(kG>>E2YCFxDc^ zHztp|t#bINxf%H8^54pvq62;eNGrby0XMnZwz(yc!07i84%t1yhmJ<2aIHlSQTH#c zPIK+m`q<_`BpDAaa^9bs{atALF$K>Z+p*Y@pqO;hNcjE;;?Ui6fCQpYqDF7M(2V^l zBoXnvw#!CH)CheGxnKcDcDr{1Q4ov9G>d#hjKAaQny}nz5_Wei0|Dawd}n69QtA;k z49f_R=@@fj+ahEiy2k!87*xT%Pi4AHA~Gt#!3r%Kw%!!oq@?qoW|t@HH+Z@j6PU=i z(W2e4ji*tqbUmUhRF+^p?)D6HY){J%D=zg=XWv3%{E9?$-{mSS+r7r zPD(D$Fl_N|651ivmMn-Fe89)qY2?B>P&_a(J%qix*klxeI=NP|KNw z?EFr%!gtfEx5mHSZ$U_U3lK$X_(@N>4Zv2~t&(^@ZR0||Rc{aH*ciSOiho(N0B|&% zR#oKmk{eF38k^bWeH)YTcyHfmyUfHxzAd-78TFp7TQ=>Tw&Z3VR#Ld9>DaoYy3>jU zL`e)e{*1KPhz=sH4{M>OrWV>P_lZTtJQvr(=U8U!CO#oaClGm8rfCB0x+E%>&vhjx zM`My1LP0~STyr6tmF91O;Gosw3M4o*IBpN$x*vU=HVtd3cV~$=jEqUFr%bfKhZN4Y z;_#`@kr%WKu8tztlZD_FlblmGeyc>EwNOO*PD0kT^{d8gJV!!)r15XPjn?HCsUe3P z>Y`^vnr{Vc0Tl#S0v$&sG+qe@zXO!YF>(L4KjDD$pgOFD&-1b~Qz#Nv8tB0u{r#2L z1#S@;C9=#jy304cP}yfj*rL9ib(mcqUi&8dV*yAn)0$w1a8iU=J;-e9JXO~ zq!nTTnGT6D2iEPE-`}p0%!+-mVq;}>L|yDGi6)zGe5R#cK2KoxplKD=?b`X1GC}vD zi+dOWA`;i%Q;HvBGkSdKD9*P=-Mk@Yr-LN^Q z2f#s8idEiN&6SHSH*@bC_l1Rpp#WLB%KPGev+P!P45W-zJ^?QxhKAWD zg)L|Ogr7}@jh~3v;xsyZ`M6zCUf7YRcQKGn~d}~nh_8Xyr`JNRESS^77Gw6 zchpYOe_bi{T^(tsa#=gj7{zitaU#ZIPM}Ot7^>cMH*dWSLoaFF4>Q z>SYNZ?{+gF&=Lr^0E2e0nRtXKR1Rd`tu}DTbE>Ms8C$n8CJ2%u-+3mf7SXhSpQGON z6_NLDg+^PT`&ux#lT_>@+LqJP)fQj>GkhRxul&T@lYq=5CgHZIpzh)Ll1i&N21utW zD#6<9tc^PwjKtW);6TJM3?~a246@l+?Vjh-jC{G*L?gEX;MN)FDC%fS7FUuXDRWkN zQDNgvABci)sI%&I(w1Pxv6_*&q&y_q1TAr3!l&@W<(EU2AFos$j6J`K@q4Al3@zRF z-lD|%@mBq3P87!N`c^>I?v=G{3_Ij z(LluJ_1gv`V0SbP^6h+k#OO~lTcK73$$Gen*Sh0f$aeD6_vYb)P}vKThWzcek+V(n zUS8#YMbciG+W}An48e8L9~8i=5_Hqp*lM&y@x+P_;NX4v$c zblcj@M1Lp?I){$OkJOzfi%u+?MlIKv+uYBV>ut>Efbz5}htfb)Lc{fLxM13M68x+^ ztiMiCewUtpA`+v1_klM=(F9KExasrd#cr=$789aVG^JrYf-CxGUQPb#f2a<`*~d^K zJ^KrKKG;mhf$eFIPgiqB+r#m}jKpIj!s9rqK3Dk+N{kC zVmz9O6a(>c<_=_WDMstuwSkM%?D4TWAzH_%JD(i94yizHCQNAkrm$cNN?wD;#xaJ$Ik{cpg>U zWwX=-EtxoJ`#xEV#I&YRK`l?~WIM-l^5i{yZ6T0LQ@l736Cf}FF-&%Md2~OxuaY1& z(oJOwgg=X9!=ak0pAceYun&X}kUhMpBi$Ta|K?C-5lX0dalR;{qeVUXf_UQ=oR3G! z`kSE#9qIMiSS==DA@+OYAC{&nNUN)B2D5DYv?;efaa{!SspYav*)*5jUOY(N2iqay z1NiuRUN_4LbLxhM2$zjDL9E}l$59k`Nrrz=44!Bs_)Nty3*KHZx~ZZ`t0oedTk9yk zClH<#(syiKX#U_~v9?tdyBC!dOzL}!A=v5f1y)hxMhI=0$$VhZ()?7v^26{J4dUew zC_(H5zXQ~{a*G@J1zesJg+-y(vtdqSz|4$H@4HsPU6l!G&kn0prO|Hh5a!-|6=njR zCNzXfArq1>n-cir2kfejL-K;IaaN4@=Xgiq2M5dK2d6!V*hz0RX_>^o;`p zGi&Z6&ckRe_79>2zYnT|?gwJ>m5(JAvpG(V%%f>M2oOn0${4wv<*AFdrB1`!v`PCVv9R_4`7w)h^steK^{+Z$2?}g#Bzlc7HU7$q^22;b#z- z#-%)ft}{{@f0(f1>l!IUw{5lDvbWo9;wjEVc!~11@j~>JK!X);NrEv;f?R*@jDD~D0(2zons*e>2%zrV8Zx4)9CZK6^9v|a&4~_KHX$C z?*05}`yKkg_H5Z|rpE=&?fD2J6l_?Fa)F%K?v^oPgR0E^0gx=usrDhj*qB+3Z9C86 z|3TUU`T9KrUG;F0rqGSGx~e7U1XVOOuvL4?6^)s5=Mx^Li*=Qw7!TOk@r zpqD6`%+ss^=pf1#gV-^t2I`lK+rt+*Ni*Z)gR1{6xU-{#KWg{)ru2sMv8_;3te;KJ zjcYAuki4!Bp&(eaYACO0HD4w@%&;${uil@404dC@)eQlaz}Ux+clOrkg}+qqh6|D@ zvryeAd*7HWE|;xV_SJnije7Q~(P6XFa(fsLxTbQ|s4|4p^|?|#xPQ7?mB{-NZ#YjD zzSMhnu{%+x#qqc+@B#&iqtko`KC}o(FASGzQu|hxJ94~~v<~l{I3S(XV#uXY%D6nE z>3pHlWEnywYGsvPH?|PJ-RB7j(kzKclF!F!f$F&h1xBZ9U5zi#57E)lP!RLkQhb2_ zZVOYTRAVBqzFz>lsHkXfX@UKi-D(y^I)Oo~NGZ>72?Qvp;gJ72Y`*?VK<)me+^U$u z*}KUaow~Wa%j6>NOMw`LfJ%4!__jy$t`l$4xiXN^t>bppzZoeIae3(s5d=|JRC032 z3N%occ6!Hd_8mh*S9B%a>G6I z3<|RNXv#w`83@nX{ILoXfk?jh|0pj{R#$1fHhxl$*W0A5VbjhD1(}^f%PUjA475$5R1Xflr(XnFdA9&M_;p zcsy}U>Epq$qDhtd?IQ7O8C@?$>4jg+T#ZKui3^(cw0y}1wK8|j=j(!=YK=4BVB4Z4U!m^p0 zRqMKPLOr)Oz>$UiO!4R_xIULzY)PlOTxi$n9%^LZgMhPG{L5*N>(Y2O91f$dHQPjv z1e$kRa!5&eEA+K6iN-YQtU`|k6jDieF8W}L#AKzkZ>nrxY^GmGKMd~)ClQU;$~xX` zPd*k+hSvLva7W%IcH+%2AtDVKE-$H5dxG)l6Q^WJ07c_u8F+bErBZAaJ^j)io4-G% zk|)a+UI>RQYieTZK&swS?Mz-d=`} ziwhrkx=O4Tux^BJ_;)x1S%YQd-TKo(KSiyZ^}QzuEl1k1+NHDsJW4!1%Q$O{i{$)L z3mI;}waU$jfPwkT6*-Yfkv0RNYXG*Ek}x94Zx=N+_IotEZ>lBgFo-`GehC2lw#{c0 zLe2{Y_*Bt5SV7Tc>#izuF(njw%0#kXMEna~sm^^D&d5BMCP;!N-$Fx*)k-2fXlJus z+wKSLn4oZTY#{|>sr@`B?V%+Vw!t92MMD6-2io*7op!>Y>GuSpHWmxP4B!)tEYJ>j znOpMe{=HZ%7nN7LHl5CMPs;!Ki^X*9{-C_3TOL2q4?GWp*X^Xn>YN>+!I4&KB{$p9 zO4TfKBci$%8~Fl7fvL5Qr)_j{7rT1RK?(cJV1N(pQ*HEO`#fgqyuXBRHkt|r0a$Go zv7{5|#D-v%t^#H{q(|Z{p8>Lb;;GrpEdMXn&y~CL{3@ zSJGBg|7N5)V;!}2iQT`rGKpyo+acn8r0?ib$A{8~)R_M1&Xgdjia71<>szmz(-rAx z_qn^4xlUuw!$34q;#VFuj_rp4>OJKl4PMa_&>2j;=P1iUaAgMe9ei;c8ELoVPqhbG z`BQ@R3_meww*PMDcj-xc2Mp;uvO04cch1+CG2$?2BSNTjTRD$OZ@;BHQA>0Yh_ydH zViG;l+GE`=9wcv^H(xP)2}mTFp%BXYUX=gl4?+(*1?*=YnNC-M?Oo$6KsbQOYnEDyN=gJFRZkfgsZUf(&2FZS_J0 zC=kR#*|u$~<$tu9$Nm%=veZ)=SDk$i9ZC4Uc;a15sB4%;og9(H>a;w=xEN^)lcKz9 z^lHApw%b<@h`r-Wid~1$Ktx(8S`j;lz(*1oCBXglt6oN~y;#LWxBPF626x(rYl;0V za1z14QjlO8U*PVJGPghv0%5hD*0>%2-4}_B%dj?7kly=n(q^`;ym0tZxm}PBl$FoU z2COPAXDo?$-4(JerVBAZ3j3YsxUr{;DK9$F{&`zleI>BBE#imuycK$cXZTuaSyV8HKpIVB-3w1m>JOE{v zs;GpTtmP40>uk7#m~qwvmRYk|a5QuZd6?XkFwf}ZCge3|8` znaBB|>d=XoyYZPjJ(FDS14WaN$;nA3pMDew6xtu5NEE{__!Z2-B)aV383tr$id#^f zS+)MR8?lj4kWbR_!Vf&$(xTRf%b^l?`A?%#+@UI`qji%sW4aU>7mFr;*$iNH1_0Uq z&7114i3SVRd5uT)uACqWX17`VQLfXhe-`tJO1HFeI-s-8;DtG#o^hphy>p=@w7p{E zJ~}-;0z@;nhc#mmSi^FLua_A6vt=TUYRz{iOcqtgVm3DR=ZmT)O6l6d@Y{ZQF?lo< zxiI4Vdw|CQ*w5*>QIq_V5>O2Y#^P&UYV~g7RW=XR_;}a2MIj~P zk@?iRDKXmU<&oB$ROkMQMy)*N#N=q!uDvVflXR6q54`uoQG=Sc4o(0Pws6o}V*8h; zo1E=G98(dU=Mb_t2$ZW*$hgC=NIzw@G$lq00S2MXCYlnGUYtoj&1L*G5H%=6rWyF79 z%KgL}aA@GZa#GhT0dZ(_5n(&}l` z)EWH(>xlXDpfv$EaR>|hS(#L${fD)H)e?0^kc=&RypUo2><2U%67UE~l}zr?{~An^ z{`qBM)5+u7geew^U9;FyXn0c1LqBD#s{ng}@|n1m38fDuGpx?fn#^30O?DQK!uo1H z>g@-H{Gl)r%sKXU9AsQNL6F;kt^mowT!q8{&v9pVU#McU^FKkY;gui#MMG#$OyLwO zC`hB?u+f)~O@Nc6lvGzTn-v5$j33-t!l66#k}&i`9AO{!eVylezOGk&`l+WPw1nXq z9shGFth2Zh<(-~`Q$_x!`kdgxAy>`W<&;Qi4Cp^>bOn*buwN(8Ies1woZJe`$WkV> z@c31nZ*Z4HVtyD1EZkvwy2x#Xw@9mH8RiW~qc-q<2T%#w4ZDB5QEE59139+fT)ECf znU|YNp@LBP;aoW`>ghgtVw#oD`?W^C)Avzrcuw)P!s2RbI5>=7e>iOR8-V(iw&qwm z-)Gw;jc%*N*?3e+g0WFd5APVJ15V;{q2jerKD+6w95Py0*RA~V9EZe`gHo-Nr8^ee zjwe?lK){y>V!y4bZUCA^R9g9Tj49OkD3XCX(URE+Xz`_UeICG|!6){7s6EyU2&}5nJoyn2jg?GEGh( zYcgLYW(GT3FmYI$N+RqeCZ*ikvfCZhc zeax!$*H8ZOA9*{%&1XU>(OBcWFp-gKH+$uCvl@v(CO+A( z)2Fmy)pOF7KyG_$L2w+xPCyIX-~X-Xc>jqDV<3VAN}te;mZ@JSMbDQo1&W-UT;t!D zc$)Q{6ubViGOZk)7S~sGK|WOkUT5`+uQl|{?0YCQFB@LEP+~46UMi0l&MWF7v}^SP z7fKrKt8lFAd-sV|+8k%leiD#H(`}JM`*Y?ep55c+=jq}?Y7q$3x&P88Z4*-J;&aMm zw|tw@WRKDU?vu2E+E3iFkIxS$=D@=drz83M2hf@cnkiAs@oqZr)=|lrT#Nag+&OEX zxRAvw$Kp7B&kBBCS*j(IEM~{y64d)sgZzCq%l>*1uCT*HYu=0BlC9T^R%*H+(5=#c z_MR?OBnt%irp6J`Vkzb8`6;HVj3lPZv^d7*|a>p($}wZVaxAK%hUnnbGT7cpdAK zn}b_CU*^|$P~^`RyT-Z-grB17GIY;5e=G0#b+=+BKx zy}4u=!b4h}FC)&UdmpvGxArY!|5e>8*&`c9L-1`wOY;8t<6Oqsicry;{&<7G+mMkRtuDp{DQm(!8pf=4FsAQi{QF`y#%A+2&!FLLoAR(h zk>%%TTCBrH@0-JwHbRaD>!1Io3UZEY8sG}CbR*&-6Ni-UlQUL$`&FiZlY?oM%l(;= z^9^dnY*^5x37u`Ir`QebnBS*idOWXV`zU;(Q!V*~HM63{@IDS{HU;!l^j>N2J*P4# zw46Rq7g1?sW*#;XO1fX{ZA{im;C@nuIS)@5AVQtsP=2p+_Hqyjvo~|+noInM3{hwk zm;J3>`#rRYwmw$R1hCbf$li0hS#}K^IT=(In6gNc*oP%B=#!kLVpM)PT&U4#_vU)l z!CVezrMBj@olw)g9htb@j)~?&?PG=Wk}B_;zt74_xcxy!FgQQksFhYjdJ-8y4OtS& zPwuyo4W9N&j6JL7U?enqTI*u11&8k8Y6mI!9G)&#hsrj8NGxc$KF@16QCBXBtSCz9D zllt37#%8|K0FU1q4+%>jYbn_1$dhS5hF6TA=9!!fE|jVSOBY-IZvwgQxG2rL)qdR$ zdlplGO1zG;r@ZvXupQaFH(DU}v52+bWvvCN#tNg2T?^_QtEDk*5x*V24Q##u)d!0{ z*grpw1}aVam?44_*?m!QOF9afjJolO=`=_%l@`n*(s4LQ{!AEpiJNxN+8Xd^XYK|B zlIF?Z7(29sKq=ASWTlPMx;8-PwL1!ZPlg5GO;NTEJ=%vc9-p|VtpEcuv_tS#{XfqCV zr5}|PJQeV!LZu#(x+ODTYs|G|`>oyjPW4$;b-(3}pOo*9Vc*1-KjNg(8X9~|Ap~9_ ziQhfMJ-OR&H<0PH8_`D+83(`;|CH={WZTn&CJ+X;z(NbG#3u^#Y1JVM>Jbx)=80bC zW-4rBTu;$|el6DBz0hd)RUMT8inUTijql~2;?hc%KlVxvIPf)Uc8pXi)Y4jv^50ui z-wg{ty5}b*x~uUR5>EanylxPcG6H7Dp;*CX+<^5tKZg4B7TwkMs!LxqlsPofns-MA zCOxc;LN1-tk6(w!;Kr^48bYI19QG?zE%WP@G?xhMNXjFp7Qe9V^ z_5_^D2R)S1u4y-OCZO(>n*2kj8%yFIRES|H<mEOIIpb){k^X)3nD}w3fL*|7w znqfJyWUwZBqCO;Gt7}$(GL6vx7R##ZlVP6KZjVN&?d`vUjHtM{V1buMOb9xd{(!sK z-ONw1{jTfCcFvMNmgHAk%L?fP!Z}ug!Ku6Z3&SRv0_S(NK)Gu{P-Kb47BPrHp{!<0 z`-3sa&OitP4SD5d2g$5mNuQ1ESD$XQR$8k>K_t53b)yqTz&0Erf(n6wN9m-kXbsDD z5Hp(R311R5HKn+}{O5nt{_qx}P;agI`es1d+isqCUt>15kq+qUR5=ZxLB27AV6i`Y zyU=4{`}6Uyet|dn7KR`gzCE0hKLe4J#>_{hy&w`Ea9Gc4<;~U~gip3stPl z?T82{-3OyxRnMU_G~To@PzKCH(BZ$Gygcx|<8iKxu+()N`t-U02((>3agNbWv_*Sg zR172Rl1OpS|5AiPGHcm+KGRwvVO&8#2`BT2dsJ=pEaaM`nH+9x zv7WTg%rif9_Vk4Bk8BXYy1k70@OY%e)d4Vxl{S1&Ww-DY``U~4^7K~VsgW3>a8W(t zpi_f`^ZB1DMU&$A)0q1E9x9Chg$OQ_+W>Tw^n_fNAFMMBu{*a(-z+liv%1~?<>5MP zhI1IMb^397UQ&&wa`{uHyF;^>jrQJx;u{DIb1kH=<{4Yv!)=x-q2&4Ra!tlzz5&c8 z!}^oC4NK*xamE4;F^p zO}}>P`Fb{V*J9D3m|Ojochg0HTY#)IQ?&aF40_5cVS9Q^EJ>3=ofKAnUfu_nCr|IQ zKHS@lP)0C3!(VN=-W@PetV&Bx=_Xzvmj)K~NhU8991TW9SC*VgyUn^owkUm69q#gC z&@-{?!*t%%o`Urb4G6wB`O$it_&Ps|?73eN-JwPlQy0MQ-> zi1m{x?Xl#S>WILZavefi^-{rbf@{0n9%ia~&kecAae`JKLD^gfDyQNDiG@F^hzXi< zq*pne(7EEI|Hgoe(pvZt;(vYT5Teav8Z_K@f!YYalllJMXZE_x=>&m`1nzF|i)rso zSn9hUJHy&zpvBdacPX^C>5%Cu!HRqEnFxw5rFi(8G``NA=LZkd;yu}KNGI*=Z&dKqppZ&T;$6&q@vc(GxBkDz*UuZChZLJrQMSi zJKC6JpPL53(dXH|E{ffI3%Qk2Hu(qjNvn~L{KFayNJ7L3-F$3sZM5Q~CX4q&NEfrF zp_GBf>_gqSDh?hV!bkyk(%Fg!B8Wn+B+*I=7pXdja^$$mWL*(Q4oH>%h!cn;?VBxQwhtUB}~-T7_-^xaQ&ZWDFO8Yb<~D{43LG zRZu_UnIRi$vwRzpRz|A94x6_#^zKYEU*mWd-%?O4=YqsyDNZ|Ui5y!MTV5pVJu)hj zF3N{b9Aspe6|LtXVr-xSG)TI;5wGk!MH=7$(Y!;gb2?tqq|>T}KolzVg`AI9rZ6Kz zMWEF1>~*AyJj0@sYH$JMexrUSt3SNmWE_G$=UWzZJ=Gex@HDE$KRldv4f}?%!8n@K zh?W)T(!ctoi*;5(K_s43r_rgR2@j;<)rl4FfAh)HMNvG z-{6R|TMt7MHu)N}LYp@)i^Tdxzw}mg`|bBuA-DO~mK#~1Z2OX8x~96)XyC2&N-KT}yG?h0IAN9BiS7V*I02jR z!ED(?y$wJ}&MGSi4wj?tROWrpjib3nTKTuzHX|5$V&i2+H?Hq|;*wpqi{?`u5=l_O zK{Co(+B!DM{`u4oq0+Dq2_l#UItZYiSKC^YXYXa)L`moevaZHNzyJkj@4PJR z)zNl&T|g?8z=q!U}0Fqo9Hyq^lb~6&x zqDga1|0M6tl;Fn1#ya%Ht52?b@O}haY}NOHK7md*479d4D>|MxhDQKUF7NV3*z7`h z3C|YuuX4Q-e2_4BKoiK6&)}aGZ&~HLSpz-naEA&6LZKHsAJ9ZVde1jeMbao1iX#THfQYnFSiz3S<%Xco zr?aJ>FdMn(J?WHpFzx(M0}2>~sLxibHYLbi_w=dr!9(p%2IHgoGOe=GX;PQO{^}0e zBcZzqdxj#yhZz#wq=Up`?-n z7M9P)eYj-@AV2wz46LoI74v#&R=_gr6_yx|2AJ*51eNFupOQHJt59FSrFXZ9eCpJg z$QCm&HG?seb7DQWH{-L={GvRFxcuIMj)}xbO-V5^McweL(5RB~I>GlN9NMoa{s;no z&rc^^K(3UX5!gGN4jZr#eNe~4<8h^XO&5b|%VvU>t&p%VGy$7!7`Mwl=gO)wrTj)g z8qaHVY#!XGvt?RAptULOj7_W7D}u)NKeSP)1sZqyL-IUD16)7rfI#yNo3$YLUcj{VMWe2CyHpT0k+K+vU;ya_vtF>%65 zFOBw=N(9qRMuW5w_ybQUHQ zBm|oleu_2vsD@rC%(HwAUJ*X1s~h3t)ij$drq|7JADC#W^q8V}ZA(Dl@f5~mddbiX zuW`S)WIS68dC6V`xz>CFI#Jv4r;>BCaR{;FUU_yqC0B8BWidCB!P=r6>mFZcMe$+( zh!%z3&gPc5u>n5HzubOgqk)a1q1dw2pO}={T64BB)@%VNU)rnn+p_M_kNQq;$OL{a@K18;=R?*H%Z0(T9#Sp^V4^J^ZRn46sW zN=B&)HZfxDxtQ*wjOWSm>S;!}9t;HR`0puN4cp;(Z;xsu;zd++P4sHfBT8R`4f@H0 z+#+_DGo)H%r6n@%u)YFj-#Om4%v|9`&UvQ8C))k_O05k;cVBHXYzStF-5<{ez;VSZ zlfsS)Ms{le$?KEVNRYc4fiPW77o75nMM@Y*KhOU^Oz{X%t9(2rU`qKPwpLUwDkGdNGM&^QbAt` z`(a?!L}C)2q`W-H_V~&;w_Nt8yAA;^Cxu1@gE>@zRf&&fmB_y~*toW2)|1zITn5_L zZdq7OD<{?UyaTf$`K)ISIZlO^9@gF$@y$>wT?e<2u zzc$s}b=!0`GcNyr=6L%hZl2|0HM=wxSDy!ZEuA%$;eP2UFP3s}8z|^Y^oU*YC1h+p z{T_euh|IW_?sY%n$RDLJEXYngB+dH;Rc-sHH%iZ-i)IOU+d?1{we^LGh|hBxQwKCe z>3WVqS{VK7*PG1X7+>I}olot4l7Ov=nejnjV(*Jk9>3&DO9*anT$)T+i`T0#5FPi! z9{~*-(fayTSp!vdICIeoonb2J^3%iF$-H*N4)NteLg$&soO-A;!`1CzDb%lnqf$l{ zg>jyl4rkivZ77GpQW#iUjj#uZ$fby`-@de&Ojkw zN?`YN9q7qbX9)NXhn#IOl@HpLq9CwWP}ay%Iq$M(siiTgF(kG(AHn4CV_S9{L;1xZ`*2W zSQ-D#ysz!c$<3`>$I5GB2SV<89Fmoz7X>UDJ}{)WXWkorrN{SBo9!PM&^ptOBo&AN zCF@>n#;ja4i4KDkdDieY-}T`Ykq7v1U`AlAW{kgQHv@>MXuls;-O=^@mj{|Rh|g?& zGpOYW9YgCwoiv>jJaaGt@;1encBC#+Gpr%i+}3JHZCol??DA-=x^qY%`ux%kC?5qg zUO>Y+?TmJ&cR$)r%9z%yD8s;Mqu#atB$gs?r{C5N5+p8r5 z2dW}9lQRr$tbu%QNX^F5#T<7>Z$bYT3IgU_`ar6rSY_A;+S(#y%}E?~>1?Bf+CFUH z+|se!VyI?jsd8Tdhz^>i$5uWchKtkL_hGB+-=41H@q2SdknjbD6LM_)1sMaVXrF;U z*E{`SW}`iEOorpS@)C7JKq#0a3cp{1M&_$zJ%_yJ;lWJ)YV+}Ghd??pOaE)80K6Cy z0KS$VaA!~dNFVTE?5+sDAV~QCcG>w8-{gKn<4y6Ccg5+IYJkyu#(LXjr1xvUzbfxL zM^J4e{wC$+7;S+6qcsYXNSbWo4R2{~Y)7_Ng0vq(u&s#e5Ohn|= zt<#@~kkTE%i>SQ7hfo1W##)@pOMdtZ<2)J5=p+J<9~v$US|}1`Vppa$v&)mCQrFzE zJvD#vcK>3`o{D}=4DhMJDRcomY(gWhSdj-X_BMp}Ohd*5oLSr%HT`3SRdg^PTMbQ& z3vqY^e$}EUbcJ^qB>zrQcVA?Q&Gv3`d~Ft`cP}3|9`7zVK{=Y4nOULH-T(|L#=LBB zk0=q4jh?GML$`!Jy!9%JTWw9rJ5k_E?{CVu`Y@YmLV!Fa1@CE$3L@f41XY ztU#*&N3jiK526jZCw?jT-G#1gRO=g5m+0^0+Ou3p^82tbQ>n;sN@`&csZ<>zQx619ZU;5Ix_15o8fCk*$OSLXp%E3A1XB4 z@H0t^yrD%Xlx8;X^EB*UJs8pdkof5?so$;3nF%nQ32G4$wQpS+sI{)PooAiKzuv9Us1H@%n$7OwJ_g3owbZ^)BgQJkGQZg=Ie3MyKKa*h%I5qs9xhRdFNjz>OxLzF8)ISfo zTL8Vy_$Bkg#8F<*!L-c-nLWmJ#HfpAjzqia53UCqBRmsFUX@yt2pT$=fj^o5;?LYQ z`6-rq*iLQme`hied3;Qg2qx8GEiWV%Ni5ze>$Lrp9mRD?k+HdHymW=_Ha7rn0vi4ZZ zfoyKqY)`b#Xy-82u;Zd!et}+Rr!V#GRbmDdB)&xZdy$iX@X%yl-nw<$7E50{#!(|4 z`#DUnSBba!mx+*)d8wktId z@r8a9QfGVfh(svw;LLY=y^O$kUrtC`CwFW+?_!X)IY>>v5~Mgs5g}@~g`$U@lwIX))b;EI5`Nd<&B6a<#9U^Fx=^R0r#_Orr{&TQbC%p}` zB{8e75vYm@c~T-rAT4CB&!L{6#zWuQ;5N|nxL8zW+w9qk!nCjPEgI&fzm!X0Ky#c_ z=6(;OtmLT$=!Sp^gkG&!iZ1l@u!A61A{3}N0~J*cdwR^Gjbq2ZK5Fo^_xh%0M!m6- z2Fm;DkZ*>4p_^?B$O?IuY~?Sd28sKj)TY5lgEQx!3Rk;4E--sk;%aSMbBfgOe#K-R zW&qFlcCBadJHLPVBjesi{syCqprz`(KaD}m?c51$C+du{d_d7UMl9o7u~?RRY&c0j z3MlB_!2QwS-tswNQ!yY2*I_A71}iPnT$nqr3Jg}0fBK|Js$&D%+dHFaK_3G`2dj0f zmOr9a{=UTw`q-q#siS-l`0>OrwhlDXxDa0561)#5pg56hIfHlIEAXnRqu?=hHv1j| zRRiNKZgzX0R5lHJe{0rMnvIb_1ULJ`FSHMJAR5*B4AtM$U8IJV6-wP5_z1!4W!c}O z$9Ro=RLW$2F(+{V{2{oEz7q_0CoFFZU)u zkIU~;DGOYfVBkauj1(x;?ROkP1AGds=O5mZ@`vT}91DV;6(kdMs5P3c@xL^uGJ(p$ zcq1N&9|_j|JQS0!cQe%P-}Te>GV;X3ph=6y&chWeX9h+YKwe{0N=lxZr2F97WlJZ# zpfk3EiFHNW(sntNssT2X3??&S>M;i06gJC$y{S24DbQS^YaHKmSRb zBYNmc%g3~CkuLum|H&YoBothRT%#pUp6g?lFpiN@p%g0Ux**r1%L`}+sSwX8Tir6 z@PP~^0baqqSpu3vo*9_1QHnm1N*GyK0Rlp$qcGblJgdIRM>15nh2zm?OU#AD(}K6+ z>|j&aPS=A-vq9|M#lCQTgrdr?Ns$lx^?a~gA@&jvrcs+&@A8z{udYOGZF^Tji=0f) z9iOAYmi9`1+a>?Ln5Rx|Yhir{x@y&S7eQsEHN@JE7*V;E3m|&8VSoJ_Ka7y$tzG*a z1=yjbj~~TCGC`h-$7%n*ysf^9_}!n~MaI}-IkDR<+=RD>3_)ZYMTHxGoN(`SsNZz| zu~JuD8}gd#u|O5cH!yix1v13JJFTZ6WP(EUX7cJAj;B(uPkpavvo1C)cvjUi=o_Dk zqMcICT203WH1Tq%ihv(7*#F8Dc)AOUZ{LamxM*E-9i%A~`&R1AsH44$dEGS){FU3&xkr2f)k zibnU=+0@#Lqy9JPbfmUnQ#%;PpHkwKp7_(LtVR5H$GMO$@D>mpVv1y}7>>BNcc-I( z8%tTk5ucF7z20Rwd^fj~_BPNc^D0{cL#YIy0sSX-eL(h6%-ML(C6+YXVTW<@rSi#( z_gWAbsT?nO@o6l1@nLUM5Mtf=@-;+tR8lqnL?!8`Hm5ozZ9>Sjy&dS z3haxltZZM~%}QvS*UeXxzEIqOPCo$VY>*5s2P9Rl1-TM?>%Tlhat;C_(Y`Xa_T16a zc#5fxiR*M4@F~{fi#r^5N{Q2o3q=H&L>ZM96cpsBe?e-0IOc))DHDuQ4`;3BBj0Tsk#a)b zUxm}EIi`oGB@T%x@<~oA>2E@$8O2eyQ8ts`Xs2>H;V`yc#>DekPMiN|b>r&sn|2^M zBpHY0%z+$a=@;+mOqSjNwhg*&T};zjR1razmfKpg8mv(06fmo#3bC4;DFni3>a358 zM_u`6B*q zlMZ+Dc*RAXlorPbyf*`LHV|OKzeu7}-0WxdYxrM}DV>v=tC9~V;Jo+#xTS>hze{F~ zRWoIH&!Du~)*bb}-soH$)iucsf#+8m{Zpzp)OC7rk8~^LhIw6XHq~O%dFuF-$~{9$ zd|Fy~qORwgF}|Brwpo-O!46-a&yHJ?;QurT&=YOarCqzrF_+`z9lu>KGRA}gf{G_N z_C3v6ckI2+b`*g3?cQWps%X{e=Zo8mY*99*o6mkQXA5Q^!~tcjz&`1OwUB#tbY`8~ z%ulfjBX-J-I|sv*>@vT^&6N*hC3Es$XS9L{nCtl{PGGcF<~`IwM0El0da@D@&U(!` z@SlSM#|v7>)mt$^Pr^NA2^eDaJL>NV`3V?6e3UIhgKCIgyp<5)NSK z&nOXETWWIJo2wvApw}|pW-}j$fYB=PSc(_io^%WIEh6zSF!XS-1BKX2n8D>l@$Mb) zj^PW}tAo7$zxiW<@KQYqVmsl<%J#tA)hu;8-40gxjEwmB0Iw6`Z&L;GFp$Lts~|vt z3H|ax5eyUW2^&?RHC5j_d{DU{`^(Yh=_jfcI%Gj=Fg(RXghP{TeAt|AB zcQ?`{oze(PcQ?}AjdXX3lyrAU3o7ON?DuzG&+%`A-DhU*-1`}5lsVBaAND1h7`ns7 z)(98vEfT%EkA0KBf0kEVRa~PCUshcmJExumQ9>_N_j1Jac^?TJC@gRS$)Hf*1*EU? z=+yNs?HH83vnLfdhFd?h48J-TD`t^g7zAOl7q$GThZ5`!(#}R^>zC8C+bRGb@<%sr z$>+WpD^2!rkbzW&K;Rk^26?tUK{oy}H_zy>e?EB?wzJ|*RwdUZE#}Z7pBWM7H$iW{ zj(ord2{US0VlEHit(MkqR@M z$o{A`>!Rk142Z1x5#>a)_anxhUJB z>~6r55ehNx#urN;k#-QP17SJ$N{?=&iZEw(kMz$UUAc{3L}ZS^7is^H5HFXP!Z*=< zZ*>tJP^V>)9nwOn-B=hZVHxwLk?G?hP_SW zBy@UV`tQr2vhou-Tx)G->-YAPj$7PxiyOv$nuFIBqyEUI?6sBfiki=cT!g11HCm_8 zsM9Tk$zxN$HJ{v}Eg0R5w#D6l7vL=+WERY3XUGt^_{6C?@~A=bET15Sx+cb^hIJr_ zS3F_A%nOHZ{Ze=-Jn{8Z_dm#ZF0bVjRipi^q}}cnwDzd^D(&EvAjg727Xqevl=2UJ zjff+)jlqETZ{ff+K`<~-_Ex!I-aPX`vq?^YuWzbn`b(|Y_3jVqBX|R}bonl$pO|!d zg(+9Ai93xK;^J{AzX)0MU`Af%9ec1?0)c)!U~o%xkh0dE&^*dx!CFfE>}s>}x^sd1 zvpV#*-4tS9CWPW-_s?oX0vK`j#|4PN+^~8*nCbyu&VHCGl(|JCnd`9OJJU37#_=j$ z785`8#;={u<4YepYcrN+z#(aSwWHm>mN86kw3$LOhz@h2XExeo28yR_3JX5FP$2s_ z@6ZOV3({-TbC5k_+h4M%>X1pKgXV!HmguVf4@QTY`WS=unSH(F?@5svD6I&d&&sP5 z8gXb%9b$xy=k_8PNStb43>j+CAPq_t?ljX`h0d@!ZHu3H0VC(+M@FpX3=V=?Fq~mr z#68(9u)?y65I@F$h6swqNLMC|FU?b-8gmgX!PBSnBKndsT#6a_|C?^ zTGOP?DG?!?=#W@`4ixAE&L5PYTnl=E7CGc%4tIg%)Hw+r|R(4?`K|?};moBlt&s(^WI*(puUBkR7_7(LDELFeB7fO~TKz z?HY-p$1azoEDrZaK;EvX>4JygF@L~ACwL>A!Clok%F!*UuXHu$FR8;Ux^!C5mYV#% z^~mw$t~AwjR+gPaAGs{TZywWZr#kWMWIf4pb*<48_wM$@VsyR>ynn{P`qXHfV%D9O zJ9bL;JCVSeSV_O(L4POrt^R090&Gn|s6sVQJ~gs2G6vW@G@)=1kPiuWa+n(d$saK+ z!LaLt?+6gD+n?cu0|P|AWyNSLfF1lD(@&A#XKPNb{UPjUIMKl(HE$t@Gc>f=lxnbk zQ~nLmOVOc(EYGmn+!Vlu9$0H$4RwwYKimw%@p~vynwgt}CF_(R!da|S4W~11BAr70 zb}Rv-ItuvXq`*GaWR(dsI;t7Y{F#M*RnyM@o6q8-TIduS}O#PyktN;5@47y5jH< z62+l2q>%q$OsrkAF=eAU{Z%p@D>Br$UZ02htDM_-)aLg+3vWsFG!MVLKjX}$D_`x4 zxZag@;UK783BOHyz_trB>_G^4{yn#au(z%IUl%C|^dMj_UI<)I&^x_LoMeU0=WtE< z2wIFH2BgzGQ~2nGMDRVwM&JEpiBGuixenba_pgDiY$(no+1n%Q?PwaSju963bR?>G zCi+MjTr2~E_c*Hg6Bx}DKN=$_1$rKW&8g_JZ8j7*j^uN6-})t+u8UBvxo)z)VipXr z<#+jmO<^opc=vGqeJ6scnrhh{D$Qh{lVwM!>f}DC-K4&%Kh_Yrr!>ZHOe_R_B15`W z=&dV|f;WY(96P!u!BGX?)NMFtBFoLwe{{Kqk{E&cQK6a#;uQOY8`jkQuL>e@?bh{eUBk~9}$zq z`#;nG*s7a?>7HJ_6x(Sd?eF+*ZGU1x6GhD^mS=u-9Nve<>!T6}uX57E#BdxXfsktQ z52j~PSOb)$pzG*`iiBEDg`r42FVZ;z9(>OVym%iL<@6t~GZh_4V6dW)t^N=l&mBOw z089(?q=pag2;EclzY&`vB5S^73+LzF*gtX+PX0a_zUJQ;h$`wz#$D6rAhkqE`V`yP z;^C#UmD}Ofv2hg|8s-&coc@?xAQ>wOTxpQpe|~T+KcV&G?My!Yc6Y9*;+KZOFsEU98U$Og=|V=QHs}e!r2I#nt188u42xkMoq$Q zU3jrt>69jR0hkL$(F$%C($}HnMwJkEZ#Vl-Q+a|qyC01doaaK5xP~)3AP&MS`#^*j=G| zX=F!)B)BI{i{$h8TZ9S*|MO|ageX^kMwI`;jqcz0qK|4NuE3fv%%vS zz3h``u1a5Q2Rc<{TC3UYG)0f`RpbXAGRc&Zx)agDxT7;;VzOm+v)0M;OpHuE_viX; zZi8oQ?Qs9uE4OLol+PAc{5`bVlthggV95aC7bvr z>g!VbUW)Eim97?+lRMlBl?|Hb&vNEB*ZGpz$03Z`^zOvtYdAKIm%6M%+QI!NG{40^ zGnJR$mU&mIItJU!98%AaEW-RBf_H~B5aI9_v~0Ua)P<^}qMB?+mm|c_MM-_8hTvI` z;%3kwYif5>-I3dQX{wpO^#r+iM{?y6++y~~>3!&&>$^c3O)Mh@$14d`IP|@(pRxhZ1f&nKR@W$`&^j(9DyQ0SKAM&A^w_C z!zd7t_#Xn0AILMajU@MV5s(W=+PYb@)vHfqALU?D(pxXUcTepBeF>Yxekde>Qo|d{ z%To41Az@fOXYGz^Y#Cc{!zXK_C$oG(1C*&Ny9Tcs`A(`7(MqjzgG!xT~*^Om}HLs7(^39(k>j*{~n?r z5qns;X}gMl-O$i54ef#0F4ZIc=PIR@OU-vK8VjgwLj01E`7zZ*#XKq{>h)e7GBL*N zz=6%e=Aebt2eba6C>o_gOYkcwSB%qAJSgsf96a;d?9dQ_yvvP$PlS~6=wxcFdM#{p z@*b!8(ccU6;GQLY1AUJ+P(3h3&9PVwP5i~IkT3Tvav%`dWR~bA^0P3fSTI;$F~xv= z9UwYQwD~^$Jx~FR2BKT~gW8?Yf5y9svAPRN(PV+e%=S^nv7FANAodJ%uvVUc{6~Qc z^k|0tXMBj{q{$r6=<3Z-{d%(Pp8^m|0h<6|o<9BAD)^>Yr|?#&2pdS_RrPey{d}7hW~_VmU!kt~BQK%CkQNYj~?4E~^lXkS)Kv z?(v$#FIr6MX>+9Z)6-Qc3{w^A6EuHeBzhv-DEnZWErX291?AJv&Hwij2yd)QD|JI4 zmN|2%zxglXOe`;<{2D$+{cB-%^#<(^sHp21JVIl-(`s73wh`6$Tw0KLM#49 z4B zjU-k#KWlXv1a=Q^F97Q8!+Qs%?5GU;HikvP6~fR)sEHL(}Mkh7EHwvH^S`LV^YTxr4=VdYW`P%IpPMs zQOPi77bF+P_PQ%8!B%!mH`gY1CqyHm%TX&_YNMOQmG!^1L~$I|6!pATU=@e=amj%e zo+w?KcwY@pF>N^dmK>+mWthlKDG%w_V-8}V&jVxPwqO$naAzhlYRHmLLe84wit>`C z9F5>dxEtz>V#u@ebY&@JaNPELnZ}y@`x?chb{9lC zu%}>@7)=>dCWV+Z#Rg;==fDwW@Icko%zxsq7~y<7D5sbM%E$|I#lKxq^qz>-^9JXI ziY|S0?)^iUu)Pf&L?QyOx3GCb60X3nHv9lnVSDY>U|}CsZayI;*Uh0sD-*Gn*xzTJ z>2}*kBL9ZE1YpI0wViCoAm>>>TZ`bL)5wwHC;o6sVm8Y~{`$bbCSgd>`Na&w-UwdR zw$|i+ebr#xh6Pk&V&ebW$P>`VV~Qme!IMR|8quPh6@I3kGUFYoS z_+S2Ah}#u(7jAuesmp$tv=?L?qZXzyq{o=UeOn*vya8i{L3-c28ZYJ_MOS!M)M5wzq^U0S~b~MDe*4{;!}tV=4euE z%{f#Wx6gbp*3ByQzrsV58!g%8?~Dj!Qy2$FC6k+|v+UXSHt@$#iJVUx2~8b*pS@lq z!jb-l(wZ6H>oW88o1+m#yVDvUteUYP!n4H+(;c4BPkTmg4@l28D|1u>Q-a?0EIr*3 zk>QX!e{!M<`Jv8o`~8~g6ZHg6jXrPWM%ObI-}^_NRZk17lY(9lha4QS$c_)uYjJW9 zer4x^DK_N&K3astTLG7q|1Cy_D#&dl(d)c8pUjNqFdvt-vj+7?d5RrKbpdK&A=)Ol zKbN2|pAaM$1O*O1E$FkK!Ab$Z9VwZDmCgLkHBjIH1!}ljj76I*^7s0W;YslM=<7{l zP`dzZ9e4-}i1_iddpJgUC%yf%;l5$AYRT7i*T5Pr)yY&H0qTuOuvM>v9Hb`@OOU8P z8zd3PU?wWq))J8TAbuy%>h|a7052#}#me%92br>q2N7aKghVHw9x;A09|;C8P~UJH zk-?ku&h1xNGK*nX07B7thVDLXO>ePCNtTDcbhlrfsbOvPF#r71hwfGHv6b&oWxGV z%)0*bX0;a@QE`R4MSBp(V1 z%$EOsNpY{{aaeyqQ*1+IRo^1ARMO|AsL^KDi8r$Oy?k+vg$NUeq49@ zOE0NpZm}?uKm<>UMJ+kGuQZYLrwP|>L7o`jMc4;zB%bxWS<%nIm6dOP9nB(xgw0z> z?uW0|+O>|<5%|oKBkplxs$_kQvSWooQb!DDEJg6|MogPHm;#NF47M?$CUFv1f2QJF zitN1epgyevu(dS7{|01o0T44*Z();7dLw~bhS6#?OTirwpxAp|hi(Bfh>!Qb5g;%N z+rWT$b_t#uq4<{~_M^|jY$RgEC28Ju+GWSi7v-yUHyQ_6Yf&=swo3H75|8aC zPneX&$3z((Kcn-EO_NRmj0mh{C82R&{ec+_6=^Xos?jNs^jn0}C}XXEBkGqK@;qLq z6yG4QkAQ=GXg=}#Q@{7BAoNS{)-sGLL)qyVBc*>eWWK#SE?gRPLI~;*Xv3BDg{_`%1=6hCn4P5iC1! zi7kU?osw}&YjQMG1mqJa62#?n84-+5Nhi&vR*Nz}MobgYNVawO#v|})lmt=Z+kG(6u5r@Xt{pd>B>1Z!H$gUtzqUo z9BfuQN2p?|F#8uz6Y2=vz&qJVKYhVxvYGqZcKu(iab;c}4pi`I8fhH}q(;E_4DDvMj0c{Q(+527CV7&HsT2N= z&*5)hhFoFJ;tBe`XgjL>NqA=}@GNJ@z$y_sk|O?h)NA=YgC)NL$zafRpU^&v+DED5 zPPt{wKNEnK$_Iv)=a8_-$o@#yPI22q^MWLk<%8H-t!Mo>;B-7^>J&%?1OC2S--!(?sZoerId!r*sy5F&eci>=kJ z_{elIlaUA~mTi@n`l)VR0TxVy>OYubcJR5M4a8B4RW@%zV0C31kZ1?Sl9^y49!OXH z-M0Ed2e2V}R?ZDd@58STCV{N%r&6Z(7cE9|EmJ?t_UzOC#1POXArL^G75K2>RiRLf zGM%fbPVBYc+Uk8;&(wi_Zh2IZ&n@B^D2pGaFNfE=?CMOQVhI%hEFOvz zvxa;?2{JJn7uY=zLm+#NN#5ZJCv8kBj=(m|DPB0D8P@{co;aHQPjnm@9^MHd;PD`& znaNerE?DIJWEIDm_ohU#WmE;Lkz{@IU2c9OcgrcO`Xre+!HvpSKp)uhM}D%~1MUs7 zEMB*K4LK8($o@K*Z}}sr^9du?Yt-bAn1Y z46hx3E~+r;7Xxn5?JGY}i=rrn)`6$jc2R;n%utMmi3vSc=rYJv;Lqr5r%~F}fm*3E zZ8nSICHKlkG((j7)pAmjwA-VysLls$`~=!1*^H_}f&8Q*6rF^KxXz$PE1#A1 z->uz3`{{h{?|)BDT(c-Sd~5zb>wU2P>AS$&Pv;=2Ihpx)d2`IhUb;b@ zeI^_r8%#h50^*0x(gGnSM&7m4i+aQZ{_UPU0CDZ%@$aHeJG!v%|64Ld1Uec-|%=6D8M z|L03YLjNE~=I3kI9fp3*HD^S5Xr6YzP^Hw4BxyYf6GDD4wNYKV^8U0h|8TqF0P#~( z62K?q?uI-!P5Fgky;NW-t5c-X^y5%pkPLsk|3RuwzU=#KEX9~C&3xjO_Mw`nLFQ@G z+Z-(0sAvMo?SWUw*cF)5?G#vbH8e@IBm!T4??JT;24R{HOf?e*|MKL9UHf!+mkw#x z%VaaO&jhsBa!3<`ZLEI4vtYDLitDjr-X|4cX8o4O({_uJ##e%Szqhn-^W@c~wgi2~ zo%PMYMPCR&vAVt~cv1nRcD*p2J%vSeU;o+(TPV)6aiW%-T*my-k9uJ$nil1b7|^!F zkT-~zO#vjAMPvxdAiQj#(uCE~$H0f5cHTDsj@+BPS><>8?_9CTkHK zCszbLt_!_fbJpsn)H_w?znW{95wnp&CQxKkST_pD9y`a@SRagGz(L->f8SCL$nFID;jgp9rcH-(fdmr5Wv*=# zB}m$3Zy`zk=DfMfF5@U$JZVp65*z8^_T+oZNz=c}^0UsfUQgzS*z!^6I1|IYFWV23 zfiRWYcbjA#QS1|$?{OhhWooahI)TND|8CwJ^H?~E0p+>hw-l81T6>;hr`4PVnUhiy zG|^<`k48k?6W@W1r^Wi-Yr#nVs-xYYefonVG-ffEczU8_0mGc`dfYvbEl9+Uj42-j z(O$doZ~)T_UU;&K-V_DeC~07ly!kAIXN8QW)l;5w;!>!($#eW8@Td7TV|xB%|9#R& zQT02=q-Xh~h@}t_C6$iZ9Y*2MozZ&xV&n9;6(7mjPmX`$xhj9k_-+fbZ@->7$C7kR z^1-$wcgQ$any=uEC{N|%!x**22x19x=CEz%FK*B2Ax>r#y_lzis!sh8xi8?rWT1Rs zC-yT%ofwCKpKlzR9rFA+o9nXr8^b4N$Yz>pOz#cPh)UG}J`o+fwbz%A*T)0tj<_Nc?PG3;cbcSC{vVN^qe<_xW)oin%%SS8?rGVpcPqMNL%~eSjA^ zA7ho%Lcx4!k428p5OiGOOD2A`+U73jiT8rFifchMagkJdzL?mt%8i8;W0E2LE@{E% z5Q)ExSX2B{+)P`Gw00)`Jk)`f<6is}L~IaBpIVOaA6=Y8kN*73T57pzt6r@0;e+IkzW+9Leb%*G{|id7jjzH*N+TI1Is|vH zxdu8g=*CXPUjf}7CwHio!+FZmnYLuq|iU3c4M`zM82F>6zdm1cX1^Ru;g=cM+nn9&*8G>5j`pVIb3t03nH zR@sS~E@myK_1E(yTnmI6VF$}v2+OuJmNEvSu@8c;+TG8G0BR3}6Ux;^s!XjJ0wepN zGO8!$A1mBNc-^9>+eB4n3-FSNt-RtS6m3aW%Db^gcrrHu<_91UKGQ*AgLoSyA-)DRA!*IFUjK0R5`VGm~GWyhDF1z&Qr02?3mV)(y1bYdS z?@W!tpHZ|NC{{igBZ6`5rl0NSpwkR=cut|A5T#-nRySTeZ`oDI76UHP@`RW8TYI&? z41UK(w1ONgh|_B~e8hwqaX@pv6M)XrGx|D5en04&QhI$u11$7dU*&PRop{)Z$J+aVnR9%kAQoT8mfEOo(iUvx9pd@&cbdPx9 z<1zNH{g(3cuU>Beq{nC6?9;45cYRP-J-{3C$ggJBtkkY-HW{deC2e7OXE?}LB|&1~ zp_OhB{~PQ7cs^(deL{u2+bT?)uGo0AN}(sY)zGiFHHQ4mkyvkrg;lemzkkgt#dfpf zn0EI7l17028uWn^(dYhFVqCN8)<$VRDSU_hG?# zbnJQW0i3I7ka;YeWR}ye{SM%k-QC|O$*QJ%0Cee&4uI~c)NOtaTM%dQy0RJdKmaJ7 zAWa_hr@aNSg0f8-RUhyH48p$92X0l%9AX6RZ>v)6Jlw2OA9cN$*)OK7CcUBlba8F8 z`Ta))yUqL?h%nevRd?Nyb6YSMDDg8&d|o$~67mUyv*Hj_$Bs;-kNX zs<4E9M=zc*bb1-_NiS)vu)UOg1X^=Lp&>_1pq%Z2yO4qMLPhz$Awc6sGp)u&1b+Io zwY8OqW0$?l2S6=eQ$|dgCg2zx7h8QHK9=V+>oj}LcI1-rapeBffzjB&CB*MVdb-C| zu*%598<`WWK*#!Qf|oMss|&N~2AnloZY#vBub+KT^AW`^raxQ;?zWe`M9`U6N4}2eSe$PMM+$U)fCt=t*ET z8F+CKVFd0OX&PQ7+;DMAWQTMPb8vSAixW89K=?Ii=N-%n0lK{4gKY2Tu$Xi9=PMa- zZ=-q2Y|L;(%XYd~7aZL{5jf>cz+H4vqE7-`pnh^GEvWlx#Ffq-RksOxgR7CPXHCs~6w39Y49lBex5xP3^Ve!k8iU$IusNW-c++oa9_G!H@0|X*1VIXG)F372D?sXK!Wt?<3;_uyr zB4LtPx&m0-VCzA4BoOPi=5-wg^Ch5>;E>V>br5_OHsb{@yy+Gv)B38|BVR?H;~f|5wOBt4!7W@dIVA0p3boz)%ux_5_2<>eW(^q=tHs0=?<-YnT=>8mHAJIsR zkL}Kbd#&*fAQE=}Ydf17rj*ibtrRZcXsnU7{7sH*Tq!dS!T4i6ZJ~YBo?);*uQ2+5ZEuG5jLM&>3dAu_cgZg)p?WR-u)T7jb z&Jg=}XniJqCh`lGNUJqRNS+ww!HUn_FZN|ok*8Nz&g221)5+#DDk+y`O>jQveIYj| z5Qr;jW(=vrsmw@^hEy=F}9WgjGg$v8gRI?rx<;A@yr!`>2pwv3&1h-43MXcVXX7&f&-iwR=*Ff5R zfIh5E%=br_>^&(W)oypIivI|^9;0W<*8S2POX$H7_8P$guCYQ-^zS{c)rvvT9hPey zx&;}VpadQ!rbsC~2S6kSqYGpgW#`+!ztHFF5pXyih`}I`eCcBiE&{~xooNR;k9lPZlYFJ4o2S1WDektCrws0A9g?D3_}< zkh#vO9O2VRM)7YpZ}^J+J`?zJ3lgGEL&O$%J+mV|$g{6IV~!H(;2|8cNj(jw1`?Wo zuNbaPqww3RqX7zRZ!JI1(Pn{M=w*B73jZ5a61IFzMskOVG{@V`oW@!WrA58 zdx-gg&7u+wfeU}+-%-V>oStLvr4B{!x$vxcnAX1v$=4Qjnd4>uAnaxK<|3YgI5IZ< z<`CI*&VFtLDSQC$a#!dw^<`7zZiul6xxZ&BaOND|u>Tk#LG=d7{`Wo?*`SNKY94R=RJzhHxzZ zEH+sh7?|gdvSvZ?tgXefguKEOJe<_XG489|-%8BcfI zfNXg->MvT0HEb&Fs7S!rSL3mrt?Sv1&UzBzb#p>mxC(6+DHC8sL@l-qYFd={0sm89HA&U4I-7OV0sFj6VPODByU zn$r#q7@aIWxzUZL;uBb$EdJQL-s$c1jcWnS!{1mtu$woWLM2~Ye!SECmP>febzUt3 zoxNMv`v*N?72108KHFXKLB^2Gnmv}~HQ389SqlfBkSIr3@dxD{MiB$My1?#aeN>PC zHxmHSOo}Y;0P`Mp)}$xxm`VdiE7c3}ktpuyk(=Ip_xvv&$B03m^Juw`onXC?=A%DQ zy&#b1yobR2yCyr5kr8Bs3XP-G40I$!;5uh9$4Bwe7^faxndX~rWDwbtd2jGwI-bDM znmm+>hH1LnyB)R6r6kEILit4^sIITjEKYkl%Jge2|7f~1KNte0J3}c?rvR6n%x7*| zb9~Nnkz_yLJ5ec8UUb*===be})dOY_x^9!mplovg>fLAvvr}vtwAdsrlUlx**$d^A z&e3WVt6D^7?2QiR-VXkK`HnZT`e(+H7+Q>d`o}*bg3Q+yBIgxeS7dFxhH;Fu>X~?m z?-}|hGT?9M3n9n7DIJ|=qc31QLI_Od(pztBxMZ`AffPKSJ5q2he*5vto%o2s7sF%M z-&ebP(kINYxfgmAk{LCi(9KI=+|c5+c79S~f}Pdr6@fHXcJkwVu?auW+aU5Md3E4^ z;a#*7*t}qRcRKVd2~r=Z!%JkzaH?_(urwj0VBlkbXUe zqgBJx*4zll3fIWuUu_nKPMbGave?AKXhe(wpf-j4?+ndOg?YXaG(6u+O`xR?unZ=l!oW4n!=Q;_ON*P*$ z#`T5eMOer7Yf7K6G6#9cX4e+1>OC;C$PTCH9*O4}2K*yhI6=+JX5Yv=d6a*2{)9_3 z-~IaOuHW^YW|bR}mjL&R!P`rh7j%{fQk6X4;EHBSkLTL>d)l86sMmKJVrY+zXfOOb z{vhcgM+M&9%@}XCsiqdx-k2V9ct97Jq;!*1_F{4?(cork^W;q%s>VEdu_@;Ma_0En z5r;e{A3GX~B99C#rx++nqQ^Dw3YxuXG~hWhiU{SsPsUeTApP|?<&RLiyq+) z9n3=hR>*%LLog5%u^IUdeFFY_A|87(m|@|_)T-U>v=2zejsWee{SDPN5%vw6!xOeOF(LMKe zny4EtQpq=xr?}zl(qqGd#Oc)Io%vd0jA@l8d@Q?#R(|*zk-GEH&K~g+Kn@~E?OXhX zsE(!!kXc$b17Nzj_C+s$l=mef0EWBP{0C*-e;{+C#lk};xaNa9yZf3hKN2+W`d(679_(0TS2%}%?=x%lNt0E zyX=5{;}bBcGaf%T*wN*0@)}m86?|;~@b3V9h6%Bp%7s&WxAn&?@~#Cse_|7+h;23S z`tk#@SLf8e+#5mbuR)IDKJXPjDh3ok$WwiTBf4FxXp>E;_<}?z+MLB>R%tCoqbXIS z<$TpR=srY_(GJnlxQk4BvY(%wt$9zKf_sWD;~@~2^L6qYQcgBS=Gn%Cg2Xg+%8!gQ zo)m%OVEjtHKAdVrkHCkrR!7;BCvP$zEF?Q$ID1i_o2$gQ#LcJGZxSUj9Gupgrk$A} z7d0^GBj6s&bi$34QS)PWFg(F`(rBQwzhjX2eSlbeA~n!;Pfxa6lBDA5hAlvR99g$B z=YjOiE7lJ^BbF@jg>zqYU#trx0cK{jHE zuT~!Qw$nFwo$4iBWs*y?@<*hOc5r?-C;tZZ1?=}%n%6yfNV0y+mF}c-R5wuuoW+0= zjzRid8krRWHv7mRi}(WOKLubfW8nP(>pTLMjti)0CiLlVe7AvKorwgXh@wRjF!uq! zV>lp~lY=juD9BVgUX8vk0R8d1qr${OEKa;edBkvLUG8gVK+!Yi61Hs;@mn+T(zTo^ zLa+LG37AdS^|Y&|GbCR}jVW;K%VY;iyhM zy(QZU0#!B7+@f|0RM{dU_bf%}Qf`3RQRL+#^U|SLXilS1k_56)&q<;k&?{Hk35H*cnLO#8H3o0d*vLP$kl-FaJpQxz#t7wKD&afd zxEmvN+Lt(ulm7DYvFvGoOAurq{ zUjTCI7BDCv0c(~D{0?*|T5MFci1V%LgZ9U%BGrX|Wk;@jn7xL{(e*O8sH?NrnzpN( zzC@RecbHD#@e8*Jt8s8Op0klxnX3f?ku>siZSVmDVNXP!H5Xj_wxeR@Uxa#&{x_}( zK*r4jPdT`@;*&R;#A2)9YlK-_WLc|=-lRFhBAen44*o!o-hPBYK$Sizh_NA5=RS)nLyt$ zS|`kG6Ype52>e5Y7^rv;#GkFv*N>KEI5blEw(0eEJ{i5(DVgZjKAW9{i65b*OBHYY zu@1iN4V^-X6~6awNs*O({<`)uwlC7RZsU>bD1Nr*r|L5C3fo9DdejC0I0BBq{Cem_U7f+w=A=NY#=t72)aY_tiRp60@BYuNSgb%WjWt;^RL~!kU8f%E zrYV!cpekJ-WQX7g{Hk8@4_R9xOA^rCrSgzR6rQr^=SuAe0y<| zpwIm#09PSF$lO<*JS;b%!Y_rTa`toWn1bLwOPAT2*8IUz3!Dj5LiBKfPn68(hB;CAY2VV|V+TP+2bt%rBtbx&H<=gGZ7d-1V3=XnWKez$WT7_1K=@m zwF3D+kp-N)j2P)=GC)Z;VCB?3<*KXsMMAPC@WPYS9=Sj-Nuwe_9y9ly`bZ$rC7ocf z_mecrAm3ruP#io#i<%ZyJ@2lD&O?1l1x62FgT-}6B5+m{@!6Bfrm|F#HX!v+S<*w1 zuv-*^*bLQ zmK(#nzkLGNxp5OzXO^0bvnNb+bj@V=77IxcUb8mXhgjAI!ubztdz8v>F76paQ7$0~ zUEQO)ZAjzdZ`i^X^th-*N{YLv;{jFxG?N$0pbhE;27|)2=i#$M>Pr3$(I50;W)?Vi zPAdi}gTLIW3YZc-{lTvM<3{|ywW8x8+q;;8pQ$ymv?&o*cFW!Q<<_wEnMW9T2fJ!t z6SM5I8nph$mNkn5OBZPQhS9qBbzD{!=!>WG5$o&6JlDxng7e1*QAIf)25P5 z#OS+HeQbFV+R2Zrblp2KX>b&ITP*EY=>L4!egA!os2QW$Jvg;&b!_ozS3Fj=w)bof zlVld}F_)x|ZkXlU4WJ;3g3a9@x9+D$m2Lwc6Gq~uIR+(8B4`#4n$TD{Hqx8;p1JUy zziy#S)y^luKFZ_uQ_f3gbF^lhtUE8Cxnf4zB)=?K{y{2Z{Vl?)zXX-=8`FhJt%ulv zj7$}2Kn_wO3m5evT?bqISQ-N_q!Q~+bt{7~$kSGCf%XMz6y^CzoK1BrlCXhm?wWD# zhHXXjg0q(0FDj_ds9%v>*U&Di{qYyDz9PCfOg#H0wDmr2j!12|$c~i#i>6CQNCdbW zMd;Y=%J!@JpYej?!g@K>?NejxF_Z)=-d!cA*yoSx<;`m+ugdbpMYj`lEww zDH$o7qU-4xJ?is5Lzlp>C7e>_x_qHuFx{94ewz&Z_yf}NsVhGJ6(x25 zc(r23vbAP;9Y_!6KY*sc*f(x!|$sfz|O-Hv`FIQOYy~1~t zDx-Nzeke4E!&YOmu;XXo=o~mZ4$iweZp*wb(#h|jiI?8&?Q(ZQ?vHN19Z=zlm1$7_ z80GmBuJUP53L^Z&IyCQIOsSWFV1uI&;e-cjySZ1cvE!gPu=x8DT&^$zVPr6nI9-5< zDh(jvepAIKgK1ms_y0T!A6@^o<6CeG@N|j}hT-h^NsxgzjMjFoK0UQFwo)etE)+GT zG0_Bsj`;<|MiXeArX>hp>n0t1HIvTOqx`d}@4I`VRoXE++%EDxslN|95g+;K_E(*OB7w-J!14(30~W5WWG#bf=j8BN z=7~221>b_)5GZj?86tX=v-!8U)`8Qp8oqF0Hb8QgVeO&K*CjnOLWEE_)@{X5q)IA7 zF-#ISvsxrxgrB%ri?3)@z6we_ODuPjJRcy+`zQ205_KP(8WkpbfiI%)iK0Fhe9sB4 z;Q^O^n+rR|89i?t3*zo>$i2#$Rbi*&^W6wYY%8=V4m-j2+qG@0EB2 z5@I3E!mr|_`mOgQf*;r@f%uI8$%L+Z(i>J+ItFv=mF=PBWzz?aK6tDHmHIB{AtVS>}MPd+Uhl3iY->C6>4?ph^_(TGsDiBB2o4LGVBjb_^=L|Zm z#I}0o5cbr|)!lU%puOT>Mq-pb_JU8=fh}!@>rX%W@N7zxNM?|6W@iWE&yxl`RSjiC zHYrtOVtU^=Xuk;cxVw*?2l;{*HCBp*fI7^j^6Tg=?+H zaRoOKGY-lP`#|jMO6p`!qKHp1zWN z7d-5K?=11#Te(8f$8=pkUP>kC|7bePuqfNMYY)<0(jXu$-3`*+-6dVp%_WU=cXy)* z5&}}vA>9qq-ThwN&-eX-U&PEdGv{%xW37GFS8%w<7|BmOBA)^wUoXoUq-w{gJ3IA? zoz080)4T>bb(Kcq><~<5|1E#F&BUMD_z@Sjj?LTuF^RW--uCjeTZkYDncX^CWU#FCl)@~OH1AOCAzSw6Y@X|FUQh&b6cXAaa7m-f&x z4${85=07=g2G!L^xV?D##Aux^s46M#zo{naih1!UT37p3EBGbeRGLvI(nj^@W|&Br z-Zm3WfzB8_)FmZ@U`G4J-Lq9?QxN45~ohJFLj_w+Sw*ShV?U@agx?dWE{+3?e< zKtrt+nwzI5`jKzJYL@migYxNQ4hq5pwoj=v%W6z~oc=`S-oG`KoYRT%P{bE}1C(*c z4z&IBBOW2&zvIf2dZgCrO3BnJ<>vQ+@D&4e=SI_g(Vg3I@AmQr zLdYeA&B)xWZc4Mq9VC#Xvv~%;MXNte!*%-4SnR~P_d&!LVN1ykaWqU1%;RpaY&yJz4BM8@Gd z#jU*hWi&$Tp2Y8HEo+_a3-3cc)nfGaP2{io!v|R?1w}0Gzb&-gT(36-fk{)am7VfX z$LMRiB7w@3f`n{or3H-0r-)k(X3hz>FLBlfGmK3;*T=2TGyEs7dloGBpS3{>vjDCJ zoZD}S?02t66ju*o19%IuU-X4{G}&4+k1}ze=se&-0|D+lc~VjM4?>`yBXo%QcNRZK z2A6#V2#lm1Q7w+4t9>Aq-1jt}<&eb!e@{U;om%O+GYyq@BdzI3ZYAq=$PT3G=g?CS zX;5NmSxaE|)cDw1-)n?u8=%(^i52PIZ2k2B;R*v^apCcXD4?GjES`Y^8|VK*41OK2 zy03MkUv>)v%@01R*=plYqrWB@RKVMwX%8lNqz1q3)H94-!@>Ng*^7090MQ21dH>zS zK7vJa%gl&Bf<+st%4=H&E1DyHKuSc4-K7QDx0X= zhLh!XL~8;pz*aRfrx^F=!dMYJieIrSs=r@T66${1V3VodRR4$Smb@(Uy$s%-#kaBz zOZXTAB@(>+t$d6=R$d`AK9*dsBz*`ixTMtmX=5&Ym13`!9i0*t#kP;}TV%$CZD2F# zT~7{}!))RmP~zKGS5Q)GyK1CO<4PfpKNxZx?6U=8dSLzauQ^xnd81-ipE_w&+RsHg zy*yywNnkEuQ4E*mQMMl6U0$Y}jY}68{F{Zb{x%{iTaVO3aVAW5n z5{REV-WUQ$qV$vA(F(juQG}wN72e^~nJj9~t}$p<;e!>xX8zrVK17qOas869Evz(N zlkkvgy#_YI42O38lbwcU;V_YxP;@h_Xka#VXBp?DXi5u%>iNB}H6(yL5KtFR&b`m^ zUNfNn!z~~7O%ukcp!!PIhc>tJC}jX@Z)v-mAAvEFCRW^GUul4OlBG{2>zB|xG&G#? zOjRRP(`XbowSwq$5L!YB5?NBZlcd$6yfy0x_4jdE3M>M+96qNv%tqdK$+S6`$G}~_ zH9`20_7ct5I{i}*5M{FX?}f$mGwEZK9auxmN90xa<`P>*%;DDWPa+xW@K$oCQrey; zkwu#8c{I{^AWH;ivQdk1TsN)m-!n9JP5<}Ovl-SaJ|}{Q(j}hO|J^9+irn2~BU@OJ zP)j)vTh77>UB4~NJ}?^8;s3AznlJ}mfzH`}Cp3GMiB!^LPDWIMyE{EssvMqZ2F)w< z?212J`rJ{#b?{elCdgWW;_OB}^^VI5;+|A&B5i@v7S*zo@57=d$XpMTGO$&YLT_jXh zQvN}Mc>g}%c=2a-)6M8R-(QyCf}~4^W@s`ZVf=j%U#s}gqnTEbuoVIL!6Yj3-@xjL z*{ZWW^q{yH*UkqZs(&lmU~z$f8BAhBm+?P%cWAI3#`@F;W%RUrWmKUvBPjX8C~h`w z{ro*t=_d)E#y$R~;%~5tbB7((4!k%5q&M*jr7+)8*3-Pvv>DMx#X+?qq!%^Pt68RW z?W+MgjJ)^EU(>X$<~{xKAXcE=goXN%c?v(u5F53&5l|58DnoV2jL%FA+DoK4KSI;e z|HJPh6v}4)U2Ze7m#c6);k_rpx6j9VY3z3z!|9J7W|%-eYVLWgTYNx0W%(6>>4-BH zoY`uJQ?NJV?>r@D(BPW@D|!84GyT|`AwKtkJKUupXVeepQX*UIsU+vdi4$T4lxLP| zHVdM20t`P=7TZww;P1mSF{A#1aIo^aFAB|LZI?m0Y<8fPL$|N&GcjP!o47-wV$kky~D_F zCUesct%Ji4aYlcOoAMNjsy8A&zooY;c0hW<1dc%Y5uqb$gd4c0v_NQ@`{Ck+;Cneg5cs)R%CKyyYO~z?R%{D5Tp+39)GaLI`PVg!|Q5d{R5%0}zjtzkRkpU=35<8z~wleb{ z_)e(PXZVwgvP9%eZY4<+`Y~X;uQD!oo{dLc{Y`RC00aG#`11n4NIL`Cg<*JI8@1(v zV}33pVOP_t$hWfrOX+f~GH-hdjIBzG_(|5Zeg!`gZf9~b?^MO1HAx}=yXn+d=$*66 zvX%*$d5d~D27Zk#()qPmyXe_byvIsgGUcoG#a$GI3LhOl#Rj|R=5e%S>^l=9WvG;x za(q(hHd(uw=i4o&9krX6{K(X=;`Xh)uX$6x-zbPr;PuwC$l?f^J?6Sjq@}Oi`kScE zit1=i4NQ1+og#~vW?}TAl|R{S76~}Kg{dYN^O-VH1wIbeJ|p=F-QSuW_uIIG7n)p+ zoTO!rNb6=4zw*6g-y=>d_l&eij%Hq7d_F6PMEz8LrLZ`)l5vlT$}?5g(*u|WC2+C| z7a@C{#-ff2zL#(Hvv)eM{arL%emH6CJ78}8)*SGA2L-2gZ2^ZgV2?n)mw8@`Q)=nE zby$t*22nQDWqUg60E9v}o)JrZh}Hd1~|14vU=Yf&X6p{`o1-{I$*JCOC%ZbjxRuOxRwx< zMBRGsPfngdZL;qzniCvkMiRY<0{G%SV(ZpC&Mf0hoxq_^FL@R)XPmjm5-tsFss)4Q>dZyJE1BY zN80Cd=pg_z(B0e{5}vU!;-0UdZnkgRsNwUqD>E3YeX<%(sZ9P~Pd; zbg~HY=Gn^ouyFRdFh9{ECJ8!Ggh!d|;>_lB%O$u#mie#v$9?Ls{cosybK3@tn2j?P zWMM241ve&NL@a(gY%2-pgGIe_36F%=)}`5@U%@WygmfxYc*Pv8P|bosuHb4vRF2U3 zS`4;UPJ6cNV_+W4l9ws)Ck+j5m_dsXA7DwCxRPV_41;u#dJHTy&%R2>Tv>m$@ zyv|txUL{mp{YvT?Ht1oa5SmM9aqkMHI#TQf83KI;$*=N?GaX@EY$txmIBl~ei&Ieq#O^Q90 z{RK6dJWt6`2LlW~#?NH8+)rOl4&8<@2IWgkgraxCpFSNk`0|bUv7~~|4+p=`_*td| z{pL}ziE*B{=B#){xj`OA29Xw?v&YXtp?L-uKcFCmxkNFQHEmGvq^b%=q?*e*pHGQf z{V$mSPX28I$#z)N^x+uoK7@>p$e;6yXfaTz35gJ|+v%7X$qxpO^|mPaKE*B$1?a4J zw`n$jQE%WAuB3WSOg@9z-h{d5!;uFBbbF|Oj9qk?EWw!k7lC4`nLd>pZ%6-Jh+E6+`1K>fi6>HuQr9p7!?h)b?k( zLl_r}bl#KbaoKl7qTg&U8hBqzG*HAU7K-ov87R#O1aX&nm@0Z4db_m)_78!Yp9ooPJ zQEy)RLVxFE1ji=dcT}JMckb>!!WXCPkDfq>!Ze-i{Wcr!8p_ z&kjrOmY!Rx)+{eA5x4Sr!6^!rQt7%kBPOn7kt{SLLjWTNozTS-(kMdI(LZ8J0+C0H zKd4Nq_6Wmt=7Anl+~XrfSsu^ zK!%j*{GeV*7bF5L$;n>j<|Y}cS0Ch6jC;Lv{&hek5KOkEf0Ke?564u0Xb8_^IK}ws zFtw_WIv|(pO`<@U*tx*iZd6fn$Q6DC2u@JtcJ!dMuuz~hTyJ6 z_`D-z;VWlMljRVhJv0=TKwM}_GAw6o6YEzzmys=66Y z72ixZx#s!~cH`Iya34B>Qq$^}EpF(k(M#_nprrM^TG}Nt7V1*2o`rOWQt9ai#nX4U z3u;J*%oS0gM5*o9BxKh?cxHtlS+%zmo)Sw=Cw{a!>%Fb5&--$>J14(Yb=vM?K=J-i zrk#9~&r?hvy@xBYeadP|Qsaib*`PwptFqjSflb+4DC(Jm;LRoE(XzGIDlaC$1ZZ#p zOnF7(HN2mPxzZzZEJS{k;!KYo8LaFYZ-bj+FronU+2sUG83@*+Y}HR#Jwc0RQ~T+S z!75&zDPS9&$*P6(Topn`CK)xI;MWY{)D^u$AIcK(%08OZbyYV;%@YH z0o4>v;b|rwI?#zs_SdPD-`_f@%LhmXJ5ldik6SH9@-h(&Rho1YIOJ>oX=#=Gg-L#D zk5O$rz4){1wQB79&@JmkmNY!v(`=a&Lmf?+H;UM|5xyT*U^mY;_lp~zi2Erl*Z%?f z^##?8jB7d~3~FUR9TsCmx(z$h5YGt~Rw0vUx9K9mf9 zGTn*ZH$5bOzeq|UcjXKH^eaBtvwDd($f)sg(Q&-R&9=(u7B|5#mrtCI`S(aJw*t~OAL|_^u+U9=cM+4ipW!5O@!#)~e?&)y#|DZN zQzt78$hnk8yiA%^Jfn8wlE9j(D|wBD*MO?6ei`>Zd@;IZH-e$8a7hg_w9@xl*Eht0 zNkU@keDEt`{A6C@lm_ue--!9k2V8Ryy$cP|Ex>lKckU{moS&AwE;7XHZCsQNKRwKrf%n&eval>*om{BFpiW=BUT z^k9rOn@A;>+{=hA0L6o*-Bo(_?A;xSrca&pCkb7G4ZhZ6n0T$f(6&Hqhbc|q8Ru~M z2RucN7ZP-sz`!0vdN3`$#Cf^XgBH3`R82%+WyOw+XLMP(D}Bx zbSpAVBqdAE7?rcNr{;}2_BDXD^u8zQZ4o7uRG|zyXOy~9gJq?~gMyf=4BscF(#=-` zHwuv{Jb0U$`@S42Ju-+CA2bVd(QOLp0IlI?U_CVi0{(rvpiopIr0Qp!m_eTdT!r59 zIjyA>hfWkx_L1+IZ7z8CDE`FEt&c6@UgwB?>pu^E<*8*xGVbBVNG47mtly8Oz6Tdx ziM1(f>8!OT|9&~FtOGOEeZ}0D9no6uJ(EHAh3e%Ar_pNv_`IOzc?)^1yYbWSP&2#L z^S(|f60QP+TmutqG2`h9vj?Oqh34AzR+tNozms;Bs=*Jmf2|KpXrSY1d1w2KItyVy zp*Zilurc}y*Rj?r3$8jiOVEeKWstJnuRJwJ#7S0J5p`0qNZd9q#Mz1kOoFt>k}NJg zu9fCEp@H7ah$R^IlaH8zDY06*?IjJvWWs+{7Jwyn&`m=$+^#jv4;2hY1dpp64uN~#tk?ax$K{tf0Q zjiQCU`q}me<91DMT#vub!+7h5(nX7*-*M1pS-P^DgCq$ZxP;K1h^ObnOFMx%E%1dh z1{PCBJkKzNImLF4Z#LGtZOF8o*1Q1;sT!Vk(13WTHKlP>?XeVMjonAV&5f66=!4UE z)sClu&X?9$8JkP>v`^v)|J>sFi4RR8?J3geXbN1ec|ww>Kr?4OzpP6;McqBO$1O-6UN2)_VBzzzDatjuj-}X_B3S7u<9CYsW#R9({Lwl?F zWJj0HY={Hs3d76>TKAgPBZfqGCcwxo{;0zehFYIM5VM7nWTAKI`A^nbC6OO!T4u|k z(Rc0Wtmk+?EZU+$+P>4y@hgS^?5;qUB6bqbpc*n_xLp$j&1;f0+z!rMXu=!)mxC8E zFHQ|V^xUKiDZx7yLS!r;bdT_w2KoZlrxt2|FR)IDA>}5_!@ubf(eM3-?S5G{Bp;U>^d*dC&)lR(@*YZ~(J~TF- zxxHU44D9UtGm_a(x^a{~d3yGU{yM5@_V@*b!d!-90^nG2S!ks@!8#FfBnsoGGbA-b zd^-k^^sP7V_IRraL$q(t`dW{(evG+OShh)33w-zz;o&qSz?WJ+W=UXB3roe4o0966 z(0n1>OHj?X{*Vj;9>GqWnCHgTsBO~rkZU}HFQ(;1eA0s&zZLC$1q>_reh@>+sw}%;-0UK+9E74g5W{nbt{oF}`!C^)=mfD*ySGG~N-{hqy zx5j%ZsS$b2L{C|>jSuG50kjwLRsVkm@2Tx+=aR%bzDMz_2i5$!8}B}m+I+eZUfaH4 zZi0MiCz>*p${G&{TU{D`W9f1oV;AM^z_WVnr6g!9s@_KX#d|M1G8WM36&hyv7 zE*RUFT%gw?(j6}C#TvELKkIr@k|(jfNQRX8G{hK=yH}rT@;C{8t5H|EInc=WSE*&+ zXpo54nQ#c`6Pq`R=+(xP1ZG47E%Xlf4E=TbI%}4Hv3Orv8M^4Pd8cMs?5|UQR7h5@ zfubgCURj>l|7zXjc42|iJ_c;&kA3MZ)4w9yn4>k*vZmOwHMsL;jUTCcS7Qnx<0{E|cot^DM=a2!-MtxoqA z*^40ED!f;Lho_caFoix0w|Zg08~ZSgqb5xY?-bR*5O;wt1KjzGj;^#pzVz-i zidzh*s`6tP-fZJ_#n4DHSXfxRkIzItH2F4W4#)QtSWF6F<9=hsc{830^7%PV^QaRG zHA5BXLAdWYcY@(ytzOaEC(H@yitdiG*ubP}?;N^&QPnv@`JK>|P0+2ewSAG1jkh-y zm+FV%PY^PEc{)&rjOaY>oOXJS|E_KEa_K_3)`TLnJ|Rk;G4in)?X4B%q||W3!DP5O zTqp=oTnFv~Zce}RFM0B#309<>4%WWh*CsiOl*yIsCaZGvTNC?sMnu#w<`^6Er`Myz z@*aC~*si>}SOw4EOm4ILBE((qCk2PXZ-)go?3hr5T3sLw-m6)?JP*MzWvhIyZoTGQ z2vzN8Iom}qTVwjxk4P*{s5C_wue1CmGB=fzj7qpl-AXW*0Uj8p(Qu9}aP`?{s4Zm-GU2sl&t6R1#zt3oUCwe2U}Uz32_ zVFjrUvI%;cN#4-NK<7>_^Ty_EwGZKxTzQJm>_d0BH)eubDzyq9f*C7{&zbp>;3?~h zxBF&j8r#0LdbzrarSv(Mwsp)xXEE+mvL`!LE zHd7X-#m=^ph8T0|er&m5R77h~QvLz_@v{9?$jwqI-(u!9S@QC?!B%TP#p$SF*7TNr zEDxk_u^D-D9OZoYW|wYi5v0b>-Vl@TfJo4tsYXM=eW&YIDaRzS$7bGdUdqdScoR{N zEmoZxWrhd3S*h_FQ>_Gw3Lee_liV21`j^q9xf76xZ@JwpP>*2)kF+)*AdpE?#D4~XAW_xe>vmM#C+Fc=%~t6I8TmV{ ze&Idq!FJls4@2^u7Ok@qxN;ABGmW8vr<(14v_K~T-dg->*S-|rbkoM^zIC`&AXT{N zV*?QE_tPv-Z}wNAAYrotxb{G8vxduPL=0T5k?4m=Mfl&OSMd!6dA38z^gI8oR+|+j ziesSlJzPA70Y`BAv^`ESzTHAp%>7qi_m(e;cih+O4GLjuw+|7*sc;?cFkMf00ZEn7 z*4F|7R?fZ}YbV0!!G>!PzWz zrZ+10X(S!Y?YHk&TS2lo2Ug;ftVVEyQIM6qZ8@mMb_n;d{Hy8vK9Z#m$irHyh zt}_P(a2Ie*SV2Or25@Y_4y7{hl#>yH79hUC)-i!nOr=LNzJWRS!O>?MCMy|3q~<$jP~zT?U}wuR6w70m(K=+erL|uS9r^ zZy?Qt(%B_GOi8OZT;jPU5+bt>V8J(UGoo^%h$ROvd|%Gz_x{gk@Q?QwO1F=bAv}cx z^Mrtb2^pv?ni+M0WSBD$Ne=yAw;8ft{eM^!aOeJT+#bZQRr|Y{KZbw3$WOKwy5`~r zM5aY9F*Eg(-=h%LUhuG~2;#nlHdS3JSs(FkK3tnr65d|4%=Sw8 zD=SEmoQuc*PMD4yYg1Ti33$8z6b{#XNE!QQ_bkzAHpoBlNw22)4YTl*)kHJ>^;Lv? zO=|A)zObAF3rlEXO!e1BLxKR+0WD{X_6MOHd{(YCPXyn7sXgc7!UnK-jU>a>8_twi z%FY9&Cq_wby3*%>GKg1H@}&KIu##sd%ca?+QNP(aO~8xId(|y<4Cwy6J5AvzFi}E= z6M~$~GQhUFSY+bm+`6i?T{ z-1_VK`EL(0J<@W(6d<&rEh!|6Ll=DH8ye3mj9Ddxhk&-?!U^VeC`y*uXEM<$Cme{s zT5~?8{;)(h^mWH~Jn`Q#kR0GxmN>-*ObzwzKd`a()x?RV-ndy~BZguIJ)AZ&GBsVw z5_`yYf0Bjc1ZuM`Sjo?RZtq_02^?0or0k1Tk##tIo_6Ct2U^ZHdr!SE>L05=a6&+i z{jVp4m0|g9H#-e<7*vM(y`MQkt8Q(Zif^R$%@PdP`eeFkl@==ypuq<88fxA>Zv`c( zH*WIwbxnDz^l}^8A=*qfK&lM3;h%HI6bPh3Ll{NS=Z5^Ib8=g~P{0NCp?Kf%S#DpypKn1LuTck;kh6vsgh~WIQJE z%9(1ae?_eHG4ce}KiN#OGVWy`O1*&X_3Ba`>$DQ_lYPvspNN~8zY5L{h-$iG?@I}R zE{ISDBaf%v&spO461a~=a~@sU*DXmH=X#JWxrdig(AJic;ukrlgD5uhrPz#-%nL3q2mM^i&?`cQi)epw^&IdXv~Dg4 z?}+dmFJUYjKcTfRZY~Nh5lf_%Tp^4oSn-75eNe%vuO;u-Un(d+K4~g!!v?^RoYF>+ zPvcPKmjBg{>cn6I-L9_yDova+{^`7s|qi;RE(F_`wXPfuI6VAZMwc$S|33v{0OMU`vY@I{< zQZrowjMsF;Ul-^B_TFz+@jhw^@$muR$0~gZl6g`wv*0K&gK=P~leAtH`w;}+E+^0l{_gk^LeOhE#er^~`yNR3*FUn%`# z4DJk9POlHY#Vz8CztNqb=F>R^B+onA?DLdULuWl8K|FKCAm-gsNb-4-Kp9?5G4yM1 zZ!F)}{9bP04A0G)<*Ux`(7R$J6-nH{;?<9QUNR)|bfGML>;rLgUq^XVWuO~vF)q--#k+eNQj*}2fqHm_QEN&6kJ?k)pE76B zI^l3qv&w8?z3-wCuc}ej$#aG5UKJ1N{}%gVU~ZJp^CIRoLwPXM%zLEBf5h=lA|CDW zmp{dVkq5myH3cU#&x3rFH{hFh$@$N9Z2;)39<4+0(r=32Jwr8Lv953vhyQl80}rVi zIQ+nv5|&UaWE80+OtHMQHQ#y_KYT8qaq8P_MP^p~Ar2OH1OBu>?VR1NUbS}XAvw_3 zH!fDnV^6bF7gyB z@NO{)6jW#v{(Jo0a^uQX+N^XmX^d8EpOn!dYmsBZJKJDUQ6h)#hc|nOzwpV|gbS~X z%(wsn1-zVR{wQ+rt)sQ%4FCM-1M&`g%xq21BfH~t-glqKOR^$t+K!{_D=kyPP!uE# zB{K%GDhUqTlKMDs`0HKK_}PiCyno1(4sPx6^xsQljx>!S`vw2Z$66?K#c*v*IxS%Qg;*={QQ34Q)hnf}Uad%TPp zsh_Miw@0+LKk2~MpK@gHD%Z{Rvh7aOZsohWz1!k&zaoli_PSB?y`HyhT`H0Runm5P z@Jt1;^OON;YGUfF5N4qpj@sYl4@b{pt7Vg7baIX#FydPfRV*YW{8%4boMA1LykQ~A zt)6fWO*bYTPDSb<1lmY1xQ1}O@+Pv5>zXmVPpzl?ifCik%mxYq)~vk|_Pwb#Y5(>S zr+juXHg&enGWKNcO30TOL5>or>|V-c2ohx;QSj(1YTTv#S;8mr?tD#LV^rm`Z4aN; zd!F*g-Ypg2zMU}W_8y(#J;j_g9s1E47|G}bi@*N|6O4>xN?@}0XLd7>n+|mvwq%es zvbR^gl4qjUeuIA9mMPrR{QU2j=il5WN{h(6nX~s0^sVn!nZe{Dfd8%0SbSIeYu!Sir<%|U8a}ba!Ey+;3DxXPy@P!78GELV`&9PL@*mqj?1LqPge^bJ-+wzmORxC21gi~s45TN z8-|UG<;OJYpnW&TxoXaa=}|$Qumk>L1$IsH*r=?n1=pe`GD&>Jpu5r|ftls&Nh)LS zeFFdmVqBV;(1fbsp6twlz#`JXo4DbTN)S|7pXLjQzM)Y<5TuQ#eN+=lEIAYF$BE#G zh|${ZW@`HA@2#?-mWNChD5PY#q~RnEe?nlHQ|?*>%YC4Qe>0n_3kBa@!ksi&Y{cBF zmHX?QRP{)?{^;-59HywUP2ra-g9t8*n1gX%S6{OkeVl!8Ec6D%w_rQy&QMb?wVLd z{zLW+M%_b8XA@8`irX9S+L{-+?YrL+lR{dBWB(rus!Qx}EgF+xegI*m&b+YH^4r~> z0L5k`GYV$w^K^VuplYh>Zw85L!e|cR^P`}U-1Tuwqb*8iVL_ON7(T2eV~uD&pm2M1 zODJPgRN@O}*_7}^BVOHLGAv{`?de^+ew~09C4rNiy{NivrzGi2I=z3LsLPeq8cCLL z6jZ#+(euTauD-jD)aw{1u%itYq@CUOe8s=pi0mK45@ISNOq`e##nnEW60La8W%YiN zA@zE?E3kEk%nDqwHI^u>8<_!)ZUe=jm!{r?gI!6+KZyMBCmkBxnNgiX{wb(bGqnU4 znMiO{FvR!&v44U$9XQ9~2=Mm6f-qLh@{w1u2b5@CIb-$#@Z;ZsgE_tFa@De zXLb$d>$8WukXcimqs>qP@ojHO&Ui0rd ziOzPz(qfItg;`46fvI=T>8{q=*$#OWwcJo<@s{ay5<#PQT*DcPg47`mAKt^L7JZRP zd<-K3i?`uEX8Cm=FT}R278w2$<#ojImoFdR|2o-5;R7Ek;3|CLH3ki>@>8EzW;Wwm zCUd_B!jyg3YA$17cORbdLN0IM(FZ!#LE&HY^@W27(jEWnANdkE@H-RC8{*LGmr7pk zPpg1=E?-g#giJ2SPPfLJ^+k_mXF*+V;l{t)2ti_K6@kAT+u|Kpg)gm<-J-}qQwdm* z{#qNKCg{Ue`x`{oeg`=~^`KiZdZHL_vgZ;d)hJz3<_l;6@Z~$Phd3ln6O1etY2-Zi z76M~2UEx~P1=hdlS$&5qAE9GeO%KsuskdHtQS77Oo?`rLHWx5G<_A~NtET%)S1lZJ zTV}JPcC7+-EUU-fK0uP-maN_#v;O9vD8uPESwQ zZuj+yC#Cs-^%Z8t_d=oGei1i}*cl;JW#pj^e1HNkj~5{K0TD#6bnNxS=8^XQHvpJu zO@Q78(ASK3^`xPf;2pmL_3bj#5d@bYL#*b$krVHgwJXTW0%XX!wSS$b z-tU0h2csJb$1BD1=m3wK%RVeG*;t1^r9Wj|0~A~tZA8e(3K7)_Lvv=0LV_yP;UK=> zK4S_!?Aw}tEQF9-Aw%+omgq}fSX{pa2Vdy_>b;}cSukkyCt*5SBA7Ms5ae=PL4nX3 zwF^kbfLH3tl67N%bx&cL8F2;!6`=vM+2xNS*hk;}*oyx0Md4FzN%X$Ar(C6Ct zT+ap4RIcSMd`mle;)&Y+vWJ)c*A)YrO!9(ne}GI!n+Oq5rg^{nNm(?QnA@rjpQ5{< z<9(vaRNnW%#IZt5h84BTVVNcD%wE)X*`h*ylE);Y0yH5M0 z7%g`R%@8E_C%izqa;bD)qZ(;u$yr6B8v(B82XOkDsN5PuR{JT8+7ANo@U20$QHK^|>DhEEgng98`IVIXJ4Y4Bo{AN>_%I6bSs z$^Z4wB;=mu{%Y8>X}Z04(u5_3(?9e9r!WdCv|@n;%#0g?$WHpNVO(61YJBepteJ?3 z25Bt@m8@T+Z<}-zagVA(-Bez4^Fm%1Zu;XjoV9wle@v(BSJ1%gGZ5Txz`dItbx()y zd=F>RMK}+$%SthoD4VsG2LkM^$&iB>Fk`X}QX@4)1g6c_9D^tN%EPN>wuu6%p3mik z0~=Keds`Gy>69?q7MY3$zOx{*N4ncB#>i|ML9Gcjwf|hKh6{aT>lBfBnSB#Gu!klt zRmEdZ*LTwLd)By@9-z3Ln_U*f_#A;5qyN4k@K$YGF@~fU1YD4hqB`NFvCuI&xFFU7 zphwly-^{DV(^7SPY*(wId(@2=o_Tu>hnj^qaMjo#x!~gfw&QI=09lh~N17;db2MvP zP7-IV_lngL#*;jn3B`kYa+NBzQv3ET4uaJ~8ea>$#qG5w@F2&`>5SrHeP*wbn^9+U zWp7Ht>Mid`Vq-7Q#KwX`a0zigx)=7DB_WkKyZ)UT%z=P`!1=L^obqzT2S+p>4e5Pe zU|wLs`?<9z#PIh8v=Sm8MZ8a>rMzqNZ$9n@yiHgddFWd?8a*66JTCtHbt!c$etzCE z$IHUwANV~N@RDSMbKjqVPOi{2bIn5#FsLd2k`Ow+eXbOr*ce~TH`<)VjC&xA9)_v( zBquA|1&Y|;z|#e6v_=*Y5dqV}L6v`CA-!??XtnvVU?qmhW^Z(;EBHms)%6lNj`ryD z^pgjX=Q0Z_@|WOS&pRHU>qFf)_NsiiAs;oyNdZ1r<>tMlakv}*T z0&lAff51sZ;1RIWYVD|oy;7? z7wJ!(qiIt{A+p#w#u7ahV8wiRju+4uY-?4n)FCP-5qKvKh(!%Yd?B7^J=t+QyQwGr zbmCh%9bxpG7CX_9x!E>u=5%+DYA-SZ3vjbO)6wP%%X{A0XlJ~~`dF;z1!p{oF<_!f zoi0xmRDS(=SAG?J->YY9JZ$QRl=klyQ#Zcs^(_zn%#l#BYxL&l=Vt_TbZ!PS$~K3g zvZ0ExFparZ!ym5eMxGY211qg=dT&wb97G{8EcN>04c6(KNwRq7R#t@vOLUuYkH8}@ zBv0iR6D_8p3Xv_|f=Tf|3bvJVZhf;hFZPN^a}IHeKk-b;Jow2KvS{!g4y#>X&7?TD z@8Cn{L-X1yi?qiqc+#szPIJcZm3oXmUs(wbRwp6sDm-4^@=)>ja72yba?JX79G}In z^h0tK?cAKeAR@64Lp2guKTTVq2~=0aEBd9Y(tj^2Jw4~R3-+U+8y#R;A+pc_Z= zOL%3k5P!x0-o6I8cz|%O#f`o~qcT(?^6jQM7#n%*FGIoSQRc4i9||_$|G92BYykOK z-EyT;q#Z7|&0A+lzp)8WQpj!dMaK(TGNleC#_vwl;EW#Y#^yV=!!a`}K|XF=?A&h0 z-qGsS;w-d1bEVX-Mit4|_Y9(PU%pl{_4M|>fmCQ#BQY~GzON}P`~iV zPjcXi{Bb?vaR5T`_}unC=Xz;Y;@q6b(Gv*Yg2m#@hFL_K+vZ^p5l!oFH|Wp}Uk;xk zsy~X1KI8*T7pLoD`fc7g5DZ*RTqvlR5s@;-f-E;y`7A@xN#u!&u_jy$VqSl&$$zp& zVXdtKtecCq>w9}pBiuT@P9kf6Iv#xGJnfZ%Gc6yEzdLtzllYgCO7jRDjsr+N3xd&E zf%HNsna2VJamZMg-E6c2MO&0nkB98wB#GAOCC9FLNBds>KJql9y4eS4-<`8ja}`zcM73 z4V%?IFKozrj7^D&0S$|MgB>6#K+m2VW6v%^dP;)FChGuU1bL+6H*5q$!Qzp$1EBv4 zDeU?L7P7^xi~+ssXT&=eFG4}loieNa%K;w8S?b>rLPOrn-3HjZBYMhkjrg-x2PuWah!dQd2FwOb~JVBqC``K?v8 z9g*gJO2GSPg;oJx{HT9)%7yzHj~Q1U#Q>n6vf#%Im`ObNRe*TDiEV$_(F4Ga_kBvz5o6RTH|l zXEzd%U`c67y*sCm$yu?Cc`DYqPBBQGH*Gt`(oQArV>RVH*(Oz5S+V!;Sx8LiaPh@n zgxEQ#y{j^HKWET_F9PbLJq($#7~kP?%R9?hnf&TE7dRic4jhN_34k=u_KKD$z;v^g zR$VLw25OgQ2x#j^6&o2@WqxQYX}?)U#G7VyXJB)Nvb4c2iQeDJ{EMji`1`Yl!YNogl6~8 zi&(r@Pgk68y5%{XYX-2t)USVTyT4MHWpF_9k(_$pxjZ?c;vKJ*Ryw4XkicqknCd+9 zEk3t!Za6(zoh7-j`*q z)-%<5jS%UR6tKivR9qb9@$pf)MxS@7%?EE~WhFd3JZ?x*T-=H)oQTurZTAbv7CUbS zuOpw$rDNTTxHmR8&p;M$KIo03RWHK;e;5N7H?%VdesiresLL7rr{WiXft?+Y4zP17 zm}vj*9=IPgf4Dgr%d!P)MOp?+Bt=Ev;4&LVfl^5U*k^Kjb|wOnSH?>eh+cyf&HyBC z{~_0~pU`%5sh=afRE*>Hj&(==sE|s)u~X$kjZVV{4XQEy{qHr)`Xf-!!4smz+odHX znt{Jls(S+p`@jxiB%F?2bhz@lN*XhDK^Q>3T|%r79a$}6!}7awonG02q_KyF+b}hO zBD9a&p9D_TFXuYQDI3+ZKA#ufT(}6t)bA-d2F7@$YZCc;ZBIkxGitYabH29NgHQ5Y zq|PhAbpz~eZS4E`L(I_`VTIRJyVoj)$=@CWZ?iqDjLmxSU87{zwVkG7U;}*$@SaX4 z;$W`A-sjU;e0RAPinkAisc_20QP=ppKFJ;Q!?~!Uov)BD6{s1VePg+-;r*A!cTcGp zs{cpRS4LIYy-y#y8>LI6Te^K{NePioX{5Vul$I7Lkp}7R?(P-^q)WP+ck}zN_p6Jg z3(whm&owjGOb%!3y$@oq&mE;^@!!v$3YsQEr7h(;C9s@mVHWdCc~=3V1UjNPM3O5@E}<$Ko3 zJf3)cMsLn~LHy1%A(O!MyXWw3>fZ`eu>b#O%?q@%>glkT%ua};mSp#(Zhz53h)=7C zPbvGp29F5p4m)Rt5@9g2A?f9rgBD^6R(O@Za8jG>tsD()`t*r=+g7QfAL6z4M76Am zn#pM*T1pXyT1^A&X5YUeqX}a*Ts55WgmhYGC9-n7agK)Gs&dyBKS@(BdoADNJck_6D zn3S|M4Kp)3$i-xNK}%cVup$w;FE?$_Vndy^7XK#PmPfNhCkPxQ>nkZ)Sy~|>(x)D~ z@l46%y?O5W01@6x@s?%i;t6(^Q#S9A1tcCXm*@%Vdxk$fB&uul1AS|v&2O>S7sa|$|VpiRJ z#G1qDmP8D?*mCbRW{V=#B_8ohX$ixKcH&gQEs8 z_j#E?^on7WWUAX@EK4V@ic+=BRQ?`S#VuOLB)w4 zXIkk6`TQHz{lv_j5BIlEl^yDoc|Z?gUVosk$7+V`l0u~SJ8TZ<`i5W7VEBX0tT$HS(iU5Co!13pWI0S@ z%T!QGfUbKER*v=iqKJciU4^aC4D9PTUgJr{c34{IE^h*+O%MtT?K`F_7sY9}_O31j z-$u7@(OVq4xvZvZ4UD-nnJ?n06&aDyE4NYv#h+yzFPU|{67jttTx^6~`vTDh14}_V zxq!nmjFjK1(*SzF?qVAq=_!~n6M9*&|ydCV^PfKDUOL_1=qk;i^XXt^)~Hw^L!Pz=5X4V zB=tv=dqZw669-76Oan4FJ^A|8q~~~Qi}Us{_iFj{G1DqhtlzMe?~8_&|K{RS`?#v< z(P^=Gh1IQ{UaK3zEFp_`z?FN{4M|spgQ56nR(@Q5(JR`x0ae;vpBG+uhVZGUS#bNq zs52|c^kM~{uH#89#-s<**uZ8O5=EHd%ZgY^zCSt#+3%w(Cs4X zGLWX;u;>q8mtzLXtyn)iyt@`yylokS;ovBHAa~9cO{);=zE9{8FfZc`XV(2Go*&$N zL!#CKM1UK4Y}n($?@xqH$1{pl@3tJR1R~Vb2yqNVB?;FkiY7ty%d;7;$Xfvw0a4Lx z`9CV$LaFNfGYc; zx;QM|DdtEFi;|%_r$`C;ZI+3zbog3v?+am(X8sqqN!|*5TOynpD+qtPZ!#HJexv)- zOp?>?M)VmB_J6?y{WnRIO@Xk}JilmR{xUe2#9(0NJy`(K`);iNb>co%jZh`B>+2}X z%E*{ppKR1St|8;&<9~=A%u`7>d3?BkO527pVRj|3^->+TUT>0J-QJpl$Rm%lZ9H2( zhzymc-s_whosbbGfvTJ?#x|8M=KI}qi9mwW@6IFdPtwx992;n-4+19*AqI2jsmTkl zn6W>8V1nx>Fc6Vk%ts45wcP#C0Oa>4t>EF|(HMp|y8eTQfk`l@K{+bj6dMf&3O_%L z?k}|n>zg6u9Fa)|W5z}cyY2=;Ykko}bE0%~NH~-t?Ew5tWQSB*3(+37k z0Drw&yf0k(tk7bGeB4tdO(d3Ii$(n>rqepE2a38gljnK7xyj%g?jSe}n)=5hJbL9+ zrY+ycp1w&8hKU$;AJq7<1hVJ;A)^R5&b)96n}u| zG5M-fnq|&(kD<$8VYF^R4&8kB`8cWsH!iy%SptpF4YrWmUVk^|4M_1no|!YWG5mJ6 zd4Wli9`impA3MS_zo6`>X25oF!Dkc{^nBU~e@K{0ZM^W$%d^;}zr?oQCFShW;Psh# zB{X{Yw-#;S0(QqsS_8f2qcYUm{}(_)N$#gjDw3MTop$fzJCUU-T5YL;13O}6r$mWL zYhUW>b2$3-hFr`KeS+(fXX9X@DO&jQ!1T85Hu=uhz<^P;7*#%)P_kfETD92{?;EQo zA>9`w+8j$xCnQ8mq*=5c)92=$i*lFRAq?)86U^o)rCBxalH_CBq;HloL5HK~4Sq_+LsbOR3rc)P-S`Eikwu>!9{C0o2 z!2X1uplX|@Yqhl=RV-V|i+Urt6}>4Mgo*H!j%`)wMIuj`VGZjDL6bZ`*;rkfep9eQ zVh$%V_#!iEl;hU55s&&J@r+J7onzo#Xp}}l9*>kjntl=Ib9YUw#rk}|C@aISmfZ`J z@r_L0D>Ezoe;K*1;uG6y68_;9DkeP(JFu5qdHEeBWeLMz>NH6&K zogeMmly?{KbH*;R^{{%3G$?e5H2#XQBI9Ln^LeT&f-Iz6vW_wjgQP2UD1UT5IPoL_2t}AyKl=0mbs2k#IkjF8{LrZ%l^Y@Ay}= z)nPOFbBkKo@vi=dK9D-fjhV;obPx9@PdPL2_Z&=ZR&1+hYmxR7=z#Pj2Ud>6X}> z8#a1ZB#QTj(PkRco; zZlh>{*?9}|wcLbJAR(HJoP#kTiyAS_h2cbJPz;AXrFCU*tT0xukt8jraXPBcMb=4H zeK>Oq3(pJZh?eIy_&>4G=`CNK<%+X$<_%F4xxg|id)te?tXNM%N$uHO;s@9AUp2*{ zkz}^5%e_A;=aY6Z!Hgrj5^2VoC+n;02p%Wfa0!pcMXc}3GHL(IFxAWORO>|&#Nso| zq`MGF{nZjkz2ONjQdyB{TnV3JgY5D4@7wC^$XA;97`yz|Us5lQFumREWFvpOUPn+U zl4}Q+bCl3;>ACobK^z=aWwyJ1Bi@5mxW0z?2{RE~xyr3|><;_A9~G}ZVsEk=e3Df$ z398&)nTXRWUaEdp#Q(7eV1kK!WhKn*nP+9SAJGnN=j(e7Fi=BBm!4IIiqLX?+(Cic zkLlo_E}Zf@|6pMIr&URr{~ZZiD1GKG^YSIn3QuBQ0Fsa%FT|@673#?G=s79X-S_6u zr>y!g5Iw$zii9Gq$;)Tj6)hd%TUw6ipckpNDTlDVQteNp-8ovLsy9LzO40nPud=@C zJ(%@^2J675Q_`nd6yMbB2R53O1qD1kJdC!-Gk-O?bAVE8BRO%OD+m3x(xPQsVKrbT z{V<%$Qep>v7jVq7W5mS4!SOlnA$kg5&C1HcB_d+zD;OEB6onqhK_dY?_54pU&<=+g zC)l#WchtB-NQ|?zw?hA8K-SD7E_Col9Kp#;1-##@_D07ZEuCT z1srT>_TyK~W)8F{D)A@~p|=7ia+p700a$gRT1?(O;o2tSh%S_il}#+G#HEvX=4_XF z&cs22N7m=EkBiu_qoFi}_+1{S0ugPBppz}h%)9IH!iq{3H~BBm-rFrmP4^6iHo839 z;?VEtDVlRU9^@5_z`}_(OEP=|TM%?o`Ky^3`n+ZPx zdJ%-tImF`J*X~T~+b*)j^3Eyq<)*{_pxNGH4FW8NpONZ&o!vi^u$Qd&_ZBa^fjBvB z{Tn%#mbr;ls`|4>cVy1K z&`K6iMz7rQLz;z$Y!KX=8X(811|1J#)NMrk@|-<6^Xg>51``usz@enk;sFd0t`E+W)1=lNO$`pj&A5 zM1UTSg<%70c31sl`mC{Bv+PNc3KlsQ=5ISXx4Aq5!|4X~#{UGe=+@dMi9=9p^LlzO zDAz7`*-zGW}Q^8;?v-Z7@GT8jI8od%KTqg;?&^oy-uNl)w zK*WS(3SZgs@me1!2BniX8u`x4s9O1Q?%!*M@WHu8y6vfd0b7ukB+nz+sq>&@=bQP2 z$&?~jQ+MLcpB*aP%}9~~bt1p;5JqC_pno!gOUCCiY%B32y1$ty2}ReymdzIQ+&n_$ zv*Y&0M%XJliMKzcP=6O_I@7(RUmZNxt@qg%VmE9Hv*EW}8H`M+_gZ^i`nNfbPN`daV2lvzCbg9i7<=+Dc zR4n)K$hcjO-f%GraeC04wbQfD*pD& z`MZ0YuLIQjD6*l*W*)DKZYZi@g9+iUjnbMu&W_-sD8vGR_z93V*^QI+Zp~v2F{Z5_ zra@H|gqZq;H(4WuFkuago^A}-ORZ~G^Zq_|ClWwXdd;ikCKX!ag^s>wd(+ruJT?H# zY`S}@_&kt%W0Q9jD(O~O&Uh_&AL071S96&RBPxF9!!V5Ypt3*PM#RC{=*o1gM}{{R zAg6TEs2z@-=n?5rw8)d@|JTu}v^zl{QEm!_d{{m|JCgzo=sOtzumI+ zy}kd=a=SVO1qM7WG&0iaU4H1slY|XyYPq1^%DLqnc6S@~HV46S28JvSTS|_8rk@3t zhX3lxy$X<3kUV5k3^H^WhYAu8waHy|zHP`y{HLY&K6$kVmr{q+V$@J-I+sNNx<;2! zNHCIiA4p_eLj3)M{I00ro2ct9kLdyy4o)B7TU0ZQ{$nRlmqC07r%Ey*95*4N=wU#e zmz--Wqp!-N?MBVT1!wb~9f7*%&Svh-eb9G?K!FkE>SrXL1QuS~laQp><0)za5x@P=jt#&4+D*xydhf$e zF)btJUFW(J^?od*pgel=$L0C)e*@s~eB>8PWYQ+jqzlisE%@3`B^3lM7vfdet&sTT zDs*|%Xihm@;qM~pL#B*;1f`!;Mo}nOzLhJ4PT9Vc!@zNDD4Hu>-BI6>d>w@TYAY?a?7gHf4@@Q23kpHgl7t*vb*)NZlsI#RudfEeHOpw24Sb$n>egEaSXW&By z)*kh9T#yPGp`%T+Rc?$gO!_6_O*|n`SKjTDkITM|^ z4f2u7R!qs`J1}S?iJS}e350McW1W4QTevdZ-(!0J!y@D$9d=WTJ37CRsREd<_&*At z%MAvt7AqFN8|Yr;dL9qF-S&~hk$*QiI?wF;G9(%*hnuy<^#Z7s++o~jFHB1@15S4q zLauP%JM2u-Ei};fNSA^p3#ZNDfpl+D^6(RvQ>16dJKFcmCVtnG(yZ6IlN(D*V#GHq z3=+zX%5a|re?}I$@8DQg`_b9zPvd5|?nNAD$6C7QLxoe#Y=yj*%Bgd;$7WrCrss0q z)yNwJ4G?`y+4&^fAAOQl`$NIIvpYkajJ88ZRuWzL^^$%035C4eCI%;AUY!f2!cq;e zaRTnjI!n44ui@5l22pT7vQofKB$d7g*ih|1&p+9zqZ6iANiXp@Y$Xkwr0HS&M4Gcs-w7-9H?W77SJ61%#ZM(tDn^U`LiwD$ zp|;pC#KKKdF>aGBmY*>_&iPGe&N>S8QZzX?e>zU@{Mtfg=Li!n|7}sM2-;R{K-X9# zPkXoYFhvYPLzjdq4XeiBTcyA>2D+8rec^h))7r(PB4Yot#pWQgy6oiKeja=)NTs8_57_LM*gt z&zx)SKJ}QC8f`r%BO!T({>?a|@cYtU<%?%&9p6f6hhr0D4s-Q}->HYrGJtBC&U^2# z$Ca8b$c&mE+hg!cz<=qYW56l%t&{W zl^&l=2d`{b$vT*y;=}wy@)$@V%qwzAc8_@_mK-;EikYs?D8AlO6f;C2%w|~m<6qJI zzxi((lkcauwu#=Aa{c$~I4pzxEqqjVF(M~9Cw!N(-Y6}`| zdxA&3vWD|m?NNn`1iFv~RuM2QNQPh|QST4D`uLlN$8Lex^K>&_(@2Z85)P!#Z+M)~ z3^(A@ao5m74|h&p0N4Nzr*ZJi2J56kEj$u>|4x2B<8;0%?MP({WAilfQeQNc>3pTx zuR41wN>Q&suvwZ7TE{HGj}*2J0vix9_<^S(-0@-$(iZVLLj^hkh~9cOUigj$WHc&k zxy_a8C-GQ)Wq$?-7Lt#ODhsP_J`4p<(nzZc z`2Q32d~zxf4eT|qtI-R zkBdec7J%Jf-!N=f31tKANhcX^HB;w?{~oA0H(RFt?9cPDj>>ag&buMUde<%Xuj*+6-&OY!wnuHDapJNHh%WyI}6y|5KF)K880 zTo8Ifa9`4QoA%aEIMs+EN53{|{j4VUDn81sOXT2h8%G)S4NyW|O}8k)I(XOm@<+ri z%vTD+unf7_7fxQF2{U;RYELn!oS+f#yE~RWJ6IG}N^7=ic^qBN_ye6Pm9;rir$0`=8lv9z*Ik$r)64Y!8-ssswKQvcLSCF9dT^l5wA1T}jyU@%ar@P0Jz zW_a0oC|-LlrKX1Ob+-K}oQUqQb$QtcD0ax;E^w~+piOXj*wZf@xHTM)p`=smRw31% zrew43lKOmKk1>pl1PS;;2r<|LO*(=m%4;t$t^uu6?{eNqK6LpsQo0|bkLovfce}YP zo1alY3lXgWGby!$o-3KT6B(MWJca56O1m=>tr;RDzv=n41Xd5>Utga*1AjHwLC-?Z z1zQRXCI}BdUis0RNC9!7!4XUMJsUFY!|8?0*27_gV!~R{=JG@d3(Q|HNaAgSDU>*{ zivA4^L-sYW1dx)2n}*=f&#mh)6ArWGlCJq2W)@=r)K8jD z|K0zUdE0u2Os5wv&F$CSvYi6+|GL4P@vg4*Nq>VALM|#RD~mY*9+@jpmpld4*g=CN z?DX`=D=RAnn;U~}`78hag#*Ihcp^(WiSHw+*ZD4;=bZ&Skj}(_a0oE`lRp4l*v2c1Nmd0pSm8wR!S0=De_Y!5U>=JdQBA? zS7^O0GZ2lS6eX>KKv^A#E~s$YkbkQGP4_uL6A=}4TyBTEyTAO~=!1+!8VDFoM<|)5 z-Pyz>>xUa75w~B;bBw>o)9}k@Rz2;ynLeJ3e`V47uH+HL@glz_Ufl`$J@}^mI2~Aq zo8Gc46w69PPT{C}KaqrFY(}^h-pnjD7tlU5grM|q4jH0mi(RTY%(!wo*82WA%I4#D z8q|T;x#HLB>U+OyF#JKR!C*WK-rkjyY)#kI?y(;Jk0SvG7XmnFu~ON8g~7<~XI^Na zc8zV=UFJ?ac3Ed%-}^sRT-d>>&*N&roqTcdB7X6>T)R29-M!mYfq?v^y zc~m&0KN5lv{(J2PX+)t)*7eU4-*-?CC%=Nbonz}<%a^Qgexxc+6UKT{ftSi;US!#l zl+vsZUxz1&9onCfijW5}&q$tYmaU#1$5lKYIsPivl+Qc;V-6P!{X~xYk(#FSA7{TF z7D1dpIJ^ou&%>>G<75!RIdUcJuwPIZx102Q4T35uBYbL#D*wXt`~B-KWbbJQ$B-!~ zELHxXmOJ!*_+|T5kxi&AYT+7vFs%mL-cV5KG2f@wAR%gC>$q}DywMj+HsO)pFxbo9 z4;iP-){R!4ekWSF9S=wQVm&_Pzd+iVt|@|dg>#71J;mbAyNJ$dfAVG&tKYC|;TTXL zlvzUTZw9*9vfck$rslthb`ws~ zFmY?>t|?`%f%@5xq+d?I#R_(?5Cx!3%-Ir12heL{X%q+{%_=zdrDxSkW}YV~C6e|6 zB5o8#FHzgKH8k7QZxR@bn1K4}|IGO(TufRVf;bt9mdM6wb*WpI;JC6lFirM=&D0p9 zzv`j5pHXlqmqC)5td>@iI+;^4oM=DvWYkIGG?oY^OPkWIbzJKm*Z~>%neK;6z&Ks(ZLv%5|AL;`e>JD2Vyl;D2c^9n2X{zJ(<77yZN| zWx=;UDZDf_BRrwUPR@5LG~24qmhSR$x=Gi6MCYJmfxJ4lp^|j>G|}g72pC6~oo!qn zzw?ls938bcd!FJ_;$_l2Q>LHcJ&Nl+32nml9(H+FfarW%U6-P+JdK_0)A=#oE2`-; zI*2vbRbj9u=r`hF_1wxo1z7XHFZVP$Eym6sp0^HVk(A>6ukc5Ag+F1t6v}VN=)wqD z^l-sH)h>Goopt@7>H}TfEJy2iG<*70xZ+V1K?bS6GXWb#%Galy+EbtYGuz;nlF=X= zP3-Cp`sKb&9D3qL|1H*LZ$=L%YS@;Zf8a<}oIs!)_1gI=_t{yZ4#SHR@Y$?@n*-dw zbP6wk=e;1|YeVNKPq<{?zeqlbbu3RU;$0=7nxwyrXjpvP+g{^ggcq+w^bpZR(#bjFhKI$vL+2W1Zy2t6+J43VrIG~Iix zMMDE?%h|`)nggBxns@Q6b~{Gr8Ypyldp?~j;HuVQ!l-Gu@bv$B;Z$vM90ymWTr1l zb3nU@=R2O?j%@gHQ`|f3OnFu4Pe$$(a}5o;SgfJe*FwK$Z~BK=c_DagU}}nbFsh>YQNO{ z^i96F2(GK+2kT1lbiX?!<>XMG`n#_^fCCg210!zV`gu5#fq?-kCZ^QLcZl~XiE}aPaIhjuuq+Ib9 zyjM`RzjYVia5XlOi8J8jLErBFYo^z>AH>!kgh9mhq5p02-oLUonIX-RZ%$GR=yZm> zwA1vw3e_k}|KCfn;Sr&z&om=C*T1>Gp$T;m%|{PI3vW%wfNWr!MX66lt&jI@#87xLd&K9 z%m(!NrpKU#{O0Uc=k8b4F9R_LJ@_tClmPFet`@scmxDPIbUd$y5$2I@TERy)$+G3!0h=3upFy_5W*oaW|&>t3ZZgG z>eRgK>IL?XhAnjjq1iI-3SVwAaxM>1QPGunY_2}=7zG1%7cQpt>s7A0o*}Q>o~Xb2 z)+hpCz?nbID(zaxi6&#^N_@$Zqp>3DU&|X?dGq|Ce)+YN)2kck{2v2HfaRZBwqP8p z1CxSEu>=my&OVpD-?U1f)~67{jvWOmb-!fpuKf#`_?2c{AdNpXza+TDTd; z{5#Cj*?=derWTq}W1X;FTG}3Wk5O2clMn4m6Z|{S$LrmK^VffYJ(i9;!(_wS>92s> zRR)~vu5k_XA$Ib|pud1~-M*N;m3tFw@V2SiCTPG^%=tP=mDVDPTuX3^2)H=Ak0AH; zIj0B%e!?e54kyXS>to}`!=0Mwlwj5 zojD-~%4}3RRwO zPar&bZ@_>x-sA67K$Ik#Or9-J2F%Ga z)bkrH4=4ya3zdTW@xYKjlJudiJNTl9Mp-vQn<}aIc|W+Ao_cu1DgymXqvEXYVZVb+cocZ=ykX&*$J88>5F4-tu|dU(U;% z23E64A7y3ESg;kcMtQcWvseT{wyY<lf$y1PK%7-%sQ? zI}Cl(-q=Xb#E1XehmGc|;pvosB1`2*O*;SoMHuI&JWUa^0wbQCWOQi*Auk4KZ_0K!tsf#tx0^^xK z;r!hR(RDw7OePA?ubg+7$;H`3vI(iddynUTg+oZux~hW(AWPE!xp$^X0SEz5%ph|mC%&?-CVp^BaLL>c> zQ~p_a-!?Yk1gbrRR;ds5{rD>CWT(s!AiUzWlgX&WMN^E|$dX zQmH*7WOK*M52EW>gL$~Wha5v{WKg;A);etJ2fk+zOl~yfz-tDvjklUJL^&R##I|8qgIYG9RUNg0_U0Dq3Wv^0OzCoxcUni}2q`alnE zd!hCjEZoOF>}KKl`22H&*S*=?7(S=7R60vMJ1W9lDMnU*cZG+ln^*RCMNK|#5*E9M^8t$(scN8EaY-& z>hJY0yamb_w?E-wVI9C#@HSHd$>(|_5#XO$wl&=uB5+VSo}?;pk9zI{%W39OV}d%2&j?b-;AE{$m{)4 za|V$)$=r9z4K#6^AYR&X$yzFb9cIf2Z#~%S{fR-!%p=z`*|R}&n{2u?>Ut-IRCoVPOY>GP);bXj&uk{kXr%)%!(u*#R{~SKu5%ZOE1hr z@u^;3p@1rjf-RAa4x8X;oscB>l0!tH&xv);eS9MI5Dx5Zlb<9ZCVN(vqXEujV<=zg zD`fay#^r5E#LGjOwe)9`ga1frb?#EJP!gQwYttY@k>vjw_;p(NLuBtaXG!JOzLGcSS1_-_}>4 z)n9l#R!j2kJVK?hAbFmTxt{A#zx%~*+-^v-CNYJV80)m$mHA-VFZU~A7`5S&h}Nr9 z*{gvlh-!C$IJzP3+{OUgV=g95WlAwu1Q2M$S8kYJWSi)dAzklw2Z9USwo-p;zD-4noqduFvgo}a?ni|X^Hy&bwm`yu=Dcf%#fC# zJu>VK$JyiABP=}RE+S)ysqy)%`|#NsJFu%b`+8I7@DzUe*SRu>BD`o=mLy9KGLRAe z`kZ1tG5)~!9T*hkxN~=Z50EkL-Ekk4R)q-?*w+t3!gy|Ub+iHwN$)@$gA%kaA{RJFQQ7c*?xW_-NUEg9};!4Pqct_eUn4A@)t9M8g97(N(^EKup?DI+e3#cfkFP zy+innQ$ZtU83ij-$4Spw<%eS;V=mOVuPu`ri@Fa5A;`b}o^FjfTF!~Xa?A$e|MB8Y zT3=Tj)2q+5>0{Zk=rhNP83fw?_?I~sR!{MFSqJ|!^Tv?z@4zyL^JPfW=X~Pr`ar6J zSc_Aac>XjGK5g4Iw&O9HPU=S#Qy0APocWDdlY+9ynaUg@#knt47IbBn;0yANT|YSU z)nvGJS^*UrIZ~-f^=EYTsg9Kvo9|83$bu6OQWb79LLWDNSe?+v0ppo;8hp_8M(Aql z(4rL9pr_#2-k40e_bh~ndwW-*7jB2j`h0MvP8$mm+ca|RJ8qYljCcx*hU%tfUo^RD zuuzX-nH~50VL==Nq`Rv|b8F3=_oIQE{l`;RZ3AtCeN}!}$kG@@A3TTNocm{Ak?dOg zscPmGV%Tit;)sZd9R108%=~!T3-jN4^Je>Ovk90}f-_O|vg@JJ6Z!BZm6o5*$Szp2 z(G>Ah?+P_%@FiJetD*&UWKWbW4@)mvtu0Y;eNX8V=J_!$E-pI!I}W*oW`^HAQ)b%s ziF%mYb*5Wt2ndNBWC&co3^VNfZeo~uY;8}7QTlJ1%J%2Tn9c&|-noGVWa0jU($o@vSu`vEc@wtlJ)2D2~R9%E<%7c@c*b!$J-7Cu}rc+M}TWWCtPt5avkCIUS^ z_^P`iBO>;5m{e3$$h~>faVoXZK^ZEsNhuCjR38vk( z_VIax%-1gT>s@7a4)p3|o&n#8npg+?w~){Me>z?xdtpWCEgju*))S4uAFZ){$B*bz z{2UU3V*Rcp5bF=*w&JOVubd_rQW(!HG{8aIekc$qdCGb~A`|_fH%cLH4Da;2@6|qM zC(7-B;T*z$ZTXIZSWqXMjmvOJU-x@&gGHG;{xaPFkK15ZCEaS7()QQ$k{zlETS~0* zQ_l+56r9$SC7Om4vB%yPS&_YdJ*1zeI3$e*rViTFqIOwGC!sC5gad3QI&G%{!~;0& z4|^A+Bpdg!u?BRbLAl0N+v5T+ybQ)2c6_iQ@d)yp=g1caU&>l<(WbW85NQzK+b>9j zAKV{3_=^$q@6vjBTOi!JIG@ROoapNyjyRbpepT}pJrCxJ-;p7%4;L7hqzN6JMFgki38NWH&>Xp}@3#5%IeW_$-;@Yb zLk?WMRT$-m>!VX;JXpEh9}B&9DqUIw_;x(uFNW1Mqh|KF20?t;uhiJF*Y+C1 zm<5APp#}4wsZ0D*57qcp+LtOgdQ_@52OS3`zYzBFr(>&Q!BB9bFiDrpHwUxSs|~hx zoUHwIG=$=DKU|*n42t{xSHh?7cx#8LoEF)c5#84QgNeZC_z1Ue&FsUPD8Es09xoEP zQ!>P~iDI~K$)S*m0wWh%3WHC$)PhWCZAw=Xn~SF7sO$alvIdST8D=g#J%3^^ zvs_Ea?CN8Ak0w2jgjQXbn%q^c=>8q(4^Ts(TysEUW`*CrLq*pHkDoJpEZRogjBGul~5T1Yl-(mkZ@BeD&o|PF%s1scGMUJs_Dfg8| zcN8Oqn*q_gsl3`Kt<_eHD|z;AI+Mf}H^g@N?!!_c-^9pz?v6RvoTN~pwlo*CeIV}D znO|;WTwxhO7)O6k4Sg%ntz!oZEcB`4DUa$hQZpON<4&(I=@XlMxTkbzg-f>GPPciQ zQQOGI-(+POwuS-%x`m;PexGJSWyrOnD>l7MDA7cz;}b;+94sx(Ab&PLC@Gp!W$E0L ziMm;0`GS%5WcbMWr>Ga!x-0ZzM0&Y`8WnvwGX3#V@S;>Z6n*2HgM|149}$+&S!WGd zU4sa#SHR?UT9S2QmE1=`?s7{%Rn;$_60y2IP?I02(!?R^9v~l> z?Ghk2uhnsDCW_lM@yAj0YK7E8G8lGraV(t-?#G@*;fZ=Gg6tQx3puS)tOZ@|Hh0*W z>Yp4~lgE(=-wd~)5DQxs`dIJQbAVusHi`Zc*__A8--e80mRb9=y88CH84%|YE zTb)($7v#P>F|Q*A1EzZPyEbjaHoG@`#EizOGSMIL1c5u zw|KFV!oRvfXY0KVV>KZapC(C2o7ZRgwr6OO&GeCt-`(C`ON>_%^!T^aRxns=vU8*h zx$M1!{!-b+L&}O95eH4u-)1>HK+Tt8@+)bvw5vt;2!0V2i7X-98VMfN^%K0~5U}8_rj9ymDzOA)k1;f?a^7q zdOWUb3wGt!@JNgDcjKH*wDx(W*h>2862AXTUfbzyOGEd=p%ShjMwlmMoGeG%4cms2np+=4GgICu%38Bo%6y#PT5FtX0ug+JP{nXG!AT;8P6uWpt6t$d%&w27D4l&roZ(BQM9`}gUC9}b}hD74W-TFhL09BqAW z{dnE^NPWXO*0fb%H$7 zSoYpUVfTZ68H*OWC=x6oy9yPd?&Vxj@4>)?iA+U`O%B7LZ-fI1P2RVB{Rf8&y$+9b z8f8)>9?|K*-#==)>i(Bzlu{x?0ZL@o=Ht^c?=H*@-yrI*r4XV`sjK@hETV6T?cat= z3H_=AH<-AXJuHTTxv1KrM=&fhUyE728@~^U9fLIZleLFxV~>vCq>Cb}A=t=QHWvxOTzpK+71n==KQiRJeB<0YdH$Okf_8>+XPHiE znkDE2eJ`d5T(%CitdeHm`CIioF}bH@(tt*<1q%4Kn&%`zz|Gm~kl7I!>=-S~W9?pL z!#yMsi9v5Q#)66ZXM=P30M9Ns zbVWXr0*0Fd2D02BJfH@FD&D@&NEk66{tD~?o|_m%E2ycdy>0PwRVw}i@))wf&hXXA z5C^XaMeibrfqd#|yxJ`)QzUaw7xDZIR*P2vdXK>4<>JBvGHVaW8^VF*>(nr#6S4pJ zZJtLG7FLZ?b_SoLA7k~H9E#wP=5-AA4~lm&njng-vd7M`ra}9q)%cTn{zg|5tDUh1 z7r_-2&puDGjpwFocV{BFmZSld)@5*Zt@-ylh#P2970VJZe`g{NS}_{c*Bn<;X)7^a z*s2?gkqzO=+!pXLB3i9is~9ru72-kLLKW4`onG(1diNl@CLN!T$0^MX{ddX7gvGU5 zWea8Er)8SfQSOyKWTd-LHTDNP)57)!Y#wp=V(khIv1juY;laV@ zvucypVsEE#-)V3EVEB5lP-9GY@HbX7F}~pKGc%T%@onL>EjFd>Q40y4*T?t-L3W{H z5I&Jx2?7jgz8PMZnbU+)@A5_-hO!0|{QKv+k!u)gKV4A2)(SL{6Y{>vxv3VM$wu&l zLXq**2@rz##}ZuA>-Qd&ZlwA6+NSbQ2EUy~1On(izq*irrma1;8EPvgpDRePZF^CS@=lFQLZ}@M0MQC)d;9<}CK|B_b7cSMl@5 zhV+BmWDNoaqw)BKVRiBE!+XfvhcF5}uag~T7 z&yVeuPR<-jCEROy!H6oD!||}ANN8*VS^|?HQbWK=7bI_~^Pgy;kkx+dd0r$iaTuSn0$&h#Ke3qx`iM-axi+6;uko(7`jB(KK=^Ib2t<%MdXi-|_ zxE#28the&eYO{N7%t44wG+L4bkPQDG#kFVNcvj#bO`SsjGu6miZ>_O7mg|-~n6FR< zm7obIeHe99q!vHm2}W&d$c>ghLKU9l97TVAT~V@mrQvxRN--?FsUG%`Ym+P^1WzJ= ze5YSLPEeWP7W`k_Ef~B3I!r*X3#zZbzfON7DcG!1S-GJ^{R7c!#^lrMG{%3Uwr+1I z?*3dEF&P=z`q>%yFe)a!It~c%bpxPZo>99K_Ws_Ajm_2T=U+CJs~g+R$MMn@`Ag`n z1ws7NX=#Qh^5h2-Y3WVGt18bwufKa->kRW8#6mS@@c;Vl=B2YTx}`1CO3L|DBN#g6 zFP1dGPfG8x3Q-iPx2C4L9$%}Z9iweSqua=+DsaVfOCkJw-#A0yr zC5znfr2HRek0!Xl<;`+~PG_!KT5FwxnBb2~4=QJ_cTF^0t(s38QoAcT8jFJA(xg%O zJ2~pkGEi(Lk|meUjh&z6o0B%8^|NeWI7}je$p+W{ovfl1*qPh|!{6A+CJDjmuEs*o z@+W#ulL4@=t-Q)CFQV56z@qFZ(d7cpuv+DBg@7QZ=1#W#GMB-k|wt$@#(pu2N#NL9RTamFO*m)a%OclcXoCTCn z=WQ`l!mBMyAf0sRV)ST5mgH+$FrVy~8qVKK{HYUL@cR2HU0hj{9e=NBB=P@BgJ0f% zIG__q&yoNi0zU9q5AXjWrIU>0wYn{YZcnNjiKvN7=6X9}qB*Y5_L1(?)>)(C~yJa{4vZJM>gr&71$CtK;@JD5sW&^JLn1+G`u0c)_5o{0#e&(rYme$f{asPiK_`1rwoU!V zwukNmTl&~;0)w&iu>Z)TA9{K@8y}9rgp(PQp2066+gl$r8k=PB;GSx!;8*d&)EZV2s9Ulf^lhgZ=$fWX+%T3tR$& z<_``+n4~HzH#bI%TUzq}+_C^&+^RX?*4%Qjk;$$>))&&*J;vAD2smuMc8Si{8WLlb zst15He}ifby#yUu6kuM-eM}ML>}W} zw`JXN(ep9%_1TjDxB~z6+#4`s$NBRYYNF=Vf*Xq3F*T6V=*%&FsFDu^g z&?#q^&643TgY__4&>;xer6)6(Ryc}i#;3hKA;g$065)6|jq6Jw8uL>#q( zbH~XCJx+Jy#Os|^m=Hfv;RXxzh%wy-Dv2D~=iWpPC8OC9b$vTBbLD81K3a?xK|K3& zNR8$Q{dLvQ;QdvlkWE1xTm-5Y2>N*tzI|#G5levqdF^Wg7J@urG@V@r%{N0*Ntig% zcr(O1ys_=iTObl7S3F^ZnyKQ{u97qq`csYqXk#S3J&hJH-T8nlbkg`7W}kE_5+aI@ zV=tb4HAgS9(VuP(@$^*pG8(T9u@WQco5*!4;IZ9dfqn1|+c71FrDUfQIbH$f$lN^w zvW2TOgH?ff6!JhLJ56V=I@heN(gzLg%u<1_*e@r0qv^Yo|85V@bsxxd?p5% z9LP2|b6-TOwKtsRLJ_6%guijYYf*>buUiaja9422#nJ(4SXd#2xv8zil)`8k)of-?xK-b@k6bW2ul){dG^E4I>5EML2i=5^KiD5Uk<7%bUu9dj}T-y zgi1XjkoJRoB(R~gHDbXqkjEe6C(KD!@DM+)03gU+0MX6|#@Xl9e6PUkZ zEM>}^U4pxl1}H`8?ZH5eqNct|;j5jlH^VcaWo!F;xa-B)>6h*=(TKD6ACz$j*x$8T=mcNr9 z8$R{xLBDE|hH2rMnPKHwVdY_EotZM^8=9o?f0u0dvBd0|dqJK^Iz=(^966ifrz#^* zeJVMNT&c8c&**ybk!=0oCSX@c8AsGpsqjxas3~ecW;~D;8^c94QzebW3fsKVdgT&9 zK17AoYgi#jd+aRLThnN5;7$}?o+$1@4X^Bt{B-&X4{e+ASvUh|RCf6po^qXNLg2|9 z>s{T+do8%r^@WSOQ`GW>{}P|?2u=?$P*5Pc*H46#1Z+}D2FCn0KFnoWHXDE{EGekk zF?-FzTj199$LB*fBgQOT>Z($Ea}%K_+($FLQ&z~Ly$y1+DC*?7649dQ0rORVopC6< zrrh2XfBw1Q;^u4&bp6{p)a%{|N9BE!mb(`QYy&ricO#JeD^lUIc zLA^d4uCeejH;15JTX3@HYVZ8lGg7F@OyNeso~B;?`C}pnhNI%VttP8j5q|!JeaXMG zEx^%YZ>vQqwSIRDI3q@v6bF=ybnpPYx`e3}QUPDY4Zrbrd@@F(K!1=3iT9R+1e!?u#xr)#qz+FQ^!*Ut8^FtXm{9^4BHXs{ z*BCw0an37#JZP&rRqhalj0x?(Y|D#qH&`?t8#w!s#rz38K2GC;Q?BfP+-72CMv5WY z>+E#;DS^L^wZ15joc4Rz8ZSNTEbt<|nrt=Gn!52ZFS_Ha(_ce_Bi0D7vn44im#OYv znz-bOplR`p=*LGAUBKDpK%az}hmg4W5vgJ7d7!z<>7WS+Q4YFpg8i|10S}$0P(5I{ ztzcc{RcrTr|4wDK);z`6*(v=um0s19VMwa-3kJ~?-^tA>v&k`x{7M(Djs{T`8w%{D zrFvhXuq_nC_}~BsTxvB$JJQjo55iyvMVZEkZd`xEQ8wQe@Oq*K@%*=fkY%0lsyCuD zS#$WHAmUfF%5E3IO>Eh9ALT%p`hpTp#F0ZlN2k--<)d`YCvl{k?xj{X?_&JA`K8|G zgcI`TxV&S3xs-MLk5xscJ~TBHUEX7axPZ6?Uaq#1nmohg)}YzVuqYY&^IgOnFmS`C z<&3h&pcy^{4DXw*u{Iu*&qjz4<$7sXNCl%3JFqodthEXXNItHpd{l8UQW~qvs(ZZ_ zjqPq3%1C`7FxlDuUdEOJHP$0;E0Cqh;cA9e>ir zD_N!~LSJI*TfZ#o;PihN`1@n#p`Th{V&8Bo2XoIy_lom{zY*4F={Ex#iSJ?}HHs92 zzd&24w}g_rIzYgZFv~!;w8lK;zi6al0h}B)jAs#T0yieJL*qR|iW>=l8*|TZ1=?_8 zM@?Y6)@*^=FS=gV>WOW(MDraaU$%+@9+fzXNhN;R70K@cdtZ=3Lh!`pQ0lK%2|yB};to5*VtaP4v z8vALg`m!rrJrT0lDUeM*qST-2c2k;> z2sJ7kH6fp2fbuMnZgQcUwp7zW^Y%Jcxqk*cYRvI2T{;eA+R2`=tbtGb=3c|_f8ZRW(!L_L19kEJ8)LHSe7f^LthuuLpI zqE7Hp-htA&eh_&}liZAz42Kk%5>b|`0wY`e# z<*2zvwcW0&gE>g5x@VMCYM5s(JMUJt@B-KOW%rzm-CY}T|9#0LE8ssS$=kutQ#)cJ z=Ypbrwc@o%`dgRqGI-T;eW_rf}!KI zBX#lfod>+|*fqMJLSnukPoRMxAm9H!z^v}|lHWZsvo9QR?;j5gQrOJb>&9(D;T&)? z*IxY4l+O>C9-e}Nbe#ow%8EB@D6t}Nn!Q_!D6xDJ_BNvSA8?fDmj&jikCsO-;(6_^ zzZ0<`lAhl#Nx9AMWdP}UK6jVjgILpk_g^t^Ybe8?;`+{J%STdh5dSxPl)2j9aMc8p-IDLei4zhOURjkA^|YOTfHk>CePe% z286~&razRJ0zmY6$mN+HUc+C(_^$v1K>6F(rnpY${Qw*7B;-RZwy! zD=!b*-q@E=?+UNs>coTgN2T;#n5f4&nhd8;oGyK$hc|AEyoEB zt2~S&pXta;bkx^Io&Ul`)ytkcBa?B45|~{1AZh=^`YuM^qs*#n3$NqJ*mA7O){@* zE*E6ss&6>5;9Xs51)`A(Kn@D9NQ8FyvnG6iIsQnvKs0E|Eu?iY&4QdV4-2h|m`AcjEhi|d*-A5M5v&kbcU04UfcDsZA0qHw z&pR~(?T7F7uVPjEPu+1vsBnZ!93gc+qSD_HQvKf%dm*YsQ=-n5nuw)al-AnrNfGb@ zX6~Ew4Yr2Y`x9N`t+sUT`po0?gRn~UiK*Bv{6y|Jwk*78ntb0?)RFYO;B5E$FSzU< zC35=MKV8NSv*b_#B@ww<$vAFlxz(y3XQzb63(Z&%nHm=qy8~vKT(}MXysySnB`8h} z)Y#BsL`GS}o+7Z|ymFyGw}l>|wL1%3hY-oW+RpvujNbo~3q2799nL#@O=C?(ehuAr zKd`2I(@Xc=_La=xl*d!+dBgsBhP_ol2m?*%tkAqcN`?pS#7T(jcqFG7ZQ?cHdKJ!d zwj}K-{8t40R|&}KO~`xVKRZlExxWnzu>2#4@^A@;ZwOnuk8qV57`L}rLRPH-Ugq@s z>x8gtn|A{DeTZSnj&}b}vP>>2kh?3@A!@6Dus;}yZ9ri%ZAzf!f!2bLku0^RXJ_Z2 z?`{4aloF7YE7>Vxf8VJb_h-vptTv6Pp-F&&gcI|4I}maMKBY8V|goNq)h`GJ?fXxOjXClaW?P z6^U_nL`=?p7Uj;&)=s4NOxbt7w*$#1-_ZF0q9CF1R!MQlkJl4u^;Va(v#Cpw?iX^b z5N$BUt=syEvx^=VqNyJUOG0obONQ$H#e98 zF=I-+{bg;8`J$n^9Z@_MXT~iDHABB~glL=8m<2)GuRiGnhC4M*l6b7hRsW=5AJJ%)^vsr^-r&KXW;pMs?}K|H}^#lTFW9ml0(Jv!j~c7 zxPO8=U-j;Z-cF+Y_dS5BV0h^cwS+bt@dPRtBN~@cLL)5CCgV~eL7?e7f%H01*81>C zPu~CaMnE>qJY(XPMZ z8qEtx;$AU%S61hNJFF8~lHztoQy)*48l_kDBkhrE4k=7DsYt?J_Jt2Lm~f&erCpMm z#_W_=aj?Q8mHR0k^)?bUyh*$Xxg0h@!6d{J)YxZ%@ggFl%kRhQE!csHdAQEzb0iVV z-7(&$uYE7N*vTR#FEo*~(24AO<`1@yWtjI~z%Z;8 zTF=7@Wo99Y8`5cA7CiH~crTeMPAF;0{rU#UudwFO5R&76r7Ko7YZ;dim+qSf8RWx6 z-EVl8)=9d_X5T0@IBQY!>eMdgnTuwG>VA!J$q728rii1))B}-m$X%kLkq_pk2{ia6 z2PuRNI_D+@`uK)0OEG;B7l#9ovaHeGbrjg}qV=oo#kknmKd+SH`YFuB_^5Xac#Gr# zpW;Y{cn+x>7iKjyPnhlyewVH`RX6{g<47y1ovG({Z@!<#fO!7h)Omi4uL>*+X8ex9XKQ-ZutayiPo4y922rZ( zBwhXz+;>aWq6M&9Z2DjBwKa8yPBfUM6XplM3XN?;%KgV{m_*p(Rr;Fo)Dt zo+knBnCj$WFCQL$vZaodr6^$PlI(1{iLY>`SlFK@38RrNKHn;}7Y`uAouUN-= zuwEdO2|_-PbiPdpH;m!4oqp@E|MjUwhzM}|)lm3j#A^Xj^uGC$;Ynj51ONf|Y&rgGyAL z{Lx5T_wQflGzDUo{UOE52^pozF*-qm2(=rTU<60LTAf93u64Q2fguy0fm=^+a}B9) zyWv?IDG-ryqvKZ#T%22^uCzcwWO5BYG)g!%3M>K2k#TW;qwerfkkhpmAD^aLiV+&T zu)o4IH2w!+rgM?Nks?sAlb!DJ>66c0_Lm_&kKWkxbhB)YA}l9vqQ{fjlcoEs)CXQx zYJ_~RI{l&e0*ucP2lF+5kidq4sMeVWc=-6|D3h_z(rE#n`x)iF9v#UQ~{6}vd`2w+h@XggYvrxm+ zhHdYxp&4g?p07H9rbz-0ot~1T=>}%iI^!B7&IEv!%pV^1{+j zZ0JyxTHNyqitwKPwYpyvIC@-_XAeJfzY$IXcE-;7@B1HLdWd)OfNYGOgQ@VC01PHn z$OQ96Mk`s0g`Y>9TWM`h%GGYh&e+0OTlcb(FIgP{1u{S$CfjEG*gjihWfUwPd@*YI zT1LLb{;I@X+K5M-HnmVXj@-$KS}?@F?fkP>N|-d6s3exkZUg22trdQD6{O7$8f;fp z8PTQol=Sp)pf}81T3`w#B(@1T4NI|jC9t#p!udIn^ry5m^Z5o=Ei{D>9X@>rd#K>J z70T*>xx7KN4H1j!ybOK1<;veAS`H7+vFx%zY%-lM5amj{AleKjXPanb=YfVb?}wiB z8n3({-hSp%%VCX(f=n^f&vIBhb7h)6!DA(_C7RjrK(Ay{A+3Q=hkNZ~XA<{b1NN8; zv=#l?=h5_PUVZb$MnXI`^A1$oG~$G6KO22^PGu6xGy7%bVj}VW{3B|_zZf_!+2PFP zcZA>hcQ*7Uo_fVfJvNY`N}tfC;ydTE)e)Por(=v3ir+#0^rgSt+cj+U9N`DdeUXM- zR{nq%0T!p6N8NKl&CX%R`}FARO05NTSD;aK+JELjz_v0(?Sz}DJmHb3=VVA@9zdZ^&P2++< z!c)Kd?U=InJ|qZu@Lk&Yo3D?Ij*3W0h09_RJx9TgVJ@>yiHh=fnA1Lwzh{mI(xbe< z=`8yryt)5CQ)k7zSzI%gl-FeURB!}eyI*q*q!`-~rcY9VO{>cOeT&jQe)WP&8q(O0 zoBM2EdDl&flMehvN5LGlEH8GUKjV_Km6es;_DbF5xz4h(=%6|Z0b(>;j5|3snMw)s z7`uLF2e((+XqS6s#ch0Tb$S2@waHyM?!ktY_u@2Po*wHTv*KF*YEZv+`n*8cfgNUV zG9w`6APH#?nlhBB5Mi{y&qIu-wdUralt?uSq2{04_Ge8-I?hMu^z%ZU!qS@B7_!e^ zTZ|!MpTKjrVrsQwYS$4CDvC0V(EAf1h;hsh6!_dg|c!i&5W01OiQYD){lw|S`=rF z(O&Lw z$N5_4H}bKb9voDqH7v{Fgj!k!Ful zRaN!vPrGOA;FWOK^WAFH1MV1ig1|3%GmR6RnfN#DNH=k|ta=Xz>{KTg<3Uq0B_bp5O`kS5R=$fiJ9335 z$!eSP6$c8D?GxQ?eiHaIHjDD>>M#5%_s3vbm3}nHIFHj10t?Z7Yz6sHOUqzjUY;bs z!w4P#@f6M|>wFMTkfW8o{BC<2I?N~GlH7%G*8JtFD&3veFZx`$c3nfg;g54$-N_`3 zyJ)C*g^(73Q1*mal7R2eFP-Q(WQ@VVfnMw(s1s;GLfl&?X}q7dJ0c=>c2<+-7C#Ta z<`lc`edsSy!oFUiYcg%@6qR;Z)}V|~j{fEZ@LW|B4PZ3fqoIFxb`g;|gFi&kj;k|S z6}layVv`W8JiinTEDx9~(TaGwKzRnEU;M7C?RICH`tVJA4?2Y~@|DkKA=m#X;?_F% zM!5DOb?!Gd&C#~jUt8we9N8g98&%S6UaKg!ohhTzG9OVRR1NmAmPh-%2-P4ER?`_k zWXYmsnR}lga_{7JT!EWiIU-aHF?;-%l4N z)kFS{nVF`NhD|CBJFBP>)6~>O0s|Ln;oHj-U&>JvE*!0Nd`4Jzvy7RP^JgK!tKAWp zuyUauR7SE`@94ie#VW?;BymwmK^#!-Tv@x9^WRk5L23CiW8B z(i9JrAV)%W&E%R)_|=%)c*%?PxTC|&m*bx%>T&2V4o&MH*epQ2E?+5+?a>Z6i(XIN zzRoGpN>a(OE4Plnh#8zNoy1}g6ymbXE9Lh`mSfABJ)gKqknUF73ruyA-&x#){7SfeO8 zgy7z>Z|F2LcymS?mtrl^!Z9Q%Ch^T3=JS+Izpbnp@um`VNir)C_Yh+^TFGKUOaY~Y zWJ$Raa` zGhunVcpv0TVeKMbCr#FB!@d(oOw3mbVppF|YiNmC4I#|{{qJW2k8tdrc^w*^?1)E? zYn$bYrib|hCYVG0RV{r=xmbK^dcQgB38IjC(oeTH>GQxYi{QE*54e0@=r>=61vzkt zXY*8^7#;r2`prtdOyX{H!l_NLqUCbvrp{41x5Ip9Jwh^@S^<67o_s4gWM^!^q^ zX10%i@sk_SATCXVFrATes+68ikNWb^;+)Blg~3@CJDP+z8d(D{5jFoxt!S$BB9^or zpy&W4Cn43y?VQQCb|OF-B~jy=AN z(IyI2>0X-{O4lz&%6uu&7^(9^%-v0!J|Es$XU*x~pWj4Qn5cbLSWLk>N%pb=nr2L2 zTN279`1A}86we)$+djwLM*UmiN}pw*NV9TRyXeCWvMn;gbKiBM z1iwdOEri|4Mu{6F9L^7=okde_HOPMEakwYht3UJIwkzr58B%8ddAU_^B&9kwkkV#G zimhb)+q_MwZq%l#CYw@PcdM2#*C|T`Ua`_W^6REIs0oxzi4W##X`$#9O{B+w9DV5v zAa14s_&AfH1kXL|$fVzaBUdfcl>3p2n zSWMkM&LLASIvzqE_%Ml*AK>3aTXWK`9d5flRR_1>9GT=wFZ1tUhxL1~bTmcBn6_AR zqgfu0_ur2`Yb(cHHl|oK z_h`C3w^Iu5=OaPF6!zk-hx$|=mplHW+LPb3QNg)atG!`GiUhl(EeM(7YgWXGl_D&3 z;3m!i2!0~-rTo7as!&&Qkf-3t=>H5LKK9nDRQ)So%PP#shVMuEGuP_g-=+b9;7qo$ z7wYMMD+@=lY?q{G6bo*UlW_0Ct50`OFBuze7pLzX@@&;q{BUkGQ5`j$LC+3T_5g1v z4n`}AJm7AgZ*-vt1)!a~@R{Qg5~PiLB>4vfNz1ro5A#y$+TdJR88HqPObvf`pIeC4 z={1|$HBm*CwS?2PaV?d_RToq!0<7*FFt^n*#2im~&=iVh&adSvoEls3e{dJClU`vb z@Sj7m01ieGUhXc?kWtP0z}(Nrk<=J{>=UnaZ%MMsU=w&ku?z;-Uwrdc-5lDHwgCb& zpgBMaGHZBkzT_C&4YpGAv_ZPYnZImZ&visNiI9z#OP4^ zGcfdx!j%nN9dLWFO%}=P##S|vH5FSQ=r93s)TCx-_2Dh~UXIKFALgwS<5=In&z}d9 zgt}Oas0)v|;`SU523ZNd+aN>6YxQkeOjAwskCtg-$B~bD{8t0^A>W3-Z^UpP**rV{ zE}T=q3m-4WO;2JI=0zCMxW|1T5{w8ilvBn-1_b~$&WB}=Jvbq-U21qa#wgsM@+a_U z26w(eu-Wo7&CDIOw&*)hL{#-ikVidnB5O+QYV`)S`+6}Ky;ofJ*1k!&SKww(>O6_l z9R4cLZU2rA8;eZwm%Vvqpb$B@C*Q816$t&0_7lE<3*BrPI!nWFxVZ;(niw*nYKU!`}q}SLGJDi2Ih%UGT;NNOSFcbjB>-Trt?5=ubZV>uA|X z*7@vTkZvFYFKDXVlP^MyFoMSgzuIiO_uxTIQ(l2r(rJH0pxX;JcZQLbBT2nf!arqU zri?CD_p|acSaDYeFze^LPG5a4XK3Mvpug=6=87vnJbZ-w7&$fz1N? zE?=nJ*XQ0?d>|-erh)nSh(%x`1!8Ex4q@7WYE(5e9O|d6f`eqPRw!X5WI;2WSN-F*DX+ss2J{3@! zNg8>in)FuH!tX4A46prVM@D>`j_b>jPkUUihLdM;8gmpOMlRLA|A7o=(6u}g+FzZh zbfV+vsh*sd_{MXuPk%|tnY-$E4bQ`RbS`5u!I^@ToP32TurZUqHx|XGpOlLm1`R=wjlQb@izB7`}M%sQlSezX-J8!xgplPG?GGiOYP%=aV&QQl3Z zVVByB-I6lCy*TdVt;*$%34m76jlPRfAnl$Feik|N?F(T$aaxVYf-LPf8amHu#5Se8Ay(_w+06Z|?lte1Rv5T9Js zXiuOT>Y&T*7-AU%DcNsg(fkG0^59q$BWEP$ywET=sZ>D?7|hgh&x` z1a2YRK|}CZjKjlQ;KdkdLwYx|cQ-bFvCpHIb9WTmB-ed}9IM`Q3b~C;T3_4gDUKmaGHup!>Kp);bKi%p+Y+^WxjZ(= z$tqyLOtK{iu?YrU4^_^8`d6%qe4V~c+@Tk8Nk||&I!}eMhSz`*;Lqn?FR4EM!U&Eb zHYK7h$W64id@@8i*<`WO-Nx*Hk5o%z;K*u|lw>p;TepWYoG-u8s_37CH%>B?RT31cM+D#@VrqEl%1fVn*;iHA}kkh$x#WgeLaw|%EaEfM;1L4)8 z%ouZ}n)Bbh6Pt({z0fU&Vg`lXwp)CvqvA66v1VSli3`R9Y1M>UX*p1pRTgN2TX?nPN=>G=(nKWB3=@tGXb3jLjU|1f%c8dmhZ`B3+N zLaY^v6oH#qI_i+p5$bR#t5|aoJGI_SKe364iOI<0piV?4;mX(w$1bbPRs}S-RvQ;Qg_5?p z-)SzlI1+o-kVr~LsP?IA)<|exb$&XgnM)MGAm5>WsdNMik&7AW67Numhm;6g#xF$V@!7)g zo!pI@t670Yf5?xg@U)2*%DM-VtsV#uL!wQd_iuXc(GbSm)&HiCcy9IZ^G8O*osX)k z8VQ4#fc%|otieTUXz9q}gY3YDNjpE6t zuGZBZLL@1Dk8m^ETpXt`u_a=$RLgkOF&W5rz~lHUeXsuEVm77CrskgymYp3|Z;S_6 z8FBEio|~Iw#i}f*o;N>ubq*z}PMOYwy!Lr|hUQF@NXlI=mI6Pi9o0H?5ed$u!Ux77 z`t|M>97(tkK(#oVa~C((g2%$-79AfS@m+{m?9qIys8;cd9xr6QeY6CJdywh8P}BPv zAgB((XU`?YlPY3ae7h9wmc)=a8SEZ?ZU(Q5V#d?)~z3m+luT~4u6b1ZzdlasK z$LoToeN0Eg)#xgI7#{0~;+PxR_2?oNfQ0+fa+V|J$r{)je1f_0r zW@aN;#`YWkZjud?S2!BF-MFF!wtuLCC=)d;yOUEm;p)h=_}#cXVw2y#)meNwMUhzu z?TnlCVgHW{1%b~qa{tZE%^!B(K5}N|{ z$tY68$voLdvbMN)tv25CIc%*I7{4_lj*pEc zQT#i@tclbGVVs~~O5!GN#z-R2uc5v(k(;^~$u6@v)+f5BO4A`BVR5Mk;G4>zJw6uW z#0+Fj2qOFi)5YsuUf>l=*FuMsO0)Q1T--FI&mUX={@`&s33KJ{*e8Ut=cR<^+=Ke_ za;U(>r`K(y#KqseIrs^ehp4-*M8ivsy%Y-4c#H4t(jX~IV0(?@zFx;JQ*}vA%%<*> zDUNl?QHE9zZ zOG}ux{TA6-Y>Hew|g~ z55dO+1|O6=6#dc5=9#A$?P!Fg!0__qTah2tc?)iG33gXu#hkv)bl&ZqH}JvWunnYvKEAejV% zJ!{EHT(5fXJIhP&v_^v!QK@?QsjYd_tHkj4Y}pKe)fiO0+Wn7*MhofDlNbw?pxpkkt}hJx-Bs$%PEKy#TW=mNN3hDF3WM3K z+p`r?m&&N>nc93F+9BTJFmrk1in`SUIK$;JXevl$S+^|i&(y-%m?-v*csI>EUt^AH zfy8^g_0xyfTgNkn!Y%o86zji@0zie`kz%Ps{P-wj~^cOg9}MB3&O~-45T3 zD!1I47SV%5YgrKro?72bYl~B^FMqm9Z6PxRZ$Rw69mTG-IbuK6s(wo-QY`ZV70n#L zbh0&6Bf3}MW(pO`%;c}ccPt_Q5K+ZTPi4U>Ykh&zslee?tt8NTnKZ+_NO+^}6ig5H zo+VgGT450KG5$aaBe!y6syO((zCm6CI83X`2aF3;WyK5?UDNP7@G=v<&P!BUhX+N- zC9oeBTDwS|5Gk{v`rVyEAgb56LC4Fxfj!>1<{Hg6F4i47s!ws~)fxD876FAbrkks2 zOW!LUDT6~oqT=I6@%%7}K4-+Ihx>=&Z9Fej)mv?FgJf4$>;c3W??A|?a>VI@>q2|m?4QVi*F82)sAev_x(0j#trkQh zh4!!Uf81oC5W2+`HW^xQaIhZl0#5Uwj~3b(5e)c3<CV_4)|4G_j*QX(vLlV;jZ-5Yc67l1W!2a z)D1&l8Y=zm(Da(kV#`^a`L324EYGao#=jhQ)w96x@Hm*0`mn z$z4(L)AcD|CICua-H#}0OKW+VM+;R^UPP@ka1(fRdW7fi4!ABI>!=vye>TMtf^xb= z$0IlhD6Sy_2ZTh-43V%BZ2T`hJ@qa-Hn;C-Yyfd|d9XGm$&86i1SAc*p*zCk-a`(ENb#E6En3Mm$ka>UNhB$>o{ej7 zuhByw$onfWxCh3ZL0N?)RR4#mw~mVH{o+OkP(VrPMjE7BT0lUg1?ev7W`H3M2+}Dj z-AH#1B}hp3(A`6KbLab8?|bk4f7Uu{%{kA0_WtZo93_?}6!*y}YG=MX<#`ja7E;+7 zNd0i9l<-#~13uG2I`h1pbprIR04Tm9bv3Q!^p32XUD+e{r)w(hAvn)l_g1lHhmn4J zz3h=sM=Dj9o!oN9H0h0XjC0vLhpvS5dl`Q`qD@!ZoWht?Uc6cyMJzPK@R^iBp!r6} zn1ch|vdFr>W}jkR;Bv9!o2D`?QmGr<}*Cf-7w70c<grog{(97AN_pLoE=F<{(aQ7bQ($TTK?7cAF=6I#-UiH!mA|W+iKvN zV{v2@4Ro<5fvcnQb*L2+S?3``qQYKX7rJSN_)l{v-&^wVlximW{Ep_?qSU2DB0b~Q zs%h>C{N-8=_ z74N`-oNp_?!#0}zGT#f(hu7R7_t@$XMf6m;?-+uTYCpoF57*iqNn{%<(k|scd8Ht- zt@a^iLNE>%!HKc+Ny+(K8O5j5S7zpZYZF_cAO3c9WM%4p!pQ3{d}9FWxd!wW6X_Rt#)P z(fj#BPR^=tQ-0*)Ry-g?br$2QB9>@1WTTcg}dIHs;D>Pu8iCE74$s zQ7RuM9C(b`U1K{zII@I7#H1;*ECmWzBsKr|-}#s;w2BH-eR!7B4RggFvR-0f%tS{`E|0 z*MuSFGsxUycjR>y9~LSQdb_cs!_$pa?oZ3Uo_w^KaA0RNL#f5+ zGhrZ%7`ab*YfbZk;Y(#j#zq>UI^LwO|4!e2&&Ojl$@rpWoZo4C|Il-nk=ZHI8w#Vz zSrmvl^(^$M_mx^=oOzOnPHQKEM`g~Y4ZDMpL_Jye!F2vT)V|Dsa;AV73nS{+q9m3+ zFEdW11z7ptf8A&xP*iGT7vRqwpFeWOXiR8nFNrlN%35gq?67ziCOGTF8!b=yaschY+ke_9>wO+a^#9#S z$jO1Pkm&)2v{JFl0%F`K*~B$!^x)t`nRn+^3VW9_te{Owk)Tpy_R;nR1ISw_SPWb} z@XKLZz_&0(;LxlhvxJRnmv5`e#Qa67qWLO|;x8my8wHqsBTujK95@NaqMJUR)auak za8srS0Z6q}4TBHPKQvY$9);QaA_fA<7(l0C!`UM#b44WEyi#}aJXYTFGi@1VegJLa zp#aASk+-=QPt6>Hz@du?R&L~iBxi|%{1JU{zt3~#QeCa;-t;}LJezGs@OU3YT*sg? z!Ip+fvRHapR_{fZd4wP#bVwar;}Z094blGC)Y5@iNMe>R-Ik!r_lE#tRe;- zepqQUIuMZ}ustRPk79;5(zy-!=ID$Tkg&|(S6mIt$-17eWEkx;dSIkQfYq$TCE|6L zVG06L)aq_ACcdoJG}dNa@XHBeoN=rCFNI@&$^XyI8UQ*$5BYs#(PWc>1Sba=R4 z+~^bDRUz)U6~Uvo`*{wB*z%1V*3;#1%;qt0e`ojbcpk%asHO(BbZ5H*8lQh?M(v}3 zlruXbIiKOjxDj6u*hLVG(VBJG*?Ivh;rvy_64avo+bGwOp0)G!k%14N2R7&}J3AI{ zmaljZUh!;;W%JNAnQ(6)&&Nd!G|lcd-HaW>+pra^@vawGui$Jra`SD)t|-)`UT<^ zEKtQG92D1M>*kG2yU?DXK^i0r0o!-~&qCCFU+{0$Ki)5Fz<6GumG1w6tesvU4s1`L zVCVLyyDt~}iLA`geYv(1`L8y@vc=wx2_Dk}R}V7jLXM>2P_SLd0U7_I4*|GP@VT|G z{Adp9^{nP|{tZYleLSp z4(UrSzD%l?)%bU)eZ1AKzPoFJPWpXK0p=9d02y^*k(gM~Z|rS1jh`*Ue_4Nl^>>E) zN3P%UES zI?T6pRdm~_n*+B5vp9?zx@xA2)zL`*hL*%QuYBZW$Y7dh5MZFpV!zYgW)c;M%0ZuH za{I(1J#xuFrm(SDe0^kQbVoM9%M;el1PPk@+RaTO8p49}o~mU6s>QWNG2me(3b-K} zWmb#V2An7@cBxw11D@)?eO&M+2Bq>vOWars{tm?rt@7X~vWuwMaK!2Eiq|7R z4gFI}35_%I{{~!O5D@7E@9lNpuk{&aVmH2ldDjX|o>s7@tv0Rz^5IV&&*vP<869jf zwbfvgeyAqX(0=&dWH8n~aupz2R%zI=*BwC&fU==Q*!erw0n0>heCLZ_TfgJ{;?1gaGKTHistS#5q zzQ0)OSRaNNVR-gX;m!JTXdr?vImGDyjXy%8U;}G;(Rsm}TfGGs4miu58>w|`&wnb> zv@ChTw8MP75)ATC=lL9a$6g7*7h;pKuP29P#Z+!>?QfSu>HuZj{K&6-NR_z73zgrr zrYbD#b6XwG1qc?K&8&FU655tKfM;<;bm&VEBA}T@=!^0-N#zsc?e~WT?M4U#C|edW zubD2&bwOz?2-FNvuVY;XZmKY)Kwf<@rh_Gj%)5R5T5ij`T2x4FPKrZp?*zQ2Ik*$O#<=|CON(u?sk^bNetl0Cwf# z!cIm=p|Sr-? zs$>+-tT$UU)nr|1HsLqr9u#SX#g2obu*%Mj@eY1e!AI#UHJ>9u77o00pINpUkpy1@ zKLQ$^_C3Y5@ySX8m2fWZ@MEqC-IzG@0$|R{L=2T#)Q5Bx&x&4XC1@!DMRK=6anj~~ zbE%h9m!h3miDyy|L?5L2Hn;I`mO)EJQQ}5TyY-@0WtkZo^Xl?O(7mdWXVs&fikC7P>$d z4g5C#ax98k{LmZtofJq~Yl+qh!NF-F=_?lzhX2gFzrL|y@_h>{UHK491MMgA45X#O zSvD?_S)VNyMSMXRpj|{EUVKY)5L|Hd#fXpRE@?7D(%nsUiIVTv5%GZ7L_T0W<>L3% zmg=STIA0{G0J?v@8v}`@JS*9fD2VaDr({b(mEc*|?cyfv z)98?!8x!t(6_Z5UVC_Gi6Ffe4OVHKJ6?wmnZ*Bf%CHxwY+*+OP%H9>fFgf?upT^$D zWK@EKaAeW^{|_7h{zC@>$UG78STSKA#XMbIQ4VHpI=3_2)X^KAtIAG}YOqooAsC=+ z8%@UL+djt}NMad}BNGCgNnY_na<|b$1NpSOa}qhss%;;pjQy7M{ng>NrTe3<*)i)} z#~5MG`7VE$lV|y9lG^-fkGNgGdP_jda)St~xM8L5J~l~xQmjam_8Eq^qQ`bng_(^GFGI7br>A1@u3djq?q&b^qaUSFxy zYWbHszLF2TbGLY!m@b9%O#E4mu6p)2TRl85S^b*MK2^3Z)SXQ3Nrv6Ba>qoJ6!5+^ z1@QWw9xoZ(x%1|k>3N&ohws`KG_&rSWa7%-#_21$yX(~~E|-@-giBCO0xmOp1qzj& z9P{mTYMhK3PJWPlCN%%dN`+kq-U{5mH2W@ZG?6vbslgGOlks->YbJ!{gO`q%-LHW| zGl-GrIG6C3V+VDu5(G1Rr6<{ujh(EgANcc#1o|N(Gu!0OM5p@B_>8-DV9!@PG$2`x zGyvWNyq|@B6Fge3Z^ggApbo9OX-6`&XQ?3xdA&1NQiuX=SlgEww5|1RT>ib%#4?^7 zt@%ZY`!Bp^xdoX+g@eC=PW6la_s6U@&e4Lwk30CLBwIx;-pNq253lvbT3QA+de>VZYi$U^+K9&d@dL+cqZeyF zHR(Kd*!EAZ()W)_ue`A-e%?}Lh}aBSC-H5w@bQ|Mi`|o$d?7P%9B_@-d_9C-+28FE zPy)i_bDpJY1y%VhF{;a*UcbsP!UbfK={fLG9NP#;Jug``5R-i3U=paR9gkALrYuL6 zYkMHENV3QQQPCZL|E|jbmeDUJ{X4TQ2Jg#Z zSXv#LqtyyloYeoe1K9`$f83trzl|<0Y8=Cy$JhwIev#Fu?hxnafUO*0qESwZ^?gtc zXqwm-ii-OL0y$x9$qlvg+PMyRjNbW_Jd$CMyiCkUW_ftj z7oEq_ScSRiA8&!76AUe0}hxVm{k-%<)W_=8OH_`0%qA5t*~ z1v>Ecv~pKdr?0W*-R0UeGv-<@C=>`=Xc(Prrqh0EP5v2ui+8!=b8QlICePR0TmGB) zIOp*c{^vRh8wtoZsX$nO4-`bmM|p@O5XJ_faYBjQCNfdv=&i4n4;2+}{*~;0xo=7y zxys>QBLBY3i#-0mDF5mQxV?e^}Hhwby@J(7Kx90lAm-)*f99!T)?9WS*7s;qdV z`%>Lcz!=)5w#M9RCpjDs3cSjr6OfdB5y+va3%BRwe>CF%m$HW5)6>IKiO^D`BFLbN zvgUDHXd#xYuSU7DwLF*=Ubgs~6aU~2_56ZW9i7Y`)R}64#`W!176JN~DLx#baTWuo z1pJV0{4|j_WYb)Bi3KvWH;t8I45P4@8yA5no`#vcFN62em{+Jr!y* zRS%$VDBfGyAH*SqbPb2|@>lx!g$zwb%RO>>8?F~yYxRSaTp2}1hH*05H~^n$w=3u4 z52=l2)Vd4fb*T<7M@w*0#lZCP2(=@n2CT>^yr5~f1V z*MHKMJulu3y~hhP?wsx83oO_3_?3iHvi8OX&!)AnEvuc?;#kzLA;dwkO4O3n)jjVl zQ5vr1wD%sA{aKlA>QK1%-tQc@+f7Li?1ZpSz&>(cs@14%^|ljy%l(_vpcRB41mm12 z4kPt^AlPa1UMhE#!!j`(dMOqAN@GBt$SApm5}6zj zbPkV@ZO#uC{h?k#uC^pC^}#zKVv3-4QSmBkFHltu$%N?GxD z9X$@*bsKB;=gs5xRWbgBSfRh6bw`&o4*j4i%}0+P2%EE_otyR1^EgbrZ5L~Ss4|AE z|HSjTCU)#!0j>-B#(5go#f``oiZFm+FdPJGayn#uIyrU8A}f&VhKw-UCfhp(x_tEW z6CcT_6j1PGx<{P9m;ZaXvdRbTDH zO4(x&a`r?MWAg;A7yfi|>7&RZ;Hi!Fuot?f6^&qx?O$dUjgP@eN@p+D_P#2QY8ie( zAsv-o9j$Dq1=>!CjlNNXPz=PVY4^iE{L_&m{o)7jb*GrK?ZGZLs684$Oa^9O@&@m19 zmaWC1meT%P)rw@f51X>9yeX$yE~~wN$jvc&V32;+1$d+&Fv!RHRi7o4R*FR>_;uQ> zD;xUaxY}YW9@1Sb$4WJ08Zeic!i%ee#j=$0F!%XshJB=7pe`R);umzwvKgAOG{Mp44GIq~hAZ zw>tKu1T*o|Uu#xmms;G1H!)L|6|;$A@etY&t ziar%$W=|3*_|Zc8Kk71iAug8Wg;rVqM?buTv^4l}SSMTS(NYH|aebmTBxb~#Q=*w}cd0p!mM zBv+a|?GF@Dlij0Ts!(TBUq}VN>f{xR~JayuTrGmZ1i~&ixspiklyB17jzl$+p zf{~X6!~^i0h2yRl<9)JXNgoWz6gv#m=E;Zxkh&@KCt1+ z$0HQb`|i$>ZFBW-nxcxWr4)|`bj0!~!8>tfZpLwy#IsVrJ!R_R9B2FkM}?-{JoX#p zS)gAh#QkvA&|)f%~>FiagFllwE6qfLkmjzd~32e<+yp2v-NI$*%Bj z@6{7-{kB5;s&8LDRCJwn*T2AOMe?SjQOz7o_G28Bi^($2zS_<=$>CA-lVF>YA;K9jzL2T@P5k068oLKuYzyDfa=X}z>k;D_c90b zw^eqnx=)|nfB*Q;zG)!ze|Ry&M`?&D4Os<(9V!^%WM5Utc87_J?0rowB`^O9l)h>P36GIY#YIZ|-=FNY%CC{)x*&^tJa_lz1hF>9SQ8jFfnm zWcM^u_I-ajRiOenIZ1op-hK);Sh3woc)HXAn6~`(n~5!26 zLpb1QI7;jUZ;KRZY*#GIN)ht!V|QXQqjM|rIn(1h6B8d~uJZ2k%@I$$Y>rz((8XzN zyRv6M)WYY}4}H-wAJ-mDuV^>^z*nlA9?$S6V=Qe&MNx1CPbP8(>qnG%rz49ZUxVMj zI#{Yfx&;Kwx#SG)Fr~m55Qhwz8MN(_#|Etwr=G>HnB7vVWBW49eooY&v6>YUjp1T) zSh-tMYE7G%X*5}GC00=d`Y|pm>}u?h-NH34^R9w&qO+_P}E6A+d3@;3&8ZMCR@eiEz&;8T++ zB=auoS}ZR#Y4@?uLj@~MM@?DzMz+ktLL!TYKGo+4c%eA%c9A1God3`inp2Y2H_gl~ zZgV<|+uH&`z6ym|>@vGj3SLzWRc0}+((t9W+6Jz?!&D-03tG9E#xQ47L{?klCzqQ& zn5E8H{}1Nla@H41O;H>6S5v@NH^QTyntBnR-KBiCX#qQ6uxuoaN^w{xL_a<2K8PQlB#zjcf|p3TGoUCF%;fpDUvBJf*utUlNa zq=1J9Jk_m3xPy-YUrI#bJij9VO)7lu7`)M+Y_H=a-_!vIV#`VIm2sk;wPr1F_NU0}K z3y6dFX}^e9Bmv&H0WFA-QqW9Wxz@^4e&dgbnYZEBRi@AWSXm;rp-5&$O&3X&T_04Q za!98H3e?^*RssJg<}CZTdFvs6Pu_(z&du8L><`qWbXu>sEtvOD;)cFMjTk_^*Da;t zN%zZf_Ee#oJJxj^O9tI#m%uv1I(#k%EPgQl%KR*f%Gq{O0@&T-P{-dQ*fw03yDGWI zgfo#xL04%xYND=6!g_0x^R?avYp=@1U7s>t7$#y+Ge3XnXq{cXH61B^%syj?wsl5v zM7*NIdF2#=OSYdOP`Q*q=fxIhe31s->Tvi>X$;4bx3>9wLGtv4g5ud{X!ujU#nh}q zR44XfS7Z7x5WysRi8sN+L*O?WVtkAbk$h=yMaa=bDp$s4raYSYV!3a@~XsK+wdTpsUKw4ekdi=f*iCp3*OR?;Se=L|(oG z`6G-+LIzDa%%e6_4qOL3@5e=bK3HnWkWH}l#z9GKbiaD_-uFUP z${qf5xU@q5>T0#zib5Dd0s4IH2=vq|FT*G{0(m zee|hW$q?mpma`#ouy}>s@~RQku!ReYwjB%CTM?Z?!a+=X3b7=scX9D)d)d@@CF#qd zcwzA-=}!;7gHW5T)uresmScn`N9)u6@n&_U&-sF-IBu`WDr>I)dSZ5emly3AefE!U zB!@pof!8GBr5Hf$(MoevL`YdkNf}xFSmU$5KjjJ6A3pSyjm*FVjeJ9e3uKpN`3emC zPYG1Qj)}b_dU2&2<4N#Z1q8fhSm^3L9pbt;wQ@j@ubY&W^X`obF9I6)wNm366VVp; zwOP7t!KnS=dDi>*J!{GR^E3bT@}aK|b2vI4YcCK=GZRB*#>X9I-AkWj6xq8e zZ8{Z078T~_M{OpL8#-I{mxDPJ`SllK?Nnq8yMV*3k=rM356h7{y;+x?n#vt<)Df|B z@#FK%U%bac=b60UhCq&^+DL|8FuI23;K;uADWLrR?}N>wJX03lk_v`xMRM8H{wjYL zcyeCFsSfy(26MUCjE^%~9KO)27*@G*oM?qs4a~~9?d7#-pxgOOJ~Ykm*0#Sy>H#ai zhX*EORouE{hPSakSxmvMrv^bU%3N6PmZ)zxY)b6!?-h`imlhgj=lusy6E4E~J96p} z#K`(vB^>H^5*6w^)huMVuzzlKg{WxQ)+>KW1OO|LRE~dMte2(tZT0(y@`%X1mGu70 zUd?28`YQd5KBO87HfyH}J^eI+W97ebK~>;KSw4P2D8kYX*w#&Ux_u+3 zCbH&4G94GAx_^F56+-5Lzhz*_OsK}H{dBY-3%SzSnf+*{V;S#u?)uZ9AWqO3HA}xH%FuReX_N%UUDad|;qK0ZhtDXY#sJ6h@Q0*l_5PSyoTp`S^LHvy!gA zbVcg37sIVkilDmpaA|urX~c?-wVk>?*y~7ZN1TI&gKt7^o@CWs&K4aFhw7t2hJcjk zEEk;Fy7H+solvV!Ko%JDBR?*dJ^I}nOH9RjAfT7}yVQRBod@)Vq0!Y(ZYle&O(34j zYv3O?RLot0QW2U6CO7v|svLw{T_NMa#d%O#DXA}jJy)BwaUCD=GuMX zT2fI%uq9w(Po6qw0-VdY>uEtLIO%kxKPaqY=(&5UeIO{<0)(qEZsX;ZeXD}89=0rT z+z+>%LehtRwrlr&oN*ktwn!xaRv$0Uv6fqYDmD|W9_QuvuFTpTdo-s|tFT-jIMis^)mZO{9e zovO=^lFl$X^~+j-rfHo=p9B7F@FBUbpYBD_R&nkz922J7JpTbwwudvlWGR0n5*sib}h1uF#` z2-!dC$c^p4#ZjM$wh~UQhAXt%4Y= zBM2O91yV9)XmrpY-)V-zXyNire+De_TnFUwqb`6xTbSIkeMGe@T??VNA2i##JXX9) zlY;aQIZRfb)&+)G6{l+NB@MsVsAyXgI?ZQcPFQA4z!*^KF{z+8BXG6lk!{!NB+|Ye zf&UgQQ!3)InfOQY-n7g%zVRt3u&p7c)1Ep9C0Pd8>7U%#5nR++rNKd9wKD5C|U?rb8K zqic%GwdGD2h0!X7WV%m>hOci6*<~STvubMe_!~cP#Q$Wv9M%sM)1hr#ICs~tR;s*r zJ+rzv9>==J{<<1JEK+q|>Vio**0==UV|)pCidep=>>bIl^!Mmjy$=!l#3ue^^-`b$ zhFtWBhWWRd*oW>PQS(R!N$$|p;I~yLT`zCTz>&#%Z^t?VBMu4w-?>0 zSRYLMs7hj{SI?u;-e8BkLBFengKw&C-!|sw=TE{N9UQuPvEWD`nVuysr;}I`hWQZS zFl{C;_W=YlzPb6wd^CJp7;ZB}^9`jw) zIZ@#(-q@3Hqi>%th%#J;nvsVW^-651G#+h>w#s2s_xdA+4SRTt($SgG#H>_LyXPuq7Jlu`~y%?OW{()#XrUMBqO;2}l z=dDC7T7u855=~a)wEDi6(`pt;oJP@mlSOigG*3N%$E%FL2%8LNczGjPJQP6bEL2LX zvYupQ(`&%QAY%I(Fj8n5F6whgG0xa-i*Ebl7lsn!st0s-v5BBWbxHI zYSmp+w?X0c3HE9G?Nv);Uu&z}8z-n*%?|ySO4urn2`ddn*zCfZ#oh{)UuXaA5e^V( zyqSB%Kz+G9W|qM&jP!7Qih%R-pMgNDSH$NjMgME$4daI@9`>sBt z#>h5G<`u+Q*E~9it6vp>saX>f&9D8KW0o^;UVO(j(Ft*IG^GLtIj!zB=TWWizk+;2 zB0<@ggx7W}!C~rkpvo;W#-J^GGT#hw8I)`>kh48hOJZ7S1Nfz>V zH2^I6zVcNK_S%q#?W&=c=lz&Uh+h#4k?D`TxI5#Q-m+;`!RI)|zg1zcj!j-$3#wyQK)7Fa#1J|(#%#)-wlLx?_1#*u#cR?SVQ(EigbNAlyE6YzqH z$yA@J>)tLy6kHb;Az0rh`9)fi6taDx|^_IlN?`G(0 z(bBgjxZs1B-IMGDoyxeA(*k6}zAkbRIa2KUvr3k07e0F7_5?PL(>qpH#0Ab4)NQf| zftUX-E)FOuK}3i!F)9Kn{`I!7UWzdCs6Hz1A+Pq2N$M(FAoL zu8bAU+hpdJmQrd27yHu~z{3Xh8%)${#8Dd5$`R3Zl$)C}^z<`l4@a2{3=0{k7m9lQ z`n4ThtO2C=ijAhqq*ma&ATJ$CH!5bVxxR2tLv$wpC}xfL)jFUjOSytztp_2$UK9_u z#2PAT@Co?1y?VLt2?2ll(Rm~3%bg2wf;zXCj7(hT#FLi;?Ez22$0eu%qF$7=v~=HP zB8({oCdOq?dP{gr@Plk8N+iJQk!1Q#_ZG>G;E(uCTA&jc+Oc3w`}rgF+rbU%@*-t9 zS7+U~g5*2CKw7yv`5xQC^uc1w_)Y3Vf0%k)M%wW_OMQqVB_&wiOu_^Jw|43rejZeN4+Dc0;n5A-s(&yoXqLa`{{e zGJRann|I(X2(aac*mX(w{*d3;1RWDiaNd{KZwdzTH7-raA|*)Lv|0OVTav zLiInuo$<;y_t@1};(}CkF?LB$(?&?#fkUNG8#1fr{8y}#bzM};Oxb-?bwkuP=v@^; zKM(pK@BAhl1B2o$^Sncs9MCX=PiIB#e_K*PE`3Vzn_r?DJv(zB^i38zdH7wYpyTy) zfiN=lB5+~Mpmio8VoLm)DBChCzEfUa^3euJyl&{OZmF`o?6b15c>z+)at=O~)n!{s za#dRP~b7_Qx3Ab=$yt*U5Kv`{oc&JJ?s*&_m4O{GtR| z8d?F}^f{FQbrvKW?rUddWJH~hN>KX?ylCgW z04FFNhKYU`%w!>R)78^c(E9APY-NDlNU;61$ZK!e_5b3(yB; z2abxTqw9FPI6+&j6xR}^OlQlY83QeM5;?GK-lHq@TGE7ig!7X!CZoLTXyr;Pf4I3z z?8=0E?*kNc;${A#GM71p%ldQCRP&vf>&4N(+R?+4y9VEr*HcwjRld8$I{+;^Z#9eu zKPP9Mm13?WstTj-|W}^%Y3VBSpIV8zSO1Ghse;>nL<$W|})!gomvib}VG%`$!D{+ypS1PfNnm3|FDBu{c zUps9?2_l0sy^op&re%~xTlD=KTUyAJl$8FLP0>7%?h-E0H-oSYF@QBT{x_hARY$HW z8A~~OnX6nMEp2#{v6tVOMtJdx_)VeLmTj>()b%yp<&vpr5(DkGG-RoD&3D_Qsli9L zLcMoZlMGfW%J%R`xk`sEjavf>N6MPkjaTKJt&=J|0>=e7u%;N@*h~t&tH3_LM1X*J z4yE<*(ruOkJ~lfWde;Bx2nhw%Ge-9Gw?HcYS)nefS^s@^ZIya+TadfPcP+K^XgSJ*7c4@HRj5r-@5ad^Bkva45 zPOn|N2t_ubH${cJ$}FFMb*#xE-{wd)|7z3qcd9>NfujX_^;Bv7|(q?ChUCJR$~NO!sNE!Io#L``ao z&*gr{60)}}wds?@+PX=;896FR)zP7e78*IGgHTSA%cvBw(VWr6@~)YW^+EM}^Y+!z z9~PiCKv;D$M7bRla6y4?LRnw>Jahi#jn|6|n>ok%Q;fob9Ia8d%Ttp0D9x#9*7baxJh8xz!LN`d~=wH|IxLQ#M%|wjRR{%in#{ z-EE=3XubT~VmJ@$Gak69mW3P!bLJi!-nTmd#H37j8p8e;as0`>P?`Qy zdmJ!NTANWrE539kZF@IybM}?y0kb|Kdh7G!vcqoS5sF_j49YpY}`B?z(C;D)8lz+oA@8NiE}{S9S;-Q{KlQ2$~-E{Tc2INeqs7YDZX+G!7zjc*j@OMZV4% zDlozP^amakYf(^V%cW5PgJ*k|M`#w7W8@aN#}6Mm-59`l`!aWEH{2;&H#cZ#sb0V2 zw%p_bb*_bfPN1#`l{R1txDw_jSa5HrcQ~vri)di7n5on=%V)~u>!F3~YJM>lDsfPH z&GksDNUh;@gZsJT1``0OPPH+i#!%sS8-F#_kPh_C@Y!$#80}Drv6CD=Olv{w?(VQ8 zV{**IE<3Ll^khc_0l{gFT%Ya*5Qu?9YLtbz`Of$&dR}SbggV~`agOcku}NQ49Nx|A z!+Db>LuY zEe+yl$IKnB~RHHXQcm z&rgq_x&VMCrfCr2>xTq_&yPQ(1L)yM0V82BRN6XR1( z3KKda4;RH4RdEUN3*>Cy_w*Fl-lzr(3Xb0M7u#uoFGXGS<`o{TVdId z*Lh_HU+e`;_F)F%wnW-Y(ruNND6}v9CcJGF|BkDgozn33t$XKJ5~Hd5g$l<~WPQ(l zbJ06seW8DFDifVl(0c zTjq83-IuOaH5S<&md;*tLfM=Vi74drjBS5M_;fJMh+pnOVj|$6!F!N-O-{NLZZ62*X=^aWW}?;a{?`+{9UrOY(!&{-Qa-=Gi=6&-mHH|JVWC}D^o=*>Pje@fG zb@;6;xApSdb;+x@QhFx5OK(?l{C_BW>#(f8rfqoBNFyLEEeI0Q(jqA$AfQ+GIo-Qo zM#UzFTvU_2N_N75TJh5&=Vq#gDC%Wc`&*`}rBpVy#kuk~G-LKl89#i55uZWU%6Jme-7=FB8xysv# zuGd~})zQ@_rESHUo$7^}#`M(fqCA^ad?aQ zg^IY)EwogxiTRutHd(qWk1yG@nx*TcJHMmsAp%8GD4pfG2T9 ziAFWh=AZ#{SpcLqh0`^vA{OLL1|}yrNYPIWwkOM^-1<|5UH>?)7l>>z!;(xLhBP zMI|P>Qp6E}LL-4}53)gP44#|408u?qt*#Puy45z`*xa-Ot}+so0^hi9O1BD#-7VLv z!~Q$xxo{JdE7@x!x2Y@)k+ia6{Xm&7W?4zZ&^i-3CW(ZA;uA=K3H`b3N05B_DS&Ty zEOW7gCMzc=Q=us3O))o?kL9I!`{4l~rpn54mlAAr5g*f(5Eei~bLtk?03;dBxVhT)9kWZ`HCs59_NS zN}(w$5jx~NMtDr=f1mrXV(aFUFI4Lc&((m0a|q#C0aZ}i#7}JG zSC{Z-=p{%u=|302IK+%JS;G~hT4pC~^;nU3sd+$S6Jo%Mnf9ZK^s@d6BV7?42^EX` zB(~1*(e6vR$o_4Uu9cME>5MN)*5CbzXYy*$&kQ8Y?3lnhQ0PNs#ifAD$xa=PuHL zq3|~9WO@6RP^TEyZb9q%ZR0I-P3^*|m7hD6h_!6Oru|T$L1Db{lLI!+V1sV(ux8-{ zdocv-i_CE(zNuHqQy;d(K|$BR^cRtF4>Ys+!97b^2A6z3Ep!ZybY=jUnG9wxp} z`dHkV=rrM!ha*a`&zd`$ntW>Z%Nuzjbmy`P%mN2A>tDKY zP=6!Jl1AlKiGx3Ws@A|F6C$jdnA8g?jjR%3&7x)USWELSac_GOFoG48 zlr=-N-A~hPigwo26wSISQEiNq;Ww$8509W9nbg=`#ggM$+*$|tFhwvn^5wM`IvVm@<~jPokq>0YTXn#PU2 z>@9g+HTUGw>6{y*#jSVX)N&-RO{upg{b+`hAJ4|;s}5JE_q%RN@l)^)?tAGMv$_d(WuMUV@ewZttUOeo$cKBd2#Kj~mD@6+ zZxWswX}xbk2I;D2+qp%Vq@vg)b3PZ+$0XA}lRV&@_fr!`uYfU!jDZ?za@cjg-Y5y5 z&osQ6Bd81)%~tLHrVSb)ijN3gj1|g0X=aUcZG<%@X<}j<()TRI$guRf`qA}~K2qTZ z2bS8stZ~lmR{387MQ|Z~BtFKxJJ%wAQ5(grbo_;a%@o?Ly~E_`3pW&o#;oUtld6pS zCKpwH(N?}6k3wH9FaJ$fNEMMSr^mc*_)C2I#{Oev5baYw))|eW4Z25VNct z^69(h64EBXU5huB-Gd1aXS)p!mH9lr5URC)o8A9PPb09(0bUh2z`(?`8&OpUEW95i z!fxC;E(<81V`KF@B5CfumA?{?$K2ac5WsmTwDx%DPnxGOFCj*&XLG3}*b%tl8;_pU zUC$Pp{#fF$+FGF{Sw$zOveqE_Bwt@dbehl~tXE=8gq%9g78VxbAYhMXc{yQna}ESol4V;&O* zTdiUeoqcCCL-;W^Hg=oQXinV-ZWw+s0c~^Ryir0N0*6ZKwdyx%a=+VL> z;?db=5a>C*NDM54%gKbP-RX=o`J5rB{6+P*C(#dFw5eL<0pw|{F3^YU8ma;ma1j}V|f!CfINDv?_4az95A?HxKx@00Y8pd-|2 zB><<2Z5PC!XjT3x@tSY+IU6bRT4Bn`Wo}+5Ue#i1&-#j~~Ms^%%adl#hN@@e6#LtoQa{Rz(!%lG6T|J0;= z)h=6x!2dft=w`RutvfYXANftw6rux9Gyb&!;^_@5`S?T!Uqo!A+Vklm6ca*39(!^! zFfg=hJJ%kstgez%O&tA?MS!?@c`ptqjx$9uKbC?@^+M8`8nA|k6NM8vrh9yR95+T1 zBX45z$lk%h)0rY9Bm_UOM{w=`zgbXJQi`%YFF%_!`g3(L7h~0I7sb0u#zgwAlh@Bt zsd@`t=ih4v4_;)ST=FyS^hVN?<<$3%|Myb$KlA%=p+`!ieGk>;KWZK*Dl>pz>rYSc zk2l#B-+Vx3Up1rd^rL!5_ix)lQ=jH^-VhD$kN@RHo?4h|!X%OnLwfK2|GGUKnXAlg z3$e@6V*TU&$P#64yl5ekd4~UWIWeU-JdNEnwe+qWjdK68Pj_qY>N$rh5)caaA3}-F z6Jq<*V}TF;J*MAOSsT)$=>Pj(Ft`AafzE}fuSqT%?H}JJW};}4lWG3H?v)!p&p;pa zf4v!rpd)_&BY63@RExO$l1Mc7S|Ij25&!k}KAI09w(35eC4ErV+PfEbZ^Hlj=runH z69c{eKZbiFws1EB45V7^(80k$qa-nOn#WCn_;lelPdr&^{C}g}y*dh6?U%{=(7D`` zCHcqr74*F0BPIU%NRjSj|HoZmntHLUEs9#KS24>?|CIIr9lQtP-82J%{yicZFr`~} zQvQF>JtLShnT#`&X?GA0DrsqjQeCe}E5fQg>73w@E#-U4|IE|6Euzn`7D@ag+24GC zRF<3jXK>KB;pVuAA6SbA>^h-}Ox3P#r2m<0d)h{s$238UN%*y#?6rG{CjBX4ZEbBv zN&oqZxB#Z4Z12ZN`o(T&S8%u1Pc=_Xzw0yn7@8pfb6ZZ(iLsV5&9S0!*pmcv+>5k7VEW{bgmT&Pz z!CV|v0wx%k@P!s!Q3+#X2B}aIA56ANNcVDb3=a!c&;=7+R*B>Zc>W&b^C-YON7Vz8 z!U?=qWNo3Oez7d-2EYO98tas z>wgI(<#ltIx`Tv}-TM5 zONN|ipcq+UOdia@Hc__8vDYC(mDeXzPhAg;zyv;5A_SYyV)bTW%kLlp1PB~veVLsI z`{ONI-pN_BQ;lhw>dEVNx)TOSAeG&0?@EvXq1oYYPZKuZv^Vhln1X%5Xyz9JsH8YZ zejluKE9vOW`e=&Dso>oLzshEc`H^`+2hvOMv{^?-*3d4oCV`YFQ5*q%VU3I@KdGKDUCFZ%i? zx0g{^7WHESpgVdTfk4y;d~+UL)(zJum8&BE}3;2UuOnvzVx3z@x4#9N%>5A5)h!Xwaf@u0DSlcB^~*VJq>+gmKLT8AFpq#B0U* zFc|F3`U}k;-&R;tN&%O^ZmEr7;*B`^vweR1WppUb{d5N%)vu8A;%arO!Uo>uu0fZj z1kM{`jv4ij02`5#lfOwekLYi^b#Ly2e%Q@}-{b^4XvJlu-)Lx9WOa9U?}N`5d->*m zgU5L1$IuE33kQwji_2IHHNfx_@Yo`RPe`n**B^CPPfu%2I}XW%pfbgD-Um;-V_#WY z8yiv*HTcoLIyF5V3fuC(f?ox`>U!-QnfoDz0AUW<1w@v~wuy3VRmkyL_Oh4cFVNRH zfKOo8&03*%akS0^d=hFrF>P(iJ1cOxQ-ixXR@4qqZ1<}j=LWg*AO3jJgIiM-xPl+H zY?sGIM>BvL^YHM1-@WoR_Q$g~7#bWzggSb9jsUR)sX8~8vr!I60Co@AhxPZsHe8LG z15%oF>>y0NQ^~tow1F?D-u^T|0nsm&(=ZY+fHpd=D?~zjjYw+~zZf67F5`!iJVgX+ z%*n~g1#tWJ_F8Za&kt74Rz+`vK)rdRB(N$N3DYA#Bo*|xY<{|G+7qK*F@ayxXBjSd zl!KkRpG-W^hJ-`U8y}i2HCK);354UoK}3~QicCrh31P1% z;hS}#190PhMZ+~~=jeU$jv#5!9Tu9OZ}Lmw1TGa+s>dO{d*6U@!eQMWMx*qtDEr47<AqPi)!K9J(2i}QHj*_UUT!pyauho zM3oDl$RP;0^PCqa+euHj?tw^ceIPSnziU^)>ww^MTpSsl6FC{#-lS#G0uUSpy7R$h zf5m{n$B3=&=nXO=LhbRW=A9M%Gdzq8@jI^JS=XPN0AO+Pv-0Z&>5up97{D`l1mnyC z-srS_H!F#NBa^*7Y;W5}YC6B3lPYCtna6${y_|{ux_0y%6DzBH`MoSm<#+GQLrwM! zfVr;Rt-p*+Oq`Ha!(R*+p*q?Z)0K;s2RmJ1!;Kp-F0Nf)okri?S=?v7@$^I&8jGfP zs&QNd&z!OP10x9OopVf5I!`YM+;;{M#o0u^*Izi-eQ&>_}q zfBWAfh>VHx117TG^K#J_ldaJYS0u3EYK0wZ=f1cM`C~S=whYM-Td+j#7_%^7f=GhZ z41#OBoAcGBBKVqRr*a7g)fqek)CJq|w@35Vr5=f(gtP%L&k3TiNVW5BIJo>kru0&r zrgam6f>6^u8;uUVd+ta2*8_Kf*t=gQG!!^Xqnb97^77a!s;!`1@>Bh0luiS&E7eKj_r40Br7ZHc%3^RDh@@0<9}W8AFl38YMyvrTK`6njKdX6x@dh3 zKo{%UeJQBXp*i8n|+`7A3=p~ z8nW+GWB>McY(Wf$Kl|*BR3aWQ3B>V!oI@cIfJcy+p&=az_=SSsy}!ih-5rceOpH-Ub_QOkw<4NO&s4m;X#z zBtPEc>e5&MztC`T5kQKthQNILr1ne7vd zMY_mzIjpQeBP>k0n&NIr#0nXggAZ~4FII#7bJJ1-y?%3UqP zjH1sQysm+RY}fTTXFiBArE#*%G76M6b5hP{HNwY%kzbr@ioeJPW(18pIQUq3abaa$n)98(& zB1D56PAM4oUKvX~9S;VGBLW1FNkEE}Es=Z#r(rYF@A{>3w?l4=7~=r2%X| zH}?@hA)A}UfmO{YFXsh_21sksflfBipFe-RtE;QQ4Wgr>e1I;4&8<J zj1N6BH8tYs$PwCG`EPvkUuXpU-*CiE3RV9=T-*a)O6i$Y1TnDwHa4#v{D3nY1bmn| z%68Cu1Q~Hs&d2Mx%xl*Hq@;DvCso!Q&bIk4s`~H3x$Nvmx(}rr;d)*c7EI9Ls$MAk zE|-jcx6Mv{#c7w0eb^8VKiK!WbbbyV=xQ-I9gt@f{;Cld)g zmT^zdyd99R+j8nuCa8#=kA6I07NWr(m*FVpxe+N;J4fIzejeMdNbMwfXi?c%mIk=xYb8_InpBqTHo`a}jHDk6Fh>?%_qQ3dbgQafQGw4P5rq9a6!e#52vx?7Zx zx+o3)gBP6|>l$lUBZF@K@#?p{j6hi_mnel=x&&KculT%-Rm_RSCnz$`aD-KpN$=-q zx70%PW1(`1N>j^HC^q(3@iVuWY>XPyV1r3qcU17Q+`%BtnBemR{9zo^<@j8SWIPn~ zH37o3dR3@^6(~k}z80PC=xQ<2Xm-|!IT=hK-zPDp6l~ls$3B9N5Xj++KrPHZH5wQi zr?=twd1NXK^55t{L-xmol&vC% zDdEJ5K?EUefA?^jhc1qGtY+z%OQ<3TgAJa2G7)~=K>Pk=qvqv)WWXc_GNN}z8aM;a zKN@bP9-bpIGZq~8h-;{qud9A&aBoJV`ZypjBIv5v9aIKJM~;fo)hi5IP-Z>n6=g0W zj*hhbC2wzm*qH9O^1Yy-X+FJ0QE7~#Q{DTB;6w$S>3=Uqm%j1 z1>ZT!c?xF%q8Nw2){ijByr!~x39NACTAuv#ozf$$tZ%0DYR}Pz?yF^P6_Nbr$DdYj z7jf56^(mDZ;~?^|($}Y0z4_`<$5|-2GbXy}G*I6+#piyk6btJ?uIC@| zNl)?h1};Lx?Xz;9u)l(>*JCi!%F2kE6g!w$P8l>2hQ}ldCB=Qv6X#g=cTzQA$?c@- zg+0+=u5XaM{`~k)DJGv+OWG>igS!K;%CvDVGmE0uG;6f+g@&B^Z#vzRB)1_mHH7c_ zp!l_p&rqD1*Yv=y{RMM{99&l+%Ei8cQF>!0rGZX*eq!mZNyQg+JX*bFoN=j%z&(lx zl@L<12+n90Y6rGllG8H(Q3+V2u(`Dla(bA z5HDgZA{;r=sF`w*OausJ1D>oy;?GgURqZxoSM}Bsg7_~NSvd4J903k;6zN-E zj?N0pyr-}nn=jtS70R3&4Eu5Xck~sDkGEqzT4(?nI&!+qdEgm6;Q6R}5@hQ*3qHc8 z@(+M-D6!Uu2+&c}k?G>U>d9!*n#+km{^BOdFB>Znh~JR;K(j8lbkhel?smQ3h?f8j zE@)P`S{kt$A@hB)rY_U-^QKSz_=pR+NRx0m;-uG~iaD!Pna$hXb<<+IrANTCTWX`a z!V`8sMoc>=pt%(4jKIKcV4_bHcK@(6LL6@}L`-hmHrCh1$6hnI5w90VB+gQlqiIY< z!ou29%6#4fN)Rlb@@e}iGWm-N9diQ427OWif|2T5q&EBcW;Zu}I?%E6Ud1KXB`oim zFD53Y)H8%(B9PYTa;Bv0iVWAUpuX2^K((ycZK<`nZH#<+WhcgFc_GERz1V=1%^G*i zcEdw0oF8QsPqoP8y>c^jtYI*r(71sczjQfQk2rVTB!9LO#z(`)-d}>d<+dzzSW()( z4GMmU^mt-L91mu@-Itc3rdh1ghr@XJ$AtG*ALuwdCjM48wMnG&I3nPYCx%04{qvW2 z);`tiNYugvP+FELnK^Q=WhA=)-i~zMegkH_!Em7dS=%!Q4!dR-YP{flTq#+!{i&KA zDeV_@_J;?1NpsWY>)jTfjWUyiATV(h+~4SCU-(s)y4a|$yHEkw;+}t$gz;V9t{m5! zzD;8|@Xla(5*k3nnwvOV;ZCfhd&Mo{a_suV<%Eau^U*NqFRMv&tcs#CvhKW+RuVrK zi`2lDKK1=tCFGXNh_ExK^IC*U^n0UOJVNVM!>F42yeV<2kNL0u))CPg9=DVAyiPgK zcdV4Q%K^S7^?#ii+}}4%c*7ZKRX-5MbU1NivR}3KOgPxKC7MbWB1AeScnvQm8I#I2 z%zQpyq9UFLM^u=YpZqTJvg6|9;zCFlr!q=ISKScJo!pfn6YnEtJ8#Zdy^auR6J$}- z?>YkS%M)MHQVIxWfg+jHDtA*gvl$iKj+V{-|uKnBGC8_oG{^?#`lC_wL6e`5P`A4=;`DgaGh)6XoqKXApz)2ZwC@#D1ta+^q~pso?k-qR{ct(AyeoN@#x8{WkQ<7g73|bH0YC zZVf8#OkSALLmrbS*{LE^v=;A!ircDt!oML(;^W&rE`St-ZE?$Ot|*{l*X@^~qUVm^ z-=MvEjaf&KCe!p5r7bYh`!iLsqhe2K@wbmralUmamo2ZD;bNz_Z&qcfn9HSvB*l&r z$>!Z_Q!+PgWqHLmrjG$Nv^C!Nn~Tc;`Jf1oQwODfMS^^viNT@i?s!>pp zm7K86VEmJJp~stJNRZcYNYczlp||_{WLN7^?Ff*A$=7@omug;izB?Ihk58-JAh|j0M7^4?zooi8PZgv_V_-!v>P}87 zo$Z|)7pPKTb-kzQyw$vYdks~`f1aQ0UY#l*dqQ3W@5ac=3Eni?)*gQpr!my6C@;6j ztnpdHXN)*5YPdnY1396_6?H9rWS1qWx!!X3@18ZzOSA4?yfb#37EZ%mrKKa`?PQGU7z(1WXGva7!~{WL6s#IHqvm_NNLv^|ZV zwE6J{j_{6Mlo`JcbKg_yi$}`G_$p%Go_$=o8%M6Fvwm8)bed*v@|vn&_EX>!H-VYZl-&)!rfsaelv=Nft#gdHQyO|( zW-a`nA>&xr?R3p2h~SaTm&}TM##A>l5J_-?gAU_mYYp*rXF@vevYVQ^r+!q1@97&^ zQd8CDXbrOCuQ%cSjb=s!X!nWdgTn?G)7$J?EoqngsydD;axxIg_fn6de1GJ*f(~zx zosXhv>EPB7c1l;TCS6aqMqHB~m+_qGO*EtfTH+Xdlk}4{8~Y@{{f2v#L?_!5VO)Q|8poVgcJP0QbU%r14f*M`sRlI`G}JN#`;`R%fZCiq;E%XUtN zNBHJ*8qSfvXlm@$pgoiXgyX##7WSX@tsD3xpX8F0w=H83mTLUzaljcI{~L>KkRB8^ zCL<$*%#vTZB3lDXkO+thAT(RAx&a0(Wz*>3;2;#gwXCen%*yKHWe7~Y?cP0Hpvgz4 zt)!%XZ?CJYHP`9)_oq^BQ*pg<zbM&GMH9dt;r?RpiG zkN>CGF@J^J`thH%w>Gr-$wYO(-peQT|NQtZe@s$wXX>p$_n_M;eyvWEj-|$aq&gvr z4f}PD(&;l|U42Yze!)KvVDfKPOf0NCQvO&}JY=uUZ3q)qaUmm9V^_@0Hd(=6($2r+ z#SADtxGP!j*I z%xjdkyugD0;Xq{fFzrRs!V(mxl~Tf59@^7`?=#+{{;`8!YT-C9_n8Mm=ND*@ zcc~&C?#-3p-wxf({Kee%GDAzujg2X#cb-`6b^D+vkRS`eT`!XEPQ~PIx*8h+tB$U%$5Qsb0w@mp7UIE!(ymwkaCH z#y&G;F5Pm(ntPMh1E1*0pQ4B-oH`;ffW{n3w%w^W!MZxfPQzoPdSD@*4fWUiO;LG~ znJ2!BLNoNv9YZ>AQunxd6A!BJEfG4`VOLzRCMh^kx}xS4xjS?Yxg6Bj{2{B7?e6Su zFWQ9>h22hLPr~X&jjngM-JC8+Vz<{E8NWJCmGD$kip z%dXdXt4xZOYV3mK>Carh8$QuzzUJlkX>TMavDWe)#Vh!z0Q3D27L7xkHwoL|@0I7@z)N z!>#;klPvkZ^jz{X&A)_IcRraI@M*-&%_}dmb*E5S)~1+uabDIZuD5N)7`Sk4M+uYGKM$P(3DPG2q zrxoYkrq-eVSz_VS`UZkONHKs}RUTR%`r9XB)$R|rOPv82vFk0vjm>g#idaOsu$?C6(G0k@8;kP^SfnD_3?DcrUzSa|+O z3U_0S#);KqUzJ-L(eRL+ANSn#CldUHQNYRRSW1AAkuliF0HlZw8XLihvl7ZFC3Q{m zIx)c^n-1Hnzfq=G4K=RY?Po3W>Y3?<4%=qz7U60O^_td=1eiMFpZX+qRJdPh7{GNHnJZl9~7aEpEtbNx`KEG{)3cEB+R_sp zo0_x25sk~_w<2AgqyW2&WBvG>V6>YXTxhbzx8Zu-+Fv|bbjBc;=8YrB?cO;`AOTLM zAb+|$x66D8*bR`kA&l{{{PQu|0oQ3cwc>Oy)}ETmxSx6OQv6sn04(dTK|j0?+vDNc zf2I00SdHoE6po`XFH{f@E~aZedW3dqAu&riOc593dx(KT>+ugfCGtE6r1RG^wF)+g zetcD*WFvn^i$uf9i9q~JTR|)-^NHEOCL7&YLFM}x&54Rs7=B=3#5OUZn;P-8RwyLD zO5xkJ{xjOYGQn1wykzZ`!wEkNS(^`9O{boYBGm$@KM;55Q;Kj;&I}q3<rT+krlJpl5XCWdvtXReDDSbOdTh2EF>27Lica`8aAo zX7YY98-jz~5IfvG>C|=bfZRy)Gi2?x^~}Tdtvd&o>u>6-jSW{`S5IA+wWt;PCzOvQ z$?eV?Fnh@dE!k6d>vom#`;{IkEfc*(EDDEgC~D}wboj?)Lw~FvHJo{9FXNdrY#Cz- zmn%0f(&4>xge?zQgAI-u4Ie4AY~x;24bqNvv8EQZX^+AE?E`cHU%k4!b)~KnTLRu) zeKqK+UQ?=Fp1I2DczrIU*eZmV>J|Vrww*-k?+ClLTd*&k8tK*( zPew@v6=0qCj9EW=-z5!UZ=R%`&QgsV|{o(I4iL}`iqaP>IQBP_cU71lrB_NOLV!4h$FYQ;+Zu8 zz-rt0J%UV%?G78sFCyJ9q;k^Jn-Ug_D!+HUV18IDRL>*l)%eqH>g4;P_b?nYM(W9< znUcgBci4THOXyx_&>wFVwHag`Q9MqSDhyH*bGOGfVb8Tht4|s@oCaOcC7)>+_ip)_F}4C@qQTLeIVL{uY{e2P>x`aNOWWP~-rQclpfKI)lsn8!18_NE;wdKRGUG$oS+jezt;08%1Liaw=x`HNiSfjb3)Vc|O z_vKRG4qnE{O4&@%PK3m<^W!YbUp#4ubajUPI@~;#RIezx>`AoxQQZe*ygX@#UR3>P zyfxevXvt%$-V;oEDMLeZH0vHktNJ7OB|b8t;*;-z83x3X&m|=!{QD6xm{p%EDg6$Y z*{#c^u0s~Te_veQ{`B%^%`cei7ni#nB8{zX3WI}bPCS=sU&=G2&#byB)wonbX?W-3 zKPeo&6Q{U^Xw3fP^W+ZaqCxy#%hYPuCrIoRAO6rLLh8*q7ZL<0)!myEX zYk4tg`gh%X#qFajADLr+1?`|v?AhUAmD^tiqr0G0&6{2LgSGlEs?Su^Z>0Kcxn6O3 zlO2<#+so5kXSJXeJ00IQSz;ZNm`^a_|5(TSVn+~ZbNilMr7-a_CnSUzo2Nq5+%f)K@Y~kc_X4RInV2uC zu9#hhq8Y3ZcAv^Mve^H<-xj?`l;yPLplm6T@BwOgSMq?7t1n^KDokxkzVV(q!(@#_ zjOA>u|92cKop&UBFb#*Y6w6i9GPe+~7nxNKAKJFG%D(3wN}AcMqdzT*VfGDkhR1utSbvUkB94AuLVvWvo1N+$W^ao zeq+3{^6_~!K2>~(}j^}CJSQgS1R9+3WUt~f6p@U+!2VC@+Zne)@{LkpWG!;FF~ zn3yK=Z80-8zk}yihd$&gHQ+z$xkgHMn+?O0CzKjLjx*o8KQO1E&^V}CPw{!j-jjCM zd4s*SG>2&6nk2Pf^c-DQIwHX^`>}x24yxT;_g3P{d5H}n($Lt#_EeAEX+NKq9ZGeV zSR?$FaEzK@3cf8bBitOxeN0ciPW|Yn>Q@zCl5(Mk#vijD&$HYx}Qnss& zG~5@{odjuI8nqh)CMDdS%gQ{ROD~gDOSO)5l5r6B_B)8bo2UNWws{_cQ_(SwgQg&7 z&Na03l26p5*kpxs!+JEjfv$Cm&^gmVZMu|u>Z^9`3qP0-q@=~4Zw}|9LSWZvYPj7d zU~cN8S-@v`qNX@W-E6_2`TKLo7lUt02dV{p?_FCt)I14qdK}iw?(4X^3t^u$X|`a; z7=$~LROD|Px$0nc*vMKbHj_&r{N{N)FAU!ZxgD)NPhDsU)3oBVp6)nj*DMbWS>1O5 zER`1#ns88HNJtCFQr`Qoq&P_5eFWVA^&7nr#($aOGOK*|Cf0GG>0iObJV_Qi@|L_0L~cm+F~h9jW*E-%T-JTo_| zRV{i)knS}LxyMcDNlKnKG%*vB2GRcf3Blf31f5>(#8G(UVS-aUNRU=XSKF{|++Ts- ztKY3#?~mLaXkQzXZBM7$X=7FWI%MeHb^J@(bG~2-iJ6%N?#8QVA?cyix`tI_ll`fx zFu$Jks5{GWtJ~b`>!DJr&!u_a+1J%P>?}Rs+rHbhPlt9`p!Gy3h2qKOxuDR+~!XBE7ydgA&(Vy!y7 zUB`_w@e$>km!Dq10i$G$nWMu=DPJ-DY_SoFzm%n$WtE^bI zi!_`zSwvIvK1pULX}_UTD)EQYTa(~S3d4ml4{5ZfvZHVu7m9K#zzh}qx3f*wzwJ}q2# zw4Pef^65mgw3EcLU$=~mU6Et?}e=|K0n{CExE|o zQ)0L2Q*`kc;wV;68MoCFVKclI2P^c%M@0_uivdK2kEA(Y|NfbAKqcx%FC6S)Iksaq zna}Xsfi$;N<)sR91+#prJl8UUxuRme_s~6BD(WxjCAxoPf5j5*jTXwTa~6GPAVomt zcMr$klOnRWt*Z|;hnP%t>|IZKNV8-=Q%PKO^*V=)EzTx-=*gh(c)?q)U;4&k+Gsbg z-hKMZ-%G*Y%LDBk@e~u>XIAE7p8bs5Z@KVrrh=Sn&fRa+VO}mX@@>99u6frOGi_;I z{v5jZR({BlZ`t?nOv4R30?*`CvuMIG-f5kPTjZb?u`4RMI{AI!Oz^0IIh2bzItql(_|2eko1^$+JCflo!yyW#&1{T znyUBLsUNIB?eD2DWH@BfMf>=?Z(H+wjddMrp`keH$>NVS2&LBeWnAN2P1uxgM2d{m zO3An7X9NcCNhJAPZXcE9rM*keef!H07l$EdG3ndppB#DR!!jIgL)2{PiXkp(tN{{M z*U_GP1KZCTeerUVick2@Iq&z{Cnf1N&Y~lEOYN<|86#)5v*KJBO`+;&nJPb#U&y@w z$oN4(;!Aj2Y&%0K{%2SHy*I)W6I@idJ5E+OXxe)AKsG+hr zOy$`P{T;7uDsHIJ-?sQ%^mYPE+)PaTZS_l%VB;aYWYA6;8nqoGy})?%h(IPplzCB;wbes>CfoG zu!ZSB&4!qWhiw^D_07$@^=Z5xSXBz&5ub`4GLvyz_6}%#5FSh@>0En?Kb_0*xve=7 zSChzkl4;|2nYZJi<>l6NIf^d)6bZt6@GzWMx)xqQH@uE4R4DQ7m~a33;eCtt;>DMI z?ytgERUGl2eyehA=W3NK1Veg0_+ucbSeg|@Ts2P=(`(ilb#_=0}= zd<}L$ZLs{-|A$E|$L^hINd3m|zY16tyD@P&qM(WA!Ge8t$AEb6->1LWQ`*ncBNi7? zmP%IC_?#uD`kWPgietljtnLLCoD7*!*N?eQ8U##~*a`Ct*?D%4w$&k1Xn(H2R;o1C zQxJS0&%}&N7&ywetr6Eqx`XQU9(%S|@b&}Trfk9p0xhZ~lhyc=E0Ivv#|y+`WG5BE zhmHZMT%jJ+c=XAynuh4!v+g^8YVaOp2tIsL{c8Nl%)ot`tunv)H$)P6H%nhe=CKzs z{Ba0bzR1AYS<8JAe%a>V&A*uQCr9~!=1ed7il*;d6sAk@=Zqqm#$X~RUkZuYIO&$a z^W<5{GK&odT>D03wbr~n!zV7PWM-e>KuZ!=9UA|Nu}$=rN!g|MhX<|&+DDi;J5Lkp zIEk1uk9(7!g`vlyveQS5d=M<3M4T1CCbMr+NxLFL=;L@qmSMOoJvQU2BWa{=HZFgF+cSe;YADX+5%V021bbtyG^&wFNqClBP_qhHj7WY zCuew4k8d9~rr9Z*Q|h=E^G)&<^v6GUbeOE^v1y0&%80hgHOurdh1oRg>?KPrN193F zt#vEff zls5w^stp2CA%#oF@hLgt$!KWUQXgTA*kwcUSGrG2-`S*2i-a1IdZgdis#6Vnysuua z4jCV@ejS;*X*X`sWmOqsFs%4r7hC*r}iun>l?^CiS zbr4KunUQYhORqPZ>9!Ia$ZYhLd8XDg{xKX&_?h901i!1q+)w>)qW-Sy*o#6BK#|ig zw3!Ct#4W#svbp6S6&5Z9#Y@Aq$0`C)n&;*Nl0Oy16wWU@a#=bLG#=Rht$Aeat$_D@ znb8+r*R*;CwX#G=YX#>jTec&~9Khf}O zkMVmJGyARKQUo*>61RtC^x7Z1u#^OL!=T6d3`5PY!q%Jo$!41##1BV1(x!1sID(34 z!%VuMg|6Y8{#CJuVM>56 zw{Z{d7M$SHxI4i~(BQ${U2fbxSkMrHySoz{g1fuB>+a|M#y`eB=b#zgYptqTSIvpC zKfZNuZ)UUrv&w!jSMabihF`VS_z1`fdo5JD%FVk{lM6*doHcuH94oh*TkU$fbkYrn z4~!NdQP-&&?Z-Ns$>;^!fb+ZPNNkqv*lj6}h|e<@cNt`xgUvGLi@+joPC7HvuJ>S7+#v;!iaZ9#Vw1Ys}_^cb#K%@S+rogdh z$ol?xS0Y@f-e!Efis1d7q~t)qUc*c3`z@VoquugZ;!d~qg+3XSy{s(aQHGnx^b}{+ z`$fV?qPv7nJY+70KxqkXm_D`pzXdER2GM8}E>i zP*&-~jyPk_u4*dg1z@+{LYUREHjdkkXzM#Rd)Vl)U(LPnW~LhhA`_pm?lOE~x94_= zLqO?#hF&H2AaO*BXp4YfRZ`}};;=^53iDsc8?X^Vtwk+cwqjmB37RG-o1orsMPowa zFZCmGZ1KECQF&PY_Emll2%&nu2Z4q)ivzeDUH6y9d9cxZ0OqMLUmbQXC?HVe5tj-t zLnP9tmP}W8|LK)UR{+jHTHI8Ak*F;!mbUw`Im>l<2})!6po}))a84Xi{|FE`BZs6C zTr5SOPu9B{2V8XZf`ZFuwOz&wK^>w$wjeZ>j+F;TUxaYGMQfmeN5yEG$)MO!7*+Q{O2&EL3NT)DhlKmI5RY z?zRUvCFs94kZ3AQo}i(~ts)v%Kylkn=5kN|ax5kU>;+NB>gb(sTjn?abKUxvf|e#? zjjX}^LP(|znfcvHV&VDS)8P4;`_P9+#)=(As;iWl5cbjFNT?~Gic9qVf*EXaATw7o z-%yj|P@*O6t`2&3csn61Qu$-SUe{cXz`r($zssmuh$f!auxUXGm5&Z<^%b=v&EjJu z8@LQu{Zo}C$zLZa;%#-cBt2I-O-KltOcD5Ux%A26#K!LRRC$iLhc5MO>B@x&0MfnDuo~mQkiRD0W8`eb&2EMfqPX%Cr zP|8CP$w}OBBAV}HItnUDHW(H4kwGO(@fzv*2>O23D?Sj-H-13AtUU!6gQyStd5G?^ zuZpMmez{UGE>dHemwz(G9`{j{Rd7>1a)SsTQVvkjZ=m&{^Wx*$S>Cx%8s#G@$VL2( zgyb~AK=5{m%8?sLWgtJu5_3zW3&^Z%6Sehkf}=3&sW7t4X(OUB;aN{+5mj1zr0SYa zKQQTEpt0!l#*v#Vh`$#a;Bw!gPX8iCM&_%YO999l4@^V_?!RWvwWj=!8>TPbqmFX=|;yldnm)?l21+WHy`&&eWz z#|vXFnJ3JaZd7nJP?vyt!ChjIYOtQ)Lr7*m4sREfs#x=7wq(ZF@EnNMl_DCQQj5_d z?>lM!PEUU@Q;z+y!-Db**i-j;SaJsW|LboHOpi&Ys5T)~=_VcvP?z3#R73B&Sddha zbf{z$skHSPx{R}Vg%zGHS>Jqr>u$m)%O!_+w4cU|`;u<}2NfQTu-4Y84sULrCc8VP zfZGGEJYM%UyGFsB?Q)Pspug5Gt>jVVZz(!u4x+Z`p8}vaWerCAu)Bs@Y+{}&<(cyJ z8q)Tbl@ob*GlsjjNb-*yfkPTA^T?lxuMe8?N+dONYy<;ayujYt_j5>xuTVu<01!iAnuNUu>w017I~k(19`e7XwX zE!W7S?dhcgbv6C!al{VEROBI$-Mce9f<%QZG*BX)5%C5iH$eES6D3+Rp-S8S0q6qV@+Z!p8s^qlN=_oeob#dC*#}WKu?Qx5-elZ$+H!g zkhq-7d$l;3FYpVysoigOXkgZD-9)NBJ#cF9GMk^ABEeggR)3?#?^Y4a#Pv zZlrqgY(WI%UBX0fI4;WE_@Vm}G%s1AdF@xlaxKLTGA!OqL}^YZY|>~Q#O8`?m3OZ5 z+Y$5ql#<3Hl1<=<--6JLJuYa0}@=Xs^>EM%*TbBMHmp5!6IIIELT&Jp#DHKlnlS zLlw+ENmC~v-rJ*6YNuzj*aTHL^~DiV7A3Lh2yfh|(SNN*UUEN;5dj{MA$6nS+0H$i z!-9Zk_P^wCV}yIhhA?s;J6Kl3K{#%EEtyp`Vjd)Vw7aX~w1YJNDSFNRxeXzY8!b`G zqKSHS<2p+%6nXCRo`|o69>DH&^TS4j_Z2v9g_2PK;Z+6*%+VlBk2T7rc(B^R09R-) z%8#~nr(XMsJ4HGs$c7Pr`|WmHA~psOMR?fY&!d8k*6~YwWDXGtNlZ!ULkjB~3RxV& zOx*x(ETvRbu4|#4#cVM#$T&+*St#Pi=K%+PAvAMNWh#MVO2kPFA^jNd)GYs7m*bVJeqXOH`l9XH8I!(cUht2*!K?vw7;#?ii4Z9 z-XFsh_FxlizYhZ#;wt&yTy55>$jmu76MO`bB5341;|N5Bm19Zb=)44>9d0*U#JEYHZUf z!u>IKZi5he0Gwz&Z46B)m%*XB!+&ixU=OH`zDp2G-FIOJEC7U7VXa_bYYG;J;_cY~ z!t)GAZiR+;cX+>0i-?8-NoIN4WF%R8uGEZyPsU4?%afl;+yFb{enZo*A0~0kS~4qH z4n^|+x9*9%sNHX>!?Qk#K6}i)v#k94w!1~0h>L#QBbJXY7(Z$Lw zOAkJIuj*j0&WGldf)KUkgF>2M)*Bheh2L^PH8@)V8^S+}v47G{;#pAuKTJW}87XQBKxChbe2L&YNI4NQpv_yxA<1m z!jIt-08Gs%if2W7#iX#GPeU>b&0`Zw3|}iqLfbAlObQ75HaDcL_&AQDmVdt@KL}h~ zEtC!CIQgwTO z=E({EUc6C4w@Ib_B(E%l!x}+BMK@8`k;9E?4dA^MZAcLESMXobY<}fa{H7?)qi0dr zKkXg$;SZTe;5@=zKV)hHMR4o!`eQLXiWMS&GmE$1;u%=)e`FE zGF1ssSM&9LwdsI}Lh*_Q zK-qfJazIm>yRguKldz4J^?2o+jg+U4y6eh5l9_ed?JJ=#zW1{hw97V#6<(^y(bL3`^E-C=5B7If%#}L5QHMzjK~xsR86Guyh-)x^;V|$*E5 z5>C-19Em*C*9Vus>QMftMF*;eH`DvBfQ@!NEC_>C6ZT$&E82X;7em*9>En;PfOhYA zzu~6A(lyt43b5d4H*3qu=_e)g8ltv0{Teg;5^?Qqd7p1y@EjbR#hv4^l(9xYEu+Lj zBqsEvn#3x@?xpTJ3-}1;+$Zv@`S^99uBvPaTZADOuaT`qxDoXs#^?0He|nPlnr~s` zDa81Y-(UBPQCzNh>t3d)mO9z*;na*?x3J;Zq1OfzIJYxyn-6m(8gN-ltAl;jVxOa} zL{vq)XNTYp3vHk(GvHb0uk>Ys;u|igrPY~;H{F+k%HgJ!4uumZ?6$6%=e;!wwfHH* zMHxk$!(6NZ9s?{h~ee#zsdw!TTNk zk%-d?tfZ6_pEgVs6T=i#IM`rbyb0DBmPR<(C`jL`CD#HS*wV*rN*o3fbsY8r(}s`( zxiV~^>}#dbiaNI)AC?WzL5Sh+hAQRK%tvbw+ZyRh8XpXS6m7DULSrY20;#z4##X|1 zGOxsf$IXkvi}Ms$tf!Nh!X5Itt)x?pi<(%M%$I5@?>N$hAWeiD(Wy;{dBj8C76IR` zbrQXvlD55lpj~>{Y$l-LjV$?9dj0L`75}N#xN!NQBY~pp5wh|f@|8Bn<|NBSB)^ez z@{TN1YNX&!h;C%wF4&zdhL#1#jxgeS`qVM+YSg+}8$LHWvgy0NF|AVfu&eZI6s}^| zzJrTNN4*tIw98vGJ~Cy<@US5$Xiymzu1#!*Y6s}6<2BoX@fw1omO8+*;{ab~cfh zX46mgGlh=RX5RtL)*9ZE*6HYo(1iu9_n!vXC#t0|VX+KOLEq!Uq1CN776Shn-QP== zN1Ddm{7~AOy!@)TnVt2)9DhC|rAz^XDVVM-UPJcU!it|?PyimV1KVJPS`eAFJ@G!d zwqT~%>gmIzxe2m9n4ZE$aR-sc%{IHSc0OOKE!8QbwXFkH=6iEV#-cO&(Qh>f#^r4SukL61qoiSCp@Ta{RAwtz<|p?ziyE z6&AJ91s_LzPL&K!U6h>NL9k|&!{YTsT*cn^BCUS{^`hWwZ<4or(;U)o!@&zL?*F#` zmw^j)~W=O#b+O=h|4)`{mgW}$@QomSX#aC5Xs z?EU!v(@c^FxQ-PO<>PSCr&DaG1T2foF`z*xz+=Rn{IgP{tiUiGKiy54?26VkYD7dt z>VaTuVtSq{r%r&`-xjp-4T$TDY&shgUqD^8Gn|{!a_8)~RvW?{$G;rqYfW(WZ9EBY zZ@^8vD!pnMea;7Y<02h>sa-r8*7Q73&-SOZ|DX8kbiG;Pak({Z*j3%>1)Wr0`JOopURW>(E=v#zm?xU^`i5x3S^Ja<7opu#NbX$Qtz9gX~DTl zZK>550;tN)4HoT@5O_4kja2}6knK7w!XP9IU91#|7J&Exu!LYA1k+^?Xg6l=4;Y^Ba0K z^V~?2Sgd`b7aBaVW61@MXrwVTQT}TUtToF)hPp4{t#a$$IUkI901r}W&86YX;6_GE zwl8tD^~F$#_*7xM!mb) zTw+~cLH^s9w=spbHw$u;0%CGr`J{6H#zG*jCb@K1r}+cvLR_c29Rf(LNbM(QdBQxe z=hPY%&E8f=qUa=0XXI<;?*6zGGoLxPRUAFtw12su@Z$#zh``0(z~yX#8$fYQrjVIO z*};~?c#Qbv&x0u9P%J`Dw9YJ_R0Y>Yr&MHnu|$M?HXz8QC=n#x52v^PFQ<21z};i^ zqMD0bK@nk(u~Pvi%Gd5}5ntVE9s%@+L?p73HU<|wp%nulOK0@tmVcbKP_g8DxNxAm zTLrVVSt?_^yxqFk=)Tc`eyKp_gl|h)7J)HxKQ-V>UCjUjeDKq#`Rh?~ok%*j_1W~n zj}RkP?WmXtsb57}6(p|bLW77N&8*KhMhlhiTT~WT>B?EdyO-V5=o8dyX>URW?;TUQ zStE-f+oQlo46)L=;?)t$Q{-^(R^_k5`I zvbTPWBzG8Q)jQE!cqr_-8EOh;bW4(dO*{+iGVnU)N? z#ffBy-o!S~73Y%heo&C+*C$>e&d_`rnl^yT`jMUJ;JISK_x#+msNe(kK@=u^p9`l6mv;5s%{MV+^G;tH?cCX#Id{!3uazEDS>Ug56X7?$4=CdQm zBeg5OyaKrQ!BHWm$S)ivcv>DoOf6RyY^Q<`iqA}p)GOd)u1ugQhY(4|K@W14G)daB zVb+AP4~$+Rj4;UaM7qnQ5(6X0robkX-f*9b~&H;K_%)1Bwb*sRZ%R;(zs=?o1s{w(9_IVBgJSuVNfr8L%xpP-l=T5kD~< zhOtZy?OvRI63F$lUH6(VJ3B(Z(l?xLJtrb+FSJ-GzeelvQWjhZX!edPn@t5U#$`Mr z{xwMy6u5s0BlpuWxzSMo!>`V39@sul?yk+lk9rjpED^OyMLw`>+~yaXcxNg#E-uY< zzB~lYH@#5Vcr;hIr&~B6qiPv3X7<3%#bNg^5qqc>eN4BBe;l%8W=wNHcHMRJm<8+C zO*1+>qfZY8Tf4sjgKTKhJIAENp|3_ELqp^JwL#?lkJtMkP?misOrzVW-2`_7tGXov zd^$tLly0Hr~G>6?UL;VD+V**E=|`n|Qm? ze_rWF*Dok8>scsJ=Fn(gf&{{&ZnE>}&`68#23AP9)D||t<}^HCq}9$1+UR+7Cb*t~ zd3d`<7jr?EK%i+W?4MF|?WnmJzYq1^e@gZ^tH`K6+4roAka%yvSB2m?(w zW_({f5|vTQYE1Uf=RTbGyEgc1uDpE1+#zctxth8Zu>{O{)^{PyZO7A_8|?fF|p`Oo#~oo z835AiJ&<|MRUSwIMPG7Wto~zj>PPQ~pE;(nwmQ|*CP1f1yBv@M6I^ik=^EKvJ-b*$ z;F8Bl{^g+Y*eq|9nV~nUPo}Y@otB34Zxl%r7r_zodS4B_tEXSQ3OQtrzxaYM#3&!c z0|tXZVu75VhilP}Jl2bmejeJbPJCYy0cAvDqtpiTLzd}ehNs>sBSnYjCnGs#Nrz|A ze&pn=9s|=S)N<#%%M*tbKSUUkLv5qQ+EDbD9w=ZeT?6=_?dhf6-2D`2gWAhFS@3ZPOa4v zZccpobA<6RDZMti}GC(6uV0b~c~e@mTaMJE<^MYQ7<&+4KzIBU=uUeg0_OcsSe7+ITP& z&S`KLv+TBYh?#eIerewp-NIGMiQm{<_R+?>3MPiwWi8tpfsd?R^uM+l_+Q^V$Qe%D zBdSWx@?mpx6SSfTeh89Q%$Q`|hWIp${{Xk=u5awCRyetWx-NSWE+5l2^}p*b4nl5= z;M_RX{uYJ&5|5Kv_EhRhF2?_wxRi28!Ia-kOhy(fdwlE#DpJcOA(ik(CmB}%*(NrP zSn$Cc$5&t}`g)KMSkHTA#Ewr&Kf{_9VOqaqT@rkxCG*<<`X`&)MhZVRx~q`&L*IXF zl!J+6_U>GVcn&dl~v?o(TRwQbDwn>Weg4u-I}Xtgkd{T ze|zZ_WMvPeFIuScV-_YbpMGLLSC;e<7Z*;U5C6zQ>Y@h>OwINWphA?1-=FjRr-ujG zgxT;}bO|UZD4JPxvYyb2ya(n+tWkIv_~EH&{Q{Ps1VvvP%JpI?gqAm-EwK|^FMDW) z?ji568F2G~th1dfh3O&mNwY`;``qslh5z1nqo&NW!(V7@s@R$5?aP8Z=#J9rF7Rk1 z0#C6*ViPD52l@YHoZkp6207JJG7G`Dqj?eG*VceQ?yG*pWG$`k!l_3`QhpiV5EmpR zRQib}h5x%{dfau7(8E%=S*zbnpGQ1szmY3bfAWhs1TU-MvpAX_d99cqGGWG9j zgVUdt?`3mA3RILFl+em?Zy<_2I9PeS(Z5^dKqnqp`!7|K(iq$RBu6t|`jv)9$X%N5 zv{)`P>c(`VL4QMi=mr-@37fv(`BzCP8Ah&;E8t_p+11>?hHX0S-#^`2u06rK4m^Xb zgK&w7n_l(0cmvfNlGs`9$pnK>GD-L~JkX%-q6AAen!#Q1j;n zV2{X8yB7Brp)DGHkGU^|B(9v9ae7AG;?xVV+y2;nBL#m=>bX$HcuZ;8?Ax=nYnA5_ z!ODCyGlWAZY?h~9>a_XEhmFBei)yp%;VBL9!jWAQEd|`)h*kIfPGpsptY8*HU2bj3 zw{zwawV{I2*4DnEcYwxi+l_6l`hwBAjJGg#FaHq}cI!0}mGIa} zSvgE-DV8NA2NakGSQM~Eo0}dx`8<~=X`jPmxmZ{`9L|_JJOFTZQ#+U6PtIAv^1YaX zNTL=dW$UEfgSXYRyE8=8mQ%pX=YA2P3G&CFk4%)%uka#qjS)1ali|8cBE1i zt!I)o?y`u+QuKPDX9K83J}!H-#q2&R`lZMsn-@beeVCzJmjg}zH-rE|L`RWWPU)8# zH2;@1FzlKEu|g$shRrDdb$UM0aeH-egE>k%$RDXg9Wu91^i%Hu5fCIJy+6iVdQjiTb}k>B*KG3iz-a&Ceug`#mk0#Yec7yv-ozuy zF!hA^P(}KByTO1;)JkKwXyo`k`BJO9gj)tA;$-{({w=0LE|xv@5BT+MHxlH3azb(< z-~yZE_!Uo@guV|WN$%zGz8@QlD6)~fop{=SFglH10(gE<+tU^lxRh6GeddeK;HY3uz^lOc9W@XI-?k5$ZeiRW?moKPv_?eJ& zV(c*wA6!xT(@Ty5yPX1Jt<8W;vPjF}lN%=qU4pX}N1O1xpr3x?0`lGwpC#s9QGLDG zK9F-LOsSP>qe#d^2akeb9I2@blt&YhLw?Zi0Hk)^(2Dx6YwU6~ah;YNP|z{Nl4SmR zInnDtxvur@K+7ueO&prOP)s>Kf94|_wuLB&>eyA9Duz0VRM9 zyTC8}th;-)^*=6AOEd=-NLzOK?Sq+Nx7r# z+?>yhYxF&sPJW?aI;O@F(bnp~23*L=I>NWw%&uvD2q2~@(ngNNw{LV0kN;>u(bJ{| z%6{Y!W+Cg=c%+gDq)T8hN3cDKgo)MCvU!O!`oG>O_fgOPBQh4Wfl67LlAFZ1f0D_` z1uYE?UohUu)t>Icm`D#&#BoMrSJ}Tp5NNJkKZ3>mhJ(9W0}Q<|+ylV!S8J1unLrDo ziu}S<8AE&0#9xz;Y4j%R8IQ59cG^@PZ1I3H&h`S?kLf}TYUF+%5u@?Q(rovS+ClK-4$qTsV+A@rcPOP>kSwkRd}lI&WS2iE7EVq!ei!9@Of!<7b#IM z=`ycCp)o}nXdbh-mTY|Iuq!=v;f(R)X_PK~;=1Vb&e=|#&42=ZE&MC7OvH+q(>cbea7ravf3VEw|_&)(p%>m z!rF@A)G^vQiv9K|doUnMDwNIMPtz*&HSxy(802#h$%ch0SnUWM_t#oGQc`4|)}rFj z`O+@zZHgcBKCDW01_KBNH5)Q((uIeQkp+jz{4y(F zTIg^^QKHQeotmVgS1%~*uH|r1`#Un_$#PYKhXRvfUhDf&frFGE5i2US59M5`|L(Sw z?fA8^+WMLw)b&_vUiC20to5?33#r3?f4^NSQfcRwODXqn3WlgOxS`TuU0&b=q2(u8 zCH}x(Z@}I7yrP`fnIDESydZa0QE%cnmrk`<+V%%sW2Dp{JFjo34ZoJI%omYHa@aOK zKZx5h&U+c>%P3ca4%}o|mj#2+Z@%0u6C*}@%P}P}sZagG@fid;V9_4g{|j5@ulxI> zH+m#>|GfjzSl@H}*YcLMLV5(t3pb}JzN$fZUJU?cdaagL#Q})Lw-|1IZ@XNRxw%hr z%u^{e<1%9{Ap7L>HnaPM>oaDh`VNkYrKN)qI;99E&INpe`JgcoplRjpncZG#T!b?g+6teF58azDlK^$A&$c3lkEg$y7~ieZ;8Y!|J$w11 zi7q)_Uze7RRol(VeV9}+#B{xOc08&qLMS%SP*9{wkBJd7HKbc?Sw&bM&{5EcxVqh@ z-_GU~i4Q(SzKE~VGGENqc`j5Nq4{NB^Ub~75{moB72D6v&Ijjb?PqOFkF+#|&}-r0h<$rkPO~e&zBh9e8MM+i zoV^fx-;wvl0&aPBV@Hn~*=_w5Lf(__5%@(tgYjYKfAt#IQ~u?*aK~dOCBb6`um3k4 zY@$}NmT6YCdM2gYP~`t`%vi~z2;d(FRTG4Q=(Sr=8Pv*xVV>`eAX0vOiW8O;7MAU< zeAc_+So2TTH-^UB`5N!voJ1C?lk6V5L6#nGXDqT1ra;2`t^0uPytgXOjN-Qn7$h3> zA&yX=lZ(3S#dOKCOghm5OQIjqd5KuXlAXX#Z~k%&!cN>{>p0)1ti3t{=kcir)h41d zi66!Jzb{v%oB2GR2wxa38bpBzQhDnweDm_iDlmi-bL3KyJYZ&4rUTM5{oN}C%T9g> zme!YgiYmAgu3AxNkt(DMg$o+OCw({|b7|`k=I1UCJAEhZ%M{VvgxrnQw&xU@we%sB zJ(F!p>Mj<#_(FDVWV0hcDkQ{^iHbt9B4Nbp8mpTb4aO*O!T4Ljy`@mLN0o>i z4&(5`g6TG*CEM*pGykWIKmOhAlyu5VJeK&13O5`*xRQxOQtr{l=AX*uaT(O5uQhlK zZzD~4fBHb90Ix_{EpG12P&%;2<67H2v^~KTJ+R!&x1xo1DcoK_TEuXRmaTqv2WP}c zhD5~))r*NGGjTTCxh+rSZT|0pOtVUAMaI~bbtM^neTohYJkXa!Wy1^qw?S^!gy!vv zj)T=o5wwFr<>b^+O|B&0%+j@_^rRtr{lRu;dEU&-$nSEy#1(Z{JB(>xDTo`-MZdM= zN-Z5d(bALP(&UH&(hif|S!Mix5j}ioH(X;$=!m%OB3TQ3;URU9A1S|U^C}loNTbNF~x(J!~KF-gdsrT=5^KUPo4{EzYNZ-Z*bN6M3xCiucHC$~dr zo0thj-wL6Qc&uak%a`-4vfwjAndMux-ll|^;XOS<=2(Ezv5{=iuULe!71yTgkS_J^ zoa>CUNV|~5YOa`05gN68-zS1#c+B+W%0p0AiF>KRWGGRZsABK(MKuW5U1e?CtuvqM zzw-csXg7c?Y#Y;g_fD@}kDBL`Vq$&_6Z*DR--E*^%fgA*HwgSP-4x#Bd%Q<*JsbrJ zw{@QfFjGa_5s3j87Sz_Ik*O*LPWmG^++OTDb7DyhzyCnetw_n@sr({=!v*%1o8kuf z8`;*bk6O##BaNQN#14kDDm`>jbMdqRB6Pr)s}b-3qYWB-F4lj zv_7DKHfwT&8LJ9DCd+=z3Uaf}XSY{b)0hvQY{w9Z3Su>K`GXxsnXbR>3l`Vm1FofN zT^Z^O8)E^!@qT-#Z$ciZ&v7(VLf1@!EwSNMDoOO;V3Xx>Ws+-XFn)(DM98J7=oiU@ zen!Ac$IF9xx+?=Mll@59{VKn85?%v5!OxED>>h3xy$w|wh>Yc7IB?0BduAvh6-CNd zLpVoD3Zt)zOXH`R&eH=aJ7>=bz<&n?-!W0yC z;F2e+xP-Bc17mcMZ!r}L=N+u=_D#)IHr2uCL9r!zE)p6V@?jJef>wjR0KYHr1^e#B zK||Tti`v>AFWugbFAJ(5Q3$|v(J!!_t9`8#El;A&kCnQ+U8_SYstI_>Ms8yDKQdGv zhB(tUrmN7NO@xf69K3*U~zqBj}ubM97CYN)r4ao5qb`$$Y(DNNi=F!f7z3+Fm zwpbt8G07bLCkC9@95k9OI4k7|OT#ArB%w-DdYo8jqHHRbAzzuvLx&LjpXd@DJl4rF zdA$$`3%@vSuJCyXk^z#Ec;X5P9@IFD0UWrczf2a95z)}z6r5@0^WG9tEK-|$1kwFPLZ`myIE;y;2?%Zpc8L-7E&C^$jh_YeC#!ka|mU! zLJs`BFozE!c4;Z%r!o`Uz`6Q%A-%eS*#4V+QA#2GLpNl<8Mq;Pz6E;qpO#VW zy@rvZ$)5<^Y;`5JSJ@5O&d9Gg#KlqFa_U=0Y_WKyGwR2zId^TEO~@NlnOJJ^T z$@RDHLctnc#ODL>QD;dWoi+J#l(a2uO6J+!6~Oy8PzPPUF{S9Am&>H6El-~xR68;P zrk+#;;$FME7B0166lHW>!0B@pp#_k~rV{(12IF&cwB8S0fz+z>_8PX8(4YV0Q}2FL z#1lWwdDj*F6S~8|DjG8x7)*Ewx;H;o12vmAJE)(!b1crqGw6@HL$S(G;qo~wwIRC~ zj<2c!ZIYied9#D-OEnDl8<>mqDg&(HU3i_u^`Lt&oTI?lS-GwLA5V7Sn#wd-O9Kj= zrOdAkdmM1_M;OatI)}{fZRK)V=yt0B*T62<6KL6jT&%dGSg}O8yJvD zi5(7Z&#Vc|K+~U7RA>}mU{uq*HI=<9e?0lg`QW(51B(7(F66bIzoR6nY;cgmfZEE# zpg#O163JxlNmRu`{|ONe?A9dN=}RjJ~) z=$unq)>(1vvGcg8)v{E@OiGL$a}E?Li%&jK#xJez!n0yd)%9NF3`O*e6?W^D?cL2@ zF-VHxUhLh~JbnxuL<66T1tRwF?Bx5FP304ytx>x}Rf-tKy;Pd8T(@KvE&qWFRn80< z6jufLzrLUKO?bB&-rp%cej!6yo@460_7zawWSImD3c66AkzP4v-<1>Ny#jA2u8qeY z&K1)F$tlvSmr`5KS!5Kj^(E?Xpg%d|eVfJu&RJ#{UFe;!5xB?P&>%Tn)=)a~7M>h= zwJL|LVBa5Z->cg?O~#0OdU_rI`G*~x|D*MFSRBESJYzq;9(wqGR0u--cHcSlRWejT|4(tRWS5kbe7Fe?27)Jejf{-K!^1b!V|}1Y zIrBZrJycujUhCfvCiA9LPVSZRJ0E+W^dRR%jgvK!ugab^mPDBPHFWp@)xqy1!tsoK zU8ex)WMC?(svZ%HF=IUiZSTj8?}!x2Mhn^Akrtf6?i_z4k-S&oL`=a8OwOi#c)p|G zvVcvOcG$E5XbWOJaqWfY&<{WLn<|DFf4@8mFSGF6^4G!>s>C%e3tbM5+@N19c?ngo=RipGE)6?;Zt*1p zsVLET!zg*xTfrSu?i&%g1}dv>q2nQjBBDfZsO&2c!C+9KpwOPi{i|X4K$2qxzqa-g zE3f_e2}J@!omVbR*Dbx}vFLTz_ki)#IA^76UhqJ18PwC;i%CW{j*VN9T~iHDVh-E( zH){V>RdF^P&++~hor0qV1F|IEX7Bj00YxFaS&};}afB86LHVDF%j9Q(X?Ziff7;UE z;3osnrnvmq>63*Bg)U)Jm4h3zvRHUe4*K~oK}kf$O9X$@D7^1GRrrVJxm41>6`x~W zPKvK^y6?z{`vhEZrzq|Yq3s=$&wYj`gj^k8MzUA_oLeKoL?yTSz(|SZ)+beRT~6o7 z7thVw^3)p(+sK=ztc>x(2#ZngF!bIse^b2&j&MgtAMKqrBl83K`PNVcJL#_~`>Cc_ zc-|6oZPP{CN`_LsKL210y>NLNAsGOhp4x1W{4YPj&1?#U`J282y9M=+0VK-Z|MR`T1W!7| zP|*78XrSP;pn@(Qa?6TpWf&xD<%O9HAs78ctJ(Qj3NVHw1MNf&gD7jAM+y0(ZJlUC=_S(5(dU z@|g~i{DqlkZ9hp6o2}kgz!cdwk%y6d}U|DgNe&HS6hz=MK1kW z+rIwj!wohhet6>tdcX0`0upqVS#fICujn)J#xd5an{48(50Ul{i_I6(Y76m4QKIb< zruZp&_8gnko3G7alN+oTlzo9U14ZkIp7pMZQyun)?l)j2+qhELeLHs`+o6oDMoSJ-#MO)1mF}j}uD}Qcd z7|}*0((ZEODp>*vRdPmi z)BU-s$0zn9>0e3SXP8F2C0ShT;p9qx;w4uV2O1wi^Oj-MWRYEnlZ%2Tzw;|Bg}4hT z?r@6q^W0dq#iJE<;h(L6^>^`q<9n8ZlWBG8AI`P_E^SYom^~c|xWYeqaPRiO8%!KJ z@n-{N50SX_9&yoNq< z|5`YXA*M~tw@!VlAYad;g@h!v>Zxs?W&hjHR-@~=QC|Gyqp3S}ATfyUW)|!{|Hl(# z6*LdSpfYPK{nJ6ARz*yIYp0zlQ&F$fK>N9cp|v$#&)yD!YGD=kN>S-^@8BhXZHYM$ zOMuKwjll|Yvx_S0$C^2tW4Sta!+yFx${ zy3VNI1mZv}+S*$lGhRYIg*~9jM}u3!;7OtHQ-F0RH%(jOj=bD9ik?8i&(#%vleH`8 za{+?CIs6<)0KpcB>|-D^y-AYY#AFFwwo1Kh^UBcTAKDwFbKBy5e`}v6?h3zL`I8@5Br5#j9v$#BA_Fkh77T zbMqD5gU`J(vkWWBu$Cm#@R@YP2*_rB)#I%Xwc4&F#f&goUbrNE{xKBAiXQhf$5$K@ zDf#|U96UxSD<)HZB@4XzUKl4EEPRa37m*2xyuUk(O_0dpke49Ye1ua;s4P;WSjFM9&IOAp zWT?mxU1JVu?u5?mAvhr))Ohkm`Mx|jLSpssuJ^_yAYUO7z+)HvUm__a4~~l03yFvE28YPMKXJlTM*q!xqc^R|JWn$e}mBYi7MUwT~>~%>1wNH$L7yAx+Lk1 zE#2~{>+$L7;;WZXBne3YxEFS79gNCTZ5o%uX-KjnzC@sj-2%4*4`W7FcIYt3@7R|` z#*e8SjoLtOzSzGJ65^QYnKcsg#dLV9!ut$>F`THnU-x8OAI1uH|C6cE5959-dWEzN zD+Ad_zqq_%J$xdN#0c1s%dfBD#q-mz!@>U#{#jY-K%>0gpyw>@Ce9fUPmYhrsIypw z_cscgZy{h09|`*pX4`nShrH&Sm`&Gq?nbiu+Ykrr8NCu0vJ8tsxR&)LOb9v)KoZ6D zo8fib25~4V1ex^OKM0ZgU}^7VSB)s1UJSKT=_x1e@UZZdkD+_~SHGsBK*IRMe{z`q zFT-`U6(16V^tZ-z#9`g{J{J;}%jSc+AQ21wNs({AL;&p&T2r~9#lWZ z*f=c@N@D&R`X=o3Q1zM%%;e@$ztEn9sqMA45%MrFAdh_`3j7m&<6^VaH2#vtIPX%k zrWI#fD__@Wy)a*YWqul)_3*Sbu)n}fWtPVerWz#&-kz{){y&<&GAgPr-g`tE>F$t} zZloJ&knZl5&O>)MNF&|dodVJ!UD8T--t*l1z90B7YcVrxowMic|Nf=CjvleYQS|gV zi(s|b+n6=bN)a;i_ved-O(91eD{HCt%(ToJxYM(-P5D>R2^F*v5TcTQ?P&V?)E6BLAj%47)1E3Ay2IQi$&dsv~W1J5r-$7*dFAK0IC7VqTzgFRf^~8+i`k|ns zCqVo?4W@=SS6g^F8hfXum1iU*Kd#wQMEao+wqbm4QFv;%^$CcLxCYczexI3@kZ%u! z7bbBPRU)V3+9Qh8&rF6{{B>e$T#R(){rDbr%@ywU#iA09N;YMHS4;iYSg=*@AHM z)}Yz#ef!28?PRtPD#UWN9^V{zCg~R)fOyn(oHWTX@#%39CxqcV9Qosx2F7F&{r8J_ zUshITIaiDfb|JI494ciA`VxQ+#J86_(&MF?IAlUDkA?o5Q0XLUktNroIk0<@zg!y4 zGbS%0l0a5!9bg|ulq0#7FIjz?MmwZV)O5{!Gp@P3;SN@b727LgMepA2g>I1 zJa(Ey;q*svDkra1T+q3IKTP1t#ncDFXrkuTdg>t8I;KVhqo<{Ehu%md>*8xqyXxbuw3*;LYyrUz)lKGDb(%rjrCT? zS0YndgCf}W%_sO3x1$J9wx-Q7+0B%&%F@6EY5vaBZy7xwXjakN_U{^J;wiNIWcCGR z1a>f{h+I^VVHWS5gO3lCPfI-Q!%uNL)gGqXze4bk-;KVwD;y3J!A90{zeViYIKfI+ zSRmc40)c#*5s0%4TB;wa*eD1WGiEXhMh?N@I zGWvM5v}r-=3$OM`-4F?-ne@KUX)@stNwgJsMe!bqtK$wM0ZZF$|?VGA4TXS$6h|@d6O`?NhWB}ykC1Hdfp<_q`Ku(5HugDqvW9_GdO1q2e@?i zw0F3n5k7eHgs92uz@vSRiMXaAE+0(>?YuT zN@OmdEoy>_cisB2x;*gH*TMooqx?J-{1_LG9b=Pt?{?9@A*xoS!gket$2L&JAl)6r zN~$mn8unNZ!g5)6+D4SybhRuJvyjo76VeA$%#=MV`bRZVhAI|!Zl6d8G8%^c$~<>N z@BLQ9vDlbnnu6%>Wnx=W%|yudda%#UGq<(H6hsy>|1s@VDBaO5E+&SoTUcdW$XocYGe7 zd$ioN@()Z=LgOAyJx4f?))%BbpShzFUCgvicaTrkkh@~2DHF)-y8{wTHPK^bIvTEicVg{~+54WF8Hg?W!N?mN7VItO zCQsiZv{e3a3TJOM`_9bIPc$I&#IuWl2kxZ7f^&rnLRXNLixUs0O9C4((lHpO`FVSZ8TtN=(b8^Z0Uc*{_3k=P)t}uM}(*vq?&i1X2y{KwFKzoik;(fxWsP+3<%F>dyz_;^*wB)KP z6(~JmuwLZyNC*NM*|SrC>Igx$WcJrp+3;>L`bn|f_?+w$N04aHQbMe#cN(8Eb^acc z50^WyzYfh`*YKkCMD;&D`;(cmD&}v^Rsg2;eC3{K%Y9c+<0dw<>h@HwP+T&Wm{=!Z zyIh|pZk0y;6T81|Rz$sFu;9X@)_@rdlTtw0z`IL8%Y7)zGSHRs{HeXrKjDvaMFZW# zq#w)l0;jxf584&a=Wtcf?DUxwJmu6fh>;o^9LMa@JuMgIRk6EnOa1=SZCC zv4XAlkX|s=nCNVGX9p}g@n(n}1%7dwIB30;4|pd5)8^D1=WgY_I+7h9rZF1v^g zRMbAgq@Ljks@01>gu55OZ8cwaFDQtpNNYkysxHUTTN8+bay@%Xcm` zXB;Mphscz#>FlKUWP66Mmz-Yrztn$qN@}cb{YqujNj%cMA>djZ=&{qa2snE0AYv=` z!F=jo8vuHiMt)9;12=I9;^S`4%xgiU~KHu;YvH*bIqH7h(mzl-xiZDcxxW~Zs#n_*o;0;Gvo4D!&RcM zYn9vS<0WFj*+$>46sick$V#uvX!>WQZ^3^=hPN1oodo=w;we}>@&rX~)N!<5qCeXE z4~SfQ6zul$)G0=Pbeb5kcgDJ!@!m_6@M_ii?#7S<$t|IR0-(0QPW5(&*}u#6Z9k19 zBFg^2U=b8#mI0=hb!1da*1@dTj_ z6f%oH5|T)U3!>sJ@vww)?p6mxa8pyU&o$oo?~jjV^#>VINbDhd{-BjKh&+eCqaggH zp4y?y3Q+~F&N`O~K&2i`p8e@$P7gG*TE+J9dl7tHAUGX2LKaTe@5tk1xTT5N+}W39 zh$h7&?;0lH7~;iBUd(|y?sU*R_WmovukV@LYen?G+igf8WE!!y!xFj43ppFqqOyYU zyCgz>(O`2n1X2&4hcGu`R<>hTCNbWzalTAknpE(eKY@+8RF3M zf-@3FDpjr9rqp0A@A7X2yUBXNt2}zhr#H|cXbDdL{UTgVb}oWC4`P2CvOW~lOg5v;P=LRvv)n=z9)S;USRZ=h^rLc(#e$ z{e^JiGwHFdE&V|rr|(y9Xnj?jheZiE)XBP9>4TLL<_~?)B15pVkiQBYE%WOoVzbxh) zgpG%L&|Q%odNvS2Z@@bUG;HwzQ0W<^Ta;$>&A)$a&_=X?QkeQ_N!SR@e@9uXyWmS4!~ z-8bc>J1%*{>hS0RT`XS#TsfE~jY5VEMRBH(%~jIBoJ$l&q@7bo=}-31)LRJOJ@SbE^vCqmCo zZBJ=K`zE@JS!%G+a4?Dh`Q2lIYp);oO*U8KX@dJv6!l7QFjGzZGY-~pCmZb3~ zTVlnB`vr$&i}92vA$I$4?qkb)(T}=q+J#nQ^~IWVW1LmC%sLzf4pZv?HiB~p`b^Pc zcwVFOZB;kgt=0^IY_QYwp}1-&J@si4s$9cQ8F;Zd6DhA3SMn@(O4mSE{EM4*3#Lvb zJLn@Bw6hs>M91l&3tpI9VF=wg@#HaK&y!8_en&)lIsJMQ+wMfz6&ky^YCAFg3XlBr zS5wR(dSfn~0#2?>KtfSLJHGKXhS^WCj;4gfW;K4JFj`xnkyhYNPjP04YR011kl0M= zET`3vakil4gVtC{#16R(O=z6{ibH!u4q46K$Dj0;P|YsZzO@8J6Z7AK-goO?C;z!` z_c&!(=~g$`Ttk-0B@trKiX~_|Veyqrvtrh-D1+(lU{TXj(UE!733X7QD#q(D7U5O+ zn}jxd-8TmVI5|0`KV5x#>9TohHuPGaUk;R(p_&O|IQH7%@~9d{V!tDXme+2aom;Hd zw9ds)!_}6IxK=rN!Z&rwB-N+i?1;|%cr!45zLVz*qjH^5&+~Q|&CmL@o`T@q-}Ecd zQ)nmB<`<*Rw#0n7j^Z>OhjRZ1s*aaWvP=HUTTOMRfGmRaF4dRRzzN-;i9Y;`h1z$I z-kazqPM_IiaugkC`M##-R6P4|ws_3{TD814=Qek4u}HhsM~aeCF1`3D6>JpB8}W*nKma^l{*03AQkz$Cx9tBCQYFG@=BSTypP9A28v zA(ps-K|l~Ih}eN-G#L{Uib4ryi^Jw{r{@(K9$q-3PII2`(=CF@2AniXH?lsd2Xd!f zxgz`wcACvn_4dW?h<#focRLu9AAI?XoI{MU{BUnu%TK=w-Q zRKo9%iBP5LZGj{k-4;DdM?$iBw1{3G9zSpc4q&L|yk8q)yQ_~tNShbeR(}YR7f*Ea zA5WRBF4U(y(V{Vk|99@{-CdG(esvDtcov>UfLWW{3kH0=z(GuZW@000R7<6#Rp?k; z2cYJ>|9yQ@6CU0P3n^89kieqB+fA07hG0w*%q3r$91d0Y-b z4tsAb^66ELVH%Shz{pHCu(uxqq3~TCI=-I*o2*hD9Tp zCEpTjioOQp(QA!e3O8V}th2~zQL?1gVZ~lk8^w`o>GN(7?K>!$SMfq$0CGY~|D=3% zOIEMr`G8 zL_OX_6|dsV0@F=@eUl1I4aw{EA`6K&i%a ze98ExSO`*j%4n{Lha5H#2Uz1yLIt7i-WrEew>Bp=a?T}`VVR~HtG25syS)7YZFtRb zG6?^q!%NlHmA1p0Aw!#+q$=iqanTv`CT<;XE1?&Kf(_{h)ej6AZ$6!+BN;}8<8TdX z4x*B9tM)`{8hsP0seg(--rJi?@(DCS*DEY4oTxL1h2hj}-!k4%0%De$cz*hK^kb9pFG{L1D*E5$i5CH^*v3Xb}0Q8r_!7xa4{8kNgnDJn^5= zg!jnSWu$x?XV1k1(n#X^GLg_(f&t6J##|oG>3UW(Sl6}Rx5-k|gf9|{E<`UdApQ*{ zZv#SbcSuzl@V9Np^2H@ zRrjM$i$T!7!*{_yqVP#QJ~R3mF2kvNPGkrW0y3R>kAl3uuM|vrZPX()`_BxB?@mYp zYMlnLX;%DG?4ZZFZ2#>i&^dqm=JWr>1hp>Mx;_N2@JA7O<~-i4P+dYVqBN|2_lAF_ z($ZA2Bv938o5qK1pAkvX4a$&Pxze8HqNAhd_JT49Hr*6;bn zw|^ej%W0#^2DT{GMLj+OU#02%fTS@vSk-D(1VWsN;kq8Ke6u8twwv%vYHFPd2HsQc zWT!w{4JbyBf6=Z`YBV-ip-xcNKc7hjnBf{HLiGjes*i{9M4B^B;`u3b-FYkIG>VV= z#=ZuYtVp(2Q)SBq=?+*(3AmH;&Y6o_z z0mFl6`6KLZP)B4cx1#+-wy~y=;)|c)u04g&+Vp`s>2LPZcJ`@F2N_2 z0=(PT`VU|it?3nO&}LrW-29%uBP(@caav2ZC~eDt4`=HR^2$`2oFdZ_lD_+1{HVYN z_|P339?m2|234N#N=dyf0a-nQ#$TJ4d*d{*`0rFp^Ky*4yl%>ute(g&wpO=p0(>1S z!to;h!a9il$ZAiX`HpkIoP}G{hofrggZ}jT`s(p(uSZT9tJ0uNw^SJEHntT?l|=`~ z|7-NR&IE=ENkYNNMrNAZ_L#P?Wh9q_lZf5#V{hM6Z0plIv^k#VVgRm+K3l*Q11!FP zftZfxypPG`z`VwE+T5I-qzR8?t0H zL`oUF%ED#UIpP2Q5Fo`6&6-D{*e>dxHaJDXqK*H-kRIBYK`b~b40^z)D>lhqUXr@I z&X-buad`Hb=Vux}Dl4Q6$PpV4qhE~wxR{$-Hwi1oj&tes$I-#9E2?bER&8}Yp>A(- z2y!Tvo8CR<_Cl6jd=6Y|6U(#v>&3yI{$W0fQ1m%^egNyL7~sOX&W|p}AMuzIv;TA~ zkFedWCo#tpuy@}ZB@7>l#o*ug&Z=+Xi+*Wq@9*oBBvpvGKKpeX2$Ol<)K**iwD=VY zJnOyUELCYn9(K5wt)%--Ue<@do{P;b^>>Ca+12KiQJ!*JEt_K>HGN_nNEay&LiD+v zwHp}w(p*nL1e7BLvprWN#esVO|kCu@F^MvZovZ08Rw32LdUo5j~@v6@R_+R zBVNMZ7IwYetm24maPyU39jSXL2;p6psb432(gk6E0yrGzP#vBe+36*BL;*XhTXG?a z#ul6TVqx>Li)}PKnoYq|*?cX2)@So&Dxw57OXbxx-?<%_6f5Fe$l;|RirUXC$jnU_Qy5!EDSif`4PT*XVs-IiDe>^ z#DS#r0%$K>pV#QPQ%d8P;R_N9VRxWDBMUcqA@Cq|a8+ z0ctgv)7&N|n%LP{xm;hUta2muTqqS+YrEYEP%>Y62I#G}82|fE2jAQ4g2!Vk9(n0} zbF8+W$ZXKD(<&Ywo(cz6&uU-9wFJT+6Hf$< zJfZ)>)_5c7XJWi@8emoVDc5M-P(im=n)|J`W7b_b_19cYr1^@XWBLB0fsWRJP1vil z!$qz}N!I#z$wXG^@#kJu+)8z=X4TF3ugd`P2x`&@nFR=4iPtsi=BzSrrQvrstM77amKZNNLPm9E_9U{t{;vPPMk>zmr{4L;gDPV*4}c^AEv zrfF`^BYU%Pb46~=3v+@*DBURKgJ$Hve>ml-5E#j)9>+eOH_=-z-M8|{`PK6`Ie#ie1`*-VZ z-0R$qV`m(sZ+x7X{4f4f0J?CXRO3d#?Th`E1PIQW3Ow{OB-129lbluApPg*y?)yZ6 zU#Sxqla0C4Ce9r*N?LvNitvJkpRiR+Gr~WkW#y|~j8R%i$kdk(@P4(EKES5p_q@c* zMY-2>NQ!$z5&vSDUd7or_dqrAkxZDdnU`X5=jSeA zSJ?M_W_~2Kp4ns_v__tP*_}U7j|?U6uv2jKG4Ynm!w#}O1N|jekl4CQ7a!ut%}c;{ zOY#F~rM4AZHvB_hKo<}!d}~4s!%&kAv85G2=BC?n00f z^aIA$Dr`wY`?e|l0cvrxcb9!T5rPcJU zKi%yiF_5?VG82q1zD1&tUW?^?*G>8QVYl{PsqFM(Qx z`Vcs@B{lj9e&M<2^6-s^pfR&toBPjA9?&N}v(wXR)tibq)_OfXE;k(LruIQ~Xj`vi z9+P~?wdI0X%IhiBefvoz4Oa7(QTgZI*@{b(ep*W!6Z8sLKkl*>aiG&e}~WJu^V2m z8Q@!`Pb^^m&q0R=tAKq%_cw*oA34wHVeU%Db-P>bhh=R29ZC8s)hvLra@3Lbm2_k< zZ{6G1@#>ZsEFy{AyLjjogz|~X7<-1=V^S}4M5fnJ*AntLM?=J72v!t)2yV0EReQJ2 zOl0X7_x3c^**Ct^O2rhNGD%M{MK?}=yeHS!cW-rMo&2dn`yD^{v_5Ne2vQSbHoinC zf%}jFm0p5mfJ&!Gi4Md`2r!PD-D5c%&w99c&+%N;)unT~Vr0(y;RB?H2yapHe-J5R#+VfNMclWeops2CBc zCWPBW0&`>i?|uQ6gowB}G~@=(Ba_2w>uCS`-(3Pm+`f>AfdBxcMUGq9spC|b(-P&> zv+evQ55Fn+SY^=0E-=#x%=T@Mk{s(a!5gSo(~*RB(H>hnM4L=c^jOSlf$W(G9*;FqXlzEb;DBVI-Q5PQS_i_nWFG{F&NtT7 z$_@XK>L~Mfd0*SuG@s_=Y_(WXvBy))Jye9j%@#8vDw z{Dc|#DBL7FvXcUpn&8j9cO7p3R$6@?*#0Z}{EtTg9v<>RO>nw^Hx~*KkN*d7{m8L{ zN(dg`M~>kb!Y~3(YciE$x$~=Z<=_(y=+x2#AKDWQZN=ix?^P_MZkhf&>Ubra~ z$`A*6QFufghT#A7#T#86t*$3HpfYd&<{M~@QLa)a{`>op#(upsU=z@a)A-(e?e%$z z4`4{=gLAjvgo1$hB>0dy5?>PG9#W*T!xxwgS;^&v$v)sxhU%6~9Wbl1`gAtesAhi5}gr5grR*@t; zCT8epuG~x+ic$25{={IytJ)m7lyocle9WX4vfe3A^M}=wB9-y-1JfQX{qMk(K%;^Es$10{XUsl z59$ff3Rp(+8*asKaAFnZoB(TCNBV%+oh>1$btMSYT(Jbm8qDlgaVs|&d#b(NG6qe3 z;7R$j${=4APoqd8m%#=D-lw|k5)d0bTxqJ{=u*lTJzT0GeY`n2K*sTnpV(^Hx)DkA zH?}De2^|=o>ox(Ko<9POkl6t_exu!^xF*#s9KVJ+9p2Y|(w;ORq2=+bq7bz#be#_D zqUPY9Ms&jp_QR)-!NYYXROEFluIC&>42WcI6)K~kus#G_K~qgW0S#&g%i5=R8Y!c;>^<>QMEU zO)lr?KSk|^)K};2hKvkCKa{-IqB#TlV8zkZ)j)v*4LJv8%k*O^$^ES^d+%0EuHpO> zm{VJ8o8d(+)9!L~e%f)iajQSlpJ@pFjDP z1kljn^)fk~q6el+LFCeJvGM2OTJz@uikkXiMpcbD4$CEE2)!aqCH1n&r>Jz&924p| ze+PmC>p`>Yr#wD=hgtvSHM`|gi~l@nI29LyoGqq4R7y_VGB2sieUkjuQF-O zJGg7245jo9hLP%8%YU(45#+-VG5aDuchn!@2PHQT${+5orhq4yz$x)zIE43o1Y&RT z><;HEs}l=_;phe+X(iyZe{H2&t(#%ghe<364AHQld?tJ#1UW_9W#_H^n3La4zVwq> zyb+?zST|X`L+JxIJHb$JA%%3jE}c6vGamEz6snz}CBsp;Uj1Dx-Sc?alD;!s*_=c% z7V^?d{a=f;lvwh|U5BQg?5XY7RjaHk7RrNU)b!ad=)a}}8|g3YY?Y4-yoUq_2lrY6 zoQfK{UcuqJ*l3M*i)bLC0tcz}xPetIQ%c>774Gp=%70kmOY;BccpRc+L=d(;6bT^t z%usWr4SyO=WFi0E>dE)$`11O^pAtIKSYGLQC$u3)?9u&v@3TfZUwMVtAYw!%_TWMhO;gOT&M#vIu%=DP zOd{?6u#=r2kE;J0xXz#RfpGbloOzwkdC9L0p(Kuy>7%FrqPeP}j}_Yq_Fl~rvbbYK9LS1{JfB(Y{<{-0goWLP=BtYIPrTE1#YRKa zWBnx%vM(}RAu-qX{q$R#WH3mj`0RNvgU&$Ak}f;l1`H(N8O8rG-)pR8^P{hlm--zi z>qoHc>b$XP)RM3hpWR$MmgqBOi#81wbYViTX06#ZJ`rls4;#aWk_)j04FeJIFQ9<7 z^&2B1E|bL87&hSSi$ zf@Ma|(UTn9^}k-U*hH+2~Yuo%>@t5(V<@MvTm7kYUWNU+Qs<9U=1JD zDIPjGgJs%GFwVx6$Qu{_rNHXGm#?=*r2pniR>BRVb?@Fdx)+bbMkkl2V{T=fCpYHO z_Caqvdg1jQ-EMd9jZH%3b3X0&KQuHT0u;xcE4Fn39EJ~;(o#H#(NdD<`qSwjBw-Rg zd%1z5-G{&HDkEp?j__k@m9$ua{og|V!RXy_ji|hkmowmYyMj?@YszQbw3@Yf3Ug1r zOQDfCA@YHmO04})yV%J^F9&3!n2AWt8rA;$G5R7W)HMZ0>zSoW&6I3rV`LW z!BYKzS}?iD6f?W8O;1PQJLFUM;18SNq+3Z{eQ*U)kdRJ^L-mPI{9hy(&akVMa`LRy z*w0l0o6cDCJjYG%lj$O>OH*7FcNntmZ3yJ>{+6%(Yy?{tXRBH^p&q&OtMp82n+Qv? zme;Pv{wEP1q`D(?qKJHH{nryDSk&P(f$otkZt zjoFl5RjqAQUIz>G!>E<~`OmO5cz+1n0zs?)fateEb+v_h{^Mar+qKSLl|+cxLVW{& zf?j;oBlEHizN*4~VOGJM-!&Ge%yjW1y(29ZgtO_8$<{>4>o(79gr~ zMi9r7Ypr*)ekZiG(d7;CrOU3YKBQPs76_);I^jeoc`FYj8mjmh;dr>66`jkvagv#wVW#b)0AjLXvVBLxQw@TVMu?0@!O@o+4{kD|!wo!#GB_-Y zV;>L@O@{tI!1g-x0FY1F4Z)2jR9DRA-IEwIA0@_!T*@qaB_kE^3Jw9mMk~*$_qsgp zI9J`@VT5l`xeb4ZQ!*zBQe^0+Wx05ErI6%w8CumRXUsaj>@#v4PZMhtR=5P4C5^<<@=w~m zBVIfrH1LoxyTq-)UuIP~>ZL7vLp~jiI{XgO1jk8ys8Zr)h$Rf^Y z9L)^Cl5qPIfL;2Ug>LBM8o;>gtKda~~jr zkWpHoRDKsC%wUI;9ZQ@~ApIaWa&D)H%7X*+^-Hwd#9A+u=gaJUDqEeEW8vbg*r(U1 z-dSm~fvCQ9Sj`neVgUk)pO#^Y`InP1x4*f7B5?XGuFCYxlKGKzql_5qeAt|do5?-{?u0grSx8%hWJk$8ScVxXZ24x3B<7SZuH{# zdpbD?tpW|~-2;TL1vI)r7b(UqU!{@BrdAf;1fxW*^v(P0zYE&38VhVGJ&%q5G2=1V z9&E_U!pIveX6S@pu?l@b1=OzW;bOXXruSc)PQ$g_C+AvC&sn=jezNRgLm z*2qJ6vdgZ(b@>$|=R|h#bgeB2n_k`U?sUD?_lXOfbfUpSULlA~fv2RaaCKW-2JrS+ zAhm!X)!}>@CT5H8lbc)?k8+||2&>6(o~3d}njaM|UIq4U5U`|jWZs@HVzd*5!x)*( z=e{#jC>7r7gKxC$nGpqq`$rQ{SF?05oQy%U+NVulrqJn-FfUccWUn^=H>+zlJCg9a z_%4xsf{G*}E=w7wouA*csgGK~iZl@&>PTPeThG?X+$rmsBb;iVRltMEQNKjyH6|+7 z?0=z!)OyYVlD4lmxDesuEyHVKwhxUY=J9OdQSUD6h^~koeCk)RhMJ#Qg)nW%^YnmRUNpLLv9}{B|9*yY_T_ zkhc6N^mKDpGgUs1pexX}T<@?cNZeptB%{xW&C-&o8yk7WX5<^)(7NJSEY32wi%1TS z@H42!;?@jbgI^S#0}D0^_`o=Y%MLF%Zli@YXz@L-yu%h^@JRp6+4n0dqSY8|^tkO3R`j0elSp_utiLH{t<-A?a-g3S8|expKU$dwWMe z7qKyO3=dM}(@M0Y>Jes}q*7B9uhcDU8nINS8=kk=slyXdG2T-@jE!k?*-AZ;@wh9z z9O8qDDiDez1>FE zsOZ%+Op|S?3nrfTZ1OI&4F51~&#fu)cU;b4S&)xg5vTR#n`IYljc<$9emgRT9Md(F zHTVhFB^JrK{HhmcxbyH?QDiEl5hF?Mi@>D0cAO~~K9a17K+eD*6Ei*sinRSHKNQy^ zmh8@IWYlGb`C}_*k}zs5h+klMAqGt`H=cS;nk(u=q*3aO=J+Wk&wKvN{BI;WU(-I% z;cHMB-poKRMdufm9k_1kzButa7L@`B$29?9p+dhyekEHn&2cd!VkfvA_j`-)@l}k&w}Qs)pN1av4^){j z&MWf*mk48H-uW%#=L5DqL;`6g)J2*abp39h5WfZ&&@QXgEX_FW?C#WB zy%2ORJs_)oQbzL?I*diOQQz9g<}>z83`RxO_POrr`2Bn$8U1VuIqrF;I96qfV$DV7 zv^&LN)*|>g96gfrv4rK@UvVs2&2%d{E1~H*`L7xPm!7ZD;m^n6&sJkWF!;>%TX^d#c-12 z1$mxrG>l5udh6gUEP~zqqY3qm%glc%6Mokgw*M&EzB30!(D(>`M$H*hq>H7FYQ46a>b%zm&dZ2vfx-bI6 z^8c4x|GM{o*hs&#RUaWSaZFzj+yHRoQUIQRESIW@XN%>d{u4GvF?aVK&6Th^?TCZu zlI4esdO%khpx}T zpP6z^Uxj|r)WA5Wb-rlPC!SUZREipL!=Ig`Gi|Hh(%SnCu)C7Jp%v)Bv7}8|{oeHU zKl`6w?R|VYyZZ<+FrWb7&GgE{fW<(+(RvLszW5S&_=L*xFAN56u|%pqIh)Aw?A4d! zXze}U9WfLH6f#F~3i(ErWACvsmXU+CdnUuNGn;TIM9HP33uW)zPS?u9Dpbo71G6ZZ zRXy8yKlSJm)5MVuJ)Z#whI_L^k0CrWcc(@Rvs9W*E(k;@aA0CzlnirM^d&ZZp-UU$ zXc9O7&yk@dnmDJ&Khi{e-|*g^ebK^h{|%L4j{L9>`4+Y>l7v$=UGVROR=NCxfAAUh zeJ`Bg7f(>TSV_X9S!;;+%~&|6)05S2MGjoT@{r!YM+Sw42?c=VYIoB2tSKg*Yy}@M zw7#K@&+yQ~qfjL+*<=iPaB%RDw0@w!Kc3Yj6l;GJ{75fHUX>CU2kSq}Am+`VM%Zby zSSjM;!*4xb8Zep7cXH9@d@!8OYBHTK2KRTR2}xO5c{GE)L}P5N-V6znXEYEB4-X%| zBVdQ}BJgfl0tlk`>^|-4cY>N1Vu&` ze?cJZ)G=bKx7lcTScBPDYMKOC@2@XRX)8NmA3f@GQk1$gu|jsUt##0M6Ccl0@%wje zgU7|psC|f{;|^hc*=PP6qC3wPq86T?pIOW=Iqn~TCss>}6^^Nbvpw~^A}L8p3Q&jZ z@75QwZkB5Aq{YiQdsvvDx*;l@S)#?k2h~htE^LFLq9VAfX)nN6m0Zt|O#N4OfVNtNqdJU>et8K3 zslzb9T}f>EF__o~kzBLR8e1VtsY*K%mpRt_*Y}UEDMoC|Wh3bQS(IS9!<@AI5>fz#`?xT&Q^E;xFCL-zyGU&22<2UBqx0|?a8E^E ztkw`8qObi%~rv2K;gNVZv=<|R?#=*e+EhL!Cds25a zi;6yw-9l2>|92W|o;K2EhdVCFQw&!TF8yZ+qNvgB+mpq0++Nel;&Udzoyu*vMMus1 zGGko2nn(VhL!usAKX_mpl0U);lgc(-on`vP`!rP~lNzGDtnN9K+7TEZ)0|KY2+;8D zKR@3DARA)iCw(hCF$ta``^J~o&7Nk6t&lHUOM4^kOJh0Rir{ra7+f<_K`I6=`w$Q+ z&9e^*t^82D##zc+DLO<*=CUpf)5-R4&{$cPZA2M6;iOL(w4n(ets>ErzrO`hZQJ6Jsg(?27(EJEm*t2|@?VM69gCXsZHgv5zNjwhTB z?*o)aUrSBCyTcnH0L*M&6pvFcShG9MJT{tv$7R-^9{>sAN~w5pON0qYK4sy5!-PD+ zdAem79##5wm2bXC_M2T{HN{hXxIse2>0zFg^o{QY(w|d~&Afj6zA*VxJQ0bd-@ldH z;{#LU>Z91B#7L`0w)o6}*;L+TI!z7rIA2`dKZk3-|J6qc$ey}KDDDTlOYe8anQwH1 zU{ON{wx?q0*zsl%3`- zJvVS3t8ngUb792;R5%D|H+f@Lt#n0zZ#F#H->kK}I{RJcibU~0G+kqKoZS|l*tXTi zwynl!qsC5SI}_WD8a7TEv$562P8!>`@0)L}JHL~)X68M!&)$!AmA=t0cM<+~BU*p% zi+7>o`X2Z!uB%J5;L~@2gCNU{!~%p>9+%q@eyRnI(moi|mTAxd`s9u0Q)* zLOubQj=UsQmV*(KTy1R2`V8F`CS!sC_gN4Hm{p2BIdt^}ZfkjQTC3HVU~_Bh!f0M8 z?O~d7rsQi}a@B~I+o(chQ?al|JA~T9&=7%fLQ&6(fcv@X@3Dnu+o)`ApkVJ$wpO`d zhu>FIS@gN&e@g#VV~1kSA;a4>^>{$MHU|VbLgc z9k?9s{3#!_WMDhz4b(YLvFHlxfPNHc*?X4%6Sl((VOX_z*h_u= z8ifpCBqOal&1W(bIw1O+4X13~JKjts823l!ei_yn@q4X<2ivTchgr}5`uHrpi)$*< zQDREKq<4A=rB3dS(4~Fy4Ll`n)DZ`7LA3IMsjajHV=)#DM8M~14>zh{!{D)G4t4_u z%>8vg;+H*EB~p<{xVtm?Z-wGMx|v^z8HR|m(WD}AWB_fuYLTvI5}g4=v&3aM0Z(V- z)<<^jPzJ3ZA+<})C`LbUGg?pE3|Wn(#SKynrP2-HY!d5X?24jn!5Hu@!fFN{0qxj}3@Z{h92<|i1t}B!g}vhe2Gd=j$-jxeH{2?HEQ@Nb&UnE>lKbBA$aBz{gQZW0YmRMN3*Of{x#AWh$qW1TR z+BL3i&uIv-Y*bjq6|xi#@&kEOm_?dseKq;$ysOJ*RDpEina5M9iTwm9nf&cHLd`e#|iNHB4eBH|Y7dsI2GzpYGgw0Lko4D}TS1a^* zZqUKBg=lCr0S0jRg3mj?>ri9`_{1V^ z3TSxBDT=(~LByd? znqvHjbg#<^w7S{7z@_VbmhDy6-Wb6Ec_O2upnwdN(n6wcw|9dz5cdWL`U$vX?Jkr7 zPCP6>FAp6DCq(28{ESs7pE_{9(aZF52iX;g!@z1jf^YP039K>dg+*)9q;TFH0fF}P zj!^99DnyM>_V+n%zBRd&s(F1S<#U35K-#uYrMS;7_x-RdOdNHaWA^Q&E0^m0=+PDU zHrX^-z6Q<FUt4G|uR~x4JEa>WyRs8@8ukZq|1fU?%iTnO>vw-LZm(ST# z7j2>)uf$CD0!gYlU@i=)NL(y}=IZ1fWTRkQ69vEK8+=fvkVW(~Bn)DcxV{QnT0n0M z=Za`|wG#o~zDfDQIm4;c+F;9CYDhxV8L6yK>I_ zIMJEBP*h+VaK1^3M=tB!XiiL`#^9zXK0sII>Le>&rr5)FjQ8|4= zFn<~8tH4)k$xF)bVb@3)1&CLU33==l$dQ7M!{or}3(ah7y`|!gZ>zEWh&9PB*t+!> zwWFA`J$ara+_DH(I|E>vMI9+`sZaj)rzu1p^hGe}vrAw@0`7y&8St;DPyh>}kWXO* z>jSEegMdir2*+7b1Rux-{e!?jR*MZapK~`En};o)_qj;8KqQugFM51HNz~AgU#Lph z4cjd=vfNW_LrwQ1{)qRe@gns;wUEb_7k23FNerOxsVWZHWAWD(VFF<5kJ`yWe@84o zK(^HSI(%_W7&gRe4Ch{GoD+3cmI1@7`kqJP?gpnRwDKiTk3qY2b7MJ4ecd1Br?Z`! zL=a@^hWxk{g`l(Fn&%U(FP}O*z3ZZZ#T1}#{f8rDTnoBv0~|-%%d=MUc)vTE;rRCI zi!Pq1H&+A=K&ICYa=%Rf!HpvptP-_j!l$40)LZSOmQE10oH8po!U63OEEojL(xd>f zoU{o81GCYKCepXu>|FD0mcCrSoe;o8d){8}M4+Le*MaadKn)nUbcrn$Fny3Dyb5t) zyz>)Rw0Jtyv`!r37+|T?bbtNTR3)ziqXUC_VT8sAaM?NK0^q=zSj?s1?PsY5|n zzpO<}B4Bqlpdt*&WyqI_zBxTt7MW!!`P@YF1#$&<=3b}Ht%B5UxydmR`^c{E$moxG z&dey%7tn@WeyagP=lcBG zBcq-v+{V)W-Ab^);~ej%BYA%B-rj}Ofd*$AY6LSQ9k0@@hO7|zmukb{9}$T3F%-e- zZ&}jO!Ta^;)McDr}2eYP8jO3QL_E;4=Yp(zA-eV!8vo`uW{K>_Gr+NG6y@~%~Fq6-`<@(?lMmci~sn0 z1Jxhwpat)rTID3kk%;Rm!mlIpMW;lCqClB!d!xLO1JYKd?pL`2IY+dP!>EFopXg96 zg0%3))_!b5EDx9y<3b4^Td0a@Cv3{r?4B6-{ahc;%6A%`y^sp2G}>r;hFTz(-*RT} z(#*cTo`btNAjY#`syaJ$%tq0w;5jJ}Mb|iGX&JR^p+OVBzA(tQ0W~>&6Yw&`0ib@q z!$!~kVjTee{R_)I4nS*fPesal;p368P2VBV>|ki7oRm?y1=bj zt6ftKorXu1?M19rq|Ys%Z=7t@4&;X)(V?_UJ8k%NN7LRbB+&Bb{OhJ7J|}xUm_ms(OTB;v)`yfJ#a-A~;ML2)u`Y zz~}HJj7+7EQI&vu$IptDvqhs|8!r5+-f}H+I%J8`Vi|%m#y|7TB~o4(%IibCJM*Pc zh=QrWWK&x~K*@HQ!Ec+aiSnc1tk9PItmd1qt&vTz7V|1+(})@-X~10JD|k){4-22e zs26o&iAebvD7KAI)dRNNI5d&J<659J7sToa-TTWh)6LiZ1(w~HaNwW;1_{@AzETe~ z^Bp1wm8i%<*-kqH^Jjqul0MK@!o0VYD5$8gARwYf&XH6|&6g8UmeI(eOG`?w18pS$ zbjc=l763BS&*fSePj_d705ZwQ<3Yr26CR4PBU}j75wKC&NHhV*p~e`%W=ZJk64$o; zkzn9nt;6O12`sOD*PR038lVDLI=ZePCnskbP)Lt-Q=Dzgdbs&8V|`Ef_cN>gh=}s} z_8*gLk5(w9ES0#Jy`djSb-PS+arYm2BxPi_{&BfF*NO%_AHor`JO$lC0|2968apn_ zoP8{K$^FdeDf?yr?mAa|CrW$0O|#P~ zRc5d#eD~LtRf!)?I1_vAl}#=ekN^zN>bt3_b#p~|O~wrwlkr=wzDC}uYaP^#r=UJMsI!>Nu(yNcfv$TsvP z8L|BNZcNBAu4FV1Is6KL%}x|PfuBG#D93SYU^uk1HDHV<@U+%?NzkHWb16v;exAfJ zJ)~a`1Dt;JwW}XNn$ISO=ods_riTClusi;2*%!?# z+Df?N@BCs74n=X#Ou9`fXuoZja8WnUm;V&A{#!Zzo}ZRA>~P>_N+Pkk?>=20Uu~2~ zRcl1k6Ovh3ujEFyhz)j7XU1Z|Lh=z%{n1?7XUO5G!Rz%S_|x81Yu#${R+_c(Z>Z&s zr4t!@f0tzs_Me!lT26B}KVUWp5u;oY%!%=i*E$W*G)q*(#?3yKJUkqw=y4Q#1v;Px zWpQxEQk22JFO>o`GI*#7G(2G`3H}aa z5Yhm}CxgdUvUpB$vc-tR+?j`y5m|k2q#qyx(X&}ZKJy4*zVeY;LMMzEnYNXqCc5ak zUA1X-tz8cW)vn%qtQVP1g0Lzy5-sUefq;pZbL|FcT((@-a>E(*3q*;q{Lz_D18_Jj z>4w<8(m9Qj;HO>@qDli)z)j9nZ#&P7bJTzk-YMa_WB~2#H>D6z{`Q$q;7^V2$!W8 zpIYp4*s1Q-G=wCzz0(Bq1CyrPjDH5+nyUGNp@A*^a!|leN6^MB$4xg8jB6MWh5ZU`Y zcG^S_^LPaF^+o5s>nLiVph?FP9wcI7Cg1&-2u9e72WhMIb<2Kh3id7!;)1RE;Oh zI<1QdIax|}v4D5K2`c~&r^_anC{n=ZvSUxFe8VX$QS8GSe3i<%YspjE0kfyESrG%= z6#THo-)qsLzuRS>r6HRfZjp<8ZeYG?GRC+Iw8sMe%tOI5B1)niV||^XcSSPfa%V+_op#@#W0a26pTx&bk}YO%t695# z8OLObjPKvn<)X)&VbB1$Cj0FAC+pKn;o)Gg6|w3<*SzrQ*%}z+yz2u8lKoDN0;tiD z%g_$T>1hF5958CrA&jrUvQR(+tR0YDtk*J=^twMuRc=tlzg?x@?iD!9=X1{rz!UGP z{NUh)8z}lvpi}dmZU%w~BJz@xM5~NQ$x5mt^t07$P)WhO`}A6QT9?mOagqq%Aw#m6 zY2nq)ed9PQ0_QUElfyv?qy$}pjefA ztKn99GH52Ik#22F?8Iv(3|WkT-HTD=)Y^J8!;8-qATKqqL-HzBAfLsC30GH6!(p-@ z$XsN`nZYF21#u!TkW|!4Vd|<~Gjg|7-v<*;`Bg)Z97u(|0&6&Se_FK^>g2L3e?4{5Xe-6=;2Fu_eu+fF12cnEn0n<>6UZj7OIB-Ztkw5f^OGI}7}AB? zkg;POuu#1{=N68`u|S$mkG`kVwG;>fyXrcYumyUtZOwAU6pCGni#r zDCg`kd;X~z8AAW$)BpEN&EtGU8vB*^Hj9>QF?i?FOySw-!?z@s08}PIlT~*P$Af3S z-|Ry~moo%-VZCs7CVw>zep+RH^Vli8Oyl=Mu9ViDbFAGPig?XI9yEZ9%ab{KqhQtJ zZ_ifv`i*^_a&`ZQPG&vseoQB%4^Sv~Ym|?~=pT0E*8= zKOSH?kpPGp#SS|j`_<@D_2N9hRf5lE9Bf7~oXP?Rs<)mWV-j&cTYY!Fss6Up0~GZ8 z9rk2E@*H6KF}mFT1DYYb1wIBssZ*_g&;)MVr>lown^7!>8$d7>;P5{EX?jqL&wVNH z!(Yt;GMt$RpanmRtA4IfWsti6q~zi7ChXNVOZu*+0&?<{(zvmzxPEs?vNo>+ZXT~D z%Sk-7cq|TdaaPZ5ns-CbF^mWlNLnd9fND_LXs&5Ify>z;G39N2gK9*(346=jCv4~_ zl;yM@Xs^%3c#xm>g`kVLp8R}u438RZwc5S7K(k<(d)C9e2n&)Tg&%og-q-sHftl~r zG2ra+06?=aQa{}1d;wxqPU~49XXtkyAdfj;;hA=@1oqAyh7M*EJo(7f0KlM$A7_SWIbzew;SsnLg5|m%Hakb#vj4; z;3Eo#z}=3#kZU8 zBLHqBey5+iySwqZzF`YLY-2I%)WZW$*m&w{XE+Q33O4PMFG~psQKVvK9!q=$T1?07 zAq>#%vLGO*1f*d`v%kKmfinTL-UvW*?W_vn_e0SJ=H=_q^zS}gGfu_*MEsjXhXMwJ z`}+Hh);ih^E3M}DRqXp*M_I>FG0ezW74}Sj|A7Qi>@PG3yaL&o!?-M>K)3GIu>t}3 zHDkr4qS!tlkD=(B{%*U%h2R`_V-mVYrW{7ZqM!B)?l0Vz)QXmI0MgJo*f(88=p>qUeJ&jaRM!+`~i zAxt1%W;6{7q;)fVwg7nQe1s0MIKekKuA~@$Oygred2a8N&9geM8oyihfw25u!+INC zGLz6-nOvU@@UIYvY0b|^@>^WPQ1saZypTu;q+vh0f?w8>N%6?yI((lF*Z@*aIyRH< z<~yX5)pr2N&aZGgTa2+?`YbZv?6f1k)OhG4@2}Sq!>*ly9m9>~);=UDNFkZ}_Ix~f z)gxOSl^@VEqEr)ay8p4-5!1WmONy#enAVn7xN_71*xnh1oZI1eB4HzZ9>@I}GcZR* zu*}CT6kSLrO?FYRMr;W+77Y_=hRu3X)wY^F>{IdPZ@Z;mVOY+Oadcvo)>2!61mBVn zb5#f|8#Iber>+zs0y=X<^sVO*V=@~0CaJ<|d?q2w^_q!!>~-gdJAfI2S+C6>$Osm> zXqLDFlM5R71#kZeI_7htUy}01(^ce#dOL(h^ZJRMzD5 zz|Nd45*3C5%kW*;cy9i#TA3YF|4s9L(cyn-A)0bRp4P&;i@7Zj~h^Jw!ij=z$$5IWya1L0? z!L@VwfV=#LPGkH64*u(H`KJGlwo2+nLI0PD7)`dDtMsC&eKWWj#=FrQjEZF>SmyFa zR52u&>X z{lO{`m%*=KWv|Y-mf2t7d#w(?|3>5azCp~rbig2iH7k3In7}_653MySf>ohJ28NUA z_g5=_z!t~#eA;TDve0dl)Si1eJ>-Stcgf=1Ndv8oWrpMr#zKJJg{Rp7&uhP{E?u_YQE;qUCmj#CVX?o%GxL8K90 zRVnG`nChW>GjNesCzvSa0k{&m{R0EWPq&>Q5O5SEfXK2F4pGsEZ6lFt1T&%=h#2!2 z9(dO6k_+jhg6OtAmoFRCtiU21<}kIwVX)Eu5KE2yIP?4DY_s$Xr$qcT-)FI%3_qZ0 zZ%hGDR-b)J>3_|Z$F$rS^dt4_c1A!y{yNYL>@DG5?xYmbl3jw7QI_{7E;h;;mLdav zvzhFSNibDGlk=Sm`0}Rm3;jDAi ze7%+W%6OID`_a{Dw21(ch`o}|tRTaBwQ?13+c1M9*=Wo8SW=7ScK-5usN8h6y07U6 z?+w#5)Fj!3P3o`}CgFp<8kaXUg6abT(zxu|ba;M+A?=NA0yak%n+HPNiq-JtTuo+sqZXX!9_)2Pe}vi3}4s?WCfyBgRdPk!9eV19f@=C$I*+P$t!7?J$tC>(9q zP={|wH@#il-?_yK=wI&E=vG=fKL1ISt)60-H8?=jN}HLW4)+4LO*Tj7#rluge*Fp5 zmi;Mj^4qrx9Vz>zcC;n8Gl>Mqhja5`YRk{RssAR^hjPBYV(w^#$N);uGjQBiRXg0p zph0W($=k7>XQtBZ8+h#pn+dE*T8$~4b#1X)$_M%l3%Ufks7UNwlux;ONOB3{^|Ev^ zwZK-Uj)GB*V05sVK^PmyuIK4l+@9yD(BASmkQFCb=eVjL*L;^b{*XB?bhqp*yyq;k z=lu2?m9P7$e{+Dlg4M@RMAvr~2SU3q%{&Q0BXP|0q;@dSQHus|a74?9Zg`rM*8zQ8 z{Ll!ep5Z>PNuHq!XBEkMbu7d2aLHL1-TM(RKyq| zwlN==_vvJ<+QvjA@2c5sc_|ZX5j~OZ9N6|KhofXRI5~e;e7)mPg6)N?2cRHck&y5n zEUC-%MR3DBvpuj?>;r#uZ*+w}i8`MFQYbYyGwP^Lqj+5k@Ihygc-^jP{oY(j=H4EW z8lP(rz_t%qZ^x@JIlXo=1b>s5q7J#N0i9Y)$yzlAp`I7PA(v&aF~S9|4vz_Z=@G2* zf@);CNmH${`_BQjqr`p+KG zM{p%_8zN%1qdt)bP9D-~5YL$~#bWv57Y~a|Q&87CljCx>N;Q7yB)&Mq>2v;|dksEz zp+Y@g3lnN}-)Lk$x!Axn*y{N7&AZdp>0$Br_R=EF~= zio+6(LUy&`f}bAZg3o>=G|E{$uM*&}nxmZdh2}j2E3KrYZMCMWl*)bgaoduwt<*hg zB}nMK**;TRf^P=JN=+97w=j&VujCvYSjvJ%p@klg2gRy+k`a(evVh1+Osua*Dk^%5 z=wCx!t{{(Uk&SkJa|g*?u3pY~*n5cm8u)EvKcSb`@tIdChd<=_n9;;n1mD)m~>Z z-cE;%rfGv9Zrxx46;Z9`$78`r%D5?)SVFH^BCE(b1OC-iEBj%@|%yrK^mU3=&XXHb>8HYS9zk zX=a!XYIatf0*gszD{&Iyoo*DS8VsDSv_gPl2)V@HUSIwc_CIjHKIP(#qT@)Znuj>U z{_QG5=jyA5)z?PWAI=68p7Uz7=bAyGz)C2oWcn}F?q~Wo|LPF{-M?t*_BIGFik&dM z#PzlHOKybPFC3`!?I7#&RZRZ!FRiHN<4?Hlb_dbE;J4HM4GB0$@6=n5hnR@{<%(99 z3wlCt?M11eDsL5H7{rL&jjoQj^PXvC<4M7*ATaPm@r8C>ubZ}*$MA_ZdzswfVSdDx z9^nQzGV`JOI^u!|dF&X^FaD*B$yK`4^!D!cpe#O zRlC4|HJx4ytZqcaXcpp_n1^RpyDdLJWR(#jpwJ|b z8kYRSUcyjQ1Gm*c!4=0^Gf`}mxAsE+@ASuZlWbdwmPw9qN-40cYP@*nop>lkL?X6i ziM-9fHiD%_TPV;>l>u49x4Fa-`N|Ku+>P>sdI{2)o(lK+{sRRW&ZUfuP((yCV@BfO zN7I_ECXNV(p#fwblPL2Cku>HF6TB$Rv9-`K<}C8(jWDw4trR95IbMYGg$rK2l+V6D z^al@{CPw$9EEJz@;F4~%w5$pp*pT3PdgrR;C*)~g3xrS-&3o+T{4Ehga!Y% z%_3xuFE^22CzoXBPfYhm z<0A3NuCQ^!9%O#{PgJ3Cei;2hWXpB<(&BB5($Nkh&>AXUQA2!3BLr&iJGzC&JAE=6fY5Gc&0L9nSa-PK`GY?^UdynyTJq_<{^Cc*iZkrRdX zOD*)@!lFF2{!7_#9+T;iY&>%?6I_QGj9E&)k-g;pg2KGi7Lzw42c+|LboA#D{i19< zeIEosh{OZ>so(yH&*^|Zt+gKOnl>Kip*!>bgS$RmS;Chf13x?JIzHe$_^=ff<1NJn zyX!sc#2@e{iKQ?M-og->RDBwkmk~;2R~g+Mz)!$)7+vl6pyeW5&;kb*!Xl1lkI8+=TSej@ue2=eNmK;#i7AR*A`QrkfTNEARl4z)G-MN==N zILEWdAN)}b2=Y-uPsqrK0$6&&Jt6N~Mo}y5F)n~uBK!Iqw#OAXaYVrX1Iu->FCo>7 zl=^|RXb=cb*8xI0j#RH25{Lc%Me3^9W(pAoVW+zc9d@6ml8w#0W!%DKol6(~cy&3E zXg%iIT1U^Mpj@j){FIIALM}egsNhpttv*Nk_Ur>6T7R^U_99y@#k33dL%=ogDTP&$MdTCZY%JC z>%NZxTkPWRXMPX7AMp&x)8jEGiRqHCdg!W$N-~+@G0Gm4qF91h;;ZO3Egm*IRu`9r zf1OCNeA*Wm7d_Sk{08Ln0I==nL9!;S1bf=jj7w$);%9Mj$ReezNPsNO13Wzty^SYY zKfOsA#Mhr)(=J9%F@QF`ZbVdDTd=?@2F)h^ zjwID#lS_+_P42cc@f)L+&)D4>*q->e)wFaEQ+T-BuRk6t1UBQQu`u>d>53nqN~ZgI zg3aPX)TmOr!USmjs11yU z(~XX238b`SSt2LEoG34(KCBL#Mo2bEh48N|eSRhbg5SiH2|j|(23AE0n!7a_hbQ@v z(_5}T$T{dlwXi7dg&9qtBwcAp+drWdsYZymtVA(Yh3YviQ^7K5ZZYRp`o6um4_E>B zy`g7Cmce-O0t5+cy678K_(J!ZBc>ZiL^?qL7nT(5VLE1!WTw7e(V5)e%Q19g*Eu%H z`2ZPGUu*&}0RhM#@J;2*?=fd$L3y^0*Eud4n4$n<91I*zo*2cqbznTqgRA?@S`Ehn_dzwOw2QqDV1fx(o25&*RX<;3eqCpr7Tq<(93R#g zj#GWdFU~%!0wg)(2GPS+HUk%iRUvZoX;D1?B2|7Wdd0*VN!K?VJr!LT5ZVEF;{GN^ zCH1yeV(S6B)J)MvxmM|-f(0gc`?U6N< zCx2k*t>rgy{nPkSw&|fFQ_PO#Q5^dcbKB2(MFFdv}pPvB>^at@$+mR z5bG_CwikbBfalxsb}9g@`{5ctLWV;?=x6g7a&YN?`j|^?5JQFN1s^04HC0Wf^|K2l42vy-GH&yhFJ3Lji)jZm|9zH0u^)K*f={x>+>kU!e@;%Xz18Ccub2k$L zMkw=o?pXq)?olDTUZ&r7;vtB5_&w^odRrg4 zj@wA~RgegsnYWn6c{og8A#F;@{O-FK{@VJPEgX%2|GGhk3e6%QYdu8{ATovO848fd zP@T{9Iq8!pQ8i{c@}%Trn50>W3n$y%EU&i{(pJHxo9|B;-9MdmLV!|roe;8pZ~3T} z#Y=u{54I!~4fX62y3jzP#k;xYoyqrd`LFs=81;(a^qE*}t6s|8wjN4)W0%frNH6iq z+scT66-F!nCN9N%wAe)791#i)ygvQQf;?9}rqTIa zp@pEtqM5g^{?2u6LWFYCVFM`qETEXT}xtj%QezlcoP?L z8oz|DfU4-=1IvI8se^8P!<4<&f;O&$2$lTl9|)0m{I~+}x$~5~q4)>Ei6$Gl8-F&G zH`cp@fC%x?jC^G>XXM-gG(BFCoOOmg*i}l}+8#b=-~fda@372_P|hVzqF5RoST{;0&u5_d1Rm-xJovmFE&b*XuA_dDxvkdpAbi)FQ-Kt`5H zelo+`ygk@`OX`uQZ%kiy5wj)RMgZ@tqjUi&kh}lSr=j*lG^?wx1T}2p!?eb&h&#vFtzgQg5vTDOQ*u% zH30_*^uAh$`^5JHu)ke4D$g8`&c*(3E}jnFFn7?{-=*kr+Dr0QF#G)~<5#5GYL5GV zm$|UaR-SPv#1zNIyZkAx{UU;-wNC8$K}10?KbScpn;3hkrCIqyQS8$tASgSbnXXYb zAeMb#cGI%*6)u4(d_96Dn{`Yk8b2WMqdaCT!esoYkbs-+Jm+JOX1OE2gT?XAl&!wU z@WAuAexvt@BmN^3SO-Alc4wQ8te$7f_pnES-8a$GZ$~qKIs)53#k7_VH!vEfC%{(zz+lfI80T}iuX}2MRMg4#qq!_b*r{W6EJjHt59>zI+ZSSA<(Glg?!?g z!4<>HivhRK>A(f1+-|i3HLXxNu zw}hFyRE3|Cl`lRbMyVRP{L3=RQE-0xuYu?V4w>}>{>yP)Q~f8bY6CQG?5}!)a~}_K zw^Xdoq9Ox>k1LOkRhy2?Kk+G!7`HNX%Wf5J)N+BTB|6TF`*Y{)@|@0Y?f&MF9)LcR zpVLQXiFHTdaedHIMt}-qYS(<_-U*ZY(2 zBrfQuA3*dzUkoB?{G=m(;ia@L zT7nc9u18#wD_$rkAq94kNaJ*qU*&d_yS%5_%}v5mck_OSbq8UlmaZj^&e~U4PEv9x zX#;V) zr>HeX1X{1b?lbDjF_OiysJ8#t%!iaMSQTyky4A~;wu@?|9)~(AdhIz&}ELSVZ^bb2% zi*+$uUt3&FoGv%%IK1W8JEYl-KmAp66eHGdPUiv#f5^M(cj-~r#Iwg{Dl%VD!zYjFs@3&|9HASSm!-7|mtF6W@##98ays?jCs+5rL@_dR$frT)z}| z=Fs8R6c_*aDZfWvSA@-KJVoNuW&`n>n}RU}1)=XGNx9cjGi!&=ZW;Lk)6&!Ji3B*c zQqK{Xvq1rIlrbKURuS}N@>5hvYV6e256gVv7GI{L$>dE6sRk(jXsw&+M!OLhEmTL} zV58Ltm9#8yqhEDzf9LBHq$1*Aswb>AJHjnD=p+THw0pVg`aSHJ^Yw1hcz%sk@vHA@ z2&b#&%wnQ3`Q*aP^PQ~!E4Q{I$}o89in9fFcL{6eCW3@qC6#%VcUY2W$S^DbxKDqs z1)&kPekJqV#?YPHW)Es$Cg3T_-Co9bU+FDAlDaI8e#e}EgK!S|L60jNH#u^OeZGE^ zx1*+>W?F=$Z|!z!Ov`{CT^ERu6upm*<=Rzq<&JhTkpyO+jx5j(G<>(>47;kJE+ zLc2M$-xhqi=4fhEBpL&UTX#8S!jkl>0Ypb|Yt9 z^O}k1&5pP?Ij*EqHE%WnA4k(4w`oa0I^@KuoG|oBe{{F+;uYhjweYToLydG&l(R~{ z8@9*CX3P~x92w7UZ_3pZI9_hX0;x27`!e!)t&SbVfcwI>&=2T;l&Y4oN1}7t38_{F z78uIrRdFZ|qpvM+0^=p9s|Js_t+ss%4$;_7YZdlur0R%$*`13L=IShzhk7|A?^99( z%Q04PclY!8#O0|8esnGOL2v9rPw~Ma6DX&|?Fh?O#_jY9eqd?4&%;ov1Z+zlhou-P zG2VF`K|8qW1bbkJ=hwXdX1fw}6hu`C8^*2fqeCj|$?ltZy)dAS+64B_XtkIkuq$Ot z0<4ZtnL(~0tGgY)k1(0PXl5;NH)PsJDy#MZRovmI;x{Egj)EpSB;b%qxXb+u{{uHw zSTByMI`h2aNEd$F1FLhHm{6{E$7LQacandBbJ1P5cXy4cP1a9lk76N{q$zanANF!C z28irI@NI@^f)9E?1Nn=h?;~l`h4FU&CyH;fa^bGjYAQoi|6QeBp_cFjt35%?O%S1w zfmXY0BU^oak}k&!7|3Cb*|*bqJy)PjS^i02?; z-)FE&_eur-q>p*}oEtFW2@zyYbJ10#4LmTTlVaqxWttM69~9>h7O%9f1$HT#Xtv+fjIYDV(HY2jrS*V1JITrOH^_pahY|3futjF+V*Cl-ti;e zY(B4->P`G)@cq6Z%10Kw;#5^XvpDQhz@9oJvG9fN%I~AALJ!7tZ0M}Z%Kjg0XSSy$ z`wljJ>UzsbNMO&e`0MGF8^>k9fi9)cZ*)q;E*Nm}r$0rip9o(Ue554_Rev|keT-`z zE~@k5h)+%q=|B@S3}zw*vl@cZdF;o|>yM|6FZZvU?U5S?<1+}c>){a*rBLZ5gMemf zxPLuaQH(nm-{tjl!Cnm1vm+qHiKzp=Mec_a7Tv(H{@{c4%FH_|OIpdnXpF025+oZ~es9Q`IlX5W@*ETh0yYAjebFo_Ewy%p5N)59Z_2SUcxD#a_ zwj}Z$qb<{(#i(#!^Yhm;!J2o0eBTjaAhkrMwu{;lq!4|EutwI$k5^?7c>ZKzow#ruZ)SO=gL9pR6dBa-&^SrV1|!Mh2u z?m^43NQIY8$|U_6^I}X~R#I7J#hG&W<>lC_?@MpeTR>o-gJ{cAE#(Y<&ti3W9a9i5>pLg*}Fd`nyVNk9dotfp4Fqt zt&??OFX&GfuK)LRVU9=0RV3dA?2iCDPLW`F1qG{{P?`;oIiHx#V#oJbQQ;>RSx#Ds z{%I0Xid`??E;Hv&UL#|EWZvz}YaKXEDv>rqeIe|9HTQMOKAUJUjRPrEMn>+fdtFL# z=xGHBJv}|1-G2xn70*-z@7loDleMQ$pKjxS2m!^y1iI8{ohplg02tf@*11HUEndy5 z_(0?I_3dEjuIFoFZ9T^kaJ~n$+<$VK70+Ja;$vUk`DSOAAwc-XyIFr;UPh+C{qg{U~f-EwQ*~+S#1a zhzhz^PKrgRc6G> zyOZvk*t)XnP+@VmN9qExfK#WwckCZ&)YVKF36hNO>j+XTca(g&!~ik*tMhg`45il8 z-(@Za1s`}_Eky(6Y+B`xs2TFfHwYux z>PXi6w%7gt8W;{viC+ZV-Dc334i`sfea=vBP$w66YqE^Xs5h5Y<{{vHv3oVS=Ja)s zKFqLCQ}A}ikdW7!lmp`6fIW83S@1Y&QU9rrcv$}sB{E_>sen9Bco5Ws%2lxxD2Ygw zK_;eu@NN7r3z4O<@j@h7J0u^ zTwD3tAHA;4eRD2xY+sa%jRfiw5*eIwK1|jF4D3PhHxNnAZ!uLI3dRz>&LDL4e|JY_ zo;&%f!o-lF=2Y8fb_2`&x-#>g0^-P)@07l!sLJM>b|LD9f49m$(6Rn`&9xfxKI;SB zYvM!o+#3~rqT+ilf7PC^@7MND?;gPSm>$cc*9NQd88(wzDJ>KYj1)U#K{#V5I0A+yLlau?i5$0{@0p`A6zaGJ~ zpa*oqH`an&L!MFf)4W!kSa)OMpU!6HcVAk{)EVklFV_lRx`jnXZuBhLE;sDSBDdIj z6p?w*sbAv;3Zcj11nHDfzv{G4rrdL;iXllUvYTV1;K#gTcW(LcYT)$7lh1Y;hCwbE z5)pw0f#wTrHzWt)K@vc?8=IakcN%wzPaTu~JR5$k1nu~}i@7R%;+~quF8>Z!N#CyrH2}#!x7Bid z+aUD=UF~jrW3|q#5z%rLFwV%Ob3GN&V-_;r{ebsgt~%MQDCw=1fH4d|X34O`&rfvB zhnQ%X6L6NUEVy%-n{Q0s_qrYb66KP%s6r03kYb=bsi|C=7x2322u z)9(nV6Qx;S@0;y$L>IsvjhI9f4f+Q2vOS$HSsI;s{AoCGC}j8q;8oWRfz>h+4_(<2 zzok)KdG;$ao3p9kArO4gtzD2WV1+i#K;#xEPI9BG`9C~&FZam((W5aNmgSyiDC2(trov3V*09oh3C}|)c~9brZFp{#1)2>S!qk9&st9{nMvD?gM}pL zp67m`*(;L#Maj-QM{En_6XYO?2FRreJQrC;I$Dc<(-S{);21HdiR8Q=M>=q>t;#y_e!*G4J#HmW&lDyAY#b9?%6ya;(+j1{P9n?qnSxKXr?EAeg!kQ_;|)vfFSd}N60mfE z*09`u1A3P(>@}xy1O2^0`FDSYX)=--nU_ZcA}dgVrwcBtJC$BNBM5XVX4a_8TRCwC zsb|*@-ZXfuQ6N?b=k^Jyt=54=S{p!a3y=svx)UowKLp&FPGm zA^r{We||-u`2Ch6K9lY6du>&I`0V+wTp*}O<}}3s%Y7nKD4+{k`qtx<)#L@gwORA__Rcnz_pRLM%4OQ$i?8i9nHaomAQtx^ z!b~i|V2vsh{j&mjn9rvgitk%k`=1K`#VsxSp|Z5yBcUpmdb;gQagK4m zjo|O`yVyh*f*KR%0{&F6ezMhsz7C%QiW%kS5-ayH{!HVD*grg>p5_-XY|7}xUdifw zNR*ls?=+mwjD8^1Pup6qxdYN3{o!=}h2t$B3KbC;7#QFtJ}`7* zPzXnWxJ5&Ntp@4Dr=)~3s1z*wV+yx{6CMRZNP+#YeN1ME5$eiWxx2awh7sxh_PkJd3pLo*W?qE|t(T){xk;N5O|EEpPlS^E8nf=aV+c?pP z&PG$U_xTQOBgEl?m~x6kmUOmBdki#FhMR*aFrVvdS2^gPR~JF}R$1zTzR7)WIx31Y zd8^3Z=o-*ZNg)0w1uZ9U)hF;pK|9^FxFe=93k8p5xQeqHCV%{=O+i$!W2&&QZ=&o9kJz3FjK~IKMw{& z={_-6n#%6Gt}l}1P}nbq2KVg9y3=yB@;nLvHIoTAc728ZK7~ok%69r8q1vqk5+J2$ zKJ@3*hsVHbPT@s70b5<~_Pna6;X4NfPU&DXQPOR8NeijR%;1qlpmRU^Wk z;Mhb0K;i`*9SZBudk(-7!jF751Y$aaNEN6nKkM3yt%oVitlvb7GA0YHn{appGKZBV z%1GZ)zJ8b^^pLl|rLF0kIN^6@Gb0G97>~qY=G?Y7b+#Z-O8W3Mai{*%vhiV-FJfd8 z1~5tCJLB>(5uQHxV_`f<7J!KT6lkAGv>440J9pdXv<@udF+yr~E-95zb~Nr0P?|lY z*!V?wUuyg=QTxiincB#z`}KL{A1(1C3B|X&#^-0C&ABcB`2I$d?)mVG|YmRob z7;qHg8yExUB<<+Fs6r&s+TYZhJOvEN3lg+a9x=yW-E3ucf^=yF7|2}il;>R#|IMK3 zE50=0>%g>?peyf+{$2~+cn5Pt&R|MrbSF{pUB&YR-68`2$>V{7z0@FZZRUdT$r-$e z*z~wKzne3HtYO-u$2U(YM5*xy_I2r+gs$|tcUD!3x^xr?A^aWdwuO(FqFebVGmK3k za{`#9DS5c~jXFlohQHBiK_!2t>vi$mWH>z(JW7YmM5+&EI%EJXkOcaH(HsfXu3iFM z`h6Q!phOsJa4>Z|oF@V)f9(KU3HgrV374<*w-6>%aua!8Nj=s+6w8W*FPh4N#7+yNGm*(h3Yih0 zWFx^88|;nI+*Y63x(vlri;%Zj>%AZ&77Y}QVJg=8Xcf=WV1B97^jm(-Nh2eVKtPDV zWm;~^iR|I_q7y6^K!5>tL`Rmd|DfmEn@uSF9WIBFdpW^ZeDI*paAUnnHQ)NaMI-Q6 zJB_XJ^eHfRH3CKh&-s~(bw8zSvt@ATJAfM);c=_15KF{$X@}I9}0L?);4#+W<$N1a!6mj$(fl4_?^{s z+>oYqJeXJ4sKb@C0ZuBHDM%Dfz$yvaQUn;_p+rCuPKE2qYKSW%7&T|~0enu#Z=Yu; z3}S2^JqY`X8o_NQOgfd1MV8khflw0Nn?f9*4B2{Fg20Xvq;4X*_NMaXNyqc($QV?d z5vev$Ck6<)aq_0F2@dQj&_FNg0Y>&acC58O(i$ABI4gE=J{+{W;Gv&XK64g^B1JhwZvh4P8XGo^tK3bk?=02 zsc&fC5J|E&2l_RX;V3R zhw(?F-WjZb8=9YU?R(6Q=bO(%`g-3Jyx$4_@ORL|h72ezRsLT~z+2fE=zz^1m{C`% zVm`hz5fntsEyloa>Hv-2WqQPsI2ZqnF6jq?? zJ4O9pm11}G^8TqecK)oRuplkoS>$3=PTBfRe9(W!YiPOM6*P1(7`T+<^q0{Kbzce* z7)%=Jcdbn(qwz3_wIy2?ppM(?6y#K>HUCnaHB*P}t1D)fWkxoNJ~BF7n;aHqZt|-3T2Pnii$vDIu$XGg%Pn!D0j#(X;tcv*s`-;Jf<3Mb_%R`}KMO7ZBdVmrNBL(Ej4~j8{^tjwWwGQ8`Gh zh(^Gf&F%Vl<@#b;7Xb#O%UzndFV#z2a>m7WBHT_%-<_*1*muM<5#@L6KuD=;G;6|4 zh!UNb#*qcFgdSpcE>)G4qfJ*r{|&?Nk0WGr&_DlTucdN+cU0GanR*X-YX+-0D=P*? z5g9H>1MT}6p??zRxW$xUz^R59QPsGCyjA=8-QhtbxKhwT5PlnwURN00*lT$l)cEEk zC=uLuna3r+i-xvqEQuZDW9hxY^DU+>qeL}co-AY>FGrStS5`x88|oN_;xB_s2S;&X zkC1(cCcaTuxW33!eJ#~lB&)7^NEN8~?Td!|8Xo_x1$}PWMwcX|Z<<7fu~|RKi|Xbz z0)HaJvNjdWak%}@y79(@`cNH=9~FA-qFTyzET&NNMnq%WpUs|RlD=<3RO&Beid3+~>%I2_mTB1U6ceS01{6fkPE7i(0N>$N{J z#2#F&9AN3xc3B9_6ta;3J0}Jsnl0qUmw)7nhtdGoH42dTb^_B;7zl`6t^H%&3BD2~ zsM*!-{l}0Ascf$CSGIi<9&2ra&0~W?@5{12d^MYn)jfWZoAK!-_bvyWvrbbsz)nq; zGi2f)$)pBdEqx6FJiZM7rGjh9R?cjiSX{lMK?omSCTcE=2&=kLW7&D~q}OMon63pR zA5+PwuAay=*yE1Ba>>W4wnA;sEL%J{pob4ky<^{>f{VHx@k0z@U?CMxP-HtvyVO)p z0N`=JLfkGs3T}(Mux?H9W6<4#nxWrw^P{XaOqR8!8b+(ES7AH$qWWmGo$XK+ROTh< zCmP5lV%F=2i~pVpf5imZ8NEITh#=UW8$PXqIMtPiCitn$jTR1r;05)ET3L@j=H*&s z6H)*U?K?ft-WPr-!hz|n^+Xa1d%7aX=4WNuvgq}%b^{AWmYuN0Z~AJmdI|jWkjG0b zIuUhC-XtQ+e<+XMe~kh=+6n%rq(A^*DAHB=tl#5f^5zC>^kySDZ7SV}I#eI@>9%iY z&A0Q=8)(sW4Sj#1i2wv!i`~vU&U-rWZhn5fh7C^jd0XIPlO-eE#l{@EvgDD8(P`qS zli8u(9wR)R7H2o&O=y*tZ$r;wXu9v5+j2TftxV_m4v}JaZ=s2oeESc?Y}+9cRveOS{PJLwsWJ2ufBnH z?t1q}nNq7L(gX?bOcKAYWjkX#T3ME6YRbqrs-C&XL_`;TT*B-;Ry*xEy|oST_RVq<~p1mQ>rkK@H`)rs$U5rS-!escRMu#j6gSAm+e{w ziBgyODrtLzqm0|)gJ9c`L+bQrT|7YtYTq>Wp_eJDMWmYzKw!HDj=7k%HH%N1>R;fq z3rL`iX9JEUX?LWm{3;&cS`qt9D`JJ&`IA?ok zM*~xRk`PBlLqlQiFK zlC;-8Q!-FF@!US0%1U-nVErLhj2o$V~q(*^AwEqPs$!I@TxaQqc8e&eUk%&1p` zFgAFb0dgOdzJDkZ3EKFK7HB5iux^QM>3sawVl0Or3c{p_BGC23hOwEAQWk60MWIbn zer4a^it8}AVn*Y@exw9NXGUV~9adb(J6A>fk8;MO)U3mO7q($%3OjZnZ*J*J0iF4U@Cy(!y5qBApV zDiC2l%&^mlfoFW&p-Tln~YuiZDje2i7BYRyRVY%Y{SlFgF*v08IPS0O3q`duGIg{ zI8Wq6&AZviLJ){B{yhB2txSDc?6XI;PJfJUAH1#R%Mk!))D1AJV*p?fFPf2}%69^| zAQCu6b2aPIViObb`^vd@H@C!Ixe*ckdX@=)ZFY8cuEBp^4%VMVP`nXd>fq)}Ryf5( zVi6hh)mCY6Uqt-_X;$QX%%Fb3!TyX+nJhKypW_tM`Q9|9=}0CLjO+6p9uQ)uxoLE5 z*TfPY2oltIbc;udYzl<3Zc1J`F=Ap`7a8}P5~y}rh{?I+qQsvY%xk0LzHANzq1umN zaq;o&UbkpnJ>44f&%35#_Ui4WP`;hQDIt#kv;%t9P?O{7@{lfAG_!m$t<#BqFoyus zA>u?9a-R^W$i{xQ-Z9ap#dHw@2%BB40^c^u%JttRhyFS zsLpzU7;Nl%3vOMdY|$IfM2P1$Bs#h`;3=-{!^Jcr40zqb?+%+VHyA(A|F49;9?ldB zAIp{0zrQ|_`~>oiVlp$yXZo{>og{GJ+ZZ^hrHHq{dIfPH0{DIKK~a(3@qb;}#q!Wa z=%Ua$xxu)m#AaN9|5ZB8QvRONM>X9A98fV|gE;g+sJNI(ki!+L^>){?Tytz_8C7*w zJy_vPqSoY^zcRCt15;a!DMemxwV&Rf zks2iIZ%V)ahm#*K+d0DB7MXNn7TX_n4h?I~&&=nXy-W0ffDrPxgE_{#~ML`qv- zDu~VP84)<2-MX4}1{0e@GpAc$XBwRto$nubosZ^>&l@^?0EC;stcee2RX%+pR#s7o zCBD~WVMgEhl_Uisi((QJgFuECb-p76LVj}2&^1{X=SwNsPJr*O)$8?CnQruw_rvp_ z_ot}d4;O0Gau|U?qrD0=G2(F4p>zTLzSsC!;I~rEkk`R}5H}epP~y?#LdQTkvj#?N zLQWI3o@j~@%!{%5JhPL26+Cx@&Y(2L@b!jV?xk%uwK6s8;N0&UJ(f@s|18L+zrEb? z2YC>L8rn_H-FHXt#ekdNiM5r`&sUBW?a6M?DDA<@WLnZ@$02EaBDIhQ?auhI5gJ)< z&?UaG9S5Szf>&LILq3QmtIzjv`J4DOoNvKi?`OPxz~@4a!Yy4eg=73D6T92#azfKF z!KP*R{q^igKh=APxhICUoGYS&m~C?-sRF1rN#A&r!tWZe5jn);u(j8ym(WIRi;BGX zv1g+>DbIJ)>2MswGFM|NKU+F0-i`fEo_gNhux6S~sUm&e1gGU?gn4Mm>-z8AQRF!g ztLoj}9iiaq7uij3CXW~Hw|TQi$47B5#+6ZtI9G=(LyiOsL{4Jmo+6Q4m#6RS=!cU> zwmQ#Pn?nE|(M=D>AqF!a_=&l#2MHDZA9NJ;iufAR$EJ{kOv}HhNAvr$E7kUinz&R_ z)eY478_5r3)D!D!(SS@jJot;ZOX1!2{>?)GC=JxFU;oL^Lkb=olmisC!O>z%>9(OE zWQf?abh@D=Y<4Y=`j?qNP(fZ^!eX|PJ}os2zNnar%6I*Fq(nOSPyiD<0f$`=yt~&P zy%<)1YSA@ot=d5fEZNap;CC!-+pEZV53Cx-POrn5^I_8iRF z`&{*6)PmAG%qX7$!)hxAie=N*jRK;*y*-=xI8F2M{n`8g*i;Y%_*zq}+-~*ACYHU` z1gg-#BsEEpf=xNs&^)c-K%bE7zb89kqM~Ai<_JIZrzYF(Zad;{ID?lsMA9tWC)x3MI1q_;niWM*5PU7E{#<2h$T3K@X zCx?KV0pjAQK;+!DdVc91aPsyI4M~894W1ZWcLldefMKw2aIkA z$^JN6rAi|i8d}j8cE-s?{1ZdptF84KzOikCoPvvvk#ZyS<^IhwW zUIQyHf=IogpLgXl`LcAm<`ax#&2F4u_Ui+y-_OVM-Ae)%3xnB^RW3O0yX%{dfQI`ehG$E=7t?O99)ysEYfA{Wn@E zu`o0a+(@}^m7KfETshXpcgA|?e2&{#LFi;*;L7>k$EqzrbkuT3-srgVNU5K@VU{cE zcM}y3)-%>W*k*5E6rvnjR}MmjvYWCIEpqh8ZCv}0h=EirXtCv}8>~i*yocWmxsq=z zyJnnE=g>3KxKxc*cgIA`nw<5C`l5?7sC#t!?^viu>`3i|gh%OE619wSkpD9egDH~D zVN>oN06^onDk>^tc`~?MRx?-^dy`@SQC9?E!+@mEDqr9S@4wycxj88aS(J(*?T*cr zvb!Fog8wntFX%T2m?7!~6|WNAf%3&V?LDyVZLEA$2i;~6GCDDwb*Mid@2M~DH!jp} zs-@`?Zzr_x48$)MIF|ahWJH|x^*ddrE9u0y_rc))1a%Q)**!^MPzfr6@eCPUDUTwB zK`|S3PeP>qxQC8ibgKp#Do;34dvsVg4i^9Y;y69KsghcW6%R;r9uo8-1~Y6AXq3v0 z2k=?SJgyFbMnh|vIzB+>74N?C_;$5Db$*@971}ohY0gFQCgq=G5f#b?1Ac*@{QJ^09>gxExh z=EYrG{&s>3Y!Ak^OylVWoNtB65V7FhzFG5eM>rDm9Diin>a|w{DL)QaVZ%F~#%mL` zGqKbw@s`yh(61dxBv0ngSoQX2DuP5)v>Lez6jJ;YG6V=_yPHJ+8a&)W7le1Wd!QcB zx!3_|8PexJwcXs))uLycLrQ?uBvG+n@hdP7lKKp~JMAz1gM(0Z9f0scc^HQ@jwpC5d8H6Q~DKmK^h(y)#LnxaJ3EOKWI z0&PcZxTFml5xzr9Nl8f-aC!xLXXQ}?273B+PH7)H>;?gHycTbd?)C{Ty2bH z5pVzSd`6WP4K!Qr>W|-^+*2^z8ZN0@4D)cmVo}eA8$Pu}h+Lm*iMgW4E1H3j(!h}| z|B<6_OAo)aMjTUIB9}?t3Lx#7(0|kb2A%>@DDX6y=QDAm^T9L9W68_P0amHHv{_6k zb_zwq?`xeL>a2kde6_A;pUOhxr~6alVe)jh*`r-iQQvT=s;FP z5>X8hs|e3cA~aGyHo}2eb2q6YWGY>YnDWMOi_8)NKNS28b}S58R-(*i=UIma-m+nc z)Mlv_@oLe9!u#)yssAkqM`#PC@HV;;EhiN4z52~P;kc<&!6nF=i2`}Wc^2`q*8lme zs{5hj<7QzlqES8ao$`w4=GprXBkA`yy%c~Dp#f949)n7mjy4?a0z}o@2ltBztYCwYBDmRpr(9>>W@WvtbaT_2RAe|H8mm#@9BbDX~8=QgbBo^U_6PB zhmR*fC)9CtB{uB@MkPuh7y=vf=ye3K0r@R%Z_lButQ?b?8ipf78^Yt#n9#9Yv?qyt zRb!^toW0-bNStGWfr3dM0e{x8<3*37m5N?+F|;bfB6^5wm)byy%s@;JS>7%<#3|1} zP@mTSGmT++n*F6h%;lO(wGp2wmDWAj$Xa2yCRu#9LszWTfCHzLBqf9LL5i#sJ21?^ zVd4t*{dzqwzWobL8S)G{!7>)uW!CoC7L;70|fI zFz7c(*RW~1;Hl!^vW(j)Kj(+m3yR5rn8@!D5?nR-YE=|j=MP~shb2-=Jx7(3xBW}= zHehgS%|bMD=|l&EK0SQ;fVCld-Q|FZ#%#W@qW=}&m}P`gIQNzL;8TXM!eGMc!50WG ztZGI1MNImfR`n{hrME$`e=%eO{d@>gqLwlM!HJ|p=)79dyi2aQ0J%afsOLTuVEE~l z|Ci6KS)ZW>Bak$*@dak2LS1Ohk3E6Kufk;D$aR9R1^v4ex!dDhsW;&UCnp8U{hlq; z3B49G+%isa$Y_yfe4DOV=YneL5iL1X$*U^WdQq<`vlZk}QCX%;D=(lAU>J2hY}h&; zetl%it|Oa3@2k^t*yeS$7~B&{!Wl@G$k4ctx_~dL_93?8n58!2&E+U!AUcb9Dc;kk zC!pMe0q0dL$>BnjvxkI5Uu2@-;-;)2jSJWLso8kmQ+QJf=aIm5*$rmCaG*4@ukol2 z$3y0OqM27p3(18<^IXMh`KqFxb!+B@Bq~iGG#LX^JK(Sl;A|XtO166cyh#N>fruvW z4qfYo*GQFb3RJ*pprN4wHq0`C+hY0brrwPbI8E8v*}+>c0g5LU0YNZeKEMPGx@d%M zTwEnVDg=-oMW2}*lGh~;8WvEpp{MY^=|j%rIPkB8WI}1wOmKrC{{qWkyIz{EmA!61 zx}pX@iHL|uGU5XMq2MuYKuFLILK7-LCAfdMS{hG20TMYNt{1vJ-wEFJU464*Pmw5TQeV*$XTF*TV7g_?)(#AXYb)Oy!dlFyNbSH1W(~B3dah+OW);>Xzr^M zT>b04OiUB##Nz*7dadI8uFedKU9Bnc_h}d5yXATN`Cs=ndY2 z^A?uj{Iuiwj>8*}vw#W%%YcyuocH@UX;`MMRT<)t^${dAF0r~Q5;JnUpi=BCTAk*n za?KbMIv3o$lG+^6Z0pv*i?;_N0^r&>Aa5uK;56L61ISPeWdatnR5&t&$Eb=1(h0y} z+YlBfvKQ%zv!@lo4+2-_r%>?Q-5OaE`x|>lZ8)||_tkS;nuyn15Uf?`+sljlL)nyOeOO60)WA0N z3RFgl1Yb3(?;+vnjzv`s4MFt%zxI-0_2} z&8;+kS(b0>-|1?y+ix4X+U=(m+DjO2J$hD}y(a+2iok3{wD<`g;kWejO{TkrYg_pb za+^R0w1q~ePJsRS#?yh^wj2rg@bK%4nmf>F`hu(n@K#5G-{e1l{-pzOl6I$Ep@k== zk>49jL5Tsz!6E`HF7P8ND=TyGJGCabv>eAI;A?DCd0HL;U5FUilo*Tkby-V2yKfb^ z!|K>_{~3>tk--K21kwJd@61daPrjBKvd)67?}ngFLyL{xywOcg zl!5}3YAc8JMS&%-Y;#y*&$6r=DEZ)z+i`ys0iF_CZNEXtL&fiOI8*|xmmkEe#wwmq zR=KDrSL+BmN8);vEpTd>{s+4NZmOpsl7v4M5I}~bS-!5WZJp8z3PIQ8n#57<(v@yM z587Wx#eJnYZbp7O@l(DrGUlXq1Bx?RYzQFj;Nf)sGYf&Vu&lTK<$5ck)Xe<+L}fpP z@mt(4a0IEYy{!v0TkQ4oc38~Rg$0sE#zQbt1&XU*c+nqOIb)D}m1FYQXhkc0{$!wV ztL0cC9Z%OgcX#5_iizj-Nf zof5^nUC*uacUXd7yiCp=i5S!Q;#WV9OVeIG&#tNqPCZSD+(V5Y>G0KG%J^xltc>G+ zzU>QGFe+YN0N|e>k?$@om0&#!B;Ud07lX*uJTQthJ?cIfSQuT6vSF2H-jb%XeW zU*WgAfT|iGcp$i%21K!TBN1r%puSQShofVui9reD`AV<&iSmJs9rPBIztse;#r|JR zAPO`fk&$15q=6_IKHMrOL}3q88AS~J`qcq$&__RunT(JPqf9gT{Rc;v2IUU<828YI z)7b}%O;HTugS`Pb+RkRzc;XhFoD7L1hro*jve?^%S0A4l|H{h(W@@CT~^%sW3lk{BpAHaK`%;WS*_<9+URLDIcR~D$>fJp2_ zCe8CIbPnU`FHtk8k+y4P-Gtxt1SiXVA-Np6EH3oCyTr{Y z>P-`-o<83Ihebzdld)IYgmKi&N%ZB%2mN*SKO>5tG~7PpshiR!eGEWyO5u1Z8`&Z* z;9p^w*}ECJhXCtmFt!D)yexe<@}sqAw(%LXqQ%H35Jp1#K4>rJowhQPd2KRdC(D)) z;qYY`9UdLc6MUB{q{&GG9-Ph}xTJ)$JlGY>PP9pq@3Bgk*(8Glo4MvT+s8kcIL7ci zdKe{K`9j0$i*R;u_POq&3lOw07#P_uO_tI3Xmdn$#L)Uhc1(k^lVtFEH_U!|o)srO zrGwyWv8szy*bRHo?m?u9(JW};wVY>->-Q@MM(dTR{}-en+$Bh?|=;>Pg`#)!*Wyb3q%mC171osM~b z8W$fJSF_;eb0!U(YZGbkH=guEbYCfp#Ge-Ms&BcRs49Jrh$Q+dn=81m(6jduJ_y32 zbfYQWL;-mU4p=V-hhyN=^So^*I(&VELFcvTDM0txtmpYOQ^Eg478vesUaB~*2&M#! za;@J!#?+De;V=FiKeBG#KrTm+=r*)x@6|lJVcw+A?ZvJEG}aMVU*Y8eM_o1RtR;Y6 z72x|(?iYIwdzUdeC6*Bu*_uCAy5dUSNYulMkBpuJKUc{1wEFDB#orD6meKo@D`?`S*k30*eQ!`!M6h(>jbSKcI^-9b)T1l zf+@&DAdoOmTm(-X0%aPAK3dAp=8T_z96lz#9hk>$ph|jg_*^{QBzk~Omk0`YMRH1-Ns)i6LP>ob_Az&wOJ4^c60h$6gUZc;9CgEgk&7J?n=7j)X zc%F{Vg5qTvpgoss)N}!g?HvM+ca$(_D%G@|o?BZ@V}ND9Z{uGk<*2j2&V_#POG;R_ zhCUu{RMBx!<~^W_^{u=Q-BM0%_L(rFi7<*q(=jrf-EL0YXNze1@J}zIWtH#&q~N&j z zcwYck0NGB+YsV(0_N*5oXuX94%i9Z0b=j{mNcpfpQ=$?b42&YN-EQW~Lw$X15P%_9 zuI|QKJY448_e#J#18id`$K$fB|LF^XMG6qqcma)t2Zd?F+~ z((t>15S@Iqo{}Kc^rPGwwI*28-VN8H)|2aTL?eK3IzU171=x)t!20nULE6#0)3o-s z!@mdMgKGSO-C)d2TBdy(@IJ}N?Q(wOnN+Xh0n1~=x0Gl5PEUGaupeswLcq3?6~Gp7 zje~!V=l06hrZjDk{%{m0`F0~F9jh)2Ic?|@75;(xgu`=q{aEgw=jS#)O4}T`&_DL~ zvfW-rfL%kErLTuAEN-AGrl$Szw~w*&uU z&wB~4LVD-NA4HU5Hs6c4JsRH|&pW6*xc}SN($Bytb9flIV0^aWFdHAw@9?r8{EqK` z%sdPUqqr-$z^IC2OBPldL9k-ADD4{)1^U*mgG3q%;Pg*~IjwP13W$l7pmD zs?l`JaUO-^NWs(asnaY>yp10l`FNzc7S~qc7NXd3k9*_d|bf6(FINc zo7Idh*XMEZ_|b&a_ar*5rS%+|@6DOnX>!`33ap#g0?0v0obU&*zA^kJ7-y_ zTh_`#G+&K=NG?ka<=}7dUF&)#Bi&I3`@mIlC;hz?Yd{*+0UAa9(IjA(S!i_bM(3UP z8DMF`1L>%*-jrcV@}=3?@7~6&4_ptS)XP~_@H=tO!4$OHIzaz?e590n45q+W=|FfF z0xDLvLH@hA^sz@a)yfLfl;%YBwL$GnL_Zp-n3|yoL(#>pukJYEt(q_xy=Ez?PV(tX zocOq~NUHrU$CzvkiGg{#=RhD_%vVnCX^VGzaUeBpJXImPxQ?80h9o%s@UV{dljY{KxhxQ!4j2A|U62(vOTwCUnS}o=L-;y$ zpiZsVAIZ@lu?eRa>=PW0TTRP$RnQw@Dh?SxWPnW86Y#waCbJKxL5apIfR*X>&y|D& znq#?KH`lY4l1}=BGvkvCa=VxzD}qYS6>6_LC;DQL%9G9_+Q{__N=CeYq7=<}m$Hk#rx_>l23$Bv65cv9Oqym1>;ybQa6IexSz^I~w?R5O;Hh3y$ncyYrc;#S8h|;GX zHN8_j>`u*FB67yNt<>vBz(&+X@D+-}rk3Q(gKK8e97eil>gMHrQlr(o%GL8NlJR|w+%ydTBnsS9M8P!Yz65P82L6;7W64CGi@HCLTkEDpc z*!$Zxm_o7o{YR-|dQ&3SFy?Q!D0c)TW8@vjPc4t47MrK&h8rx+TRwGJ-x#EP{^Bd5 z?KTv;N80$|YZ~J*v{}tN)+~YA-Jto?+iNiOg7)S6%p87~KnM8<^R9G*EgZ?gg>7T> zDb<-b4XoMPqS4uUzx(9_+ISsWyTWmM_%GIUk^_iMr^Ju^Da(`}m$vu{ri( zkpch#Hd>;|D`@-X0r~0CJhvp`eE6&C2Z2bB6Xxs@uIL7GXoDpBt7nAVzhA81Q3HcX z^Bz&3)1BAeT8HVreIV>gAsgX>dHM9OT3BTA0n1KBuf2oA2@t*@t!u>a9nR%QQ#VQ? z!^E9Um>NRE7Oc_~Y` zN1&i5jul0rNE{uROwwTQg7`R;*2K8_Rn?b?ckz-!DJvA8Tk6F zL>A2;;Fze_LlPXGDfcFmL~H1tZ8hFL*mib{ew47i-i|`y=>sMq1lTLp;tO%$gLCy7c(lP_}|wPLJ_7N76e~!S?-{mNU!9V$A3@_<_8d+rIvXMpITe*Pi2A1 z=Q#kjL;&~zlROptypU}t^V`AeG13?7u}#OGFWZyc@K$oTfN;H8~AL1rs|Edc)mVX z(Sg$6PGWf;E%(!~7lY~Eo2vEUQj;n9jc)%~K`v-V>no)&wVWnDn$46`{VsgUw}Y`XMGFl9OawO1Rr?ECk4uoi@; zCLvH}d{MuxH2OSJhZ{Cdx2+C6v}ItvN0z|V`IaFl=swA9*5`;G;Hy8#@TI`8S3w#` zM1h+QsDl_o=C#xVuUDk(6#d--8;99;h;v@%{YncYEB54V)(pREZdsZnSpgmZEnqXcIV1i3`WdC9K%=ph0Ms#&V2Kd?~*=GF+N$BF0tSEHCt9jHoN<$G~oB}{phF1zglo=a@xo4S!AR zRw7KGG=}#Ta9KlSE=BbdJ*mwNWFPMPDyXX5`QQ_icI9KZmOMuyT#&30$PKn#PrB>x z!x(L6^OLdws+L-P8_b(*xnVl~n8WG;J<0Fa4-$#R)4g%zZ!$f6PBmVlu&-X1M2F=3 zIN+^v*!}VGN08lGZ|?geVV`_Re8{?QVe^LH%9t}jZU8oUVllz6SvZmCa`_C(iLypP z0Lr@M%XVJ9hp)~8vAf(B%Yh6wlvM1oWN|>J&BYi^?RROq{Rsw92uT)IxO&}12M9*! zrU^`j?t(N6Ze$F?WS1?Lt>0geqw}X@n?q_zPxD{p_4p3EI6gcXEi%5@Wq*%Sz9i2+ zxtZs)>3K0j*5`9PIIy>NiUo<>*l&yiDJ^_?AKbSEYVzo6aaBFOTSus4(>h8*gv8tF zL{?q$22s7E6~lL=K9-!XG@P&(x_i*Ng(j^qON3)+w;@ZNYNjY=F9^ED~&wj zAy^WK-NWr9UIt0&>4W2a!xQuK^KBT5K%I|9Axv7LStl9z?75x>eiXS-7|^7_Lq*hU zES|rVONM+<$Q92BdsGU5Z%!XvZ0oJmQc+#|#mHK@Gj`MN^G1nG8siM6rl!5~NeGyo zDc+?vOuou8_AeMz8=J$V(6JHd{7bMD&W!*7P~kfEVlbT_*N_|{{rT_j>;Iyja9_%` z0oHu9Twe?xNd`y8=fFq_^labb*1IA6iQ#s*vgwb_PyLIsp9Y{*!1JFf0Q(CZ+OC!6 zi^W6&&btAn+KpL& zKBi7&{r}PQl~GZ4ZQDbmv>+Vm6xKSoc(b*#y7C98 z=o;Tep+Q{m8b$10sO&=|%JNJ-yF#LTJF}g#?54)g7@OS9CRoee3-)^;9j7_6$$FEp z?gyL`6u-T0pY>S_?XH=?M4)IKqAFbq=IFR1$L-s4n0nr_Pltx=g@-7O+|7J5t%1Fq z5kxF%XkhXTRw@vc=_49y^;h^E0R5m`V9|gK`6dQ!Sz9>|6)zr*7h+Ri;U$hjzVaR6si1H$%@W0hM0oT)?75+!aPGR?m6i}KdgE+(A znHI989a2?!hmD6mvUVWO8ex3dnIeNHR_XhjQJfO^~Ym&hmrTpPv!6Vq;T=Wsn(2(_GCkUr?v z$j6VAwx}?sbr0u%_UzdR6y&AC#qLG}sX=n_^a5r52yJk10B+C>;9?f|-vJ8tYV#%X zb24smAYHWgJReuf>tg~tT13Ei4FFV2IK&5*(11q3@3?^u=O_VPJ}n^i0Q<+z-!}8* z9;ctIXUY)(UDavK^D0{ogu6_aQq0xbMMJTPT*MP!sWE69JLUu*NrGZS4=gXcyCT|w zRx;xT$a7c!hT@NcVEN&Y;O=rVIpbe}k^9e?uDu;?I1>u!Nm<}$ z;{GCSZ6&fm5p*zc3~lxvTxCyQ+Rav)WrVDO@YfeYLS(z)FU~aV1kmi>zklgJ%^cRw zJ|a^9efQ@jvYMU8$m>xR^$E6FA{M?esgJQSAF@GD2ODkvkBj=Klo=H*dV>yx|1RdB z0j_WzB>iPjMc;<7g##{VX~1-Zzms$zFyk*%O6%DZa@PMrQiAa3z)h1h zqkRaEBlVWs{A3SB3F)10%?FtT_g<)EXZzb*tFFmd#}$uOAQ#$*-ErF*d~+K-<;>|Q{P;BUa;J4`#}ZH-NcS?+5Y+PIFRQD zO1NZ3XebNNZShuIXqD;4OE_&l9!ZM)3m>wQ{W z%4*cobec2^8sM0CXc|WBH_7}D<+cCTlUp;WtroQhwAimYnIy$mE>=4-(3(U%ox4Nu zZjey$6#1Cn$WQ-tL5ZXE^=uFQXB2g28mSl^hA(p4G=)iYEO5whGQ%>gG)$qdi|W{F zZF0&bd*2co1U{mb3!wvh%ZF1~E^I81*%$}o=9z;ldqv4ORCV5vdn?uC0%_)k$-F!| z;tnAMkG448 zAVE8Fr*$z!JPNjnrCpyI_7yQN`nEe_G0qO*+S*+di@Da@E(*HC+SR}dt-|Nt^UM7p z-_-;3_OQaV_ZdU7SyK$)GJ=(KwsEeE`B)uCDz8;A;b7YO0N3yf(KUvJbzc5!Z4MT! zHwBaUjYvX{(cM^55ps(4w@xbEr4@R7?gAzRT~?+5sCT~$WjF$A>sO<<-Zy9MR|VfC zYkp1=1Pmu?x$}Rx-`$yb%IWPh&e^y^FnD19D0!5WM{E8Cyje=D$UHnEUhKe01V~enT%?5(Unp0Q89YTDxwN&*{b^AdsB^V*hZx-Cc7C zLo0g)Trtc|!}t=&n=10n?XAr%g3iN(xk-_B(WW=jWVxO}Pt+UF^X75)dwN- zvjBylxI3L{hwK+Nflc#AFuct)nPIqEFGsW2Yn@EB!Nvrm%JC(U$p475Bv&o}#$ z-uwkN96%SZS*-iKAk&Zn=+p%P`78*$@S+w%()}CSw?8;p&V_kQC!E;J-`f_D& zmfQVJs5dLhNCquU$zR}YbiWkRX7Icga&S;fK)mKRu-h%M`8KHidmxqC7nC`{ofN|R zBcvhQS@7A>#JWW9e2A$uV4OzY6_r4 zk@b(JP~PwmE?QSJ%vDZalaNR_Ge}SsMC;A{s{1P5jmFX1OgNJj`gH(7ww9e_8#Dhv zvnjn%(HGe)Q^5wV)@^-+Z0B}Hrg66y$1`-0;{~!7zl#~~;YYjIf?dZXY1lH-kdxIr9#o}~_c=Uxyh*22Zco~1UHE6 zB((*QuQADZe?jt|IB>3IxGg#OOX*gd`vGi#uv{A4j}!~QG3^R`2ap*i-6f#&`Dmp8 zzQ3HStRtV2J^nSIo(wkA0Wxj`;4~ZsIR6k=$}+%Lr3Qr<)QI;d8;ETHKO2DpCs*N< z9$M64&@hud-XB8YEVoS)3viFYJEw!DQ)Nh-z;=27P$P9K<^MsYL$h--XHkry;o(i%%j~-sG{Zlhq?}VJ z1VFeC1i~*YF0dMmzVbocVo#lHmAP52WDa#lU-e%_FtdxDmWjW*L2;u~@opr)dT_uH`Uv--&A$)0wydxpTdGH!0zZ^=Z7csVg_ z^bgy>p<(3o>Hb_j5=6DpR-}pdUg}2@6K$q5?ID}cKp497DD;$bOvlqhTVA`)K0AK6 z1WX})y6d~uzUuNsZIf7*&7Px0o*pr;oxYC;As(oVt2LCaT`&uO^Uh0N4YR=$T zCvNUNx*QKO^yH7R9m4rCRN*Khtn;n!kCRM~i5h+0oP!H*6XL{>NNr8Lx)nfOQKYA~ z{y&!hN||unaU5FB)}k7mpow|nS@TC~KB`->Z*cH07d}1ze6;XM$Vsdjrx|vh(Gzde z0TK3F)#-7T5+79mhi|-oXYU~1#H!0EqZ$X%hO#BZSICkB4FX-UM8%R7)tT(EE&ZH1 zPK#W9JmWSNp~(ZX6~(NIX%~cs9_N$jMfYko&<2;?hVef;MvPK2hL&}stkder-3zWZ zpHgC{!w9NH!E8tEOwfpK*ya<)_i2DQf|Tbe;4^oFEm7hV5jK4 zNB4^sdmsj@dyaX#T$#wDHYA+RclnYC7LlweFzljzYnnF!z88IYm*JMXl3UBlP>+nb ze+s&bd0Mr|$pJ+A&7JY?dGCj&$;8}`@O`)?nQulc8Zkuq^jPjhe!X8+$*Ign7a?z4 zke%q!#32M!P7%!3dso2Mo++Xbv?hl4Z4(Kaf65~gLi%4Cy)W~e9hjh~Gdw@RDpl^U zbs047@O_W-X0_RB8XFTfNudYjwL^qx7W)V;eLH^gDpURMro?#%xr$oQuQ8b5>H_&@ z_w^~LkV2b=&7OXuUN?VpoP!#T^UX$To3F;*6#djkt`4S{i^$X-M@ZAI(IE2UU{e47ii6EkBIzSvBh_*W$lh-PrT0hmmQ z;EEb}A+zX2x+@H12oPOUb&ejK%p7qp$xI4Se!hO&tVoZm0wu0^EUtH|0R0R^JbY`= z0qjVgslRbaj?6=%fVMcM%gWtr{OSCctN9Vfnw{+*&EMb0u%|_4bDvPEiD*SHH+V4 z!J$!*jR5E23%cz(#1*}GvAr^=PQH55_cHlIJ`ypVpe%CWmnvHwdMb1|<)NO9jFxMe ztasnJ{?xnvbisF%wS9r6z>=Jik3`_@&0D!7*oG!1x|N86gZUXJJ zZ$0a&!_+ey5!j?n$C>8bsqjv8QEz0SiQIfM29no#o6+6_GDK4}@9EOb5m&n5I_6Y4 zy*81>RekB&Tr%#r`_1H`i|5Hk%5NpDyEmkd>{;xwQO1gj$L$@v5O<@P-wHvOLRW6| z-%WqlsGRUj^tJ!8@%Py3sZ`(YEH5FlgDSEsV%oIf=xCT6p7?VA1(HCcuzSpRVKSS! z^l&2@Dti5vF9M$W9(1&_qknp{qypM}w6~(rOtfbwpEU~+7LI<5$Z2}zhye)cKdS`5 z)Yx+$aK{@oontxXF6bsFLRt)EmGjHRy6Oq2EYWE$9JTl4P>nieO!MvDEtp<=a1H!( z$A5Am%B_rVqT)m!rIWx)<X4^gU+f>rRfq@W5)@Qe0w;c~X`k+lZo*Ebi z*Kspq(*DL@Bj|llBpDxK`Kj=C*EtKusesqgA--^KNxrf-R$K4p3YJ`MbSjP7X9NTlRqCb zY5U%D11a$~??0u|i!f<5a}H5u4H3x~qyj9}umzYclK~P7lcnroYS>od?F&i#^{1`o zT+!RDouxChuJ!1GWi9JZnxU_riRbr2rMPvo=pV}B)M6W3OARBzkK-el>>>L&x*~#} z+<-=4ygfb679Z1vkFSUSV$lDnAo;4eTKdd55N%i^O!`HBz$ovD+wBx9@yxBfwYFVr zl5Vy-UDNq$f%!;bXs^^PVasUen~98gLnFRz0rx*tmq;~T`MnrLiDnWSslq+-$KA?5iO>A9CizTK z_&K6vY}HXi_G@11{UHw1tjim%#R;ukUd$@{5eZfeLYrV#W4vRHUd79?7gu9Q3i&bR zPg=t~+I?fF`sW_DNPxzeAi{m~|o6Anz&N;Iw zEltxrrev#LWw?KRTbR~`%E#19<8>b>jzCNnBvIv_jw6#vX4hxI%k^2I>ReY0B&_53 z+#Sn8F4Afan;%6%jhC9A6m>0sRV(>7Uq`i8iDvP_Oo`c?FlpJ55(+=0&m__8LYn+^ zrZ>@-V6sz)Eg^YoI*NL|5lO6;yU*o$smk(3)0ltt$>eaPH~(z9i>!?hPqOqiAQ!R8 z?i`LK+pwg>!GGE2L2Em7;u!z;;_&uW3R9TkBVbXk&Fz(TfTNEvg9;ORyrlx%U6{60 zI-cc1D#}lymNRIRlQ!`=3sn$WW|E)tADNrECI=+cv)w2gz2A^>TDRrz*CeF87F`#c z7 zL}Dy@B;E?)SwnMnWC;2OSqh>}T<%CQdz30O4m;jsLlI;OeOkUa8qI0>9sQ};AuaT5 zR_1+$C=Qa7d8Pkv_#amhlR#fh2`he^Q@A(o`&u((qp%hOv-ya))T2|D%6lW>)^K{7 ziJ9tXlMwnBfmw0UYUFl0&5}7-!Z`K9FDupIEJNC!LT<;}<^bbUCf(>2jUiHYxpD)f z8%oq!PZq#B=u(WP+Vs#x_U)ORRUBAy)-L&w0V~dY83DJnP8Fsj54ZXY#|$GQ2*r;V zDhfXz;V@tpuAsXVvaGL5v#A`@8>vr({*6?^u#LC6fBj^&BN3zMEW6kHm@BMH6x1~3 zef~R4br;V58d%~ouxkgPVu4PdORbje^qo;7ZV!Lno`r1jSQN?c2B+kIS{Cg3 zq7(NF%vEiNR$A*fqFS^zmuwL6M2+H)b6+!ad^)1AoWe?kgvLpYu zGi6lEL1yEB{8EC%4%~sr=(hIsamOa6*ck;reykf`bxuS3Lh-{eAj)fG8(*YrV|`L= zf{DOW27R?+i@5O)HG2BmWUgxi`2uD{$rsTf?y)X&5Ei+WIE9vZK$4wRdQAwp+<>`^ z!J<=%40jOPQc6>;dS9XqCCRk!{7I&QY9KFB@k+Qm>cIWUR?nrzIAH}J*f|2rGE|6W ziSF}lA;FY2%KQ!-$=}s0E@A(Tyg?vlfF?6+IFUS%8=ajQFGR-k_CL`Y78?wGL zbH1bEw4vUP-=D+|_wm`B5as<8anBp)K(hIm=Q%`3|8jf}whf)#)pJTv8kpTQ^qxOw z*dxXOWuqxZbp$bxzdq*27gN^%>H!39TL7Cb%UoX&0W&NIv>kE2UT>gczxz#HX?I?f zQ>|wtsr%*lY~gab1A@YP;c}O~_tt>;?t7m-45Y^d|IIn9)z-6POtNBi06sP3a68c7 zriNMpHp;&VC2*WxR9Ge7sMUyi-!Kj>!Os_?Ejq2cR43$-p^lCY?Xbcm1;L&37)zOl zsFi+_S&fL3oi8&rhSnzth0_43&htw4bk{(L%)@(Z7eBSI_apCi=Ka_^1ejc$yNKMy zx(Qx3@0e(u5v5V{X3heOH-pAh4pxgzx(FE(8kVH%?~M2ch3Qgv@KDF5rpk+5CsXb@ zsi7L4OOoW0X4r|~Ev~dnMX@Fz1r%Q74cU<(B3@r63els<` z`x!nr0M=6Ff6zq15BNx;kWO*~;qD_l)RdL0pWR=p=knT8#}&dT^7I=sb%p2C#3JKr z!Tsi4I=>uYI#I&F>-e-@n~x9B+2G%FA)+*5QfzNz)`${#3BUK%sPC|5QZ1vQ+9{X~=&KoTv`2o}e?Z7iB=6MQ6vc4zNr-5&NpsjbBk$I>Xm-KBylq_;fL3KHh zxs7a5r++D!onc#V$VjeFqs)7*jhx@iOH#YY-{x?@Yv;kcbZ;HCzK{s@=-GZN^0~Tc z!hwkHs@LY=l4}1arOUC#S%)e_x7JokRrOx=R4KKiYk{EaH{Hu^HkXP>Pi0v=Z;xK4 z{@r3I|MyDrxQHL6fimp1CNQ~Ka}Sm_fn-7^skU|M;^wGjoCThB?N0YaZpOWwfw4W| z@~;wyD=e9Wtf@kq04#?(DBk1AGN_IeCoGF9KgHGH*~+EOS5xh2&_VYD`!}R=jVOt}jzNCRZLa?Mt4Cu;d*v&j(Dh1fhZk%8eU7+)+p^<>Q&YO}A03>?J_rfl}n9`1ePKfVHw$QZOJZGzSv6o;!|IT6gFEJs*76R0r z8PbfVBNcJ`zE1wAY}dt}x34poF|O7PBi+JgF3Y-ca^rlbPqr9$ypmf>tE}|FqCS(Ym5Ef^Oc_atCQq_v9` z&8c_6vwdRx%rfKXhl^~{J(t?w%+<|CmWH)%+?F-bsRuGVcX%4p;J z%fHS)i5Gh>x?}=+MHFsX^S>L|{qXdrIEp+8s>qx!qNl1&4S%~nBtH_O6InYtAPXhC z5Kqtz-@?M|IzaaY0yV%?0?a57FLT3p^rRZwVGZ}xEaod+cFd{O<30HL!0r%ar^7#g zMgS|Y=jA_}Y-&vRN!-qiG0lfH?}`NW_RkDy5wKSn!xVm|SPo;n;ZyfqWbT{Jr6sKR zUI=MQ*`#&tWU@@&{44#BQ^~oT4WbBj6EyVkP?sja07#q^uvH7qW_)CybmFCdcfu)~ zE+{d^So3Eg>LFV()BJcOJD?8Q`5J1y-0)n>t(n+pFe4mLU1kX2Z4@=G%u#R%7Xg~x zr*8p#(#M@Z;5!N+vHkg49N&!G|Ab35&2L?buzk&Z*81z*Rl@^jONB{yHAF#kO zygTV<2kOOdRri^XcW4F;uB_srQS9IEh683=T$n*_(RzLzP{M$;bMQWcNQ{x~LWKQ& z9MjU%Hxi=FV^%nr%LtbC*;zdF&Z#^jJ!eQCHj^p?dwn$ z?4j^BF$k9!tQ3GzIR>QAuvzev&lz4{5|foaHgzP&EB`j3u*4Agk)0wKq>axpIow$_ zhq}6D1BSG#{o8KVzdhjQW8|6V?BbtNYHzU1muE)7QXEFhcF277ME2XJ`!LHmuw%_1Bkr$Ojlvn%-0Q~cbJw1 z0%#cyzX6d~xb8-6s%%JpC!u*c|Fuha-VuVP_gb!-Npk;hBr0eZb!BqE^!4VFb4bmt zyUc<5{+F$1ctynqbmaW@5bxV_DVgAmrD7nVB=#5ktlUk>Tv=hQh6dkNR!P0@yF!7qMyZ)%;XKJRa? z8ZwI`Oi_XzlF;O^(!BM2D_?nb68DPbFWn>)eTQ$z!`lZ19Yt;gN|mkqVDTAz4OLoh zC!~=z_Y+q=U(-3=247@cOmPE#B@!eC*c)Ab9uNme4R?n+g-XLHNpv-)Fi!gfS67)j zJEEZ9VkIu37^|K?)Nq-~>+%Nz1W0l?4ua%hqs1yvND7MgmxN!~(K-M8Uf8}72|e9~ zYFB-V)UchaE|Sfx;3sgtL01``4nB9yqQH*zRQYVH=*h*)N%j+8!i{zAj=P&ZURgrN zj)3qaRr=EEjAUn;Ald0g=IrOVNH)<&*E?Os8pB?Gne?3Gs$}#2e5;Y*_lo$^hFU)= zKL*sDLxKA4FVOBGLVzHdXJ^Gs#!u`@ftV&Uq*QI8o5hU$^CKJNL(ti1zZ0#%Xynd% z^VMvk{4Cw?4bR++rS~})Ia=$+w4NEJQ6k04jIwMd!(>!eTrRn<&y0PXvshGwapNar z+{#ZoKl0w2#=11`EKKe>IXO9;0$Mphlp#=GINIJfQS`R_8RASCfnUX4#dv?wc=9pq z{zq44DktJk>+kn%%7gaH=zQwe7qOQ)#_^w!UBD+RS%tK7r%hMG+?3`hC1~6 z$E&mFqxY)*61gLLsFtQ;$JxlYx#p=H>aW{goz|IB012`2M$RiH7Z1 zU4lM>3Ct!uS)F9p z*DV_z+I2WssIz3dS>^5LuO%neiFhSucypDNEY^U83k7Sjf+pUZmCm)D`WyUUngZfz z9G~m!M|-pF3+;w0v_}647gq@*tAO^ex}?a!)@(?s7VP7JHq5f6DI*%AQpK5$(mmXMd>m9 zR^ATUu*h;)UU=`Uqn$(`Zagg}vT6aiD;T~Rq}za85V=v5|8fkGdciU}v3=2G=KI)@ z{nzxqP~G4O8;SlGA$Tt)CNPy!GlS+2h(qCB_>mNB1q)BB2YYFT#$76qaSm7VLr+XL zoE!nUt*OvM#bNtU^b}hsCB+?PM0?Z*4VjOnxL)}fzx8=FzO3a<|NA$4=bQ1*p?B8> z8n!cqY6$Yr0DDr#WCx*lZrO(!FfEbcQQ@O|aZy+2$%*c;rN-Ed41=r_pddvBJa&k0 zmELa_RUI%8Si><8N^j@ts(VmyazoFyMtf03csj0#7^pcE2XR3agh0{3rs(9!`4gKW z_0j-75n9?dDbs@|bvTg^+(x0CF|${-F1d+k1>1^&zFGvnOGB62qspOzF*n=py|Dz2 z$kH((s3+g z1$#}HaEOgtgiYF9ts=93VRgF z!TuNY&sC9K{sj>khHzOeh6F?)?u37N=Tz2;eVCidxg z(D!Lh>>hTqewuME>9Qy#q+z~=nI6L&c_lk*UKYHP6H-?Xyr83uajDtpKjF-S^bhb#Q_-=m zEIrho7V9vO7CrsyZ)sCpw)|rHZr3-}`FiG-h4-9u+5t+no#?sTOJYN+$&uFA3}gVh z>{uE}6E;brqaW$ct~7Bc!^R~GTbE_SXcmPK(8hZ-M_9PSj;QavCKwAoEnFS**Yp*o zB40q0zA&B`$IIk6lb=wNYr}Nd&#OYF!{bLK9=${3DSlu!!omWD8~7V$F#a{TIu-HL`GjDty|W}~0% zue~L9`cJQV&fE9&H|ZjgDuxSBEXcks;Z#&($RvZB336H>kl(h8VW835mMHxb?Y^7o zF9R$&WJGwUW_@fI@cExL+ZuLEAazxTBRDFbZ4IW5ig+C-$mKasl=7T0=21mJZEhI~ z3}3sD@>~YMw1dApK@_-~4klD&&iKtp=t#vrmq|z+B@b0xAN8vc$#X9hg<2r*-Xr@B z)8@BPy*pLtU+GR`0~Epan>{;m-PDXNu%>?R?zpe*f-Xfdv@!cJKUP(5yf&%pciKrr zLH5_kY})F69O|P^+T3!Jw1iaX>d z?{hP%xq<)(30ijYIG1*{I*W`orVQI=8!*7vDlqX$H{P3On=;Ds$!X-#JrV5XV>dh^ z(?fXih6-E^;gdTj|}-$y=|D@v_Um z*U^rSPDdwBDkiFj%o$%ApTBzlXYUXis29BWM0>1|$297U?5#`@1s$yhn`1~v%;pD# zo}s=!AkF~+!ecoh?&uho9MgvM98uaWxu6Y6UVztnC1JWmultDc)yj~c-1CUaBMC@d z-5yX`9 zVAu;EJlv?DYfo78>u@2efPT?*+WJVXlN{QPLUip@ zoN-3;AZni_Zh8%b#|RL3Fh+)8wlX%}M<70e2W3@>fX!06$5sjoH3Eup#*<@n!zclxZz&6}f%O7!l`$iC_`0nZ*^qI0qM!|*bUQG|= zo6*&IB;f}dv>Dg}v~Y_Cyt|51Dg53&1h(^b`a_4a7E| zm86&6&aqT?YnwmVOG8kT@67^VdmoX^V zd?oT9dCj=e)M()~b`+gvC&T^x4~5%;A9DlLO)I#D8J~;24F~Gcs=POX@09K(q)dex z#y_X|!xR_S{bUraf;9&%mD|dX+HEmDlbIM4|BqoKVBG;7A%sm3%(kc5jxl$|RRl0< zU{2)C4RkY)mUpGGg;)5ay-~#qsw#-h=Z$~ltM|HhT z`M2HzDk4>bySRE#FA* z`>p6-lsvx$k`S9h@;^g-Q+b+cl3)BqB1c$D7Ws=rC#|i)2YZ>0iAjd){Id}9;-6_Sv)YR)>eF!Gt-v>xB9pJ7Ts-ZPHzbB+LjL;2cz}*fKY*dvfiDNd7vT2U65f1hxeJN5J!-Ta ztO{GyT-I6DMDk*edX5B7YuFDcBQ~Chqx;?8UaU^--5L)_;k;lFB7>-w8Pk4&a$oA{ z&)$-r*Ab*Bb=R~;cs4^PY!m3Gw>gR4mtS^{RcsTDnsY$5n4@omv-2^z=$fgjVY!Bm z`!X)Eia@9s7CdV5-IH5IOowf^`;1Oy;YB6cKZ}PGJ-U{=#8>rHBWDO1&kxvsLK`A? zRu9ZQ1lI9qDF6tbTnMFseq0*J_B7>wI0 zEZVJxu18Z&Lfp>f}%IKDEv1N{?$%l|IV9nINxFW`^= z1aIw%hM5^7o_4emVt?7QcAPk$nZ)|$QrYJCd|yY|Akzowcr+cQka;r;Q?0Io^(5#M zgdc?aVW`Fd6Jsd0W~n|2aHmFtfMjt|BkWdO2FhCO)_$f(x08Nl(QAA^fF28Of`fS1 zNFBxwS9k&5iQ{i0kevpiqYEH(f)B((H^$bBA6wr|7Rqs9k~yd#D8RR0IT7$jpMPe< z{vawYH1us)le*)EIrkf>5r*-J(;;7_jDk~WR^|zFwC4HeV$GMnQ%PQgdqkoAGNZd_ z)c(gNxliiWZXJn_OWJ31+N%{4A>txd_LAc1Q7%_ zsOD#D;xzC30j`(8=6LZQwjn#}jr6p0&}W^b_#D4Jl~oEGg|pr_rIwzt!`=t?TxZ{x z1y(kAOzGM5SAHKHS9&Juq7_LXLk$Rb^wn=N@}`I5yA-VOmH1Y-V0p0fPwDAC+-wt? zSQ7M@4jGIsNt@C}ltAxjVPWwL83kcD7t@jSWT<&W(vW>le`e@9YcwAVYP@=9N5g%x z6T5}e%l;n3epn|t=1l+6iCk@*aavoA^UEY8+1mskHi&b8BkcA*VE z7gqcrEwLUtfseA#l~`>!uyw7P@BHe5;)?8R^aiazUxTAA;ljJQI2NtuB@1GXWLk{M zlhoLWDW<;uyiN`JuU)jQm!E#nt&RJ>T>bUDcBu!y$F{4(`e=jyU4&fC2hwEv$dD2!g?@lW30LhorRnz@)<|Pq zS>B5SrH-EL+O>HPBcU@aQl9p3jb-N?S2NF5!o|ng^$)wxeL!{Ccy23JuU^kj+*$b^ zGt6fGC0ZGNyMhtsrOOAOsCe%xBt@^k+VtwE$Q(31X=XBph$P)n#@h$n`(j6Y*c77v zXAzhi8(TEc_DZnX4dl$zP;1c_NLMeSd6mSD(_g9R(`Qk+Aiw*PQHj-M5HCsY$1jgq zUf=rnPnsiNVQsjKWR2ta>#ZY>kXaQjNRy`Yu{m|>o(NyYjmPb!oM-qO;~-lRGVe~6 zgagI_q&(MMrNq$^m~Qg3sRjp`8IKIst}VMQV?&_sd*?-Ug-%+N3-;k0SQKJh(u1Lf z@l)QFBc9D4Sy*P(kWo_l&foe zUcRC)rNrUh$?X8m+*EKbmKc7txz8~Zc;R8%LgGn44m>B(Y*nWEh9 zUJo8nNQ6yKB1xkP?2+p?gI4z_x__8eahi~96y;xUH*BY$wvA3NQ@O?ZH%s_JrmmS1 z;8&m(xB@?-GQTyXiyW%_)%FiE-R=cD!i#4|9w=x-uhV`KgDsXP+Bd+P3`?5_(N@pW zj*+$_*CIaEihiS=R~# z`Kr#u^ySukI0A2QvhjA{l(C;KlR@Y)+h8cMukW7*rXUMtj{;Q9pnrkphK3ND2JOZc zx@r1yeFE4XC~eJAm#}s1pUg`g{3Vd@dCSmK*OM9~cPPA4$X>8wiUZ zwZEQ|@fmB)EkU|XXa$&_eAX-r*8Mys-qBDqT4-p%Z8rh=%wrW;X(mrw*@yd4t|7cp zjPC+ZIkJi0i-%O*6C6MF$^Az>6+#eY9+EW#z1PB9IK$_cFu`+> zRxd{eG+J*2yv#gjXPMC`V0W8np2)Q8)C8l{#Taxp|H{|T;xwt=Zlg@vU1w2GKTUm) ztGxVIjIGOQSmg7xHer6t1u5`1kd46q)5dk#U^axBw|i!+B;4~F5slFyuq!i0LVRV1 z9(nfyfJ5CN82OF#3NYyX=74Z`nXZ#!(x(mC!HHAcE^X%V`FIqyQhn zq?FJN(^nimK#WHO{7dXlP7R#?)d0PkCa88$pm6*!c8Y(rU)P;sqQq)Isv>$y({Qd7bdqzc>=O+mMd=eExGm{Sj0b%kQy9$5QFY zg&GCFH68#|Cd{Z}NPuFX2kf^tHlFS)2>qBS_>sT%Q`Gx-Xio$9eFmf)Ds{&Q$#yDe zLJ9R~Eu!H%tCP(>gtgAiM-fAS0ZPm*-On`IiD6TTghiNiLuQH=N$pWm=fGT28i=u;YX4jh-&7!6A1o?EEZ5+Q@G^b|w)jZ%lSyV7 z_Wvg;8)?@Yu!oIMLuqE}d7$|C|5o9Ie|S#uyX=)r(j;cRE+#5rXX1Bp%tgpU<5t(9 zoEjINC7i*|SiH=ye*K|!l>7NUHkl!RfYvmsYi9GnB-e#$)+C8mnN3JwE+WEooMwCj zVhw(a6`uXaJWJ-oH^d!D_qHGL*WQ4sqFfIgSvSVNGdYo#7~WB`-9ZcI{%_=*tw@fU!L?E^qni@j3kM- zcyHlyBp@H_TA&YW0&BQ*{h0wS(-9y^%F^Vf5AjHpFurNnU2z28$R{m^YQH(YP%jfGmM3th896bRc)lrWzST1l5#oc&R~ zde+QL-D=pcW;J0>q`C}?bt@8{DD^-kohS;*$0A!v-%=WBFmuxUsrub8m>=J8%%w|| zE;aev{D}jz<$!;xpbU+A&X*!{V`W)cs2qxu*xHm1Vl1O!Nl8=_wm8MczogBI%+Z>c zB2@GDJjJvu)lN`4=->?$9aW_R&vj1aNSv&eHrMa!mbX`eS`bC|GcB zVa3=j$D_YWC#5hKS8@fo4+UxhdU7dX`AUz9!&X>7@e_LZAAAL>#_k>9*A5K=k!(Lu ztI7Kmu$Pd~aI*WD5`_x`#DD9XuD{aVh~PfI_^ONqsbQUMc1R`;KdPv~NOf&5T60%_ z?|@4}jKRc6faYr}(Tki~4b$ABi z0`W!bPYl_K>lZ4Sg))<JxhEY)Os4gel(PlF&-7oR)2y3{=HK%fD>Ck{bIFN9Q21 zS2cRD;AC&xzh%BuhcHxVi1p-2g}2^?_9uE?+u7jdX}h-F<>}SrnpTs)eUn#Kr-VVa z>q$1>PA+cYFUG|52q5~*nSQ=#ze$&&CMp;O7|#PRad8)!9vq>>`;(U!jxT%Sja+=S z<0xG+h;{W+GNHmM0-wvqk~LL4%mTmsa)cW0kltBCDCRa+am`ayLkp`q1>DUD2)p-> zD^20rKA=xtUl|-2kaksO3w*0y6#+4KeXlTKGlIMKctrtycp#hlWf(+1s4Jarqb7K{ z>O0+okyGq;;5@B+5&5)hR%%z%Y+eCPgPUAM1MP4LT19l|eS#u-bHaRAe#Wq|aV6x$ zv@dkT2k(WM-#`4E;43vEqF^%*U?VaJ8T!W_@#Al3nBMA082fy)G*3~jYE)@Q4(|(eWv1QJ&x)axLzSZfa3Nw*wNjV@T-ra zyF$Zhw#U3H&gM{g{_&G$|J%62Ko{n6#pHdcp@gW``XDVll-;Iy1r|0-z3vE|t^qAO zBPkrtS&MqQ*)Oo!ibIKMyXlCR`U8u0`O2TSG~z8awHhwpZ_XLUWq0u!(zUf7p0{6M zA~;M`-3mV3Z`sh}--pJGVZn$a&E63kKz(NhO33mm#!mS z6a?L+9eM?7S8qko{bgWhv8`ZMb6Goezd4r8Agkd7n%#XT_SG>;t$5*0QJw7)mDUOJ z4NNluOFpf-ZJFq~!RNO1jDJ+jK*o<#8HwYN(8hGNCIGAlT5x?<0Yf5wOgT ztbhNB#*)>HswA4#U^*`Rw=!)_kO)z$Q3K+ziU)qCH&1lrNDT`HylEtzgcfJZ=gBi_ z{l<)omq&yEQ&xwUA{4jScaa&5mmm5sC^q0s zam3)N=D7hp<%u~vBTbw4prwuW${9&fe zM=am&o!K@5&<9h8ZzZLlWcq9pjLfyb!zr=JXbmaCQl6iN$`AbLn8hEWUip#Bi zl>)BL8|8Y(lgXv04RWVdM`O@})riy@4An~9ZluQXF=+Zp(Yv2ud0w&F)(+S_1LNOr zhHmA(OSqJ~|MB7c!J591M%lrNTMvDe$h_KTn_&;m2gKLF>%#b;b{XEaeYm&_;{US2 z2oWN>@2+a?0xy}(u;;JKhu_uJFY}m>m47F?#eGANWR?vWI)J!<1y6tlkWQ{*3O*N< zUaAvk(d)v2ys1j`9{o~8IMsgPFWMH_^C0~2r29a$ZjtaqLUyi;cSZV74CahNF1BmC z>Ob>H<$Bg2ZBIRyd||VUaQE9*#7E~A?ftL2=i$kZR8uz2V#3LZ8IiN6tsVQ<%f?r& zRBxied)HWAg3V>KK9diut$gzwWSL?@eQpPR_;t$+MIt@3OGK-N}MPlQa>?F@65p@L{&)V;$^u z-rHWGQIc~Jg1oGWmI%ft?GsLC)dQ%Jl+Ac~&k^hG`+-*am6k)SKjo1WFhakSOx;)! z*-X$tFJ%*Mog0#;j}+#GswanP80U0vJ(d&mEnY)@AJ`jxE_Kj-C(P-{k9yJaj1J$- zDHvnBcR1ujPA5&Fr#v0zt~VX(dCM~})vxXag>$S|&0d)f@kLd+9X=qr`}C#3U+@+^ zcxi^JxwNh`DMR}wqU@=uqe!>I_pNq zFA#nqm=bya;?ADL=i5)}P7*_8`J3>!{TO{M>op5f)KHlbj=)=0Y!nYF*j_e2T^1G; zXFS{P)|M_e#hLJ}O`f_iRxX$;WdM)@$&y05b+MX{!T)5`u-qp_IagN|ow8O$94)z* zna5#vy`PI?zf2*OVFIj#QB3HCql?RSiW^pXR$;M8?X}+@Lr5pPu6;=t9A5mmozkDh zCH#rh3?uCtHM#Iu<-_4~_PX46=z|t$3jBSkN{&2-U8&oc5gSIR*1epUG8DA1pf7x} z(C@yVlALa3#L0noiPeg7iA@nPk)Temv5e*{A&XM$gUcQFojEZ*@W6mg=8}zQy=T<< zi=Z22{@@j{5xf3Ac?Zt_qv@=|s_MEZdTx+`j_a}Kr`7Jj*l3E zkwZWmSvhZF72ys-l2xM6?(xfI+23(&s)|lEnOc@`%^Y(0aL7v+WwGuy!`j35kgYtA z(aXQN;6|qG1rk)V(G2IgaW;A-LAdvk*ID6mRrkJ;7Bpnr_l!Ti!KxIcPE3;V*VsIB z;Sm27*tI?oKF)k>Ybp3oN<67C~X6r}Wn^eq-y}6$1=EE&GRWWjhV& zcch9hY{e9*^#xR>%e1c%tAsmKA zIL&l|Be8bedgAWfk4D6WaKzdY@zKIda%3>}(~WEsVzzjv8|7lx_n{UP3FG0aPU8vW zQNAm)oC^;tjoOd{X;9yO#8{k_kqCjTHH~SPInPO#R%MOYx?i_+&RzuMp{PTA z@$P-m!nMIo&gSXZ+;ei!U6b(R$TIWKX5wr8;|04egGU6yCiyPRdOWt#xGo~SfXqtl zRN^NI%e{iAG09A{i{)Z+c+cnf`-#%Rv6Fqq(|>{*)&6JNp1YNlC5Bg;vfZSulc%RY zcht(Re*ZL^va*|N`#qjgyavMo)LAGMw>eD~hXG<-^npK)&}9%uVHm`*}hm1 zujE?}m!A^0;v-6CD`ksh)S9Jd#dW-3Ddl)V+VeFb3l&1(Icc2fBd+JhKjU?vA@J(D zgr<7+LkRxJ+LsAAN<^6jrFVgjsPCZS_=?xerSa;^1LF`5a)FLli6RSZ;kowE8Tp0F zD5c}(exg*yxjuFwZ~Nd*(iVzr?Q@f7b|QZ3I>M{GoYkZsC!M1#n*b-x2^^&r=Y+}1 zp!MC8cXsO892gs@u7Co6UMSr7Vg4C<8^%10x8KfH3*SDRim zylwN1U)%YgTPpFBea}SvZ(RlMT|0a4cCG%@n0dON6I(f&@Cu!EGs=zj)2n`zOa=Kw zeVxy4RdvE(=DdV$crUVskw0dc_Phg3rn@)l67Voq@ZU@#rLx6;@|Wy~y`+Ag;tX{_GUix_=`h2(tmSZWy&ybD3l2*C4sB`8O`0>s5AuzNgv zq+VV54&8f;fDzL^Ou8*3w*C(=;L9quwfz$lOZ9Hdd0-aBSNI{qKme_9r8rXsKp1>3 zbf3+2{J%V3Y-tRp-9zk(b$VoCmOrPiONnOD?FtnIIq$vq_K?hB;ZT7xz9>@K_4{Z! z^kNe$$>ZAF$#!>iqt@u_m1NjV^V!{qV~kz3J@*bdNXshzXFSjhEu~o{gU)|H`_P83 z2`{LZq}g)`V+X!Hi9DZhYvgfYMzC9L#wPVa?N#Y4hl9}2 zy0qGHlbeM1R`6;`l5m0R6UDA3(kK#99DH>0UN=%<4!+^(OO_R!0k5>o~@%v0s@ z@mAFkX!#c;_m9LQZ7q2DimCI^m-87Xy(cLC%Qqu_`1^*S*432AEf`v$gQRW0>|H3j zv}Uoh@$6d&Sj?KuDv&HV?D#P^L_MGQNd!fMewQ18e;V&TsPAM6$^J!9vsjTaW^YN$ z_(sg@Al^#mSF=U8NcbUN3Nf104c(%X^9{FcX3@F2E6J_jcc-;NS$;p_q{U@mWuE!; z_`7Abch?TV)XqB^nz<&RAsmgot7kJ?ax#jgr_ad$&DJ(nBg3vg?sEPOa%q;H%E4Pv z1Gxk3%@z#e4r_6_cAre~U6wp-onKMdM!nm^*YKAkA`f;;o|8&A?7MD3+xGfeL0Ktb zaaR@mY4Jkd^n^224=|%Kz=ZYrZ3zBX-69l9qpchfSO(DOh=2)n!)&KF+V#wk3TDMYy%AUc`>n*!0+s8tD!wJReKfLeL+_i4 zQSrSgoLR59l09U=6S$NZal$JcKUb6|q--caVyYXW)4I)M>t|;o1 zMR6tX`?j{0anG9zM9XPIvchNcW^Zw&RTsWiek%KTxC%Jz$TTq_gIX~NzyP(ou~%C7m(Z=`>bDg1pZo_QOH7nAQ-u zMqCp0Wg{bc5waMmHF{ZXvxmQ5au#Gca>b+-Ib*ZQQtJ`6>$y(6!kVtKOBiSwuV~5z ze3{=>b5-k*O$GYIRO)Cyuc?x6{u{PvjIvn-2faZUZ*k^PF`c5=mk=y)8+q)TO#mJ) zzlHy$T1-MhRoul1`hmag4mN|2J8k(mIFIyugsYCiqXgVc{!Kp6m@~9k*n{VbGRCVt zjKG)y;7er4ij4d>X!5cOUt_+A);dum>1r0r@tKRLN9P-1_xFYf?}8ZpBg?kl+H|@+V>u4s5ABxc&Z7{GkunYFd$;#It0e z`r(HdPUnWRa@Ad)@^0(iimOupWUt?CUs$A^cIY{ZMCkZoPEVxDM;!5iih*b#g*6WA zEmp3Pml(h&rOzjc{&xgAp*NMZ)`e1i<>f|Pf8vH59GBc@NMg6V*W|L*G|Et@6`E>0 z>y#ek3@IweXCo;(7ik8>G8G1uS5Lu`e=TtF<;zY{TVTq{EqN@7QaA?miT~pp3tt{$ zUc*^z^krcbZe_vYW|8in+bx_a{;GfW4ZMV>W^F@@eR4syH357``cS?i7c39X)dUW$NYGEGH98#+#^cOFO1|Gh+zp-Bov9RPM5ZZ4H zsq*Dye^9)m%74T_&WE0en(&crDG3OoLduWe5|5OV{hCkJeuHNN9fEPb^8EbM!=u%w z)#FA&0%0j-6rlF;sjI8=@TmhQ6I|BU5kRs~A@=+76dZeqH+PfDBC9Wj#jaW?MkYT1 zg_=%0vh3UJV)_r=s6ewu!uYBoElM9GTEgX3{0N=*)~}7JQbup)|W} z64R<&j-cCb3mJbBm3h!&o|ZF`pm*y#fYxOlcb0PnkUxz2`o`?^?K1PDYrTwby63*A zYtD{&>mK);TL#oP+@50o^n2ieGWsc?+F$zW&i^zEHod)7ieMB(Y1B2r6vFNwrZ;@he$P~A_TJmr!MOP*AALS>lA=Z8o9yc8R==ngj4I-MRb3Hv522EP$DyI9DM*v)_C z=YYmsU+@1a0=wtS%ZYe%ygqZpw-nL-B??HXS{rIc<^vdX2UuzN2&&pIg}m!+W6sSL z*x@K@xUg`4FqPd9j(zjKE3b2fDr^>s-W!;`{oF} z8TQhoFQ+2W_Oc;DTqPrVR9oWoL|H3+7p-YVCRUx<0xukD|Jcn&RQl5d;}X%oh5#i7 z12(usn^GKx(A#|0ZieG$)s9;ORzGkCf>62wW8KJO?B;9dvEQ%NcSb}Yz{11Bs8ZVs zld*0$HdbJDoH9xSWsZpB{fch3HkiM!ClV&v)=( z_}QbEl*P!J~<4nfSsvrrJc3jHV~+#EGGwrKYjv~5=x$cL%} zCmSM02#2giH&{}s=88c9BqG&D%i?Wem4#Pj=Iv@TM^c6W=atQ!j3SCn#}YJr92qh{G!u709yc{2#G8qntv`B`dukRHzc5j|h z@bMA69IwPkyg`0{C~fIZBV9RLeLt}v(pa3=#E>yP#j8V{4%r#a5)URyas;zDAipd* zEepm6l|B+DZ;u$rb9_WVkl%?G3+OKLaEg2wZ(z~?DM#!`?P>5GArkjpuApaJHeaYb zZnm8H7cwRNc7GTEu)D|ZbeIcj$Y|P_n!iZCr0nXV*ZaYjFP|i~w;`=sYh{ANtO*G{ z|EDw&&iT1-xvQf`wq|oxcUPkDlm z9M0;_0|puzG+l>kCjTJ|!B8aP#L7{+m8vQ;=!7a7N{s==s;e~m%Y`eh!CF}4B&?4 zkJ1y7(MvVtsnt#F!9(x<9IMqc_4Vgn?(zYloZ!mJ9KdRpd-r4dM}?qg)8~~4aACaX zS!r?rR%bLs3&TW|6))ES`_}!DW&Qa&ldHJZT*qhMpMb3WJ{21qhW)BdYzpy=bP5!8 z&1us#B*0CtF;C)&_SSpC3VdY4cM5m6ufsy8oiI zM(hxGej8A4c3j_Z3OrbIi>JyZ^?E=D$Q#^F-%(JwH{Xg)W6R|STiJ@Q|9G1m!bLR* z7mI?87|-Rn@;H|kJ9p*XertIG#q?q(to2v}CHTBd?ccB(&ha)fZ+}uHUp)Qua^d6rKG=53;V?SzpY-gI0AI0MbPear5O}FwHJ$@+?FpdpxYCYWjY!e$3moAL^M@ z#%a193&~%KjaC(ER~w4w=A=d23$7$@3(ms^^F)!812BSYc}-8pzmBH@*GKx%gk#uB z*;S(*ABXx2_tXq&ZA|y7uqUC%BE_IGbj8bxZy7Jo@c!jbc_Lj3TdnWWvnQOxqkGdt zFL9)ib`@n!|CZ5&uL=XnKkHx4|M63Gu{Q<)Q!JT5j+1I!=4G0I7OTT@E#gtD-=`6t zWGSn=F(?XfLRIUVGjIoZ7nD~LQu!&C*k28Q>7f2&dSr2hdk(JqN1h!<9Ol%x{D#R^ zXnsZ8U{O_y`V%m=jb5OLLGomABuSWf8{kL001N_S{leG16`3mgdN-zT1oCY&v(CSz>$P~(X-ScZ%wfmF^VsW+tq;2&1WXVHiCfrFsl<0 zKbpjC7uGr%h_`p01CQJu=_P%^S@HAiZ{*83!f?x>h`b!dfDHwxo&^kxMJ*cSc=_$u zv)$q+nj!wkdN`C?r6yu~+s-dav+%tzWk>=hHKv#+%rGZb;0`sV3v9+8tr7T+?CKGH zRF93#8utsWN1(-wg8`{R%Y{7kzfK| zC7E&4w)F3BK}CAYsw4REpJv7nlsq%+gIoDH+hf`#R27+f9ls>D=`jToyiT>hPQK{^ zaF}#dS#t#32BylQ!eI<0FxE0`AL}fRgp&O7g#d}3ZVZnH9`~mP+%JeX7$ljoYAL2! z)H!+!*~GjkU@NHWKngMH8qqah3vX8;6yvdFA6+r~tfHlY{`R@9#xq|LQ44s;^Wdg$ zJdZNgCoONM8RMt*lRqGjS5|%Da(EQ_@xd3VqUE|PtYV)RS0sy_xb=B4DyQQShrh0@ z3aHF*rQN*6{o5ld$8kVd!?Jb9U!uaT!cq~3tHF(`;SKkOfZaL7a1umEd>T&wP*}LA zhFdgm))XD}e%f9fW-@#_gsa?-DKRD`3PZos9aZuHY{WgFkOgKoUqt*%yky+(W4#yx z7ik_Q_{P$(osr)yV2`;=UBP5x(C+h7>Ed@PH=Izc1HuISWga zR1hJs?=)Q~yt#D5`3Y8JpBDi`z$0cx2$->c_k(W3+roDrwSN0Mw?C{Q^lF@{s7scN z3arpvsVpg7BrS80+<<$%GKM4|d#c-xrKkdFMUKY|a+D6N4DenC^K8O-1${$&zI=fM z@EX}i$DVYUjBz}EY0jR-AG<7(y6n z6Vw0cxE>VzUwJ^URT9AJjWgHa!wOdD-)p7NMOh3wA_-l#3)BX0;u(Lu({2pr5oY#hHvV$Fm zcl7-m2RI4FDxP5W9!$iHjVa)+b|Z{BGqxX8-#T7*OA9^7D=-<=iMWOOz_NqIOZ!r@ zLW*XSpgNWwul=te@*QZ%#2a|Ctq=wT1V;mAun6|7c5H}^!Vwn}gV<>L6G2{!#^5c>yK=b^mn6s1EHqNU zOvBZ^3m3VfU2=YzFjwOYW$3rg?`Ky~qzK411yYN(kx|=7h{j~LxtpyQi`9yQa0mFy z3ebupV7F6@acPaCs1cT!(+!S8Tpz|7`9F#m6LOz%*4tfmbfpU@>ee%I;_^Lb>cF8F zaBH)sQ=Q8hcxhHDeXo>(K_(&?Z?h%~VqY4qHf4Z!C_-4tqe!(MqM=zr^G@hM1NIIk z6@Op^aT7NgZ!g_J0$jz#Qy=d5LPCoyuaK5m3ml?Fu1Y?R#cMFRkGi5nr15?l zx74z?;(ybpm#YB-19R#hWx-c{#(TMc+VNb|keb1JcJ`Is^S)WT`dOui(3~*M!sFSK zaPO&>J0O47tV=OLdrS-I*psg`1sE~#63y3q-?*l8&hM!KdtCSR0q)5i5!Kxf>Ye6~ z{FB9oCsqd0`JwpfzCXSD|N0g6VOcqg5sbikvXcGItt`NJrMvL<5Y8na!0h~NG#_Qc= zhcX3o3{>pyB>Pt>xCagwQG_bw;skPQm;?iiszgswQLEd3D58_|N59(sR9#NocgMPV zL(@KE$SH?N^OLv4c2@dbQ>#!5Cux#$nw7%ovOg=6-1|*^aG1C&SfST1|G5^4yI9<| z%u1X?ZaMRM9x>>{S49CdztY~GF4aw6)jpQ~^lZ*|SWx=@_%8Fi`KZz6Bqh3y+l&UQ zbt&xuU%_-^S6ucR=<}yJJ|fe@3dc z93HwdH`z^Z3&GN5lw52yqzG5U!;lII-|Vx-9xv7)_A3YR5mCT#uuKpJ>%mf!z+vN# zBp8}f5+1WQM#&xLDkVj`HIlSA^o{3ww}*u}LxVHoE!Rj+Vl2c=7NrZlu5-LhCiX@1 zUY8)@PXKvXZno+sr=Oe>MFzHPB@=0IJ(|?hUn|%ro6dTqzn2{EzD3OG2n1=9u+t8j zl*!Tk=xxqzvb+K$ma;tSPlRjaPT(i(O&bUb@20GzJf=p?yS`C- zk|@3!oM%kPsVYX(l=9@0xjG6IKJb^`gmb$Ten2Oee41VTtw$=_9jY+iaq;Q9y)mm} z4Cyi*GxbmJeW@?Esk?~vUK`}({vtK4x85&LI2_wp>WH2I`THWpw6E+1M5OeQYtws^ z)d@VPW!j|s=Elk=J?NFbp^*qED6o*nIv2q~m~(|+XuO{=+md}hMe5{6L1cE8B4U2` zNrK(8Kd6Ua9=)82E~6{W2g}I{;Oz=Lqq+vjWV-vhX_>W3AUv#T;Vs;n z@Ex-Uzr))BIFrl7y)dPB8>53VIWUtoP#S&xEOsfdx63ns$_7uc7?`kQOa}r*ylT&q z4Z#5K>2QLgy!24IZyaJd(A{Jku~`_j{cnVrSn9LE6>&Aufv- z4!H?=;^%K@z2K5#fpsKpV^K$86}DWmdamE%y*L2!rCPcI*xy+@uba}b@gzLR5*9~e zr|<^&onvhL76sYbU!8LDeV6~xe`u$?k0pvDeIX)0h7QP_ugzaxeCZh=w^)1Z%s3tH zjR*Z$EE1d{5vV5KZCc7`rR&TOU($8QqGE*|mY`eop`egtE{`K?V0`vlJ5ssvsp(X- zN1K2X9gcJPzMiyFDq9tbNHcmQ7Q;4hrEm`vfrNv5#x0K0Z?MN~@4Ym#tbSJ&1F?G= zw0lSJu_RG1?UTA<4zWbWBV;Ch#+UE=4r^OeKc4tBl~HrDHpcg(0W@U@=WfmW`XNnhDpbb%a0H-%{{E$`?yZO~Es^6uQEAhFr)PX=~ z8~OZ$Sr9c^p{Fdsx$q!;L{*wQOMf+lw4w_2RfG%Yt$dloiGG_S89qy0e`LOC~v^5niAmAi$5A@SU%`K7L7! zgp@R<*=5@GO;=af?p$fY7a1PgDyJig4ej5YTf!-YxIe#NI+72$j398pk4@WYvt@(-z6}2dQ~LB* z7JB+58o+UP{MsOT?_wB7hQ9g|YlTjN6HQe=dQ|t9% zl$O1SqVyMip~GBiPtJSb99=|?M9_^0yQp-0J2HQ5P$f(R@BUakAH1{O?%YTdjJFa? zaDIn}^scmpR>H{U-VZt$-*g0*GOcey2`Ke|>~wWCEd0NL=PKG$gRL(sNlZd!4nzz@ zRO&k9{Z2Ua*DpwcM))|gGS#1QYDT670bw!ae_Pb= zAij_5KD#SLNgYq(asG22rxEO=RtUxL@khq{dk(MH2uF-z-uysCc5A1N{t*-+mNV&zI65) zGg!OI#4O(NgbcFQjyJ4YDv=12W!B##(}8q}sv&#D3hH>5!Oi58unCup?GXuk$YSf#p;Vy;gYFcZWIDx%rlP@=z#26@pbC4)Qi4ZTj{^I-!B40QheLj z90j0ci!qD@Fo87Mx{3ZA(N05=O@FJSypR+thi^Jr9&RfjTS3En7{z?q>Z>4{D)gz zy@6=z;gP&yEUop`krj&5Vr0q|i~{awL3dRcuZ6~}a4+da4Y>@mCnQ~ORIJaR1SA7; zwufd!+fs#Pl`juPd%bcVFSqW;7uhxX$6=k;yMLRZ@Y?;6=IFLF% zxI2d!oyvtP>Eq7M`*v-mF6Dyj<~|o+@XBJXcdu^lryKmt%N_pAOEak@tA1i*iB1Pe zI#+o(?dYuJo~d+63F3&rxu=kvNFwoXV@CE7l(*EycjFYA3uDIQDL7v?K7Oxk3ZyX z_`rzsTUC8O^9ob_s@$JCtnwdVo!ThY?Nsxp;TMU0ql-_L2Z)(%odT6+liRhlE zz0?`%De!o0{8VB7TX&1Ur|9jUK7JeWzN?oy()sjw{;5jXVXkaHqX~${LH!}p*vLEw zkN@YMb}$321pTx7(Mptb=Tqf%0{hZVFV3Fb57vN*;@-RtT(-ngo%ybs>C(h(H5?hZ zP%Z8H4k|7v?$0fOtf7Z%WLaNX4A|geo^T*+*HMHB=*ZHYl}A_M#vvoJ3KTPe^=+5_ z?Q(mflXjSAF14qEX>NeBaY}MvmFWQpj-Nh9psC@T_#? zYvUd}{POn&vnT`-oz6iDuHBxO^fqH!ycL8C1rvGE0z6^>+5;P_+GkbV?%~16ZuyS2 z(QY;LA|=02zgvPc*D&UXXN^BI*N+FL^R1z-bBpXb3L331rI8$-(vP9`TWk+X zF~n9>LJl}82_|TN*3eK;etBq!h!@IKZT6NTQj*FAhP}odwjXtP8`=*p}R3)?)IF-asx}N@&XqqE}m(Yc%PHO zLuEvABxHU)zFA;Zk6v>2*kEN)F;wri_KSPk>FUz2klNCrHs>om-Odns?pCUYSp`IO$ZW79osCT6AgMP9R25ux#Ase38S(Ci-g6 zT6PvD772>jdS(U}wG9cCC1=D>!*#gsei?hVdz}LXDj1{24^Q<%`J10=l;uqP%(q8n zrB7QJ03tGZD~B*h#~q6*>B6e%TX{+v8evca>swiV*+nzy9=|WBa`?D!rVLRyjUr`y z`g)eL=ku!2mdD4l=}VWdn3ESF4&3w1imo?iMc6?fwIz+|rg;oWa>{FNr=eFAaAns& zL*2W5j_UQfVY8dig#5~_$)QsCMC&{ELZ~JPt?FJ&9lF*X8r4S-*ARRI8vj623neHFg@Kh`fliI7=W+9nYMB=E6JXo? ze7*?gt%AniP;h#64OCkC6B)4c^YdTfkBKY>x(n`LU}`Fevvd`OfdAKa-XPRI>{U)h z7}=4o*+xp87($m`mdKwPjpFgT9(Z)~t zJ~9YN&HPk<*v7L`i(3=S<%Xi%n-CT&o#f%LjQo{3+P5*Uy&4AYK09^WYt#6|`4-di zrOZUi*+O|KVEvY;Yr0D?RR({3pC4XpF&JCb@q(FWVp7VK6jdLjr`fCXCmNjv{vFO|Dln|{iPjww#uOA_r=avL5Gfq4X*>9e``v4ZjY<&4>hJo za1gQNe#PDU=S7Bz(SoqRWx=)WBR<9ZeLaZS&T~&ZqpW+|#BTEx?jdDgo@Dqd))4Yq zU7dC#2L)sUvJgF9hu)1G02&e!^1W|Ng|>yi0<5vlRM*xoDupRtdDtKjEpXPh?XVi8 zpudL9f*}|^;FvBZ7S=|pk)0jWiq{UQ#uMn_ssXXEEFZY9S3T=u=rA ztHI{;yd=0L7YS>JM?c_g*8(r1y$R+D1u})js-PY)RsP7uVYzYf5$kW!hwo{|zFJiz z$e!tjY?r)0Bw(W~!h{P{T@oW(O!Hffr}oSnI?gpza#i|x2>8uG3%=PZ<=oYSDF9on z&__m;0441!%<(b)H|XOqJ~R@i4q4m!s{sk<@0XLZGHPy&Ai)Uy_0vC%?rg!|v=^0a zyu^4A**`PtPSh7&EGsw%cp`P)8SXn{_fC@CuU!}#F`sOW*sryct61IoVFHigCU;!;}Z!*Kn z3DXQo7|}YrTaH7GbU1_cffFrMa4qpOCU=TVwo`m*tsHk&dy?|>g06Hc^!5}I(hp$` zD4@zPBG9aOZ8B{Y+@<{B<8lX(huLc#&|U2(k)WWURK=!BWZIdz#nkF6fKIiaQ%*^w z@=vKcBy^ndR!Y@oyBPjlg)XPMg;u%2$R~0Ap+*!78T9)_De@|&{^o|#d(JAmc~&Fyq366 zqDh7-8Di6%)+Rq2C5RJj0Qmzo8H4vL@)R~eBy=?CDLZJ_={mfNnt~+7lA9f|uI8zS zo|k|AAr^|D;bnm%_a3Ks(9t`tam39dx2l8mJ0%k+7*C zb$?h?BKGfO!-k<`%IbwRwrGYS#KJl@HLg0ocQ20frUbeq4&O=xPMGr>Z8{;VgZlX! zT%W*JsUu~#+1^^a*9GnU^$~K_Y}Blw59)9RudJLL0*F%y0eM`nGwR@mL?b&O1%qu9P?QT0mUE~$_31;1VGfPFs z1qDiAy?8HS-0zfLF+&&~;<(n&-Zp!vWOxFGf)f!)%NPGtRxI?#})tC~F zM=~fZGDd%zqf%b4F`4`xWOSpMJzqF?93Qu=^&>hCb)GZl>+((S=r#TK5j9Iy$&%g) z1%i62EtHo+;0mQ1+Z-PSUp7*E4{LEKOaYJ)E`i^R_qD&#eBp1jol&#Vth-LNFD@E* zb%K$NH96N_&DZYZfiC#K_|DX9=6rkAJwU+J)D(lbY@4a@g27q6y5aI^=84Eu^PvEQ z0b`h}UK0vIk7`!1?8+|7sq4Z)p>DRll_6tmFrq4*-A&+?hH;=5bh<8bQ@si3-UESS zn6fdcl~8V*Aem@CIiG@yE3uH59m9>0kDb;)w(0HM<1E@^^QHqtY(<~{47}hoh-_yN zwX5v6MNRTKo+DUGU%9$OeWdbEO7Ws=Qbd!Rc1B{!FXhL-jRW?N;#bso&RI#)zJ!Bdm#yz~7tjL%K3 ze~%1F5`f07L=GdSLvN|EK!{P~wyFs14}E0PX&mSig%N1GfGey@lg17JM$P+}`l(1+ z#|rB;(q>!Tja`xHvwn$AVeMiV1weWfY!&7+%0FXX4aUii&nv!s0@q+iP|^Vp<@2!t zm;Ex7UJ9!i*iGlM0Smz8>0`gFa&(wn3*avLC>sk zU;QU@!;vY#pG*`gte<(Rf^!wL`#c~hm4MR%WySYG=@oN7K0Xe{cZE}D6LMNefkauQ zD@samLZe+^qF5ykJbO%EDdyw(pSN0Wp zb><8&HaSt#;?0_wI$bTe>36uJe)%$kInIGIkb|#QD4#3QJV6gi?Aej;qCgiRQYJkW zBhb@b|FvRULZQ#j6J{aLeWI$@8|(T6qn1{J4k0=&X-wH5UQKzvoU&{0^sYb9v+}nO zBw$>lsK6~sJ6_mUYb0VuU>)_-!253~$;_l~(t@5!;S!%<<;5l=GE<8<0(=k=cdyqU ze!A?SL2{ojSo1%$qCuP8h~&HT6KxiyFarnU%C1J=cJ*1=?~Q{ax6t&9>lH-|f2hR# zNk}c(d8-_SF>KgMc(iqWlFVw#BqXG7;9$3+RXt(d4X+qPzp7G#P#Y&=7xUG%>nj*n z{@DQ~TahrR(e*`nfuDdnFP8QqSDx|QoGunoJVgsiPnjf_%24aEv?V<$=g(>xem-#U zOYNSh&E|<#XgQYwq?LpaeaOC!Cq~2Gps~UKRvVnzV%JN$swIL~Fi~P`L}S8pJh?~N zyil_L>zCNpa0Y&{a#lA;LWl=#jJY`S3ex27?TPiXknv!H5DYD1GxQRfhzlJ^=Q?jX z;;M?`f`fzOSquaMRK3+`x$G_HF#x3p_sVCPg$D;NNl+r*6$qYajYf~3>5674c4c_c z#LE}R4u?hJGbo$?5~-$zpf_4y$dihGr+!^DvOtxdQu6^ddoL)6&0!yQ%I2Eqc@aQJ zVKI`PoH?qo{u_rd{MGP@t*}rdK7=@9z;$Nsj)rzFl5?Ks!FbR8xXM&>O!vv-oH>dHld%9Qs z`-b(J*!L&*Qn|{3AN|hh0~sJM913|L@%L6uLAAr^XRrzs@&eemCrdw9vLbGr^sdkH zbJjRN+eN>ayn$Gv?~&nKVVdRn*6Sdx|B5%$X?CZEaKCImIJ8}h-Mg)mfR55sZfhK( znaaRxzp%CKip8H8euP))P2DB_!ea#nw%V&Sm~W79eu05*YiFL0XB*O6gC0QDWviS) zB9nm@E4fJZ3(#>__+6v}n>_BL=rgpKo1MF7lX>mU*-r{HPlDv3^vHLt+r=hNw%nf(bl2jdq03APeu4%jpEa!PDQP3?5*u(+L{~CqmoED&Y0+fW1bi$(*?H z_z~X+jqFceL7&sHX}vv}J|Wlym2G57i>KDp+WMOW)9ms|c4etqmCc9@r$2)@^n6DW zJfFbsnQYaeuCP|4*D<3H7qcq`_PYOeTE~YR33l^Zx~@QGza%9I$+>skuq3BvGB~Wj z8?eF3m|U)l9M>1j+WCOFyx1Q8cz3SK>>myPMk7uS?qA;!tX;E1ekn*JS7R!}2@}sNNHdPv z@A*q}vB{DSp2wu!s$C&^Y}V}WfaK7%gHcSEBP7UL-yZgvZj8g%WJi$ISH9Q?^YF45 zgD&7Mo);AQoqTHh;7|;~gvztROd&NHZU2oqv@g$Of8Wje;)|q?mG|{l4Yb7V8xQMU zRO}gvair^v{Bfy#4SDhP-Ak%M%UuLcfgdv*=h3m-xb<%Wxr)EjLgCu|kpe2x@~!#( zwfrQ+qi9)kP4Aqo#yjAjmwg~)aq#WoaXllzjp=r8Jg9^qu{$RT5QR(Y!a3PI`FXiPy!bGbk_k#>%I!jR)quL>b{hewqa*0cls0s^H zD*GEZBim5WL6X{JMfgti16kIJPsV-}Q7h9hGIffWTmp;YfK84RFy}_OT(#!DWdFKB zK)#Cjd2@OF!`<6!%D@ew#!5#<^IB-)9w8LH+G9iwG45I5)_KYh_)~rAcuB~izEFI4 zJu$_rO~qBglER;q_PPW0pU&}nRvES<6~b(W)492{7$^8w%pwHkOe48LG-;^3;JxOJ= zm@XHz8^^uWBF`*BhUhrb;sICzU%wiMz`|2Aalvqeg;P_R5!6aqNQ7PPp>At2=4B;( z(6Kj@KtMH91E>Z=eg35}AT8)#y*-?)pEM;$m+l?}x_{0bOrc7%qu^{O2?bIb=F3sG z&2D@cN`2tOI$rL%E#*Aj>+iye>DMVTa@r(8de0R-nk8730`9HI<6a3P2nm#yHt#x= z3W$~!CnDTvp~)&9Oz(4_JxPX6qhN>Qrpjgpd~!0GrF9i-!&^=Z9FHGFgGP{Q_cES0Wkf ze?nA{q-xC3&R9;%wJ;aV|L8Gd>(<8j?H>>cWGY7r-iU&HZDe@Ax z)x%Az$GP;T2Hy<_p|gdyDTV4brP#1L~wywi#G0!wk^}fqy_yGbTPh{C`*#Dk>@hDk>Y; z&z+8X)iduKrvKXVhG@#R3E9+x&!P`klKnsz@O<^A5AR;1y>}MfJE}($#Lgh{bB2K0U z!SXdCHug`8+W`hHZfI99JT)KRT00TL@eMlLg8-Setpu&xgQ=uz)*98+{IQVgImP_=UyJb} z3zUxof|mlr4B`j|0D6KjGQ>}yp{qOxkBWUf*kSuA2x?i7O181Qo{=6gtfE((qN~|u z=h8X5&C5%9n>osdu!fROpz6b3^zD^bzWWeeq#+~7~1g9=#Xl*--os@%3&>@) zYqpO;QM!Bsf+iD)$jjmqKGws?Dx4{hV+C28g{`I06^xaxI?^n)h6}@^{vsiZkn4*q56npflzvPZwwxh|dizlji zuT7>K>X~!c&17h3yw}=^didPZKr=$I=iiCBY71@vvUb`9Loey)8S`0I(M$cK)rf&u za=y47Il>iHS*FwFmbKk{2QuURU0C*V*{rTIwt3@E4Jra4vdEG%o&c~(&n2##8tao zQ5ksyya<_pi;C5XsM_=H6HjDc=Ju2U%JrVVjmBNj?T-rwRh-|%stac$D*2-hRJjU1 zan0B>_-IOe>M!uuIhsP`J}r|fk)+BY zF8wym{PJZpsB9d+#_m|L{N4*~mv(pZ`w13$Ep+%3#=7tJPQrTF}C1Ay{|ip!ecE>i6i?TO>Y%YRok_VE=oWe zk#3akF6n&e?nOxpNH22H9n#(1pma(|N_Tg6H~iCgfBOJVIAPX3#<<26LJ6NpK}YKf zr}f@dX8~cL^IIDhDq33Qbr;828~{TG7VzDpE1nML`wsuwbAxxN{f*f^af^9{fHl=- zjYs|CI29)+4jPe=?$JNs_WVz;!{hA_wk@*on0p~hgChVMuY(!PACrdS>DDDAg=oP) z1{msIYpDWY_Wi}y#oZb-9|-5UUk|`2r~}MaC4fN8A3***JUqOJ3Qk~9&_Bl?AWNAY zWCWzxHUZN{+>WdOxl}^Z%orWZhEkoLnE0HPij9~@T5!sp+6fN6FkidnU-ZXXexFC5 z>R+Z6jaNhN5s!(`nTOXBQT;emiH&^Mv%?*h&Nr-!MVH~fFppk?L+XiSIV5e(jypdK z_ZB|9Wa|?z923tr(qLmxUvworS~ zTI!#*if!+UIf=JL1Pb#lSnT`eIgC8%IGQbyMqi$NG219~#^jSC^k3kj?DVWZfd8S^JFU(Ye?RnLta9rc~t-V z$l!lCrf#&PB6x$ZCAT8_))tI@<PoXIsn}vI% zM1YpBb)!ZFwd&7=(?lfNt9J~dsj+?V`bWV6t=oygzRpz<2|YauWJ(zh-}8wMi;Q8I zlZIMGQnqTIJN~SY_lhGiA*g)(o4s~F+jB5gZ0nh0DC8DHWcim-2BKjYf z04gD>PBs?0!IE|gt8@RTBL41h%d_;rrbN9mctVPts7G!Rc`aF!``D{}0ld7u02|ohd|gyd zS`CPuQfYQH+vxE11?hMCtf)YK&8ua=<%Pp-$MlAhwPWo6ZRL%1rSmzN2L=ZEDvCozu98P#1>m$C z`RCp)1ZH15-h(h>={|U15RT%9i{;10OK#8aEU{Y=AidNQ9NNmu2>p~oJ(bS_E1OA? zz6RI?kJo+UWV-OUxJ$axetglIf0?5;FA2}fQO(=~h?O)rOizKQyyg%1X2mTrQh>DQ z#V)_g3c9he@u-JgRt3GugOsYy->uo{w|XEX=d(~)jAnqEhI`HVuwr}v5MY@$V6}CO z;;4PDt-uP>2i$5`OC-?dJq-!%R8$uruMMOK6`D%T-+$@mIoA6*k2-B#z|>m4|TE09e&}`>*}uw`CQAy zDJ%%M(IQsU`9tT1X7hRew%F|nw>mu6@z2pqB#IuG1fl6pY+7!LPgy=DPS%52Jjd%^ zJUGn#_JX+mzS9w+av2p0c=M}W5OTr@@r$gm$PNGAx2ha~QgzGs>}`4HIU`3NF2Rvu zs8P8I+!BNXXqRa8H=Smct1FoTr+JTA58MNp=$L@RCul%qs_xrS_3(9CaylngxM|~G z0hl#KbLRO*uHC?cxV(r!qy7frizCXJtic}?aF3{@%m-ShUZ4LOUildvo7>_Q#z6FR zpFFqv&>TW=o5=pZv)!*B$RXk?Z#f5dc2x#`1o; zZHHFN7=dVY>u6n1m&0u4s&poULPDN8qM7@Euvp?7=`~b9Sw>yPCo6L+9gTjE72=%K z_bYU{uOO!)^~ve*zeg-xIj__P{bR^~BJq=o25PGkK{VT_)V0OI% zlAuV!X(jum$~(~4k))Mx8I!hFR4NiZD?o#%zZI;xe~(~%y3-cfC7b9%U@0`M%?GDp z+5337xpijrGKp}16Ozj7);%DH4g%mZm8ZhFiTjg=0gar{?_SQ?i_uTB#y;K9e{JRs zMDQHaMyu1pc>B^w1p2=J+h_Zn4o@`;JBYzc)%zI=PJmE0PNIm`Qzd0wkd-yNg&KFuFjQ6(78qbzU}bj&i2SHrb;k zQ7o!ou@mw7bP6oxo9U20V?bRdP#h12guAKZVtri>t!IL(FfdU5bf;$Ls=XPfJOp_B z60ja}{zr4&-gxomj_#Emy8G-+D~&?0&4}-)YhG44h-@-W6m3EwPB)J+R4b^!?udkf zA`X1%4nSX(@sSr|1@(z*CzX}>&n4Z}xiZAP=3qwy$5smFQ7~cvisM(Y@N?@hahTWr z86OzQVErosL?!y~qgjOcKm~Y(mOrxa!O&U_VHShZWIaxIKhuJwR0cn8O3U^t$?@=^ zjDX~c`l1k5^lJvTA$di6gY1bG4p3i@*ncq|?ZI0-zVuUbOfNZ+Dw5&3u7#XN**SC~ z$#-krlQTB84om1z{bpAZgGY@17(+#FOHM4h{+fFMOl*T*;ewy}Zr`x(kx`NWB!)o~ z|LEmvOqiu!OQA+PTymI;JWJ7Qo0*I=#}l6S*#DB4%abw4=Ci+fZIy)9qAIF!eu80mod;sKt=~rl*V~X6v*Zm*E=nmY zlNUu1_`ezmPahQl_C7+#Tu9*RxxAe%G^TJKZf@LKHnvj6sEBQm4HIjevz6A1Cdg|B zT0Q;+-H(7|On*rR&7-C2_IYxh`xZAW?QbxkOFY`pyCt{(gE+fMfu>$d1?OMy4mt;(PWtUyvlHTQ zG7BPTx#ff0;vAjEXQ-cI-G*rAS!PB!$Om{%3B@Ke7CT53&}i5 z?19I^(E9#r{^k-4lXFhVCG0652OonPF9IJEq03h~U1Me$*#-LKf;sUQ`0Jl-@q$Xa z+&=QpeB&{A(D{P~5oIHC-M8w}RmV~P&vB-MB&NH956B3i9mJLPy92S}dtd)3OmmPW zl5cMyDjLq zXmU!ubiKXyHGxki8dN?qKJTI|hjV3+BZMbPJo#;%?}VQ~+6~;k{>_;A(Zj>dBniLq z*k(8quS*Lqs{;!;@B7yF*ugIT=9UbQq0u=oD9RMQ-7?v78A(!r8Wt?RuhL7cGP?#9 zN2IjX>$ZDLhRvafO=t7<+hSjBvOg+Tx!ttIMe5@=yDB^c)Ms=1F*$dhuuY*ev9134 zQOR@NjJ0p18%7uIpG)S}H}Chw1-T>j4rS3t5EEP-qWko9Fa2XC3F1<+sQ9%*=w6=s z)uJH><&OKB(bHHqbZnsKsie?K=E#VJ!#Wd=ZRDNDBQCS9JPVnyxnStE#cl6eAtmGn zLwKq^e3uPs>zljMZZE8ur9v|LF>ffxXjO1}hjD@iRjK=sKbCarnlV#)j#;ZY)92%@ zX+oGfnVgUFs1bN>VylrKm-^iKuv*5?3xBmWU2&%H?fY^IlmwBwzm`e3YSGX@BKGQR z<}=!50&1WK9RlF%z)ecaX+14N-%z4k)f|@4*HNj_URyP&-l^I4ktpJVD&&Ca36&&l z{l~TcRK8uxH;3r@nD7N-{j@-OqQ}rmC#Un4?;G6&qymU6ptj2)24x%sfdA9q=6(gN z$3Y?rhT}lA8WdQi=9&BnFVb%W>YpUQ^ZHS#l!TSus>Obtw-oSQnFh~GGo9OX=%7BKfi$By_=Bja1QRsTu)(+N)#MAZA& z`oVu1%8kZUodxC@zi~`q7Z2#28Yan7jE~IFjc0SMU%I$fOVd*z80lI}lGTl1v9m@Z zQ|MJ$;kifD+(=fH4n{!HNY8z?k94M%E;*|~&0r~m2KLJ&q6~eW&S2VF8d6wZW zeLBhSOkR~yX@QkNHj4~+XAYNt@vcm2)W@MrY|MR2KY$|2@FtPpPa{4T3 zin~L2x%847n^`ZYeWp>ENus!;$GN>YY?DOl>*<;o_ zwE0kn_?E`9ttE%0yooTO3Uk#3Xr7ta@AS2|hXRKtj~#YUj^j<5<++&Y5_cWy~$ z*uRC++lmG?x4-^x^cn6#DIwD(Vl#n%ygQ3=zuGkhLhAm$`Xc~oyw%_$S6${spmCZ4 z;yy-=0Ut|{FQ8)Gte*qN-wFc;*?sFYfkqdbf3({mwRH+WNH=u6(cW;Ww$OH-QP+n+ zMDuH0gIKwUVvofY2RDKyk)e`2LJcmGE!y|K!sEV_r0i0>YcqGokD{?7v3=V%b;A)- zSCpGSV@abwOfgC`slDQD^8`&DVoKR7Af%^|7rVEXWbSu8vaR>an7(01~Pk#g^o!_WTa|1 zhgW>g8HtQ!EX8U!8F6iby+%DW&KmPC9;CWrM|R!OvchEV<@wy}YsEwX@i95Mh-k4- z4-Cj~akM80-y6ubmqD`339LNN zju(C%KkiX#hzd9%*K<$qfc^;Xy~pJyZYx$KF6I-vjRNf^FdPyI5VGHcFD&3cG~ygF zXPd?;VvHAvk~ry=){7me=oVXhXgD2P-+=#HJA^_d&2-j$bE!mk8Vawm-(tD`Gqmnc zc{NO&(kdEJvIk*`+%OGx$;~b8Zte78;p2x|nZC=vjCg~ifRFEZ#69bn_@`vpWOhr9 z#$x8y*Yk*qt#Qv${)g%P%dZ50OC{0-X0_n_3-JjDSHG`m!|CrEkhK^p_D0CXzVAY> z>AbEA4-2$#5a6UDRZbj4O)1k3i9LlJlaoWa^icIQrxX_T5C~6Rn6>`tNb4@N`5`dm z^z`q6MRqs9HLe9x9`##Y(Ew{HaR8)J1~yMm|5b`0)yjK~3fo3TM)qnMdItfCRwhrk zC%g`u@S9s(u?$+w2@G23E{D@lQNYV4h3I*@n`Kxl-+U*0AeKqaI%0E^O&r(l_!fe9;#&ELUXI2tHHk`c+jv!HLH-{euNz|iK%ZwpTIJat`} zP1|D+1Ev%9>aYFDgheuB%%r4V_pT_*a@RReIVvDuSwruL3LW-*r9IbD#)TfZ_<*@T z;sGf(TIXvGXeIisFk(M>-&(u?F#d)eP%uZ|L#QOhWxj_Z6mn#A;zX@HO(zHeHZqc_ z*nifAWFg-n^ks~T(6$Hujx-LkJM0e=HuKtyudZd@J)DtfMBMv#buLG<8fR)`esp(( zHC;`6o~?}!>1R343nkLV+W#Jg{Q3O9hhZ~YVL4TVwpeGi724tR@>pWn+3_9t(jRJ^ z4<RDNE@%GQT+UmXqNeeA+3H8r`>7<{l|N)$+>l-bH3D`l?_delfX`qTID4AoU;MP{Sb{k;Z20YP+OAOs+CNN<~C|WDbgO&dH~*^QcxK z6OqnR$;IiGK@!^hPZ`614s0DtD&7ZJjQ|quZeVuGg@30krYm{1IFs)1=WYTCG?10) zat_t)ovm@sJNPvJFCACQP=2CZ;D)tR^mm4bTIVibsnClS&;DFXotBEm0}>I1ks39i z*HP(J{{)Mgc2%roGSR43>w4hzX|=Uq#-L(3-tt*fPrYgm$%YMyV99(SYVns+%up5@*j%Jly;hM-G*el6h{y5;v)C-QsxaT3Z{_dR`iXAr zgm{0^q&+FC*{uSSI36K?94(j@=6VSOCOFa=rF z2qt!`y+7CHj9-#Em0LYNBHMT0ZUy#f;!g+--02-Edy+KTt$4KS7uy*?i=@H+1=D;x zh%$e_EL{Iu|7Fwf%4ql#)AzAR?M?_T<)zmTRnvR22;=IO#G>q6rIqvF5c!X1^SA4$ z8~X3)3Ft57vG+tL7F&uzOd}3JC9W2ZD^#tfb|m!3{vki9sYgdZLn|#kQ9@09QwyJ& z@!htb8+IX82w_P#6H2RUQn*zj0gEfOlZ{r9#E9#+)jV^s=!>75a2j8w_{1wy|$xh{yqQXG6Ad4 zTP)?OQKF4JcAw`qBQ9kD;^ZjCV0baTT=h6{+QUXPyEQe3ep6%>Iy)^j4PnhyqH(rV z{ZI}~Ef;Y6DA~nPPh@h>gTiKgtE%o@#g^bpPA$zs0S~_SnJ(-eK;gGw6|nR6^1u2) zEfQH>2lu-+U%JWRldZa21sE9@dE>tLRj1LN$nGgGkZ8Irx(1bi_e-%DTm8pO zo0Dl=17V2t-elFD$aC$1lDSXHM{=50h~IiRHeu0Xa10VVf?U`3Y0ZgR&=e9ltCO{fUMCVe-qw5*gmMD_(83pk|{-9($@eiAne<$kBV!icKWyb^m1Un282G_n< zy%#k3TYHOH*>)_G|G{oI3OrKg{S3DU)^s=F$vkvEu%6xt+A|a zz*pgbOHQlG_@FRJbFw10Tq_a@tP=jNg<)yozY#Q_T`VzHr^7vd+g9e41+Vh!@g`$y z;)8?1S5%9N4YFTvzJzb_ja&lZN~B)`9li)lBx@a+Y=ui63c_IG18I}hb-xP?p|g2= zfvL1AuLPL18B_D?7D=fj5y?v|gf_ZdfyndKb=kZg>WcD>zX(|3^C&o{_vP&^{?Z41 zk2R&FC=Dz2HR4NrtF6s?Q~7Rt)e=Vv$``NupdcXW-Yzk5HEI-3ihUr*)mU&7A%7*SB#ie(yl;;8K zmUYgfA&K_Z9}@S2n$7HRcPnv{;lqpZ#pjwb)iK+ zt_j8gj5ej=8_^;LVMtq0_`-Kbm>Tre`2I{j$cad?<-q!U?mB89CgYt!JfIf-<53@< z*clN*2FMET%_nKqtFLiDl2Et;@?M}<1e(vfC$B-S!yJ$lvehV{9^Ts7rR$Y$BW9EAKh&w?7!P2Bm2*m%O`gv zeFRIRN@3Deg)8&;}6RI(0Py!Dfi5~$-WQT*T7X=yakwX{7d8EHz z@O?1n(kW{`VZBEO-+Yvs;&)#e*-hjzhELo7Xi9x4->o-K<|be2G$_ST(_anAg{1{I zk%NrZa;bPdU)%nuV`X}xd^g6P^Fjwps{FfRj?Wc2ovtI%%7huWCy@spOPaQe;>bu) zqpVXs=dY15CZkeo{$W*IhZ1Jos>v}~iS_NSp9!a=ZzcPw$4q)IgC6InLV|Oxj-pdq zyckvwgfL?loY<}K_e=B}NXaFzMde8h`J>=+m;84!HBCwO|9!bWj5?6Mm9VMd=O~nP z(m(!T+dobPoZ+$mq6&=?cIlB}%e&}cvBz~I#`{CHe?)6v zYnrx&j*!!7_4`4p$*FFxr8Z3@D`JCFcHP141_QZcftg9p z&DyROB_GXHm|&*nX&SR)9bo=fj168xS}id;7ESeWF|j%1F1(Nm($AK2F5ukMR^>iW zl-2oq#bI{UY`bv%If1@t7izT%y~p>_=@{a5Q}vmCNEFM`wOnFR*Owl4fVU zQbl~cal!va%L9wa)^w`Z#KVCQu@7D))nwJ}*Y`;an?!mwbOo?Ig^u04rC#naywNJi zh}kAAnnFFWs${gkJ3aVB7kD8B@&ETeQUJ0{@6u}1u@nJecYNrSRLu8c_t=C<6fJP~ zq*`CP>sinCBKx2?k#}7i>xHS6Re;K-V7*g|>XQx)!rZ8=&fbB;@pz3h4oBY?+i5vF zSP5CR)GeuO;fq>3^*f#RHtQAYL45~n#^GE(2o)veH6Y&pv9MG0(}>Ve&<(vDs`n0r zgo4pu{$>NfM*#n*M*c;+zhZg8hS-EA+HlDDzxHlIGAQDeg}i8Ma+&ELrC%EtB@@pS z2OR5(ZURP!KZP%(iiMD3{?a=ihHrC%wM>h{{}RZTyvUzQyg+!rEMbrVrfNR`JvHd} zK|D<%mdLe&FUfpH%&Ww?D!&5nRp_e&m&nU+jH&3ke5hBdn^;N)dL30_KGw=}1gVJ5 zp`??ZTKjmZ{3maf_yjja6llis;sYn2Hf?cm4O9E8Wk@nOKgusJaqm-NXxyig$bF1T zD=R|OPtae8nj1VHUwRw|f1k#REm)xTPj@bHBdcK&>>>$JT&kk4I$4b3ClmD&#vo?q z0piR+8zUFnl=O?Hb^s4E<(6r~63L}*43#Xp?D97_c559`bX&p`jh+~Uee*Axwn2>< z*zWsu06{5*q=~(_%9y4VfL?>R{*{W(iR+-fA6!nKKt?M`o!<{GHPUf8zw)S{+l_}p z3$o0&x2Y4pD;jv*;4y7cOHxQaD*i5)q7E%_Q>yASxv%oj#Gh{7f4S~E>-Yct zOVIc7(y`WN5B)+E##p@=c42|J>nPZAJjygtaVnq+@C6vQ%3Ixr(}=}&E={|}F(j3J z>{m;I__~8tvAip$P357BI36sp?E_y8@WZlZfns+rqk9gonISyWRz%?vbTsnS9_)Bg z+GY)kYe?g<3w5X#?05U}v?y@@03;Lm;>XOK>&|sL~8r^TvfG_cFZ6av~APgORRvK2QZ|Mqco5gV$}Jj*k?pcgGCag@0S$R{Dj_~cfBb}LLpXZ_wGZSP<`mb=fagr&>NQXmwGspy!=>0 zWnVfD^BzuxK(YQ?gBd`#)YShKSf9mMt)R=B4L;$9|DSO1xzj~N(cBn;EyyJC8waXCzSMZ;+)6x;A@*SY2?+EdCFwB8pjil zV`0lb&8N=)G4%fFTP!d;Aotdy?nvdvfHLd*xtDX3J9P@Zb4$I~uB*^OEN--pJU!7} z|JyKvMMQLkaeI4D0YHQpXe|VAXP$r=!e(V{Q_kK(;c~fY05Cq~{RRgV0xUz;NGRnK zbvEmKqZ>d??<24|K53#uSUaZ)}YBOFlD&JPG_U&BV8<#>vow^0wz=sqYVkO^2EPv)CA#}{Z_si3*>BC{Yz$mWLb)|f*QBJl-i}ME#|9C?OMNa7AdM3Ppxi15VhUO zt@?UCro^GD4RFw9W+#VK_q7L&^|$$ZVe{00@eE+x$(*>0xvzNn1x&SLegag)(c2rGkAj z;{|pK&Q>e%dv*3frzd)p(B^OcGw4@`l2X@&6#H^;r_$=wgBX4c3RC17gxl#1bRv2A zrf6_Tvf*<`u|BcsJ>(wAT{8#Ui$tu;o-v2!eFYD`tK%kmN?#y6=7049b*}IzY|e!A zh3`e5`5iJ|RHLIg00*qGR0GDic0(@PuNyQY{O!qfTuaVkloLwjW+L-4OoGpUni zRli*JOX&7b1q^D{m?gX$_V?aT4RkYff$K$wLunpuu7#h;9Me?| z#I<(jJq(t0V8Fft*P?7CJEBA9N3>>4?q zi1Hn~{A$G{w8orTf<3UIvXj*><;VLopCI{ucrej`%IU@6p|ynJoQp^oxgLO*$+@*gelcF~P&RXpxjd3)VE zF-Ds=(a6_Zx5$@QrEkN_<@;I_wfx?Qn#E_oQ_fx`zBzNYG}`zvcsNUCGoQq!U}CJu zu~PSnK|f2_!GC2D4<#!%4*cfLFt;^?BvGB8Pe(8e@|*tX++N<6`O5D1Ikx1TpK{^j z(&Ogd6pvvq-4~Xw4miY%xxai6tLrDo`mGUE1<;dHLHmVgou+T=nOS@zvBjY*YVUR! z)O%mOJ)05jRYt9{M9#~^I0xD_=6@Qc@Qu}XDV@#ZPQSZGw<)6u%l8v2zN~rYTNsn7 zOZLX=ghfj5LZE4P>%rFlMDIZaaTT=eW%GNs*vq#r|#$v-{ zg3!1q2jSPCe?RC7M_aiz%-xd3jlV*{Rchi!4X+%5cqZi5obZAAJ)sL$0i-raz0`$Q z@e-@_3aW$StmUMnZd_SZ7|_sj_b#O9c4>0wI4CbPpFoGo~g$6pWZ~Kd&6YS46Q=MPxfqVh`_06jE$ECpRQwl@g?wa|| zY|OSDhWjL3sSr-)1La&#bTXkpz?+m5go%e2v+DgAXT1C0&{w+36m;ZF#A=z_IDWbd z8&!7!G(bL1W*F&Bk{#m{1ZBy22U=lnLVY=N$C3R=zhKLNs)A6;@S{QAd)0YQZZET( z+DU1&sg;y9NAslRd9UI68e1z>TnM=VX;nrM=jsJnyLO%=!$p_IVf;NaJ?ENi`3=z( zBu2%1KMn=9R_<^KdQU6mb4dbzyTtpt*CO%W$i1hb%#f&<%RZDF4SJYoTxnfmP)JEn zO_L|@#faJ?0{D{vt{dSiPv}6+$DOrXiYg>(Fj`Ghn6L5Xsb;9o0mbhz_HFY;0H09| zLyKGA3ErK^QE;g%RN`kc-`8^E7XBIXmZ=|xX(|S|Ka~oRd(=C%x2*j3nTmK=0%SBV zKLx~1e;i_8y;TG;7?c_~gxpUZ#LNoJ zoqswI%-vOXxtTC6yvoWWNKJDi3 zr0-Esy7I)UhA8GTVmIJHT&mW_ZyoKtUi+Bnq=KQBJ8|wS(2Y@y&k$3S&luq58b`;v z$zQmPuto>re06iz^1RG1&LWMHB>?c=miwa&K@;#oz*82 zV~LSCUyO_(TRX7>_2OCvUJHqq<4s|UySLZUGZwWl&^|YIO2Yf;_-MI};_*(91TVw&eVJC>nT;Q+0~Q%kz~RT?T#rAFmIj^ZP5x{^ zrZUFnSP%oN`zW@2Ry#amjPBj##xZf*T@McQ-!H%y1?SGtU1(IGzEW=cHDsdEP3hlU zaDRSP{cjwAxkK%$Afut6e(W?3)&!WlfP8FVbQIm>Jj*CFI!v@Dso3bxb1JXnJvn!^ zUQ`j2u9SL%M^u`2{j%;i>%Hg`*e)V;&Se$-Z@d^_F7}_dMpw8tf?T%B7(387?7@iZ zt&p`{rXF95Rk1!Ho({YGMXq!0H=(9FJ;Yv#SQjj>BDfybaq>b!!UFaF5xk6`zi=Vv zgZ3QfyK|jX-fp-cqm4y{eV;pyraVuJNWx{Z8G6leEeLF$mm_vn>#<4O%Le?vx$d|> z+jJf-K(1BqPamKKiUp)~P`l+OHtdr>eoEZm)>=MhN4)@^T3_$JAGp$r_<~@f zv7%3gBw$|EEGG)ElvoUZo#Ibhwn7fT&9Iv`@`;iCp2@R=15!r;cNA+@A#An8mHiHe z1?hGg#zm|B+t1$_%8$MdhIctnRo#;g7U{%lw>ti`b20h(v&|E11?WFqCJI-MGBL?Q z)O5KJL0?Z7B5u+fEkgqF%J?OTdDn&N66BcoJ|bP28(JO5QCz=h#Y1i-lfk*+>CnR(QRNI)Y+GWdeWXAEAC-P(bBJAb@AU6t1j-u^ zEL=9($e=+F>vxU2(UiASwUoz0s-}ZyyAm)3U7I4;XA;S;Wl$}d?wJ*(Kljphe{QS6 z{mDwJK@qKW(#{E2pJ7>TFX*ocE-Cq!uaP=3x$*uoUK7W+Vz-}aE1WEM55MpKUBF58 z(s*~U%f3_ZW>4L7_UQf&IX*6FS+0jn>?Cal1_TJ7x%_qL^~Vj*hZuv|c+!JSJUWvD09C#6)PmJbY7n=~AYtm+du(qZX{31Y_UE35)Om zxP1>ptDDj552rQiEZ(M2kyp8=3_q92;U`*DDcHYfFHitCTGAl?>%Ylhfe9MRNLXy) z%4aJ0<{_(|h&!>yE*p_QYv*o!Z`1=e_qXJyVR>?v>6@ltypiN6afFBUpB9tmJ2uGk zGHAv>`7j>-GEQUvDtjF179-|$#us3}CHIC}ylb#H?s^RfF~-FWx22oHt?kahPb~|9 z*T~k`4rp^nBHLq^aR({dpAjbg+HHMTyoPx>Lc{xg{4+#@v<)j#^Qo~uHjl+}A@f z*v>aBu+o*KXc>e+(o%ab7j@cj*UnKvpkW*X>l z+nzoNdEMjI{U=Ak^WWD`WvXZYJ#sNa=>t&&8oTy-PJ4^SzfD{$Qoa=06NQ_#d8%C>hjUhOhEPlxeHT0_21SooaJBWiu!1S&|b1 z*;s`BK!vX^m$Xfn(6hLPgF5Jff7nG@XZ!T5@X4p*#C+CwhY}DL8TXHp(BOnX>9M!p zNO-U0dNiv}b?YHQZxxGa0fUc7+x#3L4V&rp9oLg(I@x=wG3sOxIX8751NpMtrU{Ahw2!WMYKLe`eRZ~97z{!~)?VT7+S#(QV-yvROea!`+4eU{L(#j+ zIm{?^_An_9O7=0wJNvD)A6Ge`kjEk)nHs#@CC_u#D#BYrJD*pQuNHfdT?A3XNz=U1 z_oFx0`%;wE{1%hV+jo&B9`RRcT-LtKnH9-M0vzlYQ}${z0b&~2*~<=G-%;H-mX*cj zlA4^p0bV2SWX>xWM@88lGqc4h{(>&n^p0@{2kGa30%KsbH&jmB?x3g8*Y7t+MV}@? zaJ7lDnF-T{+;t)2W|)`qdpsq~?*SMF#Pv$F)`!5$T#~IDQp{8E#6(C*RNR8dVc?KC zxW2-|D6(NCmt1Wff`!GtG=*qnjlilF+aKt)LMcLPBBa*9l8te67kjMFsQn8jV%Dzj zXOv#8-4S!a*-&p)JEAhQTk3Xl5v_twp*VLR5!nGd1&sAWj3n|?rDf^~t4 zR!ld1*a(Ui^b%Drm^rWS*zG3RDsJvJt*Nbz_=8CW%7qm)$m?Jp#*>6wIyK&B!l|(I zmvr(J>w37{c~@_BY;tzY5}5}vRQ_>Xmp3}w!$Seb#q+S(>}#58^4ZkdR^Xy@ZoTc& zkEA45`6HH*`6e0$zGF1r8Y6VXhCJqI``&}A{+2mL7NmP65#!c58B{lz0 zc|S#_VCf#IiMkmaA?gL&1mrw?wdwh9bd$?8ue{DzrrXCZB{cw;ruMq|>s_Mvt87m> z=urVvAOW~_{Wjrv$qsP1-D&9l{1fIkr4t5Wm6vxNA1@OG*pmr|fK-&4irCcjXW(A7wz&3$DUXD`rWslSAv7b@! zW`jpCYr7H3@_WoFX>;S%`}{W-9);pg^BqfnXfDQPv2YP3l?_YLNLolt=QH}tlL1tJ zafSc%T+fn8knrabM(rX(#$^Vm z8lhp)QdfQbN^c$KgKn_R0@p1O7eLDl9(&pfoIccGb>9wv+(a0j@2AMp(lYi=qUnu4 zH1uEW{JP6^ijdl1pfkHZ@kT%OfF4^N3qz2El~SWK2~vSH`QDo12!FHT<>dEFz&4}& zQKEbH+S!ZmLJcJH?HjRO>rsd5sS3~)!xHPkQxL-95INM=mVgTZA@=ZX=AY+py*`NL zjuTe}VS?m>ZPK_&qOmLx*Vrgos}4G$xf$wzrR|H?pvt z)pFXi#B-2j-(&B)AkQlifK`HJPy3{#^X+biEa!O9R&;qpV_ccOYc82v0*?WB1PyI$ zpZ={2l%k)o(W={syh(}u4k9!?dL+lkmbb^Yx`WJ>!H9TeDb92=<#|#X3_?qQ20nHx z%u1&Eb0tGYW2|2fG;m!(@%=B$_gAl9Co%T!HpD~D@9OA4zP$yXoKF`O4%U(4q#25e zW%2SvNtQHG)7J~|#vX_;Zq9lJSJ=a3A@?&F6K6@D#`r32ihPRpO--%z1kH>m9Eq`8 z`yQK!0T=k~*Y^tZ>7StN{IdpELbr$TyA$5mPI|I5Y1F|{nV){N&Sf|ezIr`iO;_qp7#7E^ zWzFw+5tnVHq44v{^JeV-@H_IziZ&w?4B4}5hLtsAH2K0KOxCmZ2K%oeNtM&`+{L{6 zU~;MROK^-9Dz*jb;gY$E5=^G6j(2`he~)&#H)w58PL%s`__OKuIO4-uNETt(3nE6` zeLnjq$-~mMl??&cFFndyqs7rC!|?EDVMzRLT$ZA3kYr|T;o5fz+;&_P5Oc2#qrAVYwVm>?C4>9lNw?lbl!MQ@F7H6cRc5MX zj_c^?RD1SYRGqO}`v<*=rMb%t##jIW6=fV$w0tNz3arr)AHDy<#GdrS*ByR&N$)&V z6sk_oH6O3@Eknp#f9ZUaDUW)IW-hU!%>pJ#+O+tQhs$`K9olq-J`6>+#tMqUhjce{ zWO8K&I#81H(Z zFQ}=(Hb&bV23`7BK0-G5?-m>F{LY~1{{H@Aj5;0fGZ(Y-_{#EhB)}Np2P(!QnTN&! zko?P)+iAViEZ-jGESOIZKT5tLQ^bSd2(N;gP% zH&W7h(IMSkBHi8H-Q69V?&jV1@0tJ1`-P7T%&cpz^E`4FrZ7@#-zi}02T?&z0yp=T zV;kI~qus&XX?wNzAUWgr?d=`%ue-XK2A4F7Hh0~9d}~cQ1o9cW^tqF{AAfv1;6*zG z(RFvmG!S)X5x=8m61<~UJVMa!os={N45Qv-$6}?9)e4DDl=AY!gI+kJT_xSWGbqrP zQ^3#0_i6|GQ|^MT80I*pw}lQUkX8LjXNrGTugncs^mRa}KS2eOsQIWbt^9vt0(K`) zh1e%)*4-XK*aJ!m(5&q`HsE#74bs1zhCQ5q)dp>AOLXBXrNJcJWA8C=jZ9J zxi!DB;<_P3*ZPtb+_@q}E+%n#CmU6?n)NS1?NJ?n0J){s>E?C$9&9y5}BLRPL;!yTFDaDE;Sp>xb3go#nqxHB=!t zwWa&CJbV-ZVI6^9ogNBIHTTb7SuWxH(mb1mMp#`Q7va%4QvjH^!BtE`C;Y&TQgssR zly*6M5XXEup)uH?R&j99%VZ}uBG#DgKyY)sh$L&N<`SyBTpzXf88+KHVz^MR2Iab1~@lTN#kL^}LY}0%6l@`O2LxyN6*z2M>~2Nrr~j;z%h7N8V`=d3U_NU~H`9 zE{rU~CJn9>NMKDEIhP{zK}R z0?y*!zjC|qKSpY((qeLcty{b33v~N+s%kDm7pZl1spsF zoaUULJayKvYQc(YlqjEh=|3YGE>q%&cpV;hqItg{M@41yu6W-phh024d1*(n{1(2L zqOFyGM!5YR`@_heHP#HwCWavEPbVSVfmf(1fOyW&g%0hW;k%;u2C`~6`(T7pA(tAS zbwJlOAPk&;PZPV<&Ajr-(Dl3~6G6q(%NyUEQZ*9f0Q4@g{Y@ zT#0F8aFx#ym6C@4&Y01A(9iBDqZmQo@Lay#?gO$`yEq0t(ij za8(OR+K{8Dm+Kr+NBc(99wo3*gQLD|9sS#7BE=^YF)$#_f4igwkr5~Z?wqtl z4QytZ1=*3;>}a$x-JBvy(5U4oKVftd&-%M8L~d^w$-U@+LZWy%m|YL(sgPf4E$n5E zQG$=8Rd?=wo?ma=-+%)LDmr$Pfum;NgN`P9|BFjj_j@@{79d+?I`T2y`I)Kp=~3Q* z_}4qV`FbO%%_#s2qRqUHE)Y-r9TmxFIrsZ~@AOAV5XQ$kFsE4ftIAk2mNZ#28fwpX zEM8SxRa3Zon{b$qQ<#t_HHE^%D5DY8@~xx!F?^gRPp;MbC-KEba%j*b0713afeWgQ zijFr63aZ|%l7e%w4jV!!_1>j1#_;|FV4Ptu^T+@fuDtcZ^UY(sK`_zK_D^I&i8gOb zip)jk=HT#XXYVC3ROMNdmi*tv79;VG!s9=|O}n#aZBuRHQU7mTmotWevGv*R@*dKy z`%Rgd&}3GjyMA3f2aNAN#t2Mm!lJ}FkFfsek7M-c8)lJd7`jBceSE8OPNB|EMvn{c zU!q*`Qvpvsza328Ms7X0MS_`eAVdSfIQS3uIiGKnE!x0lSWB+^qsVmTxBM}os);Sb z_f*}b>MG{OgXv6lNDV36Eq`zv4K0VY@PSs)cFWOqV=KBiFikC%MqN+QAt%%5Ep!6; zr<`i2UOee^e<13*@ZpIFOxrt$1eiV_#xnud8e;oT6&aXYoPN$crxXREB%)OFV%8ag?51~%Jbdfc?kbY1bzytb@+d;7m_9~2v&#_|NN{0Jw_ zj~qkJ$vS=o*01E~Sd_RJB<$VW4*W8s;_w_lWo7Wb;9Oqv&T8ZTC1sG)(R9lygg@~0 zhp%#f6$5>5rb83ao(GCTSZM>Y>> zILFLeMdiQf6(1<`=#ZL8UHL^~u3#DEdpH4}j+mJMo-5v)`|Z!!8l3V`#;?85w%*~= zL`0!&@E?&*{~U;)g}tfs?P0!R-7>T}%Z#e&ZX#@7vNVN<8Qh}@HBo>Ax9&UMLXT<= zB)xpts0%k))j~25paZw(?)v1#GUVi>#H!2g8*H3*EP{Kg{>{!FH6Mt;Wz2*Z zn*ZYfEa5<0wE(fR^@i!%zWx~w?H>d6I!&h%hMC@Vehogs)er~6i~h+#?1cL?loE^) z)g~C*Fe+5i4fs|8Z~3P~9-@}{r`Mj1>&va(B2cLl0TrJ&{BT0(^!|?P=&o=`yx{3uY`1JX$N-eDu1vDsC3EiM zp?$ZmkSBIJsJsO6_$b7xNOfEURq#FEIZm*y{5FjlZpJD$?Y z4S1Zl`mSn&MP0JI)gXK^&oGB$L$+&P8IW23fUY>O$qtLxKxDbn?56tWhX)w!+XC}p zK8*n^xEGyvx1s;T`v@RPlWdVf<*D1Jk>wUN${M;~2&^T)3S&LF)$2F+H_InQL{_ zCxzdVTFfCg6-l7TF0=h)ApOV)ta)QWb~%IDGE+Bu;_>kx9pZZ3yn3%RnY0GXB<@5< zZb?Ik2l(Gm%&X^0Rq+x|nHvpnc`&(j$MA&}nHS?(g3*Zgj{*W%hAnIBc%9qh`EMsS z+@3~>`1zeeTA?&tp|*<@tn(|2B$ul;16WLkLD^vsn1pU_G=2*O9D5)aR&06) z;v1e?izDodCOt3sDxm?z?FI<5zkmXuraYHjd^^Q2FgStcB-=&D5m77Bjd#1n+X)Ez z&CG5vL+5xzO5W-}c}@x7B!?=c&G)prWOh2qok)NL$F&0@IR%46tvHZ)roFyV9jQ@r zZ|{fhvGF9|N$2$*I?e5Q;GtKc#8r1GGzq!!c`nem_C}lO!WhYDdrE{cJ=b~VdyyJ( zoW%c}ZHaPwh}Ha-h;t5!?6bQ${3$8G5?lL^!Wu*{-P(&Q+?Ef}Kc2CV;2>XT+5A(h zLevEf4lWW_VV(4DMua;5-HTCFgx!|LB%$WS0dwR*gO2~resB6Gvd&BtoKAqb{+y0@ zhdW1&5*+!ZFC(}Asy-OC5vFX$GL&b`XeGA!^og+TsOlP14%H8}j3kraJ>zf*UalfkQtyCE2_g!w1hL7+xK`|VxL5`oi7Q|C{&kKFav?jB02~XA_xVVx0V{H zPO$0?3~;h!BRgE-$5yYiWCx94G#U5@3AMzSTM0hgX**aRezbOj`@&;~P8>xr^6U-P zx&KCtb%fUOq|Bo0vDQ+Vdbg)1`*@2R0#7^;dt?j8{o0%iUsQ$6dcpg>ML2GrG4)MMRr<&6pI>lxW6&n-^2R zPzbN_Qtts!Tz+F#stQ9=00eya0v5yz9Vl-?Ply|KJx-8>ce}m65D7B2QxX!krz_yJ z^;rUJ(S)O%$4+O)m>v?)F1D#RK9OgZBghZI-y3{)Lv>We^fkaWHvv+UH+YN1J@pI z4rsXI6$AXcvmqgGUEQk4ubgV(T_qM+XODl~F*9cx?TT7D*dcYQ!--2y*CzOx2 zmwP*Bh&=-}M#|<|Xp^LS|84oB6Jwml+`9&S3(;${Q5r*xJ^SqVZy#2N;wkfn@f$#j zZS?L!E`a+Rd3bSw3ZgM>Ug!Qo{LT__Ge&Cm0x9wpAUP28DE)+v-_4cHV$&}AJ*Jwc z@9Qb9V-I_wO3%}%*LXMO!`p)3O^=djr7NcSzx%eY81wBRxbANF!7eSPQ@sHGyGeDj z`)CS^g!S^DbFB`;6i4Y0qw%KUgc3k`Zz-i^4tc#;L4RNqi_i7j;VTuo4~o@-ts_p0 zVi0`YJZ1wS+JF67Sex%az$qQdCN7ZYTz@J<{u(Bbzx zwN8o%Ww{v2ZTPEY1uiM?S>irqHKwRHJG-qfku2$;hyiAlWSwH_+=TXr7k~*O;iOD} z0q6O+K#v_qx5B|O$F1!){)-$vH#hfUs~anbgq0E$2VwrFAC@YXd{#jnXKT%SK`iVVj!4r<$;la zI5=lPC!C!)s`oW1E4ccyc%noaEg=@;a;@fm>q)m9ez_-?dNYF2`%Qe=cBz+r_K-S^ zvg;EsoGqD#ei;QYZUS}^#Lt0kxN8j@ok(9aelNv7 zy4MQ-APdO)QKIfNiM3*6Bwa2Hxo2^-xbs9=iHBm1&W3MYZC)B)?mf`f!!=cCh!!}c zq1EmW!!>Nt#CLcSgXQFO1#DdRt@$42slT#~DW0ue3VUjQ30f0Oe)sn5wPJeD8%T@Y z?%$fDuf@*3i3a9-BPiRw=U9j(aS}?JVIYI6Sg$pASg9x&L6?|LuPw14jnh(L_ll=< zwHDPD;zt6J7~{HLXM)jH!XG;Gd>kYDki=?R}H<2&-_QOiZQ*!B|35 zy+xgATDAJ3td%bA(Ejn_7x`$Z-c zl*2hJKz?-+XO4F9V2L8LOos0xGf})l<+Q!{2XPs7(##)CLak6F#w0v2tsd{F5;Pc5 zob6AjAiHxb;{Ryc#+#FIXB<@Aar-nl{Q%5^seroAtr@n_JJ|!)jRS^#nKhM$W18AQ z*Z+1V3A6Vv@OEHqHShsWznO!}50$`ZDsu*afnDIjAGe_=Ma?$E+NG>#LrPB$yU&j!UDE*t+5#`V5jr%SNUWAcFoXC#9@8l_2 zDk^RVJ#xZU0g<+S#V@GkiI=>jgCP7^;uy!$XX$(nHQA;e?*mFnmaNr6=O}0JRzFVv zrk00*tJ}7`dW#cvzn}oFq{(Mc@{2NnpMy@fK~nJUdF1+xMkyt{+bFAxk`qm9o4PwE zNa`Hm`&aX9ZAsY7^0#gV*(iyHozD9EbQsfBS(SQ=J!@ixZde8NyQy2#Cp=McwpRqs z;<`A0vHGu!JuiE(XzQObTH(@-?3Lc>4)YxCSPqwwuxLm~JJ6|kzwgTj#m@!-A%7yp z41PdylMZtV@5v_^I%XgR-rJRiW0ICvFyDDk;)S#PgWGZTpa1&812KA;kR|w+W1y*1x)L9WWi@YL^rZT4u570)YUB+@=4HEr_Jd znI7VP3HT0B(41prtV3p_pB)1CQw!=DHeSQ)5Ktf!&$WnFUJ`z$#3f}z>(kt7_E9tG z36lt+1$xH*p@h_(-ywew{Rj#)0yoRqJ182HI4m@3!`DpUkx1U_0To2caIWE>+37Oz zj>cZ1>epkZKJm_RX~054;nto?O0w&??$~63t_Y|}e_%aQAL2$9Uer7q99ld){L1Hi zPS|L3;Schm)^mqWN?(A^QNT=29J`o@{wD5aLQd&So)Dp(`MI?k{=AC9LZufh7#JtI zu)mA*=bGB5Fd28YcmemwN=@H(-{xd3&KG~KUtyl|`$yWL7*OQ_Fz=?*)To9UeurDb-zgA~X>{5`HG%V7vH;N>#1CGfGKN}6}p zvJ%;B_I=c!6Luk{lO+9Y#yT0-b&I(&%}*G?s6rF)V%gc1=)|vlgaviK!hLblQ9D63 zPXD@4x;{EN;uz)EMP*UfpT-N z+P2EC#Y9S7G%Yq6Xk8^uquV#d*htUjf2-j42xXp-L8dvORkm&8zy`}95ra~^^O4TE zY_ri?oy%I)d4TEWqg0DVydIkajp;i#9w=IYZg8R_(n!co~AMrGG&{0M_4e zTf^K7KVe}};&#Kr!YYVb%gFskT=zFND31L>fq7#Zn&es;%VQ~Fa)CW(rFM`5mjBGE zM8%Bje9r5$A{O3-#)@;LOO+5hFayolLyzMq9B&Z9jJenDihwc4ZAXuwv-%Zk7jec1 zs;X-OlhV=E3#qaKUENpE3-q!~G`#X9_nq%GZw(rk#QYtE4vIx-%odnK`XaKIax;1N? z-C*k~1Ih8=JFjw{1NdHnG>;teRZ%hrkXtkI@Muof1qM=qE`75Yw&R+t)I@gF#mX&X z>g>?CSbjb3mCRq*Pv(naNQzC@X+woVtG|2}im^>ythA;U^M2y5aKCxwBq8O{BqQ7{ zAo74Be`*Ur+WBh}}$8`Ptz}JtoYj&>2_VVb1`t5~!2E!aGDMja}sW$w|AdONRN>^-gh* zk`E`^Q@HscRQGd-Vf47K5}FV4ICmUItKgdetjmTce+)_iApgez2D7Q~YpqQi?>;`; zFWf5cuYc0-Z|f;~Jdc$YBb2<~VF~6nwq)?xErJ-#oYXLLcmm`I)5Z z7v8L772g-md|Oc)-J$I=?kzk}HVW#Lf(+!U3xuc=AX`TM+zzrSp}gnA6|1@cfDIza z`SqE|`xKfYp~P-=1=Y?|p-z)MlffHO90z1Z(i)(w=h3QNJC>NrdQwOSJ?j!jwR(oZ zG;Ot(A5Irv+FOA|`tsIzHt&;yvZdU>CdUwA%pu1iV*BQ52O{Jt#$`D)eS2P7Sm(C6 z7PHtA(Ke>{3E@Fq!+lJfFTIJWRVez6mAPI+P5YN49d;T-!c%PC&hhvNyI5;N3xKkn z$Bw^hiM1`N{JeO6F-dOgzM7ONQiN)_zx-@KO#r9!`y<2YJd_XC#LF?Yu3*mgrL)aK zVucZl*WC-o8=fUUr7OXgmd1$zj9HKSd|tRLOe^`9izAQ4_NA-IiiCOewu;uOcn?g- z!WD-~%@~Hc8SwoxY3E1cDO~ESYXA>;F0>207>p*CS^iW{l<(dqr3abG(l9_~^+6iR-($rE~-7_@g+ z%(U0Wcs5&lEUK)tJF!sB=jyttFUzY2zW-Xx@DzPGH>2x?7kt}mPG-s112j@HN9>Hc ziEw1`)rsgJwI+*=+-tx~VArQBu~r$sH(Abe-_txA$0+^rQ9Qo2qZK_o;OD9s? zrzXW%X)h(*V4hfTD=kXwy7}Dg&S|!A<$J6HzjL1>1&ht{X`08YM`RCT zV+~uc@O0WL!Xs5d$sYqsKteG?SIY{iYmsI+$S?$M?jF}t)H=F0uEP0SqyuMEqVs_ zS}UNquRiM)`<*>%&gMJ9dR@t&_CF$(1RAydqtbAc+rhYDp7j==FaByOnqSh=>VJv; z@D)YpEYE@yf4>4o$Z#>e@9UqRm!jJDW%(TU9z&ucKRf1|3%Y}isgxG~7@WuzCrM~d z&UhN)IOPrd=qszGP@yojI-G-M%WQPA64DyBrlv3?_^(#Uk1g|O{5>QCTMp;1N{InTi6Q_A3ifv?`l`wO%Qxf%UwY6b z(V%nKM4H*7-w~9E>hxp7dw4)h{^!9oqEpEM{J?Y4$n}WalX!4sR4bysyeb@3q<6{! z(Bm&pf=Nh;+5rjGh0Mzrw*P6}LcSp73h+=NRXM*@_K1@`D*rP_Gmi;0^XWfSJh>;u z+*YISN1 zV@h)MDz~?oYnN(&s6rWp_0sUQzGCVq*ZP6pV^wSFD5;wwNq0ZZ`roMX4ZoHpfJHL| zPXGA%&*>r^EO0fGz0nPVdrku0Ykh^W>4)<2}R!Geqr_P1h##kWSj)%5K+-I(K6Yy46YHXLkL zpX^Gtk_n2H<~lOjb(C>rJ8QXx12XxKGw#`q5`^ZP9r>Q@qn=o{(q_K|1EdsGiK5&q zR`QV$$(%`6wBAi=!)qZ&D?drqdw1x7|;z|81wIULjAcK1mr>JPn}4<`2lBCtINl|cRG2d8M06x+^0Yzl1Ki1Lz)@P)ed(V& zVji^<9&ogKEJ}O;UFUnTQ27>_xk(UZZ@Aa!g}i~x*u$p%t#2Z^*$y@Znu72^ z{20l7G@c=9dzDt)FGSbOdFx0oUuGfP*Y09Uv~?+VNeDs?h?(YN>G81BqvoKadXBaR zgJ+apm!gd`X3o@7@@;&8t=B2rE3a4h^>B5FAL*z~*K7#+#T*R2i>5E5V^Dnj_%*D$` zP%edpe%EDMW$RDv?!0bYT5wPM6I7sI8+)3x6gFwFNuJ!{P7Z)*op*9y8M}#diNq}D zr2YhZI`R9FN=IlA9&M763{v4Om47NL5?|t^8bo+CpepArV0Cw~LO;9p%Rv8hK@3ZbmC{0<317*!l{&k*#$-k4jba&0QyzDSO* zxH86SGcDT)31TpQKnyX+!4=G`MP zP!5u8j{e3G0pW7=iWp{(?GC;i-j>JC>Qfa{QExZIply$GBnBIJ{~#?VdczlZGf6e{ zR!BD!>{hequdQC@4YS}7blrp3_C};p`HI#_#{aOL(yc&YVhoFKuO%u9V^Y_$N+_2c z^!j{{ObYmZc)3;>V;sVwA#d`l<9VHru==U4@ZHgu_a;3?9~X{hS+~{W&bTXrvD~)7 zC&)Y`l13;BZ+;0``@{EKcPG+S_|XLa>2}Je$@T`lln3&P^Gt94PsA8|O$R3Y`4rBf z-e~W8xEMFJSEA|t&R7R1lDf?D>C3f5M6Jgkyu$%b{`ZfO#%6Kt#p4n0+LVXHV>O|h zTN8%@YIo^21(#VJ@@ZST;zy}C@_M2OTc;7P!tQA`_A^wO<$4wE)x1)tgW1DQE+ajv z(3TjqdwJobqbQ#Db2uQM#YHY{w+xz5UoDJ^g&E>2X$kjGtr9*`n?Ns-%=*2>cF;V7 zRyZKokp=@%(?*ECdj!**u6Fu!LV)3||J=4xDAaEYQzz+)XG5Xz{kXah4_ev}lM*Mq z#{7dS^jYCUI(rTCieD_z24P^{F>`G!(9Jc zeL@PzCg?J;7L3f(0IUB=nN#5Tqd+=eUA%|$i2@ZVQG*_U_5OOih$dadIY7h)$n-=K z@=8@vLpc`eZ2Yzt;C_Hd#9=|xRL)K-qB`A>sUmE(N0_u{_&>mHFt;mcZ~e3Qru&rl)?#ThQLtq?`ICLr-iA%UovqZ zSn$Ppe@B`QlASgQyxi9~{TLVSrY0uYn!!3Z-8Po|_b<^c@)B;vJYH>avle?Ro*lIp zc&Uedb~ol6<#=yAL>?|=z>d57h*&b`lVti@erR{@kDZv3hgP+tI%%L47lu(Mc0R~3 z`dv9Ws?e(Xdyn=jiYQpf=C75wv?Vx?3pPZ3V-l04+IAF6#dS=~fgQ<*RLbG8g zw2NSz@q63(*GrKo`49-%MihoKdJtAr3wAADqq)A5xsp)`mW|%nZDC1UsOTTCZS5>+ z@mdV?oYQ${LYBEeLI>JxoGhHa!X+$}bF%pt6EU+v#~Bw<2Qbw7FN3G?^eCGH@}QT( zdYKfO{?tXwo0-?n!25Vs52ij#R;mxrwcuwU>CdG)@in;RKApWviXNHg-#fuH zKhEfx>hCzyqKJZ@oa`);oG0BhEp{%Ds@WjL$0|=s1oc5+PoUx;MUUUT?QER*j3nt= zvZY=IB1@q9NZgBqZWz9`kJGQeil)Tpb=NPicBr{zrS9Q{nO2&1WLG)EC*v=!OLWL! z)AB|`^mY`-*de^1$#3`&CGmZLtj+9?f+#cc*JYQQJ+4+tL_k5)B;$&jw^!l&S-N6a zfQ5u!$2{Sc0$?r<=lgv*4$C`-UFt$il$+7eV$7Ye)q4gzQwySlA(I;5XSnK6MiaDt(dN~YKAtM>M2FFaSIvxUiY zxHVGM%2dC|Nu7?UZFpk4gZb`|&o^;cgiN%N!;F|P8uVPLQs?Ug_H-dY`}+Hqe3?NR zg?HcxO$SIHl86I)X+O`UeU9GhxQN+RJTea0BkHb^Sq%B>Vzm7K_{;mbPNBYe=5gY0lVZ?^>XhTz`!N#h(o+-sm$IvC_ zll=qM$&O{C=1|9=3q|4QTjf-vJXFcOMf6HxUc9LG)dYe?YoAi>b_<#QOvfr}6 z^u*O_TylX#x1{Wj21JHJaQ%XwwB;u@9bASe4Uu2`qr6j(J<9joXRq~P((EFJa3?K$ z5We>-+zuz$?e(=mftuXhT!HSSZlLm}=%4wiW#6Ag7wbLjuXw47GnB3kwi~*BPtft0 z`QHLRRySZmh}1Rwv{Cy5X*Xr%N`6-;S{Y$3i2w8FE;8)iX-_~-W0$T32MtZY>@s`j zEp4FPdCB>8`l`0orIxy$*4k1*3k744D7vr%^AEvCdiNqETk=EcrL)~9Mc}$x?%GeT z-pfR*+ACKXBhCixD-R1D(-5REL4f5D*-dM zg#U?#p+JyKsf#mc&g}%=3Ne{@(pggc$yj?SL@mlLI=36(co3m$^LC>)(~9L2eLBHH zbitl^`f#!W0=Z3_pI-BS)*JC^hksT%kmgC5v4={!?C4KIUe8K>5IHp$W*2oV&qJU& zf)$LE2hLd5z>M{>%Wi*0`9Jd)ZnzBIFKe~Z-ftI2&*}}H%%E(c1k1*|bpr!FQkjWkNL2E zY`)#0qvact-+px}*QTaqENsWTDU`bZc6H=Qeh8QZ{ZA3H7GBcqQZLWjGe$0Ev`x$^ zq9dc%9crtJX+elry5tx&uw8knVJE9Z)VWDezIQZ5EW4>E{1C~CN~X&du~2ZL7;wP| z5v{=9AeSX1nw3c)yb>g-Vr;opnNKjX&#We?X>pXp`eP8Rw^g&r^1I%jE@9N`#V`F{ugo%+dWeI+``0Zl3E^~TpOrESx40lAezw#WG*~K+)hySKCeFC5G z7Vm`U&@9g3uP9G;m3ymj6E`B8vs5vJKFlpW%DFGz;*jS_iGn&;Rlt{9@=6p9Jq4{{>kQn|%zwQ6~QvcgA7VfSV*duD2?W+~52YCFsxCa-`uKqr4s z>?&;xQ}gexETTxHbmaUcSNMb7Kvk`LrIrPM_!by#y+0ELoM|(Ag_*t%HmqVOK*=hG zj{R?dd;9Oiiux=s7Qoob*<_jly_IH+Rj^c)Lv(Z}7x;BV!gQsd%q*u30gb(a()y}L z@M<5I!-djfW*r(xp-G#}+v}1Ph3g(A&z=??$NQ0HTg_y3Sfo7u<3ShgND0pLBzm`u z(d>%Wgi>mb7MKd;14#?nEMxSh5(ysvs)jeyi$q?iw-BLL7lWHi)Ww8t>Rb=*O4A?s zs5<)@JDizLSHG`53=*9H%wW?G2^gYFt^Fk`;ZdC3GOA{A-ihNBzjC*Af8eH|pt9IY z_*W{4BVV&8+eD`m5Hx>UrnHerr43DL)f1?Pe*E2dr2`fMwrm}>eR2&MLdVgV>Dxqo zf2gxugUhs8B}o{$hBes@R;sqp%U?pf%cb(12>aMn%qbMPo;auk6w@m6NRhC8h5GTF zb44`sDP0*Y?v9^F@iIsVTt^$-DKqSXpizbP(l~f22IYF*m1g&-kpWV_zrL04h&X@O z-JI&-VdRB0ssmRi4ctB7Gaay+wcz(<8?2X5GUTeTk5;R_sHxt$6X>)t^UFgp>)UPp z>M-oR{#^@XL;Pa^+WK_hS)bupGlUtc#4cX(O(n;oQQp1sxIabYMM6UCA|UVGREZL$ z7W4u;bHV8I8(xP{ko_Ak{zZdv{>~JL(^TYwqlSazTxg{@y9`L9#bvAHeq&cw@4eFw1SiOOHfb7j%MUr8Q2*^0 zkx<(^tH6X;kNd@2*yYs3%%s`i*Dv)rX;#_=aHj2#TynNkm5{OBu#*YpKTp)l{Rjro z`_n`sYTNg7D*=b?Wz0?R_lth&2Z<-6f%>w=I1~Dixl&PSV#TrtSKD1XW8x}4JD!X+ zlFjDq!J z>M)2y_h`wgwB10R^|AzXhEwo+w2()Bir~MFoe1!b9*W)NM8nvH&>Mnru z#|%xJmhw1Mua^1&4QqNKc5o*t!~mGaL>VdAoN+wv0!*an{o%ia_%P|U=+c@YRY_kV zjl19OC8Fd7JfjLK4h6FZk#XN<9(HdJqLZxnSu>}=hHSs4>i-|;3_$+}PD~ku2&OL< z_&ng*xr!Y7=DMrVHpzdz?g4CdZCRf7Hv$H(yW+2YRrvqJXdmc2SpVVsifFMgFUxc~ zCmy3&R$cJrb#&pmDD{*(?=oDW&qg`OG26+ijk^29$Z$<6N-yheaJ^Tbiifi#>a2hb z8WQ6n7=q^gByFkGE_U+*?hi*WD@w~oF56wj+g>%*n4Cq)v7ztD@=3lUk>`Xc>YqHUHeYJk8u zu~rm?hmY?~eZ6c0_TaBjlQF1sLMh(};uAIKFE{;4!~Z7?3J{(8%kW0!df_g_R@f1b zgssFYFiF3P2&$VdO}cL4UK(7j6TX>`D|ZS+vO!sg1U&{>H$t(PoO zl4i~)Yy)?AJ%Kk`Gp!&AXEK8%&RIg|79B8^POx*BxZv^{o4^eWv3Y)&s}ZgV^%u{er6=y&-ZmsuQ1Va{)su5XS-2BRdt z`gyW{If91&xLrwjR|@GLmcsK=+FH4$Bimmf$RCCE#~Qfn zVh;E(3((K+17*w(>Bf;ia>tDgPg(u*WUIvp2U!Ik2fhN@`a^&#v82FLFT=8WyCX9M zww;+jMU&*tvV+>rDMnBv4fp{e!pVcLpBVADaIuQ#Z%CckdrLJO$<%Zu#%4I>@yv4A zCZZ>YkGt71&7L`ZpbE1!@-l|}AirA|FoJ753OTl;2sWGfMA%#lyx_{; z=68RQ2#FGZ_x3v3>b>X^sa{NFa(1%*N`p0wg>#b}7PPx-OhHKrg&@QhPnF&zk?yfp zCbs6T?Yy#&%Ap~=@5urrl_>a*oNd?!9$pdPLExURQlzB}Z7Hqj5+ys!e7dh?%`WsA z2C0D`x;(6-#{Fbv7UbeEv+ZuE{Qy_1Zs27LvaiD|K5(Dc9Js3T#^gz4rcV zNAy~2Nl1`(p`UluAFK)R28$52vTAn*Y}%@P;%U*d%zC{2*ekH=~VHSd7?N~Rwdm&p~(|lKibd}{Pi*l`y#cG5ont~Hk0YX zoV9!7#HP?AZQ;+VzZ5IVR|3gpy?`dHr()ktmPqFj z=ie)+s5ID9gkW!QJefnl#ph7Ta9zpA@3}MSVxCM0s)&)sE>7Y;3ddW(7eo&m#1j^6$$F(oQ?IG$rg!x5_YLI1RhRG0M9prKyn609=F^7<2*GhJo2gOgDFg7j zdqH=PcH3%9V?sk#iHM?Mfg!`+mg8JR5eC9sCNlX4znPksU>K3s#}O{f zt(QqgXRh)X;6=FRr6@m zqHpFYsehXq*quxM=(9WB{r#0&>`1S)Q09e5b^O!*$|dSY9TiMQaF7mwIK23g2E#im zG!M*y^OM-pFDSnPwzgv9gVS=`z<%bSo6EL@?I6=w7s7)&yqPrYdjL3;L>7#lnDZ2Vd0@3XwF5)ldBY?@r2o3%RqZup)P|9yUB1;#;D(B=1ZZJ0LT) zVbT2zc(?jY^!#{VI?^sNad5qrw4h_~`r5Ft5hdBg-?D_bzUDz7SZ*L&#wH>x_dwLj zBZS~t=gkj-6#jZW-Pt;NrQ;uiFQ@fR2};%+5BLYedmh^3wc}xI$!)4t8P|-r;1ahtHdq;+JHC~F?U)M&)ZI$upe@3+f>EN`55Uh7!E{-gF8s#WHx%Uydc1KNB9#(uN1+HnTKlmcq%NKji)(4@eD ztL$mu{+fr(Q4#iEvo&^{kHSeGcHpRpA(8eK^{2BHXs1s#S$C5BO%4v#Q$>>xF?CAK zGqufxfoi$oU0{g|F@5!ft0JAG*&6 z(kW?6H6YGOZFVuYUdpC|($8sSu0{PYYtbAzK|Mdqs}&4ezT~d9+3h*k{N_U^x<=0F z-PhB0;l2V^_Mt%7p3NVv+TZ4R^M{yelv^Fk1rM4p?bhv{#aw6?QiVOa529GG`+WGq zWfr!}N%v=!{uVy6lcvh&=zV9n-^0nO$?SN`w~$!KWShd-bW;AXS~BliXtc9DlWIT! zqumDJjCtWbJL6M_H{TumGcRbLe;x470tI-fWZeJ)A|g3VUD6R`e&ixh$GELhew@e7 zAp@UQQ?Z{8df4l~Ai}!zsBv~&|7NPW1P^2YZg3L}>ZWbaMlP%2dNLW=Y+uX%1yD^L zOKsDjn#=xHky9TWGB8tAlbarn_@(2|<9Dzosd??83_eVdi2<0BB@t^*MsL_&%(rpD zyqYZ~awT`1st!{k5LVR)_kh6Lk z&0H&c^cu(a=r`a_#GRSws2QcX7FW|bS^ql9RV;aD8C?0(FoxV&EZS$1$~$jyuhk-& z&?RF4?a_5dhONb@J~lZTgRZ%m*RawkxF#Pya|*U%4ZvYx4JU4KNG$LE|H^vnxTvD% zZ+r;_q($jQKtZ}&1p!4`O4^lNLb|yqslXzVk_ytfbcYB_cX!tUOLzP(e!idY^Lt*; z{fF0!z30xHbLPx>&zw0kSA9U;>bdWcEU#rJ{6oEGq{#YwnFX_ERaOyC{b0>I%N+@P zhyAx&&JxvY-Ze`~&d9K0G>Sv^G2ZNz*~N^p{9ipI3D#~J#LGS(=7HIK372gQ7T;BQn*;jYzr?QRd{YB!#9K)-vxnPpUhMIu?*A{v7JiTG) z!ok__6|c=cpC6&Vo2kXbb4XLMw8c-ixEFn*>3>BYI0q8-^vw=O2}fp_r(x66BK#0c1Sx>0ToHjlZ5fH)3|@ zW~Yuyrg4?O{L{uj@e#EM|CVUwWQt`%P*vvIkG$vG-rRpBe|eIjJpbe@83w%~$HqJz zh)Iv6zuXh_T42vE%nAI+`bw~#C!mZ@G_!Agvh|&a*YP%fDc8oxtCq0k4;Fd$D~Olk zw%_m9Vc`%D2m&SrzR#Mm3ua8BvyutxPK*IO1x_N$l@uTP*S)o(9|kq&@nTip8NohD4kpd0h-6Gn= z(h`1B&$(}v+Omu!O~=XdeS5uucP!^B3d9?QYITxaWHh8M)D{Wtqf#F; zkfxkm>uvzBk91~9YwvRCqe=K)W#jl5F{h~#9Z)-v(09qMRhs?k=`bIclmJ;^a=6@O z^OcvHv2l?t=)Cuqc5W9bd@Zi+A-I*vd@|qD-u}&quTuSl-|J*b)+1@4fU@vMzOn&> z{mW-2*9x%w33fy0iU&Hs&+>(f1(@=_+^IQE%L@l+BnNnU$TqJ@tfz+H%?p#sy?NWi z$B5O<;Q2Y3M)RjC_G5P;yR*4Rr#3^{5tP}!3Moiom`^)E!-*`%*JpZe@+@`Ma%?mk zIZ0^TjJC(6bBZP**OLrQGSn24_8=OV_e9ALD`ZF21l#=}p7k1|CN~?7S5n`~O-Gbz z!~!2WM)iGyB3 zE6`(*IHFDCe%(bEjaHE4U3*s+8*vj zO{wAZV)tEGjOo*U@j-WYWIpy&L+jo8cGP)_4))Vj`$^t7zo8QttXUvz9p#FP_;WM; zG3Mx`K)~GeYSa*9VaeP6H3+>@MP^k@loM1`c6a9HPWhIeG=QI$WAwwia0P;sDt&mH zy*7uR2nJ9UzM)EE;ws^DU-80s=HdQoNtJcR;A5_A?DTv+v9W@D+tDc+;m&aF79iX7 zSJ%^1yqB8_8zSnkAI-n&!`!i#lb!7_WhhxMeDM-?UXz%~TFmx~PQnrQ!05F0=%mL@ z)q*A^Wbreeku~;_-=h=h;$XlMH`4oCuWsUy^oqw>1g?s)SISGb6kB4a>KN2kf5R3Y}LdH7Co< zi3E2$#ogS{+$)H%?PaHD##~eLj)+4nYw|)I&%iz0+R9BF`-Y$voG^OHt6+FjlM}a^ zXT7*rAqUV!NB%O~!C5H6&%pOlz3Wl^uYfhlniOQD6!R1IhaOr}v}=gMLh_N3kk==mPpsQHI*86cqPn9lV@v1%WEf?G6uQEE}iKfg@SR-o0&-S0bdWNZi2{W4BGr8udY;~p*hO?Y!Kipe^Nw=?B zx+>+Gui?S4#x^=17i`&o^}q?KQ>{EC<<384q|mF2g3Hu)aTc20FJf-Q4_*3ovyLz!_O)3lf&;6LH&K>X;%d9FjN{l{SY!bOJpc&&7|`R zgTDG5PP(#S;PXOe`)d>?OYj68=BW2x2FfKlemjW%&JU4KTjL0pk`iW7JepAV&Dmx~ zDX0_?{Zr&VYghbkh~vxMHM7c^4@GNU9wLSH zL3+E?X+ZNq8-+%iuCly)_`>68^s}qkaF0S-vA;Efc6%1a?}65Z-Ea#JI?k!DYM$j= zTv498@&n^h_9d}&ub|q@km1o$leOb{!Ur`j3RAAzb!!y+V;^psoC#CsiRZ)DQcGeJ z4_hQ4r_n_^Zbzmc@N{Rqty@YEuME-4u=wAOECBtB1K!5I;SV-tFUE(?vXn&j_oy?Q zq53+=`JUpn@cv^%S9JNTRLzvb_#RgG8U9NN=g%bL6_vCw5qliS;krQxK2GumoFReX za3{(IUbP;knm`66Wo2Xzi%f~53Kn~k^(Z)yBs&1 zipQQpzAM2xX)oMkmXhapHzPBZtZ@~jTwdX=#nvgQH z8N|qGn8k5RF+Pvh{lrMt=k@oCjSd1-F%{yrwAR#RphNV9ju4bNEJtyc{=?Z|@?-?P zSlX3e%?iXNBq+pdLGh05Tsbm9x>P{~Su2|9A``Mwh5t!M`tcx*M zsDXU!?C3BxWVm3eW>-HuBM`v9J>sMTpLks-h@$C7zFzV<>Ubx_2a8K*<6nk`dJ_yF zbuIW`s=(dayP9DqGhZsICm?=-FyZ-MK;K9{dylx2B!f@Vs11YB#wU`Z88y!gt--v5 zxlc;j$+NIEOAganSK)n;$D;L3))Ij<{vxs7%{SDI-d(1&_NKO7Kp~U|TO7<>n9yj` z$Bo?6H=Np*Sk@mxM$qA(&89$itworYdX8VWsJQU#Y>f-iUc{IK-u5dLqx(AWr|KuR z$^^s7k4qNw@~*Z9T%*Yv;jX!!-+K&=$6{;}MDMLo&gVy5)?G6pPf}IsgSX~8`X)l9 zUEM_eN_`!DUbG4U9#`(Rq;2>XN_ey7#&(DCJa4{*nkET&p)sE!y#330%Leq@hu}te zAAO*t^&K=t4K`O|XF)Vl*34Hc<8I+#ZEl?($b{qdWLU%M>-Nq*{L8ftg?t)Lc(OMO zXw_%L;#GA$9PD6M+@6WHow%>=WcoQS;i;x|lKh;%h(`=SSNQk07kzWiJnKaq5kr99 z&4KK1hwk6;HNM@7u=5dC&V;wxI#wY;J}WrXTJp9914DdVTs;m4QSIKP9#KLwl{ZcD zBh6N`_@((9gH|#tRfYv`udR+*n_?8CxK&RW+1&-G`R<~Lo-|M8)gr^S(|9d5I6!cs zBp(zX_1+3FiFkjx1lW~dH{4W<6gww#M}AAcx6+kT(yQHr^;z9B$jVv zL`3((!^o9fo^XR^WWV6~+R!9)GC&>TcYaqU#TyEkmX7_>IVl|F6{KmHP1>yj8IH2O zoar$J%x2JO7H9#!!!1(0!pPZPd*Ow2w>bzHOTPTRGvY>~S%^6;wor@Ros*MuuUnvR zo?Ye)udsGUeA=l`O1I?0t0>}Zy!OYqTf1T!foM8q(X5M|QnT9$0j|hX|GdhAX;~#B?V&`S z>gln8+81tk2pwVp7kbj``WJ_fG(CN)J#p~YJ>3m>*WWy#p#G8mo?4MQ&4J zfqt>+2omB)ltrSlcZ1NDkHZ5haLf^RHd5}Bg%QR0haEN^jW2(M!BE zysJ^1?mvN&MUMDWL@MZ>E$hqI=tS_LeatTGKnY-ppD*`(`f7+&vSMpGxTu0=1vuK) z4k-0}2zwv$Kxc)%W`X-5Ioy;f|CF_rK_NmfgZ+oMM~QQF5w&LEL(Y+urWXk!?qbDD zukpRI%DjH(I1Gy!_iUxg?ZyBnaQMqKLSw_hB7eU`wyj&;h_i0yfG0I>(gaagO%`_pFA$LnZE-`qF z=hzvFEAfa!KQ{SFQxeNe6nAXq^O}!8`(QC{tB1~G-D*r;+Ucx^Zs!gE76l%)ip`Ml ze{6fPre&rvO%8Xg3*H(SGpzq3t7s;^`2@4-;+u4TH5%++T8B(#op( zHVL89SStI)boYy;>GhJOD26rkwa83J@BuO(7yILwaZ1aka+_#wRwV%QKLhG$K)*@! z-QTWTV4`P~J8} zpnLJi5o3vo`lR1Rx(%y$Z0p1Zue*W6fv z=g&jTT9A_>u~YSKSw@n7CV_B|<>i7gV)tqT1#b5#;lo_+*Y(VgIR{UoonyE?V$pBytqYf}IM^){ zJ3Z>YyOA+rmpB6Z{oHe+2v?IiJ4z8cp@@NIWno=yFV<$S?s_i8DfJJ~-Q43iDjn99 zO8a18kLr*0pM-a)FBa0YPz4ih5tLgYN{&fo&;X}(Y%zLN+vE{#Z4vo;w{sQQ)I*oe9VkyL1SzRy4ZgDm? zoNl6UE5g*CvnPOc2Il>ISPc$9fKBIy^$9YYbOGKf78>&zOAjx&-u5I$mX7jc?$W-% z(D0(Orv1oWyAj$~Y<|WJd6ww9?Qm3^CGnnk)uuQPd8{IdMm)&m-38MB=tN!Gl=jMNntmfuJ zZQAA?LNsq&k32uJSqTwZFX(D}%uak9tB+vA0GLGDkDw`d5gvCk!=$I)bsb0>aQS34 zpBbPAr_HCQwBve{A&_zx7g0ySLs55e7Z>@@B4Ccv-=N<=@u5e$pUn%R$O z%ktZYs$WOyxokd)tv?cnFmiFxLD}a%^yHzE^Nwv?*c9tM$AeJiC?VC~OS?g@&gFN3 ze%8_v`r7joL6MlZEcQB%SBd;`vi{C4f#QcDRGjs31-XUtt)}V7k3~B(^Z0jdg?wwF zr8-GTHv`mpTdybxiAgoLDNR}Fme(?(jL+%cCH&20Q{SYba3N-54-OP=UZBfbIa^us zmlo!j3qvaN(ke|HM%lUOaS6W$(WwHhPFkM@}T3-6< z{WL~oNu}ASPH03+)b%i=?B%4?7362Kb5uD9+_lK+dav5z3k!3a2TXpnzDza&?28l5 zgd$pD!n$bQ>!jy!{|p6YtuHQ)lBG-WlB0zc_1R@^8SZJ(Om0MKa|-bClqHO=WZdZG zvIi_N@vCrqN1Hn6zB{V+9ECzpA!e5r#K=9y!&N;MgNRBb?&i-`=_MGa45VLDA`$yo zuEeux^WXRvTAAWI=b|9e`qcpD%0)P$D>zl+{*2cl0mAPp`X~nf2vwO%JRmXlx?8+! zfZgVfA4O`sY5mo2^c=j-m2P?^p-YS?#;D$n>|-`_+Jhlbob4OV*e-o#w&>5&>n`&by zU(0&|IEP2Ai5i3RmPwez@m^4msQ2ZE^e&E_>45}p36iVp_wZ&EeHv5Ud5!qiY;{Yv zLZw}MPgiIS!Nk7X>aWdVJV;ZBQrps@N~Cac4y!d!_?~+{{~>FW$|_|Y6&00w#6le` z!iOxIekN45n`*Y)w3W?IA z;IJ@fZ;Q(eNSNHj=T;>$gY>oy4pw&;DfcH0t9@Qrq4XCFl1@`vqIF(*Yu@|3gl1m# z$OYTES$H2iTp?v%Sqc9}Ge3XYuFH92nD1|roq}r8ABs6@VEcPvVAe1DvGq2zt!-P1 z6z)|Gjcq|u1-hA?rXoO(xnda`9i-fIF{|Q8Jgbr{4!YEG&x#tYm5rrV&ap@TP6dsm z&UXo=g5lH;9xQgEuNiM~;b__jt9u|xyofzLWd0mAgA-U+Cu!i3;SjePA$^{$_iq|N z2%HSeYa!=HBE4wjPVd46Dv!%_RGDUe9IDmRgbAFot7t9M0`9cs&2sI-O7e?^PnIOP zQGilEiN(yj?DXp1D!1NyTVh&l{W}cQkO5P=u3M&m^V!G zQs;Q2oL5fi%uZ=8MSY72E<}9bwwy9eZ7+)!LPb)M7EUQ`n-f55?u4pI52xz;Z4_|R zbY6KKuFO#PX&Zd;S{bFLSApYFUOs84>uYRATlQcZH9!7`i|%)AnT|F+LSzBA9nUB# zX|>#Q(YkZ$39Gx8E6Q3&V@iqbZT>^u-93xZd>vy^$bNgr%>D(VNwk#tIs~Rj zYa6C^Y+>Q08BX{5LREC}n@$};8+45XdhCVsq7L{-Z&75Jp)y!zx1Q{`Dj?cWDhQFO zorLqK_UT*t@VF90#kyTL0Jv0Zzg<=b{?PVU#vRpeb`@0$vX#ATUb`N$xN+Du2|W5P z+>9#!ghccb9=W)ma4m^Ay?aMpSmUWNG&@x7`MaXZbNOtP61|$bp&iT^hX&3w{ zA2XskxrL9b%6^Km6UmAlA#Mfn zQkcc%RoqQR7@<`RLx1!W846r%_MU)8TYs~~ir!K<(dS{%B`M@UpDCKv%o&TywD z8*5@0V5SmFo2udfMXf=8#L@@eYK#!lq{BNVs`$@vjdwj5~ zi?4gnEEdiJQz4MqGiN%xn#KDOM{72%Ia6xtSuapAjyVq0cmmb<5IwzFDfhrb!%m@z zh9iAgjZSLA;N{?0k@o|pk!kM<%ckwwW5R|rM>@#Q;%rVJD9MZTl0@5MmCXYF(DKdH zQg4Ec_O^$Klzks@#93h3E2SZga z=AlhCPx~yu?1nBUtFv(4e&3k+rFoRtZ0tbZ+v|c%qWK~jKj;acZa5T!Gd6)e7&4Bh zlJ~}n+M2dJ$O?B<$&hIDV3py|xjuW9`dI)qi$7mqh+Fe1`_?#~F|{1;&Y8C~V|+I$ zZWBc@Rt}x!ke}dzC7F<}DIr-RF)RvP% zr2j%clbDy&fgzT$H{lBPNR;9J1?*BD8H|lgBl^A zo7snxI#Mh4^RJ}PWqjA&!y!knw^o%njNYrjrImKF&x`rhJ&O0|GHX9;Du{}B^aFpm z&3=a8$(kUXC9$>3Kv-P$JWLPe$KW-AU~#8KHj z;lcTxnD4SPELIZuTQ5vFU6N~fED$=2n<;Z`S?v-vfiG|P`7=Cu`XKbB*#+|ja-XXf(az_q}Ya9LkZ(4ZHj_*ytC=28Ap(u8~2oPshz-U^;! z%e(Wvg4m51f<4P=&H~HoJ1jn}oy4%zqYK(U7RH_1Q7NhidU#lo=4&?LbIZ*if_IxK zisOV*lmw{9R^6i5d@=<;bp;XLlG>!X)U7hdqNd)A{E=enBVD~$Q1%s|{es~}Y;=?r zKi%tU=jd{D9G+cLIwPWoZ*d`f!QVG)aqMzgh`3SHrb+su2c_dqx%nz|!&Gn5g( zq;tjY+RMwEhg8$jnD?jnn2kC|*ctxz5JFQf2HU&Xa>W)-;X*9q68#5QeMa0Ls>NwR zb1we=pk)uNOL_o{8C&rvlWCVlM2P&U(XT9COpJ`jmF6lCU;y(CqJP*SRZhKKH7KlH zrG`vceP1T{^<6%-(jbUl%p%#BbX*iay@B`%f~W7rY(CANE0rCsGJp?f;ps>!21MXm ziXloq+28caQ@`sT(|jr1NqVD}K#|XAoNtzsiS}}9;T6&i$#)O3@94z4vxvVt`X@tF zODo(gHUH1UIKzD4NgKgw&57kA@rMJJm*)<&2T!28u`y4IgjlJj3^abhe-Y5zfG##y zJ3h~?*GPG%p(l?^B#gkjgGg!8N49B@{=(!L|C7IaOH7S4z%zP(9dRY7J!gR>`OX?|rlef}=Qp?w+=6Ih!!bV>FOK5v zeurp=q8G_Bes$dh&UUGxpI&B}sts7RceML?jJ`Sb);7TO*3b|kl;_27UlsOCVewKPfd}K;S<6^{&K-25HSNDDUf@3c%)2~ zO;cMszabeM{Bu@k+gw*YdieL1X5GEPnkOWS-;oR~X02{qGuXn`FbT_oGN61cGLq`Xv zagJet&Mr~I?kVc9mBQF$AnoPk*kqZNKf@(s37_AxQ8oC;+xtV$>7kB>)Cu3`l}VVe z>90>DQD_{ydZLz#V~E_QPwZRsN8abqjy2<@^)H&brB*cDw8D=rKNG#A+orF>A*O>{ zPj~_uIQEBlvvgCx>FX?xzj%5?kzjVsAu2ogGRA~z@>ex+w~VGwC9HUEE@8vNRP5^95?&-F{QQj6n=MaY_AHbv`0ox4D(xNW zD=iRX?~do87tiLdBnL+BZAdKfwCdx>VS=E+q*}T;@h`Q2oGgc8pl)JZTrd+WE1=ld z{kgG$DA>(*uRG}h`Gk}^XXmli8U}bHQY8s@2J}0y!94d#2pwtIla{h__gY7jA%2U0 zhWbmLp8xG?`{R}9x)jXAb>Mxq!VuQ7g4Ju;aPQ@U0vS)U6X*n5tS{SzvNTV^Rk((oh$#&s1VSHmIoLc@cumLXJ~4d-yh2 zo?w*g-S9XgAKB^IylN;HX-1HC+`JHvCZP)!Yc=8_E;h5Zw@}8k?x@vNg{}Kaj80h* znBi^lc*_37o0!MEo?3E;N}a!$;reJ?R#8={rAfLzom=KT=C8i*i9>*?CyAJs;~C_s zuZ_6x-s!=((=P9|*Cuv$+_h(Py1{iGm3edeT9GFn!if^cRN2ee+(y;wcr~Vh{Q|Wo z3fc_Y2UJo6kS6wgY364MBTASUsYnt+Y1F~_vQk{Z?0v_+TEAhM?j5Q97EXnww9<3v zWeAg~+cW!GM`lK43Bc1|jDT*KLEvUr|AEU+029`^M{gDwQxr^*0uTw6A3c67I&)N0 zlJiP)^{{W}oK-%YDl~bjB5jUQNUQ+G#dz|)Tqkv}US@v~^C{paeY`dP6+G_z*=%pm z(91-zoKkbXK>gxqGA7kcHBVM>eCV4e#WTncF^_fe508%XMF2aFNH@^mN##x?e)Qxy zQUGVE%+7*YAj?W5SpYo8?vxePZlH;|aGPIGDG;qg>C;YJ5pKeA^C!fQxYS4EJig~w z3$9){Iy!zvsVVO!FtC7G$!{*^QwNwD{Rc02?gJN7QQ0{;&t0_xf_*k>FM8-?n%&xr zzs`7_`~uKMz>eDeT%4%tK3S73G1=n7FLO)v4^g}&#>5i6o`w&ZzF8*X4N)8bYFnaIDkXVs4FHjiNyY}dEBHtoOM9p!@t%`%cd<$~mZSXuO zS)M1bI_7I(H)%Cn;<#6?up=u|L&T7OK|PoFk<`~m6Uc$ixF3Z?H7KPz&A>8Ol%nta z1f5m2H7^o65^DBK22QUDqZ~%Y`6%7?UY_&Iey@4|r0X?=HfmrX!-_$NzERw52$Xdvu`aqnL4-`02lUZ(*+Xe zhbUsNOTZhff-v_BYbSXRcYn(5SzMu^5Zao?se|(Jz_{<(Db2^;-BQWb66ofMef;MI zR(~}kU&vaLQ2UFad6!ujtRy^Aun>{z$n0(4_ODgmjLyH1eUDd6KKq z7si+;#tCegqLs$PV1Uv6^w;N0ZF>|Pwpwf7ggOuCb^c(d2Gp#*8XC-Ch>NJkl};s= zy?fYF-4g8S8@$JCl8+oS@*;fr@S&oT)6HtMv{mUV)xM_B8L5jim8%QSJgD|nohpY| z*ech64B^6)VuHwSmVS#}UP=B7!@A4iH%VR_E1+Tun47+4H1xr-D3!NPwd|~j!?%M; z80 z-z>%*PCDYp8>k|j(RK7Ib?I9KMkjG=Pk1)93F7YOakPTesR4w(^stsr0S~$yK zfj&7fQVZJ^JxK_uLKB|^d5^Ux0G52?;MLOa;u@X$yn7S(N7=5$A&YNX-=^4%F-Gy!l}>ttDnC&O>6g6;&R`T^+9d z8yy!HXF-_w&E~?=$cR3G1b9W&=eImX-0dSswWKGAsHkYL5_u6O4{jv`{r%tGq6We& z{33US?LQ}i+zGLzv}%GsE#S}I`Gyeow4E$_&Pbw|t4ot7jBEkUqt;;Txzr~fJz#$9 z_qO1Rk+GwvSBxo><*?xMbI-O(Tr)E>CeVHUWQ@h+Q3TjMfb?iz`AfN6G&N|hbgay~t6U>5)V!#N@b?6xyXef8ubD%Z}OIPm=Z z{P#J6bu0s@ZY)xq<6&nVh38Y|jP{5QsDEq)LV3${w5o}YQ(Ow3u`uRdp z3LWGyvkq$5N?z&a=H_=h2IY#YXR$UHpQcw8Gmqlig%w0!g#0#)c(!i~T}4NI$R+!E zd{3pKhE&~A5no2B>w9Xd{fuM>Dw8}hQoGG0 z30S~UN1L10Pj!nSetgrs4+^>)M#+cy^5x4;N2S=O&C4B84(R&xL`=bo=}c~=TbjT>!UdXnH2xI4cGky8 znQPLaELG&zL@A%wvCYa6y_DztSmSj$toj)vq;U={MsEx4vfV8CUxtUp%R8}oAkop$ z^Ai@St<%*`i1)4cXvG|{ea;V?1MtXrUAJ_%!+RGELA&&STgh>0<|*%DJ`;KV9K-i= ziD3yO>>zg#K_f!6)D|Af!UFd?_2tC9?evsTG>+{(H8)R-E+ZxPzS5bkuzPez)ZC%c99XGpDkRm-4?zxQ~U3wTM`wvQxM3O%tND>F9XZN3F*WI!H*Gw ztEUYdGV1<9!&*6=z>x26_gyTQ|My7?;f(~GTLdtjgCqTS?%izk| z8niPStR~y+kMoZXI4kntMLi4%xI=s?cykXtwkfGVl=g~q-qKn^M%*Ix+kSFa&0{)m zjgvz9LAGu`1>)>?7tO_{E%yx)hi_%)5Ix5L`S#<-kNL?*jf)=^-_!S{JGVa6nIA_$ z6;)MLowX#xsfF&%)VN?Z2`fAjciZU}R)}tc(}jX0Fd>73q8&-J}S=D1%A*g|n?BiPtLtGt;R>uPUuXbh*$;`%`P z`cM{eTLeP{C^Nrbxe3|5uHMVleEPS>E+!^Mx6xN>w9qhAIZ=4w_SODhSg&8d{s>Mw zbiV0RN|!GvQt@I==I#3y-SMU~ccj$cUnRFLI#tHn<~KCZj~N3#atA6u`0os5Dr=@F zt=hi91wM6lzV^P@uGB3xCzQTA)O@R@WqP{1aC(IX7@|%>%xF;)rR=wcESTj^U{u}@ z+(E+12Tb$B381!HsBw)GtkSwzTG$eTn+nE))SVLK5`vSe5AiimVcN{rQ8iv|+mqsA z=dTIdO^1NXaB;hGuC0YwCTweK%bZKZ2ypots5`cOy0=ud602{}r}Or$Nym3qFcb(+ z9!~qkYo}VlYyQEDCiF?LH!+jk-=iLL+b`W254ct<^<`Qh|XTW88!_PTUwa$@OWOXVQ9RIpFOp+I>@NjRx` z818E;WCR066r1vWFss@ksFx@}c*RVD8&Xoj@35Ba_H}SjW3OM9SP1r?2}!rReP6)5 zn*{X3-w!6DyH7|+c>8`r0`=Y=l0hGR0?Md$oG}>A_?V09A-e8B<*D@h?pBzO@QPDq zh3(TMaJvivz|T4yjp&3PPmeVa?mSY?i$?2iy1ToQq*v6-Co*bWz|tF(dOkNy!xNsnRUftKnklC?|m>c^XQEWn;HkV zn((>HRS`-7a{_P`h1J#7#RD;PU82X=7ifJz=IU?=TyIFQL`h_9Y@064Qr}7-A{HS$ z@PGBI=T_1Z;Rx-bj5a>zugS}yUmx%>VXyWi;y@-H!uI#~KZ5o&CT|;uHT?EuL|5_c zOb{WBdHoHjyx(ivIVrXs{Rne;{b446Bwhk=|SsyJ(bWJ(vl^})` zTaVB!xs z3j-z9h`+G!Lw?j3gs=ziXhq=_L~%wpF(1XRhq}W%$`%z%jSP8Q;T~1~!1Z+36!Of} z^w*jpPp$I?CS<(9$E)-fYP7yc+pGYgJ){joNJM1Jso`V}U;uY4?&F=A(92720(wbG zaKCQ(#w0s`0Gz$oPPG(7x{+_MtqNx*$N~zK(#iPJ`q6!J=?>B{G^D%q zZM0B}(E7Jz9;MlV;LLqFQUL;Sn{m-;_o%d+0j!l7B;84Hi0S_hW+A|5qU?(}QzmA;a7C zYD9O+V2%RH1+|qd8y1z9L$qSs-^(j1!XY`|e=H`7gZU}sv&+9z{rA6sY7{vx_Aa=v zZ#62oP+LJAUUmQmOfJABc5Xq)x71YP)V|={T#kIb(z^hNRrXfmj28zK7+S$tn4$F= zL2b##(l%c^J7@){!!aR2#0+pSsRbzW-BY@C|7Q1IIsL7^NpIDqp=+C5Zq}XhJobMZ zQNDK#G*E&+Eam)@WiB>%O%aXUuG z2m|uw2S4kwH>hY@m7Xre7sl<+V3PsxtDmfYC*1+#u=23-z2ScfgZyOn!RRkp9LO^d zkLoo^P#Z$Rn&>xvRQ;Eu5s)Jg{46d-$h5Ubvh3vmWqP{l78<-0o_(Ld4}IWBCfyI;igm zVZpY^{{JJ}APUjmba6P#)Ra zeKANiO05;Lhq8e#kK1Eg2(Rg%0QorR>t$hb9z+iI^4{-eUl$?BeTuO0j>F#(fr|FcOzot>XZf(gb; zJO)b^!9jqsc+z;|^M<+^l;}~6F_by^UUc>m)uvPBsO_5y-z!f+LBXFO_GUr@Av9m) z?EfvT)yt{drSB!ldd*>$p)TM(#wTlhP2us?in>okhUK;v+Ujka;x z@s9G3wgE3-;ZQj+V;WO{qBCo@Rm0`J(wkIi!;5zj^isQPGB;Ep)GQn$=P?RHG61x! ztgP<4%{WkPJI%Q8N?pL%>tv@D1UE!YqO#Q1HYK<3fMQyCU6`N$m#cF6en|t?U4kYo zVo5&``g}WR)r2;bs!i!U1%-36?E?}H(6y9V4M8Z~XnK2k2=(GV%(YotTYp>%7kLNZ zU}D4vv!3Cs@Bn8@Vf0e(^n|~oIgK2iCX`UZWPcze0gV2Ud4;fp*PnaTO&Ni&G#L#v(EzyweaMID!M&kr~J@yGx+6YwTdc*F19 zCo64vDhQIk8ayURoFWn9xLfo+ny;secz*|k{NbRr@G~>>E@+}s@_)ePu0PZupcc#? zaN~r>=mHjq)9C^inv2~QVhqYSwLlm)Rt_TEV_RVVAY6)q3MD#PYN1*N8zTbEIA}hh z|9@pHoP+3AW0$>qpC67qX$3jwRLamzd^!bUm%CoO_y5cNF!Jr6Il9=)9zgP?5c~EULNfw=lK%Sk^$#Jt zfAD!sKxkX&0jlf3*?KP)5?qtTzhB%@1(ov$X9=~r|4iQpEN}jiFDH3{Yv8qA?hZ}~ zgt8v+^g7x!t2Z(>j(F~W*A&#tj~_p7>P?my01XW}t+;=b-}}i5l7LjpNx&7t({RfC z_3Kv%1d4>eG&D>;I;V4+{#2k}i3tIvs(YFHmOpiT@S^6wzu!TJdTS^J7Q}UH{L_?O zEg_`Xq#5%+CNH#&03e_deroFS=+?4G8($zHyk(ZFpG;NqQ|(R-KIG$^&&fgraLEGt zsT}rNDWDsJ8H!3v9=`t?#QNbG-8Ljw#QnrsScQpC!3}1P9Lgm84@kJg9*wlL0@W_o z&sTEK--i9CGhhCv&1U~E*>zwXk$!ZN_A->$Zn_G4^gnk0`|z>V+g_BbsY)G1@1G>v zH~jdSa61B|>UG69|LItnc@K3Mt=N5tnwpwlOE58(3C}Gr;LU&_Dfchnhu+151|0NH z03O8ec4$v_W<){w41s{iT)X5y1FOLRkk$R`aR><}=(yzqkys>XjlT>fX9BBR;FCXn zrlLZ0yMRDjjEMyIQO*BMCf5H_S#{)eC+eu x#lLnM6#w^1Ij-zn!S29_8(YDqp%}iw3Q`|P6(eB+y>t-BtCuQrg)dA#|9@`C6sZ6J delta 276585 zcmYg&WmJ@3wD%wq5`xko0@B^xAR*m@G}7H214u}hbc29&cXxMp4BcH4@5BGD_pbY4 z_yEk}%z4h<`&WnQA&i1CjB=xLNZu35M*k-~PZ${Tf?w-F_C6t0#2XQ|K5v2${X)uM zU}=yLzxd%{_)$@D?~+mcjwPmg2>B8srNYG`**d;P)WkcL<=%O+dQ3P9J^hp4)Y#~f z2YktE+(9Z zq`|y%ojiy3MMp>bk|y~^PQgW3e@DhQB4Y=Ut+C-md z3kK?}V#m8siID=e3ye~{V;=q89d%h)SkI2i^qEuDxR38auC zj%vBsjMo!_jeJo}Y$9v!#+ei*O25OOm)`Ot=2Rx%B{9c^nOnj ze4Udr-8L9P2(MFTfpR_wbY0qtPYW*nx=%2YY`xtxzCl~~7$ z?$;m^@Z3Uuq%}Bu==c7_lW2_ZLJKYlNv=73i-e+|M5bSw245Bfr@Wjzd^wQ|m!4H^ z43YFWE+kT(x>mLZ({c7ciUy&PP%|qsozz+_!RvZKuCdH#n?H4mud$u5kn8$>6TwAH z{3A5T2lq^T#ho`03ECM!?s61*Pvu5>8qk(?kDS0DIfPIf>$+!?%cp2elAuh; zyQ-l`vaMLDwb6z9*?W&X|5R45gK@PI}Era z<2fAmf;eBEn{9``@aMh2Js=05Yzo}(unuV?2}}2dl)uzJYDT#JE^r^!RJ?2 zs$4OZDin-CS~r*dP$d2|X7R6auymWpB`fZ*yC?p%Rdg(!UlwY8d$Pi8yWZ6?X&b;= z2T_yI_H~BsGAgn*EKi^`2kYLd)N`BmU*EBJDI7NZUPu?rqnPNon^jhB6Ay|Y1m52j zh+6RgouR^EJ5jPv8w2Z%!*q5vQW=(@5ee9%wEE6T)apGgmJK^zYHDm3o_%b9BjB|D z8teb|l8Ok#_jEOtC z(Gyx<&n1(}HG!ggwd!%YF(oe$Y*%KFBk*`8TKv5VUWmARG?X#I?(YWlM^~0;(0HvC z?;}t|ARikEd= z=6jpvrLebse`!sCD4KW8)uz&7uI?^U8S=^VSRpbxe#5_qW1S-+y>sTiBGQfY-8imW z6F*cdIlEnhz#@JkOy#usoDL!6K)Da|Twj~O54+8S>h7C2+hU=?2xRyiM;BW<%v_6? zIn+eChP2God;1^zqWcPlhOWcDi~k8lE6m{tFeI|*9A_7GUFFM<%{ zQ*B{29JBJtsPnFb%<0+?{7M*sj`m{u34oI!z2UAYYZsX-77{Fu3k=ltv2F@vqH~ z@6n0)-BFGg>$+}^mtY_uqoMeoLe(OLCON`Rf}x8}tPA!b_7^y(v^ks%Y<}yc{TA9Y z4AU*XyzA*){&D**5GqdhVn4q{OP?MHXOeNfwCO?b{sIf+BNBk5Kbp$@SD_+Xtkg|y z!f!;0fvju7vl7|w=>3YfEamY-X~JPMeCgy6&YI1FABWXqA`zsoP`6EBXCx(5#4eye z$e5`&z+~MxfO3OMIvR09Da@QU%6=?3D?yOg89zWcIrPhiGQP8)zn}w`S#mc0hv-gd zT=+U;&j&?5cAQtozYojWJ`W`@N_Y;qDyqQ;6nv4Ut_jnGRX-Dq5Gz%Hd!7DG8w9Ri zhRCho6U_54k|8UI$2;nc?@O|XlcN1VXr7MP)6Oo4zGV;X55e#3BpA3`SCz>=)cAo~#v`JQjmxQctm!9bFA1J|wm(e1B z6_r{TL^Uz{GNX&>+pFt7^5y$ zoBcHCi+}+g%$9_14<(Sa8uEdKbHb>i;F>g(j;nPfLfTvGW<>T#j8b+h|2Qn}Ria!# zw6;to@9A&TENBtg>0l)aqtACj)AjDbx+P+}TZXCp9@wC$s3@OY>4dC5mdkL2fkBV1 z6lDkw@-*^of|naz5)oaTXAi%4CU0KCTRfa$LFT%4Wmnhc){|Fm)3Y%9Tjg-#TcZ-pjobLiVnJ$KOlVaX9XGIWl; z>AUY8nF5*>_WwQabU15pxibRt0qQ$jv(EZc^hilC(q0GVU>dtqWs-|TnE+&ACFzrR zzmllBUFtytF$ZcuNaDow?t@afp0G?B3mo}ReZ$tfP2@1|4LAZko6`|wE;&4V2BF>u;EW6P#+bS3C|za!RLPGd9s$U{jf+E`*gsX52jSqnj;CL@ilXrwNa z@gVrHF3!U>!_0t zmXMWEZquS#hq;@UYKxDw0N8uv@t8^c0U%uFE79FW0N^pTU42-U#d1! zru$xrNiG$?HBt#moP|WulEZbC9@WtP-W6j$1SZ_40Gsve4^EzSW;sc%t>3C}Z^&GH zm;>KI`*OrWvFB?na6p-o(O+C&o^F}-A6+r2mB?xduQo(~&T*Zt_`)J2>k>z@KQA^R zSj>682T`duvRNw&lCbGqlkCq!Sgw`9zG6acMnp`67PCe^;0+4Z-Ovj0rUDmuL<>e7T zY-W>hGdwRK?*+OP@=3ig&4SWY;ApM+vFiU)rr|FO&pAl^H~_Ww$(*7CVm z=4H(({Mx^kF#+|9*c`jRWwg>^b^msd)E!Ra7x`UJUj(rCMMiA><*3dpq_N%TgaZks z@i;{#Cx_@zQ%8B(Ee9CcUep@BdFNKlS=S1Ab8|$TN$^#zb;|{2Np#;r-k&)k@E{GI=lZ)K_-ba%gZ|Ix>!+MBMx`0Tdzfwj(Nt@X|f za;T;(SJ*XAGcJpVqdf301Nm7*AYptW5Xt*~8|Uh9Zj8?3@w{)M(Wz)1i*Zgk$ix;F ztW5opv%?m>L)5lMxwJ5Lp;KgZ!hZYH+f(}Qc?TEdZX+4-lJa9hWjZd&L{jux^-MdA z>OQ@5oD_Mar1{iB9%ko5^(%_ni(S$e5Rx3zKdtX7Q@=UJAS|A?J%ub`*mSM^yMY1G zs1*AzNDGK>55`5~khmXT7|4F=!%MwU{eaulT_&m9PmBj375t z=OIk{-byQ7>Ohrxdbrt9fS3Irksefuh`5{knp9K1=cl5{{d$L%g+Q!8k<(G`S7!)eZf6EcNFw6YpuDos7ry+VG7@7S72bmnP!AqrNxmlw3^K^;lf z-{=f5#sCg--&{dK{lOru9{rZD*vdL8yB~ID#Ud4wZ=(kpYSUKVf+OEM{5?~x_UnkQ zyvjqu2z>!dsjQ27IQyl)?aHQ89HY2fzCCf532)JwU}T>Jnu5E1A-Ahfr;wM>A7Y_! zWz)O^ju4pyV|V=T$nnX;!~K{7rOrT;R%&{|Mos)Y!G`K3kJ6x3dx4&!`FSkzQ?RCW z1Dw!$qPx94%gyOp*)bYXX@?n46FX?W0~O8b(q_Oh1pIe>JWF&hCe`xuq18k-oHzF2LWK4aWaTLEXV@hFJI(Kf@kN2!W<6@Z(ftry(vp$_yr2HP_F>SQEa{!$ zq@O9)&EGz1tEt0F{aNVI-?HE$oMGRl!LS(2`DS4nHS=C})n>_C{Pw+-9n&!glxZJV znAl>;Za!NO+6)iRh3f>%<@A8Ln!@z~tgB6QyX?St1Bbc<3DjQ)7(-v9)1j?ui+e3A zTYMEb{^!KB>vHXGt#&|q@zLmC>2tj>{l)2m`sc>O;WOnxeZB5q8;LwdNFkW-)Bcjq z%JLp2r(M%?hMbH)jbpiPLA`tmx{bNu`{q4Xx9YA(-UYXbss{>X6wkL2v@QISOXG!J z?N5Pxdcz4I1{2vmR63n3C+1yei1ul;P)8kAwA$~K094%Qf`|LD0z>4603RI_9i1Lw zC1?c)`puw>t;$%EmpjL}p*3ihR<}_w-@#^O)<&Y&U%d&hYx33yMkYgm5SGY$p088+ zuL4Q)^TXlZQhwz8XXqNWGQO`$-j>6*7Ka&%E!z>~z?B!Lf$R*ii+pUHaC#g+@N7wx z`g~zPep0%L!F&xW}<)+US9;_0LI!GRBEpd0A@$e;W9QCcG3T!wBu~z@wte{Mm z{#SnNqwcb{$FR?7AMK9zK7iVDXDsv{I6uo&b{U6v>xrj}1>YZjZLwIstwFOA9Fl8%2pWP8Nyde(- zNxFda<#{y`RDT_>*$`5#500nfvB#-W8VET+=F|7t6PK1`&|v;?&13h+Yf7*|2#4RY z(~y5b*qfE0!Me^GgueA$x8@ZJ>|qj-lGXIz5Ak%`As@-5?5&uETMtx>6iYQ@)Vb40 zZYB1!0FLfCeummCkR7t-{RD>tVMs*)P1}qsJd6+bL|*QWL4zqX+7V9MY<9Gr7mR?< z1z%bKk!9-B&GXu_4kP59eWY>EX!TO8wOqix?e9$8R~~k}UG*}U#~=bJ6sl0u=`KBlMjN;s1wFwG}v8l zB4Ce1n(pzQ$dhTH`Lm$@8$i0m?D}jZSZnj|9X~H+Qtt|W!d!P)z>wlb%nR`Q6I}zb zlocH8SVWL!BwDrNZUB5>Cc~rR>z%E4?2Tvr4A&o&uoBJ=fHHwO zfh+i|tx~K$0Uq*#n4SZekC>VH1EqZWTacE=agBPdC%05Q-F}b^P=OMq{;zNdkL>AY zx8#L&3Or+@H_64b<3FcGEU#2)C10HKf?HdyajH9tEa{EY40-Xs+M8H;Oy&WRk&|yc z-BNx5Xn-rK65qkF#~UuUUGvrbMYlJ5u8n@2$K;O^kRdlbuEu;@28hgH-Q^~nq`Xc8 z!gL(r)r#Yp6ne~R^T#VsR~~{>E^T}v4GsJ}nKUHg^(^kQ4PPzlT~+vnuA%I67E4Tl zWIhmAqBBU>^MV_&}&YQWw>!JGEW!K9juw9Xm|z=c_+vDSi|2UH}>UyLj0l5^ejAYnYK2?_5it&!e@1! z?}e8Ks(j6vslQzhkL%7Zh8R=0%bG8cPMR+vawDV$SIRTLBo}gq28O`>21=UUAE|rE zO~_EUeQyqHGvJ^n+co9}51rdox)n<42{kEgPuQW@^Z@xiJX>*DOR{rv!=zFG9WT}O zdEKrA?QpKi@G{Kvnu@;>nQP281J~i-z6da(ton++~O~ zV~hW4%PC5~cZoM)|CIa5;?91R!`xJ*nsJ(O=FY*x>0|eB@*l1E(lk@ct-6-o((cKf z?%vr9Z}Fy^U@p5addnrcZQkSc1Z9Krnhn+yETwPQuTr;Nt?>l`=-40rT5QW*0kt(& zhmpn)g%8(+-=m!A4m1)>rr29LAZkyz_+kyx@LS&vcah z!i7h;#V6aOg~AdVC>OLD*77vUl`2Sd)_J;6MOmm;V&3)=9byPAMwf%T11$)2Lfss8vmNYNM$>qMUIjFRp!e$Kwj@9$;h9P=rHpeKZH}W&nQo3| z>e+ma`ZkuvANu<@CZrf3?LzH)tX>a}Y!-`QY~wMWUAWn!5-!@sSPYs zi;;&i&*C_5-FfVsP=Ajka>L5=v(MAHJ?!V7xBo<=A@ESf=EgM@HM8#;o7fMp z&X$>(I`d3K#xL4ft9|iHPs0#{o@8TD>1O>f?4nQ5TWP$;h44BT$JpS)10;XE#sciv z^yK`T7A#kCG*gO$`s?6NRGNVov0f9uElJc0u0u_=lDM%k1vZ1a4fM|Agr&r^TSYLeGl529D3!#@n^_L((5-(p!ujcTFGw zqdb((fZU3X4a?z#+!b9Po-zM!r%5`51d15E-8>e!Ylp43-FO4alu2TFosAmRrZ5%@ z#;Mt+btH2~m3M3|`wX)TJqXpk8wx$x>5dSz5r!Vp;BWcWr-5z8(p!zPoH?2%(+c8M z$j%p2dGHnC6$uA1U)>+?>;NH_xqqFebjdGP%ZU`&ki9|IaI@GG6i4c&U{p9Ym)Y zcY82_bHJ%;3cZ&rCqFVFsIcN~{qm1w97_3Ap*rgofI1<8`ZELt^W@U}ZjKix_>Q(X zGiC?$N)X2^NHuOZ2jEc2*S1j7<`}-0GNT|K@3}#m-Puo?Y<%3jPQ7mR_r~?B?Z8#P zT^G-q%E<=^ypzT2v%`CnTc6MWL2M+y$#qYV2EnNJuGpvA&21*k^%{fTy?bZe2$0d& zW!qT{#hXs$p#nsa@uNws@SKAe&gqZlr1fkN7&XvjoP7TsyvbN<2LS2LcT#LN#{^$6 z2l3g>{a-o1?8oG{EJs(mJDnDDCs_*uwwi)RCMH!aP|sqPHOb8zkP;>OvA&ry+b{(H4j_25dQK`9qSh?J~8c(uP_<=Oy+VDX0`OjLbJmUj@fGQRlr~_ zYZE@(_?W_BB?9;lBU7+|mfitK#?Fcm1HCcGiMHf^>aVV#_iqrpm(yBp8@3xfqBDhm z!Y4B6zX7UJp~(3-^dU*7`0jE{ka_1oysz|2v8x02pWWu29Y|<|$0?c-qAL(H z+M(Nb^E)EG*H`eg&PZ}JhEIORfm4BaDg!`2PycSa0R3U)acj#^#`BaPlU5CL_E4j@ zrlV~#w_1iElA)8lU%OblL0h&pLLmFmc75ib&k$IWYV2!%?T4PZ&G#qJMGh;z>Hd-Y zUW+*G&K4a#5yG-L)-n)uuN~EDC~kNh1Dt~ zsWk^|ll2C=xIN{DWBtgAc&e3^D2#Zl7ca}>o|3KIIRM?I$D^z+N4%1 z#x)*G4~Va;CEE?Gn?K;OeY~h<{~~g#T`Bp3dA+Lnp7=M?YKlc)#M%Tm_rfEWGBASA zI{tj?&tPmjXdsZRzufycSuKG>t_*t-9^dx{b%K-P!@=F@iv^Lpvvp84>^~TgYLRMR zHQbPYVv{|uo9YfWjTSDX<+O2V(Dsh7;lN}O^-?lTh_o0t7=PRN;C1t*6Bv4`3zRF2 zNALq4lPyX+Fyv80UR_*|5Bf@bm>4>`bK^Ywdl7CM#4-Zbsm=pZSrF3(;pnFR=_#&~ zchT;rNGv+nJKNA=BCe=FpqlhgS6lv*BYyy{PW_)ra!9pI&GmHL;MVUib0qEI#ka4O zg!F1+O_xK#w6m(mjbIVBB6xj+W^&2?L!op-NAe>>sby_#O%?6!7q#c;9Eovq?(cVr zi>zaOKS_X>=RFr3=calSLs%o3d{ShD;DBV;mqD5U&7P@B@&O#S=!ovBiaFZUFuY{N zW5{565lz;?5~9FbfWFurx+XEd+eJ{`tu*k&y4X?i)ujsiG{p&g_tB)NG?;}Q9`_WB zHEQ326zXk1kdd!3P>l{+VpHR#XxKehBMU0@OG@Z_tOJgtDJoJpT5Xu)Q zM6A#btN<9sac};EMC4~UbSC_fWPXAGB&^Dn`NN95lzQ7^BM zan5>hboBtQSH01ZS}u(zcf8unf#9?I9!a(7MCT&;JPosOPN|$6?f3Vr6TuXDQ({HL zyyK)&Xv_K-6gc(cku3p6>#-2?)N4|Sh)nH&H^#1!QgJl;Glh|gHBzb@5zOD_l`i6f z9?xYs8PA8Ew>W+yzlwnhul~(fRLJ8^QBtXDekpB$&~2-3)iUpyLK$jG^LW6locPx1 zLKDKb;NbZR0|XE;1H*fa-bi3CR_Ha%b8IY{XQ+P41+JL(E;=njzU@#o(`4E6bE$`C z8yz|6OFHG&OF3TPc){UIndScLcxzA=LE!_~{TogvqO9@)=YNFAaV@{`syQNr>*2ze zyLM=4)uZ2?Em;K9+BB53u_ziGERCHvpaNl(k| zYA9pd7aLK#6Maax*Iei^w21KOrl=?40SLheDXABo?ttamCs8VTjUM)PBOQR zGs>rs_2!#0?nfUA$@#zi;tc4G!;T|-xE2%ty%ER-oWEMVpiL{ZO%c;Or3TON)o%x4B`2&E7#-GeVduwfiI&RxWm<%dhfi0KOvHku1 zLO{bif4W_R1xY0^gaa;8_K%(roMR=(&M@BcN);{Sm0bhGT?S7^yBu?dv5Bvj|MJ}C zjR6X;^HIA=hIdFLxU6@vtd&JjZGR45PXTZ=!eY9*V3Z}j~o|OMnF}yF2D>AxjRq+ZtTUeRAxjBKvxsi}N1x63l zeG%8TLtCX^Xdr#i-3COE7%(+2|L%|{X!*S^c~uoGa@HKPLNLJj(&&kJ5?wvVpcu53 zGf-C~YRp~0Icsx%a=+1`47TO6g?hD5%*RL!==PKZCDWHsl!P}8QKAxPg!D@3)<;Fm z=~M||-c7G}Y#vW?x7mt8n(O?MjoDf#=%M*9UXi-qt4N@~-V59HrQcX%6R&8Lm#Oxi zcd4WFF^5G=`%*a`MGyler6=)}lFbvOkv(zSQ84Kg&>35qeUhD8zfF0gTuL!E)01-ef%p>rb+=s377MPkPNVd1W6Jd zs}o$tDw4R~5*X9 z+2bVnWjwu+B2sS^@N{e?Zckn%1|Ol+CvxD)dPANbN;}U$3j1VV8cKBlJc}EBdm38< z^jSor|E4fH&n!4oPgteEo%)+W-KfXHGhU--0Sv-a;v~^RIWj1vZ6)mew{JTW<;m<8 zKkh4y{;G{UggdIkkEqG7?tjqu*@X6YfQ`e**{Ox9i5qEvg?2AT2UFc=dXMzUtuziY ztJgr^qV?IwYFYbuL!>jx;^#iAaU#+0`jf`P@oceBVj|Unu)FkVWxsK)hC}YuTkE>t ztB9U_?^RBJ?fl>mB=s>=4_ps;p>KYBI+@XY8PJ-pQGGxCu(@GQ zZROgZ;2KH5`5t4{W2`y-aU;kyX563S#C`cpvzAJH^59^QXfbOVhC*A>8NxU?p8_z1 zzR&eI{~30_LTUYP^G7;nQ1R`LLsOmfNiMSMM+hyxx*E@`N z%EcbSyIbAsll6xpt(OqHSymR7iGQU6caN7oN60P1O@PS$ZlM&di9_x|w-2edwEKBJiF<86w^0XBZW-z=)>|4uoHKirVA zN?@D<&jnQvXt0t}GHj7YI!@bhKqrTLB?XxV-Id2Q@Q^a8!_mJ*(AQof5z{pDiH%p2 zk;w{CVvW-pA&Hs|yxpgg|1lOUnz$NP!VZ}UN0_TAeVwfP` zgB@tn0#YQW4oT!y56(1dPqYj9e*kJ8h52@6~nY804wbxeJWpV$AHG8`< zJ`Rm$$k^eWhqV8=Y_LK%v#h2IyMzQ)d?fw4wU#PqzjO zl|~?+H*iRLv&G*{y_FsIZ~ofLvu_PfD-9Z3m<)J#;?hlQf5-dJ(hhpnaIub7TBc;c zq=NQQx8+7d*p8A?Ea8x*IV}fNfLr^r7)kX3BDCjM1c#krX2CDh<@%!4)?S*Hykr|t zBWYV2t{08!MpP^swTrmr(6_)An=Vw6_lA4myh@pJ>3r`%cRyW+YPE%9Ty=&0w8Q@86k8lBBHv%y+NiPd6HxrD$45Ab(`M^ZQq zfD>?M3~cmb^}z3Ap6NnWWZ=7fBb;ktJ0mtC017G=(S85#7mJ9#tMPCm4DmW2o7sh7 zwHah-mdFO^w)Ir&1gj2}re{isKLKzRVcU$4%YzM!3z^?L!GG8pg zXwh?%zC7!Enw$#jd8zg(to!Czwx=4P?CLf4`no0~xzpsFm9hvWMR2xbHWWFAoa>v^rnTRaeq8CE*5>)C9VWoO8qQE$IUl zI&ri4@pQwO;@rg)P6vD)Fa1ABFVknc(aZb+pE^9%;YoB)q|qBxXLpd(5J3umBtJ0q zMr~KnD}^Y?cHVk17@ue>R zqNDqnl{)RJe`VUt>)o7md9!4bqd8|CVrirVPp<2BmcQuw^t-IceVZi?KJ#)|-$-$* zy7(IKR#ts%AnQ*FSwNhmbkEhM01({ZKIT79D8PFB)bRl+)VQO993M4v3CXkKmD4pV z&($*PEF|)x?{Q(eM1K6pHZ!en)Zs5yZyjbk2_%Z|7#v#?*A92aU_i0uwdU=88~!6Q z8%2d1CT&BQvwYi=6R9{bBg-OXXdR`KE-{_egyH!Saw!iIdV08?X!ZI6j*Jc=_ji1I z3pV+%G1=tu>}xX%K?MPU!jIP03mR-c=BvkuRNpH$#Cc7Ka8=deQPJ!M~okocF-8sXCWXj+RtfH^05?#!fRhCm)Wr&o$qEoCz0KPgF-$%3}}Ig zWFC;)lb+wy%1lOsn5<=<_ZP3})R6B>0{H(wOn^lj2sAljAS*{HmkL-w$#{&~K|t6k zTeqz#IyE({Gr%xXTZ1FiMXTyCCo^`z4>#6`qrvOXy+yR>#n?zFUBhxtPYB0RV8sMD z5%*OaZoJ*o?ZJ*Qr0oo0_OA`S#|CjKgtzUFRZli7qtL=2cC7}g<^riC{>4NG#dGo|z% zNhM{{@}G{8n=K?6&ag- znodvh2k(VMXkqUi$r#V6*wqs#FPTwD`;1-#$rtdRu%~S(04TTn@1Dg}xyNSrJ3~W5 z(66b<)0Ha7kc_TINu$D!@e3t^Huc^#LXQXd(l4X8Pg*0YEPk1nNW

C=4DGG$%mkywUbeoX*TWI%2ySlqx{RM;L=! z7;1m$8>sJ~?Oc|rbmIcDo37`%3B!jbfhZp+V<46`o6lI2iuENdW8-aAChDtA11?uz zpWnb8)vBmzkMvHI96?DwVO=BE9mmmq|7d3iv+yd$8yXpZNvBjmDPT#A8)G8_nL^98 z5-ftUT`(fS188>Dt}MA|Fqrg8m`^9I1h20)%NICg9ENUy8o%;ETESf)mhlG+VF&;$ z*&I7)ucVaJS0DzbpskVPc+p|YQf+QjWlUIZHiV&7@@@eI%o0iEkc&P~4a2bYhk?HD z(g-C|;0AZLOc#y#OgR4@89P(Ole(m1wul7G)DaV?6}?K0o#Q?-%$FAJbGkeG^5pY! za~v={ECUkyv0Liu-i<7!>A42kp2AVlOmpTm}q0OhB&hXzQ(ST%9@K168d6er=bnMQqQ zd7Z^K^9Hr&PWPVLbk1L>2epMRmBJb_H!c$s`_v_sf$DNC-$QRs86&{|&?p05-ir+L*}iQOiAgsO`o65d(wJe2b2Sm8IPF zM&_~+A|0v2d=wt3z8*qb+8#wB%4FD&{)yWj^A#=uS+$@N&AQDj_!B+6QLWtQfDr z&GE|5CJ)CRV4)z;JI-bQgtgC+p7)Ro?P9@&APGYt#{l4DAeP6iR-zFAnB-<=KpG&@ zqO<`X$Sz6ri7Qgu0P^^(W&f@ZgLcIustf+hH@V@wGB)+7wT$PQ zAl5Urk~}9{wtg*-Rgt8hE@izYG5r|)E>)G4wx=z(dat?AH|d`tfW#{{L!%1Av=|$T zGDN%`kh<=QA0W4>PjUQ+l~(l$NlVC?pfYAv=gz}!tmd4>wo7?;J(2K)C7o}cjbTfe z$?Dd!89QwJZ{=lFxv_Y^g;GQduVcT$7V8BZ|9is>)_Xtr)0K9Qtb%*0L`mS=T}?vq4jC_+{WwcyM62zrYlsxXqG6JEDl&rrVty|=f$88zKc z49HJr>tPGNjbgze!BG+ze&R2Ga0f z|6RN^c!p1Xb}|I-l8et!x_!JykODIKa{sI*@39%QOw;vZqXi+}Z06HINQ1Kv zl@qe;0CJDday$UtWpd!V2rC*v-AdJlJYPp90O&sCnU?@Xr(P8u| zG#K@C!<8+ci!1$bwoFt?D#Xp4gqs`Brup9ZOhxgpb`ioK^TO!4=2evONELqHtdIK# z@17=$IRTV;M)F@+8R_|%7QKz3RfQT8LaVbJKifN+Yk1h2I+{}IkB`rrEHpOZ@92l4 zgBt)O4c>r|u<3pQ=JMts?ZKN6yM&2&YFQMWf3_%}N|LHMdHTQ6hnv)MA{icv_kfmr zpxdlIzrHefOn!h!Q4>Z%ANI&d#-`66y>V5d|sVE+fqHv#C~aPIyo`7dNkT(qTn zm)WEjlXZnYx31cyx@?Gm|DyN{-8`7Piz)${olWlGsE_y78eX<6yl?1I-}e0+ajlW< z&yeWLuPl-1f{nIWooXoBg~?5CisF8W?2M;)`bMVN%_%QyqmlCR9>0A+saYQB9P40y z&aRw5BNv0A>eGAc18asb&Pom;$8|^ATW#U}?0O~~-l67My0w!b?*Q$xW{ZZ`#F21@ zgQW%k9W%pgU;x^eQkI3s-hy$`<09eazLRf}_hr_Wc+ing?6ov_$2_a9%CR9TwsaB(FEM6v z=t`*=0vNKZc%1J8uM9s4RvQ=~W}@UYHu_q`N#eMG=T9zK)Ta>Q;{vke`9e?&bYVat znDimHCc|R6gR1_|mx0=aj54E9ONvF?-xS(J&vqyS3+T+!TqTstE?CG#7uud5XjeTi zWrtI_F?Pp7iIP}RV1Ze=H(fxwSZ5PC#MlJUG8DnBP5q$Zu{^~S>uR&cy@V#Q^+dc7|7>%+w=oR2gOI6_~(84B3q!T|jpIRw%zm=Iuy`L+2! z)Z;Q+JvbgM)*yq%%k{goa6E88*du8H!}iGIZ|~U4T4GJh92~uP(&%{y`;1>EC|RzA%rH-3EKr>Ys32dYMt}a;znke!z_iG( zV7pN<={}}?z+N`eq+KGBNHE`~6cKE~a5}OZ&o`|+_8CH;r46TZ=i02iC`{*ZI_QXx z28XkXra|Ov#*!)sfidV+uW$i^R^JCKWRa|dRwtj_kcE5B~;_k>~nCq;q zR@Di05xZ4#^9?q;TXKO$*M?-21oTiEnGmtxO?f;bxdn=9tcf)WDrpBreSnvTHRW3& z^^K)WI)ZQ%47iXF%~;;Hb#{>VN31HhsJG`xBrG+FhhZ(`SS(y@y8;>fpCkBpdpVH` zz|+!8@%*P2$dEx7tNfAP%@ADp%{>f8gdvI1!gA;Z5lDwa2}Zvx)Wa#_q^Xc z>Zt3KKO3yYyZnjxc4`07XEX&Idt3^uAHta;3K4lTnko?fdXcrHZdJtHy)lUh2PbIB zri~C59;Llzj-y&6+fD1As?tgUV&r4zq;J}`?N>f_)n&JnFJ-jk!d!00=15v@r;S`C z`hVG)i(hs#l=tQfFrM$AXdquSV@A!^PexJ+(OX62&60Rjg3bsFWwEio7CVwTgGv-z z5bgr}*T?+9?^PmVzIk~{NNU}Rd6}nm{$nRiKGCzvZdTo^L#xYiGdKr}AP@$`%wR#l zeU?CKB3WTT*n{l})08{zWRV<9F0q@7t?;=(>ZJUk+7Qb7lL-h#`dssmx+e+F=fk#w#HpvU`K3>Oa)cSG5C|HRp_ z%bjiABO0V~L?SXS{QiA%8~jzD0ADL;bRw~@U!Xr$#ze!xbdEu=DX>`+V)0S;L~&Z2 z@`RzkzNzWCM+xlP7w(x!-%NAz%hNYZnDiQxLgzkf-A0jp0Z zJ!wxTHG8>8mCWD?rD1!yLVK_LPMNKDlJ+t2Uz6>n#O>LfH>nTYlOI>f?rZ?9_@rU? z^U-R{5*@8ZH5EJxt`8)L$Qu!G>#BKj`9yHWrd2;_b9(ZPH*OG)+DuGX6gF}kyx}+u z&KqG0ae#Mb(O^ZYq^J9dZ}8L^fV`I!<9FKj@>yv8ckiSTU=K=R9Z#0@yNu-IUrA|G z5i&)Q08I^9 zs6AAFCkzj`^@({KO}H{RSoWy4m^o;L3d6HKVYXlU5Z8p-AZKfoJqZp^uHY>0CA1dM zADwWcFlAW5Wv*tGF{Q8t95KA+t4V3&o3jm4^ovz26a=Y0u`>@LctmlSRZ-vlJ;KT6 zTWd&Nx=Sb1l_r&MkhS^tcUg4~7<3BQF(L)!@AE16B4}WaM~&m;bJtu?-=Osof;XHm zcyrV}^Fvb>ji>LdImVqy<>Y9LnaG#MYX~L~R@nNnpxTtA!FKUK?OaZqtLpGVE8H%3 zeD9!>EJRk34B9L0HBv^m`mE{PjJyQ`pZFlIpxf02Sd+1IIMDL^rW%UdvzVWbfn?N@ zezxLIqMh#@a>ho~f|T+;ZIl;hF{iVX9Q9kGIhqQ8ih+fIDZQoKbH$&)6k-umc%Kk~<-qSXDz z&jL236#EQAeEwIC@45G%!5GFi+OByHl&-?g`_~&lun6zj$^h3r90(g>KVR%afAsjj z!?6ADUlqu5uJ{9~hVFWZ1kG#z!3xCzfsioie#zAV_k|+=>-+2XfCb}rX$%s&zt}$C zkRXDSJJj-*ehxt1wS#0}9=*`oYCtbEYR+f>DMz&l`G%hFJ`mg99DTn!Uc!-%r(?c6 zm??UVSLG=`+G8uzM|HUa_wa7+B1|ie>ZNCa!4iPv41X!=HVL(u3 z#IZ*zA&RZJjAY+sZ#HF)Hk@R~#MpQ<-QxiYj3Ql}_VLk#)b{4B&FuK>=lMTOBJj%6zYq zxgsMy7#dNYVkzrzM7tCkzB{M+7kQ5Ivz0MSaI;^iZ}k7sbkAfAPL?Q`Gp{d33Q4(>gA&OXmxYt1=-YpS3V zeP(%-4~2QD=3Cit`7phGZ{6^>LESREz}pVRObE-v=2Cy&C*u`{>n-GCNOs1_656+V z;ObAOQeHkC7^Y(!Fg29UZKK}qhfCS@aZ~%-z3xY%R0TFa&z;!46P^X{)2i<;G04ss zYwZ`2V3BNE=y*jVuihupPl-AUmw3L4k-H7Z{r0jazWclTB7WU%`4445#@H~`Lc;WN z+VLNxuUQ7gm*<|sjc5>XV*a{CrSyieH6J}fC-^YYhrZRVxug{4sfjX;b#NPX^xg%06Pk^#>su|m_0 zor&pue5nc5v&ovoqCfW~f_#}pcEDalCF=cO5@>BX2obW5oP}ovON{3O1bNhB)s(#Wklqnc+*QBXS&O}*a zB=)o96|l{z&3Rz|_+(=`yzZ%2>L-LpQSBJSH$9*E18@0ZIfhN1KnIkbUr%Zac1q<_ z;>Qtk9fy+Hhtfs6n_F9E41-sG+9(~hg>^@dGYu!)F;GK+)hv*A%Sk9zUl6e&_3VG2!MtX&;jSAn2( z;%12-?(?N%@ju6Sy<_FZ&_s@)^RoC4`zZydbyuizhwrq0i%yKQ_jM~Kre*Z#RI=6w zrljX)2SYZncVITPwlA-tR@!43d^=sM_*&@aDD^1kn_M5)-FWwRjA6v7Y$Sdmk5Nks z0~RcVSPXD+>7L$QpKgr-Gw(#bK0HO5#*QH3cY!mfMw(LX;al7(suON_O10&GO~05(uL##?xUBm|bGIjo%B%Lx48osj zl;oOKQ=-ivsXT8va{XsQAK7}m`(f0|h@cjf5`7&1`*2P4+S~Piyj8^m z6Vs-+jp5m|2+z8#yN#s#0MQ1L0uPsh`ld#$MLkQx1?D^)4T$ImWoC(r-+a2 zTYRLXGY|y22KPQC*^i0H6)o)(B8gb;d=SbJGgY6mWuW-Ur_vdVG3e(`5W|?R~=b+weD&8`c z?&92#U$@pWu@cY(nUnU0MFmQdPvV`K!e->>arN%@5`q-cEfj6w=+CD&AEv`ZN0W+bhN<5lye^lRtbEj)vIh zRStSKGWl!NQ@EpzLALWX69Ji@0knmT*HIi~SYH2`ErOhkg-?gLe%NbUv}NQBl#*{H z5&Dv-7@poztmU{F_U9)~gSS^8(!cWHU0id_P&5X)fIrY!TFnZHFJV2M^O7jnQz{Jdl z-6q^}xg}7lT-S1j)In5<5@oGvIOm6=XVj~st)pr~+80U&?W4CF_-Ff{>c+`4G?yZS zO1yJO3<4v0yV#ZyKx}k$KFVZ4>|y@BeA077H2bTp0MB~i40&ja=yZg9lQO${^-;$! z+~DVHl7fW8>6S)byJO7aKe2UL_qos3SF*fthsn*|d9HgBxE(E0tCbLO$SbizevuQo zX1Aa*=~{*B$Jy%j#!;Fq{^Z2L8>u{uR+^pIn#5S%Nb?(&^>VmUluwGIBg3W?jErc& zE{P@OfujPD$Dr2NdTux0?Z3r;XkzZIDqKBK&oUH-)MMobuYgUGBS!^2mQnx%R=jki z7wvEP(u8>6hqs%!cHRbJ|Fwb!78J2`EohtfuW_|HA zR>xqP4Z^d`Y1qnh6V%u}1vZ5V0k_nZ$!v-K^sj#CK!1LnCRtQSosas`Jv0Q053Ex^ zFZX-C*q4t&Mlq{a<2lVokWN0XfBjc)A$4T!%Jq8a9nSq8s43x14jAU3z~26@paFsq zTqeS~jQQMVfF&l3YC%ViQCKjUm#iWdu=&+7M+HMF+aKR>uCD8Q3DtnuDEuaZ zkgjM*{iQ=YQ74An8%_x3wLbKT5)}{a;(0RgW5U3Z?vA(4;y5+(JJ$j_)pONhyA{cK z!$6*`Xo5%Hzuw{c__uaX9K8FQmPyfsXo-p!dcbe*7&a4oBF1}5W#j5{UiDMP$HS*> z3L6{uoWGOvfjJ_MI4C+A118>ff9C@Iu}poOA@!gWUNMe}*7M;l_8^<6ehl0X4kP7j z!qxh!rSs8*g7{d7_6Ngvnj{ycE6~pm~#Rl`Scg;1;G=_gY ziktmOh-|vpEo=?Bexlm#QwZ;Q|1$=}5V^eJMx2sZ>v~`zgjYCCnLx)ghuOzFw^cK z`p>Z!Fsvlw$wpJIDuWn9)MqhDURZ-B6S27i9xhuJ%fR@)Jzm4?c`*MNR;*o03b*C2 zc7I@Y&5|cMunVBffCymhd+%k(Dqkxa$TtJQ z1=J`1rQN|$gpzw=19WD&81Eod`*yuSM4L_}zhJXHg5)Uc2r)ay3%EiK0v%JY<_+69 z`#726jB5%lji%8g7eJjIGUgrLqRK!9L zQNI?l?*Ml^0<8N3LFaN2N|FLSwpK?0pk?u+Ih;w5J((|%QecM7XN`ZpH^j&O_3>%* z)=BZ01hOS*g{#t`_=c1X{u9Sx^kYAg=$t5wq|dp3gLOj{oE2)sf@Ib8VrQ~fjLVNhGel6dbudo z>VN%X5P#|MM5kSM%&id+7=0eRxd& z8+1_Rb;wGi`79!cH=JxhAh=2TaI^Gqw6fmLaUP8P#BA^P4BPCLlBhon!O{&BKAY$5 zL5ON&-GixbmQ7|6{ICBoz}`KjSIqOc!QwbbfL|^zz=J4UmeOaknUcpAA52FGL2#3* z_U?=}T;w>-=We|s;qClh5Aj8xtTH_M3{0#AK6kTr<++eWe|%jG;$nhU98j@*RHJG9Pq~UiQJrZ8eU>V!f(B!gt-+zz&%0B#r}?98;R1P zF+*!SRy~7AwP+K=A$-1`wE6RDR0=!SvvjuNQlVlo6W!r4WgC*n&M`Gr(CI48iY6h} zyf89!f9Z3+c^>Wz8FDvCUr6lw*hM%C6RrE+N~ogh1sR<==-%}+T!F|U0@CVhZ2xuK z5~?!}PJzKGoGFQ|XAFfz!&#Pr#Ed0wU`DAWEYX@ zK*llYa+W|vE)tX@YKsF|ja5W!Im|_!JpA$pksTb_fLk&?{>XIPv3>;wXkU^ZPu5NI zlhAe787o`d=|}A4u5L(qs}`b!2W~~GC$yY7Rt>NfP=7}LSU=DC zJ*MdiCT1!duHQ@>O$ET!N#P43Fgf(!L9$_q~`!av1 z5W!YctHr<{LZ?X3iNk!N5!ZnpMm>gfs-*GVLY3a35hITy1*6Dh)RgK+xLIplV#Z2# z5YA_aPgBEsFL`bm_t(}}s>w0FzfUbaud|{V8<1nCjV=p3-N&DME&TI| zV1DScUQgmZ&dZf={albXNsQ!vc9Q>vvgM^0!I~{&c8;|!h6?5_+r{22xD0@onfSDa zpE#W6tmA`}vX|bn{B7SmiO$sof}vC?Zs@3Iu^HWHGGY8z3Hfm1i;T1uPryg%)pu&K zG!naocsCq4Z%y0ypmu2fv(6UX8Yi@%$!`5;)1 zUd-Up1*%3un@NXoE;9IlX=~w`*=Ii!q?%Ekup-`4pT)#i@&RAii0IAtbTR)c=Z`OS ze?FTmz4D(oRL@qT&Bq>Eg zI)Sa9O;KO7vl3#15KEKLRfLa`{!W(Uw^$aSRTggaFrG&e=%*{-m)18?wTe-G(|L&q z!)G->S`*EPU8Pq*t5Pd+U@Rg_``08XfFM}kIDD+;H8jNwrUNp6Z8XPUBLEeRF| zj9KS!JDM1e%D-fc9YSj+<%osVRq<({tdXEXO+ z`D0x{bIuGCuUzfsaG53l_`*=nW&&PuGG1|jITunB4rXOnGzr925pd%P6JKcb7`qu3 z0(}ydXcf`Jts6S6Of-ABYz%1_xRZLyq_EP249^zN<1#^(kCq(X)Qdgba>16tleu>- z1f{!P_D={^+XI%l%$wt>Y`-0z$%fUh@C)ZVyTFyhW0?1H5ULq~6o^(~!;Ms_tcEVY zMgxP|J!T9eE}ttm7oT?{S6i>w){!GCbq3z{k7}du6i^OmBUvSYrOTm{y2R44(mFr;mkQ@m;VH9D0;>S zqCmw)5%u++c+Hm^V3t=}2RLYhot_0Ny)9N(+4o!c+Anp{V2s2>5wVb=6L!4wRGK5H z+H1}d)@-U_r#=Gb6U2$Jr|0WTW#+Hf$uLY%I*frDd2}+xPXCh^iLtAwoAJ*p>rhOc zMN3nk{n?KimUOy~lr30Ac(Br~REDu~@((y%a0Z-O{`d1h(4I0H6tnLySh@)jsC7S< zS1_8r5=QrF^v|_sg@jv_v-pD|{#{A{Kp~8}!B53~XG}5PLoll`_m!>0(7)95#fF1s zpVJpeJA@!zWu7DAvE$wBTI0+^* zYRRmE>16+7&q~spvC&)yZ)qSN;2nsUH1?`gp!Ofmfb{6eZQ$MB$4w_WAL~(#h7@#Wn;up=%(8 zMKOgWJZK=9dF|o;@F5;p^FSyqed#Qc2X2rz2sodDy#r<*6lJ16Ai#09ESR0ySKJ?SM0=&}8Gt5n%X?Z*aOl zq~^SL4>vfnAtIBIFT%Ng6E^WhxqGq24*?cK$^*AY_G!E?dGhSn1iK(rd~}cM4%4RN zOYO$SNAoYd`~w!Qr5McB>7Y40^oFteU3aBlETvwc3#2elifeJ<_K&Ut#Gy4{AjYWT zJz7;{>wr-uCGa|;`XKYhTjeli;(o2RKlcBRQ8!xk+OI2>HJ@1IO!)i7^^$dW zMl+h((F-&Yn3=tcMiQTDagWxsQevB(A2d8pyx*gmn2%BeLvA5N?w&;$!QJf};uG3N z4k{kYp4M(;c(r@;c`Xo-88sP|n*s+v zg(pwO2#c2F(qz??`k(*6@=Koh)6P}iIVJ8o?qx{qRpW0@j)&jc$cZQwj@8YO9f%GC z`ZOcq%Qx?U?ar36h|!Db&vR9`({4wK(T$zAU818%n7wH3Drn@vMU>Y4`UwS(ui-g>gAPgUgDFPkHz?k8Co#Wa2rbQCS# zR8?^_2Mxu4(=uv$dSpyeZb(%G_X=X_MC!6xK@p78Z)H(P&lKuJA940maJu2;7G7`p zy6ht}w#D;%m@kurCh>Z3DtYxd`~=s^(|2pZM%Z;V}moJPlJPnil-*Rn!aD)ZiD`Qz%#Wr>>gTdxfWNyH_4E#fP^M zf3H$4H4Th-C`y9qIFdUy*z{|Mlo%KoCe~{k8OJIdU8Y8*9Y;MRlB_K(j6!}9dpP_q z{QWk%1)K%M=uYSlzzIMVU#at|BuB_8dCG*iBl@Ev8{BHK)0`vj=-2-pQ8r!Lp zGhfoj-~^g{nJG7p1H`0+ToiBtqQQy{8aP4z^Pio8?@nP~>PVd6;F_`B@ejE6a!Jdl>vgi`kn2o{w&fJz%C-gG`MRmS1o?FEKPV7vws@8Nw@lM9 z)^)^ObF4k=><}R14%-$mVD$yA1l%n(ERkOUId>hR@{=#N} znZKPnjqR#5{X~W5?Bpbr)6>}jdi?5EDsdkWMEY#46vax&`+OH^23>W6>_fKaT z)F?UgqJvBUQRs{EIlP|)?_eyMHv(^5kIQNKO37MhfdQv62NL`3^$k`uv3)l_C8sS0 zU~m%T8p)ZGklVXgMW;%!AW3o@ZLpr4<{&7?hx0Br!#-AERw2AGA%&ieTiD5wyZoDE zD){o{5j-pEB2`T`ABAf~XWa%-prS!_7=B>-#fWwBZjGK(6=F{P3uKDs2yJ3+n zfi2<7MYpV*`7O~c0f^M@V;UhAW@68PtNBmG*nc^gE7)a-FO)}6$}gW}a-k-?a`;*c zP5>ZHhiqYi{*!OfnYL8#sni>a%`-P~Ih!+u1cd;vIO{hkFz%Gw&>+9>wpPgDeGQAexW0LVgFc_SL0?cUiAAaE=90P#v zBN%Jb9w+=%(!Dk^qO6t8d;&ZYQ2_GVQt_KvoBugxGx!-Kqe5`rOOG~s*!^&rBcxYM z=}KYIKLR9$|Ln499Ogi|$w`|9`Bx$EoYXGxet`y5;gF(13NM340vrjcsSzOT0iCXO zgrHJQ1zkyn_6-h7Js==n9S2-4#U&{VG6j+Qm~Z~5^ZvJaK>;pwgO{n7;b^$eT&}>Y zRK6X11{2rTCOl|*xJPoshk!0X3_w^6*w$_yr-b!2ZmCFa1kV!QR9QU6q7ub`y&W%j ztgas0KU0q*%T(*M6zjP^w>ApVa4AWEco)apqh%yFt%^77hltJpg7-^>Hw)kM-wjni z?Nb|zXw3V=j!{rlv^Kz8-UUhwHGrTe=XZv|O~OmiLyQIh;YehJqdnUFn`39>7LYZm+1Rkb+YNVk$ET#Abh4{fn!N(XDCkjtfB&0Wdy=IJMn*Fi zJhr!4=#QE_f1?=LhScO_HHBYswe#sGjkC60F8N`D*F%LFICmh|H`?G4M>m98t4B}?fss5pKkkPPehmxkd^~ zCFUl`Az_Q%;)qdlTA%@|_P*)Mi|>9z0#|vKxm&bU)GbZLg-zpzG*{|7z0v-U#LBrh zZXOe;{7f>>N)^>pMou5d6}}s3Js;F#k1uk>RWv>9X_5}edhzy zpC3w&Y>+rvpe^7tqzVTl*GvAQ`J8!8nNNjPhWu2DqOhz=i#DXdUg4dOA(LTd-WhYx zQA-JFXc-5-3c!i*6;WwYSzId6`{z z`rDyEpIq$|gMcJ)Mc{(o{LGDG)>QQaV*LrJAEd$K4jSJ7h&$u*47r*m>hKhn@7Z<`$Wc+n)Bmrqu<*DM zC|?N!q@eB6S=0m%7kC`7ej`QS8N|o(!Emt{@GIcCCL$y6pg2?*$I^rMO4`lYAaSSP zVGAk@dOKHJnlB9EH6YE^3EGSbi~gtc06W`Dr((no+w(kLKDYC%Hqv%y_RaY4DAUS> zSZF#H63&63Y6qLrS`88Bm?maX&Gc7n_*dwI?FC|85*azQHvH8dO~K-+hvQm7f-=3o zy_apT-|+4gE3n8Woo;7FAf7#z8(j4|`~$`0N=2Nk;nK?HY3ecAeEfE(t&sAQ>Nsl? zrP4v>7pt{#HjBZ#OrfDdo>bmu@}dmwKgm>8B8A3_V!6ds1T>_pKJ_VY}%oVIH{AL{(Zl_9qIb`7)MkTMaCrkwNmYyf9DG+vZv!7(JNAKnsM2n zYkSz?P#`K;{qP8u;W4dSUmdkKr6AhFgEYi4Qiv(WJ?yV9=hMIAlo}AGlLZmJD`AKU zJl6UcMK#v1+`cniYl22+Z!I(2-jY(y5+5(G0>`EOP2(or;yPPI7oc zRTOzE3o2%4m{fMYQLi0XZCT{^Qc$>iB2>{IJ0TllxGh z`{sBZF8*$V#2|oWczv?Ab>I7>7Z}q(K=!&zXyc=X+$VtKcmxBS#5wa62C?5YZVW0^ zUIxey1g~Vf{X6cT-h%qd9C!y{N-90trH<~tX(cJo_CIHzT3>cq*d@N|oWslRvu*t{ zk=8OcpLHQEFtPq^i$*7sVtmGb={fYU|5a=$RN@-KMR~bXv$&KDb(2hfoNntNF-pH2lp;a$37m6TvoL(l!&IGXK^f>-qdz;<8k>U7vMXjVh&ifWWjvQ;gMbFfO7Paz~jo_hL=9_fZNsJ0!x$5X27hh4okq4)i$$cAP zE6WkZ`EaR~7}`}IKl{|S>n4$0&>c z(*%iF0tyl&3G1B(*E@fEZqjq4=0BnTE~i3Lv!fu$q~_B){m#rOQIu}VAJc?#w0Lu9 z3Pv5mUgHJw{9Ai&lQcHNFSb-`&?%(Ul%KfGTE5lb6fex3OW3IFOLgxhL5z|<>O+L? zDmG`pWZ*-xa?fv_=Zd+g?)e48QL~brz}YxFA!uHVwG?>6QdYl;ZcN3}8%K4>e(=bd z`1olM^;QCvQkF0Yi{1}sVo|N3Od%qW;nd1C`s{st0iqKtPLmj#GEm2YZnlRXo=lZwE}zUPs+b69930M|)t z;3}thCwpH7UX#}!ltt;@<$#szEuTi65lT~oDjZoJ?eA}b9^*ZS!PM2&*#X=88I_ao z;O6VA)oK+OEZuT&_3!2kk?ljEW%d0{ho$^f%bhd`|#Agpf*tkEj;r3qlU8@Kt z5ZuBkvs)AnA}0{GdYv8o#|b29>52-L!{nXv{BdMU7}52o2~pwI(pt|Sj+hbAxqF;x z010otIcU1Nfezael9D<_UR>_aMZh@=ph!Y82{*kun@|zC@jdBhNXW{H1)hp0O`iLb zbWah?F@RN&~k^t-qVr!JvQB7vn!E$Oo zMgf3fF_7s(0#6qK0fDQP2rBRGQ1HDno&FB#s*}O5#+f{D$HLx$?3F-S(s?!cGX#^L zp^w%70L6qrXA#DAD+8b_S1#YQODoK}(E}IU+}tY7d$GVWqpi0~ts+IteK&`wy%_}d zpx%WoH{vF2H5ZoO-z{}__V2nTRe8P?ZIaXARdz7xy9kWty=H}`a(#oLKSd^ zo8|kLsp=(2sXrlniRRAwu0az8R_nCDyb*=6c(U;tk*(Bm_MlNhjMERoM!8?C#|4gh zWEcoi=4ddi*pcGlU0fO*q*MD6%h?2>qJ+u3)vz`Gr9rx?>uRJ*V0fTzG1;pp9%VpQ zMk9)L-!d9PG_&fRi8=8VC@F<$bYfu?2NlU&{_ySj1qEE|YK^jqbIQTayKItWdcBZlJ~uNa;;76tqMb-u+P$9~MpkP+m|KW_L5~Bnm!t=e$t!$`=#5yCFg(&9lF?$8k zB;?Z-96`x>DamNuDh$G;hes*Oii%Re+;p@9)?DdUTft{paQO`kNfY*uD>7NB*zSx* zypMT-i7<9Qn}0J%E|O*U+$odQ)$d-2eW@O((Hjeq*+KG0mVKQ%^|kF#23%tedwaFm0UidmYm|jt%nk zYRtH7Pu((4dGdr_?(0T35A9f51AJN6<#_AtxYm5J{jhTjw2n@QlQEzX!`(;za}b!@ zhoFX)gL)MOlk)pK{iz%u`nrDk^oh5MhQu&MFc<-3To8&mPUI$)TTNwKDKv!J@V{7g zby#@ske=H@z5d!O2{BZO01M;Y;=H=8Y=i09!UccZ?R~R&pJ&y3VjpV^6t4aG<3y{h zg&sps#Mb%?4>RcNt$Xx~p)9V?khoa6)oX*LTi-2J&7QK<$f{E$-C~?EMK=-wj0~OM zDZPTeyLEQGSQvk2KF{pkEaPP8-wDfqQ8PGot&-k}iqNj;WPa0QB$WO5dUR8kSjvbl z_}7Eu%jv3=Pn3z=_@dOaKBBX3&aF=%%XZ4pBp1Ger+9-Jhm-xo zZ#JqzuK8<|v+vD{ztF!x!V&z|AB0fWl=!biH79BJQ+?C0KjNjBv)Gc{SC9dMUT>~cHk@zzKpv1Q}{v!nAP4Ku$<$gsT^|s5vhR$@uQWU zP&{RE5W4BB0#mmoiEl|sNj_o?4GrU+!%vg_JP%iWHU_C==Q{m=mSA6<9tpT_;~4qv z^JLAH>hryRebc}5`%R4hnZ|(a8CzikS2EUzaiyBV9m%k@(2USk zy^)@~XY<|cE$@yb{%H?B>jw^oLNpBfrAi8HF?+Tdiu1FHK1R{c-xvSiOprtqr}vkh zXZgt&ZajaRBvuOcl*ojUnGna9d!8{1 za~Z4Klja3{!zKt9JV00QME=+tWZb?{*ZzCtS?zA_VtRf1OjL`ar2?yc=&6Fo==8}5AyFfIW-5@83&>SgvWLAOdTSr`dc^{(LH-FrRDLg;45z& zeu9Ys)#$HiYP~;fKh;%StR1Uq>LQx?O|~QKsLKsyg#<}Y+7aE!D*Insx5Z}E?|hyV z@cohBavfIsO&>a9YOCpW|1_(LiNbC-{Zi=@H_5x};p^#-OnqPD3q?5#SH27~3{HN0 z5%RvV@%P-bG}nTp%RG!)EJ~jWvFTEtwazS8Jw&^)~8vtpgc=d&b0x{zC%=#82?A>BypzhlhuIR-n!Zif?7F=s2Y{g-2)!0YB!w z(2U37B1J%Ga^buE=(oR^66*Hn>P}<&&>tPI?zcVMuU!LP2D7$lLaYE{Ie{ZQ$*uP( zBZD0Fg_D?DI0UOG!xbGg@*(hOPTSqZXN%<+1{F|Ntyk(W+Ap;(5VZjY!hF6r5V-n& zLBEix{fG}2Ey=tGeY}qd@i&Jv2S-W(kDyq6gFMES^qKT=FCw;ZpI4~E_zxx!TunBK zy_GmybePLcDj5Y{jD>1zm1RNTi$Y#B2AZBuGhk0d$Qe-%^vv5u;>a=(`rmTIS!#p1 z#k_MT=HpGq);IhR<>s&tDonM?0wR?W5fgR%XDzXEL!Z6!%Q_;2BfRKvH=K;M36?t5 zRM0mt3+f2hwH~0XB@n%b_5U-hjv@`b+2@08j*o+(q)6pZC|UyccjGon5x-d%7Aol0 z1wOn)KH{m`HW`k#0eEB}Y!*sC78W~xZDC>6Gf!#$ebmxHO$JO4A^1B5dcquVKZ4%Y zc@$)4qW$dt!Jje)nE4FZ{;o>Rn?%Q=ac?Ss`cra8O-1(o%?Wat_i-&?7-f}#cf>!9 znU35IWf7fVhX3U-8ze8rgcPc_oY8r)$_Z-t>|O6T-FKVSi630#$x-o zyTz52zG7&afv|q9ray^)!O*ZtMk9~;LsV4kCkEdqa@o;go&_pBtJYRUmm$-3e8tuw z(s`%eRc%*_l=_KvS-;b^v+|A!8C4k`i7hr=@60=kU}&bkn1~-WC7GX{Vlj@YWmpH@ z3ED)_=K)io)PinOmRMmYv#BP_`sh+b`V2-Z0(BaUgK2K}?d2AX-CUuzz9FU)@#iuJB3HYV7RlU(V)qWl0ruG|S|nV(Vgy$_mlQEL3=nP}P6Jvm{!@*e+Jm)6v_B~iJ0tFQH1jG~*pg3Q@boB%cIN(W=?T(9fl{!$D!8ZH<~0Nr0RY~>aoeWZFr zLyFN!-6r_~MHf9>v4rMUZjkj|8}DsccN7sKELQB^+W-nUeLF9Ua9i5@;iY9u4VoBD zcpABvj&~>SY~`*zvYO!Y3aEILGw-ln^_uW=j%=(@rB=SsmT0 z5?zVEw(^*XU6i`PxI-EO+NA0!6c0fXY6o>;!FFHkQu*jf~RqSzg&VQb@5rL?~{``a^{N_Dl~AlJewSpLy= z4s3`MNO`II2v+>2^4V;SQ@IFoFXME-IFHIicr41O3N4Gs6d_`kzXf}(1CQ9MvF6s( z*Ge*{;)!0i#-YPzPjE_M z%C2GWL1X7BP1s$JwD?=;mEoYA){@i@gTc*d=; zLgtm4jB2MX-P08gn~Cx9|H|v~w)>v|SrQLa7CE|~z3_=GdhkYl4r6BTV}^|O31k1` zumTP5J7`#7E5oc~?ma_R--Ma{*6KU+;dVP>6gaoOJ^CU483O|(x+@DNl%xn3@xqG(oxLg`J1TO{;_V)a*0B4-nN$@52Hj+LYW6buEH(cx(=|Nl*Y%iM!BjXQ`n6K?03pd6I^L3|-9ld=jf?w*FQr|FJO7Y8H zO;>AzRjYbmQJR=q= zz`nm5eB1A$Jse)L)CfIJ{ zC`^wY&9OIU*K^OLuiYkODnaw~y~)5wrrUbAqT#S%sYsl#AaGt0Kp%=|5{N-67U(~3 z&P0GeU3QHr7WhB`TP{da=_tfxQ(>2-kql*ol;L_ksLn9Ks=TlGjVG!~-BbXuPRzM% z6gcnK9>Twy@#67%9~qZ%0O5mvGY%|Xj}!AUENP3 z6)F-r((O!rBl^T`lW0Vxbg!w`QYnhJq^1X+knATYcnR=zZrI~ zr5slNFg*MC&$j=m{d_$xOtnavLbKG@@uR|>f!&m4d|R!M7$%unI;}%ZiN6zpu-EnF zo8#S9q{1a?F%>q%fqcnU$NPCD|7Vn0DB}?jB<6^Cd^CIBnOZkfMmx<~RMhNNYUvO< zgUGSjUEc)A5HfX5hnvQCz88D%XFdLIXIgR>(fP5mGcApj(zRFx+*&F7CDpmO-C)Tn zC_nd|xj>)N2!8)CS;(VKVi8fWinepYI5Xg4M2RT=LN!u&2Jv6Kd1FFB3od8N_OHQC zXnoyI!2Y}Hy6ENPIbNUyb8{5NZCTvW(kJ8e{fI2ra~^h^6S^|D1=rnDu>4V&V=`0r zLow}PXf{Kh_Ls&!lZDJwsvV3q8IiCUvU?!m=$E22p8x%p?)hPoZoTrrf;KS&zuA+< z_jG`B;$&N^C7@0JMBq!CeH*tYv~HsbmAPh7jMTlw^n!5KQ@tIuo-X`$ZW>ul$W~mF zG2a0jN5FK)2bjifxJ%-iz6}z+5V8=}7|<#=q;gdwzd}Bl@Y??wT^oEx9*?3NFCV|8 zhKo~Rq&R3Oo2Be;ckXQ+hKh={PW8fevN*`JGrWQQTf$D`kEriO+l9F0WkWbnL_<8o zpi@)uL75XcZkOkc5mcAkUSfZZrd65OTEf2?8j&MAn7M!n=BYcNk5mGAj#4m|BOD&0 z1e11fP;7HB8*bZYtslc=V##-YM=+s1^`XR2M9DV>)UDICW1TKnhsk# zK`HfQ{)pUZG0PYF%LE>?Ekc6s+wXuAFxl7`q!bPoqTtwRfTjZIw|AOiS(jpHvGRy) zu*vVbc;zh@@U1FAV4x0+@VNlUwGF;)Spd;7!$yk_#Q6yL%+_n(1r`2xK09-V{}z~3 z)c^bY?RZi{#dN(dA+|6KxnD!VY$OX>5H_s6b#V@Gaht$=EA;72@^>Ngo;l%#+|E+T zJp*xH;lDpT76$9(*ldtPPkKV@Tg?Juek$6b1_lmGtw?_~mt;=BdcT{S^(~D=)0l<* zisvtB*GQ>u=k>%pj9&4;_?#4~-M{Gjc#5-G-Y4?6z%C0LvP_o(i-iWF`ch>e+~?sf zbEOu6PIbS76vO7hY~w)5FRL~--1=zZM^W$~nqH+6=RZOn??sNr`572Hb^*=y1%lko z$zQ3>$bCOjzkSZRWkuq^!dkN{CI>keno_EHy2u$h4}|`wE=Jd!s*x1egI{ou@`+)b z?lB`5>85g2^|1h~_a1LQM$->+KV+Tg6eU>L{uq0GR9zxZ(BYyK@h#WseL#7EYvBoe z{n~rB^jrpD$<;777XLtC^`VZ=zv9`jtc4`0%sYJBATkGB{YaD^V&1!IgnZPC(2W4E zyHZ27hgmX9T8?gG+z5Yc3a{~2udcMw8lnQ@wkrT-_t7&59FRiU_JxjUKWoLhyXx_! z<$IjT7=9JXBxhsX-Vn>?4*dJnzmd$ZW%xa*!Tqot_5&8Cx=!oYVr7hBZmy61h*#b# zlJM1f@Gy*m$;o;@V|vPSZ06sbD!+<|tz!3nuQv+C@C)w=&>ze6`4PTf4iXiqFmGz$ zSPiRTlQ~4dema7q5A_J;h)SXR$C9_eCy*kSX+0Oa=?ZkVM+{eJCdMJZ1mj! zFh6eqU&xtXW5Q_I$Zb3Q{W0uKg$V+n8N)i+;R{A5Cp9rhI1AonpoW2Scfz+urW|80dP^ExzyT)JM*|PbMuWmho5gT%%i5CYV_f;Tp37?>U9}OsUE_ z`C~ydE_4)zeE`a15YaTE6r(JhP@Fq7;Z0f(m?{aLzKI}{HV=17R!@JF&32o{T=zc> z-S<2A(~3k(+wJ)$Ai_4isq9r~N% z1upEw^g06+TjCu#`pgx68FAt17t~a$+020!=4zn_0?S>%T?tqlR3)&=Ll(pfC1p?KFgG3e*FHfg+Hk?q{A zde;UhPkJy1bL_s*tf!-*VpDYW-Fulr+%I)%qF5y&{34Ei{ps@Uej5B1|5Q_}`l6z3L|EHR0AuP^K3M?k%vCBHFUoW$XF|hdgJ1;z)s-Z03 zRH2MbM9BAOdj0X6+Y*w2FYf7_#6p2j6E)NgESta9eciXs=AiL6g>Ux-D>*<}^(v*P zu+S8U)PMm>(GyESHq*+Wl=cqLk>!0-P>TyuuUHLA4oxfy zT;(%t^xViL0k07THYPkz`(}dE$@Qb5d(MwfIueIQTIrL8-yz|7+@b`#+uzT^5byPS z0u{*zi8#76S}!H?YqaTy3oej<4qF>@TKA?aBA>XY35x}PG6eQD)l?RXOd;vWTa)GX zAphIxGD9-39tc!&$WdZ}#vu$5qFr%RWCAEf+M*&Z7-2AzfnhKW!Ug50ET{+JLL&Hv z!r6P~J<%WssIOY82h}{il4R8ke8b!+d zH+$7^;w!jPG|LP~;QYQ~wfv*DhdXSLm0(P1u5ntTgl}yC-`TM$OGSWmgmd}8WDIUS z14Uj8NKD{?i_LQ~X6x?qNyvtmVm*Y@jXqcBbonYZA)%vF9;NAppZcAv6X)|aTy)S?54K*T z6pkw}8DU-!C${e4QJO0;s*0c$`p0wYXbMmkY9s{=isFLzD`@x4(Hwl3xWw%=Q)U>s zzLC3!yP;UEKQFv6|1r@qOTaUWF7|{c!u)vJYqu^^a%)Pv*f}Zb(QACAf$@3sh5E66 zb}4(~)1hT<+bmU`oIe#Vh(KJz>LHF5I^h4V-$qHVp_s@yO4A9Uk-Qy{*GB<6Axk4( z;@^zd?$gA1?f(v9-ZczeP;*M$VrUJrV$m1yK1xh@;bhi^Yc<%8hWIQ+*g{Ee?wYfO zUg+1^s!uhCm9rB}TwR?ryj2i!%jiw&_nCir8u3PI{h!~k|nm>eCpB^MX&dueAN(JiM=eaoj;c*_~FNcxNDP7#pmG_56rEj2C(VVuHb<=C}2xyHC&xA2ihN$D0)0qGW`yOiz*>Fz^rkZz>AySux)OQgHIL+|#xaKm^eu$o^aO;zKPq54Kk~tVzii!!2 zhXwAD`TT5xBCV%G9Hed$r&0AI4*-?C=&d^5H{up%Ka0vGyHPAr z4)u3i8`M|+#J*l(iFN^>d_(E*f0V*DNW8uaYb7vzJ)~*#b;IGY{n zN%xR$zpMgFPl8Hqh2`I8!_PUcpK;9!DZWAo!o^DSX$oo#Rm(v~`H<>Ws%9psEDS8VlXy`)vCTd=^fjByR)58GsBJ#n(<^dfKcSo{BSX*pKr(`k77- zFqa!OtEPvb9XD^pSZVapp8!y!w=9K?!d5IR+tAxYy6~2e_l0i%aq43lYS2jD6u9WlosU3Nx}FRYsO)^_y#RLM08BAwio(8N49Trb zKf(G2?0`kVM)&?=om^P1Ma9a1+ALz0To&6j98=0Yx8VnRr3BoWOh$L4P5TZ9tL|q+ zc6wI&f|c-QxX4E{t+32-eY0J}F*|L8`q z>U4ejfsx>R=8}xbp_rgKU&Q=V03Z93#{(4|Vl=*?c}K0aA(6dNPl;74PIz-RP_+;? zN1J{JilCVf+gt3I24)+~In6_G8&*zCg)Lh?cawYGD^=}K*|}?pR@KWKi;rF>#{aIj z-Z2CF@p=$XmF9Tboq1R>1sl#+!hQ@hj=bF+C@n_3K9yhW@pFQ_NT7&uSyWY>wxNr^ z^ubL9A$H~g_ehX1(@A3(iN_w8rG5rI1`u!1vY^-L{VJIw?QT?=-jPTFuFX+q$Vs_v z@*pmmgZ>}M1pn_cz8BBoLxOz07MG;W+=WzpNpl5}6?m-+At4!_2LvIz--VF2Ci%~w zxlfJFLDqLu4Aw>&M1rydz}k7)ZZ8d7gHS=%t4A1mz^K>+z49M`;lthEo=5(97|3{; z3M@NgD|HvKKX}4EZ-7$tez1gqKtLA-OTsm-g9rsC5BENwwcfvV;Wz%PQaFdaf!?2y zdRt^5X{Y*AdRTg7xX(}Qc9!=rzx^<<)zvrBvB*RC{`tS!yJGb5peV1-w%V*@AH=o46;4A2RouVOHR70wEel~wibBkE#gP3{*OG!pLK9#+ux z`gTF*H=RbdN5uzim>rJ^^s`GU-7mscDbKA(H(!4-vZY)31qLg1qVx%OjbOoV5)sISpC z$0q4dVi8!`O&ne5=0B9d`-{|`?GfJFB8K_QGS*(=KX^dP9lnrLCjEA)?f?(^|gd> zxpiKwy&eH!yILy}SUZ6@NHC~okWNtpDrAv9Yab9Lo7=U03tiS$rp3${x7P62$RDE0 zYDdKw{^%-1B;=HdaG!w4~iAg5Iq<=q(<%YlqN9_H$08OHm2!!SbjkiB+r{7~q4 zIpimFO#J1L>3cE^V~Mo3~D+dD1P;rMaM2ago>cuh|uD`;nwVLg@KqwX2>{k+2Ro|R0>lJ>6f7U z>Q;SbWGgqg$xy*QVy8{Ez1musaQIIZ!8)K-1peoKa6|>d(nBD?JBXx+ppw<^>1%Ex zFl9Y?uLnbbz;X_z>|?h#?9<)Wfqvv=sgH0VIX!RMzAlIMWR>#{7M}2YkUlP5aZqQ9 zMNok0pLl?C^FjP1jVQK=H2AKMww{iRp&~ES%{K|)C&wm2v%!1*1%W`W@|7=T!GVGf)JHdYa zFxPffXI3P(+v0BwIX`tjGXU*@5bxJGpVmQfoy&o6#YVKSAbv*GpeHbaaCbXLQ*|9G z3bmLnfp~+_p*%0$CDZL#<9h*e+NsWP;?u0}k{RkR#sOhJBzaRL)K+qX?)El!*QLvK zoQ)Z+UG;ayV^8P8>DE)({@U+6?VWyoP+LGH@Kw#jGJW5t?8#li7*NF*jI)!%mX8z= z+ZmwMB`k|ds49m5BfL;JUiTsKm9mHy7))@;4V@tnI=%+w#Q$JaS6lz|{H|9=B*Fq7 zXCZSyrv8pQk-L%6`~tNx$`wBp?-wE(1XAAIOb}5LCHkoljWh^rn!qW~hN+jU2w@|K z>~O^Jx^#s@oHfY93*|GfSI*aNdX9=19h+nLeXiVLSxyc1FXJvPm5Atb87d-8L-#-4 zu2*#i)~ON*|H~gp-$&dA1qfgsj`UB}WFjC3=f`-05m{Lip=B9AH>@As7XlsPJCu5> z6&OfkqmHMROV3vYhiH$PbKxR(l_&3Ge_BGz6%KW*HXBM5(6Q6E@6#65w)Pz5<+T!E z`s;vnp|Qn)s83<@wf$&}d)gpnGF+yr6^wz2qat6Y*2>m&jk_>+0V0j$!GzaU+dYtJ z+LlS_2-3BT9;aVK{!klMTw3NFKC00SusiV=yXnDMO;Yg+=KoeZ=|V)jT;7o7@v%1= z!F(*G?{#blli*esmC7$MhZD@bx>qJ+pLXzGH#=M9g|4rr^K^)JJjq{mha&O*j_+zV z2_5!zTg{e12WwU&PQO=mLSX&!1X#k_==ZLJ(JWrG|Mp(xE#*n#;TYhp@lVD8YToR8 z#d-ozFZK;1KO~kcGFv3KF)O5Ke)$*`1KxVr;-hYRBA&@)`|=c_w08Mgy?NdD>N z`T0+#%N=D^Tc}C5!@X7C9mE^l7ff0#HZq-BV|H4vA*^DMzYVq90{S;V&&I3&u*W1{ zNLSp}u_96W^MSFeGMy`!>th!HRN;buanPhMr$|^750o{Hw3Xtyb-3}K7X|`m^iYA4 z5oCxhc(bK^L9PiB$a;AG^g(oif0L-_x=HC=OyBxM&hz11=iJX8ULpZw~Fm{@ssKY7cBX3tML_>E8udL~AJ>3FaNUjjV9Lu^3}?muHlL}QB( z?e$MkYo6^N_%5nDqKVlW zk-B#^w8>HVV zDQ*W6?jgo9mxp`0>rpdGR(qdb@9sNDaU{lUgSG;T8eW~3}+MxI;(l$DN5v|waA7yG3jN`E6rR8dy!x>59HI`03 z$YY$9^O@hbUsgMf&IzYf3O;SMw8i1kq#)T0jafEtSv-STpuv1R6X`kEXH1jB@H&zC z`5x4R2r4Kb-cLI|k6#z@cq+DN{!HCGd&;sV z44P5np&D7Iedz_l^w&7r9#$~&jg?W8W7#8Wp5 zu|&tKI+!VVFE|Uj4iUkgz(Z4l;a`i~IUuer(@QsCFo}L75S3_r#|7K~=7rDUR}if*3Ocy4l!>)tnR%y2 z&i0$5$>&uI^YUYZ`e8iu+oQh7Cln}#a!CdiI&FwJ9_j4q2MuH07K64dBd{QU4W9+h z>Js%_&yBwuFNtYp)N>=p*5n#6u|nw7&M_yN>VMVM3%;5Js1v4m4w0n@&4bI+%md`e zl6ly5Eq@PCCS^oJ^?Tj2#bK~eGDaB`ubMc0Bn+v1-7&G>6YIih6_E@VGujAf>%KZ{ z?pq4)He0M?ya!3Y5umB_Dmq~xQv778jy;We10WaAxjr<7w*O{&AFIJ%LEzfdl*sD9 z&ENhI{~HPztwwzOYIdyfH@kYwwEaE04g%f7J*)@Y)z^PNmVb))(@6|obyJc;gv<|NB&;lK_t zb&4S)Gw#gh0OWZCNhQyC89*l+>CpYX|q+m4$G`gP>Aw$3|2{ z^j*ED`ZB1=p8|<`A%!3Znb@3Mmwfce!vsdPlsB*f`I-`J;e>>y%1JXqVl(c%ohy&$ zhX5=8ISPJDMd8v)|K@(0ZggHdJ_~TR2}VAWK>qC*^m_tZ zZofr*8e_6G?eucg-Be3pyvZJB7NUYTgdR=>LQMk*_jJ9j+n&sfT0n=1RDWo1Y$>mX zCoWj~V)FZuwvxm6GWQ#T=K|Z~c?AHqh5y-@Mvad5<_hK4Jp+*BMOSg6#t3%#?54KNUwxH#KPOTE+ zZT7D5c4?E>hF`Z4vgUck4q5lZqRqC5UYug_#wNSSi%TSu7OFDdj|>nq|Z^VSpRQPzdM?EDo@G0Nb4^`3;j3r&4eCT4PXW*>R*ASQ~%qt&%ta>X88_|jKM=~DXtJ@j>!&MHEg30wkBh|?nS)3(uc=Fb zqjVrSZWl4D6pJ=XKW9Yyks%rBJ!7rDek`3^?;`&}J~MBdv!}k&yik(L?3jJJ-|n8T z$d`AuI0jEmH&_lGg6r&oY&(K2!LOJZUvLgRE_M=@qvr01X@4gDl)Nk_>SNPQZk|04 zVQ>d-2-BMTMUz{XwZaHFViQ)LI`v|uWU0(Vf9)J=4&p@H-^0_Ep$T(&_}*-7;B;2H zFQD=K-S=#5aK2j(oS7)2ndEv3{=8Ie7D5EdFnqfrdAlOTQHesp!TbQ*7fI?nj(KNM z^i>w4;m?ofYt%6ZdC2vJk&p`{e$ zL9eFsosOXPlCdZVC!{?;ZP`5x1-X?xpr-g`G4`<7Py|77CRzF`A1t-&c&r>kGI?=I zN`wc(_IDFQJ>4&C!V_M1Ip#N4*G@=$MudBk8eA&W2!B#D$7%j8B9Q#R63nLx&{g7y z!*}IwEJ3-hin}|Y$~b>6sjx0KTVn|bk+mJbB1_*Oa2#e-S5+1tZ8!5R&)&2Me|6n- zfFp_iL1Tj+Lurh<{9GSplFot>wI%jd(t8%Iux9R_O1a3>#dhD^i-l3mx!7xxzcH+k08QzpM7m8#H~qU>M8jtn>dG?1=t=9Ie=fb z>g`qI9b`oEXdtMi^y%l+y@+37uziu+Wzgk+wk4e~vwOLD>#uSupCAm7-1cHzH7`Bd zd<-iune6ytf#CJ`L>1DRWRlUt?>2;kj0PuPLI_iuah13BIMlDB&70N?KH9Sj8@V zz=HwH#QR%OQ88{hlNT^+q@)Ug(xtwS^(YxFu?lDA)DhCKxWBn+okBuxi=9-;+s9(# za?B!s_6nucokvOPgaq*G2O5GnXmoJ%nrWp#Lh=p#3YPq3+Kzi!1=xKQ##nFCgL~8t z1uT8_M}syN8#fpsolSG*nNMx7@44R1W6V)rVuLl+A2~!pfD}(rp9rk4g@p0J3R~|O zD?ZfdUY%jrV2s_-b;__GpYP>b5h7r3&qAnc=YJ4>Bz_TXN?rEuQI*{<+N{bo(5?IL zV1RbaM2~pH`harBdJ9g;`M@LUz0HWq_dQ|}Fs)_S$asBpxFFlO&OyH88+|I&1AgF! zsVytVJOUeljS*~=d$)DLt#@I9BpIiZiR`_oj5eotG*dZJ(hOa0Hg+ZOqj_sWf?x`* zznXvk^QFAwkK1wZX@Dmf!DsLYW<7p4mmKqt8Hme|9n8ehUuY&I9NSk#BH&Bp#S%v& zLx5=g-V}=+8eI1bm8wW~Hh+-91q}gK-h&O;I}!j~=-$|d6`SSm#G)2<*B%eoH+F~R zpI=9f5cXaCskUK7+={=|l41=@lFCSmRmq^KOhj_n{}z42XGGQ4enzs2Dn6}!Tare& zeNN5Pc-@8H6G*m1RK%f*(kggbV~%ldF1Y7f90wGCdZo@l!sm!F5mDVHgor{AvK@d`VEV!MVq(uL(>Z!`Bl1xr{h znla<%lK3_hj*Ie^n%qU{Q`#ENh(WxO8N+(48eF=NsZ<=t(N%w`+g75q*55I(bMIh^ zmBZK(j0PkQAQ4Uk*6YaL-oCoojDfg5%!L2}zDjPZpVOKBj^j?7B31{~);)`FtVrKD zGlpea5hmA$Wq4`$xL)zWL1Mv`e4E>yGYa`GJ}pa-d>$5{;pQ}G9$2ZWoxq_A!Ztrf z0+`-SUH6I;?LlvgirDy*#dg$-e_lL$%0^XG8fM@L%l~)O#7x&wG4Z1jZ3qB>w6)6M z<}jlQGzUTiz+i8=a?4*ZCyY8lA{6)6BY9B6-In|PE5<69H80(p!}(@NL%q4rH!B+v zv7~AEQKO*lPVj!VJE>c^wDSPdJvM!t9LCh##C@`F$A2rjHiL{%HN_TZt5oP_aGwfC`^zrT{hf^>LK+}XujQ#7 z3g@uho1XUj0(|K2?E?9HzA{eq6wLn&YIL zRs3zDP`c9Es@2MMrJd^X;M52PEF@n1@mTBEhTJ-q#JRApJljZEUv)Lzs94>^XH$*%t@uab%;gcMF21W2mu zZqmK&9}jS^J7iU4rk|+r@X1j}Z^?snR{+)@S$|ieW6euDT$%p`3G#VocE;B31wp}8 z?rVld#K)VVb&dLcUl*um0;i2uH`jwE1Clzm?}F=Od>ptI$dw^=X%6k zaM^aZ+d=+^b^rZ(ni)EqC-&RIyyo;T^E0SLsQ~ec7AmB?<&^t`X+B&5$E~RVcyE4& z;=qW->R?}e(Y;vkVE4C6IL95KFz(=i8r9XGihl#|Jz*k0>llU)$+wL-szAN7(=>?p zVcik1kzC(^MUJ;0g|3o_5SRbi?L1`vX>8=@&yE zMJLO-UfkAc3<~QPzl4l|iW1dVOin9*V9Dx7KCHr}_t{@41>g2LTdGSB$TLP*bT&~lafPd4|RVaI2~mg&pFtd=f%i1J90-55A{W)wVXdacATGF z;@m?o-agluy@BYRwZGtV+3(I(Z%65pMp`Pz!wENgjs5+!bxLZ7+4 zwuRigN%!E3u0DZBpkYwFg=CP?js`$_-1;xh5wt`O)LR*c^?-$~VHX%-4c`CJ_IrLh zu9V^(OuJ66Yf|_IwOs8?PSOtQ?@qs-e0fJhGjw-^gawVC=%cGiDEcMUg5#brgY*D` zqkC1SJ`6}o4iy4l-(Blf}K~(Q&@cnLMeu*OP(ex_jZ(;g#>4vSH2*3srErRePO~1ROk<_j@HE|Z7f8A{?Se_QAEx=1k}$`iFA_xh z3Av8cvvka*>NaG!XhCL>*8~4-EV(Pvrn;MzS`2x(Fv~qK)|9@V6nx9vHf9}2qFNY&uphe@o3oo zoFY;x=+v#KFhjlW6}IPnj$!ftKG*P6{Ot_X!#97gWMZK~KqFTorZF8wONf_f1y|YX z@k(zkeFeHB3AO}J>FAq&F|?Gj%o`chiGz#VXLo z9!rS4Uo`QW#y?&`zm3PGjOS4Pj01~;?nmI+S*S1KnCs%@E{Eyj{U-%u#TVq2PP_NB zI9o+WVa>&1d@AQXE3lm*S;|j8rXq7hj0tSI!PyVwzY}@9W@1yV(k#=3Tmh<}e%dg| zcX)X^-06*sGRrNU8zM#O<9@GUb|6IswC5W9cRXQ_ZD|TV_ zQhQ*1%g$rRtxEFCt4$IT>o5S@b=LV|yEQ$cdZzW+w zOSL_z4FwxuuMaishN9pNV#*t&b*J?x|wLVjgv^1 z;SmLtv}{##8Tr!GbhrPYi z?O}q_aWm_xqC@@-eI{C3@vgjVYjRjQnoZH!cJ4R$1Uc{=QuI#Qs#GT>~Jrd$_z#C**;E959IYnU_XzKc`dg z+Fz^9QH?4;Xkb1k_mF}xSP4G9GIqg#!oTfv%SRx>+VPUhjI6oVJe^c5>X(Yi9CR-;S`A$t{r z;@Ze|N|e{@y#1i@RZ`b6ULF$yfOc$sQgIUG+cPW>*Gk$dxRWH9mlMng4{MVn7M@Kb z=oytBB{v*bn_Vg?l{)(|u3obAME|2~Mz}jE)bFg&%{(sz>i(=Y+qUnqgW}5)={`6dr9TlQ!mQ0xbAI--vgDF6l%KXgdihn#oUc5Xotjk}CM^9%aE1cRA{9X0v+`4rI!h{VRWWFsh!;9cS-li0Z=ha{riueF<%k7~|D zV4lBL3G;4=@(>|f`oc~05)GI9CYKPU_Hs~qvG^`2oAJHRiTCO1q{lxb3y-JK7A@=w z;bj$iH$zU;0Uw{45xF3PIhvww#$cNZk&fL5b8W4KaLr&qJU_qhwh0B>^!=Q8Se?a8 zkQqXNFCzM8-vHUV=i2g(PO9VMukL8DlBmG87aXja{}xP6xXpydVb~`+|4|%Y4J{^r zLtS}XbSr;BZ;Fb{aN=|YbR2);UUf}Src=6t;P>OZ1ku}aCVw5hpRbTrj|P3*GMpj$Q>d#EHpjM$8MH>%hJmAT<| zV(c4hSl%tnAvpjK_phwnr;tm{X*+lg<#|HDpV0#A6{UKPA}H$;3Z3k94|!5%{_ zZPZP1km)IJ9!f^{6hKGS*0E-2e=sIE`wP-f{61}vRJoM#hYjM96F8Z@&HK0#g86@7 zf*+H3J&US3122)0+j-%K+F|Mye4(#*M!1OH|DHEB3()AwZQ7icu7r+jHs%AKc$Ta6 zB*1eW=9o>(n@|&3K{2r;>OA~NRe;n#lfBuSZLbBNYnEH5kD*`Figg@jcQ|IIvejXa z!SQ%eo<(UHw1VT`Ejv)lXZtNPtfXwE4YQuG^j^O%5FT?>y1tPb#9swWK3-;z6sN4& zVoBu{#LSHAx7q@#x#9Tc?LaPZ3Mf!!Go6>puEGdC9zi#VuL~o$;eXdA@rg;V1Am`W z(SaGw{_R+3g+>M;R3j&?|9ay{RBIt$mCR9P-5EAiu;CkOwdBJGR<~{Q{ST+Yn3pK} z<(F}pc&2|J_Q;+u0-Ip+L2dr$gWMQD=Q$C!qPCk;1o+~2g><=}NC2KhO4qYn;`2;B z@4N}&k*H*BoqMO0{9m32oI>sz;$zKN)?uQDS=o@kD*cI49c zvd@D{;Al0GdCB2cvU--u`^)J;uxN${3Iyue4RvhHDXE1S%k`)4NyHqDrxKvF<0_pK z@NOGQc9G_D!@kkm1BBBhhGA#)7w5iyE34j2&?RBJAtCZ9t_>kqxyqV-bTmoF{ZIh^ zi!K6wA~G|CC-NZ)Yee{+1WDVg`#4pdjenjVm#F<~)lWSYe-1@+ECOkozPD4Hb>XX& z-6Jc0Owk!0qM?C4A5<}we)WqRj{R25BFrnRlv94+HOAmF3~0E2CiG{p!1DR0$`P>z zr?Q49Aawa!R26g`vhLD*gV}Na;F;cLrQGYRBEnvk_B&Ffud{X9n)9rcR-V*mk?-+co@4*TS&PX$RE7aA4ge)Za<@syv?3hz!bi9w}+o~!QMZWcu-r#&xrJbHoch;&X@xv-ftdJF#zuRVHa|6 z-sbC0zveh!57{WzRO@^r`ez{EZ!i60Bt&x)uJlL8Qtd?Y)ldAt1e;k=HtuYLW|VI9 zA7zq)pGfOqk6q3@-sk|gMTK^%r4}EOQW=33r$1T-zCO%K{ChGU=lvrRp$tQ=HeChK z-5p8N1!2>`=U4_4!idXvgTUXdFtiDQYr~+OfPlqHvm%ch%kV)YB(Kk?$y@14*A6pOHxn{cVIZF} z>^_=nD?}3Sc!@`1`^uC~1d+-A=?gr&tS8iRrwq&e?)qA6L-jVe19*Oit3L8{V|Q_k zIm(8&GLU9FmF(dtDV-5?dR_a_JW?EM;cgH?{AUDgoho?v;}hmXh1`K?zern)iiYhimC>M1bg^7cyn9fWVG;)n zd~h^=C*+gbu z6JvXEn=-|oZpKTCOb}o4;Nh{Si?2{a?L?VbU-XtNiF#DRy~fZ$|Zpl$YZ(P#*-qEv}yvfMW( zCy+0$2JD<_+tes9X4xka_0;%lzp#OZ3<;dssRD`O&@gCNLGcWv50!Q90kYuI-& zOyUJhcS9)$p0b<9$4+rR%^WyM$G2DS&wm3L`%q3DSyPcx(+KURev4 zr5~?x^sAr4Aom`tme0>^1c#q$Jm8m?+$)VKIb65c?5@~a_*cX%x&tiuWSa^FSuVv9 zJ5kGiQ}V7X&suJXUsW0o)KfESWpvpCEnO8sOS>Qh)pd!$cR~EgM_o@UI$HHmz(@#Q zG{V9p?h`*U*Wo?pb$N}-mDbc8dB-i2=(o_D!mf3vE?=YoHIrD60*{7lrvRp(X=&jE zti~|^K!lS;id0sL+sX!xP33%Z=?rUW^E~$3SPeD{5hVERmedswljoS!%FO^9`NGNY z=J_l$)$N#3{$#$RX}Qi*k|@zCQYt(7k&%`fWs4p1!hzlWz9op{~MDTbY2H`MC2 zaCZH47&D!waeHiTZtnW#QXP)6zrwHpkA56mUSH@#2!|QLSFgthyyfXO{=b*5zsJCA z`18P6TP>8wf|iucVnsP1X*yqf^-Ahm zxu<-*Tg?7WNrrAIhU<5}$_E6N-?welX+M3AG(~Sp|Jhha8`2EL zFeI05Hyo19ZTF+vhBStvisBz)Q#hWCk6XxsD{B*F&*Zt@?i|V zORg4xw^;u@LEj}_GuZOK7lO43>WmU*D@0g*-kRY5+dE%oEYtFau{FwF{ymIl{fR)y z7q@p*hef|~;)Gz zuM%5q`N5piL=G!L7S_n{!lF?AC%-dXT>#Xce?Hh&d^b*h1LcyG8Anp0^~KvPn(P#zetuo9~m5kj`) zT*-^Qmc+;YF>?7?h~qroF$)DVTaVc&DmC}(FMv{$bS>1 z7j`bipvX;vHNey5Al}aO3OM3?{;Eqy|aCJcPQ7pU-`ya zi}?)yCzE$LVAESSmJCJ#kb@j@q(2j@P6OidtuJSJctU6Nvk+wV>yK@c2^6A&N=n!_ zb(scuFUUv~2Jkp=ajPZX(?40PR@src!Wg}S(0vV>V(wyeAgN19BY;{KH z^`C6^=8>wAyf4-w=PnW3FPr~NEzF#Dpn)pfoZTYF@s~XW@a{pp+gfjLi~b`-r<}{Y z*4j49IyBVmqq=gx`1WIZDOxshDGD*~3}@UMLzO9JOC#4*p6>n&B`){uGb5|c1EZwT z>RSi0-Qf@P6Tyy^=lJveRQ2YF3|fueb0w;<5YX)$$YJ%HC6c2Jo1ew+SG3!<%V zKo!j}mF2W3pmh&2^G+zoYZ%*pRPW@xZ}w?1(7!XLoqVX&X{nyW|3rUMCw;Uoj=UO? zcNE%nB<$=r(Rt)MAScY`=O0L`%Q<5nxixbNKAe_Qmq!aFZw^GhB~IiSvAS6OpvqYE z;&3CLw^~Ag45qQdTz&lGomx9k-F05fw?Y3gbJ`*kFp-W%X7TTLGS3Dk$|2rQLqpKJ z&Ac)AU0<$r~t3kxU;{6&> zsFM?r6}*Ebe^t+Z(50oZtm+S+DN8zb7`N(r+IwT6dT|!g@rpYAsSH(m;rQAqSpmaE zwHmmNsDf{{({mv%*WyNpQ0q1!fXEPRK+`p`b06`D7Q26!9s44;4#{1Z?n@*p?_iYe z^Na_T(arCu5!@mw?)037FY9y<1TiQXG9(bveTTcAG`v8L^Ld*|Z(3?xt#V!RHk~#L zb+&+{)pZsZtH_@Ev5;r5ZWb)C=IvYY2aqmng>~C$vrWQ^AtC|F>8qlUf>6Egb9BLF z^1P)RQtxb|;S`&Agrz9j`5_8WxuK+;G{3-t_smXhst6 zFg>-jTr2*JheM}?U(tFIA>gddDNzwQ|4WHCETX`XnMih}Fy|M##V^f;d>pinV^@#@ z&be{)gAW=8*MMjPZQR=+7(I481l}8t;07aB{BgwQ5fqJ7;2neBN>Q0L@BQvL`rC7U z)^G8?Ba+0&K2J(0rSdX{`(QM!(g-Es4JmGn0K?yrEE@_6@y`&w)#7)J=h41r$horMFG-0yM9liB>T3TbYsugg(GB~U? zyLWwtiipUiyt#~#%(wpZn$FXr>pQYnQ1JJ*)Fu@y+-?`NSK&V+^fpq}!KkH^FZ@1} zlX8+)_UU2I(ZQAW5v)v;f0td4ipJ&$>rd!%$$!n3jPBTP&Y=hB52U{s!2CJsg+Zv$ znL*QE!s@+l)v3>J&_opeIGF<=P}*vo)yXgfqv57k(<0uDVl2ao(IT(u8C~+fODvy^ zN}F~%AJgQtTVEIaFXjuiiue`LVU#`mCK=UF!UNv%mSAGkwG9`D+yZVt|dn%_XDZ)TOh;R zmMg%c>!dxw7M+q0c#@!f7$4}tNT)>1u`pWrzJK`RH8%G=RC>8ttv)!p9W!6v<%tJc zGqB-{zWU#*N8FY>O+rQnE{f~I1?%nGN}$7F_Fv3Y2rhl{hLhUH(ph9Xp@q0bpoY1x6~+cW8s5A615k&5j3~ zq6e*^T#nEHswb%Y+s*@-zJ8q0SJXNxH=0h@#XJPz8*5)XWn&7m>=O9yZx?8m5F+{R z&zbZ_;>$FeL5j6bY_CHqzdPz%O7*$k+Rn znFAdHBxVcML9^z;Cj9YYb6>Vd{|~S^t0&3^S;8zBU{FyfXk2~;`T6rFn9x|C@L^a@ z=H8l)LAw>4KggGGaLbc9d7SgLl1MM$`7Q(=BW3t3AVx-QbDY8msHr8Ey{L};n4P&s zgS0rG`pq1`L1sbEKH6>xANq@Q5w2>O9e^C`iU;YgcsSiIIYaO99~u5=Q>ARkWI1Tz zrp+e1J46367k|aTgtf9>CxD66h72YAv5@Zh8vb$z9`fAch4trmy|!GI5`?HW38G1K zH@(6C9$rSPyH2fT`p-cBQh3MPNGkW6ZtqtUo1h69;thh(HbLxEKlocN>Xrq_A(4K+ zjC|KqTi0~8SzA>>c&IOe<(`FagprZyKG+5ORB1C|4G{<)R47z$;QQ_Q!R%KbQ(L69 zwDn?805W-J`52MyQ9nCGU}p(6>I)#~KF<5=-!3Py$(8`};d)pz15G@X^u=y{kU3s% zJ_t9n&6VVN45ONfm&SWvObu8;`H8`CRCh?HFu^3NM-ii>RMK1;7v##bTB{|l5jB;r zk7k*_dAv}jiRbN4ilbHhD^8P}H}pD+fZkuS<2iRs-tF3z5VOfX>4=|5#@{V=aBFA! zy4K|+HJE>WX(troeW`3PRj94%Q@}8$yP0L-8f18FEPg)2Hr3|dDh+Vbw|&p5w)Z0^ z=XjJS{Fz+rex6Jp$|;Z=`hB%3pHeV4Xa7yU6Zr}hCfx&Z0^^)p=Us~o0&H0!UEJRoai75h$QlGlx?>GaT%Cbu*^`rb+q=2kZCy;Co>*XwJ9z?Z}wsK17!fU*!k( z^*QV@Z9LhqC!=@VO973+94B9C;{x6DO%Yc_yZjfY(1cv~6}SogF0aSdvtquk+kURz z`TvJcu7?#OV5$a9hB7%ITN_mIGJKO>C|3@prCH<|Ok+$9dv%-=V_Cg7?%=K0RShs1 zc7ZNSQwgMek1qf3mkk0Rr-lP8Y|!1lJ-y7Kci1l=wx!x4 z^iuRt#Vb%+apNaDQFt$jjB(F15O;!x2e}+EI$U~K=lZ>7ZnkM&O~iU8nx;IZqU@BxKp-Vrb1;L8n}ehm!D;|0l|=9k_3^ zTH*=qVcPaR)=zWRx_Lc3Ou^HF(8+^WPmaQ7E*n3%#QQrgoOHMrf7>qaz*4 zcSic$s5kb`2IB)K`j5b3fc5-Qj|f161S2Caot>ZCQu;6#J;(bC?CfuC)|UiZj`zeFiw_jqH#WZE~Z(SPIP8>P@QY{}F)D!2E}1KZD;A`ar0* zL=o*hQ$_(>X*IZANfRU1Kcsi$2Ip9RhIqqcaJio`TO+Q)ugjbmvG*s6 z2p2bCf8TV3MP)ighhVOB*F(*p{*c|l_}RbZH1U}JcfC0HLgj$NKmvqEL>s@3%qpfb zU{6hoEzoQQQ11Nkgb2i-t;lmX7c5vrZ?(W)jH%KWV z-Q6J_(%p@8mo&WF=bis2XBa=6nX}Km?`vIatzYET-nh}DtBIO^I3^W3A)!@Vj9o#U z%!ebo9DXrDj5PueeCXq~YGS0l5okzIP|#NStq{hbcwaZpjI4(Ke!MEK@incBE}k9k zP#55y%iZmrv~i+@^a~wD(D>6Ga+{!gP;Zcz7H>I8o2e2FCTb`QNSzq8e*sMI%zHJp zqd@9fkwz`Au1~`mOu3N2AO=ZD%ELiOy?-z_>$(fi? z$I*!PzZ!1$Kh&2xP`$80vTXw;X?5AAN-vKWn%=&-#io?Kd637*WKYr9`7P&sHUW(F{Ow#Hi-YGI zd1h&+EC*t;g=#Nm-^`p3VrYox13{I7fgK&B$m_=V190KEf%{jC7&U_+V`%eUfAmMe4%$M*ELvprrz}$zEhpll=XA zSa2KP4K`7ue<{KKs%>xOv43xxbsr69ETE=dIO630Z1d~=jJ;%>^z>MopxW@+R4w;% zJLA-``{rF_Xsz>Ik@Hb9Axmp&m@BC0+ll|}{wUtKuA(uz3;gfs#^G@l4{_q@I?>;q zn6FiTjO^xJ`3l)BjWq@C`}VeqSmf$q4N@T*4nKEG%>_* z5OK8#$&3&nddE{XDVgN*uE`p=(CGa0S{)OvFJXQq|KSRNeu+(My{<;JQaPpZPoKBpyf$tCGW+WB6dwX|ZB>WowiL276xvN)b1K+SK zY#ToM7~klqnbLSjuU6PKGo(YeTm`YBFYz=pKQsimw`|N$@^1vqEB&@)(~H>gWs!(8 zO%oVe*y$qPNX}o38^6jk>D8b+eC2|IynRO>`9~W=H@UONB0mt#vc6o@_ zhRZMSzTimom3AK_t9Rr6!{74xR6}}}Gn)iK{BsSZH@q$>zbJ*#2IqRlV)sn{6c32? zas|xq({C#Zu|Aq!(xiC@c;;aCvG2{I3C>ev*dcqvPb!-%7V?jNGIpQUJaU|z)HMlJ zmtMekjmJ>pI) zY5^64OmT3sWpzE)j4lD25f?xfl`cL%p!s@8??H;;pSF=UfuxI(hrAe`8~+pEj=|&p zmXckv(Qb9B?VG5W*oAvx1(#*Am}MAM08BsS#hQTi*H%mjIyO;&mb@0>0goUW($F{h z@b&&Dv{3F31K*iZg)_B3);rDHt##W?o8~^NFlX!Uc<%pw(vNyI;*I&ilixuU4iKn5 zW{1c;xRdmj9Uv4r+EV zzO*1iHYWUin(R-@cGy?X`B28+FLe^4qj$3IAcU4dBkA;C%SR;dN|*~eCyZeoh{l9I z>Sy6+c3pc=w$YY2Qp88c>uIZc>crVJQ6G^f)S1v+I>#*{s+oTN3+Q$Wl<730`qgaO z_ee%t&wuE^{u^~1@!oA*>eGRQvOzqPqP#-a;JDCa%^KbVhG8=0FcuJkA5Q(r3#sO5Jxq$I5N*TGHPwX2>XJ7$f_`WA14#rm7gk0U~dcvhQ6PX&yp6`s~Q zt(d=W6Csf5=&xiv93u(iJt2cz5*M?d#Oz%Mw4UytY3nSO5MN$imhQdYglnGPcDS4D zn}zw75z7E`)&XmQYrReD1^UYs0%f7hWCxIgX{b38&6-AvUnNm)XCz!ApFiIP ztm~sRczSqcwtXpnT!gFs?#eUK=JJ)%?q+C5QnH`?yDd+Kbr}4(JUr$zb-et3vlSju zKzGQ85|I^z)}MuCB;zjaD+>&C4C!XF%>8m*saXwq-|Eu}z2&5)Ulia$_#Nu)szW;{ zSy}*~f%r(jV9|o0A}WJlcs|}b^G!Q#))^*QDz6EJLIw{iq)3SlUXgz>bh3*r%(?B@ zaFpMnuVuX{A&~3gbg7Nxbwouy?R2#Tqw%c3AlQ^EI4g_tD~aIUhP-j?hMAx-eihi! zO&FMRu~DuoAm%Y_&rFU%icFgOOmfk+j0m71Nq|J*c0W^;BZ?Y#P!6rPTb=Pn zMc8U<%Z45}Oesh`AwDr65W&&|G~#yWNheN}ePM?z-q@N6{x{vU3N8*rCeSWadH+nd zU1-UDv(pnBx1oQ1I7QOlQa1pefK~SQ^dGdV-@aC?2leK?k3Kbg!Wq%3L#_qh(GgC= z!QN38hs;IgKxTSBRF(kLo4FoQ#{T2jo#!ZdGk?_*_LP^(*&PY*prSW1(}lEboV zVq$jEyN1fV%O)!P$>VBcac%Y7`pi~JPNqAAak<)Z+q7)036AfU7?gTu%vDq}7TRCY zUC)Gi&uO*Egy@r*BV~4mvqhWP!su1r?#O(isL=$Jhs07B@G|GNHS~1I5y*M-KT@`G zFGi(U+w-Oh!F%HCLCaSXj;q`whkq00g3;j;@ScKxl=Vd;;VttB>g(@k8*i)IXDqEj zX1wsrq>z$U=pPZfk(Yhsr(`?CxL0J*L0pxU2kSh4%eu&N!Um*gCE24*15-i@w>~F6 zo#R04MfdD|&tRfVscsYGIVD+QZ!aM)V^MW+YMd0opD($?>wU9g(R7s74I$b4_t?_X z;dge=T6qO|WMnK8&L!^&qWJ}MgvMz(!AUz`!}46e&G5jZB;0x=jLh3ZP~MoPH+ZI@ z7H=^KUAqZ8wjBX&yZ6AU$^9cqjt}+c6DAsyOte0QI7MCtHwEnYeFS}Cqz z2Sncq53qo3ic6CLESYuu6X%YgqWC%VCFy!$>JJ&LM&){E5h)mD{`GQ4%(I_jTRTgFp@!;X%7i7KuI9I!Ze%p;;|l_}(Fl!Kppx zLuzIBc1Ve&c<6ppRFS!I#?ncQYS8&I*q_o8sFYfaS;doSL$on4B>m+@FBT+1@cbp1 z4@iE$-+s#M$Z7rXA=E#C zF_adlVII;QgUe$`W|~5VMj_yqNOR1r2xE23clk;1RwPlx=QmB+riMI2h=%?eDVF`w zgW7+Bq*A$vN+5>j&N!a2NIu+CnkOv@n-PnSnQM?^yRFSOnNlP0x>KkHK_?;#;6))Y zTrC0Y&rWrY7k;7CA8~FV_rM;?G5ORoNig+ymfF@N&o!9##~Ob&#KZCSVAA6lpl0i- z_-qCalA6_~Fz-IR;+Aq*cP}EPSnG*`chAWpTgHDce&Vm(pQ^iq^PJ?>;6=?tp%qBk zJd@0+PL4DvWr4@uwW-m$s}r!Kb^FeNDoVsz4mkpwiQC{w97M?R5d1rN?7b`I24vDy$ z&W@Gr5!#r%{B!&75$@r55YD_$@|9NF^%K{P8IA={ zv{?p??g}3Shpmw>#|yQjoTX|F>?v}~gwpBjhEwWFHa_ghp=hKcU_*fY>B>j&RFy)c z2EabvQvuJa>#dLJLE>`OydqRw=ErJdad>xcR>|`U(2M=Wf`<=!SMXKp*LA%9CL_`Y z3c}6ZN20t7FtiqNa}>bI{3&vzAeqagqFTB4Gw8SlmO1xhch5wdvH*vjs^V}f9suWs zO4MKRj8J{Hp?u8ejU5`BAF6n2xVQELX=ye2}o@(Ys({={)-wTSC;7VJj7tIl&v2~t)llZ z4dd0Kt&kHwp;re`&&Hkk)#B;`B&JUT=McoaV1mpnOTcTFSox@V>c< z#G!4>MO&rbJ}Rbb`{)wCg~r;39!+FpGPl4JaL3Ww8p~~SHMri8Z_h&P=tMUXGj*V~ zfxol3VwyD1jr!K>FDajHv`XJs>#*IzS)^0xpISX6K1vbvJYnlJCQtTV*Xeco{5 zDAq$5zdi7$S!$G2($Ah)ID%Afjv}3=Wsh~dISQBkXdH%8@4Zx(Sr$2KrEYbwd#Yqg zA4cBgf)nPI?wrmr)rCnsCXaHX*V~c;2$^DIp3V_)oLL;E2;JxJTXKD9St$O)`TKsZ?U@p3sg_#zpWp;xD$xYpQUBYpbh@91EF(?o zBec>n4_i*1BjblgUTB<88~6F`?Cph)ulop0A9=^^PgXO!p70?;Qd^ya4L5wmtGsS) z>+IH{z*b!t$e-~nQUJt=O4!`SoCsasYB#KJti^vwQrU?6>i%k`YBPQ*}3qEN8HnSEU6x3z@fFM7=e&*F| ziH9<#7mv>+ELXW65@ax?1ESSnb7m5bJ#v5I^?t1t2~xD&Mm*G~daD%{G|e|>Fgo5d zvA_6JcrO&lRDNZBUaWOkC3srrJ2SKNDK2jD=bmWr=eFAH-f{QlwIre*|1pkN2Gz$< zttBThQ-%oyf)|lo@kxq*{s0jXe<^9{zR_hj`%Opwm`tAFRRmO{$5Wpr$)7>!{|@`P zr$#u55S-5M%mR_PnZ8r)u_0&&U7#>m@60~d`JLEdbLGp^{i7WuNr}b|^?7g@^NZ2% zI%ytXVGq+?W^L?tU6L)6@HUtZYs-yM_;m+wz&$Kpzzj*y9VI;cf!fNH)!io8GP}ww z_Oxrbcb!x1u;#2@Tr?LDCBErlmU0|`_AdGQNtIqBD60JFKZ*xUc2;L`|4Rl3T))7o@w{@$UCC@mpPi z6VX=J*zNyTV^jIEMqsHPtxiGI#8Kz_$RX|)s?_Q4Vt+KnkYB%ET@8wLi!JU>d@sE@ zPBc~I3~4kr4@*WU3hISc zj~8=C_EU}+!(VN~&zK6CA7il!$2Wl9G$$UPzx)cB0--8qq4 zPG4VNTHQQ!ib!0sV$449o$8Q8uS{e@DVyzV{05sTE-SKmP&?{oN5&I^(dSXU9gP$d zGPM}u^^+ygH3%II9nP&^~n^~SBhKk8le`NYmJmqX^ENiPe({S9re1or23E{_y)3Ze$|u6 z+>xtO$ngPY$A>{nqHAD5?Un6@z3rRxRC}xD?eJ@y`w@YJqf)kPE8+5uU&wH7up z0wql~G$|Pig<4>|>-UZ`@5QPeu$8F1zNH&*O0;FqGf`kcd40MB729YYz~Ah+^iJsD z#m9Q31qMl@ta~JQ*nVfp1rjmc4|XN6?(T)(Sq~n$7iehQ-5-#m0_8oN=u$nKVfr~W zoH0>M@90c<`2<=Pkx>n%qB79~>aB2KDmyxa2)HbJC*3FZnr`3M(!B*Dq%O;MH$!TI z^jC^7w8AP++p|QAYj*H_KJ19iO6UYDnNydc7R{n(?>786HQPF|^!6)FVcMuX19y6v zUS|LzDw5fqApKwi*nc31^uJN+Q#P317Q3(fQ^u9p=i`Ftdu+n*RidP*%{#_uCTUm$ z_l~$*Fk$vO|IS_R&Ilf2a(Ve;#eCRyKqK(;h{D!^?gf2vip7gUc@tNa*Y}>J{XX4v zCVwb>M(vlXwD_lQJ^qvf_IKb3_0a)S*+ix`N2myBYC{&sGWT)}`>Mj+YPas&-$Y1E z91Ws&vXwTwMX6n>L%pHC;4kte&i)Yh)TJoQ$hTJ4QRe!h9-ovHMuoHstz`J(vcELi z<|iV>HL6hOf-iw(=D9y5=GhY=K`{JK&Wndkp#J2Yy-{44)#E=AhYo%a31`(W`xPLQu;h4_ERN=TzXWA96{rwOpoma|mF#8_!*2sONW(U=nz2JKuJQE7p>>tZ_ z+P~!J#J|x6zcm@o*7v1<3RH?7bcq~1FXf2d4^Az_Ij5RIYUxt4rqK>e)kfx3r!`1H zFMKSg5a9o_qmUsO%;%Pb2BlV_>YtUB1srgz@wH4(UyKrZjXFzM%CMO#KFU5m8?V3o zg*9@=Y+(AGpktcGL{(a`?%dNAb+K+95e{1c=9U|;s!)iYL*WsxDw_!HQF&Qel<(kAaWQfJ-?*6Ajprqz6cc{-`|5KIG z@;eSoQ4b!|Ui7>T=XC2D(;F59xod&h$9!+&+>z7)LbvfBtzm`m9nr51Clii=fdS{< z>m=^m!DdP&;;apcOX5;W=vo{lq3@!PjIL?U48hm;-D#sxz1cwD9fP~UVCbF8pbYN$1G-(H>5zDQAQ#F=qB%OQ4D;tIJ0U*#iz_yAR~c%4q)q5df5CV?VZ<3;XQ8 zWLo^nqhJ*%)iW7UBoYc)oW{{1!N!i#SohQz`aMG@%r@Zs=7#ARDtfLms63JYx-KSz zWlQXRGjYjbqjhY0^j9{*8UdNaWoxUAn=S?L@U z$0{dzEEsIgt7zz>Y?5ZZow2`ALWfH95gwvhrQ28_^rY^t7hN?{Iy>{e2r+e z6_cw?E{|shpwRZzgQP96Ts_%jl`j&tU!GR^K%)wxYVor8_aLN7vCi>O=T1q=-IziD z6`Wk9e8Z%KE+77utRGRexJ;Yi;cjbGVG9xTIprVIZ5sR_Q2@j{zY2mMx!{0}z{7Dz zyzkNk14|8+6=yhCrjJ&&SQYC2@{fA(e7KFbs$mwXe!F+@T03T?4s-~%Y0qL7}Z}+fsDMELT*}xZBBXYcu#^Cpuspv#baCJAgQ5<=qlv3C-faNj5N_sA z15>$w3?kE=9-3GacitRvgXsk`?O$cRZ^}X5PBPQi-D-!FL2A3to(=`HSy}d&r^r`= zRK?%V)N`aW$p_l32&LX2?vBm$usa`24!?u(e2*sy$4U2%Ng^b3ciiqvEs?@4+w79O z7C8&8($7CpIHpfEr2O%GND96{^B4gU-oGEV5>ri3+{%ID)>FRw4_+@4nsM5nq%m2a zyG4HR_`qLiR3Z(pHn|Xf-*PonSy7G@ZK^?$x9+z;(RZsz_O^l^+3o(pgEI?6hXt z75TVU#CcUow=|yV^XNhQO_*DS`7i9Tsj0XyCELgto{;+0zV&21_a!bJe4%WoUP7|R z2_}tI|0Kh$Aku*%ZkaT$#tBaU`GK>GjzggTkm$Ua-M(nd`L> znQC!CZZB1Snc4~yf8+ruE2yQq(kf_Mv%tS1gYx~(MabN7TTJct(&oWpFvH?(xw;bL z$7L{6AAQ(scvJQ}we^^^?f$p@j}B*iy5izH^FNyZ*}$F*INst8j<0Cua#sq!$GTw@ zG|1QVc+Q+dHPrB5h79a>$bYYM4un6Z4iJBgeHh*Ot>Dg)0uPijo9u`?$@6;rIT!hm zUg{n876qkGwz7k2*Ju{*&Gch^U8L_Cx-Y8ij>?rjmorRXDEfOU>VF2=dP}c%epqm! zr7oJ_!26)=KOQ)bK*~QrX|*8$dYad~&IFpYTMGAIo*%1$SKogi#GX*JPxkgzHolE^ z>yjP5{V9F`7)8{dEm6~~x5W8}A_jvvZ=(l-(0g`aC=u0yWt9xYVGDQ0uF3u=4u)a29DGhHlLV ztk3J&>Wfs)7KUP$O%`ytk!foXb05=go+xtxzB0?gT5DHp%Ff%(c10%K#eF%yz5Vpt zP(rCjQ<-1AvSITnqxIJQX$GG$`D_xa(0+eGnuyo+NBBw6)=Hl&kJ|<2yK>F^9f?oh z#I{@NC-QuWIjl5s;V$<^XRI#$g9!j!2)#zdmV|WKKa;}TQ!O+kj*lGr5x7mCH(qSFXS4kw^ae7h_8xP#m4A=(OPl{CPA*0~JI(3JuV|g+ z?YQ)Wfb7mrxnfzOKyE9;kvnPky{pQ)brj11!-sJrrMiv4wAhbCsLtTgT25l+N8a-b z+<{{Etfjv;WGgJ&rg9&IezNS@tTP0#Og75L@!e|)`$1Y=JD0w~}o6@fY3X!N>oU&@Lzb1o$;DrK%5dEA5-L_d$T&KR)5chQ9VMZ+t!9 zd#V^>z7STU{(t|W!Go6wmBV@g9Teupzy_>%J~!LD`}==vAO{D)0Z+h_qrrAH;$XTk z*KYmSXTd;t6mTXxVcTM3hXac^w0J@DfuB4L*_(Kn&X?yF|3;aeA^l&mGGb)tOpLjc zR|%)<1GgQ|KX=+5FRxbi%n#X1IZ(LP5XmSC1GLjetP73f-(TrDGiO$ z^RA8g>M<)N;TnL^Nf`=_HNWI6hMC|m)A4xfrP<*t05RFlghP6lt$DFEBpsguh49nP z|I^L_axqq*M5fMQ8_qsI;VbAlWKYxi$p5km1K)6Kus_@`u}pvcBZ+^^h& z`-7r$vJ2o+}>< zc_VwFk$`elDEO$o=|NE890_a*$o1TjF`qwXbvBxo;!4d)+G zjXaFHUBCu(X{GqXtb`vvR)6=-XJgA*&=10-gr|Vp>Yr|H#K}7Y9X~OB4^NWBeG!dR zSZ%acsm0FW{nzIX4z_zIr;_VYxY_lVmr*oY48fn^M8ljYXX+e?S1Y1w2)D zXeIMS#ToOWkWO+Ef@@W@-JZ~!4Nlf`8Pfh7VBH(P_G;@lN=FqiGLD76ZJSj=d_Fv{ zN4B7U*RW6dWMKg#6NV@Y)@W?7hde$=1w;2Ae9pl zR)`eno#c)FsryMb$}1XDtBbzkH-UC`fmA53Bw8Q9?1i!RH*0l51lBeaBh@?9P2v^?{>@R9#kwzrD&MSx@Yc zosS>SpEvz_^uF43g-&{?Zh%(0jljJ)8~JA?qs{JM&>(HT(!~0Yrxh264r;a7Oxk!L zBg+pQBthP2(A}N;GCw5+1=#5coaKOkG4P|2f_sondmd1Ujsl?{TRmvssBpo|FUQTy z&DHQyz5@8)B)_j0IQ=nitjD1PuXrj3dU1ovH*2zF;IyBQ{qDC#8^gToLT0M%q-pE8NvLQOua9r zEfJpsntl408EI)+QA@ok+5~-L__E%PPt=>)p;;yl>T`aO*ekmFI{y+llY|WsE+5dM zAfwe>;4*yzya(mnOU~Z&PUOKtPJ=@Z>~+7L*;6P4U%Ai0bTM$us4H!^H?)*%y8U2s$2il)|&#yYu*qc`^10 z1bGJDGt3M(jI!q&Sss<7IISJhqp)rqD|+Vy8>|{$)t>N{iwtwvX`A94(+p27&w+;W zsb+tG>|jzVZyh&^HO520=o2E+Dg#ao{ea60UF|Mw|EdQ3b>Ep$=yyW5G!Y3GE?@B_ z&#Fil+dZz)YL`XpUR7E&2)KO|jm6dN=`D6DYEbGtbnHH_E*Z*>;vop7O|(O!8x0JC z0XOZlzO$u;*B94Pbkox{pOR-8xdg`JFr$D+&pcD)lh-HN!9?z?DiXd}1+nqG*1Y6` z8?JSiZva#Gh0>J{Qxxfo|GR)fwZ=jv>K1}W{wm5dF~rcGc*fWd7ZQXFqSpQ<`8IxuEe92bwEmJaFym#U56D1AG!`)lmg8&}9R35Twx$G;>n`cCoLquB?$$u z#jLByya^FQxS^^X@<}p(>JFo>C~e(`9}Nvw{9x2~$GYf_FNXeFi}dEL=!xaGiy?L& z7LyezqSYoMvxdt4to7J#r}kFL&#U_b(cpP?@th|#S>$9VE;NTq`76tBSOci2ZqfeS z(KRvbW+~Z#Jk?rYoMc#&=f%R~ydPIko0FT%co5y9?9H0#_EBWn9{KOtYBVJR8U!+w z94lJCpw-Y^ikwDrG$)o0rCgpl?6IY zS9-Wd6JpooYx4Hf8Y9GSg9!MjTsVJ3#*{;}ICCi6*c{mkUv8!3BQCFAsq-m!`Ik#% zlQyqNr%g?A7b=-34ZQC9Tc(h|YdRetQJLGy2at(pI=K6Nxsn{!HzkXIDhpW9X)#%{ zyfM*GDlm2^%X+2J;eAn42M9exJBBJ-7lH*Y4-~aHo*7f3Y}#vNhBCLK?gsZXiC-DL>G0^VU4Yd)tt8Xw8~*a$i(! zz@)fFXm>QdBER)|f9EE{DiQP4U;v&f#L`}hwqzC*GSxi12iIt|ipV@&EMhNmLpB!* zzAZGGvP$~$z=HQVHs?=@nf;-ZzeI|cKpdmaCTjC+JUi%Z|kO<|)k+5d(d&hq@ zeIS@q>~y6W0}Cq<{JwG=cZR+gPC2g=2XZeE0~+uuin>E-DmJ)~uo%}+xj3I`DpS&4 z#M_k7SA(f6t+5TW$L{gX3884VKfR2EXIn;j24fgu7A7^8#@Z{)-x>oW&Ln^$W`9`w zrh_;B-rKrvavwu`{09za`)52NHyxX+9FAQ1*K+8*h|?x3#edS?%SED6Sp5dsxE6^M z-N8uPdl%cAN_yTO(zDLRpH32!SxuKNrK-BJgvRrtbJB;Xu3i1~RULv93bC%tMkjpT zP8Po(JZpi94aM3uIy>)D(KC>$dtUY${k7xm@6%X^>S!%8S|0I5X-#{1IRWjA#kqO% z)c~)HA;!{%pH6x2)kmn{T;?fwAgUj}65tFc_kU$Z>AaUsVRhPMd-`Zb?s}$?DgAFM zv^o`i;&dZl;;@NQTqB6$kD{TLjSXHio!kN!TBL05C4VoI<|X&h<+*F3$&U&dQk0jL$P%&J7^(*u$mwbfMpN8+pLP2wd`ZUZXj5T} zsIa5O&)^>(|NWhUmKMEZT#27g&vVb{&lrxs?a6W{2T3JhUNLgyF0VLTA&(xKk-7Zc zZ|>nB(8DAMy|=Fq3IZBWF?8sbctY#RgQCRK6%TALJiMIDzwORk7-{k=zOld}Ac!zS zK;fE@3Pl`H6csjy|KjLchS1p7G8i^yW^(O-O}09p5^vP|kHmDEw8f1@mu20s*~W6(Aj zt(_Xde;HYcla5?7L`1gZWRt~(aA8AnnqakeVs+!|TIjlx5c2wLd)BkKWY*01^t>DD zWMH6Xmor8*euBLpxnaQC1dGHBd&Aj>wQDoTFJ$dQN>h(Bgv|h%$ zSSx4b@+eXogzWtegz&nV6dFYmKXjgPon$5Bo-Y;X79O(L2Z)O9!^j=5eQ$Ak31J?$ zqj594#@Z2BUSjdVp9jVJ79n#cy;^`|c)sk@UIQb`({m$+T0zoxs#3~>(Qkhw2`AsM zui6EVOia&ldIkg{6z}oxbXkR>Rf<{r&$(d|lMj7ubmgqjX$O-F^ZTsWOzJ;!FSOC7 zTEe(0*x;f2X|w;52b88TZ>`@C4ZQ_(D9)#^38vUe`^_9C=IA!r)WdGd{5k;iQmI*t z!P#djrs|%}(1+)BxDA$>N(#RHr8bW%idku4Tb0;C&~AE3aDPw!+; zMCfa#4&OwX3E}i)QA0e<7y%P~Q#I&XT|btqlzR}v=?~IHM&QK!MRbeBWw#Cop^al^ zHhsNh6y+R1^JSwpJT-U@NdaJ#38$_s(u))J&dy@r=%I+o$k4x}w;~3iUlTr|MqtzW z!Mhl8^6naCY{pFSmpeE6E-gt7&rt%87TeX z>H*L7CO$#5T}yz;C(rmlzMCi)EJ*<)xwjMKKKE9j%d?36bfC$aOSn_u00sZl^9}V& zzMb{0bE85SA{y@0;XBF_+wMZD8jOgLrWFL%--S~ILJz#1vx?zI3Vb|{1va!CdC6pU zD-B}8iODpD#jy>emlUp!nV^>x{3%3mg&-i~`MqQO$_8*;?q)CJcsj|FZS}0f)3wKS z@a&dFg?_`O2Of=YhART8s8YEjb(2?y?S1CRRS~LFp1OIL1R4HcKw#Xp0Ia;m4`{TQJHqwZesue+5pL`=!men_>8}%gC zR+{UkkzRlo+}U)u-U;SU+q$AT%4V%ycslPn4qQF*G}En&bd#e3PP81(v$t?gz{dl8 z|FWy9d&cjj{f#G6+91lkS4~YHE4i((IZeR()9XHHcyh^W)Ac8-ULLI6-S=+`j))k> zSmo1b3MaU}(#l-zk*G4*l0v_Is{UM>IjF)WQv}%65+bS^MK4kJ*2GSZ@6vN#H_x6D z^IiW`xZo~bR*3Z2?J5-`%=j!|PL<~Q(^9Jt47v#Y!h1~o>$SzLmm@ye{lSj$HyTew zR({1hToV_1y#-q4=N`~K+7WY4y3}a>7SaT+Un<6b1%Uf@tq3xXi0|~3*Za?PR=kSB z^FWo;yaJ;RCD)u3a zFZVylZHKthoT!zEBNg=rVIC5QUHg-{0N6_MpEw4#t+XXIfNKNu)fUdn-BBbVAt6vX z2nr3&wU{oz<8%E0(FdR6Cg+0$i;T_9OH($LFm;m2|K0(XH;pZKRYz&9;Uq8R{Ja2SCb!4qoDxKzbh5(Qm!2= zrrdJhxlsP=dB>(#MFfS~+Zu5+1WYjBZ9NGM5f;`bBRRVcN}+;wwb2dDgQem_zUVu7lW)htW_3)`kTwI*rH`#CBm_$Un%%^`PYV0)oT=6E4tBcBh ze0rKN>15E2-@e-g>AEVlyU=flbgU?5Gd+ti@m3Y1(P+~zQXoF5)OBqm5kMmD;Mk4O&gbdb5M7yNi#4)q7)$SpE|;4teFfizc9ZEYeD zsT$NByaS+>HP|fY*eo|pU1?P6)~lI`iwFIsaDDu1lUGo%<(u%LKOxEiA)#ZP+Z*uj z2?gBtIvx0ra?IjkVB`p z-kyRQl+cvprzoQEAE(uFd{JRtzL(rL!Ys^|I%Az#h{IaX@8*oQOWTWV8D1Dtm?Q(d zdRlhNmnbwc`O`xKwABB|e~_KVxxAnXcAzoP-jxkC`{!ID`-#J(Sg?~5)Z(xmBFzAV z#b@r=xgHItu@}Dc+ymES_8&6_S57$Dh94=}+hzEXWP3~}e>xan=VWgV-4hx7#CteI z0^tb=g2d0n;PMM52>su-s63O%Z$9fRbd$6#Iy+mqN|C!>NGADgiUKbg3u!7RJDVbn zv-W(Zuj8MvWU8-SuHKU^D+A*%UjxXZ-2= zpf|x}Q}K~64=A^*69mhxx-(_w+WG&*IikVtOCd z3+GD_kw>Ana*pzrIy1&fTDUYSv=N={P`!`l7~dDRa4_>j$a+Nt%x(nb*8ww6ceSW+ ze?S=WqqumX(JRG&&G-*H3*l@Dxf+05&G|Jh&!y^0=Wthgq4e>yH@8E?yFz|2sNj9S zMz{h|@<23ZId5RsNY2Q)X^~1%ftl$#P!&ZdAqar$cnxbg@!SB7U&jLOc!6v8c!>ki zK|SM&B(W*+^4BYV^_g7QfYMHl$_UI_0mft$YvAqiBud+Csaov~@B0_J@8J<1QsYR@ zg;A_f_RkM$BX?JBkRf*ugY#EuB-V%Ji0b4#7p^F8yo|S!+*6x4h)|ys5-*MhV>#E_ z;Za0lv_0x^aOoLybLrMB*8=D2*Zi2pK{vH~%%`zHjp%vMx{Y%Hq?8S(-~{S}GTfAE zCx1e4 zP(cT}T|ouZOXN}GrCss^6|r5t7PTA^dtPp47G3;4i{6IpHavF|$V`J`T!^UA+-~yM zCJ|HDCafpG&!-X z0OZKu`Lyd}b#p@s46UmayfDlZr|`}R_SaXYpEvO{j~PpT-zBCN*0(Hd(vIzo<@2gk zyPU{C{5k-LC6@)&o>9Nbj9@Hm9wV$lYz@Wu+fW){-pJKIN5R+}76U2Z$-{B`@zp0t zKGG~(hDzYaL}Q17$o80z{lhUFIa=sqc3lfiL&dO4Ui}YZ9^`?|o)-TUlpbSpe5bQf zgA!b8a2;{z6dbeaX}@<6O;%1mqk;L{8>()PLAJRj@F9&$d3(P2b$II>iAXObq$Guj zW{VO)8n3e2G+t>h;di@AiIHsXC+0K?-XHWOv(0T>BH6M>$sremz`})Qdna8^Ze8X_ zHW84H$72{wX;UPta-M5-h^4BM5zBHpLrGu)I|PEHSAnjF;LniqlY6QHZP}-S*QsSa zT0vyl$61ySo4;<$4B24Ww>83pQ$NEx z2_%3jPxynMMct#hSrA-^f6%Ma|ay{CRok#<5Z`@$Vu^mb0^=7(yAkw!xa4YU&b{etvH8qk&Ex{WEZMjIU!_j znf&HUIrTx`ktM^e@m;sbi1-4Q)bdY6aj9Dq0EiSCHQc- zW|yA`E(;jJhp-LIF*&UKLLIC~1S zz5jfEpQJGBjEfi&eGk=mwgLxnww;Ey`Ofuj-l#~WK*fUCdK-v4{79eGY{n5j6qsMB z(*E|>%e6rC@lEWn&P^kl`t@T3-teao{&;VCAtpU{X*A#WpjBSYDW^!Ynj!%D2Bkee zma*3RcX^HHXX&#Vvcv*;sisi6j_CO@l)N@K{8hOFDOnb=AkGE07R~A6nu_7Wkt2;t z{r)>Uj8W1xV2Odc=es-gqZlk0ne+jZx;2!bB2d}0DLfEE3Y!3sD>^#!1Sy~?|1Zo%8nNTtN98E@J4YCGN8@E9Bx4iYx?A(b-0mfV9ZydA5g6B z7UP+h$~*G3(;>xI?!VIlyu>LH`H|f zqLA2DLs52IgFG)vLDS7`@G8pUJaDk5^L%GC2YqtPF3?(2cfF_G^5Z(N&2vqnxvvQ} zGz|e--bgw5mH>TsxMDpF%%o)-S5rXz#0C_|$XTUDneW9j50pGnH!qn6^!mZc6I%E} z{~p}q;Wz(z!LD&Nv;ZW6N3}jL7$)!@-+89~Ha*|){h>o=m7a?aUJ#!-1hP{7v`SosRJgwVtcMZ_TtcEots93&EFDw1c zf=Kc>5JNIMg_D!RKj%MUBZmchB78;JxpnVUr$XH7%so-HrsbKw2a_3MZ(`t>N!#M| zypCjllTg!A=KW1mwMczA`%FBukpm@bJjmSdl!_W@)&62omq@1r8-k9G9i^rCOGaL# ziC5>8@;m)k=p3Lv9I1__A^0zua39`koU{Gw|Izf7QB|~U+nbOsrCUIxyO9t4r^rHz?iR-F$OD@B04mgSA+|?3ruM^GKbSTESUESi2AeLPcV@ubks^ zj#IjUTtJcTWP|?TpacD6lK{_(adz0!QMK8U* znqOo7IQkv*I5f=}Qzt8S9`STNcF_o3j$7luC~gpQC)Zo3HUIA>b`WA`e|HMKjLJ?Q z-u9c`7c21y24skK$FusuI~M{0L4DENlVx;HdN3?C_#L%`SvxXCvM?aDGoHo5q*!I} z6^)dC=3;>z`!=2yb7c+d5cggQ4fvJF>~KX}=U5}K`!83pV=JBmG;;Hgwuy>Y?)p@} z6}z8`Eh5(Zwb?5RKO4X=Vn}x0EKp*XSV*y?EBhPZ3PWAs!H4xA<_h_al;V^G?& z28w<`VCS#H8rH#FiOB4B)89nfBKd^<7907uxB6?3r@&{0xOb0D;KoF&x-vAK#tJm% zoi(J=WrPe}P)7t*Xycs{h*7c##&Wb`Y#I}rxjld}kCrTX`9ld`uT$#+pZ2NsM=n_Z z*#bGisNFFbTq3oJ3XwGs+oKL^t#H*uW?A^Kvs%Y4#MZKZQv9l&AQ3w&vN!q$MCn-o9VyT zIJ9WBF;pbj71n!`ZADJ+yP{L07W}36tNP|_@#=e84$@vd$R5k106AA;MJh{a6D1>|70b^MhS??zs1cl zhpVXOm*94vW-oDqvq6xZLSl*~M7Q1Qp_58$CxBpqmcUybFOi0fN|XODCJ+X%HIl33 zV+eSr|NZ^$$ulW4B7~diW7m^slY{+ZfU z?uvZym+JY9`M0z}M>~n`LS@2^+NGIPcUD)08ZVozS<~F(Ti3OHqH^uSNBm@8c%Q2G z!S(#H98;%Y;!J`qdLfF{YkWf+m!KE9_w+ovq(=@z0TPNVN|KR8UhB!^|^9RMWKY;=66GF9? zEWZSXTyG|VKIz1_kWY@Ndbqz6Pws~CD|T=^U0%`tw8!S$#3P0{8lA<(>M6LyI4T!J z4rRBI+dvw!CLIF%(1Q z&v68218uJ*R|L) z?s>W+z3XfmeSImzEt`gBB&+m9Z;qlR0Y6dWZ}5vm&VP)bWXA*-TgNFSm3J2~*E(sF zv{srMJvAO0xlRcqcYX0=b9idlo6ikT6uf)ZzcajnEyo}}cg>&Ib6#P7_9w*x$+~Hw ziO?>NWUf-&gz_8@-~8)4k3q^&jw+|eD9{$e86Q_h{!mC?@Hq7vvKEuR+Uh0<$dJkl z?1v>VM*jWCRHxQNDa5=Hm5*xJ780}~H&QuBl<*(qW4C|C4Ed~<30pMPmSvKT<#udx zJNvFLxvM7`G1pxomcfM#hd$@epHFs2MfH?&`qsJL;n-k?Yc1tOIR+>eN9d!pf3}#t zKUZCqo!D~S-=~qM1%#r6*W-Y1q~=HOGxzMm*{??5W~@0@GCsRr_D@uZn?gFCw~V0d zR~%Tg+1*kkpZL`zB(aUq1gw-HytZ-xO-NKI+jhpByP%hWCY!v|S%vVyO3OH=`=}%H z@PYXxVR=FUoZ0i}0fHbmi@pbE2NPwb@{|6wU;xkLtFb1OHe*;}?lVHPv zr2&g?XlL3Ofl~k$$tPztNDPCj%+_!cCLvd+U6WR_5D71XwZ3-vT!=KoOPAM$jlS^SmG>aPz&#r>az*T4So zxmUcB_Tv}ek~5eY7HCvZtTaAoSUA-X)Dih?3l#0N9{LmD(tq8&=tZ>!A+MgQ+#(}` z_m?;a{fIWbj!@9P35?3GSowjzhq>RTt(5)#T}9C=om0U4`oT4^TB{w038Pw=jyAum z<{zmy6!5`I2rD5WNH`2CBg60h^_L&p{jIV>h5Y_rwP7}2Rg*;ht{bd4{9!0G?|n-I zTyb~Vi`_Yvq8rV-*8fwqASc5Us0K|ru2EYbmi>8X%T{`{u+1YfKeRjLMp+KI5X5JxslOkn`*e!+c8-Q6b5` z6XR=1!`EtWrJU;ow|@c`EF~0QxcvyUqsVSx44P-i(0IS71OJ}ec~)ZV_49C@DB}I9 z8E|(b)BY}4s`SP0^xq|5@u&NqKa3nyO(UeuRdjDl4iOZq7m|gEC$jT5srj+ejV48& zwBbk=izhrTHO2Saodg(Q(d&Ff`6`j9e-c|1F?l$&N0B5*u`iHlI5&NWyC($XT1xU$ zwECNueqKTT?UD75j&Ue%u$KY#EjcWRKtFMAC|>Oq{lwUF z<)sx{--m)jbc}B|o#Xo#STG!bNnPeqGq}$A*lWdW{N_@#VxFO*=I3x+8RLgI`0uc> z{sTX@(7IzT(d*A9(moDQq?_EW?hKHHr=~?B%QrRq_y3^m$epsGt zR$(tl_F=ehFr5I}hj;i8aF!XSXRkSlI2XftV=-XaXn)T>S5fuFa<)X97!Ki__>jIk zGE5xlDJXyS96K0OZ?PE{;zPt`X}Gtw-$(5mkt^xCpV`EYDcU4wc>fIu=qRTq&sozF zg8Y`LaW)fljdNhxmc4<;Zc6cBc)Z4bud>RsQa`i zd2x&Ratabw1$D0>pBMp>dDAZ2M$WK*nVxnkx1CLwyS7XRQj=|79syq#QrHduN#cz6 zk019Hkpa=0VJJh)jf2fbI{&doIn@);a%t!k`yKDNeqC6o{xh67B-3`8`InB z^J(SSuHl1U(2{$g&w646rqGMnoU7;12Zj0dT+f1nKa@zo<~K7P1TB_s>xdxTlh812 zHOs<67S>#|1zLPA%NYpdh|l)B5U&#LmCpzT1JxI~E@S5VCkGN?ZxWxnCcW2}XTiC~ zF_>wC7){f(#P2}4eU`KTG2-aWtPKWbsw?r%S((C zV*b>i_)ja%q7;VMDR~lf&TR=Tad?vG0^R%yF2eXIuon>P5q(#*+gxsDVCXq<_YZD!@?~`ZSHk z(rr8;ze5~QB9f}>bE^g16dH?H5hLc5`KtP= z#7^)g`g-JN>kiwQ*CO3lo7BIq`3fA|vF`hd3cBP*jIu1PXhwPY>)omVl}jpEa}&MW zb_pleHFf$9653CUl&&)QF)LQJcNAe^3QP|&c1z242$FT)jsn{4P z`7>z)Y6T1=)sZ;lTgGE7*m*Wzva3pnTkgI(A$Z{lMb8yqsCT*H;dQiax38@QX=_ZK z&R5p5Xg37wR)fR*rf|H?uKZyD;=?A2m<_VRG5(#seu>CM@U`g&!pz7_P38$f+E((d z-=i67W;w`F0?!V!URVaN+jZSY3&%5~heq~(u5vZ+SI$L$Bg^;IuaXJFB(dd&TF;kz zNl8B}s~f1ho2iaBWH-Ft5Ov9)UWl6rgr?S6@YM=MBKeGXFiqN1%ccXUA!AM#(6P+t z2XZ;v*;H}uN6fDczTNrRbCOvlK}I>NyXnsmCeM=&+C6dDXpFSbW%Y<;>a_&zksOqCp z%keBNk*%zbap+kcyo6z2{-tTv+OaB(nvA$Cuq*zHyWnRI77FzWF%L85%9gPVrSEql*LWl} zutCOR?b>;Fi*ludoTBPpqrqBQ0Mf{FDo@M#;cbPFTNY#DR0s^8wK?7q-84HX(wJbM`E^QjMznn`K0#kZ%a;%a`r_szhL7v zqZ`|&%dd<{i!&qOp&oxPPDX`k<-2X4^=laQ3vpXtRA|9ysZi29bo|WjvqV_eFfY^c zA0^pvi38;w1mOokbGVi6~*I*gJmv(yJJ{Iz0Xc*HMslj|3>18N_|lyV`Dx}HZ;?D+jzGZ2e%5b zDuaCk9DH0D$hRoc=m@k+NlWX2X}AKq>|l|#J&CaJ^ZiF4$x7ONEW8ye8x7Y z0v|+kiZdI%YZ40W+%AFY=A}(2ZtK{jT03wt{u*+{X~}XiznT@<-7jl)^laoDzM)+q z(D^6{M?u2BB@kU_DbCYfyQTDjD};2b)^pR*QrpCc#^>wh&c@KTB*ce;>RXOe%MEk| zb73@(jq4~<^HrhMvY;xe_44uci##WEy>f7R0_tU&qxwCBv! zFZJU??Dr4o&>!$Y@BAAiaAh@aca`Y(3e&)_<)GCs!)$nejeWdt@xSQ36iRNN2Gc~W zBVR+S!{#&91PsnlZpz6t(R4C{#dCHIwVG^EwM+jfYW8cozh+Z4F1`=w_;kv9uAk-o zV4jJwGQ7z<=O`geG2ZT|PuOURr4endl$&^Y?Kv*Wg5Qzy!p<&D_v zk4jB-AVR0tt<}Z#_q7v1NH%5+wKZN%<`TyfeEi$}T~bm$NqGYN@3^h}Due~$AaV-t z`o_Jl5no80K4vCSc(>~6e9RRM{*x4yEr-OUS&u?1y>B*Bdt$j+PIb0(C_~U-s&f}q z$T^g*?D`KtAQCa#d&2zgurUC`_;?2g7I=R{F!4O3mK^u-uqzBQu?<|EG#eQQZpjqB zWJ{#PeW#55-=P9TQ05|5_`e@Z{enai&g=ssx7Te>q^Nies$k2K3|--}ShU@C?WqrjRYvb3 z2qz*lQBa02RU_9OTJ>e8m0GQ~ks@NKW$`+L^~+!lBjEC5Z-8;BaJ2LDX33_Gs^5(b ztGmCziR4??l=12bz_uERi{U*haKm`takn-D5;L-<8u|g(gNNPhc!vFDX*_4!V355} z35Ym3oWfu|WDTxCvU;?#hWveUd-AcNu5MU#{0gT(5MP4SS4-dV-uD`Ce%skW_!0Ah z6=d%Pu-}hjTiHMDmgV=ZP<3g|&(r=+N>fNMS#KFL?ZYN}4U1jb zUVbx+^m|9ypEa_s)b$)iZyK>;fE7aeO;-FGaF5KH{>-s)&?l=}?U8RU!j)cR7jb=g zt&qzp)g3E}xD3BjvU1V8-8sC^@_fh@F9sX?o#1p#`@LkQ*U?+Ikqoo=yof(lxY64A z7JqKxzoG4swO&1ae2yq!;Cci1#3Q<5k}0uNSLQOL$s7+K zAK2_c9_DBQ)k;Mhn^0^q_RWc@P=Z#%yh-g(E_%CQ3#3nZM9lt4v zphBis-M!)X|H}(M@YgkGdz~IPUI8vvq`!!FBV5O7xAH^uUe~Fn{<<{i%^{KxJdLDq z#Ah`nPy}El->yGi+|Klye3TWsA2Nz`U_q1>ifv1Xpaf zdQhxh6;>!nbWE+RQxes21q+~Z*~c_Q)YQ}<`~&xLBP<-RGjpC}OuRdNV`vl|I@QH} zrn04B_MCV;0A>e4CK|+5Yb|GUx)$0NV>H_{uQnpZDDQetUp5qT?J2-6P*A1d^USrq z91V_Eswu!m6Mjr;YAW#A-1TU&iZD%8eLPQn)^crq_q82fQpqiLPp#RmADqoY;s0+$ ze-dzQ%{}Aibx#N|HJg1$akI_0X{YCILnwH4zF}N#HgqRXq?p~h-hjzaA)W2I1m+DK zSPi@He4|vEa-&^=2C25i@n8qz`>{+v`a#>A-E8D#gXI9EV~qv~1P4c_|2{nFUWmyi zU`d!V7Bq%uFh~{(KNQ_7r9huGiNCCH=E*U+c)!WL4I(Gys%6;Sm7knxEB3q{Bjt7M zSXgh7e#F?DDQk&VdXf7^MOv=+j&vx`b90C~PB&|PbBmRSPoK!mzIetonf5MG(2`rr`=K2ZCWz0JYUf&SPb&{Lo+xxCw7g6{T{5F1n08ADMarQwuZLmg)bpHzaTheI#9nMig{Q_WNaux-|AQCof8xg3aS z16;j5Zp1GnMP7vT;ug_2Tj^C23{FV%-J6veHkqVs#`vrw{qm=7(`!&t& z0N{Q9-`@9^TiRXKDj{}0?~qgbq-P8_%(U#9m-ei$;#Ks*dyC=K$AZh^M=Rqs+jnBt zV3Ub%jwKg1*Hc(StErAOF=NsHz5;qUxbm1f{lm<2<9E^1ZvvOpH8ll^@U#y$Ms?Is zNIt~|dm%Dagx-{C{`NY=vt6C@x?uMIV_2ljBY!CYV}2ygB5@km8I#~WHN50i;O}lo z@}Vk5`s%!ktp#2LX|di)+}j(3X+V>(+1#I$SdIo*&hkZRPG?_MmM6c-G~gbzdkxvW z{tYaUw|qtU5DwWHaTk9{=kLCVn#1pUnb2t1p2weBnlO$E?$-aQZrnR6^W(?Y@y=W= zRC_96`;I$R!ZM7^GeVz)zDw3gK6pET2B2%Y3>emrWS%wEe)|VE8y?8_`--s!iIv#(UL~^o<9k^2 zuWJ03|2AZ|Pvtk4b!x)eE>b1JU^=j(g7zp=%kj0khrZ6Cj?UC_Mx;FCl}7R5#Jt)1PYUZm}~c#f=uEDn)3P@mxqD>e^h=a{tSx@{jfBoo&i$wF!_?JxO#sSUZ1b zF}d)s!lcpJy(Cdl~y6v3;X$g56{ z)}LHhBsB@g*k7;YZ-ay8{?EFyZ|Db`|N6-gAUH`0Yqu4xq=MqY8Km)D7~fUve`D}u zW;+tVx8w3%_9s!bN&!--;Mv;fbchGh>w?3~mRWXxiDOGz=}kM}Q7<4(YSt>0cY2!R z8vez?(I@-@l}}3L9!Xdy%sKwcHeiin&&(;1)FkCyy< z{`Ev@71T#*&CO~CCl?QXQF@b8zt~?plHV>ZYYxZ1`M?DQ9*ESaG*(y;DqhmEsZlk# z+E{3)q-znc-k{g`MD8rsTQCn|kbYYK7)qEPn$=WyzjV9_mS4rJBQaB2Y zu_U&K>+o%PKe1M@NpJA((kxxm#d`1Ku8hpk(Kq|QaWI9e6!0yO@`x}lXY@AD{GlN| z*U_2zJ{COx$I56ME!<*Vj%G2{xh&%JWp@J3#|87Xr*|_UO=m}ZDdlK_QrD4iz^RsK z)fxX9mF&pb+UdVM#Xa*QFlJi)%lH3XbKRHqulJ8xz=JMU^QWk(ijSEhMo2!5 z>UYA;w6|{EeS#kwnTvCh2K-s*RB?;=+HG!`)A<17*6&9vr920_Tl~Lpz=TxbR3nBT8SX6A} zEPYKmDX%RSd#M_WmNQ6*ZsZu)OQ*E4+P^c4S;SH<)qqA#fxuyM!Hg}lpsV2yZsixH zF~BLUetjwMzun^5Jy%`^4nJ}x4WX5QbB^VcFF}eH_OMvdzIX8{E}yiUw2XT>r0uqD zH!t>iwn6x_K4(uj>XoOLQ`}s1J^{lyt3V6cX6LCvais5lvt`o+6-_Qa2Z`*E2 zh<0}%l?K}0&7Mwv)b!+kgZx1`W485jGjd{I=LL1v;M1NRF7f4D5Eg8f*IB`nAP6Ti z)IQfBI#U9>_z)j@E{p8TyMFLnEpJj%Quby%-b2=IMv4D67&4dqMH zHaeHhT*%9|hJ|%LPHyA_?7~*|7nwhEt%i48X^9?=HS{T7ge=QtBZput)~qITzHY!H z`;cljtaY>rLVQ%L7IIEII*}6xBI_ja*6w^;!R51niPd80xma@;hmP(S2X!nsB9i$2 zfd6J^S2bd4q(MRC$H?cDx@mOzD{;xsuMK5x^#!$6YV9EsDZ%; zYmhiuq@e?s^&O4~@tEF2vMcRg7K13#&m4pmeM7FKhh=axQgh>QQg!VO;q%x>h{wQLQXvwP-kjl(O3F{N`RDwaHm% z_iu!2NfxjBhY8LrlCr#3oR0;D-$_$oMD}N)SZ|G4!h`!FQ94%UnhNe3zT_J_G5wd)Ky`{ zK!IkqE_GHZ)4oaTwadLj-}FRr{@VX%_s7|8akB@Qf|0vdzZ=fjqzY9!F_EOk*&?qz zce9WZwx26-mNyfTXH5SqpFr#4$1ENe*=myPlL@3qP~iJNsO~KY)lEpx`MI7A0lk@c zGn3qB1^M~e@>GlQ6OP@K+svYcf!mgIxwICZWIUd%Q&Wwk%Gb?X4V`+!L6T?an^>G$ z{x0^!zf$tj5l4x*6T){ixoqeqqtXX1_ew7Uk<^hPcr5CS!!)v~I5eU<_9qQ`-c_bn zYvwta7ZHcV4)cceMN>qB319EGfpGk)Cx=p?RJkImDxO>kucmWNNYiUwD)VwF+*A;T z_m>OWeg7X>^|V@x@br(P_xk0Rn{oAWz%!+CBS3yW+G7f7@3@{n0DbFDgV;V$ zMlAo>8?j6WUGo)alY6CK;fK~Ed={OKF)K|NNK+(Hu3?h(4!)CT8 zU$fcUy`C-VfNVl7$JzWyz!$GfLsF+dn)Li9Ps}&!j1ueWzFF=){;zTx%4OuRm9J}W zNrr2>lCJiq-i{LO9z=M4z|27)yz(x6TYI4vO)7Cmsrsx6b=*;b}`Z@YQ z`>HT-Dy}nDi4Ohu?_ZH}X`(z1*;6dRIL;E+w0J+Ll~Q9JN8w0J1i`c7Xu<4v2v$LZ zEnQWAfBu5v$@or*OezMFmJkS-cN!YTS=-370ygxvg*=g*{;o_OR09hzry{aq@CZaMMuT;^3{=-hyR{AAr{yky~B zIIw{c0*Nn_`eg^}^im$Ix+)e?KQFzL9 z*!sxe_}xV`!x$+!;w%*l=n)aLp)0!46<>;S!8e*TbuYSfc~}--)p9-^HncMXuN#;Z z7EgRanT*dEcO9iVe-?{Mbo57Cg&w)+RunocKm5_CS6Vs!7C~q#7%)kp-}oo0Z~BV7 z5O4`_X?EYIl27_`J2cGB%01TU2uFKEjp;s z-9HcU?WWMH$`11z=i+*goKzG#Knw|O=#WHOlPKT$%bRcLX# z8ZsT)d>*FLPA_)5hK8w}kBO)Rh-hhPd%*Tw7*>`IPFg`S|J1~)pZ=yCKsUo*vn}D?rm^Rb! z@@t#ppG3~?4R<}FgkscTxr7Ae#qxyqQ2dXz3OnIV`(vC}*x$Q3Q>=JVQvfEy-pf6T zBKcC6&JC>^24m85jX%Pa<#_4JxsoPTBnqi$ncISfjz8L#+TBM-Kd(+#g|jb~ER!E! zTG)k;Luo)-AZ)ahB-i^?A257J`{UgYC4tOLp|93@wIFBlbK%7poDV+9!P(T9;Us3G_F2*1$#wfRb3#{|H>5xgMTxkJ8p3=(tQI7ST)YP9m$z)hz`Ko>xZw z%T5;|J%s-A9J0V+tUzoy=39}tLR0u_>)$I_DsICjk;WAyrrn%9s4PGLmWS(cvr{AK za)-^ePCgF)PD@dZN(Z>yVt@0dY~wP z6~&5ZK#WFg9b-_>fa(f1+PN-@OU1B4f$xK2{K*SzRz(Dkk*T zJHN}{4~Okf%UY|Ww0dsPr=LALz6K6MR1`#D0y3UiKrg+w&)aOAfN;r5_rN+|B@j48 zKqt+f1vcNXp5M&y1)0CYgO%LjJ)h{8@=|cO+0Vu;nol~(7~VEV>kr8RNQE>_U%%Fa z7j7DS2w@39X;1tm`(pvz@)>`@Y^L<0j3oV(ok;ouC-! z6&Y>+C!!Kl<=*&c0VD{R=;C_V9lf;m!1o3!P4KqD{ZBFaJV@gXUtOc``viQz}pv4B8P5<_%Z`^{w zrsgZ`r=zHuX)$sJyS5)KVGYa4-A293v+szjSWUVlxG|T9Y!8H zw%#lB7^z{3@vl{0A-c%5KNrMyqBBNJ0rMH52)Oe1BLaymy3Ch`eMOn+OSDLozb3qk zu5PBHia(5a`cKuUinU;jghbGt^3%xR!Hl?#vwf8Ec>3`k$6mHV?RTA}25%amW`4M^ z9U|5R@zD_#r^1m?zH792lR_^+`mlc03W-K7h-BK7WV(EFVXb(orNTUqRP_F?7LZ1a zo*yxJ_-NaRzHk>XgUV-*^zr<_*0!kq=uQWMe3y7q1A*_uu0_{ zZ{G>DlmdPtg~*ks%-?5`)-YO7J^BMNIFV3sF`+|ZTsB;bjdRJMZEU20=3^fF%!OvN z50SKVRVRanK^PJ4XqGj<4yMd>>WdEroM9<^gfJk+UL<+iok=ZMwf3K%fgcA#uXjpr zd#9)4FkZ-{({9v`V*$2tf1Wqb*CIC~mVco?nWai|BWLm(egx(987TQ?^OLJ%B=bC9 z6i-EGz8?lysj=^pYViYjkcl=AUB^S=nMU>0l3X{^(!OnK8SF0embj9|yO=#W)bj$P z01z$yzfDi>3#X9@FMycCAb{B}9SV()GL=PD4MFH8)g3HFZ5sdK8U)hc9nbIx#~4;L z%ox@mEJDz$V^g>P9Zo#{4Ed(;=bH|XARoDPs4?{N6Jjcl)rZS|*-UmvS4cu9`cE*p zkX_hd>-(|U)smC}^7YZLOubSR>x8B|)_3qZF0anSg#Q%Jc>&>HnlapEN=lB1mqO`p zI^20Zx2jT@iTIgD)>PN~vdz|Hr+1WPo|eIT%44b-otE=F2xtCMH$Q0oLo&8fVeSta zS0@w)yQWPM@MXbTxiz)EM_DZO1<|~p-o!xC%-hE5PT4QjN_AO zOqW;8^trlVKT|<(awqw|qNgXJx!Xp+c*1`EBhT2Rb2`~FtnQGX$AExtcDBS-jb7xp zN-Kid-#@sJwXI3fM$dBrkMiGc8JIo^GfJEmKU)QaJ%9(Cu<$U>5`yhq^j{X!WV!6s zYNC4o4z4e5ll9>;Hrue{rt8RV84Z`;4@MW3e=--Ov3aQu)Y8iTQTGgn91M=mfxwOL zwPyXVjX)woweuU1KOrb3u4mll3iKyeH$=qb`pr_1KS?B)LY}fO-bQ^=ZKeSfm?f%$ zd!a`q@}NY$<$5J!v4M5BZuQ4K+2hVFlQ>F{3Xv(YY=83MT-}2t|In%TmmD6+7GiF7 zQ0YgaY-+4BTt?s6S+U(~wjw!==i~{~p&)JRWUV?XB=POe z)xP3NkI?U+^oyv@mY-z~W4Xa-jPvy;Zv-BbIe^vFXL(raQ1bixC`6n179+1B!%cik zJAQi8TbbQjz099$r{NwMN(Z;7S}Q!0gOvdR&g#NP!Bz5OpFPsjV- zm_*PW2h5P@0pFr$U}l#=hYyVXWv3cesF<9V)=|ny13vOC(f=-UF6}`i$aNfeQeso> z5pNbqr*(J-mv~1Jy=Ut02KdZd^>Xs_@@GG@ zqhn%c{l5B^;w96!#k#RRp_iaonDU%iL)TkNSTH(UOO~^O(zkV^mCN23RH6c`j+Hg) z=y}mE7tceKMoUii(F+aE9X)t_T-7>7cRZ5VPhdEKTAN^)R?y~nF`nN0?w#}ZU!j}C zmamDgBeeANT}=)uMWG+1QxlqK!pR#sp!WOkoR9xvbUZ|~Nw;9c{jCr$+Qru+a4!&Z zt$1+EyxJq-HDNEAD_ZXvq9)T^n(E0=6{d4GxVy0bnf2N%kR`Ke^1e(0FE@P|_n&d3 zBz}A`poFaN(eJeP1070y|Cn9b@9!qWO+f+HM-df>D9RhJ77MgH;4|x=c6LL2xS3M3 z&2zOxwRd&015ETz*{+1d5JU9)kh8YblQzfW6*L3}w&6ye+XmR=AHGIHZ?| zHHepa9gQ7-N`78Ar5+b=f04yIrIp<-MIDUqTXVW9+$febYpTSGl_;^?{FWoi)RQ)} zhCM%=#PXjm9~@xr)-i#QuC6a)ly6D+?BT&C?ru=De||N6Zo_{!#86Q=uKEQDjRZ*c zoK#JTP5gV<;r)kFI_P=(;(V<`@x!FunY(3eBa8AWcM%=^-Cn0hIz9RDU zU?+ya&j2<7_jhcKMuZphAGx7_(<=YDm{+i(pPZE!RFI;0Qt^GyrgXwQUcQxc*}1|8<-z_*#DM=DV3Km`*3UoSRAG z>$Q?q;iKbFxQHjWPmu`n_m5jTE`RKGM@!3~~=*WFfF!S3A8P%M=Bu{>XEK8=zBWaf%O4pk}= z*wKmkH2NsIHFm!jZW{|8m@hULKQ}# zpUI$XJZma=r9405#KkdAD)wpp$u_Upa~G8a8E5`T!I&hLcJpalTSziQt8lG;;^O;d z&%jlrif9<5fe^zmk5cK{OVrp=c07!Bex`uG3D%(Cpk(WdKBjcc831GracYR99AbWX znhiD^(}9UDjvDU#WS&ON;kuQ5gDl_1i_h)3NKJWYLad{wm@_ZyH(M7yApF;aJwaNm zt)33O_fan?S02Xmb?1~g62%F{jAw_suS6gw;revme}0+uN&>F1;;iDUfg=x!tidI#mubpWe#Xi|KK7ZZL7B?O6kC&y{Y=Wlk_;x z$H~Sw$)PkZOBpVBOTG)i9slXIvfPDvujQ4B6=s!LGN~!G&*(*CFJ^_hUiHmbAH1qKf0Hl>q5l(nm5X?@KG3EO@j-}8 z%N8AnAwuU`X-01O88qHQ5(jpo0Tv0}d)94vxgL<%(v5WYH_vLhukv z?w^VG`fv~Y@CZ(OH%o7{4&}d|raD^bQ5Q_rV9yq6*E2;+Z~U!S7C0Y(V;4Tm{VMNW zm6-%&H`xc_e(8B|uMqJ^_%6#MJWn`IOU8G7xYKEPA2)8>T8ZU@%hPId%zvUUwOb^l z`%f&QksnigzVo=wvM?uya>VNyQ(;aVne*5@)P zl?K0yr+sWHN+r~VZd`ne|Aub2tpQVxhw~fUMrVqwSCpXwwqU5q&&7IhF)fqQP5Qv}#WPy!hc!}JNr~UYWPxcVVM3I{%=g+x!>nIN^jAYBIlgGxUrf`&g zXLa^iw4p6{v!%xCv7v_NV@q!u6_o|-jX{yW{9YfLoC&S_?EKDX;EmW%2{wC->*4tG!^< z!b|XPYa`WUb;YS6%)%t%vD2|gr)2rG0mDU-WLgvt+F}NuV!T_2%Q8IBF(ZisM;XwDCxuec<{y(4yGWUY-LM4=WnLQ$#as#NrQrj8+6R)w|hi;Qh zXN&7PM!BD=eE56Zb$ zO_!6dQOyPzH2`(0wif5RDoT69?H7oAG@hJ|q{jTsz%S0X3guI}^o>#NOd7s?rQG_5 zzJ_5mk`lB5I>(wD=aX>fM?G(glm)~0X%-OE8G{6*c8R7gYDsrp;)D49){V2*Z)XYl zM;$M)R^L5#qCn9tw66~8#qZ&Q8u(ojtrpPc3>6bflF%bhr-p_vFCKnq$og`&p>ZUTcv!K0|aI{@oPQJq(*(9w3U#Kpl`bGB1!iaKt- z?M#*3frZ>yuSSSaF~PBrQjH1fOm|Ige3Lc{LAb6_Kjz+jLTWPqBB7~+pd(Cr)cU(o zMU57i_Z=nT5u5(T7xcq~D8t*zeJRTmUzDgw=9fT`H)@H0v2`B%-tC*IAg^St-$w_t ztadZ3BdOR1Ob-f9PbWbm=0*eKv;=}M$#vErX+Fr;gKC0Pj)a5+_?#M^Hdr4}Rtdz# zz3T^&CPgw9CtHd0BlD3xOBvrwm;G5?UN&&G+sln1G?t5KxH1nUumRPHm6h$eu~#ZD zePx<=_nMdIk9?B0n>VEOgCSUdt6ypBkA%ZsZ+ASO&IW_ej>T!-GezEHFUth{Q4~Fj zPs4(xxCzB5$q*5=t2f>IMs#mF2K1S)MUrdQTgTpeKPO`mvo1}wW~mB<`VswRcUW+*KN?-5tAV02rH->GD z#$2PuuRfQhr)m9kvLFMq1Q3s{r-1} zT*G$9L)4JZNp1e}EDgvKlt2_&F6I;GjELA~H>1cFO$)bIz)Y*xCjWoxOnw5C?87Zn zt?HDBdhdSeT`LzEfqv88gWWf>D6J~N{f(5;`& z_v5^P%iNz5)!W5o;mU)xnVV25#BvsIYzqgzbq z9#UzsIr%2j01;>m_+x6f zP2AeeHZ3ds6)ct6h!F^Ff%ySv z^UfFCo{gl&uh3|=KCc;nMH&#!2bzoNeu}Uk!A@nX12vPOgxpYo+(%P}fa$Z04nbrq zC%N6PibW^L_)EL0?Z(m9Z5*Q6(#TA!)LfNFi$?e7kM-pIQPcPp5J)-BN(bi471>V3 zKfi{TTjAesGJ;$J^d>!rH5mn| z;4p>xMdusr^G^E37MQ9c9p)(}YY4Vlm3;`d(?zJykKEm!W!o$s|D;&Ey+h-=M5i>A!MR!}jL z#adGE%l{%TZxk+ujvb>YFDtIl9mt!O5&6+n0bKu3adB}e%>6pX4p=4H{G)(vulQ>{ zbR=SDCbLawLh~j9~(oaJ4lstgY)2#mKt!~k4H!-!UsAUf^$l!R-S}X3-4IN|X!}$z+cp51P z*C2NNFT|sv@AVG60mKv(us}PE?a@3JlWxOn*0$UC>SemYzdxE9g0HMR{vhf%LI1gZ zMCn*q67F1$P8qPhKM8tZfI9-sb`wAVLQ!n~?(%S+-WpmdhU&8dgN6am8v>|Ot&Cz> zufK;KM%pNnM=l>}Go<5*p<2#*5WHM1i!o`pBS0#=VU!rpy)T`<`i~#o>ul7%RUQ0D zmBnZ=g{iqd=+yBdFE6(?vcXIL4GFl|>2EOKpb`E#@3ar5j8OEgbs2>?K*umi#N=SZ zocJ5*II2GiM4(_?iQ#)8q89>wXVC`1hEm>b4jDF+bk7f-S`;K$M&0}tE)T=Q47*e1 zKtP?o>k$KYd6n?T(URlmOE*brHCjN#$)rQTgLcyu!4E)7P+#i`aD%;O61l=OnT zPus^;;hN)FuJ6K_#9W=g-lHcdS=*jfE(7%vD$g4Pzfp<><~gBq~=+ zVulfMKteUjdTP7GN}%n8IYF2THJWX_9@BHWjo-R;u?Iu?;$;&ntgmFiR)_gocH`Ur zzvvPb)&uWT2LzBIKbGsL+%ZXWEZq|%=BPZke~BoMSw-TXnY;pUu)u#;u7H3(6j@!m+B^KWF{X>g z5)$r(vs+@QkHBfX%UQ8jDD_ZTY4SRZwe_d)kxqkK$GyLcK|XiVO*L4f7npW?&#K>j zSB%8#vPC56wPUJzUi=c!mbTE}bWJ?}nzLMQUQs4Jo%2d5bE`$?x8bUgLj-Lk68mJc z`xQe3nGibU$4nV%RkZ~N2MEKWT)W3H=4>29)VC&603LDbm@{~eyW)3Xes9?G!*5pZ zo0;4%rJu9X-SLuozD8DD(nO+VjW29*3W}Fg=FEMwJnx8_kt}eVZif5I*yZXS52qU0 zFfab89(Rj=>9g7brzg~n$%jOsi;?kQ7d@oIYWPy$`V3Q{%pHaAiRR?wp1ihewXaef5N* z$UU5oMZbN5gJin2!}Sky0@k}*7}DK61zXE^T%jV8y!Q}pgG*{-)l&3_S13g}a&Ck$ zoPFB12%d5xwZZE{p@!p+iw&M+kf@0^l>Pc+fZ@vI^fjiV+1pHLE>pQ>kB>X0l)vag z0oWjSeY6;;PTE9Q$^K5!v`7pU78p(nyPC*X=&@R54rNtp`Qt(TrgQtK8KOD*MvuS7 z&(1C@(%$RHTAKvIP@2gpypvDP9YAT+%@y^ETB0HfOLxQfl&9b-s5i2AJ>CKi@+yG| z=uZ0Pr^)`bR-PIjJ!|;4(IG-b%DSwxgV^}#^~a>M?8-;WvA*-rF}qkY33tiBw)dEm z{^=Ssf95qzjiC2FrG8=Owi$fF`uNz&VM<`{os86rqjjy!lkF-VexO4=FI~jmEk;+M$3!~*if=H z0xp)WTYbBeUHmr(_0}6cz#Z$MK*C8GO81?F$;6feYg}4SLkre?c7dHW!fz3W;}#Xz zZL0b`3U5j`NG&zdDu=e*lxfJ$=LGVUI!ER4q}B!7C~a69r1u8;p5x9od(gHmT6&&nu_6Q+Ia$c;(&;(ud_3v{rGrTg4 zSzy~oRM{p{s>Bk+7EZ;wZmn3(VR=m+5jgewo&s~6yoQx9-JOqTS9hi}EbP)zj)aQ@ zqNMxPw})}&)StzT0d;i5|Fg1kbQtkWfpU)hT&^FfPoQ;0YX`(&_1`zZ8cNgp*)cTt zRcfk7O=}|&1S3U|`A^gJPMdR8BEfZ_#bSmD;_KPURrxvV8EHj_5LGkHe|4KNpQHW` zeTn?+7_TCo>4Y@zxJMs(w9?4so|;;2f3r3g;)O&Wead4(qNh@X0hy zR2Ulxp%2~@?zk|zGAy|P{4xhrPEaOCE{zeDZG7;s6m+m7NGWCUnf1(c$@k@(BJDU5 z@W!gg17TX{Q&0s32=;PF(-Nw|nAF84Oj_knZ_nS%{Yj23-rp;Ml=V(WpufHXAM&q4 zOZJjn2(2z_HbTJC^KFe&jctr9OJZ0{S!tMi9q0e#c2(`!N}%%M{5-5O!D_<>mM%26 zan|L{BI2+ehHdi3h4FtQb{Y$BFGReM)W#>0-2c4& z6zg7Fu`UWczgt?+6kyh8X^EhJxnF7cYO9y^4jO7W@m`y6^eR6jkT1gL=CaWNb&vC` zZ>I(?@mLUv!h4efuoD?*|>56a+--c!k5=EO|5drJ!o< zD#DUpEBG+)cpPy!e>K~~VA`a%aQWMCz9zE4y{M`qb0V%gFdF9vN3>5qn+bABA+z3& ztB`+6L?SoR+7PDsOyk{EcrdhG!4fIbkY_}ELu%kL8>V3|Unug1&7yOkineeNvnHjcmQ{{zI&cAQ^8NyabGA3L-wh2 ziF?0De1;vbVXuMOzhgj;*pBbyH6-(U_x6u@hk`6UFCxM)G_tEp{&*ZQR0|Xj&7J9@ zSlmCW9c4#@Pk$XVlyBksKOh6k)%l78=dQ-pIOqX2tbDvnUAubT*C2iD@b>HW!&PP$ z-QwlG*DvaXT7~XiBO&tgGL1nl!nlFq{++LMG$X6>6Lk4L81GyjH|Y-r-mb>lE5^O( zW|Ot>H8%PrV7_6;K`}DP2gQ|d-P*D?GyfxOV5{vwJ6h(E@2aof0eLPXcIJmpyE`q< z9s?IyrtQH>GDV)1CQ~2yiF^~o|4F@FGSXa;(f%_3>(sr~5&Qjr_E7rhloUoDJ^|NL z3fXFdPIyQMNPw1EBI_AV6M%#8@$m`ANiUZEF9CqK-$^{%AqyV0n<<%#i@LdS>qiN# z^b#^M==WD)LV&P#iR;D0)NN$Fa7_El#lgqZoP6?-g`8~XaC6Pu4Av}@?t92jqkPx~ zjNsv<2rf?POZ1Nd*j=mqh zR=Qnr{666`c7*>R{XV#v7_@;UR8HcR>w?+4r6XQFKvJMO5ed|`D@?lGA)@NF=DXvi zh9@73h3#7Y1uA$V($k+=)x2YLX7QBWZk9_8R{HE&r!5`cOj2br^Vv^Q9u9KXhqbYN z)s^J78B+-a4hN5C3bw4(dpGKAdpH$0v@#ukmLn6=-4@ZRWAKSVMBqA|F4kcE&z62a z_nr_)|91EnWgQDmb9%Pj=+(9L0xG0HMFd5hR$dk~aN&TfPE&>t;5gfMkp|X)c|Tud zG)Lg$;jLo>V6fAmH>_%*d<~?KkJ}2`u6A&2q)7Dq&u01hE%q|R4tkll+w%&4lNLFg znCH77P7Mm;{dU^sgD>V5(9SlmxM8stAEU{)IMp zBNGBdW95J7qXp=cx(>wrWG!HY^Lr`5pb8z~@eC8g)hX*Dop#6dFV}m^0bd3t+4n`| z6VY2|WfR4ZzUaAl#6K)k(di#~PQnM#B**jX+6;BPfzX7JFkFOcl$_;mJwyv=dSC24 zx-l~8r13jNDTEc=6WjpumtV)-vh@sA#?R)uB2By>5mGe_JA|{uV+ifbSjRMpXcb#s z$X)j>zR1ET?$dX1zG`6h8Jv>*5nOfMl{o>a%?@-cS#~#_&m1*Xc^n-#?Jr)rxw)%H z^((!K-siZDfB4+asX^dr0H}C?qfu~p>_acH4Fwey!bV@D1~AB~+we0H>W&6>PUcnl zEPx*>o6WM|o+B5CZ>s^R>T;u0!68!r#nUKuL8?>MtuE{rXN2sAuV{0n>G&griLatZe5dMTd77zMj9;Dv(AOvE3JU7fon!Gk5 zE&|}CZcZeh*)0-}{**3cX{AJ@U+(9(hK(gs_dqCAH@eCcm*dY}2)IZK_kNRE6>i-( zb!)O_Cy?u(@hmpQMLu0TBiOW_xpy!+Cw3e=WD)()b~q~6g1>0C2@aM)NrBQ7E|Xovlq;)#*`SBL?>XuDkF|B4C*s<) zs9$>-l7DLanmk&_d1XE`j0`DRYCxK?S!AmrWE_N^th9L%gZwmvOu$!9;fA36nda^Q zd=NF8BSA9V!-)ti7p1zIDNWug>mlDn*$>`UV2DB7q_DnCfNM-J3|YnQzAuLWw9Y9V z?wTXCvZPqt7N2>X&{Fe*Wz+1gLmmA(?cQEYI^X&FA8-3aB-PdNz+)IRxCDX${7g{# zN&+3DIy@v;AXPxKas@Jd@jS%Z9tL(IMm|N%0=X^P>GFq{Sf-0!h>1?6r6~_q~c5X`0|Y0&4_?_kxuXPWvl_AwP-hPgZqU zU*5g%N_@tI#|Nr+V9iB{e&vn&onV*mmT4bosxa!Ag#FY59P$U^nqAE-09M4S`Megd zzjmco0Z{YxZ%Hi8ZK^yP#2kikDy)GHd5=>PebvrkI>Y8$3+l!x~p2a2cK@iT|5zDv;eEK;vZX0e^(DUiZY01Gj3r;0rvD6oFk=6k$p!g`mV6*zVmW6a4LnCXVEA4gO=H(kq7@KQXcUZa#W?`yJnH7c-2E4L6L(tKc z)4{zOK%#BsrT#l6IV2*7JRIwSf~@6_z5Ak`_tq|!u8?(rjts0Qz$`Lh$u#>N5=*aL zG1h3)=D&1Sui9AQsDmjRitea16O5KmII$=;Y*X*;Ze7XBRw`QR-5vLS#l=O5%Q+_A zXGf3KrygSz<@oI%XAU^FD;-DE$b*!NV(kk$;ETL;&u-TA&T!9%51Q3>p^YaP$t-p8 zH7pvox(&7<3W@W_>L;yA%|=W1kR1Kc2qGRhGeW|MV?_8J`V#w**Ng;;+JTX!9y{%4 zv;#IEPl%%0+$lbN*K+!__^e)kU756;dUm-vuy5qN3F5-|hE$g46T7Qb7y&!{-QC?S za9!UX4w{iDxVYXuJ>2vam$!w0+DmrRV+GC)$o9Pq{M09ExqvhdbMXZ(q_-(SR044=&+Mh7?Ckv1vx1JX)Ot*$Qzw3>}z_`E3 zGZ;F2H@Af{Q=_>*c02gJ54D>nc1!3Gum{+y;ULZmqL7rMJyXUgmM65&*StZ78_OMTrC& z#O9ZpEm>qjKop?%HF;>s{1x~kc<_DdkMhsvfAu((9L+vA}jjFg&7=R%51)itkOtjgveKG zzWUO}rdM|Z16)x+*vjxumMh(?$Osg!tnC9e=Kf6lodZ7W{ySL|KY_0F)(D^a*dQN3 z!>&%LI%Ee!-+I-Ich~!XTt^dm{nT(9&0YbS5I#fdG6R$vPsa|iA)28uDa@4OJ+Hl2 zVp6c7D?XwRCV`p1Br2?yKsFQDY}Ir{zU|ypc^4zH+wLo8*3<1XUFNCO>Mh5%Cwm`D zm^6sQQG@%pc~FHFiH?;ql3AtEOAa`B#Y!Y8wZCMwFH>9g?YjB)JUs7DuEF0-Z^+N= zb;FXCmNXnX9Jae8t*uL8^1mHJ1z{adiy|b4_xAZ=%rng^kX*?d%u{pO=pJ5GUYI)9 z(NZN6K1>^$a@2G!_9Q9)tIe?{=1mBtcH-6aJ6jNKP)X_olF~ecA4Jn-cL20e+P`dr zid)w#E`2}WHnzw@xb8a8}M_GSmhvM!8`_w~yF0pc9*z-sJCjt%P(|!zO^vsd%qNe_&3$Ji zdy9p!2jdT25ez4O!t{RjJ zBDuUyzY2-}ki(t`dH%L5&rd%c4fJWx(g-7ML+MmSrKf~x1?gl@uwIn zlX-z`mT{7|4uSfy-{!Vm952vy+l{Zh{OXpJ%1vnNScLk%23Y?>CDFf2*qk!{hl`qb zYt!bzBL>`2C+lt`OoqC=r0-s>K1c>ZqGAvJX13WLTk38sVVNNPUF#ewLrS~cTK0>n z(UF>v6rA=D|K}k|L9`P$+4gUACLW$bW;$EisK)$z>4wXZz|U8S zER204@kSPX_~+X>5roZop>}Sky%e;!;4nEAt`C&-Ty=rv;j7=Ml$d*Aqge;0{EG4R zrzY3rzc=cwmpk&wc}In@1+Sc>S)KSCmkw)&h&{}p#Z)FgRrDK9h;MW-mi@sZt`7NW z&^itMAGJyhY$hKrW_R`bbm#c7o>I&Bt}|;+uIuCF$sFe3S05INxzFDa-zTezmu#2- zuP7_r`=GQ|o{xe?BzmFZW34Y&cwQ zt?P@g5(ZFp^jln(Cl5o?apY0X#7v@U1FTvN1PMFhHe@Sb0fYOw=PTbet(o+`vR>etzf?!d$!hV7V?jVOg#@V1{SEmq`}TVBHRjph zRVVJ`Up(9^+oa9LZHhN6WN~L%)Fi#&hW*duQ@Ns$KVIXzo4fI(`UH4a{wutJOqOXp6BFo-3iwZW)Wbun%%{YM+H!{KKT$^2Z(H3} zpREoav>pU(AMp=7Mte0ImFZ`=R%D2{OoJBYK@OVrT3@lvw5qUC@S>OcZcnQIoq)X| zW4K(mp4}%V$?h!Xq88ff9vs=p=-*M*lQFnL)hGWYlN0ZKG{A<}a#8g7v^t?Lo5^{@ z_oh`#doiWtL#u1-7n1*f2n3O#vK#mpj&McATnm2nASI!{OqsNd=Q%k){=%(T+^BB_ z>8J3L^r8AzsfY>vYidW|wB=jl56<9v8}eg`7iA@ldsI!aLQpl*fNK+Bg$BQj5^;4U z!-?DYZVGq^1)~G&-cxe-vReo)_Mbi!=$F?C+8=j)4)lpu9)1nMB6Adeb@=a7PXd_wwe(wN4A*goy;_aTp0!y4_Q!Vu&pcVUT1T%VZg z2#(yx`S>B;hM^Er?Pe$fr^Sr4)BL~uTt>5u(g8r^g3c*l=5T+Ni`V}QrIg?&d8_L^ zk_ij?rC?$4ft^Q;tDCKzlZQgUxqDye2fRda|cmp#Pj;yqyQah;@=c z+WgvAv_JPWY=jsKk%XH`?3f$`t;c?Fc13pIG7Q)YMGpvE^KyOh)(s{4ueHrD4C7|I z0BDi(WuAe38v8sSrFHSUx^X_U61&Jd&RnwAtn(Df!QITqC-Q zgy-A#6&#u-J2$1B;TL^Q0p!sC)BE8dYWtj$X;|HqmUlws(@SVV>6K~8wGPuseOQO= zy0}tHvlSPZ_Nhw7JEN)beTI8ffcK+^9qM#fIzo_f8%W!jr*PS4tXzG%43O6M*gMvA zYzmF&C(gC{3`{s&9enh|2;}d+7tn{6EH@BF@e81?PbGZ%CZwfD{a`g&sxu=u`g3oD z!2FE224Gc48%BUQ?kyu=+7tKAV6{qID|+>_xvTzO7~@Y-XX%-XrG;n$YufzRM4~?& zPv&26d#$9 zIIyh$%KRE94zKUE1u${A%Zc5j9Bm@u(&ix$h4gm+#$s-5$1xEn%HkW`Y7%>9S2fN8 z9jR@Y{$)rfG2*?`6!#`tA@Z{G(R?K%kI9kz;OteSRJz|;l5iMCjm*r$Dd*7e%&#p$ z1rm`!P;`et%%_V2g8slxH;V+Z-FyaR@lwt!EbN9AG$0+oHLLmn%4E8q6dY<}5G$l= z(P{HTn4HSIP*3e=7iyAKe@0@@vg{#>RVnOJ@iko~_pBDOrY9D;*It&BM(mSZ@kj&2 zCbUnROTougKPqnm-IixezqBuNv^!6zvr>A#{&Ag%-sHnmHr>yLAvX9l+cJg*%LJwj z##friF2)&wMZX4!(i@h}`@Ct>-mJK{m&9CS^5pWIE$~yGoEU!1R{3RV$BT4-JGox{ z=fExeA!1m>pC3^4AWPgt`39$7=PBZ_na1xse3q)T4q+A)zi&20ubIqzs*{9It+9+L#&B;dkyBiZ|zlBmCDDnx#PQVarkTnijjfRFq_3|(9?oc_}CR!0XV zucKt6%RGCC&^!mu4bmn9lZbA}SO;+h zk0Jb4qve2UmFHwbFtWLg!oHeAS^ZaqAl%EfEFwJ-_XF0d0F-o3oXe>qkEG8tslI>D z@wxy%uy8FtO3=6EG`7!&l)T-ihZP{dc)6*bt$ebcbFF;@1seua8OOd4cKTHRCD32T zy~Z@F4EWJtyXL#d)K%M=T($dA6!mbvnjC~SK|sqj?LPsRUo6*jiiwm{#!2sPpMo;h zhd&G*nA#>fCkbYzd>iX`Lm$AP0g2FlHFirnFXAf7h&u5SD7&#NkinpjO0Qwc_7 zOFFIE36Fv>tzQZFV!W>XXC!9-A$x1nCQ$2!bH%&lj6@^~j*@PX%xAVKCpn%l5C#fOKE z4ed|v3twMZ`arkN3PF$>a7W*!Sv|i<=)$<$Gd+ZK;g_~?56Qg@D^o(wW!vN;iiL8F z*>2@YlazNnO1Iw*poK%Pi@m-4n}>6i=}13NdI>f};zA9c621!ltK6VccNHm1H4$`u zBE~Dz_cpvAvy2Edg=8Z``5l4A={d7M$!BrOomjL!dMRz9oobB3$~(&l(R%smI;PQA zK75|-NAGBK*Z?2UPOk}>B-1ODb4ZAi^eFQ%L+bl?UhK1=pTam z-nv$u8I1Q?SXo(F@L1?1+8h}^WYtb9)@m=zukSPgIiS*6CkXsg@a_tKzDytB48kaaTB5Qxuz%WF$;jMXi)`;U~@=qg=XW1fj z#fd#c@$c0+iL{2;QFuEbtFEN+W(ubPmO@^OFHUH=MprcW6aA`Gw#%#?ba5m;_ zHB@qI)_1mLrU`!wD>kCBnJG8gA^T*3q82&Bv+nYuYiOoNeYXc7nK~5 z7V;n`zi`GAQ{J`KxH6p`#1=cc3HZGK6V`~SR!is9r2dyGXZ#@OXUEw_U&StYFa9us z52Vd?LmurQI$)CYyGK9{?op9VJ5AvM|8%J^a^Sus?4@S5 z*_x2ZqPXH{WT%3YW1O!wc-%nnKn+t#*tU!)!Gwfo-TQ7FZS5fT?Kuni7LOt0^m>Je zHr46uL2SeySUL0wX1T>)nNMPWa4C?09NQZ%`F+#9*?j3<;S@#-6;_w-LxtY>Eoz2* z_Dn!qA$JB!%k-|uARF`CZu^8^tzJczMADC?#Ghkpo;586B?+GxMy_z=v)Qs%=6|=f zo97wodAs=ldL} z$9gW<(#**1c29gR4spq0I5C5@dtUZ`3mOidR9y!gP6ttz%@+Px36xy!;&i{=+RTV) zcPUW6RcP>LG$x^Pc=D_h52_KWo+jmAVlU6sk&B_1nD#SJAbD7^|vKYlf0wW{ayel_ekv+v{E&~1V)6yQ4!1(!DJ!ns@vLupkZOXnrmi-ttSw8nzSbzn z%V-VtG^Ro8@`m=Gl0G)1VRapr{qoaCx~_jHVuaYXrMDrNfXj^G938#c>A_=WKF1l> zd^)kzWF^KQS~4srN5n$}v8`!BZoctKH4c5`({-DbxjCG>BF9%zcH5;8ju_tJHJk)~ zyv2LxH010g6n2w9R8DB&0~zI&x=Oa&IUm@rEfA_efhdgw1n!ah^Tf|N?-)rPP7=b? zm+}^5k&vV6Qs$@`R#e{QJ0UuA6%zFeu4oP4B|NVry$3jVYBPT+cJAZ z8+&$y7LaL;itHy{YjUS!qiN%sE(Exc!Aq0fxt841CA6N~KzbehFSY^p!fS}=QZ&Ns zV-tIT0R_R|IL5YUpTki*gEweV5)bw!vpQ~{B+m(F%D$b{N3)Wyy3i;dNW}tY1>h=0; zM2qmXBj&aJlH12rU_GM4(zI|e*<|j!^-hDZ){jU6op*-4KwJFk+Kc6Io5ps2D z5uJ}Yt(jy%sOgcz)z~RyojaGtyaWy7VGpnLY{BP#PDsbZT>X?#bvN)VpX`FK zKzQ?u#X&U@%O|{ij$DIS=;Y${KiwUF7mw5GPrq4r_GarJ$)~UHz4l*0;1wDmzDmmBJ~NYI#UMt2~5SP81i_GyK6?@G(O)WeAQ z2~0lpKz`O9@MP!Js(D#@KK{1X5j&%b$hHP^Pv%tQS)4m7LT~_=IqnuibzDdRv1Z+6 zySn>m%Y|NcC6j}6(POG{;zP{wFwZko>+$V8)lIXho+}}G$WWXKGjKRozMIqU-Ti@8 zv3q0so5E3r`xw=~?p}&wn7uoIm> z7H+UPZ5n90FrvHZ>$Tz1yCJlvQa~D|kCbX&_M%&4tXB=yLtCA6 zq1u*tkm(vW7^>(rp*@r^uT$S zR3vP@t&tsgQVHW=Pb4t$@@w@r+wx}ImtS)AQur>dUt;wBz-8BwLmvIK?PU!a(3Af2 z75(&DRj5Dy8fK~ye|yInNd{LE58t9PnuL=`28iUK{$vsR!)ooUGR`;=YNWlZU61vZ$)ZC?zd+ z_ARE^Z^WM=Y0agVW>kSqgTLs^65q#@Uz^Rc=g$Q1%&$^qoOTPvmuZsGSIys`HiVcb z2ND7*VTH)xgm=2uI}#!`ur^z1q~6TH%p5*}U7UxQx+DG`m0)LSAsS*s@w#Jp&S8Tg zUmG&FBLh^~_zj&5@cXmRTo;Fu)z>+Vsqr)ox<7PA1lj!5Zr?Ooyg287EK_5>J~F;~ z)+^BbuB-1$_&dpSP9q13t0=?CEQBu>g9;$DP_t<3=#f=@xN{N&H)3!@o+Aqx@Xu_FejNVA! zeg%Uu1b8^+7Uulk?RY2K_bKt*7#YS<6fU;ix63P}E!F#UX$=8fb}18cR2^puw} zJwR=cZ4<{ArK7_2LI$~OBtnjmtE;}LK1|!X>V9lDvSo(7$DcaFSdR!T!9U*2)*01b zSxo-xd}d*p|MTdX|G~5zAF$Sn7rDOZOpjHVZ=){vWc+IreMP-!uvIno+xuRE@o9Pf zD%xeiP}N$ZetY!fEk>!@ILfpP6_xA8JiWBUG0|T?eca;ND#9Sfa2`QgkSfzNzTj{F zsj<*?INrF#u!zQJ>jBBq=bcT9?><$pBSXGCKZ2iRgc`Tojt$@J0dQZCY>Naex#xa< zhNNuyQJg+!i-<`;BMBUy)%xj=zs=bWMjaO7@qR{-7Jri>cSW63SBD1^eqdVmI1!qh zoHPJw4!mGjDVxXjAy)YP{;%b-s=^#zsZ$z$lyafZ-NC~nH*0^G)!ol$C$hiL^tP`+ z!&~EfE#g(AFgvw207-(*mA7lD13B!Ori9nzWnKFt0u>jrG8n|OoBOqgSA>jku19*jZ23rekQjT)M4x?zEB-?*+nj*Z& zTM#;uURA&ND>^OIq3xmncZscc!B3KvcLx#k{FlgiEeEugEksjY`z@ZIpIfhZTUfl} zw${Xk(9tt@XHtqA#qE;R*=R==4f;WT;%w07)R@}T1T`OD>!6bAthi3!3+<=EoaX8v zna(Ftyw8>F@a)x@BtpF-;`^5|P3dsml54R`y}Mgp0(DuA=8r1RaaR>PYeMdusK{7y zmTe2Tga9GoD>uZG{%_*^U|~uU`yAG3nbwQ)8)mz&Dn>VFvLT^4Uz~-8maE!XISx|Y zq-*}J<^@D*?J4R%ZBc6*+B)9g9YReel_i~7XrT7u_Lwqwj|PO4XtC}tXiI-VpoZPU zQN2!VGeQT5V5u;hpkdOgLVShCB&x2iUcZ0D0Hp9E>_|-;My|$-^Pd-|w0bncw}d__ODf*iTQ`_QksHv6u6OOvmGiL7E~^avNO$+pW)m!Vd5GDD#{W6_&ii zm^Y>7$^096mNl9=C-6v<|CI=;WY8eO6WLDjO`I*{>mx%1pL)?3-MV+u9*P!i3WY6k z0Z)3yN)ZVay$LNU*H+@CS{?LLtgH6B>*l;*w_@DSqE0!RqteFB-hLwf#r~(%h9^N8 zd4T(=H{%ZD$|JXLG+*-5X3jG4eF?6*){byL6|Mh!eKO^W>olIys@PG%KE8eNO1B#W zjB@L}cuj}5$%U}^vc*cA5hqFFV(yEb#nvhDU1o!^DnF^X zlqKQXHQyle$$UlxBCFiZ8`*&GHj)$Tv5`Y#N!ZMYO!~){ZtxY4O~sbF7gckTh@6K?^fvFf3{W%pj@@_k_4O3aYrVEF8%(c%81DzFtgX;2)mb z#E_Cmfcfam&PHcZ+mPM`#i!yHE zK79G{E`pp}xK2aZm~#+0GC}gsz;&_bpF`OtVRZ(^fnyITvQ`0dR7A?IDy)g+zk=#T z29u>V*yGO!kb6v0w2ocI$+m1(R?upvCwi3f zEqW}wkm@NDQg`2s`egmdwH&PqYi&e>%eI}d#bY;Ls(t1y3j1T*Omh)B>Vm1jqu=&$ zVyL!^9zJT`!3+d5iNlVGb0i7hix#Z^`|=M|$f*Ob7 zCv){BrGIJ*mp0bHBBzOWx^1Mp^I)hmxZvNwjEXr=?@b}Fy^|G5E3ydKBu;ZbJn-Xd znkk(KgOoh)ke5CY17}YC!;Jp68RGfyd;CNR)tph(F@H?u`wGAjmegetPH*Py4hE2c zHBem_FpFqS#M+~=yCr$wwZ+A zr_C6PCFMpu%8GA8+_LWLv&!TA4g5bU%44Rp(^=O1tePv$tJxWhL}47d{1=X*iTVe% zL%dM-*zLLS6F;-s63{*eF? zqUxmmb7tsx$)n^ls&l+Qnl8=hid`N5N>P9(bDtMI;~fKEJ)bxKMyFjrHRgPLUN4Gy#UuIc8d%2SMz=8=9gHA>%>H@+BZBafT4@AY0?7G5ta?Vc*4fdyL`{^idubd zF{RA`($2?@=qDepy#_QNZ*fWOst0Fvy2z}s$Lf+{VSMnN`Zpj~_l`zRck1YE3_=T4 zAlA7uHK{C+r>Medt2KRO@$~5U{&~g0>uYX~^-SOOt-JKT`T4sybPWA{1^uXdX&yI? z_tV}B2loyIlJ2tQoa#S$#Io`3rVHZ6#>f4q7?+VCoA_@Hx;E)q++Io<^2eet^kzJ! z_zv1~+aE7in4T`S^+QG3HlI#^f6ZSoEx6KoCSw5rs=es^AG|eItykRI>fSiLP}}fw z7~kF){9fbh7zu>|yxXo(Y17n1;YmGYcfU(IiMML2G#g8Y&Pu!BDW>C6nW#Q_zp)!J&qBICE8lBnOl(T~xNlH)=AFUDBV_K5r!&ldO%l$-8;xK=nw%8jS* zNb@zLJ9gRqY3qG)v!4CW`agAbL-*<6^A1t(eU`0R7T_^q}@Vu1Vw0h*HQHSC|8xCOj6QMJM|UKY+1M z5=V|<7ojfbehL0gxG+@&rzMKv+l68-M0AZ%TxsQg6U{hv+o7&*XsimJR}DYwEu0Oc zgFBYzZprMM4oViS4D4dK7;-g4GXKiu`&Dh~@-t1$>y{Th$i{}bxQJ8Mr0Nqw!`Q{D zkzdF2yU>!ReXgyq6me*L1qVs2KoxF^uFLvw7lQ4+$T0U^0b~CU%{CeV8@C0z>2iga z6*>C_{2h$zf*M8&PMgXbLKglk6%H^cMAXd;O(rx{@VxD!ou3#`)jycY2;VoIqV-V$ zh)&|0!7!PX{-`qRZXixnDOPazKb^Ag{^d$uEqu^Lfr7LqR6m;rkH z)pug6jXUWG5M8&e`1C8Fv9YmXH#-yp0iAC*B`4_r`->tM= zZjDy6$Ymj;Q6#;#Rx6u!R3S@2OY-Z&S5eWW-v4F@In}&;b9BOSoI8g}E|Wd&60I2H z_oe_gaC!mCZ%*dZmFBAK={t0sI74!iN7MaoM7O?ia&p5#0x=aV!HyVS`=2hQ932Z! zlQLeOFsYHIXqUuxt31=u)cG4) zZ|X_-tJJU54@PE>{Y28@3XOQ0Iy#{V33w3EmuKK9o?{}P*uk+%FBr zOjF({qf(|)#)bpIye6K5^!XONI9@WeZ%Rk*;*?3OW241`Cn&3n-Dw*PxiWn%Iodd!11nQsGtZXnnsZ#L zY5p7UPvVN^T!%ag-qYlrOM9!s`ug(l5!_2q3nX|KK>eCp{u* zC#{NVhP`Jg6~|ViLcdxCPnlWH0cp)Q+Iu+SkMJ$t?w*gn_5X*;5YG3k^ z2e*e@piFw~sg}*j4e$0K*s0aAJhC(H%&7Eo9+UiYIn+hZB_s^rfq_)?zCStpthZS2 zI`(W39m1H&aeQ>$S$XQj2@eg zjds!|gSOvmE$T`9%lgK!R)Mn^;@5+NS3-IUXfdP(CO<(jDqr_iNl_8JK^O-b@>I^J zhs!+#$bTCRdoR<2BZd@yT)qzCBQ>I%@LbxCS2I)G+B?#ymh;-*;>ktkz12qZB%03M z@>OkFUv9=zK;nHl)U&S1VfT00r>ptj$66;))hh4%M_*dpz{|c4s0rk!(}MHH29;U_ zf5_b3RZkQ>a}`1hS6{^EXhp~f)F1iq;r}D-t;4Dcx<1fDBTA_#9Z~|)2uO!?sdS5U zgVMEW1nCX|Y3YThUMP}sxW zyw8NYsE67MEi&I<`z$=5=#e%xWKo(gS`a?ad|oIk@%*h!63xuj(m`D}dq;9k_yI4L z$Ke4EF1DXHKD!w=Y*AAAHGP;;TN+cWkTH~up9tsCC9OJD8yCx$N;rX!t8E45DA;z= z_ee_>PLy=y}q zbt{{h)VWLBd}C2iOj_-6)zf)#x))n(ret-_3=?|jKWN^aB~4cFP|yC?uuBkXX9v_K z@XV-oFMKKX`gV?Y$EBoOO7J7?8g9LWJ8;!b>gU`3=pu8llVou0zS0Ci_PT*?Ht~IF zkFKS3rN;gkm2Yv^ z?-A8DuM@tw!bSA&yP1s|i6&kJWtG!}pauU0E<3k++t*lDIuBu;bWE*3YmWG@Mv`{{ z1<0vZ?LXqC2iR?VQrsTb+%Vpcc`s;b(L4{9!fm*Bi0pP>H6)vkm=`xd{(^TG1Ksw3 zIiwLji=3IG;OFNTy4&?--$fjyhHX{el6hd(?e{IL2AhnG&mao~i}mcacHcard{CZ2 zp=EmHni+AOr*Hq$nr+>kaJq`NpOBqcB%ZBSCM>!b9^^Lm6YrwxjW^EVkEu#>a{FeG zBQ6o=+ z3cKa?w0bFKjkB}K&*F|>TppOfP_c!_s-92ncE%)zOxcDoQD#9XC6F=7=g)_OlkWng zj8l-15pim5I?q$GBgnEV<|6DZJD3^mIFtx1U&TiR3S{I=)>1~>9l^gISPkr&Q`jw@ z>*Tzvzj3bl#1CJ+!$bS+eq=S?WamcQ`0TIS&ymo~?Nyn5qqc*#&te`N_VfgOoGn)< zr)tK9R2j~%)ea9K)e$+{zB5^+^-!|ch;U8|5%HJ4b62Msssp15>+2?~6t7{?F>w;s zMYdII=M#GiRRgvDa@_7gO-Ny74~nhXXPRs-3%~X@YU6cw^-1W9l4SP_ka?~@c-Lti zfv$%p9FpX+60jk&9f=)%lz+DOmWA@kQl(1pcXxufnM>W5kR`9BsNaPq#Y%70Sykg_ zF7JdlCf0ePVWnRpB2>L?CUuH=PrGKZXC`udiW%wYMa0eVYq?+S`pn5hg}F9Sv>x0h zc|Jq`ao9|YFJSbd7cI?d^_4TSb%&3F^R zo6lWeuHL+gRe?JHs*QZFjc(MH{Hyk5I$z4&D&}VJTGu)JtsBaVTLES+vUw%wBPn}757A%+ zKEJ!FV$2a+HKZI@xz@3Wv|`>i;7B)mHG=^-zBKS??YJ3=}gg%M3z9s1%jMw zoqv7NpyE-NRnLIJ^-c)7p^?lLf`rCMh1Jqu<^mguHlhKVb0Z1I%m3MOW? z)4CFGGZB`|%ovI3QZ+^r=M0xV=b^Qnv=oUr#Q0IWghQ^JK`u%MKIlE@?Qk2*Pqq?8 zsT|L5`IxN_dkn>U5HX_Jl?DCndY~u^I`V3TGnMzL9~(}|?g-+~$%f}`Vty}~UWxc; z3BtaFHw|)J1!hJ&tcuP*?^Ay!BxBBS3SB<{DQ&1�FzAb^0`_S(KeQwFw_)qnwW4 zLUb#%%-1UC(I2`b%n62mO(nmD{VXbKPrcoDojne*&fsHnEL9vg4RlSG)=?gJ8ylBS z>eFouoUlwNVev>h)YJaa(11ziUV>WVjDqhlt;VpRFh5EbYVpcqtFnsBp%ddu={e$V z?JH)RP(n+sq$1N?&SmOf1a)w4cCPO*-Elf9*~CDO$a{V_9PlCl!2hz*sw@ zIwg>km4yOd&<$KJOTp)Mi)JJnBEW)IDvVIyWk2+#y!l#!wNXh$FxBK|?*-z?Zu{+z zgOi;}4}+vB>}DJ>v?T}9>^Jphj3C@`#^mu^fBUXtB$v#Rtqje?#1iL=BYYMIR181Oo>s~A@ zP~85~<3mbr|1`4&y9hlg+W!SvsqK|K!@`;u&K%IueuB$im6aEwy^~6N`MlL7b!Imynb*bR%xXV!M8C)G zSyk`fMoVh1f_E_8B!Uj-*Xw@-YdJoOcLTKi9pY#0{rnlD@@q!C?{-P-i%!B|+-x(CVT2NSM-`kR8 z5fl_e?pu;wDG8y7q=lcIf z#)m_0#M}+@FP}e$$Hm1(cCg~03!&?|x6B-l?tkn|^wihEPCEPa3 zpu&OzWY{ATo(j!lV0!!Fh@gYo$KYejw;jez@!(5$@@v3UJ#LbVgogFf5U`43!s1!= z#j7@ft@FQ^g6oGdzBIz^G|{DAfOn!g!lCs!kX2%Jx$PT2JSj18e^`bY0oKpMaQnvQ zA4~Mbzn%k0F(7&!Yu(>y@UVWTtmlU*;k*l+@t&!zu&@vod=dG#7S2>rRi(c@n!7ny zSCuh4oF!v>u@<+IBY0z)v%PDg{?9)9hDUiDpqUXYQk!DS6jQ^rL;m=JSJ< zgGv3cr77doHsI~N7gbBG0V~%>10BGXlz%;_u6l3!{l|28+mT@QoHKz@S$IB&0l8;0 zBFD9qon#dx<1t_F9M5H~lLOx^DD#E>HQ}am)1iyhRLUqZftGm9L$S*7f%VLWh zqM{H`|Nrh6yTAYK1F9L=zlR3?qD6%y_8jW(rxhmb7BNnH|fz^@cQIb>$M zJx$=RH>}*=|2c7}#ON}Tzge2^U!dy$Uikr8-06YT)BiJ(VFkGVxBH&=v*G_=v+)8n zC7J#|?ftX4xR!T_Parx(@D{10L5Jc&hTOxftgQ3#szwy;i6``m8XD7W*DnL_XT!O5Frf>CSR&3u!kBC+KJR^K2vAQCV1<2gi_BC|Legx!z5I$c_{Kj zLc`(UNms|L+ z&7O`IX@3J7lXm8nh$NcxQ6I#0kgt0~pV#YyE%239zuhiM%opeL=X5yFJ$Ru0`mkrR z%<$nh96kiX8Q*Q&395kr8Z6drGDwm{r=SbLK?IbUleP8?_k59tJ+WzNX$T|Pa;@Gd z50{7Ur9e)q2O+>7VCCiKvwmKc)N(s$<35}Hg8K-y&Iub`3Q`<);IOEjmXk;Qz}fr| z21Mu|K!?U+In5DFz>dnGQqT-|8=@?qQDh=*0%62WtSpW`BsY#D(%B`YsY%p$vumvn z%2|hAg9A|y5K){@G~N2`x$n7F9}mk!8BhR0Ah6ePM*6q@NM6CCps}C}MA`QN`}YJz zlU^$-WHDKSFu-^8h}3Bj!NAzqF|on-J4dD^*o&(XK+M2iGBALIZX%o0DP#TV3R9=G z7*(4kzvndnn81tMv!<{CkJIulP9Gc*dFfk_-meFG)7jJ0%6~TNW4qE$@f718vE&5q zKUj@YSO%;t{GbQ^jqX=8Gg@GCNlD#PWDcZ++_P=YR@;J$(f&4G{jKgE9~)C|nKLh{ zL7mWXWh^T%x7}Y9zFwsj6c!UhdG<=pGxhFzL%aUDZzd(!laVj!-k=pHU0=HuXw^JE zTJLKWd#>%;Kb=PD$b7`YRcbwFuOg(>w7#tbtsG+Y`})opt{q2)tl zRgC})K2N9!-o^Rte(h3a;fVYIs|UscmPuA`f^o`&W_&$ybc)O`xFR19mH|=`IAg}k z1(=x!5?>f^4QI`n5m9UjmkGwUhZRH|Q(7X{!+b;Rfq$-qpHHj>6Z@lUgQr(Omb6kg0kRQOkZzS*_$F!g#Un0CoYc zh$QWPpS8ymFCp07k>H&VNKI77ZX+TjEU{Sef4RWxL%Bc7ILi$VlD>2wqd5Vv`0Nr@B{u zd-cNCjv5aSPo=i3>~V3!DWQ(*?(6&42s+Bl#?BsPLIO4b7ZU2DW}s;tbW(;b#%fyG z7W51ZbTC%UdiOv~b-i5sLSUSKatjMlJb#jva%>!d1Pd{4;%q^hKM0grKst6KJ8kqP zMA;-T6jdXtYT1fy5Q(C1u>mF8cE+;meqsSJtT;K2kfnKLfuRS18|A$X?`6)&_&6%8v%mi|CIYFpwurr1 z=IbLtHF3$`nX=#*`fSGHerczm1IpI-^X~Bl6TZgKy&R4!al80T%xy!y{SytWq1$@E z^jLj&do=`zz4(p?Me*N!24w(n2nh-IFX6>2V2R$Z&wa27Nr0s9;*0~zg*?3o#R})l z_W)JD>y~{X@?9f&s!Vfmkcay&#eiz`^4^N0 z*WSbHevWA6LB94$%Oap3VKAJB+|RUO&0xv@&w@i>WPBdFOnnc=6_M-)JZ1<5YNd!U z768GZ(i8XjA*u#cVL6z`E*z%F|%F~ZTCDhQ?p9Ez>wjisfy z1N>*iu*L6R`nObO(4`(m@;{EFbdn_QY;k|7wfbaSb7+1(bZaE%DC4*9OQ~_iZy{_1due=_}33M<6;&ml2nVdavznaRRLU!TsvyT5w~h zk&XAPujd0&PTeL;4B~O(eN_|gHtF0w0Hy#k@CT<5LWTCaSJm3fi0{GK!-v?@S4!aK; zyr3~kx;9%sC;_EBalV71yD z^>IN+uv-8+qS0oSV*D@LfX-{MqC$(O8yL=mlauH$?%j%!Xf{BPMh9E;d#)o{s}rf= z8)*sqw|Y&1nzPP@h(*i|gKaJ}35rnIS6fY%=z;JQe4P6Kqy1-JZ_1n@M@T-8S3uTv zwG$WC43?_9AqEBp37cSbaLz z-D>EkXJ!iRSny@@?jJ{K{x73}!j~!{h2Iqs2DHsJ21Qj>v}W|_cHZvF{Nu<`Z;6n9 ztDTw1bG&s{+k;isx`5%v{mN~l<+V=*&@$xj=jz@Z`B$S-58fs+kz_gU%|k%oqr#Mw zlwKO@xE+YWnzQ0`673qOe*MGr_-D^EQAIL0hJ`ugS{<9NfV=|QB_ABVy?wt~kYJ7b ze#}(=?+F$zlUSxPM`ngyFQN$w0c^OU0>9tA%4A|@h6uYqi+~OTiJ6--2;6Kb=x+|D zrIdk+Fx=qSv~59oZU<(9^uH~Jd&@bBU~zXS18v@E1ryc`R6q9B^>vXEj(c|)AXIO; zw}ZisV(9U2)dOoX4H)Q(Cy~eZCdoesgA@6f$~D`Ms&DWkt1;A1>~?+7ao+FP=mQAB zDDfYC0-Bc7@sLER!>-2a#zs!GP9&j;h@BBTRc`-KhD4MlA~?}e*4EZ&wPG7%aYUAV z?muf&LeHlSZ0ghKrf9z7pwvtaaxhLAg)2tCE5jtul>%Ouva&K5?ANbfU|A`YiE)B7 zBOH;%bLe2Di?i(o;EM!~8`$9Ruy<{3EtRH0R78Xqkb3|<41i6vGsNG(KL!Q{Fc{F| z)8H<^nL`Iod{ZbO28kIN(cF_S!{p@TOiWB9!2)Heh$kRy<@dhg_U^^~pFHPjkfSxlfpH@-OFjEPmN5THoiA04B3m?2Na}kH6ue}fhFAX@lVt6^10e8;WF2t^1Jx9Scv+b+53V9?FP!w3jB#a|t znP!^oBe@2ewdd1Hl*(b0;VzX~%MIB{N|hlvk$m`zyHufMf>_O1fATuJx)5L$m6b1u zu{2v|L!<4FKq|hEn`+ zhUyG_vxPsHZS$=sU=$fP&1|?>D#2p>Y-@>!C;`_D zR%J)_4Cc$Rni4?>_~pTgA(p*A^(3vI(*!9mpD4|gz%!Pja4-|vebp8bv?_BrOu@w~ ze2B||WxN_!M3jt!i5}0x@uO55wre@aftmK*jX`&OqZFw>+W@i{;iO6Y9`gQuV#ab- zoS84|Eq)6uTs<6a_Etrv#NMSlaaSBBa<+y3*gM;-G-#<6kK_nKj!Fol}A=~IdMzBamFPI3h%?x1B>WWNbbYal7s#bt8s z%NurEO{)UQ$3Gy`mmrf!88^8*&|={DS9)PvztV;Hlp_yeV_p_7^wM2+HDKo3Hta3= z*ZSWtT;(36Zg)CfymxL0_x0`O?8Mq!9+T=oCt43W+cB7nt_bztKb?NZ{lDa0SLY`2(Sca zlpuGTl)ZU?xgRokjenE$J6%LK!2y9BU!-xgJ>62crs3_QP{n$Fi}Sz1y{`-mjHoNU zn6eoQByx-K|8V1u=If{O@74;5XPU=jHC6(#j)Rz<*1kD{C=SUt>ZnhUoSY0eYugnc zo%MORLrRSJLc)5sZ^d7l^J_rwB}0|s*8Fw{NZdal0FN>yK8CvCTB8B4XM1huFG&*| ziYr&@+Z)A=;-Bt{c%*k3&WePCk5NO(to@&LR?dsPX1Wo$k&t6lRMhqx9xjuN-8CoC z#!YpbvQCuCP)7R^063{p#B?;%FKiW>df@p=P@D?{8R?FKfesY}d{56rsUZO#mDRV+XYkQXdk%7S(g{m=0sJojKKI9 zhe$Ohx`D8aNE0VP=DNPD$Oj@+C*HuyN=`s19ed{|gw?%WYjyKia_aBfg)6TJbVCTy ztMe2|_^agQ)^pv~D>xSdtnnlU&I|ezeQPg= zd3&5Ah?I!RzdY9}qGqZMtDQ1E$@J+T^cxDvG49cA{*u zV!VvuWSwt3^O5qlznc-_u%s$oW%?js3cn#K8A+4mS(QY?!7#(A4V@`|L)O|Z`^Py> zH{^k$vCs&Ifj@@BgNPc5yW%YD zCLi|9d!q8~H{E>$E;w>#W;Dm^{eaHCKCkR5O%hu;%@gSX z11Me~;QO}XWH;L28zJHSmWM>~g+Ia77<^jC1@}WdWVo{XlQek?Q4ohONJ~h~$Z>pW z)+m>wDui%ehcL#wtR%;Cd}1NJzKvt8ux2L5_k9+u_1T+3KQNQz0)~SzH8YAhj#(W( z3~az{&AsJH-Pv#O;(FoWTT_&e2Qk$QeGG4Ypsb-0(S_omPe8;Rr+6&eEwhkv zvLyU&FOas>c7+D$bj$13ZciFq&QL#|KcTqd>k7jlYkEnQ(BOKun6;78mL0~qxbtP8 zb)(THWQ4heiNW{%ylhpE0E>#oq^b5{f@`I98?$u{Y1%vVCYNiJRlFEUPF|$WS{u~h zwPwGg<9%_DIj+ZfR}lM|oO^+gkkH7^2!;dCj~18n_p0tsn;Ld$EHs-?&8zl040SL4 zsr4^bJhjG8L&Pk}Ff3G~ z=+<0318{fl?dC#>{&Gm>^F!NYfN~*E}5Ex8!(E`5gY+*mk;e>3rwn z3#;vKCvu#i0&H<9w1bh<8)8hkJ^-O?R`B>-mBDO&j6i8BL8eMRy!%InnJ)}`yfMrt z)y+Hg_EO;KGbrxu>QXeiXElEvJ(_e%3U zFX1!Rexs^*gZ9M>F+SZM9;#T^^deR7uX3^OjJr%C6{4=(9X#Ioj4>f4&OwF6g}=|3 z1c^r0wLIaCH~PohGXvpqmu2ESi(S|#yKb#}(x=tlb;JVDsK>2?EK`WT-W>E#`qDUeEZ#spZ)Q&=4 zzTcUjUi?nmD0EzCCF)WA)N`7wX0vg|`l*xr=*NsQ#kzS1Di< z7U}y7J znt=AexjWa^?8au7kk6)=HS(f@RiAaG;0@M$a|K`aaYM&fuT2uaew8uBbh^HEEpcB7 zfO+5N}k_>Cg3qU3P=QbH{vZ)-AY%j0bN1=&+EVoefFM9rIdQHs(X1;R# z`>N9%_!HuY^+o(A4N|)GPPoALiVZs-!nRU=cu{iQ@cO(@6heUYCOiA%F4}A65@zL@ zs-Ve^1UGM<-R)C3xPN#6o4l2xhQyEQ&|v6t0Y)gS_W7h@i z9F3X;!Js2KsX}MXS}S?BG~ z=e@>=B-grvoiIaw2W_*^TI8n!+O?;h4!P-5iM*N``4>u|_B(#fd(UQlK*~3t%^u>@ z=W&bvNmi%!VxgG|BNLBhB^Xj=Gif%MGsLhD+dt5!`UG02GMUgqEA*xjoQ;)*mrCVXv zw0eyiuK#c_XJ^yE#Ay{ty=EfHtc>~+{nX2iE{X&}=FA>X0;uL{{?^vrogavUG!NX& zFV;{3h`blr0mn0CGlMnbxQavyXS=lZOxcvJ=r)#)T5Z-3E1}pp>ud^^y~F%M*k01W z-IFKT1D`uJ5HKY=G#Zy;5it{wXns$rv-0-zSKJV5a(7>9Bds-tWR$-uR1I4^d3ee* zk{DK>ZF;^}4pmjU->#|Fc@xcJRkVr&LRtr9B{fPB@$a03I;IfE691W;PE)~$uw;7I=v~I<%qVW*frfpVXiAzBM4W?FYhxfPfls)1ELFij>EIM)l zX-M`1SE?LTxbj=)&P-H24zsu34Jt9`{`p$M+HV+9tP49E_{_LIqUmK-P*`W))j?Jd zNnO`0VM5|Bzb-&`J*SIdK@({ zuk@p(3W+<&ueRX-NSg7wUStq}OuJp{1MPzttXq>kde#(UCTDdl)e6VK!NUV63GO87eAd(i-4E9oI0++S*O{1ShFTne1`^~=Mun} zsZ-F^a=(e2(+trNC^})q$QR#pG2|(^!TPwpTi~jSNjS=U;z;6gOR4N`2}(iX&EBJ? z-)b@htpy}_U_VRK>v6ccdY%)4Pt#R>P`SS@c}Hoq&>G-zgQ~XWle*KP) z@2e^WWxmD3M*C-_Lu=3Hd>%VkUZ7iFyHdc&qa(cFRu`q_CEhiFSVNL$F5A3l;m`%s z>8=59d=E&*HMnui+DdqCZY~0Bb!|=kCa~z!ap#hX%h9QJJXCy)Jd6+{0e2ZJxXnL8 z@Vxew`tUeF;p}WW(L843B|TO){jR`dqKJx$W!1K>z&`q^hZ_vWVmgcOw$(jw@z-z0 z&QSw>Xt$A*fTdAJa@ocWqSZsC2?OwJd`kvJb<^NkA|5-FoEZM8mr?6a@`{QC^!PKx zXAtIt3X^yZpr|DE=Q=^XuU1|+j>+f>&y^<1k3nVc+|O}TnB7re;mJt>Q>l&DzV=-m z?*u9jh6e{>&GD$zLtAn#XPbIi3}JuHJpY6-r$DhW9@{5);|yW64?74G_kUg84qUoV zoTRpHw%pViXg*AJeNY(DJbC?%`K_Mtj^}!pyr^hK4AkG#^X)|fzF1ouKDP-A?Bhh} zE&zjv>HLV=60}41I2??MU6`vs<-gvJg%6GnHaOTlv+ppoS+L|<8bWad@o5B6iRG$0 zCzK~SsdyQ7+fT3pY!`|>tZr`mv8^RkRwr>ILoMo z>CV>wklcJ zv-heklv)d0NR?f3y4v8OM2;nQ{HA z%Gsw-HFk*_o(c00Z<;DJIm_Onitrd9V!TmQXpYZ#wK!_ul7oWE5#lzj!WDIhK1o{8-b-8Os4IaATwX^ zkc_|VyKhEuCIKbWUF~2aMZ;3w#jSm6=y@nx;EUoGTqjWPb>(NM$%`lQsff+h3D=vg z_7S26(Kd0V$JO!=0>Xq0SZ@k@s!S3`QiButFdYTwsxxfk@aoZSi3$)51^F=IOP&HtZtq=lKg2f=(`{Im{jIkgm4}5*qJz zYrS~<1XbN%tqEF*8<(oB`bMdax6Zw<7s@40r|1POy9Muxb4o40v69_|>$TdpjZN@8 z?cNpjyW&}lI+nYF2Pe}O%P8(a!jD_NF#64PQf{4=ga#$M0zPAx=!}92HCx17&HEtB z^i0Ub$(QB7Q2gbi%ab_7da}Kvx!6{_5!z}u-yH>}yWu{SDQbmVf?86QGd%upqYD*o zX}YOnV-m{S-{urfE;*YjcF#sH2zBlzq9#kboStMAsFG4=3y4)@rwU@1k#sMl?}rc!}nN_^+eZpN|Kp86}5yYJf& za_?x0%P-A}_+l7Ck9yAXp1L-y_s3aAR6Us^T`Cb2a0NV0jkhbQ8a~yH_N~uZ?T4Mv z<;ru6ZO(N0-PN1i`D!3s(^{NJ*?a#Yh^|n_X(7VY>YYlB-4zv|+}AgNHLDvZkC_@O z20G~k9Cz3R96lG$4^^@6B>j?^Qv{!Ev}YjZ${hbRv(t!s`cTV^&}JxRLSs!w_Zpep zSGj%YB8oRS$kQKsCxrD=`r`AumJ#(>#3$YIwiW$lshHcLQ03IcaUCvt)vx6f*- zGj;UxXaT7eH-X8qiLZm}M+Uz8r1Bk8m6e4=*tpVjLMx$gK_8MN0lRa?slCLFFkP0G zr4GJEMT(XEzEYoBquYaRN~gt8>m{y*D>k$IP#CPN?8|;(>g}^CCMWb7=L^z>%|@&@ zMYKj9(Xh}NftsQ@w0BteqV}NHxtKlhlyWWIqMWHULFacKLGNCkeus=SeI_b=txc6% zaPLB(_T)M0w==N|9<&-{f!l&v;YEG@o)`tkp$%7q!)9(Q_Psxs-lbsvO^>*4f14ff zAs03XE#3{x+jmI3cRSd#5P2+u@gVIUZX&Jx^r^5=3lse_rMGRU-a$c=`^Lg3c(_mT z@Wh^qiMi}4BW&?gQBWMOo}P8g_ctaa*VJi2h!i%u0y67HIdPz5T%)t1SKZm}R3YvK zcd3@0XRjq`3M+@j6qd621R9KZz6njn<4)=+R-H+Nv}gXJB2s$bR#BA*u?|uJSKtGfT*UH!`E;Gn4xrRvy6fus7CmhuAxW4DSjb+I{Yh_ zuXjMWCl^{gyD^r_$+jPW7Sk^Pu0iNYFl#Ts^C^Ogtm@37PM|FBBO=a6Ab4gnUO*sC z$JE#*H~SEEPM(=I+?2Gz8BgeOX$!y$Cmt1aUTW7_@&CQqTMuzL^*KV4?dnvw`ptp# zQ@!ay-e}pdV1+f;ICHWD=e(I5vX`m9<|LeD)r-C4HiacllNx7y)eyNk9+C;uzC~&B z2n*(r5nA{;hWAsg&|hX#i0g4hVOvq@?G)nt*RPUFxf*g98K2OSqXC-z+$|`O4smxm ze@RXz<}-3Vnk7PUZvq3p42??oMCpR^F3#33r1QD@HSR>)-&gZPCib(Byls@z8hRf6 zS}&CUx;?zE>~}zoOk-Hlx(7+gw~FcuZtNZ(HhcWk%u0_Icrv1j;gJAm^-D0nkUQ@l zns2Cs$nsw?wF_|hZh!J=l8f+@E%xN1p`l?xh>K2S=CB&~uA3;*TH+;HYYaBa=q3yN z**uDi|I@c2L_^!_eX6XemiI4}Owu>LxM#Cp)fEMi*yRcb$0pEnzw<+vc7JNsPr7&< zvDkBhJ2Lo-|D;c`JMoTL3?lEzULk3_E(EcNJ`?9YQ8C?Tn_AFO5ZcO9!;*LO6L^2d zdlsj|GZGZ&@n@qvOt3i(>8e3L`%SIG9)k0N%xUko+ZIt@^CLSaG|n$>R~tl3wTRlE zKH>NXF5PIPBm@f1D*6;V_F*d%{^H`+Cu)JLbA_SkW6ra_Y|e1O44q&r}7>|?d* z;zv^V>2t@Aj~R7t97-&I?GOFTsWwejNiGtG_fPzs#CopK zW6Hi|Tc}AapjPRG;8Q6{81FiSuiPM(#7#-SlkrEnhc#I3{B;aeGl%dnzAbcK*FT(SG=GuYu<` z8{N8eK((@EPWKU;kVldR`eoOi|HGxSc0;;Fe)mfbu+-a9aVZNlEmezk7|)s2N`$^{ z95{t1C-;SEc6|*CgE|0?=MgNP$DaNkVMxmF^0C!z0|1f&X_Ms5eVcXLt1NRLpv z0ZD$?(<_zxGOBO%>n$fy9vrq-E$!d(U#Ku)gwYZ` z^teF?JNfG=1zCqJi}-c^{9O${z{Tg^l87!;qfV7csNzm^LndoHdUkgMJTM#Qq5yoQ zWl^H=4JLFm{rdCMxM?wFEpA#GLgGJv+Ff3=do{Q^tkLaS&TSDY<(Vcs@K!iJU%zqN znqIzHxuR36G9ZP)MS89RLK?|0n6x+doeF1kwkQp3KtU|~zX(#Yy&-}A%|j)jM*vc@ z4lzrnpx|bkE~gpmzRixqI){a&Rh&_ME z^S^)==YBrh|FBD`In(lHT>M5-%{m@y&V_<={!{iiV?w|UZ{=r34hlY49>4f6?6;Qe ztJ0zry;sP2&g;I$e>{p;pLJWt#lS8idt=6A-(;g~66euZ;_X3|;0g9W{dXK??6$Oi}U+-^9*`z#Xd z(&p*HL-IV9D2HyZZ=UzRI@_E^p$?)ATyj(VhKOLUHFW9J0@ZdbOqCngkTgzCs-MfVcoU`EX*5A~YtX#Vj2`1sK4=vGSXrN+OxyJ!`tWg>Gw1;X#7hxp)L1bc$riu<&OjBzMO`SZ~$*>3rbT4Bx${m3}HM0{PKnN&An zHT(G8VRpBo%`hL5y8Cs5;!{XVZSy*&#W`)QOY35|x>3riCUgC7*W-e32xnS?yZ>FF_mmwlh?ok$#$_F&exJ7(*)AiUOlWFUaQ zGIuzy(pY&=+Q^acZ)FhH)6q%%`X2fVOM^LKON}iZuAfD(j7vfi0s(x~r=Io`mIjB< zg=H=>pSn$X?DcwA^_+EjeU~tw65#onTQ?#tgx-Rx~P>=8i>qUMAK!h<5;xg+r)qB>2uPay*1-v=RaSHx;x;z znm9+cM+qbPG)>AuHVlm>dBPZYHc%=G6_iBB9f=1Tok!;vj<^~CCFS*9yXzv5| zWMNt#)~clUuHQcqB}b^6@TFRpUK%p;Pc&X z*O`nLQ3{aqKT3cIwzyr_IkMgPo|WgwwVzaB|50wR7PXTth}&}56ij9NOzqC;?@DQP zCofQf{l()F6}sK0t|~RpkS=rqGJ9jF#E{o(eE6i3+)!~ypQgrMk2}PVmrMgsZ`x#= zs>1X05&Ya@z65K*;{sH}eD@YPh4dnlA^1kLMj#g>T?E2fuP`V2OKJ6^i@xle;QC3Z=h~}uHl0~YCRcCQY60H&RAmD_c@b<-|kfJ`M5{Awyvxu(qR4*ufOnwd~y|0^vuKYNONwZ?;6Wg0xMAW zLW(a2=%Qy{4l5&&CH;$e zh0-qO+R9DwhG}TR<+opYkCzGg>NA|-^~SPkkS+Wu^0oDCq)KtOPI1pX^RGI%yNcCZ z2-LzU@PY)EwX{)Y0`|E0p5c-(GQcH73t?Bu=N0d9z zA7caJji%*#8Zkua1dES1YxDK?M1QN?4n%Ug?6YDVa(^rBH^#pHB$iZeX2~)oZh7^W z3;Hyxa5Us)a+IAz+7^A+5CjT5=>5(g_%w5=Jb$f3Xn6a}L3t^Rnyh`tRQKbmyF2UV zCFi--Z(eVkDv@6rPM3uT_*W;^CZpRJOKcR$a-Y>1`gQRaD*F&xNNIP8h81}d^Wdrz zCaiMx=R5D1Mr$^_gnz1)mbu7wZkhXYo(KDSK2-|(!6MBwPD zbjMGAo!>rQ`|+r){0gC~2gX1fjKtnK5Jp2vi}Mc?4Yio9y2Lbj(#~pg+ zTB#{=xt!IrycALVM5Ev(?e~DnEQ(*OS*dxaXr5w68=vngRUHOT@r3-7i-j=mILy-1 z)Y4ykoX-%Ck^ahLkxZ{$XZua{Ht*$2>O@%{=RXTWCEaAh^~Y$=q&-_~ta1tO4}M3t zDv~ND&;*(%I<3~cm*kJdf^Ci#y+%ET_E!EMp58hvivDdMUb+ROI|b?P?k-8`5CM^p zTyp3Rkq+sSl&+TvHb6ag<^iM=rD1luXD0YDWvZ!eS(H(F6(uM~|=`s=8Xl<+@Zd z(d@6j&zqS~p06lW3I+zJ*l1r3ZR{#xPsGxnWBJ@%Qixp8ius}dGD6pjj6#sIjQj+P zVeV%jlp7SFCUU!35?X#qzlss)H}q@&`Ke9N?*^va5Azs0}8LX zh{CDN+;JlCYnK+mL?FX@*akR4$RWz%g&2MJhaS`G+5~bw$6@<-x@B;8^jBiq^C~`O zTN@)~bEA;B|2-4ygC8>N8yT4rw$5Pqfejq@PD=pYg-xb^5(Ugc0~dIEL#BFvd(C~I zseE8tvb^(2gm&5ixoWAWak~%iqH;Q33CmMBkU&ZAKcWYRVP50Wh2Wqg&pW!*~7dAO)c7bhWuEOdjlOwwuzqK{~KO8!L-EcmL-0F6PF)O)YNfIRH+l zVrzXtR#tM24*oY>-_D6xUqgY{zSR?9H`xCaK?)&~?;;~43_~^*f!H08{B3Z0MH*_# z-B`}Cv3RJE0D5=(#uGn^QB-k%op^VvtwoAdXEEw z_7F*IJ$R_$j${5Ec(3*uI^jHukoE*AL`z$f`N7b2=jEv_*7s=)^%twUSELu3Wqu%T z9p*vFc49_)CFpN`X0OAX^+h5njBCY4Z%YW-CmHUL6-h+S@!W zUOU>~8&yFxRTq!3kV+h5((D6dj8ym4c!{OI%#&f~rSn@?X;pIprsS`DFD6asFhzaW z4+$W2AQ20sDCU9+d(9KPJas>4;!xm4b0?SOgMZaVXt2iZ|1}5+CkjH_SV^A|{3ePp z@Zra|t4aU=HjfgG?0`4_J(!PuuEXq1O2U@ z*z=8hXY$8$7VwMd3)y04oXsb5SmDRh91opBo}y|DPS|ecw4cjPZe}B#x{dB{^V@k= zlzV2nJ@b+htSYaB-hVQhQk+xN5D;-MtZJ}Dqrls}en^u?cs6%560ZbIJR&ROET-3@ ztb4k4zs_G0lLfea{%(C3c2WDL68d)&O=Iu;;xj@vF%U>_`6BsQUU;58J-n||O(E0X z$7_4|YT?15#O7{G+e`EnZzx~?O%2YS&Rcn>d^iY%ES0Tm&nf1g9X9a;{1*X7^)2pq zKa&=*X&^d!y7vyB$VI}g?e*Y(i~G)1=<0tBC7&jsSWwasVc1O$SU~w!GvGOc>|W!R zTqIccWffr1VK+5xK(j`We8bvVJA56oGd6^pjz;3I0VJVp8iyx2a zlkU^W@B5y4(P8^g*37qj6y;$PjYBJBzWKj_KpK7x#|3noPs=(cWa3teA6S^A0}-6& z;r^bI5>`?1m0b|MKE%zgV*NK4U}Fz844?Tb56nh(sV1aF4p9kZI5rr-}Z6}VzQmMNjWnure8b0cRHOWEMR;M0dC2=QdUu*_f8njPE9N{ zqpXg>`i%htMeUzZ(+}lw%Kn6Kqw|BYXg73GKWpTS$migmS#FQ}fdby5eY-nv$cu<& z%5Pj8!L+$Rbuf?cQVPZA$o;*tU7{+Z2c*0!ZEM%iz>n3>q`|pCLQ2QJ(e)TU_DRT2 z7+)3Qn#vtBTJnS=jQzTLivHAQjR!J62=dyWdFA)i%$!xqO-DHKLh@Qg)bLN^Gv&ZdtbL8272sSV zqMTA0@LAtOYD9e@*S}7W6T(Pn`;%25&xb#@e!Vh4gZ5m37^%98tzkb@Cx)H%Q6sgSc;+5$%%+`{41vT zOP^*u1kSZuZrZ?54H2S7I*Yg92^2ZY|A|cOUfw8D(yP*oZSy?4n@BEK4Q5i~OM8}r z;hHHyK|`wy2ydg_ZB`BB3_Nm(qJi$*MxAHBOutns8V=$)OOg||3mYH<=y*@`bWw+)4D@R~IGq*FKNz!wt?Okc53B#QTu%nf%o%AfU zNi>#;5Ig_trgI5J%XB$QL(vBrMl;K|7Eu%A_G)Z$3@ zVC?BmO#;^bcB8)q(!y0WJeOoCv}N_mE8sZ(@@PnW;G2q4wy@5tCJMb$bj--JTGnfR zz46bVG{}r=Ge~_?iA9+T^e6?vAf3tP`U$MC?@g1JzrCVh-Y$gS5f3%>yx9)a@y2V1 zeD*Z7v~NKb7|g~3TBVC_(Sh_$D6SF;DqS@ML(Z37-Ns$l%T0;jVFA>sH6F{@btJkC zCOnJ|$rSQ_#$#5Bn^?s)V#SRXFv#ZAEs|{wwuNjoG9K~TMa~01t7uO4s=~i6$D9L} zR24?;KRk5{7}QMs=-rDh73O>8f?xA0lqez%cePII3I_BS5`J`=(<)mFD^L<;M5Y7jv1jn(0akeuNI^8hx7{d7QIbfscnk zk$W%=85jzOcy|&EaHsib<#b#2UW78+MBG}2SApC5&fp+|^bA`?MQ+s$7gxnCy_L_Np-m&(RtQx%b?R4WtpWpz$4w~7cL?Y zjy63h-kLX3n1$_vcd-3zpDmD(yoteq|T2!|0=4bYFJtH9Kd4pLT3y>j%w`cF&8^;JX z+mx95++{N=M8Ss{o1c^stgM#W)#Xm-rfT%{|3OZczpsTYy0CujU}c%`qh)@%NBIUW zrxQYQp_IVQ9MxW*_?46ruYZJlwYA@X$Nl*8k+&){HJV`J4iY|MHGAE2JRT?L+?Cn) zEK3vm>H8@DQtbjm#X2cz`Bu6Ota(`(YTLzPik_Yx7>php>iQZsuni}0@-3mjwr0zA zp#f#d;{>kDuw+_0errE3STH*~EHQl`!Tpq#m@ek!>c~xToQm~nv zliaKb)0l?tQ34_MrC4BdoGROV;Jas-g1qz3&RPLk66H_3CSuj zKOUBDM$F|AIlX4A{)%M^5_A!az^EKVQAf_T*e-sW^UlXb@46OC1=FrE^<(^|gy!c>nR>HjW%8LT3!-O--O z`x>)uL6$mwZYZ4N0rJfwQ3~Ql{&L3+Vsm-oHLqhw$LX!@oY(%I*yTZ=-WvN6k(@6G zA{Nb`tMs>1Uy3cL?N667x}!L4YDJrOlH;MhzU3U4!4?;n*ah|@{puuc8ND#H;6k@P z+>$em0XlxC^mMxs2P?LdlQirC^ocVZRKJpw3fRMe6xD>H zVEZMSw&eQCbnHKPw(oYKs`l0=Yy`0`GwJ{@>nP{g{vNBokB{3lbVhewlnkFmDv zm5fWKa}=?L4Km3Y4Y>Nea28>k)8LsLf9N-6X3 ztb1A{6%YW7k+D`**6xg0LA58OtTUu2-*hNeO3EXATdp??&Kmd4_7MMpxr%?wbuBZ9 zMNb*xQpPgxxY3=#_rVxSDrsZ=LuKC5(ho|-{}Z8J12$`TB{>+q;wDbA5=dX;`_dP6 zKF!KRejpY-q{yC+&x!A?l>p|Uc_DE}>b^{OI#NL5ye6znwSETYpj{2+1NPZKG{WN+ zjHm%scth^ab^b8oYZq&baAeU>iMsei*DD?e#~#hQ%t&6LzQ&U6;)bm-BRTooS|mF2Dc4aC>l zzyen9qg=B?rZ~b5Tfrk)$vfRYgLC9|BNIUZN7bhxZI9Q;8|}ft^K#BJZnCzIjP*GG znP~lS-a^B05ND$+A2~Us-ZZ00bse?Zj!iIE8*q^Ee?x>E%~!l#@TZZJpEnt>4d_A@ z=@>t^I6M1cJzU`P3a95TMycw3gv1%Z3Dhy?Rrx1!H2<Uqp5bsc|7ifpLqcoH9HSkz_1#(rTI5LjW>{tENfW!V^dSvW8C1Ug^P z;(iLUp}Sbq{OYFzh)Aa-P2EJ%0w>9Iph{RJZp$E3}`#$uB5atq4>jaPL^i2^SMuE?t%C4wO&rttohAoW+ANjTS03Cv5=H zqC3rK?Kq>}k=E&NmjnDKV$D7x*;JDl;WsD9AbR@bu2Qu1{7>l@OWu!oZkM9RyYD6~H@C+hpFC5mR{wNp(Ex+@Zl^0k(K#uC>nhU+z%s zc)jwV$qAwoeqtS^qF$B!Bh<-Sc>3e^qs?drke)K&+%ocR|5^*gBfD8seG_2pPj#vi zc882S!{(N< z`fGBBJtK>U{mq}p{dGM;Xuh+u-cIHk&Na*37eLJlt$tT*qj-R~>v#2S$SGKWjjhmHHAIK{*CIn6s#W3-Y%jEi5R0+Q?%vAs#3U75mWr94em=PX4?J0w-1oXSK)a_h0N6kRjAPL{?{2sY3z0XAIaNAc~NUG|MBdY$$DcF%lKL z_jI1A!)#W!v`9HqrRVqbwoUgnZ&1yN`Yuu$CA=~6O81>H^!mMRUyG=f2p&CTuEWcGM;8`$G&`#o22|(lhdk7#l$w^ ztE!3L^VP*`fibB7(LeX&EF9AurG-2txkm}IuzSPhII8jM7QrVkv&Z6Ub>V?f5l|2h zu62R;nBhwM$vT^Ica>T&lOMqy=iBk$RO!e*ud6puO?Uq+9YIvP;x zeze|O;UkV12DQ&KM%6fXIddb>rvOt$p|9x^%8JZ^K&nJ~7`krRuWj$6G2)_ujPUw* z4xa?r-0`JB8xtQpq5cQ6*QC}8NV0o{*~yV)d=uZ3lH9V#>+F9CEj75|+Y(k9?7U&; zV6Q49u^1vriv7tlDRlBQZ+|k)UAjGNZP@BfVe0Ke{`x$$T%G&r+|p7wso%M&HEM}X z*_2lS4hZ=unF9k4H2x29{kMCh8~OHKv&dpmcc!aO*@t8^;ref@hM#P#cZAg_EVs74 zEz?av*YVbgBWL~*?x-LY+gU!9mN@tLutF@#S*D8gJ4MH=c52_PtcIY1UC}pxG#J}X zYQmjW`8@`!D}%Z6 z-tzeE-SJ9}uR`;VT`PSlxt*}TzPZ6O9wP&gl3uFeJr!T~xcf5yE1G+!A|97)zN&jO z80F~?>gI>;qzMM|`yX}+-H4Sp#}Mxo$6{5T(g6s>Z$-Q zKGTnK?zxH`Td3n|t&qMQ5_~UeFP>F@p10T$@ypQYHu#|X<<4nFSb~cKQ+4Lg@fpK* zAIc*Q&+0?h$68}K2tQ1;Q2s$|eYnB%mi?*Diw=})xx*Y}7Ypg1Dj26^Vj{XtMvUH# zC3S9V!$_?jpL=FO!X4m_+inaGrv+esa1gYM;(4lFCH#qPwkYf=Wj2id|6$<&Kzomu zd2$3QDQ<`mg}8kOnC1_?Bi@TNEHF&10;5$uM?4hZce*Iw+_n`5^HB`y`zIq4`c<8- zQt2M?fkMNDpL)nR>B*9rD>2L5cYTRdc^@DX!$r|v0I$;`bM90Lx(vt1=;&CP-=#VoqMm1e zWe_1fseXpU57%b?^lGMO+D*zx5P^S=*?&|L{r?m$Mw=|P1epkSW5Orae_35>51Owk z+_C2li~l?>H$VdveA_5}$H{8S`=)N-jZD+L+Qu~Md`8S~5yOjAH&b#4^!#qAY_4tb z9f$M%c28PvX63IuJA*U_50e*drFnYzo0<)CTxH#4X1w}~!~Pde!CY=F{X!lOd$5j+ zDE6TFlCqJ=<%a^nRZzm4gRjYLYV@#@@(jE|G*m{&X?p>%!(xxha_K@LrDtFG>u})M zLvrIY$%!e&G!UC9RCE{JfFfB%t*U|c<*fodImym>oTV3=jBuPQiS`91w<@i zcGsrRPKb_%`X+~Iu3g!(pv^nxTpjLsN$&)~rmxF^Zq?5(yRl@snt9Gl6|;{S-Px(6 z5bNP&1>7@0;Sgy0dVxMO$fbys^#8;J#h`J-bX=~QB z7K!=N(ot|=9!EDszq~!D4jX6EP`U&APPsfhjt;5%a$0hVlRT5_Fa4hkM--uGSpV(t zt~lHl|J263rN=yX9~7tU<3-x@$i#{t;NEm(?1TT)2(8%7gH>cAhPxZbL=at#Y#sqbUs7lpy^xi%J{YBkb4Qw2gHTkDKFnG~S05w!#Me%n6bL~M zwu8Tw;Dc|wF?ui1`QpU;q0{cZ4_I#I&gqdr)-dyJQ6O(}Q_ zBQMEcB=r7m=(WTzpDP6-|5)nqNFBQYtQuj?_eqYyJJI*`nl1Ts8>=lgImGgP5z6q! zd)$H#%_r+?bM17Loh>30_8Ueyo0sc^(my6I!?_0NgPeb590u_vw;P|i3X0+RnS!a} zJ48rq+R2JrlSeGh6$&QH{M8sGl9f>ML_0`gz4Z_v4+4%PlNb65i6h;oap+t_N7Z+8GgYvAYnLyY`*u zpx;F-*to$%Y{hxtS&u*s@4qS5M%|hju{sbyb#V_003o#mMe4-j$ac%N^>BybkqkiR zCm+9^9)AQ${zs;?D3~ExRlSwRkx z=GBHtli|`r?I#wT;!epP4GMf1%&-Q9La7naPX8tQ@N79Q7PGxVYhmnrAw5+Be~?n7*iQvCqmd{` zlY!3$bv@iHx@B%87H0s>txj&;(86c|kyff;HM}VjB=peNn(Xv?@2PKG1bL{kEOfTZ zwE13|iEXX`mHvy4a`d}r8dh)SE!Gsj>##9mZWrEHv74ej&1yX?L87L611Vdd$v_57 zVPIk>_^E@HZJ)7|j6Gz8^nd3qvX=}oYgGRL9W5<2RtG zK7C=aVQM8kJ^kIRGptN8G<0soZ=DH4EIEP}La60XJu5?;n8-g#(Kkl!+ zo?_JQBEfrS6xrWWZWaCBgS(>r;w^O@Dtq)fR*SwB<}N$kvKHLd1-0lTq+|zXQV|CM zb$jD*Ajgh8veWhkC-Kz7X}SMo#Ob!EdDOJ@8(1{XS!dYcjr<(0^BrQRkDPFM+#{09{S!P7&uCDv zC@3JX>00Kz&@zfcQMHn-n{~OZxYE&;Nz#!H4VD18tq_RUvccxbS}cQZ!jtr9yihK1 zn{)o$BpM*eq3bA_^{kT+2F&Q-EYVKO{3NW9$=PYD&D=D!Wd`7tQg>0_{?oZw`Tfbb~Z8(mJCS6_} z>BabYn#_J{2))v-TG@bn&ru02Hevm}z`&i`bhZ|aV0vi|(?Hay1269fvIz#<+Fg*- z#sEauGHK%CHLB}~0MG4KK1)7DO<@TpEIqrj!+n~>{kK+_p|rHluXP;Tkz7=?4CAek zJD9}e?qYQc{4dNS?fP2w%#IJ>)t#R#60OutL1=5-ycAH_3Up+H_LiJ&!hRMMAf1rV zY(1$7;ky>l`SlS{sG}^^*gDv+|N29rO>gR@W`@C^r$hvP|H%8A;V5Hq2G8VDTW4bHcFsxj@JTFV+Ys|S&2ks<<;>)=t8|zz;{zI87TeAMhZb`WLQIcrbC87g99KS?=Mc#;0TZ|NIVkmR(x~ZWY0_r7@|Yo zY^v#HO~jN{JgWUw4o+iLokwd#`fTin>2wsgns(9duB4>%iJlVJH+9EuTT(w{*MB9T z=2WpYXFft4bV$j_7+$AG&i(jHS&vaPagX`hX{roMIh76O8gKi8Qcdd4!cHV4wzipepl=`TTE68l~lvt;nnr<4y9qo^4t{U|2HL7 zoHIKZjHtXr8|548qwnRA9-}k3vJ@dXPrd*9sBY z!y)V&hiDuh#%oW@ObI=Y#Y1>H?~c+N!pSw=5I>ZL?>buWaP)R7qtEFCH^$e&{BMeSoiABb)JBccy@zAty$D=6Sv&1cF@u;4|^`# zKs5ODh@H8AxmI~K)R0tgMPU9v3KulC{fk}GzwH1$nIM~CLq81l0D<&aO=6B5kF6Xm zPi#kNhA}Z2Tn)&3M-aiN66Tz+RHm1Oq_tkz_~p~S-MNvPJ7B`h`Sh4121dI;guNJ| zl37pG7KR59kYzR-I~9!_OXL|2u<=MKrMC(~Fvtbtt++Zp+8vtM?tb3yAo>dUvuf4&(!c{CbVjM84w;XEI z(!9~QYI}oy(@GjzKwD{&))X_!UcmOdVKr>~b68J~65vQQQ}qRV@43`d{NGX#_R17h zkqV*yfVN}Jac3(h5?bJVAWbMLIPu31-;gmACDns)T*5Fir^9gU{tlQB;^l~mSolef zE2#fOlRF&wtqUq=7q$B>*o@db$f!7$yS>m*H0fac{2g&fY3q&c2b{-ww`J`eCk(F> zu&}pkC?McA!IUJwc~D{PkbREd={`!RUg-Zlp| z>;5qTpItW8iVolU`@#=)M`y*di2?XMUMN=Zu^{3X!;Q4_LpM2lLA)3xw~<@W1T|5` z&LJYM>)&9Qk8QTh1chv&x_9ayouHe!1nf8fY|bk};ts(d#V4#y=RR9(K)=Re7b<=3 zL6rFr`2l=5cK)~71+5tJNZMFo0tXWGyFlE15yf%^vcrBuops8zPzda2rGB{(;O3-- zlX>oe!R?|N2HYFrnW%Gea$m*nUV z7-GidF5Q$Ihn!3(f)j!#mxmkH{~nxcvx2-={tj?W;DFk7o^Mc)kFwvA6)C3z z0yU%I-FoiIG>ub0_c@O)c1Y^>%txV&Y^`igf0EMErk$LM-Egr&1iz4J@@W*Im0zSH z{^EJficVd733$zh&a;#nl?yLMA1Fit84|j-r-%MTpY-_u^Y(0@qJd=wdHxk)ReUHS zm*1J}4ladx6RT#vt_|1Nv|0}387N%Hr zIOy#2Rfd83^5O$PCv=Id6woU75Knm@f-^2rTPK!QH~1Dx$c!J?$d-ZpQ%}CtibI0q z4x0=slebsC`gs+mYdhD*zYm4M;M;Q(B~~+K)_2!O6Tgqo))QbEORo1J4v=j*%I zLh4Q61DE~31sttc$Kh|Shj$Jr>8dR<82qzt_+D4*Ce(=N*JZ+40XP^{Ouyyg)$+wr z^Ox^O-coIhBZ1do*amAp)42>=YGQp^G)%2VZYY|c6H1zU24Rap*O-9Y$leDNN>sl) zOc>ow2OE?(A(GV;JJC8KXNYiTdA+|oFCJ#xY9xq9EQkX>SP$o077hPxw`Xj8ZbPzT zQqbb%8q(_E?ZXD37g|m_8&~lXZFA)Y{=hW1Iad3X3p8Ta?{6Gmi3+IN@p{G6 zf(CoIKp+GL(KTq8zD&=!S`bZ1UYCQbUk!sksw`Oc z#SZ2NS3$GO}5yHUA5LCebIR@GrYr2XQf)1pwi%#{!cF~kXI>-t8&_wSbW#S zG+yDfg#vgNO+?4GnYN;4R}ikMbC|yb zq4i^oEYFmwJf?dttWOEE`PaTp*wEp2WiWZHTL{< zkkr8yuq$xYzT-hTnViGUkzDQg9z{_9nTgLXaSEd8vzOxBEJQGtG%R5Jb!SNbyEaX^ zfTOfwYmwYiqZ|)@WSpJ`HB0j`j=CNNOl~a62VSKch?Q5%+#Z>bKmt{mw7{EGw{_bH z1NC>vW{ebUX2Y-~b;j`|b?(pTjLgle7EZLIoSK~9L zSuu@2{KLRa&??nyq9LvS8eV)NoM*Ti78J%-WRleBrXDRtVOr1tyXFUIyle@$`>h5y zd@QL}3vZ6ZgJDoZp5E%iFukO0`^LyWK(oVmo+ExB_}`~;2bE-I>!QueuVD3A^Bsz$ z*JiQmgP`loS2)lU^oR-_j$=nmQQQr$c!Xzw{^R1{A!n7GDrV{r4@|K$Z`1*v%IFA@ z2K!L1-W=pE^0v0BX3Php-Xz+02{DgKrdba2 z_TIRLU?U?aCszg}Y!{pV74vgGlx1kFG7HtGY$cdzmq420s#%MLBlwr0p247G*4>qE zxcmL?9*1|5&M~{fHI4H@X!L$+3XpHUDHefl{MN6GuIH6DS!Bz!%{`9pdY||zP-zxj zKZ_5NtRt)}j8<|PpDo10TFaNRpA@{GFFQpAH+@})E7AKBHY&7c3mPqOUPc%U=<5Hz zP`oiz)LyFJKxR31}=4vz%n#Mqj5nAx6CcpGUD}Y#*!V<|z zH>#x59TFvNf?TS~izCI(U$@Nr?ifyRU6RT@l4g&--^S$7z!bOC50FerO$?H=4 zKD9)=X-llZ;px5wgKA5hzyLl1-PH$vI~l2X&T{qW!#4-qG%?Le`csAbyl~Ycmgq}J-}SSC85|%KN*(MAMnv$>JjyzSk(_0~JjtuoJCn%`tRC5< z47YrxI>ME9?>_FeTV|nj>Py!tDn_!GN|Ny`p5}N0DI10XbZZxJUiOanRfluf`qzgj zGuu+b2{M11Tt(u@|3=4H{StIN#22N+rY%6HYqW)b8=WeibTc&7V?k%(&PLk+|gfGOV%q-q3GTxcx3@U%#@YUVU;g%g10GI61EZe z=cleF`jD%eM^J#B#A_ICJa!Q0zUoJJd05js+rNg*_||*ctBn4_=rnGMu)2>Q_eG#w zPfb1?pj_q9i?oL+5man!x^9F`83H=y??{}85>#w3bsQo<@ITcwTF<}la=gD)=Jy>< zb#jVGAt>0CS0?c3jMVRVhOj@wJ5<@mI~DuFfedP$#jDykk-*J2TnH>3P~;DEEQ`8@B6EG9vHNh4Ib3~|S8=Q5|Jb4$VzB6lyI>S{< z((wtQEX+Pf=_w^j`WVe`Es$a1h)myBrx|p2Cahhlv>%k{KALJ7tx)ch)DJlDTAfOV*c}b20^1&0%pJ~%se34Np_fw=qC=l<3R~FJ9R2#%W2ALg$IL7|gFRMBo`F?10IfTauy}UXD88A+GU$=i80e%i< z2Trn?5Vs02n=I)?p?#i5med4m7QbU|u-B10In=GOUIzZw`k~A_Ek+?X@O>st!bFzs zm%Jtr)U@q9zR#|kg(;j@U|ChW91UMp2KxFp29&7s4WzZ+pPZrxrz+VATl|^$r@Boz zgTtjRZgL+Fo0F>(%xdpyj z6S-)SMZ!>s6BMvxB$#+qnSaWInMEDRU@hp+2jOQXQ0|cLKC1U$MLV+Y&_L1i&rVOW zIu*u!8Wpgqo!9x!7G1Am6$E;3R7QU|SCw9V1xridB!!T2_D@z=+k?7b;BAfnb(yG& zNJ!RpDplve5Z?UxGdo~XEmtEdD2Io-zgFt@OSHqB4Hfp0YJJZapXsKV%NpL60Su>( z_@VV@L=Y+lM(+YutlUph-OCo8R4Uavb8(uG?>VrYfrCdD)(UwA!;;ShL(vJOKYc1J zb-~9e9Z%YA*p*A?9$$F*uN_fl>g=Z>aEATp8Tq3~bJa%+7(M+d@*9TY?g;4Q^HASS z;cfS9`#EJYI8z#F_e{Zh+h9Y8J>=~M3*YIK(M(z&EEr( zAAos0LtM8xF^pCEY`K*N^R~z|QH%ApR{T=@N}dh8Vi*G>Vi5Jit66NkmHMB@Kw&3D zB92@*0t>Kbqw!34+0iUlH))Zn_hXZ|S+*L;qWkf}XY2dvUnp9bw=A8-wuDhp%-p*g z5A=&WX?v`zO)}t zVP`Qo#rCY@Zq{6uC^Cg1HL>`>@9O1H95d-=iV+Y%<;C`&;SY$2K!rVP5wdcnC0{Dw z!XpEkWFsMBWxvR#xE?m>DKd+R~GgUAY} ztS|+t*i5LWZJRMW)`^Yk`(PZx(<%Zc)S0{JO_QW>B@2QV&~Nkc9Ads>yO&28RyEn7 z4G@XnxOoldwkrhRl~jEY9u+d#;HbJWktBIgH=%qjV-z?*G3?o}++klg*V43Q+p}-K z+o`^Py875uF8l*~c{shu1J7oXF{02|*LJFv&6C5VzDwkl;#9d#$GM~{Q|cd)^~;?1 zr=^|fPWt+>LA2__9hK)orkD9P_v?*%5~SFAT;*;`~cy(2>$&!*jDJ3p+;|&Pe;j|#zYgpG*QphO+NMHQd$~u zS7PijuT5;NLl=T%e3c1xh1G8*;ylLTz%6j`4?J19Xoi0k_MtVBr-|l-SpS)#DBTWv z?!k9MHd&?IYP$Fn^sJHHgFEfTL-&GSpUTGv`(`NGnOq8;jNY%p^})8_8bNZzB8uEC-P#J3R*yvd_wORjZDMd$c!z7z%X#`A!JwA=S-ipYP+)Tn+~ zy^DM#lmW{d*8vzhYT;U+;b&NNHIoGIIik(A7f$uRnCaViE;t{u9h=j)wSDLqVzMLV z^C0JdJ<&OVxP)&gwpknrBRj>Kb=@y$HJ20;($dnl1%<{q`1uK8Z%KIbDZo&}gFgz@ z#{TK#EAbl&VMGuoCW4Yuce*W3m^UXT9|9%#9~bOsm5}@m6TdwadcWTE7CaH4yHB3{@)$_W9JbSmtS=w29@XRNw znoBYQJx>t&E|5x!4qqweaZEgV%=*U*b|ongWhvgE#+X3&6hxj|e@nW;ABDJljl|g)AeB*_{Z%txPpB%8Om5HSvxTm>Prl z?uV5LhSd_<2&GR5EYxOXN$(@@-@C}zUm^sq9=PJ>2M~&cs36ebVWpCYQff$Izbh@a zyXZTW&j0!`+Ty?7Ywi}=_f58QH|Y2m=+!W@U+{571#B1th-C@l`nRh)!Wm7;!qb|; zpn0`j33bwNlxq7>C8y3186VFb;hgOQyStOj39dp8nSW?l7pDPDFRgJo{PPrBymD)p8bv%r`#AMeqERV9;gZo0c$x;v#oIz+m=M7l#@(H#=foq|Y8cOxm?9n#%-)_cD5ogcvFTG!s|dFGy( zd!})l;`w##AK}ZnJ)xI7j<4XJ_wjmt_I!mDkbe1}PsZydn&^ib7y*A$SEjG=dF%9$=ZLMJA9s zIo=?Y9O+(cD)+TQrxh#L&)(*jEG|8m~;8@^4xws-~lYm2)*4dOEl`Cyf+mo<#?C+&-=*f^Xombwuy+E zp7uy;uEcCb7&Kc@Mc=FhLAU&&l5xJl;cH5}>#o%}BPJg7k~R08O38z?pMeDp{dsz+ zXcW`vLy<`;wB_hm9ad5My|b0l3HN!qVC6~X%DYpZ1BAVma|$=RsjSN51t7ik-^4@k zsOw$CKc2*rf1!4s+C}C}E#B?KiP1psHrYCvKueg z9pm3tj;6*KxA2LTPAPAMT>%l%NI`qwL7@9mv*~pl!TIpo1|lBL$I5wA*r>|Th6xe# z@O(-6Gx#yJg!<|)ml)YKv}X0@MH<{CQY5Ikg?3w(V@ z#H|;rzSWvnQ5PNI7EaRnP_$$I;K|+Q5ypB?Vy9L2wZHR==c>YUs{<|M9alNWux42t zsBBQPaUy}Pa!}hC&k~G``Hf5ZzMA04!>%`dx-?HRw48VHD(-1=vtj( zhHvy3EnwTKk^>${(EHSY9JDO&xOaVa-+JV;w!ai`Z)TI0j3G^obxub^L*;}L8I?=( zB?6LsDzREte+YO#J*uMt} zn?jFmDlll-Rw*RyGo}*f&fn8YM(#PIH@o;qiC!7!lUaIzyp2GUS+fWZ;RNC0bJkay z2Xy1EF7NxUVSkfOSEV{0krFTonQI}1S+j)Y)PIB0_qwwYd{iT%3sO4Zt*&15TXPXt zsCX$xy>JhqNe166hcqCtxeE)Dd$#b{)D+Oa?u_;KTm-xqUduJou0K%7WXEk-1>Q%$ z|1l-#y#>IY9;}mO@>jq>`UqH~CORLWKx1_93*|VTu{K+CP)Bwkx~)CQ{il5a3mD}UfOk~t=~QzS_=xOg80L{vW&_(C z9{87HaWu$~Z8*NgTivNK&ZDdesRei1L&W_Mw?rwc%N)aN!&KYA5YD|wMz+dr(;jUN z1kZhg{Zw~uq3==3=0pS541!nZQR&E=3MmHK#K=oO<_^q@y+kFbh=P$~Bh5*;b`gJN z0IEpak%FtaP}M3X*Ho54G*6Kb9Qt1`YClV2&$s_=e_HrzQ-Nd9tBu@N&x8U``}QkP zY#KF{1i3sEY+C=HwT&I3{fzHIneVk$TrA0~Ox8Qv&5SjX{ z(ABVQAHq8~reBHFop_Q1i=YwMW@rJ_K%}mk!6P1A%M1~x<8;k(Efkf`_vGQXrvt|n z9X;z}DmZMCLZD^$b-9iF&E8{_CS-x}plr3q>?UKi)!;OCLhJycqxx%Ou3Gyl5&PJH~9;(Jbbalf)op&STJj1Ga zKcA4fwsT(j_cS7t%*<6v zi`ny2ziwID3s?9!L+}jMGP0A-L_5Ng9pcNPdiP1-P7=3wHJX@@S>^B5=~cEm^qtB}~BiIbKj5YhVwIFYLv_At8B}UgIsJzVE8}mdd7& zUVk{#E0)P`Hc^?qP-D+rKKJ8*t(c8+YtN&H=tX!~Kn0n@tEnfb*IFpZNPgkyn`dHB z2i)@_GC@d7-mW{aH8nDCQ4Cummz>~BJJ+KrNHkrd)Whlc)^O&A_hqj9S9$VJ4?&m0 zq=$o^Tv0z_M#hvduV$CsaTLl=RS6x4CF&7HW=Ku{QbfJp>KMT`(*Z?CzkrE#BD5ju zbf_=eZ@)W(K8@zRE`_FVHg*!^O|-{vU8i$H0#cNTw^G>w`ga3go12{eq<@3mwZ(f4 zVVq4d+RHbzL5}$qFKD!M9s=&dgnTpq@0fR*EC72CSK1xdJA{>6U1q=h&?shtY^<|p zRy$6d3M-3MeJb~rUJ2|AMe&oUJf75u)1v}ecmW~{T)Su${V6wnrrPSSF@`tD=yD?7FHxXEq z_3mD;*PFMC^|Kh_xc#0`zdpon0_fJyyFFBVZ0IWSUI}mHyw60W6GKCT*r5=Cj8-rj z3KjxVgPTcJCHc;Edk+S+;A<;xe*<;PCl5O5?`&7(o>>7(J zq^$>JEICbATA%M=;?bKoEElSHC!8)e4rJjAoRTdo4@M~b2f|qOubQQ(N?rwUz>uT} z6i;^kEK#LztMrtxn3CaOVxv*9!dW}PcRGAP z;}ceKbcEd5yF4}AQn;~ZBnn4ML;sYNCzZ-@J94lTzB_>LF= zA_n#KBHcQRi*LPg*y>Q8Z>j9+#86OB5wWrTr!V38HpJ5%GM$kKeX)7)kM?7Pcc&yr zP)@;;>{EH&Iuu9NbIQaSMbZMc(C1oEG14riJ-@jPW8kXx3!tzC4TKoCR<*>=04`+D zvsHvVLO+QD!2m<02->j`7aCU%(QE8Pe%jvjH@3}2!>?e4VXY-6`=bTR zq0+XrDJwQT36AgmIpyhUD*|LFgV(kv4Hp54nt+7YHZWZsw50Jl>O>n+T)SQ!m&p

f|_N}dH&w$se7EfzL6Gh)rjjH4r}Ut@H-^LoHh;0M zaDrr&!+*};b4FN4@8H(_?t06UDjkjH6o+Vi(t*S@K-1RK0ZfE{ZOkaN^^aD zO65^?yf0LGwlLAF;2TK+s%e*+D?RA!gE&tt73?60-9w)7+x*Xo&PBblc+RzZ`SaeE+?rE@RWjMfe^aK9KTNeUc;Yj2#Cv1H z!9jR~c0E&t3Yr`1^=ZP*IEi1yh^Qe4PV_UfDT8aTUY9Fe6Nk?f4{V779OT~l;_klF zcr-fZ9(XuTAq=7JRwlA(Rx)-a=#vLKHtQ^hynG_Q!{hRVlyJN^w=54*X|R_fZ8*^x zCXIXN0@={p!E>{jj&V4JM=0%HkseX4`zS(~*=q2R=+UU50KYb%`1kk|tNlnUB~e^Z zh6-IUjGQ=;JXJ$tIa!X0PPK4Wm{6l_Y?nshD3FIu_a26r9<`$DkOFCSgBF$D%%*ra z`NL`3V{w%%I$0mcP+wk7$rDPpJ*gUk2^H@L4uF-HszXKAT>?Yw^1I6~-wwHy{uJBr znL3b}=xOPB(tSK$AB?9kotr_rzI%E>P-M4L|H%AZ3GYci&_e3!NOXIduRGHei2(U{ z0N4-k{7R*d{}R-_?s2kNu{(CTU?Mqe{f}93;Q2y;4!%9ycijH8!F~zz2!FgJv-Mg{ zpX%>U6xUW?e^RXVB_n|jQO8yStJegCrjgdcm*TDCH$@`OGYR-Jg+3-Q0+X^Bmw2|X zIzn2WGT=S+<|=ftjz!mcnYi&Cq+Y}?fM9*~e877mgH;G(vQE+J^zx-h_VE}4S_fC< zcF+CBs!9^cJzYRcFp(rOz1mOM^@?+KW_RQ}^Qw|?9l{?6Vw`*f2Um##>qrs;<`lMq zlf}wuW<;mvCe&IidOhDVncdiI2;4)tv}J+Ay{}u&XN_ z`(m?E#%F~TOV37tI7u-A&ypZ4DcD%X+)EGTYlNLA#zt^mEV&SYkE`~PF@>Q#Vcwx& zg1*ouGxo^>;u5-tyCicfw={uW8l{ro^+!#2Rf+SF!u(HfOTB8MhlcQGI0NZ#2BEuE zmB}sINzV<;D6ae9wI(0ho{~l-K$oe++ee!#oV0aOaiUY-BGqs(2=F0wIUs3QXhIPF!U2?d83|$ zePzWH>-XpBxRPHeZQiPU#%(;g zhM-Zs;26pSPSXYP$JX@-;Gzi=t884BWH=Jn>R-8l@h?_#?-#m^woAG_w%5Rco6||6 z5i1pGr^6XY*M4>FFqftL1ON6fdcI?AEafn^Fviom`i##RDO42y7YuZEA0gxhR{6zX zN&eV!*&W6c_PzgGdJO{&{R6M6#Acz*Ia}r6shixaps?o41NI}(+RJR{tMJBDXPV2J zFH1uUx`E+{*IoxjbEkm}u*LP7_?f73y$ufXu~mmmNLp2xU=eR3S@P~*7T<;Ek3 z#;e*XtV%m(T&b`mAzo{;!n3BN#sAQ`LsEuHIj)dWBh0}9C#8*A!OLPzG_o;N;n6N~ zTT*iHS2=`+UG=;dNVQsue`p$SIC$f;@0*o-*)1QA=KaB=l3{7cAf|OS@hxfbF;)GE3c3~=5|J4h?0Gf z2lsDaAFg)saIF`6afd;wfTVLuc0gMJCVvdCDiR9|%WJ0tN)P~EpOKgbehN#ismlna z${=2b?DiI=*D(&S=uX$TESe>ea*rxyxOk#L)>onR@rkHNsE#ol-A=qQnkK>MwGlf_dUnHOrL*Xle>zkO;Nx>9QQCV#KmKkyYf`h(_U zFoMTb-!y9d?^59JnTPbE5*a#uK6Vem1CF9XWTu8cN4`t3!a|c5Tik00lD$3;{v7o{ zX4;ouwFgbi342Hdq!+(8+_~nE+_EcrNA{3@>_&H7b&ytbk^J^?F`NzN4En-7U+iVk zl;fuLy>fIWI5nX}A0C8Hi>$Vm)T~=uaHHApvj;GUM5h3~ob6kWqrXb}%+7ch1AoXQ zBW`x$deCw_X(I&6WzqK#JOlqa<^QjlfQ;4gG)sqL^|yb{#R(NC^OGEbfupGU!jewc z>(f6bOSnZdd4h9@nbZqFOJWbqs8$uZh_?{pNKgM<{zyGa_Q#AjWJ`Hz z0!UF(aWHPV@O%E~_3|xiV}0W$*!BEJL|WuCyrXYXR!4G8CO+6Q`n{@~PeaF-=qWHT z@bl@dafuBvUxgl{W*+TVSxG2C9afYshJhSbt4P{-rO6w+v5^+1UDkJ~()z!fO!({$ zjB>3*^}Hyyh-FuEq1jZ`UCZU7*(L!!-UcninbIyT;lpBr57LwE1Il<7!-m zlHwp?!ohLKMykCKp+OIv<#6flk%*sEv|eGauIvzcg|lXA#rr?KmwlJa=c9~SiK!c4 zIIi0nH=^h1VPho!Ph$i}w}ZaMZ~?i9YE!#OuVyHOYIbb3tYmbq&Ehw3`JN}15%`oR zUQla7G^ORy_YNU8qd`3PHTd8x|K)Mx?w7vKW#X8mNdKkUcyce8zS5s?F;W~kJu$r< zQbHDQ*A{|rOwe3hQJ?MB*temT~P0VK>w`&W?jA!L@@OiXn5E;Plg(FCA z#T1!_c`K4AJbsd%jxv8Opv1tt0g6M;7buVmf@GP;OHp7b@L!?m*=S;dk z$qPG9U5+tL+q&zOa0aU(e7`@oTnwNe%qYqCjzN4;dB-i6X0)>+W!9Ufw&n?>kGTwWw?m*fHXnfqhB})r$B*rS5;~ zR_PR~$NSy}v&v0is8?8QZWsY^eSb$UFnPZwB4|F?Qp}Jn7Bf;+1`m27L&caWQsO1b z;bJqp$jLv@;~$88z$7RIiiZX<{N+BLJDxvwTYn?$_;LA8KoDlf1J)8r8ZWpnWB8$=y%%77=Vn&OwE#O zG`snWt1+HU;>hj8kE znrH1ex}GbVc>0_RD4Vx~3*>ouyBFa=?C^6+>2Sf5vygy0H~XbNsP>Qk{p(On5g^qhcD5+^P8bE_BGBUdG1mt>2SLga_6@#@!WSON$d%@)1MZ3-Z*#g-<*ToAouY|z zQ}sxw2fKTTF1QZamxPHYr@8Q=zmi7_8|1=-@zXa(H@?i<%YbQD2VjVH!2O`dHUaZf z`0H&evn>e>z8`luxx4LbC5%hhp4uH6iIzPkp-&R#7D;Z_eJ*j}-)Z7t%Q+ z2$UbWW;EDUJPJRCF%+`$rAOh%S1ruIsi+q@KkjsTm040NkpcX~&ZoAS(fI6<^_>nM z>Qd}KXO0|75C)m0`?j@?Nt5a>Y`WDQK%*!JS5_E`sH;t12U9@KUsuvqd;Wa-*Bi5C z*fDB%zPG{ewN0I?j32?11Nrf(279>45W(R7Oqj0%onQxpTBut)I<*y>Vo{Pj-Qmws zYtAaKlLHYd=_KG4=-h8&kVi*MFtkg!xmE0rr>E9%JDMR3WxYb7h9YK+E3)plt}rx3 zJsxmG^8M5IzgDq^$hZs}=(HBQjT9UB<+TIJl^jyU$_B9JVyE%!GH8YH*rnqy;aXL< zk~dmaCX>VU5iMV}+qv~!gb`1Hn_$xH=uQzk6VP7hsfNhqgtX+6-4 zQlk8qJqXaN_hozpgh`>wP&>6?zx2y91WLHqoIu4gjk0}?Fz*Q~$Y(~%5K{UaNz9pD z@2Y@=by&O=M`+sPh>Ix`NKZFWUOghs!ZS--S9a

*aJt-jQo<)G$+b_XHh3+F3Ae z8G?5~B?T@Sjeb3C{&&9O+96QD5(m?My@z(VMF2E@4Ri0blg|F~y<(Kh*cFL#Zd0lY zRl!UdW`&=VP@n<^aHhf%r6`t9Iw}0yjB2b1iZLkA57EtlbOSIHFiS6*0+JTdWZ8% z0U#&Bc1p+6zW_r-_hV1r&1hjAy3Id>XYwD1F51c`!o3omYbGb}_m0xj`211Z<T}0Z+<(y42CBI#?t3i5!3aQ3DDZD(;$>RZ-dz6GgBO$6Po97pyctuZho4 zu7bHu=5bA9&E^G(Dq~=;pGet^+7KZ_DXb)F#R{PU>_QQkRP7aM-=hl>6%E*~AM+@D zZvL_xJh?1M@i;D%R|Y_Ea{qLV6E!gC*((z!)Yi!CqHf+75M!8HHu~@li=s!S768uD z!!x9}#w0JQbkkC?u#i_lU&K0VCHHNtm~5`#$5uR9=qmlI-OEGN%_T!MZdyXiocFy= zR!pC|_*B}usbY>XO`|I&b4=k`CjUOwkN>!peBXAviTUh)3=1)t=oYK_w~>p8iYlIt z-T$dTj&f%x1+ENQee<;aYZD<>Bsl(vgqqW>KiwX-~JGRzFgO1P-8M zFSg+$B;bXY+BE*i5Xg}AgVLHsJDIO z`~_w;(kT$Z|Ndf-8>ano9erJVtP7<)eZDqGebA4;SZCE;yEy%giNmIoc0BDpPNTzW zPcox=wZ)%A8djmQcY=b%^eTrb(x5ac2{tJvrBk21qcN9ZK{t*co^=zO33IyW^|3~Di_Z$ZD!Rq*_Us^c{X}cz90zvpDPa=E(?ht51nGdh}`XQhH(&I z9RDZfo;jQ8R?Io`s!{R1V#ia8*zg_=i)f!O$kr5{Vjwy{v6(wtypHTN;w)3dCy}PQ zM)K@FH2X{fKG^a0=3T*0cbuMkZjVpx{=p1E6=L_Wc<0%Ud3CRzI33-#6H+>-pEQ`X z;UP|-41WCHtxwN;8%5M}R3V~H2bHMP@bN>$3PROk&Hcgm@3iNVy}}q%=4Oe{+5sQO zAVH6p7TWD&1trm|Ohs!&5wPcMa4oVm_G3{?;lp|VD2IoV2t%dRIsYJ#(O~ys``>6* z*eRJlHP;rGBW=GMuVS$KD&+i9qvuP3NJ?$Q4c7a;OSyY1XEx)ufY-CzK1EjI&3iFm z`o00kHX4{%eiP4zk9{YU!g5J_gkjs($_n?Y(RG7ELU(c3Iuc8fqVk7*cA=i~3_)z) zB)y0{L0K zxcAgkd#fy7P5hDxR+GPwCQ3E;Fh7R*7>Gpxj0uaVuw4r(VgqG3gHRCD2NaWy>Iqs& z?hA>z!P1Y}1)Gwg$YtC@tLBMF`c=(lNugmbg?g)0`!<1rQ2RHB%mOvr1R(%7j^R?h zVupZm_$0;Q>CPt7$$9~7%<{+hYG9+-ehb7i(eSP64@7&M+dSPioaSrLN+e?N+@<^5|KiD(Co4QO zeML=~G8(>&RI$d4ZpPtpQv$Afi=-qs65NaSpE6ENlbG~nL)RM}S9?JEi4(X@o$-7R z3E3TqpP>IJ{j24s84X#8h^Yj}M!06DW?PjpI@H(0#fZ4nPTve|F?km^YwyBp;fEH6 z`$0!~>-b2lm>-P`kvvQVUm?st#U%YILZF@e^Cd}Jvq(cyxrpxK0T78eq8UAR!ub?! zBk(w%dw;f~%kt>KX7u%+p@RvgM0H&`BB8gVhBl1pU*3_GZBl9P%g=ktiAte?@8DWh0jDcuSpu1?P3MGOoM3*SCmmOHxP|g(TCgKs-wgX^kN82yOECCZ z@4lw6Fc`d};3@**tmhwl$0C~;mi9Yq1rE$)Lx>eF^)7YYjVEfQ#v8OBa-bvJH!dbI z{Lf)9{85a=?op5Vz3}q7gW2-(Ij%x8+Ie$kg9LUXW=CQHFXz?oJIZ*tA*ro5d0r_0 z#!8-)W)7p$?o&9hjAGN>=G3%Nzt&dM!p=ZT`fpd?SebnvGEj}Gxb-V^kR+#b4-pT4n?Ta5>oH@M!#UOM0lj{0%Rr?fFyYW#0 zo1!Mch#byHknpj?&a&s}My6xOY-SQ>+uiF^+|xrOv^i;neScR%=WD?x2vG)~vOn@# zkGYxVaF0!3C!o9MliD=HB09n4t$#C3t8*ga3JC!q5Qo*aNKuimd3Uykn9dhL$>{u} z={$A275HF2!U!Wxw$@bQJvOWRUU)@U)x*mu7C2mzZx(Po?XmEcadD;L=nIb9`lS7^L!OhIR<3Tg09Z7Q#a-cJof zDiS4A;RE^;lo4KbQj!|D>Ccp?iODKm*eVaWu;EQP*@Dib!J_-h7fnAj1d=>z4ytGP1q5=(v6-6JmrpOF7>iq`+Qp|uR zM3?ZruITT~dG6DXpVtdGGiH8s;&lkIbm}M@O;|&1sqH6SiUmAU(gy!0^B4uZY$bB!%4Xb@3YE)YO$Xf-JppQz zKjS23MJd;a`Dz|0V8!R>*Te&sjX&RC_D8rdtHnuZFcB>=@gtUSjW|4)8_*6pQZ@)D zQagwAsy17i;HoyT8JHt4(_&j{x~-n?{*G)S!vnRl^(IE69|qEkACcR&)>e?)uP$k? z;FvwxoP)K|4`U7Fk)R8`C0z?Vc!AYg;mako=iio@n(vdD{1d}R5dJ+J<4VvwswpAp z#D$ghyb}O8p;=oxgIn+1a)aHCDuU9tMPZzqX)KAv7E{gNixHdt@*I=RQE9Av+*^KV z@_XWg4XWan9P%4E+;l1G)lTHUy7gv_7=)5WhKboj|*gcF~-4-a>Hi3N8q`ieE zcY<=QD*0((v56eb*cYmfE`AWE(OB^hVk){#4e9Gee{M^oMkZmo?w1g1Hp8watlT`A zhpjYGQB!68l6;!b)QbJ&H{&u~YB&<@qSsJ{LP^nD z#Sm|8QSx`=zmr_3sw3#>?iA|pS>*m|V`ql!#|1jvtVt7gt=XGQU?(TiNP&gF<#1c> zStbe{Ub&@4QOainA>w@m9I4LJ_x>7|0OTAUK?RXD7&(L%_5uCj!2d`PM*=i|el&kM zng3xfyDGgqaBU~V-h|O|1)O`rlYf2V<{`*voO+QE^*Vw5cHfr@ck@%$xQ#$;)N3kwFac zRmjAMZFUnJD=wlzvG=#6i*d?cr^OfsBUVnu_Yn+6Vx1>lWp#nE$%>rl zG{68;#1>k-L@E6nts*|;ao%>Duqg=X$Pt6W@`3#l*0Ezbyo5dN3XfdGw`&8*0cC-s z*wKIU`*%j83M;+kTCEPL>hjS3%lX$HD1 zl9>I$cqjJ0c+)}$+$=MJ!jUoG;o8auhQ%_`Fmq5Y0&C}l!BZp$NC@q=UY}{ zYIQkyNY}($BboJUD?@?KaB<2frV%~1xZVB{S!o1eWmyNJmCfb5(|qqcPp9V@B8d3s zTRWZ3&um;)sH(}uKMDm1o8qvRbI6>8U$@qPfPbqQnq5a%bOa<7s zhrcsJ+}>Kt)JszmBOG78!!t>e5{$VuUwoJ>OldO^rbI+U6u;wN>#1tJEwpS!Pac>Z zVkW%#^KOR87@M0ePP)kXSf^;>lHn_^rVFxxcR3?{FW$bv^BK6REr^9yo+6v-!CLKn z!sKHsLnA%S3$;twvWSslw-l-6|HcFC{G$I zTd63A4>MBm0Tz}#wXP+Wz!>|dp)==2Uo`FLM|i=e-O^{4;J8@Ne|4;LeT^*+Nn*tE z-9Glh|6lv>;r9Ep1pLx(#3Uz=TW$5+-W$tgFlgc+=Cg}fs<)+n_pXasZxS3^XUdYO zS3FLZ^7R`X3|4MWmN_klai!78z)3s2Kswv#@wJ!w4N0g{TD2zDvz0~UL^`bo48(DfA|lEoSV!d z&($C%BPF@jmRA#y08~oK{ksc}YM(UuVH!5|D~-p5AiVLQd5_@vxCki8+>8V(9T*8G zC*rnSm#=_Qx(G!^$>zv|BFDuvloqiaMT`9B46AX9*gxWg7Rvoa#|q3)EZJ5KjRUu^ zrc;ZJcoLEAU(ZA{Jin44ygrjazHxqlx6`>q>$%)%*sEt4PK=&j7_(|(uS>XX$ue@Y zW1$y&!XOm4)*Ob-&*`ySZ1rUCuh1z=R8fSpNPPi*?EDaBCnzz`8srV*rVeT*pGTDV z{Mo^m^Q)-O^>Juqp=j?Z*)kM_ztXt(I?|le^-zlE^9t&T(P7Ne4e>k8Pney_h+ThF z<?e*2@LpDx>W%Y(<@Qu_>1Rp%-R95VjaUJm`@zN){=L0_VF zQK=rF&El1qtuJbrZAI~io?)I`Q;Ty< zQKFYZYbPfTmz|+}S)pUCo*0w~T||p0j-TPT`->=FWAu6$d4yYRWKy zvo?K*D>}6*u>W6RQmd5GmGDK@u!>?YZZh#*C-900$26UK~Q=~%uRY@O9KaAbHf^(rO)|{1t>aGJf7)MsPSs*<&9V?1M!bB?`+PBGE50NG`u;i5(w5;OV z=EXo_uH_;Ar88@Bf$T#Z&d&Bb$7kGBWjQicN`ENFH{wda7i5l%dbw@8zaB4eUhe~w zFh8}cWWWtuP-O_|e0wFCE>Z3cfPy*SpQNs`85kH4^wCL$f&ZMb$GM*5aCrvu4E!FJ zEfkq%qXV}!=5OLH98szTTZ5m|swo7f%^v{n6p z*!9@9TjB5?MV~viX(b-h+9+;d8ptR3>Ffqe(Z#if>yEcP!DF@gi~4lEJ$f^E%mbVn z2y3mztY)J_xgPjv=A8T8O3IC6(bhmdV`nrS=K=x2Ji{i-)96Gg{(4Nm1)9Im;i^c0 zHMfk{v;xK}i4c8o++`?+zx(Px zD&Jd%j@Y`mp2OuC7neaN%~r`$ta@5mRa?~Xt$R}aCYHA9JAtYw^TCfp$|%UWuijxfgCe{c znIC#Z(`6U|!o?c(1EqhL-d-fP9$);|pP*yr9YkJALSy7a_GT4m^~53~LV_@CbigvN zWf47+q*;DKWp3hm_l^Ui-jNl3O>{ji{hzogUnsV1Tl#I$jL1M3>#EHAT+5%y8C91i zdEy2=6J6$w?>t@5CL=7eP^|ZP(Riv>RyiZgCpJ@IMDqgw?4b<1oX_6r`QAB7y~!!T z3qK1J6CZ`o2pdeoD882r`lP!isLi=Af_LcVIzP48C%CWiJOXY&^GL1?AVYUz`;N3@ zCx#c^p-Q+sO~p21fHNf}(PsMZjds_6=wYZtK}}5pZu@_svIX5G!QL}x(D)WZ#0^ic zlG6v0lpGdAn1ehZND8gDooAY+rl!8w%XAFF47%I}Mwl;rZVp3Blrw!abLru9r1{v! z8+(3v)tK@49xpiae4be-y_WW>v9j0rp#g_*`x+RbNKoUu>AC;9w;ZXrHw}*?>lX$} zhp}^NmLvHiwvKf2%{4VOZo&LKJh-4+B4F4uB0k@wqwVv21(sID8;p(^4#ZN(0vN3vzkc-(O8|X8o6gRY2}^b$KD7- zEdoZeb8>P*Vo*M2vDMi{|C~xs7$A?exJ37VW3AAsYAwDuTa#=9-hB8iM$fav=X?;? z-sRh5EUt$THCG(L%PxzXbz+uSm_`0mR)vj*lXN;6Q06&$L`Nr`T;(c4LLWGUGcNF` zXA07U%1k}5p&HP?P_{*wMchQll|k&$t{SK@Z_!k|*y^7wcefq?-Zc%evjx3_nHXB>#qDA`=wVbaWlKa1CF(QTl6xuJ${^?`!$zXJA13Ux*ELI68D19K?&w&j*)Lj}#7tK%(Or?i&~z zxr2x%J$L*bG}K##Sd2#Gy=!JS7jMMx@DoCAbXe<)wbcfkc`uQv*bI9@-^MrptF(4K zq#4f@F+Zaw|BvkdRHcf;^JNa(lDQ3%Is(@dXA5s0gn!#1E|FI>pqFB}3lJZRt@4G$@sSov);U0^d^qSNUw(%`rr2V&8uqS5nnCx~3lTr(-d z9R8LWKEF`lX#1jVloz=|3WG#1@b=R9ox*BXwMVltK`>&Q&I34G zh_ltE=5(BMYZEhxaMKRHV*gX*s!(mkFgCjP>qi9lGjSK4ZQ?w#qLJ2xMu&K&n3rLY z(*+l8I=#&eE57>?{$r=Lp3vRl6_!E@lWqPWlWGp1JL^rgO~}>1iD@St9l|p#8Ymy= z#>tO79IY30=#&iF<**t2KZ4OZ9r}T>AIoVRw&Vk-58q75=V~fz(63D{v=v6K6>4w> zzbT}DXKV}U75lo0o#E-1Y4jt7Pnl!RUyP*wr#7z*4w9dhx;D;%BEKt&ra$N7V^%8Q*5k zG8?_z`kpWrw6bjkm(Dm)Qg=2x;3by|B1d;0xD&zlpdUZ<@9js}7aDf8kMHf7so0FS z%02JK$w*fk5fJW9RQV*tP2L3s2PZITGBp`MMNDsD;*iCH!}?@2RePv`_hlaF0sd6) zqpVS-Nzh!T;^a>+&RZw)#}2R-kcukxjZdyblT7D3rM`B{Y~%8!4Ap@?Y)f$qH=8Qs zxe+K%`}6K&Aw9GO&H@GmRhNq7KnjsWHOhSkjs9a=s>z>~ng?>FK*ppp7GDk%#PKZA zNYIIdNkS5RXLJ^}xXvy;=7Z+-FdS=nH);{_^(c6rATK#SD~poPMi7u%Z3(?wKaR^@ zZkYNVnClw`ZVIW#K@(AY+=+P}M_~{A<8-|*#$yoVO|S4AX|KRrjQ^!j?A+@Vb~$$K zKA<}9&*wiYToFw%qUFxs8rOK#Qh)O`?aJKrQP`P$CLs5#WT2L}_F}QvmRP7C2q+jAzlUeP`U^E1IAFY_ghMqjS#W@<)Q3LVgXDC8^8@ zV!#Rc57@bovinW{XMerUa19at9vozgicr{>razn8m6(c}O_8zsG=G66PBw~qg-L1D zJu8}E3Te~tUpe_20+C91Lu@fiv$1J)o8nOqxKnlyzWGuHKno&-LA4z95tVTCfVsip zcN5nx`K`U3;%@ABRRGCn|+kU#EREfBAO7 zkDeJ%icm7aRte+%_?%abJ^&*dS0M9q+|8BWJPdWA%@^v6b`{e(dp_J`c*ZA38SmM? z%Vv*8bOE%kC;*T7VnXb(n7Tg-??WqS5tRp3v$qDj{7rBKRqRaghHZ_s&V?;AwhJra zNDTG*uyiiGpp2GK@=Xecl8C!Nvo$V_$DGfDTr}+0*SEgp|3lL?#^o8d{d?J5wp-S+ zxvZt7WxLiL@3d^UwQSe2ZDZNCm+QUuJn!>iU;AzAybkm;A@E=Sy31D`Pnaw?U63iHeZ>34 z?=ExJ>d{nP7Vls4Sba~)^3)S9asSs0h#w-H=CI>-Uv%K%s5ohBx?zgst2GS=nTf!UZT2CsF(=t4^GCQW~ zRqdLaj-8tTE0gO#!hgMD19`7yQGs0i$FG&`BYHc-3CEivs};2zu#?4i8U#TnTHPp9 z7a7{o#fN0j>t|L&VtLzrHtfJ-Q|yp&b@>nomT(6zTej|tO|mue)%T++N|MeBKKu3d zD4ryc@E))7d?f@ia>TZTz`*tjn&KE7B>T&-q+bdJ7@oj?wMD9LLyif&Z zk;Cy4UgS|RF*atizOj7tI+#?(07jm?%ldThDvudj!V1FN19ViNzhbuhM_UDJG zf|(J}&Bw#xUHO04rd|cyaZ_0IM0b)9^jZkRex60Gc)6c%Oy$aiLc+rSh)`k$basA5 zkRu6lJ)Qrl#Vs7z?*ubRn0qWZ4-a3T}P4qF{oE<;f=W?@%-`095cj#o|V0 zu8dV9INlb>UoQ>_VB3E(1LwNPoILsv{cG1PmF_)sx&67zL=O49Bz=g07m*AX5;Gw} z8XAI52AWj18Hc3aksN)rjUQd%3+VtB*I*z6I4Lq6@YR0ZDN;=ZG!cYcmVTA(k4QjX zlAL{VJd9ci`dVIqe#^IPU`n0!K?d2bx05V4I1Jz!di&cg*HhRn)f&u1MMUHl!fI@| zXI}1fb2fB&J=Ob`wpE8QLT}RmSoZ*6ZPl=c5%7lq=2sJ7uLt>ksk8Rz74f*DMwOA4 zrsUwjdb@+Xg-qwmo7`V)*1vkaBNr4T-tLb|C@>rQfkVV&e-q$Ef-uCf5^Wy42%mzplA_ zx;yDV*d&`-&s0tgpp=SmB5QgOFWc1>?&UlPGE?YIc~>}*x?L;k4|Nucj!R*`fj~9( zRg%-^TNLp#MK_Oq|EYj`^=nG`z0v9fxEP|rX~bs9mFc=s#Vc<2YwpvuIERf9;U7qE zpA|meu6$(rUz|RsOK+nxLoPgz>z$frv#&krIx)S@;-UL7gII8Lzm$4ig<35yu^3q8 zlUSm-?Us+cs}q(n$RtxBvWS--Sk(&@Kz=fJXVbj(nE*%`0{}q?{~{v0#J{(aLY8m| zK70u2(HfLzuN+|WpVfy(5Fs(m{q>XdM>>RC+{8Dv<~uo;7HQDlF!XE@>UdpvtEXDk zIQ>NTpP^(#)n}CP-(NXvo>6hBHnm|#ruFHImhHLJi z0-BP<^>lUaCFks{MR}yGzCs^#nb(-}BJCk9Ve7Y#SeZ9ZppMs8|AOzk5LQ1vU_bzz zc^*lDL!(yISj{Cqj{0t@BVUp0Bf0PExjP+|We}!(E{1W<9pKZ8FnHsgrE{wzs=vny zEJ-^Q{uBG-YXP!V2&gCR>M}NM(Q}w=0 zs)%t|tbuhuUxTHf9x}6oIE#j_N-99rJFGPN|3h7YWU@eb^!~|gi8vO0exUoq0pC;~ zP~U!27@4y$9>a;-ND5@KF2bLuZTUQi`jHnlo4wE@6TnJRh!h zp`1Ttx)~0_f@kh7#z!n3QM!UFQ8j zw(jR$?aYq+H9bMu{u_P#%jUG1+{}fc%xj{Hgyin00BGzG0q1<}bC;0so2xO8GwVHY zH+|a!F<^c+ePTy5{N{j8JhBN=HXxeJk(qA(dx$_YT)SWb?It*h1Y3QIZTmqE3zWjD zCwfk*kFgEOF#HE)D*b9%s%G4cgROWmwUYJ=qXCWI-{9IPho=RJxw`_i0zT&s72)cB z!c-%^(|4Jz{*0^*Om<w7B%pMPF+TpI9rnY(MD1Q+7n7mS!Z}mY;sPb2`~P zFJHlFiG9&3^`bW4&iZ%Laag-hv^R0lLFj{&w@xz&YNkX0vOG$3LMx6M8a)nEs*!z8 zfGlJiVY9b`?sDODJm1wy-!SU@OYg|LpX~{Qq_Q<;_JxW+ab0j(Bnnyfq-FqrtN730 zy2}Ye$QNsRmUSkF0=VPoxUeCxpr9LNIXs;}+C6{+JvZ)naa9<^Ew9+Ec=C_hF2Y4l*L?UuRo zZ&&!qjp}C~OooYRBu}T`>^k^Mpy37)6;;aCwk$W~#TXY4Z{Hr<%T4?=hvJ=a^w5`j z1p-20*8{eJ7`bYkhXfK4Dl|w&!}=2l7WuegF7xpY29Oq5%;9Wk%A~pJ<_iwr;A7Nr z90zIfkxE1r`4pzv zKZWUawpbnHx3TecXE?upR!iNjD1CT~pid3DOLx^k`qVn<9S9JwLd56q;3#Qn$NA|S z|6+LlSsWJzS_alte^jyum1bQHyA4%m=tV0vyi*4FRswh zl5xaf6|?BFx3wj;Isq6~lBQ~PTrAH-K+` zo`OxWVzvC%nvHLi^1UdU-VMYw;(~z_()mb8-Hle9?A|Am5bFi!t9$lZtIzQ215w?% zA?Q0C+LR6tp7%$}McnEYC8!8~FOU=QCt5f5ymPP6W;bNN_G}^ZVZ8gJWvRhf_TC>_ z8hB!pMUMl$4d-q5_J3h*v3zbjqZkfp`4$_q3ey|dfCELF(#F7F* zzAlUzKt|QwHlMY#eKR3@gb0(W%b@(dr;JU!xR|0s$>haW-($*R?V8A!k7!De&NeAE z@2;NR`Ti4-8$AaB5d%e2xncODxjDvruof@F!lKvQB9V)wY)d#bGz^K9ZeTR zd%GKFj?vQyr8EAN9Uwa}7EG-P5j9_Ooon(lo+ zPSgmwtJvwOUJe#}ZX8V<>mRu{hV+h{`4?x&$g^}`vtC(ipHH{if2jua=UEo$CjdSR zS{2_~He!o{hkC$L=ANr=%(fuwE+AH}GrytV`!gQ86`!3>9{5&PR+4^<(|0qh zF>!s}>;pxOSZrB>`lB-abnQ#i0ZJiT`j*g>bnL|sqDEVVJI)WF96`YX-%b$loIK8V z)Co_mAb*QhcfM&VG-K-w#PK|>axlNQ)!qrUUKn!BE+{Qan34GiE0{RaSK1q}prl}) zq*0MiEI|B)c5}M#Iinn+!TaY2?EHFN>IG4W$U?eR86kkF{8HaJRXB0L}m^ z@WthBBH=Jh>U|6NlHNhOSm!K%(3t`h_8d73G~lK9lU6BNxgA4az$$<6$j}87nTWpD%*kPvQg7^vl5hP(nzC5?AVDHX$m=hlzbnx!Cp>pHHZiI8eenc|=1NCxDGvB?0!P5u zSOw+nV~fj9@O;lAe)c)F?+@_qFq6VC7`8*b_-u6g5>5oTKGaQRw5l@0p%IDce@qwg zg6o0v?nLCfq|s{+>#97jp;=QJa=QU%A;Pj0CUX27VVZyu1-ysCC%3e|FFKHpT2^v4 zjNk40q0?r=I`1u_-zk?cVcviJEqfK7sPya=5F8KCNv}d;tcpGU=Uipr zRHVq7KNX#E|8t)?pI#5=E_St;X?XUS=+|S4UKxXyFdkvTaQ@Sl?fYU*`}YB_ZEKy; zZX>FBD(~*3IE`OY3t~~|$4>(wyQib8Ea71LSXuLpLr#kWDQuP$WGa~`S9!$mIhUUg z$Y83S061>dovXlRz9LK|JdjPb58J9WPKFkm???Bfvpn&4z4#~;8~xADirhYeid1wS z%n#c=R51MFUidL2?Ff?hn^q>tq0WZ=Ez}(@#71SGI7W^(5e@q#!u{Vv#=Pa=U2-m2 z0>1{E+2G6=5&9h*;5Q@UVPORT`Cb?PX}j^@Z|wd3C8lVMHV@!{eGb#kqOh5Z?cg!` zK3hssfk(W5@AXKNC6KjHNwPOTV*09yqb;_W;h`ik4mAEt`1>Jtf0k0;G`MtGexdG# zXITAHeN?^k&)ewf)@q%2jSf#xc zUXB*_ad-L07A$WUp7i^9Fl9!F)QCDe?u%+xIa6lLJ}S31@zcXN8dB-Heu1xEYG2%U zMeptH*LI&Lc84_{kl*KGHKU_htE#80*IKBLYoCmijeF)wn{^?QEp`2kPlv$uY9Kox zOp6b|0|vSR;V*$kmoDIrhK$DoAhd3(l?FmUk_Vds*GugEzkW*|lw~%OtPL0=e4j66 zyl5ew@FTO^9k8JQx4r9(j#=;h5Zur)7^b;OKeV_4d zv|dB6a7qEEg@bIVk+wHRj;jAe?Dh@1qB@U1PkFJebebA#4jW3?a5JXw^F8LjUk$gz zB&)Y`lV0lUt>COxr}yC33+7=?a9cF806q)^S1RqnY3tsJkmb=^)jiZsD1_N+vi6`) z!J@j6p$6rbKVTQ=8_~Mh4d?^;p^K0N67e_~Erhf+AtQNshKFr_8o*Anco}$sM`t&A zrHuT^4iE6Fxy4e*IGrm)xtGTINUiANEGTAzcOUw5h>!0zMa(u5t)fVngNF8_U%-$9 zx|^KQc>^z!zK9e*0jj>Kg(}5k!J#a~mlmrTb5B`7{g0#4tUzG4+gkG5`eZ4}>fIRq z47h2IOOf9P;T;`x*Pa=|6>rd1Dt$rL&Q_acOrEz?4YT~^+CabDw%uC9obxKFa-GMH z5D5+%rmrriS6PCDHx4-P{Pf51S47As3=$D)tg2)D7t}scO$ETnzcYw=v{tT`KTM9n zYKv9V_@+GA_|=SIpr-#gqs+!Lobq7I*1KUZI-7b=T>qu&P; z8RMfy)grxe{vWlrQT(?=0BZKGsp3>3G$O1kP6XZPxbo-au`pv30h3;Iq}~f8)TP-b3?B;x}b^Oo;Jpk81ZT>CeZ99wc%ta zZIn@?iT@`i5RI=}GXO6}efy^55ua19fG8$^?tD2Nr7y4A?QL=MB}1L^*#TCmAu0G_ z32@f--Bx-dXDDoifb$_5R_E zYRQ`}P%Q0b*TZ_(yqP^btp!IxL6Iup&I06}1rzf*1MUwB1_mSmbd>?TNE(5{WA z3%f*`M|QKUTHF{rCM!#M20Jb`ekiX}B{%w$r@zZ6s=PMk>?CVkoNS0a4-%lhr zJ4bAn-vObrdP)E*+qtMO*wg55Iv+6iv4O~$m{0=Y(3Q-imk0k#;pDU}B2aDAn8q ze2|FWtd8KB=BZ1cL)9yNG~8I)`=05J{F zEExR}sCg=&MttaQ#l2xB#p@X#b4p44vQI`BU0EmKz*$=RE6$=ml8#3a3PUk)WVbtI zTZSCaw<AoZznp80k7g^Us1N@YGb=_OsFvj*lZ(9#-Py~_BS3&Pr8)I#^abS zvGlSzD*IW&JNiUc1$VlxTb!B4VQ>#&K-fN9v>$7A-i57@6b3d?gzwAaOlp@zFuCsE zlbbxhFH3o!z2JYBe|xJYTCP9zt#H{H^p7UFeZYmFx46QaIe%M7j*v%SSA06mtdgz@|9l`Ok|y^@bX#BQulw5H2rl(-81TGG2StuM$KiCI zmt8Te8T!J7n#s6&b645jG4Ro3SV*JnHjAou7aSq-7F&?zzeK2XJ2zxn0pfp#`(b%z z@o~f108VJhE zm>{pav-}Q51|IM)?Nd)i)z&1nc(ywhGb#+eFG^z51j%Hruhum={g*|B&JTU>@~30w zPQva}SoHftF*DT@0hs`W-H%!YbP-*~?yy0Dp)n}<7-KFO!H+B%LSnn0OFoC$U;TzS z-X3WL1qC%SKRmQpNH)62>~qr74>BI>E31@gvvb+}K>?Py-tk`oFGQ`%P-lZ@4rk;+a5;8uYEVPXVS zW?q*)6jEXDPypD?0cN5ih;9Q5u-<3ioCD8f8osij^R&w9tHuO?8v(*CICwN-QQ$u5 zH~`xN1d#tKV(e+Ivz(2$Huep%ID#}FF`FrT1&WH7cflGp|p&6*L4JM+%cWP8W8ufu{z&Oc_jw}BIo6Wpth%XV7L)+@6D0@#B-ty zMkJ!01{)lz`Dft1+_S$5W&gMBiHo<^g=&2GttpkXdf;4OlNL@x(V3e4qI&Ys4R*h< z0AE~Cu;_cI(?xHJUq4D|8o_+J$0mS@9{n6k=SJR2dXi z&6os>6%1Phz09*aaGz{J|Me&@mh;Y0O6Z3$Ud%OSc*vqPpbWda!M5A^lKE-ntQF?> zcl7RT^6mPY>$I2#>g4pXSpscmq5Z`jB>IEPN~Ou!Y##+9RiL~Zp1ar1_lPW6)aIay?Ze?tQ2@M$p;gm^Nn{KG(-r4Mi-3Nqp(bp zk*Q)ym<)XCu;6~>_pe=5e8G4x)a_bR2OhChnK3>5^m0b{=5=I6iTc~V2@Zw$6VKk4 zdfPBs#h2(6CHYq>>HA zZNCyalEV6wUQdRFHgN*S3IgoTu)x|V2|Q(zO&-fh5GoVbevd+$hN&gz#d2g7Re&EC$p=HqmtKqs!-s$~i zw1FpVpLEGm=vcXx_Wh||pmOo`;0;nSOv@5PX9W^7;than9%W_R@~L{Oz>QB+PiS(Owb7 z&F;`Z1zJoy0=Uduck^atNEdSOn1`lVeS-!LMn zgbX$lWMC4vu<6=>fq~1hzWwn}Lluld24I2q87ri(ulL6(xieB8 zO^4G7V3Ba70X3UyjVnRo2d#HBa!Fz(vtqbso_A|hhcUYk z^PKsQy1I!(2OT9nVSaKu6bLH<;YA@s359IIG|FMS0Vx}&VH<_8XdHNq!{ip@=?G|O z{XLKnbf>Le#6)`4?ttI+zkIcnfC1TOJkv-&ok|8RbLOu^_h#A}khbCA->{+Bs%#I8 zP+eMvkbf5mZSeSA4^l17JOY43q^%Y*t?hi=C?}dNj(>bVYlC&bo5lI?wrk1j+tavp zuuf_IP zoUy;=GA^{wGI2e6HM-I;Z*nHI?;^bKa?W>`KJkz~@v>6cp%VzC9PR$ld+ofn(PF>4 z^KtMQ$Q%+zuy#?@)YSpCv9)$MAa}@h z-0W_mj|24NwhLdYeS6?`A&nZxfA|w0u%#k!Bq9dBsFN+Z@e((>ZVHZP3WV5bRc`D> z5j6sO%jub!#9J{-ZG1{9DphI*BqyiKP*ww|{E=h{%)6BJchytBvo=l^fAf0%1(FZ| ztTr^9A$|mFagYVL!DD=}(KYoW(B9h#|aV49;9 zUBk^j7eZS1?+}{)`~aT9w7*}su&z1=$Cve9H=l0tKbm4x=(XqV2uK^n zvm!1*2UJ9j(zOv&%VOotc{D-9Wy_5<3L_*VAzb=a#Dj{`<4ge^oDUVC&cyoRUwX zDIE;rAsO*fkmgAOPM5B)S+ViiLeyNJQQcVwrjWfW#4sgk!B#M!A>@q40$1|(3q zUit2st-%}qn8GrW!lfYxxRcB$IAN$W7lNO5^d!xHe+dB;GG+uef67$DYwec*Sj6c? zTCo>lphzMR*WfGD#D!uL^gfxuC@bn*eRFiRgslB$;LMc=_=n*{#uyH>5oFdRe{`<*riI{Z8R1J=Gj&W+DgFlA4k(PYU0jWcg@OKP>Wi=@3-8YCCpEs|ZDDK;1~P z|LnT#O}f4E$Y`MyO65<1Aqm>GlB81IBo%%>ph^dB7l2e6?DcQX*E3sc=`FV*Sx1+A z5Q^(q3W*D#;2TfnNELmlV+cki3I_gX|1tl+1l*;8tDb-?U?3{42N@ zb1?aICu4Y5smH$j*V(!E&M!&ikZkdXD|)R8;_(cj2#F9h3cd&0oSa;JJ+9#*i=5=c zb4?Fgxol08PXXu^ah?(TRn~P!+ST8vnnmW`?Y{{`Fs86>rbXLbOeG#3O!A2}g3Y4Z z@=nUS<^{pT1W5kbn_CYqV91T`LY3yrh9N+!{9^}LCWMsXLfDoLa3}p&`$0T zEuOz^#O`fdeol%H0`7xlD@C$eB!G+CVKL{V6&@DW2;hDy z?!TnVU(e5}ttKT5{Gs8dyqLGgni_AdLj6%Q$~%;6VK%*k+)uPAbp0)rE#(B2@uOjg zXk{7wH<6##41X%f19`SX`@JZX6K;Y%nt7%pGrB%t-Aja{QY?5tCQR_L=wb$AB~<+7CAeh)0EWc6jX- zjFEEKdqx*1tzO*=k?#-sws5ZJvuEV@F<$StV}>s!6iKlhjlTk64b4niw=|w4#oOYV znRsbjmkA^)vnG)2e4OrHrcSQl3ZoPW*7Oy@i3sz6#MBNhE{ahU$BRiLgC z^F7F>)o%BOO;@7(d$t%j@I|EJ>SlDEf4FGau+=kH0VC+|2&aK46oW>2xQ6V{sq3`D zi#tOICG#no!TCIL$Jq-aRlkp)_!;_{6$%xp0U=c|k2w@G*b{k#?{u8R`{T9CXgYt~ zqo>mg1P+zm>e2RdncDZ#n{Zd^Ftya$TR9a}^!U)s4~=rXzm!f~1{fItTO{y_mBioY z5QIVn#p`RC=kICf7FGVVBoe0$m&HuW~JCZ{lG8K!u$)`DE_6ICa8WGv~UWkH<)tpsz z;)%?=$eTRNeC!K8mm`MLZ&8vFl}xg~ypxW9Pjqfh7S#zPLIB?n+#S#0W`wFx+n{gn ziQANoSkCbUO3i1!8rB1RFAdKx{A*Mnpw8>l*riS36@K%ulOjdI@h5-H_7|TnRG$tq zfi(A~&(s#65YrxvCC+8nbYng{r7P9cP~>RYkVmUOz~2zlJUi0=+!OxWY6JyR{&qHc zF}{{eW0L;cljaVUjdE4$+{5#s+T$Z(bFb|D({^MoLo82sTGA-}2faM&@8q#H$3&HG?fx2I7r7q`r_K;#eYM-U>P&%q<^@KGm zz6@lh{Zj}$BRCZNEN_WChYnxM$*gxTC_l9)_OyUr?8D`-Mr<*W(MOl*9pKRMf(tn2 zWdh+)CtKVdZVslPv$M04J(8Jpuz)mt8DnG0f7&Mij@$-p14V$6>K}9p+0TTK0)=vJ zj>m3?#TL2}wS%J2_yVf42VJC${*QqB88u34Q^d5{$mY+yF>v|1{6h1g7pQZ~!{uUT z{Pf;(*p&;+=FnF%H27&?mD57czAhJJGv27-bX>gq^D93R&c2XsvOpZ0nioJ&qnN{Nk2+Za4;?cO(pnp%W|dw z75GO4z>|4nqZyp=9nV+Dz@6*EX+qY~0JK8eJ5Q=aW?fvst)T&F?HtR}sx&y5D@|eRkV|3! zUytz=JNPdk5dRx?4E#Dwu;1qu2oUqb8T5S@b=VJif@+O_{WK$!m`Eb;7Oi$-+UZg`bnjSSc?D zMPMo|87(N@!2Ng6MG%vmJp+-{0fFO}(4VVW@WT~kSdA3hwe9fm5d5&)K1@3WSU)E( z{NYz92xfnI6FjINBBP@Z0L^%^%1GiL3SReAA%HuWJb;8w45Ka{Y^ zN_Zh0+v;$w&8bV{d*d zLi|V#6`N0g_-(#7iSG|4%SNPvd1Z%}m6dID+=K$9%!nm1X!JhjXjGGvsuv4W9I2o;$CLPM1ob_|AQ>cP`Y$7i1H87dq8 z_-5_x^$8ttAE=JgDW?S-6j_QFQmy4NWI6KNp&Jl!mvwVrEL~uILH+QF13Sl`%U+D_ zJd9)Z2GDop0I!V~5PbN)y|@D~AT)sNic>*ZrIDZ75JqWL3T}k!NHdHI(m=jI1q1|6 zzSZ$e0g9m32W!J;mld4c)8$?H*xE;3!R6n0k2hpNSotFem9xWJnXoB^Oxu-#@Kv^( z-es@m1ZocJ1y|>gEKKQ0FZ4T+$xl6jvk?of8e}<1hC);*PevcrwX%O-EVZ~-0s+I& z=xpB4wdsH66g}`{@!f>>L6GttHJ_Nm>-v9iAkx>Ww9vAIh<$dVDlA&gZ!7pC-+k zoZJx!b>c6&?k{@Q_CAY_6WykhZ-r8&PwG3oKv~yu^y5S4)D+*4P6KoW(fCCu3% zH#voRLyoyi6CBgKbDTHlFo|*O{v0$P93`r)%?lzI5<^e0VFMqh&a%Z8Cngw^8$!CL0iHsLq_M96M z`Hb{;$@#={$X<-vIa8+IYlf-z5cg2ujCxG@v!**S1PE8~G=0AP%@BCzGt&Vdd>7j& z&1rUXFod=9vVDm&G-&>#N%gC# zLvYU_jKFQ@%GP6SEVf#uLTo_O1qXtGhtKu+y;mVfU@y@k@{xj(InPC5#Z-gioneX7 zB3x1}Pm+3UGxQISxPwVSk3}@8!~~jYo%VT^yBK0{ZDr&&{POpyx(C!|Nnpo_U>r*snw* z4rSYtDCD-i4;Oj8hfZNNgaZj)bV376s(<@EU7 zh4|l)aZYZ9W{Galh7W1gD^l&W3`Bc6YPL|;?gnJpCDedId{ILszSE~KiZ}Q|JBMMv zBe@KBqrIBi|Bdh`b+&EORqDke6ACd0@ox+7;9j6s{`i3eYW#MKq{*Tank0`41?0k& z0}iKu7wU!+nPZGE`!I1xqQo(QkjylKv`=k5$UK#X1AP$?ynnz1@9hNQv(Jw5O1qmp zqaTsRc||l`i(ph?#$j|kaMZT@dbDMj_Tx<}$$d61^_E_*ZhED8=rin~8t_lgy<%v1 zOfo7x0xe=9{B2dr5?$7!RtX)IOhAn z88$a*rG;&x8Mi@43!!+Q*+=XvHJtJwf>yIlf-x1>Ma@ht_BKn?9PIC&<$l3`oF29# zD|!m9WTq#>O>v^kiT+e-MK9M|<(f%$MNIh%T>gfE^OmBPlySLb&LFjgwf1P14?m}c zP$oj|4|`#xyhwiUyGH^Q^uNgjBtg0J-9o|ihr~28JrdozRMK034+i81>{z+(4sIx$ zQDhu02C*BpZANwrRVd4yHa!kmD6NU`taep7w@A_c@~KMKA9QM909+^#8XkQMkRtN} zXTwWYeHwxTktCvm3-)3Y5=01MQXn;WUVH9onZ8>2!{E0BS$ttn;`l$*TP+Tn8~bm| zimSh`-+uKyyp0<_B`X$~&Pkkl-sPB;a$7EEQ(j*g z$pWrKpN4r>N9+WMfW#Z~7(pn}KZJ{lADjH8B!B<7A}+m4(9@p61%12iDS#yC&I+e8 zD=@a2nt0+?QpdrR8>J`~Zex}R9-44Exp%y9V_xq~z=Hse?(5k~B?czuCM9zyODnDr zZrS7y?fMU{U)HR>rIX4OR4j)XUB%Q@rDTwvye>AZ$HL|Neto z(01wu`r_zsssT6IEyzVU7aH_lWZUQF@!$r_(L4L!tH#k?la*89DE;iE{rd9Tm3cz@ zQ)T)sntU&YPk$eAOyTLJ!~%+0tJGRb!_`L1_R3;5elRrgq}vgXAxT4ij=M=>TpHm~ zoK=gNz|f*KHgigjq@S+tcCwv6|9B|s$5Ce7I5p}o|fQ4L|@Yb*e^Mfd9T=5CwM+T8O zqN<;qru99ra*T~HAUD@Smp3j#PA^LzpBuN~pKERCUhhw}{#Ap@ZoT&`YA_$I=epYN zR%4zBh5e+_g#vP%oPNmXSA_9l9x+Ik+?gkbi05jbEI5Y z=1<;P+|tO_K1C?Fv4}PQ#Je+YyZpXcE`EeGn8vItMbK zrXZalsQ{<*))VS46+^%1)5f&w^B@n)+Tt$i^F>mB zA!N{{07A7w8D7vXQdxM8g$mnN9}!ke&oYvHcvVX@Wrng=|cQaT7UTcm@{dV6-dUqAf~oV9w~TS8?};C?aWv@HXItW+r90 z%@V1u)8%4(whfJ4;I?6dqw=j6y^z-N=g({}#Db3e(60O_pR(+#D z<#?=#1c2-Bbff`~CX$qNj^&i`|0C;CeR4_vIGcWT?n8QAQWxIio>8vuNXiG^j% zx?^{b1Bm^God%*IBtD+dz0zyljZWLZjjs;?`n6QYZaNsdfpbr2iLo}O z%9J5V1M`rAj`JDS5A$?Urm&zYY16igi3ugt&^zKjpx0o-~%S> zngVhry36YYt}`O@&+SgT@p}*@eJnb~*c#_Pq}}`C@n2!-C*a}PwJmEcLun>49RocK z3C#j?RN63?yG7UN!irJu_YKgKZqxiMAwk#m%NasmY(D)cAeN9KK$t|q#iWocG_*sY z47o#E==vi7Qv&>gqgEti*~3tY_Q<2VMglwu`k)Tdl;Ztkv%c2a%%g!e9CFbZ zhswH(Cgpe7Eoep=BC&L0jzhP)E@(a2%vtq4cW>R#CdSl%<~6~8CX`T~RuK$?Afc8{ zAXLgT7psaPsQ1n$>)BpQ*E4|b0QZX13)q*> zro=R*5yzC_ZZAK+R-ZgN>3*!rDtXppES(==6HC#$YJ#O!)fr0|-R&YOj5G!C9}6Us z5v_9zl|s_dLF2Sc|5kHTjG-=%6D~opo@7s0kuZb)*8ZVByy6Z&<`@y>{WOc_$kbi* zr%{^wTvkB!H1U25L$9O!NK^!#vruuzN*V65*tZ&mYDb7hb}>j0%QH!*jL$^PpGfUf|&Vp5q?DRBCZ{k{^{! z%-6Tqm;Wrio3BsjS|cBDMY`-i2tK?*xygL?|9)u7hbgbL&%ya{367v;JnE0X;fiAN zzyAw!{Otru1$!$P*<9O#G0wktg7 zn|=IVY!?O<|CKacaTYXXXd5(7=}C*MOo zi9vd8o;U!s2LpN!0TgUNj!rgWtG;P9&p_7tOh_$9ad;;~8u9l;H4aqYoQ%%NVrl)0 zks9eQC2@1!I+T$uty>T4LYcIpqr{Lgd-hwFwz1VWE?Tn?sY^Gi#k#sqbEM}A;$||Mv`IBrhbbt>f*Brc& zynSsN7P*t;s)Vu}qu!z8$tdWvyNP~(-E4c{mliYEga)Dp;3(YkFAe>GZ}S~!cO+#1 zfG05sv5wx@7kaNdl`3c7D@0~f64K}oEHIYLtKe+T9v&+*!pNY-wc=Ll=3`!CmWZS~ zrI>b;=|ZT-rsDe|l4|W&o5hGTx(4(ONF{luY>mR#GO236#$zHXC@Op<97s~)`x*+4 z_vSh!ZxoJ4zl?2t>w=f#Z33Vkqn$xj*WW`oSSdPt93>8~^NZ=J8*th=FfBB0IAP=;Gjqs;Hd$^`pomFA#_v>{QLt)4RHe6ZTy&MA<%w|N;&`? zccSz0eEaeC_+Y7y5ulLYf$Wy*`>Hm3T>jTtB>DhxdF*69G9ZpZ2ZhGO3@o`FtK>)` z>dsYdx|^Dr8L=B}oUXkr>LvCEcU&A-b{G#QegNqap#atfz~B=k6qG8t0cnrIOI2GQ zB&!kv+ufrAL`2Nym_U_o+RhW&&Y#p{UP?-F#F^NPjOe^PYHT-4cY_b;unzTB9%iA4djuJvl+6ED!`si8Ho!Xyvz%CWk--_q zsshYGG$It69~vK{3M=nzAdDJ2KI9jg7+|!PM z#0mBri>0U|8<^jffUw{ZL%7(qw6L=Yp+c~PMzI>~b^DLPXp-LF_KjjJ5z%Iux{}c~a-IxQ5ViyfN{K)>N#25=uHhC{$s879AuzCqrp%ZEC>O&= z`Uyo!`b~UvOXQ-)2WwO!Yqr-D-&uvI*)RvU8OV-Gmol1^lKojNCd5W$$TT8bXylW7 z`xSpW-mSX3G-;=~j0$dxdv@IyW?u>}6R-WK$b8Oc! zPTR;5{vSlnz{XYDoT{A$*i;iUGI0uby(-OA00_*AoPra{fm|w5J z;oF9HR>8$vA4y*u%ev$0$yK1tjDp${`ewO)MFjadpO<1tj@@9e#rZQWCUz)9N>fEe z`=|a_pr&meED*(-aA`f#VTPPZJ#^aj zvy)#t2AGW(QEI=~`#!Z3<|t9;N_Z1Cd;UXRHue(o{Y2qjZfvL*qpPW_`u3G2v0`qr zs$gpGOqq^_y-xrmQ8d#Pkx>AoKokAO`654pOqvs^L%EUVgAa*VP<~gq<@~q5us{aI zXbYgrj*O4WPc`u{SN=@dkv%~?CQkZFqHZFloSf=%G&Vh)=7oYn3!mz6)U0lec7{kV zLZPFO@12tL$6b7rkVvtAZ!!N#4Log^&-_V0(QM29!S&^)RMuZtGx_!|i2p?hBBASQ z*^IDX1ojwJW}@@)q^jHY3;}RcDEat^!}63yvS+^eKXI5W$;a+Aexo~bIDpO=U?(V8 z@G4D>u@4WI>xEMAZ#MR5y&M{=e4dt(LG_H4KUAgEvW3SMr=}fz2_Nm+vw*YOW$vaZ zBT|!TSLeMO#iMVR{(<)-5P7Z=7$0{$H<~%N85H9G$>rH`LAlzF3-qj&QPNt;0>}8l}MZWeXo2GKNc8 zYS=aQ^b_b=SWtm2u^Tk2@2&=Iy8Fls6w~NM(}|V#skQvmW}rfR{K{w*2OVS11X}ts z6H~oXL=K6%rYuxj5^MSo-9s6o%Hq-7g-GKzOq$v0`h|k7mq(K#WO+y?_&u9i3E9kM&;xdA_@Dh|5R=4;u`4r1O z|1u|JF-}_D5zmG0uiS>S&x&$PI$N$z-f6{U`!$y@H)@lZE>#b6P-Y%xe6K)nd23(I z{q5r2kV`KDZS`<5;U~(c_NwWyAVb>_-_NeqN*6&i?8B)+irhTf)7{ z6w?=RCS?JzN|B;~|F)(V?_&Xt!1pe?gmNazScOxc9hP3fq+e)f(#qd|Yk1NfP9)Gu zMsvOR27>`d_~CkrR;gfWzKC!BlnOm9GV`IkNpeV*BJOhq)}R%xg0yX_gD=@r5slZ} z1Lk!M1YA($_T{24wDdI0_R{72wSs~YA^X1QlM#ICiY3{WO9IDyKQK=hsG2jOv z1E#^$n2DJ~k}qGrZ2wZm8Q}#6J9fbkoRh>4E;9G8%j*9BT1lic5+DgNxVW=F)+7GO zx8}V*Zg^ z7wCW6gC*(1PWugDnizl` z)wf}Mx#Q_423m&Y`fnN;j51GYB2JRO3Gc;Z`u4|L2?=8PKrNsDx4843o7_>+ zW+aa00D6y)*LFW+TrM%6{Z7XjEFDYLl8?0t_O+GJwnNJ}A^T~S z4FgN3yyi+m1)SdUZuXKidO4py)M5Jo;QLWRR)IqUUwZGQIA_ly^I#l^!~|J0NU#O} zi$(G$9`7gAAvK~JCx?MK!LwRoevKD_dD|yJ=R<#~UKPr8;f`y!FZ-k|l3O>ROb8f! zkNree5ae|faf6$v&eZ>!Wdt#BubwWu4ago!o)uwgz83|9KCZE5yLtwN$pvJWH;fFw0{vlbWu|-ze+uhAu*+z5H6m6dFyRS zQFqG}rr-ThvvAKv{6nUoeR8s}MLVoM@c5mOmsl+!S21;I`Mv{(869(c*a{_jzV*^n zHi6-oSm>aTcgjF-{y&McIm$BR+FM0SicEYQ+FY>y3i-oFA543Ddstu}tz+0g3X6R5 zQmG&*6Q{KPt zOb=HbxgRpGcAAy;i3u^^$heYTx*aZt4ageBsXbCO7yA=pP)E0}*)9 zBpl(uFV7$@o;~z*hOFOm0n;tZ_Qkja6edIN2>kTRZsOxV9$;TzWFV_1k9~R08cQol zDdae{O6jQD-~B*(PJ6b2)D@x2A*w6qV=O5ABB+WJuSTk}1=h+&7`{d>-~P+xv7SnV zw1}2+Wv=MEf>p8m1=rDh^&Niv3pP5R>?}SFm9^)VxN#)BSg7*;21QA;g87K*5fijEFst*yi86ZUV>$CqR z{fcIM>Kpa+M??rCDw6G1UM|phGb?2DyF`Fc{^@KD#`xoaIzeLTTKuJF`qF%CjDi!0 z={U`wSSz<8ivw#1dA$D^EwbDD6W&Ysk;AlGecbCD=k*`)lu=wESg$3M)RZw5;Z=W# zuf2X|UFT3eZe)f|Vkq>1&M||Nkq_r7dy52ReYHf3)<>y0xI}Yr+7v=kzPmoF_8K4z z4GotI=j7ciE&f#Vw0BMj+2&F&rIq5wFG%FfJ68MqvwvQC#iORClQ@lQX%9AsJ$CKo z26eb6`LyqB%-Zqv3ClRKrw3TPqP^E+bwmUNh~P;FVC+Afclxmc;%r3Uts;{=NmB%V z!m$z^MhPHKvis8pwH|*x62z1g^AfCZ6X?ak!q-ceKP=jnp>VV@QmWk?IXcheQ&CZo zsV|)}$wCY%@8z^|m!6%^Q7a|MMP`l>qWWTEr)DTT;h;V zcXjX)FV`-ma~K&zqg?3l2e5a*F~qY!QN)h0RfOj5TbWEeWEyOh)b^Aejh}UqwWBU# zPHF9XB&UOQ0M^douiXYgn!il%1jH!(%>sJ-Fm!JoK1?9f=XiniJoN8e)F^U>aMp41xJ)bu-{R!~pzP~HY6Gn1GthRoB z06Q_h6&(XIpXdJW!u+dYH6jp{!j0kK5KtR}MM^d)-6&+!&{!=kE^gTFD!%rbVVjCTPyYMO*853N_aFh?Z2^J6;i( zB_^vC2B>Dfi*TO?p>G&KZGlmyU*7q5{(H68IK$~bX;?OmjVhN~@WN@)dm#*;D3#N0 zlA`k#6mHqnE#y~gnSLmJHNS#`B7)3vFQ)|2TRpsOW=Gz9`{exR{Ta571ic;Z(+uvO z75NAl3}|F}erb7zKv@}V8X?Ep%f41gk~(iVKi9I6$#}`wEmcmq(^RumjWq|gR68`C zKkYQuGHsII3A522kQ6kiFi~uNsP6-kHW&<~DfKpeVj||zk-OZb*vo3~rDl}%Iq}Jr zHc~0?{NMXji{&~VoPu9F#FSpH8UyuX(nbk};90@|Ll=D6o1B~+d?*Fx%;n3YH9+oU7EHs1 z&cHBWSY?3#kAw#1OhyTbg)!9;_L+jEo6F6bCzXoGorXDSX&d~9*}SyQ{?=SjngE_9 z9sLw$bojrKp)}d`FpdUNjr)P+x&! z46H$lSkh;bExw}viOAD6NtFjvO7TL<`ty~vZ(~D(14>KZKE)4BV7b|pB19b{(qtTO zZY($d`)vUtd0~N7RlLJDyBwiVSopYshJ@qm=Z0-%-tCU!_AahQ7C49e={3_5-urtG>7>2?i4l`MF`L!T{VR4FbOifh?m7T`xqRQT{&Q%wyMo`wsMAS)2B*grh1093%}(f@P1;($^lneZ+%|>$PsJk8Z(FPdwL`Oxvb0e6yJI2`mKgTdhv?u zYm2d;SXM<8G{)9rML$m`xiwAY{Ea`Ysa1?D$ReS!=iq_?(RPW2*N}Iejoeg!hR^1D z5-BUApSV6b-*LufsQQ|Fmd*J7^XdVijU*jw8;~!+P%KO;Qk- z!U%Pq{MXUM4?s~0#3H0{s2^q9QPU0Ej80B4@tl89T%Gzk>GPM>kB7W~Fth|X=5VP+ z1BqBQAAw2;G$$ovsAIbg=~alYzZYFqojjB)XQw5puESIA-@lE^exoSkbN8u&~6I zgI9XMzhNea57gpDDSgjhQ*0P)_v8uPChycRtsHM~*iBc^;5J$@^?^BJv586d#}=)K z^rpPs`yT$Q>HfD`{)QS2mvvKe9Q{}^ZUTA6Q^8}i_7dL)e-`_Gz?p`Ii>yeS^sR-h zCeN4vD2?^U|AK~;$1E1yde3vyY2mLtjkuf4{I@sb03Qhr3*#rgjw2z_Lx%s~2wWQH z&0z|^!wxj~TMt_Xb=C_A^sD`@T!3{^owG!fWQo$^EOtP+hR&}RtKlTYJK!E+T8ZUR zA~{2r@+UEyC8zH9Lar0k}mErBXB?&U&UR8+?`4i(VVFPfwe15u4O}{@*aYc&a!?&1Wy4G-zEUt; zC4fry5@_c^lo=;%8SFv!s*TtFh*WJWFfFGT3YuvUXtzLmPHmpyKI2h**vnk=HnC&wg)yA~o46~G{t6&lP|7r<# z6=u27c$rsB#Rnl%EP|(-HjxbpABS^~>7E@4INcp&CQWh1#za0oZE=rNbNwLY zZs%3_nU$GQ07=sKRK~%99kx8c3Vl=neZSk=)Fnv~XMu}>{_fodKT6k-%I{9)-YQ|9 z^VWu_2y_*N==1%_!XHE*PAVD6g&b0o#cg(VbxX8^8qfUjbjNoURj3X_%01C%@Np0x z*Tvl;{-?=!WOXS1zB_V%3NzJ8H)Tq7zQ{*~{qdPpN+^g=z^0WTcWQ&KSYXRQ^rMU2 z!hQC%s95f6DQBuz3t^IMxQ#P~>y(?4@!(LZ(0Pr)&f`5itUy|<+^sU3Lhck93Y#-x z3)8~N-~F8Oz3m3(de{*l!k};7DdDsCuD#@awuvGKUj_X~9WQS+mcq*@j!;c2LjRaV zMS1zDN~is_uv5-qd7EfpeL=E`T}3E8%bLs0^rt1NyIXbA2#DB$N!m8bPk#f5Eb# zAw7-Me7_z@A@XFup^Z@sW9`XTv-s(Ze|8luF5>Lndjnm;y>BwM-_> z`FYsz#qqB_k0S{f;bLl-3<#e)oUOAr-lHiyJ#_1R?Tw3aUG`OszJDb4+0rTEfO9e* zM3)|r{wxp0rNKNFD)W&+2MV%uJ&ztHWz`OSwHlJ zw1fK(Y-+@WaU-?gdY|Cyup!EoeaK&#WD?}?Ops$cNi$L{AQroyI*e`0CZY)4?z)BZgIt~5iz2rs=ODv8pod6H>Pt5bhh8Z${Dsb?6k=P0xNIg#1al~?DmcuQ z#~D+1Z$F`*^fIwCW_(TjTslHJtHAi}>RHoa&98NRbAc9a`k?yECOLgXmMjX2nHFyW z5Eu~+m=PNS_0_hUgDxQCN&rj}&=3?jD0wWZS9BOCt#l^AJ6S-U$ALZ#p6l%e46fC# zTbjh|x={Z!lKs;p*0?%hHfzUua^8!#f&!zz*u6>HcZmd;v%ASk0O$+G#%{# z1m{r?@Z&J4b{d$ED4LkO&!e6t)ZY|?&TSKzSU!;Z1#m5f>X0q-7%DVX9%fGZyIbRT zFQNo~?~Co|2#J6-bl$7yoM-t;%3zkgkxU90gwhM`unv(4OZdEy{!ye7(W|(Lv@+w`7_(A1ZCk03)ygfreLd|>G6AgyhcHkvpTYC#Xp3wDP<=Xc@0bM}E zIbLaQKG%@*6uyKDcx1xB@-G++msSFtXQlNTT~LJj@n%}=iBNHEuztm; z*O$R@Il?R8(W5P23-=qGYy!}mC8KEYW8y~p?QB(24xHP{jQGi+0{h#MWAnRxDDKP9 zI#^zUkhlU-Zo6e0{L3(dFSCYQOKw4Ar;5pkNY;=0>ige_o`evVsNm_USh%p}td$S> z9M@b4;yf6)@=4`f&16P)mQHhP$ls36B5#RhI-D@Sz0Pg*9c%UV6#Ya>2ncOdDEn-3W1N4fIry^4t*hzg2h++B zaQ%Y^!}0eyFwFT~EK;Vj89h7eb-%C~oX$Q{)9&Kv&z@J`I_c=0E80H(;?i@rnPyq) z>%I4`=*7>1o05mtwwjKFVS%h!t^}11&g?Po(5jwWEEFKS+jD$o)M~j|+ zpcSC3qs#_|JNURcDsh4hjPgN=_zAmT{Ljx;wsMptpYQ|4cNl2C3&7Cm3;;P0iY@N3 zUqb$}!LKTKcm+Q1FrDbJhj_mQ$>s19pOlt{ z(tgJN0E`5ZgUM>!^?LmoW{@nWpPxtjhDNTBxS%=*`L4J|9-1R5O|Tn36NTbX;nuYK z&V&sR&M2$W2)`z$_1UH`z3LYkP`MJaBooD~$&YYC2-(M0$6E@Xe<^&72p5LYj2CiPR{@7cN9t{ zva`HMf9gucFy_pu5xLgfO_ zkNaR&arb9ZGQhxy1>9mi{!S7iDLOf!u6DxY?vE7>6|^XQTay$knaj!DRgDY_!5pOX z`U^e|cUMIhw9*dbjqGMvurZJ!0P+Eeb;idX()fuOf*NeeYBHc{j@y(F3kl z868>Lsg7?O#kXn*k1rN8?MU^neCN?l_m1w_2XGY4m;c?oF0dzo7a_WUO9bGe0B{{h zY5h@=kAaJ;1SYBfx{m!B1Q2CZfj#mT8^XH>*S;f#}sAQ^u< z{|(2KkkEPSCk;%d)^_A@BEx<**e1K*mfuqCc0k5! z+!rPdhwrFD7vTbIAUBKq59z1%JCu#37K@&6-9d_{`GXvn{8NL&kn#KDv<}eb691n7 zaJoa5)asCbNr2d@;#K8WV~a1=Sx>}#Np%2+fi3k>iUuC12HIyn_c>i`D&Ylr?Eo8hW9*;HMk%Plou@dsX2Ul^ble;4T38(5WwodC7 zzTm!*b>h?gail*^3ni(3_g7nde``kxNJ%$Wc@F0`D14!TH&hdkp304o{acEWuIc5) z;3d!eBv@BqImtdu;qEW8na#ZwfiOXgMRy(beO{#z0Srm}z)^@xD;fw`CySHKkxNiy ziJb(a!=j z!5|Si!XQ_9)?@dov1Eyq$O-w`w5l7QKvSiWx1IJ=E)A*1s@1Q;Gj0MtycxN1%^a3< zb)!`_L!>YnH#b4h0wgRa;>R^3`igXmLlraQ9VLFUvdcb)b5%*c|M_%L&+4Bt;w`;b z1OeSFe%FtU>)+zQF1>EOKu1CX{6K>?1FyZ{N`lk9c`^zL3Roam;br-EPAHN^QvS_4 z=KcK*d6UOpc%gCvyFG}^?;6YR>VAR&wCa)T>p7QTZ-euFCyGgn7l8p(Y1RXF$-KMk z#red<%E-)&0=O>F{m6rMjGv!>*;za-4plW7A7q6kE?WRo8Usk-2Mip5$AkJK(E-rF zP~&V#{MdwCFnZimOyJShNvG&Z9e1>C255SVkpL$mkVukM4vz@)EGY1XP;U_J1E zLmo|5Ru;L42PcbG?$=L3_7liJO;{m9@!C;OW)(C#9l+_&wgmP-5j<>-c|nn z|I(rWq@9kO4U1Oa%YXQ;t&pAqw>`hzsOo`P({TANI6uH_AOTNEXW-((sl=fT{nz#a z=~&NA=rUMecq0G#Vt+cZwDQDrgZ2EfNamU-3<(NFUaJ?s82L8phb+5K+DJAD&{{-o zfRg23>jk&e)jAP#D`1FJ)Ne`o#`$=Dp#8E&;v*}kRyF_f7W%Z(e+iYQ+8$Gj`ztiur&?YV|1^OO=8#lyv-xN z-cEI-6pwEji^n!Qw&n-N^aupzh`L?!#^0WifkpArL=U$izlt><`*ZU=0(%v}Gm!`V zBt9KoLPG;2+THj-nvkG;M92)c^LF?36)Hp#M#e=cY9~T%mX19+^TgyV;L`vPWgmc{ zj&9ogFaLi31Mjez)p7B_PwN6M^musa3rL+i?Xx)!Ev_)Ca>8wVP~(T!D|j0XaLR6g zXz&HxdI8!si+1pqMuRm8yOd_Ss~kZ_yDiu7I0rp$weFlkf%ptf@^1dYD{LZumefZWej z!R~gt0qpdWfSETa8k2W`R*S1Mv8PHmwwjRlzAyn$$f$5z$BS>TJAlhrDS^zVWFLL4 zd~2aABsEaeAg)X8Y~4r4V&A56Sbq6$=$p^)17=W9luXkz+Hcte_kC)(&6*-Je9k(- z`nI%FaO7VuK?dfH#FqK*47M_hb#{VoU(zzu*tcxFl@FXB_Y2nH$QIc3$-4@qoSu2g z(UU7`_2032K!68U6uE(fbnr#0O_m!Z@a8q$NEWsqudY8+dO>uW+M#bD`5C+SeGLLu z(xPgF36D8mun?E~O9wx`BrkAN?9bI>HAA-qko3c?!1=#F38$xSDao6(Kx&ngogEEm z^6UaUTwD(d=xM49#`hJGrMvG9H=-cR`CU(RzM%U>YikQ&+xL2Cy(k%}1~|AC$mb zx@A}(rI3R~XK2)Ea+g0nqF$OWx7yakrdSB{lioto3+b61)fEaHy7XKg?x3gt6f~`X zZ3dfMRkOI%i>r%0OEcafKov_CdS_Ok@XqggfOQaUIvj^48Up2@P%f-_L6Ny0S8k`c z)g#NT$mbc~I+d^3xANCIRcF7yQwvn=#mbNV=?fpj=i*aXI6mlhcbqPO9_|}lHlmZI z-L|!3sh*I^%7(t&A^V3)_sXcIBdq0iF&VLFDpe~G zJhCN>kr7N&LEzK?Gshz)CWR}*B8YRyS(F;i5`*#p3Ql5rdN_dZ;9^k~4UL|^le!CP zsYHw95o>52!;RiVPW)4OvoUZJrQEU09_X-CxF`O19=d&2;aQ~-?%$1G@NG9`V5=jy!QJId8R?hEA}o`QDxITM0Wp<pisW`d=X=3&cWc{_~m;Sjmyg)=L-M-o}qL zcaS*~o7FHLq8#PZW;$9Y>15>a@SYRqlf$N}uvCB+jmqcTj{pSyJv;v6wT2T*a}iHy zHQ(_4V}R12sv>^I}|wsjpGsQxpPaD`;dC%^bx5rwJnYL2?iZFv@pLIZiuo z9NWPEn6Vf>2?9_!`oL%UjpN$hxyI~wu+aFr1(R!aZB3`yd|G<_OowqU}sqKN|?%#RC>X#^h9SUbu zj@S0az`)3quutA&OrdVoeLb_G%6O7SMc=B~WJ8W4lUzgeM(s|R7wW}~62c-~da3pa zt#+#=ECHDtQn|Expd$y4-DCm#38u-fMlruty*2tjXXO!yJ+b>$=XOQm5sCW0bT>e_ z`%&_@%BsC_l9?>8Pj_bKtLWjo!azve@(0h}mAeb+{x{>tLMAb5mAn8x2L)7K6)d~q z+Z?&U^lXG$OWv&G!kZRgtF&6}j*6C}pdktot9@1Ie}ntY#iolGUY(+dSRV25CEbuJ zH@a-VCq;>4mFI$Q7sx?3Ji*7j#if$`$!ijbI0tmICMR{jBV6)xo3{mX7;tl-zMTG3 z*5L6MzNV|Ft?#lO#^MaWMSuxELn z6crRKhUc9itL65f1H<_44EgTnFcFiGfX7bs$5&grTJLYqwmy;z*kUIqCksM04!^$!g@z)7?ZpK34!?Ef*h+YR zDK)Mkl&X9_dc;3n3G%+c&N*-05}DpNQ$K=1p8~w*k3e$_I3Kt+0RC<+s6LffQi2aG zi2!6N{)J(b4o^1*Br~|`^R@HQ6%|$a2*>P|&R6#*8Jk1bv z7(Q_`@g*W#lp4f=f$tj^b_sy6qh2bfN0DAuj}w7%k4DljmpWE`+F_NrcaFD(iV&n#voVW zYgqMWr}QT}cFS5dlcj`08-PrUa)2Bm(zAlK7OwA<(Z;qw9#5c(s7O{ zdtgxSUfMH9?f$Wnq2t8M9r0=p8>3!;NLm8s4K6~FJDYQ?v|MQL#HS?v%%0Xg4f*PM z*M*y=>26L0=2_zO5#!-Xj*w7V@i?My9$|2n6h_;XSp8}VjylV-lT1r~9}C1WVA0$W z3dveM(8X+nvUKWGO$IJ(Wny8&&;12FZjR_0I#-xI&4vQ^+wvYH`VG!5Vd!{x@?Kul z;8qrGT>AFy$G?oqa1&_2>J8MUtK=dt&l3l%25TK?blre+dIw<8*Pzt@ovSZ&`2Lz~ z0E-u2IIp7TX;p&oS&~V({;|v2h{5GhiKBs>E_}@q^TH^hPjSU^NALzIs{}b5mz$F9sdcs?ih=@XM1!T;y2u83# zIgEh;p8^cp{VG5hTi|TX1Y!l0Kp(@v$Y5fBZ~{I}P@W$4eK)IW za8#hhPE_BMP3Z6V%tIgDOoKfUJScFTNXugW{BOfvMKi23o%3m^NTvEyrQS`Oq6u;< zuj{DIXXE>jP!ue7MP2`D%lG+9%wcp(|O* zLMcedvE=AwJ*#1+m(AqHry*S$!k^}%31FOs)^7fZuouCr!EEi;@^~NZ^C4JZf?2a4 zCBuh{YZ9v#W&wrVR$WPi3fyC5nah#VGN=0qRc6hAxmGGrSU!~Q7AXPx;p;w;z z_RuJk;q#c-$d5E7nUzxYqzBxA7iBUOW6P0z< z>G_Yug3hRqZ=F^;fcoa)5(%gQI)PN)8H9+v2TlZ#X@mL!`GDoPGA5GEto zB;#4ZUz(W%+W>@2Yd=T)_laA3ik3u>=tf~JA`!7?ywx-}X>6;tnTNDrcE`{kM$+O4 z%q=O^TV5clx(YXGqW)1~9&m@^)Owbl%x`~eJy~HI@mm#T1hY6uMqxCpiBN z#FFr#$ee(1fA?9Ikx?UO6dw?lRxj^W!qMW}Gicfz9&{t_u^l+_;#;2A5n&^jnT%|@ z4G66vdCsEMizj_6v(eMj1HVDM8nx)a3*xJ`-jXQ%Xow$9KG8e1srJYn%v3Xe9?cAT z+2dsv5)`op(e-3nIxUApJ=dv_m0UvgcGLPOVX=@UrPaNk49J_Z=>=rk*y) zkD;==z{uwa<6vYF$NW=n7H2z>vj%uExW9j{{!I)HRhzQk(JC@k!h8OD*D{|g!YOC7 z@o|!=8qds(I(-R`G$Q}X#V7WjRW2K9e)rJTmZ;m|BSj(*(=Ho=x;lkW2}HV-`%>R{ zsY8-K)&6+%kM#)a|HT9yPvTjnGumrVSBjvVLm|1&`E)fx?=6>g3o`*m^Cw_%lK`1a zp`Xt7((1tErJl)406ya@3Ht4TiqDR%F8QAZSnO1!_|vo|s&KRU>*_e6R#qMXNuRFZ)_1ESre;?za}*Pt>lq_uu4yrna@Z?(D9u za-0#_assGn6t`)U1qAwaFnxB4{P7DhxV{kK2#u4#PWmCjlN@1ZB1}}#QTmFSzNNFwvtp9umi5WW4PI1o4wR5gLERr%mQHuE+KwG8gK3Ghb{*%cX+pbQpNfgnW! zy-(<%gE7q~1sgYkO`BIC_}7lY|IWIp&bYLY)oWv8O7KgpT^AM6Ptmwaoc*Nv_P;8p`9!?2NUt9 z(NMSXacs-086v|yj!D$YRk9xkm8iMAu(rW~Kat-RrZE8OS_72tC7>tA!3zWg-l3rF z=HF-TfBQGC>I|A-#~Zcl{58KER=^XO8*i-}asGxS0OOde7`negbKM#SO9Qc-0b&)+ z4Fq*SO4L2%Z?2^7R`+DeDk?>czgk53uz;}auX9H&{_Z%DcH86@t6ryqgy`FD9WXhH z6H~d9EGc|l^gbc8Snp42Q1#lAxZW-Vri3iE7ZubHeS*&eaZy^TfpUg9#=<#GUk?O*FG0CWp+uszW6)>4V`1N~GJK}X=khYv|fL0cbvalwuS zy_)NDe+n?KE9W1|QDZFhjI;zhIPmSotW2S!#=1EXfD|w_?kH*5%-|04za%xk>~e61 z1fZmGSudhXyf{KL)hZ0^lIBq@=|vpZt)D0`Ak*#44w^30P@tN{5oSWtl75ces=)As z`^NWVq{giGJv{IY{(#`DIOxsZ4Jj!oEW=rN;BJWUao;NdBr-fvG@AJFe$nJDE-f(= zJtrrzrDeX^u@DwyKdEGl3O(hHrs2u1#okm%@!=IYuNb}N=qu%*Zf`!Gc0N;@57(j&#Pbf#cloA1SeinEnAcGKEEBqKHB| zK%&&3GS`a#imW%KkySxHJ>+Ovs7@SR*Ahe|a`hq)rWAZssjHBqAyP8NDAB7$75-|T z&^KS#0q_X0s$B=tE7)KEIeGZu0DQ+IRJV z8N#p`^5EJW&4~bU<@VcUdS&53Naj7Ri_M_uNF2R*|C8tHHYbDBs{IoQ7dLXjN&?r>zn$E^8Nff|LGBork~K;^O{_$+8+ zJWz%4v3G70rBx-T)kXZ9%j1(TY6BP_p;0+d2 zHiH&1Q^K3y?BG{KVkGg{au%yy-58YXs_00n|Jd*flPZGmAyWw15%;81vj1rrSDZF zf4M?;Rp#!1YT20Pz0os{7a|fj(?Ab*xWoDplGiQeBO-0>hMt25nhu)*=+?_d_cN~a8XoO-f-jZ$%~5#{MkdC+g=WpjJ;C04 z-t!u(Q{Wpt1Y`?4^jP54Nay0zfvuam^Wbi|89LW*?NOo446O(Y`Tn25uO`1)b{P;G zBK-rM%^`?F*D?2#1Y1$y5(JJA^NzfQbN6?7 z3`lm&2MysMS?G&8yP&I+t-?3+9=+Jd^wvhbz?)EI4eb#6)w(cmAneM5#HzzVj=S+D zJq~1)jWCLM0a*tO z%m;9G59~A5I3cKp_Y0sC^jeM5j7*4Ob=gN)2#*6~Rv}2bj6M(r7BsNhXEs3M82!CV z;D4-P3|k`V5|}mM+VW z9l-wsPxrtv_4AVeo=5M~fE{Mt3KP{KDDq}7Tg0q+fk@mm(Pq{)gMFV)zP=G3n;J3c zRi&&bDr~0ph83Gy5FLoBGm=RGX{E)gE(4Gj*(HjJA(^$Hp(Fh~N%tO03a=3rshF=K z!Na#UMQiRT{3A&l?aKkJ@2(~kVKR%?7b|fqg#`VN)}$wLUsflDiL*&IndBf^$Uh;6BN+t%>D;#uv7l;Uk^D(=oHejzUA6(X}*0EXJOxRd{F9s|IJA?5S^xlUamWi zaA)}rpc0808DRDhUk134=PrEn2|Vf@tU`fnl06OR!x^AsPb9)u8W^d>KRAf7v8>d?N zBR(95ayPj{V}ALdT9CoPUoG&VC&H)-&jlTw^Tjvpj{(ORaApkNt)uz_dDU3Cqrrl6 z7f!=^a!gWka>4P*wMA%c)}!JIJk_AsKZt=t-J~C%8x-O!Poi*w@OZQbT0TVhP}A86 z*cFIe(g&N};0wb51;?Qh#|F^-rw2JYk`E_Wmr`|h4vDg?`>*V<@;%%%!UB&;*q3{v zoan`vi@M1>12oYncD3t<{hwI^{yiFLuLDhad9H0b*)_|3KUt(vcFT``)V=5ihAZrX zONYZ+_3x)utd-cWRk61S$BgC20eAVe1`rvd=*@nucDb<-oY4Y}&`j;Cgz^^&aSuWY zh?bR>6(86WP!Y)KG@qYidD?&5V_x+lu0bo=thtX@VDNGnCXb~Q8uUIHt>H8QZ;zLA zH~M6$9*0LN3!Sv@NC`J4(u*c>Mb$fmckfvTxz8P5c~Mf-z|LLT2xb_E>&|AOe;1GR zniJf9zwAn)$}4}rDvQ1#^qM@KtMw9P6FJ@b=TV3CZ z>|;7jBs}mtt-Q!AiRF{zsg)!D5E%{AhXM<-A0lkrPM8GDpPHwC!TDADs(z(7+<~=C zXB-n`;7$6;ChG-(Rb6al`#SWvApHBOfQ0_}sL&BgsqU9po*$QpAo|u-g$xLZYkx`* z752p{{Z8UEOlPTe#XgYE>1U}cp+mfn{C12xR3q~sFzWPmHff*n(&PL)xzz1jLHh;r ztq;E3fE=_E_0*ESbCbB6d~VphsRdMl1QiXdE`5!T7%R6jAGR{dw4rUX)Pi5TB+jRs z^->-`O`q;fF?bA-4ZSYIM&2EC)HK-|E4o%uSS}xM%C{-gk9PeI#=K0aI}R}S=(rwW zZe^4IiWKXq2x3khqWoH31-Y2ZHHXs5ca)^$pa(g<3JA;Sjeob>*CI+=uUQ_qiY>3G zdd6UUtdoR+FK2O>2g%O-fTUr0Ku+kj9B_?8xYXzvi`qb@XLsXHd9nIJ?xQ`et7NYT zf%Fx5)_t0}IvFOOm4@mhzSw7R;ehL)Ib=WLqK``8*LK;rrPi5ag4cl7ZBrae=Q3gLY*XtILtvkh)0hz0%}ou!2mExN7vWdZ_mOb;7RYSEnB38Ee^i)6 z{WNV_{x**aU+M%4{J-KGL(0>OLS~}ffJkfe^+hvSg}S)<`Var5hg)-zTq>2hu~!adPZ_7zlPd7;I}UT+(H`7IF%CS5!eZ+a zCy@K9x=yHqsF-_jLHnKYxo)caolP0Q<-6>9rJ-;J8sO6K5cjN_Wy5|qw-wswI}-F0|SGZ}h{>)vE>HKmJ}I z3It&$euB!W0RhAYcYqa~<6uYplTy7zf_OcDf{??U zc<{`Zk7*^Hdq@oOfLcbokY)Kj#Cqr5)HI;TpuHXtLw7V|(p8;ceHitRfx+qYn{9Wn zrjjyq|2P^N9&KkyA01E*tfk^TG4ompgR9NQfy{DE^~Y^#mWRz>e%Gvp5@ZqQo;)k> z?4)L5$a6kbr_5s)??5{61=?v(kX`7`>|gQaB{Vv{%Z$k4ZL_9dXeLnY@US}QsXAwA zC9hQ{OLR;@7RfiVU3AwN6G@#{V|~_E+-4?ZLefYw;v7e(HUo3HPOQX5T4UAMK~Ly% zv7=dAG@p2Tot8R|CGJq7BXwmyXV)m!B!fdtjg5_$;M<^QATU(CTv1ll4!H_z`pLUP z`I4oeC?2_q6W=!nDt;u8K>}jgQ@8uqe+sSs4HjP5buq-;d+@e@wprfg`kqJoAq>#1 zdH?FC8Cd&p<#vI#4Hb`8?)|yf$-%^{XO*sRY|2FnsvQI%B2+gFnamIog~&_zYgaw3 zVTJqrKNI;r&-m8HMzy8^F4(m3ZV&)J@D*u%uYOX4@ry7);y5y?SulwoxY4jc2r6YG z4`~<)vdbrZVE)~op2mH1{>DJf;Z=Yg7v_dpl=1kocfw|!-XQ67I(Id-023Q|VN7J$ z7G!0g-h62+lg6ILXTOTP;MIjg9xy6t*ybk&R`!+ZHEe9=9JSwFtbqsF2LQK-&((Ow z$W@Fdu1M{a_l;t3teTX|Boa>b-rrv3;x2-m7`LWnL=&G`n}+q(80}hH4GrVFE~?@B zV*z_aif{MfPc1j5;^?6(cl(fE5R=vWra3A4K;S{5_Qa!$2YN%%Ga-k`_RQj3xsOZ! zu%{1^*^7zIBPXsipg)s+Dr)vdyocI-?)ayf?Bw|0M}aLZ5(`p?s9*1XX|g`6)#qvv zJ*<^1m)+Hlg$qr-!7(h@-s}7BQVW@UBCO4*_>4{}wjh zH%%;8pNc1wji<{8>4r^;j$Or<(;sCL4QSN!yG>fuHN`?QcSh zSYnuu_)~n36OJc0d;IMLE%?}O-LD4HtIle-W@6;j*dEzwz?c#c&{y3o6ENep*Hff@ zZHoN+(h8OEbB=~m4fOVYTJi#}q=D_@eenm^X2WGcOrCj^#dX%)=%jD`5hD)b@hwUw zO7DReZ5|DCA_JP3Y#q(M;|;0zzl@BwDRvCTKGh+MF@ZDS*^heVp($Je7%eb+Ai?S! zrZ9QpsETJtHrB1jm8>~Z)RM1Fv!m3H&rxWFZbJXGU+d1%RL)#=VraTxeNOnqfg zm2KDdMx>>?TRNn>LqHnolTX9(KI)Bp5k&^)p3E zl262ztC`q*FJu7?N8}^43vtS&1FYnSvvS$<#i9xUhFvA z5<&pZXNl^!qnd`^@KYoW=an=0&MTlHUQ)dbM^w@{uPP$x5R78?ZENYH88dJt5qn9?$V(81qeulCcjn!w|iu8|KfNONNi5)B)&tz|2FB>XDS`Kcu0s3>B}-bs>hdQj_RlE<#votndDwZDc@S|gNgc|n7n;VHS=X3Q zdq(ktJ@*)>g6F%_C`L-zu|P*CSTzLG1!%aj&nwkY;VYoIN*rsF!~SkFkA+IiEs-VQ z?&LBI{j6HCfCR`1Sb%aI23ianT3Sl12ns<#;^$Ql*k09m92q{32K})IbG7Cu&vAWV z6f^8-7ru8Z ztoP-B-=*Dk={WlnaeZW4#Rpv+Z964OuYYHEt}oZ;bJQLN>c;RXGka3-ZF`tLYIB=0G01*RX3|<9hMX)S|LKaWbi;GO_ZKfr_nel87et)^> z7#YNyz;OX|onR4TaRrb=rua4hZFx{dUL7okf$vt#7C5HdzDd%?&p(c6SVa%tDMp;7 ziS!vms(n+uM@m#qwbjh1dg(2CgtS^>2-$LL9%%fuTR*Zq@|q(*&yRrUw2%`a1*Ot% ze^%h@;;uqaLcu-A0}#;jfiog6V<;ii5_r)sRa*K7~f!L6y65HWAI{*8QUgtRAO}aedr{_m*5p2#Io!>BfQ8c5P zv-vU}!hCzRPrV;m$Lqhc)V&lBg zW;UHHI9WZa$$&}@tV}l8>*a%u1)#V+dw^#MM4?iB*YJ_rEvh~;xOqII(@~wS$7RxV zTj5x&vqV3o!VKTw;(!ZkwcM!PU?}P&IeoZs ziw=WYi)IBAH0c~Ng`#Bl*||%?5OoY&m|j&?+Nxg%oDzj*mt-W|8PAm$SiG-5H}e zsQ0LGhPH3cH?qE(V$3smG89$1!NL!5e=`%5Y-*pcdYsz?828^CF!JXBMgiDlxhnTp*68ti@|)uH*_wfq+Vehw?id)|FpS^%-JW-=D~rYu zP21ALgY1)Ir>@)S$r4<%+rUEec%Q_uOlK1BEDY@yPs-30Us$RHM98<%`iD2$l|D2? zx((|{5ia!c)dAV5%s(To{7>Lfp+sQxtYkz=)mBOwX(Di#|FDZnw(pA*K?R?xrf*{v>-o!HPdyZ z8+cb%L!bPgmmB^WxlQYy@4vosvrVWa3*lmIvP3A2z;qqdMjD?6N^EG)^Q1I~eriN^ z<&spFuP1!YT9$$2!}3Jl_-?AJ=EXVf=esR#t*owbIvQ=U%mV;;4bZ(PN$XmAX6MKkUmvCk z4O15>#dA#^B(F`eKw&Of5Gb*D_v2_Ia|S45sgw8+91G-=<3T{+{=VJwhPo)zB~rW& zY$PFF{9Boy$2v^O+W=6ZmXN?OuHtgsmyz3D0}{4o*oY>qUKNw;e|97reoLRj=f2sH zc)^-qE2Y2Q)Xm4EdI&G7^G?CeCWhN~#&{ik|G9?$ZJaxShzE?H@t?C$F^8{G z64N6A3rd6Dh%C8GLmbcwRr9>vGkskYGXcHlKMLbiu{fX0-}1n(vHAsrqupyigKFhB zIQK1`@FymU!nRdt>}UeRTvJJlR$`49XAjqCquw~JRw(&fLije5)Q^9lu&u5|u|XpS z>4%sGhW>7qyYGeQDHEoAJ%Cf)I+(lIk1DxaZ=7zrX(@Bs2+uNY@#|M2xcH6oO@y9K zL4zdb2f_VUxtEtFH)0N_;KUZ(gf6*ZCzjI`YpL(Pn?>Kq3eRVI$V8WE?}#g*`wQjW zz&?}ytuu1+w-BJ1|ygKat)Mz4i;b2Dz>muJ)zQXXPrL60<0 zLJlV8V$Fk>d=AXAmfqX5o^EyHU#*5T@y*O$j%U5}3lwp%u3IB}K}y>D0KL>T4tkBr zfj|O$Gcq~9w5XrB9CE;5CHb+|5C8hr?LK?qDk8u1U5B%c$K=ItO!2u|T{!-jXjAs? zae@mO(}f1n4xDOHss#2xaLjl&@zbVygC5wHeUs|q1U1dB!7o2M zgg`!iJpnL8Lp&`qX0DZ_;UtLAildYa#mVKOgvQFY-5s_nIlTooJY${ECy5k#sqnQR z&EB5Hu2AR^T3x50`YoIFFT<43R2W#QG zwCDE*@{z0xEFvg)dGW!J^Zs<>MHhI_$LlPWg-!>k;YT~49*LhGj-WMA{|E0)Q|o7& zB+y$Oww)ZKLO_X>vx5@@U#ey4j2ek6#lOw)bO1CELOY5mE81m8A_DSQsW0V zkh2=CTS|Q`dC-&K6Ft*$68C2%Z${rl!1Q~qI8gqiP4x1c^Qca_a-HMYL+#N5&Ds)) zt@d%uO8tlTDQDP_U~?$Lcq6XJY=Mfy`ANDy-RV(fIZ@zUcF!- zr)<~fzsV=|@`&1FGI+C{yJeYQcYa!(#O}BvM3=1xObL>q^?lRUFa=4h0cFHf-WR#* z7pD{^|AdNA;Fhur^V|%69%y;8KNP!&e@>Xp@D?=fd*l82?@=8oCh`fR(IM7#<7b%)^TC!v{!wl8w*FPL%dnfC?i%fx>b?!iXS&W9Eh8Tmc^jxk zg@60);diH7{K5EyR3Znrq57%KH;o1vIu;xCRB#od&=pL~pJ(_~QYN{*(#J0@)!RTj zY0?{x`GvzA89Yq6`&9<5JcsR9hIs}wSmK6;WNCVuh@X^2ZeC{lo+2$ef{ijrWI?CV zD5Qe1j$z5#M=M#Q~GsDTX8s1rWZKWjZ$g2CfBCMw6&*5 zB6JN!5+%yP_1pfw9Fm>ELiR|eS$0gPIToI{Hrz*D+|j#Ua2u9zP;FuRV3zx zKy0Rj2pyFkaNa8y|2zG5YZ6=3B%x{T2F-GEh(!i#L~s^ox&Q^7Shn!P2i2Az%j}aJ z+lz+x^$)Qf{2x+usHwviI1)J)2+H;jmdjzda_$> z6T_VuHH?Bfa3+f;LRD_D1X=NuOJ@Ju2lXE``#B1q$f|Lr9Pz&`dJ^0envZ#Dy9t|^ z?vcOOYcs1kRjp5m6ArChZh?+pQr3MZ$}_SNQ^cHn9aBGP2b;?|9s04qA>KB!Dv{Erxfc>$~-2H3&-uhYH=TYMR6LQZwgG4kz2Ql@9jncU^!_ zQVlA0xn3h0I9i^g40OS6SSd>&2RPHa9Zu!ZsGEq5bZ6wj@%=DAU{3AIhb5rpmwg%R zTj!wjnjBG1X=Gp{s{>Bv+xxGaKbk+5x_@?(b)Y~{5K4W>hsB3(3}8NEpZ87|Kl)t667`T@7fzvr)3ImsMpR46(K+#Rt#D4n?f~)m|k*&R5qe z`nwU0Fh!~r{}I}T-F?wqS}9y)R&v1z^Y?lnZhaOfG-C|G96nSz&kF6KXGqOk&?9OO|9myel0OIvMqR z=jUDuX^OUg)DYGP;n#zGwJ#QHkd^v#R>3@HYVq#l{?aGg%ALJ7q(#tq3tr?nrxW<3 zb2|UdwY$N{ys>YoU;ysui2>}=lFv1!SHKhI8<1xZ@HtVyqmdAVcL4Z$d!hiPwG&)! z=r*|TK!M}++l43v8wnZGXYcHJb|F zUhKzmL+8lT%Jd5uSH52I@6I@?3?!0vUIY3=H7G=&Q1*XT5Z&2?JcWnw-6;kQcV94 z@pgL^6_d&#$qLlXUTRwD$kpKp>0@u{Squ!P>}n=eUIEoty3uiy8gUrm(r73w0=x}+ z^0N?y$c6FN!d$OSk?^tJ;2&%Yzt?ZrYM36w4Ojz5%8sNYj-c6*FXMMkF=OzGBc964 zG&T;an6z>MciJ9EfJN2(&Bga2n*5XA-kFzg>nQGbXdf9_F#QNNyU&uq zw6Ggb*|8@kzI#Fs@)ShyQD9&Q(w;d^G)(Tx_iTBikw+dQCBvPN=Jwrm?ou62+*jj zIED^j>{Bp50mmR~9Cp;WjI*5?P3x%(cx+f3GA0&h_A z+LA=T1x`&3VLiyYoPfz!`EVYdMa6z_-Slnyt zY0V#~1`nkh^V|!V7acsnrTM9T0BS*x<4?CjiM3l;)2%39HDD-8se;%iqdLY+wiPtK z)tBLtKKy%r!mfP=pfpQC+5Wdk0S^`jAe=S8#e8V|V|^TH`Cx@gjWyns{pYuhemaxL z(Daba#!T{l`bxBwHG0?Zb%f_B^RcUZ&*i1#KD8~3QSU@Cb76&<%n%;;1u|_$-0Z~m zFfMTA9VZwud~f*Jrzb<9z}>|f`?Wy@Yij`2g*&cq5O1{&lh^X5`PYZLE5GF5IMRhr zV0+?p!9Dw5K7$?X`SQ~PI>3!wpym7{h};gtF96!)y~58N?VS?$BS?8jOu!`1&tAV1 z?ZMlCvaC63Ew%r&tU< zXhsVEEXN&2hYw;8z=)r3(J2cZ+wazp4RCExp8aBX3%yO!l8Z1^iLrH(t+= zT$wP1L8vuEjYOK+4$8zl|IWFgYP1{D5#z#81iX$RrUnA*=kE7rE1NR-_}$O<%&JQl z)?x&VQZ7@AaG{2tuV#JMfVBAIpkLL_UQ#mpwM$gzS^#e0A@}yfo?<<2|2KBUJ291E zzFQ@#rr3P92A(Upx>0DNm2X1vClw7kgMf9EwWXMzlz%PLu=FdQXNvEl3%TXw<$o-K zc%c^eUA4T-R>idB^{K-7-t6cvlo?=- z3bN?Arm9ALr)ODTawzS1FN~LdHw~>gTr$6Vzkrg8?*8;^koov-H@T@}G=3H2w}5G1pc(B$mfiTZGZ^2~gKg*@AsxQ_D6?sX|}G!Do_+z8+Y zbJbs<$j5%+J9Oh){s&`(P%`^%o$UNZD(=mV=LNbua>!Rq3D0u=KGx3eb@#_F4bJ?Z zgl|*(QPZtmN`+0>Osz#CuNOF4G~IW1 zTPU)rsSYh#QVkY|O6ZmEGK+c7-AQ=bF;$#|)~p0R`rS^FG;199f&tcf*1&_m9zdE< z^UfGEeVH3*$=A9Nq+jO(w_XjmGbHBNt{h7K0yM z2dritiX4#LUBWSFKJKfCmd*ft_~$%vuBYiz#a8?dYoq{Ue(EJ1GUggRrQ306IP9ac zlY#=;3bkx4?6KJzai$HczhbFYE~}ZLg9taJ>^(9tWrKIRIF9=Voo@ZebJ|LO6a3ny z_RH{%WvAPFh<755YE&gTc@$!O!ezq!>CG++{-(u_U|KDn{qb}(;kKx8!1~g`L_HHo zD-;HH>XXWk1^u`BNuRXZRr-o$w)B*xcs-BfO;I+xC`#KI>blBzj8=NPJ;A$T)7v4}T%))P#>LUCyu|4%_ zA|whg7civ;B1S@@SUU*7c`NO$+rn>`_cUisDP{Ej|xDXoCppk)Oz<^WmV-hHL&;PF=&@rYP+D4W6qz^pEf3 zTCVvoP3R$@ZK0(v_AkD9J#1p?fNZ*mmFSwtb-;eqoyU1 zm%8}oJm2oS5Z-F0LR1pv>Ph^xVgC`rVY)N(TAKq=lDn$Kjyu_0&o>VzXo_-~CQH^S z*u3!-KEC~iO~3t~(TcnuF1&>3Y8-Qtp5APkIk*d;e~>u_SCD(qA&zaOlz6L+t&yG) zYtXl&({5xcvUl!$FSKv$*e-uQbWSb^4MiQyQa3<%c_0o427`i%pZt;FEV&10@OGAB zmP?B52)0<8vsw3eE@1(r*d_M{_pVrX{=9ZSsZi;&36p{pjc^TVl8jZozFwXo};lhQ~cfHm& zvWqmcF8uJf0++xFy-qY>)G7n^8IZg6_v(f3#BJmmO~#tHW}7yRS=02`zIdk_R$zbq z9{Xw{bSbbjH7who8RLbQmzU@1c-Zmnwev{F8QXrKb5&=|d9C@f$hrWU;Y#nHa~!e{ z{eb&+TO0$u*@WU3`Q8#*UL$@(?&*}Kh=E`GpxsCDcYFHLO8mTX+Rr(QQO2XTy=L6l zWpu(ns>E`ZxWv#1!8gy>WPi)O^jCkyKdOnQIxMaan(OvlXXAfDg)#DGa=l=KY6M?A z$F)~8qevh=D?cAmeP2{ak_h(t5pI~s(QgDUKmk)9N(YF3NRa2Oaj-colhiu^lemM#l(ow73Z;M~Yl9 z_j;q+`VM=!1_h$Nk`bt7NJx(R^T+?zMFt<}2cHx!GGpYD+kRs{Ben7s?2V>S`bj9A zrjV1d=pi}JOFdr4zGMTouGu<^zkw*M>>-HYb|XG7nXq2{?9L$lw~DID;e5O*4s0C| zKso}9@s^eE(ZiK0fD|NB%AK6A$0%Pt_CtO)`%JsTvj07}0?1FeDFWq7Fkr$%fGvS^ zUMMPxPA&#zBsu6V58}*UXhRzvNC60-J%ANLUm5PFj|y36bTkT23`SISdmgQUJgj&A zaTNL(#0orlbERbQl^?gn?UvgJA!AL}u9<+)N~wSY9?RV5XS7g6T(ntHQPFu&wzhzz zO`aVMKTUE9g)c!O1&i(Vx7Qib!tnL^15`UZZRbgISQ6eu&~j;P1|yXe^3Fa&gJcy? zX~*k}GEvsc^&Q;1q;1plfA~W==IVpvSl&sMI5jeUFc~Pn)u_6fw0hcT^9LmmUjgg_ zpnUKUAos?e6nP}r97^V84U6w1z!hI5{V6X+yM3- zGl63~UmN11erJ4NoZCuBZH6Gyk}jsQnImMy4$4*4AM!zB!89fC*tdszFhfCiIE?}4 z&HVcp*CvN{@6zt!A>T9rgUuhXZrd|3C?$f&Fa@5^ zD|0pVtlG8Jix4v%rYn;LMM8JUBEDVdD-;99mXz3xN*Km=v(=x_vn*p3rcQikA$Z=s zfs5^}Vu2clJmrro+ard{zihc?F4fobATEPT+(B-qi=W>BG#VlXe56Ib2;g#;&W^oO za2MKH(7aOcwwz3ExKd=X{%#_44lEDQ|>5a0;LC5pZ3f$T&%u zSII(bu^sM&gN~95Ywm^hsi{2lJ}8S0uWpAfs(epztJ07>LdxlkrO_B*Rql}ghyZK(a&K!B zU(8kEe5$#xnH(yaOg))<$nVQe=Xg)%}u z5(x+ua-R51&Tq=?YG)%XJxAvm-mRf?$@)?SfSvA-CU+uTwK_9vlQi<#=ZktIi~GYq zgz|;34xBtRc-N~t%zQ6NC2WEvLCC`J+nj`IqzW~`62V-iYBj-!G{R=t-^c=xJMIUB7@Ddr4xw$f)B>R8sFFdXXp z2Uic@*eP3kHc2XNO0h`pHheS3=tab^u?e+cQSPz#cqRLMW3HdO@3-`32#&tbBn2Et zL`rt6F=G+|H^hL4g*g^j1iV^}kg&{sJE8L`6HjC4+`&MMzf+CQ(B$#X?4KO>jJ1o~ z(-RO8em}{;>oAdzL5l?Vn|USY0cIDDow$JP8Ter&lYiS?mF~X9myoRG%3Q za2QJfL>sbtK2w46J_XboqJJYq9b z`u4%KZR0I6{|l?7_uUyee`Zh&`&B#r0z3w6l6Kl_4271heL;oy|+oMIoZEobTxns zuHjAm^<`cokL}j0{h+JPFCj#9vuYz@n6#LC1_?h&ctV-SNe9i9j=9+wRbG^>1((;% zzC~LBw12Ctjk+KTS6xES+OOjRu{&I8RU!c;{`|{nN7*x9rw!gcje851LT7meVh&OT z4R`N3X9M;#I{h7_*fi#a5s)Z@RDTXI@KShio-ds6VaB>>9lr=%0P(oMJq8`w=$Ha;LM&Mu7va z3Q>uxYSXmGl6fUJkzf%MsGH5cBJz#pZo4OR z#@jLFu$vu;QQI^KPEw3{!VcjFt^UGKTf`1_W^Aejg}e>bz^OwY+JhP{MFjyJek-b$ zkt-@cZg7LefMo)+ZK{;8NDHe!(d^g4h20ATbds)i54jS;!w!6?Vr~gxWKJo77JJ{w zJVT74LWfctByBo6vTZk>6>9r~OoXLV!zB*UNcCB*YeV+5c`@8`ZVJ)t^4JgSuiWPc zQlPatEPBHGsO`Tv9dy=S_>nLQhBp3%)a3B_Z`6+M!HT`%NSht{5{iIE*3~H+?PSv@ zbb+H({xSFUr*Vp-rrB0sYfBEgI_AZDuRl4m zTW2Rw1w2>?U_LoLQOEsl)-saS)lDA?N=g%vJ%wk98jIB<=J@%l@7?E0Qz~>@Ac=?U z+iDakk+LCWX5Y$JbEPIXTlT2ISNN;e^XO*fOa?BM_e0@Rsw&sw*f9Jc{V!Sayvr-k z0(~xkzwIu{J0q3bQy~!-zheQd8@Y9{ea=$f1^B`%Nuh(lallUaX4vix2>_Px>#N;C zH}PfL)Cspm)z4;Vm~k;58?wpIUZVZ1%$Q}r2zYa_7LbYEZXSi_Hfktju2w=)XNxK< zzk^^3U3~mig8wc{X83_ZASGvuqzenRO7P+{rl69JOhn)4yzLmqeSxc;$o=VL2o|b` z%^J}sZ&C5pvv7uE+3EWUj_Z@cA;1l8db}l$PcSLEu>lSBR}x`e(jO{eoL&uItZAlZ z&MOLP4hn_Vt+!1~w-FCcB@u;A1`zEQsR}HhO`PwB!T1`!`mE4d3xH85cg@$ZFa zI%O&d(I&y zrA_H{I12y1PZRQX96_K6#ND9*q#I%^3uoZ5?7o1Be4%3e6-HWZO9dOdo;Z(_)U0%3 zGvRWe$ml5QjC@o2^my=W)L86jD9g7snNh2onA|s7FLVh>d!m@zEcTN4txO3iEwwu9 z$G2Vao5nB$!@s%#l@bQ>%=oggiR9@gNNc;~hkc)ZH0Z1%AXkbn;VRSnF?FN^R-T{N zVIyg_T&L%lo^^HPlN@ez?Y20i>D!((U!$Zfr0eT)EBi@u>TlD;KDeFlC#&S>{-4!+ zxa=3EQdK))DMVk)M?G@+cDROyiDV~7DeO>Wh4LYck^6Vc;!g-A?NYdf#E4Z&|5IX6g5{LpXy8Wc5Yv_8ML=ugZigrrK z9hu9Sw-V74*5gWJm5SNTe+*T?Rgq}-&QU_ce9nadmOIgP!p>__ncll4A@t+pKXZZ* zoS&+5MlQ!po+Kv!v7o4enMMLf_PK~4NfR;zUO8E0q8aY07LI*j(7UIY8D1IK~_-R5!0BBLEN zIn^#-7(K1Qn>cW683VPD5`W6qPrkA}dn6q9??r;YsxGu#$W$6A$^Ihk&)dSP?0<1t z_Uf(Q>pkD4^^b<)GuG#4rRS2F)Tg>uvjK?;&15anul~>nZWZT`gmq-+Ze0(&_ecH) zK@PNnE)=Uu7G)YCII0f!`v~Pp?@f*$OprnLmr@;-JmCVa$|2u(wh_o%K%WDG4Z!N4 zi$(g@xTXXJ$$vxW_4X#e^1Sg6ChpkZpRW@hTwQj4xcc?-czK;w2*!zXvBm$fGE`1k zKx2EEW1=$P<-z&Ue3hK{Pqa2;u;5`6Y^po-sPdJq)@uvH0P@FE|E!rDx_^9EA2UT> z;W&RUdHaik;&>wu{eyxAI+yJ@a?oK*5rq~z06u7Oo^j9aZ!BXw*0n*rllfZFw5ABE zg#^V4^=WZhxh65Z26(1$=*Df$qTJ*JXF(QxJ`c#d-%zFS%UA=1#QpQVXwNQNl>DKy z?}-s;kZp*Z8<+4d50}G09@2~IvoEop$KnT8A-0}_6INl2vq;UP-z8(lZS6PxlO78i z29x7&UHG>_@5s&?cTCF1i7cLO<+SBUeMLP8|yR;bWu`6N|2wX1eOK3wMUnmFjzAIi2K z!?|$|o9oYk)c=`Xh<}~CIAPC{-IhyV%K2XO$CnG&-ve%g))y_6Q#Y2z(XjAl-t0qG z{3btMKDVooF|bV7OsUnn=eAtk|isbgk-nM=9(VUFSci|K_}4>YtgsPTGh zl|{#D=9J5Ae+f4r%VoRA;}4H)z^Wz8=6wW$?d%}h3cN#DC|U|EoSO;3gLn$=-va$s z_C@s;hGNbJ3Q>c`gjk)MTq`X)R!g=?wz9j6GsQ9bfhyLdMVt4s*y0n>n@mTQ`v1-j zJ2?D>EJiYsAkRh?E9pWy&=?yJC% zRLG*6z(Mv9MGi{vTI?8DD&){-unKyjt)$hI5O!4 z9aFwZmMy)ld(Vg^W;-MtbrIETo@aFtgwcInGyZbvHzvmjw&Y!B3IPsGExCMEsvq|{(=&e(;FtnN4M~E~JyjNs9Z60lhhOV3mB4oCO{_YJyS(*_x)T~n5qu>tz1|6cxQjqVg zJHPds@U*u7u=9F}q#dVyCHnJZTwh3fDdj*gWhM5{+1kwQp@+LGkg^+;{EdCpD@j~Q z^066TXJGHA&Q`sLh}CzKWuq^=TZ{BDzTWvalBZGlc+3#ug3RrD+gS@f^#`o8K~qKs zS3V2$UGMaX?(##bbGcl9ap<|7%7gqA@p#)5e{!6HXomrZXia}OYiU%(f6AJILCv|NdkQ)LMgrD4gcZogOK0YihlkMjyrw>m#ozkQCTc%Ccubz?MnEWMsbVd(K&Ry!C85-7bE52?@P zLrhQ@G6n$7`B_j78SUj#$_$UVqSJ@Q;dH!)-`ecqY;LNpF?^O>R~_@=oWMG<7E5%R ziPUr4Kr-XSHsSF+iIdOMr)xA+nI(3r5LM{1X8slnTfjoC`QxaFrR}6IV`Qf5QzK(v zzAk%=!e<%a6o*#XT{4rl=#9>y{E*Ve9-c=Gtc8rPSM^So_Z>-YWfar@7RWjBtG05q zJ4GEn?K`tyAd7ZSDV!oVW`}FABEi>`;|6^g!u!>|Cv`l5-{VL6C41fM&pu?*@J&iY z5{!5>;;BViLaNwI0`*)6C#=ua1Bl*3r{rWp@k&5UgfaskH-_ggkISUdh4h{=&*f)i zHS^F$QReHRihQNOi7B;N5|cEk!E}6k*e|xpV~fQTVulE)0uK~T+^mrHn$SVT{DhKy z4%u?rQCBv8mgcWIe;5cZ#i}h%a)J4%c8ePWL@@HI*M{zOsB?JU3v+F4JoqJFrboXk z5Cslx{PQLFOwi-~Flr;g?AQsX

;hte5FfDrTAb0>`Y(mgRPg4YqE#jq|=HOcte! zQohksc6_@nkux8i$f|Lh?4F*R8r-U<+FI+7vZ3l`3K2dSklT?+o=KET_8}#NQ_+r? zaJT!0E<0La&E6<0a!KHC(2Nz3C9r#gZwP^Q&4ucx)!YR?ikkYkxRbK-tkyM|G@Bm=q-Lj|2d_vG4A3w!MJ{V>!?s%t^TQ3?HT#PX1Io? zL-yL3NragHZPbqLKfH1LlR4=9V#ECS7SJ?Efi4Ad=)OS_8Q@G6z?yL@?|@SBn;&Nh z<(;4_-0A6Q_34#pjqxwG>&@9|?VEn0a)VA7L5`0Id}PEW3=Dh)7N}GF^>{?HRZ*`| zck|3Y%kWi5V<)j`;f=o!#ZZ_qSbAu#iF2~q=$|he*PeL#l-XX3&jcMvCm5J#Vjzw4 zH$Xrr>MVG@B&PLRT!@oO)Z=#{yThZ#YE`|hGmyTP=F>uc%FVuT(v(%u-X7m$1mj_+ z^Kloy7K$G#7&;_m@=c70RveA zIfSS$hGVEOKPC`&_otyeu+V{cPWw&N&M=Fwx6`BZOp`++hWy)NTWq3Z)C7d+IpJtJ z2K3gpVWdT=`L|*MY{mXe5Y%SPvG0LSx2)IJFwf2(t$nrZK>4kjBLVjz69fKh^DK)i z5B>WhblWxYP)t6t2x_j!;8!nRj7Hz0^EhauccG;dxLkG4`9R@z(*|p1>H!i?PX?x2 z+E+lc5!0&3T%^D9x8l?GP%0AwWMJf1cf!71exX{#iDWQ+#}@(j($CpPrDatzX&mOr zI=Q{nY;5T3Mg&n?eL{yMw%+r#o~QGvW9^pYJ(vbl3FONj<#%NoC0N0bhC@#BSXwMV z5u9suB!{d*^8x*<)Q?nQU(^Z1$e$BGd*mAa&qzjGJM@SRqmz1mUc1hpdZzqOxMN55 z(Ue|F2)Ma__c@g$Tk96Stv6fd7#^r2Gx~?GLoRlw-3NgqLn^}NNz~dmTi}i#-4@>U zH5yjlPuE+Za(fd0sN)SLSOI>ye8hRx;;Nl1We%<3Proew&#gA+1x2jE@0e`HMZXSWj_&Re}8`$g2yqW#~E;0fd$uV7EmL4aRf1ZpCNXaAs z)wV^VTa>$8r6W^)1-eR6TA-cXoH27nkYs zr^D}8(T%)n8Fnw)uEkDUnu|C=Ee<_P& zDL$ct)3ivv(y+ZH_4g=nUg=JJ>M8qNw7R}yM9s;hAc}!tZ9e$zV?}zg&bm~lN&yFB z5>psQ3By|@B^hu%K*iwm+-ARgY$q4=IOv`^5Udt9#ZQ1L8((DG*4&QPd!9Xj5w{-AE=sjnIhs|fScm=-Y{TPzKo#?30o<89Xk_u|Au0|o2g1yo;Z$nHm-M7 zCp>xJ+$pEGz;5x0pSS%-AkKoI^sIzHTGR3 zF8{-Vong$9QR#$?8cAq>kO z3N0gZMN(&2u6iO4(lI(J>iut}&E%gf&_ z`ScrGW)bskD1Eo{6JWx2$B{q;7a~4>kQi?K|9iw`bCt&)z$R;rkJQD*B|ak~3VI%p zA}Rf=yVdatMnfuyB6u!!c z*{)~cEQ$X~=g86$o4!dy5^O`hh(uXMdC0k@Qi#4M!z9t(4nir+dM41f>1r)pBlbQ5 z3)x=qBX-MJEEzmM7`SaI7#KPnhsb`7^xZ1z9p5duYv0@vF&jMyrm*Tobn-Z;ZO=F^ z1V$YB20VW_N=mjDYDm2eW>8a&rU-FO*WV8C4n?EA)UXV`uU4ODQ~tmv8z^-19wD4| zM|CzLr#|`S*IAP5C6v)m?jlJ`W}u((YAd<2dlY^wN0AKeQwmaYxPU@tkd}<9^KUX1 zg~H<91(1@)hqeNAB^&>wDWoFP&0mT|P3q?P=k;md^&efIUHz%H*+d2i2;;6@)Oa&G zLYs^shC#buYwJv{qXzp3nOYI!qM^W-+`pdb0-jry%brh@P_~m3Eu-~*X;Esn(LZSl zCZiplHI{$=Xt(TZ6?9nl+-`dw>TJ6_dTlTLZL`wN{6e2J@)=ET+`WZUvnZtW3MZ?_ zZ&uQ<=zGUGTANGQo$x?nE62IM*m8=B=Y{!AYD@qriYUuH5?kjz2LxPGBoJ}){ZAw{ zZj5mwy$kCy6knmrUS%>i4|72&wlkosE>G6v!N?W{azV`VY|0cAbE^1Kql}U=RzIjW zY~?H-*{N+qHfSS|-8vbOVL2dnQd=`*94?$(3Vi>L`w0dk=gs7|^74tX@;-s121*|g z?3cuy_XXPAi9+J0f+(zlmB=56BUbC3*;do~$=w~MKvGBC_-JQzl#jmLY0{lpR-?#+ znrl2~(q{2;xYKA1Sv@hPLo?qGWpiXoU)L%^q5PQWEQ^;_li`PBMJq>Z*!_8Yjtn9b z#ZX+E{m~x+S5-h~j*)iJ!N3qlQ!QL$KpbLb@6eNP!Svzjb%= zYE&9Y0L3r}P6688mYrgJc^{(PaLQYx2oC3K-x)h!5nCQL`P9CGEWuo(qakOVS(JDx zD5>68h4MPBltM%SAK|mZLEg((S#d$ooj zN%yIl%vQxh>SQXn`>!tY3yC>&m#S?)?yr8QZy;TkY?;2;qAL>lxUGj51Ova zY4O_ARptHM^MPlZ9IJRJ@h@+{r~DhuV~^-F{$3_u@Nv`@z-OgNAT<)PGFbxVBfNzvW(+QuKH8NeP(n?BV5_QN}{yKU$daZDN*;v7 zbABqeWZzjvPXeYhnbC`mWC6sLhY|7<#5A(l)1{i-o6D@d;PVo)KUa@X*Kr5)+@W#) z``yzm83qXc+oEYFs3PD~s#zKQ=vUb_JZl(Y5ku@*gRc6{L!A_)o$}u+SMCR)fVk{f15Lx^nZUj6Snw$ zLnit3K=19=iottv5Cd8R$`TDvCOv)V39vOm*@y7^?w*kf-R4B~m39yEaV{dHZ&GgXp1)X(s%+E} z6kYV<$MIyTPe*yxv`Q=_ceZ7FNvKUd0@hEH#S#1r4g57U90LZBhKBQPWm%!u20m}? z3;cs#)df7eA5Z)vjghv^2sr~L+{hZ$1AH+TIZU*1@fb=+S1 z+>;L1XJ&m4itEVye>|O4R2J>m#ve+$TS`J&TIueP5R?Y#?tX}&I|b?PM!G>jKsu#E zy1V;7zH5Eo!K}prCk%V;d&jk}U)aCr-`63^ecdxMdnHMe6+Y@5W`u()m@HM3TShX#mz;#;o?UJJOQ~-pi5nEWCYjd;n z3JgxG`3h`)PD@@Fx9JzhReF*)%IePQT*Nq9a5*rTd%|qKl!6mgx&nJdfv2M2%sI|p zt>V)>!^uJ|w^pw0t_TT8vdL_~{EGgjb=Rl>xgxX4pi!XV5&J7cAu~p_G!|lz0kOR^ zxMTBM)ZdZ*_k)N&fZ59cW8%=v#? z9=S>}YDtg*Nv3>+;O0EJ=QF;*_2JNHJ!?jKiI6%q&^(Vxc=f%r3Wec5`mH*o^xUld3>S-p(8Zn;ZfCI_z zb0;~_)!cDrXgw!#OB&Pa*2Qz@#1#%+7cJch%+Lmc{l zrjhO(m$>Y&_Ykl0jp{b*H&zS@0;9TX_A2I~Mqx;6k4u!BiPU0mVApuuGMoz0n!SL? znA}&U=`yVweJt}`+VzYhVXv)e{Qm9*>>}h@zf`}m2U~BK&0DUKr2M1Ji51#hy^|>v zYr;Kt(oikNoZ8C?$z4jtkh|ll7&w6!bMN^}j;8Cuq0>LDxYe#*v+lc#bWLX3*qe0qYj)5Y;#T~abt*y*zF9UnfJ z_Bs2=h-YA+A#1Z14VOq24o|qeek;IPbqSp$P`wI4__rCrw!0ECVRvfZ-<+2DWi{9X!+#6C(GudC736zDo3}mu6T6T=M-Z!G`4EA&KZ;Qv1}V7B!D0U z!|1T=MY6}K<*$BBv{c{mvP#C=+@!=+I4S^vXpo&T=kHNz7QJ;s2M>twcvZ8m*Kql2fadoE6n_gB73s37L3oQW`2`7nl-B z_ps%6;hpg~Q04nKnuDeKCjjHIOy89Xv`Ipn1*h-)9WjOA05X~k;0Mk6?)v?}?Bu|= z!~LpvC%pv~L>L$812MECT6q658GP3h2a2}OE~IUesKl)|*{UZnx7OQ@ipv>|Ix%-y zA4O}WdO3lk61uEF@LKC#$m-~Os1l69m^}EM0=j!_jlAT@K14Y5klh%9tprq+Nh}0W zaLX(G-zN^BevHOK5Py=2a;~_hhM_B;!FRChSw? z1?pl}0Di4-f2Z9`(dCT9TDH!-^OEyNR_4cZxX0UhyvseGxQmI2a?oaZDUf?&x6Rgl zy;nUh<@@L2nqaoLl$0>w=|IoS6EsnmoWYRAqiqMyalTakSbfE_x2e^1lOz3~BwCDg z?4R@mCwhO9sZ+c00z>aTpRBZiQd4^_O#VlcfP>@+n$V3Ml4h*BcalS-(&=8z+iswB zSNUoaa%Z_lm!A$xI`%@Zk^79C(*Tphsa;G`+m#1q)4%Z|9(9LFS9Ob)l$H7@N zGB3f0P+dNZf|Vj25u~AK6H`+Ia5WHO4f@H_eoUHJgCio^2u9!amNf%ApM~Nd+~+#k z)-o`aI}9b0VAkRAfC&H_hN@IOFNE~zz%P{C_9moqarFL9ShWS{D zVCl+NicbqA6*@a|K=l{!orJtlBZ`!I>(yP|&egZKw$_%>VuZxY6qg;Avkf$!lCsy~ zcmO}rw*4|jNz7igBFC?@Zy}&zgz9Vr>B5OAKwTg4bmA@MAK3vi75KMsOVw0%G6bXt zkW@@pqzu?wQqsO1aNCMKkb763nk^E8hF%PjMrz^K+)sDx6MB_UsG;ik{fqJk#-`!)BDSdIl+C``DjV?hIXoJ$CFWI>J zBxboYGX)(`BAmSc)5P*C*iH-U(+Rl=R*U>DJi+N?Q_>HIyeF9UDJ5Yt?iFWTNi=9i zwg*Yl_pdtzYa8BHil#I%qMw_wU&^ZQaaA0f;d0?{&61l##?Y0kNH;1o_ zlN={XF$Mm-j?~q-Tg%Q?1BnHqUJU(>l5ez7@3|<3i;W* zld&TaNhFmN!(Pf`RU*$!oKlF*IExyeCAyKA#G)1Y&rULPxxuMQA%#oeYSLW?D#@CZ zX`L41jP{ALGNxFze{*vg|GYU*xSTdHBV;yb_7-1Sc_FsaxJ>hK8L--sYGg(QhYV;e zkq#FF=&l~{sPSJWj98)&{EqlJ#|y@;Zk;loEIqfv@lWT|HXLjhr_A;GvajMxe3c!C zIB-R1RUCIxy%g={(d;_8Ud5~egqdllw6sqr;>M;XqDX$2LJuga!dc+LbrT*YCPlc^ z)jpqh_1#;tXEjBcB-JfAT%clN6qtYuPzYEKfS)O4`ieQc>+&pF4&9Pmp z;mzqz3;Hhf4|?>nUWu587tfibj{S3g?0zLU72&V=q;m_4La8>ujsBv|0`y`25)9c~)b{B|u6 zgTv(u%H9uZCIdiK_lvH0T!zig>tm0rdY+eVmU1s>Rz7p@Z25o6O0j>+N<`)@%n(PD zDV1hJgu^FK;SV)fe30`VQL4TTXeIMO--nW%se~$tKT_;wvYd7@=rvvJ)#`1D|lmBYV z%Z@{;%VQsv%RJG_@<(Y_Ly7b$n0=y8helZr7IXb)M7=B{AUH7nym=>G{&ecNJwf-f zNm)lH;SVjB&m_iVoILRWq~rb(7p>LiO*Cn>zIwUOr;iHm58hzyl1i1kkYw-vNqS9} z6&Qw=h*ic&ZVX<520}$E8$FiYhn;g>8yzw2TNf>a2|wskb8`%P!|6XWBHw>UybKZl z6~uWNd&tZt8#zzOPnf#}&P4hpykC41**LjJX^D(~a zF}#ODn%l(#P+ zR(((CZ~iys4lfVTbDkLV*6%HhBB2aCDZ+_h4X^(e^)X;qr-s?Wcy9Ko{9<7Zc$sfK zzGV!jdv2$pi4DYpp0rCy#6_!#s!2%PQ4&ZXUG`4hqC-V$G7izBV;8JGnO9+1}pv4-k)ywI>8@7kq#Hl7L4j zX;iF}?N+c8HW^&`1)nuIcJUa-z`w(HsOIBK_Q%^p z8MsWq?dyVYeAu!oZ-H{_w1+bhUJu_sep80l8smxX-jV87EI#2PJ|lABPjuu zowt$4FYF7AmL*d6IASIIdW7T9utf1(?PBQmhLwP@l-+>G570uS5$*{{%54drnR1k7 z;fIU#?g@$c33?O3K)`mjIA}KYmM>7IadXqKbu8@t8xEUWzCO8c3LjgdeZL+za#mkd zRX}{p=WFs_PT9NVLooBP2E=aV2thn>tO#YyFLiSr9)u?cGA85UWEE~MkOS!lz6RqC zC)s6&mXvAkKS$wX+(1vKCnf}vdt)n}_*((5dH(iIio=Fn%iO>Z^E;Myax>gCS+TrYwu!6W^ zWfRTzG2g`LS_&6sFK2;|zKVH+)P)lZ4PF=TK!Pq3Fvb{SCm*rVC69bBUmA&{PhZN$iRiwa z;js0~mR*ELhX6s;>Qbr&$^^zp7|!Jn=Eg&bBbhR=MNc5J@eK&^@_ns&aORFph?Tpe z^r@(Z4{@&rqMre4wExOCvoBbl!8di~mpoP+J82-+Klbgz_2bTlIAr|_8=GE@*uO?> z^%9r$J|g;EAA8|jYp8|{k(c%VC>Vy5l4&4x=yTA?ZoFBqvPA= zYotyYmI+TCfn?_3cPwwH-?2YQ_!|3NkWt%$X}4V)`ShcAorw^61vVkjltaykbm#@4uXZe9{eY1=DJ!Cz5U~Sd0|J_!ZDdl0 z{NQps{`74?OhlQ_3?3HmLwkhkQcR`GTDRnaFG<${m85dHGR$(9=jAU3wbD}wM)^zg z_5CZ7TcfG0Tkjr%^5r)CqS4I8d`x7JNjU$h7aMfdl_O;J|0ao=XPyH~pTKJOO(@-g z{}|(%Oemb}<+!V`)75aKPuNBL-c}5Dn?)zQ88vBrEBK17<)xoqv(Elo-yNICP$vl% zmM;ii&adIReiQh0GCA(nx|#Ft>2GlJHO76#9fIQ(Hdc{$hw=(9$I>)Qts%FqZtS-Ev$yvl z2)nBla0{J_lSxT)y*+{8zjqg71w^NSp|!1+_*?RmO2rqO<*rtj3n?%#6l%^umx6}O zdxrOy4u-jiFIJ16-Hx{hflTx=;PF+^#ZJ!?zojanpP!%k!xyKvr5`LUXhdN_o#2{e z`C<(^{yultY`oM4_$PnT9PCFF4@L{>-++ODKJ8ydK4&%*CF2ERq3zD=x*g8|&yoVG zx8Kzg2SGRL7*rj^st7!oqu-0bZE;0_It)zpP%mO>rU(w1gf=RGqW-R$CLde#gS~OG zOlZE}#diOw^RLR;gI-}z%&&J82i5B_=yPW$Vu+t?%NJce{cs7TMhSg%f^NFC>Y13YNa<@r9Uf-qEcfl8;B|tn_XL1`PDLt z?&{pSx@H%WiujVhdT?bZvzmGPd{A|HKfCIMNZt2Q>Z@`rmzc;)RtzcDC8}yW@9S;} zCCNADCV(o}+=Tm{4|DW^I65q1iR>S_ps<$eP88dvCa@X92|CG)%TxcDjLm z;fhl7!BR*Q#9V!@!}5iyK2KfB7Gr%*jUdXSLw^52{JKLvFItf0c@#y`&WJqIHMglg zgcfhh)i`i{FUl~wJcZpshQj&jFBw|KEkcL>5BNYVomT9CnBd61I=daiVc$&ib1(1f z5ko1Kk1^)}hYGGdkdaK_`vo3lZW={Puvb4PrjeeopiedXL=;s?IxHnY*nOPZU^8*x z5b48FIm?G!KKJ+Od@d+T39Mt-$kEjZk9n?Zc!6orxeVF`_q9-ugT#;Kt4GvM4b?Pa zLm?Ot%^KtCZwU`Wvm_=iBUpPzVm4)VikD>U1XkQYSygOENYHvCuYQhsmls||#tQy# z11*wAKiC}a{@ssK6YL*}p{uTyhx2SNL$b*)2Pn>m{AjaAh7N+X1XUfg*t&~#-N`V5 zaV+$qEr>b&(gX2<&ghJYdW0y>nQAX#{l+tSu=j|tvc?=CU@d6`aR2GRyCsN-LCmkf?F^e7NfZX@CNa;9LlU4sPNAOTYH$=fy$W>Mwrs+s zh^bF&T%Y}8RsDE#)r94(xuAPjQ=r_a1(-~iY9aq&5)2wnp@R3m2zK~{0Wlf=6v9bQ z54p1ZBHEt%ncYX36`bg?2xXj+8QQJ{_HT4GmFREG{y8}9sY7h@BUG?c_TM&vTdVKAiFRcCa=R-|Y;OiN3fYj-oM!Dbh&2ds81 z_U}%(Wsm=)QIoxvtITGaVsb!>^2I#){hin5j<12t;8;6FeOrRdz3yusFPWd~X$^%& zyrMuem%r?99>U!e#%9jNdQVI!8hL@b644R~y4CF0#dGhlNp|Ijf>E8dru1^$>05=C z!Q_DMLN7ueUQhH8u`Vx<(~)0syTC*P{(WR>==XyUFp%g>fv8!b-AJv^Oa|StLhdy> z#QO3dSyl|hcs*@|L;J|!7BQ=3)n(B#PBV)6mAd*IHbeIQ-%XN{nJ^Hp7TI- z4D@cgM72;)SAg^s#~-q)vyt`QYNzB7cMJP9L2%PzWA#B*g^WT|$lt%tkpKuzB)waj zyk2TD;Bo(hq(>*GQKyuRTVErh59Xn>pMkIEk#>JQVz!rwR)5!r z-`M|p^ww{beNR>ECDhsfzQ|)^N2Ow2)%;IA<_GJ~oh7Gxk@Is`t9p-@TSdQ9biG}c z^Erw8t7OQZMZal|g}P9VjRB(Gr5=tJ-sQ=q7Ri&G^gl!6s4r7NTH_ngjgj%)a&#%% z2zjfCjzXy|>g!~oa@u@@BcmMaHl4t~_w`mp3yn#7t7fN2o5LB>%tiV7D#AV~W?+!I zmt%ibTJH`*LMca!IyaHu+sf#OA)Rxg2@Z&FsC3a_wn`0=){c9p4v2h<6wZ8W0JO!6 zJ*aSnU+3K}heq`-=_h7;q_O@r9-65w%obInudfxuS>6_4Q(05glyj$2``mkUFe zM=pN*^RSlu!C^E!V3cri;Ek_`-pO*SG^?~=j%40Le_9F^G-X$%u!cdY4tSj2#=wU8 zE2|(;#d1WvW1B@ueX7za>O0p=y`?p047I`V{BZC-6SyfD20X zw@XoQpGSqu0$bu!L!OI25ug9;{`ap?*g4(MNUT666la8K;NbcCh^K9Sje1F50UpwJ zXKCc@b%P#|xTZ9G)z;x#C@tACAeV)V>3d5zQ50^cfTFg8oQG?2wAW$m=;*Q~hu>dw zQfTxClN0;%f_rR>!GWv5g%@YtgF7UbRo@^wq@nLejR;GG-ewFg;OBJNHacSh;l@zW z|F$tU#OzlONK%=O%wVIwHh_~abcV0GzLq|J^GEE8oEM8Isra6HNmH#6Ho*UMn)5ph z#KFN~uGNg&?YjBh^S7FxQ6%ip`SKVGvai;R9TfU@Wwi|V7fHEiJ~2K}CAb!!(7v%h zrd1x@_4vDQM{J9L^tg>1rO_!dVVr>vbV?mdyg#5TfCAe7ifE%O_e(wMcarvC;N}?d z^0}_ZEC-I5*Blz9<|+hl&!aD4o&UNkpWlRoXkkiYU=Hf3A$jr~0y^2iDN``I9WQB8 zh2NFdd`nl}*a+G0>@z)ZC;O^oHXZYNiDI_0)T4{(b4B#FUA-X-VyaeO2zj3AsvII1 zo~VQX>3`{gXtfxE*F@gW%4JUnZc57UZ=tsid*`2rlQ}7(v^rvRR6b9X%G)tN+Pt^z zU!JhQlN9|Ej?dQS#{JlCNvU|G5s`u`^%r36^tIL`A0-9;B~J<1a6c?2Nb%k!Q~0K4 zHM%qk5pi8lajgX|wvM^WV1KUEVU(i^0cH;*Pi)Dm&7WhS%Re0APensmO6b2wgrIX9 zqJ@aT<90p#Jv$p`=YdWp-RGMdD3A#z%kr*?MMK}08SaE5+&V6?Z4Mtt1XIE5k7@f( zz9%s_kDPDWSn3Us?!t3AZIMA2P%TK+TdDWft~rYeV<&Ht1e@XsuPEUuu1N!}0HK}p z+RvF?zP3jcGOL(yuyAnf#nRG`_sd+XA&h>tge>dx>RN_(saGXTOVpttZ0ukQr19jp zEXRGDYq5YMc;q33#Io=ZQPU}iiufK_I;?f~1_YJ4=;R~8!on)&olil0!KCqQC%Q8# zBbRO&Zw`ssK8E`!)9n*iq6#cvqJo8+7g1T7*-Cj|Q#tY<@vfolKg`@7&o?mnMvB9-K`nfk5|vA5A>iS&qnbQ{k2LM1W=P-rAF+(sE8 zRBI_d#K5xxoAH{6$oEE@tH&>@GeE{Ko;Ra?U4w z@72m9K=y9HG`d{q1ML@9Y14Ho_Y_{qTNKrHhp2%nZ3OrW0+wDopd1k*VT{&)sMP<4 z?Tv^%u_H8QonhL>)wSrdKT0Xv4S(tu-dj`e!PNW8>nL@u!AnREp9u{T+KL-fSix_& zy>xd7H5{T4dwvC!sF<#r{lFpZx61eUCnyKyQB={BxQ2P4#RP55b^i z8Fmg3ain4%yc>6UbUCV}@OZsflE0r!EdPEy{LF2A{3Ndu8b3;LCADVQi=bXUC$x3s zGk^QYQ~U2eQdr}2*QJ3OW%k?2tf)-dQtIs;!uOuC1*o>v*J$2(KEKYw@K7$R&4l`K zD#m0A1UcvrOZ*{B%RA+C8TTHiw_a$7XXoQE3;)Sq#ofiqS@d@j&^{d+Rywo2P>&U~ zGOc7q6`$RFn=q~g$}g{Ad5vp)-%tsV3M;0C3S}(p8a)}#=r~hw;RL#C?sWVyXcVNV z@qQK+1gzmhFiJe~si`<9-71$%6YHv_DO@e)(^Ey6a~z)Yo#Bc24Uv43pQ7~MJ3bao z&UPnry?SJWzY2VvwI^-~JD<+uXtGC?@A76}Ns4+`{h{EqOkt8roW!rbN$j@uPlWBENYU+>chG=QAYf&-=tRk1*;3s#k^;qoEvyP|93g@a7T}rw!(%{Tx*~_~ zv|W!?=sone_pJ-n=_ej7s254rN1_l_x@b5^coK8e2QqZCD*J)F zTcn3<=XmEgV9421`X2msi8FNH*El!<}d{hBn(-T&OR9waW3|N=^)mN(W0W^&p*QZR9{Z3;aB0HG2FTvy^_~fLJ z#UX1aY`>}Tc#<w4@?I9((0zsghMGfny)ltCU$ee-<2&lfX_Kk2(|G@6n4!GNJS*29TH)et#A? z@wug#xk8q;I({f7E3)XXnJBgMH6L$fA!_QkA$9$_uRhH8_fE_&&+-ZMpJaq-gjw{W zYwGoW60Qyh^~V=qU{OdPYj8OERwfN2Pk620hLVwf@v`2}NRT89`V&O;*3q%OcrO73 zPgxa5x6_9VgrPW2rcA7`S^|W!2<=iOW?xQ&QG73Ra_CjGyghf;gD#ptwG%(C#8fwo zZR%xpCzej*w;EXgoDHF#t+H7Nq`p)r;6|&>az+!Dj5#XZCHEyiH{6`;v~IGf+u^jP zrx;GuSb`_fo14t;y{G2t`_uRVA~q-v(%LLZ%e%&R(_4=kcQr^$cfiy@STaqoK|$1J z^g4)fAI>N%WJ1Egkm^^=P~fW3EK|o)olw!9IzomRY_fE6$taR=x_nek{d?N=1agfB zAZp9Pj_Y{v2#Bb=y>@7)-dMJ3!ySmwW8EhEID&tNUOo}Z=14`61z7bFqb5};nuL*) zuAi**mb#>kH?IP0cqM9tV_cTO*zrXMt2xwYy3Yd5<6BRS)z3*M4$vJwFLq0;{n_$h zXHn4-n!5n;n1&NWg_oGiBg)|#9?Jh)#@{IVyQ8n3pDSTVD&(cXn#8Qhf&Z4>=x#lT zi2X-Z$8x=yex2oo0i)dJx&b0tSK34pa4Nb!R{~5Wvwe*EppJ*2@56CfJ?@5y1J-Xp zg*#chLsyouQYi%?Z?3oBIg|0g{?Ye=YrP>;FhQok9JhE4I=RG$e`}J#NB~-L^;Vv4 zDvj5OJ*Q+Qu;RM!Z~sngB=c#1+`uCu3LTTWbaTplz)EK!>qz0jSzc(%oAo~LMY|+s z9|bmvoY&AeO@~GNPac@;+Rk2qu(0ogI0a~JdaU=})A;22SgJ#AtFyv`yZ`dV5}4Vp zgKo^*J2^~W#fsI*p&nWvmgw~>YIw0n7v22bF(Em1Zw{~BIb^TrksJ5B4A=)hnV5zDPZL9ClMOO@oP< zJs3wuItmq-zZ;mV;x`{EE{`ywmpvT;(vf>2;yi+()`+S~0QHTOo%j)v^^3BBFhLA#npXitr`nY#kF46Qu7lSBl1-asHH_2$8H__?IJq~hcs&Oc|{CG(X6n$snD6vI!+w8{DUdy?r2`k_gY zf;2ot-hB04nf2BS!5J^dq6Jb_fWgn*u+UCVLdazTItqnfU%GII9+O-jT?XQ1^X36U z$P_XUU(oGj*zFKk%6BOcEq8TJd?Rj!`L<2MGyd0pTiL~S7+uz+)$@Z{lBT4L_Mk(= zWdGV4amqpe4^9)P->T%SstHBcPbJ!}4VBK5@#hQzdeye%YW;j|BnlflAkXef6!kJ6 zmw$#eL+&hYA2&64eSk>(RgAAMB$8wf6|R+pB0Y`miR6+(5L=6;N`u?ch+DKxpQXw- z2ww@SwY3%xk*UT%&=WbC-KkJ-s@h1})gxOwJP|+Cyk7jR?VEf;>!DqwMw49p406~< zF0+Q`wcbNs$6T`)%F%~SponfZW+ggBESfeHv~ndkYBJzt`G#$Jk!z86Pp_Iwe@!72 zEZ(p9^;dog(L!xRGXu;`W_x;3^DJWfTnRTEonlsRUj_e<=dn)kIpXnL%dL~_J>MG7 zHxT6V=pdIPWD=A7Xg`At)LJvj4HK@cV@14MrV|*%gwOKK2bl;)Ft$Fc0l`zZ}8= zaU`dh&8wq{^zvN5fpg>AIMD+_e*jTUio25$+ik%|BMq+J-tW(Ag_64hEntkvN^Z$CQ~J^DcWOIOI%w$?o)Rp4ox2@w+*wkg zl|vu=?bA+qOl+yy6$!iZJEfjF1eEQe07-oyx}a<}cgPnT0N)sn06Au#*E2prl}-Ry z$K}fP(p|ZtPzt&E3EFw1-8UGb&8byQ)NUy6I&fMA=QF=<#Bbl~rCO~T;TFRi{gBgd zNKW4VU*ji=9=-!m=+XPU+Gdh(u6$KpZ__2z#5TjN zx#BG14cOOO-RLpjAGy_MMXm|a70Q^Ydc!0Ly~Vhy@Xb-^9mI&PP%OBN%w^-CuWQM@ z!p}i2c9=rko^#YtY}j*i;F_Q#%W3!B%7BZx!4eNXosW zm_~$V#Rshplsmz;)}eXfYd5-|;pntDBSVzSfG_V7BEoCZ7AEC9Q3K01FgrJ4a(|=! zVABtL(rdHK(kwSdRdzcVu{z`6o+-;GJg{+} z%#+WWX?F($`8u7j2gWYZ^Xw)9XZ5L6vObFHJ#qaUR#RH6@A3nN;o|Cx;lU_SZSnxH zq<7-I+rlA8g}Jg+>y5aw%28Q1i=#8cNaViwr8nL=4MSNir|>zUi)o$JnmT0O&T>fv zcg-WuJr&J~?UmctuaJ(kb7aQ%P%^sF^j<&e{&Blacm;BHk?sH)cRBtb=N0pA?Y2Vq z$6JSXojP+B7|cBN85wz6`=XcQW%oM3<`0fKOW8%0b2A;A$2~U^8p=9p-t@~-9roS2 z`3^&E3^|2lp0`NUPL*FjmMA#07W zajGol$q9ct;^wzM{!GB6fWAM$b@qdk=l0aDvtF*FV(Q3yFMD zSe*hVw8l}9foqJ0S1b=~AC+eso1c*zk0gw7&37sSX9nAh$Wv8vImW!q$qLT$uQ11} zoV00HT0t08AuQxo)Dxa273W&dmv;<8R5=pR;40i=;GhMN22Iwm@bQC!(MfVaMCD{d+6S9%5Li&~0P}S?nH^IXLz%Al#rplF z`Rr@l&U6GZq?P?k_UH`0s7x{;cX}t*m9Ut8@J>D*TYB%5>$BbU(-$Eymz?IlDo)&bmq08byGtoMjj9dlUk%f86j zOxT}qBDaGDM9wshcz?E@;E!2C!G!nPu3lG9F-YjB@+KuZ`S?q@RDB$#UiQ51v(}He zX)m8HzHE=ik!7rU4iyGPY)bW=vU%~f2|wSQqq@K3J@bG`BAC(iDQ1LiS#ke;80%XP z<8b8f1%d#3r&A#W^Yy%<9x{9qaCimaNG48OscQKLilbdqIGy{Hoi-Z6;yu3OstB`9Z^I-6 zv{5hsw^B74tcj&!$fIIovHBkkjf~Dl`A?GfS@k<2K@SH*(5kE$Tp_-{K4Nkv#}&I= z(%{J7DsrWMaP6;FEvh!&q5$Wh<8w;vb`u4FeoCoBXZWYDx;%1F~llf3VH3Gj; z3*FNAId$ou)2V__rNPB{NF0=!r#6j`yNr;h$Xa&wJqrr!&DYTNn_&u+&_7^wRjv|J z^XT$jk0+ziuOGw>gfoLOj#q%u3(9Lscsiv_Scrs_6qVS+V7=s~FA{v;)sR5g>@_a6 zt#6nVg0|l2hR*QInkQtR^zrfFB$nS!C^}}MOyE<}^0i8>QSO2zmmV!&;*Tq|_OHww zueDyaBIzSX)!DrMlj5cPt@EM6{-SX0?|aF}Fj}0rY5wr>PkYFeL_&L@mY_v{))nbv z*2-{11d-~ltjq1}m?Gzh;%2V%$Owh+lBIix70#tIGmH!PY(-y` zm5)+TZauHW&`9|az}*jwK&(T4clghG=FxH9p4nDuUyzWHd|`8?-tXI#z=Y@8JVkXE zM3)^Z@Av;&=Sc~Zu<|DZ#6wQ=7%~E8871M!bld?6mPgo$6$usYb~Px@1=9tiT#~2Q z%g=?5cF*I*F3*R=03^Nmw<6zDdR59y5Bhx!Bvf7R{@NI$VQvwc<*~<|a__0@C28&I zxAK7h7^$wN5kd+}k;y1xgFV&;o zHV%FfrPCUCOaa*B@`xwkXEFg=7`wOYwOub*nSnnLNb|K6dT@k~4UYT+1K|&snsGP!WBRX;7SBMC!}u0z^|#${W+RX>@+rSF7?5-^x>NlM(eW zuIdSj!t%G`HneBI=NQ#(3j|A-_2K3V9*=EO#dO|~4LYR_u}hQw^PS;LeEcEloO4yB zY`LKGVzo8oz>pv<)Zf}Dml!1hU41ZJXKK4-KD_TpiIxTMTHZAHN;JHiQZzMw7<_7J z0Js0P%Iv{%iIm3?Exz-fpq;M)o2Wud1)HQck^7|qZWQe3=$MZrnq z@hn!}0)?P6QUHBG&DdXT_d-h-QQ>{5Z<8EI=0rR1*w1}So^WK_rLx)>Ub~CWht+ad z1hgRHDe8-b@>u?IMlU95v|Ihsh=-7q1LQEbhxrw2cZIR<@wL3`+U|7yJ4Ob;LC&`a z!@vSx?u>nPde{5Yj&6FNU%a#oQ)y9}_At&wSzBA)_ZhoL+)+Oxv8o-?h6IHfzCW`H zsRW|bgd8|{qa^lsNTeI4t|08Q}6&W)#OD>7iOc5Rdfs&nFCF-wBMja*@Qc|vHe(!E) z!BFDV4aHn5BDkfxUi|1(Y;jKn=@#Qnn;i9Jt9UTIS2Y_o?5g=J^h`XymcdQ$J;Iy{ z@nYdO6(!hn_hTu@7;Iaqhs)CjME2mHa}q^DZeihx^t7}GSBzaEiuHX2!beT->BQ~# z&hg828j;W3PI4^{Fy>mkV8Cv|k=4KfhK=}}3}#!0^8 z^GrmzMuylwt}9Ep{~L3kYVf&(06mnL{(#i?Rvapg&5m2$$?Qg_Aj8%0V5TB8I$A24 zl$Z1jyrrq3H^(-mn8?VYpxr~9kT(>Kj1Lc#IY~zlyvt{J0}dj-eJGv|6z`#=r^m{Q zA`R^W)2sf!>^$y(+K3JGNDA$rP+^r-16IW4)y`NviHFmU#C5qQRtf`kH%A~94GocB z$HvAPG%nU2(`|j@pEAdGr2`y*RX362MwHD~Zk*R!$tcuChbHDIKevT4eul*#{4NE~ z*iY5eG&hGbRR2m~YZ4O_23fx+)Z#PkAsNT0kN0^y1H}ITLSo-z^ND`-Di3ts507*l zG(|^N3>lNg_~rC&1r8FK$w5}3-4=eRS?efOH-l{VMpf~f@ek|0Vy1;ID?U6bYGv3| zsm)(p-(i;IWkeb=SNk*g-9S!67Jyx`;DSZedV^g*G(p7OZmk*Pn)~ogVh0*Ffk>i8 z(3iO=jaoNWk%@U6JC+93s)?&Puv0VJSuk+&#mc%KC z)6!C|@jSiv!O!RR8*i`o-lU|Ykzh&(6F3>ckz>I>6l}iG=yb(BxdP-7gA-zrS>l8h zz{D%G4#U)$w?O``s0QoLnE;LQ&poXXMYYHpu{mhryZSD#-$^yxE;9rZZIKn0rwW94 zfgY-K*lM&s}2=@2Zx=~^>0+O)&dm{+pAgSDTwhI)m zUx$NSE`#|RleszzT=2t5WPTF5+l+_4fSNA}&<(TZ^kam%a@nhA-CB5g^%uaeYJ0f9 zh?ojb;8@|-lfgkyiR_i@&7`<{f1iTm#z#}7q zkd;cmvP7RCOab0cIZ{=;P?OpH>Y5QJ{rNnL9Jmyu8_u!IihI0phIm0_4C}ozYn<2J zU$ANa`o?hQx-=7YxhEj(^DdxxH8XcpHiF4U=k%{@-ArL^%%8$ zqwG0$=YNAg`Ps_fFy;F3?rS944gZQMRca!M`78{?Ab^dRWGi(DL|8E^bk@=W?;9ZXla`S+(^VuSP||XQp||{-AG$D29aFWksglUFWRKN{{=uHbqLP0~DoEbc;>Z z(#7Cob+b0fi`ymtAPE2RV-+uo7ANBq3W{_QA%mc}bn=8sePW!MGgnpiJEVNxx9^dQ zzjD%Aj^KS9I;-8sMbYB&fWaS15|03`1y4^_;7;8Ib_KmiU7uSCIq9OO^(oauJMl)D z(D|pidtc=5cvgzhEWrre{%9)7HI~Vcz*{$#VvC~o??ycC2oWDtMK?`s+4WC>136F~ z2M00NXnhrq&j|S-Hm9iM_{0JIW0mmEgNKI${;*!D6FEmeD`BOebe4KgFadC0<+6~P6vsE#UTfm@Tx>8uYXdx&+ zb2h6A_6!Sa^16CmsnZ^voW_mqMn`^j=C*bQ&8A0zO@M%TE;ZOhHpn54WfvgDChm!S z%Yn<0uVly8sD*r#Ufyjb_bFxgDM@1#noIO?JcX0&D9hv;cpWc!w5N01 z>4lD_IZGv@@v5qxfmPM6o)|3D^(b5T(0sWPu5`M*mnepVI_X6Jl9A|uCMZq|MlGe3 zP9oE4R{qTF_)es_Ms#c`;=JapE|>jo|DZ z*{$|JK*UegF*T_i|DfXbnluTs(1?hsG0>xq=w?K2u$d7-Q1uS#bSI^Nw*8pfaZQ1T z7kYitVNjSy+T5n}CnZmm$#cYot70W7oB1_rr*;C4pX?Cib|*R81GRzI;Z_X+#pRr+ z-EvBC7eFh#bpLNqdIpZC1zvM*Kchiz?`HA;X|V}L?qP$@cMzVa*y7IW9&Xc+sB~Fe zUOOEzXDsPm}XS50ORC6Z&KZR=32{q5Gc8ENg`OTLMrf4bcG zkSpb36D$!Rxq*3kFUvY78{+_w#M}IAnk&*d%dj^V? zbz72gtEYINm&JO~VD{?l%ryg4XonOR(}TzwB2bW?c+_Y;?+*gxP;X2I6OiFvq1n@L zX3()PGb`w_o$$TT&p%P=Dr22*4*Lo4zSFBpeCHTVAgrm4$OTK6RoAUwR9Odv1$E;E z(FXRK;zYr(MAJ-Ttf{Z$%vvZSi|s;_4>cjXQzhu8Pjy&fVdP$%CjcSZ?g}<IgwAtw$y&84AGblwS%_aBNrri4->f~z7Zc#M(9Pp?oF>zT9O zNs6i&#jR}b?d|Fabyl*aCUFW$PBiPqJ0XgF7W1cfdNqn#CY6ypE@Mb3B<&|Ag zsGO+Axpxz$ME4I1_Jc5xQsVK_GpBDgywits$#}HF_3vY`6aW%V_%Q+EAjPL(>a`WY zLtcBN&-&KUlB-^RjQCm8DK*%YYG!S=g~4XO`}loKOt8xdurs4wWiOs0ZvFTXWu^UK zr7W{J%^qd=zX!KhW0qAG*xZ41QYy*`xRXCbeI-NXpctW7KCv(OeHjcS1e08Zc)!QW zncr|Qeo)Nm7=W$8LpOX7DJIn1ip--^G8($ph4>2aI{m%ld(X26aa}984JD7oWF$FtrUmi?yjLoarl9vE$&j>-Q6v?ySoJGot}HnL!R=KnS3*Auf1fV!I5il z!QijS1gnBGD;qZR3OFRAjNoaeRcup{TPTJd?+AUq$WH`1sO$-bq9T(kH^8IfAMQa6 z!5Q&zLCo#aZsR)oVY!4K^Qs*bmh#?HNs!(3+7u}le>eIlpVTctUWao@N7P(px=>>x zux)S1YMjy1mQns`1Qt>cC?8~ry-rEm9?sWbzh%&V=YH32c;U|na2u=uG7UX}ROUJF z41Cfo{rMjYCnp#ea9E22BrP@}A+nSj76CyRses2O;8PjQ6!QIS+yi%ewgRc?1n`{6 zXfkt;Kk(+Nb;pYOkHvr=BswFkv)>~%qx#v1tj=!v!^Ugd2Vhb)IR*p|GqX?_IhP#( zocSyr=cvkW&JE){nrwUd`_A=EGUfdxC(w`S2sa12TAIvHx?*`g)7Dm=&T3A*^v0>~ zAC-0x^#&?Op0|?IXhMJ|h1$k?r=z1wWPXt)=-+R{6i2ccdzIUGyUf6+U(Sl!9rR^R z&#@ccwz?0n{O5B@sWg;mhv;oRPY!O)6A%|Rcv-)5gJaFOK2Vd^ovufcO}96y^O9=^ z``upV&|HDE5glDB&s_b<72yIKjeH5k#J<0)OpMAGRm1(afcH3Gj|!HF+X3MDA^SJp zj@R47Ieg~%f0t{%Y(9)ECnv{h@p4+1{2y`4N*g=d7#2IB=UrpRArayt{=i6aewfwJF!CGh_`A%T8*ih@7d)l@{BR)I`9H53#SpvYVb zCMGn1X&nGs!C*lD8mob6BaumE8HO#lt~@~58yTFt_MWIkIZ792W|p(BEz72;>v8x< z%G6^jZvJZa!8e1{v^_&>LT|3JeBwa{eLI1K8il;xSBn+=^cn{ahg9fUU`!o^4fsy} zpO`@5YhmdN=xlR5i7|y)XfpHg@9y@H#X(hvQ2c+{u@Ce3%xVP#wfX`ZA|?)HvxgcB zi@hqwXZ2qSPq{`6saR?`mxUXYqfWU3?6T|xoX)ZvAKnMygkgTW4s@_uDQ3s_7l?5_ zPp-UX`Y{fEftRpk5=|>AJACy%f=!TC&6PxkLoCCjz&u^sDUSJOgN#k!smChtRvcF_ zfv05S9(4A2AaW{_-x04~s*U{xT=;G!cDrP-N%t57dcftD3K@NUeC;cqwFN31@ymu3;k37nHu@h^gTE0Y)nzuS)<{L^E>fIM;DUv_$| zT`oa3jyR#Z1Cz+Q2tEOyZ$u?cahK|YQWDM0w-ZmXMy=*8-(Ch9$&|B%?N?x5ubZQO zBDUfao?ve&bNxArOyYdU#YEZ`q>!G;a7&C=D$HGhctet1sYe(Smw+|)YF^Uu`n5JC z6awyaUVE=B{Qb@Nwsd#!L^{jRHQy-IKI2$fK5V82b#&~z$(d7*!W!VdrFt6K>XcS|1(6OS~M;aXk2n zj{gBnV8k_ma|CuYpspFUQu?dx>}(pDi}&K6fo=u6^)^Bvj8=RyQz-Gb#6K`z4E%Y4 zZoG(KL`+@&bRurMaG;(F^#L9ZZWG9Gh=rn|qRwWa4GawQ>|9Yi8NyMHfwOi);Wff| z%%B035iz0Nv#MW1dGUhrMA*om0=k8%=z(haZ$73MwVoIv{gc&*2>dbD$I>{cX=vcwh~A}l zy5TPpHYLn9zr}4--#=)Az=7e4X;Z#aP6MjAL@~das0LX!lI2E4597vF9ZkirRbZ&!eql2FW z=KVR@EhkPriP4pxQ86(g8wslJe+U3H*3rx57%Wt%RL$qPQx-0 z{-HKz^Ul36l_A2sy(MmQg**nrwc(qW#u^itVhj6$r_-)JtzBBBoveKAF?d}?g(Yun z^>O&N9Io%D%fJHzbKXIdR@4zLdd(bqE+qO15+TL+(Ym^s6XSNJ&t~gwMg)X%y*CkY zhuR@A8cV*@D8da6#0okOmfpHf0?Y(usJK}#D3la4aj;9nT?GnQ1=*ib<*x_=RS_ju z7M}4gFAv_i!u3dALX&bS(M6M`wvFfO2{|n0f(1=XO#b`t(0DHNUKCpuk^qYMRj)d| z4ZXk0lS!}}S#Or$^HRcY``SR(ZoJ?w@OKHYt8LEulh1!K$Ss52Uk}n(NW@B?uBd|dsq-`}n zLY1V|d>xGZ=)vyV-SLNwPB8`7fCf&W;WfgDpyXch2O8m&<)xEcr^^AO@mo43oGFiO z>nH!dm6_!YVjhQZfx52O!~g$m7xchbFV~q5eYoBmoq1^iSkrePz>P{H=WjK!hEB|# z4fw|oR~j$;rQX4cPUlKhyB;X(w|a~xun2+$S0CKn*edj!w~Ixf7K}un3;x!^J zZe4M_`L&#xQoG!=@aE&ISsGe1aZ|e6qjh^GgBtJBe|j9JU(+%OQd+8N6qXKM{^Tc^ z%OG91|IYR64QTg$dR6hN)xp^xiZm}wm5u^25F!AGSb=AqY{7m>eNJ0_3 zAv}E$f^<(uOKUQh9pJO#(2hu9+i+^J^V30rID(4k8~}K`L_BrkgRDrdtOw?3%BAI_ zqBl7MBZS4`)C*b!`DA8lEw)zUW*7&YuM0J$2Y*H)Hyr8l5o-7^i~<>~3NUyzUcOrK;MP zia0=@d%6A8- z#_m}s#7h8EA#l>80bsRL-qo$}e3N*3CrhPXx>A>CD>N3Y#9Ena#ep()pd(p`WHz2m zs#U2!FuXGe3@2DvW?)h@D*gdazo%gsiuO-Z!MA;v(5OF9?<4v4)!boJ66*RfedIc9 zu@iUZps!!{N!p?-xPn9O9xB(boQ@FK zS@24%2duEzd61khCAzDjIC=l%7k8tbH{o-M9Kh!-})q0G7`m zTZv_cJ1FQKoBbHp?FBT)kmqiN{L;8m?9!(lpPAgzO0BKBx;i{2LeAV;p9T?yKqJrV zVm)TC|K`B1xIbJ4NVW;VV$!YF-3U1AM;Pvbgnod5fl1{vh7;$=M+7lxH@xqxF#I*5 zE(F&8w>Z?QG=v9zDf@aB-=bph*%9OZ;uRD0U#F;8+eU}R|5BXy|3H0YZZ-hkL4m;C zS*rUBOp4kaete*80N5A4eg!t|Jt4@8RmQL*DXei~!3ZCtTpzakqgrNl?&BN6h#pAW z72re|-!mtOF0e|K3kA#%OM(5df{W;vlT+`bZcc*sMlW%0;vZC|QC!93N4_@Gw38jv zWvA?A?0V1CT$yf9eh+AS>{V`r4|)eX4FxORCA;D?#gizUf8aP6R{11)yJzLkArni& zPsiwxoB31>M@haR?e{|J$L~>RxCXps;R+Qj6>ZPRh3t_BCwbj^M!`W7&Z7sT?g_Rj zKsRXX=>AN7F55BY+XK(QolRUrOzF2R9;3~#1x48CnfF0db{mgB_G={;Sj4 zh#PYrBCkQ;?w|SNd2pg~n;};2+ZdliVk$w(G!WfVc93MYTQgK|J3?OmT8C_x^K2iN z=3y0al^JT+o--LEia0B;sD_L7+Qcz{aay8qIo@V8b-(ld(nn}+nNDD%{P2`_u>*}L zH%>y3pdcOcmX1w7`HZjve)E$iTbE&0lF&u;O;bA|3Ag5+0?#%&O`G^UJ+keH<%`tP zy|LhRmw_MC&NVsXo@`;!&d&Xxov)*hohq<25#Vq3b66}RgpIx4e}zQcrWRTr3AT7N zkDT3>Rgvc@=0mlw^IQ>bT4KpM=c_+|FVX-@q3rfvuCKY3vb=`vES&Lv=8Ls|a-M%X zri%Oxu4J+P4_stMF{eNeoYYQ8#WO5!@1XD(CWdQ&TS8l><&i!FIiay6)0Y5egObzNT?s#3U0R%-k8kVoeHKi;d>6 z5s_f9RUq!6;$Ki*Kw}8KkXcafky#b_4&_hpYVP{bLv}~UkxWXwM&+{|P3LOb6_a#1 z2$m);U9>8U+xRt3bh0bLq>oZLsF2~yk+Th4QfFre)`C@16TL~XG&Qstv2bc3r&yJ6{!|O^8~ZvN_nlX&v&uShTf*?ckF}g&wQ|L`T|Qbp(MQ2n|DE85l~Z-mNq-~qOfn}(KA~R#M%kmBe21~ z*X+T1-F??sl!m_0az_+BHNxSaj#q0+{yWmb6QTaTu;YYGUMg=zobOo#s|eNEDc5er z#QFSvxk7_)Y7@P`lEo^cmWiZ{N)a+Ta*v-;@#sr0M4Sb!9r5}HiSIi;O9jj6E(y{? z_Y?4JZ`+lK=MSttwJR89we6%-pMgKUmI+DL))Gifd`%44I}9~{x9s$pEc>eMBmb}D zHIEFAfk`;a&*ri41snYBEALfAayq9j&9!TiZp5zO$0*+#b0v=8GvmBle|ejvi|L(b1o9!1${=9LU;?6* zh8*+%GROecDvJ=6=W$Sd*8gtz3@{8uU#FJCv8ksz!Qg+jctE|c={*6HHWtux&R#$3 zG7Xri)9-Y$cOeq~l~qk&wfyK~!Vp+5YGoV9!D7xcQ1_zPp3ORHT7nd57t2X& zlxJYbnNEP65_4C>?vxo#gxG2O9c`)0jsgOC4X?RPXiQy zqduDVr+iEnB-k0wu5okT6z`S=oSjqek0kw{^8$kXp1_CEU&n-gR4Jy;{&|q-BiG0i ziM7!A(VRK-7+e6O1iLwLrl;NRM3y|B5iv?I!9GY&Bk}2&(;=2%-O7F!Qh z|6Zo%s%A~*?39~5dGTeG9{k+hqeLJU&ME6ThuloP*Y(8lP=r3Iej6Dsi@`$y%*U`W zFevZH=jVD4I_0${tupf+zh|wY+1UgR@s9(^y^}2y9Z?mH9nJCV9FqI#lWrT_{mV0^iK&Ux-H5r_Xl8Fm z4*GfDNAQFUSyVtC_*$f6?z0rs>FRQ`w^47?Fc)zRG3Fge5qhj=Hl9G}&)=wEK1@cI zO47zcM%1U@8uYipUUY969V?;#39^TZ%TS)8gIMX>H%LrRBpxKe^eJQOrNHWRy{Pb( z{wFKz$j?s*4{|-hy&7uf)BEF)tNGtgO)072hP?wOSB)BHVB$h!yTvN8&LZH_R`_H= zkrbN&&G&%N#!CA*kStO#DlLT4`|oC$1`RF%H$$h^LJE+`P8BK<%iP|bKxAH_ccIU~ zNe?SfNTJn9{&%&lXA7;C>q>;28U88F4p+gvfZwZc8NL?Ky@tTocPiv04%R4(kC~aV zgWtYRqgxPZkRY-Ft)jQsY%-&lSdy7(xl>lc3E{~5Nm^>1Zd`hkg0fSd^!oX*q?$SN zzA;b`|8+D<0hYA*CAL(PuIanc~s z`_uAZ2vx(60vsi}ZfSAxcO6iZNFe>WBA2~H6Rx$Zy)pZ5)# z?(yF#CErp~0!!eQkhCL7b(-af6v+fNb0G+`Xy}%8G0I8Iun-+@=AgF#q9hNeY10Xtfu7AdlNxS!1Y zc5iNnapd?7ufE7P09$4SOny^*Y^Xfd7+1T`t3~^2v4fIkr`OSUUS@EV8#C$n=#fPx zq9Y_Snv9=2Dc~lMzSdFvL(9}uyyTTQa`r|5(r2^$JTG@bUf(Yi7<$S22X*vvM{CAH zt(!$38L?b%Xp>(%Fisj@LYL>uWW8`2$muv6dcyC;IyTh9mQR|1&byO6e)ki0q3icF z1rzilq9*6Vg<0@f1_Iy))2D<`Adw}b<$Nt_xKeh3KS0glTxxo5=-^At^ zh6Harz@z7bpmMwSit8rUa)98}?gz6?uh)2{BpXOOkk6_(PjRTqtV^10#`W{ z<&W-YxNjt?=q?9CKT7ek;6VD(9YH__6gFFJ8smiXT`6|=FD_edSKr~eTu;@+@q?IZHUhXva#ffU|Pwd5S8u0dDco+_jQA;|vx zITDHK+OObI{k7(@PbirsYgoOVI^k-yDY?h4F*$H>YlZw^4AK}>OW@tmT5vWOWOscdhh{h`K8ll&!<&t2XMbH)0o)eX)OiY11+ zR8I-cwC!~KOwxvKt91}^fFpAWX&Wl!yk1{ER-yAADS(dUo^Jla9vfUg3<&W_PyQ|p zw_UH48J7Y{Wca7gd0P{nYxIr#0PKT&F}H@ulvvVt1ND3VJyBU96`CuKcjJ(VWMomV zVzGm=y;if6d1eiv#-G@rzLWmq?$_I7@EO0SOaloYe9g1hwL+YzMcFM{v^DJbzWH5T z069gb%ReWvaS-4n-a-OmjJhMqEvV)WaJB2UJ(+Z`T9p_DWWUlMDgDm>b2OkhwGgiX z$#&hJD|otg!KqNG$Yfa|tS)>FiYu9xX1Nlz`l-i9%JjRhZJL?85Pm@5?D0P^DWV2x zlg~X3kF~Z+rA~_rx&mu`U4jL=LA#0MWu!Xil)=)(R~v^*jySBKGz}4m^@{t^#J*lZ z*Z{i5j;xBxy(O z9Q#ZqK$ z7*-;J?UqphmJ&B)S(6bQu@dR_**+}&7Og(ZyQ}}mNO@xz78OpUXX$4H=BgQgf~)MP zTmI1fPZAR>I*s3ZOIK^jpHfv`Def!&-m_arolgGIA#?sq|814GDfY-PFE7veaEcNn z3gk@13wS#Gm?#NBU^YHJg*KKq8Vcw8eP#ZO2fkRu01c%HV6NMOH|T8>2P&9FrdI8-zL0>=du=)htHZ z-exxr#XUPYuDA8!Q%L?f%Gd6Hf={y8hzL?GmQ|axpZe4fm6y{ObVW&ox=S}gj*Ul; zjdG+oL-QvC2OPnT{dMLmQvp-FyUbt4ek7s>x?g4ro{x3gBJxW~APi!_TLQFF^F{g> zTzJ-nvW)T<1Hl)-R?x+hD4pX|Py_<8@i=ZLd9LjEDB3Y7B)1@;pv+{DA53O7zDNg0 zA%+3#71cI9tQM`LE61^bHH2}dAO<~6?|R7)83g>|RC;6h*LquzkeSUoM@vP> zN`uWTnY<|I#%&{)+SCu%l^cq$==MjAjsaX7-{N;$UVc{3IGrk7!^+R?jZd7+Iyaap z%!#I?=h-*6r+20N@I8DiGf}QrRA#T{4n&!NWODpCV@-mzhR<|SM|2_z22Spk>2MFn zx$OCmi~U-|=N@Jtbe zq%EPDg_AbgyV2o>yIsH10xx(7w;m-pLN-j5Kj{)GeJKrZiaByyhNGv^NFT$)unX1C zr7pl8OsJ9blpmq`l|}z+(tfm%HRlT(m&(ms@8IR=wdO!`;&t9cCGLAtK>wsfh1+qe zLG|S|Sp=|J#SjH`jN_Mg&O}*R6aS*cpN*R3{Y)D^f_u~PD>PA9uk1^ji`BIc>aa_% z^a_eO9q0xDr=x?(X`Z|07__JXG)}^&MD!8tzTy_6ZRf zkIMgP*mV6qIg@?8&KFEh3rUtETRzO&3ymOGP#}(ee^8P!o3uR1Pv7LBJi@OMXBzgS zATO2e+&a(D1&2aDeT0Mu`VtZ6p4T#Yyr+4>Ew>-3VXA{M-c5{+ozrQ^7&OMhlLSBe zxZUik{kp;q64mzNHGDNvh~@K54KY6+7lLEVq9l zWeiA1kCTbn$CS&0scc(;(MWlX;Nia$^D_IIqoGApbk#y6plLSoP5jK#h32L z5erCF0}R#gK+0Ky^f91Z$9Hv+d-(sSB{=Qc_q(MFN|~|lNt3yaD}y;k3`q58BikEo z!|B;|180+#ki;DB8{L9N)LGM4(vL%x&8d!}((~I~bsu+j7bc2^yT$pqLhO(3WCYr7 z`TMdIj$fcw`6ZZMKEpmJpru-i?gSkp3Z^gq;Z;7p7HFh@38hqqY11wJNHy&3!ZT(B zMI6v@{|M5RNdBY|GnWKgDJjf;qev5~Lg_3y1d}H#Ab7;#jtHCb7QU@D$fX7ieU@27 zj!$v9%oK1PXj-LM0JN#N=#-R{`mklTy?$r*k2kCtHOlkyXc3n=e5QFW4Zq)}GDWR0 z)uB^UzI7dmhq@(yyxVdZJmFrbr$ zmr#Y2%H_Ij%GL-Mr(kKCJ5Fjko*Zz9>+wukrxhU&ACdP?s?#0r*}p_+-m(Z5pX-L+ z^O+NMkE3>396VJqNv2OG1X9(g_bSyCi+FwOSh#q2*uK57fw$|vI{d8)tScNFe`!p55@{TLd2KyUKFx&91I4u3){HSWqCqqj}%g$}-GH zYo!Za&BirZu6#@}im8c+coCbatxEsWJoC-W=KcKg;kK3Gc8b)0+=Ri_>8VdEm`$}R zJbu>$s6%`{a3H{Q8e^D(3GO=C81K>yCYN4v!K0>fZ_lrjTojLNJ$Xq)GmOXbc&^wB zQ2xdXX>ds2gZY_~5@jIZ8IFm9l*f-ZG~t14$Rio-KvL$GPUZ&xkZGWdb4N>F99fsG zFrQ!acr804l3CLSJ9d*036uemna&hW`C=bR(Vq0k-quN9L=DQ!fP?6&wqHv2xzI}( z^&M$_ZxW(Z_B3JttDA3gmWcM<f^}J8`n=kdb*ux~nmg6?Oz3W(q~>0a zTVC!%R~#v4LKh;Vy962~0YyIg_VynFY=Ep23kb;cJpVE_5DmkH3 zZMioan-wm1#^4{_&rQa3s_|FYYrf&*``nd5ied6lJD!$iCCO##M1PmFY95R^X4M)w zm$0;M>T{N+pn$ZieMkeK{-yUdrIo(ONm_kUQ5BN0WI~Z(fVFs6WfBdce0b-$Vp)pS zI)qDa?`|SYIH6wPqOOxQTlui=4Xcy3kb;+?d=bPkaa{4qHVJEtz#cr8fpz*-J#Nr7 zWt888wVhd>GY1|CYf~ifaOPYzFcFh&dirpsm2SZAnon`IZ&IUh3p&b5f93%H7?*ug zLW+e$8uJ1UdZ(DiMqFr3h0EV?Q3`lw`-EtL&`hx6iO08eT3Ubp%)cGSP~unxfx-6z zjdlY9lfps$wS_288NR>c$`D1OkLxwI%I!$9cZ(C#e>}OChq^Z8J4&Nmy<@^~aY63( zMKc{WKiG8&%JFk~zuf5HJa#N>D7%pn3SY)!@-zVcvsgzBD^PQ9e33&vG*OR~xtMD8 zlTi;rlaYqHKwgToT$5BuLu0DYn23QUiTUh23-{k|0^;MP+aB+qL4h!ynB74e9b)o# zHU+D-V6wU3ht&6NF!CXXslbY11v|=8Nor!6TD#GN9`MN!sOb>p8V}y*8qEsIUk>!) zgPYrelWZZTf5jLl+>11HDOtD|Xqgodk6PKU#_PNvUAIui^y*>Kp6}S2M*9YH&D;5T z+cizy93k&48|-5j7gNDijf0o~Md)QUHU7bCBM%ga?HJP!FEq)rXGluSq~C-Ee^0~& zs0@RQahG_WLb?GfDI36qJzgM&344Rp$yEYDFz?{t90N^qJOL}bU@vg$@!~eopAqyv z7Wu%FOpMC>WGjw7udmrOz+Dd(|7l)9GFzbJE=)hVG2;@aRf*=PH4#R&F{=U}V)x!L zYS{WYjI#ZS2VJXgOF!cv8mlCWaACi0=XA5W=St!j7YABwrG{zN;Y87|+=D})Ym>6{UFIgx)qpqB(xUc-4A&W`p)_N{tb5BS4 zb&oM9&@}=p$3a%n_0ud>JX7xIrA^R}>zvt7cpq;!vFZC=dR<=(rttRxd;ah(@*#h* zu;N5kOtmD>pI&5ZM%UhMV4oGRpto_1e@nOuv{32L4@#Jeq)IdRi=<{q*Uzu*K{9k2 zGqcdHn$Q)W0zyV$nYn;GJ*_4g7+*7%SP4lir2R8paF0qz3+ZlORaDfeA~90=6=t;0 zA_c8b++&jtqII{;RXCyLE%Ik{bKH18Y>rdzk*dYYp390ZW<=y9lFJU33qaDhPDd!2 zq1e!ugLzL%YWqP{R=rI+{5EQp1Yx=rtwNa&vL3j#?)!y@gLWM8_6Poo&vADvo9Q~$ zSb8Utj_<=3v>S6960vid;^Ow3FyHaZ!>n+;;K1q)!<=L#Y{@&9jL4Qyi=MS_EzmhY zYCseTy7~tD6Dt~KeAyGc&&r%KmJvGeXBnB5!TrWJCjFdit=hg!>(kif&_`Em{ok0~ zWjBjEP*34W{=8sGW#6`2Ib*R-^;hczKdN5$@-wlM-QH`eHxH;_+tXfUY6H!6|8ea8 zuJOdkU23h1kRkL`h9*U+tH9Eur|V`?e9!{@o0GpZbyH#>HElImsYIC)jz?2#VQ#0M zk@@Mrm7m8%EewMTC+Wdd=9MjverUu`L=9)FF=;pWCKEmVhi>x3TG0*EhZd|XD6UAr;}}=XU&>mh!P&bb4i-k^HJ7`%aKh6Nj2wnZ`VMq^>QAYgwY@G8 zH2%kCc5ofHoOBTUR_M-7J2weA$1+P}WW1vn1x|w-64E*-K6p_Qc-d5~JF)*ryCINP zZTh6qF_3=$QPlMtQfKh+5EHv*tT#oJ(sv3$j^6^u475nH;N`S>AN#B;`i*d?>iOa z4LfZJ4T*g)xhvhtm*0;j4RQsCZI%mbQQ=0u4>x&ghyCcjwi1i16=q0QWOV(M`~1|b zx<{}@lyL|y&PX4a)oZlF$1MJkWo|hNSBc1AAa--qzFYV=Jx6y_Ml)VHPF=j4lIM?# zOGX`qsn2W+fr$>l=-)PWnw~y@P4xB+r8*;H=Z^%c`27(F{2u5`^75wJ4N&!`^EjYx z)^U9yUWbN3Y?Sy6lrGd1zJ%g0+UV3~Cwk3fTGC(&A5v1KVQmDY{EF-ogT1rc6lA6d zNiL)ihWU*J0+FnaCo1J5Z{&MY-tbO~iEf3wjX~H(%sON<8GVi>_t z3&i9P$k-kS)1oei4kxQzY9Ud4EVM-9>Kgxj&0{G2Dl4t8$a#&5QvEPxY`4}hB)#-pvu@MtgMbTX=cT(qK3L2Y=S9@>zW$^ zrp+V30`Cje`1u^@{eD};z#!9a+`p8$H*l@hW4o6v4+fN@iu52P_(hMMy#@lVpDv+r z@4-4Xwc)8nGWU;V{dWKY12@rC6@Mzoi_iYJt9Ci_$#-1&J|^U=R-oQLgmPlxS`aNg z(HqFNCmehZS&*|lbFybE`K$OdVu)C2j6l}lV|*Ds<9`EKqwm$3Pp=|mWF*7mN6}WE z(Y9e5DSdt+XX0|Z7IT)1MIeB2Kv$2{`LJ{Za4tiljT5BbQ-CN9Aby>|b8T*e_QehJ zsR#Q<|J!TGCbLm@W8G)|d}p~Zlr`7KOG+S{^Z^vu)Z}%0KAVEZp`}i%VBPN~dno87 zRg*F&7h#k*vSJN_hktrBIX!vwyJhr);Q}3cbekW7C-Y=WRfipb_UUj2S%dF&=EpaM z0!I87`dn$gE(2?KfiU$pkUf#cf})hB5t3`UDyd6pm)z6~m9EJC^Ud;E{)phOPHH(% z%DR6xQU`sbk0ny{vBC}1H{N<>*sc>VE}Y!2L2F=%H-ap?AJ_cn&!F_75ZX_qf-33A z*6vLxS(89V_%pvkHTkLnQlUedCnFp&>e!43`h5GOHFYw^0=Am2WBZM1oF;lKe-;%Iz;8Qm-4x{(w zNN7@GUQ%ZLH!wCxq0*hdpx$gqZXoE|zRD~N#X1JU!cpxy8L)bH&Fjapc1M8_NR%|P z6Isi@l{>2sFXf)a7#!-Vkfm!pGD`8(65&J-f2? z&mZxghA|cP>CIB1mHpvwiKjGtfG6SlSGWUYWnjb>Tzgjyqe4m&|+D^Wdmyvra>`J=fm|)hs;faq+{KN^OjJ#$<^T zsCyS)G|cI;FBX_Z|NF3O^0({LkGx)UEh(6m2b=BWh5tT4EJ|V1-;o>V^%8pnst2%0 zG=N99fx)0VEDId-67&wO$r8!pl^2<9`Y$?19iZF=>|>%H9v)c14{#E0ifBX)g7A#{ zlYQ~Mh}GU}Hk|g;?721bwsU=@dkZES0-kmRNb647Td~_Fz229l@$vPDzZ$i7O00u@ zKBfGWHOwa$dEs^>L=e2eXTNX00=pB>S9~9YK6DqX@l()7mPc)Jkbd~_9f5v+ilTtu zp<|QDjEpx#CE(Y0p3XPYqt;pw2o>TW{?MA$jkhLXI=A^&{Ixo$qX!3TFEP<+&75H8 z43iER?66El-bi}MKIkv6F>v3XuSK~%hq~?z#Kc>%V))e{X%E8mbPVVezM>WW^5}n%@1fB(-OxwkRLM7x^c$r53g?WrCuI_|{wf4ws+b-tG`rXD>*;ubwSc zibyNHppOguW?5jJqx$fMf5`-EV9*!}?DlGVhK~0E;K)4S978M=Cg*~l(&o|X0e63_T zeoxB<^DGR?6o@Kfaf>cn-XHX7$Wzk#{Q{cuL%%$Zffo(jR@I(r6cdUvVD5 zFd*W>*Z{uc@9?{30qd_82<7JeX2lM)j?&7R{EuLIt5D|htgiW+Mb1I$YSN+mppDG| zzTsD11lj_^qt12}o)r4Zc3%#{0LUbVZ0gUtT|=TbEE`BU21vWj4^O)7se-d%m;2>- zE{0>7rzE|qMP%$P95+&T;4@l&em)4~0Vq}Vx0NnXq*VQ{!2MC-e+LwhE#IscM_$ML z`g@vJeU%M|4aumu)b%G%4FnpZk~qv0BrZ2&A`rU1E&h5l#1a5H!PPTYs_hjXI_9~` zYr2aA)D|7C@1X}GTQ7gxyN1_*cSW1q0hhMmy|uo#2VGwXn%rMW2iC80EK$DDj3`a= z_P__S7UU8xaOq`*%8Mz!uE7IEM=HAlFNVVf%1u~#XPVb(P3G;P@_cjR>@HDBCc%)$ zv%A)HuBpfq$rJq3w);K(i3olyLEm#$Lx@LSZU< zNDZ3TP676(p-DuLe!#eUv1U<4uF!Lqh3sWR+8cu!*r2Ca(AC~Y<3CFK>lP^izJKuJ z1q-VUCKB|FJ(BtGi@SXCp?s~XB*gt$aO#SwGfdcX0S5`5g-XZbop(;fAXl4JhaCyS z5Mqi@A~&OvLYK4+QsTJxkflXVa;oOMBR>9iS5EI@d^O^c07sRODds)7AX;ses+~ew zLpUCIzi;S^5KS?(oQiyRT;h>8MQjAt$;TH`^_y#hi}&@%e~!Z4--B7Y%dR(LzSS*v z{tI!3j%`bW1|ktR-N(zc6lvxcop1dTo@#}fFXtv%dG=zx zuzMmq?v*zvRd24*U2XL?W^3xAG^SdC{2Z=kse$c;`%Z!45 zRXYAz;oqE36YYIP|Mr}gvN1rk;&EB7!*Z*{7UE|5<@uKQe8PAn--~H$L)JMeaiT6P+V*!Iyiwri+-X7e6-}UGk^_ z!5A@u2T?a9!whq3chIcWdu z`VdxT#Pcy8;Y0Wz#pC>Zmiinaa0pBAf0q26V(x1HXqe)wjYn>lE@!dVIex)*$~DJn ztR>!gq12S<@9%n*<;{BF>l)a+zNs+}`wG6s*Wic3FrIS3{6WU^f zdiF0-2z+rbPA;cSR@*Hi%55AqmqZ+0!Aog0(jQ~A=u=hK;oX&m3h@5&S$3P;cD}r) zB9DN{PvMir*HKVrDdJ-Hw>49Z5q^QrVt!((GFi^_eUl20iK*@7_{>75ubl39kY*-0 z-P9UO`Drv&y<<46K=Zx}+#%$(^`=*C(#^E)P^N6~`LIMwtwJ3yhfH|4t8WWMddA_^ zAw5$d-pl=jMuxcz{RlDfFJw4tw^*l)dxpmz&J<0+^ISBer39OdJlJo8*D&}Pejz`% z$FY4PlWowzuE4g)<5C}qA9NUsQT9;$=4Sq-tS#>KO$E)zn*?GF9yWh}IkIW%tQ0qK z56g>ll6=>Ct$ideaGPrN;>2d~$B&Av8TxJ&{N$Z_IcV4Rc?*%O((~;&<}l!4_Rv*Y zAi8abD8Jwfbxey7F0HJcVG}gbQk&fko6DlJj7KrCtpU=vx3`yyWoi(v!nmt2h_QH5 z#W(W%HY$@*DMP3Q{QC!tmVOW>Z9q)hi{I+o)Xt;nOxQw?^UK_gr5&!PWxgbhBv9wzaAv81_MN0K)3@)4Z=~1i2Z7WCSBFM9;zL1ma3_2=jAK3!|*L zRa4OG!;v>w)pm=>+_%dUKGhYi`blV6TsLERjF_F(LFMpG59>8P1@IEVco)PYS{<*u zl7ZD|$Cz$Ry!n_t#mVN`&##!-{sGLQ9s5+q!w>=1CsiD%;xff-usH3W>o4;Pi1MkQ zZq$>x6=`Htk6<%MKU_@4YKuyKl*K1S#B8n>NK8nKwO;Fdxg=G|@)gT6)%A3)GVdi4 zX&bd$?WPj&G}OkXDg~j4mQ=t#?j80>s4@$A)h{d z>RSa%E%X|?m{6s2**Ga4$KXb@3BGn~d0hvydK*3uSf^e-W)$nS*GL|}xrRqY3vBhXU-R#!#2ciVdM$S6B5+Q3?;yJD@_=!4F#ru*O|Uler2|6w&Ak5zOA5o z-}YpTv@Toaw1ff@RdnM_=MfHoK7^`t;0&$hKn$V*$b99qPEMEqwK8a!n!K;f=p{&I zP*GgWm@?U6F=(PuD3&#=9Wz6Z6U9i5KF{&O)d^y%V*0mR;?aA=?R0EN@~)KbhY;8R zmq~BGzAe061)?`D)`Ez*Wk-@Nn}JxtD24w+>hzF0>%~)Y*Qo zUFMbmZo|J@uopvV{50^#%Vjmi%4=`!@MaT9(Wq{Qm?bD+pB79;z`_}P&EKTjpRZ72 zh{si`d$p8YX&6PKqA20RY;CaB7K27Y3+L#fo+_o0`aqW$z}tyQ&L0BqEK-62(a?U( zafLX@Bg*_IsLLj#)PjY6yx3r}2uIiMHL>AU3?^4TPnJ{c>8%V14`^IP?G4{{A942S z?O)Rz9x}JiDv2hMf#8UIh#ovF7nIGMiZEsaeyNL(S7?zr2m=$(;&QlxJ6A6P`?>xu3f(jRRWLh3N# zA+X5pob0KH*MEKFNtE>JDzE1l8`UR-KU!Rb-EsA3DZEr9r<%*eP`CSA-Qpi3xU%b< z%aAFFJg~^6vz+ayvqG%GBksMYcbfl{``RCzE1r&28Io{0&R{vSRio_m4u-2PS)q^$ zZZ#f#;J2Wv+yf62v-VHD5iD)#&hh^`R?w7X@0gtSiclsUPt%lb!1G2 zKr}K3v}d6@#j+)@E~Ee3|AV zmVxPYmGp^W9x$Xo!Zy_;cvyY9-3}mo>v|GY=Sms3b4BrfTgHKYj0P{& z4rX3)MwM|#DK0(FhsX?B#E06&;eQ-uv^!U%)r@iDSKSURc3GGhom^velYdD=P?SxV zez4;&_AWTS&S>u)j#}$QTRo~f+RD-p6q2!mRfv5zhwI!e+~}%eyl{m?sOu3pOiK$0 zxvsEQP@-a7a-o%t3?dakI0gy=cpL2R;%`JrJzzfIlaUFN-(@3)Ys5G~+o1Li$4-{q zkFKpg29dfV%5blH2qFByf{$#y!F+wbw?ALGw|5`K(48b2Uvc?IK&f8vJ?(MVSNxrs zgR8h8?Kc9-jLQ#cW(1*8kzFr|Y2ntuI5%3`#7G^+ zOJJPPbG-=~A7fF@*8-VXY;XBYOint#=d5)ULc^r&H9!Fg+MBi?yKpEHZcONbBSAdY z^3t#k*RrDRfY=#>WOA}sIxZ%1m`vZR`g=&rI1H#Hkx?k|bpIXZz6f%kAHABMKd14$ zg5t}cNO9WnSBRELStzaNBrE6ZDc#rP*yN&gnxB77>{-PTiOJX5t6cPW>^#==P%p}5 zy^IFb_T!?_%TMua)XU@*I%@QQ)xJW7_^w~vp2f~Jo=G=*%-258YF~6)K5~0JG};^t zyT5HNJKQws@fCDr=I2_1$fx5SyT%a733F?6$aa;`MD7Wv?*?k`9L!I%v7Uwi_SF@Ojq}2KItv zdew0}U=?{6in73``dZnTevfv9WYNT=>@ST7dh?)@wLP;x>H>!9F38AdqiHJMjsEfWH^xS;Qvit*CeF({FNP zzs@FDy+3TDP3i*dvisfNN);_BG*djEDW4G%`f07#m}aWVylPR+4AIxd_2t zx0JQjrm<}xsRfuoi1dXI?+B zzXG!D`8S5hKT1E#XGyXUAr_U8C^d#pr+`Jog3v^m@NrbA?o!(Qtn(Ma1O#>E^%6m| z#TB2x*xhx?mzu?XkGjRDRq>BV(Pgx{H=TRbGls4a=Mx?G zM@J}hJ_?Kq_nofBppry-Kkd?kdU|?sMV z+2klr467sJG+r#`_)j~M%xQT*!YQLht@qvY^_9F-v&z3fhuxm>^h+@RUW4F@M%L7q za^lO&SGRTV_wVcXz1vI5#p{yk(wBo?&uiq>Y{hp#ou<#W7FN|JCriH+*9m`jb-nYx zc?F0X524&b$v*d^!vDl05ue0F{XTReO@crzYb$=)dON@^tzUl@c)hXz zwPHJ6$tbbvfi?xzj{>dN%%|W|{N%YZy4V(Kp{p4jJ^+HdaK9)^;P|9gs5=%Wt7DN& zaI=WPbu-sr$)k6o=gv1-o@f1oQNIlslB~Ai^4>vIq@@l)n#3eWMnPe@i86ozDMQsO zo<9!>%-4PR;LrHj0*U^Sj2{JM*((aEG$H3rWq@8YNn2Zf;=GYMZ=Jmp@?NR>-N|du zqP0;VA8xE-&LwA1P1q3SneoH~)ftFjdU5*Zzd_>NflEY#jzbbL}^3 zmqRvZ#{uN0A5ZqeSAA%X)^7|T&5P6!KK|;zUM}9sB?(?k_821UsSbsD?JP#e6;^X) znja>8EC}F>7pgpKSlJrgjHw(#^Py2{fg<7nu}-}cnqq_9T;srQ_h#F?U{@c>N01*B z)INU=;C%3XxlyEKAxAgh_W?K`T_K1a6k~S2hU@}w&Ohny*7uhy~KU#f7t zzPps_?k+8kn)u(rNvfN{(?q@@`nis;dVc6#BjI%-aEjC(r!M$;7IDh&`S*Y0x&sB} zCwEvJj9b~;;&_IOQYdqHbD6GD_LF8j>iX9l{g3EcopdDDbnit5Ks~{*Sw7OdaQrT+ zZkUgyH!#VBeT;DtuT(o~?M^VH+zqvV+*voO2}(4qIOuDPc7oKsas%lkP67j6F;;hSO^zR07#R{-2@l{bj zxEgicU>of(EtaJK9Gm;b7sxW_&D(>YE@L8VM5)9?jkM0pm6o&*q7!x!za&NZc$InO z`V@>wQT0@998+A+%Oh9Wc4kKELHaH)``X{PeH4U0iy$6b=DF$`b~r<^r=pY_^9+t!M2`VWK%Y z9!(8BiSct`5c7r3LO9=nj%JI3WN?SVop{p5UCX@AT0F-U5!h^2(}EaCf%osf{dRiL zN;`l4s-OuksnKL9vHjI^>t2PwVCoQ=`+F?DOYTBB0o!WH({@hI`A7#ek+mc$J00v` z>MKl6>V9Pm=w6;n9rHUVS!nPHIvHH|Z_yPTR1~@$))^bMUA~jVj7l6d)kSRqFo&qM zm{8l_ZWQG*_!W;}bGe1zXEcc%ixGUrsi&}R z8EXq$*G)9jH0y_`-F4V45g!CLZZ5p0BgLYKUelv6Xcgc8C|hRUyx>(TmrIrG z4}y-z)wbfRQrFok4Om{v6V7%#-{=FQNSkmIGiC?d;8EuVU+{d>Y69o!`fk-A{AbTO zu~3~E?5r#@xPTZDHPOJG^o`^2X>fy_B@FGb`Kf5g0Ws&3dYSg%^k^~%wkKw}+nPDY ztgnwp9(R_tfg{hF*FM_Lo;FkS({;-`C||5xpVjidm%;Yssbc@_#!aIl3R-mO!s^7&&$v!{JX3KhYogAAt{R z4@Vjox=7(LlUGvXEpiH6^HPRFab;$8Lxna`owz0QSsfQva|E$ro%w&cz3-V7X{r7y ztLK!hR$b*C7Xhzh)8@Aahee`0Xaij+n2JGGY(et!Q?GoMd}~B3f9|r(+g}@BF7OQl zv%8|3|FWaECnd`*4N=vE9moGRcmR@AtNr2?FOiU!&U$jG{?dJi2kG?`91IM9 z$7&U*qeEy+sj~R@lsTUzFWcZZ{&K_{ip!Jmp&iQXH=5mB53JY+DO%2Rp&64kVv&b5 zerlGv$sQ>gC!l5j8(xHzVwNR~@o|$R(3h#HLoRixmUIt%1y}BDB>C>%aSxzyX?3Y8pDD=*=s%{-H9_YRjuxKC(9u@l2J03F)_K+)RwRd(RdVLruAP<4^R<1s%KkzDLoSz{&!@y32@U)<9n=asJIq{|62w)1BP@=<984px{p z7Vj^PCm~{I5+SI6yv}KK-IMF!MWM({x%)Hj7j5vb5hPW7Nmc~9%Uz&JjzIQ0`QcD3 zQ#ybh8X|}Ys@HQnk`+2ex(Kln414?UD0iQI?ibYGuX9| z;YzQZ{Ud+9I0V(mU6vpG0cL{_5Ipw6MhhYOYwUP!(zZkM|O zG0oweAv}|8CcCK131#7cS!NG6IblutV0FHQ9)$ z-9ijc(>s zt63bs$EKSgDyBW7t$BDNRy05tJptFwM`lVY%qm7f>bU0DwN}rq`Sl#lwc1>!4t}y3 z(sl=M>8AvMUmt^~pJ=S=BeT*f`gEn6BHrcIWtgj2QQ)7T1Tm`?Hg2LI(Y+(ZQkD}TISlLb8kb7e6L`*IhTwh z{3bB_VB#IGzP=0lJ4+{F*`Eo;ZORDe%uk*17(4IUU$e;4>HT|igebvh;{Pgd#d{`( zr@2~}Il^gPr|+5KdS{HsOoXha)xFmraffqtM2MZopoPzQ^y6bIW%}ppLGO6sD>gc1 zh0g%aUmz3B7T=tf1+_Sdi73TvY?s?|F+YKeSNw&TItD)6d2L%{~lojkaSPc#ic0!|4U_eDhMNq-P#ISYq z88>zFw|~C0U>skcpD90}tDJzizI_DZ@fuXPUKt~yt{3BuP`pptiC6IQ*G9A9jpCo| zZW>S9>p98sLN&BptU%cKy6s5fJ~jEj)eicP)RYND-d!0Vxg+B`p#1oWn7|mgH+$r( zJ$?hFzuoihEcOA5_gtvpQ=`%ccdFC&XTHA?8b11Zo`ru?L(kH|81r{d{1c?4{4n<3 z7gvH)zQZ)-SrUR;UyvdfSlqD(-&s?4{(5<;a+wqX3C$jHOjJ5fGgC*BB#gZfRq%P@ zb0&>9A2^|U9~{Qfn;ilq>~er;->F`2aX|S3DUps7NwvklmW3hcHU2>=f!JB2yx)1d zeYbtx>)dRnSmmOzUW>4?S<-yO(~FC6zKYMq>GIOCo6UZ^vq3;O%QC^~ERt+%dyDz_ zwe$IIn^HgEfr)?ZFAlUS%`U4BlowJ@!3@vXKh2T&x=8}8=Yx^E8~fI#Hk9VvIZA%#Xg2#jbw{e3^C;@q;did~~lR%*z6 z$UX+yTsmEe39ql65~3O?zOG7M_H;g?5jB!57Aog3Ptl?~IXj=f9yHvaEi1$>w0z{P zH=Ih+csTndnl6lUcMD)(ei8!lzmvZp6U~!}??Qoj_wc=3mm4IssbG!8A%haHsIYbT zf`B1iA;fRTVU#TXNpz}fsnDl|%{$Rr&LKWriTfuFG`keyfG#56eTaKX@nW;cPd?fD&E~75w3)CUp zEi@20>?HZd>XW^Y%0Ktki&=Y+pO?WCCI@|df1bHpeTzkhL*z0$h%%Qeil#JvdTxs~m6jyr6vPn%J^$Q?r|w&@QU#*${a%dc{v4)Oa)BZLo?lgX zvZT|I@*ka0ECE9nn{=~*~SdNzI4?&j16=xmGQE?UUW*)$ALO_3Z@W)emuZ)zwFEQF9V;I z2`Tz*flDLAAYG4Xs)|>nDoRwGS*NoxQl9T|S4TL8@X`U0%hyExz!<2YekmfQ%KJv! zoBx4xcsOFWM{{AOyns02dG$gi7^H_F z=0zA*fHgPwo0zH#J2DD#rQr>7r?Z9<`Ob&$0LI#%KYv0)5^DyuYZGcGA$jrEa~Cc( z5;x=vJu5jl+5DfE3y^hw4lJJz=khWg{+he7`UU(Jx@n+0)Jyx7T{rzNS0c2{<(P8o zyZs@qBw15)c#sHlDhh$oX9F6Tbpam?sFIY5^^UBot1gAvVn*|i&kj04Fif8uLH_34 zHsI35yefeBt%y9G?a?UpPK$>T+O^(q*S~17^a%}Z`p9TC-=$Qu2AycSfuhWg#NOvm zIxVcYie5i*@XdO2-Dwhv{)k9o7>&&l?dPGn}I}R46^NCCp zs`(Oga&&YI4GVks0}B>v5mEQ1hXwtmggg5c5TZm;Xy)iKr_RDzjNtmvVDl^?v{P)- zx>I_MX*=|^TKfZ$x39`_D`)V#;~Np#b_W(GsR5MOao;|LOq44Z+qdh+{uz?$2a5R= z1u`+VKEap%aKVo4s-1>sKDsS|Lg-Odg0JsEjhl-NG1@uEOC062n0;MMENILr#~eG> zIk22g(9o2hU=8(BeOjb}_g$2{yeS#;)uz}!Xp(|TlmDg*E?xG^%{@)ucTbY+(=_$j z4Zy@C;Yw?Bg6+>p|Dy|`%8*qcQGUs#LvOqdof7Bk-@L#|x>|_*_wAX7+S$W)>(pk1 zoKad@saCJm5viG9tOjw|MW;CqM(cP8y_0oNMuAy=@6c!V$Zn(#s_w-r5%dV$h=_nX zor+O5D-5EDNBf}576xQlXAO0~P{2_rdt@A3m&@^lKHfA%we=mjK!ESJ|DggS9kO~J zGCWE8;Nj=Bu);lf`FS+M5b`6bfCF|(NtJ4Rv9phMT4n@712K+o2!6TunsVOH=zLd$ zckV~s_8?71k8#3lxHhj>pirys195P-=WD}nj5rwb!K+zR81jOv{9*Zk=w|H?Nz(l? zV_F1Vb25?&eG-zAz-E=#S%+%?861f3VT!16rF35EU}UFw(SZ?x@7se&+Ky*-)g;65pXmUmukZpLTns56E7X=nS+q@evT z8WeD6>7^r~Pi49$5#4V?W!f#j3Q{=Hc!TO|ul*Z(Goletl&BMv@$+XtdMm)#DOUEJlnUj0JSs(2CZ}v`PeA;P zvM7B(L!o{P4~8BPoAi(McW0&7I?T#9BUIm{rszA?B00WvQumK`O_@a5sG@(eKHv3< zCKZnTvKBtrl0f>Tm;{en2*wqQ?fSH)A8fc$*Ew(GHK#8JrKuO2JQ}?ZhLbq=v{b)j z*kA)>k%sd8?uSjv9xE(%azxis1QWG}K5MVOPb7m;OvQSy3?#yS}`#5=430{=TKb9tN7`sD_X6#}k8v<>G_o z7tfR8R*xX|PYJs(I2A}JiD#;rya-EK^l5-jdk8We3G}V{{F|KE(ra>Z(!c!c7j~FX zjAs|Dj1j#S3c`L?C$;d>Ag_)X!(k?ay5OjQZ(oz8SYyj~k)?<`wh}SJyr+ADZFyNP zr_VfQbI#gfkg6s>9DWkKSwKNtwB#s~HHaManJ2Cn{>&yRIhCjm|Az7R-ZU_`Qc4Zj zFK4+QW-&`*CKe~GCJ4v$%YtIvX0G2Iy06YCXqm`l=L)v}@CI-8$6#bxYTNpz6RTN) zS`}oDn&-KD#_>`91%`YwVM@~wYAgI?;D{)fD{N;2>(GAvY1YDI$Fsb#59m7ZYtHXw8$JN(i(OP-SOpt1F(6(?%{`XG%PlRw#TQ!S7C@ zh)VO6C6OPRb$0ygv$?+f)lsiOlI_UfN^($=a5J8&+yu9KgLQpi=>N)tMnp7#^zj$^ z<;2qhp1^BK0kO;2(3o0?rF<$X=#~9P(gsuiK@kh$qL)N?u>0m5gTtE+R2p#VaU+_# zR7#!ypFxc4RAqE^#tiUjQva9c)7TvYkb;+{5Ik8 zQ9-5591s`Ja)qq2%x4;Qhu#39$~&zU4b&g^zGrmVrCvTUEmV#nn@<%=v$=4Wv}6;w z8cocXRa)UZ3)aP_dEcSPPU@S>JV2dhe>`4U)~*D=UOi!e!WyZWQne&xHC zAuN7vx!cIcmw57W`V0DPI-IExw!7KL;=|x{C4*-_iUcLNOf5o#jf|jyGNn3TWRMbP z8L0s?#C@wfF{AVH0V+>MGL1Vww$t7OnW;!oZ65M)At6mW8ns2#q;<3 zukz5NxGP$i^J;y(?RLyDg;NHFc8kIqMr_9Npl@Kk)+{e7H%KPFiznQJYat1S1qnqFHWitF9C zqX{9tWwpTvKD!ZlD>={$SE>z{hgL3#7A_VM=I4EAA6HDq`2DhE5JRA3@bizU$jTe$ zS%?~N`55=xV`$ddHcD%@xFtVT5m+`&i=RGO_wev&e7nJzG#`*MIc>npla&kYKR~eS zX&7G#(2gJWI@(}Bxx)2LyUmN(nO0lwF25` zFPP?g|FXOc4PEO76Jg&rT1<6Z{-FEvrKb-VJ$&6}LXC{(+%o-eZQtyHVZ)@BHGJnu7JQ;{RF3Ll#asba{djB%WX z#$yiWmy3>=n~)j1Xf8Z`8uo-3@96$Mk!pC9M&?N`R&_;MTSf1boi&2P6lE`L>sJC8 ztO9b=p7Tl&JC|qlJN4YUdOJ%OTbpn-+Z*> zP1V01&@?Ub-OXV3I&zHqJ`Zg*A%|#Bi}gb_ORq~xGm2Gxoo%9$4yWMpjYRJ7Kq4tU zK`@JV&?fr-X$g9~-Kag!fR)|Zet)yrzQ9X=eA&IB@Nr2iTL|@nf8H>QX`c_KVe1A%k{+fC~gXiRhw89 z%}q@TCDT>sF4o@f5VRIgZ7reXlv}x3U<< zCB0q#X0nMtP3q9H{p(UkHFi@0i%t2b?_swTGN{4B{36fmFw|BT_!(7=WP z;yg4|RR3iLx5|nGlS76mVkYyNB5gIOuhKG4 zt+nf>8~mISc;ZJ4ysMlPt=i zb=x5%0v`qoD8#*l!oTprvP&cywPR#`I@`x+cgdjpm{$xu68o!C_)WW+W-h07pD*QR zcr@^E+WQ3=r}v8`QRtWNMq$H}le&7u*h=3hWG!p$k=Ld7uaN3el!M0hR5W2fM z0KTOdt=y!`Y}LB3dR%Ui%4JOoAF>#tf@p50?<+VUoYE2%jAigIJ4JGfh=QiE&5{k= zHBem5*c=#6(pWydHP?-Flp>=atoifAw@uCOX1^XhPwJ+Dc-$5d3pP(m5k%k%FO!^h6B(W|J`_Baw$AybiaGt*;6b$*YY8h#q}1}k0<)oexi zDeRf*cx(4&(`kt=0nx^fxwQ!!RFCm2NkKHhtuFvw#nlCbQ6hdvtO0#iv|VUL1-#pK zpmO9RJzjK^O;s^jl=}GtdtyjyTGr-zg%`uBp)V;cz~*dIrx- z9sY+O_@x)sUkpEM(2#DEHQ|yk zJ73)}`S#5OF$@rYI^+^m(U>sKck#g4*XRBqcFj|RqWH#Y+H8gz7!bv{HJUJj#{(q? zSlP<%r;02s5rZr?jQ99;Gt!U7gQ*~?Kih}cU13qrqI~1dAcE&+J+VxQJzh%{*Z`(I ztxB+BUS;YB6BUh0#h{|Zg+37m5&9a=>Bz0&TJCkc~p^ixEvQcMnG|$T=Im!vwli_tQ!!%vL>v zyr&CF50CHeR$Px<6k-+<5^Lj^mC-O31Lgzk;*VqUuRC%LW&$`F*eJ6@=(u^A)~Vgg z#OI*h7u`CUfBfK%q>#O)s zuTYR3Z_ek>FPNrrt^&6Bp0L`&AX)LVYg%$O51kBx8<(<5MIs&`yu(iXahzZZI!cC# z&}Djhiq9phyQdCa?8ocv28c*F8yO*1zS8AN9|v(uXAc|fbb;_L|99CaD_Q%0;JTTW zCU3V^YiLpIyd8a7-b2Sx7ga3Jk;*asjSnEg*%FjZfgk0>e{a7|hTMW*hI+_6eD1Cx zzweVikMlD@Sg5OHuf6V>`javX;+eMC>r1%Ir7pufwr28g&NtNX&y8CTAKV4o1c(gArm3_#+pse0dqH{$KtX9@L=M`pg&{gGY-(cb=;Y-%|*BIVnK%Zukt z{Hn5*B)nRAiwjz)7Q5-1W`3aUrBZcr@uxUh)l_@XzwC9 zG8|K0)_(23eP8)=(dhYMl?p1RwfXY6&Z}y<4^P?(13=ZSA^*8;jkqq_BhB{zIw>mf zG54K!^{;Prg-95!lS72dtsYF|NG}9Yi6-zTmETTvCYVuMUC%&N%f>s)hgTQFE7bk1 zBuBl~cH1=IA8?p_?y`*pos*Q3l~%pb_@O#Nju@4apTCc;x;4n4X%i@AiqEmb8a;aB zt-(q_;b)9U7zI_R0@V?ts&r&P!?Iw2nw(JPLx9^;`6`#g0VXI@D+j(OZhs*&vl3G= z@+tPs_oz=6i-@EY0rClY6KwN370{h%`U(oqY`$1@JKx+Xf+k@Yhp-}DG`>8R+@ERm z`)Y=Pxov_U?ma zD)hE`Ke->q)_%i*EnD}1{R+Sr90E~8UjCjuGo`BNQ3e1@JI@{X8!2@3Om>#%qk>b^ zOTN{A3bu6hm}{0-KwWe2g*oz({7viWEz+v^aIV? zn)Ud_R$mSQ-|hGMt0&$1J0+)&AzDBDHT(`-OJqw#SqfW!g0nuV-h^g-OaiE6`N>_f zNM`gC$L{GTslSj6=u-v%8d56_@@8dY<0B>Bl;8pF#EH+XC_=j^%GYKOR6!+krQ1!T zM6+&Ziu5v)Y~TvYiP4N#R#>729mCJs^A!QIEwuq-(=tRM8qs+V`ryK;jHOBr-F+|q z@#mvSh^yPH142`(;!>UI7Lb#}?0EmF%@5NUm9w20e%m)`>#&B?w^?L+Ej9)kB@by(IFzdtcY+^>80l=Wy=1X^g|w4vVip*WaB2K{(J?h zgTYkhnL7mU)eWbv+xy(G$xRn;1Yb2%a0}>PkEtCpaOe7CevZoqm{+a>vO zsAt9Q-nO-I+q8VvSctg>euF11-mH_3tFjn{CtE$3jq#Hh`v_OOq&^H8)wqSvG|Ni8;y)sP6ONLJ8sItE8zr^>(`p*z$ zCgJDBFVVGK&Kb#nBTcHdwTh9U2( zbe$f_+1Gu^r6Q@dm3h#$e~1QmInrMjViaYeTw!9fGQV!>IGVaW^!aYm^5vFrA`-21bXwpRn6-g7Xxq#jQ0M)O*^j85)EJ zC;lb_jGZk$Pq!at$TD-8<>_3a!Fdy0t&q6|lSp2Jp`2|0lTH*(L&7d$sQ;6~;PYpD zG8)ugv|EvCWo7c*xpJ6t1G+#j1D}k2s1PN5z3Xx5XiQ+0+JohbKS6Rh(&M20q^z1d zk+8wgPuls!h zd5_oXut5`>Gt(2b993i;RQ~Xft4OeU$r5+0ZA;`?CFT@lk z=nv-6at6H_R6?DFC=fH#$s{JKNvX8dV)4qCZa3XV$$9ERExtX@a*XX&FIDGNY|{gy<2Wc2t!!Mi3~3Nr9xG~bI4FC=2z-#Ymc1i$X^RWa(- z;@ulzms_eO=0%rftAJ@DMIc~E3(u5kU{O}fXDu#ru-;gkj5r=H?&VwbZ?CO&soL=? zLF&T!X?&g~G^@ydJiYRSp5J{i+Oa(svtQZHj<{X5WJxpou=kW4X%Yd9SNpI-Z+i)4om!G*hyAH(%}euO)FHG9@le3a?q)U;m3 z#mvl%(QMZIH8t?LVBzB8$=IX$twV=`GKsp*-d2v`uo+yL)y?%dXrjs@^ zGz^(9ydf|$jNtknhc`?wXj&IBYOv4P_b(u$>$cPf>i-|w{cr&Z$1v<@GXET*^Q-ZE zSOcq_ep{W!DbIxqLtjfD9332tX-1f{w&s~Wg}SQE2?P2sEyUjEf{mQVMWSgaiAJ43 z_@&vdwK$@O;07_cin*pn4`U6E_h)5xLM3#{xp>D=uLq4zNUpb#zQqbqI>NiroQ=sw zCyR>*s)#aLxnt+Orn|?<$yuZsDyU55o86PY+7Fo?Qn1(B*{40$vu_e2Y7sF$`zs+( zEWds&RBb|5Jd5ZMl?!ehNM?k$zwfVr=3t(?Y{M7nrZat}6ikhke_|c=tt8{v^wM`3 zPy=H*UDu1HtLSA-DZk5t);}y^#NQ=FZsd9bNwsH_F=3oI+7)`g;LKFoLJyB4TW&7wy_{-FiiB%qE`)1o=vjdl~ zI;s7H6JC~w@gkGWY-U&S+{zmt?9#MQ2xZzG4B8|nl%i#HVZyVXqlP;Ru7m1rW{80z zr7ViX^%i@@1!W~E#5DqP_zKT2JP(ID4c+7*2v&+D6&AUxy6N|S?r$MRNOm3p^X(uP>ZYf9sI*6RLJ5d#_Lu*+St*=edY0 z%0ylJ&sqABe-E6XOsl%1gT*HiSFQG7>0P6bOp8N8wyGXdpQV26%^ANNSOTLMfZu5B zA0H8dVVUon97Dc^m!3hFC*pr;J`SjuWc$9SL~{Xm27jV_(}M*>B{nO;3xMu64+rzr zXuqRpcrbKq$A*RSHBe8~Zo5JHJ!K@ls3#7_-;LEukMO8mK{$U-flkROGTwUI-t;fl z!lhO%fq z{L#nHF$}N&|Df|T(f^_g&_$m&dj#J1jQe#{s$dY$wrCIfi2Nh-z4DBk>$2&)0Cf96|jbd#jOBHDil82aDYJlOH#z4AbqUgtp;tFj+-5(o=@khVLq65Oe^8w0mqOAC2|&Q1p+Oe2%!D!sqo{S*Qe) zf`F6uHIQifyL_>;|8{(wJ<-_1lS#qt7M2 zgZc{(o(^O+E3E?dy(N$G@I3g|5NmV48cl3&Ui4quy)bg&;|@{7f_&3iNuWMaRJNZS zqRNNLx~QMS@LVrAI|JsivxQ&sR4bE~E`VQln+l-9cr$S7_~@X-UO7p2L%K^DgQ|n^ z`RQ^Cr%g*xAai8CA>LnymdS=e(Q-$+r|=+!K?o$?@#L}jUtR)XP~rF zdexeIe{en_3x(XhU_8A*gI2^>OayDEWK)Wa%K@DBKGYp(jl=jd>P71x_rS0`6VrI z>2kP->O>jS#yu9E5JPXeAYVj804hJ0Aimfbo&C@nW|?An>B`nPr*zI{^81$rBEk*r zkKEJ{8&yo?`mBe8gK_4aG^AF}#I!{$K>bYD`F~M>PmG)2L?Mj z3zh2fF^Wpa%|^4an=VDH(%U?SxKQ#uf&H12UqW(lC@FPj}!Jxg9_=EYurwe2lYpvR7ajPvWw1j$XYC0BlPLe(cX zSZmZ*fe8BucnI$wIQOEk*lNPhI=WvDwq_yEe|j6oZW0no(u&IMMLSc@tbDU~;7 z_Up1!(b7`V(IE+8W@oW)j)-rqKqFG(FF1&6)?O}nHb%|9{?-jD{cv}IP@T4g$c7S_ zDG01w!A(8~&>hICwdA{(ifB`0=;Y`9=isPvb(Sg}Wf;5pt#|rj~M{TOmcA>~>K`e{q0U-tXg~wbHLEkHCbP_&9 z5Emp>zfhe`ymp+@#(+Cu;7*{b;z0>i6(yX?7FZ1=eAOKw=mX!w+23}(He{;Q;rQ@k zE+hS?$`kxAl=K5-Wc2%{rU!vofgf0&TF4mjs)2jTAJ6jdWMavTkeV{x2fm@mt-Kr| z5L_I(wR*Yfu8{nPUpr1JlS2RMuO+tv(fkH#7`HrUTI*cR0wz^M6LTJ|MOz9(;qLB_ zfW@$z^&;j8ku))J)m9^RUVs+W~D9wnZe$x?DTsW$Tg8u;122=!%% zQemLg$6H#XI(5Iu4k|f{$os{%X1I^&1bu?g7!u!sif0Og*5+4AKS|Gu6(4XgcrC}i z*iI=plL%*rga?njggCsu=?m?(0kBgy z6QcoZ4^u5daT0=(T&XMX=e+|5_yo@BIcvv5u-pAD@nrOCeTorX6|PhfKOfWn9CYhc5E5yPy(>6oCADtJwv zD%;Y;B@;RN>yje@gJwInIh!gGAb?#~UU+y0XB1pkrK1K{QdEO)N~D%&{jb^XM3z}O zDlD$u3$>Vo{D6=B_)%7=@2Aqg^C!74dJRpXk4ibP@sH7CQ3_M>MJmu9x@9K$d0QDt zi3Fp_`3SnS(+Ewn!&3_efPviI8U?O`)Ia}Aw%xTV#cV-oBn-9yVLzbH;<1W$bTsf% za+=`DIO(yeZ7Fg+^)nSnH|%1*t1zhYe*FRxY6^>Yob=-jeUWcwM>hq;#b z#l*6hJ7wh)s5um@t&BPItyodu;OxAv4cx8Pj37;KFBQ`6$NTFwj87E&8RH=a`ffot zckB9qAkFUWu^Fwq+nMciV=zY@)cC`_>2-dDNPa&1cmw)FqyV zOMgzM-F;$1-+oeYQvT2y(FhD`gI(h4`XW1@MsL&RYGXERn;+o?U#VA*_Y2lPV#hh@ z)I8UFxIb;aVXX3gOpLH!b_2z|z5Oe+8L8Q9_|}!|MX85uY!(z4c0oL+`rJr;Oq&UFxG0XuR|fV&QYt|Pk+jLu>dAoFVl;HxobBj z-gafvS~xw0GryLCCELmMx2svWIrzqNU9aa^HOL%oB~39{rQ*VFl~C~1na`)@(AxX; zpChhPV)-ib<5U$=c?@TJJIuJ?tt8~^Z0(g{XRl8FM?`30o}qtYvfOE>js{sdLd8Cv zMU~_YISD+o5?mDl_g{p`AhbYOr z)sU@iss4MrKrLk4cZv39XAC+Q6o_m?+bjH^CNR?6rNaB~{w`kB|}4}`Zkmkd6AvKQAaO?12CBAJKAHgq_+%wv^*Y9!sHnSnsp zc%S}a)^`n;7S?LTxzDRYzeOKxcETW?x}NtG7P;k*?*5czkSR*EO&_RcVBcGYAsFPh zes-@g;%Vg{YV30u6|c{~$nMBDBl>Urq`f@DM85qTunOc9cf!GS9RF_ky6r9Y*?%!S z{A4FJHL%|$?f)2lAxmkn@%LX&a{iv)M=Rh0j`g;*WkWrBTfMp;nD4mcWzN+}`K{Wv zn&|EW?yNy0DW!*dtaoT$Q+yt2i75g8&*+aByG<3yi0x#~38j^0;MZ8^S%O`x#GQ1Y zTE?b+D~}M{MRX~U5M8oqNaXj4(;uh1uK4BAp$j4R5vIToDS+eZ?tpy?0!Z`xkf-$F zQRgg)roT|>Vyfvi71G!3(S1U+TBd7_ezBV>IHZXKd&$hI7v2(_^j-0H_TK-i>n-D= z>e}|5iF=G*T)c(n|M8cL_3dGjw-%*Sqn$ zpZotjAKv}RAI{#Zj&-i%jCG!aKJTS`(Rk%!QAQq}ZWkx9X)JK}UO6MrLA4t;07h^2 zXJ`mg73pV5X|sO;e|-3EZPYyO(aK0RMU#Pjbuz;#>Kf(2YgF$kQ86BdAePm-;m2vL`$5V5y!yNo^%(c+d!C zUM-v0A~r{7c(X#R}jR5DY2V%5P0R zpm+I@jCLiRFcxSPTF5HS+4cY@`5`%dwYR$ik6+iK#$m&+7A!>g!?*vMV_u!oJB^LU^&X3lW7h!6MXJ8XLuBGX=wG!HP>v~!mA)oixsdYM& zu|@e;UsG*R_E3^c!GVDRlL;gS@+c3i7uaMzLhzy4Yd@Qy^%03(v}u*+9YKisNc5Z# z^mV$*%zQdDf8t=FGM$Zwr-4`XLMoy|*+Sd@6djN;fB@w(@DiHzv!>>$bhz1U6LR4d z#+)u6*Jg_ia%Jq2`pWt1mDsu8xOK=k2 zngGt(8FEF9Bis*M{}J1q`K+jl!e}jqjZASom`g$4?=x(fuz@-6D;kxj=M5uS#$=CG z4GrCVUW&5|K6rT(BIfGa+$pRg%3#?@Q)Ty|v$a(YSVGB8=#hPV0vUCtgJC78@BKiH zmaLrN)(mZN=NY1Iq?eM=Lb=3PL78(wJvGY`{{63akfkHn_G)cEtBNu%8Z+SM1yD3km zY&{}YfEn?cn6B%R3C;>n=8Ioph?vqBOBI%Vl<*+wSy}v-)z5lE0$BO5Fzfd(W(rz< zHlkI<)NXIcBc*l|H9L|x@?T+#_S#?GaT310O?0^V6oslT8NLD8I__qfEPJ@DGvD(o z_S2ZR2pgNM+n+J{@wT;bYl?YW`BIZI(*j{z6i{^+@8dC3ZoX-vL(j0f!Iy=?k-AQw z9FBWSBSCq>R$Fqlk5w~HWiu>QB?$eI&f=I7jZth&Hj@`{c@~o|S`DX%)VXKlW($?| zVJ1IL7+s;GIr}V^mN^WAydQiCKH{O=lHvNvP^5^*_1an%hjuj?owwWEc23IkM{{Cw zfcI)Hy&BU`l@(~kJ1LhK3)}&@)8tyurGYXgIdFMp5q0HL9xqx6#o~vqdGmLfE6A#$f8j z*?jHE{0sElL9ET_Kot8g`OI9Ur&4(|fO&AX%bMoh1x0B|Iwi%e4L)m4+8vsz;SdII z=RSFfdI@72#pG6-D*`uWmRNL=a1w4r19Qn3c;s&`>NfYhKg7c|z^^H|zVfo}DKdC} z0#>O|UDeqZB=uDGQ4hak`}0*lkvsaO22wXK?HgX>CQCcN`|j&IgIx&Ot$qNc;`V?E z7m1HT=Hg=Yl>jh{4NgB2@HeOY2q9k^Wwe_(s3+LW)JX59G%XW&7vNXa+%y%r$S*#7IUW@PG(w72zNLgrfg-Z?`(faHYfbYcBO!r_v6s~`XC*l8U!iA8Sb zsW`$?3i-U4I%1S_(&Sg_=|OJ_Z`J|+P|-HmY9tz;sB*GV z_nU*CQH>lI^V&ofQ`IpYInyJztvx33YxrH|PnP#r*2=Tac${_a{A}tOki2Gh{AF)s z;Lz`O!tRnARN){X1&Gs}du?eShpnj4)6~}H+VwxpAR`0Q&9lAq(iXj-yf*4QbfaDh z=Kg*{Ezj3R`tj+m5{Y!dw*=t}8RfYCKcFsz=YMK-K4_tva*hJWDM~0#Y|aA^qnko5Ih#E#y7lUFn}q5Um}qJRE060^gxk2@Vb7M>1WvEw@>s ztntck4BFm&<+kLwg6oTMp3GzXjHywR7`^GxEZq@vIF$cl9u*g><81^@dU~kQ&S*3* ztb91t^1+=^3_#>cT&(e8KpThr*9vD_PEzsY=S=pGw_nhDYxj-f3aRtVMl~Of>V~Ql z!MPoT?^T78oxHJ&=(^)EhVFQWbD6hBSKW(sTH_C8{~|0O;cvfxS4%iX%hBT91fyo7@N@h=ArCKUPmu(mRS&T0rPzc_Y19~w5BPs!H+J03Bx`Hp zaI6l;y!qN1;zHcfa3-Y@p5Jrxjp|||?|3s7`JFJ#63*x?(vTn`GwM8CJ?M70WQBc$ zrFgW+cW1r(hK%v(gk8>f6(giO*`9@$!68io{T|(2sfrVuNfMMYR|$R=;TiBq-p$aI zVF5^q`^s=HG^OaE(2ZT9zd|e=yD+&dHB~-&L4&HK<+syzy>3;0i6K53nEfg74H+7q z=g5pT#Vn?N=dt9{@_N``Lc`4}!9}9Mp#m}9^YlQ~pH7z|IxeQ7%YtV_Z@Ed~!Qbqh zK16I0x;HBf;04ur`(qLA!|r(RcRWxe=fO`So(NZ5lDO* zXdb$3^HDC)ccp4EPG~5`uoRu_ZeZ%#7jQZJfI%94jZ-y&0OGLR`z(=oK(gPh>2q?` zF{avLT`4sjBdg^U%ArQ@A*WkYp6Jg0A(_^@^g|bKmB`{G#B* zIyX2_VyRK{gw=u!1nWOb`bXfk{Te%r&yw73cB|`s%XZnmE&EO?$|I9D^06a-PD)~^ znl+s>9`vF|^Qc`Q0-7h&Q6Ltc{o>khSlEbc8{6>ez)>WVVc^Z_*P+f{4cQ7YyqPQv z`~B?faflVu)bv?Dr7nEqvtMqgJiK4) zvDTEt1P}rblKS0?wG-`2{fBT2JfDEHZuZIecY@#P+Yms&r zT+EbfyEn)B2@_Yy;Q%LcpfLv?8q@qajAw^mjtLRpaz{*Tf`yL=Gw#eevS5l20jno^ z`d`4b3lko%wr_jv+3FRdTvCZ-s+?q(BefJUod6N<2pLrJo#g4?^^cy z9V^laot^8K$g>?Df7>eGVxG#-YRaXdeNyB_0j!b%Ywc)Ry7}2!nMvA-d(PcGv_5;> zCs<^Zi%epmx~vc}XtSmrbG}_2X{6I;Bvv3dUMj@@$~md|6DIGVP+?R}5H*jU^7`3!u2qvj{_K$9es{}Tv#3hA<^J9edA7%4U3F5hzc@#4wU-gN7loJ< zg37oWy@Mi0lAZI8W4Ktjr1`cIIhpdUazveWLYBs=qx1ldDPHa8oQcfE&Kf1}l;k>m zZx~t>yv($vLs3l3y>=?Q9lqg8knR<=W+d)GX?os8@ui7B++^j1pDlM;J1XbBx-LAG zh3+7d>Afaxak0EAwy5YawnLS5w_`yW=P-GJPyd=I-IcGsAWaFpT|etotbUw690k#a z?w3i9d&dD&9q&OhE|EfDjoE;T=mq|cOU=$%Ugf2wHBtGL`VsbNJM6N2;N8oECmx!M1o^@^KPF4=Y#0nQ^GYl9N1yBfO-74DbGyGhgz z`-DxP@FAb$!-F13Uu+T#1_bC;XvI(E=HxUAc1e0Cp3QNVZB(qN6|Gg;X*1!G9r0tKdm zO{(lR8Jl0IQrHqsjter6{UXhqD6ym9an)6}Hk`LJQrNsX&faJUYwvfJJ374C6aI2} ze}09L`^W0EZT$~HHG}u}^R%qa?zvNFmH}>ZK88^xNxx-~k`jmjLd*r;#AvFR+|{Ms z0J84w9L=>-{S<;t&!>h;kk9UMS=HCp5;CuWKF)f-*G%{j@i@bPAl_Q}QSaBH3HFCJ zk?v=Hs~y=Yr@o#>a(z@o$N;}1(iTd}gLjrY&a}_#L&;4s6pY#whH}t1^WoOCz_KB% zsp>Fty)u=+I?UK>a3%k~vclF-fjv{rh*iX3ss63bmDf3eEb0c4?n{1^nmVF>zQ1Oo z?f?uh)5wIkJM%5Q^EDPP?`q{}x?dwPpO~7j3+3Wji|^g|lGdcQXhXJ>|9j%6qjm`K zxO2Lzf%~>tnSHCzlm%7U{KwkqDPa1=vP<%6{OUm4VDe#%fNB7lplIdd11@X3qxw4<_Iy_D(wkwj6v)4 zFTQOKzdLNT2k$p`=T(D8*LMPI<|G!n_TM4E+?(_Y$7Sy7rQmwJ(_>@FGvI9lM|R=w z?{DzG#J?$hSB?4h>(VK-lIt+E7^5CO^ZCxAgKTphElIp~q@u09XzMn#K*7j)|0V@X zJ7^<)@ zMKD%U2k|(D@$!|Hmh)`R%G^c@=^*6D)Zmj zvFpd-Ou!-;M{S^!)aZOgJ0mxj%;$3}`z;Llv z8h5TOFH7BV-^iGdlYoxZ;Y7UKHZk&2XyHqE zsiE}LydC4VqL|vk^8-D8Lnb7f8->ElfzWv_p5&0kP@h0Lg?TqOPV?%o92*GlSsLoI z9EwYqhn>rlB#>9=IU4j)w??l^)pQM!#3$D_gey+9F%&G21xu5motS z%%NXWo=9ZaSa&^=V3Yr_gxONzOGth8h>@v5WW;0*MW7viSWUj}*_lg?*h<^}i(*Y7} zhcPmOYcBR1NZjP0!67_Mxa6dC6~@q|*G61tPkjCPx?_fIUHMuXpHxH4d=La;IZ})r z=tG;b1D`?rt8?Xe-cPZ|-cPb7^}PaOWV2sxC5YNyI~=p8xb_i}rV{^2+X!`ccd-kb zqg&WrXr}sMyyC8)S?33w-R$f11xhrj;bsF;PDH0Ck}AnIwX$3;!^eBK+>Dz0Pa(-S zJV2BCGsZj^=E@^T-^B@q#ri`mOAR5 zom%c6puI5>7Z=y9pIqJ|R??k2`62&eW4tucsZEM@>7sBK@=ZzE&L^JQNyrBonOAShX32mU~Sf;O+jcV6&Ut5{%8`D$|se-rcTenh@ zk@XJTUFl{s&Dk?7$*Q2c>mLGbsIGAK=A$VZI9d-qtTZ{^$I2p?DpoU_pic90fYX!~M87|LK zm!)OFOJtqfn;65e+76Vey~o~*JwNH1hoe5)%>BI)Z!Goe{wyX2dG!UiO-EsLyxAhL zd-Do=hPP^{a0ztFRQsX5wJK5PfUfNiB10!!C<$+r4X`x`$5RIeR^f2+;KM1b@Bz8s|ZK9C8 zi=vut`xDA_KR*SizXZ`-bG#6{b$U(%b9-!6K7Vg+Zmw=|_h&{1u^?dD{>05qTq{TC z);_zv@&ge=M+@>76ccxtyq(W+@Um4tr?3`U>2uyD(x48}thsfsr>O3HZ~l!TEWPmT z)^>OZZ+4lz0)})B6%`&yn;o9k8efbZQEqDf0hO(1j5Ek z8`dZBY@{yjTShgQ*Bk)7_)!pvXvCK3+qZHszui38f$|SG2R?FsGndncp;Lt&qj->U ziMz%_(Pob`B`vBZ{={z$Ue^3MIZa46YHDJ1m*M`xARy4Y9lsrQ?a}RbB=`7~Z9?eM zvm>Sk_NP*hVx%|8xZT!(t#(W8>8*|2gx!{g#}VuMQ2$bty96MWC<=A;65-)FQ_U~1 ztK{iR)xc8PS1`hCZyI?ZiYPdxeeJt>F>TvVNX4STM;lm~7Q9=QqtvG%+_>85x;}B+0=8GpE(0$} zLlc&hnpqPMz9Ow~BkXsxaDOf8{aX zcc<6BGS$)AsxI8~DmGiUwUco)m&wZPV0|+$?l`zR zZau2AKNhAz-5GDmBZ6LeoGOxYxJ5GU)-JvnnBt#{M%V6jN6tD&Oj~cw9@Pu1P40;S zCAnu1dg^LFz6lR#VuveTw%O&~Y*XovLKw-wV$3D}>g0)tFlvji1ak&C70H!-Hw^6` zz*#uIIYvd=cB>a*yb&a&4xcxA4Ru``Cw$mI4y|k#Gq@6%{lGdsVrqRjHyf^K$rt@9 zU(2{2o2Xl8h??G-I<+v#Dpbf4&I82ViY-iHVfXStn;lEUjwGV`478p&*3=e?V0uSi z1G)v`HF<6f-Q=UmAI_lK?qw7DpE}ejsy7YYpjHg-c#t0@HF%T2)`R|m0VFQIkUADF zpOLK>Fzn+HSE(uTD+K6)W_+ zT(km-{gX*U+-4SE?L_bQ?t9l0PgN7ovGv|}aY`cum_b4)R$QciaC*Enc(UEx+`MdM zMN!#Zb#dj($jk`5E&sC8NN^;H54O#jJ@!Dlib_|^bp^(nVizwf2z+l49O_$%Bm_e+ zdY-##?Dh!PM^d5wOek#=UlOd0o> z>gzA6*3VhUgrmcZ%=X7xyyhd>g2_)l2PrBnvbUa?z?2X zQuk4jqlGp{i0AL5JQ}-=XIrv5^U<+!Vp>1=EyE;`7dpj@ANQ+Z(vl0*y&OEsN{+(x z>2~q+e5rXScgQ^|@rX*%z6}y0vr7}}qG)5jYa?$ASffUNLe?c5pZW8eG1r!H?o{qK zEt~Bx z=?Hii{r390>KehOGcQeAqp?uv^&B0G1cqzIoT9k`spc8B{d8$8I(&{|I$f~<@v3$F zl>9tHbBN^s#1c?h1d}#NvQ1o{P5g!6^Z?8$>&q5*k3EeA6qq4Kb>t~dFO~6o+h-v; zeOs)Mqjrs*UhGb)?tnk>(Uem7Y>I9BqXypI6dE4J?2FCN&FN$^{a>Xs#5G4woKUwn zycb&ynVwtql3qtuq3KeK;s@o6w|+|qnn$up6Pa`3=tWLs<`(8aPb48p6e@UR1kjs% zYbIOscH$omjnJKnJQWthpr13Q8}+NQv$7^x{+jVmW&L#eh@3yjKQTa9Ztg7T5B-61 zm3vG=g~0rIWO#`Jat(pASlJrqx?syKsJHe6!C_R}4MVuPj+$wO;%!SnWR{}zIm zzFW0zT`hGS>bVuYo3874aN}bv)+k{4GR}KtM7*lz!(Gz}R}%4iai$_JvPI>QO$qzb zg}-+Um*RxqzB5g_jIPeNZ7UtpMne=HhE4BzxR3Udz)j0is~A3872wJ>7J=q_UrqT( zf_I8u8BvG2vVFfeN}k6loAS{(-nc35WY@*_J2DwfJicH-UP*akFFJ~C+8YV`X58ji z4Ds~p{6iygY$|bsdp&(*^Qi^J1OKt9RXim9?6MSVFA~gA(cn*k4*@fA!{}d|Vm_^p zmWn8qZ)a)Pe*wi<_3flI(Ect;-Zesa_s8Q5NqTriiZ}oH$laPAV4@UnqnQCkki$O=MJOh;!lXcyACpsT{Kx4fGQ5(hFZTm0`Vw zn2sTP|2{*()Xi`Ic&@3m_o3Z=!ZFYUooHewF;jfI*jNCV)NtDkQuS~ zAN&3sqZWHEm?{29O%ShI zjsN~+EpNhlR8-XIL0ng3R>3GSy*sQ3PZ{2S=^c4=*Zi8)ZefZd|Eo`lGR?cqfvZ!O z_>f-E`(78bcYQT-l7ss4Y(KYbMjIf6v>zRMyrgp^_uF5!=N%hPT3S-{^Rln z>w@vI*(&r0I2k7yvHw7>YR{Gh@zv>*M9pZv7W#%NxT$U6l%GZ;IJG*F94P&{BkayH z*0w!8xL#i?tqK?M_Jh5muehIp%P-KxHdOv+DW8jxzC$_vNAre@t??!FRPJ+8;I$`} zPEHgPRM;^8GpjQ^S5m`e?vR#kvtzu|x5c)%^>x&M>sqL>J{f|oY3kUVcM25C*?WFM zi`1RT(Da!<_?hA>u( zJW91}+{*1Lthd=fr4J3al*nM&@^d(x@eB>fIhR?!D9_L7qc-yR_$=t2xe>B2jSRiz z-P{EE>OQ})us~N$7P{;n+~R2d{)LywQy)r~qsiTzTBfpnanj9Ee9Fv2UG?ZMTgzbp zJ6J3>nN#IWN1A1s&Cus967f5ZMebWrNe9uacdL=msva=B@Cmn%Dl1+F*ksn z;J`*04lWhMD8ZHQE?jN`zH2{6(|!n!IB$fOTz}Tw#wVvzN#5L!3<_Q|NHKe>C*69s z@Ay6=R~|l~*q3F;RPRr&T!j*)T`)V59kXXQN|S zd;Vmla_kVWM9GSh7DI#M`prZP8QAC|dYnsyiJuC&_*LtkI9~b7-F9{}rTT0aqo!dN zCs)3<_O!XR+4Fq{Md2MG|2990->OP{fBLL>e5EPt5SW^vMc{B(PMa%bGZ?77W6fi5 z6J!nE^Ctw>w~_6XWSPu&O)L!!p?(n&%rHTcixY8%iQ+$k_Ubxs52td$G?MW|ul(PO z+Oj%O7;b{Gb}h1xn=RvPLKCx^sP#d6z$X$_*{;s(xA^EuHZ4a&m}-jJclV=!N90iy zqRGb(@Axt)R}pkon~??`_5#1dLI|t{X~!mOu6>vl(dP#d7Q0@pjIL$!Cuh6{aaf}n zFYDT#v3_5d)vGAi$A)hEWVY^@h0tQT2s%Bu*I?C4 zp?gj!wX+b?x{rtm-?kZ?RQoCf8DjJ{m+& zu)ui8@K5(bE6xIL_(&;PsrWBHom*>}$NzPdzkKv(whPf!-*B{q}${j~reB?Pm?v zYW?L@exyr_Vu>o?+8DZAh=FGL=jzrkns6=205Nj!kmMo++U$$N}dWmn)3 zytfN;KUY7087iD8%aofiXY6@5CAQR`PQcj35ZqT0AJ`t3Htx`QR*?0OVp`|eN^@iK zW3qe6%vPSY?b}0<3`}N$>1uzeoUYM=V!=Z1_rtc9mK%ApLiI>=FqjYfY||T%_dg?l zv03~43|O~TO#w7uT zVTW2d4b5oSn&_(IYoC4++=0GZhO97`_#5{W2p}#y|%qO5R#7aCogkuVr#aZ#DiBj4OC@Y zwzDI)x&Rpdv~`_WL)Xstw$RMU+BY}-qDW<2kMZK{am?7&dh?D890w-}*Ckq7YU0=R zkA8&j&fux%4~rFx`8CW(#r-I~M^x?t>eN^gH$7h z9v6BHH3&6pdp>AbKT0_{V$hC=wjFV0_tH%BFpnXg8biY#fdNLr50}H}u|^q1uj6HL z|8%(<+@SKfy#f}~Q;I?xNG-4)c}S7cc2g{pGp!smHU)X+Lyg?mg)}uanRz;Z^H`0O zlnxFU44>p1`jhiyj6eSTJ1}fyTqW%LSVGiD*+heYoIu!<#QKJ<<~8T-9SuR-jnpVM zm>?C}{?yxHpE0h3E)$jXVAPPeEce)>1Bwj%N9f3>8~RZ%2fXeyQ**i@tEhMb>aq27 zU6NBJuI{kr5C*BEx30RS=2Z$jm~Sdj!PH)eL(xZX){(xxzAl^9hriMHRQX3WXUbY! z?=cR$YiVmMIM;nB8Ru+2^>Gm!p>wF`jiKkqYHQoA+-==W!pz8 z<_`pV`_!#c3Op%$-({(7`JQR8B}GEf!=DS~)NI0MGEH^EW@Z?RV_|-NRDaMq`ug-c zZwocw%c%;%ziw2sWDlGiDl*_1Bx#M zWv@aS-cP)g+fAPdl>jzn)S_79dd^8fqZ}BjMKx576oE|DNRQF)z}b*Ve~cTD{Y~^e zFG6Z^TmpBu)74n+D+*A4DQRKjqo}v%W#w2mHd`0yRev;eTdmOC5wt=PSbM$VC1cps zgu}_|$|8im&RJ=+g}jRg^OX5=Cb-C)>S<|x6Jn4PEiW%`2HGDtSx7prX!bSXLh`F5 zlc|kgzkdB)j6VHhE;A!aQB}2+H!>50DaC0EJX&?ynNyr^f(GK!lzC}j|j*S@v)4S6)g$Qj(!8Pulqx&_-D;=L5 zTEY3>71(Oe0}jhoPg3$#afTIh!g^n7j0_DEgg(d=tS5uHQz&KbzmvrOYAyZfu;*>C zSx@80$Vu8bF+Ux6lsuD_l~vDG5KO?6|2t43oA(iZ ze8QY*Vwp3-KykUOnt+MVuoznuI`n=URDAUfJw0Id(A&jgFpqI%b(I#$77-cg3);_9 z48lCWNP^>&>u(cb$K6)5cn>NwUe^$3nV&x0?8^T@!5|8OER9R-OcTbaTXC=Um)n~; zY^0Z0U90*vQ^j)x;1V5Pk(X;>;W^{xQ9`3@9CzF%Gh-=$FEbaZr&On^q>J9qCsXyef>APDDFZK^(8vA)?o zWEWe({XcEqDptw0A~mcl%N%C9JW_a>D8*Q4*44B&To^g%zPI$z=<;MRZywyzXXx5R zm!c4!(#*Eo|MRliYX3v3D#$B!buY6-)IIyf&Oe{-a@ou^-`ZR5BckHad|5Y?k0=%q z4{`<-)BiQlm$}0AB#`UZuODrP0l{Fy*jpXaR%4>U^dh_q^Gz1ItpD>^!1d;7s{T7_ zIUc+yyeWbQz|IX8xg@TqEsN!g2@Q*h06FAn9|MHs!7Dk+$8>v){S8=%zPa>!b1XPz*Z9=||HigjhBXQi2y6^mmN3J__1cUz^!}hvm zMQx@akg0quR8~)KC%!LE2zgJ~YK$0iHM!!Bz)Hjv9ee2&>)OULfkoS0rhUt%{phdS z5wTx*xi(Tv0(pdJBAr`Uxa!b)5MNDQVV4vMRmx!W(J1=#C_UZ4R%(6yke zOADFmQDAx|FP~i#<*JjdjcHQit0sVa2k9%v%kQ7B+L%1wa25q+31|b~+G?QStPq!r z@Nt=A$>q^6nF?2x!h^(#J3g#0Rz!o5SVc8_91q!c=c9~F#w$Q0Sw%$!!63&mBh0OT zq{(e>>C4neieH!XB?BPgwD#OVNM@wSJiOQtX<>2OAa>4qZHRyM)I>m1V092vs=|77 ze)0EhDUYF%rjx_X#J_J#oszzL_pZ6U{iVZeN!0#;wobL15Trzmm8Ddy!g({{f0`IA zvBZb`y3};Jd-pC*SlD5y8~m8i)!qT8jP7Zr)B0`rUDg-sEraoa0snnie@8**2t)wd zz&r?hx_uXHutL@hB->a0i%Lukd!Ug9fe=zaLkB9pu0uO#?x|3jCC|j?Vjaq6R-QB^yii1`s;kwEzHl zVrpvYg$zIY6od$Jl?>~>t8MTRjLKwz!C*bFw;x;d|KqTVBZG`Gd|4VR7aV0!qmyvr z_9JJ!33;xBV9K%qhi_$xz$YYqe)ZMY+Hl{jtcOjHSa;^AWCG;0z5YAOfFx{RP?Xv{ zfe`TSe#Nyr^pTN~-zA)Rwek!C!IC;js)7)r*0qSV4PgU*pYv1Vx|JlRZ zOVrB=wr{pMW?c#IK`r)RbFxc^9uGV|_;K?7`N9Ih#rYV7(l|x?j-Fjk2)5#FP6z|S zvahILR9;RQOJGg%%N4v7aZp*y1rT%IBU5 zymS^DHv3KA$`)p%ruLCt>DyHt5RI7K{f+WP==w(uV!YZ(rX83M<*h+Z=uEy;>`A$P zpD-cwn)ADrK}_Om`P+#msnaz<$*ns4yv6y=yay(X*pd&!N$m|ft0DQqZo$PpZ?3w> zgRo4|bIS=C1bh1q$hof0hvR4ymY3sa{U;T?&};BA=CNYLw{M?7sbmO7JqJBuW^Qe5 zZH#xGc<@3=>5nW0tUu@91qrLT`hEL%ep%26|AIl>;rZjoj|0HpTUb~$Vwr#s)srAZ zGvMgso%y7qBF(DZPKo@Z0DfxB{%}Os_((~<4M5Hy%6#?N0c|553nUVb;oKE2aaz~o z2Zc|*!?L=Xp&_r-`JRTFp5DWa>ca_+D8@EXEj6__=cwHdk)YmQ_3bHd3IUv(aZU&n zVYxFHlCKq(U-E99h(`?9WDgg>HIXB74V6L=a(5&cyhZ+0{~vcJ@W1_?|8;kMyb9mZ zzcNO0Hzr%Hj}$jpqj%|p8N_abGXJ@%s(;ra7y~8DTd~A?w&4uQwyKkrY?dzJ^i=l6 z4VumU;qU*B?i-IjA*5{P6W8a+$ayg%0V@P}&IqG-V<(NlS-aXq04Uc?w4AtShfX(Z zt_b)(>9iUs@!9qJ|CV+eJ#Sx&A zG2R#}hZsjH=Z7FVYvbDla9K3S3pKw&28rb?7;qG?=h<=lweE2OrRNrmUDa5Rm~)$5E_9N;rMA}kB? z+TP|;*XhK*)U#k3CtP=c|9Ht zQ9sBsyN)M-OH>tjUJX?`^X=^J^1EyqgNHS_EZ|4_v1>6%7nRh*FsWOV7(4pi;^OQW z;tAfW@Ece8YD*s!rPIU@*X?{qi?jr{5F#gp9E6#1jwSpQ?{h_dNqG%l(@5HzoB?E} zZE%1|(2a>ZPG?895T_?NetJVkM@P`Np70c=^>@pB4yXW`+4L6$C>~_YaS+ZTx(FUr z*+QtTsizE*E|f}9yw5;==nCkc5wHFz*?XAvpslPBC7>Yn*lFQ}c!C>BbnUFAEGl%A zfDTAFujA%?jj*&w83h*y7jJi~&(hrbbC0U(K~7B#^vP0N^4t^yF#+pM++XkgC*OrU zeP+r!a1Z|#o<&7>Kxy6G2?j>`OKgWmKY{?0^hp=>@ zL533{ELVpq)M2=$(^HeHzo-9&ZOQj3|YWWl9#M4 zZ2CKA+J>&zA&=g*Mtt67-xa>cmmiF%D2M|AJ^}(9t3%KDFTlhPyuVQ*XU|dmI8az< z{FM~?W9}L{;Nb~@%+Jp!?1Ewt|Isz~6tfwS&|jge<2Ben(@r2RFiiS_hD{-~5rf{( zmJh=p$ki9gzJ&;N#l_t&mXbw*Yv%`Me*2k|L#nH*OC#|1d2*ZL-v57Rb`HJ2-9}QC z^MV&c5IJPIsZIQ>pLjT2G<2Rm1>MO>Nit%q91>UD?uYZ{#FPJky+ci;@K%+_v0X3? z|5ap?YIv7dg6l#w7%rs{`ZzPL>G|Br-pN=lie9k#^5PWY+1uO8A<}re{9hEUo&R9} zGa`pkY8vULLCXF=Ab(3tv?hbr0+;A@NsyfpLgIjnzr@Pw| zq}hH|Y*zPgSBLyj;s$2F?^-Vw1Q$R2@4@c+2v^b$&I%}a^ZuQchW|G!BIdpKL9+?B zehDe4YsiCUZ^+dDR$Cg0*CD$~-?;1NTEcBXdqq>xSQs>fl6QWo0rj+U8^BW(_^W=3v+?xvZ=#pQ%!j?lj(aq$UPD z@<)Xi_D)tY_>jEXi<4jbioTXP`M*&{AhxyPj*(Il%5k0~Q zCu#;1cj#ij)i4#!>Cj`!>UG+5=t**gLT{<99r*y1tUZxG(vEl9`1nyL4(mvm+g@Gb z<(U~sE6WV^t6cb@z?p4CM8s4*5mT^kxiVaM!sHyaK3+);G8qZTEU9@$e#_Nw95yG$ zPbv!vZkLYPDK(#v(>#JuNNzzuo+H~ytr~LNDPw7re>9eBP9M%F;Y8Y(ri^bqW(kOj zh={y}e?`$}XJ*!ih)ldcXCx*thJKr4il{xC=i_(Vd;f0uc@4(pPyYK>lN?7x&cMH7 zcn;(-%5NFz(L%H=@y#40oB1Pb`R#W{TLHN z2kN44nm@CY?e!}0zwiDbKbb2E($V-oGxnu8%e4jyX#!&dnY($nNK=}G7mB-@yF?BQ z-sa5ebS1!g^57C{qorZ_!leJjnD~{%?y(5WXBCD{QeX9)JJQ6m-~&ih=-29%{;M-a zB#9D6G0hRUkPZnIVIh#~j*4ciJ;k};`Y_I`yIL@cS)_P_q8B(Bz#q#=6z}93&X`o zYPZ9UkSENZfmEC`xup|cU5m(#tiLL=S}>RghScGSKb z3I!u1qw~qtFHQRK10vzPA$O&0yoUeVG9G!)lfUnRJE!VsCg`7sU^>5GMu~gi35i>E z9oFFxWrTM!~5{QLH>6 zi^qS}F1>3UE?581V`d@9E1S8=ZBW+jmZuym{2!)3r6c(70NDTYy9`j{_z7x7xXGY< zJip6qXMx)~Z2$g5$S_vn?-*hG{Ti*4)-}hbUKTQc2ebVf`tM-6V}y{8?B-t(ZU#pfK*iajpO~RwvvXX|21FmG=gESzM$lS)KQ?f z@FhW+mFK!qb800CTpZ0pJYQ#P>CW!`ixUeIuPAUzGp=W3WyxR+t8GAbv$rxp5-VaY z51JQ|HGY1Cq>SQsjDT|?kc{Oc2YHLEC#CR&IQ}-NGet>uHE9o2h(E?!*U%DCKnWn+ zhE+PTW7j={gGs?9q69Cnf`e`>9yBDOP1xkTdhZ4N|L&S0?^2#Qg3oy0rU*3eT~chN WhYD&GuMa@LNAa18T%L@v*Z&98d`>$6 diff --git a/reference/link.html b/reference/link.html index 49bf158fa..d5fd750e7 100644 --- a/reference/link.html +++ b/reference/link.html @@ -174,11 +174,11 @@

Exampleslink(mod, parameter = "scale") #> function (mu) #> log(1/mu - 0.01) -#> <environment: 0x56111ffaff80> +#> <environment: 0x55bb84d08d48> inv_link(mod, parameter = "scale") #> function (eta) #> 1/(exp(eta) + 0.01) -#> <environment: 0x56111ffaff80> +#> <environment: 0x55bb84d08d48> ## Works with `family` objects too link(shash(), parameter = "skewness") diff --git a/reference/partial_derivatives-1.png b/reference/partial_derivatives-1.png index b65b5202c6b1ef4cd26d3934f29fd25649be4d96..944a4025a8169b03a9c371b200bc1b01a11eea7a 100644 GIT binary patch delta 139960 zcmZ6zWmr^U7d1SHBB6qWAStPUw9=snD%}hX0)hh4T}KIN5NT-;h7Mtx(d9U}qzF%_*%*?s>*?aA^*Ivhj0i5n3oT8YwkZSX2{oYRI&$oC!uy2auI5qS& z?;nPQW+R$o)GCNnrMEG#f<{$b#a50Q>y`8?eBny@b3Ltre{t&;uEEaBi3$Koax|gD=)q+=g~AH zjUxA#5W8N>SrOIa9-mxFozNI8k(R+r*Nia}`Vy#^p&{O;=OTRejz*CIhJx@O1FWH; zVRNFKR6#*OYS%*xa6y|%Bbfr1Thc(`(vc=eH#hoz(2I9gzZW67Mz@(E!ygVBOB{@@#U+S z_8v5LiJdNmoi^#zg>PF;>-jpP_ujcIog%UbRCy!*7r2O7{;@_uv!?enWM3OonjC_V^0YM>j?7ZJAVxP?~6l@S8+M9jTt}sqwSmXi*mav~` zClP+^Q2JvpARUd9=A6x4QT6e%L58sU@#DDJ8v6Z<$DS868dFvqR79|-Dn z%|=(vx)muez8FgS2Df+OhJ-hnxVTm_M&ZdnHKU`e3#|?!d3I`eefoY#e{KQ!-6C+H z;6-ZC{p+Ru))_YJ_3PJ^g2bV@+RM4}*Ei=9nX2QX7|FY3uryM08=e>WS?vFfDZjQC zJbMNb)ao*^Y|^3UU1u3hFR&FkBQI78*9ohV-bB7POs5N|arKSOjp0K4UWA^XypUyQ zKe-!O_SB;@lK#oF*nuThCBX_pjMpT)yU!WG8TrM%NH_2Ni@p?%s+@sBn+>t*8%qeh zrzziTygM^F^;HL#1ed6Sc&OiiK#+96vp7p1Cm!Z6Io-a%@cT1Vb-IL{*nOKzlo#+>F+@z$WNozAf_E`LLua2^!o;{A6&qX<@AT--IwaHRIfy(?>H5QC`~#~cWEUC150+SPEV$!#In=(_ z&r08pE|gsZFIez2`_m3$R!!VNh)4xojaq9pgYyo*jWzyE2zd*#leBc>m(zB`tW%3p zn@|Kas8sTpgH3Ap3UL#OHN+5GFV1vRlZ4hN9wRRk`B%mbULw~R4(e<>X)iuLqp|bs zkeI>SRx3S;V?`e@VRs);K4D;x_+KvaB~rQJbM8mrOYyNpyNnbT5q`QJL=r};#=+U zj8r*spEfnJkWl*chhm3%!S|T3!oORJmG{r634=9!@2$qqb}#Sj*^HjLY))+Il&T`dZE z;~;lwX!YmUFw?NmTDD+P63(aQnp3&Jc8lS?A)@NBUN^BI1eJ)(uVb zaDV%{rcFlSsCAG&Xz=VvZ!Ooz3oam#Ba9Fi=Y6^`#mF2VU%Vatyi6;9$g1zFQEMnB zr`44^wQH74*}__)#W2s>XH~D`%W}PyNIzT3 z@gda7z+&u-R)}GU1RWXm?6_piA}{WmqE6u~xy)>$dn&Q@dPhVMO%=Iq^W(PF+n=*? zy$^}1x%!n#v-Q1y|CRvAr&(eo_R$;a_{aqif+zT+f!Fb0U%c_asg@85b5@rf@IEFx zK^O4TrS~oFr&NPi3CY{4=dkPf>f?18;p3+CY*t4*kwTEBPp@w(%;SoV#(j^iR=}t!fMzc;=1EU>Klwt{b_fu3v{5UEoV@qs}cA! zD~NV|jNPe2P5agT_sa_a(Ws<$al@qvtL5K15M)p`%dQ$o~@{7)^+~_Y8FU@T;jFc}v!HNT(raK_+CK^|yEaeC%AELh-@8 z@8XZ3=WE^oI@GoH8)urhBrZTq)^roDGLWdXv$D~+!6Dk6+k3xhv{f77!q^Zmbjs1- z)>^wuA51A00F7EnIe83XIm8L0Mb?LLq08Qz^B*}MGdH+xjIfED4=oO4$iVV+%afSo zei;h@7~uJJcUVC1I0?n%8fF6(0y< zri`HVl)T&-%x5_PRczc9oFRLDs9W}2AG`jn88R#xceWftJU&)kvlm21oX5U_f@WDE2bDX9?cV%w7=f~@?y`Ejt zXPVXi-}(c*QY^dIp$i^6TytKll`RFPKP&aLu(GY`cM%1bE_Q7OqRt-wrr>^fehX`B z?Ykcu$&RMozSjKHa>UUK508Pb0V31}ee$7)TC6F~Xf8q2_Q#_3SfMjJl}_9`bQG_guSb+KV(tYQJVpkP z7!8)E(|N1!PJbvF(-2sJo5-KMLKO_td$@qZuc2#Hsdg-l?^u}_wc}1-575dxv@g&slG(s$f=bYsG8WY^k@o)7fE7^jc5~tj-u|>FL=T+1%~z?anSPHv6i#B?Bif@6u{E#P^rd zKn7h|d>CYx?WFgd(Y{@+gL`FIk8T~i*P$X{e*O4`rKP3pxjiwABBras1iAlrwbw@J(wg1J+vT?t4O_$c zF?1o|B)5y(`Ukr9%x7bDmehJ^+RJl-bQ-Jpf^gIwtA}NJa($QoqD4j)_iR;1N5{F^ z6i&U?7mfeay%R3hecNECq4CAqK$y)=sKGxT1(RC7LhEPEL8J)N-lU78QGEs`ru|l$ z5|_Q^nBWJj`g^;wY`n1v?i;mTYbz^$2q?a~*5}zX<2!{B0U;qT1$~6IA@!|Dl3~*_ zwbb-8GlS+RZr3mbFC}mM{h+a~QeMh=n+Ny@p)&y9lK<$u+wrVC>-Uiq$PH;?j*jDnw}ZJdcQNUSdh&D+m^M$Hs5$ER zqQxGuVHym#aIQP7wmU6|rdXBtV7+_xXCUXJjJUWszs>yZyXYOr^mu#fg~dP4bLiRE z>%7^Ts02xcOO5A`WOG=caJXJ0*I}J)SD?PVZ%3kE!%o`fN^3&L#k1fUaQ#?o1!H5kO`9v%4ZMUZN({={PsBV~|arNTlU{lOp6;(;pYA$$A=&?Z+(?zJm3<4e4Ei;7ZmDuhE~~XV z3B91SHyg`wqugzc9dljh%)y9*tNQPUgEr42+9m$xYv*Z}yhRn5j~2wChfmqW*DT(O zw08762VO#S@=1%yk_Q-BqeWb7Zij>Rm{vbE~;^q;=>{zChwUhcF_HM3jr z*sAV{eyi1q;3UCh=3F$7;a?8>_3L{dSybgmM-zUEpM-XIU7ZQ!m-#pb=J79cphSX3ClErxg z)DJkgRse^IQJ}+4@~OD2wv$#oqQZRjL0{i9>4fVbQ$9@<2lA#TrW-aAsVZ8TaVa(L zS4CNNbcwC?L7Xv#dOV!m;Zff(@0#J9_~JeKK{sMEeH;=_M)Dn6UQM=UXFML z9gEV}{X)^aQ|E(~*9I^Pb=Er1D{5Y;&;a+AiB>J*JW9k(nW*|c z`4Ar`;fKh@NMfuovgY$-kBl#){iUxVU36YTZ%VC5BD#xW?q5d~j&XBdcCg2aT7KVn zG?m+@3q^f2btFw2l~Vgp#?PZYwfiran7$uvj69}L6rKNR^07+N0pFR{=RF`~;{KEe zU>Y2mI^(j~G-fLN*LW`0lYY*_p;9aTsme28k8iBjm08UFh1VQDpJ5lChXyxv;KrUL z`ay9qH1!yd{+ZTaz2}SnY7;HaOp_WO;)L6U(0nRDI$2B^8ZgkYg7)g;=Z8r^K=8`I zAf@49z&RWGEhRp_b9kJcO1au&^dV7RqEO;&3OCkUCJFRSw{rVu_dHQRlJ&xgd7owL zOtgEvd*WWGym6+}&a{@>v@moUoXfH`LjP!Z-Buhs{Cm5}V9jElwZUk?(zVJjM;#?r zVpBDDv)oOk-@?)J-{0k&)2+Esz*P0N*U#lYimDGS1#OO1{}Q2q+k9Gir2LbIgHSP3 z96MyfDOKvcAQCUTp!Vsnc@M*;Z{5EuMibj_f4F9!M;%||!|DvL+(k_xHb9!W4+qWV zhtv9V&SR=_(1SMDT6}P1@g%WT9%|lsyWlJYAsD^>MGdNstYY42TsGq_c3i?ug8p#8 zM9vOP|2nDIi9{<~1onPd+OrQO!m-T*2n6T)ors&ybC#nfC^M!5Bq@_EuZtyjubR?(X zZCc9jd9x(qs~uuR_o%3;+TtTTm(HQ%{dH5ZflO_&Kw^dKWw*UUoJW`Y_tUY2nLRqKDAZu5LDBQG@<@WG1P<;=pH zp6K)G`T1bk7w!65xkkaU%+~=jzkI5PcnQQ8zcV;Pz68gzv{p3aWoun_J03lJGETd5 z96_slU7eBH1V0P(7f*R^0GZ~&c0=wIwL#0u4pFTU&!dw0z(4M7#@t-1`C(jBdeIdo zS5`o(;pS(d9U@^@_AyI+iBVyFMH$1Ma03EyB5vqUTW3e>kI4cml6fsBq|R%-D{R?t zp|9K-w=~3#;nZfM1G8;DZSnJxK}Cims#Ol9I|pVZW6mEBpUPg?R26>53tojfu`Kt04fHS#d(wVj*E32;I&b(hj z-U@zV4Ml$I2bZhYMiV)V3@tM+p6x?!2w7hO={st$O?}0Jce*cmpU~Q7$$Nj-I$zu~ z!g@=$ZF0P=tA()}Rfhh*-9g~k}HLeTyiXVpy z5NVMc)8q#NV%@*Y4OHp!x2I6%rl)xt#eYBd^mPW=12JaH8cZ$so8_D!9|Cdxk1RfY zM!TJdWEb4wE4@V?TilNM`V9e?D5NNk4Ij34v40UskhOyyed%i+^3v`1m(f(a_+8h& zALx3M=Q=8}VeWXO89ED`(%;g4anEJt&4qi- z+RWt#?{p%N>M5_iu{Ot%K|!P(wuh@qIq;a?N=+#gn|IK9(!dRTfsvHpJ03P}=0<(Y zs^Bi8VWH$7x5hf45P3DC%05lJ0F?HD=iX!yBb_I7Dt-^B=120iVK2=7aq3e)^{qn2m-k<; zr7|#$MWFCsqig+T{+38k%)OMxbj;P=RT$xd)gNfX)v@q8MkIdp2Z7U@ z4G8^@wuf4%$t#&jDNllVz3kJwspYNg;opom{TJZ-1Lv#H&tb49Fe!=Gs`7FVY@Y)D zm8X9ZbD10`D}1tApU@gbBshabZx)rCJcStPH&Jnaz$%}|oz%nkI&NN0jb;aZ?cD+I zD-d!*%NG0{xfJE1^ZTqg{Q~vbX=gkeNx^I8&#v%%O8-I-KWjkl)F4d|p(Qqa49|dE zu6ag&b!)F(9diD(e0mtqm(x4&SY`KMPdP~8CGV6~?%%sqiAE}>^~_5hMMveYS$ zN)JsjdTZif4Uv4S$Z|^XJ3dX4VBtEX@-M*A@jXbPizlo;ZTErVLrWPQ6UHG&vnf4z z7iGv5FHmK57d2XFJo+7~blpoB;aDCg8DTr~b_lwv2lK!>nBWnbLT0?5r7AmyDfi*K zY^;wS(l}9l73>}-R4l9hPal8%S^iWELYLZn^Ov2{6u8q@^r7*&Yv2WBF|qA&NGYr% zvt9OS@+)JVXIOG!m@Bu8zk64Acht;NNIneVR}7?aFsHBWb#ba+?eWO1k8%kLgJI{h z3N8tYgt9Ur1)pV;1@FQ#)0!VjDqh0LrhbQA9J}@DI}KWi+S&Exw;|uVvA^^MKh^U{ z#zTY_yDdmNT4^+Azk%G4_>zxG;&!Rxab}7i!)EB3v!+v+Q}6#$Dt@cmp;qu65mQcE z-jML~Z&C=WX89`q1IvjN-I?N@CvWB-(iH}h9Jh8(#5eie?nw-1Bd6@{NzBHX27({k z5BcP}SI4H`Kf7(0Sq%KRIay&C*N&YyS@U3{w~LsETnq+B5%jT@dY*`IjrY?t)ODf) zn77j#?WB{El3rL%C%iNtv0iAokKP=6v(OR}@}Tp>Sl%BmICeN#-jIkd3Nb|lq;w2p z=jeoNE8st6X9~R3<}0%5)I$5^530A^nyjX7>_K*pRXSFptuQ;`zukv_-G*nsKK;)G z7eRN$xT6VG$Mhx{PNj+cE460_>!yKAwhAq|b>S!HO#v6}#@uuIX$&j9NpmeB8)Mg) zHZx7l%{Hrw{5N}7)v3dzsgb>~Xmt7|lbFNFjT zX}YGp{Q?35mf}XIKc4Q0bp*cmF14Kc1x^Pmf;`S0fx_2dQTFKzDcq$Nm)2JPhDf^i z7RHLPRUM{D#^`B0H<9<<%v9bL1`{L)6uQsEjH!GHTW&l{%yZ=)95CgXF7f@0}W#*dmNJEMq96-c z;E7kMD|B{tCMOFZpsRy^g65g_%ErMVT{`i*&(P=hg)92>Kr~wb5{LV>d=GUwsRsGPOc<5soA1s%keLq%zUEkix%oLktW2Y)+c-R8LNbp{Z9Q}$N8M$bjzr~)M z;X_dQ1gL^L&d-wm7;X4?z0RT0z%!KVnFc@uew_5s$`a2rXM|wwpf4(}*;U0jW^H(I1G1CUGfpQnk%UnUzY% zwd#S3&m?;sQegGgU*-ng^71OVzAHc*#K_ZE^TYMEH9lSmsxre9hfCWwX;v*sd1a;Q zAqUmeN|#~%mx)0J=O`ekh)Aij7Jzloj&&eJbyT2Q_}-z~;w*oftYyOJ>8F@F@&op6 z@)5>^i3|;6IWFFaeUG$->JB@>lZ71d(|g#%_~wBN%$``7BE9@o%WpDp_8_Zyy&Fo)`MT@N|-&);m45>37g z=GzjdJ+X08@UVo zoc|PpNqH`|mYnBj$b_Tq*@ff-Dxn6{0u$F>diYzvC3}z$uOuXHgA%K8J08eUV@-c! z`<}Ty8&7Uh1W0=XbSn%CPkadeDe!QR5=vM8=tH54wb%eY$w1)J^VB=H7%aJjiQ(SZ zN(7ZvnNqvIN@g-8Cf%MYBr&jb==Bxr)wnvlcIbKD`8H-Tw?|t@L}kwL>Eu;omF!tE zL#|In{NLKzt`k?`FXzG`K(F1<_e{4cXMbU?+Pm79Al-;{KbFsm+F&O0#zY|_b?}&F zYfMQTJ5`zmI>)BuVTP$f2jV@7wgyV!Bkyyxd}Jp#Hs&9-5NB zsWqZ@ko{Wt!@UelX>zh~g45Qv82uEBlp5^0vZ?b}l(BEaLxGqawOq3;qNnm?WC2Dp zva)^SzST8|dCTrIvpyC&(~WmDE?f_fd1PYEX$*eqfXlN$!DG-kBOJ zAM>MH_5AX@aRnPK-3`3lmz|Y!K9u``$UyqDZD^8`(z1SR1Q++ zc4QS_1X-6qLPc-6+If%e;lqcpp)Ada`Q{*_j^F&oSXDI3!8zoGEB*St0(N3Mcax#+ z)P<)QS*oJ`3h!zPL=lF3N76@vGf!Pb^{RSs{;7(^t+r(z+?7}BXk2qjQG`5t#_U}$ zk~hbqq*P4*?DRdnNq&^R>E8H(_n!n!T7F%_P8%^1{lco=xbUDLyeL++`M$YEol0BZ ziVA-B3)~ggYA=>4-08d?|AD=1W}jyoHyICZvF(zX@$V6K%~wR#r`^ll@yIfZ-XGUr zCd(oM#IA6^0+u%ZTLIcV3XjqiUyKAFtzUip-O*+9H?vlQxXq5QYIYh@AxZeN0YfOO zk~-jJ`|m+#1nJrq)z%qP>KO192J+GA_f%4nuJ!*YA%)D99+I z2u|UsWx3Vy)cY{dlTP+q*IKX^C31dC?RN0qkCTyo53y-!XDQNx4-#L5|DV*P6s^!V zzYEW^FkqMubub?_+X4(7Opa~2`X;?AtL=j@pYN*kjeOrJD1H#!sjmnx{JaI^ZF&L5 z=K4)TdyPMbORHOGg((Ey1xJJCmktMJP z-3yjj9F9?emY@#gGnkfGpFjK7$CqUUt}ZsO#PTm2tsu~z53t`y%!YssZ>D8S29vW# z26iF=9qDB7gIE3uwoYp2x__I0tBa{-BnPS06~mqfUdjgENl*)-$(_6xWQJin{7;qD zc|*rQW2tOB^67A;7LvvHvs_?Uty#mD2*Y9m{MQ51 zUQn+JuZFYK?keCbENR*{rodC@H+HXj<21`lCwNM&M%n#r*4zn3P*v6QUztR5TXJim zHTM}wy&%KCk)LnEM)d)DK6vTovBP(CF0nJd36ejMMwVD?aLscjThYTv+0$X8Ci-1( zt3I@{@^jr(FvV>JeZyszaz{1FKMK8*mrxY>ZP?SOlB9BUlEMFLn&XY`M{1pH<{W6>;wPQLGB6h?6+soKO0JXQ>G7pxlWs0f@)ZICMQL;W=$oKVPp36> zPEf%4H2t3Xv=xu#rRC%RSW9f36D%=*s_> zos`8I&o5B+Tn00L(>@d6hZ~Ynfeql-5)H(Z zs^3TO|MiLngx1KP^e#aY~eX_I?tX5bpIiT=ogDu z;>6bZ@tKw!r8ej6nDjOeoYZ%j7^k@T30sMXsPd6Pk2ow17{vWAWdDa3;f#1IpSRw* zikKNN+PM?uX1_&XERkg(>!bHHQ?=)@wXP?EI^+c{{IPmtk9?Fi;`k#f}_a@Kpm zi8mtWq1WvB$Q(^27ntFaAGoB+mZR3@z^e z(LC$CS#=K_s_&brte;UTQE@Ob#*xT8!ay79;fo9`up6zpUFS?|9WCT09VhklW>AX$ z$yLw)Ylf`DaM+%FOd4}ttFZ23KTvK~ZKU9K&Ga&!&HdoOYJBM-$Gr|$onb!CB|gE# zbLLjWf`$gE!teR!tOE3S18bP#7Zw-t}RTXekLxWG9Gh8LL|pa$487Jang9}<9( zqM6Hk67%e{wS;(?Y1{Q9cKznH15(&0)$&Jd3VcI z`P00%dLG?3P}mJr?fluzH7N{6nJ30RoDh$3Ww#eM&gEUKSi!*Fb9EAl0vbD*dLDY55A}eYRQ~<`p*KCDfWz{IB+w9X zUKz9#9&QixgCeu0PE}+1yk!@|PC+KsU|Rg!GHiZ5r;5$ruEOysXN3KsVh12L1gCNT zP5abE6h~d(xPl0DOz7#0M?n?>1NPHzBVf=f{}%2)`K!h{d&Cp0nkOe2_Gp_fm8(?O zdEdko>U5_e+Hx0frjE6>I7x*SA{WM^R^Oi(82@Q7z5gp=i$p=s;EzEon6pb;-8>u* zUh>m@DAv%Zu%HNxtQTla${u)~upA5(oL5Qs|UKhe$F);Hz;D3%gr$fz4JeM!tJ9#1SY zku&)q$k66ms7pgK&J&lwb5q?V%^fz0C@XJ_19GIc*}rHuiMMZW^`%MT zN#D6`>{+iY;Ga#s8X4y23U?VTPTqfgIX(0A{4c)UjdE|Mnk_r^YuMgnkjwI^{$0=N z!)?Td>1}UnuHo~4smI@YrZ69pDTk@iBTn0{eKQz-oMqVDHzPb4hAD^aa^ayf7Y{b>sxdROc+!P4M&<#`C?f~K-`qHUL z<2mdQTK_B^8F@JuY*MTd%LmmuR+@%UW4)!fZ3KVhw1&I2zaWAlCkr9yX&)Z;{n;FZ zVZEdk+0ajHAtfaR1A{dgWq=WYaGe@6h27}qYSNyoMB_h}F1ai;5KIlP_sMWS+|KQ;6^o0{ zCFZY&gY4<=?}rg{=-tra&fzvzpsd1fbw&b(05D>v|1uC`7G9=bVQ*_wbv@k<$!^_} zp&$#dFdEsZGvC5c5CzPR4k}mwf;~8%IQ{~x=V%kO-Nor(qSMn@@06{lsvj#Drd@in zYyL6jtx4=`K3G9Ij-}ZCC)0QSm+7)09@kW9>6?RL7^}Xs zGibWT4cxA_5H*VOPg@W2Z{GGWPn{Jf0G*EsjN{O~(9GGYk~PvZwoDDNp~at<&Nt#mmQ#uylu+tZp^dO1?4 z2etndfMDpqX34S_SaA6H&R>?sY|)j-^R;Tm#DasI>AD;{lY`?s2P@`oj~#cWL;m_+ zwwZTZMC{kW-K~b5`0vIXP=p)umWW#1h22=RQ$9?&y)b?4{Djy@#RK1QF+PVfVZFa8 z)he&Y_L#}$X(it5R@5L#_1mqeoMWLpDCy0|>Am$~b1jtdSFZDu3F5Ga$LFiR9Pe1I z{dqlxcFKe!3Fu7!N9hLwLIUQd9&(t+=)0WjvZz1c;-m{7v8ubwL?%aWJ>Jjwf;zV0 z5ykkbE=MuPEa~UN+0^ZG`^HzZlh~FEM>>9ejidK23Ve9npZ^GXZqR=@w-1xR>7zz0 z{Mn9-AlOijc9^L?xuv!0d?@|9(4vn)mqS;;lr~taD+J)TveK>fO!Jt;dAH~xBwm5kJ%fNthy2Lvh^cCucgpz-2Z;bwIhchPC3TA=~$zc3i?zLm00vyy83$( zf#oTH-28Sz4HN_#FoBb77IS?(76S~|Dc>FjT>lcVW7^x> zgB`B+djroP`AzZzSte960qaSYJ@d`31xzjS778a1^QT@xgw@Ag#S7@D0l=<0qF5eN zhAdRL@6j>0x5IA3BhMv)*lOF8(dMKIMHMke>y)z$(K!HJOR_TAki@pxGiM?D&`v3j z(_AfK&``ylQk8Wgrv<@TX!}xk>F?xm-LF5kR3Do|nU+?H#+^F~seCb}h@U#BZLTcm zIKHs48-{j7ZIs-b4hzD*bY!kX-#2%;4uO|;zR`0@Y{e4wINl?shSwz9Rn0uj*;^Ap z7}j+OYUPjEPw?D(EJz%&-yqfdYvLGHdy;7B8j5gN939=-lTQ!?13gB{+ubBS%L+Fs0|#E(F9cU*+voKVUm58@J&7atA6z_}v}ORh2(DK8KR|(X3*}$L|2! zLntI;rGOz9*x5eVENZ*ayb&U@Q8te$)Q~D~r#hJi!ql6a45sz5Hk>;@=5pVKJvZTj zg&nRp`iJjOP)3~;sJtwPOIM6EtC2~jTcGN^My9-F>PuT+)Q@n7M@8)uUJ7l5ELGcx)DK^s#M zP{JJ5Y(r~_&;{Vxis8_Mr5I*(fTx5&BLez%MB<`kZMt$cb$yOtlDL}2lK2u6GL)&A zBDJbHO|3W`3X(zZRcZ`ddaHeS4ucQxS(ueKK;97x{JCFR$5CZ#IDnw|3n`PshiiGt>;1~XD7c;bJt(LOHJOVNc2=$_Sp|@ z6znk{sCiK7QEiViZ7BJ}b!AM^<6E|SLK>Ov7!XcK26_XNxJx)8%t$ z>$)DzEZvt`R=vS<&*aSezNi7s_iWAcI+KRuqK}r4hzRTxbV)&rqam)NSv`$TKM&^( zYk;WgYFtS-CydwHP1<4zX?^8oNyM9;rK0T1 zL?-g1+xNqP?KiW2i?p&Kz07ql2s&a-ZK1+}>XOJzj`=UGHA8K=fi-_S0T zl0Ec<6=-;FTQ9*pdoTF?>eAWP-|vza7m;uW@MS#P6EsM4eXYo%gERh@$qFTN885z? zbiYQVW0cwO4RKad1nXRY6wkiSTTb41VKq-iEw-xuN(OSyL8#`hwa1FA| zFDyJ3u>B3Rw|~VaXJ?< zI*v9*F+NRIA*;hOJ|MMl#K6RMc6O>%D=xWo#wCtz!zZ6O8xj)o@#^9fcrc@4W66N= zaP?m?{g?65hNB*=-9TkXS$TD|Fj;v9@H8Ia-nJhFAq)>7dTMgI(-KCbfHWJnC5FHL z?MfaMWX8qrJ2!OP2M_Ab6il1GQ%Ibt#`Y{jD@7Jb7H^n;M>i-h`_U6c&x`w6rYSmM zZ(M{ElkEZ*vWedgqS}6~e^F%qlOZ@=^DHS^UD}pg-vf;!<_;ZXpf4PccW3jDMHk~p z{pRO?9*vym9OT%m)ZUk_u~mMiXd_H@zNTa12QJEX&a38+BiKc{PB!u{R4 zEy_4;Cu7EgI|dp5kC$Kxe}*hHtUTu1LlSqH81~_DU6kWXPQq3@>lwX#-4c0SOb7_C z#0>Irvmrr2M&Q9ZjePa9!?ibco|n6ahl?FitnT~mPZRiUyg>9bg+#c8wzs#R>s6l= zKw-hbi&(=zwhMIT zg*DB#`c(<}5X#dBH}euCe7YF%!gBK8%_dh^Sfmu4zi3=tv-pywsfO2KL?<*!`2H(k zb$>im9#f1#V*OM;VfJ6K5fFgCD}z0jH@)rixpNhwYUKK)2p?GS2+T#wU_Ii{3vM<{ z1tAtb8d4!Py>S}3$0o0aM!~GC6MmTzM)T z7gsp5zFdm#f}rgAoDf^3c9jJI&&p+Y8-A;_EnjjR*yP|ndJd}}9gPWV4FqH@VXNHm-Wpv&+$u<{mt3+KCdKamPa__||z>E=~_Hj2#NV8bufklwENzz2&>t51Rjl zRXirW`!zS06*Jl6+eeDB{ya4b?{s1ThtvI!S3B2wBkT~T>x*ySzG1y(*p9+o>M+~# zFIfRl=#mWTpVcp!4sC<`{qnh`JI4R8W(35QLd-BfK0*jNU2R zxuaY0{vpeERd7mLiPv+W9sT>K0lZLmUMVUrM>WOse!;B=$Zj8Zu-c!_z`!uOv!h^b zUBqq#)EsMeu#bCr&7Q73VG`{6g&)qYJ9IU&HfQ;Kq9PH$#p7FHa`HDwDR8oA%;Mf- zZXW5bak8x zLLNi0P*22cwBQTi084!hm<)_#kagBfRzN}nD{g6tY47~p+&4eJyXVI{y~hXqf`|!k zJ{A_-uY`=!v+K~W(`*5-E5PcTj@>uwPm_clt_?n&z$a!^&ocn7c z_=R5OYat^=gA)YozJterOwRbgGsrNOUW`7foz*_D+2McTbIR}52+WIL8#h!Hm2o5!+{?Mb6@8Jp*t{EU

^dQRuFSdrqV9|jj9D+K(f<0GV&KaXaABWTn@OC_7^f15`K8E6s_B6WPfs> zZu!Ogg`8FW`Q7^7-mq*j+2R=vUi0PaSLu#JT)?d)Ld`gCTVq!@0MX~m>VITS9=t_{x;^-~jDUA>@G|D}F(HC&l|hQOI>`LpIp!7Zww zo;T4HqhdNq-O?0XDB4`-%yl`>!z{{oRGSY#+nsLURKT#LA^0&*X(#602VI95zpvJn zH?sF_)jZ=>Hloc@PQTgdI=2tBmk-ak*SF`OZ+?q0_aBmy`qkAznGjE$Y$Tg@^!NJ| z11KjeSM+8;OpFn+udwT37COBfxS6xSCMfz6crTclnU94F&R>S5+g07(0rm2Ah{4ii zyW06@LSI9Trf>LDrbD4)QeilnEf4+su62Ak2L0^N9g(UT{g(4`xO zj#B;nkUIE^mn5Rv1-gH9TG^v%B8f+Uh56`E%~2v^q|^zdtsZM`DGX z*a0MT#q)*vQM;~+zKy9Y4N$Eil?kZ6Id|gIbOd5W_x!iqDj3Uz8J){5ku>!iqlIDm zj}YCiZf-p8C)VUI%_Kl(bH&Mm3N12feNcOe)_y?AU+;rM1d$2Y8W$MdK=uh3pjfXj z{qzy?AI_uY)~?jKQ-}+zkJvqu$^3KU7VSR(3{@nFfS~-A&Ohx4cKj`Rc<` zL0d1240xo`jiuSbq>mK@IS_01+jj?->z8BbWzSeKs1}_xf;5K*l5$vrDs46?`HZ6n zhr`!-p-;xv5?~Yj{(TB)P&Z@1+mQWHJ0^a94Z?=w@IglBsmV>g(;m_4IB-|BgbLl} zFHTpIVTapOLYt zk1MXV!Qx2#f>$xrHB`%ocfcAmAa?Z`bal1`H<3FJkRcr;)nn1h*;R!ticbracK5n|JVTcCxCmbTrmg zPE~Z0fiSe$uv0|rr#{@&4uplzw8=FZR_VwdYVOS&=p? zuOVE(?61#a+SX49#bOoF3NdZ7#oMOU#N;+5SMi(a&L-GpsYJOQ}SZcuwbTCqs zGo)R9LFUbYBwsHhJF6K28?Kme2&kwM5jMDKjCji*xZ*MVQ8wN)2)PVx)Fa{XN;W&s z>nNWI1P%_ZPr84dON2m)Nnf4$U030MKPyX2z^KIgX);nu?Beg4?p>F?rBQ}T?Rtxe zvJl!g-#^8?3n5=3IL`?2hy=E{1np*mOZt3psP(V<+6a)zY z2|>EM8xf=z-6`E70wN&Y-QCg+64KpqC+Gd{5B12A&E9LxIp#B-7z_4!JMKb{jh>DU z=6*Z0-`p%Mcy}wK{EMQEnk%o|dHvH%2T1<_vc=k67@WRX%DTzEbFp6L0^}oGy9;lC zWnUj+q6$mnFU&+guU``tXK6}Bd6w($>ta=_ep#DZ-fq^fKcGn?hMg_?s(?-dJ->he zP3d>G&0A~c6M(1A_ow^xnunJ`wuj3)d2N5CmHrL2$%rG{$2>33&R1t4d%EFyxBSqS z7rFAL%5ENK-mHRt`Qxg&KHrl83=|Al){XxP9Q;zNy9ZeyB zFsTEBoG0)f^m63)%}RNc%P%`DfWSb@JpiL% z;0!!SA%E?4%ikSEy_u-zm1QxWdwX-xOu(wmKi*^!ma9>uYC4eA`VA8C8#}8lZ~O;^ zwSlh{PM$i^-`59o?p-F2?l_|eud-#m?ykjsf{ZgK_g5|0Ztx$%r zscg1c_vB}*DPJhF`6jT3<;&7{F%;U<&pgLyO3mrwn?w;?7gCHHL-#LrKJk^vYDhGk zP^Df_Z2;fnH|IB%e}DHtwu=q#Gle{B+|HO`PCJw9dHG5w4NJ{E&@W|-T*WII*g5s< zzJUIiM}hp__`e_lf9`$8b))SuV>apSbT*R2x<&`SjQ@?eL{`~w1{2$=#jWZ~o176dS0yQ?F%Vj_7%||a7?C$!kzv|!& z+L^jqPt@c0y#8FbY6m(Z|9}7snPYNbXiMfaiL=Y&VkT5iF(_{AtxHEvx}OrIa{Gb& z+ai>i0!z5123vBp%+?9%)XN`SQhJ8heJ#NOIEWwrR2JO6a(wMawfCX_^~B{alpVq! z<0c)SS}%b8<)E`dc%$54N+dAC1^ofSD|@gMNk~f@0N(y-Xc-23^5n@tIu|naj92?} z;31&0OssoY1zAVTaEajCUeILjE^v5U>;MAg{eycVG4eSdJM*UF`L4#sKr*^_Vj?kH z!{v8i4a+UT%%9`pwi_R6krMa9qoy8K)AP*H!9qnHodu1TQL8Uvn!pz*5-cd2cQ)GE z6pxo!h2>^59BjN2%ZXpso7aD1(6+y_~DcGnqTr zt|#kl5cb+EUh-O(?RegD51OG0pQ2RW!&ZUf@$XJrb?3Wc-Kb#l-=DnROx)83VbC!v%qXIU1>!O8kP+(+QIvcyb6H0kBpT%h=oA850jvg z1)~NYP5GKt*6#t>QOcFg0s}2(hTIRAMVM6!U8b7z-t5RmagFSQlCfbx@t*Zx%9e|T)vH{djDDPgl_U*X ztCxpjTB#mo)Faza?MmhSUa&aPmkhm0WaoDShjE!^(e7Mh>_mTJncJ1(2cX_qG=J|5 z9nW!RyB6e#t^?7j()r#WKYkqkQG&m^v2$Oc2JkcY?ifM`tphJVr$Gf)Q3^h=(Qs|D zl^Gp80Z2u%T!PdJ5ruY40Q!fvkU`bw(|&%T6WiNCoV~OfznQ_w&$;Qiye#7N;7J4v zKqFi|<6C6sr|TwwP8jB@rlD{q?H?#-&W|6r!tR@tOUes(NAh2J7r5jua^Gs1XPKaxV9$^n zqo92Eh5O**;*b0hLi@?|jY;V(T3Ns1lNlL_Cs}V>S+}Sx{$;%1hYq6R-rC4C+psmh zcV8@4)2*$&`0Fvux3-unKlBE+bfZUGFL9j9aNaNj}Jn5o_t%wa1#=& zaQ+e)Aef~F=GgcKIjsY^b*wF0}GU&mgzkXHB z8l8zo7k{LQqrU z52RKy>b|Jqfyh-&zAH0xcwTe+2UyhAg>@PNCorm!L=;>NS&9mK^~Of24DZRk!q1_3 z%q3+%qlLth16-D(WFaqZA)QjBE5rbln>B=0ysyjaeIs^0SH{6dg1$#dLmn@1OZU^0 zu3Jww(S8m-RmZ&YI}nF6g{%*VyH}&m8qFFjE|NJepgU)Zabfjf-!?j!cVqfXEL&Ya zEF1Ww?sfGwqf(>uPB*xABkO^+$v0JiL0j1Z)E~Fn53kcv!9}RPgc{3=_wUVCr=S`Z zzvr#Rq`|Nk{I4|P;^s|DE{#5+k@vg1yCY4W?%|XIM4+Gryd#vm5_-6G1Po@=;9snw=WM*m{?ic0=V+*w=X}Y33(Ba{t#=U)FP^X0y2cebG;(2e4%Ra_XrjSpV*$1_`;Eyke zvUTzjM=sVywgl|i8yXtP`4vUgMuY3eD>2oePD2h}n^}K{wJtOY(kv@=4fW+SRHG8h zH8zEU3H5SUfu^e^!K`*VlyuR2<(0LZrIooet%{vnj|PXW#LVS&3DRuQ76%HU`%9y} zHAHlxmcBkT=52dD)Jn|=F38f~zEnMYztO6RF3DW8;qH76cunMrGRgsB(hT|j;t!N3 zYoX(cdt%-f-6DfCtZ!}Y+XRo4ec;^+eteDl4bcb62l%|m2cPV5A!H%{oUjysaHQrd*6AEPET}o3deJ{?frtyDV?m^M%>K`PeM>=i2UwRPN^?Cx5$yU}HS2-gFxc=Y3Ex0XNS9_sd zh4-%W+3eYayB!fxBOf~6`@8yTd?$KFM&y&t;VB{A1{a7UJR$-yT&nt&^WSgIwQ9{z z;3P!3i3rdTkNcozz_JTfJMV+zMNgeVg~zu5vyL;W3jtKz)?mL_t}glHz$2yHte-zo zrta&4(aq#njss4D8yxFJ_wxS!5T}?f$45TjMSx9D_l@N0xL2TB2JyC1;^6$jQk|R> zl?$Lrfe@ZO>%3$5#ZnT07Cayv|md!tp=d_4}Yx-#S0v@Z|fm z)AasCHlz6(hn}7u*f)#d(#I>3#4ZA^i05(`;=x)qzS&1b&Crp%AP$2Lnud8U@vRaR zQ5;a8*w?Z<`ERCp_FhZYQu6-3q=0vG!e@s#;b36Wh<{uh0qiZ%Hvh>FY=Z4{&rgu+ zDX71N@+6y<41Ji_*nKgR1r-%l9atTUjZ#xmEEC7a$5##x^vv``hq6mW;n+m}Ji*7u z|9r5ZrX}{AQGk3Uk!|I;^Q7j}KhbBc7Y?cnL$|K7S`aEsF!k!2jlDYpQl(@n;9pke ze(_*n=Bx9I{0V6Dg`00Cf81*;x*ad)8$HoSvFTVo>lLAamqtLUmQ=WjOwkI8RCf0H z$n89qXs;_j+{gnXdCQg#3XlG26a{A*k!N3LdZ~Mbp2(Kb9#NoXe=%ee7yobwwY4Xi3xUg1h+Z9Yk z&-AiHsG)XkV|$#xSr)(L{fP6O5#DIrz7>Z~a6@8PP_p|^5_t+W^!)w%%gy^Yw~h}P zs(&IPA`Q2{?XIjt5zAO)9DFrmk`?Eydz?VXWrt^1=oZ;_p)pGGeXsiM*~O)EFSHF2Qw*3#k`7so|I3IRuv`VXc?5j--&tx2?OQ(KV*Yc*%3MqUdf zd((GEmrZ3|NZ-ItEH@n-`<*Hb329c^>KHV)xCQH18F3kv;TsOnBVSl0B4ZU~c`-#5 zr>CSW@K{1W6rY<8rG->lP0?|2;R8!W@#H?3H`x7U$+LTI+SJq(1qJ0XTNO$5<`fnwyX7&PGO$6$+k%gQEdG8#F>cKgZ9;V$7xd z&bZ|R#e(CpSa8Dv6`#ZT`9ioP1Q-qX4E(T}C~ErE1M(UtdWvdW|25kVn;&_(%5J~u z=qZVZ(jK7*b7A$D&%SD0na5g=E<7hbW?a-YweBt7ZmGF_M@;@$@{B3Q;y$or9XXVy z+yprULnL8Ar5ppItgsqam+vb2S`pvoa$jm9B9xglh3Z zUO?1S@%(wZ{m%X^i^t)HvPZcy7k^~?DK_!yYAa<)RRzv#Zr^3X$Tj=R;c@kv&(G+x zjVgDFUJR6Op9CavTcYgAI|YNmH3sW_@c^c^+*UZh3=a=Sh?K6b^*llPiiZb-70n^F z4z?>zdk0GdgoL0z(l{ZY;nxRzNX@5pv^eQ!ufObvh|+#WIm><9F_{y8bxamm3_D5uk&$>W zIMmm1SO3NSVV_cbF`@+suefJ4Ws0sro3t@f%Nr{YDj&9dO^rK9l2fwU?%O+Ec$XIl z?EXD(NMkRLtNIx)Oe@XJeH&dVx+KF5R;BDaB*m|IF=Txp~ruFomdW*awq&RAHhFC@{9al{=4hQ*Z#mVj zX%uqC;^pFM-^k=M0Kz9vRIj?*6U(YyWZk>cgaG~UhzV-`uG%tZOHj>sO>3ehgODx4LRCWv_5}xzck(;>>FZyztQzes|

?;6R5i#GgM;i;ocqc+ zJG#VIS{Ler8_0cXwwO9PCDuM_zld|X%p2(GAsMo@1;a0bz12N^e^nw`)ChKnUbn>O%aYZsNYJ(H+xCL2l3ppue zSU|t(%E-|;;pEMgi_z8hDE6Q7glEL>mpx1GElXDTvG5t>+I;+iAQ;T+{+=JY_o&PX zmRg=rP*e<0)U1B{!I`(ikYi{e+`lnfDgAC@G7>@8*E~fb1j%>A0RscGpNsI01d{P8{LEQ)VXo9+;CDXczB7@ICF(byXl{VU*u$Eaq4WyCz8#U zJ4TmcV$(5UDE}t%BpecHP<*kO{HjAnNjP25JFQ6@JQzFuY1~MwI92>7L$?CH#gnY#SlF+(>ghX1W5t3ZWj|)GgPnGkPd-?}Oz&-oq_$fLzHe!CayQg=ERc}bb))uP_T*c)4 z5wyBjGf^zJhriUc>2P-`DOPs3(TLTcFZ}g&Z8M#iA6Qur4^Mql^N3|-Wtpd^On#r0 zt-ZO4H^723nLkd)9pGd=Fgi~A*Kg>&IxNfqv_P~kXC=HJr3H1)zmu>3*vxDDbkg(e`O`u@l=NZf)Z|ka zKSOG=KD%7P8R5#6ew%f7$pzG_GG*79@=`!S`SvuFiUr+qAoC7sLpBpbDRwh+ z%5i|SI$Tn`Z`q`(*7Jt9?qbX5E8d{{1)yCGwylcaOi^1AFwY}?p z?4yU$$G8o2Dh*hWFvv$~)*zv*pK*I6ySCD#4G5!2G(OcKB5-yK1E+G!MIm3m8vc0t zjP^M#mBsVtQgU(MKn2|h(aDk&-PuB}BJ7}^ zidlt)1SUBq>lZmpEG%LUA8|v&!aizi`+r?sJ??(T#L6uC@gwcy*OV2!eSJL${%+C> z3u7Z*U9DH=^gN0F9i?VNjz>uU@KO_ji>o}eNou*M4eb1;E}e1{ zV&Ne1wnMq6K5AdgMd9qUMe&++>PdwU=A*uvT}w$}Vsg08KD{+N z$LO)PNe+DKbJ2NSFvK7%Os!Mr{7yM;BeWS14lA33T9Bn+Rcs6TkDRC3TG7G)LlhPk zInb_BOXcwJ{Q$+z_ObJoAANJ?M-xmO856@H*qGWnUayg3BR}K`iUlVjsg%rnP;i;S zfAtI~(Xdjz<#qf`~)_4P9-X>iB$Dl z6Af7)xn&gB9|>Td`#z__`&R@(k(s~ko9OXE4!5_m)Q?xJU20yVlRRsM>vl*wJD<~Z zsMaVqRd2j@XYTEin%>u|%zVho?&1ikM-ajBcnhE(jtX6jR3GA-ZZex!k&u|}2P|-E z^$hk(fcgQhk%Vnz?u)n1=SF`MmNo^w!fzV{|8B0+ftBX!>RL$@r17y}0+0<7@82Jx z9~E0Ar6k9jiKn)Y_0=_K2lBiqvhF)5(l;@|l(Dk1sl`Yhz?!V8nvIoz?ZlWuXIV++ ze!dGR(K~F=%+8*5bwOR~7520nL+R*ZVq#C|pQgO#Vr+&BQ4iNZ7ea)k@jnpHHVPtT zEwM=svr-~PMw<5#B*y7)whEVAZz{|;U3_cfg@;FFh@uLNGHr}Z7!(XZjsD34n)Gg9 zD18D`%qPg2t7>fKR_{)0~2VwLN4djzZ6YK`AW`&`l4LBBa za-Q|{_ltSyQlTB6^dT+_%31g_K0^x)RZvzBe&k;Xtold^2noOc_(=l(Eoe2wEHv_+ zuijtzjg2vy;7DgF=7aSHe$P_0;93=YNWqF7D_Alp@N1Bi-?;Z6LJ8; zi^uhh*=%=#T9GJF#x%lexLrRyxDxb+guuLcTlYrm1u33WTj_rG9LsbtRj_WXb*P`vQ@84W;!aSk##m6AD%v@fiM6JF{-p@2z2 zo13k)3d-qEparAs%YD6)a9?-)6uBG8jb~#RxW?h5+ZO?!y8OVaqn2f5y2_0-ZI}F> zUz(dTG*rg^aQeOVj3Sp$u^T z=YB|M))&9-W+R`pNX#2>k%3yIP<+ip6=EwHB#g|V$N|JO*UJkD&}{9 zwO49A!zt*ZaHP0O^fJY0v@L+-joaq{YnR*&-NM0ge{-fT^KL`G$4Hi;pHSRP-O&kZ{y_Lw!TI9psb#p6Fj(Vo_qr>RQYPmEN@I%@bLW>#=$`n7_qT6l4kC<({)9tf z*oC+~uhH*kjU=3;)RsW6#lK6_J=2PR0(fkgsliTJ^(?{l&Q-igH`m7VLasQHU#wfc z3B56Y$j>u2H8t!wT+wvkqhZGZD|S4z9)Wz$Yw?>7m9PmJj~YE00idLT3TZf9VqkA? z@2$)npv(+){_TxrnI~U>wpeByyY4o2m*etEIvQo_vft+6iAq4{y<((na%ac zO?LJ264I8g2rLV$zMsYd#Gc}&ef49mU;%17Mrg-B< z@c7|Ivn8_>RT^YDXlP*IwBiWFq!gVo70+%NpFrF^w`;E((YahYRsriQNVBLbvV?vb z*BOBx`Mk5+aZy~sN;pgG*=LM+>4<(l+m)V>a{l8@QgMlHRg2A~2GZO}a-a9vL9HJ@ zB#QA79Uq@?7}` z^;>cNjt_YBiWKK3RQMysG&GW(lYau$=6U*omI`T=@y!pvjU?z$?N%2HNc@b0ZnCJsE^38D3L|L$l{PC}w^ zmC_{zgc6Ae(#NrjPhg-uD6xC+0z&1x<(|`6ow+I+7A2)-{vVQT2uO(N6u6^*f&u3O z`@Ab2IGN?nK1Jv9r@Jm}^?x?m+Qd zyVe(k3Jf-fbb5w0Fi zzMI76xivW%sjRW^-)U1#mfVF zY7Q&gPJf9BR4uIZX+)|&>~Z|iW5d{=!xzff=fTi&%q51pQ`Y;dDUFE87!3)ENO$=; zve_o5)w%v9-Kj(t*VK@#G01kzIBvpV5)u;Ajb?13bxuLouk4C<92#ddNc?#LS{X=P> z=w3Mh`aA@YfRTKDmzS5)yyxfVNrJANxgV0l!HAzJLEBh7m#b9xC1=@HuBEl(HwUXlX$YYB4494 zJj=W%(p#6sd>(6&ZKuE;uG{;ccSS9?1fpBd~C z{n5ROt+U)2EZ5F|<9V(j1n1ELEbJW}{iJiocp?0-&iE9%;Z#0sJ&z+qB@8*l|FKze$n!iqd zyk%tPsq!83hjSP9^sB7Dmzsj=vA-*v%O;*eMy8g&zbSo33#=GVx5rRzw60WG8^O{d zyeCU+(NcgEyumZWmWPEO5MU%EB>F%>B4IaYk7Wa&MoC#Q^oh_AzSVFbiQ~^+DvhO2jB;oU{Egw?mZqK_k8qtJukNbG zAp5&>ogz8V606$G2CAf1iO}I@1FFgYGm69%mdMr^ElT94yL*lzHseje|9UCEuze@g z5)te!YsmV|HA>HNiKwy89AV+yMoUanPJ(!pTY-rHfJg4%x{YYCXsM?tn3$c*t$wZF z2=OLL+~iFs+C8ZoQ3hf-TwB0%SA?7+J3#yBu&r(4y2x1 zfZn82WCJ8h4)B0C;7r2%eas56>Y z4Cbw|oP)6#3edwh$(y&v%-l=k5zYIO7mJ<+=aQriYy_Tz%W~4vz96{-Ynh!T1EX^w zQBW6X68k~#4X96FuxnScqlCs1O#T6*yr9qw;yE!k$iO6P1%)bj;m zk@|}GA-DIIUU%Uli2T8RAQDoaZ$l1S$_xMv-6N>nWDQztn;?((@7fAF3icw20;B5@ zVCqS!SteX;mwa|eOB;)acKeum)NC%bwW4&Rj_Z&&G9X$7Dh4}+Sa$IqF^0L+ItJ9QSR#LpZW_quJ;jw$Ka7ACnv*T0Fwac zDKfBy;RDEHHa<&Tv1+^ddttOljT!*B(};IrL{DJW*48-W%!rGOP?->$>-r4F+@{Hs6T&H9MD&XOC9j*8i_pUz+T1fdRBIcX zQQ&4h-JU4W<-nH*ixxC*Jpu7cHwQHbYfTQT2KhK}t1R83V-cl2Yqq2brB04(yN#4E7kmWkh zPQt|t<&5&^&ix*out@!MZwlTq? zHuz-mH2(WLF8QLvoqyYK#FFkc{IA+NN2xYUa|IbU%U-Y9As46?KrvVR$QGn%pihrZ z`Th2j=4+~F47995sMz>l51BYfpRH34X>f3}zYR0hJ+vFe@ET0D=$LO&9S*L&HQo3m zXnJ^@k=$?IU3!0MHn42cm%Bbr++vk`0O}$8S)~Oq5g~nh)kMW_kJIGkMY#K?P@|~9 z?D}{o>X|@dap6iKB#Y=)g)J`PB?QwS9vw%)u)IifxEl0g{WvLz3viR2qXJSfEG|C2 zb$Xfv&^KVvaA7G$+oRTqhX?n~uxOUi-`{^3q_IhOED3-KUo8haM={@~u*zbbK{iw9 zE}&CIQ4tRCp0Kx|DFf9r;`Lou1N0zt+jpT669dH@o}Cp{M&%-z9O6Az-d1#=Z-a*u zbjJ55I{X`Z1_E$Yz@R!Yo0^)MEqSrOHC~NiWK zNreRK@konkZhPp;IV7)f*<;wBj}rZ$tDCc#5SKtlcT6oVey@~cC*$Z?!#~?TGYW~& z-@cx0*R@5ql0jEbN^Tu%!yqy>1&8~TATOU^a(*v*@9J|U=9t)2K&Qa{U06@WS#IKf)X_BVF#+A)A%;t^3<)t=cua_yU za|=bE=(&CMy@7IjIW7-e*g_e9A%{wfaip7eEOwrkbe0?42Th})^=#Or_u4=~_(cc0 z&h2A7eV_-m&DT0sMm;f4%=8FS`IYC}D4O;2>EX7f_Asx6*Tdb#zxk#oz}E2Ceg|s; zU0gO(|%lfB#bHuwV_8?1$ysZ! zE|&Zk;vxm;-gstw0darm10kZ-HLmO*ztvU4hKE+<=`xc7XPt+8HX|)_CNK&+^F-D+ zUeciyWuXDrdjs#};-OxBIT*$B@66wzvg0*;mYm84x*xoUgld|aDbC4dw&X{NxweMh?QSu|j(DrKIJ=6fIwWRUD5_ONlJpP1B4@ zOw{vkxZQq`vSbSXp~yeezoKu449q?hdnWeLRtXC_Y0LjM(6H6al1LKy1jqNzi~Td8 z*tj_VBYXxrK|$^M{mQ9agNf}4cJ^tBdW?Xts zs7o{k0MsmP>gU4TD z^FF%2gaipo#58=xuq#9|2fpSK9Oig5=^QlX%DT2^KZe^|-olB&!vo5nHl9q>5(pdc zVo50^?PFsU2^%S?DpzFz0i=a5Ug%pGV4-s<^_W!IN6)qPieSk&a$T=TLt6;MY`Pu( zA+gahSr*sNVx&p{Lowjy&g&`R`nQ9+d1bd(_z4WywOkIG9{}MYs#jVK%~G6z<7R8I zvEF-BFrc`Gf+7R#g2Uw}AS0`AFpa|dzwSAhL0Uor3DBf|0_)ZFU-oG@ITh#P)?GRa=0#GDj!mh#*K#OjGx^e_a5HpCm~X5Eno`-G9k)BANgS#?eP6(J4Huc1 zncK|JH?63l%Ok_-zSH7b;w*A71IRKF;C;1pT*&1rw7;|SU3~)>;~<;Doxce2mfpwA z{rS(L6!RZ_I!Pp4>t4mx^4!{z3ZUc zW7fzs@$io*rZ3iM3@H<9xe@#*3)s4*#XY)^VEQD9u2|t>+uW}k%8}+|1}=n>9{3VQ zspX5)QM`S`;ozmN9Dbhl<<+O$ISe2(Wb7q%o0!{o8^U`V*(7vtMR{s>Z8&{T*-P@| zm@U0t6E`$AGg}+vTj~IvNac>7uP;>MIQ8gW@_}#3x4Ng|Op2Q;B}H40>ZCj|_>ESd zsS3_SLps!;SOLblRCizwrFAiSbE)bqAalf!F)Tt)|a6-J= zb^-V_t7`xRV|U(Fe*`8gD=X@Q-Zz8)-%C(!O9@;@8dk#4Bj3J$diweD=4NVEQc0|% zbU!&*;z|Frs=COr7Jt1UAns3dyk9P=7pC=yY0y2eJ zTK}a2nw)Ea_k-UMevvf)V(#+v4|Pan^>41bb|WG8^H~tEusYZma6_~4ofGT*Da6&> z6@DKY&m;e%Cax&Kk_-K9-zCSS%I@wdSM2pDlr0GcE&LJkI%vFLcifP5adT_yky5JH z0SrAG8}`SKANifO2{14)4p)MxRgVb)y#iPuLy&8kuC~*QUwa-eZDZJ#E0@add3{Pu zrUpz(9v&VQQJ`Z82AHq4yBj$$Fc3g4ARYw=2e*JmFC5r8+Xn^;eWQGR;UF8oo+t1~ z`hajPsjMvGv=s%N$s-W(&j3QU-}LmfnJq_fM8rGr!XQ;2 z9A|_Ka1gKWx-kHF5aE6Y%q^v#XE;61CZ=ZlILRDrdzg8^HV8k02^mfLDQZeSkE#fY?+7j$Iy#ut z5p{Vf>2i5UOZCR>M`LGaCoKz0!KBb2G&l&NjObwMN?Wyw4EW>m!QCKp^!t0cvr$hS z{o`@o)!Eo1C7XHw8Vo+j(b)v^(nt2@RyH>o^^GG^)0?fka!$-(ut{Ftq-yh!Z$~&0 zVITn8HZeg6IN?q}g9KnnqU$+$9=0OeJ3Dv9Jy%jtRaIrA2F|dzo6t}?OQHL!Opal{ zvYqs!*b1&aCJgu}2L{ANMd1L9h>6CD0wJlV3{TD9%`;M#ScPR}(SkW5Aodyo!kSs& zGanuu`4ktkfd@{J$t9CMer$8;pUTX@c|qn;W{fH=EnTs*zC5Pj`?|0{G@gI2=5Xu637<6Z&bxu}bo*6taYKa3B zdB*ztXsz4maJhB5{tFvOl6!;xYz2G?9N;40|8szpb2u5#FGbft#?RY-P=OQf5-eYP z5T+A<_ijStg)N!_<{|^q#Bvb?_p9~Bu=~%7Ok+Nh-2x`~?Hy={IJpR=K75#5RQwvt z17~YU2Qu8zX>tAln@nyo8<_mfby#DxU5p}AZ|txIHT!P=xXQ-}7eNHO640F+X6N=hi;LP7cnJb+@s)>v8D zhp@20^TS+f+Q-M?srJ|Vsi+>eVFTOP{kM-(hyCw@5Sb{6Mw9MJLx&^uv2L5O1=~+M zP0u=OxD3v_&AT~JeCRTk*qSzf^&fh&8#s5b$}{;SexxwD=1ce(y$(E4BnUIJ;r)N3 zHa9G^ue5>do!55S8&n2bVPU;`&oSu7`%BYKG<9`N(Lp+MP7gheTl3wdfy;~TnsI|8 zHFCXFuWJ#O{n@_V!x_GU77^u@k*)?}(+n2i8xxzn@VSzI=1X%5_aD>?W{`aU9+h)! zJSHO}vZ8|fhj96AVG=M)hk}P~xkWMJg)A>G!}$34!1?>=&$ng$Z4Ws)1r!vZgXK&c z+gkz0T8<<2Un`zXd%XB&Aejppti!S2vAxEZ27_c6)It=ndKbw*5zzip`xhDEDWUsj z!h(Emco7`8C%}~s%GM&l-k&~wijIl#!FU(AA@qjwA1NtdaHJrK4mmlQN4ee32=MAH zqocS(LqibQl7TW;jpgO#ssm1+KKX+C&sq@k2;))GpYicwKU{PYUmkT+vv6aPaeiiM zdboE794-op&UjWy3C=;|5m{fK^b;6;m)zSWA2Ec;Q1GpQ3Vakr(#48Y=>-JHL&GtQ znFzpp=utMNp%?QR7?5QmAY0+37o*>RmA%OVU{wy5Uh0yJwE_m?f z(KriR-dzoS_iz#4@)6J#C1Q`x1B4?{P=bMDrDvc|)YLS0;~WyNc&X=ArW=!t`c49= ztgP&tYPl+X0VOXl@1sW7*!X65Z;$1rz$*)J?OJW!#hP$`t;hO8bNOs_h_d0~9=XQl z=(}^e`irhqyT$;}m$uK&YPM{pP}Pf8(^+490C}w}T2;GQBpDVKfS*x(^@ILbm`Nko z6cnPNDA{)mWqpWqg@v+vM(;N8N2@$MYwfi(HLu$YIeB>lAK$_5E=}TG`=TyZtu|I| z4^<^#YslPXNzBRMj}9v2H=P*5-k z*z0n87v`!D|M|Mkc0cGGKZEm|MJ2(7fVjB#KRY^`(yCH}fvxJmT2dux;taKzzXxBU z=^5jn8d;!R{Jp?5+OF1#qWy1M|5p8}0`8~VSA*2}oZdoDy43`;DN_@ZMuk!$pr^f{ zDKGa+Pp8~9=8UknGD8+{y}B!(`|Hfk$5l|H_cLGFcl-Twk z1vt0PI!(JQnGC(;$nHG}|8jNjMlEp!+`tDI7*8&%>*0wo^j|!6p;E=U6tci;quxP9 zPNihTE59OERc(;BW&A=UU)ivKWAJjfeE6FJQ^nlg-rmvr zSdNRytxK@@ce2!4DOFWvZJV9x)we3ZwJ}+5E7GZj z%voY*EcayPWRpC`Ew~?75l#duaQj@Jl0F`tZSCzW);k~^^cdj0thRin@EHN-@i53q z@o>iuYnh%#5Wa1|G~L*MlYEDR@R9GopYPIYzCB6wHcNqqo*tf*^q+#Uc>-Z5#t$>& zw=r2v#CAN@pdb>H#n#qdLE{6O0`;FpC$Xw{d4cCpM73$Jx2&k>r7UxBrD#0P|1Qah z4VY>YE)NFe*E(*p6keR2b%IcTNFp-Ew-+CMUg1>{7c}es^1Qv`V;LPQCRF~sFAV_P zqt6^ioD%rmK`2Ai&F%I7mFCk^j30SK!IOB9TL6Ki0lX0Q-@ zCZ7-y9{$T&g0b?yjfLa13aBOZvcF2V4yWb;XJFX3dW|_Qn0Sc-4}Xoy$~{%A+jaY@ zydrAJWm6}df={F+7}SVuY)T#QM@Cl3HyEf^QZh1PmX?-RpRPPDe>v*_K`mei>h>@& zH&@S4VF}W<&C{9WCK`Vs{5)JtH% zQ<8VWLrKf9P2F&{&2OVrxyXjyAsLo{LQWvUO*=L=b_g8g;9D^A^YhK8ep5cqJJ_Mt z2V@pN+@MfCf)!0jlZ?&Fi4wQ_W1Xtf>P%$Ob*PkOpLtIv=?u>atfCY)nySqMW6$*N&|W zD;CY8mayUWq+?;D>9Q1@c>1>NMlO}JNA^$ zL@Mz^z{3b4!BtwhSn?_;83g<~wbv;wXtRKKscMPUk|&l_Ib6>wA=k!ILa)#SJ;}(3 zA_r|FTaZOL~z!0h|$Tr&~TJn>^1x z0qQwAK3;__^BV54zsR~EAR?lB{`@`UE+Z=|e$3SmMjrsk``1PgP?g|-ZCPBT!a6@M zsjd#1*Tq7vy&HGJN-?owDPXoN9DT6-->SSu>2~0E+`v%vxOw>@Lr}vYwYK(^>2OBG zmnC;>fU#lIh+?aqUo#Vf_!uJquLm-C>eO|4zBMv99bxn=Ea-3Cpi^(@oHjslm)Ng} zCV`fMl@$x#jZG;mzU9?jUNfU_utvLxM)Hx1(PrJZCb-(1#D;T=)0Orl+8wSl(KO zNlGF^kDs-*6&EWKi?G}}Gade9KW-R9NoPnEC@IY02qdwbE;|-I)Ii($!sMrxiG&D# zULg1yZ!W9JAyD=~_{yNOdg<|@h=f%d8X5*b8g{u!|DtbXq+~!|&>}76W+IxHd{cDVkJumh1Yj=b!vqvBF|93m(Vs39e zZp&=QRf}~aB%`QQrWs;;5BJ>UjU4y@Tu}Yz-zOWgF)at(@BN%K~3XH0WrUFkfC9ckibkj|vuAsXj1fn${}!}Kzt0mp6@0bi`{|7wJcwd|d z!ofm9&JYj8C8)GmB)RaQ_PTZJ*0)zrR8>_0=V|%#hXE`cDc=0~^T$ljcowlSyW^kD z@xMSj)D7HHao0Wez>DV$2@G-x2pwSuh@7$)bLa$gq6Frdbq(bF5kSlCdnd&Bmj4|( z3P5=ln^o^uN4{hsB*>Mn9pQhk#Cd&4BBgl)EPD0ntovg5bSi|M5PGeWTE~f_G)t2;Jx-0UlDC z`5xH~(3+eOSu!9!LPBMaMKXDLd10Lio%x@5`+B#+Ko#Sq-mNXClwJ;(>pZD1T={&z z(y?v&=P2@K3YbN_PXiz6&hK6>xb^9$;yyipR>FRuf=Ie7YNNSmy^#O%y%-zih$?(> zf7F8UwTV}KtM1BcikTJla>eB*9B%#KKzfnDeV5QUp>}{bvC=oqei=_QU&qj+WeDOw zxI7WAj}YVDxxLPe^<0;lWH`8~FAiy|6g)ZMJErFV4<^tuu+;>k+0am*EE)RgmVx@l-= zP*9k%)*gP+)zw8o0XBNJCRaX1NdCfR^{fi8pn&$bPW3JGzjS{T5S!4cYG?3ZLQ76g zt^q(2j4zYlzc=ERtE#F3$65(Qy4bP2Th=l_9wF7$V&M2}>gY(Ad`~kL%j$oxHiiZQ z>5C2RB!}5XhSO6D{tWnTr~Pmaa$DxPwK091hvTsG-T2qJ>0ELY6m&(!iwY}j1>Zwb zaiI{D3%NAnQq42Nj0q1pwsUQz&~~;r4~9IK_R=>A);SRjvj1}T>tBijN;PSf@}Prf z$GFZN7pZN9h=vU%N3r-J1CnB4J~mLL^a{{kFayM?cAo9roT?@vB@Kr5F4FSrcVua) zp#B}_gk@fR+jc`HCKzBzFDu*614KnZDeEzD!iUt$~Fg}sa_h@tQUE%a=P^B>Yd@$2^Cqt~IvFqmeKdy!%xMKo+~ur~#^DcT41|T}_(7n}AiMHt=rHz!8V9z0h21C~q>+g9Ns9x1@&Co2 z>^89ZF^7IrmE&vzz$Fi+AW8{>!Wc>GdHneCpt(N)mkPb_mxOQQoZw~;M74!0h8tY!s2tK3qm|R^CLgN zqELdsa}x2z5&9`XIiwe_2;j(a!N9^ArZ-74T02@g5-;Tb=V*DbR=Y)%$5iI;Sh(&@_Zx?b zvlzyuN(7}!#K#hcBcFjYW9Hxx2T0fjc*1Dml|>RWVW9vrQa^Up+mmNsKTy?B3NnLi zuc$OSf^wQN-O|J#wp}%m1<$A9OEABFvGsJn#$}fkSyF@`LPAz#yGBNC$jdtpWd~rp z+s-nA&sF%>uaQT8wGSNbV^)^Y@}r204AbjAV^)$rw}?#~rCO&yi6b88yaIp(%R(NrBzI}_Y$Z)X??EF%tt?*P5 z83`Xg>x3Nq^A;K?6Ab3i#`<`uVDFKP(t7sDdlP|K#ydi{C`mD zyLUY|yCFf`S<5e882#~TsLbjiSHsVHfc)6&6gJ23%MBOdZ~erzuigx)jK4a5$3n}` z8jZsmeT7@^e?f}u6D+J3H>ud#-hxhm04NJnQ&X8)SWv<4PXT!0Kvu?ZcHVw42pK!~ z48nVn5YW$u25+)CDsUa@uYVVQPwG)5k$enE9Flki81_6|9rV%i#>S?n>G?gj;);yE ze*`xc93rA3@85WC%eVfwedSjvVJiV(*P1EgN8&k$asyBKCLK5zfHUU}SS!`pz{^{u z*~uCpN;vZXkn_Ag0JkmZUV6D-(d(?B z|BY688v0h}OBmja{}Ki@5YHAO9J zj$tXJ7msKw%Vb@Baj9=q)Y3~&x0t(AerP|KB^!MVB;0o!4#4cwj2Ub)jus?0wo1pg z=QTV!4(>0B+f}}O+hW~Q`(wdr2e(xTDhk3tFz!&@!JiV!-fYEh6yu$Qih9~_ewvM> zSJ73qj^wBPG$RqYn;RH5)50|^6kDd&c)~zVkBEfC8Ck>3UR|DmL1Dhg;qiW3@8!8g zbI{w}rz-vF@h3Lk@w|x1tjB9n=nsD4`r0d4rwelt>$syyN`CHg z+9-+%T>&?VfGDl&#yy8cH{Ip!(5*(3jb}g2Ts6NkK-gy_*F3MT$Q z6gT%SBv#(m*Rojt`*#jf>@HxeyTVJrMb4MtcMd_v;lA{CcBhZ__syM|wMg)f zitZ$TJRo|uwVl`!?26?b%q@(*vM;^g*VkY(AW;n;vwmAXzuzd{bG;69;wd4@)pc6~_VM!|rXbs5jJNxv6!7GX&<=aDd#1`dWizT-w^OkFZ zqZOo|y5@SBMB9daKKk!h&3_ApI;JjZizZ`C&R+yTxb@YJ<6A(Htv)m2Q+?xMcu?C z5!zRCP{ZGazFI|P-PTk!|1tt33ShWr3T(o@!vB~YkSMmQBU5D}2FWmVY+_bXQA!F5 z3jIxhFrdlm-j^fy-K9|zIICO^GS#ltWIZU2iv`>EQ8(+Tw zb1-=J=^Bic7*ml8ULf?J3qQ}-_qwQY)x8>dmMZIL_e=z>Seab)nwITYA4o&ufqQ)6ZlYyTA!Q7bAKp~#2DOL!W`W_1u|ms7LR%WT+(eQDI_LX>~C z?PG{&@YfJ=Y;Kl6`8yR)EiR6N(p+82iBsF4OB}xRLR3-0!c0r~4h0Q}-O~dF}>{>dl zrq=kkO#AgU4FjF=6T`&GI{nf0GlcJwn^Azw8XfIV=9G`w@_9#f@?6RRi{+YJp@w{+ zY8;X9%P`yj&5tDr14-OBdTeSHZZCYi-=`%&f8UKBJNe%2qZf0`WYIvEii>L(Ad5A# zw8UU5r8Y0cQ88y*a%U54k1xL2*(s2G&y-a#B0Bo>Z$DgivL*XxHrIVT62BwCH4&tyTCWcu8Ri2EV_y;V;g*p?q(L3KT+`v4EXC6cV9Abef| zdIA(!bRagg*X%9|1WtQ<6I0)H{DDKbwr01x)V03TCUzZRSj2;Ddp(i4)+sRDCx&!L z2)`4<>{gW0J2OO;?T0Lv&>+BqOaZVr1rEC|kW&WiB|gBYX=#|en(*%#$FpsLk6q3C z(p-6Y>W2CWzsg>`*3tY}Uxv4ol5(r~o077Y2p1Pu$~Jetx`tl(UJnIdBC0RLJJQ7sFBOR?ADKH4z%7B#Tl249hKGpBu}Qot(Xo>9(unvvVGe@m_D_P|&i(n_1AXd`+>UqJBTby{s@v8O3A05-#@=HOx#tjs#(%pW3tFZK zj3GRLCZ#+#ngPy>%GW8*>t7$ur^@tQyNw*pk!NR=@C6f1ew77e>R*;9YIHP1{2<%X zF^uu+xLIRh81nL$(CgPJ`HWwVvwFGprGF3WD6wi-C>K)0)3;J~EFU+Ap(2N^mk*}T zhn3##HRKtgd*8ZIzXk+K+q?_J5=Bxkuv>c`m`PY3=SMBQ$muaJ2cOr(}_qKAWT9vaa>5oyf&2+m_qqOrzWTGgX%tw)RE zL=WG{g}%MM66N9H^+;uqm-ZJY#ZIVP`OGLEc@%-{F=vCzGM2;dGR*aHL>zz*x&7<2 zO>){rhS5mA4fJ+ZX~>kOcBB)3?cv?HF{9Y>$FIA|kNC9Tm}-W*vtfbkM#}3C-#!;S z?9h)c6-4m#^&_cyK!{Bh^K*P|HZx(@OkkY@`eTvs+}!B3O1<*&9FQX?Q!AQJpV&G$ zGYdM|c`yr#MwFJ8DTm17Cidi+V15=#%d1oh2ngM)Tnl(M6}`O4WoKtkm$j}we$tpz zL?lw_@h!JdS<8cvE8kvS4>+=GcADB+pUMz*v_g94)ujsAcQx+cj(PAPZQ~I;*_Vm+ z1q|fEN{BUhdbz4N6N?>qdWGX^c>(sY8dp{(^!alRtAqs2j~`tw%Y8K6mSvQi@87>C zrJ;%b_8;^JiH4axr$lCfwK7TS!?f1@94Nyeq%C}S3&l_C=YH)-b%0yVY9G3%g%4BSF7T02Yn33!|pUF zlE^GY#3US2I;kM&2Zc8Bh|H_Fi(EYWYD;IFZ<#ucid1$x-S{Eow#6(ZMvT0}43nto zdbAtU+>{%Xh==%Y;*(RkPL*C2)uFFKW#~6jiWxQ>hk761UYMMkyyaIWG^79dPYkvt zG2S4-y6)_uQI1xtB-i&3@~DrWKeuBK$t}Ezpm*d&t*PiW3Du1G_)^G9#QD>zU-9P~ z%JGHLd)t&ETG6xIA%-(&S821l_X95u^!jE{7*qEh~y{w;` zUz+9m=pCb;Q#+IsfG6C&w1z>2Qx*8{;N(o6HAC=6hW=ga6pYf-g%0 zn-K1mMiOIe!&R4p&c&kUd(w$8JcI4#P31$-#oHbV14RS}A79Z{ml`y{0=7w6lg_-v zce8EJGrE1d+kG*H({T!y6A#tn1^Hs-#!#D6-7Ji}&^oR9<0Y)i$J45v7TUaKl{Q)~ z?L^C$Wn#u>e2Kxp%<;^6An`{;f7&~QtDaR{*ZhmlaByksPF(tqw|brJQQdym;7(Gj zJvBVl18;awN__iip=X=_hzVH!Fliz5$14u`c7N&zBo5t6cz^#cEoy5MIax0uA5@w| zZFf(78nTlFBdq}{(Q5;@|Ig&Q$OOO)vueoKKA0fGV(*}G`zd^fW!4Mj0{g6aDqpM}OTrctn$at^wzkb_ z!Zsrqf8egP<%SU;+5=3A?T+T{wWIA>lj2Eg(ujx8?+Z5SHxV^m9iad1pA00RzeBz4 ze}b!$fNMq>hx_q+OSJMF!H##z6(a#U*tRmlj9(({LSF`e2cBlgA`#H*89!<%Plw@P zK88>dhP!-k7d-<}ovf`Ft&!_wy{KX>3Lk822q~&x+P;4(BcheifEn&xQ`_9uft%^X z=i=oW@ihZq+VK7S@&X37l))jFs$>#J9Z9J!hE1fgi|1;%S!h7zBoq{-!5ow?&(Ov&OzY?#es)%ug-=yudcYGu#BO}fHTNb|x#)(<#HFMm~aT_xt z>neToF7n)Z2L_I;yu5_atw!ejz#N8nIlE?qMYyL3-r7)ol2TGefU`mV@(?K+3&yCH z*2(Vo;@wNJYZa~Xs>g4%wevg)%-LQtMP<=#7 z-}3G4_GV$x>yNj-gWnq>aJ~APtQ{TV=bW{*Xpv!4G`Eq)+6JWn$!{gbHa+7Pqj*q7^UY>^aJ(z@e5U z!dg`1w7RUUHGMRH7ZBC9*I0)v`q}ba9ft;KpnK)wGkF$MtK&S~3I^jzy)@@sVhdXf z7~72j0w0{24%Qc=0~=)KmgceepLiXVednKh>HYA)kiD~WXLFXS@$wukN@bs|_hs1& zFrDw!x?8Fj|dd?IsKjiW{dzD*sLnzW|n z)7zd7r~Fr!KO%4S4L-{dGwX4$KT7l(8utrIC}YF93?p})BT%1Px`&t-!2x{07X!`- z-P#v7i5R6nhz0;9_QZ5kzotfAnJWA7e1o}R%?rX6m>nP9tK+)0Ct}#(ns4Mt@s55& zummATusWrBbJ^GLP~}>dQ9$fdEH1{7#)hM#0Ef%1P7ESQt9%&sYJJ4rcJT%b>*Ega zd%7CT4`pBfuJ!3g4D`1ZH*KS$9F&zSudb5f&@n{elCta^AF{N7GYp@6w))&!r9Dtb z+IB0*?@1*`K#E##*59| z_3)^)GY)E!LpntRb{Mos!^KbGFq%gHi9y#KZH`*i#_F)EjGlgw`jwBK-eu`;K}Y_v zntC*8uqZfUcqfa%{YS;O*w>LSjyR>=qO6t`zwZXgK&#hXxf7q`&|)tWg1s$#lkG;x zRS?h3XmqQ&+XjW#pNQM5X{TOGVqC419dIh|~h?wM~L1A__bS5ihF3 zO%u57r;37y#&I|;QfX+8I#{r=+w88BuUpEKbl&`vjQ3o;s7^H7`1RdUQQ>P+^HkgF zeKconYphJaD<5_aj=Y@T6BBdqy=GcsmU_)bCjMq&%wU^)S_HrS-|2LA+AAJ#MfUjp z_oxH>w;KA!Eq{OY|0YEZ^%rPIz!4RI-RumH=3qZ0y!Q7cN?h+hOOC<4ZCSZV=B{~Kq%b=4%VFlLB498?X-yhYyO z(!Rnlek94g1ME(u6)12(oC|(FY8&O&nY=-Lez+bvBrlJIZ=nCu37(=)Jo@r_;&+=; zFpPPxpq*A-Tvw;>aK3HraIxkF?oWCBbbsYA_=|n;Nf$Xlzjk!kYpIM@tSv?6Jt}Y~ zxO&cHP|++1t#~n(sb!+$+4yr3N04|Ef8xZcmmXYP$wGA67dPZ-S4!EB@%RJB14ysh z<($FC!KsKpU@=yd+wr5E%4K7O=JS`VHsp`Rx^6Bx*n(h@xiZY8h;?cjHG7v}_b}qR z8P#QDv=ikf`;MLPh`gE#SQeU(Ut-R1NLd9|dK`*-Y_Tpp{Z(>pi(KxB1y$r(H*;`b zSK0<^+UVUKe?bhZJ8Z$n^6)(u4qG)cgty4^;${OQqj=DPiqR2lia&U0Ah!$xaYa*8 z(@&{%VH%%8!8vWh(}{h@a~eF;`Wky4JlvUO5?gq?-K2QG^i1NjNwd22b+wM5*&0D* zOYZ%-(J+je$k=S$dM`PD)EKTda&LyKIwF+;(CbOD?;Hu#eQ!1Zu`!kL2|mJebMj02 z81xyC>yj#LlQ4E=$-W~?G8}|CGku}(`JI~TTCT9Trw{|gzwr8abb0_Q2RY@bQ70 z3mYf^pD(iR9xR-u-k?BWm6EO}VZpuK;lr(`9l(gck5Q&A?E+r z_QGmuJul`DHcPM$z@SWg5ga=+2G?jevjStXodb@{0+!BC~Ai017-kd({5*-QAvC;d?S3SQ1`)2A6T5vQ)$d^?gzc4!tBb z8!W{7pC?9rH*r3yE^hq9?_C~jEjuk|PWh4aVvXmhgMBU$T{X$)oCsy9aNIAU-E1&f z+#69>JC_^2zw_6^Z5|T%2rtNlNOLt~z@MD$@Who~E;9l;I*ZV)DGcmvZ~rd`uyd#u zki2zokxwY(CzW?*S8j#o#onctc!d|$4dNTdVMXeEkO|T_m5YR-`F%l6K6YBzNq22Z}D@VpKbEyLaoR(QdWHn}ZBz7l z7K_%7T{^Bohu19A;65_?h;C2BUvEvv?>#u|F@{$lH(smZ)u%QD3#I*c7bW;+Jbd%N z=TA|;2BkU6X8V08vYA1F3b9xT)B^cgt{xs*gkh8*+Xi{D0&DWO<#MeiA?jc*Z>gn3 zF7MHAu#q$0PnDcTUeEP>!MEE73(*`IA#CoeJy!qANTH6;c*c*ug&fbD+lCw7%xbbt zqCnJF4f><7aS|JSm9GGJLndyEc}HJg|67AendNQIjfreuImoBME02TVU~6zWN%_E% zLRc@{6ctnN?%YLchfOx7@goGh5TseYXs%t4nfY!K!s)~tXvj{+((HwzphEOaR)s*> zS?sMg=G6efM9!bbrlpnf1~RB6_?saJXMQtGW1|ZST;t$YR{s*?QX7gXrMS@**5J#3 zN&*|{!O6NCkTaH8%$i-SjBko(20pfEjLa9#^mX9NG&sBU&i6My*)uvxQt$A%-$aNI zC!OqL(#`LA3dKZG*EAMq9K~tno|W^ zaA<|G zN8;^+0@5l_-)PGS2@AtEudEb-d|O*<1q%xcjQo65(~=Y@aO)?{*NRJQZPAJWI(lF( z9e6q~)y^RMtu)T~vZ3^Eu8rXNka9^y*_t}|=1N(pqB*M~PpXUj6s+4j`f`Vi?Fq}@p%V*CL`APBrJ(qdu7c)_ed$r!?8kjBN<{yt{VLU){d1;LfMV#8H+|&% zRj7cd<(-d;Ds1i}F1q@u)f=f8GYP+w;YL6%m^XDm5UlgOO+@>Uv9;60i}~cF$|R#TCZp`;{Ko@+2jN|dW|?x?!HireTT5TQ$0BoPTDV>G$q zQDFPTVmI#%8Z<{wxJ`73yFgNnk;kjoeo~KKijG@{=19-Tko=%jV${4Xc6wr1s%WrRVB_tX$-N^qv+ zXC3VCKO=XSk&$VsGrB*wY5TvMJk*;@5xq0FnyiRCRqjQ}d>new4ud@QsrWlut+;O=G=0oF_gG*A}FhR`{`7dD$!w=&31wOPPf_no_ORkvIXm)D^huH&osoIoM5zf zbg==U7it^c$rvS8B!>}{s7OEuibxQ)h-@IjD3|Wd1qg6_de4X-dvrrRYFY-qWK`O(gdwI35Qrk*pW>^hwXP{r&AJP1-CxSPd96M_dhH3W;9UTNL z2GAf-VT48!P+Xyj$&(;9Tv(4<@dH8v#R$;ykdY`7IOlG{qKe^hKjL!9Qfgnm;mw&# zsjYW$m3l^K2$*PqVDv;S=m$GEKUEbOw{=c*;|yhg5_B}g$VrI5Yx2Zvc{=DwQ%=~B z*TzU&V(~2HA?9HhzJ3e0;gT@Qe15tIs@u)d@x()Z7PeS|V0_9A>>R@~=;Czu5YlpOcZ6K9^;$y%27NpD`&apyT4T zDI`rGb;R{I(!}-8P-=+w)AG(yJ!L8T%CaDe`Z>_OxWI5adWjbpszgkkFvgtuRpo_= zq-8gWlCmlr0wzJTw6K(vRXOc-O*Xy$@n9lGYk3-@_@!lqMRabt6-ia=>sgpRqJ*@u33?5Y#bpT*f|PkM3!_-UdZs9DJ&&OTRP)5ch38(^v ztdylx@~adP0W2TDs|=(joA9jYrIHg&JU3d88X}?1P-tdh<`i*zXg*cS11(1>vNWFl z30!r*ps+|w2ER-!CnZ8UL}&IKN9u?H3k5=a`?uTsrJLUe1w?styY$GE#@qI#k&G&s zB@HGuq2kbgk4;I0#&uMJHeMa{b5@7HCK}HQD3CCOulN|&dNaZsAKif1x@+uI6cL}B zOT(e|KH)U>E$c0ILk}?=`xG8|5+m%c=*gi)lSJl#aRf86lZs<4nT4yDU6L`Y3*Nm(vQF1PNP#b$;sgeM3EkDseC2|iBj*#$OtnB2iAAb?ar%9Y`~jxMh8Hp z3Bv$8+wZ@E)(-Bvb$~k(echMlGZ9Zj_w$_pIb)=+Y=lBB$x1S8GuTz3y4*VSqc*mj z9p_`Y3zk%OIz&U=|5mv0U6Vf{UEuGK*{g}zyfxbR)3!@&vww&SJ#U9)P4T0$VfhHS z)<5V}s@U|u&tx(KH+daS-Q-Z_UTurc16(VLI3?xL#iqK6BLlPNFwT#ent{tiN=_FG zg2O^jV7Ik`+m?XO-VF?llh8%+9e3^f?$4k+fY>Kq<3&ZZaYbJ3R#rKAdU9sg`OYU* z){jy89O11YMJ9so#Pa{i1R(;JO6EuM@rQV4{Gu2DjS&zV4_J2Ip4ezM?Ygbcn5*}P zDucn#N#Wf7Pf<}Lqtp?7b434?i}dBYPJaN_c`{`PA1)8H&!HMW68@Wxo}c@|V%oT` z?QaiFb$CH=OU_b#Mw>90F<2O384r{owlJm#Q{;bFp$|%`3E5wZQ3_`#a#Nq1xE6f+ zS#B6_BlE$czHBgh;;PeUj7wc`(F`?|!KQt!H?==ul!^Lnifeh9Lttht$tNqD95UR% z(+iWGe=Ff$a+g>rCFeUDN&`kAMj4F}-(z3BX?B?9PngH=Tt9b}vJd3(jGKL)i8%Z| z=+IU5X@|$N;PO1x0jZ zFWyObphX^SwPrVbPe!Pgf8|4<5bPYbeFj%upH3e5(f-WeuaP`t%dK^D1%=t>&g&mE zo79xr95z#DJdG%K2U0WMjWXpkgrB5>Ej8`AV@?ZR^NeQ5vy)2`Ub38}C zX|-gx4~>sh5m%l}Uw5L;@c7_ljYTH1{MAR$h7$m7($(%Sw;jaR6n3vkAUUA0NJw_> zU!vJ~PO*S=bFm{QMz*4zy?v?ktn7vJxhK7>J2=0kQyK8`@CdjrDD|aEkOK!M7>u-t zgl_`ei5c$RcTLuoY6{9uy<=&|aEDA&-1) zA7T1kT3XpA&L6wS?mAn4c$KE-L!%@r5KLVQi^!vF)M+&;IaL~Yv00eMGsZ?4><}r5 zD{b#W!%`Ne%Uys78k8h=O--4>k0BV@?2bB3knup2mX?aU%%LF3qM+Xa4Ko}lc!SdW z1RzDg>@}L5_a5_;j&hRMNIv53j0j6f3;U5^(U8&1LZs@0=u-(uHRQp)0gl?}Y! zV-E<%Qsb|LoM&75z6>t*^b?>2eUBwi*qlLjbe5-eH@%k+Io$10R1kgZx-~~weYOA0 z->=HYMr>?2ARNyAab6rvIoAZ7S4HFkEe<&i2>=&1-woY+?CD+3YL58dltbU7I(Wi` z#L9`5dsf@d`7k0Ck3)*sByKKp17GZkU^Wn_KG?d#Oj}?7OBHv-GW;V`aklQ@RB-i8 zS+UFi_GRgEC#6nas1@N0k>J`=v$xig;^M24h;UDh;}xp8^*oRFD&Gau>d{yYcsE-o z@7vjNf%der$`Ox%K*`*k75J!Wm%u}787e(Aub>gOMsgLmle`^l-LG%5FLjEkVzoYyilcF5)IHfd+()fotpIbMi+gTHg3~xp`-7|TD@M6Cx zr6Db6>Ke^Rt9j-^C7gAFPv&`r1{@Wmuc`-kL2OOLC>gdb=G30u7AX?O0s1_(OlcZ? zD&E%5_g`tI)L{%UFysbNlHgn)t7d4up6zh0&L-bK(Ut2jaQBMFgAij1ieD8d?U4zw+!}YiWP_{E>D^ zOXOBzfy(ogJr6BM8M`$)^=BrD>2+8mB6mLI6v;~zK9(phGRDg_#HtO6G%N}V(fMZF zOVOk1rbLeotS1%o(QkYnXRNz}&)%$>O(xwO}2mZQF%kocn z04q=Q=^J|K(NvRAxly~S60!0wQGeDyL#Uy{z$3cOC+RV+ufDS0;-M#tzCTr~fmw{D z*^=MSq_rd3^^?nc{W#Dfm7<_N1_&s)ofPjGN;)R<~1`aV`7~7L~zEamleW~nVfw<8XMioMH z`J5EZd4*zbKVHsa(RoVaS^mti{fIiE;H>vqsssz(N9@PxQ^Q-q{Ryr#pAXfn>tD;) zV4_e;QIb+`l6vcf8;osoO*j6qFMaic;ucr2lKgW_yocQCy4umLFUxX{zx4RrOc}kE zUt}h$d*=?%qerIp&x!oF-NYvlx-FwKw0xeTE~VuWu?>WV8;&W%8;+(P*9uXT;w+fP zD)Mj0)sq|(8D4q!@=TZgc8&8Cu>OwIUH@2DNl#@rdHVxk7n}oz%@deUvWtvQ4Nj+D zVxH`UR!{KV71;3FP{=P)DB%mUYQgj|iu!HD{FM6!9ujE<3MtY&1z|~i(e)eRG|Zj# z-uv-&PT1vxuc6BCs3jgH@8Bo8*$=u~*1~UW=~{3tmi;a*kTNO|vzm@9-vWZ0tB?pL zZfRsPi5oRM*NRC{$_1IRkqA1mc0NwWYYJk?o3$~*+3cwyXvDaZEs6c)(DnL-HiG9P z=O_N!X5cMLk_sT?v`t!&_<#uExfu25W^S4$f_ENW<06CmCE#85=n)~9G@>972*M1Y z8Iqa=p56o*43d`P!C^%4G*@$!HSfa*6rf3joQ&$!Rr^08kx5#o(bGL1Rk=7?G48fp z`#AHtTfP43;#Cw5-e-eZKNJcPkM3H7xQ$kGODe>);y}aGztg-en|?RD8x2BQf8i~^ zB4QzNrSE&h4s1eZ%4i@LGE=&eh&58h)^ysC!bYoAK-IIbDpo5BQp*OZfKH>6|15Kg zpiWAKl9rK7-r8ESf#zgRVR$aC;YQ*7KU-q{730;#5qx7lqSf_ht6{5;-U6hPV{%&5-Jf<*qJkG$@2{Hisaqvl}K3+AD0FsUq8P!4EzPvkBM z7}I1j^aV)%5M{0;yDmzSEt)6apbD+9_`yq*WNqabU$VLzMJtCpZ_)MXEjr-?H4-H{ z>J4W5_n%;~L_|bTP)tlV(Xj^85hD1=HTB${;Z1GMxAkPnk!c7Zq(CxtGhC#KQS6&e zD3SThp0!SgGAk)%UuxWVrXQXnw$HkN;)0pFzE&um7oMw3HHp{@d{^FTlh?b7Bonixxz{D#sIBq&|4P9pCZs zO~)V+QCa7{G_Bs9oqE&e-7Xqaf zAuTmmN&fnpEB7{|)6$<|$=PY8kj5gS)s?lVK)wX()qiMBvQ9hei+BdHTL^V|X+!HL zG>OEs-ZaI`ING(uoe#;9197WFV4RN}jn*=5f`~5@I(+l^&6_v6H7+b~ zigkm7f>SBHJknZIi0`>gfAsdEqkz{`#RNw-;`$0D=-}YsF`~on@q-6=C||xgSXtdnH3mfS5lS1A0r-iZzLQ7eBh?~VjG#dl%nhE`uA7c=2##|~YL)!24 zBII%0MmO3DNtVTl!1hi+P&A}rGyt{oQpU|EQ}t29jpH8fWRs@}AusW6yne%N@>ZL< z=`$MU&82RBUR4brt=N=7kwe0o*C~+?^?Yde4gG$%7FW|<)KAkYn|KkdZQ>a`Bn!m# z)Rt|yLG!!1)NEfAUUC~_Km31S{h#){7qO{`^HBs34{xWVsi_?_0vbkzfm5qML*Idu zWpPNADLf^GV!v=6BYUvhO6Z-G8|{x>)nt0ajg?k$=AUpYbzBD#AW)2P?2$Ue0bWr-N zkpO(Oaa-@+E-q-dp%a~lgWywMUGCFo<_>WyZ)GFKO>|L#b|cxBw2Gs=lv0zL78{#Z zTjRowO(o>C_`;LXZjHu|Sj^nfDMogHXm#_m!_A*lLqrYleY2}y$()J*Zfog$>LG7Aw) ztX(|fVHXw*YTNih*>ifi%M0PP_0jXWC!l34uHVB0)`p^^4FV@L61vnj*31%Dx5yR~ z7EVf)G_|VygQpRz0%59&s@@0U1WXd?0e7Kxh!Q0srkh=+rY&3NY<~OK>yZMhV4Ug` zo3eF+<3wS7`&XvZ8`au;Z@nJOrSvW#bE&=k{oRFFepYeunzlv)z*BKsn}G=0Hl7jK zp=xr8{RQ~9dH;Y%_=G1lUd-4ZsC7mjsVpsMgZtfFw_0=UY3?;;hU3%J)+D*oo!+!^ zMAVQ9ljCC5NSOtF7sm1xw?KZ}k)4uJr^s_m68UIBO&2}Cw1L8JgRPYP+%)?%Ex7j`u*^ukdhjSI`~NRf>|ms~Ii#sbmmSHC-k(T;gEVzGSAy10^O8kf13H08*S(|jS3Tfi)%)0g zr^BneDSzhLt>xgp_c>x|1=w;riz^k^&jkv}$E;F>v|o#kR6C*vy)FA4Jd|Mb<4I=~ zL48HM-2Ay`;e{U6>(`4ThYs)RlEa^kmn8+VVRxiBuzj0^7~cfr<<4tb#@lGnr|IVx zw=k-WMM-uixtx~NN51Lg>=dNb7I;cNA8OWB?$meWFfU&euR@>`7#B#Bl;1$Ta`N2q zDsL-DeuP${$)~DfmiF|Z|3_EK6lTr4XF=;;cp@sV2Ka6%c*>J{a=g=gw14O>KVj+| zcIwC0ia?oWa0nLVLeUg3MqO!l(0Wi`?Q>#UDr-G2Uz5+b{~l-UevUq@4*Q)T;xy&ykL~}b z-iLg<@tn_dYdZHRKuAcawN*KGr-!@Pyg3LJMZfXzorn6vu_l~1&T{KsS%;oJ+EU91 z3DkXy_#gEI1+{{mR<2)TQ-k)jrw_=xTzLc;Z%4b?+5$$2jjc!}*)ee_B8ljEVwRsb7)nP+tzMTK5`X9iSM2Ir&7DBOS8F1P8^u z%SMEk7hp^{=yT0O?Da=iT{XVk&m7NQm(F`%S6;W3$fLWzrpb=?%_aC+WHUeUB}3eY z7ax9PE3Vn>F|;V9Zp`G{IewuW^i~SXPm=9JBAwx8p_G_7O*R#EY{$-_OGI&`@NyWz zvq!~_Nec?vjE`r#J9FtI!~=w}b=>ep9wXF>XZV^&r!Y^hOodQB6vv{sqWhDGxHESW z?S~uiPw^QsDje5epFNxi5GMk{mblkHF4^BDxcS#-6-C3W2XP>WH3MUQq($0jo(0TL z-vw>BIShbxIo=sDi@+EPgbI@HyP2Dlzq&jz-Q zFu9Jf#`sU)(c&BZMq%%U0uES?CP%jL?@0(5NA>K#B4*rB=x~J8&j{Y zU#;|Xvi{i5DCHpIxGr}t^&TMbuB%-Y7gk^bHNwQsA4G}groN`;9Ym#j88EEM2W4a5 z8vEYY(ohUlCOsp9@Y_=p{`#}`XZChB5eZaCcX%hi*ZDBB3*nh1TaeN-MjI0)prBwH z(Qx7@M7qehCy9W)N@{xg$D$&l@nVz6tSoDTQV_hMpnyl@sMB?S#~>A5m3!cxl-k8g;RY%?`R~$~&zpEc7_zu-By5 z8{74N57{2K&yLMCNu^+SAThKE;#rMqi>TgeVe1TmDiYI#-uf-wNjjVre3bJtygGuy zsdhvsV@G~*LkzGAb8~3u=obHd&FTb9Vt#e?oAuz#Ob}e~ghp&cB;~%78R}Cq@}!s4 ziLXCn{GI*bI5Y;WP>WVpQd-jRq$t~ie^XC@08@a*UunCV%1~+hGqI+iQDOR#nOVWH zOuBwYON;#Y?FxLowQUDp+5>{!?&otn2uZK%IV1*xlx@Qv>VhdQqsv;_+N~`us38BV z@mQw>VoKKrM2|V-|A#>t0#4K7P+Q-1Y_7`0fG#otP=G$DqVW&zPt@5nX*XO8?%`$l z&O?)E{=Lps%*q>SJT^97&M4q)?Q*_r=FTtf`nRqp?teyR{up7RFquNoZ6J|UQ7WLg zYU-?nLC{RybVWTmh`!9x-_A}mCCLGNE#IoiC&gZ`|HwHT#$ zIVeo+4rwf`6Ly1VPtRn<1ma-Ewr^YnR##0+>YKAe?y&^F@x(4UF|Ml`#;o?F%)c12fm7FmuIJ1Zt$X+_==p^3%kEXMX zs;UjwH3ldsor0u<64G7L-Q6kO-BUoiK0q2IrMtU3MY_AY8_vt#Xa8_4#{eB`&Gp6; z_jBDZmb+MaTUmLnU%+tHtjl>^j6h640#QIvfrrotQcTsSK8ow}u@PCFrE}gLFf}T} zbMtJu|EpPasCx8p#s)I_0Ga`kHXaAApIy zGp);vB5QjFmAJOD@ujubHSFWro>P_Y>T;{z^;JWOLG4o-i@iy~hHrH(P5X5KFw~|m zLud?j;zwv4vfTZ{K$_eV?wdnPO#XmSyn$7D%?_*uLh*Ke17Se0auE>G5Pizhde?5L zWmyog2sJidJT^)>y}u)0t2V}RaRq9mNp$XP0f}^xd}9o2Hs#0wzFf1fy$E_eta~&Y zMwY>UB0rTf*}cx+!$omF#C6KiYJEh(Qd2+wAw!VGk{mRY2)&T`e@OAs> zBm$lOgXi~Y8JsJ!@EeQ-#NRg@KYrYyXVQkNL8>M#xW~KpGON5TR$$z+C;REan^oHl zt5`a$X2qWR(ZO~!8=o!}V!$c4a_>&FtdcOX>!EyIv2QQM0w`53k2hN&#VASy@JT<` zB@grwuDg$;yL}15GMO4($9vU+`z(oQ>UX~0@2xV+5xznXcl<-F zxLl(;4tD*8C7te!nX%TB&;jw6CE2`i-Zfd3@9$(ZTYp!cvDw`+ET0mwMy;o5ClteEGuuJCIf)1Do8Q{Z=}cACkr2`l~GVIlPb>6h@%6%O*5LyfV?B zVxJz4JG1B{-{B${SQhDyuU2b4^wSfhm8r}p(#@7B-~YRCOxGgU#?28?@=&u4dUU%#NZ{ztG8*Ievg_7C){-xvC3qVwazE-VLruB~%_ zt6r2p7&IE*hYgfGKvl$ZU;FZ_d3??n>QF?R))FHeb=VTF2O=WoMg(oSR#lr#3j;Tl zdLS+$fCC$T-+}0Ob91rn9Rv~H>7y%-g@%P5{|ko5T-Lgb^}d^q;;6KK*S(k;f?d3i zvfCHq_T`NpLxm2_LVPoC-0F4jkNM*1^ikS9nsoL0iEzO|(*;gxW*rW268$i@$OF@{ z!{66eBYss(qd{mheqFfL#Z4Av9(FjfGVlQn0`H3Sfci&7=Jg_8!VJzwZgg-SV{IBi zz1)~N_q;&PWI%tRy3Fm!fe%pN0>Z-nW{E8=yv>id=D_4eof(LG!n-WuK>LN@({XI< zFOolH{H!2lKAaym9i43dhsH;|@-{q>UsMpI(bI$Zuo!^c(b2epd}tsa9==g4;(lLm z^GwZ?f1MD17`$=Mfd73i5RiDwE(4b9^z|M~p>_j7jM7B@4w$@Ho^!M|`0whKZz?$P z#ooMLOm66!PH@15$Wyyds6MD8_ezm<6srNScw?Aqlt+1#_cHt1qS>GTy*_}f3BenbuXk={XUwFZIDFC}`u zACFH{0Fi^*FE?<5^#5{vEA)f0;-4d~Al~^lK_einTt8eb0ThNU@D6F8o5MpVm;7dE zNILep5IG9CW`HtirBV`LY3TuJhE^h z$xOw&W8;1`4`>@F>gzmA8rRBFv>TSY{0Pb49Asr~4p>G3e{*XY{%u4VY8OGW1%LsFuE9gbxt)*4yHX*_Me0hjc+fzYNDG ztLH_W|9e9DDJ=Yni2>J}uPxtuG`BO5sNTPQ-XetjN(d*KBgb1o5S^dAkQN)X@ZZ zGZ2z=wLD(X#>Yc+%2!P7XC5J+T3Bj!`jg)-e2VEi-tm`mIf^ckUHYpC&tbsN)O@=v zRolR}L)UO9q-2TjL@3cSISnkQ9Qn-eV5hXJ<1raNM*Ix_7GI(=*CY&|@kW=DzS}@9 zv((1pEhxC?tvw1_@I?6p<9`41xU)B-di)4??NYm31se^O+UT>9hE8+{PBGAvU<@NQ z$*L-b{v3}6BZR&x)-qZ-LJm=$cUNl(#=n&~7qyx_*m-WZKgA~{?a?u2P{^vw`+zKk zlpst<{$37aeF1F7t|9#N$kDeUA48S{MbGVQ_S-@0&qVsvd+B5>z&fM28dv(T2^EK4 zZt4qaGSvI+joK%_DU`oSScqsrf@G6G?t=&DOTKb=6cIzbW52s{3ky4eStoJa0KN4h z*E9N+oSghKHFf7R*wOI&_h(HFpfs6IF@RZ+XD83+JCXi-|1T%cOTCSAqJ?xJ->|>~ zC)dqt*!ML970AD{5BK)}P^q5m&apx6JHcj&3bgJXCx?`SNi69@a`Q79w51ED5x3QI zl*bn|DtfuZpv*Xlg@`=M+-vxJ)l_$J_hSxV%=wh18r%Rd(L5d^jS zYZq}en2s4HC*OryV&bUt93xc6lIdEEf_`5*cjj4ntIgk5Rrda%pOzb~5d9)|TM*7` zneKN8$?;`P-m#PKQp(LImDB5sUnE6jgE?1&NKcgqlKci=eC1*M2nq_nwttd$ej<}t zZwqN~<25KnRoBXI87=4htA<_=@c0bZ(71RWfY6T@B%h0}8?cRar+xth3a^UIW2*!n zRXype6;YU2za7-%0mTAj-FO1m{BWr;!32Ow9%lz|T&KOCEbIR%?ZFbSAS)bGY8((cNefPq zfQ?oH(Xy_l>Uh0LC=~ml!Qf0j*>ZvFHuE>az_>2n@y!yUWcSmmOh#6@x0xlJt3(uCuOpT`ZQf zubq+e`^kU)q2on{FBAC>^!R$v)?~Gm{!E0gv0WqM|IgD7!3|mYB+`*P)5WLE7fuuy z&{0)EQMR%WXL}?)1`{A4(6hP&6&e$&__(+PcKWNeN%Ta*d21sy0?kG|o!-lrub@|v zQBg0!SKsDw#udT)q^RAeUaITe?O|_kFPXpq3r;E_d3iKo5KpCQAxFq8Vm|jN+g|+`A zUZkU+97tvJq5B-#NkScp8`MJry|P^%C-bO$cq@$#hWft+w1I+yuei|#-A5%Z4ECqXz_&yVP;_B{6SW44Q8u== zsGZJ$D7L#7Q{&?9;$l(-kATn)26ZE2V<*Y<^LaJ}D+zyZoh)oVtyEp{3R`L^;CX|j zJlGL}K0vWpX4&DVJ>znZqRp|s5jFUoZPESj)PyjVTuusTu6O>@-{1KO;V}P|sOdvfdw$7a-)3n^5K3SzrIT6rrb|I1xMB4TC52B1+KE*Cn0I6($2=pvQjpM!Zo`v;||G4{d`_xECnDCTo3(tY&J}M%tI4>s6hCN9G%hb5TCFjudwt3AD*Jj)|%^I z@%+E|qZN~Yjp+7L3w}Ci9)I8AKVrUf&G!2y)XgWAtfk&Iyr+@Hgq|J;C)<0@Xf3BG z(Lf?p;y{|qCNmmwr5Lk3t9;M7e5{Ek_56IL-GfszIdvtW3F!lg{W1xD6!@T@y_9_x zR}Tj9q*5fv+n#yexR z<1Y7P_Qnw=BXeU&q{X=VpY^)xzW9y=AZz&+S+G6m%Bh*cmJA-L!)~y&y+c7k`R3rj z1l*KJn3#SwxnH3X5P0eEFfuUwxc_Nt%f|k}+Jp%g(XFYuGs|2ISltevJKNbY^!4=t zC~g?QBRwMt^{sVkKZLLw%!2AS=%-UTCB(&H0|y+aw!rdBgo%Q+I>%4`!r5bF+NKAd zDfzPkB7(g+-=l2LZ_F~t?}Uv~G$rf1Ar(u#$J>-H<-JSGg`e2B4jCc?vh0|eBxQ6o zKCe`-X3Ps0okLk$o_w#@6)}YL7H_o*=h6GdvHj`FohcLy1p0h4Coqr* zxcST=bq*!&K?+wgdfMQOGERS8e$1@#psPN6ZuMO*C)^#@P$iIE>bV^dT?{QI`kZQtW-CMbAJ;M zm1=}3`5X5=+{$a~7)m9ViwPBYaJ6-GyaJS5zz|~xY|&>|PB0y&3q*d6XTzMx)Y%9@ zd^+`rZ};Z&1^0u?`Ud-r<^xhImLp=a=~=Ojji6H`8N^-lMzODLHR80nZ3WO&@XW@> zFRNFdNt`m8nd*!x6fpd>B6H%;uAG#QIJL9*0$SoIGTo~T#ybvjSDXqm1f%VHkOqUR z&&91#@nC$@QZmrzb$sr0Mq{jU-Vz{-!((y^p;VzCKr|dF3hO)~$3~)m&?N8Qv9QU) z|F%|Q`J*GR^DKwg(s4pg4SkM4s@<&jJBg={BT-KN;e0qk@oKe zbU5!VX6oXIAC4X#cZyLT4oEDFr2~AL1*cq7oq!Qm;>h1fxkg6>95gT)k7%3H(zSI31T7A`rOzuEx7>sx zJWnh?x1lX{p@d^`pT=I?nL$usAQA9c{V%(e-5_7701Mbp(l{kRAt3|!Q+hPN0uqM;1w>po ztAaEGTP$$*#>tambv*eG63JBbB@i-4cT}s_h?WYk12VV1Vyb7$=WU}e24deiWEl_m zXPapW89XfM1!a+}?wgGK%DH3A&@k72vi&d@-UU;7H0V0p%n=j!#=)RJSH#w-@yvlQ z=w^H3&#QNFP|-`>v2bo=mNqI>5^io^VgdhB-~>4~{WA#tX4x0{JM*9QaLtz3_Dx6g z=~^R9)CT7a+Ld%0UtuYiTw}uet3!#*QX}V!Ibo8>U&!T*V>9v8Du7<={l0w;cD}nRv_a_{BxJ>`%0yb#3^QlHuD3ot2RX3YWW{@1)u? zJ!zi@wW?yI0;J1K{@F8?P+SNSzFy$0ZkoUm(;GN^D-rPN1{=h@=X+DBy}hEXY#Qdn z$?TyYFADuWMgLMMCMPR5T`-#jb=Wp2kh|*F^!PwY1^PjpwIX&T?#Ibk~oY;&W zGrTt4;`LV$bnEEokjwn`kl_KV4epz`xOk9yHK_f<0v7A>qA!hqS{e}&KExpi*kwKtz0rRF=AleB)}0c)}Um-^WaEhuWZF21H0775Nho1 zSn3du`P=L_0h1NVU=Slcq*OgqP?Z!pT zRi!M?F4~yf>dy>S=nYXK5l#ol4ArM=wchUcE*qy`LK2OhukHiTTZ6po}`GY4C@e-nLsg;Jl$Fjc7}(W zw)Z>pQF1zi(ANG&!MdqqXhKz>XO2O_ML%8sbf3M6+CR!ddWvD34F)89?!HgR&Sf@& zQI(aI{C!%$!0YkR9n*5QvdqRX>XcvnRB5tEy>B-LLUY~zR>q=K;5u5d1ao^1{hB=B z&^M>RBNHQ?3?-+H^VOKDaNH;nB;-~#Nf7^&aUtLd?eN-BSM9PsuXX!0)jUDp40*9g zII6UHQWsXsSqGI%fUae6+#T2J3dVH4;E53z0)SoMFU=0hgwIZv)RkNm6aiJ{Q!oFP z8T2%qdQyN9PD!y@fI0pZ5>NUkb7JTEIK=u||L{rD;IR1X{n5Y|uF0AJ*iRKmUrEU! zM$b{5`*K%Wb*&fprZK_Z_@KRJBQwjyr2Hp@#WGrybW+6SfkSZ6H6X58yYo~l;Du_b zDXoaXni?=~%-!3vTzO0NbwvvA0i15g`v+>yP=U0gKleP_Ifb@L;_oYKft38 zD_m0F^xxRO>C#PW<&x`YPj@FQsupNjVIfXC;(eORlL)-^T(cdkR;(!HTd;x*_WE^Z z>NaJ{8Q-yrR+d%$QCMm_cQ<<9{GxkFG6Qj!bmKz;Wy~+c4f3`*@bMeGMu2Dwkc)e@ zTC71qu28ki2QVtvwzhhJS$$be&DLy{`7@0;MOp-i;D}Q+e*gsn17tk>5s+|N4F{m+ol@0d~j_DB+ zwe1Qe%|p!{B2orLNz5xX);S72EpQlPY76^i8yjyEzO*~B4%H4vp_kLct0Xi#aJN`4 zL^QV|V`$(p%Q%`tLqB`mQPz#er?XdVXzvr;r5&l79K~en_ zqu*>GWZ7BSi|FIjCG|kPC_6ZqoI9@1Dv;r1hF6xeMcGF9mon)eZ60Nls$WdM@?UAx zxe`jgA-B;vsYsc_CBzI(OUvtvqR7g3c|%z@Q2H1i8!nytkIAL#_hGxa7RYV5sfj$EOWC*G5WX4v7U!Q)~!vc zY75n#-I#%CsO}<_M4{}3hxvGxbBoB%0z@>_u92x+AhkzLGy2I*HN=aYB*_eg8-g!Z zU`qJz#OVzq6 z2I(iz<4wR!h4EOH7&>4_Rqzi0LnW8Z1mnZnVX(F6KzR+#ul0y5yWtNBliC@bVep6D zt|-fV{U{U2@;YFvr$t0v__2PGfH7ER(G8PxE-IRh(s!C8L;?w0qWBws>}m9JSh&n8 zG`2KZYSEL<&x&Z8JBOJzpEMyB%J zoYu&gmOL)4nrb1nzj=Lc;`dnD`_xB9F;)gigQJM;5qFsjswf<+6P34ooG~CmZBp0~ z_Gw0$M}F%O2ZQ0be4 zE6kX0-{T0j@I{PeIBJZWU9@-v@w`S8Aa@BVi-uop-}?o>cvs5!Sc3CDne#&*)C?(C zI6sJ&Ga#@gD(5NwtlATw^6oI5O~+#))3hE+SoX|y!UQU2nXhR?+S-#7zce5+aV~7x zaM`;_f$z>x-Dj~G1uIGyLMEw1COuF@dfxaTLIhTxc`79YKmn2kYBqy+MsAJKK9TJ2o4`huPG2G<{Wz!#-DXAS# zV7NI(B6^FH9PT>uXrHr!U97Y1-#)q+zU5ZEHPl0pVZGGc_X-p7OI^V~5=15e= z&5E;ic9?8xZ7KeK#LZ@wml|#B4OtHR2m5c|A+9WsqH9V(O?>>TN@f%PRd^GSf(&dk zX-%q63s`)C_*~t>PDS3f&f(BYLi-^mAC#{v0T`Va7 zLiRmeMg;z9>!jny`TE~@U&MqgxEejS8FW9Vyx|IpvzB@%_{Fs}_dhhmsPc?|%E!ve zhOpLhRw~DkHn{))(yy_(oc}aco4PEz`F`nO=HeQBlW&OG2x?t7_g9V{HEx`VFO~9? zKJI@QE1|M}f9M@ip$g8aWtwhjz6%JLUmqz(d z0AsBbbXRLLzj5?&rF5~kvzsn;!yNF^=zQdE2&S&7aZb}1Sv-gKZ9$5U{^pJMnbem( z>cGO~CS*>vHVv1QavzoHVcoyzp65hws*BfJCoi(JB);lr%IBH zR5`HGT`_)1uDC|DS|g#nEdSXu^3v}aTK5b*G@NX%ai5&nz_t=4<%p3+p_Nw2F+TVa zkDpwuhCMxoXM8RUXY2nIZCtjRsRA9_`0QO z>Gxw7wzK4+cp;&X9GlZR-SZPjjDezJ)GF!Hg$h{S6R`O3ynGa`*@cY7^A#z5I@(PanrMsaFX)*iqZN(D+od$gq8fz2XPOMq zt+7-3;xuKQbutK%KbMlmROhgy)NU8z#?LQa2t1C%$FzkmK$9z94N(R^H^F?a`*TNS zl@gp$YV>5gnA8%Ld)+=O_iC0&uYc^}ZQjnSA1gZLXu-O$B^ju{Bv39@>IHrIH0lw5 zkeEaY`GMDhr2ltM6 zRU~XnvJMbD76$a0#NVtfX-dP%caF8|EVGR3aR=FO%Zv@j)4Tb|V`x_9$v2=P`nb{! zHShg57q?^*n@8*gopw(C+EAInVlQ3%u_gK~T7=tX9l-*H&i$Xw_C$)9nSqa`Xho%8 zx^)2RJp$1yIf*rFsuQ?UoNmN1zEH|*P+8B#dI(k(cZ1rJ(a<*3cwR2M7?QToB=;m@ zLQ!`mchqn@CmW9?q$u=YNHXvdgA~dAqivd-Q0N)G=0d&xOeE ztv|TS^kCsyPo;ew6TZx`^sUDVmL!Q(WFjz|{4tT**#850Gb$#jC%wX!>vZwsBy@>; zm;shL$?M5tKk+~Oau~xrrLV0w$f&47Km{9${$=PvG@C2;=~>BKRMdD-{Mm%bweZ0l zwq@|{H@e8D5Nc!uB)wjR5xV7*bQf1V!XH$zC?#>Cq*tNJ(IWeKnS%H;BRc}_f$Uyy zJ>fl>_VH8RB3jFgT`ri+{J}n)Xp|Zdj2@2?_o>=+E~4KQ4{RL!rAUb(w30&xoz6lH zsTrfV1vFa81;;J4JVronjul?)5g8TR=l)K)Tz71de+|*>=5(eaVzfq$J3U__=pu6h zuXl30_n%4Mc>F1>cI~+lrpTp6I?R6xfv2bPcr; zIE!%3e>|{tuI%X;C3vGzh9D)@l`R07pcL@B#yk>wn2Vo=U#}V~OS`_(Xv;Sj)BA}3 z@onyoh-N$HS8`3hoG}Bch#CeyISe4&?>JfOc>zqfpOX?`s@6w%gZX1UOVCT>no$Dl zaqzst1})$ly}o_J zOjM|aomhFm|B$^hjlh%^BN~d^IO)PmRbo2%L-JUh5|O>GuJLj)XYk3>&$Lj_h6wWD zjrPBINrR(y!=J-?+>vf%oT8!g9R*6gBEJ=%wMhS#4sB zfSu7-F*SBl;|&{T#(*rzHE5}($F4GCY~%>p9gq322Cexp{Bqo;z^6q6^L`;E?1@Y3 z*x9*zh);P&#bGu8uKYGyW#?|u!j4Iug@B_=V~JL8l1fI6gQ?(;GNQqAKosrqz`>k< zr^=Cq&^KRa|D3A9AR!@9>oh0-_yO}ygse_S8lSgYW@*UiAU_5YqP#YnM9`h?-E#4` z9#bqb(ps1&i|QX7DwG^1;FTPY^b{02+`8Xc+dd*%THoK5USmlK6ps7x}1k57%0vaT&UGGX)RMqV~G#tv~upXGH!*z$~hgXm-Ly<|8bMRV(;hiHvC2rjd!maRI^HJ5MP4 znBSiEw;p1J4|1Fo{N~kSIx!2DJdS+Pw6bz?D zLy56SKZ=SFbsBlSpF#=rl6i7yb;Q0(*sznuz%M-wmml1{3%fGM>yD%4;nUJliXWlDK;X=$Ik3A^FCT+YmEFKRMAL)(~s)M2rqnlw9gxc|;7!7}abWo#bqorwBV zcSfHOnX}}Bfmv!0ms<5QDe0mo}{AX;IX)i{;;Bpj-{-W!Ppg^SzK zZaZ>JmZ)_h+Z);LUYGF?Bqt~1oOjy%_IecZ5x{*ZucJWnC1PH*uD?zpGMN*#uP!<= znb$M4Q9edp@#RI%N`6p+9gjjW(kFaG=(sicdK@yyZs2 z2&wk2j~MIG;ke7+xw)5}-z7LeCKDeqxId?%ZT}Y@i1e6@rHiQ^!H= z8)SvJ!Zjo9=59r9UTY&B-au^{+*q6d6AS-F&9Z4GeJ8ju)pmK*QbrSr$qC9pZP|?0 zc_%onQSDuX=Pe%H$I9k<0Y?#SWy8Q8_Ve6=IJL&su{5)~fwB3k^mQ%7wz9S_>S!#| zn1XNpa3EMki8P%rAsUu=*PPXA<^bn+u8A1OXl=*daZOA7DQO^qoz!rC-A)-H8U*Gj8I2t@E9bV+UbWPDZa0daFI)ekE;D zSoXox#)eME^T9E4WZ8?e7Wr8_^ELMHN7ly>j<&F0S@KI;T8ux^x`*IRC;k81EIKK` zcPJpvv#o3X1vBUgeGI-uFGJJLiBITERxJ|rW$xCz`41dsex!FsNVK7;gMd9 z1*_jw9c9K`+9-p;zWf=Qq*)u;m+;FWL;fFoN|dk%ebC_`7mbEK`V3l9@BZ6Txjd6y zl^AJ9&mF}a4jOYe5UM!GXn;a&MGqIBra10gxKLzFbWbLsmqh1&-?dx48V6TFx-P7> z{3$aCi=qFqnpFdZR+!7nOKSB-95BWJE(ugJD#mCb-iOwE!bgeSi}oJjZz=U$te4Di zi1^`|nbCeEzeGuO8%3HTqXae2zkKaZUezBLTg{NMd)SSUU)o#W=hro1^ucAND2+UU zR3h?`iOhns=z@Ql{0HJJ-%oiqo!wgc$M(?EilJhtsm0)!%<+T>dRa}hTLZ}Y63)j` z@5|XU{$8VNwcp&=wT;_2R>~Tp(e3^f)~*{~tSu1Kl-&HgVZOe5sA-z3ocFeHSKI(x z2P#)tkr8kBl(g~=R}JeuWhtjW%4558;=0t0wGO24wB5uctKrg&=6w(Z&S=fXQP3Eh z$A(+_)h?#rCzWFR^?t|Z8G8f@PQ2O4({y_+oqD)ixg`m5!~r}|9=U&9lyJbkVh5G9 zI*$Xm5l{BiiPUEQSxV3?m#nOuOAOI z9^KduPi5s`#@?IGSAL*vuwA18P*8%N^*Vyc`dcuG_@i=Z9EOoZMTh0v9a-{5Mt?_!l@45Mpp z+*6aTnqu zEpWEhCg7ZGmlG2gC>TBNDS3C}w27TXszXy*Xb@-Bcs$q=M&@T=eQOq+5JDsT^ zB!~MSR`o;s<_ES6{+fx$$t&bdTu&9rq`*n#&e4U|BPPj01=<4p_`z6(pXLAiCQneR zjHvA@R*7H;l3-$448IRc51sC*kmbL*qMf~ta>C|p$1`%Q@Zr-=ik#C_YpimX)m+oT zw&7Uk0N(Z_Uw3_Ds%T&2r~);1ix4TpfjX*vwp53nY<=MeRqI(;m$BklE3RyC^?Nol$eN(3#Q8l!@XD+vOnyn7^gAg28%<)1U1FUHKI%Aa6M^KwzVj(?WW-Q zJ-J*eZ7m59i=-@N*f(bXy}os2yyE@j;&#ggL;lq0VO{=>Otp!3*X%jJpVh zYomDK8%$93kT>dmUDA1btoO&uKT1e`mnbX~KkuBtBfGV+FU<^VVYR$(GC9~1 z%B|sKfwg|gZ_kRvpWv#tr_!(WcQW~4;^_7}i9K~erzx!Ipy=X* zAe597G?OQ|Ke%96^xiA4zG0I$O>|_Cq2Y-nzUs@Y)^t$e)cdloo*qWqRnoCNT4Rr( zRGl!+LyplV%JEQ%%9Y3uQ?AZVMiyM;vls=h89@-+=!K{wlhj_foy$L4*XI{)>DO@MLRy1Oe#2?;7uRS-=ge1R3(0#iH;1R8 zRc6QP27@uk&ruh*fDXY(_d$bHNl*C4?l{86Fi*0h1cyy|5(6U|{iTGnZ2B5XC|nYc z{fDu8WlVj{cga{`psS^_U{CG6JQK^>#7K>IDy>S_qOsAWZy-evL{;p#^R1%{m%VN@{H(q{3;G{mb!Jy z;J0p1P(^1>mfgO`%dw~F-n$M$sLVli1GmNpeo-UpWt6*s90oXLVh1JHI?@)K?&$9G za;QJXWU*Zy&UX)v@N5F(JpQ$?M3%LoKEly-305^nn#lQ2!5RN0CX4c0ypN;p{0{|B zs{IBB0NO&xRSHoAi>lj7q){IEh{yA%{`;Y8z%bR6$O?Sp$tlS&kz>@MO>wgC{vg9F z{EghxG+51j_u~4f5*g>B^V{0XCRHU>A%TK1M$k}{)G9}^d_gny;vkWJug-fschjnE zo07$qgIi;mqI~z9tU6hST27)STLZIx>LOD^OJ^4LdYP1rp&yZwu8+NOBAq@bYE0Fg z#d|{Tgfz=IX&;F+3#75cuqBj8p=PLqj6-`X-zhi?c|XtRvz@N($&79_vBP*YJG^=| z%kn4hm)U1jP7RHGHg-Ekin#O8H?nOS-fE>b#2VEWFIpECQmp?l3DkXJAy52rB&|CV zZt2(k%6IRqJL_uarWdAe1h8~5gowE>Zi`-9F~yDapb(nh^HOL)_ims$a->XB^Md&m zu&o@2Y2o0To}G!)J4X{^7x=>;IYlV;ff&6JtsuwojLz$ESHZ_@DnqHFzo^EY;}9XU zMf2NwokVuo?r*C>ukSdoIGIFHJrcj7iWtjI+n-QU$!HsDDe{-O)mNKg7Tq_we*PPA z$mjLCzM=O`jq(o!$Yg(wZwyz-HAeynJQZ#@chR8Kpe?i56V-Yr#t2&HRKvqZMy`oQ z0@`Y%wxxxca&Hy1=TZ-lUaa<9H2mE`3Q3uB6#TaW%Q)U5g-mwnhvN)dh^J4af1B54V=G;j(cl5w6Be)?n%(|5~%VT zRSST#H|~GVY*7ep(Gizb4sWr9tm){CH?VkAYl9nKx|NMw?zUELy;<9lSEN-0UMa1S zKg|mvHq4&gvAEh;Ek)kxzG43WU$eigovB|PJwTSiNE7|<-DCiQ$)-w-k(W}!JXz?_ zR`IcfA$7xADE_p4rQ0TcpwVL+4uPd&Qkf+lND4d3`BYwJhZFSXdxBZ&Prh0dBPUnQ zYw!0bhyUrtXne#ERg!moiMtWCn!sj?wzC86z?|>SMC*0<_kHQyfDw{Yt*~Psk(ce zg^w5FC8zcx7Rn5@-YQ<4P(}3u!2o%rqP8LI(L{kt0=)qol}a(`7wAh6m}or(&MS-n zGj4ITM$lY%uZ~VB6luKD-wFEK)cEb5YY8bthB;^x%M-EMeOc>9ab~Z6q@_gJD-3?E zG2{f_S*a=V8VD|xz2USw8mdT(ITQ|=eK|DzyZ)9s~FuTr;2Zhs86=D)Od zaMh17=8)Raa=U^B-R`8)wWy@HjtA$cD`3H zL(p#ereZEsdREmoNb^sK_G1RJyAUO}qEFxFs-kJ}37^>>mg+wxtURBRZhG|9?CxLI zJ^j-@#Mg`tN=ZafoWRs@FH3rd_oi%mA3@Q73BIZ+zBvV&ZIk<}v*TnYLNBEs;~Sq+ z|C+O@?OT$qj?L;SCQP%-3H&0?e8u?%NAQpZN*VfvUuK#|K+*K0ykJh^98L?xklXuJ z!(S(Qx=#D39}zrgF{-AWcdBVCy2xv_<)C2o)K0Gs1tW* z#Uy#KBaNosu~b)I!@ZVcq{DeigS&p?FAM%wf(uoJDg}_t5!Dy@I7hWVzTO{fl9m%( z2r8*@5Rg;x+^btP+eqnaYHS4SKr4MnTtu$Wnk6Tt0*~_I;VKMh+R^}FZ2Lk83aGJ| zi2|}s`9J*&BRpTJS|)`zL=7ZJF+;QPJ^*#ge~xdY)&**bx|&0o7aqqQ~M+EwN0^ENgIdytv8E}YV-Lqosw zt*cqB;}8m4u(>CI^N6m|l*rlP?&A3cs&ow}BZhT#*JcLp(Okn9uuHJi z4mWjs(*DkJ&D0{L^T_Kc$^XiiMhZ12?2lI_rViv9NcNO7G_3@OlaPpknk`3o4F~f| zoig!sKGoKcmKyZ8$kda-bRxd$jZD<PtXnlftO1m8|H2XfrlZ^^P- zGlYyg28!_LD#f%m_c=S-dsQ9aUd`a*6Iuru-t>hGZqFrVdnwW2oB6Gr8NVq9$N&eo zS7(z4x~rwlpq3K%*e(9ENz40ni88s1Py)ku1U0qppZ!4avooF-?e{6)1FW8Y-dNzZ zJZJdbD6u;!pk_03hY{ZLpQ193cp;V$Bqv&jCZl<7x~ea2yR-k{YYMlNct$)m{x>B; z-uqVqAbD-Q1-o@wgY)g1oM2uuHIgz)${lMbA-QL)C=6h-SVe9e9v*@bet)p4pJ#qS zsvsD0Tcw-@XH6zxc8ukVX>v@!j@P4*`V=uub(3P*@YC;j>{{;iwPFYKpYu~juMfP$P3y#Go_C>xLCcVrQcp^b}{h_k>P^>&emzNsO_Dufl7Ic#LkT5 zf^D)R{mm9_m3UXhN}J?9ntzC=9=?2Dn?@+_iw11tr|x!|JD7N;eN>(g%(?3q%KV6n z0sf)-Tg?J6orH`2sWM*Bt8d`f8%uzGC6Ar=2pYs!D>6jQ>%TA- z37fBw9hkF#>_0h*Gyt_Kw2k(jR`F-sTK4}#6ed1wBtld`V>`LDJH!}ua zh_A9$m5QS)-aUE~k0V%#7NwzNDqG3E9WE9kGzspcaN~t2>Wk!kYichLX2lQ}-P>oG zT$oua{2a>$&@ZKxenKIbA^`r$Y&H(_tzZIc?%V>%-nj0MJ1VW0Ks)miL+z31L7fVu zy(C|Ha4`I=mG3)1-z=?ZqPA~%(qBdx$#8!8cP`Zqdh6YWpg!f2P_o`cUo8pi?Fw@f z4!(9O)O_(!Rza`b>7Y(8#Tb-D`f@(y#_JoWQzymQu)hEZ)D2`Yyf|<=W^-Md_vicr zeD107vnLp>{t9HjKDsKXq1Q9f(M{sv!7;rYA!-+NoPM&stsk17J z)3r{_N8wP)_}&dHf)?Rn-*`z&S3`XX?5Ai*RGfl>GbCg*hmFuNDrA(R4#zWiU~5F5 z?w2-7ntZ{@ySJo7wZtzjaAiTI#B9R+Th%TzWsz5opggMVxLJYZ@gl8CF-{Wk-Q68_ z(ofj82na8idm}%A{aN%}2iTQ`pT~GXG;IPC_RTL(7c-wkeS)%dxPX-bhx-At2!}!$ z!eFP4SK?}f|8eyaR@TiI51^Dp(QxyO_G^xc>x_;F#!)HT77d|SjBQ{pKEVm;-T9Gv zv7rntO7!`~<|$FSO%=4gquCw1zx?G|L~u|PtQl#h3)7_gy_k>U5`S{yF`(e5iBKi}C%-3j@R}FhHw~f=_|fq71<(DNg^bYw_s|nkcbW=D6oWlZnFD90 z1{`CZ1kNRk*~*C2Iip-&S2$jmu!V+D{uj(P`%-bBabvq;&D|OLWZACT@Wf`S#(LxW zfTWE#-Q;30Y9910$6$0#n9Rc?W+?3GK#=Um?vf5e#nwAPWG^}A_OTb@%yQ7{hPI>m zx{zl9W%m~^Fyc#H-7a25=Z8z(XeB1E&77fPNzBRdBh*#P=GGC(i4-EU0_X z9f$aH@#nXyQN@}pP?%qh<;Ye=OOEmZN9;RElDe*;!S`QAwo6EVn1DWoZUs zyOrcb5IXYXqN98iFS<>CJ#HQtm&%(ju0BB#smQXwe<0EHX3VV0kDxz}842los!neM zS&!-;@xJo^$JAEi>#jzf{y1KWNF)9 z5<XbsgFMUnJuvX@-iKX`DO1X>`c5_aBRf{ zCiP5%^N-suCqrN+l~uVjp9TuM6WNRziIut~vipsDV$qUbIsrN3>uc1olt(B^ed5sY zRb-gpuGC!)!q+DMVn3xWk5|}5USte5AhO%s0_9Zjr4<9}ib#n+hYkF(w*QW7RhlY} zphR6`zcUs7shn0Jvp2lC`N?aEe|}{j-u}X*U+{~Q3p|{c zgzfL=F{KC$i`36d0>vHkk2()5P;IF%Xd3tgjn$9W7O#+doXmBlXziEyK?qeBaJhb_ z&;nxs?k52twLR1~oG2I0rhnIwPblfUVDAbfe}!ddYr=f$Z?@xom#!fZx!N@@IjNal z{>PK|MgsbPyV~Gu^uW(7I^9-xe+OF1U|#1$2iizmwtFhRqqJ`k;P{1ee=1wPlXaiV z%u;q6E^uA5n}=Uk6;~9big+T2C?6MIk%nbyEXcS*IS}` zSP~;y{8P2bhCqQTJq_|vr-y<-*cVt};43q>fw3bryEF05<#hF@LBc!N zQ}F5qbjt!PWoh3hRG;0LY~+(RRUAXWX%W<|{SM;Dm(kTCZ!6naOc~PfiF{&YHRJmxa)DR#4#=m^L~M?`ibxWw6?0V%L}jg~ zjNiG#>^Z@kS+TLaExbW%WMLYZxmYY}KV!y8$ehY6KNQ^gI;x$+3T%k72d5ZnH)Wrf z0t}~6P_nK4Sj_`DK2SSQV+I48@^rCkdH&~e;e?!!ddEHGV7~ggm}+prs;*w&;+0B; ziIg?I=W4o~F{IVd&9S|)Ht>izI-dQF61jm|f%6x>lFv~^u&5CK6g*y2aA^E`(P=H z^5g{Wl}Db8S`vO89zH2CuzTaTYYZRE=kBwc4bKbQ<7*G6<nhf)p7*1~K@DbvCxq%5{u>hO2YYbjsjE0OfXcc38_zgv7z6ZE*vvtuFO%>eQ;Hat z5XQE)7Dyx|*Dlg%d0ZP#Z~m6%A(hoxOSaxW&n~{JD4IklRZ$!eDZ{B^-|Z3w7eei{_3mERrkWcBlyf3HY1XzrQ6VCAqq}{T??93J<4d zWgT1hF2T@hXNc1DxeAnLyFUQ*C5ML+zPjG@FUyk76A@{>SEa8gNO~>p<#w3y2ya|m zOmrI+Ni9_2-^7%FyqC@X^1oBpw%grt9#VtTw$vLmFnCs5y&^|@$}=+)Yg(}Ho?EKF zD<(WC;Ymt1w2y4CIa(1K?Ugf1TbEY&&YVFx%G8R3>#CDNFN>G=%>bIvuj$5bZF9Tq zVI)`Bct)d1J?Vr`6fwd{A!%urp7aearLg)l{?)(}dmzqDjGgT2sp!?ibo zHB|XhGW$O{de;7R3Tve?L`e=n7cs4J!hjIM6eNm);sc59SNm*+9dAHVNl;|uPDL;2 z=j`#ZI-3=vm~tdMfO*p&PcKua&f0RZe%#WVfe<7CMhSepgo7btc?=y*DCP`zYiv9n z)X8?ukESIR1&KJ&n{?(2(uE4SqjT*o}0w_hdd+Ush%iO;E4Tsvi2QVFb~qGM!%H;X%6awOKEuO$JFb}ANc%! z;AtFx+bb{SxF|B;I~@>=bTRKwS$X-f3>X*;w`}0+snC~@f^?+CChB%dqGBAeqG^yS zs-LXa`ttnHfyo{{0nUP7hMximRD?L>R8( zN$dzjtZdF`dVVwiDuD7b-L&+mLFbAajeZW0i+*8?VvYC_q#p(^RUjO8}7EEUIoo!xBj- z-&IQTrvf8efAdK}xVPuuKl-9Xs&9iJ!BL)c7U&*t=I5z(;RlD?)bsaK$mASNg5lau znIfUr^%^y$qLHE$1wZK$Z~Fa3l_9TVO|s;s2`ls>by!7?2Iv;)y1Tlv;za?y^d|=3 zP{5MVLOG8{X2t8r-}DiD(SHmE4&2UgKaS9Ykv=A9*u#p6R71yAi}QnHtF-inhpb!R zEIu=Fif&Pb?y%Kj_Y}s8`z@s zXs_oAu8ew%jL>gOLuWspm5hdd$xBQFq~BZ4G=8Hy8PP{<3-F_Lv(e1ZdHyMr`R3nu zA^mTDAz?$01T@yA%x$f$p;?G>7xV8sXc5^vPO$8tDvwNh-o9`8%xzWcq4U zC;tn37A^W=a{_WO>+R2oqZ*&rZkMf61-ClRabKIM%5bU8sY6&9Pknnb zcWUsIq~@Ke8tFM9e$C}MpS}Uc3;tBgFFObc6X#eM%-7CM<3h2fI#Zn^;!Lsiagy}( z%Oeqtq>I75@s=Cz@}x@d_ri2*L#aFeN|WqknyD^CChL*vw#4^8p4bQy%#;H{M9vx62s6dSV+(?VU|vvnw?Nce23)4M~FL zJ=*RSUsePuh5e36#Nnc@kpl;~Zwazh%ob~r<|&NxoV*9Y!jwg-<`+Su1*IJt`rC<-O2acv77qH@KAm z_CCc0rg|m}1#REbxGpWx>-#oR)mkqNjAu(g)gAcv2^fXt6xe5hcYKtRB-F{M5)(?L{f!Y)!-PBo0(Kmy)U>TFk`JST&B-%h49r zy9upIg2+IWqC);P53yl^2)AnGAKO-4%^x;^MIpZB0eY>y<~0q8L3s-k4)&%{O0kTE zmHe*bSMjLM`8I4Gk0cNF<2VYA`y(T3O{YSZ%uwK-g&cyppq2#|KFm)1j+1e(MgF!N zumnKBosm*y8rR7WZ_Q8Na(NWJ8A=yixV)*i+bFMztLwSx9PbQtb>1caFfj>rik3e- zD)W*hZ}1D;#y!()g_{Be0mEDm@*!&p#8B=`K~LNPr1IqupxNH)=J_l`3L z(E_df0n|@Rj7dY;;O8)L95k?mt&s+~+0wlbvOm|>{R;=E(5K_VQ9oxcvx3Wto$tAi zh~Sc+m+_q1eWNrZVh;-bv^E#2)fiB11?K1fCu;N&RJ|ZzGy3UIS!sv$g?O%^VK%2!+v>9)u9ew%F6}V?Roh_Q zA6N=d&m=S_bUzovkohh&t+{{UFdFvo~vZ>l0jY#6AW4e6=d{7Kc zg1Q9<2i^WT7O4%flG@Iw=@D@Ux(-L;&yHp+3U}xb15j`NPiqG7>;{&2jQHVBRi?dM zdc^7eY95gv+_ya|?av{$m312r z8UY`wRUF1F6s655$~J?$P0(v5H%2h*L-;48=6{RMM$IZH&|gY$+9MXSmR5(urOPpO zy5uWxjtH4dXy^Zksq*XOGJCxcS$266ydgs+aD@AFZPs7Wr8<&lVt*!%Yf~cFwg`3f zcJFLH$Xnsf5qi0b^scYol)Pmcy%=jP=3|YOVjz;#3;^N5cKShjB!;DclRdKTZvN~x zQ?&0VSsXOdfnKE|`cxn-?2HWyZZqD2gECMEx@sdgp$GfByV3FSc~+hw2K)G2TGj$N z-mj^;OmG zYFS6&-QlK}?$(3c1w^t*6Jd#Poyrjo_1>9KVSpx#s#qQcNnBlMoB-LgktM{2LLTQH<4n`c zN@(|A7&rzQ+YY7<1Jw--L2shF=W*vt{c3-Kv~-Y0<5;MYW@z{$x3vLtoGXV}rj`sk z>;sD6e4q|b7W`n%6f{ng+BevfCbx^eem0?1FHrgE?Bb#d43hnSYJiLT(9tYllCW%T z&1!)ijFNWtrMfuhalunX8nZRm3eF{8#Lu^6#NvyrEX}6=Vp+K(XS(H)^6<(Od;p_k zL%0YAkf@Sg-tmip`W3V+Qt!?2hNYgdAE2eOhCA6T{}RK>$SZ7yg<;6+`eSlFKmSCm zFfapLx)gQn2zI{WHQy*eiV7z%V42i&v6(l9ix4Z3%tlbU?xV2hm(AFV1rW&1T;8H=J-!e=?cptfjW~(4+JSm zGiZ^Id*tNg$~fq`csLcAbpJHEmqpq`j`E==mByU#sBC1vc&;#v z{`D)h?QkUi!Yv&T7)|S+b7*C6Yo4Bd$GQ|1r2i7WdLi;@|c zEML^mH49|!iY95GB!^i#{EHv$g)ck24A5hA?$GxJgq`Me$SqP}<&>Zx7;>B{%XW>$aN`6o6eHW7r7 ziA@V`dc`SzHx%MbA`Gt-Z7_B)_Q$SS`iTBoW-RZr0&@cN*Asm~-)M{4=wm4#*0h?b zgmuK3@IiOznaNbhde>h_)2mIgw@1L^c1{IpTUjBiTXwhD@|tj075D<(t-L2>;m*)M zJltNe(Ypye7#k>}?{hZ6&PR`yM!V2#MXEJW-*W+7-cl{ZE2e5s+C$xaBa)xx#rcZ= zg)o#fHF>R)VMD)UqVHY>BwtC@sNQP^J4<^9#gP?Y2-Ak#92BP&{*=DTj63A&(m^x2 zX3g7LuxS-b;VP}7XW->0^F@+h3r9Hp@#6rASCAN3y&`AUZwcEqD^(t3Meh8+Utty( zpuJwUL?o&CAh`qYASX;y)DPlSCs-aanE(CEe;=O3KuqTxPqSkrUj2q2VEF&OJUTG+ zdLnFn68^v61sEO%A=pb1Y=~TXUbAbFz9jJ^0D2!i{5%L`UW-m zX4%7XdCG~q5+HM4JE_da%16n)7f|oq#}ZS=@W-`icyT4VWZkRzHXO7}7ch{9)y@8^ zNU`B_xY0XKUv0epD}L&KvC8TTP7kp@#8qM=XIsW&oih4fKUi{+=HBmzYG;;)#F*lU z0bh3(-i8xg^1ZorW%$Q}=#LbsG(3dMV1Ho=D=kOEa_f#2@nFJFoxx=k1+tjv>FDV{ ziHJZ!+RQjQMn@H(${bVgKsmXa&xmdf4dvQaH}2u;NWcrkEVvt%#DC)EvpohYb)%a5 ztcGFWo6v4#89OEFZYxe!d=yrExv~8q0QMcjk;+-2U*YQJIleeX7#~WtbcBOyo8r-l z!_KE@Dti&Rybf4u4+>)NNl{QzzUtA|d|TvbH?566%*pah{$l9X*kDn&(a*9o(7tr~ z5uj@NfSZ>5kM>gQ?%G zbK;{z>AUF_Phs(mrOc-$5x2fh`6&tZQwE2=um5$bTDj^9EhI=3$Wm-hqf=zCW_a`q zCA6e`e$IRIw%T1ggAe8?uwX=1Iy4_sOc>%M`;C4M6;)71c zVwu4TakrY@8C(5)pkebt%_1APxPEsx;r=jjC@b1X|A>>sgg|^w0m&3meSn&WWrF8TjOc-MY~=SwfjoU&`ib9T+QGajSyjmvt&TNK3sJ!k1oX3 z`jO}HY@rMSdGUb^Jy>xjFU~ia18?`U??32#hu4v<&c0{IQmFJ_jXVr>J*kU-V~2EZ zUtzMwSe*WX6;ejv2Nx)j)w>Em?$l;`3$G@K@$)>bTpfW}1NF+|^D{D*Fm0Bv^VDyg znsiL3V)68jEQ62Yd+-3Mw90hn65)fgJG9UGle&%M)zwE&a%jX24O1FMMp-LFeLBlT<4X-~n| zHOB90aSRLpI5_`qHX-&$=-i#A(;+k*N4T0!d|_f3+|T<53P6yXEMc;8Dt?0%lSMCn z!ERiMo?a=ENvF22ed+f0&+cqR_CScPAB)hlBI@z}rMoA)gc8qObP2u#i%JOnRNG+BS6iODs z$LQUwbZJ9a(KUo*~;~Q}a~(R~L{K(|=TtKZ<<%%whaz(fhJO z;_X4*XgwGz@`Ac5^ghnX;@tqdf;=C6x9( zEaQ*km7$#=mB9TP>fRI*tNg+?Jo2fU^1cdvf?SuyN~C2jMB{led?g|^;bt1`y0`A8 zT4JLdfM^cKwaV9zJcm3@Ob{t5v&=rKt*}fde5}d-qessCfQ@+I2KMkIrS>eJyJmxG zlVc$oQI0>2VcAyvI`$|zwAUYUi(2%R6m$utbz`*I0*&0P$IUdS3 z`PQ<{=nv3D%|xk}MW*8Z6d1^5{!%i}OwWM;z>m(ERO`tPe#}mkGlp;H8VE%G zwvsiCQ!9{BmCRghiH+aGfLiX7sfCo)_5UpYWdNFiq&GP1bXeAIC6ImiiU3-)7Pm|u(XeCTwgil$mi*?L*cJ;B_}J8|mpJ3Bat+^D^0qc3t`Rwr)s z!F#^N^QVUGLgr&yZf>Lw=~l>mFdYp{ly^h>eTA+8-!R&Odm)RNw5R z<9U8{lk+JtY@I|D{`>*s z)fvx_Iz#%CJE=+WJk`m;R5cW?JYnGJM?9%H!qGv9BpcK5xr7)Zxifc{sZI2ZOMX>&1K64u zZI6v0|GgbFFo8grlDQ>PxUBl#;|QQgMW6P_V1e7LY>YQGl@Sl$EO|-RrK`}RWe?djyAW&27VI2x*-TlY& zSU>5!ba4qOYmJn$-oeH$%b+pB!^2J9#a^Hu_(0 zH-1EFYuLl`Bj!%@Bm|-z4woUohOBVY><662ty!8dlWJAM(hhDec>OgNcs?1~9y@b0 z!cWK%1CG$UVDHvE)k#rvzph(~Gw=JmZio2$e1KYBgw{5c)!3f8L$YraS<=Lb<4G?r zW@OZj?#iFwPk=4plVHb7#eM|e@Rd1!p~!`#BT>PGqHgSZ z9zurY?dC@8Ue|!5tmIuYNHuV;+%=L_ENoE`_mvhKN$nZfnENVF;fQ1(Vzq}S>rV3L z%G&Qa`~ZTB@lF3rT3NDrRdRuI7wG;7J)U6jhsh3`kpswz+g7N?ENsG(K$zM;n&JdL z6EZQ)vS1ulcMY^-=c8GQW^si_TzgA>y5gl5TesJWx^Ib=nU>il(}&l$sms;X@}|NV zT~=aap`0Vzx)3d4mM?*mzts4$wF|M)NNg|CMLe0@85~+I`OqfwC?7NUqsS%!+|mdM z0TppY{NUcv&8izjcrWER6`XFxU8Vu#fEpqfxRu~~A2nVci=EZD)6$NAcWN9*3bv7i zIa@P7@}Qe1Lxp}kh&fjH&OcD0Uj2FwJ^C$m(wCZa-CFexSdTppVI4_$ptaLbx1}95 z3wA_vNc`eXy_J?#8fP(t9WEDW43WiK#1&5hRRPh@un#$(NQd9Mq)>it0m2n~JH7&4 z6|5D;A>1fe>GG9Ev0Z8^9Q9cy3O`|*!gMI5Fki(4mlKjCNI@t1aaOirsb8 z?HG9(B$)OefogT&fdJev#4uiNF0twz+pozppP9l>+w=6#254vvUuv&zw=Ql5w&tey zE_c~34KIP)@3*(`>SyL4(CFFwMfELXf(Kb+>d?*BXQmSY5m8B}L+*15B_ehb6aLGz z0OM@A%JNK&7$sxkCH|);!8+f^8ck7*7%&Upy`;t8)C9ln243JCMKzOhhmbq#xy~>| z%+S{(CkYyO(v!ng_IS5GicE7{p@s$9yC zMpp)aBb&J`>#)>YzE4>Pg_L22_;nPltmyArboINV>i3cLdN@}hWr2211z8Nn0Ai}cO(`;-XcXE z2>piHl^NFhe#6{uKGkg<-Td+L*CBNH!CW&}R2>3j;eog;24&7Xr=1FuAB6|O3-P$#VRWYcZ&WgAGS zk4=Vxj2%?pdOorWEZ2oTbvK~ze7ImW85d7we*fpswAty%64RN%#eK+jqY-|OTRkQ7 zC-B8eNeqG4;ZoI#+8B>Vyo_>CX{4haO%2qH)~S-}ynlTpxS_D%Xc1z`+aM86m|ID) zlU-hpJ}S&6Kho&2G6rgKCx(sz1yb_jgx<;Le9omh)FBY2shRu)Ve!GKaG!h+(V zhFORAC}>Q}Kf|DPFu3)zNmrI;5M8*#d~^G@TOZ8b|;c7%i0y7|-T{4ockvtw>5&r1hR7G1j(%lgzw zb;7AF@N4RXKTyw0Dc@bbD%h5uVo+)n(rPO|MYWd3mf=;54tSP4Sa5nL-BCh5=1s0$ zoR|V_y`zzrZJ+nbYT|6ssxTE&<6MvOE)gNP-ADBMgVK$@T4b#e%_;x-9>QpBkiE}w zE?o_fIkc^E%d)Sf1Zi>id2x$wB)nSRRFd?EJ+?(yYc!)c;UKs{3EagWH~q#9?}!ZV zuS1~rdQCBPKlUT%R8h3-$<7$*DNDKOd$_N* z3YZ&>5GBV$jERCg+RWH_)}Q$_m1&h2JbD7AqPOQ@0$2F`InDM^p)$98&g*@%r_T(` zSiBq0+K_N`-0E%tyM5z#%F)F3l(;y5;pbZz$m+pCC>Wy(i~AZRo1=X-(!HdCW$*1= z4Lgs6+RaSjY~*Mv!-M_Y#D_V45Z*=I^wzb&peZDWo#t!)PvXA*gid`9!))3COH1NG z1@?S&)8CzVMYi6MM%86_WYP+`e?_nEF4b3*e=jQ4v0!n%o|o3U{IrG z1qOLTsyZCC^TnVM_pl)r2zWQVU&zqc9xFFaj&73O@Zp;Z2glhjY!sPz#(;*eoWr11 z+Um|u9(a63qCtz$$H~5-a-0+M$rj3V&tvL1H8)aNw}!;CwZSd>qhwD{w(~jevAtzO zUG!gIk_gA3>ZexWU@e7`aK=yA+4|jFVfeyk#zE+LF+?XYb$|*$Qfas8O+>obTdg`z zBrvzU&}uA$T#o;e@I7O+vy%3f)c9^)_g|)hH78b84aZi9(n(7vc~!UX1~t_3JjNQr z+taY5`HP*=gat|od%K#-$66k!gPCVo$mzvRZ?z5ZQ+{W@+etV4f;Ph7K8H5S{w%er z%q_4KpZojUPn1A5y}fhmeCVe}K!fTi!rOYT#wHs^{%?(fRek ziGqkMi9F0I9hFqa{R*zZNS;l*_J__zgUgOYycLnUAC#{Dm^)d+0#^&)6Y{hi5Q6om z9g_IXZf5P9JWA>^^z3b$wl!=WF_ScLzXGp$gVPcxwdrnf-yaNlQz+-1{g=eL{S2Zl zb8q#tfgNP{w;@dS3k#bqP|GmA@_k3Razq9BU3NnXlD`%@1JMH2Wf*?#A~xO35B3dI zhr{!^97VKKiWh-VkB+{tS0P5fb6ZVj%PXqlip}o4E`O3$PwX3*g~GBiL|Fa~fZ52w zdz{VM?<@14Ijz{L7{+aLEAxpw$xBmC9VutPSBtLBi-r;}8`tU;DM5VdL;}f<4}Ol+ zbkfhN?+h9`}Ec~Ix@^V2BTF_Y}6!$aLA7=Q@p z-oO5Wdk1Arnl_n~cA3Iqx$+<9)J;XZH>=|@_Kj|y#+6+udsU2>XZTc=<%LJ!Q+b3% z+C0Lbl+bgj4DQhHB=ki4{!HhBV4jin=J8^N{(st7V9C+MeZ(L+7CZU*X+N~fyWkG? zefLuScO=Fx3(n~g>7ns2aV>z1slxi8M$g81gUWeJt>AHcXPNwux-xzL8SS7OmVMp| zde39J6TBJ&GAB5qt18hi=dvipib4ldCn$H?S*O^%A>6q;5f=HqADbi;d!u6CQB31q z;mS6TDvbum-(Ral#O;RI36v)L&0Jx7(7bkuxs3s3xE@9@JATqB2Y9;{kQ@RQgBL6U z+my`;wx`1yf3duktWAvi%0=0)97YvNpuuROTcQ2SoHKhjnXJ@398O4^q^U-mclRKB zx&(WX&(7r30QF7c&F#(AqlAovx{Q=dTq7e81aI?c>Jed?i3sMp|1NU1>DqtTC7PAiP3u0fpR%2V^$v~@i__nZ!V zGSU07hiUW3o)m=8uuzt8a3 z#?Op8zm&iVB73woKt4}JE6yV(A;!O8R8W_>eqnzk{dP4$jd<~OBC?5%QhL3E0D57+ z)^G^EJ{tO7IG{l!x)?^DLWVdHipx}PI!X$zkNxTK2B&*)Xry6Vg9#h!-Jz^0!{Dc0 zz1*tJFu*+u^swefzC14Z(2(`ct}O zI=O+3H}msw)$mla9{EiEIK{k~Xihtl5*(#DQ4{)iRtjYs(fW1>V2p$4NR46;MTH## z{Z9QE{=hW9#;hR_Mo$tFHTYALT5h4i!X8Wvy_@|pTTfR9GrfQ0QeKzWK=S7Lh@)6oLpB!zFeWF~D<>9Y_A|;A=^$p5L{#FRb&9&d!d{X^X2PPzp5G9OH5ZZ~W zb-2lkjgE(SPaUat>zgrn15t4|`I)RG`syLhm z?mV=YKm~o*H@r!E5J*fUOSJ*R}$Jf+RKO)5=42*=zc%VT13Iz7GcPIdAV< zF^cNP?|VV{7?e_h%D%Hqe6F}G#g7^z8vA}xISOaDk)X_KV@DX#zVbNd(u zJCxKL_F&ClA1P3isn);Jo7cEc@T!lgh1d7!8_Yp$q8hf{YEs>FwdKrr8NQjE2Ef9q z>NBS(BL{1&)8{L!`T3#e`dHDcVzaA75}YV6~^ssZW?tD2ty#t%gejMnLtgI zY(AO()=|(5jjxtkLc>s)qwLcviQB`~O=A)f2aV$B zqtWSdm-yGzgN;2qkkI%9g6-*EYemTi*!rNjm07{Emn@i;$Wu%-87J6c3Mr) z^bNWRLj;PRzJg2xrpdh-b$G)@Nh{pz4*cJgRKFqt#Y-M1&8HVGqgr#EYcV$~;vi`h zlIc*_ICiiMMbBWb|Bo>}d`@L08pg}XI=B}pW8{-9PafjyRGFS4#HhiC!g%9+YY^g- zn3#C*N78I86AEH*vfLbU-E6hM1*vVTYz>X1ywl+BtS7I!PVG7X;^15!xJ&&;A8P&e z27&f38q}TU?a^VX#Y578cgguyVdF?oFr8za!U#gzI}E=z$|empZQuO8g*Cd5lbO{w zL&`j2=<9r;PVefiK*1<y6XIVWCf9DO^0*EL3E()Rt` zTZqmN&X|ORfsjL)j*bs|1megVO2H20zzIOgn9dCkF<>P;`?vXhj80H25^Tq7a&5>k z-mRFPw`?Dsbg7VeBiZN+)5r=hrXv z#ii`PzrL#C(NvzN;(O8Ey&2vBs@)Y)etw@I*ul7Hd4v>gKhd2W*qiWHk8$Wf4zGDER=P!^l&IXjnKYq0@b)#PNjJ0(~w;P^e}N68`-~(a9}=^iZ^PCb$v_L%Oz@s zdd`RPssEf+ke4_0K;>&K`Flzt2)Yi#6q6~}bK{NA;_+CaKM27_!Bn*ME#fmAT0px2 zgej%bqta*(^KhXV3-Bo^pT7-e$uI7ELKlM)_ToS!icc6JKH+v32oSgm(a}=?-*o(M zT7UqK*z_A2Cg0uiPnN!PhIh|{)ALs$bvA2k+2hwlXXd2K1LfK>#-w~p!zJc8Vjw(- z=+XzC{9kSQE=Gl0-vcAiuWy3b>zi_sl4%Nmmz$LR6TAdG(%=u8sDjmD;BDal7&K z`s^KmS7}t(YhJOMQb)7C()#l21|{Y6?R^;GX#1B}I*I5Hv5)U6>g%)nxK(IEmlj>K zK#i8!RsisS0u6o;kdZ~fdK$6{R+KYWQ7I`%ZMeuSNpp=^bVfsoY_D|_95TMo&!2D1 zmZ+ww!OKthT!|fXD2l{XM%67}Jr)9>BjV1>YX5LmYBlHfSb5)c=JNf+83xn7@%d5- zxt16L=P51b;Tt5YJJ<$=b$e9K!@icF9nr#`46W#LRzX-K_R==(Z3!0{8(~?5Nb%U; zROLF#+G-SUT&eB%9Fhrm@qU)NcjBkv{psOoE5cKtoHiFpp7u=`$>@qrgbU&Xk_QGU zZz(m3Xf*sZt{0<*Vk2r6S0A*U)qUiPa$8Sk&RkT`VJN2{(rV7Z8qzRLc2R~oEmBX> zgFjT@BR8WLwrY#jp&D<*^BoIUXXI zapZKw4Cu>X*ET|>khR=l_9w6aP!Ma2X}FtZ;WyDF)k-$JQ%Mr)zG)vlXQj@ZvYofH zK6!F}7eKXKAV2ZgeOZ=>Y)9*`#F3&M`^1CFaHh%B^(G=#2&zq*@%A&X%hZL;QKTC(|jF6JNiq3^vXLw6%a02V*m4j zJA_UOorHs5L&{pcV%pq4HzZM7RTUdT&c=53_s*#d4gkk|Zk>Fka4tKe8IiA*9B2X9 zyf@c2^5*rW?oU#YvUQOz`zPQq)6Juu82P;)&$eIp-iJamuXn>xX?GuQ6O-53<|m!s z>yzkWNRX%OHWL410CZa;7Z8@xg+c zs*%3)$#VE)o&pR+yV@+`kKBfudJgH%NNP{?odXmEE*pcT>ADugxA0Qfp#LB|%UUbb z3k2fmE|~tXNefoeI{#Bi7a7IrUW{Ti-L8j%=hdMoOJjp5_yFR@Kji~AmN*fKh zQpU^lg{R8&TRSXFN7GTV$Agxe8J4rl;9T$+HGQm?{u*BGPlJAZ&cdYNAcv)s=beST zZpTj$ZY=8EboUwo(kEQRi%#z%aBx=p9eOKhg*BFESm>}Exf9AwO_#&!Q` z4>XA5cJhBMHkiZdq|tRxM={uzPfu+hFCveA@AIED!H|)Wfj!i&o*pC6bJ%RT$*loL z_Z!9=DBRH834vD7k+BWj?Sw$UX}oV)8B3VBaV{VcL0qu{#Rmb04`DGCd$J!$zkn`` z%E0(TyqV{6S(=2}Ul*s^oo^dkU6Xy)^C8D>7Bz|3IDx@6Mg~-PTJ{R=@IU*GgdIK< zmVZ5za`*o3!#&k2BSsyOojDs;^?ncH<6iS=ZDUK1Jz-R@{-;d81_V;KGXDk&9Jw)6 zqNx|*$izRsZNtqc`wzxXZOpFuX*O5_h>*60UU&ten`rQj2+U<~=}7jk+B~W#^ZZ%s za;2h#@p^VqgR(ZGflc#bZmBx!CEK%gG1*ie3^q2lu{}q#l@>wo$1{n;xhi;XZr&Cg zF(uSD8rKrc9Y;C!`V)vfM^Qt#ij|%+xDGX3s{Dm4?TllGjByJ`e-i@&aRFWO4UT%-~%~PBN|VoU*#Y8Nmg?8 zqJ@87HOsmRRx+Ctg;kOT??+}S?0KM9P_SOPb=KVUjkNr@mG?mO&k6#2M}5`W=sA;< za41v8PTLQa6Qkd)$Mnrbz5_zLwizJhr~jEQx8eK9Zg)k#S_wlPzxQ5HZ()0aU)JXr( zE4n8Wyzcz-;5QXihWBN9li+o59tI|%-kP#B)0A9V3MD#;cCa%xpbkViujE~SFWDw- za_%Q)ythtn61*+Y7;AW8)huH#%{(Ytj_^eO%Ad>?`e_FhQrtz>J2i^QnTAzEF93^$9s1L zLp;rN^wtrF3Q{#0Q^9%>(o_cGBE8TsKW+{71h(%ryW4m_r+Ep`sStskuST}E_4Abi z5!K+G?G&IA>zuwshKx-OPq0JUwgDN3T9@x9En>B}EP4QAT_7?+Ru-nyT%!X#SMd`P z1RWpyr=5ZvIN9_l_ZmJ)6ugpiYaGq*9k;o3#ld+!md8KIrI-o8;S0>`X~MD6${l&7Xt+KgdEe^sEkf1 zJse4(pCxv|kTg1LZR~E)enAkX$z#(wueL;0)kt{Agxy=mWH7jJa?f@pQ-q#DIr5bT zXf6Cagmj9Apr{n?Vf)yccKf`v13HL&xku~#m8Yksh4Xr1q*FjT zm2N>&N~9ZU>25e6APv$eB_NHYlr%_}D3|W;?tC}T_x=4T@`1y-d(W(yS!>Pcb5Czc z*+n{w~k)|neusLY*O&EepZ*tyy?9^cP`kYxZ(Szx6 z{tz7^>?^NT=)oVOM1skXmhn<44Glurw`+ZOYQUm2liDhGzSh{8sKkZg5K;z=EN8sc zj@^#;S%g?cy*E{~t=yW7Fy4JbOr5IW(T;EM2TKLC#-1 zVg3a$ssXY&g0{Jg7db-|6AAIbW$_$+)90wc&JMMt+%QB}F94Ilg%h|yWXp_7)18rl`ddSD1dGQnjt*fqlihPC+ zM-6X*N1i5J64!4*xHFn2f8yh49C_pE>zl0cU-t}WzjIl7*nScuS-Z-!y4WQVdCw+M zI9-<%`{rQ0TWd?#7NAwg%Bn*R3=OnX9O zlyl$sP_Qt!dJ_%x!Lx-fkQcsuwtQ94Oz3HL#~chfY$cSU6;Y#H7c?0R>z0?EoW(O~ zE#|N6%5Hkljbz5nzSBhhSFD~qmChWWv6%G7-|4;q7g8sV^@-gr2(VP_T-e=6{eu7W$+OlvYV|Rw%H0G<`s0^1S7AYbNu^e_t`n81K zNoI>34G2iIgojurxD_n42Kj644Vx-^QI|O_`Ux+HF9VhK5>-$} zq*S>XZ&TwdupeeN1Q!m=2&`h}==@-Y=QG`cq$3ktR1%)$G@=(-%bqS?VR}tHHZzSK zZ`Cbsp{|{Rj;luBno6hj7L}d=?m@`#w`%o_Eh|GM<->W{- z`W+FbQ(H$MjtjQL9Y9Q6%P*7{%*vxzdJOd+-*8S{}#JB^>2YjwR^`vL|U5qHE)Sc0?| zgde?l-v%OcQVXw5>~zyFHw~4rTdn3B_msgJYiY15RODs3r_sED&0IMa3R%$tNN;mw|(;&z{WsdOdAYV?{oHmJZD!`+FH^Mii?sBmTQlUG5wu(KQvag-+e@ zQ@@_biA)sV?lHz^WWCn&Ko(i?W9lx-g8%%KzROF=&s5t`W2tuqv5io5MsLY4VjpI1 z^Y!n}%{HhV3ih2xjOBknu&5HWjYwuqEDOdK<|OtC%T#YMmv2_fyl)Dvi|53Z& ze5GI)LvsG;1`*LHcdjKAl^O@L5DI=@;FhrbjndlH*MQ%byh_wlb&m3Ghb+{J7}CEV z^(*A6ND$V@-R;>YTGT2go9=54HAIW`@ar`Zr0X`4?8(5-pw3@8c9z?e7sHM9nE$R9 zvYDUkh0}-CzL{To<;OG17wO1fLt|Y^w3r+1O6>X&C?;@K;sAGd+L0gXta&at+KpZn zG7|>lI8QZO-5Zcqj)P$HPl-QjgfD^tW`1Uh6R|%!Z#Q3pqZ^09`WQ5 z{+;1Hd(A8~<+2~K2L%EqLpVr$huqIc&eokAe8%Y>>colI;TrO;Db-{In1L6CIp*HS ziD!md;U&y0CRP%aosj_2!(d`RuyQ150Ml@4hpi`Z8tAjgNrF51QxXthp$9x!cA+hx z;XHhG+r6r-HLJs|p&eD3Cf68hl0O84edR8R0gOK=k$`D<^Pfez^|>AF6&OhWY4-I7 zyxxq==5#Fa@*h~Fyld1!pMnA1f=BMc>k073M24Y76a|JbC@wZOJjA!<*ymE3Xq*b# zfG^GIwlw@S85p#59GzL5|9s7YU7)oeZ8JEWG~I+Gf%ENOVL?~59MeT5ZwX+_EU$VtL2)&=LS(tqZ6Px51Q=f z;?MIE9O?CxD=YxI_*8+!@VD}Crw^hyI=eVi_P=Mz$TN!Nkw2AluzI({+RzgzlVl^_Jel+`khN&LO?VZKYi%T8-=+$%aNq!A_gpZ&Eu6^9CVAh21! zO1_FsJQE+6N$FI=n6ut7tv#O5{=T|`02AbEKyJHh=KGHQgwh&QR6+etNQ4Vr*L@!e z78LU7n{V;7%4%}*QTj8(=a${iaAQur+`P|sJA316=uQVy0sq>^NA>S%rdsN|#g8eY z^B+d9lNk{-B9NUeZl3LvUH|4ftL!wL8Bds!*+*pny|VdyuM=oqFfE#Pa;L$7I0vczY7dal687{X1TQeL~L1km>H{ zo0j)|geJ7u@W)X1n`RE&Ytw|+cyvdn;qR>;3P5S`M{@04&`pH1cFi{ln{OP=oAU@> z#(#nwMQc`*hUS_&_*!X`s;46c>$Q7uZLmdkE-z@OHUPzR`2FqX^Avf?xf4rbWR9lm ze3DC(Zohh8=pHDhn)Y{GC z4%*olf+S)xzt&RmA=j3$ZqBIn>AnwEQyZ*U{q6s{iG{Lp$71BhdPENu;75gNCp5Al zdL&pQrr-^C!PAcl(jZaub&SmbNYk7Ak86feL$3YrfSP9m^fo2jN;n4I-T&Rfa)|`$ zZ`8c=eq}fRZ>R~ek)>~Ces{jA!AoK%=oy+;qen%f2=JXFfb&zz#c^(5ZyudZFwhqi zbn*1|_qIpI7{u06dE7{ie)uE#AJ)Mz(N&SxR^PUHtNG}~*26th35AsOyZG)Y<7>tC zr?C{<`JZiUk+aAFBRu$`M!Y@5Gd6H~!F2j^2!Gm1PMS2*a(%rFyhrL`n>ItbOZO;2 zwmqx2x@J;m>leYM_w^2ebZ^3I3A}X8_&wV(8|TU%N>|q_A)Ak2N;-gW+H(CZ}lfFt`2BiKf z8?m1}FnY<~uljr0$-9nBh~8ROu*vOB*J0g6qC5Z~DKhuo-8Oxl;s-!EQ;rNC8|E)M z+tAuQ4SlbqS^d&t6fi?6;PgHG&FK3*<@@+)6H1ExBqsiy-umT`a=T3q7%bf&rh6_0 zg^ezm7u44s6C`rq;Q{Yc$R4;u&hc7OkCj=c!w1@pVXsm*IByKs%>mIoVaY0%Kq<$^ zB#n`|^(5i%2@LxaT$;=4PX`?Zvu)(h?{M3HtDMNQ8#J5 z$i*D+;;z8<^z_tf20IK}`}@()u}Q)6cE`n~1&L|ip}-E#&bp8~WI%GL{`~y0b6yi2 z(b^W*l8PkU^?sZ3p$|Y>XA_Q+(YB5EKoS&?EG9_cL8Pe&{q(B*Y4EmTkQg*qPSwCZ z;S_d!KVFH){*i#zo4H8#J8@!4jpBDq34Vp^fj&SOH3-6(>MC(jjZi{d@M}#Q z&+p40Z~qh;{9rETunt>)6a+bEXAPyOPT1W~_Vau8)-<+gHxYCH&|Ig#rb~NT#Nh-# z=-YkrB%=IiKMJrdl8N>Asm=o!2n9PlvJbH&F4N%w*`&2+L<*4yZb+~ zX{6=)u)h6F%gB<3wCEmF(Pzs1f+NBTw$0knt4%yxH;wSHK$xQd%Qj?uqKWJ#m}+Nj z&-OC6<^OS=G!>)Y`1pd`C;T7jH6tmb&4#_}<0RMWuF5jMy^SK{!0_w#aVn7iGCeWr zsfN-YNId-5*^-b$FDFm%!+8z-zCvKU6b^DP6ohrSVZiZDN-FYif^XfMwCOGw4)tR3 zXav7r0yga6-{ygvf{4dci}sg&Z;dL9Cr2b<{z^G*<)ZK-~1xb;~BQfLT)K1w&`MkOLhO=p8tUj22Urf>6*i*xwyqy^WCY-D1NL5OWh4sHw$= zseQ)PahdR8p|9~d_q(z!ZJWx+`*jRzEL&8k=ey#<-(+2Epw}t>UGCW$mHe*lo)o=#u-JPm{ zyW==Q2c(jpWubTc@YLf#?XEP^I)MpNngV8hf~r5?_+Db|e0>8!Hqy*~2(L*FV4nd> z8#3#CU+)5J-<1(hsxi|C>-xztY{Gm`lt=}&DNq7Vu$ILt=7x~$Y#UspBNd$eS}zE| zU}Y~Q(WHHT80_HirLo8P*+b22=%)`+*UHR7F`(bp0a!B*8-J)_uR1xVY#WV<+_XU3MuUC%TIqf)KlBtG|QY;!;7OxkuAAb?Wo?&r81sN!|Ay)k%oonBQcz7pOox?7cdr@qc!h2uLbac7J<3oP)x`P^0bG$3y$_Mn}QyD>x zB{B{8A^hhs=_PFxZ6aAb;>me{D7*RuRcCU0;Csw!Q?^`Py6&&MA(BE?EpPI4vi$PH zOf-$lXb#C$=b!XlD+@o8MwW~Pg#WX^csX#2QtHp2*U%Xtz#e^xOc~4nk==pSb@|7d zyH4nth7? zG2Dp(O4$nY)JBm~LrnrWqjLL^x)Z1Agnu*V#_aZGBY;eG4q1&N#=p@3WXhN)WcUWS zIIut083IEc2Fj;${(poM_bft4fq}FO^63@pc`Ff!Yq|MGp5~;#<4ADn ze-Q3V!aHn5)rZqecS0U5Y*War?mpg4eYK4c{HcaYD8hv39SnBpsIu|rX8?#Tuj8IW zHl%4sGv*q0Xy~f_JRPLE=bMJt^~Rp;9CZ+FBv?&PbOs4)s}TYZ4*?+&_8|K zEsA6>Hgq}k5ZgK8EU!(7y;}j^qke|q;k!$jB0u(cj)*D($T168!X1f6q&Wa|7PZx#HE28dUrzG7l(O^IajX^* za$f+Q8JIsx6lsoax6+E)RmKpcJH9xeG97q+F!f@C*D|3m9D0a6c~+8gL9z-R8)^>G zD9H~U|JO#H6_Pe=a)UTayTRvPKi_HNE?ahm$Sih7a5HxjkAjZ$=z{ z)#1$N8McpPBYI|{w6-pAMq(CY((+I-nh>#m)Ur!#Lr+f?FLS*=e}4D|CCTm<8@6C^ z)E?L*R9phdYMZlOsN0DN@A%a|F+h5y1FA5$D0jO#kD_VGK198)|ls>=D z?v7##tiXu3-`t1E9-H)>^<=y^<{`au4t;zsw=%Ok6tvBBrtnaR!$0fVv^8ws#n&2c zPa(F=-O#rGE7)I+8mC!47+Q74p?K6%+NG5*z~2A zBO^RiT!bT|dK=@WaUXxkVif_l)Dh@#|0APf;5IcebtT~FwTRHOo?)O#>x(sf8;acW z160JPg2MbB{@`q_1Ac~-iV7~wXrVO#4GrT#sMpgs@Vf|b^*}DApW!pD127y4c`d}Z zeDZ#Jg0;krTkzZ)MwrO+Fym5ibqGi4%J>g9J}-v#aXqj2RoO4uP)LTg@TPgU+hV)m zt?(c2vtRwp&zGFg!*p;{JPsMIX}>V(@#<~%nm-|Q7`F^lpR2AhvE8Qfe_WmYLS&T& zW~6JtJM<;uKw3?I^!TNC4n(Uln@cE`m(ZGKOhGD{= zE$4fCp^>3=UC|zxIeLr*M0uo?uU!Yyg*5GAC`HzO)e>$E;D%GoCRZM-m!8)6^h%@* zcEbFdk@j92IdJNe9a6I5bfb<=N~#HoivyhrxU0EhzSQ>TkR>9wAcbTE;I#`gjH;3R zUZiK7Wxf@CyS)Ux-DS)ax=K;&tzem@f@g6lm#ZF4NmAs+@6pmFy*V!-UP0g=R2cn( z!IN+(+@$+~!*;K|7b%cQ{&M z+L@?8g5f-U8dy5}tB|E9KsHW|U8JKN708Ucs`-DpqblvpW5UqF3ZjJeNwWVJce5bs zC;l5Bd(Q;ucS#R8Tt3xf;rWl>&9I5R_pVca{X4{a=&#D!)d?D!v+)6}e6nCKf2o~^EHq)7eg8>dp1mZa_4$PE`=yCPQ@ z`h9b20fTw{AhbWX1m%6V5rL$_y)V?%c)Q~(qNbK3ETf#>R-=CngN?*i|0%X+c<9W{ zRCdCpqdrCp7EfVL)Os^0QHnij)zJIl+JFVxi4;l6y-nW3DTjZ^1Z)ZaS8nZL7(6JV zJzV_xSXj{Ili53?DTF?M`4aXClPnuhR5qrnw=r*fdiwg>?{2RvAfP=U7xBUeKGhFo zI-uSdX>hmy-Q8_?UY*Kk-(`{MtC4%pq?-55ak-0UtaFC=KJ8beJ`pe(z-^{)bdXU6 zW%H1vFNeKC*)-a}IJ^p44pf*!IVTk=`fzr0GT$eZken*RXFrw5w+Nm;z9(3Fh&wf_ ztKaJ4(w_bBfo>m2^FMs`IV^V{Ydyjp{&Ay8$tXHjio1n!U0BlhV!*KF>ALD)1emQ| z*z4|#s|Vb+Z^7|-+9U+isbL`>Zq=Cv%nDR;q>Br>4q30vYGx>Tuin?PWLR+EDN}aF zicBNG0;u0_@P0lfYO*X&!SX82si7cAN zM=QMtN7=(_U`2LYAhxL1d&tU)dF+R7i0k%PmVSd<3ipS1xnh5x%YA&NmM#?OyvdIR zTFQr?Ucxu_>j?a$13^R2;DmDQoQb)$rqRYUQLfD)5tdG=e;epv@4#prrT1#^%caxwdvf_f zMA&bX%cV;tknmQBOFoA7qHFd~mtk#hYampnH3|rezZyiy24VX{D)?=koPEIcY zsd|0La-uw|FPvOP+)q@4&F~3~6C#ePvu8wQD!gg>&dup}z7S%xLX-K>)GJR(EY36{)cjk6H7D*#Q?>@QbpIlPa7fw6IM*(vtb z2R$%*J3Ds3sd%J<|H9+I2!lcp-@w4&K@J6$I|P9rw^%hS+VNcuitiBxG%`Wgt)RWx zh8)1z1siNWY7_@g+H`?0ys(|(e$Z?I9XByp0_1ute7O?8F>TitLS*zsz9|4a}y#4!+hw&nz>v9gq%{bqRK*uL?3<}Ae<81PgnfH2?z*?q!2o*oNj7r`Y>Kf z0pwtfhh1a?seCcuqbBewf^tU+Ceo(@CvDx-#{7GQgs$J*P4A9djbAtnvag@63vssv zi5Iezd#xMgmgA*V|9H)yv${Cir5epeNRdBHeHyxd_d|2fzBjdrxD zIsd+L#pFbaTR_x@=v8t=1^Ta7D1QdVGxPiq)G9odO$J)N1@_UxF`&=JRdGhuahCnm zZC0x;ZiL=kenLbkJs78EyJ*$zE~}zxo)mm{bo?arw-oXsLluMM#Zm6& z_gjs(tJizKQ_d}$k4zwk_i0D#??)WJ<{e%RP^FDiK^u4NVkK%~zwwv2J1f+L3avFg zl!np-I?7rE%#Bb{kVhKqGie&yiy`}8^m!hRMFG+Ggaemzo~ri&=lW;xl9yk%YU{VC zVvDN661r8RRjI58|5Ei1-5NAisA3~_Ft1EUZv`=qi=m8n)*nHhEOQ>=*JV!{#!ByQ zolgBrDU`}+6gR1Y_!vbs;QMH&>qEgY>W7`0eviH`oo8)%gp+Ap4pp%1CCh%PIGLPv zL9zb;7OpRh>DqUiR~;@zaJ;me;lH`P6>!@j_Pstc0|!d@pcRQ#t9;>WEe}xtfdJAC zE`NW}YXbM`Q!Ve_B6#98c~uyxGN8M}~)Ez}>F`%SgcY%G}?TZtBbeQ}y*T z)pzfJ&4*)eUHd8Fp1@FS5I$^6=8>~z2kAsrS)X4AdeocXDdc)MuKLWJlS0=Y&0C}t`z`lv0$e5wBIk9c^q(3+ zAyKgCfou#3M};X4i=toCxiv0(a`&cUL1_Jy9f{qLP!Wwfh;e(Sx>c7nK3eQ@<*AdC z6EU|X4p^|@K4;M2W;<4@FAq5I=tS%*zr&u6mUy08Xfe>!zXW}Ikn+2L;Efg8z&$A; zLGpnS1^kCuIP*XfW7Yqa0Nq_e-Db#hja70HRW)Ud`Wg*g-P`B+UXpd=Gd{0%giOu2tQvgP#*kR@15c91Wn|fQCN!R0B-)2;$cCzR9}on_d3L z9ra~?MLT0LE5v0W?_@Q-X?lH6JVmcANzb#!qTetr%8mS@K&6&iMbRF-UFsm5?28%q zuupM=+W)vMvRr5C9Fu3M$jJvxVnn(CA^8iJg;IdUa4{*c<^p0aAF$(5Qbxw`l;142 zdsjU}Y=5IpKT84%mPnQ>(5rt51BNHAlWynx^Vt01ea+3y!Ch>I&1t~;EDB^RTs%C9 z<*q2Os{tJ>ZBV(QALiuM@(~gFzSXXjNbv3bG8w6JRO|fV6KZKpw(pOlT2brwc{h#WPc10sm-$A2 z$i<}gjZZaP$B>(B?pwpq9zR)`Yi@EG>Ny=N(e4KuE)gupeij~MFen?A`XKz zdqHml0%kR%$x5@%f#w|n!;dDzQ+~faJujk8U9}e9cs$_<49IIn;6BBy+Y!a=shpOp znwG5+GC#?i-Zh=Dee`9ZK3ESOfj+)CS%T0k1x2{l2R)yiA4@_i82Qj^eEU?f2)~mq z7vl6g%LUmVlj`l>9pjga)!L!{7!iJAsHGhnt)aNA`ECmQ&d|lxJ)Xw6LFm47Q#JYv z&7jWQtMp%|ATf)4&(|~R-&%!vcwYaxI%{wll0~EL3jt!McBGY^rN7@lxUCw9o4t9g z>0x4Q4C@Y(b*8r9gBPlz`U4@0J{;ox`}c3%dWJW^{ zBSM#5w`(2(k6kw*uxgS$EG+C~3ts#u2iMQfFOpK^tHuuv6auYrnaC(h#cs$0Ye)ARK;05X73)07s%vcWr*8Q|s zC_xB?kXSR}Q`P-nB*0SBC#r?V`Z3X8E`+*bMRYq`4dwMnJr>?Q@+v}hMi3$4;NY}=3kY->DsW*-^ZsyV*Aj5Pl@jKCxqhCj@yBF)F&3Iu&;VmI)6 zv6pHal9q#IXF$f0GvaCJC}c`Rq4}v{k9jEXUAjx|FK&OUw2|*q#$&Ek?FkM2#m(z_ zDXIO2sGaNMmKxql3WS~fW!cLK-IWCt3C^Br^&9*-5(?teGlb3=ht}?bWi8Xy1emn1 z6P>vj_h2OWsnA~GZxV&@2I436yCR;S%KY@v{X1=znq*KV-U}4$?G&!%h`Mcb(Weh&ve)QdRha{0e3i5{V;}Hu|BZD+E|YQ zxHk?}(nZs^m`4Ot`d$%TIWsq)6*b@%`ReIW=3o3Og1EnGpZCWM_3wINF><5Ac`y^* zH}%N#k7f#6u&e@#mJ9dSe(muoxX$a8jmvM^W6maxs7;smbyu^T%ZYDtMB$OXT@6&V z<%H+|{&tn2ilOF*&C*nEWin~QTU&;WU)z&kdubwsN9eIyRoU%R9VX`1mGnkb)PTN5 zp{}Z@4%%@3K=-w~^J*rMbYF)x`Rvw~SGU688mtCB*sYy^3GhD+4gOihY=ZYKZ`~^g z2hIhp)qLF1 z{h!Viy?UQPT1AicA7Xd|mx_A-_^%G4cdD054fXyeB8~Y}Y*19U4KLZh{9y+JDq!p% zJ(QVdaA0shQ@YO<8wtf2@v%jg3mK+KR4gcn%Z^u+dWe>!KRRin<2#LDGj; zBG1v}qR17{Rx#HD-i|CJD_fW6DA`81AF(EpWU{7~T)?b`%?_d4p;ON&*9;EPirsc@ zqDQBvU*Wbj`Zid&#@yTXQ)<{uqrTGmCU)-*U0MsD!OxH7l;n^XN=qpU4c7%9fKDO{ zv<^NP|K*b0wZYWwG?h)A&laAank$6^D_vVy9SrQoo5Vg@Lo=C#5@^&Xrd3nDDk8px z2@`J?BZJB88)GSi-1OYMeSC!5+S=4gv~c}_8#SSzG%zP$U_>FZINP%>YkE+&LAR&l zJmafQ`d@l$qNxy0@I<|kzgydnkLD>vY#dP7{zquoX-3i)_;bLf zOCfc?@$Lwz=`kjwA?m_ku9sg9Iu$^nLP=Jn%TD)U>L*89atJo0)vfSm{>ub0Z;3wU zD@!f@TDM>N^FcE~c^_=FDkeOeY|rlm&1r*hAPK#OzW2=@Ozf_jNqW{p8pZHQ?BZwY zMQR8O0K^2BmU&lPOoL2oZ0mjLB$G4tG3jMfov^jBakewzA^3AFL58oWQPJ?w3m7-z z{8OySLCC7r8Y6bs!!;}%zke4QiQ#wIIg;A|PD&@K?mupNg_KsXuI|HJqd-~n6;vc2 z-T zNIS9AeVu*TG;Mhxan050U_zhH+be}KZ}6+kjAysW&S)u*t(|3-vvjHZ$%YDOX(RA^ zAVlQ9f0o~C{E-nGkPzdOppp#F6{b_LDFYa4>gKVC7JsWUMA?Oew>U(9x zNvzf6#ZzT5OfNJ&^5uM{-Z_$t533mdrdZ<#yY99dK*1-w)5+ro-q3F4i(^7}pj7Gu z`h`UGRs22I6B9EK}u6>k-dsaGz z^M%S->)D+u>v)&|w@LE4psli zdjL#wo%&(^7n~=`UD1(!E-&f2GXVk#l5gH;`9dWy0Jt0T?>qmluWP82_;#f=PFd?d zu7tJgUP7(kU?)P#V{foxqA^*rRU4&$=-pd-VWi%z>PbO9&33a6h}J~wr(}X>j4x&2 z^6EkL!@zQ0=+&xeRsFq z8C(|V)Zz?3&)b^E4Tl;SKQT3x7MVO&NfN%^sq(p43>o;!69JaU7=jHlee39`XaFab)n1Q9Ht+}bC@)RAFbLW8db*;> zNora?!}O`j*j?0?sXl?;Mq(~Sy?ngze_qYK0DKYd5YvhZjs;u0MmKk zD)AV^(-_K&hH#J1rKcJ>E7ndU?|=G~d_@ zT3?^rX|KD?DYKc_ zEq}LX{qSs{nW551SfICnci%`tne?o*T9JYLd=Thbp{Zok(9m2g$A|%Ia$4YS0uINW zSM&A54F>ld{YE==P+Cbzsb0%`QOCn3fasa9a2hVHG$$C!=3cPt|M~-LVi5O1ARq;0 z3_ElnN)Q_x`w$J9eXk8piL)=2Pb7o95Ng}YQN$r?)UPsuNDCOC;|0NE9v+_Ug z<`hA==WT@y75qi&UyCXo?ZyR}^;5_IN!WUK(YoRT9F!qH1W*6GcnV5eiG-wSyM~9# z^RNw-s@!2w!q~**1H6a;pj?Zd&(vDqW7-HjT>>;iDJs^}lSOQO<*6knE0z@y&j$_V zt!hjjeGL1!@GZ#X_w0HP7E)O zyr0lz-vyn*X?%{&K2ITIr%~8ylyP0qB4a6^isRH{!~6Uh1(zeo`fwWk)qx+3rr1!% zG^&4m>M01BK{%J5V;MC#_Z1w<0k!Xg@I8WYa;=s1@B-J%pjC;aoVsa7njk8anKz+j z>uf|-=aQ@(iub*qzJCkE)v(aVUgX3%UMY~rH{W47aJzV%cjkjC$MoTLJft8TeDVP# zY|#Q|>0A~zp2Wl!!6XvEi_7vZ6BV>r5v{qe?(?jdx>gc2r+GNOi=w7sD2s07svGK4 zAqJO_T|-`l*wBkW=85izHvyuZ8#i82_!i#_hxGo};>1HT;%0JtrzyPJwgFgR%=!Vl z(yK|+Z{B;Q&1Kz8rRtI8UtOiLhC3Qa4QueQ{N`U8ZJruk)~lUujB%E+K|CKaeC=e; z3&W(5Nbxq5Y4>S*p*>pj;b(yeN}#rF6T}>-{iD1DFTYt4U0XoolTwPD`5x<nyWOYH zLnNlLdaUlkUmr8h=+5_OnN2Tx*LUou6Vkav3}V=_PT z)}sX%*^f6pd_ADvI?Z}-tQhC_4CHN3J20-hK5%k)#TPN*_?*<=3irK_;FUo-7c&W9 z7?^d<1j9VESsU+Wo&xCdlKuO4PLnkSSfn&&p-aTmrA#@EL`zhW+zOx41a;Lj!yPf-h6otr+pOr>2Sk^~;9;wz8!1Aq`DBkSlo1zjNYvQ{<Ul+d=GPTsK-{M(VX0*E>kqzCXkq{hQ;w{;RnwxXcpTak?VHTG97J~Y_P z=ZVK^?dyAcU2KC}n`!F3S~XrGEjP`i{tK?P=)Gcbh6d44Sx5&h*e^sYIs z6CHAC&i6jsWRU7Jo@n~ebnJ9oL*Y6g`S1koO|k)yrbmfF@u27AIBUxjiO@Kcf829A zRe@jbDIMp=3Y7@*&OXTdFX^0EdZ(aM49-2%a$TyBR8t~e1{ z3Bm=VTM0+_)_g^uV|XXkD!=MTza3eku3@pQ>OZyQD~;*WEoTcpLXiyd%Zgv6$DS38 z!W!_Uur2T3mhU3DiknL5JLlQrb2knZ!nyPn4XmcQS-SN5-G+0`Ub-woUT(kX`R%=P z<>jTTtz9~k$4lxIbEIrPpawdnO%<-7IFC4LPOYWEQ!khJx(g?y0|T_nBD3F^O5#qd z>2*m^*BW<{;LmMqh6+4|qH9IJtTo;prY@J`pL!M8R1rIVl}O9G(00nLTV>I#^d6>! zZH*Pn#AJws->_@go$R(l{F;13!(`{jH=aZNO{f@;0^2-Wt~{hlEgGn+*Y{l3c>(HBjKJn9?O{h`Ci1r#02ax-l8uWFUhr+ zuruEuiyG?3v_EL9PJL*-QI`gT1laxL;FpV+-8;y<<{_2$MUbbaA6mN|!IJ`qrmcc5 zj(7ca3!V}dn3!MnS4G2&oCi4yk&|O7HfqGLB zfIcOM%k$0#e#R%3jZICi1JYENK)RV@Lchl#!V{cg5&{b?c$Z!tp5e=>@DKp9>%Kz| zA4(Jsw7wc&pZ^w}`ZqjWNy%h%De5NGqG{HRhvn!}-xlrow9#180F1~^etMQnZZi6y zUU*a;z`M~f5B>>M#T_paaZPw{put?rirXf-sm26y;;MitWfGeq)+sr&RDMvfisH|;mxtUOi5Atv_UeR8-u@^xPGv(q9X1fvjirDkpX2#|3y zt#^$O$GF{$Ubn(S&Y+%&2cHz#-?tyQtQfO9EeC~>=i+-mXh!le5?UrKN0vr$eZ_Cc zEOzmR>ornVSOsc#Mpu6Y!GPIIcyMn?W2!Xo!0+7;3R3)eTItTy0`+ADLU>&^8O7J8 z(XZ5Bo8SN_K+8|&?LeDHsM!6b$u#x4E$*GD&sZ%RMQKFimMv*hW5N9H3=ON=*BwDx$^!f_9$z6ft6rq`%7??alx8@Lb! zbr6&)5S!4qEPrRd{zY=#cGp^csj7LmfOsLt#N4dAt4W6Vs_i~)T;^=6C#6~6WuRbX zFy;EGHLo4YHwi69s>Y)dc`hR2s>TvlY?e?7N0`jij?`k_=yNXOIPKpfm z_Y;C}%(V1$=s8%Y+lz84hNfw5d{cZZtnGeis&^0cC3RFBo)Gf|e;;*$=}5yD8i=Uc zO6q=w2vtt+3g7HN{m=9HhtM|XHVIMAHwyvG*&gJZcgc7o=CmRUHXA=Yuh{)VQK)$z zAZWgnp+q^g-l$hBuKK@rNRWf!>Ny7+HU5c>L*cgu7wG4I;p`>m#rt&?!8?2H=A}If z*WyZJOU%>z5jd;Bz-U2A zvpnAeulkH1H(+90>2YWb`}pyr2bdu~JWzg4&g9Rwkyo7A#&lC|b?P9JD4#6e4)ybF z@s9qlenUH_F){msLjBt;nlA5X>4?oHGTMEPZ~-z~8ysTW zod2Hmv!aJqR3LG$%vZ^!(L{fVl%PZVZjMV0GJ=fmT#{?fy3+YRs3L1 zF%B-!sd>K{U?~%O4r#GAk?J)o1hy5_D4z0H_Z~~fTaj5+AF4IzG{kLT@CK?>1lA6g z;#;VBlz*LiTX0sb?(3ImamvKgtVz~ia5bkU96V>L;e)M<-(8gw9{IwXfMPjR_1cQd z+fZQ0s`hwwEl^>mJY+|BzhOv4-J|6?N)7XjW6UFZhV@N73E;ntF0oow8S7J6Fg{~v zVha?SPwUDemZ27Y$(piC6_snX`bH`;_e{&tHEn5~vx}u=->F37ljtdAYHo(WS)i=E zS6}=&mjoj`6CUnZw9M35xR&?ex!eI7UMR0XM*}oSns<(eORr9I$=81kzdu!4m9mvz z&KK_InsJ5Bzp>m$qGvu`UDsq&NFGNZzf2$C3ar&hM@e2SY2060LM?9W|K8D@Sm#yL zVD!fRDSTD{elDvmYOI&!{4dOoqZoZYRU-6A>Pch!@~O?+s9aR?elFHoDt#l6Jr=d^ zq7)Z$6Llw_Ca*NI4GgE&HvB)T-a0DEH|iQ5KtKr*=@yVKk#0c*ky5%lq)R$(xF$(9y1N@;Xc%f}zMJ3kyx)4?wRDa8hg@^TiGB9jr#4G|Gr4N@j>0Ty{v2?<=Wrqw zLjLgGo1)F@1ktfbr?Ekkycs_KI9Fvz08wUC{x?~#I}Um@_>5+P$+7VLIma_(Q)qn&g@6&xWRX$L5tSx6Zo(IDeHYH4Sx)a5*s2 z<0*;o`~+swpOS56ug#Ul!Q={9t&A+jwA$)Cx@`#f2gDv5K*8FidaXGgV4 zG$$itd)P_m$P=Dv-?D8l6(Z#ItZ5nL!v(!MpbEwSz^e^&sTi(x*d4P8*yxAkZJCk= zpFMg4UBu0*&4{a=NZ0EgSvG?NyO)bZy@tq_qUDdxsk@gE-{u;R?N2k5tU3@vDA>@^ zb;m{dV0f+`#}!-KiF3(hP3&>Mbk zI1Kc!L$4z*oH0?5T2Pj<9VCtzw!5&QTSc?e)Boos(}FJE)yV3#IsrNpF#;p3|d+G*Ut54%en`qkoko`y789Kl&nio=aC?gvrZoji}O{ z9b@;t=4oy0Ue-*RehOPm>la;toumXlJa>HWJk0&G4kor8Ft56`dqAwSS$+9oIGGES zD3Szg$|4pj_i~-r4v?j#2NgzDhDVMW>CU7W$+QG&i>aCaXQ7O!H}9)1az}=3N^S_6 z$jSQsU*(#>=fzP*HUEwU?4RK|+mzz<5B%14ZS};yV zfsJK!44U2nn&_2N0+;g1|9egD3ToSOjx2(H+bDMxoAjoN9CP+Jqz-^H8FU4| z_N*J1FJ6jeq@b|wL|G7Jvs0ty^em4sKWXY0$G`M#t#g;qMdf)*BDRg<=HpmhHV`P4 zIutVHEbdMkGDlX3Q61k*YME-SlF?CJ+1^Fp^=Po>L5HXJIlCWLe9+mQ_yzJZwr+#j z-HWYe53Cs8gGv=D&y=DRnSct0UiHvO0jF(2=jKbI-ceo?HV}QP{I{InSSb21?_9Jf zuAc%ysorqO%hE-K3}>n}KgutOEIPeAx|bT+SL|(*X!+wfB^(%0w5W7@KcTc4{;3y_ zYo?~Wb-ODh22x`=|H}&r#Wq*oYFb`(wil1A@Mc_BO-sRms*UXt4}j>3i2S)c$c_EY zL{Xe~^+pLxWgo^5N#%1La^5_R&9 zY4G_~TML|7*22AKclU)vizJnAcYC7OUr6||6O!|gnka~@HI2MI9rJbcSS&{p4$V(SryL0M4`(*978nY*3X&Qba$2#5 z99e6GCGgk2c|RuK_H4N?wN{gmeb>Fp!L$sJP%iDNf~V(7$MS7O8Rn zNG{Dg!{%DYnLux~^Kc3Gx=;;5n$ZUK3KFM}L0+af=cNPj(0`HI~~_8 z<1~(m1TMOuPa-3BI>#bA!erHUfUD_Aprp$G?+xg{1kp)p_WKh&OX{qdHsG<6j~R7e zg8%}#w#P4gJ)2#!r3b~D@`znkj z4GKW2TufPP6gItJuSEB3M<(y>(su=t(Sin}kp8SX?I+k~NCz1(KIBj7i;cLwy7=&; zHDT)eyRm#bS#q1jV2L;}R*F&0L#DA3-pSb=?epCm2^*W*%4RJRQ+ku7fODB6ibt-& zx&x;bofpMaolw%Y5qoFntucLL8mC)JC4|g{-np)*@LHUc<-e+Zm}iq2Qr@h>-yhp zuE94Qv*^qVt|IujZvru~6!33LNCFwd(Mio>JAL2}5ONAEuOl@YuH?8rI(tt`NZg&m<4vg;dUA#88dR@!myNmPL$#0BJjK?3N^Mm>J>36uyzD6a* zto+@fl)msd?WarSwL|m<`3xK&yArHdA>o6BMg8^`ter%uYg<+upZcmNcnP#<4fr64 zm-QT8SMX86jr+J{1))8*4Q_XGhb+8nhvQQfnExtwPs@!KICCO5oN1HfO-hYFZiTRM zVAX|hMAu;!;;t1d1|A(I08a1E|L(rcHwLFS8S8JK{oPr7a04xcHg`%IYVRFIO5HKy z8$nM0j68YD_EnDLQ?O1KjwO5rmZHIYw8!Z5Kq4Euv8h&TPXu{DK={#AT@OMHZD*#e zWa7WLT!8<9gd#w*_Vr{=_Cl(EV0|*e9YM`*hf}CcyPu$CaiU;jE8h#9QcDu)fe&vK zt10Si-$yZ_w47vnfB1xCpFPJIw?kMbv01R`l_Qs>lYq^zPLKPUD!)zfYx2&{SqVGO z1-cODA&mOzSfQ}|w)5fmo#}4QuAm;Cg-1V`7umJgCntLre=Dv?w&&f)36Urn?=A zM(S4ANATLjoyh`PteS$4;1@`%Viu_*k2D!WwTK@3MCbl7|SpD@4C0Uw*L0w$hlneHN+P}AN0u?D(qN9P`MS3ajOd<2 z1PpL?xE+w$etaJQZ-u_SGBwq-T-$)%C6yuPG;}y;&Q3Ewa-Y$s%=%sNT~TqX4qAD@ zH{b%^k#qB>2=5|1l*2^Uq!t#LpThyb*}WD#eRm#9O3}x_ZTihKlMhtyKW8*Jw1}zK zur^_<<*LEd5&mK)!DbUcSUdSgx!&wVC7emGd3W&9UI5rzpu1hGos;Pk| zl%BiO8`^n>;60!Qk*L+iAI{`WxfQVkKqzPXx2cYk4#oA_8y!L}(&2)4>E@eUag`8R zM?t^+4NgqZF({9u3!#4iYs`f2jdqMFa*HxL2JFoBStAthTN`=j0MldQcmEcDf;FvE zxYoFx69HiQ=jMWu@nqu^1Ja?77y6uS|8Pwi-k}BORdVLQEOMtU(npKx*aC)m<1%=< z4~tso@yLVFgBY>PmdZ}mSR)m^%9*+f^Jz_u(f$VqY(Vo_!52QTqh8)u-E7q5vE%0Q z1CynC)#J5#zkHWQLkA7zI3flyt^WBbjCj)EZK&t?0dI`${Jjn>wKG>q2hyyh-N#xq zHRjW}Ei|(vbwLg*at=V%(spI0Lc(}E!aerp+w5*)lGi0EuXx+K{Li!)q;_p>0a!oD zV^D`4{aG%J>U@nE4k#**2QjHm0-M70lvp?u40p}U?y9~cKtPV6urt;KU-l{G^&KB{ zE7LZKhV|*v+m-$+H>NBT&F7E1b`W>g(Qb^mZi+&x;Mx`hJlk|XLHXlce}7ao>!cMY z4#f zCK#rp76Qn;S)I?{{?{fm5cJOI zibB%hdh;`goG6(p?W1rO;ubmNiS2BHIkgIDr2bYk;X`EPUyb%EvT5J@-Ps)4`;;jv zM#0ZP%!Bn{4+zY`YqO=XO@B}uJ^@(1fA6IxlD@Ote~~}+Cxn_VtI>1p-AK9s)`xrk z+AG%^+&hoy_*Qzh6)&jvCCE;H66qooxcmgxCk3`W1eRT0*X}=FNav(V$xAuuynG&y zwEjO&_6zuS8$Q?nQ8v?Uq2j0`+c*O*V^Wha+d(E5^E}1y@HDkc1E4Hq9aTidBoJaR zi{#Wxb9x)C<|RTrxN?<)xY>E%ysO;C-$F98)}w*91<&#)6(sAe=y#a2Kk+}SbDL&l z-<%Knu3VlMnY6p#wM%GC!5N-d9XUT#YoTP=eXy(a{H(;!4tzES8*~|v$Xbqk+1uA5 zA|hZqPU3WYm7x6ySfggT4Qtf2Y;lABRB}Rrm6z=nxldH+zq*M9h{jWD_01X>fZM#Z zn)Py$n`~{L1yyR)j`Q7(0DpR0_GTgZ!RT20Hw;UKuy(5CPcPP4tE`+Jgz-iPsM+tl zIO91AiI95&u3W}Tt(Dz)3Py&fnG8m(Uek4J&w?!n09%61T6dCk9|%{k2v^NoHQ#>K z1*ggX7?RV?A4$y}6#C2O%8&0&tjttxfAphBLIp(QCgP%_J5D6YyVC`V-OqQ%YpfPk zpd|y5k0UE_Tp`+Eu|wX6=j;D5o!~iurZYTrIaD^(4lS34y8AYpwQDq`vYyLO9N51Z zTJZ8G(!|0}&^!TaADgAq0*MX!SV$=|CUcmMg?PkYZLLw7CN4R*pyIz{{%G5!Ag7w> z>ZRK6J9zdeZtPi47nLV;nw?2Z!y7laBTQ=Hf5t60#v0L|!XL-p(F&LmsoDGtqM=0q z%2=rv*HM9PgvvBk>Zj%AA$&z3dXdw$(9@WxW$I-rA&a@i2r&rAg|B?dn6Wug5V%;~ zIV{FCJz?Z!<)%!+&hY3RuyRkUSAL1UUQ9rqXHiTW5S4#I2(eu^`~-7sFK8H45ZAKQ z60BdnrSHv>NBYWD^_mH^{U#TC$dBH?ci?$>uQmy#VNK%fa*j{5VK@W*rS zf%$(HDbPdZ9GNgey4!<|0`80@V0&Vnpn;wUuV_TH-o8GC6ZjuO{ND; zdIp)ODwSDv`w1SD{h(v?xrJ5?t7FltgU!b3J@I*`8!>($E!gmHKkoxSQSZE%$u43| z$!ocKH|5=xOnygdUt@}f*zd_b&9$1Ydtr@lb8`4>j)DCM8Gz{Q&AKQuh0H0g>Bi`| z6cnat-6O;Lo$juU2f%*WtJ2``$HEqfffM<9p5#K+(p9sfjV(svrIBsDreIz4!Y4XI1JDdV3yKm5ZNlpmQ|EK0 zz1Y{vcQDCiyY56LC+5araYkDPIhEg+{{Xe1EhbCOW4E}O9ohFI_xRjvIglSND9w2_ zK>UAyA1WkU!&P@`{HnEtHTyy5EQvN&35b*uhU=F+a!zePh@oxt#xg{HP}E&L6S& zX*V@VINfcJK`zynbJ=39g0j5zG!!ffv7|h%wEK9vLkg7XLQ@u3iyS1j}(vM<2xmgPj4*drr2}Ak7akhdt zTm5XISpGuvl3J}V^VXZPWE8NBW!S4O$?mLaiFmsaQ-NQ3V)!>$4=i)AzYI4i`BbRP;#@9dk^4It3BK& zaGKA304Xs~v--A&l90UX>p_m>87A1hIrn3vnECo>pDitcg7C6{Q}cA!k7mj& zz9~hLV|6Mto%gImmpqqfPnsdT0oqG(lRqGXAiN0{K;eAsk3 zasMF6oAC4bhlrv$b#_z!Z?)!H(=M~Pf2V~4nR*Mk4IMn>z?S=1y54ZR72sh+;CL*@ zOp)^xVAHl^CQHsj_{0r3L^?+-3W7wQQ(b(dHR!Q?hL2wyNn9fp&sX4#}O z0K@y+%s5N66GhZ|5*xJA1S$%(!mkd29_@~V0q~WO@_Ob)`|-QnLD!T1N+T$*NfA$( zZi2Ch$EU4OY>*C?F{|Sl2X>1gd@WRZy|@jG7-LU4Jv8u9 zjW_)-E4Fm<1!0bp<;b2Xkm3{EG0jF4*T>{=EalgY48OzYg7Algzy~q`j9l}5=E3-Ze_~oB2tB$V*CI3d4!g!f! z5-h^?5z+qf8fR~xWnZi@SbOsGP~QevnKxBnyAK27Goz!ntD2!K)Eg|Yu*<*h6REz6 z=WC|L?oS&hR*1X9#Nn{4T98W1=<8}dy~KHnjG@-6oWXdf z272(mH`K^*XYf1Edm`wVFd|hvW=UPcoY4?z7!(9db&16i+(c)-ia&@?~gygc)w;>85Vx$TKxHrEH*ZoTV*%|F0 zi+J2dKei}VP){R$duxTpgWcNG;U+#a!hjL6!aI{t-$V4*28JBI`;55Sw&J|1Zz@*UxB2*-wkj z&BL@ssJDvLZ@r22!3&9LtMw8yQzBTTL-<wWT6x@xvT^h%~0Cr9#hs|g0a z^fn3_oM#A6awUO3GoXKitaQ6{*~x+x4i2AC86AQyj@Jn@4vO zA1hd!BkNBCyv?{9GUnwid4xfgYw#tQBKs1T5w1HHnVmmTgpA8QO}fjKPciXv)t%A? zZf|F1gX~sAC-BH6yEz^mRsGnG0QT$@bet!x>b*;*-`OnkE{{em^qQK-Lsh!}r!*{2 zQPqYz4hNLwzTH8QfCKEOZ53igA@IP$9Y6doZK z_7LG!D=v9{Qk*wc@x+jr#=@C4Rl5*6*n=&l?(OH27vSgpWvkmWw7ocP6{esKHGDxi zfcIqc^bwnxm%dJqt1+=P<=N5F_-(!@O#S?2-Nm4&`idkiRl-1G9J5)WiZoy>23Js8 zEdT(|t$aQ_BY`MvJb37Tw6lVo0&3`%$lE85mMt}e!x?YJl>_SaT zc{T>dPqVeu8^$WN0-l1f(e#gMYKdUTEQ<<1HeSvEAg_q=gm<3@`bO?W!g7WzUvpGR4fRn8Eq-x} zj5{YK0cY^BY4-PNQ&ECX(5%&>A3ovic35o}U-?MK~%)6du_0eZV z=b2l|i}OpWzFs@ch77FMcN(SbGX=ifnI|w>qxOlzj8VAH52H(rQUmpMMgTM8PBvA2vbKoBN0bTl+}~#w|{1 z5kLgUKX5=eIX&He(5DW)`p=Vrd`$AEcv@?~+mL1DgAMw$Ls$nR8Y7_vZ+v9MB3FqM zX7O^Q?BKl!oI7RtC2zdlJL2|OfsZqG%eK3JI=mC+gBj$pRT|$gIcly`xyyCE^BS=k z(yeD%bzd1>45PdGvC)X2{o?ej;|%~ZDY{c53{MK6*A{2oP%E%AY@nq77>6+P!BtU* zhL&~$XkUIy!0V39>G)dUoeseDyRm`_CdBogyaQNG&kf*68`hzN>JQewwZjeothR=B(A z?ADboqmZY0&-3q=NH;ot`#YMOWV!HEiL$_I;JF)yQvgC-n~!#CO0ilqX~v|AMdVufPX;UGFBR?h^wQB+uGf4$I=5Aiq z$#o3Qi|GNaCakh^_kugh^#$Hp8x=VgTrpe_cp#_cm9vUv0RQhZha%7D*>Wnz5QM&ncf7X_hi;wMF!YsZ?&Q!93srV2_b_1~3xd+9W`U z;C1tM$E5CHZrC%s*Z!Y)%~_o!1z}egk51>J^aEkzcd%=``DTD=WS_$01i|^oahajS z(Rwd9$&`Iv5Tx)r<$asn2HSnqhZ;>a=1NpJWn*Y_L7F#G(=zJ?0_6ZU!--pBY%Fez zVuOh}T_8H8`0nS=Qk(l*07>G_BEP{X9khu}urDM3W_iTWd1d>1`gUpExi4My;-O69 zC1$zD6@yB#;f0zmg^e(9j7d&7rU^Y5h~FBFXE(UAghW`Xv&Nclw zX|oibr$l7cFqv&ZR5Sak>XToi65yUVnwgnheOGq_>&MM4)A4`;PA3i+D?ODg(Qf6_ zj88YF?<(9lh~}?nqf7AAedyer4ZhDhJ-Zr+%r8=`GdrKMn3eiVNgIJ+@k~nT_7Be%jU1vnxWA(N<{Ma#Gr| z^JuzXrD1PYW+nt;JFD%y4ql2GKBoPXv-+@~$*IpE&yPuc9DSm$pN>^?;NgL<&?40|_LkWYe(`(iT6=_mlmsieFqyed^q>v98!k!hj<5YwJVQ*(l z`1t0p4*tTKAH8mJ6ZnL@U#6yNvw*|kbVSLphOW(zpT6UEfFs1Y=S6251gcqyTSPQh?ZL+1x$gtQql}2F0FZY5?k6Ms^ ziw+OGq$SPGo0D36fu1ojZpYo(XcgfL?F8NkNPtc3u&$cfihppfc&j5vE!ez3x#3iW zXuMU?J92-hKW{o!Fx`>jTv@oVe7azWzH?;zRyYRagNqBV*WIq^+`{}{tpN)Uv&FEw zXze#S92Zh-(kANxQpJP&QDOL0EHp%rG4?DHe&>C4bCe0`yrK)?@5IuZa1!Ef2H?Ff z&zX5rf}z>Hc6r<$X6V5;_UBIGAXAPD=i&uqqMTY;)N7)C)ug&kx8!qnNR+uf&Tnd{kO6o~mYd0NbW=lerZ@-d5L*7|Ke*duR;qp^<0wvl zjf2DfWA0r}9==!qSeCIgvvwURYgyUWQjGV0wzJJp%k5OyB(Iw)T_h_{ufvvCM%rGGldYh_;fIKl?yR>iOaQB~R!1z) z>R(k6mRd`PAGYm_<#!~Y0!{p<`qw$Rx%zV_>%n1RNDxk|1zaXC-H<!Tukw0FYIqK5La=O0tkBGRikRDd|E?oy1H`Z#hRT*trKee;gYUp zO_^UB%hBO9b`-{bL}UjrtmfT}$uwiti8HxM|K7ab@b(pRDjeVUK4m{q{URY((VTUj#tX#lNzq7rpV1KU!_!!6~xfk$5ki+3+e2JFD zyosqr*zTxsAcMS&Zv-a6b0#Sb4dOryBHzEWp-trEzMF=969U9w zC+%fB3lmro%7KczHaL+D4p?qhP`Qzg^!Qu<52@r zWo0D1))fH0zbVjb~20l3eTrTsFF0b6IM&x>TF17s|vneQ0cvt&F6F!hlGdS3Nj& ze!uUqBLbj)Ir8f}pGoG8McD_o)0J~zmInhihD@o)b^dCN*T`xu`)^urIbK^_l8SkW z!YZTEG;Z|oysaC`Z3r5gc$cTjEn%G9a$|B6DBcEi%#4eC0}J!dDFpn0gKGtv!*Fh1 zmw)u+DL2Ga@S}Q5C;K1mB#Ie-aj=%Lvd8JekRhFh+NAO>as|FlzqlcgPjhs*phT!) z-_H(S*VDI<_T^OFZ->Q!Yji_xbhX^QB~D+s{f(=v!N4l{^VJ0U3Ak643yP?q-#Z?^ zIvZ&{&cTKL)sh#4IlUDnipqCXlpYT>(BCJ7(UU4k(bd-Q5u7Vin4_Ii;;3 z>c0&ugJApet4NT8g9F&o6cKpzkTs{t=POLacrGE}*_(6VgP#RG^zV`?S+?opt;0HP z7U7f^3KEsvKw0EV!g*@kO;orz{O223Fau*oQPGuIx4BH(z3}T_iY$vHTm?~&tq<=d zUXt}8?+w^p^r-;Ux}T?T^_pFSh~|gH96Eho{p9Rv4ka2>PY$t-1sl z`xE$bmrhj-c%Ll+8RqmPF4RTm<7!I|LJXIU-?y-ZF!}7r;;_hh^`OH=XT5(d@QsNl zS!ZxUWrZ)%cOXMKZ{!c&3-X=rTP95>YgyI}r|bo3KBUB2ua9Pa2oV{ps= z2!Q6&=;GY2wDxl)Jr0==yz85A=K{v`bJS-Ic>|cKjv}i zE(C-nUaa(9od)TK4*AUS)sP9hZQ}XYS(O?(%^1#~;<=ox2R)60hdh7&ygd}JEgKY) zx}i16MLRu%^@JD310om4X0{H_vj98wpQJ4MlXLU%oQ9ab&mvKG&02G((mYQ_Ota_7 zlc)!PW?Zd>4u#2?tm{vUq%9X+y&8#MR;J|VS4X?#p-oMWA3kE?J2FW-$*~IG-3T<_ z_pA0bxEW5>sNTD|>~BGBAN!j0ewLjm4thRxC%9Z*Ls^hdLB`?TD-hWAmjAzbgViIPUpo-&7|As%R<|Sap{OMJ3g*<3GDl75oaCA&8 z556pUFfw2=QDXsX@V@VsQG5t7jaLe*D4#@+FIjBmqimO^S#WhQ{Xem5&VOfzDZTV{ z*-&$OS>=Q>zgZj_)p)v@G;DHLVTotbe2*|fL#tW0xG=ZY*pH>ZFd5Z8-Az=~A#wSI zWd(J;^6ir9t|8SP9RF5H84pyPVO(*Ak`2~@Ud3>nXBFt2pC{V3FOxS}-zskA#S5X) zDbR6AD+_lqD`ZH7A6X985!tk&XGfIgvbkmu*W-xvZ1GIpl!lQX`wO-ZYd*C8`~@k` zqakqFLZ2QaCdQp#`2saf=!t0_jc#^CfcS~dwQ42oEP7Li=CXYBqw`Y}SJF<1oMjs}T}5%Vc{RIhd0_)vU?}MU z5i#Xd*(3yqzus@IdrC3BzZcU*a*LJXN0Ktk?1U zF%tYm;dmR-#y%@D>FMdJi+K6Ip`z06&doDie%Mr_!1I&6HH?mqQ#5G0u`VNA$Rz`p zBDp%IV(YZ#5ut1>e@@waIwCn&;b?U= zn8I_>abPz36kg&b>I6UlP+Jy>P{@rFG_Vldhqq_<@YP;WD6j~OFxc4G2-rm6Ia{kS z8E!8lQaGpCsTrj`k7?uE6gNh95}t$G@(r{7ztt2W#t^y7r*>~6^;WRo5IT_xhcqK9 zhFFn-r_>_*1|_hzlPpc_)m+2xEpC`r-;YHD`ShNYGhv{2lxe2jW3svTV(*XPf?^D8 z#}EE@rR<+;OyQ5VhZ`nZ`SkFT66XJA<2~tcU=Y0*hYpzsJ`TH6&_xljmdq@PK%xZi z!9Kum>dh(Fnh$cJ0=b5?PdUd-)Kd^N5!-Ls>Po7a@7RI)!P6=_Abqc)VeMLyyo=+J7)zDF6xR7& zsn6*BdhP6^pYwLB={!CmVK68|@dix0dxy*;Hzf^W{Qy+s;l5`HOW%e~tK{d8^*tSe zkuvMPW_uzjJG$RRi~#lraCJYzu3kg{C6NX-K6&8K8DVLqTN;yq^|!Db>E69bxx01w z_X~>A5zq0WNVy1WBTSh^(y3T#UKk*F8^q$N;-!$>CoAJgZv3jaZvSSvKoM4o8nwPL zU7~QjzrGRfrC<8)T{9+%f34`|4f$8gXZH(z65)(>ozc0SVCrI9rYj&S(C>u^Y#tYR!jlXkton$+uQ`O_RVr6!VjZ4sKW4M?&(m_G# z8fdy0sC*4sTd~8+GMqiiZB+z*w)(vie!JmhqCYy}lBKo~Q1oiN3G)ob)}fbj|8GZ;o^J+Fj*MiFm%N!m{EuOt7Hf2#hHmFM@r z83j9(I{fH7J!%E>5y03|nC-(< zB`;`(eaL1Xn2Iy^jckIBC`EUcTWQ?tZG2w(w5CeeCxbB>A-F|+UfQ)~eNLuyl7bIo z_b%3_f4_enRm?vqSp`#L+vA;aUZ;aZ~t0IF7EqArFY{^+UVymtpZNgwkxXFxAOSs8=XZ~Zd`;Pt{kMJ4_Qa#z$n4a#p(28 z!Lhn({(*TQC@O>;CG@~srqhB3v9-7FG|d2fqNyeKQUUeh*T50;imPYd)+z^dC?KWD zod}+}mlJ&~dv=n}7cxWEw4aCxzVzse4LWNug$AQ2J9<`xrpWY;qioZP{W+qpbZOQz z;mbPi9o!{SAu+_K_5Cp0MGe|N+xe3JHuH<=`=m;b!wi?l!B4?UB-;PD2~6n(FHU#` zIj)8Dr;7J~D+Y3X1V!WW@$2 zF=3q;5f9MydXLpa?F@kk?zwG5G@dnlPKbkHT3cP;4vl~OpLE4&`ARA>xFDW4!w&Bo z-3aBLkzkfwYWLBmX>_btk|=UB6l)8k*j}Sm7u0 zhS%f#)yNT-grsjKAfDB#NSzw?zHmZWt5&y1`K!^-BflBx-Y@n@5dGic(KR@ig#e*{ z7$s5e?VGUt&k~C9RbLfv;$#j4?C{;Adi9TCyy9dcVbz5M1DMe5gJ3z9`|-kD3jVqi z$zFV~g5x6%L7wc2cBa%U(de4$TAZF=wJ|+7^)Z6wolu>)$Q%~Rl~%tX*aJaWh0zu< zE^cDz68=IRS32w07P{na*Ih$-%N9@?VRrsxe>?2hU4PUzqpk2VIuncTxJXz?2%7$1v_ zoQH-q7DVX#9k%3x!o-(-3YTDA2hJtbMsttwSKo5=!5p(Mw6G|&3q9eLCnNbd-6-YF z6yJe>>r2yLqY3W1B-sd*!$3-=V%YZ1R3}!N#ua6Sx*;q`qe%D^9qr`ykL;C82mFf3 zUOsve_fyA-k0)&DU(i^K12527_8ZJH^fq#&K=uAgOA;Xy46p$rh~IJP$54rhJG7RF z*YS=Jl4(}Go%oPZ4+bvQlGSV6ycG@{khP(4zizbu@HZu4d;qjDCE`B);n}eMfbOM9 zSg;o3suJGl19jPtXKE*dX$|!vYW=d zN=RnaF)C))Cid&}?uZ#ybc0m@m0Sj3#%tM5JLt z7?AH%bEwC_C0=+g^Lwe8Pv3B#)gSx!T`*-P5A)j5qgjDcX96rw@nmr)BAa?zXLPWy zZz2`5o76Tb-iS1#PV?s_xxb#???n7+-Uebi%n2^G(xC5WgZ;hoME#p1qFBYz@-*kZ=oHo9Ji;?&(2cySrUY;LQ;=y0JnkQzcA?Ia z+|`=dyd0hhou^JM?ft-}ThrPl8Fr%=GGy)QVflGuVLMYKjR%_%@YM(_RP-)hZf1=B zdmh?tY!@O#`6^y2F1_NHOQ)e6_9X-GV0@SZ_0LH=zkUs8u%Pw&dd_=)aq~xUs{A|^ zpBwkMxIy=DAtYgH4L&K(h4z%~)USPUdKsB6-4X2|H5=O4A1ss*KG_fLrr5v?MHD$k z`M$Lu@pD`FIqVD&P1zPc0p~_%cR*}^Xq4J>aiF)A&U*)hCFl>1EO%QwgE-Q?Ggj|E zST^=To$WU&JG)rME=z91^{h`Wt_y<=snPoLZERlpVRMkeiQLH%7W#H#@Hwc0^pt+yfIl-PuQjsfTpY*ZO@KW`JylwyyoK${k+5( zzMydlmTyw~t=5VuG!DMX8uNwcrT591aW;m}cwoNSEw(ib)1OVMi8QI4Hy3riddrJv zC^?v#)KY>^Ub?sp2{kxh={5YZ)fpH7o3Moi&kh{sT5ctx`@uxDTE`PFO8AB_AGtuR zKLmK;aFJpx>Qji`S!K7nERW>=$Eag=X#bEvuE*HUcTPaW~qUF#r2kD$r1h;AFnvEFcTs#>-I|0 z;3XnNaDTd&V3o}Wh~bc|m^C_Vy7)AuyAOff*4_6%C4<=CkWVJ5v8ULu3-4Z9$?| z!r%)rez{f{MM8Pc3Q~}?n&C0EXb7gOPQr_(OjvrPyw{kH61GQD=ssVw+iScyWc(h37tZh&l3e(Z!S~ zC<xT3Y4r{zAn)mnJ0W_Ry!eUv=OiVZ3bl0>UZpc^d>ePeK^Y#u1`;?NMi&c8at{T)jwE8fu3JByL);J2Byr^>aDG!4?S-${@xz9 zM73wWfp`u2DmA# zKK|{#meL4e!`^_3E;=P2`Td~dq+=Oc0s2O>TM0b;G^vliV@$jrrGyQ=;It;E_p&j5 zbyUome$J96icOxf(6WpxQrrWi>*YEzd&uO#-d5(=Oa=&8{7et3GRcBU-|*+7VdbGU zd&YH7a%EsDM21x2;TGXyW}Bn=_BksDS^2?KP9Ou+YzfXoz$e<;^39s3L4m$IB+ zy`-RI6x9-=^3uGgi!WR8PIHGR@czngHPsF$7bXOc?-O*)sp{K&P9u|Qnchkeg~3X* zYlGWGk#SQ~lR#eEDH=F+2}4tJB%?Hcm}F+XQS#B4FX{;RPW5nHN#{5cTBH$lbYF3| zANBtg_tjBRzFoV60U|150E&Pjh|);6h@hiLm$V>VDxD9dGzch&bSN-%*8oZiNJ}?J zcQ>3p{@(ZfzO&Z(=UZo;b)J8iHO$O&KX>eX?Q0hbB};&0RJW}L&4fFG7Zx${{$6*U zqktK07Q`W79D`6k6|G%4;-7vOj& zBZ+?`QSZ*Hn!4msL#MfdWBLj8{@Sy(x$M_Rds2kmozMCei}v`HJTI5c3VcSlh$kz1 z`t+TJ-ZEmwZi}krd7g<^gC&;+xQu0}Z{u&*4~gw18iWTj%^`D4JUnDXWXx3p%UF+^ zni`s-fE#?5^7LCgk(_D9D>GVMIb#Zp`wvrLH z^ut{f)Pp~JBkVIr4S?4`ekvTU4snnues(ZZeRq30#P5L2k>y(W{XE7iX%&%vU+o+Z zE48UvIZb8=2arp?ug*T6lA|jNiSQ|iv>n|(ua<2*p`?zDlGPCysT(ey4m>J0c!F4g zPYQq5{jGx0TT9EFm9o=z)6upcK75e*Q|NNIH@4ZMDF#K)P`eKW6zsQ_hdht~N}q*= zs(A^!=`&78i>#DiH{86&(Dd6m3Sxb$7S5uWemi}2eHbHPh7O+#J^`C{Ua?k_GVbRvImW$>-nl^ zwzCvZ9k`xW+J^{#@A!)ES)WbQr#!Zrc81u>gJIystVt=2KfMDpi7@ea4{LzI*WijN zZyh6q6fM#(y?5I$rQTF5YScT$MhDEgI1EEb2kS&z#G{%Ty0Ny1Q#S&iJz6LMz%b=A zQOw(~#W>E5lVrH*CvCo7-C8ERuw$u)kyu+>&(U4hOz+FTnszkkUR^I8*4t87=dDQ+ z{dzjig`7K%*vT22#w*Y83|h5S9Bv^3*!`t4UdJud-~oQyC4Xhj2q>n$|CXi4cxon? zT$0?H>^_SYJ}UpLk=L#W%R9fcw3Y~2YoLuw&lO*8BD=y;NLpfkT0+tZlHqWk3Ji~u zy0>veT3-q8m2g*&dff%Z{CfePq zy-O+lP@x2z3cAfxW-5916i0)kK4}qj)awwNz@B6weU7@9z{A1TD>72;s=?FAhMzyb zyPqan`9ZJ_{pr;I6haN`@lV${+Oo6|Qk%5+nUwM!h~aoWt22gO5M@?<`D0uU=lTH! zEJcGqba!P*Wu0PauM$a)e}dGC;tnkl05t_Mu&48kW_e%p;}J-aky;T)p}(glt&hi= zc3E3XFpfwVcKxQvaI(1amZG?<>SpwH*C+ISd?mewCam4Z1(!_Rr>4G{=yJ{a# z6(?Fq54LBKmD*nLV168>{A_h~b>~m0^y+4h*6rJgGlM_Jkg+kyiNfF8r2bxI&Q)MJ zPbzqSOt>lkoC}_t%1+Z+yWimD=fIdd9k{opi&I4T2zmbYBlAng^Ruq%{3e3{dxI&6K#81$On@NphO!UGIB>TidnIy;&6ZUhZpq( zuFtEesA%v&sqJzgnU)jDN&G8;y<8?jKDUf?4(C4EIdq8qthA9TH0>lMB0I(>Zr$TI zkmypZbgY|5>yyVrQG~lR;b0ppiR?9S1m@m9eTPG5A~fe1M}5VJ{r0KS$+XH;N0#@_ z77VMOXe_kEW zgL023*OeBMowa?s*BSDUfz;{})*u*qM?F>U1~D7GMP?^EJVWD}69$yb2#(heTQ>fsS682Ma^oO^0B?`NBcFQ_kmTV zwM6k>ns-cUrHdUqEWg%Y+A_Mx&3R@5jd0%dp%3`_w9+p;CcEQV>8Pumr?c_lm)kB! zwOLt{MI^DE9n*^4l7l%6b~1mQpHdj7@goF3M~FydtpUx+a)ktc@)&w7eH(b`qd=hm zq5785hZ6co$+o5Kn}=Sm-_D#jwOKr_SXnohIS@NXcJ~%zXvC)gO2@62>wy|Omir^# z{C4D1atxLdi6qft13C|~mdM-kD5?hDeXo;H>7^a2t*sq{hEG)>OI{xx?9vo*8MdK< zgtvI_NPY3dh24C-rT9owlSRK`=Z4GSsz6g=nFN@fQN+S-8PU;!5y4!uwyk#QYS@dY zZ2R4y;`Om)?LRkWL7qISU7@@b95~*|qNDbU&{V+cgg{W#BT03+lH0a|>QNW3�V zELZwvwloy}`2LBf_hRhvb?f}y0yO$6h2!RKbJ1N365*uU-&)8ub^J3sy;`;L0;?qZ zZf7UF^!^D0q^9iU$&JO~)6QkqqOs=D?KJ~wXY=SXl9H0N$9P6Y)3V4$p%)A=89iCl zF)n*X=g8T<&AL)MszD5t;_@xqbAzrZ=2y9dr2J@@ze-|H${Sr?Z-}{5F5Qbdl7|PyrEy z9lbdf_eoCis#=j*HJO%+k6x2<+$eP`4%|}Njp9j0@sfbF;nzbKO(a{8YIpxe5|PpV za4;gMc2=6+&GhF5wZKvk&2yaza5@aWVta`&Iq;V8Jih1V^%PrG+-h{;c#i8&%bc!K zB_mIwb5v=;g$IeH=HljLU<)cd*C@8THn6==U8o`3 zTgrHLUnejw?z@8VKG=aXNITy-p)$`zi=Gxk${VQa&0y6I@%9u|w3Jkxt;m6^_=uCo zM7{KtvT{i0`3(KjBm!>`K7Ra|@_y35r|@vUo^R!zu@x`0`??dX&m0xa5a7&1b`A{T z6%V{>YKIeA*eyYncCCBR#ePdvDS!A-mZ*&{LzVN@=I0D9gPyF^ z9fSV;b@Jf)+rO#NCa(G+oRooX(NjcTv0&jc>SM_Ers=~=g^T@uW@QQ4W<8oWgk5rf zv2D&d!~t@iL9VqEmecSjSic?(a9?K0_t;`f?g?%ntj#G7W%g zN?E%n{YaUsO07*^-oMH`CmcjE2K$!mPNC8u1c!eci~59 zo&38+{}7w;=nq^T8;-Is1<^L+MI=?aD)TlS^7S{*7?KL_uJy3K6r0Vt=S}Z&uRXif zH~91)CaEIiy;dkm7ZbS76lFTzHSHWO8y>7~iyl~ylIOl%e`;zVbH(#Cm)LPz*i0Wc zd9>QjkJ{CK8qtmfdRZ=g?#MSgy=c<`%6B+XHIP}(8~H(D${Cm)CZ-viS%7Pl?f2U% zUtb}8Jgq9EsQ6}ORMctva2qG)-+-9XG3!V234hf199KDS2Z%#)2o&nhqR4rQb zM4EO^mpaV9SZssK@5iyw7OjYB=r?}}igS@G9fR~*GF@P#A=Vau#Ti(a{IRzMRB?GfzU~9VXB)(3J`Z9gm^5Vqf;sv%%^a8bi9Z`@pQstAF6K0k%P*`2Bh1Bobq{{X6 z_Py-&2$~q!*2hD+eBQ>_Jl3c4@3(Vj3q?RWtO#`ZYf1dox@}avp zhG5Cx&`Y2!Uu^pWs&|M}gwe0;RR%a{Ua$Oov*68-BLj4&V+^C7GdY%?1`mkUTpOuI zHPWAGVn^`E)7I6IvWKDZGp4^QEkdPaj`-kUT_=5Hg&xQ?-vYw3XRC_$m+PIkhrI4E zZFSxK=6=Fys~urqj^<<2a9n~4m{j(A_JW(sso4d2oqS@L^Lzfq)F#N@*;GPjGO6y0#m)Ug=GLw2zfIKNk-Jw>obTWKLP#mT67Y`;;v1cG}0t zNI&a&9_`f-I+fje2553aX3`ladeZ+o-zAWs;*bF~xfv|dmc=(N6MM<6mP6%#yEj`qLZg!g>BvW3(_x5fJUTb?ZLE^~cg-!r`1U zy!W%7`^A~uwj4+_sh8n2>DZB2mAWS=)yO=KrWR<>P52r3TyZdYMTJZx%iT`3?!=!U zpOw$@S5L03tz{ccNI<{VD~w9%-zS@Dk!G1>Dat`Eol_D)Q~^{%h-1~1YW(pOq2RA7 zK9%OGr`cPp`SZ$I&TK}#7U<^?(B3-2QWMFMd`p0_Dc5;p$7^M6-g5NMg8H2AY{xx2 zE(?0Y+Xz*@8GUrPN{8epmv;%fJj}VQ#aG3WTu~f+}~|beC2A)4^-5H~Hv+l%}xKBSb=Xtdz31SfsFqSXBc8 zUrk3^ipw0yv4V3mAL!Z_%bX?|Y!=J7yQ+o_4=Als*OZ$y&t(Mq5tVuth&`&8u{yu( z8n`GXQ{VN~_x_!7J!o259gFDfu3d#-V?7^n4FUoJTm$pOdVkSlyp!4%7FlB76cj@) zjk8bG8syj`)%;C`xVVbuV;6tnllZXtNLIeD=e;)UQM&SHT6f(yw%wpcWv9?M>mLA8 zD*Ict^w%3xQznQw0Kemo@&Fzl-e(Sj7G`I=^627EpE{dcVnqsE)T{1Qkfh(Gd#}gUc}4%cm*T#dO_ia z&C*cN&lJOj!7^&EplBxBBo%E-Evpi4Tl1ckEklw~v+n2Y*HTD9;`$F9^?V?lS@w<^xRt@D@Ikx}!kW8l4<8?Y9E8?C@+^4#NQJ=af|;L#wa`=ZUneXG54)pI zSjfp;Qp(T__*9uyQ};abLwLAy`r>B7lF;^`2ej|4u1?&^sXAdXS&Mlhm_dYvu-a;h zEXf}c^7PCD8i=|EyX96{vEzoyU0=DwSXNRy#v3rJn^*1D-{!J!9=Sbv&P#+M7qp>S za-6yIlb=$$3SMY~CzX(cc9kaub}iEPVhsibe;5=1@ZB&>{Y6y?6fn5~_aL{xIES%r_BIUrJn(PrPZO=+iR zbcwv5@03l?l>Z-hBLxkhQaYzEWd{5Ts0$R@f5WzA=aYC~(fzBQf@Exwgru8Fqk16m z`Rj|Ortd73Ru^v?noLpa7_Q%WT&eUl9D;&usy*mz0$3+TT$)5x7)5fn0#F zFU2?Cl9KZbq$WhNBx~ym()>)RZMN@9I&PllG^LI7ib-MUvWL^td5R5_~ zcN8MQ8RpKaWKlhxR&;9;(UAxqP`|Ob`r!DCAUl@l1O?CUdtwP|lYgdOj>O;YePd>| z)KcF5gb#`Twck6?cPrh@1U7=$BNaz0LKA|ly@NYt+)YhQ)rE@#Icq_RDka8Oq28RD zP8mNCUU2_GA)Qlu+sniT1_rw`1{e}3{jgd|JX{@4-72Q3nThB*%J?aL=9<>9TIv?7 zU+``u`^|Ern#%49wT| z({0V&WVddl^PHiunVUBC*@;ggzVbzboJS>C!z0ya!{E(?Qs)B;;e zV!z$0e>SzUu4f7dU@wvk<7rf#eLPc0WOTj^@o!;XHF$BoWnV4WW-S$;R-SXFjsB)E zw|ISMSdO`II3%M$SD%;7b6qOX?-jBDpPDXDOn4vy?=Muh#f!C#qD;``nIFEeG=s9JwHX_)d`c(Da(>?)#w;h(=!xe-E(l>s*`*Z4G=g0hK z-oqie0VJ&|x(yqh6ug5+N`=B7TfccO`AiGtEQ|L5np$gC*wC)x>HZbr^J}R$l89qo zQP%`zt!&Ed{4y>fUeoWd_~vITIzjFCluCE@jnpHqXBp7_xmbqxIt0r(ydygMDGs{s zpTep1&N{MlI!MaC;1@SHw?w|h z&{>UYftxf~p;Axl2bV3fXv}!WLoU?U#Ni4{xa^!gf4=sEz7F+ok&#K5aw^O#jLg6$l@#)Hj$os9}hrV|ezZb}7s zBM>;)_`CGP@}}(b9-?n_Z>*SZEJlQf6NEM>7`H2?6{}9=8+S1{9jw;V4J#FyYdAsY zg2@H+rt(S!rcA zLbI_yGjU=zub!*sn-WS(OE=Mc+1lEgL57lv-=C-Sy=6Oy2Pp`vDK{~c_~*+nySRzs zLX-M!&eZQ|@XRF~1l_WW??|e&EyF zj2!@BJ>Tb-awFzrgC%aw&VGTJ3ZC-kWis^H$XC9fBp!t{3G(}PY!2JAC&|cKM9^RJ z3i7IZ~^kOB{bQFV59l@B5rl9@k?g?T`^{`vDqZUCV7 z$-7M0rxMG<=IYFr*VzGb`jgAfE%qQ)Cd~^JMWw4n<>koa>d%%ImbY$l`Re4)CQf5& zIv*RB7l$WBQ#prqWgm-G`~<)9upEXqscM(6elKPUi9ZM(7Z%!Nmlvw2 z!)=3E3z$696jJnOD;mEvcyT{fiX~xLiC|E1EIaW{oT7e(|=xERbhJiA-3CZ$S6YX-MCS)9dH-H z?-+Bm#ATU0BqZ-#2B({NkPlT)q;LN3e64_OQKa;9(&tyfTvslfxF6a4v#BY~mxYlo zuF$}<&}w#*I+~xK|Do)a6pV2yBkI9Y*E#i{1xqB@&AF~lfzt`bi6D!GgoN;}Wb$hE zs~6t8cMpXU5vgSGQG3DHrBE`=%!~*Wm;A8Hxcf0Y#{3?+O!N56%o#@mgIAI1$3$lt zBHEE|lJ_RmwCn4Nf36PJd7Wz>L9bhjGfJqb@%Hx7&@*11y5##%wQY~lS5+*Lpaz0? zGh5!tOX=xFd{BgP*9sSygIXH-I!a04_sV?;S(5J1_iXBkWl>Hv%mB?_&Pc}H>JG18GNh}4ND>@Li^IdfB^MGQj#gB z8TyQ(P#UFHsUZMsalvW-TwSI(WgBiNcY+!L-TTjl=`+@FBfA@wtR#EIbQpV0Xli^4`6B7ngF1#bSL2EufQaF?g%kO}d$$PYf?P<7mmhSBj0Cf{7OQDk-I) z-NnviY1UP4EiEmaXA9h(cXOSgKjqF!H8IJhOYTt9Og^s|;G;6;4pXiT2e1-NRg$w& z9z}I;t*kur@+<=cTxRh>>o)1IK*#YfOdFzIK@j3GI5_xvw&Uw1GJTqP1K4HC%F3t* zN=n@Ywd|?i?nQa@toPr*@mmAAoM2^7XdG$uXoJUz94xSv)j zwYq1sWH9Knv0-)U)G2Mqg_!Bd(()mSR?F8=K-5hprgM-<0)gc;rHYsDNIsB~x*#YhxUxGPJ0E?4MDY?u z`Q@9XlxQ@XSJHa!3Mwux?!mOF_l(llLGlhVQRk!2k|@$hwLb_qL!WqP)+n)j3jPmOjeWrVcLJrZ zu5N|V=9b5r;XVvf^Xqp+M5u9X&^(f*n`iwMEkG6|v_%dn#0is2m%3CwS)a#7uLt+&CHQvEW6{r_*Fu|m5M;Q1WWYu@ znPFKjE2=9fTtUQ!M6ubejMCI1 zLy@bclC)&kumAlu+vQq15;bTlp zYM0spf}HSg>Hsx05_@UDYH9EqYGr!_QN}@2pOiZ=qb2qgtS!spR{NLtHU76&Z^<3WZC9{C$<1g3I_63Z`4(Q%y#C zl(+Hy@|MWnUKP|k3nofR%4cRpHjAo|V|smc6wozHfqI7;$hx zny+O^!u0K|rbkzkd+hU^cHg4Dr^qHYVk_24C6h#OM?naV!qf2JV7$>fKLnjgW(FLa z_Wg*b<*tTT!KkIdG;Hu%zAL>eu5Cv?(c*0-$a@;}dwS;NXIUbUQV5r+c+^v`>r%k*LW+=7DGUT)BX8%skJ zz*O16t#c3{1EAe&byL%Pm?aJ79>cnNMc2R1)dY4I#6BKsd(cuk)yz)g4n}w$lG!4c|J;8LcYkQPCR}JX@E%%GkJXZCVJA!WoSdAP zs5!G*C!Z+{%vzskns-9}SVyFcWA?Dn(EI_=Cx9{AfVxrh{Q+XAYurFTq2J@QCdMiz zCI;o2pPx@NvNFU(M+ZUF)6>dy|46!YhL*_r+977OLg*5H16rP!Ew-bMj*d{S8ygz~ zzG4dUGxq117M=cms~2T9%qU!#L6PE5WDM>NyI~xP+}^%Koh7_3kTR`3@iBoq zactA~PqnSTzK@E2_hTGiDqB{1^2FQM*Eb(DAK#4&=nhCF*o2sv#>uz$xVQ>BC5Xyw z7CW|)iftjG!;MbSjG`hvw;vdu<$%iVzROEXLuJYh4GpTm(H2z?y?>8yHjI5iFBLVD zjrZu;Ge2KHzs;YwgPw$#V^<{tu5sLHtH;NWs;~=p+!tOeIk%y%g>9<9uD1w;BvBvE zJk}VlpT7Tka+QMvg+9w(_xWh)6uy@S0r6Y|LLa+fZ+5V=>M-_>>^tpk7%^*NRpbUF-7>oehl<}MRvb*SI#y<8W%YpX zrAH5eF{T*A*h@eqtHm{8?QFuWvK#)}!L536)9!~Xh^&~H$9)bCI`lxHnLNN3WW;e- z9sfjUM@NF=a(T~w!N6h|J;q36b*vql$%EwM|2S!&j|$;TsuXbjahs3tMdk6~@+QnL zrnp!IgsY5`6Bfu4P6#P*MJUU7Hn$XGUER`IM<{gtN1oph>>2P24D5!pqoA&y9ujhi zQp81_nVH!)AfOAPld_Qfd`_qX#z0F;J1UHKQZajEQX3oO#Bx%or)Rkt5Di_y;aMBy zvCNw3`)w3QN9PBRoZ{BkcOtHl?|lA2g!<$ zwa;6sJa$3LnVVL`x-uCsk2V@$2W$HaxuHP@|X z(FLdR>h;Db_*GO~*56-`Q}$#+zLt&!APw*}2@5I}+>-PH=H7_U4F0ePTfp3z4Lyg^a%i-VQkLeBh4U%+JU)N--_Zn7@>@Lt zX%&(LLe6A;syTWI*V-@t&4dKHB{B-bH|Gq$>RIcFtx0lF&d;S08 zC7i$ieL(*=gV27Unyu}&VB?s4n}OkJYHF%#xkCYv?E@o2A8DJT_~dQMG{6s3GHzBj zWF9;5J9=UbnQT(VR?etpVb^>YY{~$bbdPAX|4xNm-1AfFHdttl178}GPO@>p(Q0qF}13v*f0J0?7TevwT$Cb6WX&rw=KBf8b`{RPVY zAR(Yy{yhK|&isj>3hv0~6ZJto^I4UdFRR}-jJ-}tNJxlv=+W=V)VN<~2&VgCf6!Kc zP-L_Id_A10(ud#wifQtXI4cRfVd#!pKQaLgTmEQe3svaUG~i?n=a-r>snhEgt2H~a6;9*XdZ?;)7_h& zH37vo+D{S-#V7(6Al)+R@Qr{%H|&_#o!F@qSr!XlNpIR=9Wnz8vfYht*oL zGIjZqL)GRZ?7>p%22ZYnXrHyL*^@}q^5@dhtJWF`fBMFJ?=qZq^(V4(;{C=|L zD0qCZE<7K_Z*3etIX5@Q|LNJYXB;7t&!0cfn&XE|enNzZh~wqUmvxhGH?4m18yXp{ z7RqaBjf{O6SmcIew3HE-zC{~(Rn@t?;hi13+YLAiwN5{8m+5%Wveo+T<(Xp53a35* zod@al7Hq8fm5%D3lhOr*;-6 zLg=1xS_}(D^I1xm7pc{e>%DrlVVI8FhLAoY&&9&=dT+}fj237GLybTl*UEo5*|or{wb z;6f9e7Qre9PUnNvfW5+hxt$4NPi<>D9YkY04C6{pI0uAxvw}x#0(MnDq1w zVJ)rZQ8QCh&iHsF{?45{b(}mr`%L>C&seXar-~{M&5})G`ck)t9kW_auHf5VNm!x|qO1h$Bs}Qoi z6BRE6?a2Sao};z!%AIuG$uh|aEfk875LeH<@zYSun?)!REiz)1D1!dNV-Z7}nv(Ja zGEwGaaVrkDr9%@7ZF~SR*_S$)gbu~#>3gJRXUl|G?JpKN_LSpa6#TaghvT6+zcN1r zyJ~ZD)8P==>?ts4rE;ER)M`(Tb(zas02#Q^s~dEWj!qnQ>hSXzU?1tw)X2dbuYDZ$ zXJlfMBo;Zyw#`celF7Pc?32ikbjHtuksi!i71-)J|KC7{laM!LYoQ- z;PE2Co@vb6q9L2ek+;q{2R|y0Hd)cJ1ji_MKUXr`BC*9ezu9^Pz!Y3Z@g#jjr`zlE}Ka&mGZz_n%h`ksYTWBrA2&_yEqH@D>)U{VmCzY#z(o7L1uGQa|_ zXZATlDOU4sfzQ^bL+Smk%7%tlcgDvooch;!s(X9GxIuFG#5`vCof5OY`V9m#(k6tm9B6 zqa{dD0*veqc6ea&8tw5|#)iGhQUuBH&=zvinY(oxOo+p*k+(-xOG_(cGmd+sgbsM8 z$o_@XLRhpa77i^zYSGRrSMGgUs@OLVxX$%d?ESe_eDj!y4;qBT#66&O3P250^1l8q zpk~+ng-d8jjctA|BhtJS9r7_Y7O5QwD-|Kdp;Te5PLr|7b9cug#k)fdr)5R zYHVzzMMLpJAq+>G!TQI6JcA$@Q}=CiYVI<1me}nZf_81wyA&2!*^mt&rgqK99*myB zp>=@mN}{4}Nri3MhfGgf)0?3DS?nY%`^ZEgsASrL<_k+1+TC69;AaU*0*U7g84JqT zL)JAOgp@=7*N5NTt#DXv7<$^;;eer(DDB|anme-BT||op?;pRz zM#{+a9u8NdHCo;AAN&gwc_KJ82ZowSLkL|+61YXIKF9OoWK0L3^32@a`xXzt0hB!1 z6h04?R90RrgQiL28MYGxl4q;sEo@PK`?VxeW>wnR_3U|sFewv*VOW+h(8xdrDB9cG zRm*HLXmMxRqI@&#It4{}kD96~4nSpSlqms0ix54Hbn~x;r{DrU5^Xlp3MXax62z@<;F0@l?b^EPy=^)gVj{*#F&nWkjS*A87UT_2*!-g!ESsU%h%28RmBO0fGQ} zyVIe;XO@R6;I@3n0nRWcGqancRj|{Dkf0UcTCLD@RgDLQ#+i30SG>C%9Gy?@he$%W z=Jra|`VGiUuLE#=ciyI~83Ky>XEj@cLqjJH^D4QWDIpzd3g&J8cMq@)w;O){{=I?8 zY;1hGY#oj?b)~JE0PN{j1lVKb1o1iDddIW?58S0T>+0+G(>oI(82`!rR-tY6thy?4 zT^5bfSLP*?q?MDU)O{j@>_FxhO9z&5wP%#X(PbzD5a;Srq*2bj;QQF7}Sof|> zwb!9}N>{k`X*KvIc&=oaDk0zlpGrxEHS2c*QTXZ07Yu!j-9tcSrHh3F?j4$Ox+GLo znSjq`hipd_B_!U1*0>7q%yuTv2k;Eb09^-Wf_ukfgp@@+1y~NFmMA`7A0H$b*bXR= zv$0)ytRXG5sqlRhf6o*Y55L_?91d}1xooE8_L)9?`m_T~0R_<98t;EV(bF?HX$jtu z>pbRBU|O3OkKvc^jQcULuqXi|HIfUDy$9xaGN>vUU;%nS?=eO;e}lqwMq^d)zyRn1 zQQR4CA_@2pNRnh6t^$&!0Kmhf6~CT8q1~XX+ufQK@KfThQ;M*R;I329(#j&aEwN#1 zi#n(M-|Sl&oFV@A^7}u()CuJZZ&f+55ccAPI_V+xqmFbWJG$f+?ki| zo0rY=x#8g@P|gh9_7dvxCz+M2#7vZKD1IMsD(ailviUtrxtfB+<6hH+4UPfCgYJJ3 z{bdg!+Mkew{DfjZWW;}2(AjSn;C^|s{mW_$%inIMZXaR5SnnbefIws1GvhCvu`_S2 zQ1iK&VYm5L$E9~VIac)YXc&6nS!1s85e3zWuk^9U2v=!K+T{L-`MKXl*40(F&G#~W znL}Qv8Aa&k5311VQg#9!^EPb~cxP;?J66NP(7+^CopR^=LswC^EzJ;IawatWA*UrRGY$=@LpzkY5PE8`u(wk(>6*)|z6?>z<+ubhO~fQW zV%)pQj|HK4q_UAz1fkfAiJ@eC;ayrdcZSKP9`mwJ{Zb=U!D`?SU*5_mX0Jy>_Qun5 zxvVL|Bw{Ffr|g?cj^j|uu$AWq9dzqwhN949JfP_ym;RS$9^Qwq5#FR1Yk{xqRYea^ zL8z>J@_y`LAFl9z34R`#^3qeFz6~$uJgw6xPXRI1aNec}S*ez$F#W$53S(9;{%Vc5 zyoZ5}ZG3yX}^6~-rr|^il$F8{jCLi2=^cHwMcmCtY65W8CpwsH7rp7+@I(4 zbnpnf1bi)!&e}Vwb7*JVtJEI5$I16BW^U~0Zn-5t$b!>9PpZt)ZrCFBFz`D!rISs8 zwH&RFtWQ+tk4s`Tuz?KfgnsfQmCW??a2WiwHkcICUnls6LwSV%mdeNf&nH@e7-lr& z(R5LZcVzp#;o(rD&-J1GfNsrwokHo<37eJjn?{e--1!71s=K%|UMWE-YoGtBSASwN zv5@Y1u^1cgu{M8CdLM)Z@=rHgcMXe&}g5r;Zr&W{0PJ5XF^Gnr4x%t{w`h{k-(r6 z{&|^6`Md5r>7)|zIHOLE>*|*;(2x|a19AbUN!gcfoQ|UorMg1-dWi4Ncp(iUAt*O& z@{)tJ`c|OcK?>B2dv$C1qpS!tWKhuk$y$Nzz!W>VwReO>2#^wthIyH-l5}{rwa5n0%xH zR>OL&c>u3#R=hcUe>sQo>Xk3?K4$o27Ickuc5oFRdvNfRp5gK19DMLre#%ofP-<>& zF6}RlH}PH%e&POC;adN9^{%*{{tYuvIb3}OyV3la5F+=bHijMz`6{P{SsaUT&nx9h zaZb;bj$>2af8p{A2ufG8eu#Z)TQ@pp>EL`sJY7((qn1lo1PTdr&ad^k!GYqE*$g$V zoxD}<-NO1*OXVl}xEXYE(M_2VUtMyo&d}~NO+QSW`npTg8m86b--L!_xuGKQPRx>M z?)t43S+yAvnz%X|97J)3}zaT{BjlB|WcF^=HHM_5An*`=&=> z`E+^8`SB{F_5R7)NLD!^y(z?zXSA<$E{Rs<7xLIc&A6b+&ZViuEdJ29ShYa8qoZSP zcUM7N{64WvABlJTOd-z?`cM2Hn$M&e4f#R3wKN~%+NyG*yWe^SVcBN?;X z%2$KOMExQAH_Y(AWjf{q>wX??vnXW;c%A28TQ>jH=>7a|gw-ll>Fq5&RA;pkW0 z#4nGT^?2ms6hSlI`92Mwclc>^Vt*xa<7+$qJGq({?|N5mXI+qCaPcX~Taa~0g2W!# z*|e5ZU+KcbF*R9;)oo_puK(mrjbcPYx|HLvKzo^j?(%8nQ#n$EwsgC!pwn~Yg_3x{ z^`hni+361Yvnof@t*D<645YzD+X?YRKXDrKb}vc?@R}dUgZu*Vd^of^`_gu(?NwG$ ze`w-LAmT>yc1Q2;ts)H6Xt@SK#W}PTp>vIphvew5`;%DD-PbEv7D4ZCj79oJLe%s= zm@OT>BWwSb{SbGB0)#>Ed(h$Wo`EAovFw?3eMuk4t<0}+*dSe1pj-vFqs!+TRoVS8 z4&PnRGTr~HSVEanXax6$@;3>+K8-S&_xsAJ#1C^@(jxun2=gBX9}fPQ*A;RWt8cPB zy%7>QFt)YC1(^i$=GQb--h#b{>`6YqUZsY4Y^Baec|o#t#^8KAx=Nlujurr^9SQqGP*8dkoiryrT#HJnfk;nulgH+{Z<`IUmV}J#iN#Kqv zYxm@-`Fqx%UGn=LJxv};f3Lw86WJ(zmgE{_ua&PyqRN+oHMR-vijrychYXZh7mdM0 z;S_48cB4_M%_@>o@yS5MGY~g&73HVbO%~tQ!%Wg+sf*+I;GVZ3LR=p$n`PVP9bQ5! z1`RhS4>TK=vc>g(y6@_j=+(Q~qH;ajvAK;i7CN~ix0xj@)mvTM9{lqL@8BXVpMhAn zr)5jrPq9qh^(OU#l6l;IzA6^#DtF%XgYoSClN%|$_B36?M|xU%bXB$oPwgKcn|ADh z2XZLaQahooj;0@%nPt$2XC97e}0#XubJ!T>5E=gnPnvO zl#wH#vK!C&q6b{Y1iYYqK3%9LHiujb?!l&2N-#$LDqH(HPqkz=0s2IcJBqG8AU7Km zf%B#HKYeaJ${r|OrU%~{vtVokH@U;0T{gJ>=4GkW9P3)GX z=9RyNdZ9R731kiHMr3?}?k)cJ2EK4(`x7y%|7q|%eB}#%E|Npo0=;a;Y`H%x?_j0d z!08#IRDBb4$`%DtoeM3uOV38-pALLTZB66ybVs+B7UgoQ&k$W@UpO8%=6B~1yHvTJ zmDsumNMI0p+i!BX~o7TB6=4bk@#L(x(yDGHSShW{Gr$-iqVUMJn}oA_ZA^N z#)8>HNBpQ%P@k(1rM-1H%3MbGYNpAF5MkR$&o6Skcv0w~zZIIk!lFt`Yy|hFQn{J% zwm{loUvPXkpQ9mOH{RBKM?hn`07VQpKjUDtYw4wTRHAc+V0B0W!@H`B!5`|)m}5^~ zWs6TlNOwxawV7}Z7t9(qG&Gzo$1A~#^q2rUnsk?mF6az zb3{9)@JvWpTgPlKVpKj|r2i8M(r|ml763+>AkOteytUEaxdEx+S3@>Dhl=ph>DhA1 z-I(hTb|@XIrPTL<#bhqEG14P$K2~_a%T|A0{bOJtz&7;I^)U%1Cg#4$>NK35@bwWi5Pq2rTPEn-hKtBnn=vr+Slf|np7hQOP%TGL!1QR+_3 zh#411S8btFTyprko2iXDbHJN7HvA40)YP*Su?4rX1{qnK=Z5I^KDsR5zI8hCQK%Mc z#_jF-u3s>~KaF{Jl^jGFQZ6zMiK%6A7a!V~CVKR7S#s^HI`ExYUa;u<-Kvy!=lcBi z$~nD`f_7aa9y6_NP%nE;Z*_4&>q3DVdOY&kePw9`SFT>!y#f!%?2&>iU7?j9*8WjuYte|!9GLI+6`G zTA-6Gm&RjyHlgXNUX)pBsn-3AsVOgUk?|yr!}m$qHC*d4^kGQ{$M*&zLM(3Z1yAm`{5%4m76@L7}16-A8qyZti4_Ly*2pUZD8tX~}`N+lmDeh}iC6%pY5d**M#v%Z>bv{~CI@y_MkD(N_82zC%eya$~1 z^yKyG&D$TyuVgeYq~{&})p{*+#;8_08X14*r)r9fz8x|i-W7Fl=%xeg4ue56tVQgd zkQ;yKfSr5rhARU^p#3?+I{BW%%G#UH1cX0Eisk6`op{ZLE*M?wak)a(#>v$))(h-K zh0U3Zx|B2Y3QrdcuO?kvnz2`{N$OoTQx`eZs5RVU@=q6Y-5cKBUbQcDM&4VSZKf{K zu9BvfL2#?szZKFmciiwHcQJUT4p*RCzhpJ2j~l`h?#)DnLVs=6xm&?MGS2?3$<*(6 zUptlR&K6OsImglWMa;R*z{~vE8+YqN3yJzGjjqiI?4QSZETUHu-^g?%z8CmyIq$_D zs5?O6Hlsk3IkbsN{#T(}lP*5|jGPxJ7{P9Oh*CYhld*GFv2_~>VPjL~OrvJP?wEae z!vt*9el!S5vYo1@(&*;69&}rVQ|$E38qht#bd;gW`otboLwOa5r3`NQ0@pjbntpJl z-qhQ}aSQIUaZ8%pry)Ip_>wMrHhwI7yYW_k7ve1w`V%tS)hGjct!8{YZXMcAfnMwb zPoL3zZ6&keBWix%CI40}%sAgum-UG1Sm(Un3uuhFyvUC81x~s5DO-tNe$2q!u_|cg z90m#tNXg6vY1LOSG*r}c%jwX8FRwBe-o3>cQVe^Hce2(t1ep&vnR0E{?dIJ`b>FnF<#GUrf=`jT1)jkbYI4r@bzn-+6urzRlt64L2p}tbEv7l@*MaKpTzj4F=Sy&i zC@QNtjvjdxWk$K%Ey}R{#P`vv{NZj^^ImUd?8^pc-zGBd^y5e~LUKY=#gm;}0HIuV zMDGLfV5lssG5l?WF>UNV9>!O@>kxluIV7QEENOaN&%jUy0GL3JAWaE>DXr|p`BtBz zQzy%>fE6)Z{6W}_Eo1O&ld$l~07O2I>`P#3{cIUedVO&?DPc|idnc#2(RZRs!SAWN zWM>3Xwy*cwpZ>n3!K}R56zY@2GPywb=PO= ztH(3BWkePWr5lgJnl$&43(aZs%vGx=TBPr zC|S5kyY5L2Q`O(u*p#o0A@bZ85=5|@uTGk7&HfHpdX7lDe}@F=DC_?GdaD6wTxk>C zmjz#=j)1|On(>U@`{cJ7-5u384_+(A;fth|e+Hm={bHRvZv)C3F5FDkR=r%W@heGw z$C{M95Phs3wQ4)eo@vE0HdT1z!RV@@A3A8nS?;t>km-}d4immOGT-Wu;*ZHqNLz5$ zMgCoEP89J9w+5`5@#g3?16Qj zstW(|$5GZ?vpJs8lc6BbB7m5fC-1QhY2o!4_?#hSN%fAn&_cBp(P(skn+ z^9{UAxelJwbZEUNV7DkxK|hq9-90zICF+C&qqevAyLQ6y-Q6vwQgC$E$({OEr&OwC zYv3GuZ=WKf{O6CD^>-#F&zJiW+s!JKa^63Z&kzW%u7=#d_^EfYv-5N&C(F@fB8>~X zRJN<_2Cb-kzmmi7aJ|w`MXqTBua4?V-Zd6`q#n5O|F9VTE|LE~VCryE77fI}EJf zS(hJ_7=-SHMaP(QRcDc<$Kg+m{8-nUw49j&?Qd$bk=fKU>E?Y% zWEuI|kzV+e-J2YP#z_pY(6FgIe+U*m>9!IM@ro{P2J3j9u6iDMY-xe!nVx6RF`>ee za=^O#aJ}_#X2+<{-2PFMt1wT|LR5uNj|nHo+87?kOnOoc;GW{|bYJo@(~J_asD@c_ zTitWm*&<_07qoVeXiLlxbhp|@Ki(uy39pGCw?j&VHU-wWyddyb?tH5ye5kQwJKG1% z4FvUM%(Vq;a@rEi>^PPlZaDCH>}t&7z1=N*7mUlw9eag%*iu6Q_IZMvHu7^&_?5+2+$RE0$T%eoaAB8~`>+5sY?Zvv zKIg=qG80bARVS$`^tka5VHgh|=IPctp_@Oqt5zfb{l|Atae=&JQUBT*5ZSmYwa)l-c_Y)%8@gF=&*58jIiN0A$NGfjzRihdv=G%#XG-580>G zS?60$a3?u&d5RK!b;c{~`?c>>cGIHq9Hlh*R~9pIPHK>~W##>Y&&!qhR$NaeT{c6L zV`E#%Fyn+xm5DsynIfjTv~9W9m#5B)UAV9(yjBVHvGHOveaC6*A|9le;X?~MdeIw) z@KJk4Qie{wIMGyDx~}F^0fbT6;nY?$fXX;tJFY0#sB4quCBy5U4FGvn_|_k?d^YV* z0vsG0Dlq3yQ*}=w5(`rWBH96kJ$n_eQ2l{?f!3)C(m`jxzfy1i)r)0EBDk0Gdj9Wh z?tet?X6myelM`GunhccGq_08pf5Laq5KdFCG(K+L(rx~D_Rm`Tb|m!F`C`v+<%%zp z_Fbe(nW^_i)!J*o6&U8Izw#A2IayUG2}(iIvWCZ3Z>ReWVX&t#De*U|&FQxTCJQ?c z(9J~4w*H*+kd|xIUQGB}QBpqaT>ZJw!nE(wgxUAlzR;^7?G}$&N56h&U|oe@TIck# zBkW1u8`qKIkePDdv~j!9nn!g)6zNqa1Wrnkgc?;umUv;Z5RLoHzl(*)-`$cYV(R|lY zSR}JHGTFFla_#i!kR|wiK@b z)2y;0dL|L|o=oGIaqA7UV2*y$8^2Ew(f{ERyMFekpUm9db0=o>%C$-;CL0qei;^pR z2b+OfDX~f7yG&5Da@|_!2^sDmeD?}88?{AdYRl;;G-Ur(Bf}vm&ee-HJ8@2eCjs-Y4IFQfv6>cr=_>zfRsa_vA@=yE2C)*d^ z7Y{j~Q`0yVy?4OyySaJZ(e1pFz%o2%_uE#6agQen#lFeqV2i&_lt)*`xms;Rg5~U3 z2L(0CGmEf|p-Dt!;9{S-4#y(`SI3=zB$WL1&YEvjR8M^{$`1o6Q>eE9H~U4MEm zDhLTmnde;ZYFJdvy{~>HPX@At-bB>58Ka$DazF>-0w>gMKp_DCJ6{e&h5cR8o~ZYM zzbr>WfGPv~Y1$8jJhL{dDob0a@?B=x|Me4EI5rc3SU+2%#Y8R%Pg>&QeEmedC+=mn z{nEcCN`UCAngBA#&W~}PA9dtmzt*X5Qgl%2Q!ja^nOpdnQ9E7i-CIVAg{v|}2aYS0 zK+bFKnR$m#q|}l&vX9F-;ySv-3Bd#dFtXLT7`gR}2gFQbn^39m8jnSa4jT_IN~PBj20_R=-eM|1fm2STuCQu9^4NpamW)Ag808Zwbk4 z+wUWc`JIno^myB=NAnKylt}o*Vkjr;A3FM(xxU~^-C75A%d1hW;K$j6*Vh(^Mp4k~ zDsCQ3(8RwO8TNg4d^N{oH?Xsuc<`dbs^2d_!}C4RYa|+6T&TI?{nM&*nD&@}pyK`c z$4*Ttg~F^u1m!WZKtIl^f#re~0!$bo5j(~Powt&Mu_Yd&TE;P+yK`E$?=(DSktue4 zCAJ7_nd_;lXfb1#$`uZ}SEi$jH#MT^)O{gqc_vlBT12Y17sum-`!~x~APU-5LR*0l zg0haf<)}&Far^qZ=&6oF9IfgP#Q;Kjd55(>G)855&lngm#@xk^Pbr{a&Aze+#viA2O?Qx>|M&sEgI)vx}1UP7Y4- zd7cPe;!!V0b1agbBHw3^9Pr_i^DK3Q69Sil+xFzU&GC|G`Cir+^trOH_D|vfGzTCt zA`o9}PH3<}cKJy{f$p<*5x4s2O%3C)T7rNXyLzn-){ju^f(<^+!L!I0Y5|BZ$DA(~ zPC&|##>EZcQf?y3V#~^kmy@YAOL(ZBTo5R64B_JLTB*kF@5+=*hebWc!7=I6pdKlO z8*aG?8eCtI`_{XD^IW`;$X`)FbnDX=%$aKBq9QJ{{8$AINr2bT8}dzzP7%I8UOFMH}%}K zs%_V)!5p)7Ep}W1aWwhf69E=ovdtWd4$WH?(Acc7#3#q$p9ZIqh>Pic%e zU*$O>@Hyf!pjd{-II*GOYc~Y7PK4=<=Q8d6wwU0I&!lC4-{2Vd2HqGmwxNr91FZ*K zuTRTkor?W9IAv}LEfdby^D`bGnSL+bw`71J92^{MDT7AI@6<78Gp%7H_Hq2=@R0Yv zRm^%XLa2M{`)|}jEyrxt$~Fl&Pp4*KTIJ++gkU$*&X9HGX0=YOl;fiH*maWHCF8@Q z&6~Cj`ZY2armQwX>2pU#--l39*GO$vsqqHCg`Z>EPJ9n@a+TZgpM`33{6MmOm%hQ2 zRe07a2s7Adv0CKZoS`fmPpGWqX?wa-eb=Vv5yE*OJ@#_Q+@SonhXclxgu588#9k0v z@K4S+-z#I6P?(xsulqhlVV11R(w}qGe5PfG3rYsnEa!(ARJW2|Zjk9!PQexu`b20x z#JP{KY}i}+Dl4h}UOsucVnzXuDB3a3Gv`SwP%Wpu#G{z|n=1pFApySX%kKSyqE}oV zT!PP}yYut#X`#E2zk9@S{f{1L*K3Nf?yYcx2u4g+2)#vCyB+d$UGFi&fHY>%1&JH_ z4{Q<8)(@CLTv^S>6>gqKXS=^}u{*alYNd$)4f+QjzM}yqXP^c~oXr0os<>{$*Q&^n zkSBo8y*NI=VFmnDh;_nhITe8%;|cOQPwlpt$oY1saJg664=0pTPNCpCvnH|orP$br zQRPS%qLi_W%xR1H^yO8dVLb+oi3a*_Hv*JssVBhK`5`c`S8G7W zYL5?(CMX2l2>hQUG9T>pKZ}xkALe5?&_nX;L}A1{E+O5w`1!KJ4n7*;t>81?VJ?G`^=)djz+Vd4?<$3?0-aSb5UN(Vk3lmr$zy z1nGuBbHK#7heZQ8-W0{d2lds0*bPV#5!Euiskx|*KGjNhz@h97>1eB)QjVCDOT|(C ztdw@SKV#OJ00!Em#WQ$&BY=i zX?fBkCRHCwUlQnQG{yHN(#!lHYcI(5KrHP=*qd;824t2&5Ugxg1;DpySzSy zhA0`7iWx}Q4L*#8uu=$lecVW7HW3wG=1_|@h!{TdBirb6b?S`FXWG-RbIJeQt74== z;^AB`9;hQK8H8UQIq=_#^Lu+T)5I1U!y5cX`tbE~_toiZ!)m^2l@ zknx|q`fZ!WYqjl#n+8Nd7zQ35Lf+fjmVz0I6mW%Hh z%|U)Q^-gxiH!jmIAse1fvN>8w6jao`!$VY)@Q|o!?Ldw3St3YmDdA4T1X?C5Pg*oY zGV~x_M)Q^R@Lv!$KuNE1>yDws9S~pY;uXCO8M8v2EB9;sq5LiV?i8Yl*v#+J>eJ1$ z#5;9gT>W_z$kjJ1OGo(T%^O%pcQ?R|m$i=1;yOC+!>~x$j2w8x!WyLi?>I@U0-UMVuJ}IP&NVn|2wHy=fd%lFixc$v6ymHf2ish-`f$XfXBl_Lvv8eGpL+I zqFrxVACT%1qQm7uNN7)*9)1}f6Y+O4I4}@=Q;fJ|sQ?X7iSQ>X40l*ce4(-jAfsX8 zl9H8{BXljbDxTLD$GAi9mpb6k0JLCb!vdSxv81nb53U*1=C=J&g^&t3{mrb{QcGqH zv{#5YeEtE6B&#m2Pr!dn zeZ3JF-RJp%Td?UY&aLm{L?A;vfSR7>IpYn*;ux)a-xeaZj~9zFPcw($xi%sdz{=2W z=iYSBb!VEa^1OLB-is>ZXoefv7~1F@s9nO-bhFO8eGsXD+b2}IL7s9zo-uTta5%z| zq~|KziD%t+nvv;pV+eikGf~?5@i_~*E9*rqqrAXvtw<8t88v8Tew~!Tw{?|HoSfgIN^qOJEo{yS!W#u@HF_Z-riV7d`wh?iiXF#y6Yx4Ydw&Nz^|Lt~z27 z#CKuX&$Z<1Ov1jnG~o<;RjQGeB=-a;^!VonhW?_**3cMi?kX2UKAqp(LE?O>0s}!?o7#AQSfPQgm|(8Seu8NTn|@t7 zkPURCq@=W`5D4D*%uMHg^>d3%<8NABsChC5|Yvgbv^{sq-c<~dN@+>3X+-cRz0%UVxkv)X$^_zvb ziMt0$+Wl!12q-?Ev%$UCX$pPFaYzwA#*}&O{=EkND;TTJ?;H<{ADgI2eQ#usU+4P> zjd~}$Q(iJskJ{h*RtR3f9>h>R_-d9YJzAtDhv21LR6lTC?3UT}tMik1x%Nnh< z*f0?HLL6IKAT0cOyh*-llCMPGJ8!X>Z(39vlm*X%~Ad_Hs5|*lAuFK+Yw5 zDXCsq%Ai?8gGr{Am4@Ao=H}uebH4#5jg##EmB3HH(+C~ulo?k}z}9Zp3?qo+kLRPz zDkoC7y??van#WS1n>};7bGtat6Ko&66|R4h5{;kPYs8_#QiA~lJ^V?!+)YwS3Wk3y zP%TqyNYVzomivcL?8_HAmqSGW4*tJNNT&S?df1uwgf!3?wbz@QnyRES}U>1QoW^#GIZxd&>vRxpodli7v@tYCMUb~kF z&&5fCV!Sk3!A)M}mj9a-0{_B*6O&}YUi&vUx27%`Aj-PqKFu{`MG`S5y|afq0iSq@ zoZzENH^$9W``ZcyhhG-nKQ5mu^K-v>nEd!WT-1Je|E$;oJ95?8z4 z*c$xUhx@=Z=kw8d-M&{aBQIbX`Swa(Ak|&Xre&&QKlficf~2nzC}u z^k(d$AQj&s#?ui`DFMB1wMIy-dthPL*$h5D&bftbBuB$WkDC&%lgPZQM^h$-E~XxTgk>Ydm1uyw*`=HUiIsXiB@g zt!;s^;8I+-skrJbF4JCJ(I}UPOk&aC2#>%Wg&GY!}`yke0n`yb9EJfCiJ_BmR2hwOHZs>1`6WUO}(=xdSqu?yn@gN z2MOBz6KZKjx6wlV&KB9AJ;VDld z#P`zjD|_Mbu%JX1`EOre8xs<=1#aVr{-`3C1^|cOr-NT*5;EabDlmEnvYDC$o<+~k zi0SV{wB!d^OH8^m;{nB(!3&BAozf|y6WS_31p@`H8Dv#f*w{O_bR+m0}vq=<+O z+!*>@c+MDA)R0zs&^7Ju$7{Omn?NM^B_XTbYCpk15bIv!;|Em?Cx;b3DCL0eG(&IJ zDJi>qT@J}<9ZmlU43>^HRTm-S;Me>ElITG#d0)V5`k zIjMjRDcy2le5SYE?D}=5z3T{9=D{011KveSVLz;*G4$7ig^7Am&%AS1!!{zd5s0@u z47vG-s~E?q!?RuNwh&tSzuG3Qd1)k6U<+%^AXDibly+sH9ta~-h8r1p}=Si(v z;kVkqT^PG#_?t1tfeDG2Rr}73P~cmTZ2m)g;_iXlkkGObJ|aVVU1F8EpL7hZ*hd0g z%&?Ll8ZFeWDr}voNmikcvEFuC2DS-&T6ZuM0?*vz2Y|~QKTmyuM+oh!W95p^#hCbj z*}2D@9e`u`Rba?bD0HY*lhxj>V72{3jfo6|Czajy%C>fW01Mn|fftp%@B!o|8F|iPgvpy@syrW*A9=Mw?+l{F92ZP*O?Pfn=>+#}y zPhY)CxVgT(%fx7ArD5%Q)qNw-1cS^koo{GMW(sU&P`P?%f7{FyR*}E5Ff^pwS?y^} zSa5%lI>Qpw^WwjCo62nbXqI&U-?Qg0W?)~z#*ZwN)|}Gz?j!DyY|X)b3WxS(XR`lPEX~egZRUYlfdQ$H04w0vro?P zqLIlY%gnu7W;EhEcleGbXb&F<=DST->2YwpcDriSKY4E@{W8vZgRw|B_yp(s#!Da_ zttKCS*tI>45#E%;nh-(8IK!$m{zq*>^7-AtsxJp;gQvOdShgW>E0 zSTGjRcj-hXbn_*V((W#&{VRSLlWK8Lb@ko!kmf)@OGiBdOmXbY{;MI}17?^ATG8gGv}j=8!o z8zcHFX18AqQPxV)x&Pq(-wo`(x@9rU+V@?LI#<%sAC5D_xr`@Ow=e7(FZb3pX6LsT zuav)e4Je(vY990FpVI9XT8}WT=Knc#TR9nC8`i!&U(KR_`Sj_Sff}9w7Id93yLLp* zDVU%^G47}vzjOIXa4{EPKgOUf_e``gl9Y`*hlMupF@3%?%M4x^KfKfRb;gBBb{4XF z-mJ#Jd|<5f$!N9tg-b#R_u6l!S$eE%mu6_B3wyRX3oJB%fday&obUGQ+4XC9HN{Nq zuUgrip23OwIbAv#g@@4FrUVR{D7ydfhcKU=zrlw%8wf5r$J2825H7ccSZzm*nyeC? z>i?j9;IQ=ED9FbddcQ`)*{3ykd{CkI^5xS=q!>oy_5>6*Ge5MTy?kJ5iL7b53z0Nl z`|&&dNxJ|sTcWJTb51C>l=)xAeo+4IaCPqlVum|%cWJ3|rVTb+59i~M(sqiuuK)|c zb`|1RKYPO-3LvxSX3vkDj#s)ufg(>Ol^y$LiKH0lXz| zAGB4uC0dRB2b3H9Hw1e^f!@6ss8sgyv}}FS6%CunXsd(T5>hZnkq=M|iQDPp^BrDW;=1c-8lOG-d0 zCXu6~V-y8Hq+BoLbz}jA0o;N2YrTkWa({9uDRgs?*nlZCT+C~3AN0QsRcx>b3m{vz zPvfy&*cdBgagsePY97J1Oqs z@+y&8VkV~W9C&od}+AKu}o65F)Usl_1$N( zDf2XZNYJqmNnb~WkNb~7mIFB}qfu_x54pjlLx>lH=^vOA7{CLYk-G#J!7A4~pW_Gr z-JGvdm(0{UPjZD$pI26Goc@|_V9pS7Oh2pPgISFig!bBWeXH9~kGaglqB$UmW!<9! zxCvnZQPvADzt#8f-~+xf7R{gj9L8O9o11ARDTVoyY9-p-Gp-LqpMkxc`VUwTP;7ew z_8J>bnm=sKraAQ)R6VI8J)MaKI|2PI1LAJ!BEABHcumHlw?h4G4Lv$O4H;Y>+irq9 zX_S%gs0?WAc#Fom!1|U&@~p4t{d+fl{ zTS?vV9qNzE@-PX2czJk|;+{zwRZp#ty^w=us=7f>1!O$@e#b=wv740xkgXxb%*1KN7>3ul=4)n|ju1E_=TIz(&2u z?Rv32bGb9letuJ@1-Sp+qAAV=!o$O1LI*w1?&izT-7we3$M@Gsbk6gu>m_ccj%^;x zN!f>#f|#!dnG#7ytCPzymz^bJsh0QV+hM>p8;C{31iHF6=4`z3T!|j<;-o*mm`SLl zJXwk;Bm!XDc!|#8T6V1R*Vp%e81_OsVGe|Jmr?mo$3+QRT3Q!!XE48jO?&S)o}EEw zCjS)7)fl4zq?A^Z~zLm=FoHyRI+jNOD z&>_4%QT5guaR;cSM7;#@e*Wy?amL)yNe}zesPdrB#lqM@{Tu>=Wy6ipg30AOaFG&y z36>Kp%|o48*FFYgDSdMy0-PII<*790qQk@IK`_9Af@_gd4O*3EFdHz>P3FL(6cG3E z5u%}?NnqBHPfh_}TIg8}Tc5Nw7kjm;*|n~WJaXJ_K7%8Rko2*MVG=Dp z5Ne^fp$}gO2t0Av;SsU<_wtT>$bdcJUnj%y`Jjx4UF{AkG#P2!O}&#bO#iM@GuSgQ zJDGtvr4aR_@CW;GD*5u1fm}}Pm#5n=IEK#y-v)NcE#m;BZYN}ii|pxn4~rmXjp$ss z7)}5DLI%acUbdFo9w`L7I9iWiegH+hkV*P!F$_Fd!ZP!1!3AnX#FT<=IbftHO)UXD zf}Ksim%2BD2!sZ+{;KJUIB?L^w-s(B6y8$JMhK2EcdL+VnQC~4E_?;dw6uo*>q1v~ zUm9*<$B6f$*t6enRA*bXW>?euoH1V?a}`MojqMs2Qpc<95-tz+B^oV5HUoN7)d#)a z_T{c4A+Hd;R$e5}X0QE2{F-m zDW=M~O$oHrvu1TicI%6(WAQ|k4Tx!H_i9jftE>_J3AClme!K_?NCs@Lm{po;VyHi& zriO#(d2cl;y_iDSOVwIZTpS-18{nG<K7*2F6$aWL1yg9(EzHx1FAj!)IP;_T2Ri!mfI?u%+HI{t$-z`UdqQ zC%^6Z>}=MfXA*yE9F2h#AJuC@Dqp>FEQYu*vJ{Qoz8Z%or>lVR5;? z(^m(p`eM`SU_~u3lUz#NhyRch_y{CfLy=`lvvg-5~P-+M$C~O=gcA(CA2MaX) z$WF1wkZ2&v-*pAu07QSkXS{t$`)0(T*~nzA9^oon^!EN&AnK%^SWoj^w+cUf>JL&x z3GBcETSgbC3$-5mMtNzaqlIeB?XH(s*Vo~(v2ckK&+an?36G;e| z`Zv(ewae#dA7f!HS#7zt;Zf2|SanYIFN%D|cGUT2<3{B@kDFBHOr?X3j(#wO$m+A+ zdg#DMA)C(k=06dNTIr|&OhvQ{3uv%HL}4`a+W*`6bBK=42);<@alPzZj{hXqJD*wg z2mai2iNG^V%%OMi#)bwbZtv6ST4&uFhkGChtPrIci+sR{^vZ5J1>h;iy}4Fd?1r%- zjr=Cvbn`a{pYJ@$KI1|xZLi<@Q5=;_vP<-fcNH|mVI(=-S{ zPa7oXHQev3>Y#sKVR^}>%^RW9YB%@z_qeexemsD{*%fG)&%aJI`mK|(-)(z4v2 zJTfpUw+*JwFtBpOp<&FbRdL@T(fh;vj6tM52fsyQ=~V5#nYAFt5g^6+NgSYY4_B>FVwd z&B=K_lqIT=%4zx+I-aO7#fG`hxN5|7gAE)2*I@qg8BVvG%i)CdF!P(to#tj~9KQoH zCiTJrY8CoQr*)$ehh}%g!;_uK*0pN={7ckp4~IG9(NYajJ)zJxyS_ehwxXS*LSi=Z zYgPA=ac9}8%tFCg=3BSoGsCrWonKyd0uQmI$%!``?-xN%LO`U~m|&3gq_#zMQXxZt zonGrrx|<7MtlNRK47*+mdur;P-E&xArbkE}?h}TX0tLJ3XPF0!UoUMHDK*No`ckaE zwam{Aq;ef?PbGu1!MskkdV3HC3YCr3Qpbkh8(!4-NkYrULC6O~4Z^KGjAI$@_Lu4n zXQh>mkj0p`M68d9cX`4tUWNgFp=wr|M8h!SEd9<^UMbl1F%SyfF*frJas^h}|A(rt zjH;@Qxn6=6X-he)3-Zc(58iNuI2JH%N$k-irWVWnyc3Det zBRe<=ZLbS@E)u#tMU4b4Ts9V=+*A-`PPL_=AZnpd@=jV5wptpjp%Ilii}~=9n5`G^ z7WWD=yN^;Zb%{8%H}6%}-r-8Wj7%%j{Qk;J$Malfa8s~uamrIJ{=^(a#fMt8-ti({ zC$`5sMrhD|wFHOJ&bwD5A&0Mi$OnBgYCszBmw1`&)Z@>b?lg}hP0Ij`%aM+*YRQb9I8zN1U&}LQG{QV;1PV{fOwchFn)$aR@ zqL*h}baDyOMaP=2UsI9tS^q6xOt|_@zu{dtqw3G%q;M{8iOTtk~dAwpSk_ zhjpRmO8!@8JcxHU=rsniy53A4?zV*MehB)+&2I4UsrOyCc}_$&zT+EaMSrF>kBHZ$ zl`NU?_w$MNvDnj!!`UcN<09izk$1I6j1MJ@&Iitz85ll7XHa0Wse>l|_C)1FS02tWgS8>5BK z#C`ykBXM8Z`@CFAd$E;ZbfEPMrUw(7I+b5~Ks9;GlLlScqIdfyHnnXZTF5zIVfO0Z zQ<%T}D}1st`e>c5%(3fZeI>F6@nnnOY73EgSUMAnlw)sno>TXxd8cABpDH6~S4z zyOZv{T=s64thziX(y)4kSWNgUnRH*xckZ&`w$ajMN!U40s`y!0v;AlD&eh4B-KN+I zp}hL6Ig(Peg}tfPE}gQcrS%QN9@AT$#nPS^2U(8~8~=O%|)XFI=Z|~27byjUl0Q&G9@0q2#zb+ z_?VC+`%~QALc>Do)iMZT4=GyO$AsR`8Bc7!du^YJJ-pR5SIY#L_MfG@ezv6K+SB__ z6SSsfI*m&T?anpsb;UP@Uf(Tq-Z!rh0i72{U~FHK649trMHJ(JxuJSA_)c_5{cA_G zKtH(7yhYpCQiPbDlSHy<{BA>t?B)wsowx$-;mmJ%Y$orX3!6@61|B@rangPSb_7vG z;CcW;Q}V|j1RUZ>FM(S1j(_|W!lEGwNMy?I^W7u_dnC{{55%&s3);2)*BI_&&3}2;Ds&f~iiW+7rJ7M0=xEq3SbWbygiZ)$T>EpefMtKBk%U zxO|f8nxHa$ewbKGEHW%md3K@Wbu@dL=)eI+NlGZzo1VR6%FI~B|OKS#$2^$ zdv~C1?c_|5D&bGlm{w+RXS|1t?6ZoHDq1SnAj+FzaYU|p&CQCrQkc0dO$NNN0>#$1 z2!Gu@(EQyLoqNh~&u4$~t2RSIm*!|CS9R190Br}>v+^I)waPlZAG!|yLWp-}>1Su} zpkc9)bX4im_M~_}U|&`%yIum$Bu%g78Gj4TEvvGt1kUHXrdL|SD1##c>XkPjnseBi z7DFeDs!(DHT2T)#VIZg4V=fjvttVx}HCZVBf#xZ){j5Uti+3V<)+~qgyoi*vN~P~h zqWqco78j$?|GgO~qb%LbAg~(A)-3TMyks?=uMH?CvumBNMa_shRaTnyCsDJEIZ^joe>`>Gn|v;M?3yMrydYO2plExX$CM82y(RTn^mf8AC^ zJkayf%=T7?f}y^-%I>frO^%$|X>(a8d1F;Ir5c|!7dJ;Iyh~1|9=n-X*Dd<(yXY-f z3qPzXAKP2ETuJX!ZK=F=A%0Rc(vft2dgRracUH5YioZVPqE0a*L$x%JP9dDP`Z7=C z94pw=UBd3kwaJ*^tgPCT{QWy`OKVKY@&48n#I}K z`LaU;rS<*C;cuVVFb>hn*w)2m5n$%CH3H<`-Hy&I|nweQzdfw8dy);~o6D)d&{-f1I{c_CQTX&a9llM$a zK_O{?cUhXMz2@|t(UjNKe~`H?+P$h8w(8u?XWgr(w{H>C#1~wz$N$~dVK}v+y%t!4 zRdIUQ92y(upzh%tc1PP|@B3w4{oVuiXy*q8j8?xGu+U7p;!q&<+L*~LDl#&%Xg-?+ ziE%=|_8#W%H9UcEeT zyqhM>aosm5C`-{PIy*btKCm%eE-@%JHWrV%&_?BQ&)3Oqx+m7^{zpoz{RgenR(j7d zY-pT$j;`Omdp95M8?Vh#{CX^(-QQ{N+&!h8n^-AIr5lbbeSxyt9z6vw`CzIjGf0Nh z)nZ83YxqP8-dw|sypxddKXd!bj(NClbU8V(7C6!Ru}n0}(`)tTIy^pXUYNLPs)^pN zZf*>LOvF|5W~RhdsA<3;p$$r5;Rk3YCMJ-5TR zqj_JfP+ODqluFi^8`BZP%{Saa|MDfe@oKlUNyAU=LBvVFUd&VQRN0z*c$0Gu^$AEx zSiO+b$}5rcJ*9tJ832WHyf7d6mOhS2vwL!Qzbj7Udfn)QQ&WBTfwYWccluu~bk22FuQqYsp2uR%l#t5R$=b;ZOaYNr5! z$EWgQrD0I+nhx5|?he2E659KN&AF|&USy-S!E-G^dYszockNwV=+Z1)DjYY>%J-Ml zbJu*#Eh*7Ab z6F5@pETEG>&aw3ji=X;crwH5aCB*+P7Ai!f z*8-B%PitMZEr}Kv7dQOViu8e?WBw?7@r0|FW9`3__|SSp((be)V~%bJ)z%=q>-r3p z;&-tOhl~rip`tD;<$K;2-nhihI8Ur=uG8P~bg?uzjSO5HDffMXU>+(d9<*Gd)6E4t za9;2;MKYt;wT^s^%o*+Z>Gi%)^V#Un&42XdZ3n{SAA1ieV>$O628Xj;M4373@HE_; zS=L@u>9%{2e|G#*_Qh;>^84f6CH<@nk6Bl(2s&jns#uC;yP zLuBFpj1u|<$G*XAf>dd2Fl)!E=lrJ*4c_WY9k{^qaHfK^|gC(J%JmH5T$Js~PqqqhgD zXvh;1hU{PX*vj;B2k!m38+7RwUY7if#b+a=q49x`xQ=yhXmEb>jN^ypr>D`fj!S?1 zyCy2`5V&qLBf?+MuaSm+{n{!VMq2=Li>nndUbeGcRgSp2YSFGL66C%q3cHh7wT)f< zUak)Ob|x2fx!_UUTN!1)Ou@qQ?NhwtK*ap|1eu~f4Lv`#;=`X!R%g3KlKo|gQtOV+ zY3(JohzR)`aA`=WPQlNUza`Uulp0qjH2SU=>p)Ug7XR(rw-F+X)6@R|ErvoXJEA#I zeJ$eRa%e%OKTjs){269*(QT*eu8bC&IY@ZO^wTpi{Mawc9pFC*LghO=TW7n6X)je_ z3;Ul^JiW>ZgBf*~2l+N~%%=y9_Dgl%eTtz>RfEw9%?qO5Cc6~1QJNID2R|6IXhcqE z^kz%E{H{^_=)2h?*{&|aGc2EKeco6pL`I-}IqXK9EWdua=-0aCFgkuWDjoIT*FSvt z{M}31B;ecF53Y5g<_x1+9C5ti3MZyRxr7eDqqA3ayq9T!RaH03V9+Ae7W%pP19Nk} zvpg}`j3g}4JA_5l(>F1=H0Q~kYua$bC=ku=0ItcI-`jTv_iH9~I^6apHb}S-t)J=6 zjey6xF2=i{1sv`5j+d8JWJYvaWUNh9zE5dwIHS&p@51!dh{sY zaar;4)+#$$ZTm9L&hnCBh5hn3Q!M&)ikTlRAd{GYi(2&-@Mdwl!793TQygyChbV@Q6r_5x(D~N?15oj;5CuQJz!a&cO24RM`clju1;%pI z(DOgVkM_6lNi#FN-!COA*f7HNF-`{lXzW*c$hZAR>Qg{KrnBP>7mPaFjcS8@oio zP~0f4yTh<=!bS7YBlv8N0sxf$W`z;ab|S<=yCyYr2iM2;`p)hyF|XzQO6T?O9)}x0 z@zXV)N=kkr@Wcy2s3pYmsn4#Wm*-C5X$^HNe%5VJteAP~N!Eh@VD@*?)kkE()H98C zvNUnuibIRMp|1*~n-y@Qs_Em<(w;CSyCoL(A&uL*FFe0F@Wu^`r>1HKzesqf9%bLE z@xGu!$5Cyx`h~u!=I7Q>>+Md9`#K>6up|NF-RVXbW0AYHlukXieE^VqtEiT@Gg^Nu~ z$sb)u+1@G2{-y4l-nj7Nr?y#i(bBYqMP0CtzXg&*b{D(sSQ|cYutY!<>{C*bl7S+P zc-bsk`jhrL+Y?*U`yrh%{oI+vAqt(QquBy7$uW0`2f()X68m6><&LG8jt+TV%?|cv z?PU$Z<8foUpoM&H{_+fykB@IY&D7`Hi^YRp&+`j}c(-m27HV;drKPtwB-mJ*C`2{G z;ZbVJcH0=m&an$qo7FSY_wTlLxPtAw%R=}Ch%N6Y*$RkU{8dsp%fF50;^K0TRgKbV zfl#e-g{Y8|>&lXlE&D_Lz<-zZKJI)efgvjF+CzD;IVA@eUu%2&98^;x$JK1HS6rOL9h->;FAKZNUO{OV?Kc`?GF*7R7nXKOSS0@M;YNJ^ zkYPL@b3G&Ok^K25gnn#!+;37r-|)QU)qeUyk3x%`6NS=;6P}OWXSgv*ph&yZ*F>ZP~h1ei!N#Q#exoC}Sg}`T0jtyz})~ zXij#=ce^zyx)aXtStXBYZy#EzOzve7Gj8G&uI0{0zpPa^)g~93u}&>eE_GydwwHir z2WW^E3M39e$wl6jmGNiQ#YRLlft#~hr85hvzy`+c5gS|ZhYwg#|F)z^g_X^`Bh4+# zgFcZBz+QA_C#0dFLF)PQ>3%s;Bk)OzZjm< zl$NrS8u8+wp`p2Z@2+H-yVD-!x(4~};qTlR8(VqK=5nO*BhKAdP0rSN)F_XdqrlKz#1wKc1Y zT+CC25Q*s8xgepk#Yqn2tm9sjqTV9XoruZo_3(^! zSwBP1_?TiM?5>Y=hbAzQMtAsXwE5Ww>HHJ@*Gx$4{fCPW;#FbYp*g?Oeca z6+;GYvhM2=yLUdx*F#p`Q(~0_Wo3~nbZQa|R7vdeex*^;OgpPy^o85D=T{+ zH;7-5g#A7)KMC`pECwAJ9tPQi2jN}#<}vSD4Vx`F^*LkNze-0Mh21C`G4#qUE-sGy zmZkbGd(zd-;|{_7r+U*(mX?=q{z3Qg!NSHbz^X{n_w(cZg|rM2VGgWuh-Eyvd+*-P z%zKKRDfOb?U0pwOgO`Uxf<;a~Kyft6E|`?G6I?s-v0iVwmq_QjUVUH0qx7`h`8Z-f zjO(NPf@1mK%t&IL*A^za?-km2p4hhtL1Y~lcD@FBGx`u2k}>udV*WTzE_1WY^Wz%D zru6`c(~USFpO|&ea`sO}Uw1r!Cuo$PDk*n&F%i*-VxxPf0~M(=yIvid^X8=Xyp1c6 z27}TwFALsZ<_&y(_C)fUju-)BlOB8c4R?LzQS%r{XO(-4oSzL%j6x&6N-Z4k8k(rL zp&J?}&{eL9te&~qJJ|p3Y^CMndk>AAc|b#EbSGjA!JC1RF`Mx6;NAOl6LraAB9R(z zqj}D=t?hlb%EP9ip_>$*^@a(9yFo6ALTvl28HK1w0u0`P3yX$M)<17HltR zk8KHZk2Q|C_;{=PNj*Jou6?e!K|#`PlYQf+>uFYwm1bl(Q?X5NR#RiE^@OkkKB?^? z4S7|kBjN`XA}SjKbaYq4nkSpNls3|30_exC(Z7lXolgOjnrM&~)XMa(OosBL`?VU* z#=`PDa|a$tAcF|2Uel$Dn!$j!|i93m6(j5bMd zdk13S0+2ck=4!Y1Bt6OFA6(5tYJ=Xt$J|}$G&tItIXXFsWc!g}hWh_dWL!nL6@V_$ zWx(N&2RR-w^tEDT=76wdzkclmCH3qqmAyS`MIW;9rAwoA6@=54$$$U$?TycwCsce4 zjK$$x^z^^eGroO+|N7$7OL|s@pRBs{h`g3(60SH0ZP*PZ#EgM9j5us0)Xt;a*pbJJ z0v>GKAPmq1)xQZV;$n?5yd_jwHvRYS=a=OzvXO;%Ha5yC$|G1RXI2*Ao4?-|;B#?- z*tfN}R~TLh)1Tv2Ubcr@Uz&w{*) z;XQ<`UpqP7uIs;~72=D6+`~6{?2;yY!OkXf@Fm=mAeZ5ziBk0Cy5a(hmbAO%aaMt6 zRaNw3JKW$2(T`~}eOj5U+YL8Gw-Qx#3PikJy2u1wHa@TK9~N-a2MOgX;X%My?}O!M{_C!nZd1wuYp7Dh~w}D)-Bv47xXXI9nk_|ps~_W z`Axx9A9UZLU0$%nroE9tu4u7NA6780>ii+8OJm{m#8+e>L_Ma<=!GCZTAYJ)1(kR7&ag@x5HLl$dX%7kJ@yb zen=`w^#=%sO=0k-LoB>38ID!AMVFy7QOdcDB|3SAQWyarrfhckM@ddwVZ4NJApYOo zp464(p731B3M+f|rJb}3{j`FP_vAs;p>#8nf>Zn*SWD)U8P!62d&XhhWf$S#351XD=GyyYvGvW=w3y@hZ{{W$qZn;L z$C;80B2B~G1H+cna!jW|sIsHipjlpC9<8p2)aGx=kZ!4n4e#oSh4N%XG$CHZf&Ixw z4I{zjBF9hbldEr_sDC%Xo2k;Qyq$hzhm5RkM#+aT@lZK9J1@lA)Xbc$=H+c^xw{Ka zA(z6n7bi@Z_+*p<0_9rCML6w%JKrTDnwy=qAF%_)lm{VKnc!aB|AgXeVgxHZO-)VA zI#tmSx9rZ2w(Yx1tj69U!k3Q?T7sJNd z+ekLupkQuLL_)>SeA;C%;a{((H!FNN0$GxE`!{tkG=&teR(VjTOO!lSAhfi5tVOzO z`WuCL`A}ksS0@!0hr>>S@2{J0K9Ff2<#NSwZ8(HT$Mx-)ByLo*pEy!YnJL9j8pWbF z`D^p&Fk8TI{_ktv*kj{kInSaqI?0=)zi^Z&QeM6{)qKx5OiUa0b=~7&yMGk#Y;TN$ zOu+lQVx@DAhVABq^&qf~T!Q3}bD5Cm{wH1j6vP!GGgX5OIejqOgN z#L&bj%(}qRNl7(>F~m?D+bL`XI}7qDpgQQ&3k%Jl_9AB2Lj%ML?#MJ?x-0|5p2zkO zxsyFQ+~su_^yU8d3ghWO#s_+XP=B0zuYM0+UmD7z072^fpr#Frs(~uO%2ffmvq z5WCXJ#_XFSGjsj{4D`(CddGPd>KCgT?-0C|7P!E_71r@qWI1{-VgIQY>EnpzP5jPV z|L!*P!kdFxu}SV%2hO8Z8%c*~l*8&+Lz@;zZVr$oFrc zGAqF~v5YXN!3uZYQyiTIbdQwjKk;Am^78u1u8$r@Cr3+5+l+Aw`&S9gBlSk=&Oa4VCrf#*gj|#~%rpP|vyfBvBFCIH48`W4l~q>mo);QL z@Y>$aT&TRRYs=c8SX;JFp!(R6T~8p zl%@E@|FmbMDdTE7VdSO7%x9Ixzixd1J#4W`em@zZ7xJSyO@sIOh;GvnHV#g?!EC(c zaM>+1RaMn{Z0awjyw5pwXVRTh7u&{u5NDn!P0rEZ&s-q6C^^_OIyaNo(rTom=m3EH z=%Wc!hm03~=A4RE`pCJnQW*)%3gxtefRC*`iY*;bH@KI8jDoG40raYmAuG>6eI<;A z;NljucE|7pL`UC8M@KjR+a9@_k@r8_%SRjD0dLhGDVzr@O~3whIn{i?KlS@kq{cnE zAHkH0kuUxA`irf@ef`n7o4buy57}*IUQZ?*UT0X2lrCBu*IGbMg?DSu6O3=opt=DA zoAQtYD-Fg(v^$n^3U(X_W^i)>jWZi!Inmm@UJ1s<<=NG>c{dv{_Dzm@u8g}r(LM95 z+84E88m5tukN`Dl2*{Vwr)Ot>eQydN<~RKkNS#%gt#-x&OyiZM(IP(K+*Jc;@ZP-% z2*9~1dd3cHF%31fFUae42RB;j692B3P7PXF0st!Ly!IP;dleI$5hIkhoLDU?j{m1u zXE-jLf4ss(wS^5~)JM++a$XS(FL<9U(|Mn4KLf40W_>e2QnA(*A*UOa!H_pc>}`L< zePqPK!(@}Ync%%-^VoeV{;bfN8YeRMZ`2f@8ry)}bLrUumxY}bAqTU2x4KXmek4+V zzC2NKCxLu}H%O z&(63&$3-h7RH;}A6_Q=cgHkT*asK547Oa6fNa?)|@7Udk3HrFYb|>0&)$$!_Di0|N zN1j>tEEe78Ejy1BN=6JoS5Q8-VTQ#YU z>+L3<+J{}ZRg+Xp&YR;`q6#}0lk{J6lMo9zXO~EYKpuo)fpPcMoan!Crh)2J(>S-~ z;;((vZjs%ic>L$*l^b~2fc_n9Otiz$!O_uNFluTsH8pi_VJT6HjWmlEXUEMyZ@q;pdn+~R##6gBRllrk$$25X)DJ=M&$Ec42jw#VI zEtP!2NR4h|bb;jNux0$K!8mhUyKga+V>B`9WI0+Ka(31hdkKgDyKN7pBrZi4OX=i` z8H~$c>LzE^bP-brJFI(U$d8Ni6gQp&=nx-sI)6!5l~u=s#6R;mUTE zOC~&uOtufCU&i-NdY$afAByT6lqp`D97Q#2WgoDr@IIeVZbkA-vG9vgSkj=cG9W=K zY~w4d3N~W>_Y4G==)bQ}#7HH3(qwC7Q}6Csul(n<8E; zdM*RQOV*EQI)9J+;g*e;qMC8MO?M}Z-)VtaqN(Xqfx4};{dcDT<6?^mpCuI*=GWHJ zr!O0|y!b1O_iL=h^&x0K(OmJ>mQ z78e#b|1VB|!+{NsvPga~R$An=s)Cxnw71_eT8AluvLpn-xJlgcrQX+Uq%f76{?;|2 zU-k8G5#w7jruTaNDqAKo{%Jmz{@HX9oKxU2Txh`tnJFY9OlhVBu91v?hYmC7O#O!q z1smJ1260oed*-*i8^p10;Q#7wA+?c{^Q;oyKG>NpU}UlEOUu9yCAvr!T`@Ja=)mMG3eTg1 zSQWd&v>EC=`VB~1S}#Gc@%AAxN~qL2ozs|TxTP{H#|cjxWv*BwIX6KLE{3NlF~G1(0Mo@5!J7#X(oP$K&Y`ldphFv z_}T2N6jwr4!BqRq`uxZAN+DTUj5yqX;YtBwc777}@ChLS;_Uf{FJ8Q0U}Z>3N%5PV zz5U|FO9ocfq_UJ0#L~+wBCtL8o#ey%r#yJ2*z-h@$uaNxi|vwkd`fn@+3(16WPD3>$#okG!e)*+34esv%RyExyJ+F>*EBWXZ#EPdKoVdMMbU8fKe_63YiTS zDifjvEmlwA!Rfnjiwa?>3ncS98wrCvx; zGtfj7F_^`*cVr&83ZQPf&c)Ndl3$P=;rjgEUAuLU?5Wyw%B1?p`Lvn951&jCal^3Y zvdRda$3>ePXyOm(K)ik9&gLUt-oc6u8>BK=V6!G5EbMH4=qlX(?%IUZlOki}aE%F=(L3AQWc;pQd@*n4y97)8Q7%UZ7qa){!tOl)XS*M6$iO|{ zIyk7oP{8rg3kKk;HywGVda}2qvAlczxq=hQylOa&g$*<@hQc9vK7H@6pOJ57ANgJ3h|V&bhg%iI~X51b*1Ib!lj6!J%Ml z?<}<(85_H!Yh&YC1;2TZlArxe&om{8KgQhLJOho_+nj&yiskC*hEac&h~KnZ(xP@q4`h_qhgX1{=`jX$Lv_M&EePN2XO-5kaLar z^O0Xs8-m z{o)+>z$RP4BVd0LWA(D0@uYvb+5s&N7bDKLw| zhEy(Ijcd!4?*&hO?L>i{rWE}jx5GA`lthH=kZT`TD{ZCjK8&Wsa=Q9ohnqwWFjXC# zCo155#@`Z5u{ewr7~E4hTv8}3{IjZsfq~HnWtw2|=09+b>vCTzS8s1`S((;d*+18x zqZ(v_Jws?<#<|RHgEH@DZY;Oi?ahv_zLk~S$%C<=3phSHcgYUcJ>DDx*5rlqfQcA^ z-@jr_sN&_H{cdZU1!gyDd{)42_8Pb`#|e8NKQ(Hg-un;~lmRIIYbHnJ|E6eOsogYNI*Vwmd*H(crL-`21=L8mtNm%ugxIx=vuJU@?e+%+5f z$znBDj4~ELnQMa(P_<88vrqvQ5b7uSJ^x~b(M@$jlbpT!JvmwZXnzP?0LR!1PddNn ziI}XWiJ~D}yN8P-W^7~J-(tfFX4^m)Z}qjaDZP4SsD1fc z^r9Xl?LGi2;3a2G#ff~aTV<=(^Lo5~X)I;HQ_rei>+O{~yP^A)!$2~1Hn;k_83~Y> zgGNc_ph|^>@XG=PNLYyeU+8!SHFrje?p- zXZ*`+W36rt#*!EfgfT_G#X{g-Q2t>+O(I`KkS{PEr#a;R{{8MYT_QWo-I7YGTtYy6 ze7f}H?%&0$wsS1h_XVu00+Vn1wH<>vD5qVs)H)TCO$4|2yI|3a`}vm7prDh6*+RQl zR;~=lpBwNsSIB~RpG)*tlBebrF7L&?n{?=@{F@#lB%JdHZ$BW`l-U%YztCpLIp+f3*aK*rmqo5LOtG2V?R~CrZ|hX>-xMyXpLCPlTg=FA{yb=+E<&y%?;e z!vQ!qQzkXX7esX2y9W|_Jxso@$u=df;7gkb{kfR7V7wr^+k~#HAl93>VCx?i$8%q` zH8FWKIwn(lL<{Q2MDLv&aJAHYd_yN^+Ab4Ku|+0x>vO*9`lJ?$M@V(%QNb|7*X2z) zyX55AJi(d;WKX2h#Tt{6lKq3~WSsB2C*|6)J1S~!xu2Y|@s&j}WpcMOiO2-(uf0Ld zgiSaNDVu{SKD20PkGC7|fG$HjYs=39ifz>H^#w-#!(K6WUtiyI85x@O(D|25k37`WrhSj%fa$0D725*`amH}ppkrkG1kpb0O&0UDe?b6w8-DvEcEA>m zVBZZo0XSK8S&s=0BDdvfxd6GhO#Y=FFRyBv8@ahL_|8;@C0W+0`|3spAm{ zXbC}BtD&~2XCoHSGu8IL0yFD8ns7WaS6^?Vb3?QaRd%p+k@Qq%L1Q5L>hwol{Re&HFYzLd@W+z<)8D4m2i{uz0cjXXEQ%T zs6;_9prL36ZVVoNTCjqEooTn4Q_2Z0F25QYat?m{_yLktJ%zJWe%>fr+WPqRMmk zp?q&5sO$??yOlxk4~_1GI0&OrZSRF6fh&)ULGSm?qahr&FEL1rxi$qES;yFBRRb&o z)7i~iZ%Yg9@+PA|1pz^YS+@%7P4kS5S~(bt)YO!alfpMwTB)cz1=R@$w7&}rv&+jj)UHkD z=jSsba~F%Em#q-0vx+UAKmUjUnIlOB$la=OEzYiZ(ET*nS5l5bqoHQg@w+ZMp{v|s zG26s)zZnMVVeh~>M_4y*{Ky~A2$5*S!^dY8QqrQhi}!D`Ui)|z^X3kmJ%R$BRs})s zLGHQqFby>IiHV7L$ri4jrCZ3$tg_X=YaNxlL|Q6P(Fz{{1Xp%>ahg?p3Wij~k6qcp z#HfZ=rg!htDykF%Ta=tY0{?bO;wn5`Xl#i8zx`7-SYQ?-siVV6+>F~5apNaU8mdu{ zai3x`W#mKizYfH9pPJidqM z?{88dX8(4kc`MPUuWe~#o+l>u6)i~+hkmg8*J=xjyhX?f3k?I z%U#pzba~p(4XU#o@tj`MzvKS6V-AbVUrUIWH9Qiment9z0AuQDxZ z6|?ZO<`z>wte@CtVab2WeKmDER$66KYUy#dOuj#5R;efoU{JPHCReRb%%sdJs>`}8 zQ_j+1EODBdU6j|*Xd_BQx}bJYWL>6x^vG|>q(kq_?*>0V|0{-yOr7bJ?@PXWPD!%Md6S3l}3tol6ty&6)9f2y{j4>IL~pk=b+3?SHT z*bR{$lX~m~1O#AAw33yF<~kS1m#Y@k1b{bu4vajLfG!6%nU24-sz*Q;B-o|^qN_dS4kih% zvmZ4+s%HF`e4U?rdtcBvnFkIoyaw6RXUX<+UH@^5hl3cAAM?e;;3CwXB4c%&GrSqn z#dx^5`J--HNlO>0zx9hzMHkjS^kZtZ7dLg>kBwd%7_$QBZfS2mxg~s6!2t12Z5jWg7DM`)kMFJTofNo`WQ?gm!C% za0r;HoYh3n9Vr1s-yRS6DF}Lz1%Gln8%()F6B1h^s{r6nlpc`9H;%tl`F{d7xS#!=&0hI66>H7ctN-2`KXaH zJEih{DA4EkhjbUyVr}mAHZ-3Vdopa+a5S8N{*iAgNygD*ezd|zUJ2awa@wJK4G$03 zs;_ulqRuFnSEi_>l+z9$FhIvI_8(Jd{s#IB=mS8_9FhD*)sfq`sy0yF;3+_ zuWQ=aY!-%MzKt>*#)!~#(LeW}$B{T-qQoru@B8t*zPvcEI@>{zBPSq+SE;F}T;Urb9QJPD-`KN;=5(ljC zc~M^yqn??YGeYV`apWvf6X_{xL#gTMLv39nBO~R^S-jEH)0C@)sRhwS$KoITaOMeE znFdjH6#}o4u8XUf(E<+lfT?0D6@je^`z7I&RxWamN9du*h8%g-DZYhLi5MT=+*fE- zcatbg+Yc7-d& zZ7qFm&{B!jiygUo=sxcM5iXPUR%%A^K{b~H;km$YO1N)e+p`RBogvSTl|TH093m4% z^$}yrtE+Nymir0x<%M&biY1aN#Hvouz6)YXATeD+NQv0KqY5747zefxSZy17!r1-c zXFt53?fp~|su+AVtF-Qz%b#X%iI2g9eA@nT)K5kRV=YF|Rh{kZcF!m5?(WVlnZD zqhme;_HC_tQDF~lb`cn*L;U#4XG0Dpu5HP=Yj`}_)#oo?CTmwB&I1R%JtzVFwMxl; z?!Nwh0k0Eoa7c@jee7j-{D%S6C?XeCtOnNsoG4SEhO!$A4~H7uK=XU#K@)oe508eM z`*Tlz&yM=fsA)>ft^YRIJ}`HRAmHdiUDs5N7hKo={ygfsLXb1)BoR^xdaa-;WL%3p zn*ZICQI+Op6Fd6+sI?kj2p?Uj*bfP!^LKIpzTVAlAB*h2zXop0Rg}p-gJwc zeukQxsJk}K@&+z0^8c~F4ku3Jm@;=HzCYCGCb4?(OL=a3R-xq9^N;UHNJhUuePV1( zOLylszuV5E|E6GUcMQ?f+pdknMrz7nZ0YY01Jw!%pS3jD2eD4_nHzeMk&w9ikD7jp zVr8_kfB+#;z|F4GAN>%z4vxhPxV}`eh_8K&d?<$z}xgz`COZ!=IqQ9 z9`uIXbSMaZnhD*AqzABtR^V>2Zl>QEHfh$LqJY08&-G+Ea) zROw^OQVx0mGr%^WNUgM1O<8%XgV+i0|4~AjDZ!B4`_Rx(l4I0qflHurFLxsH9FT0- ztw2qGQ7$zf?=F4}2%x?=vJ0a4*Y&DpZuUcPFdZ9Po}@^_uU{WNeE4S8pT=!9T0CwK zkKQ&zmd$}`iqV^Y6QYm&0}vAe!8g|syinlYNpj&{U{Pv~Ot_x>NH1IeIcDn^THALZ zt)TGm<`makn4?9XURh}#zaFsURK0EykNxX9#!YhR7ca1(|Azia!g8V_0{m40@4W#h zZ31{VZrgx|d!PMHSG>?rc^R#AbX`vAWE*%Vsl$)5d9sBGQr{PnP4kf`H{_2 z1s0N5#McP-ST(=p2@$?;+uTrgUdnAu`aeXy1z1(v^FMq=Q2`MtQ4my8QfcXnbc=L1 zNJ}FiTR=cSKtM`TTDrSaTBN(Xk*X>tj5qoU_l`YtNdQPfYFnSfa33n&wTx zwl{Y>s0b|-I~of1*87Tziq!ITuK}Nz0x13yc2KKM*mmaday!sLo?k-dL?5aBn(1ev zS^9K&W-8G>6SA179h79eOGretyTgY+dVHq-Ppp<^)Y%~U>D@<~2T|N@X=`~Iii{)$ z8H%aGUSo-UmqFzcP$y<^{xTI4yvHP^tbG6MXnPs5Sy+Kja3gFMe&IripS4H>E@faI z)R(CwrCwqo0#}7<|G{Q$>g~k=M8)(qHS)h5t*HRSgnGC^CqO@J@9t(iiUsc(q-vWV zp7p2D&$1-=@mdS0+R-joe9AV%ppXr&Jrw@d z2ZQC}tZ(|eGg2q<50l&|dU^1KQ)CH)S3|oW&eC(;`mZG6tX3p=u*@AFaM1FpQe+Yg5}iw_zn?p2S63y*ODz$OO3IJ zNlwc#0Kwq?7>Na>`b9~9fBz0XYoHBs*-{4c?%f9M3g35o zJCH=t#LZs%%^!pIw?4FjXL-K&q1#_Zj-W1g5Eixcrv?qbg@&*{3V)7`eWc-=c)bum z^RB6>X*1Hgr4OE$fCL&1=c+ zW4F?;mtH<2FImp0pFhX3lb=NRqh~iaE!W}lte`*Qu{&He=zu#&ZH^6L5)XCrz)f_t z#O_u>L4l;v{py+;6uLvo)B5{ozlh*42wjMU8UMG3Zl8XQ3j;Ftc|9yw{`3k#H>*Q^kJW8m0Lk&K&pl_HvJjad2=d?N_CP zxA<22YIp!iq^6_$vrO72m?**^{-6E&V*Ax_9-W#@@G*Hprh9w1%YkQGBZ zYVAuE9;ql4_Md%JkS|w^_wp22WFFl3)_MaoAmGHZn;6wKcwTo7$f+KB3|PA`ug1GR z;BjQ&=dT?)Sdw&)HTE{}>p-+sD&oHX$P@?*njbT}>oS^i?vByRclaWeiVDiXQU8-^ zreul!UzmAHqzcOv=8I!>)GhxgZ4{eb2;?7;Rz10$i6RO)e1;UjaciO-A}b8eyABLT zfK84I4?zS{91xTy)*ElXJrzebS=GnHO-w^`vig4y9pVrPc@l@kNPX!40=1eNq10Dz zA0Jyd^zDvPBv4MvlFtY5*<=j1Vn@5&F9_EwbNO7Dof@2uPlhkzSz$x63o4Qc=G#HO z3D5;Qkd%y@e6ZmN)EpY>>((m_&yt~*)Asb^ zrIV|!dfl(06P{1lEQB%eZt%=N%*$3kpwCjtF`jSjG+6~DflU)OCrUd58($6}MTQc) zMHklpW!*pGza}K~jybHW79e|K?%=a5+4=k>H}{`91OeZwwqNqew}BTZn9^9H-+$`B zyZieC7&@TMY|J%gSywle`#I4M52GS)FWh?&N5-Vna*8VKrEW*PC5EFE)#=@iWSmF9 z64yv(Ttv*SKakV*Z#Son*7g*fubOMOyVA_qc(cZ2Ymm`s#_vh16JNTO?S2;tMsLXs zZB(0%vmd3_@x&y$Uft1PTgX?!e}V4XVKA048y??8cSW{w^!H7ICn@K%A|;DTWcc9h zgJ~svA;`)x*lN&Wur_We(~@AOP~6el1(08)jyu#y?z(X9gj4fE`v-*?Fgs4X6b&X5 zLf*c8rfZ6pHsmJB$Qw?_1Mvlci>ut`wXY>X|Pn%Z@;z1|h~H_hRLxGDwz z@wqv-!)B10LX7X8NuHAeuZR$z05Zr05Mv#xkjQ-sIx+w?&$dTlJySWLWpD?^4**xt zFUIE%H97elu^8?Dg{%^!7tm1X)9D<;FVvF*6c$1YkS9HOq5GsPusU$INlaBl>&InS zL}*tSj-2suDdUiXmh0hC%?Z}+yCK^iPXsD_7GG$8HyhDbXe1PXgKc}^CVP=F7ZH5PKPEQxGzB{mMBu{gR!O#>9j~l=}87nNhIMK$%<{vZxJ1qUeW!CTSNs*M&f@ z<%_qp}c(2a?Rv*s=IJ>xnf=e}Y7)EPc zQNC?+XWMmknKr0cn|6b-xl;hJM8kRHC~2;Cz9>oIO%-qYbzOV0oWmGMXu5{%J zt}%#4P=OaBwYsntS04HQ9+Yzd*JaZCgV7hey53ga6{CrL;% zI~+x3o32sE#B24tY7{a1(NZ(F;PH4Sj<{}r6qjF((_6Z{d?5VwnzpX}180F=*50lO z*QHoHq_eB$Z_RRi8DZixOd7d3Fui@i(b{}3;p@Nh@*(mX@IzEo_=Vn_FccL3mRU_& zy(=kDcn5oW91%j{t{ zwmr4K7_5Y?vD9#jY$mNeELWN0nTycsSHCPn?=1BLY@J>94^%nt={&vnSQXIo%IkbK z6<@Jarv;9_lvZ0cpTeT>Pg3N+v#mte^mb~=GEYA@lLuF2Df0zE5q{oBd>i9j#Q)#E z?MSZDtd*m|;&ox>RdOcA6fBy2Nv9GTnbFMJSqfoL>2d{5Ux-UkhB~zzG z|9kZxm9DN7rJP|qwO}&t`syph^~<~%j*85BUl!-Kd+lfK4_^PgCLY0;uBZ`~^LFs0 zs^)H}PjYneApN(z#4nqxa@H_6SV%?Pc#DlVP)zZ`5RN~R#g%7tOg}QpqZ*UuGM7XDumEw5KB4m&E#prYEoEb8CPwr@>G_VZJ3B)et z7#bNHQ*^cV3JjbriOa!0baWgUAJ?-s3Gk}ngD+l=jLzy>n>brqJHO=Onp>CwM;QtZ z+b4K_!sH=rVbB&SrKZLM>G{M`_oGaM8Qp&XkP}6Mz8LOD^?iBB=kt+_Z$TA5RQ+2- z#Ln(G*2%QVV?f~gUHMn>!xnma{>EKEY6+$H^3<9fsalTPJvS&h4N!Kq6+p0}d?y*l@snQ%n> z#_#n5RK@_LW82@qFgBqscLrTkh3VWU^_nKi&>{b%XA4C4L&L+wWUa{FpP(-g5`?O} z{#V=m+KIQw!ZOi=DI$VUo!8>pf=|6gS_p_DU#rqCL`0}fDQ%d`6SO!}iD9X-k(isu z#yzrev~O;2|FKpQ6ToVf%T#RVfHVxYL_6j(l~l)w@>c4OJC{-OE@ z)E8^THja)r6G5~El^(aZt^qGdLBSDZ_u^=p^SL~h7-lt|1fQa!V!p*F%aqHy4Ahm{ z2HH@by`S;8^&e`Q>7Yc;c#ksl{%7qx8k*+`QjtX!-@;o`vmY@T8QC$@$85xE7TW#I zB;!(nM(g@HQZ)QVK5H8+^FA z^L9JPw~A+Ta`H~zcv(|h|Mkbu<;E&1e@QaJ&O{1cfbIoF&etflqI&w&11hJaL9_MZ z?THE$`|Ara-}jZ3l}wKy!-mup{x`kpJEvHGNAq2j2HCLN{PYQC7@8RjG6c8kuZ!az`p6~a|RsZc7kA*1`t<|92WrcAOAQbhh zsi}!(`6f;*Do~sq1G{Cv0JXO zk4WFyzN`N+W=?9^i0~w*vHR@D`977B=IB|!N(Sm;u9wDiWq9OsL zm7Gj!ek}^h${k=-*POmxBc!HgI=CP2@xl3`CinrHVbsH-FmbC#JX_T}?kHdf zS4qDVVk09Xx1-52xG6j(Hv?7x5{Hv$zI5Cwtc+f&OIE}?sJKh|&mXW{=$&y>ze$uC z&)Qz@?Dr)gl9v8?9n(E{6hvcj2Bb*ao&gXP^|Q`#5oq274fn ze|a%1M&(~wZL*3Jbf3)4SWPCQ#jh%_pAcliA(1kGMVYFdG2GME@5K~%zO%g`MkJtv zRNC()ONmWq`qsqNZ|b9hH7+?TYZz?FQY#rscp!v)afW$jqN=KRK(kE=w4{t*;$Se2 zVb&)&P8pijY^m8xj}=cvwW>MDCPqVZ$-IreL7sgdO{y#)HBB8i=#(_0uQs%uhGoY*2p9Lv>{RZWvMbJ! zqqF0?bg!yehg(jZV`Z$L%eSm@ed4ptAVFa!$S7BKzOUNGs5MQfPL-5g7{m4~<4%O5 zvf0K&?9Mwov}%&YJk6{{TgTiV-^~9Qt$4^%N$#PY3ar~Q}9ISFd^De3GB8eu`|6|zyo%B+$^EWkJ*06w%OlD^36%f?aFj)M zaPjk|khc|huIbIre)9G0uX0Ktt}DSz9OgWiA>cE@L-@RY%6-b6mD^X8d6D~Bpzm7! zCp&yCC+aWs-(%i&ndSAyadpS!RsY*HHpp&~CR^uaqT@!0^L+c}2drfpLy9G7n(}Ny z&*fR}2Jd^N_`l>9sxTR^1}fQ;hBD+e)DrKFcJ*^Z_45zloNF6-u8S|(!Rkn_4l1MT zcUqi>5!=PB0Vp>!G3TJ=-(QDZwNhCNf*tUmC9t;9z4Syc5j6)!(KD?347!cSyBXec zLR5In_S}55rmL|MlC$=2Fx!8c#!5+0(Xt0F3}9(?;8l^F{L-dQbv z+R~X~q`mOUg}R;SkA!T^zqV#BafD0$NnhQb1}o~J+39i5${W!5^_f}BtbPW9SvO+*9Nud(q?lGPEiHL0OU zLgBX<8Wz_=v(2#JYkldPJH|L%<8rjP$T@BFYv;9IgDSbHWk!x7XZD3Bt?AZm16c0x z@bE}VNpZd3OOY3JT>wXr0;Tl{o!+#x{|~u@+>cDGJ7$p6ay zfjI@t!=-N5AQc*vRj`wt%48Df|7yl(0Nb_bF}ry@RymV4~Woc>A}@ z%0(UW$LUkWXnDsKAG2q2dJZyX*mLTZR35AyxG)(Tg~fiCeY1w{h3vRqqDjCFgj+dr zGH??JkMp|LZ6EnP?%Z8#s&}t>IFW}_tpa!hQDoCQMf>0+K8{qmN=zdS~C-znQR4c$KB%o=@Vugv68KF%Ykp zVZO@()St%fp;YWkK;i)BlUuMOxa^kh0Njk2D>HlqVcs9uAmGmRtKC$4_gm@{dS@!j zAZSdYB!N@zXN;0kc^oIGlmz8lhCx1M^L zPtz&o4%%{1g@>7hzO-3-MAJ)cGbfD)e>^1J$th3yX6brZl%w8x3o?RJ^h3ju`X`M- zo){$DQL?qI>I&kNcoLn$>HRkQBh01N)7mE^eY#cXBqSs#62pHG^*87w{f4rds~1Dqk&2Ud6M*Zw)Tv20NbbcehcB?l9Fl-M*Mi1`(mNjZF;{D)?>`MhlE!k-vD1D_UD)HzaV^|;sm}DKRU@hz_#-v+ z9+OpQeTo7_T|M3N3(=H8vAo?O_~|d-ZQMA+c}$U)nC}w|nQD!1Q2xL_6FL*qjJUAOgkNwN8G5HRyp zFe*W1)7sM`t9?B}Q-!WrJ>p`Y?{Uo;b@Zw!`%++>Gk)*oI$`C_{TB+3s5_G?pIlmh zZCu7Cb7OW^5K2$TZGnE%iJ*RCU%G{kzWcXVfaD@uHeD!duryj3OGX5}kERTw$Er2&uKTS|8O( zC!0soF>}_}u})UmH#cZ&OMeyr?hm^myw3j)vWduxBa$)()3XK}JZ|4=eD4HH zsj;8ErcO@_iT&P+Q%7>@R${#@I+EY;2?xt~nap`%khS+;gN9;%a&;8rovRyAakV{{I= z^z!Qd7AK7SDU>&7D7uyR^JZ_Lzdt!UdpLmU0pL9nx4&->GPFj>yrcMBIfficl*4X6 zb=@gtRu{t2789d+%0r0hQWGzt^PvTnPEKB4ey}>x{o)uzE4Q(+3DQ2CZDBF*` z))9;OPM@sC8 zBqw|QE8TxE!xrUts6zOKeqeckw^KLbqIsdlL8hGap4L#rr1enB#XFfx61}+(vd(e; zx@(j1IBjNT_DojT(VV=050ip{ng>vYvR_4$kN1pv+lfd{RO5wG%Z6~5x^1uK3rqL8 zPTesF{f^p-K);`Z5r3DT3+%S|Zrk!BTt8dS8-#?S&U%R+a*%6wC8(-~F=f5gKyng) zWN20B;%Lv7qg^mPyqTNh}qAbIA5tgNlC;ZpLS2BWWN+Xt}V1kQmYB}^W} z4?vn`F>d#fkq6szd+WOIdUjV?Nx@Uw=GWALWbF&g@(_Ae#H49=IB1P2<|IAJB3Q#( zzoA)Mey87oQxGNTI7Bwb>FMpA-kpyW+(d5$SvxZW4Hs4bAg9|spf{vK*G_hX$%r1Q z7}bE@8N=9z>EJ8h4@9&(rS>WH(n7T{hJ4PcIw~;OzUH_o(43m>Blf$vq~M2K@7DhIWZ2~gmp*|PAKhzQ zX8LM9u1TiUn|J-b!;B7nMBJ*O=R4XZtP3h{)LUU~{A4B?)?_d?_}`WH1f!1j{g_r( z^RaH^_`U1U9@Ra?-_n@X!{q2w>OIoLVDDdInq98}EaZ4T*Gz1m(wYYOs?E(!U|Qr7 z*aDdkJnF55jX+(l^_(7~X zykH32yp}&Vro4{i#A3|fqgf~5)P|xVdFW5jr#M8-iJXDEa56qkW^%H(sMJ4JX59-b zQV;OomqVH`v~6}~M%5V^nO;hpVeQh*skR|4SFU&aFDq}#zT4QecsNqV*>G`oZ=&vb zgW!ji(eB6eS!UhaGPJ+oBdtGHxJSD~Twv}Bv+oSGd-N?g?kl%77d_<!BpbYCTF)T zJ0-#@)IyJmdmNqWBc^EEAzZ8sZ)ULFtZ^OVs=o7JOI-gYI7FG_1Fn=yUGKjZ7iT4x z%SN+!%L$w`&$%B{VJAPs6qAdmX0oC2gUw-i#Q%mVW%%qNWCl0)u?@wqYH#4Ibbd|b zzj4z(i*`wpGL$1w#rLqQ_5)p!b%K9sl4tYe*@^kQ@ug}J;$gS^{2w2UJQb_YMvFeG z+$S!7>$UnTu6jU^JwN!-3EBXDZ%fqglJWj3-5ejS{csCqjtLVh4%}OZh9bb7=M~*J z;~gxFpAJOP!hhbpCh(r*Dczk^(J8bVwA~*j(Eb$kWvLJ0l@WjY>CtaFa?`uSgmi)D zcO`Oh3O&n^z&>JnXFxjNFNQ~+K(W%kOMDpW1=EpZf?#+07c4Tzc&WT3xMkW;o#s<`U^d7 z^e@W=T_^;e;1c?lrLA#VwGloP~k8 zA-M9@!>4WLn*HHHt_m_^-Sg41byJ$5lmmQzpD-22xVc6v&#<@s__aXzjJ(lzta&b3 zJ^xe_&OBq>JJwzG;^dAkRH+PS=6qKdvnr+Ae6P*j#U4K=EC0(uR&#Whcf2ZwGL5;5 zeCIohF~xbbebslD$G8)|8hb2JieD{-HF1%~v-hkKPr;O>Ss3w(sscq2Y%kR;&jN1Z zKp*fL1*lH&*Us#xJC9`{GOqFEetxw4DL9*&$V!IhL5zAidxHw&wK@KCukSVeCvOdWtU}|@ z^lRpyBMF=lYc`roYih#x5i~oF$h;lxj`~0F>>Ct|dO#{u18uG!{j1=@$Mn2rpB!#7 zH6dSW{rDFcp9NMQQlH05!yIfh)au^Tr-!{+V=F8C^TI;+ULS=IE-p)yp4aL8n-A0X zGTdRVA1*}xJH8M~O#5BXr`8_vyZh%wT(%;iMkQG;o0}HVcEk4ZE(`qd07`k3J-k#e zd`X8smDa}YTc7n0%C!etF-pNGamCp~5C+2%$t%T?JbLscENnK2MFCUk%?$hfqD9Pr z2w=D$xZustA8#Q#cvbF}IY#^2Wc7SFyp1_dUCr&I6%x$w5gzftxri=>;5*A*%v%Kz zI9Jsi(w355bjSImT>I~o3PgKepCqCKncP2+TPgL!UkW3fsxy3HZt?6fb80orLEYny z^2*v)yz!DPiBKvMa(`L*t@jC=^ix=}&}0z23mfWPntl*Cn~xP^dkpI)XJ)TywIw}{ zD6s6kBrdYFL@b=wW=Ffu&YBLctk_vN;lKAKz<=-ij*fcZp-9{P&u{(S37kru4F&%mJWU+>CH5hviT)$x29k=m;$Vz+>Hy}UuI z-H})Dz55RXD}(-`!&y)SCzn(`oJI(1?}P!Imsgz$y6ela(6FUx(5NqU-??OQ(PAt=NC!C%>SiV}D|uvN z53X)*TpVY4z0_PZG zIOL39LOLZAQ%b+14c!B*>fKo)x3l#<;vV98gjknkw;yG*Iqz>*?z zey&|Dt~p_>58P20(!hmqRmf^U00#CTP|Q&M0if|uOir4QMyr2=W50wMeN#O@e*_PG zDy%K2a3g*DWIi(0&}8s(dbmmlF`hhnqL4oAQRC$$af_LC1mhH+6_yhpl9IB;(Bc_) z*w{RLS5G-=VF^}Mqj|1DLx`J}j!CaOt^53Pm>anqdmtun<0oG=#aGE}YEK!KL~)yx z`?fj#NfBj;xcV(Eo|D}{p1j@TjjE`#y%>wh5~7cjx9;NGtTSxIa{B=>z*Gp)hPJJ= zpx{#s4LtZ%6igfuO=3XMrLWbkq4 zF>=sG^=PCjeaMYSMa3$f!)x4RN@)7>x-{aUK^}5Dc_`yGTN2S%s?gS_;RJpyKkzcT zZb)lf&~0SB`qi@#Ug5tw$B|C_vM&E?9Y^cm@wJ2OfbmxnrfQ5N=JfPQO+L);_r<9L zi6w?*-3QNU6Z?k}65bsY)w`2dW^*?jjQ@*l1s|3?2S2e`QHmaN-)5DG^|M-iRw3Uz+!yjS<9ay{3D-%+XDe&DZM;;($Fe48oz z*S3to#y*;3xD!l&{MtqJPCE+9iWa?T9?#Z3EZcz@ zIRzVe2QYWMG~frm^>XC%X%ofc+pH$EArsD!Sb?G_Tdl~4-F&Ffq&J?M{xKgf@8=+n z+m+DkfWdpIRMRg2iGB!S-SPNI*5z@a62gQ~FtV3K1jfbj8PH5NZx(G)QL-hiS>5ZS zQ}YwHoqr9z0Z*0VB^p092-1L!ENM#8NkuR&pBd4=jIphS$A*T+ikPI`GOU%EiRF$f zkH)UX2@1T$?t}hlYC>IS9?a1`TGpYVr-=ldhI;bvZ5XpcZf7jZ>Ew^sjyQ;A4Wh;^ zDrc)U_!H7gNJ+7pnHU+hpz=CaM+O)OK=JHZMjxXLC7zU^Jb`T7%hs{*PPKzJmY#Ti zrB2ZtWqfWH|DXNah8xW>@9b?^gwo*M3o_Wu24Js9dJp5lu^>$TGt#q>JR(QE&8hBE zqA_T)WJyEKG>Xj(p>&d~iiT!@zKOZH4+C6zGfTxq+OF`Y8+K<+cB7>x0>C67i#x(g z@1fq^Mz`zxe8>_Z&Hyet`W#jhzxD{}f%AwB9*m-!Zr~O9(gtn5RqhE33tptz2Yaf} zA)lmYkNE7rveQQvK|Y?G#8m&Wp<%*)eN(!2k%N~Psj+$&0T*16F=&?muZK!U)&KUk zpZr}iz6PS{i{@Y?J1IY-eCS;8!o_ZTQ=!7HgOV5E86h9k2I1a0T&ed-)!F@B^n9;B zA?up+QD;jmcVt&+@N{Ee!&KKpNy={EV8sk(4d=t?RN&UUe)C3uz-s09j|uyJLj-6w z;uV;S1OJaq@cOj?oY?;78nmXV&-@!bYFt&T%(%>z&)ZMF874dOT|ayGw|EskfIYXv zZsaEPJ+VY{2X^AyYvX(BzzWy)s~HW`3kG+WIrp?o-_08PDpR&xmW!uWQoYp zY$B3!*E^fKUb1xNjJUj5A8Mbl8z(qD+^ORhQgPKS$(*=R7P<4Hg1f;|UYG;8g&&hw627Vv^+YeWU`weNIk$A-+bd37R)qxB+d+Fwyk^6Oezp1 zis?RXon{Ni;FL=h(@n@quX{0_P4CA9=*N3hE6llN7)ygaTx;g zJl9x1C^*WtV+-aedsL<-*h?@Fq5=_K4<(YY+_@dG*Zmy%8W*Et5FZ}y^K)Kwx1O~B z0V)3@`Yg#^Va5FjxXXU`=)(OGzLRIyw5oPy6rR7%9W1=sVHTaRr-4y3jfsi;*gPTP zUE&fF7|6u|k-*g{{rYfDa&`4#i!u;hP_))=g|ruXjl!T9>Hhi?f(y(ydWzWCF^~*j zwxl_j|LUo<7|50T{ZH#OZ}@Il`XBbhHte1^jv!%hn+mWOa> z(8$5BcFvb&FlVlG9l#gko~~8icWpVx&Mr~3Qjigtx=uKB8lLlcR=EW6efEs=#d2L69aL{c1l$eq@|v zEa;a;H&sr!6cMYRR$WBkx^fXaQ$%6mn`Fo9H*xeDyA>WH{fg(@XVW8;RCw*!VyW^! z{-`FZnt9NciTroL%*BL+=hbfP*2pEhnC!r#dco8;5HP0;gl&Z$ zG8;beqmN`+oN9Xspa#0Ph;+raO=<42Ul6~oE~(#%gsiObF6Z>le6WBm_}Hlbjf;(q zVlhIYB0PP`qIw1f1`x+WNq_(s%j^D4W_>8%X4CBMMh@Q#=8vMudH|@k%2xZ?gju&j zWrhs$&Tgqd)YF$&GkvWwhgkF}%iZ_3Yn6Se#Li*XgU5P4 z#C1DGLo9)tSDcdbW=#^%Q{E2H?OvAQNzkX5(+F@}6?_UQo9f=}2ctBb5ZWWYo~7y9 zrqTAkiB*ZQC){yCCF?`GgGnOU8c1F3(4@f7&=87rSY_N}2>e5(@Jc)h^OW+8&z2Z5 zegSH76Righhy4!k>?z4z?j+Mlle=sSQljZRv*yf`l z<%BsQ{QduE^VfcPDB#zJPmPO5<`EdUiQS_}9#Sl<7%}Fhf{+a}qMhU^>)T`qql=4l z$x^YXM(Ik=PTaWl48H@r{@1U8<$ji9-M%u0(_IyRDYL^1V(!Gpf60)Kv7O)%qeZqG{FVY5tU4V2mhzK6yh|tj|o}9HG?bXQk4=8RL&zg&7y$`5xVH zVIeF!OwQQqS~N0Rp)$SvV9AJxAVfovf%i0@wvQfjk&y74P8;(@?1qJnoo)T(sK?#h zp5yYWnaLi)ZAT{Os;aroE#s)AE&spaSWiV?|3q)*=%YVOqH!l~0DAnUB3e>;QqlbY zQ>xQ5x|Lq_9gcVS@fum5Ze`u)<*IGfDU+>bxH`ob?RG{!!kIMGO1%?IG(9uVsZGE$ zdmn>-?9Sl8@=+u5K-z?cfj{#Gy1gKe$Uwp`Yrmfc#0CB92Qa3j;B6l{h^s_e*xO!$ zrNvgwk@i;I)isyHHHQPc*I9*SJ?FxrUPKq}7-OsGzJWxCCXEeiBEvDX2E=pzqkh>u zcG;Xl6Uche7U0+;C5$26%4)?KGKC*82vNMtF~40pwW$$m(kBRwQG9O@ zm6>|Ut{X(NZ z@LA-!p283Qrt?15wtAy8+ZnSgioA4J7!^A8{CCwaV*7+q2aDlmg zXN6R^^$yjrS8Suctk$muLo@31H>_MC-y5QfrBXDz$kX#jD48c7ZNhF%j0?M-8+2Hl z<+izG=)Ae~)_K#y+85UDuyp9<$e1F3c0mzyb=gqH`h5WrHbkA`u@Zsz9ww@t)c1nFAh1#VsSGVN<3B8s_uL6l1otD?`i2& zt3K(Abi15<(@~?Abd2zo$tLJl=+Dw!|f<(11Tg-OO{TGos#8DbWV@Yl>KYn_qR5@V!{aAsph&q ztZ4U8CoBh0)d5`{N8pUyb@(c%Mr(3Y2zC@G3TB#u`Hevw3#8G(s9}(wkh!cp-725I z3o~gJE$c#+J#v#gub&;}Q(56_%yHQ0BHLE>qGNH_UUKn39rOM3N}q5LSwOH(Tju+{ zvnL_q!~v;?GvSzdMkw-K^Q(6HXEPVi@BeK`vZiIIRR3veVA*YOG}Ug0#CQS3VBGck z4*}CM;k#o^I^W(@+ZAa{-=6s8x3h3Hq?&PGRQfRw@y#s(M|-qs@vhiBxNewzI>y{Z!HnKs^H;}HT8aR>=t%6|hml!i z4t`s!Y85Fai+DxbZv$6rNynI+I2wsRM?77R`x_<)Q#xr(`QX*2KCE)h;W^oy zcX6SJGzU2R%Yi3$-ErTW8xfR++QpH;OpJA1YIe5q<8#pC`~}h{D;OY;I*RJ=0#h9N zh!%e1!fqSXrPhJz`H5Rt3l}%YOM8xRA(P&) z__*;MU;2Wwk{`YaG<@gPD}#LjWbWGg8E#fiGD;Z*01nACjY)ddsoXlR;l9^0=;57v zr!YcCQ}Ru#QBR82!dpphv9SJ^-$Ps52c(M$OUi`C79ag#THYBxysp$=>FlH{o|k*$ zXx~IU56Qi=wHh4Ywc$X^tZQ!2l;otXw@^F^p^rz0!T5Ax_YUTo!PWrf{d(|*e!o2F zq*}SZOng24ry`wI)g}kb-{gYX8eLI8g#Wm~cqMV^&jwPO3>Y@dnlF)B+3~w%@t?#3 z^%pV zJsv$BPe(eBsNvIoWyPJk1o!zsoI1)2{qD;IRx9FODLFpLJgdutHbUGJ;Yb)hFR!jW zl#iYG7OKBu1>wD{dm1-W)z<@DHtik8jSQCy&J0DRqAPCpaZ<$@K+O0a+R%;_{VG}Fmg63Ewe>%<7ibSg^KQA5PQ3Pue}Z{8=JSw$bfHKn%~SU; zDX?U`r-GY9ja#CCk&CZ~B%QE7QgP$?%;>dK_}%^?al$wpMgX9x90UKxtT?)$uy8In zn)UtZstHJy#A>#LZL7@HukvRDds^@Q{PyjcNG|sMe%rx%Abx`J)`nol1=U#nc9C~ z#wize+|kvwu!)pSFM%!M699t%N(6J0Ca@waFngu*x2sE9UteFf$QT>Aso?cW3Xc6? zAnANO>kE=1R*9*Ij|9%+4*>S?M892a&Ox=aVQy`A-c>`}9RC8SKK zf?3U8A&X`mUFtbW6E@ISH~{epDUW!*b3fu6vu+n7Wje77e;xm}#M0Hpk1-#BfgTnGVpK;mAzE`N zUm$P}%at!MH!*1gs(&w7`#>L0;#VAs=^)`T%)soxO)7Sm>@k4JH=Oqe8&pLDgo~? z`9rUlmzQ5~a2pK%c0)qJbZ35cRz9yAh;m&pl5>i3cjV7kG;0ze_~>sN(6)cFnmC_^ zm#Nm!*0%2N=y|<2m8&gStSR5bmNHsRhQ$FHv>6u@C#+Y}-S~{eB zXJaU&;ac3oKgn3vtDI8O6I24PbrXbK2*WFPF-Xm@iwYtY5f>(Ju#>ji4IL9Cmo!B$ z>ZU#7+#ax&a~j+{(i^D%cf%s{LRHdZSX{G{$XVWe_U8*?H7;?muv*U1qq&lJV9tr z{8NPaX;5>wjm3Cj^m%nPx4`oJc*yTx&l)Mjn90dQ4@r6H=D%A#jsVgKAUiB563OD5 z(sPP$ZwjhjYT%XPFD2UM=!jc>-q^4JZF-+sNml|$g%c1m=s5t>u?=NG0b~IbORnzX zFebk@JDY**!@fL9k`C0oU1Wd18Jabqb-z4ae{S6U-5WlH&ebUqNbAgs_R4Ocpg8Ct z;;JXsYp5!A)d`%(9$lSbkC_c})LgIJ9m48;@szvTSR@_x%5jxDnzgn(hM$;h(c*83 zIh;7%-u4t1>w@8o!Tp0HBk^D+pZLs4o&-viXWBuVI0{C0yBe4LXQ?Z; zhse5Cvmdq{yFuwS4^C=h{9fL(J8i)U8Q+a9ExUzq_?m$`VXjmqj=GD?C`hU;iinj} z{L8d7qVM*|4+6Fg;iOIca; z@(FCb&jS4z)r3Yy8(h)Nr+i`~<;%Bug5pF}di_$elKJ#LTz~Lzz-#tHC2OLF*dvQ9 zKM6wpbyXnK{3%7BGH+*kroUcbXEBDv2D%2@vat7O#D$B>EPVVo?Gv_s$7e+F_9tkjUg85b;0mGM@p-w%=|$)yEd`>Hq|}78sj4`C=GAuQ6vNSAp+=9 z-|a^{sr4FRLllS$Y-!rslzP{BTAd4TAaQXR7$@7Bo>nRophGGS4uf3wEB8RMn`QL3 z8DDM(Oc{&b&AtMw;o(^OtlwrOBO?!bPFPu3cJ}sI{H7!P|1T)N!oqbvdd%O7f8`R_ ztmT?F_EWK|Vx2?#!yyLm*f3)U?1iYhB@CpRmZ)f?eEr3`(_{Vqc!~vr`@^Q|vuo1M zujND!*JYjS$YAG+C`>=LhuDS-%e1Fr`L$#~ipyfc>09_XPQG4mANTktR5l|y@+3{< zMt@dVFZ+LJBi23VYw^yr*!*Whud#Pm_?&jy>#iII|F=#vlBA)Arf3G5(*2m z+S-Bav%6|&FS;*jw#JcvU{N13 zv-r>GH5HVVSu0xkprWGeIxdPPK%nzxre^nBo|Yl(*o>+es-3j-^!fF5tOvp>2I0lb z%*|J1{Ddimn6mplL7w zyKs;|o(sfXL{sNquP%4Y1HRQzcUXwvwNFQ@vT_1)O_m=U8*^3?Ez!;@ zXG)3gI}`R}RwwyB_@N*rKdWrN^HgwGl!|We_HCs>bTx&M!9k6(jN;g+>olqb^Y^lx z$f&D=um~FXxLN7BKRu-I+I?pyO7&!dc#eZ1DD#H5hK=Bm@^4wi+xHJ>bZIJUZXoEj zX`gdwiTGchnyIq;?+=(}G>>W46aI+I>9 zAg#Uv+AzvBOg<(hg|gCd(?2ebl#0sXF$uAdg#`ncW08Y#Elk9VZbnR`(i}Jwzly8B z@K4(5>?rB_VEUA(s4y$-%-JP>Cqa(+?@)UN0W-74A2wUe0JWOG_voD-88*cy6GRXb z1zTpbeL7rijq>pL@^B>JQtRS%fPWyK-$Q8s=}C&;Q+VZ>@q1N*H>7KC^OkDC=9A^; zS{F5=c68o+>2I75v!)*)0x390fjB0%zy8S%Eb$B}e*6E?be3UNbUXEaQPHuW(mQ-A-ushd*0>b`y#4<(daR_IHd8!cfT|1bdrX`L@`&AUK=4YQOa^k4 zg2dISMiUmdni}UB*P|oya>XGcCT0}9rqGu0zo5%(h@4Y*W>K6bni>y%S4`3?3h9>5 z9ln~hjg?1j<{{o)0njEmATuAcaLTL&w5_LgNYl$+dTMF|5X|8!2BI~A=p$OZ@3W;l zC3L%Kb%$M6W0_D9jV(7&6R?>ILolNfYZZ2rwQ>Yywt9^XF=RU}HTP}R`E@AHRCgki zuRDv2y$z1r;t?cW@^; z037}&{{4BF{TEJj*`NQ4g!l>!$mv5j-$TFbAe~U%B2bqLoVG-U>GGwBYhu^A9-0FG zcmSENwA2Jhx-0O6!e_SzEcwWC=MzUq@%;Ruc?h#A5Oq^&r`-9gBPltn4+dGrOCo%c z&x4;c;4>tEsNYJ^Wb^yT&|y+V!Bw(UzeoYn3pI>ZQ@@%K{+{OM-?D_emadQ6Uu#W~ z>p@>z%dR0B=%xXO6It{p-mtM=43KJoW<1yiH5*26`t*42`tf7^j0ctHls>=S6=nkj z{}bJt)Z#se!dLVL=-`}MFO1AW78K3QjQY2}eUpB@Jy_k`u)Vgd%O;2M?xzf?#Wtv@ zkz5Yj{v9O4wht1;dENbq+4q;K=x@7dndeZVQ=)r!1@c!G1s>Yhmf^2v#}tIowaXIy)oBLxLXU3vA3nnH{G<8u}g(%=nlHe4=jMyu&%rkB$Hs;FQC8%Imq z*{L{|ogc#EB51w7R<*U4nw(Mspst$P?sF?bLL_uF5?0a(aAwE^pI*w)5)W&WhKv8r zkm5&JN2?;1M-J^YKslpwWi`)uzdV^HX3w*(0gJ?ZICQ23gtq^OjLw`%TudbT592FWLjN#u|4FF&zgVj=L-} z*!Pp(#qrYpfO3DsYF@bh96LXK1wgHw$1e zf&q^y6lf|E8JQ#!zbL#NNaB|GV*LuaglSKDPSFl3YFoI!!NS3p0lNHV60*NYqLCT@ zz*xn$yAM<(w0G z)7=RwV6GZ~LBEZpnFZ7HqS#nm&4QS^gW6zF;i;RGXS zrQbjvV-JX7pqJeaJ)9C-+aSKs&lOgP;uiBRuyrDHiijh&5kWlkDv48Dy#HQ$k{6xE z5f-umHX;6j8R^gI3I-G@NsSU`Mk%Ap5`#;UjD1|snK~x6uWzcpqfzn>>0J;+i+@rs zUc_gMs_Xdf$jTKWlDDc+N)mo&*b3bikPE`kRN-T^()! zb2gXTSf^vRn-x*CBzQ>XiRYo0V7azBw|wt?6;7~Oht7ZX!LH$4wV{efl054J6;EQ@ zcg_`#_Fr$R0)LjHI?uS@-e7>kg{^%Q{rU5c*NZU1xLlwH7>HqOI_*znl!r7Gzh~>2 z`G|$GxJuf*kIqD?@25LDIzLDJKtYi?@KCGPwkYU}#75nL?Qr>IDFLka?k;BSYzoCo z+wXXKySoMR#!modFgzt?0N|cR(k!8MmXJ)~!i__lJA3SeU+a?%5gKJp2Ov^79#LASmZ|gbn_j>lR#iio~r&U*B zeYy{tM`$UlEiP>paoV5yi`pXxR};~;4_N_dq}5`Ze^G=X5iOd^9Rw}bkJyH@f*5m! zk|M1xK}X&U?EYn>=2W`wWR_I+BOhRfzcGmrK2x}`sh#0aN7pV3{UwD73TBqrPQ8@h zZrd|oS~0?QCu;DTgZ;6+|358(+hMa7%w~c-#(JjNqp9xZhJ zE-RU!IRXB6RJXMZya|=OQWT|O|6L*HEM4GoBjXmldJ{rje_%PQp^+QUwokcmdsqbr zY918bxLXcEC`_3Zrf<3DjEw?{NK`yf&MY#tMs1#4M7*7CIQW*uK=Igyv#wSw^ zxE&&K8OAFudTzYL>g4T}pc~Bli8yyiW0!UzXIZW*R$Y48nRMqc;SyVu{`PW@l;o*H zYsgt{XP4Z&y|g7RZjP|5kbxZhGfV37KLUEMug>}6%X&4G3_|k{(p3k-MiUjU3#*cK z|H)ES^Y>N0qU@8Ss%4CK*ARioq_>KSi~qA*bpK|v`;eb-v5R~7>4P=vUv_1ve~NTC z=XXe8C>$`w1%fC5o9+QEP!J5|=o0~)>iH*U%hf~vJcff%LXM}!MGlW&>{5S37>)sC zLlEqL1AIo?z`(%TgId1&H`#0e67~V!0acc@RaG3QxD3YNiTz~g48VG-(GeW}SyBJ# z9VU@;;DCGj@frqUJU(1W(a;p4&|c25`?>0R##|K2n)Ves+jDL|*DIKB8FZ-YV)*!% zZLxniMN8;~{brYb%dLwTnVcyQGA)~%wPr+;#zi2abqTV?*BK^e&w61Y-|dlT4gctH4amiS^-*50Hve{^JF-GXyw}z>=__&eh-^} z&y%8vs2$q=y0=&sTphAIU2Z_s8TRh)=tOU4>`{(_uyB~Eu<+acx!J{kVDujN($9^H z?%tThp*L1lrmVDbd4TGnJ8l~&))sJlByA80Q18Mledaeo7O??7;0*w4dO}iy#Pzj@ zhsQvwz+gFKG5V2_(F{b@ue+)qOV@Ty_b~zQ-G6LtnFA`E8e2ncH6b>3J0N-%SK`#S zmSB&NUgiT%Rtfaz4Nz2~fb9E(f&%vr<#BRiqLfDbGR`5`iX*6a@<|>4s{*zMSN(tF z(lb#>tSY?qV~oA)D=TV{k=Pz*e-63~&$kF@rDD2DT%HWkMGrH5=!96^w120+ zd?`?5vXuV-4&12c$G2)uyuUB83)FjG0BMQ8WTxTBkhe6!Oc*_tBJ4J{B7feb`j<0T z(=RIi>p;5+oAr#Qeq(b+=#-LnIg}b}8d~FovogX0eGkE>WT)=1F|nlwMG^vC)hDzT zz#8`@#kY-gcMlkzW`zK%fEdUU0CiauKq>a|a0o<&&+u9@#4v_f-b71Mybz5P*ZdHa zUhLd|cz4vi`*uxtfAnCN_#fz7f64yuGm9xzuAeCU4GwiH+vihkT|h?!&0OHJv;i<@ zPp?8IfsY@5q(acc^p+XuZfX`(ss>L0o8uEO2z&fgT2n`+8p#mkFq>Q&Mcd0^woChy&E+$OANwAuOr zsG(@VE&5nt*|e|E5$G|@rZ;rxG_gBFjm*Gu(bjP2i_QQRkG+?7+~rSlIEh(`dOgtg ze;8O$qULS7LTBG+Im2J{pz-@rV|TLj-Cozj9BE(2n1{fLiHSQ2@zIU=KM1T3z>cY% z&X%KDV~PNL6kvSw3CgR$^1?RLSCdc#< z7k+6N>{X=rHuJA!g>r?;{;X<6&Osv1xaLe%Z=Q&SN`>G=TY|upeoPty$xn{VvtUD|8}-@ z4mW=Q{Z~oEv%I$E8I}<(PF~g1?v$;;xk@j4UpC2WdbsrYk*aeP8iT!KHQ_L?Dl2Q% zWlw6Xn))m3>qqksR6??bhNMsQAD~ksr=Y;Vz-R`644=&s8Y5C!BnatH9CY|l*lgRY zJcYpc8SG_)O`G2U4fh>HV^C59hn|BjGk|B6|MiI;+01MdF+?@5UH{XZ<7NZE*u_1{ zN=T4UQ1}5#{u6O`!Ru0om7rfI82Dq^c%F1qQ81idTs%P`tIjlNNNdY?Ctm-4na6(D z?gSMz^+%Wc(EJFvJzv?CTlnoCn~hCYCNn^#x7?IhS*(8_ikatpTVd0|;#D`4=Ny-W z0x&c2swB=Dm&a213m)Iqny#b2N3jAy*uzb7{v1(zxtTxnUpZVSBcbkw3<-01%ki`2 zk#*(Q@AySE-I2!m=&9T&O6Wkf4-yI%$f!g5R2>kfuE7|9o<@Z-_ioFX`}JP1aj&S7 z;$_SAN`$JunF9SZNxVQ_)PlSKjO343f*CO}FurAH zQvp@p?qrb@AniWkb3o+l0GgmD$A|+B>1fOha}F3&vR3pD9b|TmX)bBlr4r6V?FjG2 z!A{E@`ohvN@IRKu?asVX#YbR+q1B$)z@0etRy})*!K0uz!y7zJ9n>)oFf3T#QdDJ}fq;-^|hOOpe?wK>*oM}_Q>Q#`pAdB2rKG`}v%|_pkj^&4BF=ikB zewkH4d}U1}I_Rc!{qA7M&U`f z02knj1o2+ZRg;cl2Jl_r=|T5sU}QnDvmeA>iaEYhQ7_P2FuAh(mX|4Tfl>3M45jE) zuZV?(d8)*jMT@=As57SD*wi@cil2Bf;VaPo)Ly#(7pmj2w6bFv+Mg3e{GT{1eKBCm zOogt#;gyd+(l>NcLnm67^u=U~N!r@OV8}lRx=sE;yeTZ;`zs!3gOVLWR8SiYPftX) zQ0G&t-Biu4dhLcw2KAL`qT4Tp0udEd_EB`Z=QCTVV9!8u3tXK3066^<-4yhqnJve1 zW>1{U5*K9pXCtb)=wD0A&#Y$=uFc zlSO)gz2MvRY6ld^V_342p}*hgj-_;BSWb7#m#TY-NYUI={Jg` zl-mHN0@3495zIC26w&=pg2sLtQrG=|@;yS!qB02%dWVf?d!u2L%k;L}3!Tr1lHU z`jenG_ToSU#yFQbp#47rAoeF)D=^1U&T163m`mDlDWi3eSldXT!2{d4^s3Z^z!3&` zGQL26PtMI6EY ze+l`}Bck`L>(9w1E!mOrS3iSV)bc7zx0jG{j zCa$6n`ZVU$%WRZDc_<*)ETD!o}9qGG%of& zO)pP=`iBH*u5mRN55(lP_r~AFrhf|`mDB5(`I?(=)s)fnu$$Ag=~I&B>k~Y84hyV> zD5Gp?NBkG)#GB2xbsAix67KHq;cJBeiUmE?1i9?bXmBx0h>)DJ#Ho@I0> zWgB3DtG(nU4Uhb+HoN6a3CHbv;3?JE3%FU+Nq=m{auoc2NGS4iH>9|l^%-=iJ+g$kRo9#} z4e~>4%iaz7c=0y&4Ag$x!H-Lw{T@TxsN3Rs%CC0jf{=yWM+-}GX>`gh62Wc2M@oQ% z$#sa9a%;%cba?mTKflBadrpRLCWrR{HxK-(p~WYd!}Z7lbSyfwM7)mDl&}J6KxKu0 z!EY>k0FJEGc3xAn(%NFZi+qBTj4v6@3VJX;4_ch6^AHO>L2YEahb=XK1EhJrcDTtOA3{q8Q+10VRTWxCuT!{!>0 zMbPfroUOD9MzlhC-CeTt?=ZRgwpGT)fd%MwV&_bydB>ohEY+tpoTwp<`IVCOO_X@* z7MDr->VXlg(vrYfD8A8}Prz$91?wWRyko3Cnwm-|Tb9tGD)3Y!Pqjx& z9wBL(s69}tn7qj=R-8tip*EaVp~%r-bl;~u>A$ZNj7n@Ef&?1agI@nWk1Jh5KSLr5 zSj9BIyz`(-I%B<`VW&0Ad}@l7D@`N64-om|D#;c0R*02-xbXP7i%kNq^36L=$9g(5 zAN3oQ#g+$QQ=cCLI0}J8WN!uQc5B>T5#4-z@j8yC3~-Iq%UtN`H7jXO&V%0th#Vdk zm{HBJH5wLTL7&Sj4=VXX>{P$Zk2QB2%#LAvQI8j`?^PX8u6o@4qE7SxIJo9bcDqnTjNso0b@ z1qi==LPOE9TKEJ3vs5_=!A|-MGx2@B|N1OCPEGO9)GX7^!$m#&n)lAND(;0^db0G< zL$4?$%x-dk23dEwtsp0PDM~xKL9|bnj50RO<>O+iq|(i``xKgizuV{VJl6w?}WLLEt!_ycMsv!91O%!k<`a!9x*{!tsWY?PX3*!yq-1TK` zhw5#U1#Ofmzp8u$P&+A47TnPH~_rM*3($ zt%#tIfH0qu{(DuG4cj_<2QkKgk}~<&Gnvn83F?L_G}4HN4Nr(}I`!iJz$wnuNSr5{+}~3plg~##)rt%ssma_!R0>=OWO-X zZs*IlIXbOnuh4-elV+HjF7CdJip6o`xyw5N_mqu3{Z2Qp?qgHck9+0F z^=j3vYk+K};RY@3aq0Khy%8D99ZB+NxOQ{$jV{3pJDtvN1-msd3EF)fqqDKL@Bzcl zOGdEU@yahz5gtL36cB%;IBAW@Uu#4=6%o}VoJ~{2L%(2&SA-nj-~+LiK-PRVa#P%8F`>lJOuNDC`yk}Z}-v;%-FLe(O}>%rjZDi5=g zb%GEwKKDz5f30Cd`wt78+(9a0;N5uW0~AJZAwa#M%;JY28NMdgf1GgMTdYkvqor=Q zR)%P>`)6EN8=J|@hij-@TrP0w-^avk(4KC#kq)rt5abX4s6<=HppJLjGZY z4*xY**VN&Jv3vihSIRMk*P$EvQrF8%h>)N7ODFS25( zG?jC+ow3cWqDNgv;Rt@L;B6MH;8TBxd+$@0oPmil$3o>#)q7pvRn`ggJU z-$~V*^UM*~F~8qOGY;#IMccury_^Y@|i#!G8B7|YA6E)eUt0HU?2H7$;@5+d@$o z>0cNQ3?GIts^1PnM_(OqQdAj9qo2K$TL1XG6B^#9s3-Z}d)2l_fP|Rv7|Q3NJ9L>$ z>V8UUN_Cb9|2vUVq});T3@IN00-Y^h-ZhLuJ&t5755S-7H%sk*uDoum8{VLKMrBC9 zjVL84u55h$^1loPg}Zulez5y0y3kZuwg>XA;XL%PpD-T=KKBG?XQ|{F8-s1M<;6fm z6YR5z2<2j*3h}Jrhr)mDRrQrEXTbu`6asTIZEnP!(;25ws#`oN zZe*VkV~AUlT(<0IvP8|d%b)w)DDUmKJY+kt@ehY`_)~upe7+SuFL)adrkvJulj#_F z0iSk0gUIl%b;6fP@aVX1oS5>s4ZbjPgl7<5;@= z%^Z{=>h&^sg!!cd}6no3?y@~Gco0^R?_{FnEQ+~Eq8fBi_5GWA=L<=f1r$--{bLr>2-Ae9yj41vXn!1v|?%{;xdCP1%9*|)fRH$ z$D+=6S68_K+mq?v!cp^^dI$w1UOxz<-SC-tf$$rfD7pLe{T-kCTR1>^1&i$8 zfbUU#cM-xLa;oFgbYk_|trvy567;2g&4NOy&(N6+&(t+5Y#58e$uS{9GJ#9(>@Xry zZcF0ffD4BW;kP%;rU*y~9HjL^90?VNqOBVkQ>d(I(ch))s9q^rMo zHM5G}u$hsz&nQI5=juFBgVpwpaeEgHj=OKT-Q->WEqK(1D}5RtYVv<^82YXY>(ce# zf-F59F-dOR$IqrIF;MSx%^&0S@T6oZNf(CLGDnF4=6f$EIyEZp`@4amsodl0@S}74 z5&7N*f@cTgFOTe*?oS;l{g7jwC$kl{#n#=>K@;jf*m0gd6h*?GxFN* z4_U`>S4bo4`Zj;=8P6O0yKS}qi-tna>m0l3@qq^pblN~^0UeGNXifGgNE}3AiG>~A z17m_ZOOq0m#u?`wX3w!vUgj0#zmvrUwg;FJDjV)DIN4zjLTA4lgvIt(_qsFidVbkC zI-I6i)G=iJv8^kF!6xa(RY|FdG{9rSqYI(vkEA{SGHDp^DP}+zE^7nqtG-@i;x$R3 zEVIVf$bGSN$jxMcRJKizIgAVN7mmYUz--Lzw3FTy;3coQbajn7URdHdsIhQ*umzV{ zWI>xFrCmL^qpup(%+W*@oleC2nYQU3ol1;(T*Y2EjZ_t8(}sBRStyQ1$`hj}5v^Gi zlnyb`{qQRaF`X)Ze0H>`qJ^Bh--+t$uF1tb+ty?XHGduc)0@lO?1B4KSM7}DZcI9D z^pTK~wgh2F93Y4&OqHG5H74>T0;(6b)AJ||NuIO%5 zbh*&kU4;=kS%(}sJs&)rhX;>oJzwDCPkf66ezP2P2?i^BCbkfX#k-c1rb`fE7WtC~ zQ@o;Xyh}+l(<8&P*VPpXIwOiOk|S&-*L3^WcAQH#oGE@kuh&V+(e1~;4Qbd;^@T4a z@c26q+yCqEuf6%bSqR?+4Yugpx9wkzK>i_(+tfh_f!#(yg|3O6uD;Rm@N5b)Juz@? z_IbjLWuyL2Ofb6J$4fLlQ+nCm=Sf7RG{O}hp=+#Nps3JPOK8CMbmqau3A*3M&;C>f z1bfQbX1v;&^0AOZGu-hJfQ!32SKPV19V*hPjwUPPL!tT_$&ejmKwSe70lEipO{N-} z{{;~MUpF8QEOk_lm~Bp*Ec5QCX-wuw1Q+I8jg7s%z9liSqqZqCWMrFH;$AX=e@Gqa z&za3+g&j83- zN&hCA<4ufUwG0G0G)`&{?d6%f)hT*uOymC+12l|YoPN&h$m}2?NMsU_qhl|KOIDzb zDON_<`&m2UT!CWrChB8xd9s3DeQfP>?SYj;E`c_~0f5iLZal(JidqlL|+Muft0G)z{K=VW|98 zRQA@tNgz=eCjYtH?hUV){+p=x7}KU;2vD8b@Ui9K?VTs;`!Stb&ui5MPpy|8xD5tx z8u2kp%9ak_jhcA@m#4sB7B(_8urqqf*!b~+XwKpVbV=HX&}1;2nzYU{qh;F=bEXp0 zG&iTMdRO}W`Y~DT%iT3;@88KI)3LHa77(|2c^iVRO4eMdVB~@&M;9Zg1UqQ!V&-qo zOxSpu4`tg@w=QW360htehGf^yOpd+u&ZdlO&P{dedTNw9IbC;hw)M`K3NtBVZ~0Sa zoRMvznTR2r{g*b=IFIhl90U;wF@!zH|3+?q=LlfXE;|Q-*<+xjNg}dt{Bp(r=B3o# zH7UI;SlK7LJ7^mvuM4z}cB{&cF3+h~lT`u=6tb@d)ugzbsM~o}`xROV!!9;5pUvAx zDb3R&aQlY0N?7d4h#NpF*r619t3A#o#!z*cFVQ*Q{L3>(imu-1&vI%$_0mOoJUlV5 z1e}KI`oyU`m9d-+@es?v`$tUQfRKOP-P4j7fNJqcCqq|ChLV2+Vbe&?p8tmPn5nbI{aUf+ z(AmX-;$uEyvy2wzSld}sqS-m*@uoufgi+a2UgObQN|7rZ|D8l88N(gPzN# z7k~Lk4p4>EolgR6 zG6ZvV`;Ail2;&1^vb!YZXfGQqD~O&oqaoXlL@Z^|KYh_W_fe2cjHV2qld=XCqao_k z6Iijt;+Ek(u|T*LemtD{yLVS7s~q^JQ?06@eK#M${zuU)`pHgJFG%m0!vee20Fv-Fmv(sl`Pj;kH$->3wg3p*N5qXxXz)+RN>_+g!#d zlP0uj`_Ym6udt(BS9i~cWog#qY&i^KE6ELAh?g)}R;w{8L9&1{&0FX>AzOy`s{Ia! zt!Oe*$S@}kqq@SM58wWf>X%{HpCp<8zXJuk$1F_`DOLRUE%{YivxBkgi{65>G5z`P zlFuc?B~}HTa7x%uirxB#n^QQ%c)Rk1+*kD(BYiI;K(=QcEM?dmFJ&!|sBNmm1Vwu1 zqZ94i4E+veG-yoWd>euF31w?p#d(YdJH(UMd~LNkM-@%}KNdBXY5dfdfzxhbU2zgI zt+ZJJCko%VoYS5*SAOGkmyO<*)^Q>y#XR$0tbyw zn3`cnhP)4l4ke7fr9~uo>TXgG4yyKm9gVB$#V1rr(vTzheiMN725?6OD38B*hMVxY z^Zg6P${wm$u{u>pl&6ajr@?kl4wc#*{PhM`#&eGvb0uAZ6H7=_GGgeKh&2=eApjF^ z=KH^)tT9xZFUd=y=1EEG4>TqC(&*^x6+UYRAF%2%XIJ&m21}g^SuQH7t|6O!%2V}h z_5$^b@vnEDmQjoLVt+`&WQ$8;^@W&*v&_?7Iu!T7n9bFy@^r%|+ISPk(|Z%Yfg1?rDy@B%=k{K`oxV#k5JEh6sifir)_-6e-lV`cm}U{$^CX_P1=#b*6d7 zH~%@-62b2U6AkanhGB7KMFO4yJ=6JZ;gu1T2)(CFsD>?BuJS#}ToE@s!T4_|gBzh* zjdnvx7)eN-MrcU-Z1|^wz0eQu&LD9$3!W0GSJ67#=$t|MqxvV8ka=v3HSbU;h1TWo zQ9_ve#B&#~2X2B}!d;dib~&svrNAkT+U^;hYNc~~TG;3C@Ms%{$M#}#rE;xeER7;W zLn+`=ak56eF`3$b_X>K{G_mC#E}$9vDOJ84jyUON<$`0)OF8p4?=1^RW$V4s^gK^U z%nZ|m>8xf;Sc+sJuh@TC~<2LQH?X`I{W6>dGNP?BD~v z^ytPI{!A^$2IfVbZza@~ zty-NSC76PV!N0AnM4Q)@z$QhVA?y)HqN(4A zWvTCl3kcq;{w#C^t{BDZ)|h#+y86A-e-taeTGgp#n0Z0&W0x61I(kW&!lHhH5TYqp zz$7=@y09d-l^j(mH}|);pYO`%AKH{JsIpdc`oo6{zQ9UN;OmKu1I|7CJlLojJ+2*Y z8rjhQlZ&;{*T3Chja5X;T+z3aV>6>F+}sEccSEOv(K4ap(i;M1D;x|4JIahQ&9QH5 z?>JgcjGSul(h5IL`{iHHyl&$T32@5WvIGjh5mD$8uX88+M%db3Rzltu|1j8(A&{zk zdEOM>Wh1Tv7EE?TJp%fOIXYrs>!)v4Ef7-f9jGhlT#BHj(XWA=?inoyr?DDt&>in?I+oPf zXoXsyIo49Em*B(Ms|%{GR+SV|8#wHtvB}7diod6#RraCena5^lh!4Ec|JJQX8^5l* z^QZ1}52)SB9>aQ(cxTz8;Lve*k#t3>i$us$_GkSLCk}<}J~1EHty@2=Vb|D2e+x(? zoOC+v>We{Mm<2j%KaK?$)OvSwssR$n%PK?Rh?X^Dv(t66)Y7H8N5dgpX~ZJzH~*xH zpIV!jtk}VXhiV@DdtHZgsRrz}RE*&sYtJ84gkq5ID#`e2n^UN#1hO zPW-d4y!ISQkn|tZ!aqIfioM#oYlS35_TvpcjfX4Z+nS-Yjoy>FZKv z;##9)54ALL)8FN>RA_M~4r;G-=5V^SCvq2W?!4+-ByRPAFX0{g^L}-fgEBuW+u679 zxilfm4;7Cv`)f@~9w;nzd9XnYe=NhdqV;bvCs)GXKPQN+JrooyZar_AnVI2WX|8sF z_REm{Mhy;hdir}7uh-@|BqSKjr`Et^hfy}E&nP41ONIkN8=DhFM2|E;vEvSQI4)a% zEB|7s{R${2zI$=1IFWqFA$iuEC2afo-hT|m@`s1BodyXb6dvU-=|_5r-mUWm)P$IX zo1Tg^M*P?Q$(?eEot7RR$4$s`BV{T^w4$ol;sZS<)8S=zj%#$slR;|#rt`+nUY;1q zuhz~jrs0ggTO+AtHY`#dtulb~B1E#kJ7H^_CT`u?LiqVqUaLFY@zRE-W_$b+z^Y~a zE%RSkg-{_>4nzRQ4_NSc9G6sZS$D-g-a0dHS+yhI3T80znUCzE;f08fk@laEn?7;Z z!G^>0{~{x?%FG612*R=d8)X`<=U8V0Vw_K5V2CgJ^L_7kYP;p-b(8ija?IU`)Y#rk zCo}PnQEf83guT&g!7co(p|WFz#EQSV&@9FG(;$Cvi!sJ@VcX15hAZnBBOU^Ydpwvp z(yA4bb8*E35ipC(o;p~j`gTvG-MUEMRQX*}VNIc$#$mk&+bY1m6Shu{GMy{T26J9l zW2SZk=?B~g^XxTU+_GNUd0BUopGs4D-7;MdA^dvzC(=dqO;SZ9O~xT}q~1SK5|2_;mV)bpT`1_{kJR0ra|iZYzbU~w_lr#P3}~3T zP!!Hin2q=*!NLyhFWbcv~@Lx1Gfp+)i1os{jRQ{ySiYJl8ijK{Y@q zo67D55qlmzD{6Y+hC^yz%@RENXYr<*6_@u6^_qD)@EVIR_T??sb$(`C6lLd7x-;Rz z^;aC3mc-an+2JYS{kB&kciRXY`Lai-6R0<%sNU)rJj?gZnl91* zN5t!9lU=t=6ON#im=?@IQNhPQ77k{oXIIrY;${R%KoB7XK?M8`Hu;wcLmr`s z+z%kdG~AQZf7D{>*iU2I4FUi zG7xU*e|p=5{$$3>9+%Lly%mX_EH*y;x$5`#LhwgUY!oT4YEQ5_B6=iW;qE`HiHY>83^dshA$FFw~j_>cg6(qQdna~91=g2Q#XR3m5 z^BU#1i~L76_~#|GnLRS9z>)x`g_1B|!@nFVXRUSK^>5bK?}?(Wzurg${^0gNTZDxE zzE9xst`$p;w;4!5bV=@JNWmx~d>3*%>xb1eg+4@ zH0*{{Ks4E4^18P@rjCdE6KDSWFg7&uKDC`Z$sncq>2PGCR=i2dC_jbl_$rPB$hzRQ zZ+h{S{X1IOj#JH8Q{H*Xw;s;esi`J{?yPoQzBUP^7ylfX9UZT)vd2A{ysJT&wx5eK zhZ~9+dZVbw43v!0sr>VI?m%B_ZtOTmL#`uj+|$cgY}Uzs8mC!c+O}0v&wDL1$G2Xi zq3Bkf60Py8DcVqx~0@NH+0NDN}J-DUSivvbj2oO*uA8to=@GcT@= z)L~vX{$m{y_L@z9ELZpRJ5Oy0nesKfHum}2({nh_M9H```3~&@S1wmK^eJn&?K+?7-Uc{n6>k$Hx$rmmxyG5sG9fVItQ@t?1nY>=epRvz=H5 zo&0wS<3eEU`cNAMp=USBx~Rsu)td-wEhO>>7n}B*`2B7(1-lYsQpjzcN#y(&2)MGT zD=piRe<%aHN1lOzP4(g9SX3Vf;Gg9`dY}6^d@a0FilD$u{F3?Hx+Lgwz!5Dd{gc%M z9o)i`OF}Q;5aV&!zI*bC1edhI$y&GSxmBJx+5(L~G`2+ljBsd)?<;por@%e2Lo5pS ztP3jo`fXrF?yeTR(L=J;&B7lVizCWErr^Bj1c>;BE}HhNl%0&#VQ*SneRFmxvoL6A zhCxqTstIJ^KA|pSTp3^&5g(fBFrGaCWy*8VOvv?+w9r6RhUP|&U zOK>H$hp69k^x|6oY-wat3y}?WNXVT)QoyF{vw}UQX=&L9#OdFNs~c zE3NE)(5CmlioxyveyL^zrplPvEDY=U>FjvtPhIVBy8w-q6K@)metm=xN6l`!551A^oPSzqryY$ zos{J2ZkJL%_j|p)F9PY697d!BBSi*quU)bt<8-OAEGWaHM0L<{8}X;U?ITmymn?q> z#OK1p_B%vxD`n^YHh5F9+Ncj%1z4O)<9ViXHQ)B{*hmvhpsSgQ(Tb@!eDti2B*-*; zaxnv)Zt`j18Kt+K4PB_PvID>8L@YhZeM`Y_UoLMwSoVV%L5t9=y^B2Jq`SX+!?O>0 zK$LjXv(I6mGieDFfOsM6xCl98mUp{I3LJl!oC#UeWqmt98S{gH%}Ql@dmd9FGc9PL zNw3z}(*NC?H}Zy8}_0Rj3?KjT^XbL=c1m7E}RmArr4 zjC#GseP@p)Tr@MhIEuZo#Cn7xy-RRI>kefMLiLiBcUQu+P9n5kB28FcDeCPN40?UC zsd`^;39iB3lxX51?f{P#(%|dWcTg9+Okz%3sQ!M$3viX3UM!4 zEEZ&xZ>EJb9cb0}jgF~$(y{BGyO*aUQ&$L%$sX}Aq4YSutlm|fjBQHU>EGj{x?cRI z{wnB;qbdVg?0tXS1c~%MPH2ZL{ar(#Hx*6Aly}BdpqFa6rKHFaUOr~I5<=*Gh~@2( zex3cq0gt5ng)`8BCdVS3(@m?NX_F3ZtAA}$Kt6=@T(e78P17+ypC1y(w;yr{yLv0Q zwv0aOVN*aLOaj|ekzje@c^0)d7zZXZsT9QH<9mKOcYaWR z2zJX)2u8K@JVF*CCiaueq`AjUNz%(7- z`q@LTJc{CpL?XA+`SKV=claEIK(qL-%Gl6|nPqQ>S>~>{M9oK<3VSv;6JThD{T~t< zL6qg{hUpGfRBUX3ZV@A!fd}K=3{CW9BqTx3-k0S|g~xL0p*Vv2bt7eOI&?6V$R{r{ zU$hPz1YKHCe(ZV;Cp9K$7{`}25QVWL8%yWC*>QU*n;av4@XPY`1^6Te(mI^uOSg#u zxaZkXt3dX2;;)|A``KY}%hLt+A~di6DsDrcfAVNIO*vd7dXbxq_BJ_%#(B0E>M&dAdcxHE=YuB#8&!i8ogj{F;( z4pd>FwT-taOGI(5=riQe;CYYy%0_bj*QXuUR~fdx9kAOMX^MF!r81#*?lb?>@cX9|2hF>Gwz?2J780(?fm|!7CT(3j`G(A!s;JVWJ|GX+ql19 zyO$cadG&?xcF%Pwfd~;vy?u6j%xH>kKKQS=cH;FgB^Vn^-*yP@@j=cpZDUqXnRuwwM^_SrgrA5=4m?s0<(;qe2F3BXA@*q9O+8-Kd_G_aDs)O|dm_6kC%wXZ)E# z@*fN{)>)*}ZUaJcy>RoO(cL;t}^~t6Ka)_y@N|3%s%&r<5K4%QY?=ERo z*eIb~?!Vs4JA7$5H5C*t@fQ>L;GUmQ07W6`MVCMlm-i2FdkydQ#BeF*DvzO9f7S<$ zW{AVmQgXxp`?7LBE044?4)Cg1_Wl{9xcvA0P=vR!u@UZxpP`(XPdD07Cz^p#SlXcF zZ4=#dzfF!wXfS^8G3DBmWP3V`N)9X{NwnUaOk`ZH%6F$SYoH)b;YD`!?Tc}_aW!<6 zQZ}a4&*UOsa^z4Dj!MvwgBIu%XDu#Nls`KdQ%w%XKkM)lG?}mKA)cf^ZU5+xLEyTx zf<=5~OP;41c;iY9eDxY9Uia>-&HoQwZyi)s`-Tf|xlxFsed0$+KGYPC9vhkvauq0Y^y@sw+wCclpM+Ys^E0t@ zYO3vsvLcnA;ji;_Ph>{U2QOU_V37|Ie_9x+L5q5dvF&BRw8r|&kT;XEfSJ0Vpgvf( z?0aKtu?M=I?(P(A|m>?Rt7gMsR>D)gucW zq{pzb7N-pDOoGsXt!GEp=Z{lGgQ@oF@`Y16;FkByJzLQ@GYXi@O3uv3o>>ucE7 z@#zLR-1RMd(_MIhcjGz<3Omu=tLY1;jg8vq?PX3_6E|LyXUU2%(hmwuVJ?vz6BS<5 z?&0Tx2kN*rRfkNH)o}?*#cDQO|Kk$8YR#Ne8%&;4V}(d9{sMiPu9$Tm(?e9bhxw8#!WCfds8tpk#v?flh{Vh{HEA>wKMn;@Pz?CwY@of1G|9fr|#Z- zs8ps{@Oh}p*Nya0Kwj8!Yr;b~mlzumA1jhxEmG(pG&(NSek&Hi z^Yt6~znEGv<-z&+dGPe{45m+PDg&k`XOCj2-PJ)F04*Vll{z#9VWLDPS)O0mMVp*(wml5l-vmgyXpaYKUK=tHO zzSt}G`r~!$oQARpMk*OVSmL(W#U+x>WYY9s|GP7&OxfuUN=OPjZTpBxQ1%lhW4rno z-2J(Wy0>L?{9s@VO>dLl&9f7U|2AOoKq7TC6*-qpZ_MPl^MVr94>y+-4uvsvt@QYO zjhj<|n*?mM(bf9;eE;1>7&EmIfAM;vqs7mbvHiKRv2YxT^scY+p+-X7;cW39`^e(dNb0P%P^s9pf^ zyaV`G^nq_ZOAjD5(66;I2tNR{`Yjh>7kb{7X8g89EP%Kx{KIA1P8H?a7pbKbe?zsq zK$k*aCJ_v|;sEsKXpEdNi9)z5YG1w~$>{5|?Lx;DiG6aOT-PugDf)uXh@K6VJKs)p002uN|4)#uiID>go_lKqi<9CSb+tB>)a#?b+5c!pjbw<{)XW)*wA(|`2WXNf^I$T&w3(ejO zQicel#g5Y1{3Z3arbN*t0q+m*AFVWeHc4TiQtc_t6yYlDj=|Lotowcj5s~av8!q|Y z5&9Z6c8g9m7~yQ*+T+?$eW7o;t9||Wi7H&NKonj56XJofpZgTix9MB?K3mH9OO1*z z9G;t(h~NQc>1fja;Ls?jm~Xw9m({Eems&F?R!T?!#Negw`LR@0solL^P?1TI{Kez? z;s)gWmsXQR3_eZJpm!Xgb)+7clKyHxuYCQ3b*=2p$-SvYajfEV?j^p_*o%MGk_uRE zEV6*r;FYnrpYq_q7;@hnjnA}gJTQ5DS-D^AaSeC7*o~K{lPGSRmWJOAKS@L77g-t%%-y*&H(`B%c5v(n(7!~JVlW3*w=Lc^jcztdn<^W9k9%!8a1(-<5GV3or@oB=-QprXiM)2?8zp(^1~#) z7bwh_{nWvG>_bt_-5%RMDbhuHS?vMe+Fy57SGL#e{Uz%LzwO^;pXd~8Isd?tcgVE~ zJBjQRix~v;$a4N8&+v%-#M@5MBRaPbnLnt0WPm(85lr`^H`{Jk5PB=^^^Q&4a6dMR+A1E!&H_OTHK-)!s7axI<=b0pP__T z8L%BZUs`r65hARAgDa-Bai8M4dO$&JTA3*E1=v#!n$QY}9qLbjWB7PhYM!aMdW1|8tNz#eD7GCi?A7 z-Sf23>f%fq?@#3`o(~(lBa=md>kapml{E_G<^m;A>9(GUBz0?(xQL=UqpWFzE4d_h8jqa%*p0P`O&k7TKNY=C2TzNbUnQaT6`J)pn*D$BdmJC2dMbM1<$yhTdg=cA!5_kr^I2@XJ;gO88twsAdb@vb5p{3#EOlBn zv78KupCP;F7B?>OTH=uMGd{*xz-leuG7KH}M&jhnrKf|u!L0^7s5e@1Ra4u{iGdBu(Ws)_()940`JyvAqr2Ebkx4)k zFEI7&2T{^+`KE2+(jk}?PBOJ4zbxe4kK-hbm7CE0^v=j`p@YB6sP?mS;0lYw)zU{Q zd_;sUFXXWIXH-7+*5K_gh!){5eG>s1nH<0|)^@&V?27{wvtNtSlAB6^L;JW9u?A^q zTweZl^}akYJkbXR36B#j?31T%RPkCitc@aO{ccFZlEA8WYNjvnLyO^f<43GrBt`(q zrAM6vr@!^BoZDV@v7xZM_p(0~Vv&0-rdgJo`fy$fX)H#> zX;Wn9AeF{jk+;EYiFq&((^(kI?pH)1~t3n%vtqY(Sgr`Zj6c($Jnk5 z#bQ(DPgTh2ecvWtLL*?JMH|-S=+rG$@BPNGZ=2H%1KJ;je`1xt(*D>z!hOFeotW?O zVw**h$HRYZR_GyL{8=9hEQg3~&~*|=Nu#~-Nr#Fl`+iOn^Nwwi=_jar*5$^8PK10` zkRctTNfA3@Di+0!9X9*Zkk~XUxCsZyvc;5!!JU2M(bv998Op7l1sICo|NdFq(5{K$ z(<>!Z2|YiL$AN>jUhlA+ECaLAp4r*ih_M;|raJre*|^xaH5Qgs^LfMWJ zgPKnJdtQe%GdeH+`(LdkfZv;FFxV@(uHYVOC*uP7C1rZ8?QMPtRshyZ-Pb=YfnF16 z2mz2ewi!Wcb<(|eJaCcsdChwAOIK8Cjo;paH!On>+xw)GGsN`+8Zw&_lj@&D?~0kI z?2mKafPQ;ivoQyO;>tzp<8^aD?t4bf->LP!65z*>1=#JEn#$aO7u;JejWau-*4H1|S^WGld@=72XRkvHB}0W<^u1~BU9xux>cwHBkNwPh!L#5gkP-VX zhWhnEuFl?aWl~f%OV8duB`=U>Ce&VBFg8oOQDhdSCWqUh1P0f&UD~&Ji>&(00hDrV z_Zb)TdbhgToc_gWHQkQ-VH5r_WBuv;^*7Vc#l`rtuHON zg-`UqW(l;Zk;%H7v4K-9*BE)UcY{EhP#j~#f{ZAIZmgSU^;bDQd6%o#XchphAkd2z zTNa)0hl{NH1lTu#Oas`rq*PSe_vuAX_N8~LP7eb86m!4n^`WxJyVy6x>q7Yfb&bU< znG%qALqje~bFAnw>izvan1KAV)#vIazUNG*!s9}s**@}{54YsQ>^enKt;}&Bukb{s z?|X-91{53Izs)$+WKycmef;*S%>Qxzy(4DQ0WLXo0&%KSbZ+>MYouKqaJY~gy7fQ1 ziB0YEthE_j`(;s3S^>Q_Bm7)n&1z!U6r1x4={gpOOfIY45FaZxF7EK}$D=cS2^U_q zmi=;C@F#aAq1(8BVzY`eoO}~NC71rNM6Pw_;vPxoUR00#=s!Pi-^SJEd(`uw&qX%> zX1sB-nfD_Ba$tdBo5eJh_sP0uMv)GFY5eCIWaO4F(oOf!>w$=atfi{c#Y?72@#g+dz^g70NBsRh=RXoJ+nAJZE1+?d zdjia%POxLMudgrNtUWn`=$@qUqA8(l(|mpYREdMxti$9`2N&bDq^$PK({~8l-!Pts zkdOXZ}W4GZ2FEv?yBnEsn zdb)4z+T3%z+;mUQx&puXBWu19BDKp^9nE8BX(*d0xUH>LglQh5$9+oG+mz%O?86_s z7av{Zq&KBIU>S;X`apZYa>T9Jn#R06qQkx##EaSusi0X(7fN_WAoq(u|GO(Eg4W(p zZDUDfI9?p&?)BCD)3WMK(Ok?YsesoAFLLM^2EJYwSw^&?u4HKuKU2cP^rV~gprf_2(r$sWdp)r!7XVm|Hh=kES zC>ZSa7P@uVEveI8U?`F88K-~AnrK(mSQnp>VUr=|s@W=mjEJ0Vo?!HqP24T9nTM&Z ztY3Ak?wYn%`S{HPf05O+4YQQ4Cv)ZaxLL*LtPdNj8(3}aW|v6F(q#hLWvYcge%~ln zYaP?+w&ux+suS2xYmn=pCGJSF%-WPEQ+4l|V?zUarar;v`$>m>#as2PU|9g3YCL=JpKvl3E8u-8O3tp)~MR8A^3qokN3(_r; zbtpg48=gYlh!oXEZCTT4V0$YmW9Mt03QsQnMb4cBbMjn=)>g^%jw!gW5P;RAG}-Mo zOef*Vk~kx~LY|-5#dB7;G;TKwr8cnnB7(IstX|Rmwg0TIrDR~6Sjz-A%TSd8tq;t9 z4GmrT=+E&3W0q+r((r3(MBqDIS<4LrjA+tR;Y%+srYUx`V2oIeA(ginPEI9AN@8MS zE9)1SLuvjf-BFoUI66u695dhv~@F1mL(?~8FO}H)6l96z8 zY!AJEj%}_IMSa+W4#lkt{`Semj=PKPY}n{q@dDKfzxVQIdfG24@zF6L+qo2U6*|)o zsaGk@j?}(l&az8WbludZ*n3e%T?iYtn^MZM4<^{kxNHvg->!Mkg7`nQBA!I!NG8tC z+Tp`wvL!UiZ5h!gBIeO095e!LyZ=H;UtbjE)fDiIQmMGgr3q%neJq;rnz5T+kvw^G z_}x{vsB)eX!lKo)Lx3SdUS4J+teD*XKKhnotDWsR*DV< zGw{HNs3OC7V=(*TsF&)t)ul)Cj%?KO=9~T# z_oIr?ft%ms-jebn@^qDyC9fLWBKGMNrP6C{9d{-^z}h*;1y*Aqk$?HWlY{jUgAG}G zq?%hbZw;Q_w&nYbYmnitR-jZB7li{SMeuL^}VmP&E!47g#6i?%F&OtVc7bq^K3cM8!d~0_ySY-gaZ>SN`{H@yHyknoB7bpdYN-)P+ErrxvlmbL?K?Hu z9gMjqma(oV7``w<*#RFXN(pPe;MJ7LQFUgU$;fRJQz>ap4lG*TXnMl@*7oQ4zh8Tn z%fjGM@ATvuczLyL(I%zh>5g5)%nLD4+(~muBnh}&m&m*ucLY4SJ&i(O3lVh^yPrw#zsoM0%2l)A26lbSg`}x+}ICg;zU9rlq~49 zm11FX`z%ES3LjcwUl;uI*K&Qj-s6(*9}4(2`Ks7Fa*Bj=lgp*q8AgY7u}BhPzUN;R zv+^bb0m44y#`G(0hL6fTV&gxMV7K@B>Lqf^d4&sNOh3v0ZWPAzlSLU`iB`SY&|vuD zNx5_|Pt;{WUdR$-p2GbeCOTEh^yS)v1GWNrshgP%Tr0iHbr7bJhX0h55W05@|A&a6 zV|qZ^R!$J&bamN_e~4GxB1bh4yG4J)kApa4fU4wZPGz1x#k`e=KN4ETrT+ zgLjSl_u46DE`iT(_mj&lj}LE>5t?A)g1HC(&+QMFi_&!~n)Kdi`> zC0)UKM(mrJfSqO!A* zc%Aib>XOMzi?#)h~Cm3H{@0&z%uNI*K<|hhr*g_R!ZDO8Pta zmZ6APMZ1p-{C#(RLyjjeKP4M&R?Rmp2TQJJNe6CL{P5PldpRCAs>%C2eJ;p%n8`LT zgX4#?{mXIs=Va0}d54;nV>|U#Y@afEC0Dgi{%9iLRXh|v+bgPk#UdvuGkSv+gP6rU z37T6ZjqA|9d3Q5$pZQP9x}xI23c<{@;J)d9y|dNv+dc^CvKDe# zwTI6(8Q+lf-fz{Ask|RYJg^gihh3f@By2rdRDGkfgGWm7l*3eU$RQGDe@kfXH$5

z7-UKOV!kFrI4LNObamx^ysGiDzxtlv&VCw>8oH9ue5TTSuRr+qw$=05l<$K_14Yxm z*IAzrcy@P2hK1i_%t=BTa5V~h|M1ColJYe@qgZ*ffSu42@V5;z4xr|@ORjUZoh2E_ zQ<2e!RdhCxKUKgJU;0Skd;!d7CUH(X9{wFfOyNp$63?k!v~xyXUy@;wco@-o*Bubh z!FC@a`^H}x=CdjDbs{L2o_v^X(9v&1CdN}D)OYT>Z4z<&*F9VH4h~jgVKLU~HO!b%%dJHTJs5Kq}AO&1gS@Z z*DaUp#)@Vu!@aIYJ?+D&hrEUPQRXS>=08igF72|Qv&O&w;7la!)jfnn+Fr=gpRX?T zxO+4xc9DR^w(FJ}`kdUSp0e_hHl?WRIQXQ(cM=`uw`A6yEWs-EUcbJ-FonFq`<~bL z_75e+d|l4q`Fzt-_^NtKfT!xh#q}^J=K59`o>*Ny?W%XgB0gAc?Yf}k(zSbmck8b; znPJe2qOp~AM!A(#uYW5jt(wL^e|7}vh&bkIw=TYGtZsie3k!~k#qScNFWE(=Wf&z+ z=5}^u6+$5X?L5jkBleEK+gf5Dy_@n=KzfugHE_V2U2_UpF$r7!xo{W7Qa z5X<*Y(RBOoN;jI_!^OoC%5lM#j09*pzODOGcwh9Z@MmSGPBFkHBEG#$g^)gpFh~O_ zJ=C$8PENk%(%28%$J;~9=ID?~UwfYN;a&z~DC_+*Z4btePx2?{3(DT{85)+G(#(Ab zPYh&wX*v@oV@pQ4e-n>5FT^an=3)?c-4d@nsZ27HP1U+!q1sUSugzT)lD}$>vV=aL zgd4ufFvQB(8qwW&4{^LB=_oQrJ8({hl%iKkxZ9O)RTBpMi|mQ(yoJy)N-pl6Nryb; z{Ap+w=0$<0s239Fc%Il)NF{10T1djI6BZE7@AD3+ z%hr5?{qQPCaito%z%bo4ZAaaD($?z8%R_>lt0zwDVp6wl*DXd0!-W z8={C8sgCnqy0kl_d{0P~UkV9^m=l=4jJ$U8bGr+VKxqEwXSxlu|3V(M(z4Y`>;88n zi-RMUTEaw@J|^;c#AVyNFYSqlyODl!b-X=ZRhx%{3@EFI|4Q_4XHU63KDr8v0gwX` zf!YIk$rn!NRsd8T>+ezHWqhCYa83q;x5Mk}e78LjTQV2OA%EwT^!FcT!NPxGn zmO-4w@p_%*en=F5K}#L6%@y*?RX`LslLJVLU3i3y(0PW_Q3Ro*8_z^*=HJkS9>Z8NxkK ziUh zj*hwV0W1h$O8dHNghH+DZAljj1ys#|9QZV-nGcpXF; z_yf~pbKt|QprRrJ><2y1c7j3N1_k(Y9e0iie=qbXK~WJ7229fML3|4stLsOr)dCLZ zr%bRHEh)(EyMgN9=Cmrr2Q9Xh^ucgsd%zCZ>H*hn6wvw)1U8;}BKcWqP#WMBAD<k|*xkVI z-cHzW@3~-|XD?fv%Eq`#SbPvp(tUs%@dK;9HPtH=YZHhdnnsHGmih`(mF1uRKBK>{ zA|sL}(P8;))}YY5?&$Q5=*7VD@oB4qg#94&mE150sr_~3s1I%)zt%ot!reY1zO51z z|6&OxO=3){DgkxeO=gFHIvBx#jE#*oe@vq*H0blwiJi{wf}-pf#a67NzZ1iPhSOyB zMwx8g?rNIKBx7}PB?U85VyqjG>QlZ1hDiGU*bCd&+}qmd`S8)Q@LbO`SA0W+R+9Zk ziHl&16~w5F*%4`bRC z3-{^GLtERy-_%#A%a=}aW4 zFEI7iA0l=R3OR??7RPK-il#CmY)95#uXB8h41d*1V^xd{&QM1_k1I=v2@>m-`JR`T z^KRtJ6GXVOmUFUPwM=wsYHH(p2RP7W>hMQE;j~*21eRruhQ54cSis0Q!g0O#ap0rk z-~jfiyrd+Y@6`rz6e$lfImB<%e!4x*1tgt@b0RnGa3;Omzc{#8#|$vaJ^SE1<`*dq z%{xwVaNA`&Ex{&o>x_P2FG6E)t;2{aAwG(=D(;;@()()5m;*Buzl6!=+*FZ;e8m-^ z`iF^{;IVWiUWTBTiHY6pgqYmn+qZAm0RJ8ic6oVecDT~P%;LlfX_nHjPyWq{pU(2R z;#*iM4b&315#|9q*>oR^C}0QXjjEdvVpUT8%YR-o7g~U0i4{hNqWs~U^brV; zz^$IsiGU@iz+N*sL*Ny~^=Hg1`3t9{lxvA}Xn>0X4^zn$u37jC2M3qJ`vDL3)0OD+T4~cicvx6= zSamfo`BV%RD6#VXCa&InoBm)vx_h`EiL;s`PaUK-kazB?5)j5Xz1MkhqxNf_%5EHS zc^O(J6i5E+Bhte;IOJ%Z`PAb|#qc9xQfc@v&3LDtt#11*TXXJo$3nq&BLPuN|NDPj znqI&at2ZbEJH?{(Jl3aH{Jfk?A|pd;_Dn z@wtfSvIH4fkJgVzxG^S3mRWIpMFpzKgtDJ8F`)q?beJE|9qGMy_R*cXKHDX6(o=Ukuw7pXIcZ9+^*DO(&+7>I!@5(lU9++^Hw~F^&{p*QT#D^4D=Klh|vn0t@Fl zmf~*Cs6++Gp2TIUIfj_qX)?*e?Ko1&>4%pWiH^2rQ--)3?%PPdJkQ-ELbuPapy-po z9A*bgEik|Q{O6}zW72PAW26nO(3hYSJ`bOrCTf~Ss#t;=VlX0i)d$KQri{u7VmSp! z#5nDac~8kiBM_Rv71;$mjbN7u+fazNdY<==YFL0VC{D2XclCy}%%ZQC@3=PXI{I)Ss_Z3Hriek9$OSIpoS}Uj z)n0lMZfQ@7Cn8L?!8$??A?VtD=0{24e>)QI2hA(Lmfeo&&Q2+PRw7xa4I%;pvq6&G zs4@`WGC7kqS3iPJxz4SSOOuvqASCk3rt*VUCckX9MC322N_-Wm)U%UqA(E=sb z=3k%;k#0>UouYoDO=`2a)#;W}RS(lGPjT3$DJS%LoH%?RT}DL7QK0HY)4R0rX{*A_V8PBpW*ubS$ZqJ>xLXkJ~%V#T>jA%~CK1`R~&m36Evq6yM_GsWQ-|MSgnEznV51WKG1Q z$;v>mbhuJD{eGonvO~EjF|c*Xkgd5|cwuuRGKA{O`_+vuN;%?h<}ZJp=KO|(Ox(V< zYcImeoSjZB75(a52i`s&;K35!b#*e8(N$tpCkJ30Fn&pZHhg_w&*by8Y05EC_U<@~ zI*-6esjBr+R}>Q`ArTDb;wdI4_Q2pN!5O2)4c3`@CXwOXZ+?P5e^Q|a1V#5H*vw(s zQ5qBpN6A@d-Fi|jMU?A9O2}=K)ggY*BS^$NdHL;^qCk|)TGfbnQJjRlKSDF+y3}pv zr+`phTX~bCpUa+b;`-dpjATe6ZtSGJ{7ghV=NoUo&P zDd4`>znS7sB)rrqvdbCP3%zAn_w}{Zz^X~!5A#oD&E8`}$A_DQP|{%aq0=htAP#@3 zGYz?gN|jtd+dy?+OIWPyIeRI?$!zGjBO4Pg>2bf7N5c2@E>f*Sz zym2h$bjg_4X3S9b4VcDJbNVuIqD`G~`P-O**p$L6hpSy1wRzvaKYjZ2k;@D71HpTG zJG6|9D1Lk-#!$joQ}*-Mz&}vG;x!T=g9g>t3nnY>_Q08hIO5j6Qt0gF6VcE&e%AWN z{V5U7uD^pRM`9vZjXx?{9{TZ-+ef1Fjg1v=4Y(@^!NoI(JnC?A=jBuM=TBq_!f1|V z;gI6>?Sw5`-;3q_W_?>cqSA9csExRDTHnP1dvyozm=4w1D@LI7GUaRSG&=ZHw>5o= zGvelcIy&9JvY{)dsl;5$VC0-yV>5@?tEn|-j0^Vq#G+y*)M`UEAc`-or0H9kiR;oA zUM91g*mk*Gsp7B}T?;NqTi8?a7CIxPxbX^YF0qYmbzj>z;B-G4_y`~kH~G&ns}Ql! z?2t4zhzMhFv;Q5wyquvtYF}TUUYhBJ=V2Wv1_VmhJssS@UPr=d2GcDw+1K=;1@KK2 z#^L-di~CyMqLW%Jf7~;D(P9*M<9feOBJnJ!f+@*?ZO$dMt}Ns~tS8>%zTu_mT=7>3 z#C64@3gaZf{U$&&Xy#WDf!G+2%&8ntaj6Q`q&VhsQj0iO9hyphu}NmqFX!UHBIg-j zDH{J4X%D+b3Hq8?7rS;d^jzKVonkvj(`buk-FM5xD1B@Uhata#j+}>I1N*OEN$7MW zEkp92p2D!NdIw8kVPP);A_9&D0uKjBVLjXKR32*|c&%rZ-sqGL)vz30US5U95tu^U z*YCIR6S&-Oc}CyYaLyK(VLuiR^{CmX{weE=arqxqfH#k{jxQB1E*!{;A|N7K1Lwu} zvz6Wtt{_kc<_CfkmOqF5?pXA_yEX^abp(NxGo7)uJxMQ?f!iM57sDoHcB`=l6B1vF zIfPcZdygh~JU@%82L0qO5n>IHK_U@(I48;2uRdx@pF7;$4UDd6TC0L!dKd{G8HuYW z;#RS*k;j#p!b=h`g7$`oI)@inDTYQ8Uu~kp*INFtP}UW`Mnqf>KUBEfy}Wg{yjA|e z@RQxC3}$cju>fZO)f(9y=QH|Yt!p6cpc2Fk%f)DFgs+!-AHh?IyTc&Z%RD=}FFqa- z{Sn6L&V3I(#mNj$MQE{LE8sB3xO_#i?|H6eK>B!76!c|wskMlrbw=>+VOkzi3IT-kH?pmhZbtqPl2Zg8(qfR4Fp0Lcvwj2^Wx%WPG~-1$_S z`IPY1!CD^(&d)5HUbpT|-P#sX^XbX5(~sqUT&{=vDU1+2Hn10TOTh(mbOCKN8r{?eq%)sVBQOZHH72D3bxp*#R`x?VSfJ*Wg9&F91*8Juwkq(y3K%wuvuOC0aX6iw_-@B9I^Ypisku ze__5nwS3rC_a(UCr)L>9c3EVycxLQY@6Z4RO22PPcuphOOI2!ed`%I3v~3ex%Guw^ zH|s^pKrSNl{1f;7zo+r&4>~?JvMstaX+scA$IBvz^oiYPHLtFd&6~`z962Z&ME(|r z4GRb89fc+wt9%h91#E%twVuea?~3@%$NelI^H@sivB$Imm!iUeNx}&O2K3AqZ6LC? z9fVVdEVX!90JZTOaTdyj{mCL?e%m?3z{p&Vbd}t$pA}7^w~uzP|jHSx4<%E$sA3^8>uA=fXO5l-F%w z94+chN@mEL?O&XVc$G+SS#*pmu^E`nc(X7`_REhGvqu-t6SF%Ok(L2LA26ejFNorzoavQHqu z8X9kFo#2QDy_J0rOWc$(nt83o13Cp&QNf(hqLM^J+%eJ-pk-yngl>;eJTLx5gGdMS zx#|yZ#ABnQdqB<#%dM&86EpsFmcbJ*kUh$3REr5K_qq9ad8z#c(mY&-9!faHf~wlp z7ctj^1|=MjwFHt8-B9?8LP!2>!&)M>As6}IY;TK|)H_B}KX5KSsfp(IG&6hg{Q459 z(`eUSaplfMI0_YA-fbD??YNJQO{|abKJ?i62s(OqsBy=DI@Yvlr1`FAK0j0~$N#C~ zPv-0)Q3pazr62E6A=eIyW<{og`gj*Cznb>6ywK482&?4!jUT^xqR0xe@%mN{I}r36 zET6#uFKVrXH0wo|EGMCSJYDgfIlr#YNkKDPtrm{x*}dN3@sW`QPMrXzzq%!;NJ2Ko}SD+Wih+c2Q+&Pduyq0qvjcd1(4aEc?PZ{fdqT3jk6cj{bnu?45B8MP# z`>O2u3e+kszp-&%Vy~m2xSx=b5mjRJ01rb%L0K;q_n9F4&K*y;+9F_*I86<0B6agx z6=%~-%f!>MrruHUvtI#h_O5l+{m&%M*L6}BaQaXo42%Esnbv*(su|?{1cSDZ1&HeS z^N`AE4$q*H@^bYZ$p3M>Zf7I>7t9%j?`Q!zxJ}qbb2-02(T`wdE_`i=jMi>%uze}M z!R!R&bEB~OaS2pf>7A!af4$)}u>4x@CExFC2Q%pn(`uDJ9WxF}ULOJ1sroWGuo=7S z?AFxN$w}~U-l(PAYfmEBSKrJh$rQk9tAqB~>2GZbC;)GYjTkR(~XJf9@Es!iw`gmD*d#j_ozw+ z_V`>X{;rsNG&ncSYlF|v8p-mji~n`E1L#kwOZtk7`(Chl+$9Pu`zO~4G<+hoAdj4l zYOogzZG#?Um-yGV^&Br!6e$+o0N6wUH9H|@MaKvx9e~jM*fEcU6h3CBCh5h$X4L&u z+(d)OV@p?JmK)90Xjiu~YS4UiBk6IIKNZcnURwK!l_tpQ5+ChjK&oy{rEmn>z zRSM}MN!cs$Py%rM1w=e1tuDyaasqNbIqwWS5TQR-lYguZ`>90YQ-8PQq~Dq-yRo_& zvfX)=b(=;%MV8Bl`&_uUR+fn9Zb2iZXqzxcpNx>VsmPeTz+UyX0(AzuB!RUrPZJN3 z3&4N%m-#Kfh!VVSSGfq*@Rb$GT^}w62IR~p7dT!r%~eHcql_MXelcjM&iNhf_6{Me z-PU$~y%i-yvJlC3!U+3pHIX|)@oDSc_n5FoQV}h4xJ{MRAljyf_&PB_-p(Ew4K=F_ z`;PFQVJ|eezU;046vww}G#JrqS-1X*94gZtAt@h4Ngqab=F6MB>ujm>T(c~-9EY|b z@5raM%9)nX_h);oXLIUQqSE5-6F1+S?yD(cob?eIDLZ!AnXoe`ACH`YRpkLI|3dHP zj()ykT$hog5?54eC)E0d5%D6EDmGLuJJ0^B)}NUcQm_($dy80RA{1&_g+>~HRAwp^ zTec$*+!kXmX{B-PKbxt^Yuuc~&e11Rzl6H9o(By-ZTtEJHd>*|TFq|k&Qm(LwTNpc z7QiiZ#N2wb=In7dgd_fO`(SA)B?A3u`w!Zs{evCMPq(OR-F~Q(@&Z%i|D26%1VKNf zS7;^AI>gZ|iMqwFfMUVbiVnsyUbfoznJ()vr2`&;Yo<=mt zq{$mwZ%9_5Dy4#-2MH&1LdPg=Ueoh5c`6#dnK)h(@Ln9IF%JKUAC@)m^&jFlBi5Q6l;Iz+x4nvOVetp;Qe!8lyzyGn1`L3Hr zXdBg}28omIagD|9Z(L74R4RZnnrJP^ogAir{Ul&bNhOIz@DMt#^#qLta-wM6FXGJ1 zRTAc&2uo2mevBNM@GzTS!|gLGsCE4hI|`NrpTFm#ow{Uox-sI#|JlerqX83CWN$(5 zSgKsPY5oyz+FNd;{yUtB0IRm6j7(xP$}d(BWB(!%s-sK{2Ls|R$i(x`7A!c}@z2+q zE=+pcL>^$Uty9nD!=sD$KJpx{!H?LCTI6%Z4C<@`zW+Y@Py4*GmF=3QKMN)ob@{P4 zH+6qkO7R^lIjIkf8~iYp6-_qb{GX)|P@%P~d|9?Y$YHcQJMK6N5dCg2DEKKWFh=~| zYQRZZ^B7}e1LC9}VP<_5iGKt-iE=rTRoA6_0mkukdkX;f{yJ$j_M-*hOzUfFe>MQd zT9_~6^A$BeHyT5Rb8yXoc4GG6cqvdbSRE=H~U%L40QJsFOcV+}P&2@!|PgtU6w z3)u67!j8qQ1L~-CBoCOMY1Oz;dlB9T4|=$zC-yjmi8HacH2Cavd2leYDK!7OQ0r*p z)=v&ddhw-jFuS%Iq1#8~Ra*ogRmFxiWPW@vCA=>k5(x|Xeyz(Noy_ULU>x@LqR>_6 zz~jJ~y5&S^W6R(b*X^F#&&L0MJ54_*5SuP|-@tcDlhV!fO;`+TRM4H5V#E!&0@`WGCK z+j-B=4>7__t;@c?AgJKrASWf_0q4LuYxA8}VJ94!?F(LkZ4Uaj?=z+;ZUwl_pxym@ zf%o0%n*K!L6ybbkqmybWY$4lMhEK_7zIOb5K2phN>l3*qPz*IfZdPuAI2)~Ha|@w` zpCaZLTP+w%UsWMjHUU?rpZQzd5+32dCB8Cgjl?*JeWo*;LK)vUJ5m=7T=`V)l&rbK zL$^^6_mnu~n|L#z?#aygWXP>`vuv?M_rpBkABT3juIIFJc_1)}Uf~bA*Je+nt`~(Cj)*_P_U3Cae?fL6j z1w2pT^gks9l_?>)&3oLKztzT{;H#ff=tx^Bn9NQtbET_#3MpQSi2%&^pQ07GbKd}Z zzv}6U412%*dYcqx*0)SAG~eXrv`AwF{NO$(hhmZEX5+aE)!TF2&sCDbo;)E53s)D* ztE8DW8?oQ5FxyN&zFxGT>hr5e(AMsL#GnY`5r3gQko@(N2MalZ0~0N=#aivNyq}n` z`Vu=%A_#LAPJON&qZckrS+(~#%KF&T^x-L3e6Ymwrk=|`_?g-CtD(U5h6Z{^zjA_w zg8G*S(y>#1`uF#OAGZse_tM~&5RRRuv2DR>;{}S5+v=$_e4V`U)%$1C+PdGV9@&A- zx0h8M9V1!eixZ!<9{zPbEO}`7e%cpzKn#Jz&eHk|{~PL3Pv}wA1Ouk82$~oHL`{;Y zIKYhhzu2Q zA;UscluB#=TThmuJP?;mj(Am&-|f36;jCz4H{M-?uF+$v*gYG01oKN$O@9ip(kZ-= zt?4>2s(s4CgAW_M85Y4MBP*<=ah<7qM|}Qo0U3shh0!+?uAX1~^GZ>v-cQ}smw>41 z?8`*!J4p1l|7KF*8K^|MIBVl%(BknoS2j?~oIxKeL?g~P0ckzSzON_B81~|On?O?3 zUiedv3lfdL=r$w6-N}l{8)py-e3xLoH3F6&ER$-Rh=-e%=pRRZJ3!oFej0hQEimfD z$qDg*=W8pZ*071`aRk+zP95FvtVV7RY#bNcQ;M?mHOkwknE#B8ZO;dBHa7ac_}~IY z0dt|FuTvE_=Si^qr*vB%kJ0=MTleclp-(Qt06qhswlw6q(w~e~2vh6T_pdqRC?ou3AT8vry};t^D~^z?$Jhy1^OrY6f1m+H0ZLp ztr>Vrc9z7cahe%{bsFW=r-suK7V4%AAsO)2<_)!V6QOFmhJWX?L@xRV+&=CWXUEUc zN=Q%xOo9j2-qi&m1R}?pKEwPUnE*@EAWnF1z6!(ZO8t?)9kf_n#$qI3Y3+$MjAdwMe|Oyv*6Oc*qbEWzxoqGJ(;L*AMZdU&JidL-_c-wqf_#SVAcxF?gJy=ZRpK z0+@!@&s88zgfvw8Qr{N~2DZ9i>PyeQ++d_faiDkv>f~%s*|wS##5wj_H%l4aE`MX5 z)Ap3!J4)TnqJJdHqDjN!cH4V|4Nr%`rpoGZzuXnXD!75^-bY6Vi?~!-58**y&-FTc{WPSmeV+w+ z%#3o&i;K%RVZKIAdfiGXSs3=sdS}tUB9*W)Yfv-|waN$vn@gAAUwh7vHkNqf@OCFLCeS<)Ee?x*OO&$lN(0*lfPG!gaQs=)~W7sIi; zaqZ_jq@rcG5CW`;Jel9U0`Z}g*PZ>TMZY{_V{6C;^D2C0xf9qxV5#1Jl56>&)YO0` zjK*XkXq1l}ui=gmYZ|lP(K;D0U3piYKf+6=>fZhoAQ=U9QgshhnHJ++4wa zwRv^Wvv*Rurry-aF*iyA;^!?8)MwkrCcaryAo$V8qRvla#k)?BTLd0xWnqsU^(UqKa4k|-9n+^L1+6JqX@1Z`nA4!n zw?-j96n1r{h)wl6Cb*EC4Hx#;A2Hm2m^JoTbsRViS#I^39bJUt-z*eCN6yU3* zDn7}$%xnwErY!GpR-bmpiQ6ibnw{L<-Fp+2nCu{<$|JDiOn04=JX$L8X4;C+UP60s zvRbWkT|Gr8m}E4%ndT6#;nD$h-+H*fm+omBnAPJVn%6VlSTU&N|AhNv2y)}N`zCjF zldeW&Ia=8TnM(Bzd8byu*FEW(!{aWVc;4RXCC@bk@{wbrWe-bdJO8@JtLhjF- z_g(HBqZ=fjBUNR|L%I>rwD}d4d;Cs4vmoJEuiz(LEbiKoBdl0m*e`_aDH-P7~)`<9*8!4q_S^iH$N77vC=sD=?PbNS@idpSN&yKs_CeTSg%;7;gglizH83Bk{h@L&(|S_~;S_%U2GN z+VA;N0dM#pXW=4-lMezWB3qwmJ@YTa*=nwL>vcz0X|lbnmEvB&TyyBTSc4)m1HN)D z7E#IYN{^n`t?$C7F#-|Cq~ld_t>>pP56@6Dg^uU`Su;M4RDlVJC%K_k7hzqg5Nn5KK%w_PYy zqGGNfg#x6DZ3$RnSdWT+hf*dRYTIX=Ej*-*sv((ls)oIm*N-$3^i9wft5BhTjPUCPz0_A;Uy_g3aUf}YT7}wj{A8^oLCoD!u^Rr6 zr_+5jk6@mmh>kWma(YCLUVd^r{}$>pe(#(m)IvTZJ0Lo)P_B5@(Z8N?BGTlQ-`1(P z+HOV=J5hFz346u(`enk@To^r6`)mHmC56Z|Qcxv4YLbNC5!0kkHFKoB+}t1Uje11E zecIHo=lK89@LDqF8pMzbV|rCjS}KmpLAI!oLDGuS_!C8YaOW3eoAL`C6q7pNk=GWa zdr(Zq8&=aBhm{mlbDTtl>cE7E*1fE|tk&VIsjLQ~^pL!ti5$fIb0UM1^(T?Jc~}_F zt8{4&J<>0R>2Lk%G`@$i1i4;--{ulcXL=PP5((C9}n z4w`1ZBqY-yetg+(;3C_(G0W$9Gpn!e0a+DM(!7}bdfFa)v4o?I@awCyZ+%aP+W;IZ)xQvy>cVApf!rB1 zMtrx+>m-spJ^hfbaF;H-Sl&+R?YZ~&AP4It21adYoQ z4ls@-Gtf&yQ#Bb2T`c;ltezr~C4F}fjmymn1P)FwkL`Ve-4l3fzp8GY?a{k$a)Cm9 z+O0DUpSre_W{0tQ-|@WK*okM9?O#Y8T~z)=Ml>d4U9nPw@Uj_!}dxJYCcfMXy!NpBHyPODi2*ul}sKP`dk z9N?pYaz^mViXpg{*0Y$q##JU5yYm5{ec>rgY&={fsb*hE?dNgP7L@dgK>!yv@VfjB z;&(4iBXMBMP!-;O9iP10i5;Ni;UNHe@ox#atWg0!5^%d70|hC+R(-kBgn$14$=4Og z9Y86v3B18q$|$5w-7L?4#3Y29jPL#&PuiJ4eLV9w@F$o8f zngMxxGdA$GWZqCpV~f!cH(4-)f{HKD)_sR5Jw&%VQS=xni#NveI6PF;Ts=I-D@mjx zSoHrr16TmeXHq72er4m}8E{DCcyi4zEL_M_|2~5a2-4%+kLO2qR~MW8s$ z3T(-VaoXN)L!=Ltb>jCa9F5zS(AoRJABl-!i2@#?m``fYrYt%-JHOBdbA!>)>J`?Z zAR5*MOqJkLTzvchpnMGmzjOv)IN-QG0cJK34~16YiI_eX+814#=TXs5?l#~p7c@L_@qei34{uw-KW*z)2*+XaPa?z|5U!|WgH=m=&uFjdK(&CKV?za`9 z$x#dG*MQq`b92oJ^T`ZA+S{FuTT^&| zPTc(QtGmbN7VFON<5xJJL~5Z6(~GB16fd9^4eCMJSA_B?EuU|Lp6~^u>5i6r3=_ul z*u!9$FV&GiUq*Snu|EFydiZnF7aUWH)sR*WbL(}SAzuVRZ&E5oGo|J?D*a5wRJ)yo z^d0}#LuX1u*l?ZK48ref$>ftTXvf7!qn94oAtGss0(-6hzv#C!j(h|J1gE zpUA{>V*x6wylDNqMoL$RAOrEnjR{LNV1YDd0;GcRk zvv>$UL^DN2&%P1x?NszLLRstFHlVhjo7e)~jUkoHLO(!+dJXaN*YUUrK3qcXz!ZnO z(X(gTQV}b{+auWu-Cm@am_AaG)Ch%P_4&x4T+71G&(C54)YFjaxhWq4rMTBT6|jD* z2?n^(Y}HeDuJ510+S$|R^N(61!k!^J$TKaw>XE`4RWe(q2hkSAiIKZ$-_=ikEu&dN zfboVRQbN`iX~ij1lD}T(O(tI7+=45^XADiv8f)~cjO2^P`9I&^83j?I_XvP9@|*!Lu0w7g*pt) zhwdl9uNRUFShx%?2SYkI)FeNjTM_#6))xrX=uQu^y|4U@4%-;WBPSd~10KSDWd&N( zYx$iI3m#@W*#vR$YEVdsuPVQl9pGrQ5Qc(&5$2F}F9u*APmpn=xXH!~sCB!8FJXCR zPHi6(_v};cAE3+i^{YTtlhpFaQq4MdTo@(^30htK5{uDt+gSV+5(a5)HYZRFWdZpA z$@Vw~kgwhEMRu}%fZe#Drkqs#gKM>I@s9DV_L(LZVfbh>D*8b+HaMMKubTrGYfeCP ztT`$koaXv$rG_3N6>*?U!Wok z=Ty6FP_y4#O7xXT(SC7Wx%PlS^!d~Bn4!<9haF_3shY5Gm<^@L6A1}zvl;2>ku&0b zNdL0X<+_!f`2&bcw}#-)*M3F{05FBZ#{$Fk<=!_`HlZ1y?Og7z;QVi|>LwZHS3pri z=F4*4?+3e+^-r$jNr41B;l?8P%uEVLsW{V)avbYs95c zM|TSuAx1)&YGrs+>u`IK!>~F~X&6QU9i1XZ;?RFCo3$U+6d@tB)agTcLd)TWOw9)C zR#^;ZPUuHIk}}NWkMfF!B>xt6wcIpYby+&PzrIprXsq!#w|;0>nfE+KYi`yw`8FR4 z;yydx*G-SQ7+w#GdazMrchTvbQ%PfMPZZ+wh>w}|*AYtSUgV=m3*u)*`I=2_ICnux zFO{p`R+!^{qhn)3<|Mhj!AcvZdgJ#sGR6U2%8GZhr!2fyb5 z+K|IlPpcF%lBdj&n`rcXq*H&)4(&ony2Yv{a;UqbSO|CZO4q47kCn&<@lRb`ms>aO z_1~87m9OEUqacemB+kN2U&$%7eV$4;SD4tlQYw(KD`Pc)g=X~ZzU%aOv8tWr7^gJ3 zYG`6GD{aw)nV=n>oZ2=yl6CS%^%dY3JEwfTvveB%-FNv}Q2SOw)=%gCOFw!jj4b_; zfbcw0j?hp&c0*y#8NAv`xtlDTEL$@P=P!hD;+@@mM_Z4xhiZ{UWkJ_VKRI5?`+7=l zTIucR^HZ*mJ2luExIyhPS4x0Py~M;qf3V+*TGgOVK*uA#vTgs76`A5JzTSAaKuKm7 z-M%{U_x69pTb%w8gwOf-i;%du_)E|yp>JStxRDw@SNopE=i%<6t&#Bi&K{1T=>E;b zN}8D!8MH=PcHQ70oLMFN9z`&=e_OG`&JQM78YzXf1U2(}#=H!-Fn$4w!GjB-9f%tn z%kbj2=c^SZDt4T`yj!(qrm!n^=i6f?0O=k~Tf2EJ`6SZk$R$FRPDHD^9f2a}jh}nz z%)-K6JyWgstOBI(5n5cpX-`saOaA*81{=7a6M2RPPX(#^;)PI+t>Hd!*fzqLd!)CO zUH_E|Gwi+Z&*|lhQAU50QwCxy@7Eg+Ti+y9jASMJe`&a5lixsRC458@l9fe=S+G>{ zrn)?6Cu`O?qt!U?ejRHK@va$r@u+2lVtvSg#b6rC?bg?OcP7OY>+uOQ?!s(*R9Lde z;z4rQgo7NgPalRNikK*I^$!D|eagohrXX74+|QAeeOngrWTCr`X5}=Kk0W3V?K#>m zj~fEbiwFeR8-0qwDkB3Z3YRVbly7S@6y3j(pJ!)fI8RhNF#wPZ1_K>K;xaPd0hsXc zKaQ5qkFF*T3j~VAq=1aaOf=id>6hgs|7hlQPZMB;iKFL=usXW9a^Iv*U3d^#5-ELY z)&DZ7dFd@FA@K+X8q64a;T{4P(;1<=zp&FMYu(sje~qB-->ZTzo@|?LegBT?hI4p# zdp!uiPxG=x+J`Le*?U-qH4jYTkiY3#W2C*&_;dufbt05D6-mF&)2{RtKHxa)Yu5#y zrx1$lBYr6%`U$&-_T5^Wm|n-*sG~|B472& zb|>?_Z;;i|(LV%%WV__~wPXVt+np5xnDDR!t0|)$FUnB+8N8DUInClj9PQp;c zexZAQm@a6#S{3Z?>};B;azKDlN_@}yy$^<5h(x44zYil5LWey=Mg3L5v%R79@T?6Y z<0;tYE{}EGZch{Qu~u3!AgnzJN(|;&Qmn^Tc4MnT4sQ>%m&8|slxR9#p4y8cI4Hmu zsu)acQwm=nf;fPzC)Dg44UlO`!}HKL4<*A$Xie_{_(e#eFu!fChm1qg^|5nR$6$*W zALT&B(N;~Lgtj-r@7q!=*x&K(gMk4NdItGKpz@bmIPfRL`d|TSI>2}an9{c$0ElXH z0uA$|q=}W##CuTo!e~F+ZLC@zt5-9frFA4)&}K$_IN!F>7=E|6fPfBEq`$k&xk(m$ z`tjq358r<63+hPZ$B3xHV1{?s?f%!ND7x%~gMfx*Ugjo11JAA1UB^KKtShxApL6~O zmv=F}*foakoSuvy)hpAhHfp6( zDZ7oUpO^V22_+W1@Mn%-ehhP7H(pH1#Edkgm7`OWEJ5by=xrq>MfA^*92vt__YHuGUNeW2@D&Vcpg6TX zfm*}%K1{k1a=B|%nDfYFP{0}meD@@UKVxbU9g;F)UL#Qql8uI`-^MNNOAtv#6s}$O zubzIv5JC%;MH-axSdKkVq)U0xZ4NHXODV}OFpEjWg+vg1ncE2G+4x5EG7+u-cSkO^L_Jeyr&^CT?Cjf zzeaD51r%NISDMo|qeS;D^zWcXMbSzAY5o2*Tk56Do{(p(ryBa+76&0J3VOQ>8Wfkx zMPAuG5gt1oMv>VumAvWl0mq&FJGhcws)a^e#m6C%kaHCp8l~t%*?FE>gO2$V@v1u| zPJcX4NS(*|BN))G94t{`c6vIAB^A+?X7ciIz!ji7_%!ICbk}kXibji0gUfSz^b8Lm z^-Brlhu-GyXy)ar^~86Oqa!<*DaGrw=t$_qK5%CIW_c^+RWE)OqQ&F6fiUd4XXe;AnC>b#X7u8ia+{CzRTMGBy{v9_a4oaowB`7P@?v7b z>9k)dW<@dTp1k)xNt{iXCnNeM_Nc1y8zkl2zwYK5GbvEzVC=%iR^_ptSnqW`lp-E7 ze76I1-uhNLBl{=rfI_b6Sl*=CI#pjUvqxC*;sGnn{OQ%<`)CCQp6C0c9pg#ogq*9r zY47rSdl7X%h>&VLKN|+061Ajm;^E}^wgXv>ltSmJ`x`_Ug z4pO?k+oflSROmDqZ`3m(ERL(<40hD>Fq`s*J0}4og|Np1MN-5G8u7ejDj&}lP1sIX z`MzNsRrYv zxAQak6M@7gV3VuWd-6nUITO5~mdQE@Xa6{C%iYKC5<}xv@_CkrBXnH(SGL4_IM{boC7QSrN zEIa5lFHG*!;6gmLNs>-KjMmqVCb^Cg3RM|r1hyo6+3XwAyMOA6Zw<%cQy3_*LUDK0Xy1t{&>@+P2? z@ctsQsRg^DBQG!SdOE5C=+zjJG@p!{{gAt?{>V^QJMT(|lM0y5RI=*n>BaOK2Zw}A zR@zb8)L&9>kHbyI^3=W)a%`3Eg4XYEpk1fRfD3AMyC5h3xtm=e8(Deq>RZ`anEA=l zPOJ}6b800uV?+|$aBSR9X?ZDDm=sm3r#mC^dx{DDa?09Q@G+N~~7kBQCGD&amof>zdh0G1NlW zMbGC)TKhKe`f+ShEHhgPj(6U&oDd_^?NW<*dVAwfM3dJKnE5ivT_ zY>f*u*A^!=^*4a$u!7(o?8Q5Cb9$)kPOIpO{j0C9+fopXtlP}^cyxhQ&C>dMI|_5{ z1Frg#RsWxvO&NP!`kt0iJUR`%lSFq|pI{`Ua$Uy9F{fH3t^?(HSTM%?%6XKlg0B2L z_p97AqGF1YwQRn?D!0R`T~6yl@9(bZqZYCIK-w#Dv0{3fkjZw12@Tc1LQvAOZ6P&t z9EV%MCh=EByr~PM!7-s9oCqQ#WtU4POhU=NU5Vz@9d;8^71N!zzkXO(IHXku?ml&U zjkBhp*tstweP5#GU*Q2!?Ph=4kl5bGqY!T{A}yXc z+rEGH7!fV6l)m=LZCHTS1V58-<7$6eO=lAWjX-egmqLlOMUII>roOc-)VhtodiHC1 zWLmvygDChze{S?y?eB%aEc8Grf-r1RFQV<*0vGaR@ME>r&O?e*#&;_P(1oQsO~ z1@q#^;OiXsu+Px$OeH+nrv8Z3G@iRZIh@g9(;RYls)wFSZn@n z-gP;p(Y0|$hxMks zt4bDs9N%h^DEKb$MNdp61^4&8COu2$j^+JYV$Rj|;?!3d9OU+r&zQP)CHK}BIVKl z8SZ4B_fCeNL!|CorKbPc;<=t}URO3YHadFxN5ILIH^+d|NRjCv1;`NA%hBhGQ>FTi zt#$15CKCv4;8PiJp|LtNzzi!ZY7~t(e58I^!z*ut7|cO~{PfbYuiwbQEWoC-f!CNr zlcb?4s;51lzR@z-AA-HPeQZYZQjDzucC*3+eIc|_ut!uF+Z`CE(E-!2WO_d<>aRz^ zp@H$FnpMQ`KOXqH(y>eC=ku&x%V(d4*^NDwjorWT?Xj@Lp}+yp>Pw4+ZjpQY{@&$O z`5}fpww8`3yXnx^3(n}5@jrnbAQwAba#D$E53ixOmJ}CHaQ@>5q8(VF&AecR6&FNK zj$rFVe|>$O#A)3bL3INrMGy;m#uOH^fLRgZKp4Pmwz_PH%ni(|{`E_dznwa3er_+{ zd(z|AzdU}}RGZ&$g_(da8+KxIyJ3cvFs9YxuLJRft$*73n)x}`*NakF!WnB4G&6lY zGb1$v9%nXxR8Wo#dX^h%8!~#@FbYXmwo3o%4KVgb_;-0_uRnwoMaLj^8m*+-GvRM> znoVF`#hqx4CSc<|HXM6&Ymc5$*|!|c9<)5w&2OsNQJc5*BYYZm=lWj1h%XtLtX zpmNs+3MepBkZ5}cL~;So(Uat0e-%njDXSAH^6UJ%ndizLPF~S*nujS0RetF-j9AXb zB>e|YzobO#SSNAgUw%gr`oz+bJ~4W`8Q)hWP;NP1i?exju;7c4$a?mVOaPXhot>7E zakLF@7y`q46Fv5LJUu=8E{;J5obJ-UAY4IwT-;XBG1ve0dTZSkxBPZ>-s5z&hS|!v z5mlvG<03|;^bjK@6oLlm`ptxY4of8tizE(nq;Sn|rZsN9`ltV9x1$Wpu&C05-cT;o ztTFWir#<)dDV3t9*t7ogs)>m-roSUS#MFKKJnk+-v+o4Lm{}X{lNC1V*MK#H;1y z+eCm&?q>qTLnzp%ja zp9#*w^y-xl&{!EPGLQsW1WT;U%KCZ6sikFR(JBZ~{QbD7l#o-#Tjq8X%}FfhzW|K9-?qKTtkZ=9F? z$=<>u7eS0vZ}Hx`_{4``gpG+*vb!&t-mLV{gR5NJtzBnskslkX>XYqh(ZG_7y=3lP zXVpp1Fnbuudjo0&j=NJ)4{d^_MI?NQ>S9zZEI)D!s)H{sT-(A(x`EWnds$g(>a~-o zp_Y~wv&mv3|9*def4z$7es9Sfg5K<8`C|nIMpjRXy^;p!?ffvy@n>WuYS12J$zZ5L zsuG&JM<13UPvV%)%i&`lKbfMgKjxL%{42LR*L!UyCT@BjsdaqZQpw{ZBWEjjQrGgY z>EdiJy7e<|i&Z;g|Cr$N%kjZwnH#H;`aAQau+l(emA>9I?*2@vqK!o9_|6p9pyVIZ zXYV>akCts<6M1;lvwXy|(Y{+yd`8Y?_CGpOO70ep)?Wl{bWFF{S7Ss-SPBg4dNnyn zuyc~@eww|KexY&ryKfi`KNo)J?gU59Sajje%)VIOs|2dUc+8Htmt|9yntZk$W{~VE zw#eyQ(ZkY}JMPx=(d>bReId_OW|v)|lTsdTTpgU%54>FM*|pAF`@ZVXUvrPG=&HSI zG=;iy&aGSdD~*<{ZnxHP)m7WwJ`4HJS07S?2^D#4b2u`tB4QMU9D=`eCF!MeDd)cl zXpvjKM#_5|U5CjFzqWdYhW7Lo56@*O>(i%CeYSP&3Q}D-J{o`;DQJKQ?q;g_-cIVV z6L0hGduV7nm%a%JZVtvXO&q zuI1cadyzGurK!|x`1EuzRgx&C*T3QIfwzCBpj;9~ijgEAoE*!hsQT zm^;c&a}QS4*sn@IOq~U>a-YR(7UAW#0`&Hilapg}Nkz-1FtOqgP0CF7_VILY)4vbT z{0RkR_N+92kmc>#npcKll45m63BWla z&SXN-VRmI^t-o-WO|E~9mJbGjJckR!OdKGFhw3a&`nl#k9lDOet-3kz&6RcmSxjG! z)yLrnv!+^QPqjN$wmfN)p8KKy2Us#*5Py7myHR@H^|Nqmw?HYZo)ErnBQt{K6P`6_<8 zuVBK^{*=DX__7{qiT`i}w)o)tJblS+`-(a;2$w;b#bHe*I7BPT4FXV9o5Cjn|5@uy zaQ8ENoN7ET?ST%)KZkw+7wI?}UKi+kmkYErw;cBi!Dp)CqM};Cg$9^_aMh9xuyJ{6 zCEpI0+aKE=w&E**t#O#OvLkFDlS!Uf%t0JVl|$tWQMpqGTlzD6e19bpjqmi7>q@IF z3O~X7&gm0Wy4ol=7kkgJaAohj)l#8%TDa^07%?`UEFX2Cvqe@!V+AL{s||xX-{Ua8 zy~g?Z`GxO5R(d#w%Os;-(01ef*2+6c+!!BZ02z(6h$3Q%+qYsP(|+v#?(gv^^-*B>-*b% zC|jqYzP`YW5Xgm?+}&Je-o^rfCV-*%NuT!;naZN2ca;aH`$q5b@F+>{w>dFbt5;PQ zip;3L*UD#&RD08x_cBurry^H%YAo&GHcG85Ctr)Q48>Ni$hZiscK0EIH#Yf4(SIW}|cFzh~G>h1c`&xK~WC zI&O~~)=u%kxw&i<7z9{X*Vm6HO`xPI?-9eLy}{Jebxk|#0;U4YthuqvcB}c~*jeNP zo-`s3GwE*UUHVy*sNS4JtN7q`s?4Rnobt>g!OC)vt0bN4V~ndSDuIH0cFEE64{kb2 z%fEzL5Is3Zn`fr}nk#ILj|IcwOU6o+QRk_I3X@2YOJ#*yWJEdp(MM#=){rOJukD0f zZ%(EAu+KNs;;b^8UP@LUo-L}?7W?A=1v(^Px_koAS1K~@B^(+a4gyo&KP3sCY>jT$ zW+)G^@0LEDk>%G>R#kloL=0I0l%;nI@Ur=aB%soyudi=9Tg@The)1RS3U-m$h$g%n zzA%F4`i|?r9W0R}@nwUsMO{lqgR=Q+UF+(-r>Q==7HTj&W_#O~DU`3T_0?kUk{2tv z)^;21A+)@F>R-G0G}UFjC-xl}o(LO1*_H$vAfX>O9$oLnsa+j4>QSz$%MLbaO-u3Y z(07ghmHJ>lZewaG`fw>RN8`tx4<3n0NW2ABP$HZ(A7{P>a z*A;X;>DmG1d+mE$|^&Ru@#bc7fAB=bH=)BbCjKDpHv~=e8sth$o z_{nw_Z(FMYu`Ey#CZO3jbu;O)6@7Gr+LL7t{1Kyk7zxr}$#Gf7njLHa$OwHLzOda= z^t!EwJoY(2BGIo`QL-={c}iM9wx#8OkraN~{V8m2cj8$6e_s>gsorowEnDD)<)ysjZgeJ8r z9b;$o$hm7Vo%4~wng^+bI4&E+zYqu;Pk4KyR}#%fyL5Pd|9&o!KkMaCkbvfP8#jzo}nNCZpe6x_@%&(jN9#RxIlafl?@C zXLjxNCcaPZ#zZdkNzl+%LdoF%?)IUW@AG5evvt*}@`GhRvUgBBOSSlepra#nr6n}* z--g_)7BE-rj}14<{O704(cy`{DkCxl6i)Q3@A_{_6^7rxcMARc{PZ~4xo}=h(OckC zFy)YZ*tC>%WLYW9H>#Y#JJo%e-N1t7hm#JWG1va&tf~^*^$X6Pa|1?MP zBzOt$zR;&xbX<|L?)sF5h5KdAQPn>I)`jXZJCpb=i9yLpBUXTr9&L z>XPaI+!Z61m6^qEa@(U2bdbon}5n;u~qtY-6 ziqy&!NoKHU9_O2?-%BZ?Fm;_VwA_z&X>||bl3#QrR&BEdNjJkt^tQjNvB_8POa^>D zjqR`a>)eg)2U_TGMZm33`gmLrfJktq(-6&7zF#={b~l7;6-OAaG^$#wk1KRj`^aZt zK{xRw*VKgHgXkXe{%^Vh=X>sc;&e}Hlr^tZDB~J~m@(m(iQe{=(v^J;lK^Uu*nh9m z_$RvdV%hUubg#*I`iAGCfjP?q?Aq#8K?L_Zqh#BYl}rjq&gAdym~=lW0DvWf%lJoP zB$6-`t$fH2#hKux&@<63{=}aHunV+dT{PQf=`6=3w7)78a#^JHi}%kzQ^uNL71R7l*nN| zpAxk%%iQ!&n?~gP*f^Q@icpvTD*^H5v;5bV^UoW_Lzm5_7xf=b1+-d%E>1q!PgbXF zCHR;B@_0Q-@o zGI&QTrks^@_%f@G&Sbi-x9YprO~mzdl=wa=3Q-z|=*KJVdX>@MS*uw_RhY;TZ<-&< zO=l>v?s=AbFv5RKujlJ4$XDM@u+{Oglv{MO)z>MvTK%yySKx~Q@TN5tml{Jj-!P|1 z{zPMdP!;G_j+61c?15Cb+Qd|fKen1N;@I=$az{3s+@i8}nzg%bCWN1yR_J&>*D%@- z=;G|BkW~mA#tEk~3DzjKB+#g^di+;vI74&#eoDH(wadSL_T~EC(vs2gLqM!U%Hqf! zBXf1UwW~zB>3HlW#Ublf6YQZ;UPS^G3}=>`P8KofiVlzDLQO=6YF|rSb!IOn6<7DV zvCp|wORBC{KU?*u5xW+|DmKne=e1q`rK@kGZf)?-Ikf;ThxWl9DC-4%U%(YrOb}>y zs5IKMA?~yPD56^u6Zes8?M)lRy6=?_Z?Ij*EHIyMZv37zrU0+|Ae*?& z#FX=3C+XoG z%=Cy0Q{C#v6CHPmCQ43YHB%w}YaGic&a?yolWKbz3+~;RX zwpHL1RGYLg5?*bI+e(n`Dz;eOJA~GQ-?JZAqxSIZmOS4h!4K3$1Ziygdl?xySzeI) zWzyPl$5z_2C0Wj-3yr;E{LvqvEwUV_`$d26@2}piJEE~-8MSPD8nY0KHF14?J)*I}_oGx7n}=6NBg7SudhPNU zQ>#J{tE~3An*}UHG_Zt<1ehR>XDB1l7*TVgZ~QRV=~)@Vi%`Z6KE+--*R+i;`4aKI zSlstY&@uu*ehXQz-l{RA((dvjiTOCOF8tsx%$F^f*T$1OVYlY|(ZEUyBEQ|e*2CW1 z-Cf(@F{?ci2Flo}lHr`7fCbOW`q7HA{#dt2wbHp!7d%2W^w*Yy>r@q!6@4bbLExM1`q1N*g~}MIv+cSy*^QY!Y}b z#sFzc%Q|zyWOcA#j9 zVYHe6vCgGUh@=vmINJc!N%dAUeJXct?J`gabh#_%jVkK!eOTQ!ljVvYa^~{yk2If8 zmO5n9SB?)?=jhW{2+ge>NFJ5Uf0;6>vOAJN>T*&vmo{;v%SBhe+VN1)q+mv}U?xd6xk!Qt1tP$<(?O z8=dF3Zk!jx?4d>X=h&@O1$}qZ-_TZa3Zio>PT^;L@5=j{2U>m713*FE!oY7;c`UP}*wZ^}my`MK6yjD{ zn6tXy2oP|R?zy)CjvvpBx{TQKtch>fLW%b@qH zZszXl{^XQ({9O0u?;N<|ry2dwU09R_Ckzu8t8crX-yHa1i=nhghy6L|{?_l?H^P`u z*5Z=l3#Uze*v;cd8>8py;HJ5qSxrCjY1OAHFk|p7`Jar}?IALLlRGI^Hz15|v}Vtq z=~JuE_eJoC0eB%cw3sS0GUmJLpc`yHm~mv#z2@Mz!&xKgZ_tbK9ibi{llu>Hn-(Jt z3L10OHhxD5=p1rOgNCFYhfZEzN5`Kq2}q4~!O}o&+VecY!qNHjXUyfcP&RH#1IEim z4{2%5R`u(%284T9e}YCey(!^0@%j{o}_PK=S^WA(C60>B6lbHeU( z8auBN=t?5}7{PA-)&qImd`yYnLLeNi^>$Z9AjD~?7P_EmNl3t$5%=i%-hantr zf#`X-BA|+!d6pFvFH7TcyKYu&uj%4YqZ=)*g7v{xVM3yTf4Uv+hpDe9Pz1M69Q+R!aM8( z=h{zgzsHN-o~g@_68Jqg7r=yOP7tlh;c%f!eVZ63E&VNbL2-EEC0_jvrpR5Qh9T;L zZ@+P|=cqcY`Q5vVjnj1j_tGPvG|=)NCXI}|2*%5Ef;}Uak}0Gkhi;`G z&J_FnHCWf5QkMVL1(x+e!eZAj_kYO*;J%G9)H8W2qxd@`b)QppvZILxD(v)ktaefn zsYS+Q2;l)-tW}%jIKlELKMOA&mqvk@li%m^ZawA~St3r$301YF2SucILk8y?CQ2T{+EV}cqCKYS_=k%3sP^WRG zOVsE?i>?$&mxC4f%GA92iA`L1FI`)hsNr)GcCr0h@Qdo&v3>b1f5b#3kAfLX>8e;S z&ygid^Hlv=5|f-)Ol)wUZS9-DSxGIDMOLh2Mz>?+tzNPM<7M7wGUM0vulg3%sf?qaUuE^%6;5ldB`E>?FBgWZe)#7^T}^1T@8r1gs4nbE%3Sa z^Ny$@5yq95X0o-v(s{`!GOt4*wi>-8THcC}6G8RR08t1IDA;Z;;A+F6V^zFe$B+Gm zwB$w2zD|Nu7jwgPa{b-rib~+K!=Xa^J&aBAY@vY*a{S#l``mC1*Y>tVLAA^Z1LiHrxhBB(?L!8xng+AUw7Woy?Y0koxj7m)R}$X;z4^6s|S z%Y@&UDZyrq@aqWI(~905-rxB`)+I4j`$NXxw@1GI&=p$7&TAVgwy)ZoJijo3TLcr` z5B1G5OmlK@m*lKF@>VVX%;bsx^+-4luh;c9wjHf6XUmK2T;m~nfh{d9Rjwy`p!Rie zGeqCZTpb#~H0O4up{=SBKIKS&n?s~cAX;|Fg1~<5+rna+fp8hkebKn9;yr0yNl(-wG zauk`kxcjMLp?$UCG3LvK{ydENw)y^&hG!5hJ^j+;deZo~j_abhr&pbe;^5XPG@j-9BSzI-bGilU)scT>e_Z1;~H+R?k12mYB(1 z_ryN%Msn4WvRgBc&hiA!GZ{nrV=LTV^CfY4&v(`CB6SPQ(Ne^ixV!wd8J@5Yc$o{I za?Y1oK;|E$lg3m03Q=!3_IqT?0Q{U@5tE3^6|PC2q} zF@3p7_Kg>8Kqfc=I=>IrI3EX-1$VW)yte+Wn@*(_P3cYKRevm-Xa301r|G6?WaV|z znMus=`pgF~=~-MiKKD6}C<|0q3l;nyqTVvB$}ZX(-e6EtQqrY#gLGJg($d`}-Oa5c zT_V!m-7VcB-QC@6x_KARIp_QQv-P_CvF;UP)|g`+NsPU}-&*G2$N^*TdPa91Ak59N7O6h{cukjGMiQ%h9xhas;6J2ZJT^_Ot%Wn@60=9aTyqrLzaK=WaSx}jn`_m_Xw|ViNMexIA+7j zjvV9Pg~i?r1EoFbaPnD)Vs1bsMggAr3&qV^~_8CweC;n2-a&7X5-M?qxnI(VK5^=El;I*dhTI5p`!vbp)uS`}L zKKuBY`N&Tcp&1+;5?#&jum2V-aC2PE71Obh))MIya|hAh`aP|rM(^Kkb=|J4Rx8uT zljA?wAFW0{Jl}P7MsV7d(A^%*g#leSZMQWe2>3_iNr@;HSLW0(M+1Yc(aO56u*D*V zu3sQKGjvMy|D`r)!D??LZwKE9lRG`3HopDzHo&{mZ}>%>4Kq3BX7E^$^+&w4EQx>g zi!0~?mu|^_{yZWHMa8noHw?_n#EDC^g5FtXf>)F2lY36E{e}%P^(qS^8|BV8fn!Ne z*TX}3!0{s)^AJAYW?2P&sRz*) z%lzL}uW0=!4P>93s58i9^=K)%E~a_~%gN;ONVb7!Vkx|+&{RQV-9^#Jnb-AVrPia~ zCGu|aYtPKl6l6u+9K`R;b5ri)P6jFKKz(oXDJMd%y*am3p`_4<-+|q@Pqv#rz2On($vpAPF00BgWHai+ z@S2TP-G`W8$4fo4!0WozQXejJn#|q1%u7Tp45aq#Ym8hdvOYUiGVkeXu+1tD?&)V; zT?fIt%Zl=Ct?eVXqfVhVAad|&FV^!hBG z0YSU_+!?mqTVYe0j#=Cpg?W|8v%5$QFoq{g2)g^xcn2Kz=lEpbwBRvCUIogi_T~xO zsrL)Ej9zM;r4@=We#bo-3(fZx<=uNE)f#%iGdesxO**LTX5F;nhhpPJ?%I}#(g`!Q zaCgW#j8e{0*0J2mGbEB{dwo~&Uf5=3KJRY626`6o&U^o^m!3PPQO)`8du?&<7g;MN z2)mW>BT_Mz$zYSKr}oZ7c$e2KtofwL31`z_t#rT8=w&h1Q@bxjO?$HI5g~P|wIa2% z_QLp_)YCl17905OKJEh&3eHpRT#=NlGDg=e=~o8DVPu^bZyW0KkU#6mW53^b^-rfS z!e|)pi|1IG`xw(;n3n{)Cj3AbD&k-fq$OYl^4tbEc~2XeIfdE=F00>rryy;w(N|Yr~sZ&t=aZvCc9= zo=;5JS-;U~XB)u_z~T@9$x!sX?!2tXBi(ZK*xR8Q&CzB7{uC~0gPRLDU>hK^*{w?O zJMUWnyokT=Vx!iAvA=UW~|WcDGU=f{{(J1fQDqQ!rJ{Dm7}#=9YdIgJrs zsuRQ9?m-o`t;cRAfmOvdwP?>%Z=$*(!=WLz69r5;yT zSofwV+g{N3qcF)h3JQd2&_XS&@lM-eT4P0oI>LN$V35>PW8>gzk=yU*Sx{l|{lT6J zRGTMB3(%va@%Bt3irL-~@I!py)8@s53E^6 z`@g9F^5JHbv(+e(^rgQi1O-75`s+FKyWXBNGfmn5a4u>H7SrQc$i~`e%QdqQbW51h zz$`JH&#I z{E9uB06;VI;EN?R&2%;ZsltZ{qWv-gLtB0&23P!FUMCd$+D9gs!~2oOM1byA;>6)H z>;2(rRs+lelr|57GX>`x(4BRraf9kmv2&HLltS&5iPCc$t?)YnXPzo)s-QSHe^Xm? z);hiY?84fc*Mx~bcCJ*Y06T|F5rr{amOa-HFjRA74TJzlk4-_w1URWnb|-a={@kKH*_W1llc zwBp=~C#HFOv9ko(W{16;1E9MQ@eNSLt=+4FW@Pz(=aG3wu8g+M5@owJ@(oQki66wV zMb7*@s{L>&sN$M1(zhYy+@urvBq=88p?@In*WtD{hW05@ujh5DTddjV1`c_2g-N7p zf)z#J=vL=n>8VU&@^CA?nyuAZtC}UrzEM;0SCEz7|A+f-R0`ye+xd{sD&d`#(Zf)& z8dKO^P15gK#D{oEyEGu#iFAF7Y;RLlRxAVAmWICe3Da!s-*bWIQ@<0u{v8AC$fxFi?))zRkNpZc$(RS)5Z?rM z@jyfn&(F9WC{OUNXa1$vRDAns-wtP$g1!0#1`n?SWw0#>JTgJ2i^YNbI8By*vF>!} z+B7{%khHv}qWuamf4_D@A;ib+II_Tu$1^7%)w^q*I~A<6IJV-wLRy**Uu#hi6t8IL*s>Zg+Gv*y+x9{{1r&h^%G ze|z$G{>sk3UYXW(v*z*9XI}I1?E}}pCX=Gn^fV2>vQF^i4}8T(uc^>uzHG>$ste7q z+ko85Cu??6?o>x6r={)(NF6MQOmvMq`CV4&9+nO)=TL75QG1!gI*2~&cY12T&<4)S zL}%Eup|I(>GDpJy>+u_JH%C=&ccN4MFN6uul#h~}T=U%dY*4v_ z;(3kj%I0up5J>xz-;*^k+CQ(8^|Asy1KkJ91U`dT=b@rU5}{VAUMk8ilWN}GJ9R5h zfWO#-qf%+)kZtU7d9IyxUmHIpb<43Jg%_nBSBU zaF}Cc{!+0hW~;qzzjTO%5%TT}WybhAnn_ zio!jc3tYNCf7JZ^g-B(=f%O_E3?|2##oe>sOHt%?8wfQkUYaN<_}f-yy0FLxE6f!+ z#hq!pT;e(H&7;5Zh@cTXp0(#?q-V@_^5qTU)z3I0R5-oOTfNK!W)9hElBz^n0ey|);((d7@5i^pB=EYu?`q$>;hx>I0h+rVe<)#4?8<$$} zWTAg3C|_&tfIls*!Q8=FiE*NZk=o^d;5Ht_)3ykcOvX>E0}bTzdAZ4iJ85^Ek4B2x zcwbLke2u)0P^%Ro4C?-neKm9f27Ttok5Y3>SmGvQ?Pnnb#JWBpvpJ3y=^sCiV;SND z>+-=@MOAlGg?O7p4Y?3Mm1;s$TNlR*5l8Nzam#UXH1v~=IrcWfsdLm~<_xFS)bx)h zgnV&ANScXt^4LpE@j~9lA*>uA7H4G?h!@ze$0>9`p1XjrYysiFhldjfQhgP&r>EG} z^Lo16Nx3P#xafBr|Lc3W0^fs@g#yRM%mSBmN6Zf56$m4Q_VoY<#O*D&UsmbWPxBwE z_Dwg^$2oFW?Gu@@9zxS#gfE?hybRAPgLYF%8SKo!DZiLGF? zbKUS_u%N8GUTWiRKL6|B2NUyyml=RWD@7^gH^I4d(lDJNU%cxG)%qP|kpsR@ZY|w1 zl|KyBUXt`IH<>%j3H(jYC!WYOfzn^F6cW5r;Pbc7LI4Le`FnZ0_m+`9k<=`9k}>w8 zRMde^i)b`WL(`I-~n4 zhApyRpGROh3XE~DbUdYc@cYmK69s4<>4T;-*u~Y=5n#(MZEX0pE!IPM5v;UeZ4{8KIF4~O($EI~tBpVleQg6|mRGt~bqr-m8|3r= zgD>To%d$Qz_7wsQ9_~ zYMhMts0zW1GxG}?RU-%M?z~Y+2>)3Pesdx?I&p%6`vzWH?Ot2#iegY{Z72?y7P!&^ zUpej4ab3exuQu}*yF4ByoZ(2JBa$wm&SIEA1LSCkO^Os9E~0>;9bCES9zj z_9sV%>)vzG_)2@avw1f!8ZUo>bF;{Hb@k7BXsGq%*Ua*)p%iVv{Nxm~wPmk{=Izk< z_=*OklN&t=Ih_gsfQaZo+~|I7uajH{595(hF+jy`z(N0U@tASjOX`gO9~9-KW50s5 z7S|R@YfV41L?5H!Wm0;r?*pQbhntHuz+a z%1YBVrJ!kMI^I;)(7+^F3kUzz?wIA4J$|e;o#$+&JHDuGK_$zQPj5f%)$G+G4sLXf zSYGv)@gE&J(7QzSp{FLs2^&eCQB8MOXkVu*&5yfjp=6ia+1`6)P0jX#f$W`~^nDUb z0GfLrkcy=SC?3M%C9(%ghAz)I7>2z^#Ga-_4FmO_au)UwN59W%z->xenOa*8TO%mj z)}_qH$l45xm8K!`eKI7-KYGf_@LLSKg$o(|SLnhQ5Kb@JIFSWRU6TCkU6E5}2~cx| zNP2q>bi^=3old7Xs2_QlX$d5Zvtw7};>g5LFSYw1bn76x6Qn}BBO zV$-=F^q31W$4}?^s4A%d;CCY3uiVqfjFa!omI^J%-hcHkkpp}aKy*2FJ)d(%n6Mlg z$&#hzpLQ3hlQmhYuO$&#kIS#!!Gl1z@Ix<@D~o$+K1D_!jgAy(fx>!M>zPr;Hgi9(p5%xIZ=2iBmJ;`kOsK0omjFP7(+dwW)bwS z8KhWlMGQbgW4I-dsA($hMO&khkp@@We>DN|m%Z$rWbeNfxveIsqQ3PX@fCqczE5X( zOOgD7d}>7=p4iUx(kRARba?G+MK5_kA!(jAX^(w*x4yb8y$clYB9_XE0?#5yP2fz1eN`DB-66t|O2JzcS}h%7Uy zcz#ixroAMaQHs0= z4pg)~*Yz_lY5v`arp}xAhv4|XOONrNpFJV*!3T9DFw$GeH|tB@SVv#rn4;2*Kv>>7 zeu`_NP22x8zIEsDTM*<^TJcYg&FhViN$1$FfM*xGF7&PWXlS{@<4hAlokVdz~+ zafl5>4uS{345sWI5Wg3hT~L8^FwZKNtjm|J%dvFJpX-({o4j5zsni^l`4sU$Eumww zm0PuuQ(dNLYL~H+N0}0RwTc`4Q$h6#+A^R`>S1DXa$H@<`=u@Cpq*G{bg9lfiiw`_ zRuv1K>bpN0rX8~%(h#6v$u)hA%>MI#T!N$O@;E$kkyz$0#ZbOlX#}7EnHg8*B^pa{ zYpk&=S8BLQ2Ug$2d3xZ@03vXqVE?N9dU!_9-E{vCvxQQl`*6E3%<_Nj$K?DWQ0#52@e0!+d2LqOIwz2 zWw^1#zbRBk^eC9@H7+p5!7oS2pVGX7VvQmQ%cp*1nVH7r!(z*OMf}Qp%jUy(GD_Ot zrp`e!sdE8yYPQ)}s(-g5VFo?fb0mEUhpWUM0evkP)A zL6yH3oCLgQLExW|muruUcAYbCqDBYtZ?)5^rDW!3H3lTPT_Xs{&ki?sa;-<2&Otz| zj9w3v|9ay$;)+^9lYg;sKNS^KD+K7k*;*(wm$wr-Fyt_Q!^IFlI{kaRMf%Tf<3CdC z4~>Vs9ik1MZa(qzH*<@yKkZ}uYnS?d5u>w+pY6~zkGs}aBjPkv`A^><(DOaXSi}3J zq#C>;D29mlo0pEx_Gi&xswOF53w(Hb0SMBPhAFJ!2#pW_AY7D$0F7bjo7X7)UV8}g zVTT3B$2GUq8F#Lb=*GKmYK+{!iQf`{s#b9*S?AXxf}V{vu5~8O-o!9LX}$0rRrzWs zX8himch^;xz{PZ71g*dKLXLI}zSRq^(V$lPB1ZmH*=c`H-Z`{WcD<^|jyBtrjTid? z;E^#YL7o~JdIb_%rK#WAeV-y`Hzg`OvXjha`^!Yfa@*wJ+p5oj8rV%oN7Xbb;}wv2 z^Bn2PE83_%+}|?cKXvqJnEaNdJAU##H*d?=HrQ&_0>XYi`!W zkv|--oCNN9;Fj}utM<30Rm}0Fx>wf^Y7n}{V{JhDmXO`ep()l?HN4fUB@|C4eyoG?v?|^bI z{rv+d^12u4;_9%uIZ2Se0+SNKD<;Ff`klDKXF|R+c0lKd2F6HRJi877(|U>?s~|(#Nr0Jg2#D3y~9_pEP~He^whe zy+rxcNQJXNv;14Q0r}X*3~1#OJRbBE6uJ{$)Avp)$jJB+FBYPIz7Ng8MvHZlt=D@y z2L>?xT56S4)CdWCZ!Cxw8Ty^iZ&!e%fV-+Bn)!545PJWARP$C&@E~$*rHs)&MOsRK zsXo`h6|?;k80qzTEN~`otT?jAm(e%T+!bzb*&~6PZWWool?R3nGQGD{4(6nHP>*H$ zLaI@;K1DhZrbEN9S z51*rs-#nY{O6LqS`%~>^G-Fk`0%>?FToH^iBV1%2n~Clce3_{=F`*J8=74|5RnZto_nk1VOeE zf)(FWYS3d5^VmElB)*E{%Ph{WP)v@9(*ZMe%uVZK56{)Ga2(@-q`vY2!SrI_heJD> zBgM(zKrjA+$0;A-Ru{tNd^!R1Oa78o^V(9^TDiid`PELY3g+S*b!9${jqEBsU`{*yGaHN^w0QnvF+ z@FujnqbVPr2BPEQMkkAx=}F;YKHKcx^ZfICm6aeV4}&(-W&(nemZFu+PrKq?Tm3D- zoX?QXR|cfd+XC$dUodb_zj_AY1>55NRNcWLUEV3?;=+_P>r2YtFqChK>U1CJQqQSU z=#Uxwuj;-w=sfxGrU4cIBycY;<*Sz5;}3su&BC7}V<9KLaie%cAyjvKOiP-CghTsz zvU0k+{~3OZ29w1%xl5ZRKYVy;sH}8Tdrv2ducX-SE zJumOV?DC7??Y2hEZ`+kFofA!NW*^CD&BUY!B+NH`pV-7}Lzx1)5;^N=ozQ61s`bZ3$=qG-g zEH-8IcD5f9$2E(td=nxup){-~SA&a%FSP)1?s z96gwb`)`>vUZ?kA0#=v8V|cqK>P+O&Mt1})`!3XvU6T-w07J&a>_n8rk%>q4R6DR7@oF-(k-i|8K(&&yt#7BVE8M0bv9!$einph#sz0* zLZO4uX*{$~Q{l%c@N=VO*2S0B*O{q5&?~)|hn_c8z06BgU3^7F#qcdJ&p%iw#MRt+ zIl9S-nx`q{?_QFlCYzZls~~qh50UGiAhaHbw&}hZ_N7chiBHny7Q}=3#OqD>G;uDk zN7>7_>Dk$_-}|8h&84O2XUytV?ZZYrOUv7kdcP1#jj-E!=WLy96IW!qv*Yrn-AYVf ze|??-%?I%3^&_-;@Wb=5>6Z^V_g?G7juQn9OX=j^-i_zs8C)N3^d#<%L~M84?cp`ft~=zC^KmoA@2=4ezGLH)66 zVFv^rJ{6<5u!q*mp-c_gM9#bhtE4nafsrc$7!7a}hlwB7ptuvOgr|W?)}>gYI5*%n8A`i?&-k)2pl zxv0{jyu7lbVuP$}m6o=((--YW0MiXVR;CE2H+p%w8 z+hOHgQQnb5mg-`ttKwj}h2-ta4~sde$px1|wI+CGg2Z)4Gn1$rb1gN!e*E4x1QZ^Jcnd?E?y;rj0l_-Y#_7&q}xAK~kvDnS=c5>8%9 zVg@=4dV_t2@&pct0}*64nIu6Jn2ob@SDsQn_Hb3=TIMWMeN@crdc!YMv=ogb|7Idf z_S&3Hz_}pvZym65yXB#u3{$>9zS7|GtvA>86r^px7ACe54|kw)6BL5b4TU z^k-N!hT6E43TOFYa#&-t-*Mx&Zin<3;Sv>R4-R+!o4BjZ7_CzRFuZ~C{nM4+SWXxW zl9iPO@+7E~l$NK`y{M!?Lcu!F6&Xy|#3XDZTWW%j8WC0DY?$hd_M{nL^AY%hsh`U2)xRnJ%a~hSfCD8(u6_UHN1CSYGJ}P%%a0Q-kC!8 z0&ART<;t<8gvD~n(3&>>^SAc1uJ+}r*z!`(bKxFt@0PE$N5d% zq}8?c#pO%fhLMLCTIR-d_#8WYth!cJwTkw7+~f3KFGW+?Q#fH*`h_ic;H6q>?8GFVs#bYCA#+>Be#jxg z5D^he^g9lK5M^v*fLiYN8;^QE&%ia1Z**q>>vHV%x5}bJ6>Ny3px=MVF+4ym!b(te zGaT~ZGDQ7%ZpxImpYW9r0$ToQA(dJkU~58s-T!UZCie3m?tlE2_pL|=2fan-{{2df z2nuVw!#MD`!s95KfAM-xsfldfYt2=V?TF)%@V zxWpQI0!v1noki_^*}sNMIF4XmVZoheF*t0oL9C~ia=0f}3y9|@$X!}<@0P~isN$AK zhT!AKu2J~Yh8k^9n4A0*YRrQ!Vi-k|08(Q1DC=DbkS}ZMml@R+hM*@UpRi>KAJb`S zC8QEr7#5E%dt#AEwt7Yi)1-M>m&z(BJ+P#fz$C*;3u0ha@I8;26A}~kST?Q<@wFTO z1u2B9F8gfzOlGU z)M&gps{cgJTevW2?%KM|#oTza;(KD>&Yb-@_(X8@A|mLb97~0RB5k1Ue8DE`Tl>(+ru%;aiWZt!y^rCezekeXjO29K|6FV~;KK<_Yko;8BiZp`?~Yg!xnU5*77gLN&dous{f9z?Q7xeI4 z%q}!CMbm_Sgy}H5{x+A5(8Q6n^##OKuN77j7wg z4S|K!=kgd!dm^O!qUSZAFg%6<_1@;qp>%8l0;H5f+E{XrLj<7s#18Po%hk2kW$iMX z={^ZaBAG6aD0T(sFr5-#L#lT&0V3%-RR6hJu>ahgTvRFJT_K<$iAmDdxoI$Id-3uJ zRq9ohV@bK_Irq00FUC=w9Z=uuM)*d}wA)jTSOy8X5?S}%B8n8NK01~@CY#9L3C)$2 zZ8g9Yg1OiI+K|o~zP;Khj^Q$UURG8HgE?-Gwt!t@H~fnQC35By-tnA2N)XhpDN(n- zhY4Qw9`l=gFfyC%T{v?7JWKz7TlF8XWl0%xVkCTQk9tpAayf`%5ylt5I8~cjWj$7b zedSl#+B`VEN%kCBTwn^(Dx;yTOLn=iR!0|pU>?MDqS)2(gjMhgE9rKEx8@IT5P?|w zI|xX27dOGl>jgGEd#V7~GO3k4ZfR-ZH&(VM5g`hay%*>@I^fyxcw&hdSs78pa6=J(q#}(2cSyRXqPqpUmy`yK2b}XW@Tr5MjJq@#jl*~w2TS;8cGVZ z_>kkWn$wbFDQIvB$2Cip#zKQZxakEod6To769sv^&!d0zA%*IX+^Jd!xjX~rHPTH7 zlfdKz&D`hPk|i5O0eal%`B#1F%?UAXeZ^9|P$s-81q&bh+i8^Pk7qbo1aeAC>?+#z zG3m62>$3y!9`c<)T&D*Qq`l1mK8%C2xV5BR>g9vxlRIpndJ|(xEDPCb=YQI=WHB*Z z4EH1N+GY>G8mQb=# z$pp7n^4Iv#foxSgk#n9USBQxE$K`+lPxR{o2?6@4#^>^u?GHY(k_7PI(n34*+)BH2m-a_34wuto*+R1Yitr8I6TMqCeZ$6G7 zd+Y6%#BXGB8%iqR>nI--zb3G!AR=!(j#^tNzq@AN<}szyo(;)EI(I{t?`p##Bw(euGh~u_@&sbD`$YbL zC#Wj|bY=X3A?SuX?DCNYh2t@y09 z<7e=&=g$s>m+AD9YB}_`pJ>h!d4n3dR!YuYNYX-+JElg_)_m>lojzno-EEEzW0GoI zq#K}#j5<3rC&kOFF5<}yr9cwXQ`0C0-P0de0%krYsVvzSe-a;#!m?gyX}ehL5mha- zLq|oG6176JoFM{O3ccX7z_O5}OUoHH(E6DiRf@WTLXMMO1^;L6e0Sq2m2mPPv}`%G zzOxeqASKm)qaS9)gm3+vC6k5uGl*vvQ9|8SEUl^1sN9r32o3P)J&Q0OjjR$-M} zRG!U4S4U7}OV8neq(8aD+!V-X#jx2@0YPu388N`b@~*YtNRuJuA?3`Un)meNmmw8- z3NxK9;LEz*umo=G%n(xzI>zqoGZ?S^iEilw=3!$;hCR`yqGFWS-)o3i^G@FzI4}f3%4?eX9F12?1Rkd= z#S(8nNi*7k)QJ!onI})u-30IBCyzIO>-9OT{lk-r=SDwXAE>slx3vZ1KFF6EuMZ~J z%j2x)$QcjkCUg+#5}vNyM(2?uzC&yvj@~CnBzt-9#I9^Pi&|d0X>HuFJ<%2s7Tl3x zdydbNAQm@WW`suOxyv5SMuGSO<#MZw>E3I*qdQuj0#O)xD((9U`wV8-L6a zDjHN9Xrui(ukmxgPV@KVXuUhkrbQ_a{n8nCFibBUGqh#y{Z(6N(4rX&HT9#BY}u~p z<8`&FsmV#*f2-XW=RFa6Jze#mjZBP|0HjbJe_8teZO;9?V`ZQz!(KV#iEJ^=Va|b1 zkXg6C0KPYrP$0$2YpclpkbV(8#1X-k$NSsO+B|fDOClT}+_r&wPj1hRVw=0QEb6;m zSDRvKZ(VU*2iHlFHJFqjaHh^T;E089R7rT;9$Tw6I#C~?1%@s<**mtpPZ!r+t{E~V zY#vIFTsx0sgwNU!P_o0t^ctjh#$!YvVL(hlf;b5{a~1RQ^skzdJ4z?pU|+?g>)|aM zIxVXIs3Huta7EwRT+8@tHX-Is1>^Xo4Q44|HjDt^pekc{$c@mG1klm>)3>HY8}`dD z)n@=D-u6ANStR6RPKoQ$szj4C>uvbR>~8|Ek%iFD_F;l6l_UcnoIMViLKH($8l*7#5Wz@+O7YRHT(8Qkj_; zd?Llg{l6l+iWf1g1g|PW_`cOZHL{)aFWG_uJV5mX>_^1~PdjZUZe4@HHJ0GjqGyeB zyBsecKmM7HaTCCisN^WLQr3y3iGa1#>~*Iyh!2LFENmXw!! z6JjOL7%x5FitECu2rqhCY?M6g1*x9Aj=DeMgf(}L8_%~oK^ zJKIP=DJJp6bu8|LF>Gh=`;7C4)hj&M*OhB$>eVd7V&163hM#Yta3Q*i*cQX`b?8UhBlc1^5>yLy&>Xx z!w4R!*?tI?h&5DUwvT?_iLT4_0v0NhuT*4gyj1;~xWop|VKG%(H@PPhis7=jzZAn| z#r19mpXJj_me9{Wpd0WZgI+A<`6QZ?gK4G>Of;Fgzw_g^eCNAYRf!R+ zHq|lx;fHfGwZVQ%LmN`tn9uQN*s|(k?#cH+Y|9 zq@d=NuwYRyZIB3>oJ&7!rp9puPJ(weghi65^)t~Gj}}9W@*%t6r1|1Eh~y;#0RrZo z+8v|WFkA|Tc4TWZF5=_chIM9BP&0~?h|A-0nQsOtH--$gcA+uHbHD4!3K=RhOHT_* zvTZ3>msV2xKpxr=)}y?%ftN3t@EuCk^OOy4a(+xkQPR4(5`^a_Y3H=s;SA zxOXXUz2Qr`o@M92Q#tRW82Xl54wA*VV zehnFd-HV|QmYL~cpS!X1)R-CYPNQsOWvXvnY?WVZojZaW3puabkyPo-o`og5#T)Bj za!1;yr+ZH{h_%!5-+?|ud?ro*32k@Mt*x!7J?S{Emb!!bkh*=c?lgToPCVSjtpLm= ziQc!cuOT}&+111D|1qq?n0Hw|D;~?U|w!7(}-RHrXg*_uniO3 zLl2uLwIs$}kPp9Q`3(N9q?+J+194idiqPaV?tN^P%Mf?o z>*or?B`#~~H#Huw&&YT+_2c_fE#ZrQ1=!s#bVnVX8E2ut2DzUK7D^BlXk=TKsKuFP zEG#YIyX6DA!250M=y+mW^yYIW$nKdd=(I+OOgw_^?vWm@Ig7N_+Zn63b?UXkRZJCvU=_f%}E73%wx~7^+UP$c2aS zw6AZ_aYV&)9jf*L@2WknIixKaQyKM`1}HpP+>;f!ykfIMFEU;wy0U?gr5tiE*z##l^&&=M<)i+`g)B7ZaS9l+pwExD|Q1d#p0 zup%i~D6yLg?{FklteZigB}vk)uu#q-hDjcLE&fb&W6-Hgek!AuQKd+CUz}WTf3~*9 z?KID*uC7iXtKo_PC~`g4(CAMt%m%60rOU26Bfc;)7Z=3419&zFV)E4e_HRpWcES)~ z@<>o&+n{aOc61RvA{Aui-%Nx45Ky$tYa<_7AwM9ywfmk?h<(hjPH>CEak3xkMod}_ z2=YjhNeb24T9~s!86slnq(8K=6Ll3@J^B=1+u7N%-RBn{p3PXVfb>b!9yd3)%gIm| z+i9fA;?h#8f$w9OK$TS|2pC1*dM#C^3^cdIu*4#lwv$F*pz|P;EkKDqaYVu-*7%F#)pd!7paXoiVqzXqFo^r}ieWKf3#tK$FftiBtP1EQHv*zP{DWnab^?Rs5Ks zg)Y%L`3r7`?wbv-wvw}rVgy+D4%C%`M6;EE_u!VDb~z7xP}JM5G{Xx`kYqX1*{wCy z{MTC~S8!#;61BW{djGwlVXz>T)&i2G`$bO@D;c@_$(Ux`YW~{-k3~yfh=wiPPAp*0 zBc7{M$oZs8=>1i?yVLVa`q)=~nKlu^z`iOp7GN9F=sk?E> zJE_NRN0eK=3=EQO&x1lV4zK@6Pl5)8qa(>A?hMO({2F^*@>#wda{eC`3`aP2O2frb zUBwjxf$bw~B4a6gt_sRluCA^R=~Z~YzySqdRsFTp`s2`Yvc)-ACrb|FL9H`hvRk8h z%Ne1DFFE;)M$c;4<(Fq<=dU6n9qsxyq;&XUOkH?nEDFpNA|MK7ZMuJg5i0Yl=q0O` zmiDj=UJ)~^*0*^kkHd_xLN2?;l_+LFh?wme+UHGHvw0 zT?B4q=Tf5?2DwF8+jMEg2fr__x!Bfo61en=A**W}23_+Fs^w;kKndUp$jLI5CWeGy zH&F2}^}oGkvv@;(;l@+6ES1YtTU&c^HUk|Tf|b|xIBt#SpKpGVUBt;}74MIQ(=@uzz>E<9P2oeg?DIwk63JM~P z^w3CmH+*Y+p5Oa^*Y*AP)%-If&YZLNK6|fquX|OG_Rn%R3REXc3eC)EISEH@u7I&b z@9QMyxjW;sY`XI00~&uSNy)h;?!^^hKz`<^n)UTe!(5};NqH@7qIlV_pMMkd`&asv zJMqX+H;fDtKy3~Qz&M?S^V_F<$iq+<+8>0sB<9yHCH=DJ;G!_&wQ7iNL$85_qSeN> zAL*Dk<$vAX^~K&-5%QiY!QtWIOnyZcIp}dg#-`>;EE@a%&!0ax%Iud0F6GH41VQhp zqI}H@;*!fh>Q?ITBRI&~TWEWdNXgiA@c10TbDX@u@S-yido_rsZX)*-amDlBQl-Y| z;F61QB6vP!X7a~`2c3arT$74di@rupIG{Y#K9;3jfA=?IIzfjul)Pk6G}* zGTn>OB`>|X3w%FBU-XR}L3e$z{nxI_EZrjrPsh6*)y7Ym;N{NLaed*t`jV(K5xy)F z24mVG#`uE8rbFZwu=0yWPdkUo)=n=BYpXL~&qDNgW31=q2(r-!e0`@SXV4Df*ZK1P z+>)TCS?a1XGd{oa`;7PDmS`3G|5FMfk5j1;I`y7d zZ|NP{G~`}9j#jI4xfvg|cY7l*E)>G0e$@Y{`_@oY3)siLcQxL6&$N#OOrX@ULh!oW z23(H^xIX&{I9~?#437->H~ zt9f#e_0z&X>G|H;gwsleG05G|iarb_dsX6*C(xKS>Iuqot^HEu?(yaFz6^RGwM-Lo zy;tgqZO6nMW|WuErC9SBQt9Z#kE15=+e^wae=*n_#WTzj^QLQFUSD2?If_?f*W|y; zB!@tEA%`FFjVt=b`G<2Rh**MaM;He2Zhk)3L_9TUyl1Ei2EC z%1wr42l8HAtTmUKluC>06yTfhIK8@J-{#=eMKUS0LxB3Jokl_s5luZJXLCW1V?vdijH zYss!6D@){Yv@Yqk)g}2ZFpwQsl4N>X>eGwRxZ&2`{^1$alT+r4(yGt+2vxKA2qvZ8 zf0`V&d&lI)|9x4BM3!``%e82|Ikt=)?Yd3n8C-Wl6ciNn*5Zv>kqq4xDQ=Ue>IZw$ zS0PG|weGVWU2$&0d!(s}kc!3duH7p;325b_=W-gM+T#2fV=jR45Oe{Zq zu20Wq9s+e~C)avab@je^;qoIVZXeNv3H&)>Igg6uHZ<;5P6-(yKI zOAFX%a7t+eUdKtw-eBa&82%_o%OMM#7dzV@9o89MtKkS|ZfxmW3GQV2jRCI%wFm1& z5*O%sZc@kz2@xIcEESr&Zbe;Pn=Fp1R#ABCbQrP3jctsiSU2F!p=$^a92UJqMg$tt z3RFprWHh9tq`uW#)qYde<@^d=_}-67vqYjSO<|f;?~}cS$|e*AA29x(vU*Vm=bTj6pS*fGJSEtgL*A zD#42#9|_W1{Cs7A(iZ#HRqkKaa=-EHFLiYjYik^QwtYYIa(0za^W=N1>4&T+^#Kaf zz`7f|FKqu?+P7-_+Urtf`qSrIu(P&e^tx!f96d zwcy#2kr5hVk-ck-Pa-`N6R*V;rl8k<^AX;*{Z*%cyjqr_Pi8nO``xssol?cl6M?%;qDcZ{4{h<>jBVl(=g<@@*k= z)IRar^<8CV#>GTLo)ZU;+2@m9D&;URF-=v`d3;TRXRtoST~z5J`C<;S9;Zll~7TfVtjmjKSG=GK|&n1h8#ie4Q5Z=MsxqJEsZ2o@^;#kZ!j z%lABWcQ7Jq$oPfD=z7Na%Drf)r60Ixk8>zXa+*iD9oR2+383t?<62hwuL+8iPSpC(Fp`Q|)<+PVSV~w--3oC6a-L)E2K!GL z!mtpB`h<*3jU57%bn~GL?{xxYw&UXB zxQtreBl)dwfb+#{P)EM8a5{vvjkQmF6O%#PwPPJ6a=DPW zJ&QMMx@5xFR(7}%a5{cUi}=%( zN-0VtU+@V;s0l|sa53A8>Bl)t?4no+1)yC>bHD^8Hm#sPKveKjPwoKs;O9hDfg6iO;^Y4SSKL%^W|L2ITzBHT2Ye`5os zA|?T@1!=!!(7Rn(7Ps-OHR<7F;L+s>aa8z3sBj4#-8cyIwpN=!^tivg(_;69Eu>^SDWrdNfagQ_%C@R-%cg>FX|O;;>s%1j(E-`UKs0 z2}BX|p+GvmRf(hh_cu#TrqO2|C5oH0wb9AmqR0!`KF;HALoo>&pfb7!3iv3il8LXg z3Z}Il-Zhmc)@~3?G;?ybTO{{!$kF_&Qi-BbPX#kPTeA-9eFWRLU!p441qG|rV!+-1 zyyxe~^3#*UOJrn5&hCGRx6Fddf2XZYh9aT)R=a_3d)(Y&^xBO#FAfzH1w|=X=<6H% zT60$W5bGR?qP}t_)cLj_`_%U~D9*NG!m74@$jv}71eKYBd^2$9o(& zr?~#(P@Y7rc%v!q(z3O&m@Cd*3$~W0l}^zQoE8YCZgnRxNe@ZwJ{U;*QeiS8i&Xa% zl`boJ`>f+wK+r)@EPr5}YjS~YhlrSj_|#l;_iPB>Imww4lS_6M29~aN%jEt>B2O(z zRzKBGr3~MQ-zo?dV)S1cQ2OD1F4%s3ljU8SA=~|v$g{!sl)0{f(2U`={N9}^Q}E)) zIjvf)qyF=l*c5GgiRW8$U2|PU2$Oj(>48=J*9Nv&?_RcS95Xt(Dv#|U{7|3c@E$Q` z<)fqJht)j#EKsDH4J{e$N}gBEMJ3zbo|+rU5$C14>EOk?G5Y}R(CzGZvrTDUl{p&u zedEweQ^9~nrk3+`HcR#8uE)!<9L^o9v3J?56lxoTf|0kEwIkn-)lj|BMY^-C3)iyVGGS$Y<*S5svo^6loIK`Wj~@C=WKYyn6#SM_VWVz_qfrt7sM~S_usdOu=XbMM1V($n^8k$0eO81C7 ztslAW{a-W&r~>PfiLOnV4!-}~Yi<1?BH!WN9Jzn)1Wjjw`&kHZR8nY6DO*}HH{aWo zW7Dn029AxbAboLs+!I<(z1WK6niG8vB<`~P&rg}s&mIbWsoSFFwL;X42UYSs&Qx!Y z$#oikeNMwok4@{Q$4In1ew}d>=*m4a~JI` zCI<#yH~==7OTY2?K)&89d-hm;b+kfGw*&RJL%A@7%Zm?iu`Y6QOT_&&?uMex`D%Us zQ8$ERc(Lzyc1OKKlF^2Pm$eL|UOx83nONP%$x!i0lL4y-1goT0JSuuZ7U-yyeS*m- z=|NxJ+DLChT?ArFyM-?1v1<@$R&9(i$s{0cXo7YE6Sj4;H~jGq%UdqOpFC1}@5FCC zDn`oX^u*XjYs^HvRDIn)>}Dz^^g7|JrsN-_FTNbnKUwGvt>L_$dBN21_3p1QR&r4f zp(l)2BmyblLchO;(NWr4w{Au9n31)|-t&I0zH-I1LVz2eeD({t0XWA8kk}Xa|RvRI6vSKGw8)t7`CUMF$GSYM0`dmLxwj(jM z>W=@xBjXP)R-Ubm-TkJW>+jB*1;5KDnxh^f+g@<_G#rULdifyd9`0yL3PZL=4J9k0 z>lYZqd!3UrPYt~&KH|Dkf#2Xu?CUGJuKyNy^w)dkk4_p_o%pQOeED?a0=Uy;HA<_H z^2!&D*}JcH=MFzJOK$@v7|(f?UGLPVI^NdO-1rF}?*`8V8|wMm@s(F&MS16J^k0`b z3BjOm(h^pejfTK=m!oiXl7^ZqJ7zSCLCOHB4Wi}DRMVK6bAL+z%lB&lfo(M3zV z6Z{nkZE&T}mwFU;hk->`){=IIM!<`vUt)G;m@4qo=G9--!8LI-^+Tl+Vrqh7Z%QOV z?5t&WSQVNTRaXm{qibncPu1RrWvO7eH;C<2Y$CzB8`B=iX%ufj*m~P83Q-`2TTq zU$XM3BMWMeeH0I>&on|qb-c!I{<4iNq3CxfSJ#MvFA(&CX_JU4Lv78q?SR?;JmF8f zKE5ODvuxr{IC)p?<#hucbTv|;9c3!HdqkgG@XE_fyi+u?ib^Fxibf-o-oUf z*!aAZ{s0=7CQn~>d$D7 z6rQCME`hS|35F*8t5)Je&1sY$o^&NXO7PqpAfb`dXspc8hV&Q!V!k|Cs`}7B?&r^+ z%mp=NWqimAxxo2HzC_}?w8H+aiL1a`f{~`)VXyY zJbPGeSY;L`;IuQX%C+;6078vFMf%PJ=VeEI5!EJZr@c0+x-J*Xa}-sob0xbO*<>)g zWs=HlxVrSyuY=y@kTF)+8s}=rm9;5ftLOE_yV4d&-Z{sIn+_oollW>*DDpQQ-nV0K z@H?hdU9)6zcS}7?oY-esR@}S$GtK6M2++?LMQiK(A5Cy)O^f`DF*%Ip7{v`l*jStc zQk(8w>SZg*{pvdLaN;F+brwiDn3cRhQemM0*el<(W@SwqN~)=o`#5QtSNhG#MNId6 z;2eTVE>0I$`Z065Mo1fTq!DFdZXWP*tdjH}Sm)F+wosNDi+xW&l6*2YdeiJ@$vED;&+#qm%9+4`p;7dkUUx` zKNCpBM;3f8X-KcLQ*h!c`5VQi;LjGm@~x4=_!fe1K>v2Jn^AUNrl&jRQ(@a9pVD!< zaAF}a7YFgpC9^X#K9w9h(|im!vgBbp`~4m#Zkrv?PUmMPGX~AcNS&;ha>MT#8XME| z@RWQ<1J*mwQ#@CJY_u6aA5XtqcwMq$>|hnnj`j5smG+apZ)s~|-$)IA`zq{QFWZQ!I`@93MDqf7J+8=I z584#hYq~hnm$RH$y}zOG@$rG_|LSIu%DUckE)cmY%=MDI0}gv6!;rN@p!%RQe! z33>o(pEKXq$^YUso_O50t6caqdad~?Co_{y@zbH)Bc>{tm8@7=oxDwh}+ z_cT^03;Vq|!NzZw0zr1$EP0^|S39nVG8VqNxs%20Z`k>++2En2W$qkT*OZIm$MEQ# ztaTCO@zqe0P&ROkKe%sHL;)Ttzu=axY^fPwiBNe)zujd!nO=Aggt6WE&`@fTw#2E-aiW( zN-;H6N+Jr)0TT2=LP98#Tr3;+wu_n0@Q)7(3BmA;?&33fjM2GSOPlBq{@ULGOYx$p-X(?zd+;g)T$|ShZlCm*a^yFof)f`j~isZFG zD9@cccafHs2vF0FU39rM{YvejC1f}#{w^q(F6VrZ&_PEm3 zZT`&ByZf&Oj4edhn!<=ah|hv*nWQhGi1G&mL)QYildKttc=%o>M?g zLo>0oq%{?!Tloz(ePN`G1R~+XNa!xVt#4*V$j8U0RcV*;{cY6GgF!U9p|_V5^&u?G z-opAxrpdY4@$G6}e5;_*dHI{;)YT>UaK(Mo4eDwwgTOf-jk2FZ_SdgpSId8n70#jU zk3n0)!ZeWkNDe)L54IOoCm$Mz(UcY1{AWftlA<&Ef<(r1a!iNLK8F4;H2hv=GgD_r zhhEb=nTh<*fAIe24>vPuOsA>;@;(1P2dpwrX>5F~hzx0LYJ@~tS-B?kTBWj8^*SEZ z=DYA~WTeqPEfc{pAA8}_NT!z;c1y)@ ziqw_3qU%X$>zP&x14F~l;PZ1ydngw6PVw%=2+Jqfj1hq_E%U-IK`i*vY@A~f^iM{i zAiEFqGfi;O>)rE>;W7JSbw|vu1yaY>pw21^C15v~Xv-^RmjymXw%Nkq;NbSbi(ShY zmU3frbK=jR6h{^@CBJ`H>%X6d{}joEh;?af9%OJX4%N#6+rAzQ_e&HMe!wE#xONQ% z2=;!SPR;YXl=gm_>Y(loG!ppWYY*-0?(VFNya#G{9IV|}wziKRKRyer#~aA>3wIgI z(JY(vCKh4lK0#a61&)u8pPLm|`fcLQVP-9|QTas48ZNWHzmHk{^kv~)WY`;@&aHF9 zN+-gPMOIlE_9Q`5Fdam)RY9%*5oW0XFhz3d`!Kq%adRuQHDYrrM-2?9J&C%5Yg9B? z$j-=!$Hm2kfrMa}UT}3iWC{GQ@j{UhFrwTFmLXes0FE24C1p}Z zBs8N{1U}3@Qqojvq$sb*0OnkDLa7u#q*mQOm z+>44zpHWoO(at!hRbhiW)fDUtvH0l<$+w{!?*JN257H586xKtdXsF=-Izur{MI9q3 z(iyV?HfI2`??Lz#B5ZA$X5-7T!POjBlw);fpPHJofWrYLu8?ogd>Le#y5*F8RQe<|yUF)B zcClL-reN1B{SKqJ?Yd(Qoxmnb`gN*u298>hCrW~^h;%Y9v#Ml!g31V{6~66pYh3xrY&BCXG{Xs&rZ=3v4bt)Xnzk=|6uA_8pMLz8vMt82OUf zyd_{YRAyy0e+r8l{o`h4PUaL(zQ7E4eU1`DiL&LYHt2ux&l4wD=havxu?m7zrj(c3O-1BxzqX$A7Z*7Xk_tepdD;gIpJ-R`i6#yl@%g!1yD6_N2(mBkWe|1Z?F^A zPYzdY?RAsPST?n4d;e<;egOeED7(2%tlZq(o23)5rw_*et~h24yu5FH2eQCg!_r-m6MBtc zbl1Im6goOOWwEei3Q@BgIr0CTE85w%4$;%|C`_j#X^V!%I||$1I$Fg_@H_5j+F4p! zqC5fIJwqE98=qmWe)qU;yXrspSH|3bZ`_+W-vZ4G{ay(;Yqq-CJ9=lAmo4@ao!vQf zlZIsT%$}>yBJ{AXq^iBMPai(ShDz>`sSN?fL&ML`gBZ#r6Xm|rOUt(QEOm2h3kQ{w zno7FwvuXr15DFzKTF3IQWLCBw)#6;S94y3zDMkv|FIWWQt?lmOqf*k+##E@2q91mc zJvW*C&#l@ZCLRj~Y)xIxLsBBRzp`&al%FXv@Z{YURaKf~3K3r)W0N2XPgMrtAp95s z{rY^U*d#gthu{6+u4ny`-{Z0Wz91|%h(0)dSg14jMAbQJ7@0lB)OYc(s~8Aw4lqzu_GY#nGc(cf6ooP_8?YM-rW#GF&x8jk+D?b4w5t?ccI3T& zvxD)9={XkO6q4<00@l~oAFV{3SHixI9G*!{NtyFwia-0fTvJnXao;yB@9B`|gi*f8 z8CMs&eJ#9$6`NW<=tKZ5x%>kIF(}|T*SEuPd}Gqs==t8Y@gIg5<;=p&{8(IEe5lkS zW@u=LL29-mp?@k^lq2nTMo&+VB^CS9XpQFB;ppzxpHMlx;?a|ET%W9yYJ{(L)Y!4S z3Nv*kCNmHKHAIe7l$6DWEo5>0)=4dR7jtrQSU5QP9uW&ONL8L3ERtTiGPSCI=283o z>Xu{ozazA~Hfm@!3Q?R=-N7DP$+W9ewiM+@dr=gg47e2ft-P8eFUGsB%oo&!GRgHR$_b}JDV-fBy4%Az2iip64}Z?y>2!SU z5mA@^`SXLQsHl*XWpo)U8(ZsajT^_A6B7%IUO;2Z`$N-+O*2ieUHsk})Veoo44}~d zwZYAelA`1qp!vG?F*Y`P?DS;U1=d$dODmi3DhIQOhz_XmXdbihvAUp<1Q+~Fxv%oP zA65go8fO`aRu<#LB_vEinZS~%NJ;qx(TV2v^{I$UN-jb+x3Y#tG$4CY5|TfT37(%> zI#&^!Yon4P5wB~h=5~!%M{7a^JaDVhUa3ynQp^$SLUM!K=-9sVQS15h?ycjvsDEDX zUtG}n_hUXY_W#jG*!KUYm-yeh0`&Vz@W}i~k4XNqEL&_6J+ThnJy{P%K8;vLj9B)w z=2hE7xtxVbo~L`zNX|VF2Om94%m)j_p~hbx!!15ORd}u=D;c@DeJN6*e{3r37IVr> zft~?-rgB&tvs6BJ|E|qe6qf`rbjmt9@hL&-HP+VF@ry*hkNo`nGBY!gXG%(+WMlb1 zgRS&!fdhVZ+n_+~47?w(!HGil8t6!9XxODN4;;>wP(S2cKLDUF6>^S{kiZcOMuqhd zst%w~bpez-(r@3s4M_pywfGvJ_5;K6aD^gRT;U8kx^+5=X|goMBG~ZFM>H~b;Vn_O zu&4+L3k$m`rBP%Q2<#k_Z^P#pt6Fu}oh5}=w{6eCmpotpy}G9dSK~_nTp%VRX0@E0 z%4HKoa)88putWi0y1jL24KAEG)}A1mXFb*ET+6}CEbroSaBT;(gygh)?)Tt*3;mw6 zc3^rDR?zB$3uYweIg!7+>VDm)@NiR*ry(in>CC;oy;3j8IN|?a-Mj6572Z9noYs@> z+_}Rb1*<0DypdvAInOB7?O1oHEjNIf{PX7*F(dfO*+||7eZ;r7w+jc&K_qno{4BPe zm3`~ulUY)tJhtEN2#*S{3c7xxzJ3t@N(lQ8RR`)S3&cRkR+{Ls6d>mifpC+70)1bG zjFc2Hv;N%wS~s)5)9fIk6Uhe9CDjc<(j|s1;g-8XTb++!_@N9Yrzgkp_Ac;TP&hy-2k6Ju zm)J{)PYOh7G{Zdv!Mgbr9i0i#Y619Z?~$ic-SBQ$hPDTQ(=uAxGQA-nTXT;C;%)~I z-@o35+y@w&8Q6s0Bcx9gM6?~Mj!)ZM&mAGKRG6EPhs4UJQ4>j)C1+$vL3@ag@opPZ zAY8K2(_iLSEzF~zmyIF)XC4K&Oouc~d}x`bT*`GC)k6u>|06uko8;9VWCn1>H_Y2D zeD}LjxtLqUVBZ>VVPQe@M~5gLj3bVzDUs-y7~$QX-d^V#<3B$?dJov5JjMPUz@(RV z)ybS$(coPW<25@&W|TappWtLD>F9_&u35}?-9;=Z^zV8c?LAnQG>&U(Gb+090?b0; z>;H~G28b|uX=#6kd9c++as{c0Kpf72rtRnH*Gh(lwohaE72cDvKT}ta>Tp4$(IxJO z4sB7~l3;x*!_NQmBq}N4c*$76Wh=e^PHo@#c3N)BJan~&~j9SUt;)OM>YPQ2W49^Pe7F1Shv_x?BgRC(qLvO=ycTLR&`qzP%ovS@uQGD_w z0JhJ$5$h%zgoT}bS$QyiXm@rnC8!P!1X=VQLx0TuU@u(Nar)fE6DF9~yVN4XS;J5@ zk_%A<{jN*Uql5aPGj!Gw zS;Y6bi4jw%uNOZ>MVT-}f#*N3KTbA{03jyk3u%SoV^!L^wQdz3|7<$#3>w$;-E%+K zqHTf0ZOsFpYFTal^p9C1p$w9z?x8bJ-Y$xEfmWYv0&_daCwAxwZu-ts` z4!c_ZeVE%vIL=%WfIHkNv%v0Jgai@Qr%wan@^DfyQbQ($*7?sR8X>37yd4A<7aI7; z`4Z7Qb91W(VaWy{u(XtSd9X+_O*XE(F_7wT%dKX+MfcgpL`tg0^IxE1S-RE7Zxrly$;8DGA9X&LG$8*xB;Ze7aH{?-!yck#*jS177Ogo-o- zJIHB(Kp8BzMv^Na^dMnzw8}B>qXAUk?3z9Ty%oq%1V)ZX@2mx0o~XUDii%6TRie=v zq_{+nk3YXcN@@smsR?%nYAcXo6Shq$nrC;Z2l6w}W`oX~GZa#cqM~EA?Fj|&n6|f0 zdorLUN{$y4geZlcE~bReT{3n_KE9@4q~a=`%FqM6E`C`$yt#3>RsD6cV1L zq@pST8DzOQy3+^@C_@I?rC{Z0H+F$ZjHvxh1_mdidqh2rFI|zHC3s%l`oY3q;2`Ji zwt_+o*@FCl{cPjjOl6&pF%S|VDd1M?xQ8^>*QhLOJC01-`|N<+`HKb);K59m1odXA z?z%LVaCV3snwJHhFqlAaZAeN|(v6bh;;jsKB*CI1UN|HLpVsL<3~O1%^4F(Z@#;md z5hXP>W~s{UK2708oik^t{{e_!KkUGvaM<6rjA%(cIB>y;?gKCfx5Dz=#37nKqrVU| zq066-lAl6ClE9VU!6*l&TSyAf#pNx>7#K`?`EnA587ms>1s=g=c^Kgf0QT$8s7B|6~Xe5I#NH zWZ~pgRa8_gi~1U8j3YzC%@hpqytVbo@=%G4=+Oi*k6t}aM|2Iy*G2ofGwQ0>DjFU~ zv`kY3bJEr}rg7_X2h_BwZ5y*KEG#fc=^k`Pxb1use!mS=x=tQ?*&)Hu)og1H=@QAS zcx0(-MtW#EHf9VY%)knJO?6{)^K2Vi+urW(Brt24nV2Lo<`0nD7?aGl zbnSjWDf0>iQfgJZZxj*LH5vrt^ejR5!`er#-0q>;m9SJ$P|(TSb#%sSc64HOrwy+% zezRK&d#UZX=5Q4`T`Y>t%dg^d!#!9LDfiYvz5W+>c%}e(?0ll7q{JX~(3|IxD!5v$ z2(7|@gGWo~b&M;cfgI#b@z^gYv1=ohN>Hb0t(8m%sPBYBvY(X)i#}AA9~eNt>h6@L zjs}Z^v6s@(k8B(5u8f4F00y!Ada!6VG7drtWSD#iNXC&gfn#@G#|rTLeKo$PyLicw zC(!}m;56iyU6M+-!u2kbYQ zg@Gfp#G}cpb!(L>Yo!)w1}R7~=?6%App;hEF+-ab8%wQS?Np#ykt`W(3dmCjeg51z z1Zq8mFsz!f^4-1r5?Dc<7LP^Eu@DB#;{>)&Bw_LW4SM>U+d_nt|Fr=?Ds=HdFb&{7R?^o`o^>%Z%L<|u&V;l# zv$0xtER){}1wc$!&vYbkW=tKb>*%bgznFq!UsekUsb%8T%blgc%9&_0>~1+LB5MA` zgrh%9@zVsLFp7au!YHa|O>FD68@tHh3w`}ZEc9}PA( zHY&p`tgQIJ;jyxQsHUljPHhEdqf)DE(43Kh;rpN2+0(oUd>>LHj3ERTu8?oT#65;v z92~N+ErJ146r1%U9AKg(wH!m&c9+xR-LTM5jQGRF$7ki`RUsr}lZR4aD|M$HdCTUb z)#|{SYLvhF49LTvXpn@0A_M%9(=#&*AdN-%dMJNZVzYxCOvP(14}yu-eMuw$8Yj1`={Z~^J9bwAvKoI+-L`bVHbfamFjQjnPKnw6dX z1S0UU=94*Lihba}Q`L=#DEf_5wLqq#b?>0T6ds4 zXb%3}no33nSjf!EU3dmz)(T*C{RY=UE^rUe06PFG zyVJNQZ2485#K4Iav)U*WHp}(v80$YXFRvdQKzLPxD4*N^J`0dP;(yKXi03BY4J>G; z{O30)HIjdUDq!`bv;Sb$|K}eucJ=?q+x;(h)BnFZ(Gz0-wC}dBWsQw6Hwu;w`;F#X##O@RvgEt|hI*GPJcRH<~W5uVgK8Hr=DHa5yb| zp=Fgc{qb{4M*7i4f6|zV>`dCly6}E(5;|{N$~7H4(Xr2SKT)sHum48g2Z}MObBbAd z&7CoRqcdu@5^r2zZqh^SBA{&>-R8BTAzWb_ovl7H*Cb5Nvq;)J@wvO#G>Ia*jn@2b z2qJVzX^NWJ@XJg0`Ad(h-J0otSKmJ9ZdQTs=k@jw)rxbFmD~1X!KB$ zK$=ZTs1aQf{XUs1_Po5jQu`eBd5uQIu;$rn6x^yyw{~eizdcV8Myzu@U)p3t@*Gd)1maPt%(8@m`TO7 z93L%_j`Or}FC2~rWx5`CgIl6Kp;sUxDy z&oCBxP6ggAjZkoQ`HEg{CnmG2lMJRlfY~;D{Bt|;uRcM}sd%#cQd zB}zwPaw@za01y44Ld+XAwH$3|zwrof%wjdXD7xK}Z)L`!sm*55?O$5dmA$|dccrg1 zSmM3qI6)mKK^>C1M_eIen_;IUSbiHcQN9V!!%~aqZSNdP{+=$126ZxY`Dkcs`kHzl z&|iCWEjmFhUqL;J#4lD{SbV(VBiGH&x_105-MuR6s%FsYf40`*L5;fKaAWc(hA2mX zLT^+}4H255J5;Twdm0%W#u`Fb@3f5pV_{_-d;M1O#Z&4Vh7kz@bi$A1bi>&zGpRyLXh#)3eHK^gkD00|kj-fBea{ip7pwM)+hUH{;2`{BQ5&*-=z z6Ev5V_!#n!Xf{?}qGFdy*bxbdQ9i=fIwTVpW_D95-slyY2c92q5B}Lovn)wUN}|BJ z-~}&pTW}wl6|tuoOJZGC_|ahbVNr?t7c`wMc=V5145kY1T+xsIvE7Eg zCj^bFsHqV%XnlT3P+x97@oaT{{qLYKv@flys#NrLr>K0(5u7G7w{i z-3O|G5X)#OoM&`w%>R{1g=T_XgX8ws9=#i^v0?=I-emDLrmoweSgn&g{pT{KPfK|( zVLmx!C_QITu3}3xjc^Y|;@;eJ1tD35?@`#-YRvr*m8r}=hn4bAdtJRsDxq7DXC+H7 zylXCr6cx4wUce@)1wFU*RPjg6+vCl#`r}{ZuCwm8k7T$uhVFygq!ZY2Zm?Fgp~S;H zT6l;~YrfF%Y)aRIy&>rO`e;ZhCM(J~DZ7U_AfiyK>i&aAY>11N`kit(qG9m{FH-zg zV0|E^XOA#VnYp;-)}8yX8w)a8V#J~&I4)ZtqzwEdD#K8N6>2KTzrVxm#>X(dz07*W ziWZd^7a*LW;g^1cxtvCtjsdg0ImZicttkFqw_k*Jw?%o2A3F-&61q^V#QvlC`T*Oi;d>2;My12$DH z#uSncjgMBUd+o!uO=wC*?)cj?EZ3l=b&K`oj62m;@rBDz4~tX8dve3iG%mQq>+YM2 z?om<*E88T|z*#nuhpHjCN`n6Q#z+zV7pWD5=WVs`DQ;s7!@V2L=%ggFcV2!p!LD*D z`}4dOlbLNtO=8BsNsf1>)oPlhXqlNY6LjpcCkr+G*Vj!vZ!iAUE(cQh?e$pKbeL3- zJ(uze=G@xa@;oksF2^$d{z%X@uz&WDc3pS#>ocH_tC{JTgC(LnAd}7Zh}toIIr#3$$toL4k?hKjb>{`>L9v=%M30@}!)}-_R@94O(C-R)Xn9q?T|D+<(N?q8 zmKI*3RrQRa?q1U%p=77cVe!q;DA1Z};zR@u&1kI@4_%=FfY? z>4-i2%qWn{0NF~6+3jDBin~f4-960CGfUosbk6SSLYMsd7Kf))BB%;fb-w-{&aD%^ z*%A3djpb}P<%g-uE4(jQ$QvJp-owVm4vd_7WCNXJ!xudVJQJ15uFB#*RsFqW{fGL7 zDlehrat^`ehGwIwnZ)6B-_JebsTMcbhA;B+^GAzHtY)jrBuu=nPP_h;89HqXKMBNx zC__-tFkmp{G@&ma8=UXW_gozP+hnAZ+-U8g_V@s2*DU`&FTgq^a)bjf(Wx6PbtCj0 znVVBeUPYIzRNT>e8?3Cd+%H(W-JhFfxII6L63dlw-=@ zRZyLGE}Y6XA;*`+UhlZse)#a=LoltxuD99SUV`0OsbXbSWS!B%tdibYZ8ZyH@pS|c zph&UBoy+6Yl7W#C1vXit7x^3dy`=XkRtZfq0H1nz#E82}TQ7VH=Ho}%jpay*GU{r3ra>H@khTG?e7v%k%oWVEh7k*%RxNamn>C?$ zJ=f_j)fBnsSbjeD95@VS3q&QS!m<^uIp7~sC*+R!)f*cC*7%DH0bGSdD3@2O#3lVB z09KK`?_*@Qd_fXVhu?iFdx)^K~ju~u{G%eGV%!#y^RgZsK?770!)^+Z|q8Rvf-H$oq zG6e2R5Bg@+qacxZn3RD;;l+*U4>H4TAFo-s#KwWxf6QZ7d0MAQDES;HL_^AA>41d z&~2*MI9l%bSKAc93e+^WuyBg)`D|_7oy@D7lyh-psJ6b5Ur=mtF(iH`vaz2I24F26 zfoD985YJZVBbe)M)znPU5T^RdEYXNR4nU%nm}IVB69Gt=J8k&)+_hgCCK202$axK| z^9hjqPvvd@CUb==Kbf?OknNywrPvDRMdV*LNE|be{~k>3DsH-h!J6tHe%fgD-mMI# z{24~=A7?}XTiSj-J8ZOso6u+U(%G307GnmGz(%ip%<9TRQjYDZ64RU4iO*5)-Md$R zQpAjgiD%R{`Rdgx#Ld|v<~*j`0g*K1I%f}OXS>_TiM(ulV%`^sOJO@CdS+J5m>o2> z`Td*>UGn@3PNQ`Hr!parIqQa20x$L9uoSesHZ{Ln- zG^_VuO0biYH)1Tr#cjyH@Vx!@A#mj|G}nb?8dlKdr>Pc~Kp63p$?{M2*zmzSC@B!Y z`j37Cj^7<#6QGZ1<`oq9&+bkZiR-4*syDg3U++)qIK0JCw376l#G(_qJlzew+Hc9Q z(gOfGk=NppdZC6d7+m^id-Lar4ty?`Jslm_)z1*0K9tyk3o4N|xa-sz%tG_wRpbw@|LFVS+&`Cl_!0vB^d8 z)vGt}-l4;KdwW|eKbPHz3W26);y=!~yT%+C7?7-DM&pzW7;+dAg@RJ7>%Uq-Hz!lI zd-dDTt>*;7D1{$|K4NbNYm&1X8p)P%J?J9M;d-BqEC17%1`7h9I#|#hmppKm8j7%j zF1z7>Dou>vc#W$+Ys-5(%0nBnD;E-ekBFF9^VT!A44=CZ(?*K~2W<7_?bjiy7ZBwJ94`ZpXsHLZ$eU;^N67y~9;TEWco!lamuk3f`N8gC6RN>$L{eTq^KX zrKUL?G6G=vPq~a{Q-#R@d4D1oecmZv5SoI50!Dur&EL>pYPkk0-Qi|tW)hm3WP|CV z4>aRPekhVUZcnnCA6iLYz2N1Q@wksJ+4&sIigOu~dm8=OF-i+YeAusu>Wqj)6^0k~ z6_|LjKPiv|sDBubVWU&kEZ?>*ID%e8KZ4JWsxkSdGH6M`YA3&>z;N~#yUpJhDgha2 zQ5Uft`yXOj8J!kqUTaU=lD0Qu)$#4~V1x0^X)KK*fmRr^M0X`N z6!Oom0{+FS;nXaWVnZPP5;bAmOEcBCrurv_A1q+SQ*jgrU6LSL%4UClcebV?GlgC* zLV74&)Mz>J$jEpP44TgIUus-dGcRz+Uj&CWaN&Ih z_-7fp%(y~nLTb1B?j$qX=wYxEi1)pQ{r*5+kPjA`7spamqc7~b-l-i zTR$Uy%IIcGelvynB#|lwpP#$8DJm8&nx`sB$;x8DW2MnR+x1A=?0GaN0?Ui8mJ(2qp@3B)WR9KUcy$WFGh zDiHDQ{<#N}SCVfH;G(A!YEDKf5sw%mY$w($d=0i z<*g2ecqBl=U7tfQwWnHXdSd#gq=KmXCAC5VA6hdwnu zdhoE(PVC(~G|vB&m&c(C3k}Fk^7fwEPbEon64W1o9kq5VfAZtz8(c8GyvZpj!V@2X zmk{LZhy(%;z)Pm|^INzuDgrp71aY5HgSE&o|A=pFjW#RgI)=kgqIY( za_Tc4qbCdS`sU^Zm4Vhbcg@n#&8VQi!?K&3bzPb|0apRTy3WCASC|OQq(?fhW4>n^ zlgkGs(9i{KaoH@Vz6sutqC#%vdywcnF=e2bzueLpTs>vy6+TZ9XB_%fp|AEN+Egem$bbR2(aEWbEh9bMWJ81f=}bk!+5^%8swBKMmK;IQ&9e2gw zBFc#@riyX%**6hH(nd>$VmObV(}eKQYW>HEX4;?ZViS~wD-e9*eY%e_=Qp*%;*JwV z^kF?-#5mRJ?Khu)`DKmjNu#>cQO1Ky*gpz0Kj?{ETs|v2ZxPoYXee>p>JzZD%@}I5 zsol3T`bT?1TNpBNwC{ThRyl{(Y( zDYQ+O!N*qOWPi@B*^M{oUt>G|9QL-r-A_fl=*;6t%h)PxKJcw~pH;TgC<4uUjXJ7Ybh%K@f&N{EA_m?k7us>auzU;lKZ8MsR}*V3uS+ z@WM<33;}jc`IWySXH#W|`$RaED0xy+QeJU!_uhPis;1`Got+(@nfiHaZfgQ*;U{9A zwcjeRs1vtaebKCn|Hd$210e2tI&G>sy)#`Ap2R{(1Qsq(1)czH%?#P(ctKs}UEyb< z5lS74zcYO)ZHx$Y81^>o`5nXODgEntL=p=Vkz_T zs*BT`a4!yvGMWLKBcY(9d6gckT>#*4M-6w}Cn4102nKJ*A;UZ&wZOtg)C>t@N?c!$ zSPpnMx#)$&$7r14!yDfYvqRT~FbY8>uIJ9Sq@()&7yy9wmWSE2EZX!w79+wXF@nN@ zsPdDm-J_ttby>HvLhLugv z?TYx+csgUTvDgAt=Dw`V{PpYWe<;@vK*gXQ%d$+xGu@GN*+24gqfc{0)yzey8o^5a zJ>a{_?I`liV6ek!2SH0nHf9FJSSf7GH^F*dy|TN0M>Z&&2GrV7C!Cal?!)l#W%1zx zs>W3m83ObyPFc5o?~{oSY~miLJ9yL#4&!;My}y6IeEE3+k)|R=Ei3!Gdby?Yn zTzxPNmfq>k^w_Ix{ng&+o!gSy+S&mR2}y!a71IF0yykps@VE)hl?WXg5u^cFtP^Ws zj6kfx^-8@@(^qA>)rI2J9Z?cmsrlmnbC>5m917 z>3o*I@6-{u`D*uOFr%z&XUspmZZdFvP3CpZ}dG^dLxW*kq2GU(Q{)kn8qMr-K4~K z%KY_nOQ1~j#XFf8Nh?F7u;!aA2Xa@;;{*37YBA@RXSeqxCEmvTeK@yj8!(?SyL+(o zl%6CD2BU!~NK3wZ*yo=>gY}ZrNK^Lx`)_+U=|WETug+K@A_>3_9`jiy?iot^Z;s_w zT=Oq_62F@Tb=j0FMUa+E%&eSQScqe6Y&?>FKMU>k>(?XcsFDO+SYSPP6Uww2ymb_k zQ7%?HmhY^u<9`JwIopSihGQO4rA!$V04RmB(|m})rGL|uy> zJ-|5L`!%k5M>SJhUr1eaU{vJ3yFm)y>Y5TT6q6}Z30XS&@Gnj3Zb4M@B5S3`YqorB z>2B^^yd)$waNas--rD9&t0qYMt%$3P>TtSw;teqbkQ;qfRaKzJ{$Mh1!PrJF>7z$< zPoH7~5I9?76V$Q%iIZoxW7&52(9RFbulQqwtjGO;h*}+v-txK6&G=Y;!NgbPD=qM&cn zv3>16CrkQlkvn4a15A=2fNFevyc8Nz7Zr5cBEh2;!+~u~7Exf}JkGp&Ji_oU2}2>q z_V)VXloj|dj&C=3qG*EShsQ)guQ)5K492Hg{t;*lQq3eRI9Xa!E#ceE^!B3Tvu4Xw3x%sn7ZVZwR`2D+kXrEtiYXOJwFtp)l#_S0t;3Hk^QaEc86kELGI-A83(D%)kS z?^843!(L#hPE%sy`0C53x)g)Oi2YI+JAIX%_ysYl;x#_&)CT6lYQ?gGWd?H(h3jkx z&aTypY3{JBs7h8OrcL~_5gL!!e&(t~)+3VpI_4j#i%u!m{UT3xC+1!H)uENcmA0WL zj5aYMUu{?wied}DU572294ht1ZpJTu8K^A0wk){uG?)tFw8ku$Xg^$L>$7hVKr8vx z^+CeNffm3nl4q=}xIEm&dM)W|xhfe8pPj}gBrJRkmT-?fWB$9bF!)+_f(}p;h6VRY zu(Q!d-+bsfd}{UB^@IijNi8()cF64uU4z}}(bwr2Qjyfi^p@a1d?cfJ+AO_7Xz zBUp4s$;ruYhMH0*D0I{HyTix=!-=iHwi<4MAehAG&rgA}foi>ND-f29griFIaQi>L z3D|xcI{)`7F-m;-rzO5(Eco;D&~sxfjG3}7K?pYbty%0fGFYY$4OgZ}A?ZnI*v-!4`8aw+NQ5vn<|WPIvu&w&~qkFSno<#f8VNf>@K zu1KMb>*YkW8bTVEEMJaSz2 z7g$(WBme_qBRnrQdpbtI@*hPIkpe1--%{2|9XcU>Y5~F>b#2x8K#iy0ZZOU8Vy6%b zsWMe~4*3-mlJSfhefNNxinnnz%HbBAuk1H4bU#!~`A=CgfsIHY%`!eYNj`H26d?`o z%j3(ed6Ly z*HYh6E&wjpu`WxH<_ml{BI_EWpygk=>D-05gqrQqR`d{7_7RY0L5DvRM6+?Wj<8#K zXP@3qIJ(6Cl-2ZtYF_AASymSFfNYd&#tqS5uvndC&1f?E>gYLU1!6q z)7?af*Fs&w3fM_tOk`6&;?(VL6h-5m3>*y%W+O3+p~0d8n%e)h<9qIufJudMdGZsb zY^+$8_MO;9DsUZM2($j&qf|I!>m0uW*I$zXO@qK)|Z8nDnKWc4K zp@Lm6i1Rc~K_`ZhQ9);vT&^hh+l5XolA24n-+&tp!I*t15wePD~BS<4|E z^G;R(ml@Qg2+)zTB_UevM~?A#tcoC5V2?{77sK%G!v{22IUU0g492XuB9ToS&X1V9 z_(ewEjIr>Xfn4YNe}X9-afky-cdTmv`*()pjS+gbkMo6vY>xX2A6M4wU6qnweD#{L zT4)qpg7$?*ml?4P-=v|^Wp%s`2xNoX*6HkiV8Ei|_z#%DzWsZqu^@jl4^-;YRuTm? zo4(H_(o$!B*TLZw3ZrkEw0x_aaO=-FLl!VMOBu*LU$`p#2m-Yc56~I~zcqSh;qbD5 z?7^j_Hh+{VH7BquhVdt8(P3Z>5_Ba2 z>mUOlguMUO9?bYhu12#(jdftSLvg!>{NMoZpjs`X>EJWp`4}Q%5|t_Cjn9UPhg)~N zWVSh79?V~kYzHw7OErr02Y^NHH=N*)iC_LHuOkq5Y}dFDNVYZB^N=IgUjFwVKbEgA zj>OuuJB&xaE3j~I0FQCD8W;RLULGZ2gaHX1LHIR?=Ks>r@SX?^ubcWZDCFhkwN?y? z!GD_!rCaq&V}D#)n*!U*=D!Kl`YV=MquFCOjUD)jvO4~7}L^* zTP@b&OVaounr!xx28zM*KkFugtroQZbv|1goJT25=zW41fRx_e&9v}B9v+@$Mp#FlUoynPyuIjvjJRcV$mK*$R7so?un_D{Uc`0C1GjM!9pEES)kFZ4WoF!3tE;OIqfGeG^7E781xb-|mP0@y zrlY4velP{06R zKoz?i8SMtcb(vHW_D-7!8e_wP()rC$_usQzU1Px7R-9OTc$D2v8|wa$5xIzSxbiU! zX8te{WR{&YWZ{gG9x6i&q0rr_kLI|T|NZ?A_Z@NWmwb~8cc$>>FOOQZiZTrH6!6uq`t3Pk9^~g%fXA&|Y!IR{f6CGkw;m+uO!7{-a$AlKv4sy~fe=tY4h)>fC=~u0;z%y7(sZH zs4x-|l1#9Q^v^z5Y2iW$QW-)(CRhM^X2r^znF56ZlxsbS7zQwkh{tkOY--XN6l{gk zR3QXou*N5#A{Id+JF*c^`fkNTLqlheUlOCE@TK5RPfyE(9xWV80<)lWb{A!$N`r03 zIB9It(yoP?446}M)-{oP#USky>x$$w5 zkpmQ&tA#cDwq;vsPs{q#Ha!njlAJ@bSIDJ<@(NO>GQHRn4L@2E?IF{qUu)AI3{T3K zbrW?3rSCTpa-__hieaXIfIpzG@It^1U)P-gJc8hd)znsXumw9V&#AHaSKVt2MaV9{+v<_u)Avly9Tzs@U0JQ3s`RSVHH(uD9_ zPf+eOpTX_NMUE@GtqTTAf$V1!EQN zBGsYW?g1j%H$g|K)&&1C1p|K|Yw`?DwYRsI`kxsY8_O{IRZN&qFk<}%^{knmP>cx5 z2Wo5pA6}3@qv$r#NRZeSmLKfDPyRRTd&6c^>3i0n!~-LtOqs({cmWJcP1Oj`&o8WN z%$Fwg(BOUd8q481l zW6pXn^6g_oMX1dJ+F>3UPq4Gl@;S>5+>L>L`D(__WP(mB)|Q{ zw1F%8Q+BGUtgB@njpaW+ZnJY8Dxb}XwIF&;esxnbQP}fPI4xD$pBk-%4gSjSH0k7I z={3&oD45Cw_1U@5e=}JcwV>(?ONc&P3Wr+k2^gUuv$QVZPbNcOE(nrYVi&8#@hwM# z!m~xtc2Q0F_3v-2xJkrW)X1hnMZyH>H2+lJDS7;bz(9=k=@@F-8=%1e!Y1lYB_o?7 z%Xcp<+6;jmVS+o)^IPFRw#_A@pn(1A74!-Q1Az!!#g|V3egwY1`)hWiw`yt~hkwD$ zQ40FAZ2M`k*$btL9_aYP>tNv;UX0|()o4RNS7N)n_?JP!og48RT+qRw@ct)wJUgJ1 z!KaQembccP5+=Z6Cw8c0mLND7#9!RKm+9JgOf4`By5eU38f$YORRVIdqJ*0|8=#fU z?l%46ewm$>#Dhp4$P#~XwD9_WQ8q8v@M+s77qEdl(jqv3p${;4s+gD1s@VMp50V_a zKy@GmJLlOvp~hwN$Hda6hQJJEqYhT)yUR8MLpHGgSI!~0x>ARjxVQk2?^wCqE{W9e zjIFki+g3hC@*=;(gN&Tf1G927D>WyLKDa!oPb zu195d^snhj1KLf-?u3K{ItuG8c}bFM-N-M_WPyl_j#1J=5#c*!P)5VZC=>Z~6G^mV zVqV^*Cq36kg~uZ84LK(#UN|O^PjrGCv^?7_3Te8>mzfBS zkE{UW!Sg=}fX*YN5ueSlTLq!UM(mqUD#?4HaLBCi-KO79!P4d#q)WTAzmD4vJEG0oD;S`426h+>q3P+!JS1EEk{__5iN z>YNu&x4gsoviUz(8GG5DRQ3>Q7~E7PyqF0=jYIckY;)l7iuDm~^sTW)MVDM668XwH zwgrfaU?Bguc(6(vTv&Z*;6BNoQ8&z!vKG`en^~&7h#AWwmp~}=jG;;`QgI>7S={$H zSCW3eEhVNV$j{~P7Fq$wVD4NTgmzcKJb7JXV~Ef|ei2>rF?vahu${pMr#lZf zce~cSKEv_`Dp2mqJwcjL!U{-CWI|Zk_a2aP7+vJ1yXVhehLUocT!0+sXq2E6#{K*E z5g;r8>@_IEMENmTDzzXvsS2dzk-8{Mu#(E$nB@uVe8$|U79X;r-Xv*GZ%pKtWUU+! z;ez$R@5lzCu}fxe<9!(^G8HKl$0=AwXcUNe`kAKNkMNvrX9BSQw`AWRb0aW_UOd$T z@2=GVn_VDptL*`LZik|7Bll`{qrJB?~fGJ6LH6c$$I{f2$97cX8&TjCuXgwkLk1;99#1x=t5uZ6nqoMQ)W zuY=13KrArgM>e^O@8m*uT6n$tK5pvYwgPJBJxSiDVK57c2e=E*ujn8kIlTM_)|G(w zwE!&hLy7LjOqGTF4Pf^{Y`X+c-?7hs+8q7y00o zQ+FFiiavtMS}lMUzUwS!#O6n1K>Xj`XxyD7ut9B=WPy<`e><^Z60+_>qx6nB>#Oni zoz`I%Fx%?U1{_vq;Y!Tu>O5e9UyMI5Nt@rBxUXEkNsSYMjKhW@!)~4e3k{TQ=P^Jt zEGM;L;l_2gt^%PWWHPiBTu5tmC=?Q6JNBACtF)8=*!&&1{Bui3b;mmuLw~y|Y{I(V z3A1@!9=9D0i4U!kJMa8iHq_uJkNHd!d>)9T$BcZ1C*-c2sZM2Ch7;rCnZ?zQV8abA zHc~x*KU+JOH8OTLg|`1_%#K+Dw99d~p}0AHt%G;)hzPg(`5F~?^uf^Z@*3GWOup?C zcT!Yg1zQv$mLEZRSSN5;HN+lM>T)|0NL%bRo#{Ria_p6htziTu-3d4gVps}a#?~Y) z2zW++U0SXvXLaixpL(A!2L*?{=Eg-QTy(S!eL%?P$NcFr3=yg*t%6|Dtyg#yhbwvf z3O6qwjfh#CCghbUq7R3aROia0pD(J*Hty?yxpW9u% zHYfWqVrl+SWqx;znpKVaq3O`lRZ|ztXHV2U+ve`}cim(nN6B2~x|<-TI)2emgVz3P zU#z9x{zB6dZqC}pt^;jWnhjU_sn}w>Ny6FkZ-bG86I6y=U%|8=Ce1C?2#}0=C1EF5mgBb&}0{77tNZ_D8HrCOkTG;(TKU zIV=|Fgf)Q0e*$LJpL!=&ATN&qQ4ndYCxS((#H7~i^@xctF%|K7I^8K6VUy z%!Qf&>AF7+#336snZ>2xTLSC+Z1#?4NW@>#ALGl58nb2ly2PJ_T?TC%BRNuA%)Go4 zwqKc>72kXYA16e=PI=nI}luRGFMH@Y54cJlwXI1mE~cP1B` zq#q5=JCgDbFnvBx%b+H}3(zK}MO{@gPPS0jT}Gz;6JpKe74g-w+lX;Eo+dTAfN+iR z-R+GlxJ|EPROCVf;70#sYvO!``Yt#iARzOyu%Mu0fPZl=zcD)-)dHF-?g9oJc7g;ZZ5QvlpTCFa0<1~lN zKLti8AeoqrP)*kt8iTl!yCcr{3|^0j<(0H0oW>@4vn4BMV5+#gBf4pF1@RZIIqK?9 zKYkb@b5%QuHj`(%`RGR@u(iE?&`Tc)n5K@y0BS|cucePTD*Iq-d-P;5Wq=Uo;_=iVN@*jx^iv=yTH4OYQKJ)qd!fU zf372ptlDOA@Gxs@p9zr7LJJCZWFDRmff|ChzuXao!t&1=n2o#DSrQiD)I==MysoF9 z8&d106LtBRfQPK=pB5V30)MYXgRn{`KXNk={>*Al-`rgN5YUJO6B^dSAs0(gw-#^&1Y_+v}ktX-K&fs`stfFBrF!*==v-qJwypOkS zdzyGSu)e;hNCho;(4P&iQDy!Rwlrf=VpX+hQo*%94cc}?s5)Pi70$g?=M$5b1Q-Ca zaA7_m(iW8<=Mu1Ft`$JX%4I&zaynlRwt^2X!)lizK{GJI@x`HQ0wApg4VXv)pv-Gb zuIqUqBXW)C_^*SW@C7f`fka!9PCo$I2+%c;`QjNA9GkG{B9Ka%bmDtzHq(nQQ}YDT zT=>*)aT>wt9%3VQZbYE6?pRrc-LW(_{A_z(?d-O|70pInz4zt?pKCaCHJ6J=W(OXR$GlZS>86#k_-(-^nzVGZ5K%`F7`bk3oP4pynL&Sm>8nt z&|yHoai~Nd`(!VA{_XtgGZPJ9U?Yg4=>wr&qtc8Jm`(p`fIiZbaeK^sy{Q7Vr;B>f zz33QJ(9$CJzCFsQ*iF!N?|Y*HX&?S&3_ky1xInfQkRF{4!hF0h68LLVXq2 zWgcgSg6e@F5VXObK4NRXQt2fSX{mc##eTL!=kAi}O{~w_e{qL*LFUC-S-#&O#@z2OtiubR|$+!P(wNO^;!;kAcYG$3@w?{CXoc4 zmVw?vRh1B~k}e{+AttRVt)->)?%3sGg+FD0wq(4ozh4@%va-5*DA*lIJtUIhvODuI zYBdhNHo7Zv(8Cd8@NNi_Yd+1&_U4mW>5|Qx5}Q;n{|K_8mJhoDWII@mRY7u)WaY=C z9~}wdCE7K3zySe}(bNbG9Av8iwb%y;Z-xWOyousaGp!*nKmXN@7$b09h-xr&>FFSu z8qzgQUx;ofPC72<25i!U}-hK07)`{SPFPEhojzY`M4gTJQ#b3rY zJHd#B!CEdSO0Fm0qgkS~LemwdcBeariww#%P@8SklZ2d}A(tsE#P@7H z#Re}gZNOw~p2nOnWjbctj9{S57g6k0E3KW(kZ3`zIAkphx$OOSKnzxkXn$}3qDL=D zWI!Hgz{By@%?V^z&reDq%Cb6VSzsMey3F}vQhO4>5C9u~+MBBjY%Em$unJT#sPl*K zRIvOAIHHl^efc@%3JjCbS?dN|KblV&6GBN^m6mZ5y*me<|D#D zi6{3?L>HSb9!Wvq;&<0ayT899WUm4AA1rn2B93Kx-3}~mK&Qkr8yn}IbLx%)=6lY_ zf!oMVFH?{goo&;2Ap7WPyGFGk_d#G^+EiqKq;k#Ej^_rQSYU|Wd*LEUkVq>M_leIls$LeEHVL2Y?Ci`$-3H+u911?1Mei&2{QP{|rB;+7y2EILo`-R49h>BpI^P9b3~VH zwW^snsU=0GFr}}FTrd_S=w-lR3~~xS%T^E;ErKVqekewV9S(-8fjeg9>xc5PQSxG@62|8Go>Qh6#i9RWlrKj=VnO`VEJpuv|ik%X8?H z9`X8U5?XNyF5r-oa2`B-Vu+l5{L_1r3PHAgRq&hOY@H#XM@alY<-U;><@Bt$%=T@aVDYMKgPiL1|G> zr^!brC;u*PnYV`U;c7}J-FSgqhTTiEHZAvHK$f1~=lx(;r&BL~A29`_dzrQC+#f$c zVg(P{m1+ahFi`4%~h9m^~FV0|oyNBO;h)LTl59+*TZ=Tkj6=nJhWG4YSo^`v|L0-QUO8`P6@}!%19sRIsp(FgA~G!F_wRZ1A;A?4nxn?$9*;?xJK5940oo zx~NWG`Mh(MRAU-?cl74pn(ZIE)m}o_?*UEoSKKrnHe2W?VSSJobNsewBSpk_>5?Fy zoW8b!)I06Bkzc75R{{>Nj89MxjMGE3_!YqM$bIawygx#m7L`vITznpeQ}Lk_1Ux$F ztnMK`Z(#~|CwQal9cTqwx~7&c{Z1+zp6j%>wcSVvS5teBR#NjbnOKJKYrJ$gl@K-X zIIK8kAEBen*F#RoFmtKx@=N4VS+13@4xo3D%n(-Uvabu$>%BH!n(_314@iF5VR?b4 z?lWNy_Lwv{4B!ro?feMx$Cg0npbc-MaOnvn^Y5YZP$HFDD>LkYos)AKd_!8kKF;*A zVOX0-3^OP{kC}ke$ilf0O)tkyx$v<8a=!PYBPIFOsI>GoH!wCluMc`)aS3j_g188( z7GN-O*_+$-fa)3nvceN_?{R>))QN6{~X+rj8PV>TcTgq0C z{|ADAJlWoe7q>Vu;L19{eIo$FZa1k|&WRLj2XmfIK-kJv%A8GRlSzmPtV6J~kn9-I zhDp8Ti%A6`ruS_H5Ywg^$b?uLx#JxWgTcb$L7t8SF`MPD%<02jwc5J6gE&K0_dbU* zhJ=J@=qhRp5D29py}x_lSRL-U0~;_M0SJ?`v*QBn-wv25K8+9hS`|Bmks2Vz0!Xml z=bv|sUAzQq$oK*Ta2JN_x+wf0nor0RHhXm-rUyjQ=bd2TN?#MY5aDJ?hu{SjY1g2> zdC_q_uaU2sz394vi#%7H;dzK1!IN#zPtFZd?&l}YFkxate>TmISR-93+}C<4JD zd5}@k2sm+hZ>jM6KnNA*)7Z$LiXn*G?|`ZFk*2Z52zyAcUpYLy_5+oMMUunsco zUSTgNzvwo(OU4@megdjQ)YVodlLTsT{7_2fRXhNZIv9Pcxvz+Dz#WN>|R{rZ3wsy5;8#pQc0QOke z8Daxk=u5$!;C9G4;PAOknT8|wqeqIt*^etnlEq!}S7ki%$Vbr$OV~6@fw%oz^rFQ3 zgmv6~54(zioX=(<>|X{% z3WI^u??OyoFA37V0C7m<=6K9?r>v*c^TM7@uX)ZC0=pNHvUn|9Q3O2fhWX@V=*N#A z8ZF+Uu(cm6$nm=V}?+gBJ}$@1-g;pYEtup2x<VveV>)E^uEu35IdP3fn$4{H)Zo{)Xq68g2 z{7A7ACEMMV#K22;Ht)Zx)i+CqEs?d}Kt>bR^ai+=kW!nGUKH2Zbg+HIk?LQXGJ_-C zevVX+^55g>`>@u;WvS@6ycUs=eD130QkCFhB-GY(J=k(vl%xq}M?@Np=GDy5|F z-|aQ*C(2FyDT559gc8!09n?d?^9EIq$gcxPr1TQt8@yLR{OPMf;N0K`T{p&fj*lV2 z`;Ell<~vlNOSOy}_9|yRuao!v>7v4ZJYk&0b?fBF7Y1TS8D6JkNRqvMS||^Wnf&+o zYmC2}6;W7<(#fE`YJCAqkBcRP*B8gz4RNjXM-%9ME{Sfw+@Ty`{IR1C8ebsX#pu|=`;{QkPeXsk&=!<8YzoV zX+-HR=|)6K8l+XayYD)`@B2Q_z5fq7v(G+zul1?b9-R^vJffuLO{xCpj+R5(&!4K0 zl5jhHKG)pBLrRG|*G}VgJZ-EeRglk>5_|$?Jz~=!# z%LanbzEZ1`=@{4pfU3?V-(Ih};8@UB{!Hp?6x9VLj^ z)6^gRqXofWo>2`2@G{?k+yYLhmBYzuCQ9+Mwd@;D#A$|F-2QhzA89O{pCEDXrD23>kvo?RO&-a506-8zc%t^63+z7>wD zzgWL5c5|K@u-Nl}+HYj^sx4>x~FQO3QAsm;R8|$+4LSo|7Q2NCpF(JJ~ z5U{qUfJYr3G+acLiIl}0rP@Hxn_oHA{LpWkG({q(Oueg-`YmhuwvXKsTjxPxFxt2S z3VV@REM4%-UNsQmiMtCbno$H#?}Mj!1qClT2NPINrj9xfpl}5k9c;*w0p(;};Ce>sk6T$;f&?#{yVSCCW5?%#(iPYezeijg0O}+7hyVzZ z5nVZzkTo&2JIhZ{ti1V@oA;at2<3w_ubGWDX*uGgcU8AGFQ=)z+NWfzMzb;c?vnh6 zs8&ADQwdj7Rc*e-=W&JT=2_Pck0Vw8N4AGjFXvTaQL1NH_^oF|G8D0%S$vqNCq7yP zwZ;m%R)U-BGS2u`J&!M=uBs5I*uFX^ng#?SHueV<=z(y$I9J@h^{JZ>q3-nl9JKJ; zQZzGTL15O3{2%lv^eg<{4n}k&T!s+i@K3|Q#3TkVTGR0|8x$FJ2w%f#$z(Cea={Q3 zf;{)3p&>Y6q1u9u4+N}$2>)~;Kq1 zx2AzX1VAX4aa8}Mu_%T!$Shqx-Hoo~Q-Y770ELxJdmQQ;n*Z4f)sAZ}xd1GTsOBnvJL4Hh|IXFntDqaTEw9Mt`}p>40Mram0Gvty zw5#ITSODBlm=MKsw`SfCuKz_iqd%Q^J`|OTv!`{X1796qFE~k_tSMf)H^B_bxV>b` zH*3&{pV!UHXT9l+o%(bHGd zQ-#4HG54zxkBat&*V*wNiYz=-R+3lZ-uuN+h6}!D(5L{NSc;i!qG@4y>@Numz~4h zkBF~nY3O=}Pb26ktBVzPk3S6;RBPEvNhO+Whiwr2^i}iCXqEXo`QhKdhNy8Ltzth^ zmE-YlR`(T@7lns#)_pa1LL@foipVn^$r5lS^4qL(DzuAxwc5?BP4Ly;X&-uQe8oZ8 zGA`xgR#d~^X82zsK+1M zsS%dm`!hV{C~c1sC77>vCO(AwQLkHYFQL2(Bc)_H=iYC>mZ2<3ol7Vx2_M$;g~Gz- zry}LHT{3E6$!K|vnXbdF@hLuMN~PS@PY%Pyr;$sPmrF3R7zCt~1xk7ghiO0RsAZ^5 zwj|L$tiSCrbCVmpZ!YHE8SR^+3c7$r&BAU^ynMlNYfs}6!42}aM_$Wxl0-o!dDHmu z_XS>bC%Il%wT$>0HTty2&^T8n#cSEmp^il*rr0N?-uF7X^n$aOZj5a&?#E3(Zcpmi z*o9FyOYMsN*vu-u{O_Hea;B!&DQ@ZqDMe6R7ZxsmoCI=~tH2p6Hu{PHmMCw(LJ;4Hq0fB`lQaX=kDx>6hl^A@ zq#+qCJ|q#`+b(!bT7r&n5J-gyntPl1*`sAU%C#O0L` za!96*j41Z~{5E&*_r>PM=ssHJm^Sb1=>sw=GAWwn6{jv&JjcUEKQQ&gOV;l9oD?Yu zjF-b<9jHU51k!S0TbE1z1xC&v&|g*WE-hvCDDg=f?~r$y_-*00v}@GWqf$+#Hzhsn za_WvdKIn~^?hWaEJ6W$Wz~s&a-TOyjJIAh@mDV)w(?EQtMy9_cCK#h-i4FwY=h>t> zeyw&cxuwhLp&7<}eqwBF+io9FFyT2ljW5mt&c5Tq5*8f^{Wd10A-XZL1_b#nkkK;( zTz}c8uD{KQpw9Wv9pDfg!Mcg)`Th;g2LP*!;K`_jH=>4vhjJkCB7&Myes2AQv3Y1@ z8b|hMyK12#<;i??xkmD1^X&ZWCVQD`8qg0Jvt--Y=WfAj?c6c9tDfBM7t zxY*d=pt(&b(ZeZ=U24Vb6vPi^eCy7ynB)Y}1TpeBC zEt=X7daV7aN<|@$;R=GV78DAgye;4)X5doHDlD`*uFFaD04;>Fsvf08mlLERHm@Ke zQjx{|P7NPZIs!=JP&c;AP>HcbA0c9+R1votsYOo`MU{oU5fhaM-4~_q|7IU(O)_5e zm@oAZVqPWtp{@U;{)RdyQ;7ptM8Nl)!t~4QUAeXOr^K7FN=iy(Byx_IEU=a2xNhIP zpVk-mX#W@HVsgb@OF#7I4{CQls{UFPPg(f(&|&?z_kub51IwPo6Dd#B3v|yUTvzZ- z7_dg91wY`!t)?I^e_6tP9Ulb_C*(;RJ}oW09vASt0o`Eq?=z=yz~;^GP4`DJTKIJj z8)6sfX|a*`zqZxR`K^(SdXi4>`SHi9R024-9MPBhq+HJ|2Pn$yrkmR0E}iT@W|vu} zTuJz2GJI`HabZ{64lTq`*iz%#%gCv*^tKG2zK^m=F1lZthnt)9+FxP&aWX$YzoCyC zQ&+ALDDHK3casa&JB$`x()_BXz2>`7mfK=^H=xo3-s_=*@G;ZvsUZSHIB7yizcTLcqMFmzm9}q`B(4JYI#dHZ>En; zPdEJV6Mg<0!okqbMtmdU1+1*BbnJL_>WKn2EnE9*D!$+1$De;kXl$1r*G6cZEm2F? zk(QVD)3yeu`CUK9=4Jf7++1b}3H_}5XVc6%q3Dzp#^e2=X`Nhxx0VxE#XWGFE19tQ zG;UrF4A|TwYDjhXEsOHk&S<*5W72w2cY;+$6BUtP$|u=tcJ`5Pb1)%IUdY@!qy5ln zVPkQqLyFnL#l-~?q@b$*s7rb8I~FTRq>q& zIeP+pyZUij0{{MqR#27$MUe42Vf6ffC8k*PvZNXX^B zPMW(>9^5x?mXApXY$`$)D92#tDHL?~3yy+TS z*1Ql4DpJfR)c&Uajg2VC^1P)S?QKZ4Gx?E^-@cj3+-eG|i~n_TtddNqs-Z3wWqZ|k zrM5p*q)G*RbH&F;trL36oUv|8_cZ(%jFY#7&Nn7S)3OiKB3~_qnH-!2@1q6i@jI3y zUap1gbn0U9;2D{=D*5QJ*u-r^0pVB4y9=KYdp{bN~1b8S)73b%w!3ikDqmQ=fAhkGAOL?9O&MX6idd= zH;fH=l-&waA#7BY_W|!yGcyt@Dx470#u6S_#843O$Y=B;u(I2vySn=8*F#>=2xS>m z@=}Goe3Tsoe+#h9Jjq5NQ!RP&0wy+JS4lu}rf@<;)`44IIC^e~BWO;HaG^IPUMK_t zOiavvS2@YorKUDMy@0ADO&SWr&{0LIfjV#Z>)9LOq#PKN<_7&7LlDOx3Om)5^8Gy- zo{Kr!+HE%rihZ*Sy5>#4x}ufJo4jRt$EU?s93i_)(Y`fYN#679(&3Em>k2K(QAXAu z1SXWW@iWlMG0tghA&TYsku!C#mp_oy>fa3AYtZ^}OO;-xM|ES_W$TdzhJ%)tR{6rf zeWAmnBbL67xBFsmq1DEX*48{cIYEjmD*h+_lLz)(OX2$YA^Q0h5_Tor)7WwzETRrm zifU~?!XbG;HMc;$&mAxaE&- zb1^5`)!mh^_h)`OB^C(|XbTxvq{lJg?7R+)FQUb7nK}AaIM=oJNxf7X4mmkL>3==R zA=IQD*QJEpn(^0_mNU(NCQCB(KJ>lzO03U$#4r6U}0mk6rj-~G_2{SXI@!GrC6w$r}|#y@8rJ)B%Kh=9p|f}WP5q(30?%# z%Q-t|28s#_mgu`|N%Dz#tPJN`y%vhdVz_kal9=zY5R$V{RV9Xjg(aV@#VjI10lItz zZS6QCRuR?yQmcobzkWr?^RQR1&~Ix$kC7GlFk;L`fi&r=tHG}OrD@foFCEjOj{>@us_!gpZaQLM zV=HuxG1$CMiRZY3j!+K}h8tHthrW&NT_?ZOoT6%??%u-F)-V#wg8@jo`m`Dr7S>o| z(Z#cmM?_R9)*v+%`u>bKh>vM_y1Y$1U5m^25n6|+s{{uJhhLZ7nEa-GnXIEDKkziK z6BF^v^=~e{)B&%>`+J#EKiP09IiS-6Hn=H@Rups$=%(escQ}c; zp%00ZQzUQ|j$gDC%A7Na-H^8(Y^=eHe=@LN7iRr8xyJ}KT4frjo+8v)OnAp&aqL^5 zx=b>sJ#J==*TwUm6d68A`}ns@qu0ds+=ILV`$C42V?d6a zMU%V`jAI;UVSmhU@ORNj>X8MFZXEdw4|G>om($WOw!8D3{WP=pgF-`D0I~qFI>k5= ztU5ZXE95XmH#<8EU46{VZ%ls>RwJK))hj3=-dD-iN(TZAKAEO8$Z&>mt`3A_pH+Mk(nwZJMUsN~P!u6f&@rNxzN&x{k`hy~JB^5y;4HcZG#8;;e? zgP*~TY7En!+$N9uLNT{ZR5qNgNsC8uK~!q&L2aj<-x@QG4&8Q6df#|dYUOyoO6{|` zZ?`y}L3!Lxpt#}Z6t*;~=ZFJF8x$umS97PCisLFeML}${!}#;pOqxBsY>g`qrEXo( zw34!Vtr+Dw!^b2clCE5l9$NLlgi=PMe;{Q>1dn8=eJO;$$_~cdjHof1wS;LU)zOrwd2VKZ{$tUgs3BNe`v9SX9xM z2gOgeF8CK>N4C0%Xp3ylDyuxtXi$^R?V24ibAwwMy7eB{PtUFplMh^4yX~sHBJ#$1 zs)W4xqliKsRvLwamsqrdoF79;>H5s^5WZpE+vveRcw*>%{9gl)a>zc$toM<`Jp35Y z!H$-;(X6o3(a_co*;^UGE3{HgS(mrV`C^+WM2*Eh+*0KE%lt z!t9qbGfz0lB$_;;7gD)yyv+HGadhO_chB(EuN-?;26~Ze3Mx@@HTwelp6Ea|%a!5$ z=0|-hSOoGA`-n1Ld77(wtH_-%u`YF>9WovV*XV0c2+sp$F2u297gGItnmUJ2lS-q( zc$6rV>&kF>ME!>}EIufm1DNPA;ZTDM5GBhSPpcSrFF4(u8y6p7+mnNv`PAr=G0 zBq1^W<>$hmnf%u2X-51OoFkH-{l#V~@O+U&g&xV*S-%5DzGCOU_fUYDA^5sha0NIyCto^w|rj!-yxqAk?uN#1Vgj*;X!TIl7qJlO*!tuILQ0S@mcRJM! zyyJK{2L?khrpp0+yEWy#AwJ2V(+{A!IojMDg^FgBiH5%J(?5;eA*91*Y^>0V4C-x^ zJ-BXqiC@*i^Z~iHjyXOHSw;GHRq|nX=M%AlDp)K~MF?wyn@!wBslq?iA0-nDx_-iT9_=T7r zJz4zZn35)XPtwCGb%X@6c;aLPi1y9#f3)_u1mBc-KCO9_c1_xbr;&VwzJU_A-h+k|xBD=-@IHwMRvXR`i?4aj!(S>bchn%bE-)K$sIW zKagoB>L^JRy};f3dU2P^KCnL<9jKhEv`?R;s9@cq{Df-wNzJTJHM1!j56DfnP*2Z{ zg5_T>E?Va<6e;~`nt-F^{U9GJtD>W#g{37k2Zze9D)6{`vS-10I17|qGpGp=@nZ%m z0Hk&-3%p1|zzp0oZk^XI;EFc|JPnbS178yg>Ot%BaWvOE3vcc^uf1!_bKV$sX^M6j zv$W7;Fi8^UYexX#i&J=UwqB#o691|yZDp_=HIu4r)R(4 zm?ZED^l~kl3#?46*Z}gLSy-4H)mQCmH<+t)W<3Gtv^6*LB&`U$CzeW!6CGe1A)qe0 zE`$~$yO(+GUgodG_Uy7UGGMUf0RmMowS15^4e?M$=Hhb1Lja|50~m!$jd;DYvs3Ct zs847g`6&B6o#%e4Y9JW|OKxIyLj_@%_?>xy zau_c1-DQqPj5rs4qx15mU-%AQFC@L{{DI3vFnPM{YA&a zMB?Q+j{Tw8p0x@2I@64Hc)9oqdUqh}jS3+#KqAa^`!?F3&z(%A0d$<#nVB!U{QKDi zh87KVb*5dH%J1*NX3%t$chPelr&(yfZ#omlc;Jq!4>UM(S@&Hx({$t?(^%Fc@Oxl4vM4_CV zoT$W|KS$gwNoJ3d-&%c-i$dkcwRki7J~c%Lncd*xZOXDTZtVWagOZ(y>n`37bWeQV zF&#>XyH`)dlTsS{Nt7#T!Sul=IQN@{K)pq>tP)Kp=jkc#k&NBD11Ze!iq>)q>o22% zf`cLY^@@JJ_M+9jE6{ixEXbWr!rNvb6)K;`?^r|}d&hNClIzq1mwc zX4kYbg_9tK2t_4m9wt|FXgV;It3z)|X-d77Q;$AB>QL>?aSD!xNdi5g10$NW{4>U zy>axs<7_kL-5t$z;pPckyj;V!$A*SHgL~mM>FMeC#}PVfa0j+D4Wmi^;o#vZfQTK1 zvazxG-qTazMHr<#?UtSHo}}~Y;y&47kB-`Wg-tV<8*x;c(uCb5 zgN>3r8O9c?KEviDzuKaK&lg!d$?HVQ@{C(2S77OAq~k!Z z_F%+g#|t2*`7hmyX1hD#J$iq&iH9|fjR_ldC)6SjL$cNA7nW|5V$AA-g)FE#|G4JQ zjPOHQSrl~qJo_tsX;;oqcOIe|q0S68*Z2q6{FM%;-NvX*eXsr1@JM{4r}F@v%zEET z`CV><)@)%yo!QEvz$=*YF7E~Y{WzW`co$WldjlZKZ{5dqcPFfYF|lqg9R@oz+ZKT< z-V7ccg)$W>G4T`4?T1oN?>2(s^3y^w`PmjBF0NDcvxGxK?$~?r-&sm{@G>7Qy~XCO zcU~8dR(SrL7b=3F6K9a?59W&>6uhlKvp`$wfuid$1qUC3FQQTbpZ9Rve@1EW*!N$z zAC{e{LleB3iFJ;+c;^Sy6m`PAH%@qA$!EVEjc#>ZLRB+H@1^i66&55VmRw+qv%a+o ziz=TE-tAr)3HF+2XYGFy4eEwd$Xq~P8;E`hfCd8^eX~aYO9^O@8&$h+JQrdIVm?V? zN&1$TZz-rwwW)2`qmv;F1uNNmU3}IRFij`8SZhWtu0pvE3-lDa1~IQ)PQhEZG%BAP zy;vP30sU(S4E6}dNr9{_A^Y*c85`FZFUtCry6p<ufDZS1JZ&}R?wdLkV)OnwZtDO~ z9$zS|O;Jf{tZXj8Lhq}D$I0%J+02YDXeOV1?1(#9{_gp#Y4EDJ74y4J!?=qOQ>u6d z??&%lUq3%-a7d-~Tjxl23@4(!bvo1kci;Q7-H&^+kt~JsPfcaYJKx4D3=_n~C2X!H zXPkQzehaEn>Emq!$-#V5W$9}ReP>hE@1pMZHoRi_`ob$5n4=XLy~vIbBDY1q?k$j+ z;<OS#J*K7-7pbHMxBlp&bKvI=gCuKRl1XeYo5{e@1_v0XDx8s>7}M7#SKd z)b8Ey42rgbcuq2!X3xr3J91`7yv_qz`t0EALqovfkct3PCh%}z{4X8wyIdOkAl z!1ISQTYw`gUB@gU_kSVE*x%o;-J2Hk^LmbQ4xs~-6~x5ED0p(fB6hy{1|jKCXV%8O zVL_{;gVh+0opu1S``~CZouGB7H627Z1z@6H5GA)q)32~{hvy(ggl-(t-GK++Be_!l zOU$)=k{|)eUY6UpC!P!ia40~t>ga+1gr^6AL_s4ULd>4r)th{IPISA#2mP44tI3=obwpH(Z%~Jd0Ku)J;w*t5U3~V9*!i0yfRMx%ihH2Eb7&3+Sk z(=`zPtBtI~^eB2>!zv^u1=@H$-v;<^b7E^Um7{3ncb^&?ClAD@!tnrg%5{LHrRJ#bDI;Ud(!dwoLKD!Us6s?iFok$XutQH_~qc9cr94{3-*w)UU@VMpa=&kF z&FPUWdPn^QT%Z{TrQkK2Ad;5zz=dp2ie=W`8QssyTkWl_IIh`(1h5>u13y`CtcWQn zkeEf_PLO*N8szr(H#8%UkAR+lxm%6!++JFx_zq*njmN`tNYg8|WWNs}aT!`a|1+~2 z51{yY&z!YMR>D&xJ%254fwcPVbZ$2DxnEw7WCg|9miyoOy@e@qkt(n0DYx|@TVaSB zjfTjm(hJeKUU)_#zLtr&2z&SdiCJMlKTF(vaoB%dp~(7;yR8w|ALr9rFL6i@#zpnn zivRT4n@%`659Q?HLRavQOVUJ9OrO;f2wYK_@7h3L8?SMH)s!vO@WioUYUtxM!)n3n zX_UW8C}HtL?MR^|&Yoqr!Kl5l!yJql!9)!f7}0|54ml?Kvou(~|8?fazXCfG!ecN` z4mLVwnj{uZ05o-Q=Dog@{$UZvDJcIN=M(R-JuoOZ_U95G`EHV!}ed;7B&H=vy8*D#TMbE&SHMKI4LH zPV;YICIGC6DH~*fue-{Wam}jT*0=`uNQQ@HuMuFRDKLIpS>^Pxl6AKWLhDhXcry4R zHQs#S%G*=oExtpaK^*%-1V_nOgG^AFlf6TyxUOVz;jOJqWJI`GypJ?)hDsFK+i=iZ zlF~Mq5Q$&mPaIB7P3Nzza(v=J$j>R+xYI16Jcyd(&3NN6Vt z1?3<4{NYxM%@!f7Rc}2ju>6eIQKAH?Vnw|%vLZj`KMeF4MpH+|qKkbHPMO zw*@Of#FTE1VLhBzKm!L?BvR;)7-9UcTaKr@;-^~&rq3?UQ1=+aT9?K$S+x1ih{jba zUiMsC^MY*WfM)q5mIjlC^S2JhnX)9c|G;}p#v6UHhb~0V!elnVD&66cz56b$_uoUx zOjNqjWOo6aHh61*ROH63)5sBj`R4o=tRh!YqXiAzaHS`c|JDRh5D2&CE1NkugoMf< zb%0m|n4%O8r8>0Ty>q8ru|HN7gabC&wYbRP-wpn*=I~={HVRnGG(ZEyoaQjlkYR;L z5dnVboZsu}y215n(*m}y)fP?jyX#P!Rgr)vzNO46nS}WcRN=)g9d3hpdIN27;zV&4 zTTLr`v&FN4TdTAi^MhkMD1QU@l48fd@w!#p_xbSy`@CF9uF+v+K6>eWu=B=cgF%QU zzf=5nxkKo|LW4g^;Bg?@snp^hbaoWW3agwDYN~UKOW32upik)}5X%0muI2jnL%+(m zx1CiIFdF^b;Jm|iwR?`j33wf?iyqPT#CkPVPH1ON^J~DBTMv5tP?)BsOE;fSB88C_ zy5NbpOd)KBzi({BVW1SS$5}7)+2&yR05&TPef?K=-=W!Y3-^dBNx_Q;fL)p|0BN8y zEY!4jVFOU48R%vd3gTO}O{TMlY!SrzKFF0}eHLtQ(Y^Bal)!0L>9-d0fWQNOn>od% zZvgbDfQ4)$%cP+`BHx98K7+-{V}fXObL`RX`tyXckOfZ!Y1D5QHoWsK^5-g=j%DGw ze*+_XG(*TFt@iw%^%Q~aH)53$PeAi9QQ<&oOhhm7v%tj!v?QrD)CG*=ea~II@rc+7 z94S{V;t!R(C#6p?W^azNG|?<==!Xstkyfzk57?Fy`ueL77kmax1w^7Pqz@%c|NQnv zJTE+YB^XfTlbBm4Mv0lCH6G*E``i?G3XbVu2_)+D#h`7u+-5|jJPDuyPoY$VF*3Kr zJZAj+L=O8^if2#uaBy)2$W-?P;Gjglc6dhUb@9J~`*ercxC=cj0yn7Lot-tb3@?LR za7V$qt{TT-oT{$+gO@yEr~B1)WvPtV>zNqB{cyS~&V z+zDawxO-r8`M6|*meS$a9))8=?rK~WKZT0Lx9$-P0% zp;v#(MLvL};5Jlj0mgptOS|7xku}-T^K>=$_V%V{qywOa=q}c$>LQ@Qdmk5f&-qKP z7FWvKc4nWzpdgHAxmD|tQBiGRWojznF&F&MIeh^A4_6^Q5rbqH$W4cADy}_ddDq#O zt;vCiaiI!;98e`Kt@nSn$Y8n>GoWeGEKQ2n2!fQTG=uxeheMc4)nD|ti2eV;1b=wU zTr|GFXiLpcPk0`vPL}@E`8jE(^KDhKezz~_TD3hj?v%H_KJ0k+jvcpWl15k_y}1+f zsd!TcFV&oyUq7nAF%~nCVx|ns9{($5i;XMp=ufgQ>8RQ)$Li*tbmnoxq=&d>hKH}7 zj`rN+&T}&TdlmFm0dG3FC#XL-nR4UxYLN|BCD3tm#kD8clTI}LXcXW988R1L6D-cl z1y4sw#bC7Az*6fvsXCCz6SA(0E#^a{^-*XxR0P+C~ z5EB4V?tL`ii&Y|?YK4rS;A0^18}+~6CE>{nxB^u527e6>dWEFxk{)-8955gw>bXM@ zQ14n!t@b}0OA#)g;R2%w$bbP;wx0FIz*nMZ#@itv*RJZ5X#Rkdpn!|`t2unL$knD$ z)>S#SC=RSFH(JLP-EfwBrgC2=@0!Z;@F2`QiRF`dfc>n__M-?sO>7TxES5@-vntMZFQss9UtP7u1YsZ zH_<>5^$&-Lr~@n`K_g%ql{GhK{jYQb$&n$i%G!82xS)t?Vc;;*I&_QJe-dgVw$SOm8QjoL`sfcbdv^Q zH}2HV%z!k|db+;u!OrXR0(-#mUb%YYhs%Q^vU#G)YcEH=a=+>A;jG}SS9(T9nmQ+H zk=Y~P$@mQx-|T(Bcd0Q8J#HiH26pp~W5|)yI_Gug=Wk>0*{-ZeHKTj3aVpXuZRmd}Cm&}7IsbWmyK1prGb!Y;8zn0q!C@;ppe~-p6 zPRDvvXuU^LuaK`_9#nl;S(D@a8ZO*41_p+8u@; zJzkb=w7>`ae>d&OB^2l{nP)cmZvN_wrxtT$f=~}6{}q|Lr3_6wbdJPSRAC^RL2^k! z7-UJ!V<)4;O}3|N*9XxIs8f}rQ2f~ElH>QzCSiT_7%R5GTa z3hyJr$5iKUHx8D+&dS6EidRr}uyz}PW9a4tPV=P4Vp?cWHCZR7Q>z1D zK=h{9GzcWzRyharV{KAlB`Cq$c+eS2{eI{r)9M|dLPdNU7(hjh36n}Ji}K-{c`PKk z^Sdr*@R=PIeU&PV_p} zZE<)N-lU{B6q~53M$Vwb-Ph@0Ls&XR`UPK3Vr({`5`9khKkT>>@PcqOoCcrP_|$$% zfbuAGTv<2f&>e{*gij^@DYh-wFZoFz=fMn!HqMuAVGt)I1e>CU%l7@++~%V!tMOd! zQJZ~ZH2dA2@S#^;W?+JPel5~e6u8^dLxDV_lbe3$M^-G874QUW_kZ}PemEsNeSZ%n znfiF;{4$Z47dLtZ-@&pbv*MM+m3ff_+uUZXQZ?(XO9SqtDMC|PGGFFm!p}a?*b#hD zd);R~ry4O4b0lNZ#SzS)Nsx^ZeyLPuEi9h7ds5jRUlG&Q^O;flG(#T z@PiyPpIz$W#Y9g-Z$S4Q#`evREq5I2*Ug!l%3@N-_J)Q*Xw zn^&y9Jaqr=$7_`JlWZykp=n@#aFHzf&a<8Ai}MjvGn|ec1UB4TUfke{ zz2}`UKL83d-51(OfB)d4P!n7RPJ7Fb5q~lu7!(VBj!G1A9ugp3ZwDRf7DPrO><|n} zj%?b;E_WLN^%m51xBKOrX(U{igCZzh@5c5fiU&eb1!Av&_)W|8=>`XMIbE_ST)qKC zvR+;iZqKHHBd(l<{*e6ygmJSUtdg5xVugC88of~l5xSW8%UF#AV}aqf%d+ko!w^wb zvjK1rYYLD9I9Cak2R8rhdc0)%?GJ7d+-T119({v$;^+k!ZXjO=rrM{&IB1UP$Ut+HS&Ll^)ejgh@A#Ix^s>2B;c{LU!-7dCU;58(> z=}tLN`~MjNGH)y~&v8#GC{rBb3DCv4bN6)*=;;Idk(Dn&ea zH+|Vb*0OrJcSjxrl)EA;8$Zki?`~fX^8Eb6?8Rc=B^U;FDfOMr>$#b~o(#~Qy^jpr zl=vuO{|cf|7{DDu@O-*H3XJ}8xkJoCLS$fe%IK{PWja3-N1xYzNJfWb4cX=jdE+)~ zZtg7_`NPJzgyQ7kX?!Vjfn=r?f%E_Z!4g(S0NaVAzD)tL{1)&sV|*YUnw>CV4YJ^6 z<(O}L8Hn&Is-ro{nlw>y6%D2fepXY}aG`%ICrTEGnjh-$?*$=GMLbNC(fF zTPRXh>c+Kl0Wa*LPL6x&B7tkQ{g!8V?OHPLP6}+F-#SxFKo{7}ect*a`YC0swVq8z zU1aZ4>0mom`4+xi6# zQ>{11E2+giZo6G3sxL`d)O8^zbxu!m{37j)Ay*XyI2zs_jGL(GyC#nhvc}zh6%u;- zDDQ*yw5qDnFP`6<>|`!qzRZ&x3fq~5lM@F5ZeU_gh<0wUD*LV5SgDG3K?k4&QLg@F zdzztJiZf8KFx2xKFi7&&r@C7jA4gl2`fUIvl6SdSK^ zDeQwVf>%q-vcs9~G;p&(F^bM4Iegd-0e( zeHvu`4RRXZ(jh*N+S)|A?Vt|COWD+902Mr3{MDn(%?w&9FnhB$`1I5#;?aZp%Yf4s zLcTv9AWUe)c}0v~B!-RK=?6&&P=9cS5YWG3kn)n{rMx+m!D-a2hM?2AI%rH;4tGI^ zbDz*PZk98_tK7-P%I>Q?Hb*x=+;ty{@cQ#(JGfJR41FPX3$oDDr?FG{UtJ#AmiYmW zRmx@Lo0e}r<|2o05Mu?sg)SPx5>Zb;9_6v!$zk-2#FPj`asMAzv$N0}IA6u3gA<;; ze4fB0Nszpdi$Axc1XYL!CeXQut}EK7+dI8t&VRx>BYZL+g(D#y|J6{O%`Ght?53^- z(d!jEXBNHKg&Z4MK(d;|7cVt01-+B6GmC#tIED@R|8by1i|pS}hX{tC#Q->VwU1Ch zmw|Xa9bRz&JDjTWgLbZ4rTLmtVZqu{>^Wy-WMHZ|K6>;P^iY-WH?iP5Bk7b+;1?k| z8V~{*XxaVo+c;19$M5EXWp*QkfOR19Cm0x|U-8TaHqGX>DS5~dLi;8w0hF+*3kaRt z#(3}pnP;u=D+qQ8_5cyZLny^C5*7i+8C10%d*TnMjggTam-{CD>$m=WBAhg74mX3} zGO|uMNjZB2;}ZUt`-7C@x82uMp%6jhZj*hs?|%Qg|4s5cBp$*k*0dvO3ndD$lr`v| zE9<}_QkQp{3pO=Pq$yxqeNwC<0a38bBf46c*ke6b!p3V-9|`{)6EbdUWu#I}lue(d z_9c*UNY(@5UqcL%NQrlTyn=+b^ubvj(QpFt6X&;?LFLF{3cnxqBA4{Dn8yvcdMSVg zHna~gkRwDJgRB^(eLDA&%dD?G!TJ^rS1obnhs3Y11mHk!fwsBWbAbKNC*pzUeW{n) zBN>8=&6G17QlwwwX*`lly;+#`F)Wp=T)>hvCw%;8kC_c4`@h`u!v1Mv+C?r5lfj$lcc9#5;Cr`@vVc`+_bK80HprBC3sKIRw{W13e z({Bvt&2dJ%7an{j_4u-1b*L#Rtq`^FOZ|NIF_z{GTp}XTV|oaf&X$vZ=%ViCb)du} zgE-V9sc*Hq%p+^A^wL;+_J|#Z1>^|is)Pt5! z8ODL?q3bc5h5&wsf^i-0^Eue^ieQfgtufS25-%400b~M?X6E?G7}Suk09!w}3rCy4 z#Rh$l{B<8%5Chnf))Hoq&9vc$=e>ar3JpUCUoKnL^`y*R$**=f8(8i`FCOfv0n;MO5G<`GjD<^@=!XV8X?+*WUL?w zzWJf-F0Y(c+beSJRP9^==xux;l3CsxFmfa(14;!m;C!l(^;gUNjRlopj8(EH7FZ!6 zMC{fk9N!aNJ~7IxtR;V5mwe%y)q}g@9A0H`TTcui}@ zF`DH787GAbrv=^;r?X?%KMM;Pa1Ky(*a*?wJG|?`17~zr*7qUHD&(sB{T6U<^wYL& zi$g#TBhQ^+YY0^MwX8vI6Q|Qt+bEL0XF@(s@JZW5|P(50FrBdV0Fq$&oSxrTI|Fm_Zc; zZG056xs0Nc@sspJpKhs?oc+;rowI7t#e5y8yZzU4(-Hs={>?8(pG zE(Pab;wagS_6L1=o_Me9DjUnAWCC;Vod3OB=H+BrPFGv$FpY&0vibG-UX-)%zJ;%{ zr5!@@Ld_xob0+{D`E7Z-?u}868}6Q3l7!wWpPR(mRNW`KTI0)Xx-N9Xi<|8o9as9; z)??>pFlECH!QWvp9{za-{r25EwkYTrW+A8OecwXm)&F|@7fL3S7X}n6b577)LM9WNA9PTLnxnV_YcbhUU?%6BS ze7$$C!=O=-tKtTwP>3OKsB#84;sk-UueuPy%)iGZ84GZk&Zn6QWLr>!%mNvB0Q|e9 zqW}Z#1m*pB9yyM15^{`LUUcon`3Zo&n5bPq<^keNoa2$QWa{l3!S&wtsm?#bh*Y(# z=*?_n%-DRCqq(XvTo4@H6|cM6p`}@)nR3X-&tLRIdM=0-5So&}1K3$eB${@iaTJ_! zI$dfGf1sL>H+qGYd)Tfj8Mpb2GhoE&KZ0B?%wnuP@b!PFsJm?*{CNpiVy4w32? zfO$3*tPNEjTg=GW2x4OJ(0-%`jY~6W+8^70*Rcy@Ozoc%O=1?F8EGx_av;iL_;h+7 z_zQ0Nl)bnCQ^nFI_-t0MF(6o5v1K<6%HNQ;W$!&R%F3SE2~o&Q zRv~0YcF4GFWhXP6gluJI?`)~8Y=vyH*YEt?&+mI2&;1oNyd6Wi=}Zk(ZDfa^#+i`AhsffZxoa}y1hycxgg|`$B2WAAl$HMKGzBkEa!at zAQylIbrsvme_ros0Z&yZ%)96imV?b1wB8;erJ}NQ+W2qrUfOGl2L6~G@MH3gVu*_4 zmGt;wTe*n+7#)Bnd>OlpoP?Dfm!fc<9=f>kZVV}zwl>Zc`+^hAtgd&-~hG0adh!pRq z^7I;>8O_-&vZcnpZ|Utxa~&>xxN!b@A>riV?rJ&wWGFw-!Rn0PZCX<<&{27z)PR0( zK(+*O;jc$pm}mtzLp}5-QMt*EjEn>yLmcp$<|NM2?Hz)sF1oA64eGz4d5BHI!!!M^ zjZMKgB_Kv5CD#xxEh`_QMm#9!7!nN$0}Y&L}cb4_dG5fUPEXxOV7+4p&Ju_@nh zkqOGLw+rHhP23QzYMSUHg|9T=H|Nc7mgTh2i9-BVM@#hzN(k!M&CNlfA0iO0gb)4@{*bazSjGS777P(Xc-$LMDZ%WZH0f) zId2`){MNOMwEB(5V)L%xj!jX4w{AA8-tqdlfkBdh(>1Vfu-m1G`$|I!Uer5LTU071 zzL_EgYnzLbJxr|k@;mRXSqVzKo5p%D=S-FOD`ot->c6z7Oge6<57~tl#y?9fJhh~n zsmQ%!^XE8OCEfDcS2_n4uAyjeJ1VmVXpdTZT94p~DZO!(%w^E8zZ24WzI_^Uha%df=Z+t`Gsp~Abm$Rv1_>UX2P=@Gk$aWxVf|I5PLHPr zRiV_)(3OOdU{P+`hIP6mdqH|j0Ui4Q$>A`4MS+J2>;kRUYoJQQ&egu*DIu(x{OfJw zu0Nc$W-#@(uAi!5|C5Q#~Wxjj-uCXJWxzxz0Sn_Z_c3s2AT$HIRV)tyq$OE0jwX% zXj9WO!s|FvWgtB?z2Wub33KD4rs);2&69%-s=sGUZ#EpSa{;^#b#z{uO};*-koSf1 z!^B6Zm<0q#2--41ktXi;H_rd&??CHHLjVQqSn{zTeoW}ww{HT^f7NjPhkpX;@3CGv znrclucW=>ILT1sC3`U#3$|@?c(z576_PG-M!ZuYR;noMto>(?9z`c&1Gc0Q_^csgtK|OsD(?=w&-Rl)E2KYNof9WQ)_zJP(T2$V zn#~%Rtfg-5p=ri`TePwQ@#W8_>t6%Zj#dDp7i!Q53P7?Yr0T?zJr_>`#K#ou?r10p zrh}N|WZd+nVF;lBL2z8GlP8O-dK!2EP`wqi;=7|MNR^}q;2~fDx2pobK&uvj1?u@G zb*^8{5RjXsqd>VdIOtwU%XkT9%`Xr1HT?v0?xIZws3Fjroi;i!!7x8>){B#UjE3VE zIMm4aUZq0_J}hD#D=$*j#b2)#Num7sWL{?Z25RO_&VChuAc-aaaQ!u!OEd`R2IIN) z%)nw3+jT$V)>T-6s%>T8ym|B9zyK4_4)p#Hkj0Aa0~~CH9fE;CQySR34EjD#eSFA` z77{^}4m!LcE(RwP2ffTC0{@l?TMzuP??TxHSlJeLJ{sM!t)!D49Kj=RE!HZ~8M3V` zx!HCd;u{cCs*r;3YEKnsPL8>_j|CZS5)=HNnBWQR)Xvn`w;5zYWN-&yOM&`wq?7Pc z$vm#9;uZeuQH!GayGr)*CgpFN@6%s)qIdX3Y&k*j@k1&Z`-I)LQPSzv%6|(z_vm{8 zR+!WXYVmqgC2I;u(PeV`gk!-E6Oq-sScrYZoQcz2C;GN`G`+gx#Hhotx{seJH14Mc z!O(+}oSKThWsJp}Cxvkw{Z`!aRiX8whf~U2)yIj?A3Ajy)~6;B$|@NlyY~W7V#3u4 z!cDTKL>{t55t!0LEvkt#SLTvobFBT3$=K@o?F>-M93+OK%WeQg9?Dik@WWl6RT{>{ z$B*~w1UPaT7E@spGq%H81Jy5_Ma<>US@Dj%==w)|5Eud*10A4f6D|7a@qSe_Y;FCU zYVN0|GaT>`48hIhy>)4SbB6eKo@$-_D%EHEk&vgfIME=4s)u?7D5$xD0$~`j?Aa+k z#^uXt&+)qur?I_B-MPJ->q=5nSQ?>k1fTfmXZt%6|4%W7sb>D*^*YXG^I&xB6G?J_ zC_tT0c$;H{xuXSt`P@DC&IwtN6k0$AW9D^m_5k!pbZ2uG(^ZHN?ptZvs0B@ZeNGAg z|7MBS=jfDmAVMgOOV9q$L#W~u5==!JbcU`>0rvXJZR)*G+o5V`j#%zW(I;`YQ_hNw zWxjzg|CWQ5Oj!{#UV>g4KGiJ>>7l2sBmn4J^rBfL6gA&tfr$@R@DjJG9kvGJ_w$vr ze$+^lbSq*6PYQq!gyCm!Zd>P{J$3!ZU1(Q6ZhV5pywV_rhp5ukpsmMRT5mc$(czY` zx?e`YlFQn}0l#Npnw0-O(^&rX=+;4$QkLeuTjv|B8gYP3z($u@Q=@OaI#J0D2doNl zW_<*VmUw$0y+N;Ib_i^X1H!HLaAY|ZZfd)SZcyQTaXi_#M~8iQoB=ZO1Om(vidq0O z;UsBmy*QDb+XKvcI8TEqaeQ}DBPIBFYdE^Lk4PirjW9}MTD!FtL_KC(vn{WpqpQcN z;nD>m*CiC%H~{B-N>2Z-4N#J7d*%kZuZVz-0X;TV7rcA-(oOJQT19X_#Q-b-IQ9Q( zT&c*D$LY_I?Zi3<)up?sPUrAmTAr*{`dpg2$def?(0S~+5wDWFmHCx?ph3ozM#<67 ziNWf}wtI&P{{sl(P;uM<$>A)-7ox%I@ZxI>a!;SW*G!DHR%;4-#r%tOTj55l5WEB* z!Sv>Fl$(fV9RF*TDT9_tBRfjDvCt{tKb{PP^W&8+^{1etWazB_N|FPzVl)YkexX{1 zw$2EbNESvSI4xzIJ$WT^Ws*SwfHyD&>Rj}L2SEXQ8s%kdB8tE8*|1`l2EKVGuFC!v zm&mwSm;3A2JRPp}cS)*ziEwNsvYy0#eHsv0{rU6v722`i&S<6_0q48r@Qs$;`i|X& z&CO^yxaMGU0zC&8T8jXec=B3rMbYe&hXp!dL3Diiw4rdY;ry)bq_$d2DEYqQ7I5TW zyE05^K(YnTAzJu4CqEwtI3z0ady*$eGV}XZCkC)CSW~7@ddyGp z&LNg)i6Q_&7@3bMDmNw_Rx)C?@Xl|!;=E0N$MbZZ0YDmv$X(V>IUj2vR}2nD`PIlp zXFqQ^=Bw;_U}_O@mrBKXtrny}PXOFN`GH1mZKjE-yu3W%)0!N}k*|LTan~{dIWD$b zZ-!CeceXBIq~*`##wlF%Rpyltl8rmIwjVXAkw3Vb3K`afLoyMyzGrpg=fI)VaD#w! zh5M%T)ty#SOH<7o4{rFv(3bbBX#j3mGFyo@ssak1wTfJk|RWixC(;= zwTfHK&7ap>)AN6RO(?vfCy^kwq4(Q&&nI$ej<-?P#pTgN=`v^m%uD(g%SjK+BTrT) z@9d_DyWvI42pa2$P>Vf5-MVlV6&IKlHx?I4ZMhpMW;-(4Vb~x~-+uOyk$bw)Vpl24 zzAs35wk3t(`nmP9$~tPqQ0uOb(P@f}ZBcb=H;rFx5vlr9SODdH#w#qS$VD)*vGMnS zh>+^zYu$#UTvY$9wf!icQKr1MHW?V?cx@iTHr2cL1XrF5%~AtuhHk^$?<0!zN&6t>q>@irN&(y zpIzUZeDRN16?0lur`lNjrKrAPv`9_G(o{xbSiJp!4fNRWSXlBOUcYiNIPDoKX76Tt z8h=$PoParqwA-w-taaH>+j87%Q0S40O;#?&p>8iX_AIA;InnGUG)!=ZI9Hq#z=SJ@riC4CroW#AKFrk{Otp!Ew#|^c;QfZe6 zhi{8>16xSh1As1d&mWGiTg0zup2yHv%sej0LebEk_% z$ig58%A*T}0T5pcL!O8hqbbtvNaBdaynMux{@cI zt+Dx2J9Ukl*nnM%HrpwQ@fR#QF@n1zVsx1=_P9iH_%1`YczD(N#2kQ}C_j)9+;JGg z(a_K!=YM=dzuGc>D|4dO${`=(h8HOz>cUtVgMUsO4w36|#c4dT*Rse==^sCSygb22 z8uH${wquSE507jfd1&0|O>0>EGh5Rf1WUD@RjfF*P$$eOH#-GpDY%hX$`ylTNJBm0 zk})h<2Z~>ZD_%zJ8ct#67J+Br72|6fThh_+tHhlI)#BY?yx(k}yI+Dqd5%1<+-Ax6 z)N59NZ>Gj#yIds~x_{^Qwf9r<3QV(6)D%)5lBL}g`<_|({S;*lH< zjq<&jv$5k%UzDFodG_+qj*+YF6hrZhPYPcJ86=?EK%O5Xa1~cjI)%npA6452Mn^}t zCtONSrhRp29$Yr%b-QIdArV9AYZ9^6HTTq^r}CktrTjoCUb*dlCWv!y{Ivpb@bVIz zoJjXM7TF8`diUIp{(fFPe?0)6;L;U;u|@+G!H&Cs)W|ohQ=#ZEQH_i@p!>D+?;cZ- z04)Ei6;_I%@dw#(_8nY9HGRz+kE(d2rKMwF6hRM1;ATpf!IgOPeo#U|9~-y+PCCSM za)9{{1I1}j!HP!7U-MQFq$5jCJC6x;i^G-v$zIHV{j@Y*wa2YXA^K70PGk?I7K=DZ zmYfBt{s$42qm!(p(r86SdY;}n8?$SS(k~;VoVIJ6mKaNB>H=L7OXj~-(ht2dW0dhp ze?_^PU2bM1a(WhKdiy|~=4UAfSIAIc!*y|wEs{F7O+FsX0^=Mk@U|gDva+%U?eC|m zSm*}R|0dWpjn~}h(fi0#d$(LTN0Y00cVO8~5wG;q4!ZLSbRZ~K7HXA5zBF?$Fk4nL=U(Z%L{jnPy7dU#z3PgS?HE#6MrjXM}C=V;PlwiwH8 zTdBbihk%n1>knC!L6mmEZRMtU&J{fdH(Nv}SU>eENW1k`5;E`b_G{rY0&|6LaonzKHy&8?B+=DLYx6<1pjfDzpsWq20+669vEVW3ygY z_!Qz*VIs#Sy0|U>3=a#B?ea9Pj7Qp_S?g_p7cl+ZLFWz?R7li1;1xV^UXVXyuQ!Qv zBF9+&&8*-FOH`W|*sj78OCK;8%RdK?nq1EC;sl?J%vH4Yt>f$uRqwkPCMw%pxOWG^B{L%Iu%mO^W5$(xsM_F&-Hf)!7kV$4Zfv#o~cHK?G8%hC;7B@fc1V zCP}}o(?w(UFHC`Yu@1=)J~R2H?%lR+_~nF{Twt@BQlGT;($^aW?4@e(M4STamM1<) zOo%!J%%T?hL@uL>u-LOpat}oBCj=~Onx_@UwxqZ{o9(X?6}0(IBCYEE+TM%UOzofU znvD13i*FvM4Q?Fg({q5DJYNJB_^1m1xz!`dlAp-5sy=G{y!uDu z^6mu9c!br5sKIih0iWGJQGzyBhyUKuYBX&Q%3ibsxEvgQ4~}}02Gi^H^$jh~b$ZVZ zU%d{rr+MxwA%?&(3l!*59%z#nZ(YuUCYRJZcF$ z{`t2_wJ6_3!QfXELT>EP()8Mjf*{jTzVlsFZO65=qx^#ft6?sC#)Gwki|&Ro26MXoy*=s&#Q z5S)uf7sji8gz3hU1%20auB=zU;5Vg&h_(SFRibr&^udR2A!ChSfp?Q?9dUJ)p5OWN zOgQ!jSA5qjW7<%)%cD3hmYtzE{qJ1H-?1-$ac^(lxJv4^c`KpFAbRHK9lX|m$Hh|R zZU?^iPd!*Wqv`PCF>!CdTATA^MC1PNl{@vn^*{RDJW)wnaW9|!&w&Xw?(cJjyPBm zaO=M<{P~lxs;xmxOdQ8=!iWZk;QU9|u0r|Rp9wM0x+q-qaLZS9emgmnMXKc_+c zU-qxfS0CZ<(KA;TsN?ol96Hall9T2F)U02(=01*lqx$*dt`ejQL7_lEpY#%cCfJoUnGdPGBPz0)uRgSfAHVpnu z{K2Ora%N^`JY*i1reHAhz^z4tOz59j{@%S;WdST={)zG;B+l?PC$ZA87X@xe;uanP zSBM1d0E=L;pxnJH{qv5!QhcYq;Ode^Kuw{uQ_EK4l+)hqO=yO~b`Z;7)#+UhJN8&8 zEN7bEIWJxwsV&w-{+=oZRd0l>dl9Qxr@27Uby(xsamuSNu!*zi79T6TnCC;C);%d9 z5y~3O%-Y)7PZ-M%xRmoGbthV0L|pbdq7=~#Ar;OMEs&6 zaL5(a_Dn4aD+~>{GQbZFPA{xX)s16l4Y8ZFt(=f2&5n$brATK%;IZoW#d9Z?Lva?$ z51s4U&AQUtFLf~V@qmQj{3%Adv=`BB>ckhC(dsqlGcQh$&-8GR8%w{^OD^S}mLKOC zj5S)~svNJhs?>YL2Ms7FneWY{uvQItdh9HMv(Y{jzC3(4w5AVKUL30Yg;M3!WjDCU zP_uvjM6obMp?|c?IZxq7)+2(1DLjd zh*ZwdLC&_g#~Le4M<*HT?ITOCX%MM-pMw6kqN9*=o5yG51p7JIo;Se@kM3gE4mBA_ z$Uu@Vt7kA556-e|anJD=jx`TNz){1mpd~9t<2)BH1KYc>ufxf|ujR#t#Y&EjWwWLV z6g-PHT<-<4H90f23!IGt9#=e72DEGrUT5e#!vauqc4H&`*#*MW3-PJlLL8WKG)an!Z}*wz z&m^~BBlbwaeB)T-F*O0B6zyvD1_mnP!t&;KE^2R)n$pI!`AVlqpIx&qA9mH2f4M1> zgGpYzea}uJZoXReP>r^TxVa-xJ@Z)Bav-sBV+cVm&{E;nZ>&sAOm~VNg?6gF^ZV^N zSNyx3iFBA-kjFr%dglGWeJO=x>b>q{+Gwx!v9T1fvXxCY@CxdQ5zz19ibsVWzUBVM zdu&J7c+>Ewe%D`mTgA&2A=`aFvS!;jY5m;g%i&_-vm>ApxJ?qPI6(Oe)|0{San&#nP+1fVgBysSt{Y1CV}az z=;KO>`Kx#MVR7fX49hGJEtAtK#IQQ(Vx-jaTBeS&t8Zy-`_Hf8uU_FqFi2?Q{A6Tc zh*(}G%-6|v=DVxXdzw6hXH4#cMd+W=WbxG)r&m)mR+G>v zI36R|rEXl{99>6@zx6u>w=ks(go2`SvnT?jqE}31LX?%?6*(Jw`}oW)EkW=wbV)^# z_lw}F-NPN+U2J)FxjQhiJ07~&<{*&<#nmac%~_Hf%==WFM1 z2fVXgF5poLu7KU^x6&j|+fkxfWzb=RyPI45VzqIW=IvtLN2i{ZY*8MY4<0<2TUoJr zkui5_RXvlwxYv-_^A){2>OudJ5qI3l-g@DeVYesFqQwavNy;N-{|4`6m8hmr=|h#N z2j#X1lSVa37Tkyv8B6n#Q9h1)f9!wN(TCdXQkng{=v1|T#|lvoIS?#0wzt_NKLxod z0_K128=3JYufCV6vTI)RXN^=xQixV2I%aGt9F5)kr zMS=wJr7x)s6yylhJN%3=D$r(QkaWdCLAFu69p=gB&z~6vgW>IR1`Bze+PyB&*RkwqNPL90_YzGAX`{P#8uIMFjUZ})gp`!UZYhEE z*H6G1uzXG>u8Cb-|L$|sS-?^qp<9ZZh-ld2ntx3x+t>Uk?HKYPPRpKBzI3$?+%gn( zk*z^(FMcI=UH+xGC2mE71wUOIHdyw`EfU_miw&SP(V(i5P5CD(&6-reL$KWLyY%Q* ze@rX47Zj<6LE8vXG^0!_Oc-bmjmI8~n97i3q(<4BE;PJx7?|$f23IXC@CqZZ$KI27AO*FiuA1@tQ%x zbr}Wn!b>PH!`wcKVNN^X%S(|l6(8ma%Zd_vVD%ww41ert)AV({l;qd`=Il?-ex}vk zBnb3;aH}in-~@x=8N=PRmS>0Mq{t|(<;%VaX4|gUNWO=UyTpY@asx`}&y3d}1Q&95yluX?bu8C&#`Alsfe8{t)rH6DG=UzReM7|4>Wy7W) zDJY-*qyUNvU^XO@LdIi~e z)pw>0y5FmHk>Qar0^vA=Va#Di^|8lxy!iqum^63_8e^B--Q6EI`J!39F4zmU<~#5U z)UL5M|M>CHmi*Od**`=E7ayOz2MCngkiNNN7t7UVNq|1}jZH>m=Z7>eCn-Kg>eA`vY)S+%`}L|tJH^7?jL-NE=wxGJ zVoveEyASXzGQ5CpqPda9H= zU-(-@MPEOO`A)hlGR^wo1Bm`+=jQz3V}3-rR4JeZ*b>DJ?K@jfA^jR)axNIRZm7Qb z2#~$J-8Sqp{!2%8n{qIWy>F(AcD-hW9?Q@HsNfIlfVHBhpypX|k)5FWTXLyMzK8 z7C5r-Kk63aSsi_bcdoRF23wbDByaPiO)E#&o}AdEwF;j1Yy@NddtAw@A*?*}W2TE! z24z5wuv{`hNA0rsnyY(wd0ht^AqWYTyuGF22aW=vfB_<+1Oy|i@)I2S`yiOA_#}7)dGARO&3tk*{?r2|#qlC-$al=bW!2jA|C)cX?aQb8~N*CGj z*7S-g_j?>qElsD^yhrj}6)7u~qMv+?!#WB_aVL8T=lM)Y`)8g9RrSAv#uSsr*3D-* ztO$VKrvUF-PnM)HZ|D|iW5NJ-7q)q}sy@k|JSHJ;yIhr%!%pIx$N2NmH%F)zCoC8- zvI-#tEzSOyK^cF$K#he}J}l{3kcIy7^!Fm0KGP?W)TOC+t_F~}9Bdd#du*YA1Bsc4 z!-)LyMXD!(H(bb~r@Mz7(h_$N`Eing0S0FcWK=T=gs}MMHZ8|`KNNh1s;5ce`~LyRF%p349C$Ox9$8*> zcjz+P7gLgR?}-@S+TVUNl-+tMM^o}QQ|(qy2GEU9_Wu5W4#Udt9}p5Umb}oHtrV)5 z#^s^rVAgqz@dTkSv?hUE~*vX6T^jzXI3igm8 zq!Vcd;X{SdbA(k7wxPpeCLH1*d&w7alj>8fRUczAH6@pr7i}7 zO{*~eUOmM}%J}my1(U8<6QD;Ym;mS8b@r9tm-%8;_usj6-}9#%prbOsVzKRdT0qXU`%)IpLixM_qo8n5-VuV1o{D072ctJR9WzR7_wT>W;zQm) zK|a|p*v}SS3HWUjD>G6j&dyf{#eL=hezguMwd%jjZEN+7*kM`Ydvv2q@v=*isoP0q zQMr1dY|`(DF1j8Dj;h{YRt3nDR!$1krngHf#m@)lhlhu=upz?Lpt9I+aF|KcyVIdf zJniR)qTtpg7|f9E0@XkaBt;SvM?a2U@MHwafTArnCIU_h{DV~3uEic@P`CS#{)7?S zAFy&3s812P6pw~Jg2{@;so)8HH8r3*lTG)NI&c%r;zEM4On(#4Xi?33f2(zK35vsS zUa#>;ZFIz4;xm?8ryHP5*>?lKV*tD^ETE=CX_gwx*iF>a8c$Cj)!D=cs8SD@Pe<6a z?u)rlXIhuf&!(87PbNCt`k<%a0DW}m9ljaavFF(`zVYdIh)S+J z#Nljh%_%6$*P?$ljg5^S+w+)EPSWC(I&NhTLrVJ64K2<&63}ErL|^XBoZE63{45f? zRHe;L*lxHV!aQCw+pb_BhaQxIdo^rDN(DMktYeJkG7{_yPSPIumiA{~rxZ6lve6IB6*`}}ca0nT$Bw2`7(#uHlph&cXn;9MQ zNrEnWQ%T^l*?BSrqWD4%L4cs2J&?jZfx%v$xKpTWY6_XUmQ@JA{a6zsi!#auv3=WdyCyEct|gq?C6N4=4+{G$o0LvDta`iy-nhQGMki5fZ%tNn?he&hI&r zJPc)UPv6!w(?OVY+;+z7elZwQXNPtUUv@rzUCiYzFz~l!HNs?8UI{)5P^{puO*hb^ zV7YAMv4=pQ;*G>B#!G&!!rdA5?Q;!|a{(P^d3kwnu^Cus;l~wTwNEjUnbQY*dQaR9 zw4DC^ssg!W80Z|(y(uWf)ybnDw;lyICj%C4eHCoe@*w#d5p?XM9$Gd(BUqWDoSLW- ziVRj=4ENJM&o}wPuAUw)2Uu;5;}MSPj(Y{w+;@f zMRY2P^gnsuxOul28@Z)E|cLT__`@mN~2qtQn7^iC%R*z9(e^upn{WG)`$joS+^!Gp})h!mjm*Tk;W4I9Y#8R zs@=w8d033uuNvO$`065v^6S6YaFO&s_(Cg;_VJ+VLMxV+IrAEnWk3|ucr=sPy1(dP zm2KE>NM@+>%IO>Hvmlpd0RoU}y@m&h=LVdrK9%pzqH@9g>uUs_GGCeW^z;M>-WH1G zz}Ex!4_d{~%7Jh!fTJ=0L0*)iIW!W#LD#QW(MWqP%+EJhMB%F51DHbs)L?UEgTAH# zurX4L`;Gc+(>UFKJfO)@hg$Cau-^BhT%6nm7eAo{BlAmhaq0nK{jVRW!b>?!#AX_} zy0THaBE1DaI7o(UE575dU)l;`A`;#YZwU5$yQYiOe?{u3{fm~(J>K!O342)Fh%@+0 z6THpc#l3eDpIw@j-&eETt1{e{okaxKf$)DJqN$gY0$ z@~;&c#d zEyEghbX(Zp&qXCgzuVm&CrIkfL{W~e^cIcz@~s|mCLTqRr8$64(LXUr>!yn#{&OlS<|7n0;xk*lg=uUpa% z#v0&iTKC7C7KXlPFyZp{NYT=+(1_MBrAtH4x=x`1{!HK5oB3U395ntF6bO-hao^cV zi2S5I(k_dMiG^btUj|KIbq%8OS*be`ERoqDgrVSgY+3W=OB>9$kcjnyskg07;oi;* zAPS+*CnYV8)j!ek3cTFdFYDMC1_rq=+Q*Z6QGU%rxq@!L?gG4D2L8#bhK<~I4wDTg zlIA>jpf?I>ZuSSniV}sUXp(NO)4wk6p$9OjS3_y;ls2tq;n8Ors^UZP=g%o7RkUe> z2YRQ+&+Wu-z@Fd-oD>7-Ekg71I6wm#fL44Ds_sHG>K14#OhGFPDc8X;@j(NH00h}W zqS2V;3n^|unp>H)1R!f(#Ayj()&FZ_1OhP@<0?@MaJUmi7Aup~q6M2ZwY78W)ia&h zis6n&I~Imv{N(iw4Rh=3>oMg7rs(4m0s;G~vIa3t9bj^d=X(L)M{Ab!%TtO6OT1)N z)IMUgiqbNypYt7u{!DesAI%CLb@`q??<8R9eFiXLnb~g@PK1z!H#H*IrKu}n8PbDQ zsHu4j8|g0n?z1XpV|b;#l8W4%2hlPP4o-8u=Pn67Jv!P64N<^NDi2g#QTjvV_}>kc zc&=bD<)OPo0V}%+IdD#pFM+lpdqA-rI@?IKGM(AIfBz`1yn%6WZVqV+ zrHYm_t`B06@IDl{b$)2BQ)+y*q@)DuVXh|dn?%8uV~~0;rc8!SCC;%Mk3@BT%T|Og zG$?`@j~}qnf3%;-L2*I!JH-?KO=jLrS~6n-bj* zeDeCRsI*%g<)YxY?W2X`|Lz!KTxUnC635AsrRd4=a?0>jq>D!3a8d$)XsftWfNA}Uy>5)tQ?tm0zX`tKDV`F2%d5w191OJ(J<=uHo$knK&V!AcHRjfp#igTbEbf@^iGQ*zqcC9)JFX zh~E!1-5Do?J8AKbogELjd!fc$!&E2?pqbr4S+qr(OD7)%yY;`nKk!LONneGBU%%Ap zPZL`*?nU`ZoXFt?V?D}qn`}S0Bziy441PZgJ9{bm`=c*WeulZNsW&oI?cNYUO3D$U zF1?@D5Fk9dIoo-5w}vvquYHk%#gwv5V9VKEfwn0-pH2tsN8sfx-F_91LQ9L9g5MXn zmyF)yeAFxP3)gd)-8jScI6TBvIqB69`C%<`==l9^0CvlA^euTnirA{S92kC8W0T(! z4Pm0)ALdvdcE}8(B4y{aTOwT5-$53TrY9{J;Dh?NF+~sZI}T{A~m5bxl!hj52 zbixjNK0JRRhIAgBMLr2Hfp7%f;$_7!3OP`mZ9@j<9AuvdgsaXqoIzd!Sjd6L;DP}o z&LYDfNOP?%DtJIIvu;@pV02WuGD1dEVPT;naWZ^|hbM@`lu9a0Hkn{u0|1Y>EGU%| zlpHYs1w*0_U}0!*58?;z>gZ5F&h4eB1yEGKhP1*vN=g{0`uh4INP0r&LxR&U?vRqg z8yYvzhDU(RzC^N6dAPjO|7lw(Vxv433Gt`}YMf<5BN%V46AT8<=}%z|(jlu7XGM%0 zB_oLv_CH_ng!4D0*;8y_9mrYJB7Q%fuG|$jHox!N^6Z~epGdZcrJ+&sXc$JzsujJb zuE=V)Wo5j1rMTxA-}!_WgVse_%L29X9}PcK@AR(1?;`bei6Mmtj8EEFjHDlmRpM<1 zJ#6*+6FHQn#amPGP>Elv-0vx(Z&*y>^P9jz>+1EFeoF>tia!*+qxRgmhJ`eRB`I@8 zaXjA8{8O4Ttnt``LbkC2m*sCCg0;BG28U8cqVHS2P39r=Z1YuGB+TL8otJE@CH;YW zLW@atetMm+y&Z*uhqv|PyG_8s=dn8jTgUBg-C1efF9ZErUvqhyn*2OR+V}oM3+mGC zo76ipG7)WH>t1-;jTCTqs-@%U&iEn44PR+2J26qkjpe4_F8K{*=zf2b)mJeu$)_sy z(~+^JXrCG@lyK_cJFbcKtv#dkC3N7}e@5I-YX{rgE!a%neo&MHpAZH)`~B`2zn37v zzjS+v7xGJm<9Kc$7k8rfnL%Cwcw=jpLa4KS3h;w)7eKBze9B-d_e#0DNRBr=`xwq+ z1D-9qvd%!g!XH4I#oUwcCW1{AjFdnonn5WWfRQ!6XOjD1{De1TjxHXed*FgFKic`d z*uTmQIU6x?aZjrc-@`@?WuLR+MAeAH%s^EdSgpTE{l;gpi`Fg7rP+HC1Pc?9>CBsj zL#s_W)NL&$hx-gN_sOJ$Dh-P!+^)DOsoh<0`n0|J^Ri`!k%pLPfGXA9$V{R92aOB> zgIE%4c0@BNB-(oK(D`#!_NlnzxnTx=G%iqG}uzu&@%jl1nus8e88r3S+| z3Y8+}FmT|3)$VjAEsf{!Yj3&%q9F&+YX?*=#H>36dhE5rNqE(i@r}m4ZWlhD$hK7w zxk3WQWfVHV_dP4pySdvPp6G}L(lo$)MI#tx=)mgV4)!6$RH zCSO1WFD-B2emaT#W-Vg5ihHeT$ZfbW26`CX;X-Urmc^NT+ZBQSjXfwel4{$Q*4>m0 zC@2)Wer}Wf^%iDZrfED${ew@}==4hcZ2faJk-DVnjaIJoB1=ELf|Z&V7lP_DHQ_Q# zrKT6bs~+(mQ%rr{#D*McU6^fG1=5VvO0fU4CnnZA^4Jym_>96MI{i&X^9dv(;K&|A z;gJ&H&umrQFUc5hHD_*I-CiqrZu79C=i&BlPo<-q+x0>0_jzkCSpwN>AAUg`crxfVCQ?q*j6hT%!a^dTJ zUMt;7NBXCn{R1}(F%PB3r8MeIFoJ`}e*j{JpsREwdjZY!uCJ%*k>f(CF!+c8H-jw$ z9n}g#{x`XV#R#(#4ZM!6MRr0)?yINpBu~OXUEK1oY-Z^*6w^wXvHO(=O@6YZ;o$Iby zYIP|6Up+keGZneROe6BAWen#MPNan|m^z{!Ac+v{uKi6|KzN?%4ukmXnOj$b+@GZ! z#JY&Y%LmTFMPtmyu`nM`4~cJB!_xL`;5RI{pS+2x%TKX|eX!A`VZS zP=0%pf-OUkF^PG}Pr@Jar&QDE4BUHDr-+gTqAV}R;o+Z8+sw<1m#J{F|Fes{Noaff zjYg@8UOawX{X=@A>5}l(1M;o)j7OxNn$#Hw`)wT7GNyb=RSsdbOnZTaDifvjZ36j* zHz>LWA2$~+K5dtM8BuY`g0&KN$VZ9&!Xd0o2BQ2AkB}o}>o}NV+_0GQmF_Orx|;l!!&ex}mk`V90)5fqGIp zPXKq`;tx;nAJth%ZvY;;1>QtKkWke_FSX}J?>IyomTHz5H`HkkJg5C|SU)K|hi6L9 zDUAfW%)`V35lfFJe|)rfjOhmQ&hV2_YY~dj#28FKIK{y1PIC2Xnxe?RgM+(J;WZ5D z%di%=g{G3ThW_UU!sRZtdo4aZ@!vdL{&YT^xrj|z_$@o}8VHqwjK+PoaMV*9P$*8l z4-{DY%Zo8i=4J~5PdXiHyb%uLeC{O^f={7tcSoyNG3tFKiU<=Ca2P!@ zTMUqO)RNtwFFtYF&}F!m7o|jt*aY|fjpv#@q@6l(IyzP}z^R8hwPSms>p#5wa5j*b zwW?cE`+>H=dkwM2VG_hBklu8Lbra&?;l_^%UE>(*4UKHNJpKmR?@1CFo_3@wqbB|{ zao?c&VPlA(2koEF(kk8$N_GCo!~wAYiNO4z#CQ!K<^os=yU#K)A`V%4e*F0H(VVbV zn#H8ZM*|ncI?eP93qnu5IpWO=MAT?6kit|B>LmQO(V?4$eriGwEkM!hBey z6V^E`TMMrQVvm%w)jOK6mzVu20#h zUWiyjgD3ozQpZQpX6FxGN&8l19r`w9p_H`je9!hQTuv&p2L+#fNUJrMP`JupYV`{*#<&Bg2vo(+P&opO-|3dnVvI_9sbuAD+|H;Au{ir;G2PS@*)0;~B1HV~r5k;@30E#EQYjG9T0ix^ z>;I379xgSJMXv||w_JBHN`3%*5*S1$L=pkPV+Ke}JPPUk8Av@eRY$U$!w4Dh0^Pqbbj)!QCLLEyzNPM#QU@c_Solr!9R`Y2l0i)!Dhf;+wdY z;ba%9Q&^K9BITX6tM93F)}$nsPQ~>RU0xl%K)c5-p@2{d zX*@>6(*|xUDM{X9_>SF0v|7(+WHeRgp~h-ETF75+5v0f&H*l1w(Gxf9xjqANT$8`u zv9_xrVZxKHjd|l7dZy5(G&mu4e&asYBnizcV_gh5%vvq3o~{U04kBdnAC)NY+y(9W zg#7qB*0_JSRlEHYcq6}tC-N8sfVvB58y%&l7M4%9^GS=yAI5(BHf-7>&rs{FO6K+PAs2rfzfoc*7IGO>pdiw3 z-M{#7--(X&-rMBY!wOilO=opU@3`XWnu<>G38%dufx%MMlmc(*wAaeA9c0qDjL)F4 zPi)c>tez-8^|YdlIP&l>=phy)={srfi!+Bn&zDsXp!efRxXko9A&z8{?raR$-&O-Thlb( z3O`MCs`Pi?iMEk7Kl6VpKNWY&zT9^QaXj?6m$4_>-9&Sqc65VIhX@Px{=HS^h#tO^ z%*LBG`x_4`u7r8~jpgo`j&wOZG=rL+@Ls(V6pBl)BmzhQ72D`sYf-ug#+x`N=RYj2 zzR)Vz+6@!6{#F#+oM1gfSwuUp%!F%#Vz4>=G$?-cl*WiAJ+<@ zX5oc9T>FF!Y8LA90!H`w`4k;I%eP}Yt&!RVcgoE=>?ZQ`XB*JnBR9c_GDfyD?M47l z#|zWp9Z`ADJ57vRpWhC>OD~X1EcI>OCl^GaFxzGi2C?Q4pNZRVO!UO)wflO>A2rcN zZmG1O{1R{2Ebsi#xOe}>8u3-&J8-}Ae)T*?q)AI-oi%vO_`&7H83!1adES=qH(Qaz zr|l$b(~X3F8zHM*9o$>bdk?f-f zt*w9lAcp*0GRDJpn)WFmjk0Y4AB>bPy7&usRaxElF9uv%bJ?#pAVE{e^ z1Dl$>5FC4&82rf!7^N=e1cuAT-1_kq+#|!ubshCxc{_Kd4PjN1HA%hIpm^PLAHnAbr$oTS+5kd*y}Q z)Ro&yZekas7S=xoB&4HXkH$=p>t)l_`|+6~p)7Zr;Z*>>>=Mu{5H<|P`7HQV>Q_Hc z8#gyd2v*AZT4A>fpPl}f7rdxh$nxC!_b_~wZ_pn@s&L{{(NZ;@XPhJF^bHk_eB^!G46uZvdqa^}x)~k`9&x zG^L(<^ECQNT}iGKsd`&ilLB5xWtZd6Yj^dQ?a6 zFpKhDMGfT`abG#4zT6gOc1RQT^}ZOi1D!-D}8|gb7Yi@!p(x5=}F*Sf$Q(6hrPZ-KY(Nd zP8(;W3BSVl;1s6T+mP&)MEsc8^w)@nN$YouH;4OYe03qIV~pw!f%yYvs!wUm2l#xh z1{RgxsIjBX+ED-H^F3di)K6Ua8J~e+G7shgKo#3qh}WGLw10h%_Vluzk8RC4y178O z&qZqY6$OJ-nFQgHfwGzRe0eWOMrkKr4sWD43TTzunNsliC2uCIl@Irzy9VZAD+WQ> z&1@tm2OKgAFwdHg0X{8at=U-bAQ5%B%K~|BpNu;}ODkx01wOGrh@(QItCo|lkjV@1 zrQoWfS~oG7M;bj?vYy5b5|%f6p*ivebNcx$?J+ueKJcP|Clzgwg@5v8h{)>4dkICC zHm&)Tt^g#rR1~S1Eg)D}Sa?ld;~lfPG<`wEpxG~5rc+&Hs_SL83)AhLua>+3X-Pfqk2(#_U539?=bh67U65nqy}!-8Uku8-F7;Bvdc??t9T< z%ywYU>nqbIQ3tbX>grU|XRzTzY3sev$tQ$Rw>Hm_Q^sa1gP_tfZ@IMaFAr6Q!g+nv ziYp&5mI8$HDI5idfFJYTvIJxWiV5!}9D^)~;^o{=I;i6eh0lHgbQ@iP*u*4EOwa|Q z1qx?y4B z=>v5VFtRvs-hZ`WQ-t4dWf-N4ii_rE=R8F&(7~k~9 zLT{M~?*EAi8q{@kL}K5%l_QX#iAGbTeqpJCMD)R}3kv#cqj+vJ{U$+t4aWh4{UgaS zVi9DT-5ly-TH-)Cpo>~SA^!aXi6;bp#~vbDC;;T$TwE03?&RrnlcJ9pgr1LoV+-B^!7LH&gT4uz9DPZkQ9`2~ZotfRe9?HP{cpVd z)IU$zEU%eM-)I%bh;qM4T-?qUk?Y3YndYU@Cl9R#QUV4)@%n#$s`tJIVs}3DdiYVN z1+3;ya2W&Ph|Gabibm=M4+xg;{fd`>OYF`pm zW`49-U1g=cSrP|-?9lF{NlXE8+tB(t;7{Mb5H?F%c{9Uq?|br|i7q z!1Ob0RjZgZQv+U)biqgtqZlntXF@Sv)q68+I0kZN_gb3i{6W#rfHv?$buPrQF&t#6d`~+1@v2GZ$NN&R%)7iHShcxSulNA)2jZ{T~-tL z>d*oR&}hEtU_!MCdVb~f1O+%q2WY_f)DC+~%$qm~~p4P(&BoUgTw?+@ ziEB;V5cL#Gn)n+)f2~dyZb2i(s|D)Wu^@&}ct%-mR?zqye!7J9MDp(CqbaHjYY8T>xmsQin zxerLT8Rfhw<5*VtX7##ojRUtKSD$s0P)0ifNfmRwb^#ep^77J1jzv-$D2M zy3|tx+X?zJiKcV2j8Zx4!*JR^ot*gmE=0X0M)NZZ0Dno4sEgU`Wpz+Xq29|m|JiKa zm?=W2@p9kXWTPh_9t*J{ook0XbKGc99Q!7v02mva8q3gVXvd$Js3_6;$TXlOj(`(` zN1s-gpaG)STcPMg6BJpn9(^nD`y&gr01{B#`)@PVqB%S~eBRFoZpHN2Emd*dzAx^> zie8@C4&oaUkBT~9PWHVSUT;7QPb6#u6bHYBmz6WCe^mUgqQ zey!f#%ZIvB8 zFi_obEH#t01D1)`Wx;%nJm0}ZfotK`xOl()*g?jNty(v3Xnr#IMnb4Ep(dM5;qZ*A z;GZqG-32L9)%R&BiN~RV%`?k-`tGaEL#vbLIWtgz6drBEn<8k`5*~bVv&cB?`v-em z)$Ef;Sq*!c5JbJVGl$?!JJxIn{o z>xrTF=^kl_J(T-^K)FYoWl^#L-07v)DUtEL9)t^LX9K;Wf>ufienb5verOFjtgo@XMB*{+d0|+0f)c%$LD=ms#0yv=Cxo2SXVymF zGB=&OGAuGHbng|_IImCHhiF%CpOwEdYm!$yI*OusPN$>_mnaY%ST`ghG#kAy*T>oL zk}U0*TW4?I8Sef;=y-^;a#l@rFO@5x;<~mPOJ}eQ1fF5!Bx%$=$AaL=qz?hj{)I8E z_Xz}V_FN)IJPE1coexIF+_#G0C*Uz|l11lh0qVof#)bhtyh`f~kUZRTQzi|0fWH1y zUi|ZBoUNJl=DVK`&yn6o#h>_TLvFI={y`)}L>;i}qOG53F*>@W+tc$rd9~SlpCCX} z=z2JaK~O*9==#bhBR@?#L%2W=m=5;LW&KZSL3WHD!S(>svhrdr&3^!5^fr(v^lwAu zd^-Rg#oqh|Dg+bJ8Plg_v$QN8Ch#d*x-iC(`0GTdHaea97uD=KY+aqdUPrssQOiSh z_~uOJb%TM2JThC$z{f>Y)2GuJMK4++f4{Q1rQW<-EoTN2LXgQ2xjT2&x`vwM_9SoC z+$vTHn4ToVrR$$E)a{}Vx)QN9Xp=fb9lgY8;S$JCYTHE{k z`xlG%+}FR8Z66JSAqK1kt1cnL-srSLEl+Q6#r%iMQgmron9)yN4|^TA|oCwZ(Uv23>YYDp5rKtTDUc|q8b;S0FbE(E1u zo#3b`vmR!L8_;C7!3$ocx$SmeBxFgPiv3NNeiNw}H~H>g{jYZ%kC!H(W_=X9Ju=z3 z^q5dFY|k=T;#H@6>MyS4Cy2~N`|$nG=_?7!jly|zKJz1wT5O{weuRktC%@iJG1;=A zY&N?qjoJ-k{ms?-^Qc(4>^zU=3Y|)&oiPhVB3UC#^uvQHGo9h_`|oRRKMmGci4kv@ zE5>Ltte^rar+Cf-vm(qbu$jk5;IFx8?hr;pE6$$R_%ur8@3Cj-E;1Dv*-_7^BqmgH zM)G3jb87MB&NhDYVv2Ap&yoiWiJUHsj)w}ih7KnWrhd(d$)}iKR*un%8g9o~SYM7p zp_<9hb&j$y!@rEe*1#m^WtFvZuB_k;V;F0UrAffKieB+AU5V9yE-CE_nc*q9a}Z zi=qYD7<~*5eN33_BKr13t6qO|I!Zn1z})$ddqMLAgrLfCi{r#7r}4xv4C{sgFc->~ ztVaqiLvUpT=$V(_3(}mGZ)8|f=#23oZMqb}BN-lLXb~0UPZLmGHGz6>bh9||W zPIeXm!|t}@ARlHK&#qiNV76w)m0s{#Va$>I3)bhV@yV_gUsZ>OJ8GRj=VI1LfebeQ3Wg{fbv#&8&#AZ{i>LpuLP#B;_1&XFQf69mAk5NnuEi` zanTVMs{>5Ch-|h6WMmf6<0%q#&L!a#l7fT$!CyG6W9DzLD#<2Wn{kJawKe*-0TkQ=LUHEH+v?{d%mKy*wt zC1nU&G7F))FpTrS_Ia1#_xzRLn4ugAIE7^JdB%-H5d~UKpqeJetL_I`9gHYY=p@>i zPa<6Ch$OG}sRXnl7=Sp>^V2#zyDuc+vDs~~d7=U&HpXvSn@sv1crBfCoR)hDqHes` zm=7nDYY|e-`CU;&MINB?MA2z;-M&NNK}QbZ_)z8ZC+V>ZOHXd`G~X*%>#0bFXN+K3 z>!bdp`?dSEZVR$p$iIkEIfrlB)<2M0@v^+kh$=!W0`|vI+rb(gh41e6L}7xXDI^p9 z92qi)S*NBqXuN-19>J|XL5-G2%Ef^pILSG+(2fQL>a7}@>Y zQZ)MP7jsA{L1Z%@Q?)yx8;Yp?zl&1N=XqA_!Tvr~LWZ2@ZVO_U4n6NE6gZS#NpMuO zI$^DCY}h^&%ztoXx)t_OvCffZ+H*%uubf!V??w26{jAFVn@x&As;9>d=0pc%?(=W^ zPYwjw$vr>*{a%^e=o5qK2!5z7X)QRAsvR)9uyZJ_I*-Ee;=9{xl`zG5$%*Po?^IGMs!+(-Gl!;Xu9J~($hT7abb-wlFumJ9nUmUE8|JwA_T$6+$ZPNn(50rn zn60Kjf$ezcqy4#@sJcO2XiT^o2C6}BMmXOw-S^;Ev71*OwY=eP36P(3U8tG2cAweP z7qs^EJ>ucMGCdgebGb+xO_o5OEGr7cKsD1kbs*7bqW#0RxYG^$w<4cbTXAUfFWC;l z;kRyj_rPp6TZ@_7SE@e0@PsxcB0=NcL^v>J@rC!Z_VY4D)GX~{RF>yEEnylyLj?T_hdnJm8 zA=h8F@G+GmNBItrb~xIN*3`*)|1CYP1y?J4V)SUy+pll==wecMBgpz4Q$nvxfL4*R zr)TZm41*i15%6m%hJ=Qi0zAoef=&W}SOqcLF(Sw&4Fk(D3N5RGdAl>{5+Q2M^0@C6 zueoSCDA0o!{)#@&a9(j3Zwf~4_`cW2`BuJ{&a<#_DQx7aO*qkyl=ML%|K6kM$7mB` zexd2_#j3V{wV(PmRP#|O1-5;*ZUW~GJU#U}u_N??k_}B?qb%vXw2Cg~Xw_pzQXVk3 zxq2~MQAYP(px$V#-_lm0)C$&IZ_*;N%}};5aI*-Lm}*&Ds*kYX3JyUwj60}eic-2; zqa@;A9uiz+)<=7^UBx2!A=#iPn{{3lNxm0oei?^;`j;^)3A0fav*KCN&!6K)^->Fc zs+pcPA9B9ZDqCl1&hHMJD>Bx@3b?WEHOomEkYo2rO?#!{hhS_f-}jp{1D=~-I-AW7 zViD9@!npFhsT5c~jx~)Dgb!>UIms#3fA5k-K zJfC?n&1RdqCVN`t#FeEm_M)Ea(YQ&fTw=6CrD?FMd=n$3$f$%N_YEey3{NE9!{^o(DSo^^ z?jKy)_=+?jsv@E}chf8q#QKl^xysAATCPZOCq5PV^VQW~U9@3;O#d}>OQI9yVa?Dl zR##K&0(fR)t6PZ3I<4CK=3x0X08c_dEH$|)0kAG)_Ljdj|4mh-+&jwL(0z7i+7QWX z&!SkxK=G%CE~V}%k*(2f?=XF*@KHC$L{O)d2ehm6NiTMV*n(;b3baJ(Ysw+HplQ2r z?a!m`i?;fU1N!FEl?0OEPmONfBgT@*DcOCiAx+*7I7B34P_6VqMJp$h}weeSqg8Mnq4^f zVy5up#@?4+)+VD2AsrF^_ZDa8{0#T-_y&uVC!`7(y_REA8A)8d`-r(K1ef`5ua56s zcV)WsgU>Gwwg=u<#J^6nw2CVrUM>v1!Agpk)%Ao4nT43!@B@y!)_Aj=PzeRl7!C?u z>(hL3E)zyXXLh|#0tGotfh9YW72OO zZ0j{@v{2V_AR_|8Jf$nofKwE9`FSUO16@T6aGYv}s(HynBq^||EnQGk|!^+CaARi@`Pd#ql{CKJM`LYP=jr}v9hO$Gs9g;S9SYyX{qy;b|@4D@>Stt{9< zkA2Vf$9>Pv4(1%4s1;xHu>Apxo}9QqP$kGte-GCVfT%PCs|EQ6gq>{mO5#leb8|n#I@xqOW^q+mE?zfG#55t{? zd9z%0vm~8^vIj;A9gXGbC({|_OG)N@_Y_bwZd3W$zOkfLp934ym^b8oNDAYeTHVvrdbCDV>Lo@39 z2H?#L8`Yl%JMSW+8AqxVzEP@NuDp>oY)t=I^Lxk{gL+7gk^^GruSlvtalichw2THt zO8ud0!gTvV{hY@-h}IV?J(hCWh25Vb0aqT~_!|9dl+MwA@E6m?^G&gp2kvsxr&`|& z(rG@b;f9%Vj#J1&NQN;q7{|S>X3F_gO)V&QLfI_4flMN~`h(FecUZa1cK+>8{O(7N zmbvE+QO>=*)ORN^$(u*#uc#Rz;TF1R-x*r_kKR((*Pp>dONuvp^ZjFKs!QBs3;ocA z0I?H{Q|d*d-aqN1Z`3BiOMB1VDzEGA-vR`9rNA#=?iKzclks$%snv9p>nj(cB|`WA z?T?Y+Sz4$4vu(5l`3~gSEy5@YMR7MB90bz zSG6$`KKr&$)c>c@yCjzYVX3D!4l-*YcR0o#Te-I-GHMkqjPo1Sr8-`${%C*mX5VDr z;1+KYjQ|&I(*sFUA)HSG^tbF0mEd0r9M6{4iTIz18kSZ>uZ2y1X{B$wLYkehF#fT)v4X1H`0X!H1(A>K312V9#)8YEdIGTk!ao?uu;!Bc{wa3a&6~Yxh!#U z=S2b0ea3uj77atg7>M`MLu66#HO7!c<^lo07@M^NY5ZWFM72oamgt5{aCS^xrwwFf z0tXtDLSnZ_nSLevT+J--B2%olw>OK0^`=Nra4?YN#m|0CDQ?bJC0Rcx9J$F9Aj*ax zyA(HE57z=(FbWn>G`Tj!&Jz0?o*fW4czU@FC^6F@P`FZPFLQW&<{_1QWr7+p{L<>E zYlTAbwxWU6Dkdl zJDhz_maR~=(d9d*tB-iFR1@ay#%}taei<=Q9XM~|*lByqeet8k2qUD#H9wxw76V8a zT13>62yyt@)6)W^D!;=gfB~a`0*Y7n{sUGMbBMZ}sCCX$?^0#O78#HrM)!%BO}(z)0#(x#9feX%17a>Ydo7t0XwJl zUd?9Rz0B4ZuANDe`j2CSo?U+BLifr=Dj_{Q$M_-Nt@nN#w2WgPIm9M`gZh)4C)4y3jXmNbK#LuIhV?$rkc*w|-f3{X!hz#fJdz9?Y(Mozb zk;r>GQ%4sNmVG)4GZ|xGxxvM8JSOYUH+*AP8?EUFsu^?MAsO0w)O(g)zP=U?Rxp;^ zZNc)K55yI+8ejxEaiL;RzI}7D0_}J*CeFNpcPC?bAtpK+9o_(z6!bL>bo6iCUkq)= z4lnS}`w?h&Jq!=6f4=)>6_WvQ##=%#bO(4 z+&U2DbxP@L`?kt1Ec|m#d%N7D94*faKG9NFnI`|k9lnIgC%l)!KZ`!{WMtzz5p0^R z_i(&xFVXRNM|<(R^QVj2Lu^?Ik0$MtgX_1SpG99Z{kvw(!96)F-$;{XcuE(9P!dcW zy~btsf0J{|TCdokE}=wFzdAbZTa^kl8p$88aiAkMKmdOm64H%T2{}m9$Oj}nG2Xyp zQR^0wZj+Rh6e_dRFp_iTCd~u`p^q9X>5K)Gb2nh^uv{GCcEz!vL$!62tRz1?!#=ODZD@JpvF-9!*ZOFguz`*C%7Q!C8-HTMNy{opccEoq;>`GG>tX-J+5BKCS zg2K1ahh#^2Q0s}7+2pk2@4i2C-neWSxm2Cy*Zd9ZFd{epy|EDED z9>;0jq#~#7Zjti+Sam~;9y10&?IwW%69JWCM9!H#t? z`$vl&)S;Pb%}qK($`yyj|Bwwa^4(J=*PKEPd=Yz5mHbgzf*e<{l{x0RcTIUYIXb=) zPL%D!j6K)^jgT{UyQxZibj=)~0jKN{dP?ZqL+SDO_r)4HfD}w~o<-9yewJ0rREra= z*;+}-u3LRx)4htf8p$W*aYDveXZl`-s(`5Njd)otKg61fAtYG+RzaLuL8`!2eJes-eEA7nsFt3r~!IhNWm&0sy_39b7V^qQ`#(&4pzHgjPM zbJcS+K3q#EB|}t6BXELJ$ke@v2nm-q7NhPaTO2LJBHi@EckeFDQ1>A}vYqTMY!Lyf zTtF*A=Th~dyu>X-!_1KZTFh46?YdQFUuZ=`7nq_c1=)WS=F}UGqf=*4R3q$N=_93W zkpKBJJ-W+;Hmud};72$vvnL8l!QkEWU;iokVge}*3wm=>?^-31 zWlBp+gj2}uONJTVwKm`X^=n7{PLu7P98>^w!{(mYl9em~)@U{7C)D(!cMXsDNvC@WQMp4MIK|P}=ZG zNKJIKwu!ikWgV_$kzX3^dw{s>k{+iqHlUmiHM*&R20aG*6xSg6NIcKpNO|bu^PuUE67unmLiMx)L$5b7XAb5_cTT3SLVaXds zefRfziw5U6%dmV;=Igh+^91>Es3@^H+JB$C*h-!5$R<6nWk>Cfv$!cRi3dhyeS5aC zk=fnZxkp#DvA*uPT5_2C2O7I)={RL&n8ui@9AK%4r; zh8|vlU6nQ?hyMVEL)&3Jq16&?*QBJO$$L_yCC^}G-ldxw1~+DtgWxbd%&W!2uPt-o z5%Pt{6EuxXa|MsDLI=3I36ujpI2J`z(QhT4<}>Xk{~e}J3cX+-$+nyA6jYG$f2Y6n z3{S3qK3ku}bZK(KSHEb;q(Q8fkIVhuvmtFojI|MIdOt3er5oE18g~20D95bd2-6C? z|1i47I7%@hD((6AI`lon+8-V-xM~YM!c4!KbSL+wV5X6pvht;!V(Ymkx$lTk!X2IC zF-%(3*mWP4j7Z|l&tgYCqxTET_xAYI^NMRxs8v#LlL6UiKTqlx+(Yuk*~~1|ud-t^ z&`IA=RfNh|DbSr0m6OXiM0u?Y*4fqb(K_JMe) z{Ag&99TJNHeLrN~NuAZD&_g|w%?KCD;P(=o53m>)S`U9%m z*8A2krDLdNp6^^jgqS9%5 zc9;#LzA&!SFEf$NRV2vKRB!S9&BKs*q&X_%0Bp@eY{Uaq-5SFanY%-K07Gm zp`_TpJCh(NydBS-{?m=3H|OcOE}nm107g({{Gp{SG_3oc!5H;(V{&rRw-E|sn3)YB znh5RB7k8YE7Hm4iw4bV6C~Wthu12`Km086?Dte!ENH@SM+x0^UY%zt^+@v$g?;q#F z97QYX#0o$JI)LVQrjF35mEF?MP@m-P@xLuXl%(%r>3G+2+>0wY^ooqFw*I@me7C|= zOk%o32$w-84jUoP&P+ZJ0a%PX3bXy`Ne2uAZtapY&etd$BUcX7fY}MpECHcGuz>*XgOMKO%NG~Da0w_F&&(mmdw}^zX$Z=A z)5&EaJPd`d92B#5+?j2B_x^p^X8}m+GwoK_csVZ}-?uEEDy69T>X9E>We^r7%{c-n zJ3gHrH6r{UCrFjs9u-zq!NX zx6T}f)sJ+vBKl8iU%V|Pg|Xt-XbbbF9MNyDKd-1WieGv3sK?^atnEg$H3MISpOmuc z-kOon5Ybp>6AtRIdqrtN%JmwOz|Mr+TfYVwGhHzWE<;4YqT?>{!^3ih*Y-%L_;Z2} zHtFJn$5jeEd8J&}vHj=vL{Y4{x}vi3CvVjr~uIREmVygwL{dgrnYuBQj-oKKOyAuZw{Av zxkwkwRQHM*Dw?;@;X=i{3fxN0>)&}_rKBvueh8TinMx-Rzfj27h8-jdV_b85x$uTj zdS>mI&~5sSeeWSLd+n$*a9Nc7!t;k4f4DRBgbG@hHN%tTo{FSCauEO-4RN0s6rE86 z?K!l8?7u56{>h?}F1$+a#3iEh%$J{RL4Bu@yLP`(LdKwxtb8ZajFIIZpmqUYy&b8>JVRkBf>W%9HCEYVG(0A#@)1cE#EHRx|GZRq~|L9yYt=k7VBqu?qbxJHv;`5_%TpV^yIR1QX;1Gn4+c4 z8x=P$zuCtr#gQ|nePkN5XhurKp_3OMm0WQ7jykn~i<;p8M&A(iCF;-xg1#|9u4@Rp zXN)*w1EoczTjfI@zDty9QGXqz-M8DWNQ+X1RXsGhIyP2j;3C}hif3y~JnfRaza#J5 zTwX<1n6xaTSYH}}YUnayyHn>4LS?I`wPtTZ%^Y0ohlc2E)?%X16vqW|9`V-e23<6q zipghP5bexdzU&xgMU8~n2=Zg*YqYf>LPA1B(PDj6VPsU)Dq)xS1y;=2pNX=Ja|syQ zwRNq29;OLuCa0srheWH7T2Y{G(JS@=56d119>?l!Qsw=mq10k=kJBtGgBV_GA`eoA zXr0D*ljGxC$1BRq8E4~6X^SFg@v(VdL`Qdkpd~&tb8ElnzLoIp^r@3=Bm$vnWa|Ir zD7h3(wXv(0X!FKK?^_FN&;>Ms`W`dQl$r)BXN5sU%_3y`rp(lB2%<-R!{bef<;-K64)R&D0#obrx+jfKKMO<=&Ab|Ef zzP`7OXZC*V@Vp;ge2kb%QEy8Hb}o7Qylob^tBCzOwW!WvnvfvDC^yhTH{-gLCk#$6 zU%y_{Yz_E>WVbp_*EpSphMP}OsC$)4?6j9(i`|@xbxdiDU3XdN^;wM$^jsXZux2)w zDGwY%y&__Dp~e<8->^)|<&lNC=QVs_kx@}O;~S87x>RM2@Vzf=zh^O>xWrD%ctuij z;{Cg+=xCy1oia=mcefe;@eK9#>(^rxv66=V!u)0Iw@=k{yv}#)_HDFl%yTU#d|laE z9Or8VQ9sFHG23$yaWN`yNVMxzh1smP=BSmZ)7aKNPYt z|9UqnsYxPxs|z3?Uz@DpZZ3wP#>C#uCc&%@-_!mICf{L+7G=w0#i)s-1EViZ{c{!i zaR{^xb+f2_fhzK(dvF0nHGvH+u4c|Z-Iy6xXLJiKK=Zv_Pq!JI&Kqe4U;m@-XuV&0 zzvh$S?)E8S+Ab*LJje8@PxLg)oq9Z~7GbC|dLOYCpMcWh{z!Y^F`2UKKBnyNHTim$ z_SJWS=wVm&S9)PMOG`4xi#gfVwM|LvTEQJF-7=sGxPj-3KyLJz90QB&W(qQQ4@_mHKXnxKip}c z_RZuXR0rT+cIhcml6a=p6hExp_eOTWem6`R!*-@jP6rmdKl>z%lB9s`LpFq@|EXkK zdppy46NnwcXSILNo)9BeO$RZ&5C7$-7`4Fz9{2gP^h|>n1z2=Df#pJXnSV`}An}-S z4%aTwkqJ7I6S#J*vM%Q6gqD)>`}~3Jx3_o2$H53JwK@OfY7(`Y{OyB8a;TJjIOkF~ zljGMh&I*WLg;PRmKE~JPn%Q$-cwNr6Zfw2K-1Bi1qCG{{8&fbVTv_IuL}#Dxrsb0X zigGb~alBDNJ@7-=ylZ|`+c-(~i$RXYF0ql->nW3&6R)6Epkr^f?!agub8 zmh=>Dya^D_#$fHV#5aJS+<>x+($AOU6WCtwB~=c=-Bb!pxc||B%ua`GN#lz1 zT~Xw}A+KxnEr|qQQQ6keU6g@K)d}vwOBbDYqYQgLkI#liW;%NgE0J4Yw|{7Ulc!&t z^v^6k|LXozYi#l9O*-+6rr2INru!cjcy7ATO?9a(_&Sgg+~WfkC!5&B{;ztG)Lnm( zVlf(*)xpfA4I7)xO}k(6Maf93ae(}8Pu>f=*|xkhui)|o#cRfYMb4*cq8uNEz7t+f zO%e88Y&ZDsu!N7Z=lB%4l^b|Nh-2SP=e^y1sZS8yHud!tGZoeHSQL;vdLm$+a02fQss$3aOQF0K6!eR8 z7v~ZMYMIaeH2k^?5r2>}4)-N&K#mJ;Vy-`X$WS+${m&ZYWScp~#3(71uOI#(yDv83 zvb*jkoy~pBE=KGSv$Br^`FSlcf*<_>VSf7_fFUyOP=RVA+bkKv)t6?7YbvDtViu98 zrD>aSx~I1^YFy!zUrdjB$w(J=#ay(3d8vRPVQHDoP#EHTQ7wyd+)f_jPeXX#o<&wU zG6nyso1xg~^Xk0gsC^OCt$9r?h&=Q4psu8+-0IVkqN4Vxou*4IhP&#SdvCjsf8)SI zJh)P&v)_7g^O1ggjmIxhJoSd(0=dW%d0O<}TTASvd8Sd3k)@#Eu$!qR1IQZP3!LS- zh%fthNXg;HOYbkAnw@|`I5*SINY$U&CiwL9=sD&8T~7U(GUMBlQ1fQv(MVTk5DOD5 z);xQ3zXj?rwHeVqN2tR$lVdKXV0F& zzK9W!o@J=7Z|mw)rsM%>3$H~iBHR5YU6~UPW>0qH=8q4ua(U$GtID9bJPbsb=CH>D zn9vSQz~LbL$gQk|rgAFJ{ZVjb7M0|}8_4r$Ib{zL5xni~D3wh??~0Za!bBI2E`3JK z8ddUIAp#Hs1&Lz6r)p2>QdPqsT@9x&1sB>20aV}-5K!^=Z+h~J&0g@idVrMe^K|ZBKElxB}lpLR5inCxhH{e>2^Ddcg?W&FF^ zrgQ`!)=N)^yktiu`YS6Y!>o;6DPOGJPE5iti6an6vm?}0e(xN#GM>+H3Ay7)LGn5e zuVWZ{<%L0!1fD^<>fb5auEkOP$=ly8r{b!Nd{YI?5}9$S!3={#uHXqw8DvZ#Ys z2AF$MNcClfPbx{jRxA(SmEF1Hz%fx~ssG(l$d8z)5^O0|bjl2o8K#EaPt`5m$xb?+o?wH zT+inzqPRHrt8|Pp|C3s{dv`T@*_MROZ}s{iBbqJ8BSZ+59z3`V+{~p+wNzur>mPM9J*E##SQjiD3fW+h~I$7{h~W zYbDXp2kf7g$R0f}D=^FKuOVd&zhR zaZ7HIU*^Jf1XOqQ*W-sRNcpvqH&AyBAKaw}Bj~3e&Tgv^5lAYd0lR^MX|xC|rc0pp&+Ec=OoMV& z+gfS#OEHU)CUE}Rz}ha1ZARhaCPMQdek9)6;CrBiU1zV(TF zkTh|z%52coB9$=hu^HkY9On6!Mg5_F1>R2Mu4I#uEQ#J_iOxq%)Pbob&EcylelaT_ z?X04??}V06;7kN}*vbWd?>AzUYnvm7k3K3R7=axe4xk0mkqvpiE z#6Y$}96WYQfWnDS8}ep#$TCr*;QrEfY?K)>{ZY2n>r93>u(%oX6o87OV{pW5+39Ad zrKN>Qu#toygO%mALgx#J zDlmQjejT1(4pm&xsLbOaHu9M|3~6b}QjPzPhEXNmUL>{=;p4Z$;Lh;zG^T`&*eIx5 zh}NcfvR`=&2&?dLi#wJl^ZRDSQoVOYd47sqV@MBVO%)p;@kljKwm|Lfmk}luI6vb~ zutsVZ3xc&bZBy8Lr@o0drf?il0gw-HIP*#28yF4V zzJ2>Wk%+XW{-JDt2tLIPO-l5E9YuWv zIeoggeH)m&guMS+7go;WW5&w9V<&6CzP&rP^Brt?WzR;DtNus*5_9MdTJ7V1w_gOO z%nSR=c#~gAGWcC#YBj zD8-O8E4_xd&LBdl1&~aCe<35d=t8aLPayw82MoXvu-i_e@CLa!Z(`uN z`H-Wv7sYuy%=GzAY{FzRCP7Q(_IuBzxMmb;&eFK8<=;2ex|F`B)=UUh#73t@ zPqQutNB^6$o=6{#Adii(Y!e|lVP=nmTkn{@C;YxHA&}i2{wGUu_DL)L?0YMPuij@^ zw>n)S2xU`mL};*x->CSQ=B`RXPHrcC_3PXmD9X^E4b3072?+m2CnoUl@hJfVuM)=` zL&l*}OC`Mg{{eU>9B9do`S9Utcr{?O&{EO`0D4RLwes?Eu&U$1Ap7y7GPFt;DP195 z*L?CM6gc*!Opn=C06O`{t~<^$qTfwu;(lSCLyVu%dAAw>QA%Y%4${dQKnX?E?x~s( zH&iRkjkV7yok?8MbbM8eUM<0O=*>;QhQn?nCw2sILFQoSXhidB((*@s9dyhO{!fHIOnOdhGfTv#jPg4BJaEEKMzi%iQj zx28uO%db+H*~|P$od=9vw;AhVx6@i5DEHi7(3a%uijfV|)Fe+Ac)sW`xN9!A2HK?5Gp zcjM^J?IkGkQvuCvM1=qVOv`Y6S2Os%e|!}!vxRAxO+jI9<$AhN3Ac0)Mx?|~I{BlY z6b8aaG?2lFl)=QWL3u^j<*?wM$qz%ahOT5ROCOb>U}j?W|GCQoVY&SY>O`P|E+O5{xV+{4SO^f2K>eKLQ@ zkLvcWw<68z%;boq>u+ont8m?A?*kT|_f2KrY5z+5)+0ZkoYw8%ZsL~~z))}8H?G`z zMcnc&{=u%mRa}8P{loaP0l7SIqr@GUt3{d$1}S9(1Q*zZGnQqQPruTsv?fy+VLsUS zzhHvpDNU=7d?%!M*nEznV&NHm>j z*wY-68{eO~2ajyPt)=<+F?N`#hK7boPM>XdaU@Ls*_Ot!$9o;~adctsAwhOM`(DiEP_Ys=vNY+i*8C@#t8+@Y7vO z30}RQ^GO=v;Lk%jYsfl!W$q z^s9j%zLFwAbeU>%COI}Z*9c3X8Ht&p&~$9nv3S)%*@#8G?w@S`~nu!U1Er zC075CQ&Ljyx`@Zl$D~^;&Z7sSgIOOEK&T|$)=0TjU*E{6$B5)WNIrBB8|q6ptc0J0 zv?vyh{;uegy=h~^3&U%N;NWXvtRJ7_PpodEU-3UFleA5>#S1sSv#Z8v%1PffJ5V?o z*d5v6_OOqtT(eA=*rz;8&|~L{u6L47(+^stw+ZQ?$PNAFT_?e$KH<#waDC;m){~sd zSFYkU_UY_(f07Ci8@uUUie}k`z;Mng!7C|)SuG54G2Nrh>X@jfB2vuW=k8y1|0=v| zr&kwgxe;zUi9<-}StTjLM`!WK z8jqb>U$g{QZENG>wdIfk&~6i>N9&HxyNNU*^UdJRKqMqZZxpxN-49QJuR0OXnz%_z zU21fM=v6*G3@3aRST8i=_Qxsz`vi&yWxHh9xh`*FSiC%m*Q-ma(s9jjWyJ1%>gjO9 zs>a@%9%vTT6HkAy)lS*Fn_X3;jMJeONdnVk>T(_uY1_wDw!U&#@;~ZV7dvn6-h4%g zz@q{O;>BUe@kj_g)@_fApwNo#+ZK}#P&aP<;c56g$0B6;<*y2pGvf=-X)nGf&MAkU z+ZnGC6V1sq`MNgWZVl&!4{qUzEH5wn{$Rwo!%FHKSD_gPoni|ckeBNI%-E5$oDCc2 zr}Qh6u-tbz`BWWb4FHLfF5Si5iDg1${;SEGY5xwH;h8;@Quz2eY zP)CwwTLvq*m+i=;rrMe*2j}C#%y-m#+ED>!zKg}Tg#L{$qq>g^>+D6V>qsx@&Bq37 zyS1~r{>j||iYd0s8j@ESrf>w z>6V8$GRS#XTH)MdCC%Ux6Z`&tYh+{uW@r;3S|>y@a#H&UdE{`9OH$s# zWgs@1+TqJ`^m~VX;!A%cViezr;8l5Z{4?Ue<(^6WO!;LmbB!PsXLNRcOs=#&eNkza znRSOUfdqAEJ{8K=`{UwMjTLbjVPp}{=-#F^>4`V5ObmPB?r72Yp?7+4fTeCaTx!cZ(GU66hfPI+8jqlPLI>rTkTm~oV1rd-NAAtYKha%+PyJp zRDc4nX%OWZp_Ri!fYwf-mH*d_8fZaUP>$#TRvmM3e$b2x05s=h=2P>xI#(+rqgHww zn*q7abWy9=dL?#JJaXs>$cW@2fczLpl5_sMrU?zc^MFyWeanw(KN|d-o@xNIJ?p{wbjiO4zo$Jr&dvEvH=(KG8Ot?56Z$?9LXeA8VYz)69 z898|n1e%xt;6AjmU%M15^D-gfq2nBA-l);!J{n`(C~3ZDnPQR&*|NR>fWc4h?`z;; z2jNHNl9H1zevfegsMOOd46;#Wmai4TY}tn-^Jg-1vkCp0ZIrnP(A*rNs;O6eGvNE| z!)unu5j(h@z@~u5`OTX*D8K~86=ZG|usNCNCXaU|rNJ1!0K+XR05a%-C5Y?!Qj14c z$$IC@XN5~%VSqNfO&dAKP}`~w*WVPO%Nkf)tA5}pdR2L7;mi<|X_mE)XqXH!pmcPu z+fX0wAnO-N4I#hWYd=%&&L>k%@SDhdCyn*Jy@v@Mj&ajjLa3|9>C)iha0)fqsz8qB zNV&y*bZH5y6y_)}m%!QyiF%AO&xzol)j3sV`*HjJh!_!0@F=05$ENu8SQdp?Fs15K z?gdwZwMk@lQ9`s-hb`J(DT;jhAd~Bq6V@{wVhR9OiGx${1wrn>t?t@+A z(_%C_>XL_t2f%mqBjoPxPVHj;P!k?t@VvY<_6L#i;m>~TRW{i1dTaGGf{F>pQK;UJ zdT%^Vo|a0-=!-YYKhMfulz)F;#H{_4+k5;g^782dw)?Jk8K=0uN?6|8a-30m{z`AG zpriry7}HzdNVZjWjRt!cHX`ocb(kavy3hZ_i=kO|fknB&nin1JIEQT3TW7biFAkz=61W;vM5hkAeZ)m61_ZM7coR zSJ8M*^%Q;}S|n)5Gz)GaMya+-j>{`6dYN7~7#jkWo;*pqz3C6CYgmD>P;MJzmw~-X zxqXaJ3AZXM-3B5Wx3}+uyFdRFd#1Edn0C(|&AvT%yoj2;6hO^(wK6WYr8E6Wt)5oQ zBK>8(x?k^N31}P$?iD>7lAbz>@=v2K_=(+Xs6YkrCya@~5?ov=4>M~k3e|I2<3{|} zry9A<78e;N+W)|xJI?t~_*CP?zldw9CFR)89J7AQf#6+k$0;T#xdENHd)XJKxEDu* z2;;03fK5P^e*XC!D{y1HZb4DKJz zsi~LuDn`}t4Cp^w!KX66!8xpF(q3MCo3Q~B%iC7^4L)T8ht=@++kFSztV`0!&owg6 zC2qnq!-Hiye_1aoDtdv1soiR4YZj`7>IcTd+-zwjEOnZT)s55(ZQonUKhUSx(JDW> z78GQVLJ81xm{#AF-ur55&nIKA`(G?uHOafoWF|~KPm1YNXe+VR-t8MX@0aqx_s7{N z44tomaTf>hyUv$vO1^TGn1RSVSF9@hN*+(?p(zT8EKwyjyp`;v+A1d9Cg}jal6#t&;0i&KD)ruUJpm zX)<5Ue31IgU5%YLPB8r|ltRILDx9o}&b>{!=%y8Rd->t%&>Z4B9lgC}GDP?PXnG5% zs<-a@`v8Y-P`VpwrBk}4lx~oeR5}jbAt4~3TtY%hx?4(6K#=Zk4&CtX^ZehR!@;;? zFyMN=vG>|*&H0(;6Xf9mXW602lh_F=vz`CQLtT-dD}Jx8U8c5BKds>^d=?$n`umMa zfjURZd#Rukx+H2x!mPqCy|*?$6;}L=kswsBzfIbOAHi^q4pU%5o%_N!FFe`8TYovdG zX@@j4c^@-0VKNY85`fC&@NCm)aA0704;b{&hN@pNCzXJ8`t|En9V;e+VZ@+eY6O%= zx&0UPSNKolr4b>bRoo?Pp72fZ)-9kdQ?&w4MRH=|s5cuAPx|~Op#8ZXGYA=pfxN5n z!lWQ}b~;eYGu<(%R0x*9U_uQwG>7fRL8TkU)(%^`N!vNwOEaN<4Qaw5LNcI$5MrXJ z*uWT1NJ%b;U2Zebj!S{PMS-lY5|2ujw-0^7Rg3dmS04STx}E-` zEJxHktNo~L5OAnM04NFJ zWF&x?1IC^Ilg9i%hI-&Ll7eee7%IfgosgbawYAJF+Nw?&uYgjn>xhMs5&1gCt$MZF_ng0wj(Xf*I%BGVfzR8j$8yDjVRfX#E_*N9`1CzPwd9y z?px%**plRAOJea%v2hU})!jXeT}+vXMStkdiqwquxn-)x7kVE(p4BzIubYg;=nFnP zhdHO!BYm=T1b2y;#4Kf_0|u&34{yDot2n61_m(prr!w5MF+{@R3$B#Ofvb<1|w8n^P9da>eeQW zSP&(Lf5;NrhAnS-it^A}cs5WVnW!9YUPV9AF6{0!A-=uu$9L`0dUqHu3q|-z9pSoD z&DK5U1kOYu^WF@so^Tc>l}ppNhlEtEvWjuZ!xc3Xuiq@+!+rS4XmRjWIL1ibQq- zCKP{(ghrl&RnW)BN4J5G)sT^20_?f#HHwI#0GClcWSOW`0ytLI+YMz9%}qhdOU$tr zV)QfDTkM;Fi?^`F=;KZ&CxU&25B658^}0#^_aAco@D^SRly<50WABp2gkW-Cvvm_++`ka|KQcu<>!9B3E5q5+Fy>xh4LL7ScfA=nO1& zi;;}<-bA0(Y%V1=IT_#I-*{xrB~hWMr+`OpFuw)?s+F{uL%|oP+rcqM9$RKs>g-cX z3I$l4hIc#mXr+l}o9VQ>X{%{%eLtPbGf&TToJGAEC%VTczl%-w}oWpda zoh#t{Q{-GCe#XLHBtoh&NRS-~UwUzdZU*Jg4buXX=9rwrUyRSK0jYB^J>c0^&sshh=dpQqrw&NkkY>MK{3^y8qSHz5WNE;^=R7G$ZZspf;p%>HvA zfl1pDhyO9{|ldhb{DGu3H; zbw);(SK)`RPFS(&y3&N3(>1ERu=gergsws!dQfWYkIdL8d zjv+Yx&dpGzPPpHmY1l=tj}nl1D3I07_m*iDm^en>@?fTC3LS?UkEAS|`m}ykwn)ew+gB^UYhcpl#XsD=luP`jYCrMu-Z|RHJ8e$A`=$=+e}hxA8bQ+Ru<9#Yi^S?`6S@JrYo}y^ zkfr48Y~v#|2XOsKgvKuDZakqfYqBM;Pn4smzJb~lk?z#FurCUlgPi73F#xZFDc~W@fzKXR`^4swI}trib9IV z04F@Ml$3yhg_JRu;V`rdhAUzi&R)*{%-HehF?rc(_O7gB6fZw`V=giRD?Nfq1qLIP zkuq?)ix1wtmtGfCt-fuh+O~9E4M5XBNQ?}w1hrnJDQc&La0Xy76ZrlTcn@ z;w?tsk1tezGBSv5)58Mpdzsf{U;dsI<}Z?rQ-upqjB>_e3&Bc91t#lu9!Q9A>nc?E z8!NlAIEdz477QuSE2S&@lTdCUkX?ZP+51c%ZDOl`{*cASwN7-;T)P()`4!f)XGP~t zl4q|97X-M9%X83p3_%AHnOF8}ef|B6ATLxzM5NL8{25U3fDYUm`~jR9!1tMspLVeT zXj`QaY>yF0_q{s52)iM>Q(~t5#z)ZP320~zkE@9M6M-aHgqR*|erC2Bc}S2CqF=_V z1;YB2feNnRML*C=#HqQVvPkyreD9isl74Q#{zSS98U`YiXJ9_q5qRc<9Gt1cGuI*Bx z$CfUvApezB{Y;L?mAcchhzaTg9; zVcUGCZ(z@D80P~~>IuilL?Cl~Pdt^7TbCT?; zNxUiva^zE(lL@ud@5nqhH8KOKuFQXzGb*&3Bbkjx^$!&!Qs%SGhN(~1Pwpu{h;sVp z1)f!K(3TqYkLsJm(W{7rZ2Xa5dpsN0*q(2yiBwp}q}ZxrWaPW_r zqQdpMf?Lc357MgxGb1CAk^~!w5MqKdO;+@;yNeuks%m)gutkp6{>O&(?9;FB!yz)c zFF^v@$?2(slj(YJ0*t z^cH9oeSDha`%Ku2%S*a?-`g_R?ci_@<*{96j@(?b-f{VH%kq0;0>eHX5}?L`9EMlS zQs8_-i$JbGveiOs@D{-|2}}rtgx!#aWBa=I|V86fMxM*yRG_xJCUk}V}Ln>!Y; z33kD05{8BS?Q{n+k23g)L+@lrL*hL3U#Cb2KLM^O*kkCRRAo6Ni2vZ1%#L3Ly+(9e zR4<@_D$4GG2rc|GCE`L}(Oq3#y^^7uk7*m2TVJvzGEsnnE11BQ z7p?&-kxKY%P8-^_ta(N=R>w#{!BepfzW|JVK&6I4sKmVq02c}N!Ql}!eF4BmWPvA` zF5(W25b`SDAXii0W0ZxN`S`q4?_lW0o}VHa@)sZEH-aWeGK($>VzLbXOAB7JhA$*KoK(_%&;PUI9?ZPLHq$nZrbtn6F11+!_STqcJ80h~+~CkqsOd<6Bv4RDN8i>Y53A|Fg>AH<42?Cv)f=nzIC>gO%E!*AXOQk>%dB@_*E7Tr^nR^?!vlp1X%ZU(_;^+?JMH-Z!s|+J(HQeh;(kU=Vb`eIrhE7)UPTkEgNdXk2$@fxF+Q4I6G~5>IT6?2R z!Wq80op_o2Kd(s}K2yVX&>Wo#yLS0KyaWFd1m2fTEh%=4U3Ly&0K7pp-gw+6w^&nb ztM4y#Y2u11qq53tZBM@Faq>%m?;3_JUk~7!CqqCCtiAx8A6P^U#g&YKkOAQ<9@v-i9<92^?AU6lmA|s=KBCD3c+ojjTLV$snr(61^Jz{&QL&{9bbvbA}01R+u z4PjH2%$UI_;JxP$lV}o9BrZo<&KJatr;M=pn3Hov7%3jTcuuXpl%?Q`ot6t0MmrNQ zY3Zq+Zccm;*M1T%=2yEl_2LdD-4V{Wy<11F_7_{>3t`qP%yIEUJ6inwMGQliWUJBW zbuzWTI5q}oZj@ZfPP{_`ym{=`amtKAu%};I^&PkP#sr$w`nZ0$kG=LgJtg4K#!FS3 z$jLSu0j{;fthWqgXl@MXF3xYc^kbWD^Y{$2=3FKdQL#TMJ(DT&lC&!E$yr`D%9+&G z-94Yf!h)i^07m+>5fpW}GOX?+FKyWHH`3Vr7<$^`|%tumv;} zo;J-6br%mY5+mJ!h}mb(Dk?WYb4l~xSUbwU$%PuQ$Z(EnnkB4E^2N@T4QLus@s<7& zbIUgwMAe-DlwECa(q5-BgEfO%p2r7h5XYFAMB#(54Kl=hE-(q162}e!`y<)ff$2t7g=$|NxrwNQ30x5nfO}5CQo6INZx!Ns9 z*!s!Cv%WS_KTmo~P8&by{ql|)>zO&L3)ArW*4F9ymG|X0(9I{NYF&8Y+u~G7|9wcc zYAtf(?gZdPHjX7R_P-=WV9@Cy&87;6l(v_)fx{ND)p% zZTeW$+B&}ijs0^nt|66Mk#On;_*bcNN515U;;T0er8|6U;r{PJ_4!<{xMZgL>lR@s z`b=Q*e5xHa{om#CCrLDoFgC!HPG!A&tghR@2yNwvV4|2zMqF{I~jD-~ycwm1$ zi*f?|ACOp^WcF3iDO2%=ej3OhFhN&{Q}AG9Gkw7W?ktrvGe zR&>!TIi4$n1rW5L{{Q!Mc+Ms{aE73afZlU?pqr@%80J?!fnol-rFRTF?1x9SF57VFi z@AM}HT4*qnAj5PxC_kydOZd~l4@6{w94I^pP|Nv-5;m{V^Lp-8Vn>ba$x1$3(YNWOX=avqR5F~XiTQAbsP6IAc3@kPl$rV4L*<)|BsvV;q zA+iXtW7KaWDKm}bx~;j|JZ}4|cF=s&OZTi$F#pd0siC6P?ap3n`55~#(94?vM((Q2 z6bHWlQB=UOpd(sxZo9Rr)P0muZQ<~ofOhI_z zeiK{D?=LnDyXjp`+`!VOQ{wi7i!zM8bPGqW6{Qc+qb6b~LqSa=!rFgQg%~SM&W>78 zd@pu|Sw=p^UOpQnZY}3FX$eINv;0f*jXamkuJ5(Jmc{-Pi6XL$oYp^J$#943$5 zzRL0OxPO)-^&9nNB~h1k&n5d_%dI`njOlf>46`(axmZ)Maq9i-kF`+`M=~pOY|UIu zfw(f=7}^96ynxaGuk-R=e*$La8W;uBn&p7Z9z>2e2dI z_RVOad{NJs|xa`LZs$!;Jl_4S+Gw2AN9$_~m$l_L@X9;LgJ zpH!ZG`s#ahT4?hTD$S%;i_xfWhib|w*&3AHWE{8W`*A>iTP!LHJz*{H1}-Qh#Nkn1 z^q#4&uWumI3yV)!IOBDtXP>@42;GKxEm7N?$`s%cc}F*H@6&cK<^%ZD`KND<9~9bF z+M!w?S&SM2;TT!_PF`n&!Uq3#EU)7&FE2GhK2 zxC83q%im`<#?||~%Jl>4oFuV_@8t`VIFf$- zoX7=20EfRxN3h3RyLaz?PPJbMG%h%Qy9Xz*ejeEMP{D07j2LRcjf#p2zrA;>961$7 z5H|q%HiRz%9FCWp_bSHQmU46HQ`6FR7ZU~o5qC8mr36|~XoLgAPpKx>}j_o`Y_9N-(Qu=htlZ_qwFXm-( zYQw=ushMO>>%seFpJwxTsFm7flZ=b@=MS=J=yIY_Bu{(XsA#;Gk>){k>-Nr0@Y0c@ zy1M$rIc7-4EI4m$Cv@Xtwg&C@ILa+Qg(A_w68k^m^CsPodJz!f3ubXy`VEAVLI6zQ zg}oF_B05l2+2F>GVp`z<=(KXfp@{EUvE{9{sVU9oo_YZdk@tI?wumiVt)Edtp+Xn5 zXpmGA(I4*)IqA$fcT@Sy=Z@e1TGRNW#(DEXhy9OEhj3(DA9PX9)o>AFEe_Dg1mci>Jba_V{2nA=AV#8wfrH2ax%EDeVDCJx>O53 zg6DMQ>UfQeoE%Ya85$Y_((W1N-m!QNT;vm7)Y+P=aOY_&H~uf+HSBh7=sCJXsD^{- zFtozc^g)&o2vsHvX%EGqyMe_A_4O%iw1UT2&|Zq3)-+~Q4Ic#sgkq~+2<2aHa2VHd4s3Sp_P-& zp*wN%IH$`3ydH(6Ek-3kowL1YIz$-XDQ0^pk9qzy7JP6hy|9TpZ0w)VfBzK=+ut%2 zN~)|j;cyfv+mg`w!;vFKDnG!k(95L%FB!eGFJi&u(C zNe2c>0yiw=Wr9CoCv@Znv=~2#FtmV3D&4)G!Y^AED(15fcXwkPt_ef<1b0N` z+~MBhWMpJYH?TloKffw4>VlkjO#{_PO1lPwAh^aq6^N?dZngaCq2%cx7?wgC5H;uYW*O(R`;zxO_TaEm<>l5pq#6D+KnhmhK!K;tW+{iUi z&FqTUg{_Q;Oc(n6(KL;`+7QJfPE2yoV63yJ@DZ$vXCLA}x?T1YCzX>^wglIN=~Ku3 zflK4XtYbd66jhkuDPEJMuS$qF}B&U^jB9rp=B-DyI z1^j-~svyMj44SOT$@fHKxfrGlERr3xm8~4uu0Dbs6-{S%VK=u~eP36;lQ)T>4%q9V zE)wPwwqout_7jIwB)8ob$+S|4GbR`4p715F=)U^yvWwrrdt61o{Hg}5Zffpy4(cB8 z3`Ua?3INk;;RJ|$d>XiBh7PD5&HMCG`$fPjWOmg}ifDIH2AUBz$=2Fm04dGu-SYDC zl#3LsSO`P**RQazQLC*pSNDM+{LR3p6~u)V9HnGXf!#1;f4YX0yOJu&@0__tE7j%h zk4ugp&h5xK3S>7M&sg(wiDN#lZ@*n0{H zZ<`MqR`UAV&E_PoN?RjRAHxuMJGW#BvbI3KrcVQQqXU3X&L_Ch*-VDD4Z6$mtYd})k?BgfpjkvlO|1?a zY&Q)!f5ZHFJ5N4#bbdY?+~O(_;}A8t|M~Y-`pA4xtnI{FpM3One!E+>4k4@V?_yOp zWPx$^+7jf-ewTf`tVxQXt+Rks35wu`ftO**4a&$zRg96JZmvmLSbc@)FuZMVRUwer z-WcYbW@h$V`sdinmgELe!{z`ksRFz2c)aakRWo=m`o(*Af5m;YOFir!z#jik|DObiGJ@CJsTF>v9Acsi&MKlNcQnqWq~HcQ7B zOdCf1IOL(oNM{!ph1Ou_VM-FiQb*eL;&I=yE0Or=B2@p3-}e^mRw;S zI#UESs~8eQ5Ia$WUpGsjERIQS`DvUyJSG!rMPdqDQk>bC#A?0CSCt{b1u6m{X_{;c zh+0#86YMp4O-cqnUW-|PbP3PHdc5)sdXkS4=L_s4@@xq|-4eyv<)NV=6k2hw%d;~% z<4C%w;l35u?e;)tXJ-XjK&f5+8BM&sy`5nlyRG?zC%4zK3ZY*@xHT0N6^}uS$`S^0 z(0-jrJsc+@nAAX-0$-zjBy)0d;`3#!CXek!F8wE(yRh@&879@-+}v6-IYD(9Lvm#Q zS{LA3|GH=Z{Ht$)Any6uRE^wCE~E6%uxN&iC00^;?PJH7c^1sTZ_qm=@VC{g5WT0% z+A_BA4MUC_!M)`^gHk2~nkpB|rvA*(mRZb4QsxR_88QFIWN0`R3}^_B zz1RJGF~X8&tkh1gqOFi-EG#$^bRpbJfk78sewJ zFYIZ!lJN3o_-kux!3HcHwTG5UhrzCV1M)kPQ~$mUZf}=tz23}%tY>8aEqlPP3w1R- z%vL!S@3q%O+8xz1SE&zjM2V&Egj!Uaqd1*U7Jbeno;LP)i^~e(0CmIe-47X_j>5dWje3v)r1U4U zpBvO{fu)QhWGV8#r5*)RCMdSab6c=;3!{5Z%GVoGVo+fD6O`QmrOPa*^%FQC<6L03 zbAb8$6TsH1JP!;J`O=s3@9_nl=Zj0t64rh=FNh)@B>f-eNMPP#QD?Yv?tA=Km1hNU zA+dQ{gJ|!T(XYDj13wnnru()(oL4HIvy8`ZS52Y>foEs_tcnntC+(fk)ffZYngnRyXJ`+Joat-A6Q}9D$xQok#}hDl5Fw} z_{pM|Jz5sbdCCYB%75{ngkjQ{4mxNfFPc-d^^OSj*l*45`Q+HA4-N4u}_M5^mN{4c7G zL6+-u>t2wZ@v=vgc-KMMK+T*-w8AXLVy595VXI-V*JjyF#BFq6zu%JXJ~_8xE0qu4 z{os1kzI^I)on>`Y4asXJsB|Z_*L<)djgOpt4tNJ)h$8j%n>QAqb!zA2B(I~RGk>r9 z`t@?t*4M02Qy`f}Y=FhZ7+^_}2)tXPT{C+;H)CYE9FY6!f1<22$q4e5o~Hw-?1Gjq zP+~Cy118qXdVAhyaOBQ`0gPWmLp7VNDjAXBqBgkDKAcKD56#E1&Ta7gpy^6nx*!|TEkjow71HA;>)M-=gU@=H z{)lAoK36zGu8y>ck`wo?gCV;N?VsXa62}L)v=vV9n7Ebi+!KF}PqyanQK`0wW0zTN z098NckgAFb{2NaICIk+)&CKQ_jX>>$>U1W|`}_OC3w7{rz5)}z4=y!8jo-Vtvf#C( zz8&e$7L%@oGia{a_qw32?kS?a(9+ThmX9R~k8VL=A0u#Kl;HJ+1Koxd5IXHpvLy+E zjpL{F4ppKBwIJT$Yc=@)};?%St1%Nkf4(j_c9P<|LvA_HPm^Hs47X0 zw<9}1=*+C*cH=RmQ2pbaT*otUv^xe*D5@!5=hO2~7|@iM=*)G?6%TfYET&>o-Q4CU zu#YTd{UJ_||9nV7k$hB)QzU4YDx8sdkhMocNvR_J;a9KJU$pyQkU{(4eFJpu%axJi zmPq6C$QF5b8@>Dts>NA9ckO~Hky^w)U zvYz%YbD!S6uhjqcjuC3+QZ5idV94^_X?BgsP;UZ(_a| zjaL~s@!v438?h~Ytq)^WzNRWl8%s3x=gbN=^>`*DVjsFu^Qb5Tx5%TG@^z zP4L5Zgr>}(rVmagqt%)OrL=Uv3zPz?4h2Q&#JD&FuNC1k0I9^eKR|EpEuvU<13k=q z&`nZjRFfcyWj=JoJAm2tJJc0!7>2JkrXIEw6v#DkWALt{X~mR>f7L`?B(M4NVqSx3R)b0!)<6|4u@<7LX>4lm@F=&*s7z$QFZW-* z?87`iKfl~PjF2)j^Aem9vzm`SE-eAop0@8;1`c39{gr@*&=nF;_2+`}8=RJ!>stUE z6#=zD{^&$OMCi?|+`YU^_l#pFa{vRFNh27lim(u|kN|ScpL_p4=a%Z>nD&t{-Ne&C zjrQtY_||pt;7?17nwh^07w@Uizi#QU?2Ev@yo!=GdRY!T1otvE3koEJJpi!Fa-f>b z1TdD&cao|NzeE2ys1LkkXJ!WWY*u;rxt5-;@j4^SJ48207=v9?t4P0w3+~j z3IBlI8cWI(vEUrJbkq+J3^3x0A5=6n3LxE0K{2-wrBA;PyOpOE9-<8*R{3ZGuPn+; z+YCQDoVgAeo04JS9kUtY=2f3K|6 zn-l@Kcbky}o*WfFzm}B|EzmQ}e#HuQQyu5@=bsV+jEpGJ(9mGo%}1apW)}oW6+98+ zJ8`$~tBXfM?9BsUbGI-_;x3OQee` zD;Vum_lWEX2ASqo;L(EnwoTXA;sXZgTW_3PHlS65t)uZ-Q-X)eX-wZn3%H9aVfTBD zV1Wm4(DcI<0`d^9UoIl(UOe9yQCH76ymSAkJ5l$dNPy@ z6MFR<w;s2KS#1fbV`7&PxSEN?CWEcD>#zt&~(j$ zW*Q)3kwP@Vehac2zI}KMlXM@ki&P><7e(h2XJIup;w?wUbA&mxI^g*l3$Ib4K=`Lm zUBA6}7`;-(o)C+)*NUge1A zPGqT*c;g@3iv9c#BH*>64HqrviJI?ZRY^$;`bk$kkqWVLi)YsY%nyu~1PF%98u#XJMs?r2@v|OeqWX)HnIS0?V8yxircD z$mH=!vroZEE+@H{PiAUbpBVrDv;>4t=auGU-j6-qnN@u+UHIE{7uS$qpG&+9rmK1t zp7l3JmosTjfuE+exAzrq0``(yL14(x?Jat$8)%)gRm!hVp%Kr8Jk}w*j1L=_bNWjh zcvXK+}BU^7#aF-7oMvX1GFRb9(qw#Ky&+Tp|hKWz~^I>gU;n zcK|LLviBVG(#)?lZ{q{baptI0{jv;uujSOG*snpfCV{lO`-6i7z;@0D=kNla!3zRcey9EYZU2X)icZ?WZ3Kq*W-eQ*(FIcRO{@$U^n4+)tm zq}K{g=ZkoF2n0u6mI;k|MljF$>AkZF12xXC-w#diw-nw9h1ByfG(>s+$pqAHfcr*! znXvkg5#HCrP0tu(Y3M9?FkK}ZO<~&nfxS>8=W809B}3pg>C>bqQ049=uvSLAj z_IL_=zz;l0$yrWPr0D`tC;dM*K$&v&uQCu7(s+L{=W)rSOmx_O>x^jLG~Vo%9f+nT8$h&zfrBg1c)EN`Rn$!;E4r5`o z#_oJ?eD&c**ewyYW?^fO_`gU6g;ZZWRqXFx_?05`drdROu6RT+6eFob7 z#<2Ty3(^aESm5LBQjc?k6_7O&r`h)|a8$MuA)+$W@mVk!ws=>2_CAPK0YVI@(pr^kjDUFRzujsHG6LZrYCl zvDhA1t+Pyy-J@P2v$j@R3;O;F!NC*9DeIA<(L0FWQ3=rz}fS^DWi1%JO ztidvd3XJ=t<@_*0=%Up6S6sn0TNu+vd4o&4S^c zeV<9hOT}myFrv4U%)KB(>lvT5+v8T#VL3K7o}l z&cR+2-zHRJ&jYGFvzEK&JQdn{#1^cB^(edi9`5A9+1Hx1Dbt$Ew4(Jy1$3oJ{x4_d z&!1Puf!7-dT_mEz$4uqJp{zX|`{ssFg+Q*p+l>k=mArYd&st9rP{GJv%V4$`Zv@#I zpei$;W9L;?;sdey%Ajv_oO(rJ{P(z`FLW%7hENwLC$5u|ljYNE*PAnY-o91IL@?Ah z+1q-S%=C@fck1YpWALAFLjJk~ITWms@8mFtd19m0H066WP3Bl`HlnmUq=MR<+8{yx zrKqH^H>od)4(IvZc$jw5bovJ^wyz;zco2dx}U9^yMxFGvGatIDC zUf#h^09xIs-C?UJ4mL~eo}6+fjvCGR&uM%(68~D%v7ZiYu9m@~_?i7zRk<(ER{i)q ziHllkSuV;Y{Nu73V+UMJxMesiS zhtz5{5Tbu{RV9ETiNxqtb{c7GhcK(AnFB^1IDL8pJ;M*sNDb>}z(9_Oj6@XTu4XeK zY%$=QDV0hDB0>Kg;@N(lh$n7t+E7XOKI!S{0qc;QkB>h|bTU)d-g(}Cozro1#unDr z&^)$R?RkR;xLnK+kk`a80h^jC^2Z6|8wgG3{SvwmN_IY&REfR&OnbFMi~-Gcnu8Ue zqeg$s(u?e{l(Uxv7gG@~dgcp+C#GKrTP41CI`W4!ydmy#ER^r5!ha*xJd^%W0);T^ zV`r9q__`K&qlSx$iw88C6pbpZ{#bw(SfH}VVHJ*ShUxX@9f3> z#6Giy?wrox&&kOt_zuK6c_zrxy965%@umKZHR&MgDwPT?HX`mS6&jpf{z$P?rnh{s zqdcFN+jbtOowEp=cvqaLM0LMx@=fxVsM?m!GNpv3wTaLS(NbD~i{nT3EmD8s+5Cr6m{PU}wRkS6a-UsiXqM870D_OQiX;B90|nL3&eE zi$?lT_^)3RcBr4DhBjg{#u;W1lPW}i7;=62zzUN*j!sPs=EakT6BX86mOX=Wf7ke} zd~1~`8t>4~cq~VF-Fi0$uUVgO!&geS#9scfL+Y(4zKd$;7Tj*Pk!#_jLN{{CHu?E= zz8D*v1ZdkVTvgQl981Axvo?huMWY;XoS$y?!rY|SS%4;H9CS{gNz`P@??96gP>5ma zC?AOn!jmgLgMktTX3)nr>2dn+OqAsWik`}0Kw}i($mk7f{QOc4X@cd;TUIkZMRKsq z4Fv+lX4!;G^A3wX4m6)|2Q>G)Uz4FMO3VQ|-w73WN&HA806}=qs{~Xq`r2H0SD_XC zMos>JwEpi3pLF^R{cj=*9UKpahPwFIlGR)CK_`eUX8Y&}-CW!kR4VnlJ~ywF?a5=* zetmt0hAix{Eem-mt;Od+Y5S@gHEEBR#gE0-FgM2PsZkBy)JU z$mfc%0y}7dE?}WY2@9|yz#E~;)0WzoMDSUu7Gn{3+Jvdxe~gvr6n$8tV+a;{z9WkN zc;EulY1aY%;{*UvsErHd#VetcdzS$vfe*=#rUtP|bMJQ=!hb*ySBEijJArZ(n-s*dV4}0jK+c$nO$8 z!%gYW4v@6CXFdM48~jz7_Iso9A8(zU|Dp7l1{O!I-N@i<9}fx>uhK8AL1&qxcr_02 zjL2P>|19iUaYf8uA=c9Fpczn@z@APFtN7-q^5Gd`92R)lvV;U_hllh0S6&4xh$b*~eDu~j zT6pCjTF_g;KUMxZdhP`zNSzUmrJV77lW+ptR+g}pA{MQP@d0BINj;$kuZ+qu(R z{bvCx$jQ4(FvHTh9frzj4e#1@gPq@hS0|^t<8G*0W&jQBFyoe=-UEUFSk0*Zm!OWQA`q{I@XaUY zkf<&(rxH@{#Do$vEYeIS_(>FDmm{(%Js-X)a1Gm9+|jhk_T;OymC5DEhw3hum+-N` zzdGqKdQ(#TuCWvL(SPcl#RF#Z8QBmk<G`eL2)k%egg_uV*{WN|!py1Irp4(~`dO;uC znv{2IWN4Ur1MGWMzzt$@-)zjCx_xJoSq_G@f{FXbV=cb!=L9&dR zdVzG3+`N|^l^KM8plx~wRFP;|3#pO;YHgiE8r_3VKZ|T|xZJicacJC2@;pDk2-IpZ zOA*e?D|VKJmg4j8ZADRNc1Q%|Jm#J*+) ze)9xQ$J^dl0wsnR-)IE&&m6_QfzE?LlAYuM`YEv4@AYl`_ubte=_&EIJX4|l2IHRf zChFU~0~f zc!<`Czh=SEmxz3}C9eLnHIo zpwZQ$Ir62ou#ZoY?N(Z9>VJ88$oKcKd&Ja{jv(q8nj+Z3MONlB6y}tkCNo(8X!EZ% zBj+aO4J*3gP)-Vr@#&)Y<^PYS?~dnkfB%0IB71MimNGIkvo{&pBP1d-pq{~_dNRVbR746zs7YvujeS$*2P=D+Ga8Um76ks1N6*B?xEaFK}x7Ze_} z=(+afthpFPv1fq?UZLXP){m!G%&>o<`WobZ`Qc-6CQZl6=yy3EF8(lozDpmTdWDWH zy^kKDY9q|Kj^}ZG_A(#eH3I`myd0vu{yVFTOrJ89_%t~t^jL5(G>_BsYhDqAq;Lt_ zdowtgv!bEUy2JV= zw%sdxD?sn92YYbd(XH8?fI(<9RD_|D6os3$_zH~jWCzrbkC8~AaXZA0q7ap(yk&q_ zgVmkbo9-SSy%2)okEt5z;Z->JfL6%#==J)oywa)udGRI3Bi3^-ZQeT4-J9+|Q@~_x zX_pxC;!1uncI5s17H)8-m;=cf&MnsdE&E`j!$Sh&(7LJu*|k*_0~K4_^B9L+5{&2C zAM+yjbOgxWAp~KzA(vm&Sy41&cn6c7u*8aJNP8GUo!qkFHT~sh4`V@ZQ_i?2OUB#a z=-K=7ZXCAKxrkY7B-fud@O&%dZ=Bz2x&4{xc>9F{KBjr2leix2wRBlcV}Q~KY$3j* zfhY$Bh#oSv{UENT@g2_m`@M-ha>odzT}^4{%gp$K^Fh? zmLr`jR!t~;`%dbI8^zaVOD_#))Z2H97IXc3b#YL#s9c}A!_u6sO|`4N{Q*^j9fDGe zr2F3r>@*${CAn*QS5${$SC5X$BFvOOzQnI}ev-DX`T{#{trl>=#B3c{PW zZgFLH#c2TfTPjIA-ZpP&$(U*t&U8F6$qA#-5f@dMXITUp)%My@;0-0W{MW3CjPXN+ zc(0e;N~u)?6^KWrxHLNvz?;I5!xu>0QxbkkE=?s54;%jYWrvTP`66bFNfvNLQ$?pH;t-@^Dc$rZFue*gYP zWe*DekJo3Q4_@{AZADW)@H>A0DGzh`SUj4n@E{0DvTX8mL+?@jwJEj>+ZQx%;@Byr z;AsltH2hJRd~w&?%dzCEU@T=E7d?*)u~9q?%nyu2`64x4@XB-M9}aBZB#B|V-d++( zoL(VeBX}k?pu@tkPGFT~;6vmP4I6cQ)m{6!aL4u)(hY?oHvkVSv=doyforQ36=FON3-*x4hKRpShl z1~Nr-EhF~sb??-y+MT@4U*&xp+2%*`wSG(y36>j=C^*n4>AiF!s?tNXDVv592^ai( zJ#dO5RV{m*k%2NJFt-Nf*f#t5pre6tcv|@roP~nDHDI#jH!GmSBFUK`(dEE37!I2( z%lQ4#c()Nl!v2)J{PS&PBO_|eRgppig>kFxIbBolS~wv2C5gfACvIY50&FS6(XAaD zL8(G?w()2Db8mIF<#>l9vrPPOrVdN{%Fat;GoV3HCJ{YQStM86NSEI7IilT5erX%j zc$q1TzWVV`N9y9to`R;=+9k}1v#YZHdL?5|X*woDe!MjFaL!N2Q#sc03%7qo2S+uB z%-sDDL!ocZ-#xoXwf>bO$iLU-%5fS{;rBQ-?HvT&(RO|$2$a=m)I17?N?`i~G7 zl+!B;J`Y8DxI&a1qkk}^y5WO-Hg%IZo4@Jho(nWEIgo(6X!aHs(jC!ynMGrko9dV6 z00o|bJmo;SfTXT-4oNZ>2Zgb$md@4;eyYddEIq=_eZe|X@6;NzI7Ha|_}!+Z7`EAA z1KYc>>a)L&OPJMZV(oT)cJ+(OhTdz*;a=>7{zp1emR`GwgugmVY41rFV(#$IpS#nH zR+rj#`K`=*7eGVvO2-wF$jehDFEGN~IQQm{DILd~H`%9@HLju-8-~b_SYDn)I-!e* zA>zOSV(>fj99QDa*h(!z_dwVYbx1xgws3AqtL!aGtp!FL=F9M~3f(1h6w>Pq`c`7Y zRrD1ZEB)|mWr1@hSVE~uxOmt#?WWjt-h)#?3c-!^6$SE>|GV zXMIkpiqldKcHTVd`4U)0in)&5m>~>o<(!j`{T`}oUoCQj7{w=AB4qt`?5oGoHV5YH8r&>61bzk|LQLLW+yhE9Ij{uLjatX+{BS?mT_rao#g#9oLAzU=}0l2)AwB} z5(iaWG2Y96Gt2%i!s+T%)CxLA!^^|}#V8&R-FUO@IMfgRNizdIaQSCaj|zzqm! zbj*sLxHF{lA!%aV&*HR_LppCD78R9A64^>qU86Lj&iYKZ0mdbC-nGX;`Mq7k-?y zX5C2Gpwc-Z=$ritbC}Ab0inDJL>t)S!@+Lpwwdx_)9=mKRc?6Ci8~%2 z&c9vSb;g^vKZ^we^9`3dEV0xyZh`pZ$M=58?M%O;o0g0Cb5h&euW=y+N3|j+ zgIG+pT6a;EMQGz=wDM8(lcK^x<>3CIWlo-CDn_ID=lmTKcdV}Z|9U>*G_Mmw6&9&` zY4DJ(P4?>*UAvTXS!Ojx%bf1ih37TH3^{gKOPp;rxF?Kca&Hn@iev9SulSi?fc(Cc zlKEVQYRnT4-Zoy!2*b|YPbo4OHWzN~N7CB}yJ{E!?U9j|GE2wfM@fGwxAvm|*>vKG zm>I~I;c?JViK6-(oiQ)PeKn7r5J;<{&B;2o4+gtXdidT|35nt$Nf?#^Y$*J)e1dVe_R`TXxR3$QLN|buQZa<`6v2`vvM;j zETMIirE7&{-yW!j_k`D3M5`*-pDmYU^ODDK|Ga%7n#SHC%zLSkZfC21{#exnDz?p)b05^X3+9^%J0) za46X7tsTR>krp6#42Pu{I(62U-NeJ7DMhBX!M@7`S~~X&fc*RG02-hw={QN&!KQ>t zq7Nf6e^55`1u|s&T<##0FGK69ORBQ)sv$si{3{?ye6;so(M?3SZY;3t-aeXeLjNj@3 zdE#qHjk)5kbm(+2x<#*vMoA?__a5Tm4ga~WT;#Z9o zE2ciL!IHEAzdCN`BbdEHGGHfqO39wG|Gt5i1g0G3)Mt+q2R4ykr z{4(Yrk}o=UQsCvMlG9^iTdUrLF4S_ZE&9E zy;RlR2ctWy>9P_vX5{6=6(DY@RoVchq{~U+Xk0iHEE|gUlTY`_e{On&{AEUqP5$$| zBDJxOgCH<@N!Sj~Y5I%B1Movkrk{8#pzb#* zx5?QK-jXG6nj(wt!U2vI#It|9D?yc>mf*W}BgQI9gn%v~=`rr|yA&?1#e2#5W0x(N zK3xIW(d{rP2ci8P$JJj2q>GnT_Dl`zzyf*|8w^KDf@ zrRWZ7Vh@9{z?s*%cZCG}!s@(d#rp2Abh|0RuulF@+gtfX>POFgNQ)w8FUv}O4q)*x z=w+ZOzZyR=di71*7vIN@58f5rEwm=j4-w=20^M?DHDOX1?tvrO<*{7Exp$;V-1ZZ= zP=2gDrw}rj%ae?K)nZM*2YgEgb^SA!-vZvxw*M)@KU>C ze|+lx>0RV_aoZTRhTaE}P|VD~HGdEnwL>4o9>)QOC$KoBxRY2#eCMRdOkUFh1o2}F zm~7;z-x&L(7())ux%c+Ys;awYo3RCi$-SZnaXiRhAo26>fSnja?u9Fi&~?Ju2NvTG zrreYHeqHHdC&UesKMw>4pq;?TH}y)c6%) zW_g%BtDM1(Xnua*r+z>6*uQp{gp`0ISLO&5aW!#O^>x%BN+W$A@*$wWALptUq87t+ zhO#+9#K*r+bK=!2*PE4QG$|u|Q%;tT1AOIRX&-(t4$ZJ)g7@Wp27psoWy%Uc0v9h< zc>mNw>poUF4+uxFJTFZbTP#%BLF}be}5h1<3l6VvQ34#`bvUv z248ldu(_E9-VY_Uqzk-bza~~-N@kW&QMJmkkpog|VsY>JxhiLN66#-VuZPG-FV)0; zIi#w%^KH&BudkrSQ=Pk>bSXvjk=Akv2|IM>! z4=>;eVO*Arm!{>Z6UsE;@k*aLu4ykO6n)WF43x*SoVxGO$=G-tbU74UG+?y#a)BcXoGXNm? z#aSy)#r=Y&VN}4D0ih5AC1%-mGc#wYb30Xig31mKoX{0|ZY^k{26^<>J}DnK6Oz~o z0UhZEUNFh&qU6WLMQ4*(B|MQ47#kR5)YjAhV?!hLR$RM`v~=7@t>8A>7aaM{3&vnP z+xdrat$v=&=%{XT^<{Z^^oUE@8-}$`EykT{9)vTDC)xY!Wt!!1UC}OGB^2P2__g4n}jxP9wf@a zTakgPZgP%4xJWK&dS5`-Ye~Y;k_v*HE(oa2Mlrj$Bmc&bj^BUnpb$j%w=&wYw<1)5 ztp_;o%aRh!Lvd!PV63efrImYG)*61ZaZh~KL8AeOj<~L5eCV$IJD_kLWZU4le$^Wf zoQJiXk#UN5Sy6Vaerp&rJ0Wg{wai2@}vYKe2z>t$JbLeT78PtA?!dmg@6M6GGpL zTLc%dWZcc9pYI<4WmWmbta{d>auK`9eIw>yfyvt#c96_!DL%#m9LJ`GZp^ zxaAV5$|S9lHG{Dejqf`%_I$E$q~93THKlTlJRIclByMr6ZK&a*yMs`V4JC&HK;yuhSsg`zrbf!lu#MSh4lhqT&bLoY&i}+gpd=eRKVm9pM$V}Cz6HjpNzwzT` zVE)I)$5#T@4bp8|kg@^AwFAg39$SRGyfh8zcT?)1E&OTp>;)49glWX>2@^F((7hfx z2WBj4zimA`@V~uvW5&#ar!x_8GvZz+!!Idl7&-7;yMcEfT!kVy)srw7aE*_m6)<@+ z>VN0X(xZ*u+VQ(k%JB)NJEakaJ>zm=W@hfwJnsjXuyXd|*f&vz3MS#SpZ$i}n;#5{ zWvsiL1(Rp&=f*>P&y78eySSm*yy$a)zg^#qdBEtXXNC7-KGTSyb zP^;my^ID=2L0_-b`oeVqO)<2{{JG7b&t4cHya}bG$czAJs@7Zfa<{(SuC@czVpfU_ z?&JuCCk%`*e}~fa_;|s5j53qB*Q>&!)L_Q--mgyv%V5AS;g|<{em((ho3iX%sbu;a zFY-bxO3H29rfdF|eZH-Ipz{!039p5pUr{qQ`@9y7YC>Wn>@0q63c}~Z5_}`5(FTXk ztZv$=d(6*%s^dz1x(hQbtlic>M`q`RU9gjgctVT&!F`%7x;KKlYq87Q50_cRh92-u zi;LlkFnE14EA@C|5o(%zc768CL+^a2ZaBq-HkekFwFep`ifqhH^E?q1cct97P=Trx z&-zG<05ka@j?($goeswg>!`gM1k~aGzEwJ$EraP^nmWUI*y*gStS?``7WH*mN4|Wa zfO(bfics49`9#SA!M_JVC~E@;0~@<@J!DTt^*jDTdlD4x^Tf9MxPI%?iC@1OHbWRa z*A^9Jtnd61$gzy5N+Sz2s2#Cb?AGIAKEVvvt;aggOG`;P^BL{7$jbb)Y~z z;_VlUgYpjCBF+(yG$XnRB7|X>wi$BhrMTSlskFLtN;o|Ah?_yu@~!3$^$A1+Ie2(96z2MhnWf($pjp1gas(HJIqFJQauC{6S~Rn~UYR zeG0~ReEkZiK~wWu1TW4Grwu1m6^JF_J#ioCMLx-%)jMcjq&WU2ees_{ri9kj!EFQU zG%TKnZN~DFrGYZ8kR6GpePiJu^s|HHN2|x5v9Zv2d<{&mK4uSXi%UvQ0A(X{l2=y;!0_(n*1a;-Zi!%Cdx%1XNMT zEylrfvu<*3Q$^2v%~J|s!U!n73SYk_h8GAtT3u%(e`zl>s&`(qp`{zlifKZ(er0*6 zDJphwS$3-lJZ_4ls_6Du!o7{ z;_`ABFfSp0d2=C6|3}{GLEhZo1D_p9unjM{J257&>kfCg4E^@tWx@}>46E^moHxy z1k@E%xqSTn)9RUd!I97S4I}D*p-16g3z*|E$iQ(hLC2fD3vJpex2o=?juy|sxm)~4 zH07RJqeiXoCv^YW1T1l5ww&&x6a1Z=rP#G?An|}21iYMnT+Ja`xP=F>BVX%IsR#VO z&^b%plpixtK)=AVAV+mb7aC68O|3s`Idf?8J=mKC4MMjE7B2db5aox>Ma53=d7=?8 zoamiy2}G?AP{DQF1Tvs-+@*jTJ&DfW;dwl&2oR+qgTv?H4I(EZK_8kQ562Qu9y~bj zU;9I*R`>mU=eix1i&A0P{hhka{bN-+;**9l5kc)}m8->-wNh=bRQ7gDTb74KL5Si1 zDf&e5zLvIfj{K$ya|akMp~8{%wABl*TwQgqijZvs9*Df^zI;CBJD1UACPVQbU>kE2 zUbdYiH@bOI9|{n=oHs&VzjK?OQtZ0D=#SYT==y{2)X8!$%sG z^xF+01*#x!eDKa5r`HROv4=?I&7Ae1vkzo1U$meRdcaSA_?aP`OUs++FeN7SxylCJ z@A2z*>q0ekE|Wa%CZgQ78R`>3!Zz4?tHxry@ z-B_^9(Iy|Fqowudx}Medg?CA=WL;lQJLSFW&yfKdeo)t=6DWkM zXr02Up8<|u`2EQw+=$ZB(g@EuhxmSwd`(?lA@r?K4gE?`P3{dZD-#4VcY3#V&L0q6 zl=Q|;ljhe71OY!gO@xc1D^tgLL~Y~g&o1C}TTrDlzMJ3d9uE1`14T7-5+CPf)|-cF zTA@q6!V@Q+k!j&#f9_@iO6kBnm~?M)>)Z+p!{rY1`wjUk#jF0P2g!-3T-VCOv1o%l z7DsW~Ka5J*>wNA0%UO1$xmfwFde7buXRsCjxK2uT7)~}n8DwwW$HCl}OT!5rI_r&LK;w^{l9yiWxKyX372>1yws=ctJLV_10-CK zmqvhFSj4UgqcXuLcAnfrBF>ks$5MudNj?gu6dNV&Y2f*!3)$i)kD@vBM(ciDB0Z z#M@;<>0{Ra$=uMWUHiM$CnP|`OMlrS0#LHduc%%{Q`5zVnnT1}Zb9JIAo zykfz#AhH*6Ys=?T!xVGTXz*GDM)mOD9C+raX+YFEBTT)EJbS<5L~=gRIvCu}0xxs^V4Ny~` zWll?WH23{MG#T?<73tm^=KByO5(OR$ESL?Te+G7LK~QDTlC~`_yZQ`IC!`NV1B!AC zXWO$F1Oxq<6rZ5ppNSpRa|bVj-Zscf>l}9hU+1cygR?Us6tZZw15NkGG#W={fVFE4 z5opVJtQBY|#6v8@2s^y2bh4tLN&N(Itq-!tE^x{6#DT%eUDGrzE={cxxo|E`EJUz9 z?ybD5xMOH&s5G|BsMJr?{O?~CqG4ig&M2{?qRnTx30BUi4NFCH7VRx9o(3+f~oL z#P${sbpGhHr`cK-$-O<$CHJ^?ANl-yK1kiAH~6|kxu;@nNT@%eLpW>I_NQ>t*oY{e zOd#1W@v~A(KcUU)wj3uV%*LJ)PQU8LpANOzX?S$Q@JjKm4zFy~;PApfWVc^dHtwR^ zk^c=3O(*UP7nmb1MLeWPXOI|oq@<@IP*Y`Pr2X!ct*@?b)AREm%b>w_mT9mP3yYoA z6TXWD4+=Qm-=^q9cp}6p(*GRge$n&~MvTY4YC4rHFRxu15P}!IY&>$6_eP8H%ue=E zri&W1pih&U#+9JpiF z{;Zhk-rabi)-U#EDnhg1Swh!`vO$wru6~_+%S)n{-3|+aS5N_*Z!%KHaX8LEMqftV zIL-y6+=djh|8E_cS$f;h&|u#Hj$%bcMW{Cic-w9q{~hqZstToCYX=xm{qdOs7uHI* zNeNX4kI|LE-4ipfQW{d3_iYZ96ImpCBJ+`pQl1z=%cmV)*WM&2pEWGG`dcM-oBI*n z)oYt4^l^QvDaaGtKmts?A$x2=<)A?vvP+9+6W;OcqHD3$Ea_N#L4Kpv);lt$mw%)v zrlK9qBO-hhw3;P3uyNE$gzE=So;)FYG0n}zg~5=Wmw)pr8O-~>g9Ph|jK82e(A0g; z0DE>Z`iTxt#*4n0`@eI(wFb*zlbW}0 z{RGV8v%$aw59V=X^aW_+w?{}q;=><*A0nXOpNBa)Xr?@f;21$vKI$2vq7PwCL2v?? z)q7rOJvns6T)Tan=ZdJP%y%cKNzgV+_cM>Qie^D=8n3>~X!*mtm!-^*oSpop%^JC` zN{8O=d4ar=eEc$tGnBbSGepgu!Y{IOU@pt!}-HCh*(=2%u4#Saa%AmHtJnKj4qe-Lk-UfUA z4vQbN8rKA05>77N^6eC{Gd~MF!$AW#GL@ry7ajpiQl+gLiFvP~@nSqAp-Aqz(M)lf zFYNCp}uCMSf0_+Jf zKwnd!q@ybic?PTDAC0LS6K491oQw=J1~p;-9aNrvKO$N2Jb(YS1+cKf18|3`y+{rH5M${gP&&F16v|R;1CT5T3(3ZeG`{Vsh z)LqXQdGfBiJH~%PKTm$Dq+P~$*;pq>y0&Wvd{$Y@?nQmjR0{8$n5OQSq?R&8%%kZ9 z$_o<@QlZK9uy_m##fFEhN;9>meitAaNUM^QA zGcjY0-V1yY-52~pTne57h0f>2>DZ=kea&l)Dysc$CPrf-ZZ~|saku8*rqJK-(qtE? zlsD#lqhtpYQ`g_WHZ8ZG>x|UUMzmM9qj-T7Fet*4rb(kSoSK`PG(2p)u&{t8Zh)qb z)%@2sHJnZ8oE3OFATUF%^0;>y{lO_BCN6H7GK{R(3pGN78IkA4tl}}~rcozefMYhH zNWCA3jmRnEz>*T2ftbXHX=&lmt3bh3a$-GB7mvIPgQ1UJI90Q%wRKMK;m6OPRUxPj zBR{bVr~c?0Hv~aGciVT0!rPY=6QjW>kk)b{)^pl|^71w}&WF`E!FLAg=uL1S6FlnT z;^JbLk(n&wH@CQ8CRRZbE?m90N**BHt6h|kUsy1F@bnj*X6G+qd>~)wUS?uCK$z*m z3mI zGiNav$OJI{-fHz7?6fbLlv$O0uug``46{f%MDcMM0TArMZEBxOx|2(B%Bo-S_rEP( zzu}hs;di8AnqqRt+!TGR&`c`E9Ag7X`8;KvL1vf%bnqj4V$*01MldQ#;|>$?U|_tBS))smiO6zdxVacQH8nX=^K7D#IT|ZkY^y^a3;vmD3Scyp<(ht}~RJY5(>*+C!- z+=o>Sp~Cu7Z#-OFs6^%TGOwr_Ov2rZVn5)qA`-CeEDPRcPh*;3AX2dS-5^y$fAG7D zsspitoaAsu6~A9}m#X3LPJPEmH~fn$%mx;p)jx3~q)8Eaiax5-KXEHrB&F00+VJsH z+nHHQ@7iKsmcLfMCyMM{33^!7?a6w99(#tMo{dsx+g74A?zG6}~9g4Hg^wWXeeIAZG`}Q~s*vM%24bTWKX0*&Y9)Uw~ z(8sDk{UPjoC`h~-L<3bjoA@ZPwK_FC*R8Fm3b&-OUvn>riW()jwEaxC6eA_r{`9R8A-eem0X>xk>FZ! zQPCVwjuv#1-a(u=ClFV=v4xeNMU&)A02XzC@vJ9b6AnV{voO z{J7JH$>c=WUgmjg$BUXiCw@v<`LQ3FejHRO>l33CI`)om`bspO>jYtQ?Tw(Cxa4ti zHR|zjiilBR63&p{Qt^)0^0n?}nw zhRZ>QsADfAJ!6hPB|=@*|5Jh}Y|++-R*spGk^fvWyy=l+wGeR7Os-sm#kBcE4h?~3 z@PUPh=ELYtLSZmeBL~M;6QRum{hHm3wqtzgGxlCX|~f6os{xN+C4%lmvAtcQFx zggMdd>ZvCGGqW$)AUVFi>trVAukKk_Lh!)@vm)SRmvjX~tW_nrEef7Wmd;^3ckk?E zCn~EdSJw-6XK2sk@SYPLi^YE++mQL?d`8eb4kMy2qZEkfIY_iHPJj=a8Pnxh4U@BRvW zl1kB+OIZfbX>As88p|Gzs>yVoyqUIosm`0?@D5x-rk~Z7&yq0^GWTBH=KZt&C8^~_ zSr78D)_VN*!|b$B;?u-=fuwuiZE!l}NjPWB1A?{G^>>y0z9#QWXC&XimU~w1GW3T0 z^i?_i^?^698=$8sG|MG6HWqE`=2lkX;r-2`02qKw?#-KOAog8vJC#ROhnb>Iap@#e ziZK+=?oL5|jC3`>^FXGhCqI$9nC$mSRR!xsEiJ_5EB>*Wv zo}UH(doqKTGGK#Vx3rw(d~<1KWkqguj5jZcgWw{-EA-)FPeq6~W=Od-;S& zm_|!2qk&`3As|5f`0-;69i47Su(!_5?e@xEV5YzOWOM|j;kgqCeftEW12QoHT)~# zH13_57!60F3CuGqx(?GFi&PXJ3D}f&I{lBrGp9sz_f#&W%LDX zv@}Ft-m9@3sPZtK9lr#FrFzD~LBg~T3&KbGq~Hdq3^`RmP>2Vl28Df|C?KtBg5&c^LFz7-@1l6U!41=YK4`3n+YsesbH#lrk&S<`KtHx+yBlq>!R^G zlS%Q)H<`M8zUjNUl}-+jb_u^k%FBDf-C9s^2GFXfyDRJKGx=+e&g11u6&016P^>^*pT8_2tLR8g)T||C z#;0oXa&rd%fl znCZ%1kzTDGNi}1mkZu3D3#VT;fH21cr^n=FRt;fbTK;FI?L54u731$Ma1sJ&52KI# zm*o(G2$au{A3v5!@&M|H*)TA%K<{9YlG`w(lK;1Dq&vY)Sg<~nlZBkXw3UYQd@`%d zuXHc$5mQxhaS!J|F>&!Zc$E|xBj2Abl0?~wZDM(wmD`eL!D90Z$5gB^^U}{D#_`CX z&b7>~w#+hwZ{j>pmZNxROpMQ9Qtb&kUba)0K6_3>cOjv??ZPB%!P^F?A1?;0umf#z zAQw{-`*+>Dci$}T%%o?WU&P^~j7d)Jg@2WjmxmU35m6U2%ocI3obmRq%1B(q5kiI@ z8&=BRe<>aMHzVFKwWcQjBgY*ft45A=YFgsv)Khnzge&VdI1OjZ>F7v(#SJ^9Pub&U z;hq6@79k;_r}-@Y186?&p{+zmU8oN(IOWazw;kmWaB>l<=VE^l!O>Cs+O{pE#j?8r zRjqaTLaY8V9Y4h<>N_d!EM!kwPf*o|!G^vQ3_IVaqdR);m+<4QMT+YU)qPzz_oo2`Jg;P1FB;$zI%1xLw^a6XA+PSm3+mm`hACkkBAw$kL0$@&$XN%6U;ZEW@t*>^$

+E^ygRXDm%Di$qdTeUELh~k+) z4s+dS$J}+QLK+!fZ!q~!5Ihg7mOfBkEME=OXid83{QO?_?r)#?ewXaJ-^W69m8%N6xXH1(8QKW>(lw)~I#!hv?iJ8z{o8B!}a{78Mh)OXNkeQUli3aJXQRE0#%#+fuo?Muo+B_UhHSl#~>A*S5!WZ>2 zzdQ{@3|dsXXfw}x)SL?)bEz#T$Zl(5C=Qi7gBiZjcZmr%T>MTN`pyUo>ZmCwp2foG z6NObFZak^DnCH*;25K}>560Yunv)SnSMP5cmT$%FcQXlwdfWb118=PmjPFEpP49tg*! zDsrK;T)gR|Zz-mi(w(|Xn!=s$)c)w^K2esv#D}50@Uh^*z%Og6J|eGk_|pQZQg3*| z>RweT9TyOj5}P)+i88qq5!xvH-!8?ppYjO_2n?xwBPJz%*RnSq-*$@pTgN;=-P)i8 zY3*C3%C&lq0bd_tF-vebjXv83>@I>4bSPAuwtHb>fhdo4Pz-^77?|8BICw zA4ZroOb72-dmX@MEByrw>)96_Z!9~TfKkEMc{{E3d+n*l67x4HGMO#xz?F16VNsi@ zjJ^+j?<$a*t#pQ^E_>SG7|T+LgptC*NRrP=p$UD2pdQ{@Q)tb=xd(&gmkJ1HSD7&@(HN_H>}@EDpE1m0Hb zv>RooHw<@gSMy3cK($cs6H7j=1x3;$ej~B}2VD(ps30`GOozE<7?fRr1iMKvZ>`di!c(G1 zPAz|vtE~wKQS+IzF0>s2M9r-yp-*cx|M#TyaL#_wnH6Us!NQmt9qL2#1CFo&+)%>g z^J$HKeKNT4ZbL=$-#3bb45`^Q>9jy-}Z9F0uYFFQEJQD3Q8f*e6mO2BO zqHWRsFmgx$6ff&8BK_<|0b-2w=w`U;es&5nExhZ-F#a|nX`sLFqpGr=#emlvhu$g) zw>NL}zS&CfyqMYiQGd&!A~||Q)Avm-ZJ%^gTSY(nYtiM^fmHFxX&bsxoc1K`RJpG2 zb0S|DeZNeORTF6bas1<`_RG9`v1p4DF%PTVUcg@Sz!cW43r*@>68&m6@Kf{0#zr?b zB&WjWEvu8zy($MID~pSIt8t973@lR7^B-#!{ut&2Hvq`T%tR+RdT%%N^M|Wxb-yd7 zTazg|T(>tP^~%ro`Z`#78vb4o|FQk`df|$y(`Q28grK78^*c^0O%3F{uGy5+wusKr zDjyA~LBiMr6vA}FO>7>f@#uEK$F1;Rw*lAJ*~g|f9WXv9LN|F#yb;~EL8DD*v0>uy zbJdRG++tF@%-wIo|G#wwvU~LB+h_W!a@Gpd;UxLMzR^55~@TiIMY{-or;ZRq-zG?ih|{8Gv_ zW1}m-JJXj@6cSBh@M}uox{V1>dKZD}OP9jSN_ax!qW`TDMXAoJH@`pwB7g9lF9~8$ z$kCcw%q@>Srd8QL2)h#?2U8^s1_0@L&p9N_ve($gX6@};W+tXD6BEmCcSCmvo8X{( zV2S6I?l|S{q=+Q|=9ubxP65psH+`>QYQ0CsIG8BBBL}l5(js5Ky1d(r$HzRVn8kEQ zMDk6X{)vpYzmE;8k~!FdOYJPVNlp2+Ol3TeC$A(HYRt+C-gQpe>;pIeVlPDs1aFY8 z6n|PVoL~;3I;b*iLGhKW@`e&`@JRUm?IOP@UNtL}EdQ+Mss04rN*!0u73wrc%E=L}^t z55HdJK!3?SL%elZU#dqojR=?O2AK(C#|qStYXPaXJpoJcl4pt56>){~3D1WwC`^7|{=VO;P6*0@rE4U=`H2X}0AB!OLP4;QO*3CQ+%&K1Juq;5)Ardh5jN|R?%0oyCF9wk)faor z+louC8UZ8`53UOy)$ukP7W?tU>Xho3}KX#&Uf|?ccpBIN?1X#z-w0nLmM27hE`0tP5FvFxw@cDBVW1$ zOy56VVp1Y_r9g(eMQ8)7>Azn)o0@?Ed3isJh=>SvpMLlA#%q)Of&!(#A%uGn42DvZ z;JjuR7N&rv0*bi4vx+{E9XA(EB1*>Z_CTmG1_KSNJ+6qq;f)*IJe2;la619+T_))h z`W6a<8KiBpz&}yVp>9jcl4j)+gV-7IHSg!pv@HC{6@55*GZFQg!!Oqnv3;J z{u{}qjhzFyN=8#Eg%35(KYA1V@tniM;xa-xn25s z`rZu(dEb+Q`itBMzrma6jiUDVNll*DZ~1!1M?L9KZufdXS~WlYYdpU{L@)xvQJ=9h z=*l|>MlIO%E1BEyDipDdnau92`;f4Xyp3GBm6J{=B-5{%<3dhO`YbINj2Ppki|t03rPt=j8Pf#$ZJ!9w+JO?v%0l4>+ONu#4~5fuz`Fk=T*zc zy6h``8mt2Xq#^tnTB2qbr$j&cr1|R>7IBNtJ49dbK|vu_7v$Ur=;b@}S}!jzG`QmX zDE+wT_GRG0Uysn#rz2(E{CP){!TQOo>D>G`#ccF(2MKwcQ7U%V9#oM}r8O8zd|g27 zJ{BM_*Td3_b8sl#koXKl>>H1Rxb8Wd)&KaHGiWAM=lsQQ`NDsjQN=dYjsXwPSz$8P z(Hzyf6)-$}{5TR!|6Qla=h$F`0U)MOToWTB<31Sku@vAc94t`F?hG4!S=yfoL?GH@ z1I$xqVZgib@k#7-^oLu>@~GdK)xAVB`JnxZrl|RAz^Qj}b+vW@yVLxyCo*H!W~C;{ zs_VD_n24iS{2V7WHcKptWbt!0e|g7J6%*!KkTXV2E$XGaz)H;{b~nSgE1`54z1+um z(~su5XjZ|Yj$<17oEyA`rjIgWe}*yR0thhoL6?OIbgWq}d z88C2VMf12X2UAW|1@5^+`-6kw(a%lzceFPz*>;e~_Y1cj?7XAk5~76$DLOfsd>a0n z1O^dY@KM_st%$Wz^AE-X`qAwsH`gTqZ1!KNHlg9j3s^L{uQ)sj1zs3Sv2L0ZWJ2~UjOjd-aN5A@c7f! z%IsIyXIuFGUW<#5H~KD#d6c1v>tTkCcqK9M_4_bi`iJn0{}s;;Iy`BUva}wp2e&U- z)l*>3f8K>@O3HZ;A^1e{2t^;vaQrCzCaK`m0cAY(=g*(f&Z5sCS>?3DSPw&KF|Wj8mW0-2Z+F^<%Z2V7_~5VlC;&t*xC1x>hc(7q=oNM#c2F~eV*F8M3! z!!DAuzbY<1vLxX>s7?_{(1CGV=|2P9cdcj9CIGIN+d@gte^(k(LU3B*@IO#wWznq_5>*SNv#=0$`3 zQ1D+LNLV2H-EjzZHWC1uoz!pYFg-+>jdk(oBLd2-QmCYkjk(dmH=8XU2tci1U(Xqd$BBRKKJA@?$fDO^*tTK zm=F>NWJCYVKd$-hxSrQH&6bqa`9!Pk;JxV&$w%5XeXT#oP{_Y$FqwE*y+xn!=B|@o zbJU{|uOzpa)3CcinD%oHBbxJHmmKFMHXL&lsWf)fa{ieIOfKpM9P|Dzs1MRtquSN&RuF@gJ{X=j~J8;v_u+2eD9*_pN-9Rn2-*({IRlZ=Ez18m8i? z`F)Q>s@{;fEpG{GrMy>;?&;vkmLI=LEqKDRd~2Y{4c{g@_`oTvIyh(h!mtcs^X=-I z(&JjL1Y#H^(9Ajh-e97)LG+dz-LHo$DoT^+Wf_SI{7VR+PL<0=FySu?^l+9BcK_XoD4-NoO4ffQ#b?f<)n+=tf1~kaewxZqjne|LX zsus2!y|d|#O}^E1<4-Yy3~ zx&rIVa~Kq$<kx`o2?(;>p?T|+1 zcdl15Rhp28px4X>xHev{2Fx#C4r$oHH3NoBCX&1vfa-L%o&NLi$}I3Fs{QgpJU@oQ z9AwtYM!c50QF&?ad=8LU%%*EyGyc0M%-9q{nzPi^fsyk~45qd*Kl6~2an`OSn*ZIAKYsl1 zGw6j61!4oQ`tdnIiM!t(z<3aIoLKEz*3h#WoZ3Dq2?GA>NJOY3C1pErL!;qPj7e7X z@Af@*CQFNy``Pp=j2QnHyne09^urS3^RwgenEyxDd>hhkxUjk(HU5U1kVr*n1^A zJDa3z$sVUrWG14rLLoDmaaq}vl923CWbZwm$=YKGmhhZ zyhrfrgd8VXtjW$U@ z2cMf`OtND+G#qV(6E^~Es+3Wix@2@AB2_!;4>DkQ_T=~CqEji>XU(Sc5Xi2RkQ?#| zoS+3Rd@~Fvp8}i+0fUg%Ix*E61wZug9|wJ? zF@Sv4X!B89rwz2EIVq|-LO7LMTf$fS>TWq-qZ#9W~cd34U70dBLyKVAb+{6q|C7bib2=C zu7qg8^xtQD$R1~v6^jOPpkwXC z#bHaS@EZ9FYy2WjlD3XZ?zbdlsh!jR(U7ew@7PJpUyt{{5_)6KzRdDD*_h>eD0=Rk z#o96Eq}BLN+yvQKhMbq~waVP9jQ`(jr31a@w}sIRqZB(6se{F`7Z5Fgv6u;jmmB}` z<=^f4C&k3%m!XGRgK?Bt;lawuu`3oN=Sw+GcnHw)+Ev zs0%Y@?960GJjXJlVcZ+=J%dMh0duS}1Z!t5KBzN2b7kt-)jw;;mV%S^{0(Y_x7UNQ z^M0Ri@A;{;%a)}ItS6R@E4oe1u*c~TfmhnYC8QZ0Q zEVSTT*hCH=MfdRb|BdnUPa`+gGFJ(@h}gSo==0|AC@bEzaGhGYC8BcST$;|EXl@f0 z%R8ip8{3wgO+G!lz27SdhGhKwO?XDn{$>a&RQ>2KF#Nv*cCB=(g|?ixA+{#L&&{7+ zDh%}hi%c26TRuDmY@z@KT&Jg6Db3dO8E=tWlloVFh(>r@G=FLycH$Z~@>+J<{!q3Q z7BoYJB}mXFH029@xVSa(hQ$|CvHI-JOo}-1`{hlpA!WJB9~l8=TH8jGRpX=PscEkN z9sNS2%`o+s+w^|G<5$NDhT7-Ri#6aMRR9*)X=P<)^deB>-4tOVc11{=QJ2kX7)d~J zO#<|IXoWAFnI}?^D;W&6W(XhmPrGFO2_J~fcz{WQhb zEdwdR?yk==fjG3o>zzDnDAs*>j+|900+be0VA#XT1l)`e^y+uGAA29wW_(TRx0H^0{4w&h4E_CXx2HYEBL)sU9J6P_P$X>a2cIbe9Sw> zhPh#f>$>}z$N+H`Yqps@Wr3%>g8CBR%k#D+=wdN!t=>?hPXqQuMZHf$Pd^P1P8+C_ ztSYuM#wqZ7$910FC7VNZu|TA!alY{R&gJF09gDavP4v^Q!zyUTFvyCLj;D62LQW)y z@^h#fKMyyr6Pfn>;m9qAtXa;V6j9$F*B~Qz65gJYj9Z&3%!niIQz0`NX#RkTQyL}nQGx5=DM^E7VjU3BeM{M~^jPTNU6o_wJ zl;PHR5ey(R0|q5%2^ha`WNDtKg6SO804r1LXaw7Y)$i?S2VusT$X4$xZxm38B))_N zWL|bQdF%AUL7R%nii|gNqq16Oi9-pFuhzz9C?-6jx3{-UAW4wl8(weJy=OMv_U6La z3HEdPxqCkm;nsuG9UMMaX?-pN1p4p0$!ODNKsA}vJ;PW^b zVE6vBi3bV&1Pr*^fypZ^Q(WKi;*#w*8;bp>r)$>zu2w_r!4bvV^HcCst1FDwrN$_u z3}AodYZP@)&^8W)aF}2mJcPgs=y|LQDT`6o<8lyw!i! z_@GR`YWb({%-}U#%=hz(@9%0zZOR;)UUz*~-(p-FgW<3B==}8vQ8VP$5!qfB4SyTv zENY%2IB=ODq1g+AnT*mB6eyEmK2$#A)1DiL%Mdie%rV?!G5fxxZh28$Z9?-p^T4u? zB(oR!(!wGX%Thk#J!@Bp?PizUm*z2z&_4GE$@39mZ6xP#c^-c52}yY*vB#I|Ea}u( zK7P}uL~JOQaplSn{%H;(1Gn8OI@JpTJH>Ev zYoHsuFsAK*UepJEbL;Pel?Kc5_iOma$9l)C3sI}&@Z|HapQRy1vRSv144L@~c;g~j*elDqF%%ccBZ;%-VejN>oe^2J{PYsc36H=kXj z!cJ1fC*u&{LRLMHJ^*4@1g6X_Q&TX1H9)&4QSJwJ`ZQ0ZqdqimdxiN@_ya-w-K^z)n9;LHEQ;Y2?nC(w%Ws;g^~sp zPz7SFI-Z`Z23C{QKoxZIPHFPUrVO<5`kA&2w_Cy{+fOUsb-JhQC4Z_-b!${|xnSq_ z>zbDh4E2JSNc0}_Y1{mXR-7$){TfSx5q8V%Oa< zKrLsvCZM*)fIow~(zv>+Rki}Cn8dq5wrna$Pja==KEd|| z^48M(QJ@lBvg@L$ifuK3ElJZ)9aam+0GKc=Z*Rj{LeMM>LSZz*2Et8LOU7o9-d8ZC zL)~i3f@b1XUl00g#cphDEazJ}2@e(VnY}gm zL!ZM#@gEvX)Q^vk{~Ez!v8qZQV8u3t9)n;Z6N4UYAkv@?v;j;v0$fC_zP!510Df|C zu7m?;4M?9>s}oK6Sk59teh8IAc~mG@2T!EAWaA)VS8zx6{>=3_dLQDf+}HXO zWW>bsH*SysPN!pG5ew5Fc5(5c7>-Ru0B9I!MluEi=yV%8;`$}Kx3W!vt_lymbt|bI zX;;en-=Ap@efN3ihUz_y@>(65FORd)aoDiEdKx|B=KR47D3V_h2|hugSAH@1UO`<+ zzpA}b@5ldisECB6J~_!CY!#gEennI?b0El_S>`$uXcF!^KhL3>N465T{u`evT$D)b zDX_ryL_|e3N7C}IqFOp&Vt}$eMJ(nl2BL=6zKD)bfH;C`fq;}D82UAQLPFcdU>xC> z8t{^>Oz2?L3qwhHh>3>iC|D3NA3lia8yb4~cU3o8$pQHXMjB6(k{+uiaBp>9n8u$J zM1F9prCf7nynlapL%c4lHy z(nVd|68MIAt107$1d#qE3hb9y{@4Je$rr{KbY( z2R_bX?o=L+m8+1XU|^x>I`;pZhJYQ9FXL3+-auioCRx2_&gh6OV| zH1d(9qoH93elT?Q5#NN}I+$ku{INl$GrM0i;Y9s8Z9Dh3^BnOKc*DCrVM?%lzb+{~ zf3fIiP(9`E7$Yj1`6FEKb$7cl0C!BytsASag+-NJizU4Nfq~i209L?T41@;8-$v58vS3ER2B)CeC#P@I1CTr>>&IGjjr>F=fLvSS#@hCKvp;(*s!tuO z?nIDnx1aX-!6mm4p*Xt{Z)8S}VMr6hC+?kZnGPJf8B(EkcD~JLzdX^(dDm@=j*P3d z6?VK?tz^+U@PH*K?l7V>Tvzli`zo~&-5gh%n#v4{gnxOf&=FaSTu}EiF*S|yU;dbD z9the#bmj&8b}Nuz7?;{S(!+TjFI0B$74kkIK;yL1gCWk46T0>OL7moKfTI?15|=wYX%^TqSS+jTDgkACcmMsPFukQOCNPVV}R%! zAcq1+8;E~EX+tMNvXI7z2_|E@_JHx-@ZixUV&oGOT%!K|L}5(mg9c3tGwb@-L3qIB zrb1HaF@;Kfz;R`2+hew8tF1wP6X(b7>IZ=+6b$SJy8E~L*I8|wACrN>83qG_g{dS{ zm$fdj>k~_sPj4waAaA~|Ht^sBr`dz zS%nIh9qE>d?AlksG%h;cq@v|2yZn^PntI3k*=@2y)^_bQ6;EeSYa)Z~s_O$(&)!#P z=KXn3-uMWse%HYP7xU&#PKYgL=v}beD%(@X3?cp^G^#jt{)Qmp1Im?kC|XTgNKkjIbmjW7>xZ!&Ev{6`X(@4wEgzO zw920b1XauAD9x6r-5r(VVJ1Kf>{vDCbZr_nY(hm_VEhLa{`Io@_UN-5v6;oivG_b8 ztL*>g*yvPXjFlww91OM6xWB1u8Q$sK2K2B-f-@^4O9g0@&;v}%o*;V^7%P-<1YlhS zbJFm;!on7S09t;d78n*Eb)AJ7I&9u%$WKy+alZ*8oEEjE#=*cr_SIQ7WNaqFXy^g0 zy6otqF768k&c@Qmmy{J-lN%BH~*(U+J}!91vh{CTU^7#G{o;*?mEf z2rLXTTU>f6C4@=+LUD?&IdcY?@=&2h&|wR}PsU&f$eDjm&K5r1+5?rxtL?yFE(kRZ zH8q~4O zAjvaBJaF@-egzI@%hPkp{KiF4(u?^#E=@#m64{9qJRBVNm{Q}@I6G)sjG|g`3;Odr z$wUFC1o{9T2DLo(u5os8DK-{_7)bCh@d7W9^71QEV6|tig?9uQZcm{9kT4f@pHdnD zA@L|mcQuyr?XowXJntiG2EhFQU}-(*fs`H^pN5lT ze_-O9Ell2v1u_ow$%6Ge5SXS_!WD6uf%MAdd^QCK;>-)`ZJbuqPtpEK~cWHfw2DCz13Dux_G-<_tbQ*ipn6wewX5!xNz#A2NhM-rVKF?|>}T1K zli+DAxJW_)s9<0~`{!%McqY#q;I`PtNk5K8qDkQd2MieO2dBW#28Ax%4|m;BA4ez- zlyi@M|M`Rd2dzOe#w*vKtFm-|u+TjI<7_EiNAFtWSLP~r<|6jdKw$WzN0~J>znVne zUdD=NcMX?)OicB6CC2ttKluBsDqL<;4@UHS=(GU`h#X*S0v<=KTIqFkt$_3KAVNT*ykKK-5)bzl_kwrL|26AV4prWgjA+iiqZ+U*xtkT?Q${-7GO$ndJeXkVX zx1GBwyFTO@fEJt5$6n+2!tdUqZIRhGc89i_ z`$L*0NnSpZRHve5O@-XY`R#gXF3H}^y4WTilI2n);6r~y=;FTUJy zuW`3Oe-BMNupsr~3W=Q-Lt_?y)_M!I#j(ZZru*I4`hwKHc)+xC~(&$iO1LzJ|VZVLM z(U%0t=$2CzEr9TEihkkyd4h>%uFIo;KJvT0dxLAqCPt1STHk18K`U`X zD1^wFCx=Pc7((JZ)c}{|F0;Sx(Hm88{Z23CJ9qq&FWg7LR#-7wFw)Jc0&SQCt*wIM z-Vi_F^(^}f5O0LROilNHm7!`G1Zz z^T0c|-0=x}qi>mO=HlN=L`OqT*Wz|A%I=Kqjz0>**-7-!mLou2t+S4GBK?IP-YG7C1b1>!gSxm zV-3WGxD&EMsCuNNzP%$o^F#qxX!6<}9+gS9)5WdXmg{t62ZGNhj24haQ&CO1?{Z(c zawafNpFf5*9Qu&{j`^Op@aSEsu{3i2FnSqbD%`t6;k12v`t6|h!;H9np&FKhAUY)P zYnqJmR-Em}MoGU-hW=@sU>k4b)|H<#bfA=jsQ|C?dy%<_i3ux)7!|l=ppCErq;9EF z9EPVDnR720XQq~cJXs1hyPDW5k2+@Hw&|q}HgC%^& z6=wQT+*kPcXyM&zsp?X?Kv5eRTRFasq6!yM40UC>bpefOtXad{uy$JS;yqQ#*eaL>_&rWEv9S^eLx zOb)v?w3gZ)uFQi~o7M)24}u%oG-0q}`AR~g9IzkJ+*(f3%QFIa6WCCM+<}b)Z9!(OZyh_(?F|XrSeKD?0ts& zvCzxAodzr$j+foFb~YHWU$}!6{Qv8ONpofXFP*R{Peg64_lgRLEG#y?-_}*I(%v8HK}KS=}sXv9uk_D!s6DOuV8%M;4~q5y2Y4www_@ zTgldD&m)M7=`k9Zocv5l!BZ?oA-rf z*`)lDQ3e5xn5{gepEea!F+$#v-N^3NmSBbV>QCZC!&RBk!8_5)bHNi@8(%$@dO8zq z?mydfEOzhYh?o|m`Lp4DQ&EBNQ+xO_1MHv8YXDZP{y)USUNt2XYYPHvy7KZ5#VXcY zo=^i=;2w_HqWw9`;yK`T0=5I%;T-ZEH7%_+z>p=5#sOK8fqn`H2ggF~ylQnE3~)cE zh*F$vS)M!sBJq=qzhH`~0QX0-^~zfI{}KbJlH%ik^?c*lr&qYIRe_pqpu`PO1ELe9 z5x`*q5oV0hl7>nVAqNLc492!SwiN`ibyDXeh|fJa1}|WxSz%IR7Ph%Bmqae~78BkV zNjcRc6PO-9IMfYUGr_vyRdg0nrh2w(-($1I%%LO~Gt!MZIhejw_$!ck+J?a9mKnZC z7UB6zS1@=-0jVS(VlEa{1N~>Z^?8S2UZZyM4Y$k>M~{%ZFES`tQ=Ub>J=<(`?Z#Ot z6P9s4e)Zk9j`NYm2g>9e-TR$$G20SfCuF(PQz4fi66j znRcPCukRcMeO9G-;?k^k{+TM;bjN(hZ$BGP&r|94Cik-A$|9Kyp?%h6@MiHqV*4IQ zju7uj1*BGRIqpRx^_&GdHP7Vi)98?oK z(-DtGiUNesYeFf1_cNg405aINw6#SxvEY`}@AFv%ZuPq?l?=!}XU7bl8iT<+g4T4B z-pfi*m7x+UFly7x?tRArMiW?^qQM2!rNZ~fuffvLCkv>1Zj{#!M*o{=;%vmSo@XGu2lcgk$u&boBK+{qmr2Q-VJTG0kvDN|h!m9p zbWD(xMdAm)ZM-7uJcJL08GXA%N*BW%X$k)*?)^4KuF#LC@hcf5WV@NV-czo7dz7ec z&lyfQ{rC;TNTL+OJF2lAii3TKqr>XsW3IEFd6ZJ(sd>FlUnh(@_bZ4#0O0gdNS&`F zM_7{Z$4YJc%o&ZW>p53dRNAY5(EWqQ@h1bJRMxe$)Q(RH3iarS8mC47wV82X22-=jp?BBB$ z>;c_ z=Xk1)-LmU8LG&3XXro-&K5o@#W>^!6IgR8_^*m1Flpm+CF(it8f6n%3$TI zFewV)U19^rrW_;uu}_~AP^Q(?dV;ph-V;d zoR{M8+$#D>2!eG>5}Xv2FB`;BRH)Z??4Jtv*jp@DBz$DkS*RvhwKp`d(z+hPl&Xz9 zS|V4CW2L2@jAoM^`|VCUqi*7pO+exPv97X1*}sR@)$|`7i70CrI-%E=|{B;hBG zo*-2J^Ccx54eEmWZx`a6-5|i$%;U>d#+dhxaMnUhGUqWHXxH1`s1={|4B|r2H>OcC zhYLRwLl7KiL71R1vV?S&-*WbulpT-g{yG|XnbdFaOy3OsbM}RAmoRcaUb|^@>A3E= zty)zYvx)|Vs88bthwuaET~f;DtjhK!<$uTvfG@1_xqV;`w=9Ky;&6nw>wV|h6}6P* zM@esaUoW1Id>s-JDFE(o!KYsnMTSK3PlRL~sx%3`n(dynLw;L$az_9<48(7L!;+&>IV1am)t9#Q$xe>k7}hkvR3IMf>0ly z#}sVEQkEdc&++yNg3;Mea0JEJdlP}>+tWMFM#_}L*8P-g=~rXV5TIyDM36AMK`{hnjqm!NfsYtmZ32W7Rr4mi&SbUyI*=kZxQc87r%KMu|<@2+}X%CraNMF}KV6 z>hh#`Z(hV?w7BH*>E%F6X}xXTKk0=o^d)~5AMr3}D!eor*xoZ|CSP^qh zP{em2U%vE9lAb5=csMUJ0S-rPglqx9g@pnDixGmyxjdA#csIQbU@R?c+eZ3(4`^5M ziHS6uUXC#IVth6;e#%Z?R=A1D+QFZ$UaC3x6`8vVM#t>dVyTL$ety9co=jG?ut8pm+FY9++XS4G0r${Ushh%s2bh8t0FMwN)8%$#q zNgG|4^HK9m=r##34;Ft?p|Qa_!v$m(b?PpQS?4|_K7Zan9~XM7zXu7a7@z7;o28#9 zb~c@O+n%B z3E7NRM4z`FA*fm5t81QKWIkZY0hW(~>|2I+pfESCaG=G^KJNfd1i7EW#~etBEYE28 z`|fyx7E8q~E8Ae$ZMwM4u6*<4_MKUqwsy-4Uvp#Xx!8%n#u){^)1auSr%=)@+|aQ# zt#r}LskQKXml2PndRZj#Z=3uaLXW{W@Ma<>+(A??iZRFDV&hk^-ZTc@A4x2{UC^wA z;T1nB#bTv4=lf5O7t}a%e1Fz?JHMB9x*w7lY}=xnKv{{wY|MUbhl3{;8vKR@8B_3f z&e6^&mSO{Er|*4uvhRJ&hny;3o84BPd_ruIh4Ebo`%sI?n$9e4V3BSLBF*=Mh z+!uddf+ZpG)b4EH{-*A;TCf&wLwV6)1FNU@3xR4Jb!~(m6t8wPmYACRHJr14k69js zu>YuDxw%D~Qeh(W4cT-sO;bwbe5d8Te+wEydisO@dUhg8WvV*Atxw=7yarBf6TrGa zUHqVVeIn6s{v4I-n3(iP8Ujub1sCAF-_F#K5jhVxw?mEW56|I)=A&0!YTu<^76mmZ z>lXH{DX43#o;lUHT}QY!*>-}1`OM8>!6+@pzIBWo9>^RX4zpo`H+nSNTj|Sp3hKY4 zRPpxVsI4AvREeaGEiyMOY$)q<`7C*D6KTShcoPn^$eGz$4k01!6`j1K`lD@Ar{O=? zgc6|RpmPjJ7=`Y;h|?#W^lS+56s<<8-S3XpTt&H%;OvkERkhz^R(*odOs#1|3s9WV zWs3yP3sy%%0|p>8OB+Z4GjiD#O2Zw|0}x)@Of){^h4u)_bz!#&IkN}7%0c}lm0$sc z`cPp~OSMz3kz3Kn}egS zG4VE^7v0~SiC91Equ01Im_48I z(7#^(YF6INJ&G)xm7-O%I{)^G6inw_z=3{=G9B46{p(vs_rD+;iD_Ljq2oa(EEqSJ zjX!r^ydtP={&~19%hiecco7=BC)6WKVD7>-3w@cuiV%N~w z-CfXUkosT7QJ&rblt1wQK*gs2OAAp~Zw8L|8VpA8FlgQg7JL|R)aqwvW+vX{rr%6Q zU1N$&t5jg94a3W$;)Y5X=Pr8H8Vv*B4ID~&S4qm@F*fMnU|Lo$oR_P zyE0zM1Q)u?qd3*j@eD-$=pB8c5ve~|mb9w@R7)l~-p=Hs+`qd@dS@gQykWtdiUGaq zx!oN!lYUEZE%a!W-&2Nbl$qT8mmx)^5eR2zl~P7sO7twpmIKSM*b&Dgz?!FFn1G<5 zvpaA){}~m5e&DF)xn+`_1}tgx_LyK!4FHP_Osehkxn49OOPAmiL2A2ALg$;#ddfU! zOQU6H6GOusN7>biy%9nOxu#zvF_|Y3pMH#+aZ-k(9rxu zZwM1yP~)lWH+`jHei-@C+9%!oJDHHX$$$6Tc-MnIj~?RdY4+3xMhs#;6-4096kiOj z0W?*c-7^lB$kA1q z{W*??dDKU#6gDfSXIU&qD=ptNqx<=jn3(>e3?mZr5rhMxk&^62Bv zyzlv!^8Nn!aYx@$GpJ0~YwKMbjtpNekJZ(VZ*_i%Rz%qFF)crxB4SQ!AyV`FK3ntM zkI|AsYHE7AZvWGpZ*(jyOJ!0Qzj)xNSzBj{6lop0jMq#1Jk)I=Ryk>cAcc0AjO0jJ zzPCbfD)W~uicqBysd|!tZaQkodl#(58qN`}y)T*1U?Z1TQ_Q!(ylhYDHQNJ?Gt1I_HJW@n=B-<{F4|Pa;5MbQ2 zHt6J;dcRw3ZmNHlA3#5>f?_BE*#m^>Zlpsl(Am2lsHr^>32Zt=&)c^=+>7J0rQThD zf8l9}=jUpGwB6w;;<)Ux_NDw&83^eYv@|e&E#$7rTCN17p(kA_ISI)#&oGx(gZ~El2 zoe5({=|e(_=V8?Q0V=`ZMbWmnVWwPHoBj6`(e-%bF0}C|p3s z%sIDCf-J?|bXlq<{PY{diy5$yw@Z90&hNe|G@jd%*(EaDi{34agC&#aGNLvHPSNHB zL0PXp{(0rN3hx3BgN{mp`iX}jpkR%L*Mx&N|!ATw-gD{EWVgME~_~-Tw7S$47i*ps&=E+7>v;``zLpYgl_{OnH3q$7o z>(R`4itJwFd&&GtP*L=M|ACaW#SxN&(>xcCRa00zj`VKHp@VcRi!2VNI?K_%+%d3z z-1FG`$j`23*1+h%oDFm6v8oM=%hMlzX8(qvU?fIsTF{> z4Nh*&ur=0T<>6qqmq(+Zd!_(n6vT)BOjkkdJ&v5PKsAlr9H7O=L7A;^3n^AQK5J=F zs#APT*@iEqdoiG5LG<(SjN|V;NorM&Ub>%`@17omnCEe1);VY}IX?d3(E$Yp^Q-(` zC>R~>ne)Jx(+ND~N=c2)FUVVhCj^`45wqH}Z`JO$%-|pmRx*C!iuM4?mR#AB0VCWu zNN%p}r!ZpR#^`jZ#xSB*)QUGpPpQ!m-6<2#GdhgQifCu`Cry*^{De9H{^O+q;}3hL z#sQ2{QC*7rkX`bri-O3LtOzKB(JmAH>|`vTr5;h0jTyM``1*XSsIJi)4z4Wq|7sCX zWlN7?FOny5nIG(4kw6*@Jp~bi;Nr1U!3`%8uQhTrJX{**rBn7v#zgvGQ$oF`L?fZ3 ztK{Io7p;;#7;H|%$hUCtBd~u8njSY8BPS*$Wmz{rUNn6fq7oGd7Xezb8=1ME0{+Tl z2yETgSia^u#thIhzaONRuj3_yU3l2y^Hv(h7ZY4a{==;ABBW`1boWV8Il8=D$Il2w zV=qiBeGXh0@7fIX9wW$Z(lHqZ+oCY`i$uc>GisL z27|z}$sSKZ{eM2|DU71m zwG+07n}bvsIR_hNnbZJ^=*Nv?#Sb_)#AGbCprjV(BiR|b)UqO>e&fdbpwO}tbKpBH zkCv>~YGK3z=Q;0xO+|1psl!=zIwuzx4Jw*QAYmgbRGb=^NTUDhoZ z@t!88PGS899a#p;=PRlg;tje#&bkiO)9&r{)R+5hFB0qN({vf6^i2MWB{z8K*Vk3N z5dKy0U;}BC`Mvk?VvpXRf*q2b^1+(>t@}L|$4)BQxfEmT-_H<**bh?a`@ev)wms4= z#OURM?q)f!xY?yVFCmk<5^=t^-+2La?0J^uXPlJlEgoF`cz-1>W+MA@=R)>Ww!@D9 znf=*qDjbahp!%x*o6|V{{JK8v zj+~>N9kbPr>?S5?~O+bnyBHL=G)|+v)=kQ2DGKI~I2S6!e13)laV2UrX^7$jgK{ z-0RO;;9W;SgL|X!E)!(P+s?QM&~Pwz z3QLV0G<2tHs6>7ck9mZqPMk_TT1`rRo>|_qGuQBM)7Rk3%Yh3&SaMO<$yYk`^U=_V ztxPz6e?uH}v}FWpw+kB?0qbgJ0c)*48xUD2&Qa6K5zj$$zenm+T~62dc^LEVXYroD zbV+wbm{-G%Naj47*WsEdLBlTh4Rdqu5uc$)K8X9~#Ow0s$(F%J`k@8V?k97>KWca4 zWZ%1Hl<_};J17hK51e=O81lu9&ENf4cwZMj{5W-ns-iPUYEasKMy_bCFLnWs0P1KMKfyH7G~~#ss-}n6aRJ#)_$H9 zNjAIxM5GR;|F7GX9w65eIsv~-ymTw?yxKFLEx~>gXh3f6?X6_bUHWI{5i)tm`h$V4j9lNH*~Y~i^#lM6 zlGWBR|Hh{MD;>rno;;BUBNoQ14q>2=pEr8VXLtKHFVbgN&_62z{TQV=o<?nk7_6 z{7}-JnXO~&?Ck8CP0Zu=IVEJENqO$Nj2Ct0Cnc^0qf{YYyO5RNBYpWIb}U;sT9oD# z`69ac#@rvfNol(|zdBpXvPnFy|5_j0=i4JwaOm(`$9|q?H z^^QD5J|a0}`KCQ)&fQtS4%tbgs4E_;o0*D|3S7Cd>`5v!|IV1eVDyApCWOsc~pb$iv5V#SQX&j1EXL_wS?`5krIi)$ioi#lG?*GtU;pRRZB~0bfF}RUhii!5?hms$e@OiZCY>V?(MiKh-7nJX7 zjEdHm(=+phyemAd#x3k>lnVxM3aa>Y>Av<8zU5K|=Qi|j{}mMzqYUp5eB*KVS&||Y zP9IDbnSp924$*hiSiRYDFoiT^jrVONx}5!W%`wdNj;4}>joDSlefvKJuIC#XhyGkV zI>@xGZRApyJvj8R+iyc~9xkNgtZqWE&Bd>S+)a)*&6`Yb>gTDYpnu^Tik*%%sGG1c zDS7EOA%j&vfBw8tYjiH3r1tLXi0wbgDQn*r*qWDXr2l4Zyt3XpO~KA}&c|pOx6v|O zWc63AI*yoYoZoUOA*@rtsor}o7-HlAJL`qJE4ZHw4_lb5<32q1_kdy1(5>ULRskhF_#`N|qe+mFcPh0`l1diO|OcHj7Ay(%I3wQ6xyj5*EE;9;)jo6&@2|wdnEE(ceIj^V2D=nBc9Lhp2>~9zPM<` z&>xR=+G=RY@;y1(?3M>_mVzV5&y8W%-M_O?dSAR%-l&`=*}TlCHaO|fGI`dRKDO)+ zPVnLrf@OK1(d@Njq<)s(&Z3}lDbeov?;i{42C8;;^+fj4mkQ2cxgMQLH*aZo;m9XZ zmM_-f{>W|DU$ZPyqGw^@O(MQPrPMFO?=S0@v|eoL>lzlh5;GXmPlaX0lJwiv?@t+x z3H3j#H@%~J3=5g22G$DNmV@)IkLM*hR^an~lf=(rL~KI_JFSFYLj~&>E^i%6Y!OKW z#buh1==9{Etb*FhGRH6mb($oNdU{c>+UsRvV?HOghm|WB%*5fJS*!a!Q4;@42{_D? z#JzO_VXAL5D44t)8&$_1pukema0SKPQG-jW{xx@br)dA(`c&O_uVeuap`!!okh^#9 z-PmMUYIXGlIgKXGb&XXCAqjO&O}jzT8BuPZcVpeKi~=`g8|_WK`R$5c<=E^c0-TC{ zcgLMsGU$w+#j9MY3~5gh4Hb_3k1s0PAagrO&^%v?O~T1I*kOBjDLLc$Uqj~#^;JLb z-lrudwdlsC@dG>`Ld)Wj`1mG%-zT45aH_#jkt*;5x48IOy8hQwN}fYh{&Kq3 z(%!znLeNhQyQ)rK|JGysG1C|$BiEZ<0^A&TMkVpRcb3R?N2`hn1`3D6v?G3QCp zHaLa}5p9E_YQjw{-|}IELTsx|6L_a@k}G-`7Cb*%z9|Ri$yQI&`0=N@(=u%jvw^rM zC{}f8EJcjb`Fr1$IG&Q%l+zhOy1vJU(-c`I2`V_w(bhj%+FeT z3V1D;*MaS)@hlL7Ea%d$2f9J&InLAwTJ6t?FveV0SJzv<4Ku|vPe!TXJb451)_~8< zkxuX=`tfl>W`1en_ZEySP_2S2jqt*bM12xWc3*6?~ovxbjNhU5$1MNq%0P>M46I zkU%6B%)tbgf3i-i#Dxi{rF$wXwt5=;uJ6VU`tBT2JU8ugIV*xc@Pa|?_E~ZR3Nmsd zZ!!zJWeJ!AN}nWW1nGATo4aKW-d{I~>+!r@qFN%7*R8{a6MVHTM@s0kEZ)x8o8xd^ zk`D{I6~&JAZ}<|-`z-8=kuN+2LWep_LCbEZ`UA8{^ILDPsCxQ;h}$_G68GVT{#J?Q z>Y9?ya}pUk8a_H-nSuTm1$6!1_z#nGgW3N)J$Keq;K6}Do+GD#7FsOejYrG}5b9!t z;q85zUy_|}8kG6et0#WLWMT6Pc5jaNfisfs;vQJxj$9twU!}$bH?dpyF4E)4NaO(Y zF}#A!mSU6k(~w*6KG@><%I?V`VL_94jW4@Cr0VH^bzZLtfQ`DK1{yK*npLK85uj67 z;`GVR&W?Zes%u8@)G2B#%>X%zY>evoK21TTFTu$(C8{`zrEnhC6^b&o;d-xrt=ZHj z7Yw z&x`jB?g9L=uju`H+IM!IA+3xb2|3`Fj>|Ry<`}*CH*)HB!r880r%D8Ma#Z7pwU|!r z?d?wQNMnM_RWzTbe#dOHbH?-ARw^kzBYi*It?R~}yHAUL7T&3dtO&qB+`nDT64uK& zn50#vf*FjGhDP*QrjB$wzR;sI*PxLoRl2~)Bh@&5yK7Nr^;9?80})XRvJ3bw-aa0G zb9XnRN^mfA9yDVqJ3j#&K!-JIm&M!Sb1;`FAc} zXMLEix3us~e11pWVIP9F1QC4JR~X~r*}xdNPVrn8BkV4)#6<@Zl8M2QPzFOo7p-!% zERfUDguo|@X(-d0e8RTn=r@w?g2A_N(grhNCreGISc@62G`!CJ?U6TS4WzAk5O zRXMRHR{nWcJjUX=Gb%5+vDe(Q^8Bw>@&mFPO#M%ucyL2bH;InnE!^x2H+tU$}zO)!P-f7Bof4mZ>$tYZrkZQH2p((L!C!Ti zx>U#diENF!R3n=K=_Nk_feWpv+5?j(NysA`18-92S64srPp>o@&{yddE&O_GfcKL4 zS*=DzZEY?YLI3^yGKb|sC!Oa{Za%8Ed;59MSjRZbv|U@l{Ylw)iRu1M^Bf9rRv|oo zQ_sv7crRs0k6*ob@IokxAXUsA_ax{957UFN+b(}J`s+WqT%-SUdDZOlV!Hm$S5t$t z6N?~?3fdhff4lgpCt2?CqUY5QA8PWy=~X#=EK8R4=gLSucz#hv_risaFY*dE_>|z@ zKGGh28_z2?+dVH*YE$q`(WFT)Bd!+N$I9RqlPGODdP(OGdo+Z9;1jm2Gu`#@tomw^ z8kRp>+x?S}1`f%7RS+l0>FQF!>}M>KQkU#U_&@Fi)WXnf?Mg;nK^K{TNz&R-(x}f= zk+%D8%e}W!0}f4>n_XLuM7KhE*$s3=KnZAw-H0GS4z6A@fY;Dd9HHnb&dkyzjf#_pNRH`F<=vo^5;NzVGY4uJb(3 za9_Fk`X=n&;oLs(|`U6Vz7b)ZCOr|C!Tk@amFzC%Nxx3UADIq7q<49(mwrttn z?zH1SDqknS_%xY*_s?y+8{c;EP09Mk#v07Oa&)lDza@F&jG5>-+_${~9$%f+(=V=5 zE8MlnyjpUrgS^u1cA8`0Gjzdyla`k!gt8sXf;Wj@1Y|sc(8v=Dsz4boYEmlB0E3yJ@t57s}Rca5|Iaub5~34hId@2>NT4; z$~wdX+bK@1+#f*o6$f)tED}V$x=O5sa;-1onl<*fHE-7IaueLA;Uj(bIz@igh__Y_ zO*Z=EWM%I{Z@$8u@$3opOg_u=m9%$4?-4iNnN79VO4U(ofwp^A$F9Ava3L&lhczK4bJZw`abk?dePU3^`p&%&zNbQL9ioxJG%N0yG>3TE zd9L?@kH?DAe5qHy0q9vtWg=>i?@dbv)IwJgxDm#GAe+;0859ne$=xP9xXv$K+yN$)umF{Ldtf19-Bo@LLL<37Kv>ZTJRSnF{f zcOpuL0>K;Hn$y&<7f}-6D68Q}cUWE4Ro{|2jSbSN_r>A`6ijGI7Qf1DU8u5Co6eM1 z*k1eH`>BU$Jndrqr@r?tr`vki&?R)cOZU!WsE*uO2+FX_R=Q|f9 zQ_|9s(K_;BfG2eG9M?8#2}9g=$A2EqK0!WR>$(_9m5gurw1(qeiO?wl&FCA>-Kv&9 z3r=82$gJv{UG$~R{eB6u%$S?k`r_U4+PjIF7_#t~E@FA6GhYn;q} z9~U4euar6{c>)3!Y2QmAihvvm&nrCA7gsX-s3Xpd!4X;e_&Pk&OMusPtCo!-&yKaX z$y2?)^jnY=Yv(Qp9aosJQGXxKsA;`^Quwf2xU#~N1v494c}_pUkJw5Juec2`rN7XD zNvixAa}P|OS0S{WX6)aSRhsDjv*A=}fbajKqeB{av|9vZ;asrh?be@~9*)6IeAZ@c zD8PN88^HHA+Ey6Os`y>$Z&h>r=?$N(?AL{JW6wlmbP^4p7H>2UuT1ObR%Ht(2rRMd zxG01rmhMl5#NKkaLV@Y5K@b%ByKB{9U252px&`teUC-NL`NoGdQ81)gx#%FgcOciiVRB`}Kflf&uWEFBXSx2Hz(pKeV(0CH z4epawZ3)N$j)@M_TfLDjo<~{ziKUw4P`LkOB-f-mMze&xm>7>82YGbWs1m(H+Frsa z^Xh%!B$A6{WMt<@KcDvXIYmT0M|VmoG@y_Bo~jGvP_DL<_95}b60*yw--lOs9d@RR z@mEe~{#st=)1Mxb{qP<8cKDQGGt;#1SkAQ;SA(giKbM>_Uw`KBOdY7#E)nBmsz>t_ zW2>h!EPTiG6qr*|aBZtuO%&gE!f?#rfFLdZljKDS+Q5OiZva=JlRl4L!s=PQj0na6 zpI;*iCuNMcZr+pTe}y+(XzSFjT1~?K|@4l(2>#6Sd@}8 z3{V$cOveo2%b=FP3?7#Q-&tD!tT^>%-TbU0;}b6*xSl8WFGR*Cv?067ZI3@o8Pa~Y zuXl~0-Aq%pZ#IsbKvXZyTz61fMm5AS6A{gM0WNQUrjE?VxDS9 z>a9rgP0#i@x>j|r^uA*590TFttJ1$n1bJ59hV5}C9=MYaKKy`KL*5E zq=ux?AznXD;_la8A)AJ2@%_7RzAcpO@hu?y20yC85-UgC@Roe4t5(}bVqY_VZa7m? zewCB69!j~W*$&V@0qe6|3G?gmqE6?<4#70~^-%f^tzs*R-fi=%N>QC{qTzwYf(>Y8 zA~DIq&VB}Ry=FXGi3?ijX)}%Gd3X-GbtIBwn~IZuke7c^8}|OT8Z&m+4*9%cseM15 zh(Xwi+uhkR+u><(>E1gcO8XTiHV$rl%;x+c9YeJ!zLXz7!+TAK->9q$#s_e5gWl0( zJ~Nj(0eyCj;qApf8ebCX0iW^;Q;d&_W}{=s^S@_bEZhyyL4aD~6PzW##33KIK7;b! zp$mrX=2y~1)*Mgb^?DqSzWT_`r5sT~anr`vYU7H(k^N3&@%Tiv2ZMS<#eg0N-gsHi zC*8#QnT#JDzTM^S@UiBuIp84#dzTuWD&^Sf)DEY(B2BU;v(Tt{6;0`XUw|$IK;!Y; zz;tKuzOYW{0M!}YA(nM~cr@2HG_1cOZ_8FVM)5bRzi{%my3@`Reajt>+Y!|b9j1Sq z?%2|qX5!+x-JW?}TGFh$8cMUn>BeNRM8;f)nQ0xjnW(rT-PZ|WEBrb57>UO36YKOM zd}gIrl)K~DdRW7Z<|xmgk@$s>(uKjsD@vz>U%l$DQ~-q=NYdOYB#sPxkr-Tqv5ZjH z#q0-Bv*I3(4MFTXwZ=CVDAh(Qx5s!w+&8H{iRI9E=)WMhsY=qcr@%y4?%RONE}qZE z;v`63-?Z?6XOPqy^{6_-MgOh*P}MRvC52Bd^#Q%nKjp{)YKe1`dW+aEz!S|iDiuC# zezdLMS4uZeI*rs$?0rk7rd1$e`LwtMs8e96n9A=_2k{4$>tB~%OOTShuXvkC|45-Y zCrz|3XO1i8YV;W_&yFB=n195HAj5%T*-Q<8z5o*cctX-1JIE zl4~)88lyQpgVIzdI~F#IbCj=SM3XVo3Ob%9OYpqU?t#o78`oZ>2($Mm^tZ6&^EVIS z+ad39W-zLbR*7jU*bxS)7_3^e6 zllaX2eDuiBlC8C)uE~&!M9u4j&`Fa|Nf1ByI6qgAICCcww^nJY8e!Gmj7pN1dT6b# zjRj=f^G7&NZ^@~T34Sz)9nhXOtQIJn62u|7tjk3P&kgTA<7r6U8uATk$DgtFf@H}h z5ZzcPHMtIkvR^HUYl3?NFnYCe-daaXiXrcgKdv7@!iMHRw<0T^e4n(Pn4C3sXK`7f z<|kQcLu3na`bO+Rs#Zk7KMDu4w^c#xhuD-!7H$Uz1?8EIYG5#hgT5TO%bzrd`JxuF zsUI0v2!qb9JH7hLF8W3nDJbz#E;G}0l+`2Po0_Z9BwNbjpJ{+s4|DAKm$ogq>N%#Z z{YZ>sj>%)*Oq8sVtzoCmy(ws1si6rzTEUaSa)j}%?tOdlGw;a>%xvO07y?5&HxCb1 zVk|&%`6TAcI#w_5`QveMy~9;{kt{=IG@hvHU(Xl65C<~NS84(WbqQVw0Vt^WD!#RL z!YufqvN|#5%Vamf>Tf0&_fZ}OVte}y+tGmiwWgumr~;};hT0HThCX4X!wsO3eohnC z(8{l=iPNWfOOaWM2NUfa8gDpsjl#uo#-zT(onc6INk*M_Ub zKmpY6d8x5N9o>n23wk+kZyaj|QCy$_VtfE@oxDg?VT?h1vRVy)R@#YR7`ZOtXlyNK zn(gyN#f|C2whMYbY?MKxSh^@WtuUMu%loNF`K7dicAMNc@Iy$hN=X^xH z_7WA|@w@xWPzQrR7-$IjYSrAW%b^PNedck!Z)n;EdC73>=)EQz7+jmCvMgS7`~GY? zVs5hYp=u5pIC0DQfEmMhWhiLIrnSCULoY{8_?)6E<^B!>26Gb#47siEGu;R|Vi4FU zZ;Flt#&ILrn=Mw0A9kh3vetSmu82AP6Lhg;kb4605u}Xg=BZ3oFv|K+Ki(&DT(WvAv(cn2`!- z4XOJ!PrDVN?uXaBxm$ap)*zpepP&En7U?{@0U0XWRG%Un`9^{PgAP^zyQMac&jOvO zKqMsls)up@`GSlsJj?HnaSArxYgFkuJ+TnxPYL77?|lL1Zsc)XuqFWohmnTU0_>1B zX-`XAASLOvQ+6JPGr!%QjrRl7k;kA$6xUCa^%mD?W;IpL?)v~=tNyH{YU#mqY)Jd%r1Q~{9|iG;t!713^v!a zQsfNyW;e||s~4F77B>Cvqy5s1Ufhb$s_TAi`=5<-bWE2ZV^7*^c*;|2+%xNftES*} zZ}2K-ItM(kB=M* zkLL)pEa(?%XYaeLrHwtVzi|2m%cp;mksfIxiRoV_+oscl$XdL_AE`SK(bX9yAmi3RDy1 zZ&+RyqoOgF+-l(?s7foLsEMEcaE?62<5d{JccU#I=MPG!IJjicU3+&#W6l0+aVPjD z1fkRw487-SEp_%rw;VTkM)%BC0*&t?IK?Q0VwKK)GeUS`9q0EoE=WQ_tjk5HT%#wF zW9?FN42J*^Ho#*800+nS3&8u(49&&NmYjQUm8&s)XQ`x0NB0|(%w?|HciV zUg%xzxHLp=(VtZdazW~C`DExCKkGI!zqh@nMDu|RRL#~&34LtZ_*6SKCyd;mv zHx63<`Sl)e&dp6jcUlV?S0CbqHQhuv^pEc)RC_rE&$jS!ggY&=!9&f7`3N1Xs)(YG zR}U-sn>QySg7Y$rN!$8u6swI5!nS=v&5XQdC^s9&a@ z1c&mvKEQ%J>$6dC)Xc94qwpBjy)eekty3JScT6LqgD6(Ysi3RivOWKbEBl?rkhkue zs2Iav;*oEDSp(PIvvgg2G!pe9cXj=Ca>lZK?#9kU^*+)#Pu+tbJg%I&QaS5Q$MpIA z&*#kFuiUcveB0huY|+_^I1vrTstC|JbaSYgYznR!!m~iF&^oiV1wgPsYvr|!$v^z) zV*0u(H*Nq?aI$NZS;)8)F==P(=N-sj|EDno&ge5z_sFISt^smtW2E=J;} z)!^?%(X&4uL@?u}*l?gx%od3OPzD`tt4kN4cl$+3E9{zgZx@E}KnGOoqfWFDYI^Wa zz(y*DOLrBhhXYqX?*QpIIV-DmR=99bXWGi*CUw+P1jv%$0BTMvo`~`*D=TSVym(Pw zwA6kVI2@)&H2qI`F2TGW$lk6|n|7ENgE@Mk)Y=z(H6(Ls_I7t^6_|e4_Jta=S$S}# zmwLg`9y*T5MmWwZGEJSYT=w%&JIqdxOe6MBmIBNgOY~H};F?Bx&;G^}8HJFIWnO{r zPL>1IB|tjia0 zeyUXXYPG znW?##I?+k5-ooLfIM&^fGQx^cy-T(4;*hu?5C%|-U6GNIAtbPJ{5P2Zv}l#zk5$Nw zZ<~KCS}L}6rq4-TsnIJVKjf9Cc0j1?cH9<%-IMz^83M$%PVuD9&CT6E4rIA zq(Qb&j%zW|<+SLh)KuJ6`U?s+lhGreCNh`u@8wpf2L#}LWr@jVxMHO|5-^aJnh!4XFA6~Muix~xlYJ?uE4yMpr~UDY^eoWIHU%Oe za27+sDLWTL*$hBD_J{REZDc(qqo zY0%vYn3C3<7uGj0h(I2jBQJPgPMv?yEp5vZE2(DCsrEbSnrbu|NA#|iVpSI7SJRu{ zp7MN5{OV-f$8nB5@!rP@#oXtg9JIEzh@-e>Ka@PW_F6}SQ|MUfi}7twW^px>h^HF9{=l1$kCAU_pto11&oezn5);knM=M~xYZPlZNS{IDX9HQrnHaDEqDOaAiZ5&MpL zN2B2Kwr90**_6j(Me9a#)3Pm_$)-r~p5Ck_*6OVOibuWLn|lB_zz84&>4cW1DACUa zd2zSf$)uMVEQyj4qT*_&KwUu+rCXWvJ4~_>Z>sl!N*QHcQgi{DM|VkVf45S#4VMgB zHi!KJ;mJ09(i@b7s0B0d6s}gy2tN92Q&ZPqX0ohoXxRqy8i`*N zo-`;O4u7A0$a($DnKd{BB=0^eF?_6kFsZdqYKFGDv6(~8M0cE$6_=goI&nZChBmT7 z6;L03-^b z`78~#1yQwmBmKGhVZidA_9fr~!&pS+gnDkx0?HhPpBEJ{{(x$l0vAc*g7ambS{hLJ z1)|U-%))&GC4+ph-^<+vzAI{%q4Y$=zYYfi$$TQNQ})2P(_dNuS~0WnNIr#2Ld)X% zUP9p?)1(oC( zk1gyAy()LS%CS4yf7^NJc_EExroWTw4g#Y~CS(SfAAO7{F*sr+$=gI=U zUHFIp^Y>jOo*+SgscrwfH0e5Uj{bQO{8#BMG;7dr^Y`a3weO!7!GABL{trLzzkeL^ z|Ml}&eiU3=pgZ{@Cq~cxjK23S3kG?7bg*kWkn_Hs)ND3fn!a{*< z4kSTQ>3>kxD>!6&cC7_{au`(Q=TX7az-k<`IT)7tGW|*jgV8N_tcBCs=@mTxO%&ln z=fW_FQ2V(;XKaf|#*n*m6&I8-Fc?%s*%qAs=4p{|XJQ7?5FlsbVz??6c`6|j5tL5J z2Kh)sNZ&9%1NsPEwpMZX?aNE)HB3&BV1giU=k3egb+D9HTX})u2KDDlP#%_^4JhJs zUVOATTzU?JZW<7{de79|4q){vsHosztjlIDqS83%`-55~16&y%g2~dWq6Q)a{PI0` zN7I9AE-r?U`sQh1r}f-U%AndL_PM7G=><@3aWKvmy0fN5Y7ZYKR&L7(or?hR7Z~Y) z+__4;E8gSX?99wOZ6Ngc)%84J@UOj#1UhdOqx4;k%^>SDvn51J{64u$J>@t-)n;F2 zUmOYJ7f4)e4Y0c^; zl(HAcs%T*H2p8kHGDe3$rw;wH*^|FtD~yLqFR=Vu&4NPhemjTzIK(eg)I>3ye&z)Y$XpejEbE4Ls7*s}p@RT2{SXK@W0#suGQ)JqO zyEIhXbNjN@=Ta{y836=`*9gIQaUEXj#txx=A7wZ0Sjur22->rBADluZSoAxIt%>=MnwK!IAT=JJ`Axx0Z2iu zU(DgRe35xyM!(%|5O3#-4dc{+K?Qy}Y92sH#gih{LQH#$1Y9_FuJ#yIv=oHEIlVYu z!vLD5$0+3)sQF2dJiX;Hy8nJbEpuT%qtE1hKzgdLo6cFp-_R(DF67OV*hX= zfI{FYK!0v#qi1%-d)O8LHYDBK4J2dD0||&M{Tg~SLck*P@@6yAdv z+}JF0Xnpee@111ZvF$r^h#2l4x}#%$z4#qTR(A5m~m%iT>pcpS9vOWC~*!owY5b-#lXJu zGhbZPZ9PRtPmc@!ybMyWu}ZhX?lA}=D-MSM`Zj?RPE5_8DS{e(eYG2>77n*qy*W!N z)c5-b3knMUeju3fCF?*^{iOQhu-8D2?mC3Z86p~l{|Bt=b=Ye&=w-neaMrlfz?N5^ zg$D_#+LgDBpKNcbOJQA@T;e}&AF_>8 z)?0^v7`PHV1h#9nA7p72pLMRFLkHocLMU+x2MM7I9Tyh_Y1_RBX4Jw2_Q60F0-#Td zq|iU$o&*$GuK|{njbJ>D3(pd*Foh2{doVsQAgtylb^n6j4r$NSVYb|X*7r|+)lMZK zr;m%-+g>7wY*L-KuN_&Y&~+pQr^W!#LPDqVKn@%yvm$3DQ*YQWAf2aHnCnGdK96z@ z4bI2izuyKyzYgiw6`5Zbnredm`YwhYD}LM?eiT+Rf(7(gM|8(pezpGuP@(}I!^b2X zCWDXd=EA@^pTct&IRC<;Azcg@Ipa218}0)tVAySY{-4^IyVg9?kMporNI%EOP#;*w#ag`4i9A4y{Lat}ec{|@kt zfEv~bP8B|VG*_{E)i1HtG7{k&|NDtD-aKaQLw2n6VXNXyu9dN-$sFlsIcb{}^T%dj~9&iWDYal7ivw51CnPGftj`rlxv{Hr9a1-?h!nq}ubusu;_e}L3 zmg8Vn?D(#f)G6SHcID^_q2rJ+aqiD}+R0i!b3k|aPjP@;u2nTiJYv31Ahmv(qt3NM z%eAVoy>gfCN~p#@rjPkH`H1TCc&Jb3fw$^-sh8KW_sQptvmfGNd*%0AeU@xq6T5)W zEObR{dx;(Sx6JB=o*8pTogREl@Ym|S5<$k{1#ECfRsz+k+93AVH~Q5;Q`xvJ4iDqH zT8p2lvki7r&6{h}NFyo*Z22<}hfdJ(EBLT!8;S9$bzz+b_`Nx&x((k^fJ7yjtYCaN zaw`jlX8ejr{XWBbqDR-$)&IJl(naA={qtY0v`n)5UsrmOgsSlvB3R~)TJ3Y7e}j|0 z=X`pr9htJdWnQ%LM+J?`PeYvkF9fN$V+N?csG2Jnn=k>r)t3=we@W2Tx(ZTfQ=qC_ z3z#!MzwIzF3XAUi-3Z0jEmhvkw8!GRrTO$$IN^3^6#KgteQGC0 zLY75EJ(a?YHG4=25C7IkJPkt;MpS6O;hflk55V(J;Jk_A8iwNetPx~4bAyGfS|yf& zXxaluCz{cLFQf&X9FnK+qB}ic;9*+UHY$4b#}M-so=m0=*Uw>*x3Frl`69sdJ>NR5 ze2bQYX{&;iByKzmyHJ9Ed3v9egg(K;KIqqYy;do?EJUFrGz#5$N_$*4mc)i5Vj%=qh|7bU+&XGz;#9yBKz8kDS!1Ls+d@Vg)P|uX=%SiU? ztl7TTKbP>Zi3O!4J?!+Qd%W8h&EGVO>`T`K5-Hq|brGrSPJ6&0+g9qBQ^^Y4zFF?dYSPcd$94&P0tfTpoh+9HB_X& z>udxCWdwGgA;k8*K60=nMtgX6HecwyV)mo_dl9I@R8o0LwEoV+v)h$Ydp4vP#S6_} zp1ycQm2Jr6GFZYdYW`qgj1!4#kdENf+exQrO9-y(y0|u8D&x&h?4sRu;W&G}+$P{8 z=&O*CC4<|nR78>Ee7DqaW@ctF1TWd{4&-Pfua_o6wAh&8K?7wnEOzX4m{>oI>Td#6 zSlUbjTX0{P+ZZ9}N|683p3AN9udAe>plF%2O$Rl9L}3hvmn7S6RGj_i26a=7BxaT6 zja}2(d>*YvDdk~WhT<5D@rO4?hPB8RXMKOJ-(gEueyI{_&#|49gnTGfO`iy<>Xo94EL8PU%uR zBMxCHdp7!US{kPCXM3P8y|@cKuTP*5G5i$S-$EABSQ9*z!7Iknz9#ZaJ)ceJk^gkL zKXzqwsq>>aH8`lJ>{UE>hE`+K(es6JkinjV`Q+_`@frIjx&|8JB6`;e93m>7@0pg2 zjr5CdL@)!6!6k3rIRiVOU(`D@Bs8VuwrIh6u}ymRt1#vUnLl@LB3#5J+1smwkIa!n z3!A8p22QgUY(m(l&ITm2a+&3`cxK=8fEM^p`p0`3RU7R>SJ~J)VFO0R#bp4ipNK(J zcrM{#QOelEE%x{&O2Y%LkfpIP@Mehsjj42iBmsCehwYI84-SAECn$BzfOG-C8o{1o zGSQ2vFWSk+lV6rUTI(4?i4uWDct_89m$_g$<_rj8PG0@LmSc3I z^m#+(-r(OI^&hP{hG6%K()cH5n}!~%yN$}VPT*nw`DrmAim3_3jf;MT%Z%K)un#M= zFW*plaLF07#9&CYLc%bN zdnEf$oNhEwlFcKEm;hSC5a6GFf?QHIJcWi^Qum)seubE*TZk##= zS{Nb?(ijr2fWthV^Ocz1G6xg*7r6*$WO?Ls#Y(kyL=l7zPMt3gBW`+ZTN2UUx^Ccg z5`#I-`tO%r?VrG7lof=mH!40p3%d9E0H~>2T4uqq#2nM|Nsg;b0+dakTEX2SQKMwQ zxwCm`yDsRFABB(q4JjD!>&;Nh6z%|xi8(NWg8?1olOAwg2np4{ZwMc=D~{`64N{{h zv~;uMcw>G2ros1s)7z&3doTF+8{S9XFb4A4+J$3~!P0x~j*G!`@?Zbg(}}tc{|w4k z;Q;e7%idR1><1}Y<~0Qt3?@MDKOY230ltdopS*$sq<~o#z0p-3rJ&MsO|~3h9AO(% zxX)d_Yor(n1_QCIfRo%9D#M%Ry=-dg>dnT507*Q63ar05ih~^5;&E}*y1;m|FVhKM z1~Ys_Ug53y&+Tx@8y1&D)q57 z@H~m>^8a}*(||~ivDs?0T#br~Dk23$vvqST*Y78Lp;4|a0p4j7)KlF0FT?jX4g2?P zX_i^VRm#f9xDTE)8R_W{@81uqhO%pLXgIrx1tQ!?Ywt7!*Qo?O5 zqE!6XgTg*a!U<4rpd1CoWO#$W3uoW+L}UsvouDZIp9O=Ku)}*nTjaY7Fkus`p8RvY zVcR}bmH%uj%wwh2m_#RByOOCI?Rj8|mA+wN27r|~0h5n@S;o>0pJ)Yj-u{SU@tZEP2S5i}( z8JvYYQ*>t@YVVUC#BgUN`XRtN&3}q%o_E9o64?cq;4k~KO+cDHLo;_M?};A(!aY;Y zW9|!_09PqOW^MIC4>^I+x~!A0Z(gyMaFY8Q7k~eLN4s(1XRut;s&vg4bXm@VNMSWr zdHY^f=1!M1Llb2(Z-+2%(xGYkq#Ib5kS?BqPw(Ux0pIbA%*-CZGx@;j0OHV92t{Bn z)jw2hK~6!D25=?}2OHfjT|3+vw*XB+YBBH1Wdy)a^+lkMQBDRNDi6OpW_Il|cAV?H z3px$2ndwA5zjSSvmkWaM)O=3GayL*#l7Y;-h3%6AZ&wXanICt5ub38nO*09;Zw4qB z-j|Vi2qfDraF;X#a7wi0kG7w{+wyN_&`&I%{ov7C;bP;2?Ue;xi4UMQiRAdx9MTjw1e6=en<2H4SxQdqU%FWSs_Qc91dg0% z(=An&5PSzq<->7jMa5TpJ1e>H-HeQlv1PL#EbJ9#URv<&Z$N9g*}8NA(5$VB4mdw< zg8eC=exTsxqzooYe4r?n%dH9Z!PC-VwMUQGWU+&VX#+zYU>UlpEe5+k0RRQcPLa!6 z9;0=sl!iCK6-Blink>{-E8U-vVDF80<1!fu&&ab-@odN>dJlFt$bn4n2u?PVR-8xv z9I!Qnk7yX>VF4L3#{x8!Fic16FDoN+J)#v36dJ1pVYh=x}=UG*si1(^BzIft!-jq7Qq2TJZlbHo0`m2e}i`C z>A8clg-3ahpN((Nw#N269ZVfMxBU~?6vm*y>KQHj;K7d00OWXenejdIn~Sp@8y$$4 z4Rb6UseD7f9R(lk#SN9(B;06t3l=w5cC@QK-R=y)>Mmk{#b#sgIpGavAuqBYz-nZ* z5)cy`m*D}xM(Lqdp2^}%bQzM_#$1#PRlJ2aTAl0W+wWh9rGB=)9`%er4=w!JE1EEl!a8trqnbv zJlZ=@Epct_Oths2N~T+yN3VpIJHI1=WLvPbnr7_x)f1*M7~+qz5_eTUgVR@T)pi{7 zJXoZvK)yL}RQq87Xhvp$RANEM50z=@>+5Tpv^9ka@4l>T*DP!$ZR@i76+6Yao3beS z55UTdec96xC3SW6!uCEuE}fQ&2Qpxihbu--O-;=m_6`do3uRa$ztzwM)%{xfVKDdp z4EQt~ADlcMb_lKQ}np={^mTB>>j%57x%*Ggi^tsPzgqH%CQ-lzAJ2)M~}3Fk3YUTjF6l{ zTsN8mjF+h@BZLdJXxgboX5Of@!i{fTn{Irky91Fgl$jTF!g6O#THiilj)e@3KT}kn z*VXTHW>HbEgXc#iuGRxeb21tl3&`LU)YPIn3ZT#98~*fYJ*LK0WaUGwJLm=OUvmMY zaaS+#nTxGuKVglg9Rmx{tmqILOwZ8S5zydb*jy|C71%gXE>clansm#_ZkxdMkqJ~iIOnt|&_KPG zKvfxVdM8Ugt!LhC8Cn)vu#1wNY);%T?PQcSFEY;Bzhahc*HiKsiv=J8nSGb_aFo&t zB06M7?m*1x`>ev6updIrcRyki9M^Y8j(!@3?Rp-8=VK)E($D<5h26*PFFdk`pjW&f z|8W#RR)ts|$%xHdqlE>)bO1ffJpb5=K-JDVil7a4;AsBI zhj?*i!C|~65hO&scOal`98^r2ghy)Dcvr!a+ya5SxuPCLf-kff2|t;JV z*JW6TTXEl3?8{U&H9tdh7g>OkTO9^5H^J^@|C%;zY&36y)9;~yL6XVZGMosq-4yzs zlf}o}$gUhbwRyNJTO*6+Fs`UDw%VM~pOwj1W<$LFgDLEf?#Rgd*PQI^YnrgfMLb2v zq4tnfhE+ANQ5#R-!t*DHBWJau0|?E`ve05$Ekmko#;d(sPML#BuV?~5^~rDyamzv& z)GtJX=g17&R;-!1mw)hV--LbnJDezY1Sak7M&NGxKT23S|k_hsL3U9ApO60+ia9*Z67DveUSuE<0S2+Vt z9@LQOF9LB6+x;onx@t=u4*y5cDCvHFh73X=gWHk-aMO%TOawG{0>{4tYghq{A9|C+ zL^I$%T0rs{kplGLQRMh=Z^;J9?0Fz2lIUG(tef5RKE$z1hgE@pe?PTZ-)BQWoByqY z9q47tc84L}n@lx_4jl%gP*HR&Eq@(7pB8T)G;Ss0zS#)^`G+}bi@Ar7!?lH084*g$1cgmwKca!J4~z=Hdnl=RIZna?o9+`{6pAFLrCKth`VaT=|!(ClGrAq{?;3C~^O)U${E zeOe=hmd9XJwy!%katOlb7RAo*Jh(<={?&7NIkfn8a)gj(>k!I1GofrW=tk zkA7JoT81*u!+BSj1JJzGe!0m#eimqA0x7r`0-O_2M&^LQIsiuqAN2C7)|)xnu-X>= zeadL(f*IxF?QP{Ebp0itzII4TNxe+j+tLTi&FMm}ovfwbr$&GOgoaaxlf8w7MKNgE z$|?h@@J`Jnukkm+wF4$T`v(=#2f5NC*9RAEZ+UtX%-g(ABH;RRCt?!<1r~bv8)fG( zn6FKcJwb=u3zX)xz~2iF;yAVH^mLgD=cTi*$AD}&&3D(YK($iA0ap(uKQM&0HJAWS zA98z>dSq=~wz&7N591_OnaZt+^}<7!IzU)!gZWwDWLNg;&YQe;en)pJ#RRWdqTZm*}-Xu zX5tZ8n>YW}=nHXAH38mjiHXb1UZpI5x>?6S&- z3*j?#aDSjDw*}~uIdomi;TgTtMQ;w|-2|@3cl}QB#ZH6*3gNXgnuglf0)svJA~&M9 zSbPYlL~~P9I5Uc?;FMO_O-t1b!$-gQ@F53y%vX>oJj{};cF^(1pu>pzsT!PbPeFE8 zDgqjeRz*t?qm9A8wgGruvc{01>J1NMYnw2~0`?aN{Wa>e9ggmBnP z<{{R8;8^6%7rOkQ)v2j&aZqcIc9%ppv~JBvhe<04>6pITkqW7yGuL)Qkf` zRx%0-6IxxX+=yb-PN zp=0SIO?yc zzB8JkMaIW3&JRHP(v=SL6b_-xJuvpGqONWn^0|f>J^%SxkiW_ARUw!q=ooJW7{Lln zm*Iv?+P($OK`d0`uI=H&;^#!3GRLBhv#BK|C4q$y_l7D!1&mtO7^X48mxE%0g3dw` zvp^N5uBJw17%~V^p{cd?W<)FCu=9nBP&1i>*oi#kf?t=c#;Zp+ZsTE+?rI|;$CIxP z8;Yv_fYjTQX~ot1>&zn4Q&W`@g%PcAObN@13&gY-K>;5Dae)CE|Dt4-M8lg~pzh5F zvCgVjL;uX!V=TJUEYBJF4$Roc!t4jL4 zPaw(hZWI8(Mzry110`D9Fdhw`#|N%=yFejQ*v*$?pzcIA#83sfehAoh*6HsQLU%BcR}v3#5S!Ud=i`fLUv4XpwUb?*S&$Kb0j zWEHkYv>Eq6Aj<{~Yfwr|@8Tf@WX)og)4Cg!P&Mp`lVSg8qrDq@qO5 Iy~n=)3tup4EdT%j delta 149026 zcmZU)WmHvNv_E`MB&0(^Qd%kL1`m=_B1$OTNJ%#v0qI7%K>_KKZlt9|x(*%E-Tc>i z?!Dvv@Q%R;1_x#Bz1E!bSL-0-;a=*)GEq6m&6D`#=1z~tgQ200ld#=u0rk1IJtm#+ zLyXRxk2{O+tc%}!xK^Kjuq=MxQhjpv^KS!*YEcl$_iEo+41apMga94}fB)GOtBR?# ziyJPHj2rOXHdkdsZ*zv|Jf677QJu4CrN~kG+4{+;h407xL_K5j(Gtxi@}p6ws5%HD z%uHl+;l-qmbyGWYSj!1lh+m8`$*?x@;W^1LqY&^q-d&b=f8$(D%&jq^k-~8>l6h0U z5?jQ`{Gkp-=;$+TKfdvvOWrssf~(X!QQn6QjtE<**$Hi$+>DstiJib&XtKey9F4;? zAQQ(vrqUCFqaB<%HvZmc-SSA7kIUi4x_|jWs{MM#iVDTrkAmLBy z?T!BUW1$my1L9A=zLGKu%=4!Qh1JQlbi0)Abu+cBuG>^yP*^XsmcpwLoZx_k8#hK) ziSv#<`ZlYGB`e~hc&S;J2D7QnQO;({^71A+SH2i?m?$?6q0oOTSN8I1G9CQg*Y~PO zuZ0?>&-=K^n24_JorcbrS!h-p_KJ8fLC>4~jYJGXw3;6+#=R8;BL>~4o;C&Z2%!<* z)tZIC_4zBxSW}(0>b-?iJyH9kI6?2%9#m>My#48B5=BULGJWvlXD;7bX7rfl@LF+SV%Lr?c2cM&~hBV-S` zU*25*P~~o8=6oo50^(~4N_6ftR2Q#3a!|S&r_jyLTJqGA>RO?9jWwe6J?oqh6Zm|# zV8>b%|BGd?E2ZkI@%4s>K*a=S^J!#I7;6aK>^5y@e!xa)r&D@XE>xqND-Zl~h$#4Qwd~Z)0({VNb@{nSb8v zY>s6h#vHGg7raP}6+}4CpFe+mxpPMHduy96kGq#STOze4Tq#{r#&7RKUQ6D!yzX=s z+|2r}@F%d=dE<^vBRz{hwhTRerwq9bnp))1T-MsgidFQk6P{Hl?7LVACZTKe^z3{E zN6>+^w+$}IKpGCryliTJBrk=I+h%XRN$cYJ;wbs}hp?+igB^)9*T&vM@RW3dpuG7$ zxLelDXdRc(VyK4BzpJ~w{5Qq7!kpnr7=!(Vt^cc7IxVm9T)mTKwN=u@!Wgp5x~V;| zBPDJHNjPk)t*CEW}{^J(q7HuIO5>|1CpW+hqHyL-1HaeF&2JX>a*r9m;|v08JH zSeQA!oL*jjwmHSr!+q$nUJgw>TJKkVZXnrR)4sU8{sFu-J7u>uEm8ujZO2%+)`;kKVOe5a>WzWp z;`buYT643>8|fIU8pW47*5RhA1Gi;%^5Zcv*$Kuwn!QiA=k>5*8wnYKbqUJh)7te* z1^s4~8%uXKbHRsLm$J}@e*|rXuSy-)5OF3#|9i?lQR1Q_589?e3`3DtWk6H&VgLp2 zrhs-lr`}-Szwu%$c^i14kre*H;EZj{0O4~E-A1y~rqlQ6>l~SKMb`^=H#9bls=Z?1 z5%wb?DW)^j5uyK@IzD(jsV`HZrQIs&?wHy6=1}tP%=J@UuR~G;%R^jBAzYH0+13g4 zv-YX*iiiT?#tOEa`pfLy0o_0CtFS#8{$lR2zJm9zp21qM{=1cFo8yn#>dB(eDc%d%qG6aY9G;-$ZHW}>J``l zZH(k_Sxr8NwbnVTmqB;8nOEl4*53nzP_s~IJP}&#XR{7NPDy5~n|T?gt3NF#w)i>e zo=;Ct%f+VB25|)Is5~55ewx~V?{YVUwSCX%ig|CjDIp!RP{pq;$iRkbo?n)AFhh*s zRFq!YG+s8araJfi*}>oK+L5sNI1}>Q?D5@`I41UJy4~Y6I10=%`JfJMikJ(s zCS>f2ufsoFLjOkh6p%Q8kAts5WF(ArlpjYe7` z3EzEqd;MrI-hw`5n7qmT#BjBs;F;3pTie^8@%m3Nd}-@*o11kj_ZBM6h78Yk=U`u@ z>aj@JhQH|*YnJJwu#sTF^ubAj@=I`UQTRcpl{(+Yq{2aanQ$`Zi34MqG6;LI{+TwY zgchBXz-dj_gD8Ifl<4#_TtYP9rJfZ|UCaFazzX||q}^qNl6-io-^AjlGOQ+Lq9SRZ zlMHLq)m4pw$Z1huI6f-d2Uj7Fruj5SB~JI%l`{Kx-}#eU$_eL?APKUq)#ms+F=08r z5J=;091+i<^Y~paqaJd0fU1OjJ!|&9xy-x*tAP6tfy1J8vD8`e9kI2wrRg?XYghj} z7y~y1G|_BUaJ0{*iBS`A=zgOSF!{A6ZSW8A=S+@Z7hqB;5sOtX4Q3d2wOgXVVpiJq zsOu;mnJG)+L#UlF)RmZ+&(|m~C;N}IQA^nzx^CNy>}6wJ{ffwH1~e$N;-B<$GYmF% z!97;hOt5I$W-=ECGC0q_XUWU=&fB)GTL~Shszg0H6334*Y@V*SzJUZP-PoEvmAk7e zepO@In=Mz_E?a)+zV+NYN^I4-s-F0r(Xn>3H&kP94gI~i*eYGVZtzrz0u664n?+b^ zzy9tys!I6{M++oC4B8PYf!5Z)e|)js7M7MB0XWo2IgAR1YHJ(QmZc`ED&iLm8w=^+ za0=2#dB)Sab8q>Pz+6}3H0q0nnAMkO2uJ)V8&Tno4}@HU69F=ry`8Ki6S8{-%lux4 z$q8LAIZvkePUQa^5OVDw`fAQE+q6j4SS{m4)mU)LfiD&uaEKK*32i)w>A%SE45JqR z7D`gy5!Oo-oof65=Gkl{@ZPZz!A*wU)7N+J1tAV99Dj~nKBh5ysoeyeucDTxYPqx$ z5_*-*KaJ1#7ulxXTUb1epcEo>q(PPjw0Pi0bL|sH zddb8-+At}9N_SgIDkW8LfC|66v0zEMMBay9v_vJQ-ziz5VP!Q{`ZK#;^$~ecA1^OI z|Hl}sToEcuFl*8!U%tGr&5>Ywb8v`V{^q*L{7_{nq>2 zC&;<(cC=uDH4ntyO;-0mo9KkHDmQRUL;Z=c@ zjZ%uysqvq$GTg|SDzl%|K+p_yaN8$=HmCCDqcE&>#4wP}C6YN!zBgU&eG~n=#}s1G zBO@V*xmqt$R?cJ*)@KeFfvlVu5e7>Zxj~_qs{;O#ay#`%sW4EWG$l}-r zyyK9N5EyK8b2E{TwHlHoWz%e33AnvlxXUgsX3swb(;#Ty?Q{w#6}|?)ZF*IT{BBbP3h8rr}@3SOk8R_it4toniX4AVq#BUG5h6w&ptup57kfmuVVa%Tv(JK7-+1jU}=f$VU z6l?1B53a6GlB@hqy%~IN*d0Ly|7mM!IeL6fASG(}oNRymgRQ|#wRIwwkxbr6u1ZG4 zK#G77*d)Lk2vVPnJVMUFcADdGrC$uAq-;=U9g*HLcmh;dh{a}X2rY`a?R#if zj7>5N>Pe*0TO=!+9*lou(CO;y*%x%R{8->b4w#kiFuGPepN`iP`IrRJj-}2}KIq>E z6%r2ZAf1MC|5jaHGT!8P zYYfZl@;ErzlVXIAot=Fo{T>{j3tM`h(3?OSg()XLvCT-vFEgcX&snI^}JPC*IgqeiSliK1~op2nSZKqdPp@JFc?L!n%7k(Xk z8j1+Txf+XZQmfj2GsOokv?7Ul#VcG>2|uiBt;N^pn!P8OdIxkmgLO)vABI-g6?|nGF)XK21yUS3z7~AIB`N8r?3j_^Z zj>l?Mnzns>NBDJjwvNZV_hF0$ErzwcvU1n{Vh5MQn&R(dJ}i2fFxqvSu$PZP6Fdj4 zQ0g~b5Z$i#62xV`A_#+C80p}oH|(kZozOm>Dakz)Wp&v?gu@95YcX`w5*8+{QqgQb zt}s4+>xKqP^w|6w6j}PHvFg`2=W(nBz>H=l{}I&Lmnn=X% zx_>bdl{SG6d#&Y6GIV`z%KO*3S+VK(7nk~A&!#vbPH>vk{ubO%1J%|T36%}H=Wk#> zzx+`ZlLR@kmo0l!EmHk7WU1z{2ippzW2lkS(KiU2KSgPDL#JUbVo3Z78>Y{l^yy+X z!4evGVW?dSVbye7?h1$hbP`Vc)}es^byPu_A0|1ECN40bAYuV})3%qMPmgiT7HK->A_2uIO~ zejO4133X2(Z!mMP7)4w2ISHnzgH-oK3{*}Z@PtuoWNr@Hz+jN4p<1ju=2c%yJIcxy zc72LOUXprM5%YjfvBo|FHI4QrJBo#mlA~^gu=?3*>*xD$nvC3BCb3)qCPu7jsdRm5 zg^`!T8o99GKtJzwaHbfO)j97!k>O1?V6(mo)mk___r6Tc z9}v=XHA|T%h(9@@W;S^LF*zf7S`8p9R+Xe-uN)VY7sS$G6nvDhVROs&B!|`dunt)* z0e=3=ALL|P>k7o1%sybLoR29e_$Cx)%M9+ba=x#U8*Qfl+@`PJJH{aY@O|!QM^{Wb z`+R~UCJu}*LE^~KE(Mtl&xws#lkwV9df!Y7W0P^A`7C|AJTGop`uu(L;JjF4G5rD2 zC;gNPE3T0k{~2oU`VZ19GJo1;hF=f$Sy=q2j~GkO_6yXkE7P}<>K^y=Ri)Rw=%?>? zBB$qj_W`~1vBhlKtvC`75+SjCy@Sw?*ZCzSGOE#Z=wH**p$Lo7T#ISw8^VN<-+G$( z?T0^$j6prE>z-u%R+_|SlE|S2&_e*%G!d;Kg&%FJ)$8>8wWyJ!s*WPGj5a9>_O{EO zu?4828sTwX)@swNoW@BGu98#n68g{6U7e`LuNP>_7I!pePmEJ9F3TnwZR_vsj8|7Z zTFApawUVI=-7)Xkqop+K>o-uT#g!}G?R@-@*S+CZAxt$jwYpx|y>o#L-M$68SPcsG zQ>Rbo))iq*+Nib$ZDg=Ff$qK<5>#g%ea+QQ-2DHv#h%%BTXRFdJjL#|&eOXiPX%2= zBPl!nu&%oe=#hB-e$>3SXCx@#DPts^4NuBLaKh} z+rNLuD@0Dx-0=SBWkz#5*?Dbl@31BP?X@T0jAXFE6pS(<*P|B7?I+VE#DTsVhl5$4 zJ3{YXlbwAp5RbJm&fdX6_4VuF;?;*7E!Xx%It?VSGP~ujOv=WAfq^-WI!d^stV{%D zu!gSNgO`E0;iqqdoU5v;z{+TYaFOdfB~xNL-V&jm9>t&t{viC_#rRc|i0_19ISVOz zjM~J(+mJep33($~X=6pGNxtI!^Deo`G^T1}nk+1c6UP5AiIwtoCc$I2Q?{q5KD_sshY7*Mv^3tU~_AcE;ynvbzl zMgKGl`~<|rbSCVkkH*SNwcIF~t%v8xh0S4Lf$$iHRN)T+(H%d-9p&WY^i|sjoN*`{ zcLOmc1zhm)QQ^NHhlhtJ%kJHq$DRLzMf!0b`bHp4z+*P}OP0n9RX(Q7s2f+?VSo?i zXB-12ZeY%g2vmnD$te>_J!g;t8WryR!E(2(`z!yhjRTRF_)=$qC1DtJGUk>?g02H= zsB>L*f|OQTX727Q$1W&{{v5H(586~CgwVQMi7u@cMrx4+bi;D{~9@DTjBPl;2hqbc9c=ivz zA7}pl{dVGrkuB@6_M7}`LPAbsqv*8S6W+}4-x;UXz==W2!q9E!%*IH>4pGkQ>2(UR z3DzX7QHneI%J6w6mkrNeKHWreR|#J*NlO>=E&I{4D*J}Jrhc8E+InUt$JrH!+0)h2 z8D1!s-FhOjW6_P!k{iAB$KSE4zc-DyHt*j*M%6lXz__7wo0^zHRQFQV{2*vFjm7Lh zJJs0A&j?f=1Pu5eb(4tgd?R&EhOVY2Mrh|f zFsj8oPFrniDg%>r3~FX9E(GDIaG*`*h&_;enX1fethLM4f{qF|zYl<)R2bq@RYk?r zw+t!2XG1WU#gFJvL~%h0TzX6z*4mThXTW`$x~|ZH=0@4bteiGxg8eRVq67}1mTv22 zGsmC-tHPg2F6NuUF$ZD?ktP8(a~Mh8kL~qW;A|rHshD*I8Ujkq&zT)6UK!9RmmX-v z%jW`p09kOfhW>IE`|d)nqi>uj^27oop^m;~k0{amFl_Qx+5N3$#JikiplPKJ?#C?E zX@5lJyg>!0)f}#)15sVsjKzSyl=bZ!Dr`7Ug$A2~U-5`5TRxu~2Z;Zmtp0_7b|yxA ziOYbIE#~N=g3l$%lgDhP^y-f_rFc8baM;-U{S)~}{+*yFesugc3}nL-mD@Yo7j8OT za*F#K+<~z|8cJm-umEaeX(jNo*#Rc|Xld!Rnd0I;2&IYCbhCG-@moI*+g-?}5^+X7 zfyMH&VEP7`wb#+mVq}{E0|(TnRke(|vhoOLMRiF@L>vzfH}XlDl|3jM8^6I-Xxpr0 zoK1ycnMK)e61dLuZSr`I8MQce*QU&o$_2hu?cecncQMh?7{Ep z89hfP{EtbhIgiJwmHIqXZY0~f^B=l%4N&L$-@ofk@BoF$Z?Y~pwXTL7$G7{};DA0t z5&O5)XC8tOOi5C-8n~e1ETyE6$Ghy?d!xKwQdtt)o+% z5^IOl8br+oQ>hD6N|@f?Cq}o;?2ZM13+y8(6|ogMjP}rUx9@WJbNC-#-e+ZYSwf8Y znwvxWeHCG`X0K3?{*;`8d_+%e^fy004Wr*o;=Tfo=$xgt=Cr|p^ae#)SXjX6&pxSJ zy1E7Qb2KQ{*kvo(*l+~kQayyRu(G}cpSN1?I5+5yf@u+CVXTEKa=;BvF4q5SWF;s( zdEr;QfD4mvM%(SDmlpZXH9=wKpYXn#!F)Ui+dinno2YEPER?k7KzqE3qfJz7WUj?gNIx1cTULMnr1&iF&F!onQe+S=HFDX*-Qtfs!Dn*a4nYHm#i zU+gn;y6ZiTgqXm^nwFbl8|$DR!U!u0usIOkf4lPa-_;RJ=Bj0+GJ{&@iVA zTFi!TUb=gTv;mFkL>UIl<1x@MCG;FiGbR zc%k%(qDJZ1;G>u-j7M)q3Xai(it^jjng|s|@{0cjx|*t9AwlEQA3p$G@Xx$mzErpL zI9a70qHq!O*4>2){2&2#b{wWyf)`gQZ>Itu)X$s^{^3?ZwQaf9ei1K{JXGyT{^ zeD(huE3kyo;QIF_n?~x+5jM+mG5*uj?_n=Y{a46g2rDg0Io@xFxbtWdd@nm@+9(2~ z{9<{LhPNTPZo0vEkyi4lb-Ctnsa{K&qZ1Dg&q$4}9t~bXhtYJE#deL&`d}K$-8d8t zO!^JK$He%UR5oy;7RgR*tGv{t1yWg~~D`BahM{OwGku-b94KMzG6bDlYu{r^|-hcKP zS8H9RnP#2s-RJsnwaz(z>Ap5rc|s2Dw~o+}eMKB&n%HogJRx0(0MpHRT50b$;rt-2~wI5%O#s{{r#a?XU_;jgmN~w zy0l!)`AkpkoCYyQFa~Lw9dC?yr+I%C7gW6%iS#Dk{c1=8$Jc*OcCVmcr1JrM`1rF{ z*}joT+Mv(EOTqm!8ZstI#Qf#Dpzpjan$COu2ZeLJ=3U?>tnm>jJ z02S<3q!n~fY*I-0ZuEjZAS`V>dh)k9aC56q}D5HG+wUrVut!I0EJ@RRl*?Tc!yw50{|_ngEO?xf@8c1XMJytM<~AnYW!ies$+M z&|KG%N^?D1$U-XcWSP+MwuSocj9v@Id}wMV-y4F)N3)4;LSh2G8u+LTi38?Nf{0-_ zR%nX_u0nQOo9zCd@aNzi6zzAC>XjXak@K`i$h`YbPDe2Ikv}CGIO2VMeI=|eZB-Vd z`@k~2_c}tJ7FYL0@Dd|{p)7CfxR`Sx9OOpK}hB_v-H?9vuz3 zdUo8v`y`=^)1#nS^hv3Zr_y4;UMPg73AtXK{==)(YDp=_B7LS==c1c#v%LTJ?^vb1 zy6I5!!J7=zEdut{m2{GTI*LX)&R0s*qt%k?P1G2N}U?I&5zk-Q6wua1k>3De5 zLVrAvF4b*nzs338)6*J6Ko5gGdh|#T0B2Hm;;z6Z!Zj76AVh#^0YW;uq9 zsaiO*q-BTc=jfSfMhc0j&iU1&nPBm@57(nyHNH>)!}2HJ`P@ zVpZa#3o9f1IS}vr%%i$7TkDRd>eHs8Yu}5X1+7SZLcC|OuF6AgH-W- z1_$N>@@~XGTejAGVXOSF-IlVbp>XqjV(RfhG(E-mipD2S?>gi1c$;?Do!j>F;E+aV z<^pBlI!nW>a2?tR!l>o0TKu0~3Kgz$c3Y&MM!ZQU;;P48cAqK0`6GY{7)m2=AV>fp z1G?Zb;g`gnWvfrRBB(+EW`w<8d$Zn^-;6e!KZ(WVlPnHN0!s zHu++M>3hFDe{hK8y6S^jss^GnytQCPlMA5t!?;P{}C{j#G;%CZG`b!)qtcQl!2! zl#}N0yP#kFW!9IJyz~qKWIXG+>&@IJO7QA1dwYA|E6Nka#Kszw``?Q2nDh~=Gl7c5 zhy)N)9hU)xp7W@^Cfo%w*6Io-VxF`FYKzMPFj>`$S}jYPNXA&+iw^{uR$qcI#K^7^WohHy+?y@^@7DHJFIeW~d8MkAW7A(_BD zl2M%lXk~bJdjo?Nj)jyPbqCC9xVyW*{psR`RFUnWnKnOk9`=QfK)jqEKYoOgJapcL zGr{p6{uk+ZLHXI*I(-%oP%W^}B6R)Sx~_{4M9&d^i6Sl>4NNX|+DNB=AK{0WT~fmF z%wc(m{Qp}s%XCLyER)KK8jt5-nn-tFwB!EtY((!j>Rm;v6&umm*s934&>B_aqJE7B z7&Mv}|4LhSE5vEOKQ3`pQ~{YqZRaVI+ST|E`wa^>xR&EZZ83}rlecpXF36}X zT=S4OOEP#IcoOKG&>|^HU!W8!?ZbOKa2P7e^Pqb?vG8qunVO5~b?bXP^b>=lx^sIu z;Tv3f)><^$Vhcu?KsDD%imc9tb<#d3n(h8GgG#wV)%XRdg7?e-9U9P(_$ZcP#x>fa^^ouD2b9UTn6#6!Ak z#&9wS43yfv=Bw9gYD6P-uTjA`DtBX)!?*M47zrs2dGz@<>^B=W8L#HVfopI;Gnlc$ z@gWTIWi>4QtucN}z zlOz>z3uN~1wQcUdA?Ad;R=OS-k|^B`LC`G`XY)DT3ND=bA!o}q_q_W*c34RD8EAK; zJj3KoVB`AAYU9z^ndm1L0D^OJ36EmTKNX%u4Cm4K~!A zBT*R{rT;Xa6*!P?kg-pXnwuYMw>~9koF4f-m;b(9t_-be6oFI8iL6-2kwd%IXEaac zJ`4?;%m<7bkP`*qtLG1HwB*Ia_N&E^0OC$e#QeD^2RHZR7grb)l zIRpxgO!5$+Hiw2(7aay2KS-H?hbhj0y803Z>lM!q22+LZ!{k&HUcUF|_)9taCM!Kv z9|!iVP^`bKqEfOUdMB(a08L#P^Jtb&z@y5SU419E4d^xapUVMzQpaU>uMrW{1J5EE zv4kU?o0asTSQ6jAy~3n_za5LSY(iREx^@+c|L|H4pm^zF?QnY56_UOUy5zB z&||foYV60n7()_wqOq2f4KAe#Mmn}~KyzK9Kx!6WC#!px5ouz!%_T-4Wt^4OObvSu zWJI0rB@0W3ii)RQIic;}vkQ{r0O{h}X~-&%j_Yt5JR-yyf4)i$_AY36ag`i8N0n;> z3@PecIa?I?DSk!RFUADz_P(}_{g@m-T4DPIud_w4o`XLt)N1KH>(VXNt|OFGd;(64gpi-@emrjegk)^J%`hG}<4|>MI9QgKiz1{m34-h+3H1<>x#| z;!QOJp{g}jrvIj8BG`Y=zTp7mujVJ$j%T%#6d!xf<=aMO2tYGyd|87tm%?T9kG#H$ z*s7^&b@nwmIMfz+Pd^mz+UH6LK#TMfU}PL~m`2<{SN4J770kYX4s_`Aw)fr`@UZju zRmkAmDd^}>jhE;EQhI(5Xx3!Hjw~g$OAi5JMMv{{vLYTD8d`3z@$7*WYSrxD;GXkrOd18?U~gYV;xOr0Adi1;lN@6hlCf(g5lKM9Geeqjli+9UdM6 z9Yi8#|BnwyB^ZJ!e7=zri2&(Ps8H|bp>6X;?rz(oWBt(oJzK<+hf|QI+u*9khPOlR z$7>Y*_s-F+o5ve3PL46=kK(9xS{rpA%kJ9~;{32Mc*R>A+ol!6;ttrmZ3LA_Kp?ey zj{@gD2w@;a=kZd#$c4MxGax}>6buCfpv}7fE2gT}c=yg=G#65utg;|N7H}Yq_hjCE zt@u?Az6X9zWL@`UBPii&v4^eJOTU7jh`jwXI{Gs;zJv@L?c29+t~a?4BmrAMJ}oHc zR0)V0=s8#$UH~wG;_#;-BqHK6?!m_<=Vq8z+l-zCvmNj~+QD?O(25G~#>=h3TF^P1 zsGwM&S?0Z6Lr){nf`Z{GN>XF$|= z64#$z-vCLFoX1oj#8t?+jWNI@K~F;HfHMNZTKgdAY5)Q*uIp(o*B`}@A$X7_Q?0e- zF-2;HtbqkBmS75x{|qNVD$ydYDAIv7z%=o(-v&1Gun7e+pC-jyuE};jVC(HSW77cq z6J$C~v(dG@#NG{gE#UwMj0P$Haz1|Uo4H?7Sh+(`a`9Cnvr4-0s@%tYVIlO=k>N_J zMw4|;*lRi<*;a+;7nBy7t$zLcHR{T)P8(!%QDB<&pH1oE_|Y$QWP~lp*^t{Qr>N*p zi_z2H-!ZBpf#cWa11Lonc+vUZYH!}`_v_sfs^uyogIm~W_W_2Kl)Q&juN@ywKt)>1 z0&g^kJ;oVX_Yi5KZuW9s-@f_Zdf>BzE%b}Hhu-Mq>b7Hu%*lk9(ql*f;pA(4{L)Hq ztnS%Ef9EhVu6zCQ?DsRZ1Ml|kZc9rFl{Z8pX_r#coqj6f&-p-imQ?TGf6b4%2rZE@ z%$7dmYI%AQ5Em%(iCo=fY>2e_l!8NJM-3y&qeC(#NRS|b@h!yyn)@-R#?>6 zuax7dAXg{UQxWF$ZyW^%w)YbEs#u*hG#%7zO0H4ibR`H@L@TgN#g+pj$9@F^MhiH_ z%Ie76le%P1$>=zMkM&!M8(j8qC*hK+ji`&^Z2jl!5_iD>4np9PM>dE(i<>;YJ;-rFF9i1j^CefYKm&A#+JroA(mMMchew$?Bkd6l#esaz&pC3 zk0LipWXC!XK1KlQ(#|#&l8U%c0(spib&GdQ^4{D{k-A4yT_;X2~ z?WAwHxl4f(*H1id5?ZdMC*(eaEHOes=ifba$v^tXCuPq}PkYKVof`t=bUx_Py!?}0 zy;2Gbe=Qmcu_hla&r4Fxor~4%30UupP)!Le@zH z!eS`%qFP88IzwN*w=-J@SP0w??P>4e-}{}oiDyaMuj9RT_JrMKHdZKI-ZaDto!1_i z*d|K|$DE0mdY@a1D@RR7B9>d-!`E|Y;4d)O5mdruEoZLjcpti}H)`c&y6rgCH7b?J zlakVT5bIHgP~gX2)5>ydd8qek)aoguHh%(~D!hWZ1ukG~r0L!v6>=D_UYE!ABo{2i zXv%QYQ@TGd!#f1rgD6A&01S^da6yI3d3(}_@*9wp^NWj5>Nr_hv9z?bPzDlQ6d(HH z$He~3&ku^rQ^}xqq{(KX1H}H>WyAHAz~k^IrGI;SdR};WL0%J&gaD}jO_y=Dw?GrM z@^y6c@3!EB(JqrU(6^$N51zXHr^SY`!OK^PYocYb{|#AP!pczrq15-??TI^(_C_umh_b{p1` zE%$#Bsh|qDu|(vq;5RF{-2lU4J>ajbscA0LIxVGSQcXy&@x$|aEt9q1Nv=l&0w^rF z^B-)R_%fZ<3*R{e=8u>gE0pBGdArftMa~uptu12o2ff(l8g^!CAUveH0MkF+o;u&I z9HI@O!$$KPvUI%lb~;)cIG-BwCMmBwm6B5ZmF6-Z43e<;u>CFyTm6x2>3Y#3RgD!O zFGIh+`)NcfjsV(A<8`c>cg4!eYIn6$^AaCbF;%d9)Lo2Sfp-yvM;h-2pPjm2;4A=_ zbHQ`D?kV+S$O63-pN0^JS-B<>F-8JBPad8!79KrbJ~xKPNKMHC%s$8lW>r^{gpzWa_V3Lgk@zyr9T|`VKprxX7Q@`erVHTsgQZZ;B_L{_BR~Ah z_ks5*Qt|k4n1a0S0WN=`&IOKr42S9+1hk9PSqTXVeeIgOo<}_3xMM&6QN8D z`Gz^_xF8MRAVyt}9X@sM+l%#vukwp&=XzczaW+RG%(+S_0jfDNi{NTNet!Nidq@KC zfatt~70_+36i1$Y%-sOsHOVAocdRgh$;f8DksMYABHb;jO#i(VXt5IKpQ`&BFHGO6 z4K<96kBM%%pkEJV)s#DCUm#Q2>{GPxTyPF!I%B`P)V}(=cjfjnGBQ0kleMTa^73<> zSHd7_mC50Dx^A_c!`{)P9&JKCBz8u5y0;JsCOrahl)b8wQj<1$dHLGyivH)08?V8j z7n}TPN&4*BvnVJbVZuTJaGSli0S4ecgSmwTrunWf?Q>aImR_rK2B*DJiI8bL)Mq0(EE-mX!%RM(H9m_$Tf*2m2(Qi=+;Wspilfyt|5p`HB8xgZ0?7e?#_ z{r<#pk7BOB4271juU`Z-L8`y2U0W{UoPUkq$>eYqZK!I+AAs^S0vdiIP=&og#y+q} z4Gbtf&~38w^D#gK{OBJ-r`fY^p&5A*YB&+i@3W9QNO^$7&Haq$KA_Ah4}q@hXM@LL zPtK9hui(u527t)2qXlsPl~Vm%;^S~CYyhQyforgsFrZ84EjW}cISTVd6C;M2MmNLy z4pfwszR+UK-GcWwVlD;n3sTOHmHXnp`v4w9j=r}!nx`eCNiJzm_EDB0+cizutd*4# z;0CQN!!Je^P2pKZow_dot|7qaotu60Qe?4_R z_v4MAa4eYmZua>6A0AHJp|5GOUODzz65huc13CNF)l8FCO0esHMril;5K78QIE8>o zyW@T|wgf4Uy!w1n4sDo^4;_=pOQ&6%sfq`nFkR0V13PX9DFNn$*%lvaO2;s4FSOA3 zfc%}->(}^>G$f(lXZm^a6BU_&bDcSjyaiC?iD>=oGYz`t`wGQhVnBi_qU;I57Jrqr ztM?PMmRaW+>+O?<1|@(yH~T)`!e)uRnN>@3_C+OGQNFDVY~!TjfI2b zw2qUDi=fhU;Nizz+#vi1t8^~{%sk1ViYw^T%y3rvaEUl9X!q=igKeNKeI2yIP~tE( z#7_-LL|%`mMKZQ1twfZ%7kAjIgiIT8~uTH%BXR zLsh(c&z^CVY1GxZ?FLyn$%Ezy zjJuA-7jAAsmnZ)wvLcentRP@UKM8LWabZ?Y4FVy(w?wk5Kh*MIt|FXQ-HQrEqvGivY4(G2&Unwgz*pn0RfK!g#y9s-@9TjG}B zLye6z9FF3BQmALKG+UgXuOG2K7*o|r2!=+{w!#NNeD`EO0f1A%WJi%L(_$kDqY@zj z!X^Mooj@44AHu-^g6w;2dd;bjkSoY^27R!p>$JUzW7ViQ3>TQs|2MmY1IPa~=QOc6 zWtQ;(1}yk2Uop7`WIHqbotA_$3R_&>v!9 zd3obmPgh|A9@GJDivPpCa$VmA^1^?xWqY=`w6q&Y+5uq5cv!rsuBy_XDA7S`jxbm( z6Qm^R?d{ECE0PU-p4T>ON=&g#z3qmWs>FWI?~l5b;~F;}zI&NWyA)^AAOD0)wT|ho zSH;`c{fsY*GdLhk#AWGXJp(g;XSnKb%1eP|J`PN$3NRLWcjQj+&*3Udv^I-D87){N5>& z;dw58y8DK%*1>&sX>HFX{i!zIUnzzcj&%RA-;ykX>Rbd-?=RsBHlw|^moS(g{8u8u zOFo)q0FH}_zN6W`dbKV;4x(6>4cU3paRgG0(I7yDysM3Td*EF{;oxez#|{mMHhCX} z>YZDGqyo79r83Y0*sIlC{Ue{lVY|+dM`?qLL(pwyf~bEonTArtztCuUStRK{y_z>f z3GDdnhyIBD7R;0=x(L6q!W|Bf`azH5wd(u&6e-YyeAontwT_!mu&b{UNVzb9_|jhE z5pGEnbow470}VHZvs`!U)*I=>Ty|%J!IGANziT`*@3x+n8CyZ1V^pqLX535Icyl@f z($mNSp>{=}3|unffNY`)vVeOPM}S`<2;GYQHs$FGSk%U1QaDClgYi7yS3W~eW=uAy zHtdccI@q``(e+4!y^FrZOStwFvI{FNCs4WBA~?NllZB`b6zjpDHChFph>W9E+;P?0 zuX_3ZP2V2@50}vW_3;R}!m;FO@(8BCG&xen@R26XtNNIY_3BS2cEIJOu)6Y5j>c2@ zH^U>!NGo)A2zd-I|CJg6tbl|(0FWRN5SwNh49r@$$w7!qD_%rtDR*}r^vCXn+f#w< z>7Q60r`v6y3l4)@{~-5^tX^bt9YDMW>D>L&=F1T2w@S&pPrn&WskNBJ#>CKu7`7I! zF)63^Y|#jQqGh4djiI4iO8TylUIk zpmQ*&9w9XsT6se8+jFDq{XmJo+q%&p#Y86_Eb0gZM4{(md8ghH}KD1_{lkr5G*?7jEi>tmI@ z5|SAy*<^-fWY1)0XYVb-|GJ;=|98%Fp7WeKkGeni=l#B}>$NUKmg=6AvXqk(S6BQ1 zDRiW_co8cS0$S0?)m1A%RRD-6LTI(Mw4m{CdFVdn$$ay^R`6pQPt1$sHZh-J&2K(} z>Ar_!C0j4?F8(YmphZ@rWu{l5&nmSXWYY0H7<#w=6wT&kw4`Adzq?0)>g?+B9meB- z&Xd_L@?ZK0kLNCVA=|*&=|P*V`}9>rJgmU;PXKM>*zlF>fkv0?w~aT}+@^-_Uf~SC zq<|fh{GXc_PbP-(9lk6NdF*%1tbveZA&Ti3E2ix43TZ(B{EfDH6DY~rBXc4wlC6m# ze(2%+o#uBZ%rim;=-hVG&uAqjh~EC4+T!z2z;yVNU)(=8T%V?+k^Acb) z`p?gPr*T+unx}pXic__0xB}W*ovq6w4Gl-1B_A7af9ke9aIJtVX#iJVMWyYqtUD5I z0E~7ZF3@C4EHQ01N;b%jN0^_!Yw28VUN5q++w@dX#=L$7oa43 z^&&hXV%9}y004jzSMKaI#*=UQB9Q?>At;ohk1rMCxk-2($&hq!va6a;Iy#RP?69jn z)r+LyW}n*wzJU;|PdDmw*9!1O1FL$KdNv{4VzlnLU@J^$+)PTVS<)7%5A$Vt;Hu*e zOMa$IU1w>zGHE2o4Xmfqce5|(kqq?)cLkac?8(PU3Fgm~O(`zDOiH`*s}Z0c3b$R8 zz-2`4ZI2l~l>ia9S@-w#u`dsdT0&`RRp```ges(;nP*A(dNplavs zOYhCY50eT&w%MzO=mA3W6e zSi|O>?~*YxrZlUP7^t^YFK~*P1FM(?J&>3*0T4^%W1wDQ8OOaYIO}nDcIM8@Pk3;0 zxJH)ve0ag7X?PSDCEL~BZ{mk0bVsNJ9Q*}uUcGu%@`QbBMUhN`DA37>jD)0Q{#I@f z8A^Lh3TE=Fp;@8Q;FKQ8@YxMB^F=FV=3=S`XRsdik!*0t^Cbi$QG11G?AKvp?lwU1*)soS%0-L3I)yawC6XTrO9t zBgGYV-!xh1xXp$$6uw@dp!Ut-K^etQuicf!jRKJ)6AY8~$e(`_O=v zqdjO!Fy~BGP|z!2jz`|S36?nuYA=$NlWUBBp8R3f0#?lTl%AizD&i(}SW@V7 zWfCB=2Y&q`d3FXywh|u-90IEE3F>-drl2LiiO9bGl}$W+Jy;CQ!vbj#wUD|JiFNhn zOvQ+vWQM3OJCirzeMW?G7NamxG;sFXK-eY9OOM)>s4aJS#)YMgrV{z8Uk zqEo)TT#a~v@*gm6n+H7@Ia79HTWZZw-${Vdo;$uTt#M!FeQmxk&MsrAWBzOyWxoez z_==X#0&V|nS3I3$;0&5L0U7Y_71P=g9Qm<7`GqR#@Ml%(9jNX-pSDI$Jw2)t1dS)8 ze~3*lBmk^LyUiOXP2qb$#Ow z65^5~x0#p=BWZ zF}Ra22zn>a&rXejy4ITj$l}7FKT|@%Ew(=iG6O)3iGeEdJ%zX`jGUA6{@xwkl_TLm9NsY}0$JSv@>y18czl zKP%XP*ieQCjo9!tTSR7Hxi?C&nwyzrA&7LwIo!p$pYary^slzzNx1MsiFaJdNh-_j zePG;*<_a8y(iFyfR$R~mpj#kDLj8~s0LS&HO{In6L-rC4Hndq6m03|i3I%K{OL`>t zZxHB9XyIc9w>%{LM>YL*#|+hp#I)5{A!HN$_Tjq)^zy#C)OHt#RogF0Z}7f;{VsfD z%9Gaf?|^Qn%UlZ{*uXA7+h6&q@;qCJ7BU3DA%a3J_39fWx74!s9NzS)Q+CW`X1ZfA zAaZtxQ?Z(n0-Bu*w>Z+qChKi8lh;z0;rhM<>J6y5Brc~P?uv3oBiH~k<%TYxkH#)K z1?^c1=nXTMq=_ceAaZ~dcoG!J(k9d}PLL24>##ua13+aI_z`F~aZ!*j^u3Ftfhs|; zjk1p9k9mggN4X4iu*D8KZzGln6GKV#h`IaiLo!tV_;{eOeGN^@IWcWqU5>fp{4484 z#CSTL19ewPS6}x`R3?g=b|D!f!|%jiEuC9CF+ z%PgkY?l7-pM~#k;civb4C?V$l#Fd11ZvUlk1b>6eJF}nwAH@j*SCd!U4)@dlkgEp9 zSneQIrO{XOhAZq}y~FXX^V&F}1B#E>glsL+~+0^oi7k`d5r{dBepP61Hi z%L{ycpPiRXaf9fF{x}f4)$I_)QMbQ1@m9$ACMvVAC&2p~!ab#ZVD`rRBL`0S;mlX* zJqOT0VxVs3lX4V$5rPOQYPXx@{9xd5fe!>{NINpo$XwfNze**D_%$i@j4nm-(w6NO zj@%!zO@)z7qkIpgqlsT;vz4Rv+6L^GG(OC9>-$_Xc6XR?`)kT=5 zGY1X|sZLcCIT`fcscrMaOBy6!7_*E3IPePh8dzQrk5Y3!?Xi1@1slM7plBnxAVzJu)t{zb8`HuFNwUF(&56NOi-NhBfC&CgZ z0ZNuTbQ%jQ^0sUJXyYF0bkxmRpf zR+gJ@YfOr2)DjnwlmxTS^_3johODg`e*e_#;Trrs_&cer+(8mcf(T3MHwFf&Cibnx z)M?sgkyJzM#coc@=Chij8js#DX{X&_6sYMt^RdSjnHU~U_(tc2H|Iq?Cz|j*OpiWr zqS9<3_(T2)wP14+ofo+^hj&N8$qf@zj{3@<@fC)THC^`qAdU?0p&OvdPWiHLTBqpK zeR9UGuV`|*N<>xQt~Gj9Mvt`I)HFekhsR>HeR*XO3!{$CU#;jX4ZrwG*}lOW1@~vkE(m87 z4*UB0%#j5kFUDG!nqnalf=X0W6y*E@&k-R47gcl;0|tLxtp-5aneA$(;Jde z_LZCYys^aZp@YGZB94}t>$yNVt)!P&Gy_g&s<$zdXpHS?Os3qR89{k>Y(2f_tLqGp z1^RFJ22LbNZ|gnDOTS)6aV_RrLt+Paors?T!dRc*yTEFCy{@sb@kP24^T5pJCPpzu zV)-X7@L?xQmi_CRoTLs22)M;>M^$MvHm90ALQG7YoCeGT)9KYfRE3u9o-Bd)MhIPM z8eH`ZUtAWXK#1D?Ghyicpix2C+xZz%4*sMQ1GyX(Fxx0+dmId@YkEQnVLIKKK&twd zHJU2#l3dwe`}B0Cdrw3=+43GEdu3K4U1%pT-G;mZ(an)#7p<$;w4Z&l(?xyHPNnKj z7bP8Jik5it+LAe#JI;j>V^E(ehMYqzrl8v!Ro+`F&HAl*WV*=hD`hbpsehX994jC} z5_fBQx)eLi;%Dyd)uGSVP}QlaNK;m1KJjS`OGB%0ysrFc*$J^nMA8 zH!uSPJt%o>aHHoHB6BsDsa`MW!$>0g^yv?PXT#Cb4xl$%fWmyLWP#HXt?Hyg6uftx zn!2Ymae3Lne?L8OPI&W1wd<5Ukd6(qErN&01x#)cocm%wJl!{U zz4+%!(tIKo);E{&)j>2z1Bpf1f;v~QcD(fS86Y75;T z>r?e9i~wPn;QmJ7%e7G^r)s;+lS_@FYY>0=;TI)AWxEKb#P_BbVkH~=dhaX-&qzr~ zW~{>bevVB~PiM7rJP;(L<+8l~q{K`R3kwS^b^@Ar%i?)=>-02jARSl{D^ru{a3^jL zPgk@EW~s7Re>g89NLFAPYn;q`!Fnj192-kF<|xl~4Uu|E4-Pc&q&A&yH4s*4Ma9Mv zDP)LJU%!4Gup?|B?7~2XUE+&RKf(7US0H1&VrM5QqQrE|5VO*9%|M>jY>=a&WcPzyrK`BsqocOY z)F*KRL-Hwm$1BbPWZ6Rp)?;<|m?EjH2G4%9wSD=tp~6L-LeI^RmOy#R_j%W+s1s}& zZ;4fjFtzYBtS@8lL+%P2!uGeuTcV}(wyrZhmm&{WLQeZMB zEP@H%aiNn`ZvO57g>pRnUyp$mE=5ODRe|Okv>Rj?7#QeZhAwOeu+`E-z`U}c#k2ru z<|Rkd$=!q}E?ZO7sAsCG!GNK?uoOk-d%U|0s^*m2@g3Vp(=YWMa}zcur4)ji6iQTK zw)@EN(_7rR9Ig1P5l`2?jW~;s#anmC9ouyn;jix8(xcd8i>49%PF1_PN#k+QGbZ51 zPQamrNOGhUe2H^9U^zcOwMUIBR4EZsEgMAOuT15vV;{AQEkE%RqP?o-)7N?7H&1%^ ziC9-9JyT^jf-yC4g<#4%>!MiZ&rD(L?!`P+TVq$()yvrU-$6!@{ACsbE2>?)YSih8 zFlPnqepd&a9&KGl3ArpA#C;$Z^ynF+L#O|+wzalCp_o_rx(Y+O5oEG9cMiuw98i{- zfi?%0^2V&d##NL+TzB>}ElY*4=o7#HX$cSs)xXMj)wLV^9(L;=BOb1c6B@Nvf|a)a z$bbCMx)*RfdaI?ab!Lxn>HQkCyD23BE+%$)#ch z42*&unHfqO;~s{cJazw8*SzF@RXZUV1KtdIZxRlPMpioKm|29VRgsBBv9Fhl@lzx%GT8s8G0Bsd zcicsUIowb6`G*yP1e&j5$SB56$qkN^lKWn7Q%u7`9rIcFe z3@{~RU7%2a@HF@J5u#$_`UP%ya29g>=5kn z10SRK#%2N1jOygkm=^%0>Y1)J6zNP=7Dnleu!cb+V`C5(d|W68qeP}!KKxdm3A2PM zPTsqMh!PISObCXv6E^ITUZqhPvfm0f0=kIW@f__n|9g26!awav)?jyzrOo#*cmmr)Q`) z`AW6tL(-Ar9J+h6H|C6uV_&CgyzlpsZBx7@Y{`|La_p19tF$4ftbCo%X7qdIr~2LS z%Y~oczB*WY|6G>Hh(otAO4!|TW6`!8IXrSM*JS#;fAP(9VXRm&;8RFQ&(UY_8S2O> zS%xxrQ1TrX?)va109aJA8-zoMvPh~Qx}Wg=o)W;gU~edeC|&dkZ@4%YhA+*wyqMjR zBJ+%30y2p7{QjEOiwZ?;?2@gD^ild0hf#lDSmJ+G5e@mld23rNBv+8?F^?Qs*T#tn z?bkt7kf!qNF3Gd}p=ytvEEDt4Cpq*u9LfwH|90A_Z7&&OyRQC{0A>fOTT}=kEj>GX zuhAGET8!KiP(*`QJ6NJ@Tz7t`Q+Kjxllsv0Qpn*(W89f{Y=URZ!96zB+b24AvihVc zB9HcWAKlLw&wY`96Ct_6i1H`%$h~qUljW|sVy~pd9d>EUK~dbG4D#sr3^MN}E#A5k z^!EZS5OE*N*K0<++-m3w5f77$;Hytw2jc6Dg?v*3p@WKBGY_5qVV;XPV*3wEoPCXo z&&L>#D3MQM$3Ri=SQwD{a{FG>bK2H=qN1YNf=KuFhlfT+N`dt5>^do_Dc2>pgViB6 z6^%%46A8X00o*L6rackBL{KOPXXh_>Ki`(#N}OF-_(7BV;?<+~;tmd+@EXRYq(ngG z7Xv3}{~v*bC(?ukxPcB&(3g)|u#}XP#`+8+%=Wp0vDh55oF-i4Ui+I77i=fk%p6Qc zN3<6PQ_RSP#Mb%cXSIrHSK2(yUCWb^@1m9wLMzp z-@jzH&2?R9M~g8SJKULLTlCw;6AD&UT7oQMjjt~ECj6kcqPB>#Oy3-dPjII>o(xEQ z`!Tor))z5Wd8wV9^^w`<@>)$vd9ksv-|2aU#SW3TFH$X5ve??9d{fjszrB+M10_0T zw$jxJ2gT`g?C=j1EY+@SH&t0kBXWTvmNqfDGpJk(M?9M5p_P+9ToC?ANo0ILqT&$J z_%C_l2KvJzX&xCN3#2OnsM%rVCu98|hd{bzM&-EhikKieD|&FccpPuvMiu%X;UQT0 zqV0{^uR=mX{!B}h?ggPk8*(>8T|Jai1q^pYKEU9{ivuBMsh4VWqWcxou8)7_)=S~V zfm7RhpsG_t5@M|$@5pdK!S!YjW609U^TKw!6?CbbhK^dExmq3JPH-cUKG~FNxqHK`#gu;qc_OnfDHM^g6H7^zm z?vap^-UdGi+Pq8i&>MV&^5wFd0+nyBnLJdauc~6=;S@WMgEkA1V?=#)R#K^c<60`A!dW#*F zn0oDH3T3OPFs?P99ZuigXz2y6*;CJ{0W*V3pAQ58_sqa?ykEiyKMdzRI3D1 zeuEvgaUpw#eXWq|6YK6q)ih!R$IeDOd*D`rG|<~$G~m!b@K4`mUr5I_3Bk1x=8rLLwJ&&0a(C0aS#UpO{Iw% zzepfuRa>sO*zMwYE8YF!7!|BX+fbbUAnYA_W^IlWxV!M@dX*GZMDU|;Mt~qja%dPG0WrM&pB1v4CmSnHNH3s zqYNgdB;WELy!^SD#jt>H`|CpBt@YbdLTvL7-4fZe4_r5y?>-~Fgkp;Hz&`UjyvwU> zV{DmB?29cw5p_V^a>nvm7^-*vD!j=ah{VP5k58X`QmS>;KMhzoaOkoQ%uIZ^C!mNe zk!vB^{PmrTX&6jrSdd~E2}u_e<)&t4|FQcn%S7Orj!jJbn6#{*&VKi~0@MR$f+hApzY(>ByP@N;d4-C{r$ zNm*V~P-QFn@Ih#l-Z!!6jQmrtwS7M&e?g3C24Ya`4By-;m! zGWKn!+3(*HTol8i1cAAhI<;;{lw3{lt5;2vlauR$%h##otvofdqrVctXjn>DY zVa?Gz>-i_e^~P!FrtW_`%(CVS%q2%BYvkQVr=}Yj(Ico#$q+j5mrOO@lzYq+Z@4>p z-Nt)t{2D>IOvYRJq@9(K>9UQXi1Q$sYpWBs7jfo&Wlk|P4-2@=PbO;%et)p!&hK8y zgHVZPI!#UZ7NJiGMhCM2l!0vwa!vDm4pNukU*Y$m=dhv}?wvzQcD|)5!*`$G)B_X`<#sjkdx)Facbtw0=cXzh{s?uG#gd8RhT5lyTRdf(1 zf80=-aJ#)Seho((^LG!@-jb_s_P%=m(sGU?!vmv!RP8v?b>+m{N8ryZ?@V>y`fX;a zA3jq^;(QU~k=_PMAe2QK7gV@5z+IvKckO$-M$duWVXVm{e})C#_-L8+8Q=BNHnw*# zthdIk{=P2kG5;jK|49H$r|L`a!WXdV>7vu?3kui}g*2fT9G2D~y3qU!2JgcMuQ|hy z)9L8xo3aGam?iIJ^e5sq?p?2Pyg2toiFGW_B>h~!j?eQFN<4cOz9&|N z$ddb>f3a>oFlDm)WQszKR}p;Q?Gr1jrA99H2w9KrRr&O)*iExIr}H0KTP6tN-PhDa zG~0W6uri-2?!q0}4AXms)li@U)8OF9&BwhODS4{{f?bY8Qm!AtGi+gzJLU{`!9$#@ z%CA&$?Bki9zO7SMQF(3Bb|2}{`uxsazL#rjpry1|w$`6r60 z0?KlI;hCMbb{%OhX99EP+^wEY}CtEYmyC#az&tb zvhv%{nxjA9oJDA7)+U^km*8<~%{#p*EW9~ctDaLsg)CXd zzB_AcPtVhz-kpwAgR={sG)e5buX&(VpH z(6jbH78Z(5P;e}3?HkNQ6iJ!UbZjq|JORl?V;j)yfIX&wPet+YX^5Pvq`;#1AG%xR z6vG;?20W3-OAG5bi1-Oo@I!1o$F<<8D$W2F-cTtQ55_({xyT2 zX7+NXF;IG^GtQ|~wd7A;K0-*COCBUnFc!^eXqO^dQF7c(u zeXHdORrZO}+kAW0_b9Sclw5i{wItrkq^6yr^)~hmXRqLkXdE0QhRzMRvC(>O{wW0P zruiIE*8Q0n=)_c92w;;sJ4sxT7Ty)WZF&(J9&ThRf8gHHs4bYDo(={h?y+Jh3&(qX zVi>5>(o!}}Ra0oS0+u|(GnsS};md{C)09zxd-uY8mC&L%)XdT+TFgxC`?x3&R(y$1 zs5AuaJSf%q#}D@hl@Mc4xxQY!j);wV|Bwd0v&6LPCQ2NdT$#vQhDO1+;ZMEEc=79Y zA{TdJQq%DAlszpiZ9NXr;cx!TA!)Z@kt_wKAeGN9?U$3@Ic<8TKL(Q%ji^uiX4is~ zAZv>A=EfQM5ZWr8iw*D(G&Kyu!j!0fh>fV@^Zgox6y^yze^hGxS}5!&eE)K3Qjx*= zMPW-I;o_+2^baduraDow({llA@zdtg*$I$9ReaGs)K5pGcJX~rwYDCez{Fhey8ug$ zM}s&=C2T|=q0Uz?hAk?U5^7eC4G^7W0E>#$CaS8c_#sk)h>R=*tmfPy#gE>jP~9o~ z$bWM;W6whSJRPojz>R`28fU6Hp%1IuU{!-i45u<;_AZcxmb$K~p}vFJ8=VZ4`q2BP zd0+YuG-koi?_1^1UPgujG|NC;yYQ*1>YH@z(zi1+1`%pH(5Ti5d3Yaf7B0>BLR-N0 zuI;sP#ezs;X6>*miBrvQ#PF}ZK(zC+H(~W=O+3kZ@$Fb}viawB zJ)M%Teetv?6b9pzgu)*F8k*3suttB3 zVWm+tt`;Ozp>+*#?L#j5$22$+s_q`?j};W~Q5!vv|7&BG>77Hv}t5L9h5l zx{|@>e$r0Qy93}C`}$HtZ1a<5k}-xIof4NuKg-r}?&<#S8fJeO-(@;lzH1%n>sIpg zkohHi4YS~&MxhV{!NkPWFr5k^L^c^3c*Z|V%ZE=@V8Ody4TBw+iUL4eLj(H=?t?)B?H(96!X$1y-=QRVU6WE|AKp)?R$u&iyVnU2{! zm0(Yj_saO=BnVqW9M1oF=Bl!QLNXJ2DW*2%EZ(LBWtIdaG&))aN*W*;7#6f_h58Ls z;s`%I{iUV83>=gXm}!RmTpPi6%vvY9yS@D#5Rf}P+0X7i%jeO^s;(a09G|2Xa`_IZ zCpwF@x?14N7TF&s&jhEcUq!kp(j1~b$Nih~{RvMP>+0*{;^U3o9~$n|aImqtv~Nj6 zXMsTdnu`sIgLZ|jb2_2-Vc#A+eY>2k&nYRC-rsdpRqy;gOX^x6h#N!`Z2}Jfm-aAS zRjoWj=gfmMb%Dn@Urhg-7-cD5m;i+%MP3tqWIEqf>zijd&s$^4#zmc+_O=JIOf~8q zh4b_Es|fu5x;?-?*s`rS(`bsDMlK8%K4nzuk?gn*IF%=26c+Lovl?Ez&kVCJXfUlV z_*rK_f?Vh#hGqrYqrZO_XVWZeF%g6BFn&*aUf{!e2%S#URWkMxt`6?en;aY*a@YA_ z=2?}jCcC@e%DP_DWsv3LhBJ@qz2I^o1(cJ5_tbKd0r;*3H@~RiSQ<$?^Xt%C$24(X%7i>a= z9CVl;EUnb|N$l)KOU>?K@2?U-$hOD7wdo9lLv#Jx{2bFQ%boY`4a_W2dlMc0l6|sPUQ!eSLRj{E{YDFGMWqWvjJP(;tbP#m7%vq@1 z6brvxRRU+0gD0Y&2ix?(=?FO07D>(oO$ZCKs$kIC8aT;LgYd5a>tdnan_8- zBfVi(U8|7?@qB!nbJCGi4^kP4LHsVvXZst)Pk<^Oh?yZRS1J{dK=yg$N9;cIT8M>a zG?9DLn-?g$@T3}OFfX4CR623p8n)_!S97$=koRNnInl(CC3QM0G9Eb@a?vuUv}o8J z`)PJj?VSF(y<5Fl%`eKg+->e2K|gzN{hY0wRYsTE0i3eN&?K> zY?PmgTeR9!>&g}F5REqS{CITc+hh4JzTO@lnfB9u>vHd>YEAx}{TkZI(sFEA`)t~^ zqFs4~hvK*=BI60g7zp7pl^-k|xI^gG{dXbs9yw@a><=dk!<{>rRB#Poc7zS1Y=bG& zKsS>bS5m?WGz1A58H11zg-BmYLi`WXeQCU`yk#DDME6Bv7poM!(wQ+B-Th zGNT@ieL3Za8DLG=MwiGv=SD+;P9z`3{nF)H+Dw>Ip;Gqs9rM5Aw~YKu+)7tx?(Twj z^%aza`cjd;`6o|U=s9#+XpxMTsp**aB|3K$kJ!^n%ytkWieCh7-Zu;b0j$2wufEp4 zg^Uz8{JF!W@?Tym?u66EI4^p}^j|osh3Yh`*_eM7@CdjV*D*Y#f91Wn3QQT^^AqI`hpH8`DiQq zLrXQ2;!;1svCXD}h;WIR0C&2}L=aq`WxGVIo!qGJ2fw(k>0KadE(3~0n1;43C$)*4 z+|OEaJ>N16X3FDxf?yi|7U%arx74GSZ{t$Kx+6IMZ^aPNL1)sc6%CLA!%{XFNd5jY z%9ogMpqV+#pLZ$?2PY$=qg#O@&^!ZR1;nScwd0Gj=-C`Nx=X06tgLRQ?Mqt8*zlQo z6A&Q8em5Y~17@QnOS>*aQrB|8)1Kjbc#r96YI|yJnviP~E%Gl~CW=CERH&{0-W^=# zBb!zZR=Ya9)W~Z@Pp% z2R<{s-h1q@|9zi^>n0FdsB-@1C?6z6!`2Qh?uy{OQ4n`t;;5Mn zAm$?>s|pIJ=Fxo3i|Ak%fwxeAf#tm?;J1D&pOVLe({HRA!GPn<$&#ui8QZn+R$!N0 zf$A^*jT^;t{iVdAUvI@MS7UxKH@nTDpK>tVJW2iS8s+!tVsbB+m0)I?wNQ-u+HAK% ztsHK_BxCi*E-pN9QDcdx2sHNfDJcM5TrhVxR(SCoIz@c#pq;Hr3G9Bi)=p z@8zQapjwfLMF`T~i)Ar2k;~#0r~F|Z_6u7=Kp5O`0AORH=tM-Q_Llk{dw5h26zu;O zWP`S{fw}ifN}B(nw`1{L5H;VRptxdhZ~td$iGh#rCfJe9q}Ahr>wgX=V}BWApNe8u zbPMc2=Y6!^SinCm1tC)Tr?4&;e3>6R=8~5hT9l?NiPjd2^fWYi(|&vx>aMsmv~{MwZv$AW0q>8J|uXBbY^d5*#^i?!#q zm4tpewKtLG3|}?v_XCsFp;_$m44i_FWxO{NEJu}6-$Zc_6Z+H$cJj*0j8k&lvYqR! zP~+@u_d(xBb_>LVx4z^QC>k)sazyr`c=9(`$%bjw8FZ|!oz70pLdkwe}09bchOswIjC@2I#umt&^Au`#G3tdT!Y0PP=HzlfXK=88h0;7DQxEVdO z^!>^!1fel8=$Zd78mWOthlv7!6OB2sNEE6uu(Ec+QNIQ;skp*f0~RF+&{x4PU#YUprxpl9U=#v)RVYl2#%SxdMqbn^+}P4r8=B&pLx8HB(O|%;b- zVbr)Ro7I>OlN_Ls1}&l2w+{|p{wD)!0qO0*3F*z7Mj-4!1NtE6Xg+}nv_m417}Jmd zEWJ3z&{M*GDJYry@Yl}^QZHymtQAS>aF+Dxb^4?C_eTbG5730iMLLP$yZXcVT^P1ujrCQ|MbKC}m}3KC9C6@Q{!p z>^kBQqOJ1$`M_6KYdK>q34g6ZGXkSPhZmR)<7#ndpIXu794gUSL2^@yU zk^Rn_m9`T%!3g+c>Y+E0Y;+Yx2N<$6jOw?y9ghG0)pR}oS10oOn=3JDW}1bFhUO=I zT4( zy*D^{1M^Q4;LUe>bU_frh#v7gAa_qwGyG4Wz+9DjF;@=i+y4a<=v8ut^R`nt&xV~Yl#XZ4f zs#!0a-^W*38#L@zzVXV|rGD?+2m%GP!rMM*CjNs7;ZY7VRS{#_jFr8t@Vz)YLis~b z7eqIb0;_(&%CR6$6zojz=P&`$$`Ome=|Xk+D11XEj)K?rGeNOTc4<3%po2_nBtqWY ziPji(oI^~TA=>se4q`N?mImPm4<49B6^1EtWSPAmu+ZRq@igcTMd*`R0dci3+MjRZ zf4-Uc`Br~$^w!{+K(kQM>%R*7+*7^emCh%VP?SynkQZnl5yL2Hm^rbywH4#J)&1iS znZAFm3Z6KM$7(|#)gKYg%8EEYV%+$+`lO2rf6btv=n~~K&6f1zYlE)D89a>|VnzEg zacAU|)&#AJMEP7!AAh(O=p1&-_gixml5rTb77_~#^we}~q*b!LIqw0u_kD8+21>NT zdVXxSx$){Ho}9Msh$&~iebMFBgH?mNO{Ygx6LurF-`1xE>WLYj0*rQw_wgbQ?=rjo zaHkQh)sp8>k%^SaHtc!{OWdB0PhcuvyN($kz1_Ls^bjXY@Y%lIQ0$GytIGiC&dqHX zv;*&y^mU z+B@SB!@VbgFvSCQ1vjK@Vg?4h>JGkO>)moGb3Y6O2U^o3G?RC6+HfI>rrl{oQD6rC z<-F;(F-}T3T>Mza2LGIQgY;`0B$GD$D+|-rc*t)S`G-=~xj$lNgGfbAsp;MF{;H3! zFY7u(qF}Cm!?zw=NWwqSsd0MwFn6W@JreXMSbyRDT43Jn;v$pWW0h1C2RnPw>+}A$|mn zUi>MA+<Xn-I$4jLdsbOGp|f!FugZiodRm+)~&CY=u)BSiMe6QhmI%}rQ3M<0E5 zz)XKG8V$={UWYzx?6=@Ko^1nd%o}yR(hk2O9hNSVsc5RY*Z1z;{SLVg=)|P=Q0KSz z_ocwa4v9hmAaNq6j*pE+<*48R90qOvB@{fYAFG`WE%M=?o~C>pxk*D~$6FiuUga}- z8w!GsoB$1#gS-1(us!~)1N0~e;DFE`G@Fa(~`}2UTW6?;yI^lh-a#ZFJsqg-B+=rU3;&%Gb z)n2%5llLD1MhKBtSxf6{$WT6+T9HmiC3tcpAaiDHQt=T*VkZwYjI*8a$TMY@uHATvK^lGz0Z_? zq(<|3;QE|jYpaHpx+gk+NG_gKdJxf9AgF zg;9Rg`vjb;qc-@4-r!R2?L?y%9yH)ZmIOO7hs-G`=dghZ($w~Cz6$w?O<@>M>UF|lBByPfAClj<3q>Z44=0jVjZ|98JyN{kd{~t zv8bj0T}1S zR}m2CvpxLNc zTXSb`5A8bIfdz1L&~sEfFA*RxV!-hnC^MBm=~gFhX%O4l*-6;{^|(+iC1*^YXz1lT zn)<6~=mCXta;7!x+nn?`s~G7QDj{t;J!ro0J*x(}vUeW1wU zsPX<~xwn5*9$kV+HUgBzuiWd9ZfhNX)&h3Ifh%}xCh@-2HxTrjuK4S#>)IShSz95Z z^SxR=JV=mxz@+xp2i;mg`#``a3Zwmh%Fg}uF=BtdxUiz4fB6tE6a~eyYAPPRrr^Qg9rl1Rr$Xj zW`(SXvfGa{_n;+1=fx#+o1=5_Zm#7%-!F27`t_QoI*j@@8He5*5i#J0iylGh$3T&; zaJlV7;?TW>CxB;Wsu$}mPgHS?SJ^A|PFhJpf@*)Ar!#7Nt-kIjitt{n!sS^jfG2+) zFYEfl%Y(O^n$(Kuyfte0`1!ERPiB#uE2h+Kknh&vOyvIM@}E~j2x!m+1rR^wS*HYO zI1K)5U%&O>pQg*&Fe%r~&-oZUIbzYR=_FyR0+aNf*-CGuT)j2|CBE1NaS!(`e`9@D z``VA5#a)|H&@ymmvD2Kd%)aB?$(WY&xofIK+V_tA2^8Z0f2}*M_0pd#AzRNO_X}g! z3$KG0ci%h-Rj=I(Jf9Blgsz>XXwZ4vZ51Emk*3&ycTbXHdxTpPdHt+ zV$%#nZ`r5-(&AUKKJD!6tPHPz&bYir28en}%8p;9IS7Ao%bh_Kp)&|;ln%Au$ zY3^?}ZbEW0mv!$$H*SRw#R6R>ArBX=`>6k8VFb`|x=$HSI=K)@9|7enxAoSqT+H_+ zR25`ou=(uAF~uouc{cPf%ErFUaU=?Q1PQBP+M9v~#%2`O{cOVtM% z$j`pRUxXv*Q5ZskQ>?%wX<>1fo`FFbf;)k1DZ}4Sfd7=0PsU54^?CR_+OAZ>*a+|F8P8#Y<<_@u2T$golaLAEU04S>a z9bylI&kNo#2QIKIP}v4{(H+#|U%hJF;ROB79YGHuv|x@9{1ax?m#dupHs}cKZMn}s zMF-udFOYZvC`Z!^KURD4{LP7#!ZD{*}gn69uNaH%Fg5-@0LqkGv1fscXc({3Z z-Xx$ug!L={bkKeerNcF8*cga~@`s4^8Bk%nmq;VQytw%I!SEpA#+#d;Z``Z9BX=Lh zQys9Mu;9<-he2s^zyqvrd!k79*tLxS?4q5^(%)1*OM!Io0rU&#Pj`NP-pXr_*A&jC z{VnxAH8pr8;2I*Fk|88wmr65UP}Kc8WqG_MMG?Am`=(37 zC(A`Dq0gfdaX@MF**C#=8zm&YI$RiG*!&8TPeSHDf5PtX;WAVQ7-gs7q7XZ?9>Z7Z z&35NaskDd9ooQc1?FM~oE=5uDn=Js(MhQX{*GyhhjV^YOW~eDDD74JS44OCTf!~4h z7w~e6Gq71GyZ$MCDlRB7Y3~u}Apjed+f7oy(UbP_5`rDT1G&l!@y^eIA0kD^q=F}T z8@^>D_})P0-UfL?5liXb1W!}=UZ!X-*abizINqVsq=T-y%gYw1@gY1FDs*a9^%X)| zQI~Q&MYIv`$toT0Rgc>IL$ATz38E45v`>K#$Pf;_7C3Yzu3b0(+`2vp?e+ti zD5`*gY?&;jttkg%WtB^r_jJG6um!46ZKzp3;#Jno*?T)468D#7*}BBOQ8ul{`E7ZA zR*fKo?N?%x4~m?-0h~32#;4qBrUg;l%Xe>m9=zyZS?x^Z!JDgbl~0q}_*(Y8P-&K)-1&RaSVCTp@hT?7KTt5UDR^Ft8#r^oDgazY6$NupY82cojcZRyB_XtT4T&A zbLUp!kK_P%)bNCS2gUiBibP$I@|iCHAT$4xOfOgE4u_@o3-~_l1jaF&J z(u&cbO1uuN0C^M`rN2L1hPEC-`*s-?ej_w6pg!;>0D?>HoBLL}v>TDuJ3iY^J*ghR z5`@MvlO(P9JRBdhYTGTTU}cll+WgsNVnep@8Ta0Krx_A*7vzCK`k`uf($HVx#L-~ zLBtsNoN!QR_XSjrRzRJi{ryolO@R)J1wk-u_Ca?$ARBHL-l}_ke;vYRi_xN`3n zC^Qt;2lUhygCDPftPX)$8SoEK(v<)b1{*mqO-)VN_yl2J)9?h+dwH~!0U|)`2aVlL zTNLS^6Wnt?xiJJA#k6SuhrXzU6#HcHeuhK*D__0O=oGhUAXUWk=KvECUzTE;ki}RT zBicff!f#Ix1qbxo4+KH$7JS3+#?a05rMM&{X~}{+6}`zoQ4T=a#QVB|K>be5(2jsY zA6npM`FQ3enMZNlNe=8;+1cor57f9tcZ}HImc>&M=Q&z6zkeNieA5~EGce=>hGh(q zHzref$12~pL4XJ7dq+*RIChTf1X zm3VP}WyKP<;=+#xE`|(_z7zmh0NyhS{)Wwyxtp;B4c5dEG6+xN5{g~_TO_D*-%@2G zYWXV|T*|hd`^(om+Q&O6qIhP#@XY795>D}c>6>kY7 zd?QfpcX|rd@+}1+UPCcLpk@t?!A3ne5RarwC4X(sNdQg7CuqE8ASCorTL~wdR&}hP zn_zS^7-Ajf{>V{@I$?tHJQ0-tl@GW#+7aRa1NPHUVErJPv9;7XR z1~h|t7tM@81wuGJM-a3Ih*q!pZEz(UEC4o;vOW## z2-QSk?0srOzfQ=g@r62Zc^h1}%DeyGv_k@xc}|e*6?RxC_VX6Hq9?#Aypw-wC|H=7 z(BwFtRMfrNNd9?{o?C^vw_j83(-|c=6(EL@j*m~xe!4G;JSs-)1h}j~Q~(Z-)pEy! zWn|w?T2oi>)^OuncKaMxQ$Dp{(YqUgaYFHDk%j@kgIfwJ6P9FojT^p(VH$IXuVLO3 zel@V9Ak%hBkX44eAzaK~MzoHzfLI{eP)yP6+0DpbVW$s0-b5UU9*5c@2SblMJVydw zk$sPtGSW>ZOB2>jYn4r_E-wB0<&#p`dg~4EA%I6m7 z+tIy6L~DkCW0MIjKR^E$Ze{f$c50xD-a<4AsA8=SNB^e=`0(Vkctxk$As`{gV*c;( zafy?HLgarBaAj+pAno7F&x|Q(^C4asT2L7Lc6Ch+dgCS@_;0{ky>&iutDGeu zw+JL_aFu>~3FA=+ASB!HvcFf#5cTSvgiwi@?v>}G<&N3IH87V3qt}Q7+Jo{J^*uHE z7mVjNwnhHX^OdOE7_$V?;?7LtWwpetMwV|6h+`P@_(>YAg$1JH?+g-=khG`;frC=c zZ&~fxGvpZ~Y_FlSc?l9>IN=x;#pIpL%>39;vsZB$)$^&Gwuiwvk*vw_Ti^<52e;qcppxjlA>;!sMV41wMn;U~puQVWH|wU+Q_BD%*eQ6xlX~r$oMh=;z%r?PvD-4 z@8VasQBmuqUA_Z_8@d60K^Y=UfFPT?y7Hg!feKRprpVcsKI41@B zzFt*4r4pm*O5%`C8?V67dxKTVbLBO$N03nHTI3zo`8$W)$q3a`^Po}-qj-5u7R#zT z0-C!l7kTX%w>l!t2R8BJlaobEo`?NpSu41eIxy^xHRo%S9@R&@x~KYWG4n|N z2&$jG;mmJ?(BWvCQ`W{zZ~ zJKxMXbDZ&rJDz*pYhCM#TmpBB&zOUGwW(JVDMPNK5xN+RUPuT809`b&2gocOs~e_|p*%G*O*;RwQ=nH;b8K_!1YJL2?=l{SLwEG=A0`S2HM|pUrZ6$5 zCt;%%C3&DPR4gF)qAx%L?YH%l=;Cl|?0LP5A7OK9FjCI3Gm`(2+C+JKcE^Yz5L1GnL4Bb%(>%cK)|w6K=^OLqL8KOnQ>0?SG`G{xnEaA!l5 zUj%TACx)-KCquCw4tA2ZjwLON1&UkmUZN&gnG8hMNi{Bsa=jzs9%M&0s*Xy4i>SuZ z$HI(^QUDJR*I#prM<*vKBRxD3>pf}L;*aJ!J}N)bND)SsdRl$gOexTrtGhjl?}O78 zB}2#uuA}gMexUZSB~+{vulBLz1eCW3?XrOo2#i zkpBO8{*X&;C|89D&s3FMAGRFQ^zsgr4eSsLmx36THcYv3tTS)frAiVMHu7Z6bL^J~lxQAWYoi#^{c z<~&W0Xc)D%Nr7+(&?F{Wq9)Q@4xpzX_8~&5_F~;)Jy<)$iszYtW;FM%B*B@i<6x_kr;PSfR#$9G%ELvpi-^G!TH0nnF zgCOio=XQq0u+goz@lqO`5;5no>Hpl8^JA8yNA)M~gv?glLQknx^9_Ux;xxOUNfXA;Zf^KoWanzNTelGSEZVPWCz|Mt*9 z!NG3x?Ko(#@tX%s+Wz~yq~5ygz;}s^EVUC*9H)s<4F;jEEr1+wt{({A(gezbBi=^D zyLLAO+lYhCPwBk@OlM5IY@8`DROaZ6_bLU6vZ;UIWr6Z-unZjS9)J`u?A>o4{+@hs zusInogr&O)jWgJ}BI{06wl%sVY`l5V5NHMyGcb_2Ssh-kmOYw?_o#VZbPizB986dL zQO6-A^BHWtF2JFE2kf8?88@wwT{S*Fejq!3kzpBY;wDEWj6vX(Rl&^%EG^RNh;o{( ztykD1nW=dlzVKdAKso}*LE)rDkY_T5{ml!gEa-i3y@#I@D;`?TAq6&ONe%#7>SoM) zvqC^^0#(u2GblnvG0?;!){l<9Gl1O#Ugw#uf^97e!5e}9DUMXn948xbz)97IqWoY1 z#KiaWABzk$zyquGI`NdiniY)ARDoT-pDeAStPJW7OeAPb{^AsUZ#f)|%os8HEBF!Ye;bc-R3wNgDl;K2#@x z_TO@fQObFFdam&zx{40Ab-%t95J5(ju{qoT!&Q$BeNf9bl`cOslfY+F6p&T*lalX` zo@7#JYbYu{zvrJKCH<^M!6+2Eq=oN>mUdH!uPN-YZ^t);Xq1CtxF49`e%wTC2|TxF zNk2V2Jb<8X$EByFpMr}rr_n8FHaJSbKq>*4Iu15$_%Hw?5=CT*05p)^L_S-n3i^|S zaMA&ae-5^kct8F2VvDVWJSbP=udU<$XOJiuUa@pwC5- z(qKQAZ7m|cbLUR)o`<v|ET~U_*m68Zc&5HV}#Lb6$I#=X+7GH8pxn@$gLx>`)5M z+$Wm@4=zU1SM>y9We(K~5@hY)k*?0ByE6AMTLF{t@42*1LCO^nvUR;_fM-t9`ww@z zn8Siq3BD=P=zh6N`FbPyXadB4PnI}a6?faFsFM9z%3#chZv)mP=u~N&u_X& zAy5*K8RUWn+@K;2Kl>BN4wup%zD7! zYfb0%xnwPycU>JSaOZh2dN|vfD}3^%o(NcQ42;3^_c^->fD&W@LPt#%D%m4ph4n(G?{BJL#Yh-=f_Z?i~-y3f&nF5oUx}Y`oS~ zcjORQx*vAd7JTCMaEsZXWv)+TD7RRoHARj1Yvgt^@cy;YOc6mMyMm~c>gn*9eI*m4 zu9~^=>1zka(vl~>Jr+AWj3X==(dZSfaD^?qI&Bj`f1_k5MdqolJz2Wt2k{kPK`H|C zr|hRgWFPc8h@0{2%KqRdYzM#6Z{}_Wwv{o3N3x73CYAe2z7#5DL8# zLh1Nc3uPdDqm42|&!p1+Ijn3j6++2q34Y}GpP&ywVuYFfosJA2zo;CsHXMkg7WP*{ z(oFRiK!J#a0i&|-WX&S1e>~IsvSN&^mwvb`Jb6M0GHa#v{;Ar;%;)=G9q&J|Nh+tY*R{`e%@tpnG7Wr{waKRw%BL8H=XIqv0w)6i%Nr$|N;8WNj< z9v*a71fmcun1DTxwEkt7*0}!JwnYWj|6R|!7zt*1bjN*V;p75CL{WNV!hUr7wwFO$ z8xZEqQIkwfkp?EeJ_@U;slAp(gL)tICUHO{NYh-`9r=$DcLTve1tVQ)v$DQ^)tq8b z9cM1MyJk=Ih+DmY63yI`ra8bF`yl$ir2@SF??@~Wg6W_FA;>Q-Y+5)*09mNo2z&b9R4|Ahy0QR+L>WUv@#hLjJ(NIC$u`oGrx!%vS6@Tm|X{6XP(vi#5^PwLUBGC%c_SUGhZFA009QNr5+ zZ|qs^zA?!<;x9gbH4jS`s%i#w$6-f6c=C($+cUI2a&rPpBeI(fBVj5Bz+dc1IqNP2^`Dmo42_6$a&i!>80acpf%-(q_)pt;~P~hFs3FputUNengYu^Zq-HaK@pK&XI zMHh(6{VZRImOm=oZ9|h)7pTO%ddh`)kG``tj?18(WO5raqb*`q@ONfQYHNz z{0?KOdzO+}J^)kJ_wPbY(CCMIcyQ1|8yXt8ZN6v+EnUa{ zH@`>wv&l&+r>|xjfB8}7y5CV9TK(9~)um!(I8qD84n@~(b8U1_6u?>dHt<1O00>N! zo$c)c<4+IJ;}ATEs^W7GC8QyA6xlzzF2>8R=o7&*Sy*ZNYmbeZIs_VhOr3BCQN1}} z*l}ZuDBk7f;aT?m04%K|=!CC0O^U%>k5mj~r(tGpPBfEgIqpKMXfh-g^XU6K4p4mn z4IY@)SAglV=TxRAK%%XAn?02&cJU=TGxKsag8>1B7lCTWN1?ZOE;8ja#hudsEhHy7 zx98RrI~-l(q%4teYU}rtaWBF7m)DQizZ0;CYd=s#iD#R&7j`J~!?Lp2Ug3tMK!|%g&~co1YD2p|^4=nQ z-%5mnWt*Vh91nRu!X7J&B99KnZw$fALD{=}cj*7(d|!|HgZVwa9N1>J^4-LJZlfQ_ zOO4S*AQwHkgB>KL&Xi|JU!Og+SGsl4?$J&Z>p3Z_?Gx68hL7W4gwrjJAAgPV92onV zBoyLeop#(_)9xG&n5Vr(1?noPjb7ic5BqYD{8|6|Pf5C7Wr{3~KiKi_$uq^KTyiO2 z-Enh_os8)y9B^P|3`cux_sBOK=6SA?8tu&dDAF6&;3Ma2Sr{Pb6wqQREF>M`dbC{f zbXhj%!XbM$4b7Exyg%xOQ!VJ_sBOuX;W6o1mU%3;dj4-i1jt`u7ZeVYm6vj>ox(pw z5z!l**;lz-u`Vas482^k3KRs}zg3IR1_=f?HhkAkjm;X~H7k@Xt;W$hO$9I4PRn67 z7{$;;TRA4JeCou<{gE8HX>`7ud>Z978HQ^lNX;2ljJ|DHYD9tBfoa2UQS(vPPe^8A zlGy@zG6o80onM}6pG>IVzCGXd0sKWcB_+ot7vH}b8qx`9I6^pO1sZg<_3J-Lp8VT) z09_t7O6RR61GLxO-&QC!u_?T|R4%80CV|E$&j`AdAu*Ch>8^?4a?NOOI5-@f)%80MGz_)Kc6-&8*A4Ua|ytGHqu6d><#T zV7akfdTZ>R0YeA))&*{5%q?^53{e}83K(|N-1KqpdTC)!aro?KuhInJffMM<=fc|G;L!j=aFh*V65=O>u7yuYk5D{7pxl1 zOW>=Ca=N*8Ins!rt7^QjFN%&~d%7;q@Z}7IIiRuuLsN~jOvSt9a6s<%?3pqMIqN;Q zV_&_Z)+@e?1AFRk-r9(<4Jg1$jp9$zV9Cojb^4y3b0;z`Bf~t$R2I#`%&b%Q#BRFD zmk#*lUI)J!zNCm){u&qRL$*nfJoENyiUL0^+&+|3*g?u(hI7LBoN!c>6?2yGac)v` z2I-z2{=}H|t$|YW>-(qh&$MfQc(2nC8GPvab{)-WN^W_NH;>|>E@CyIKbr*8>aUP#cEnZ)f!YeEmVcoN!TwfejR|H7Z()W5My{JVfPJ?>BK zw0B&&2=(pp*g3}yj^*JmNQYmT(hK1msrYFnl&8rBp7uJF3j{bfUzztQdHHHkmHWh?n}I-T%`-y+$8HiApg80aH-2}o0 zd9CmgA1JM-q+#vYv)>v~cV5z}7u@9=7HyOKtk2iR4{3dL&8N`*sCOr#UoJ{I#!kOr z?3cDa|5DXhD_B#}R)o>yx^cuBa#2stCgyCVKlSTNZT;MdNY6s|@XW8DO;ncX-Sz7s z&wjmV3XhAj&u~wOkAIGVg|`;bkQE?B%Or7r>4qB}D!{Zpf-G$a{R>~Rp*F%%7pa}4 ze+H=${(ZIsBWz;3OVP%kj_4DBUTZ&7fBngmCqrORhmDK7vew|Xs0v;aKw)XySvBEu zH6HOSyTKsqBL;h0JuBi__zz+tz-^t8bhFX3;CaqMi708=6>G*@%odo>*;Qb!KRr9c zC`xQ-4mg98C6M$^FT|ef78_+|ju-j!6f041aBZDOBZP5)3V(^j=ZhRJu;uu-%+rgz zUCnIq!%-0CCf=MO@zmbxa*_9s3sfK5TwC8w&oT`+Fb4MNPCXR)**$#{s!99GKBF`0G!XMR}?}KZk zuZE?^jT<0jO8SrJ#juz1+{##ORxsf9rl`d*7BpL}boM!ac4HnprFnkQ|0Wy~$eYhQ z9>nbIMfc)=&^MpD%Z$~p&_KIA|Ly!1G5z87*sLa0nDtIC-MyU!eZpquvlrtlw_)wQ zooi}OhVaG%|8@I9`v>gIvhn$pJ-g*WqP9nr`G48O(ac+{nF*03j{QU8Vy(CDjEWH7 zJ8p$_Ib|H1Jd)8u4j2&Nbl{R|we&e2*uh2Iwk4)S@UirsO&@GqTA79_VL+Qc+YzSo z@#VsYm*4QUMqu6zy{wreV#x|$t=H}X&em*G6bwJ(Q&V(O%^venelC>xU*Y3EM&A#X zKivtQ_(z_v0`>k#xfW0=6^JTscIrK(S9GKguGLc<3w6_#5O>E+DJQl_+r81^sYmM*(|fO~TUMs~Uj3jyUl#j)<5{%Jy${iX zwElgwFA&HrYNVtAk6u}{+)f5yeA3Zaj{-)Ih*GTN+K z&jH`0rya9eEjHsRBjxUg7^v1CpXd}gYT9b8vBWlid9L)cyrz=cy5~(;PiFqpTm7eP z&g_kp$HC>X*D6Jl9&_ROul|k6TRr|Znjz^;>F;y6Qo~zUU(e~+M=R!n9>yf@*%c8M z^ozo0ec{oU(7Y$*?lbAo_r^-uV9@WkwEyDPOjj&=)Z$fjwFmw2gmSqM-n?`Qo9WK% zXn>=hrZY>PLlLFsXZzI=!|(($o;-z55Q7j1b`!7)3IlYCVfx|G&OE)GU#;#QP%GT|*!K+` z0Tol3p5s*|UJ%v;%oo5C!OS#9qkn(9W z{+8X0@+S@A`&SwJ(5aF0XFez$UpG?53zEw{v*Z(39a`O(1OBb?AsUOuS|%XAi0g0MOIZ6NNZ25SrQZMh#(Kfo+-BPC9C#O@sdxlf(x^=8Zy( zJ^|nA_~zyWB$R1O1MBW~%cJ^qeas5?{=p)OUk`On^RZRXx<)rVr7~qSx|_N9;*&CB z&TU)TlY*t^tX#(XHE$~y>)Et-z8Hrv1Ir&xzMiFLi)7H?^RCR{Y=!7Pwzwll z%rD7LMf6Rlg`a;_m-V_t#cj-$nga0m9Qcx=0z*QukB*aOe-?csCKzNF2GSLOq`-bn zrAgJJ@+x$d3ecXw%CYTZsiIPo_%Sy{J3db+bTG|A` z#4Hz3N9J1D+G2q*Sn$bcsWB*J-=I1~!30A!#*f+j_vR*%-(`TDEj`@buTR@k<@ld> zjSF-5Z8|wQsT_YObYLV5ZJ#?Vxm3~lI4d3-_iayGU&pr-_{u)9HDf~>B*X|GjUx1i>L z6L5TvTOkw$79X}~;C1?$!#1+twg`Jpa$s%@YOuIqsM)#utNDAA>qw3x z$`!a{emEyzzkbEcB1(VjU7-=}N(vYQ{6pMfG`*{GH2FzLaB@*%jIF!&cDKh;D+2?M!azU+3tk}egxnobEH2gPR>Un1wm}O39)soyeK%7a zXkuf)Vg&+uWeZN2wx6 zpV|q=KJ7cwIdtU%-Z5$)1o-f7@@jU&MDpEm;ieGlV$XTDj)MKMqr|-NT$QepEdFR&nQPc8`I{9m{^(pv>JrYaP3L zOJiu+h&VOy`Eo_P)z+En+?m94K{;iGUG~13+s~n@@J8ES_Js`g>Wz%2sMhAE+J$AA z9zuoEKSiQexGwEpL9rKEU_INMiF3(gQ9DX}n-NYC{r=nOuVU;lhrZ}JJl&HwqM_p^ zcXR9s%ezyn4eXydbj;4O!dkrz2rB>OZ!`v+nK&lmb6P!qELcqt;wid+oq&{dr4QKQ zzsBCbRy>1_R_Qh{av6;pJcQrr6?0Z!N@$;$NTW*wS~d@xi{-BhfQV-NrW~56dbP2G z3WmdOa8k3ulth>htEp7!L$y;-KhWDNZkvZ$iypDKc|!RRmE5gAjpp_mzeywMsnG zeF9F+KqGQk#LxGk;a%d!Wq~n|>sJfRwl~UZYO8K{k@p|^8G!or&QWS5U20|+59%Og4 z4x<9STT0tMV>HbzEOCFdTUl%mCHnQPN)U!REY!}yLH9sLgU+W1T1I^hp?1b!j~-Cc zYiVX-WJN1w)c<`Z<45o|v^uA)?Voe|rG@s#?)Hi0__pB`+XD%3Xhmds{G22d$ zA4gra`@(+h8kLA;^Q%{C@>(OX2yX?I^3F;>Q=`vcLWn&anKCHWV{D{XWltr%qGa(( zuXG#w3dZC2`|0V&fu6_}?h-en{2XDey zj+1&laJdDfHPReNPrJ=WKz4i%jg}Z^@OF)Fu96n-kM`g|fyXGj`k^=eq=V3zR|$ zPubc|C8ea=!Qt51+1chbCQ7Q8lZeu*fZirZ=OYD|o5}64X<UYl+riKiT)nvQ~u-EeXE45y-s@$#w-)jBfo|>=_4Q$M| zs{kZ{8F}KN{((Sq&dqru1I~nDJ z4U358bEZE0k325&0l~qWTl!5L_T8V>NP94Eq_Mg^C3Sg_U~(<`o(`M#(B*AOoT`(> znxxui=RzoV`!I$*x?qaR5r*)QSU!5SYqjb-at2q|_XK!rvZU0|Nfl@n02V^W?jzNHw9#rw#N1J>unn#a03b%az+siD`itIOgBQF1;}TdK2}5fqx$!id z=k{3s9Uya+gWC|Q6%EAlAz2gbzCipake1L~Ksw6F!-In&r)G}MXz=Lh>iW0v{cT~veAJaJ zg@U@Ftzj4TNe}K9*upQq`|KoaIQ3q$>BP5~bNZG~06kPFP)awZjSe@be*8kKe1%3k zaq_k(nYg(4Qn%3pR3awsXAo=hUw-B2i?L{UyV#iX=J2@)7?egpSu{HJ4a(&Qm}&uj z63E}$fLk!XvGKYPa2062?&l;}QA8YmSmS_YiC%8mOlLGs%T5nk=p5sE2{!FeuoZEz zug5aZ?B)q6+NSrb%lo@*>fnJ4szORCDvY8U26%upuD&3Oeh$~vg%2m|bPFD8hpaT3 z;VIn^FqcSF<6dj?)CYj+P3sAZBaL)JM z>zuBQsaLG(6z}6VO74n?&m(8@B|*)B!yJ51VEs8Vq|=*VvsZt8oS}{e#hxOFE_eKk zD=8_p-Z{pT%J!IKSmC9dnARd>j{}wCKPJm`qc_#W!~~LT{F4_}*&!SPIWMsvf9uhC zUISkyXoKM!P;_)GZR;H|N``5!x@|Xr0Q(*#Ik}adY}+5Gh@de8RjSc5-+v8mUVt1G z*JRu+=C*!-{~#Q_D9O2#jsaJTvTb+!a;=*h%aZKS54@FC$dzz(uT4 zM(YWfg(KJ=>aUiuo?iZUi+l?iPux!}C&}`e2OTn_gzp4%RSM|R-?sLa>R555SL!S1 zQUlBz4y@>1cf!!1pR@tMM61x`N@ZC1%kA%oc5+hG)ytWUhSLN3-EKIJV5uDjcUCr} zP~!H-r={J-20-(rh{>HEidGXo3T^I z9G=3xF{i?^*tX^I4Ws%8T&TZf(_vko<{isW^gR6ie&jytqUqjxLHgQByy)kF>S=|s zE@Xit0dBu4EPn}qUAtei@a+7aJpNMXT_R;nD2JdfN9EEprzy&xB4(tharZgVx9H=v?2>;NjLV;IHSM)nJ<+xg zsn%SMw0~1AR5X(8e)4Vu@tH&eoU{AA<@_I%7DsG@Jre?d2T55GhLU3H*$%hU8`hTE zcP#yFpL!1v^*lH}@Sc+OO%HMF2oD@K6La5~A@dK9Mddm4(OR{*va0G7+Kk%K2$lj0 zcO~5;x?KC-gOGzy-dSY{gj)CJN{50|02v_9C!zS8Po_13JUIjCfyKT%M>jRG z&$tiSgJ9@hF83vbmXU5cXFxCqK%dEvwZ@WIC~@(Znx zn}E5$hYm@2p_Ic;pUoykNx*hWPR_!G=}$B4i<9kowdt-wNeWW&PCv!GMprf$XLu?} zq8&@hX`(0fYy8~}OSu4pe-&)#H_|DCf7X|mL;o^~15uggn?|~j7KsvLK?F@H=#3*kseg%cYKxh zve{?0GUEm?EMbMW_bj(MWIiRd?n(&SsFqr5*2O}BET}8nZtPgi%u9nB;88%W6~KiF zw1l1F;X0bPyl^*w^0k*)7s^3Al5OrMjtws*Um54=MFmY`_DBc&MT3?f(O4R%(Sh87rr!Ox#>eFY^DpdW)zwfmrF z^Kxkee}mTUFtd-hStzHz`DF7GR##W!&Yf7@K9dM3svjL4 zffr^o;wS;vil3hh0eM~XJjqzh;vc7u-Pq);m>|KtJPXSCy$J2I`@RDp#fLl-WatdBUdVP}_{}_XY0U)b`D!^y;wx@}^w-WEy|{ zXpcH(SullzSDY3F+JW%xhXV^ug`vD3CwurxZ(?j>k3B}?P#fs9ES zz}sqX(vp!`oZsX@64|Se!%AnwowM2B7l6aup=#KTA(#XTu?WEjE1w$g5E~^WF(Wz; zs6V(wjV95%bNa=4Is!?`+#I(^)8A6i14`JcK+Gn`4E|0CS_A4-_`cgNR14m|wSf!6 zsK%LPccqH_!-o&RR=#fPcNZk@|1J1I`WEar1)e11Q;B{Y*dt&GdMn!J8vK3aLhbP* zt2^8Ci>Sb>A19`uT)gT^i!nXgpaqsdq#yN!?Ecas!st zi_F@vwl2qdPueZ>S6nQ(6Q^I`o#JYBzjD_NjqI`+uC(5wy?H6j0g=%HtaP+!nJf~X zXFI!Uw%*w@(-RRO`?9!~c=D);OcZ^*jq8HLV}zgfRqEna1RA&Gtz}EVY0OiA86ac6 z!mouXq=62}_958AhsBroSZI!$m#nO;ZjKTTsuh^&FVi0#xwAr1G{SSjT1`O;UvIGLh1C-3~o!S7wfIR=Od3KIyM&*=?qS>@<2nn z<126Zy`B-?XjKM7I`cB~YaLIR#aSd{(q4OT?y5u%+(^$I%2!3n5xXYC*fS1lUhiXMCn*82psaM?YC2Xwj_W^8TGk6R&|Y ze(?MEEW1$c#@5?3$}M2-2my@|3Wd-}q2&&G>j3T|O)s3@>6dh;fKOcH#n~N3>lplh zS9sqFDl*aw>|j0;nT7Qw)FjYkEjAs_UMsSMd%?`iEb{bsKaA`HcZPH%U8v;b+Gpo;5M9?Krdlm zwrIjd?d*Mss0cnWdUpOvVtcXAvcq*vyHVReM&05l-~8JCnJ6~Rm-=R|gI{z3X3W2b ziqg?+jQV>Wg={qn;vEY=_kNV`&N2LM@M3{`S5}Z|ul+*hsDkL|EGoxdzbx#S^$mp{ z(SC4LBGv5Mg89k*hB-;?tIdH3H4q_17uE0z8Ob z#9%1c|1n7x^ow8=gq1z32wij#4@XPXN|*Y!E5R5{qNz>ydC+K?<-OtZsv#{MgILOI zV`?^Q@+a<%e6L6GJD22GhAP}xhPw9WJ|#`D@1ObLpJiig$pnP4olnL@EUq6lk|kv3 zbu6jSkJRzd#xUWV*445}MCR&zGGQMuk8PW(6yuiD3XAhp(RIQc-HvzoskIsh<0CQb z&nSeiX4bgOTA^>1ZPj{0d(}GALB+)7nd284DdpxtKp6zBs!=L%U-y<_e%M<+t+pmi zd_ciPSFwko%|jOBf0kaLdwXbkO`_uBeoS@03>Cp_GXfIE=Y{8KFuS&u zGRp)ou9iVOk%0bw`^(uGp{p*AW|+8xXDkv6RLAw|nRXRc_klE+jnV2*-I4AZ<#9dh z&$yU3Ht&Ib_!W(d&g=ctB_3=l`)An7Uzy_~GZzR`s!H_R>iX7yEOux3CXl1O;O2}0 z+cpdon2GH|`c6pU%c8!!gy;K9gJCy0sP-ui^a%|To{rM>zq#wWTRJ*MFj@RW z%)PEefjLk4Xqf2e*&6Ttm7JiIi97}UunL@`W7~6OJ_8PW(|VD zV$pMdx8oowi?rZ8uCXw+oJajqRz@>&^@|Qpz3)dSJMf9AlJ(O+Htz|voum$rXoC%m zX%B(Z+@Ep%yE*s8P{ui%+~EW_#bkUBh=JPHi3VG(9q6L)wyr2N{w`-#qVB|$1Jr6z?>Zq3#L7AkgEyq&ni zE*tekHDFk5N$xLtol52tF*^#c%I1{E&o>0HR6HDe$iMm_Y?l#e@?x43n zLNwSUTWZes1b4H|8>W}Kkc^Sn;!blbD`9Z=A?du5z6V0yW$NU0X3kG2EOK;O0nJCf z1hbj#4d>XSmXL2GfO`hO&@9Rn6>t(Pa^dn-^i%gNx?i_gbgA&V5u^LdvW;m;{MF=u z;KN6eHTuo&GwYI{mL8Ap8ip&~>i?>gvexJJuiIbucd`EatCr0HYRpWPaIdOF(eC#A z)XE}-hDJsp>BbFuc|TEYR{fsQSA8@`?j1kvPiOHsO;tsy)pb)tkd6>R*n2C0dk`Ffm23wEA{N|Rz}5o7 zD2ad;E#7Neu8PonQ^;7P^z2W-8*q0SV4Jiq-XMqaiV6-3Z39BB5!|S~cF9JdeW(|F z)Dv;0$Zvzo5=rm^a@K$ROYroPuE4%hXp}Z{_uK>%nV5E@`}`hwnSbZ0U~b z*UqG^H!5{q-O1%QO0pgrpGs^KiGA22L){wuqFRIAPFd7$zZW%ix1tg64KoW0FH5JJdXWND(rYV#@?e%5se}2 zD{fv+35E$#QZgGZ1PGkLL(PFJnoyMlA|W{S*dd_eor4t(YCqgw(*K`WvXC*RLdNX| z;KC(edu1io>HDMpikRk2V3}FFlr?{V9oRcviFcjX64-Ho3J6|jqHYVyv$lt)$9t)= zzT)o;%Scg>a*F0`{3#DSEt(1I5GATwHKR=S9T5XlXy}o z0r+7+o?#-vF{hQjJiLRjytI(9Qf3~dmJ1x;!5&PMePlbEB}td(nRJl2b?KLdyJHg+ z4G-hgZe2&CT3_#Lh6G#?-TfwXqN1itjp_FtZTiHbQN3+yw4Ev{H42W|-ZC7_X@SNp zJ#`-5Sk~BJ-$Rs+rK_-M73=#+f7nD~+fqzX)qG}c>^e1Blq3ak2p0wZU&4dlhK7c6 z!`5D2Uc0 zkdb-Ay^|oRJXhrmx2WA!1~45WQ}EC8Is7osQS&)QSTS;ZVgg~8rpozCAq^|kDkS>_ zarK$o`k+nhKi7%@LJsx&8U!#BseFlh$rgs+*g2J8rT$r|(C%e(u2Q*_MN>eqe2p^} zYJB|j&O|xJgEX{SvCR;AOr7~fGY(2;e4O2H(w_Ly$f(dB4V|HyuVdDA*5=G*6v~rL zz9aia%T2p^wmWDxm*!Lc-4`mWV^225oS2Wc;{)P0WD_)4;^q4v7uQXipc{awjc%RibSOq~$ImHwd3i{Q zbR@VNZf6Y1gv@~U)p~3)S~U6aRNuzN5Yu`+8fD18)7{mz=5!NwcyA%M@()_toB_fWDMSw^&ulu8-*Cq^322~RGrn=O{muQW z8Pjt%IRbr&`=JNrj7-?>llL$?`29T`*7=wGHP7W}j_Z#?dW!Dl381VWj46Gryz5O^ zHyD#0J2J^tu3>t(;nK3}&ZQO;^j~kWH%O>ClpH!?Yb?82x$3@7_y~<>ol%`JcTD-l z@EyIMYyk0rV;pvN^JkOtC)efH$FKx44z*^Rar%4kiAR$Ee#by{ETfx;>M{R)43cu1 z2zlYdLuA(;VwYUtb-D3QW)Yn`*yjEDi(PDH%u+IyN}m|&2F!faBVE`aOu#wd0^zAD zOaxcl*YzA{8;xDiuVJvV0__SuF||mTOq**&>*E=iS>^KltnqPX;htk;W;109|M7~| z3tPzz2>Ew1W!j4G6_z_qi7Tt9tkLX)JI(hHUBR{5+S)*C`oxg>KY#x&w;cY7YSXY0 zrP4$;5{fMqAqzJ@T*c>ADX`f&$*P<30m$vM(4HpnpK0dRzF+Zlh-~jJ3(LM4oE}Ki z=|TLbEaI;vscM$@I`=ao&VwBDTTYeu?;gY-p9v6P&-NrA&S}BM$jFEur zCM*-}eSQi8X!Rt4cz7Ma1%nP6;1--7w>qNDP)KJoUZ<XlMN4WQl_ZHJl zXM>~82eg67Y24U1t?xf9@w`b*&5OXVc45|$JOFwJ11A;mWNT3R+mDqR$M*;zxvFUJ z=YZHnOUHZoD$2Zx6uWuYRz`+-=ccl5JVW_2=*!=X^Wc-l`{_C(-Q6jb+s48|}cpd%b2^eoA zlJ|dfZd55p^JQ?q@?t=xSsEGzwKmr(xgcIn3D54*e3)%(xA~_|8oEbU(dgUjlg9?xIje7cHVeYLC2TAV{UK!K$jsV z1JKL;VR%0Udpa`t)ACyqRlwN2F|l*r@#DmBDkO&f&*Jw*0H zz7gIq?{g^{|9~T!8;rX-t9T@$RhDXj6$3{K)lZmC<=oiQ@0j2xFP4k_ktg09r@Ve9 zKs#GU@8#{Yvs~CK@>S|b1DYF1KSyqVeLKPG5H#;qzfQZ$?{ATfmrF?)?CjSO4L=+b z-6g(yjW`dpN4I`AU*oC)Q*ezJ3`Cfp<#TXR=Wi8rbf_nMQ|yevJc%AKN^rFi1c1Mn zR$l2vh*y0QaWq9OB(NdIil|mNUF3|6k96cI!&BIKfBK>K5NSyqbZb<1o(T2;@d3z+ znpddYPmYgwmrolTv?#Vybm}>F@9TX5dJsG6wld`52}j-Ess^1-96;+fu(rW%!Kn0) zOwj!U9u0^y9Ave|LVJNIcmRX%0k7{}a73hYNP>m7DDk~HHbaF2&Zpz|Z;zqRnv&3d zAXy4-%2+cv0W>h;xFml6&vg_#e=FYOW_laf>1)N5cfXTmxjW$~WXwHYPsjf>Xgr(8 zY#ES#I{kZ(;6=n||2Q}dh94zvv|RkT7GPcc*K&LOS?W*~Lwmq6 zMI7sr$axYqz{DBouG23iMrhrpKu})i`H8^V%LSY$qQt@trq5s{G7L%-@C8y2c%GoZ zFVGoWbDuq>kEEHm*s?{g#fpZ7#eA;ntbts_BFG4Y1bATf&c5JG5ct*s22&beW^1pa zQ_A;i8jWh~^jOC;pM(;04I$SM4CQyh(uDKUB?9=WLM_nt4kz>);LZwu@R-SV!zG3y zKw8FwLW)SCRt7SnfZB%)0;yN+)Z|+hoQ0R3-lz*Y_!xn%IGol;1tw+)w>Pt* z34OH*#P1OlJwba(Ci$?&>JqGv+G+O$7~JDC(j2N&_>qkBC{oop$KE>QAEsq(%nP`iTQ+j7V%1SB);v-%YVOfc3M%s3v~ybCA32FPu5wywR9>5*p{u-e8zVJrM%u!T zSSUfS+Il2?){-nB^G4f$0MA-wTgv`iDNS?v_Mz?RCo0B*wgo!!4NYph0>}ALwWl8vdWPNuu z*8l(aMP!T2?46L2y;q13iiFCJ?2*koJ1do0k(E*QCYzA#Ju?%sWzYM0eShEkKEHGB zbNa{Uln>YSzFx2A^D!U)U?x$=82@JATvS@R;DSXS1Srz;k-RX_?Jq!-6sRiDsGpn+$;s)uw&INqA&dXv5bFc=)S^vnO|2Vyz1Xn@*E%_T`v3J|E6eTcNpzlGr zii1UYG2zF+YT>teSm@E=T5mec(cbEJF%gt0Wt@{*-F7k6_v>YWdmvkGR~82K0{X?3~0$opEpajvetyx}Xk)jm@Yg$$80cKat} zOQL2qo^##m-WB&wScQl%sBuDRWvWpCF4M>IkEUfK2jO4%sCv5Hr)?%M_wfshf%&z8 z>3ZeeFD)pPB#ug%)ydl*l#fvFqh6d-kcFqCw=Gm-t>?d;qyg&V36T*46zmCeNerYrGjm-A0d$2(*vF|OgdLV~#{D$)#3NG^g~#aknHlGqj}f#S z%zOE;pc)%$CF5U$-5t@86;r=&3Ji_k=g=!ic^~l93(!q#qIq{L^7c-S%)jBC{&0?bfi=%`0 z6vglIM;!oxm+73~FQ13zQnYs+Tt&P47Sxy3GH><9*TJWQ32LcOj!@eLnoNZ>t>rUt z4u+^Q;P^9%u$|j?fWL~Ch(>6Z>SfgSEcOpKT}*#t8t{OCIwD*+gdZVB`-k|2+0F|V z->yw}z|=KW?~DEyasVYbtqib(lQnZ}q2A}{Dr$CP((4lquL@sQ(tgu<_izCk!XNI`A*KH^09Zwv^U+~ekRAauDl>FFput$c zU?Udeew5hycj$De$)tUj!HA8_!qZylO5#xO3;2cp4P62;d0Hpl#OzdvubFFAXzu z>*S}4{dGfBb-))HFwiJHn+QWm8@#Omfne`)Vf@I`ofS^*Mgz1J zw%6d%wRB7YBH0WKah`u|hH~VC5JQD8fo%HRp73q1B?*I9_4rg6admb5)F*r-Zx71D zfG>`&u0ck;e9pIo05*V@J_T(MM9*Yu7SX+<^uS1kEdgCM3~uW_h)!SoRYS$-`(PJK zZh|i-fcIzc{Ef~Mpt#`}q?YxQfOTx~k@iZnCd-vji4G)#Pb0o7<-*Xdf@RaL8$7N1Va zenYLu4oT5#Ada2gOBI^TxuLwZ{my!gbgVI<+K5)h`WLmV?Q|4?YZVOQ(MK(cb2nZk z5Ohz)GP$C%%jFMiZ7QAb2eh~MpPF&Al5FA=_RH1%lVx&T&Ds$m$FF=~pUf}SqCzkE zm+h^Y`1*zCD!ydlpyt1Hr{LXT$~Z2HwrIL=LqxOypjTe5i;hu z#pC>dEAN*#=Kav14}PC+Rbv)bOM8@-mL7Sez;9{QKd)HY^NjPKrDjX9;spi@=BVwH zteZK@_f^evc{+Qd8g>@1Ecy9jU5`uwrxO?m?^tu1?d)NW6I{uZfj= z*EIz+>rf&9EwX!2d}#e`zzs(?w>GpAjxDRYrlzH-30;&8Rf9O~PM&TCG~aUl`;hb&Om<&x`?k9%u=X%bPMJ;IM%#9qtmP!9abIMLs$3O#a6o#ke+l!T7+VDKvoR|Ad>Zj@zy-Ry6a7WDw zOeY?a9WAKdZ%_&C$qJ*l(+Ikm(jK7&M{sbA&9&q=RO}4zqclCpbN!cV-etqkZCQPd zWpe6sun}*9x|C;tb#ZB_;282R2g0&Ewq_yjgQ_sigyz*VGJiRKb|nfAj4e#QRN}#{ zf<}lp{UMCo#ChlR@iu?HVJbGL$KX*!+tb5`W*{)?p7TEVSnBM0Qe>~7fO)g-kLqD@uOy4q6kH3kzLKv&P%O9k4??Tb=X`QuR^uyW##M|P7$)+IP+s9k#>W1XACE6o5)iU4)s8_OqM53Sp6Mc zUAGk!Fo7>5p+cyxSBM9hqKyo{i}-?D=N83?x#GC)N&CNH{O4G3FofwbL>xsCx8vqWdz6E$eA!Ns@-uE5 z=y0~*bcw!z12}495U@-Lcbr`IL#N#X<%QM`!q}J!pA?WCqZ0{WwTFVNU<4!?$zs(n zC&-+Rb{5foi@Zmru03_0|1Q6M`xYIF!Sx^q1r}SB94Pn;jH&}5NfT46a2H~0`pre& z)4#sd`dn{qY$~qilFDn=#V9Y@J2ihcn_9ZBk>_>|5|97ABASqV!n{->=n5K(k8xqv zxy^?Qs?r7-c}bT)enIRzZw&LECo%n~sd%lscs}d+;nsD1i{I37e=erh$MUlBT*#jM zI1^L4F9DvNoA)CMVwv(Tv)@PZI62%Y;!3m=ZZyR^s*%lTEJtU@IZ7wn_Sd>u&^5SS z>D8H@H{T`lp*DKF(1hby<|xx`M!`0Jsnd$6%P!mVC7qay(ei>cfO=N zM(nwY)!Q=Dxip99mK+C2r&{tG>`^Bz9q~v{kZ*^uCw_uLk75lbeH7f{QXkYffxHC@ z7h+0004@gyBH`b=peM@g`1w--yvt6IzNs*2X>M)|p{=NwaMrA7aA>}v1R=A>(N1Q6 zL|1)1ED+$N@tQ=VuY_!bZ$0iP&z7?lAO!{J=L!<)LcoWDZo*AEfR;GuONhy4>?|>+ zR^G&xZg(21_mza5_S0sq@bjU@*cvp0er|bq5{0_*@LA;M z4zpT~_v79xG_!~0-l&(z>!%M0YB`$6Z;S?t-n{wCe4)M*azl@iV4%tfdLAd>&lm(y!~Jj$^fo79;8TCP@P*Jau7;JjDA z4DGLue-7cgT>U<0H#1%I*zWh`L}P3C4V4gF5~4Sv#KK*a>X)c<6O znVcSO1Ht^Izj%0R>b!T_vt32o-uG0rX8f1=eO$I6HWJLVz#z&Gt}9&lImVYLu(Wso z?mZ!G4dBD)(l79;+sEW$3cyY0WVy3U@|j>j@eaaXXPf-~AbR}9-HPswHG7d0pMRb* zo`2rI7wp}CFSvB7DJ65f%qq3jcnPiV-Sv`&o+ywE2v~MeqXVDN-5n(3w6DaEB0fw= z*iMxYp-8?A&czF~_?EPn@H{c%m1s*zmY@`P+mn**Xfn{qpn#D;RC9&t*Uk?D^o=&MtXpy2%Oz27T>}rbwTH_X<5*>8}nBYo->ie&O-$r*|9_YQPw0+ z5VflOAK2PY?2so|7GbYnQ>HsDq74qvU>;~f4Vl=(F&|k(GjKmC(A72t2VEjH|Mx2- zCHQ;@Z^5Uc@^@~O0&+)-4bLsajmc4$D)8Q*={UJx#?3wu8 z%5;NK#1XBSflad&q_{zib5X>6jVll1V`DvP3#T4D$80i$3`F;|83>F@+gv*}GI*fT z?i);Zw{Tfx$&H#Npg}q1>t7n%b6*@36Nf>xQ;YB2niK6UYGZ(xvmeKc4bP250Pw?5x$b<5v`x^)lh zU~`6V_hfH1JIC%`PHukdV6-$3Pp{uv%5uLXcO?ERNFyqvq^&d;REpJW0$WQO%; zLNsU2#L7%YoiK_A*H7g*U9G!j0FirXB_%o|GCDY$z(B6%KYK(;8G%g`l{GDa=^vW4 z@@B+Fgql#yYnKzU_{9oJ?XvERwbi&x6StY8V_r=v76&M&l$;muAdvSsGOlGylSoh z%C?~j8Knrb*mdy-#qS8P&pnV>*E=PtI%&_m2-v6rV`Y} za;6{tbpH=A>Px5(!Gn7R(8_QwYt*xdkL|iPK;&|9CPW=iF(OF(iAEXO@ljo}Dz{U= z>y|D}zp%7ush*Tz-u25X)GUwxLUG)1YwqIAP~UxUH?-ennOgsvo+IVB?3KJ<7s^C8 zN|bRihlhu$g`da%BI3vN@7sdGSR%P+VfjeL_t+TZxTxPyAxJ-+!>O`lj1}OFZhwSQ z0n}2^9II>cFQYdSh&5D%pUKJxm;%?LkkKzTH1Hy1sA)B???Z=qfDEA^Gu|h%@ctUe zmL4myAb`LFr=8!beODP@$Nv5M93BR254RoW$_TuY!;SWCg^Sl8rE{;YMauU6nIBCe zL%p;t?oSlAOue^H!^#!jT%=y`!@pI{>>#W(e0=0CUF{3R@6N>{JvDIEzQ?=TQ}x`h z8O0;{WY1BkhuZtY7qwUH5>>Azb&UtQr}$z;c8|{>L*0p|eGR|XC^WU0Cvem7J4>h~ z-SW16B?4yv59H)yBRG6$?n&>(YVH0ByZ)&Wv?I~bsU^ed!a>q}O130RDyR2Y59 z!MWQEXj(Lo5aKU3Fy*2c2?n|G!e-e$)Gb~J`;E7)c(f_2n04{bgs0VQ|0-0tD8^GH zrT?^W6hz49>LILk)`XYKG!+75s$MtENlTdg;w{i0f6bcB z|JD7WsJDmmgU^Rmu_JRgd3eYNyx}qqr{*h5tp%P{>SAa?A$H%llu^EtLHd&0X-H$g z^yK!){ZfP4`VeqY^jA`9eIar4q7OLNSF$9OB)PZYF#5a%!%ix=u9}i0{7K?}^v^{_N2{QRt^Kt}a7@ESlQ zCW@Gy2E{Tg?}*$}EW8H(W)X+!NO<=oYv1;lS>H!faHEgi>CqeoSnHUfBN;Rg5O1n+ zXX-Z-@soh|(<+M^&R%{mF_sq+FZZSP6xE)nUK?4L!a}`zJ!I13F;nKVXlkKvE13|j z8`^8u`JP(=zn)b>)FGh8yP`$9Zc?!j?^MN73mJ7&KxUpUQIzHmh7-RKzajPMjmz@A zv>6bmD%sVTb&qyBt?|2`#t!Lu_)G`9WRM+On>i*sKRGo^%}+8{3GI^cuKmlU$;m;BMT*=vpTs64pqrD>Qe6OFlw|~Y`<|DVpiB7_phyIrbr2qo z|3FuFSaK5=dkLXr_R!~{Z1u%g16(%{9qtcYPBE>+NZvJQQ;&iy>U4Mr_FUbBX*BMv z{0{9=$CFp#>UBKP$nY)MYr0}6KgklG70vJ}OTfSXp9#jP_m-+% zH!vH&yi=01d84Gs&W=nA7a z%dYo6zR|9&t_6F1-OjtB+S$d2q!b(m+B}`}3BxH1zp!1fm+VLLRs}YbDf*Ox+g+G& zC?DJrQCNPLoP2XPlcFp0&R*eljrs8BG*QY>CAV~*Oy@6`6Kaqjud8*3ONISmmjCVT ztEU>Sh^r7hVA!aDj9n;=D|7j=HeE*>Exn(Z%+>qKZRZ z)6$V=^Ot%2uE{dS6TZn+%~mT`aF5+8u<0ka`_T5%^3l-yd@%$45b5gHD*c|7{0G!s z9xo{^Nbb*To|8^u=!O5*PE~9rc`9qe&FU5sK58H?Cz`b z$xs0&ucEw1L%|kZ9FvJ2+405V?A8&SA>9Y-ZFz)xfTAxUl2|diber+y(bwuG%zQ$T z_@v!tVVRS8*EVNvk%*Sv!$#$>d^h$`Zc!Th z%KomsTzzc~uRY)49!&VL>a~P)1GQ-;abJt3JNqG&(-cpK%x?DqH1%)N>Tk7hxNB0V{{Vb*RNdB6;Z&p zfCK=Y^Gr@fwE&+v7g`Gyp;b_8i_YYLZ^;UQuvY(UfBcBEh6-G=;{yDAQXIY} zQlcAVt)|7Z;38}JAZ!yPkgpd37bOXt4|YSFCi5@@e4o&0z&)F^-#ypl?JV)w49#!e zF(V5(65?}Dw7z^XRYf(EQp*;}uB0-*!u(61`YvKqiPua1((fi&zk?2o+tTCK7}-q~ zlPHYK5fp|h?l$SRpUoe!C7kwF2Wuxi(xv~gwAZ|}MF};Kw zBU4yVCLdo=>z|zZ^#beVu}s5^Xd`LwHIixHebQJ8D%32U!u>xz?N#2EfmY?;n9YSf z@X`=9xYD%^4DeqLPW|4K78Dc=-s~zVxz*If{o)Wy?PL|g>%qENueD2r(o7OVyBpBD z@b4hbs?{9cYpsx7QevuN;o?#o*$0BK4Zvv>3Np8M#+o{9s6_rmXezWY-@HkLuG@iT z%y6idL#yf_`T~3d0IwnCYX@}$u$4fo&ZXJ#=qC|R3frN_V;eRUw0x=Zmz!Z~HuWu- zH1U=gHXN__qa2s`fJP7Rit6*=|JYq*kiQ{Hh=M{x{US(VND^a?w6L_CAAgoo9--rq zM?&-@!0<1jvh8%mB@_w8mzJ@)z^S#lq?s$p-@kMku_3mjBOTOlS89e{3KCM%9QdAu zCc7N8z0NPPYZIHA7N+NVPwEqBaD6V}q%BoNO^|uu-_gy`qWRv^@SaK1Gl_87s*`(< zwZ3QRIg_S+&KiLGGRV);GRxHMvD4`?sjWc+C5j|-bRq1h7yEZrQ-Fz;?cscb$5EOj z){^}LS3-mVIWvoFI7m}|Z=WtkPVpg%q{IlR11mPwKmX7b569O))gc5AyU* zrD~2>ioiE<)9-K&$7i=+1%{%iH^j77ljliP{cwAMaX#WiE7bu?!d~K4(oTfVIj{ho6*+ETvIQi}Vl`^ zpV^PBOBkqsqKvnl$)wxdc6roVXs<4qz8R^#!J81fuvyDOt!{Z+mBq1+S>fHl^Gs_h z!=DS^J7N?rzHR)XC|wPAkLM*l7Gylf`Eb1)_lHxUyr(MjG%|+=%L8jz=ugQOn|%WY&d%pjjw?GLV!cFF7f!4(pE`t`T! zfqJ=0zz{$v4LTPC>X^rV&8BmzId1)Rm5>~-l52b>w(KfV6ZuJwB)qV0p#6{2?u22! zo>h7M5r;+9!+scVO6iA^zy0f-W&e9IFkT3DOH-0!Bp68e1eBEz;Z@TN)`3=!% zz(4C)Q1P_OF3P(qyY`#qooTvjm5UW4>>i;VYGxB(Ws7?C)HO2n<}$(p`Ah6dSoVj> zgW1iF8x;igT~1k0vy8H9t?`c*`Xr|R)qHkd>91gicP!A7I;ko#G*7dB#Z(o^vBIXW zy?*ZwU9BcpD|bhjnabAWpA;~=AQ~TL{l%k?&k~znT0C0&RYICl_YGA(9M^pKD$IT1 z6HOLRGqCzRix2`8q5 z{FaVL?S}B?_ZL`<*0v%aXr}PE|kTX>B+X!Z6 z>3C}}FyDmt%0%R&_M=BPrXv5$+2H*MBh>p~eD`+HfX(KW0^fDdP#Y%mYsnVWwo_Hw zKlU-j9+d%6gF+w`GMwRQj}0SF2R=)`oMTvr)%R$_svN_7<-m{1gqT*jV>}#b#T=~q zF>kqZ+%(M8FZ*mSY$|+S9%8N|%`k2JKxk~Yx+-jTW+_Y)Y$>2`41YutgDrD}O?oQF z16)LJDMa;h7)8(B{75kAa^l$Cp9lna>krY~zC2|DB(FkX;T4u_+SPvm$H}Cb%gQ6F2uEn37iZk4uJ0$pF9Mwb7`88J-F~ zw{olt&p)KKHM81H)wJ}JSkb&ksKJycglZYV!6wEg)cV%(HiWF!Jb8o!1`~%%w{Nw{ z7DgF;OAOQrwqyA5CYZ zR13f81w$;hJ9@{PcLL1{-HXfJi%{pA2Q7yyn^LGGo|bGfoVnwn+pW>qsOJ>H?hb3w zv<=5Z?B&XI+&t~+>ncU&K2ICy3Jq-pFps_mOQozU-R9)$hg?q03A$R~?qHtzXLI#* zr9pFcRD-r^0cmYBJr2BbvTfxLwwbk=Z+K66y)lLt=aUZ#9tCdAx^(uH1^@eTIe?av zLQZ&%fKFkdWxSP3N!w$q2?xTN31M7nu@ynz<`@wbIui7HH~!})x&eEBN&55Bl9DZu zlV6(stxuUohz<4RBafhrG=U?kwTyvMg266k9o& zgAHK$S50M6-tcqiPd2LOJr6?8k8DGY>1bzJ1OwBQ*1By9e-|J1j+gf$*4AH;83#?s zI05uCQJtg4tPo0H3u|}6i=@1Et!htqI-$HPygN)*gz2I@!BQOuABMcD&ngG@9(GL) zMH@>utIm`+7q9tsiih1*nhG{Z*|%Pf>&MSieI&3^NSxgIO#E5UqJ`OsspBdezuv_! z?dJ^iVd!_QreP8T-snXntu)c~G3{=t(mq#Ma^S%{Maue@brz2}RpuYPy(*bE^S^?% z91fe*v%^cL=O0aXH+FFtm5qF)5-&5v&EAO1WIsGD#}=zPqcTy?jrcyKlF~{Pvx%Ll z6PzAkdnw>^<5e04K-W4te*VDgkY91t&$Q(hFUQEi?F`^@#Hf0#Bha=WxwHYC z#`cs;?+pfvf_^J3oWi7085R~6PGsf_+f2;tyV&36r>kiv#WrW7iGGtRG8P`=m6Y zpgL2~)ujMjc0K*4i?g$1JH0y-Dfpu*niZ_W)aE=-iU`JrA6 zo6**TN|Bp4HIU_}Ac&e1xvdt`NXBEG2H>A*KjdxgXo&s_f(o4J&3Be*C^WnoS*gTW zpr^OcUZ;ZL*gCvHJ#e?fI&H+ZRnf-1N!{f-E15*LiRO%EI7174G|_TE`E_3A!V*dq zf}cejhwSR>>yNaF?uBXP_KApNWd94-(lYQj$iPGT-%e+5etj^%+EMf~=|{563$b+j1D5}yKyibdYILH>QyVJrKj`i9+hmMth+r$o2$0h)X}mawZIAk@AJwz ztiT{4o*Sl$Ot_rM#JRC{>`TM7o-WyO1E5(pSMuJXBH`)0+)COk+ha`sQI^r1nq!vK zh+GK)ECTy<%xBo`y!zRrk;QG`Rze@wxM84RjSSrQRpafG_w%P(HfhQ4@++q{y$jt- zpDe~xKQButLhB=>#!^F;zRcO~eRKzNVf`@|F{2D2O260?8~Pml4-Y*~Pi8LKpphoY z&*3-*zDA!k-If5LPKwie?r$P9S5S}+A(xs>^$wKRV8N|Sq5pC_P!OlE!yMl$jZdLv zcEV~}&Jh%sJo-g)hYSo574^yU&$UNOSyAsp{zWKSmE}!*!=W?`T3%N?W@^L9{>=L6 zgdJz8^D72wWm~|**GR81rtk&nb1A_x$v^f}1StKIGhF+XPp>(nN)X0k;+uxEt_<$& z$5U~p<`ua}^TG|crvdoB0x2afTgSZ}PaZxD9e(PI_fm2$MdTlI13fnhJjUy?d2Qj9 z=T|}|XiRDi8=5?}#z)^I4t(vub5DqxlY-d6um z4-1T%9zOLAMmMLHm65}5;;~V^v;}8K8fcC^{=9$Y2oN>4tv$IC+Ea$kh6Y<%AlzpI z_H|9hjXe(e~_f`a@^I(?|Lt z0beD2z8wfLY|M;T2s$X1@G)Yu9QB*)&M#!J^$=7j$R{m|s|i z^Tml(Y)r=Glsuk_q?RVmADDdQMmHWG`N($j62VPk13NRlj*tNvyVb#sw!;O1fuqTa z7?~@PVuiWE54XnO9f{RDgciwgNnF0qX!x>;EDM4{6Rnj!dh|Oab*Wwc(M7DX`Bt2B za-TlyUlxAef=C6pgJ}<>imx6InS!$)PSR}{Ig9~dda|N?)M?MzU;6Cc-1qx`KfitL z9VpAnR-h63;y34>2>l4ve?}c9oN|MkR$HD5@J~p|$O1onprMm=BjkwbSV#{mFE2NK zG_u+y4)7*46@LdiTNK#x=g`*enTzvK*%?UV` z94GB6<#UJUAqN1RGLfgp4bfTw>4uD7;k@wv{l;eaD@Axjgh{!9HlUyoP5WP{GpJR= zJHQ5WWj6oo*Jc>CZcNRtO;rnmabF?5qtL(V8=l|dk0Yd0%=y#ZO{;`=ul$oIxh!jb zTD9GmWh9Bekj|m~c3oJvtKNtc)k{puDk3xCj?WQXX6HGP9o{;5-s8g1{~IqhIq)HK z*yWFbqOc8{NW@)*qXFnUIsH#ECngPa?K6(rHQd{4k2BVjYTl1VH*(L|HWe9evQW97 z2c-uTW+SV*|IX49;^Iu8V*+3~7!XvTw*kVJ(IENz_hI0$0C#Yt|kPS&dy;|=-EkT$|3X>Ur{$L$3LarZgDwAhGzq?-iF8>2G|ek62# zv$G2z0$?#~1S_4z8E+^7Y|mTVot0~p$Ak9Eg*h#MGiR=3|T{ zmmu6sgKfLYx%D8uryiZ^gQi*%`Ask(Mi-{TE5MDTLMf8t88Y|M(Vl|i)59pUiMdX3C`GBxj zr)&Sy-B$eTjNa)6uL*Y*WX%+ZDs^M{A=?t{l^6S zj=_F4Za*m#V@jNlmK2bQJaJ~KMK8w_ODUI8YP#tNMEe6Fc&6ad z!cRy{C-xGD!2{)0#{dxP_l;ksFMA%l|FWp0v$}fqDq01EmXxFQSRh~yfyL{`K*04^ z9?luzx3{iJF>5v4hjG3ooPs-OG2w|99{ea*eiSQ`!n$%8_4Epzk}CVdQ(KP%;yX44 zkv^E+B_10~fz1%;jkwB(28k)zayJ!KbXbk@P@){aeZG3)?CbJfP1Sk?i}d-1nfI5f z2Ld&*n6EQRmwNEe0Bk#01QXlOPWKyzJ;%S8;3)YUdGcxX4>ny$n&pU zC`W^u(oauA+MixfZ-=Sc^N^6UFY6FI5n+_g4GsVttYJe+5ZdQ!14EUAW0nz_b~3FP-yoW7Z;b*G<@i>YTr;Z*FsVIRWt$w zY}4-Q=oM&82`(xCttV{x!tm5?b&Jjz&o=ruedLtamiiLd2b#FFl!SyOM_Po1m24VI zO>G(Qv1V~56vS4>nA=wS1Ci%vw&*bbYr2Laa{BSnl6Vp)7Y^!kd%H1u#vRwg#BbdF zH2*l0?@IEE`eOAVE(!yV@0U5?YK^rh!bU#O);WRo;#<2D}891dj8$~)?D?{r@*F0RcB)GG7CQI z*X)7FlDSib3e#%Y@td~r^8OUj$!)q>CmB#~ z*Ev?PaN%-x?3Sl2;M`<7f4r!BnP=QfpZY2PXGu;$B7!RM0m_ZgkhkQb8TnHPb?2+6 z3OwC=$$kRkr<}33_x(p>b^GUiNhusc-+sm`Zv6Z@f9v_aHZ_lsNb8D(zY z%3^=&Sv-Dc`u%HbG@Ffg{f&-)J-37gK8Utv&fn@I3=DXuQ}*Y+9osE}(}nakhLkh2 za>GTTlaD1Q?Rz&)E@pStdy}c?Yp)^K)*E}3cghHSm7N{^B46-x{E9d67v^r=ja0qA z>16dWek{!{kkWvzbS!CJuY-D%vr>&v;_u))vTFv^xv~%a=sQae%@!*4i`TFd?KW-V zBrs#1$%Z){bY12Y?h?6lx{&aROErb8-sT`2pWxm4-z(y=cur;X4YNx#tC#(C5U+|f zWmQ!?RF%42c+}!8l1OFNJiS~MQk(OV$wV}VZ1v&94)PA)&g^ef$k4mNjLEPm_H z!d`yzRm6=Y$Z6r%J&8s$W0jkug^a+Js-Tk8TVTSFp02E@poD?AWa$o**nGvLeG7;L z{zl1yn$qLUAU6z8?O*C0p z^Ot{nZ`OtW6S+_T4Iq6TlGbdliH8kH zXbznnm;Iss#Y<5(?xbeLA2$5l(Is0bPu!1GFQloh!C3&wue@a-kk76|Rn2Yzd{A6O zzO-T(u8uWUp{%QuX0p_4GL>i)YG7b8b+b`E{4XtcRI6=sLZNJ^tcV|XBzsm_a|_=E zm0gWFW|6x&8D>OJAlZPN@^x_O=IgB zA?#n>vmaa?92fxJ83eOhT3W{0otD;Djq5k}G5;j!{4suqpwXASRwP^e8N~aAHxsmYai-F(%5u1CP#w?E*W@Jr8VS3K zQy3soTi^W9Gy3&wWu13BUL`gUeQ)kwpYgmG7%$9u^Jjbdc;%$H`w1aqXhQr&P-KC0 zfS4d=bceayq}2zEP8Oyp6aDBvzqxD4#)re+KTsJSWZ^h3xeHQFBD`eH>#N8~uJvG+ z!ehv9z?AQ4Bg1qe)xc#$=WB{id!TE&(IIe9sCg%;#w7f@=-1E0kDp1@o_6~xc$f&$ z1h3sf3d;qK{wpSUpk-hX4m>BAePkGj069E&ov^6c`-VZMcSG$px>v&wZ1C3mOy=c-6gpZHv*awTrUk+EsIfDVvkL$Zv=){<8X5`3A zn1gVBOT{)SYR4K{V_ReKIYDyY;b2pZ=RZqWCCE_jf_uSKPnd2g-C|}I!fJg~rxB@H z<)Y6>W$4v2DS5Ba)aB0faK=r8wiWr0X9+Z^CcZlatn*q8q#a4Vin`kxoT%%G`XR7|H?UTK0^*&~4j?D2R1%l^$ zf0nE!0&c4b-EP+XBr7idh_hfDlzzbR9E)~(B-a}{)Cn)UDaJvx$cW3&JA>oUgS);P z6lX#W@8T>v4WkX*s|!d<*i~Gtaag4~CuE?{hbHPWs9AwO#nKpLn9+x}IOUADp9jcr zv6SFb&&zHsrwSgML$YU->!8y@2U!)-Q01cuItQ}ud+^mc==wklG2uY^^LgY z!v|l)CAkc0IUfpE)U!&G_wk8i^iSXpeaIhkN+TJ5!Lck%D&jp={~$;ViLX#QF0(6` z_Xglb96RJ=yy$95AH*q63% zn6SH$v}-x28#t=}^(oc2ww*oz8g8!PCW2BU|Lu4lq;jc%ZcoW@?=V^jLcWnfa;)RBbGd`5@-=QxsmVb3! zpU{2t(DC+C&yM|mce3Q@=nb%V2KTMFmav4whZeob(}_8v&@k$YzvOTi{z10S;Vbku zmk1h0{H54|gZHjew7+Yg;Sn*q&)fid4#0O~>b5VJ`5Gw#+2GUepI~^O=E(>DXkrG4 zb>&b`oJ}wjc0-?YoHpkq_j~Obr#hE@XrVkDdR3c>-JVs7+zBH}jxowN2sT-{xi$En zn}3k7B89s#D%m#%p}O<@DnWQUFf{l-R$o`0U_89u4E# z7YH?=h2TK6up`(W;3zE55ZDDfoa2W_M$!SnC|Od)z2~*Rc4NvoR>BpqJGsvaG*qWi zTq}F2`IYEQhxmOz=MN@ z+@hN?U8;p%(d)#vu&V8qZpHF1+&KJb^A|hgmZ{g0--f5y9@jJ^SI?IQiYnwQm>LPG zaoK1YZJa$8vc6ramYI56(F+cZ$DMzo;QLg4yw3G}l1e|Msl8Atik`NCqK!jpi<+a` z>sDTJXqtl9(`+FSqZ%S$pL<2hiU!+wjH>$VeL;3q3`sqqCj&x65v}uEw`ZtoqM{%N zc(jJPr>l#c_cPSI%nK=fOy^wQ^8)`FI_P%O1q`%1Z@CBi8W4mz!UW8(i2`OJ?I$}k z)og07<(N3J3tLt-v(n%dA#6dvE)W`(TS?@!g@jyr@#2M>vzeMU2-_-W{0#N|K-CdE z-;H&IzB&YW$zz$8HX~3kcRyP$PcqZzb{Fe~0?94@OcajDz}ZDql1N|pkY;2_HRfM=wrTQ;WTUVjRB>+;5<-Eqq=&u*MR|D)fS{WaHn&+gZMvv=CMzQ^3H)JU~8lNtzwgKb6Uuo$S`FTYY(t1Y_gk=DvrKp`loH z^~m;<(V?MK7&)A?RdE$HQxKW;ExPt`F=n5z@NnmqSvx?p_BWRTDtC5v=qwpXa2yV` zbafZ0YS0SqpFe-1P|{wzjj+$HB`gG?Q5dbr@^PHtbGtxqr(2jZV}km5hqsm@i=?t0 zmX$U!UA z*2V?hxv8L?TouzEoj!}7eKJ({%%SX#s%}?BB^ukB@xYrDef9R$fpj@8tHZ}>ur8V*gFup`H@ZV8|IT zfjUiNg!(e*ej&3dvAvxYgw5GyFIeLK*d~$GSJhYov=`TRJ$R%Unm5koXQe^A;CJYs zTTVtw>JMV7iKWI;$51J6tk%OGO=Q&FGsGeKiCnNhca=!G9liwH_aj?D{au?l&fSBj zyxZeXO7#VVi}JpGa(}_JVC!C8|MHdvbc` zEPHbnM%wW5@Q!Xg zr8H}v1~S5{tIG1b+x-R-CX%n#eTMqV%H~F=WIN#&zwt3H>1Cl41FC3KN5Foq?`m0G zX?A*R`*$^Svc?1Ur&)RVDb&{`JPP$5xJ0Ymq6*)vdBs_7(7NDk@aEJf)2r4fB`!Sa z{{H0RpYS+c!|ZH=UOdEng`9d4b75|5(@=!KjI}MYu<|CWfac_7q+4(MXns+eN6$%; zK$gQ(R^P5Scesgqbg8Y<=YRq{N+JAy?X3R<%m?cD$8C4MwGDWRf5_e1Y3!$FGkdq^ z_>!+jhN`4CyhyF@@br9lw^sZA-} zrL=T|G=kD4D4}!>Wq`oY4ey%syuZIapQC&BUhA&w`rd*LLrR1pF1%3FcryY4I5Y*f?rLURx35RnodYG784o zG_!M_De}eD#M#;E4m9PY>837yj!{cMj7RaZzj1xKIxPnA*+f6K5TZqcU`0jE@)6&U zg&(31CoL^}446CYIcNoBp{vgXA*>cXiQ)EhiC)OJYuWc`K+8?+^#L<%$on55Jp65h znOX&SltzygTF~oJ0#_68=h|eT?ZjtN?R`ImW57Ve{39XeZh6CWRv`St8;e?>Ergbu z8c~s>nYeEhzQ_6iWX0FzC?xR^{~sREY@hupObFiu`bHXw9Bux~Roo;;diI`A4+?H} zcVWuqh!r^qgLuwB? zswY$ZtLs~0@RzNbRJfiQ@d!U5^F6c_w60pu{mm12cP-?+Hu+CGVT8Rx1Ce|N_#CmX z0VWvp1uF)c+&%W_IePl2|Id=z%;~{7`o-~wm-0pPsoV4P zlM#O=-}b#y+n2xf{osMW%<8uvTJUN5g-P5a?qqgCOYOyjnn;dEPT1nUPoebw8kuHv z#8Jp40k*atW4bkRg(6p0kCshgqQl-r^3z$MVj{^>BHL{LWCss0Ts;M04GA#$XfUt| z;?3gXBK=q-@PI~J)MhjV0^uiu^!xB|$ee3>lT4BmJqC99uV+9G8!AQ;Dx+6!z*CD} zEFb_Pg6F3P<^1Df@Z34l<-gP=hjJ21aD?KOh14zfE z*|=Ahn0nd#@uIeqGqJC2r6qjVkJgr^Dbq}${pAzcBz}TBa%KL$Z6{tuROafGXXrL1mqUl^D38q0?*4LX!%CZvw?)D`#~&O)f_p4 zbGjI@)}nEc9MMU(7!FL;i990cK8IDDjUtEb+KKgO!I{KLsg}*p(Eg$8vMXN!3YMlt zX>?Oz6+bW1Dpsy4Y@RW<4jDMlxHTfs%xPSu_%i(!3ARc(d;2Y{ zOL_L6s`k6HSsSOH0>x{;y2|?bm8<1vF!ojUjIG=F=>B$CiK@(>Ske?4*U(Y0L?;?e zPtSR_Y_?U7J_AjxhFY|(%v;#VDaURfvR=ze27ui@zIf5YefC@J!XRcF+DcG+(E6yw zgW_U_fcg%3RnCJT)4z`$nmuuz4d?fNou+Fzewe)FXt?n1iD%;Dh?mH3?X}n=r{DZB zznQjB#49{{ht4GV%n<0@o?+Q0&x|JBX1@AFPx5s)FGuQYxA!VNCSirrH2i~xpP%1n zoHCHdE$uSSlPRvm%Q^xv0%Y<5o>Y3Yw?YR1y|C68b8&o&;1s9C%4-ccFW9EEC18)j zGuQw7|K$`FpPrtcwhOxIgN_=3glsZrrf?oGhOZA=SOgN-<-k*O>(`*J20$deQR$Gd z><5GaYY|cq_O+OTaJef6_^;*BOp7|4^5r5hCO!0OF)**I$_hN z`ot~ANTi4u&xDr+Yinyu*U<2;Y}+4m*zED(o;);OM-)AGocbx#`Req%&B|5#9shml+y`%A$)zAe4>(h*$ z#yt3qa|iaVz>6|v;%ri{7I<;q2;Uv&jsXwy!N1<;?~OMxUemI=&{<7)78}um$DICs ztlECpft#gApl|cX@#s%ZaiqC;mG2?Ml1lFR=EkIHJG?#5mml$x{)~crm8e|PQ4B!t zxn>`OPF;{}?aavJfR>h)5=g<5va(_TI4U1>Xu&`d>3a*B5TFv#KZ{1BqRNz!3&1WS zrFpIrPZKh)7Gi%>f|3&3*xFu88_6Rl9EG$6iNIJn{8bGz71%#&Cor5|%^;)+^s8jw zXawJD%0Ivk9>IRjrlpL;Kd5?31eEoZ(gCeifd<@o#%}u9le~fHceDh>(niF)!PMjC zeE2l~dE8ZqmcHy6fm$X9(*tyMmpB`2nc#bs&E4+kvybeI`Nzy@!ov6s&2X^HBgEi} z-UgD|lqprj8xJ7%cDy?x5e9c z&ZL#p23!NDpRMa#e5rGu41AeCAkA&JTqZ{08CEAQvHH=kLhxJ@CWb~-*=^M~pX_Cff%sU|D8UtQ2qji^VZ z%kObrTY!nVXeBlIt+$#K?3k(L0MoZ`~~tYkzpkn zAi-obPS>5UZG|s=K4w!Mk^o<8}adu=f)S$WX z<@&@F%o4)Pusnds45Sfut2`IUgv+3ik(io5)oMV+0^FORSW8S=ZH4=lnd1;{^@3go zPTJvL*dpFA@O0vgprF3JUE-kvz>R?-CWJreo}Of(>HsGd8uv}4?q2}dRR(N#RL3>D z7nZTRRS`)JDhFW0k!K>T>3_1}=I8fmyc3B3e(Xesi5_uqaXr3ju?NG}vZ^YKF0%VO zQJ(iJgz(jvN_iKc z0de+dW8|bjk=o8VLF!^0P$s_&>l0(9J>~ z(_i>Us=!5sUdXItV)cR8b9$f`16=(E2Liv--G?r>Km`Jt14Gh0A@pyq?h?VY3!ega zwbwYHK$`rqY(Tv+PQ`#Zf0$oJw zaFgn0jgLqSvXS6|Hb$38f&jJGEY7Y}ZE9LJz*(GTP)pom`-aiwxH5PDWNXwg^`|Gt z13fC?%x$La2Rdct_6`d{IbPkXJ}IJ>#`rjuQ z?cZl?6jR~3+sfHQ1$|4_|Cj4C9r8qHMf<~_gqT=eIVQo*-lyGf-eM<7(qjNz^hZEs z4FC>6)N1g-H{7Yxe_09PUje>%^{4n+ETH+)0;&Wn%nGz_-aYcEsj|)d_6!JAGm?BT zdYTef=tBj8hSU_Rwi0d;{1V0g=a3Q(&1(Y&Lrqr5ew*$Ic-^2M0~1^611=Y`CY|TF zsH29Gdvu3k+3%1za7J8U8*M}jPou#`$+`61U&CSfak}egx8(HNboZu*$Z&0OZC3f- z(g{Wt>@;fS4Z0#|!WyRSY}eIll9AiCmun@l2C(2M=9@0#xNN3L=aPPY5>aJS$_c3X zjVa(3P-Y3HeJK8JjkT;2C75!$Q_hU9@dTsAqFN8f*17uYiRBf$(x292svNPBsGsm# zy7L#jL6U*@VaVYfg~X|vH?6fB%;pdJmBPvj3i`zrm2qs*bIqb<#$I^i2O){qnDuB3 z3~)~^>A0k&tG~Ud`9<^M{!D^`dk_QENQHN^eA(WbW74txb^hW?`oxy1QQHHRtAVCC zaM*OxzmAp@TnwFBrR2I#GIM>n`hK=9{gT1=vXGb0_Y5!JZDBIDFWzm`l?+R(`HNSq zPmw82+#IWmz|YmFVTCux4VJzn=mU$`Tuk35oGT?X1@V79e9awOJto#l)D5-1P<|xg z;k{w(je#jD${4;wQ(d|hho_pLGE;IO*#T_d_00q#nt*LmFV|?BEK0E8lbK`X+ld3trzG+u zmN7Y)pnf7rw?~Z~S9>J5xNyKQzok71SYw?fkAFVat!$%ygQ|zV+}`*H++Lr`y%!9T zMbdca&jOh?^U>?JyW*h80zj2hYT_PR)GrVL0+3jZemTa2Wnl8l&|86?HM|dCdqW6@ z9w5+1FH{2~Ind!_1-I8gh!8ANnY-Qm;P*@XcHzp8eU^T+6jnGPgLGzM#QJ>q;}+v1RIKkLUrnJ@uo+Ww(G$}CcS?k zCp#>ofPL|2WmsJ<(M`<7!`OZUk5QWxi@MAzV@Ol;u=S$?XVo(<#|EwVZa}!KQxini z<4*FMFfMeB2=gVU1`L!Cb*X>pFjiTyO@8T_7Hw)Zm2BXD6BLQgfu+tOo||bprl<<( zpT2$%e=8T=*PUhb6la>p$5KE=rR};Sh}-xNvpx}P!&P+gm2^_l%yM-x&CzxO8EGW` zGEn)|&A_pd35peS{W7HGh0z&;VyQ1>DYqv|YZZ5cTiB8`>xQ|EnLpu?%lUn{c{jJ} z%`H-O25Wj^rmp_$Sd{*=sfV?xf7D&bf>oY#Lf0~WbzWR!kY-4CLCO|C&}ULEBN?a4 zF7}gm@foN3ps~gJ)B0NEg;008RRN*c|9)#Tw z!0gSriAqRo#+0pk{pJ3e(j)U_m-5O=1uw70AIITu<-k&6T@4jApy;Xd-Z6nO`-xH} z!Yx9%Pi|k)zOm0%_;WN9RmmsTTWhC)~UDf7|-+F_B~yr z!E-SbyrIAc9fTrcyN}i_zH6^Xf;ID8ul;^S6KaWnGRJ0Q%3e`(*KCYUaYPz&Y$DQp zY&?<|t6!$YuSZ{nqJ7|PVUGlLIdQR!-^qSwn9mO*x*kWwB-Q2eb0!m?KU=4^#8mhY zE;X>6egZ?Hr+)_fB1Kh-IhH14wlUOJM?tsm^0harXR{&Udk1l`T}yERNCwHDWk%}p zahQnU`u}?da|#RU?rrRpVMQ-rYCiCU;C`yGbfw0<8vulVJz$UXk84kw(3dD*JwNw| z4UmZR)YsxKYn2{`-#}UqOwR4NQXQ#^6~e3YI}N+s|4&RHrxtfgr9`FXoYrq5lQuwh zw~+lk(G#75Zb58PFJwJ;42cTC%fpH7iRM6+ynk(lH6>K0Gl7`Fk3QyzZL4+CCX6Gk z6dH--DIESrPwweRuidHZ{IlHdQ+vw_QbSUO(jRkV)zCq>%Y%;PA!d*S3@t|xTA=O< zHg=+JU@D4QbIS)>{dO1M;;aSNTrmGzwCXvyaMmcWvE5C z+qUQcm*lS3>G1TP!=ZJ(QqJ-?59TMnBnO0Aow)Bep)e|5?4motCZ6^7#hZb8To1al z{uPFu&Jyi^o~l;S$#ZQBYc|>c@pUYGbN_VoWP2ZumtytE`LkR2xDpjqY-D1>c{CVD z&31GP*4uo5{v^KR|50lxBQu7Yo4pYgzze&JM|=TD<40Sqphs)5qXH-R?^!^dk}Yxc z`u*L)iAa#HWPllL%PJ5-W5kSplJZ31ihwTq&U4XtaQ&>d*b-&`Hvev#*=jY=vr}ngu0bdBz#R9RlO}JuqOwu)r6Iv!Uhv) ziCkZ8FO%TXi=<=r&Od777P57zD&ptKY+d?pDN<1NF3bONl)G_vQo-1vuwN8Pckzywo1UhG$3Yf5Sv$h_&}A5Zz_Obx#C>}a zP?J*?V94nIp_62Hjt9o_9eD^aVt^PM9fjlp;T3xwY{bJJWd>cpF|nB7mu~_Ua06XTyr&is77kxa#S>jQTqY${$?kc{1-DcS||1`ZRW*I;e{CDgu z;2~kCgQExBKi8$aXfVixb)e8_A>Cr3ejK+s7c`?4%M4mSxYZLs#^oSBF5vE>GaG zenH&r!aIR5$M6V}KBW=?%R`eazDgo#WIT=kTndJQ<9LfqG-ncYDOd;uV1Q zV}7hKe);oG^TJTeqnO05N8fcJM>YjHJEu1$2=gXBVVN}1guIMxu2L=4@>;Xv$r43x zT#V#fwEVaBsnn&0IqDC(P3@o`JV*(eCC|@aq?_RX;tzlGQ$`uIA=lpXz5fO;Rr6ig zuRmBl>_4`|8kt0QkyO)O`be>KI0bkPXxC3DlE_n=#JLLZ}i~{yOSL%_o zjC39WxZjlVV#Xucqo0e>IJNAGd;fdqda3{P>HYO>EjwCb-@*GPcw^rr2{HyFS_o+G zz)~p|>U@6*(O2 zEv$F73#@pKMp-Dp^MN{>=f9^07Y1L}v6QqJ4L*PdqStobNG$vBNqKpD7Xaxg$Px!d zJI}wpV<|dU^!I-R@EmtCM&3+L>NhqvS^;WnFLYx2RAkeiQ;u+M@L2;`DxyW33Wg40 zQ_f24q$toDLHs%~_Cuys&o)&Ai#L`A@3aVeFD@j0P9@-H5Wt>01!8VN z>I1*)<0wOn958CzFZOisO^it@Q_yl-aL zwsBSIA*-k6H2+;GHqcWfR8&D>8Hi?p+?rT$yAv>E!0ZF7^J-ub5s8IpA#sZA2!T`0E~zCKG;OM?kH3 z1Mdm6^q78G6DfhzAec3~K6vc60t7+xV*p1UhT5c_2ub%E)>`D8yd-8)_`%UP1ea{y zJ(s=NAK>D7ONlKD$6QaT3~p0hRGE70Gt#8ekQIREWwDfsMrD1Gz`=$Ma} zQz??vLYK^mnxZtkM{-$zPLR88f12SEo*2BVfK$(`7RtoMHY>AA$_$1kHr)uXyg%o05gh6pMz6zB#-!P$TzxDpWQ)|2PgI zb#-0}W8QbTP!fE+1#uCT5Zi+Xa-lO zO+EU131nhHngvQ{@NcvM%{K7hv%uS3c0V;j|H+d#z~VwAy5vPdMNJJ-&MrRO=;-J= zKs|5;R6rQAA@ADN_@`>k>glCz0VV2>UaTu$?Lt1Cz32pzu=XV$j|Gr)NYd(vyj3Gq zq?#aUYWCf?X}baSkUGhY#Pc?@%zq4#SD;R!0jUHpiVD26$7OTHm)V6wU;evG(-?#B zqnD45!-lv7DRw^wN0n&{&(3dV

N58%6J(1%d?2%>B<7lYiMcetI;oY~vxU{OKTA zez$wC($u=o+d+$bZW(W^w$#fX7x^Xeh^Z7iEw_opXrqj=ZKdrwL0E9qx5v7vtaLLz z&2Gut8XohoXU0VW{L2VT1~!dP;E(}s93@zrD|547w*!V^XVWcUsj4sVMBz!DQgogv z!SpxeEG_ds1Uh5_2z1t%5vrC;X!vT zYh#u?6k+rb$(rK$aI06t?rzHhUduvG(#y~1zp^|9Ce9CkLxpW^;!M9;l;5I$r4k*t zfdu!?I}Ig5YXj%y=Y?MaD;~#7oAMVk6vKa-mChExJH8}-xi8WLv5z=jK0YbOSSEfz z#EKt=vAnL5Fm#N=tF3e7{%#$46F&okv8LR|>1yB`dfpD^dF>|TAoXEzf@)g`T5a!E46q|%cLDOycEBovIi-$n7d6~ zj{*n!KV9CB(z!jr4{!&(tz^s?Jrlh&)PYiJW_mH`jNzdY;g#p^?5zIoPgQX-CJYHp zsLG@x{8dLzf<32^^yU!Je_v|a)zhE5Fxc4a%!k;oCgS4kkK)kE`OCuOvL zlXyO?6NCn0sg(Z-iYw@O2|8b>(}p6V0sm?rF#mI`C98fdV0aJO-mX*vN|u(Enw}e8 zo=AjTHrR#%<8zv1=7x+DGqn{du|P62SyW#o#T_~Os)7V&#sBfFr^ux6ZCXknyd(Py z+#=VA_KRj!vy6QL_@JB%NJ&Za8ygJ{_w}@C9%C#!QCJkU=OxWv{^Ifs?56P0 z91)LPEG#J^Y&uDbiL&p;8B)FTPH*tB%Zz(zLE4|uTi}W(hIUEFEYU-Pt!XWTI_WIW zzZwA!V!g1DIN&9j>5z6C>FUBnJok2YcjMa$P>u_Yy}ur5H1*o+dD z(k12nFkmNOy}#Cv+LOBIE}9G7yavsDmF1fVnX5a%#U~2}SV1QZc`Rv)F+)0&7-RU$#*wDTQm{uo`TordqAMqNgeS>13cfjyG{cCfL0MnC4G zn_-HlfDFa-$A6h2Td71L8BudQ*QL%W+qjYzX-d6(Ax7fLu{2B-^B8ie{U{-Nu(d&F z!(PZ@r!Xyq|}|H;F4oz^!Mp{nEApT*s~Z2 zGlS>LU9*jg7n~uIG^L%-vn6h*lT2v^x)mGp=yM8qN0cEH{S&_25OE!89FZ?xzSfB>S&yl9bA064JDZ975BsK{y z4gTGSABM@&@$I{BHE|`gbtgH~kYe+fy*MlJcq&c*8 z_tUo5QzD|jVK(JavH9e_MnVq18D2(=MAhbrM^~eNhDRrhQITS1<6i6={f=L)25Q;Z zcsA!Bh#yW9gjsf4SzX&F>>BpbF|L{MJNhifmDN+cNIxlBuV#&rcWgTEv13@i%sl4o zxSg)_47{P}boISe-0eA(*G@M;_x=4X3kyurtH5#tG?=ps%Vjp8ps@X8%FLN@ZYm*- zV}PUWwyxu&ayArb7u68>0sIf4^auD-mO&0c&UJ9$3F`s`2Sx|ZnXtk2;XfG=qmMP^ zUBH6tu7$2$0zm%m=&HE~qJFd5MlbcZepn)bNAs!}Pw^O5b$G9}>s-Ck&FgPe>K4+| z`EU!youk+2XQK?SgN4({t(}#ArTh*+yjb0@5rGrE3damnu7t?kKG2(WFxTDGZ%bz4 zxV|$i(?b4?VX^r^IDKDfs64sh>F9jHpG8hyX8)*WUfL}d`f|naY>5|UH9A?+3O+JX z2zfc003R$^m+MEH?`ynNLHLY5t4ie?ROR^MH24W;N)Vt^z=Efzr^8477Od^rXPB4x z)9Af81&GA{V`#=Q0T_lkHXRWkzY+u?!@_%-E=*z@>0pv4v$k&&;^-rfY(E-XeKZz| zssa@|fO((_b_}^LfHHo*9(Qp7k1T+f{@~C-5GgWV1<5Q29BN4(3^94GJ+`?jXx`8l z1X$^|AW*p!T^#s*CRE}t&X(?b4%A2j&ebh1q2Jm^CnsnyK%pi{@k*eiPVCYs%8_#T zv~e-{>mr=C@b8*kS3+J>URwXsd4KU)Wv22^(<;LIh})*Tfd~sx`GR1`T_)_-F*jb| z@x=}mx2pkS>USsQuVb7J+5}$D?E9F8m8=u0pw>64l?es0OFKuz9Q#ivF|-b9}XU#X&U>cfOAjRg=V~9yC3;RV5K4m)Ihx6 z@nweif)L{l@Y8oeH@~4Z=oUJKBHTJb69c)l7bJR!&Q!0>xaTfK)FC;>M2TBa$9H`% zY!JtyR&@Q|zk+Ewn9^SS6UT?lN#4ui#j}Fk!*#bcf;0m%jXBEnj0F}Z2BFZq@z{&V z2QYL*1YQdN1ZQCItCtK{&8ASZ&BJ$$U)ubXN)kCkXDaqXq^P#p?C?)pOzX?cwe ztF!LC`|TPhG`KeCxY8GR)L#Jm$@-L$B};b*ePl;z5%YWI%#VO8Zq)1pZe;@f3r4!Q zH5PrQykwi)M?Mg~49H}F#QzOQMh7F?Q6~+MQ$S&h{MRt{OX=XT{3F&B4xk7DneD9W zWb_O$bg@2=tC)((R*hwk{`2?m@E>ttDB<$wH8M1`nX9+IFC~*ib5tEoc|RnAGP%A& zeiD?Ad5m-6l#t9>hT!7ndw#KiRa7I@fp@q~Na1>_90 zLL!rn7Yo!AfYEl8kTE~7$6`^86p{Ho@dlt3NH(n7Oe%&T*YKnA<`6+G@&K@mR z=Md51_A8BR!?ty3ySjM*QLtd(axyi;@`<;W%J}G~Gw+6@)QL}2hey6MwV|GBqi0l9 zgOmxGf!joY|CW1!LhNuqn@I$_9QZkUQ7s-6T^W$pdk5mE zK_DKVo_;IrGEGm4y$U8k3GLk5r=7P_yPr=js*FFPEr8wCbL3<`dH%cN+?HQxK(O@d z*@W7(OXxoR*8w9Z2YceR&_49rbQdVaAORIDbY1M!yON-JkWi!e^Hbll&f0NwB`16S^Lo@B#dsq*n;@Jf z?DQay!?S@e1+zTp~eC9wObdy8Yu>u6)=Sw?R019W9+FZA#wzE(3YL;p|8Y>5M^AorR=8yI%*!V86$JE9Jnw(AA!J;J2mqw)Q6P{aZ3qOOaFF&s zUbKCk3)bC&Rk*o#*J@U^d0Lcxl?=v@!t76d%_A?uqb_NA1kI^ZOtyCmAww-i*)fP-a z#e=jZhcw4AmJ~sUF?zJKS3d1mhi{`Lgp8HHFf8gCzR7V{BV1lp@9c6hy!WYtzAIHW zKpUep+M>zvEb)bZ>L$PUgZDX8 z%Gm99L)1M5lq5+xTz1ZVsNN;GllF(05>Su-=ik3RG9$AP|J^n$pQTquUz1uw>0>E} z)Bn!B4yBLudTd}&^b}zZ7{q|kBmaRAEDX-7kJv;Y=s`34r3AJxb!5T2Lo|<{GIWW{ zMg)>bSk31Xlh;uBweOpCF_5ybHr0Zrr;`&0jDx|vgf zCHIW}YETHj&I3FLnjfWX89K$))mo=3dcgVc&-+ofw!^@V{=j6Dhnt%c=<+F#U4!1q zR}mRd{RA($>s;&&zyVs|>VD>57mts|8~LOUXO+H$}0j8dkJ7=0B+D+@XWy8PT87hi`!7Fl~5b=igKg3jIwtm0-b?HzNRblN(}3X zCC^v&Cf@4jUd6lJ{$HxeR#n4S48jV1?jU{y?hPH!;bc&QfN~jrZU$P)i|?qZsrNUa z6rHtUwmi*?ckO#$s8IySMK{4`qx7rk^pP({R4d7e!5vfwSSqNNDyR@amW!gCcqK4U z^_xCUS*Cv*(7V2G_mFCY9Y}?dVRTn8fQ+W5qN1voqG41~75X6aU;c4%^tb5p(C@Rj zlwPu%Z|+!h-lzH8)n8eV$M>`IsEzy3HeX~cLnPz!Betjza*`M>D!~S3O$;OyXnrte zSzQ&zlJv10`)(Ia);+bqFbZuI; zPx7Ob_t3gh)15wDs48~-?%vmt!GNWvKCwD@lR-HzlSGG1f-jpvT%dca=nh6HUP>Aj zm`gIZ=VKfRw$GpQ)x_Z^z0*{#D2|z)HoQV*^-=BNti%tajDA~#)uNh{*XP$ase?kf zf48?sd@)>HT)3pq5a=*ep-awdkv(xpGEX0pp-P?SBf(FZNL>3ZM;9|oT+l)|B7im< z?=B3cHI2p%wXo4qK~Iwyk+ernrpl#h2F01Os{4p3MkM~H)RoGgHEwr>LF1&Qg(5h} z|1J!rA2st3F-AxQAb1f>H+_P?AEH%GT!d26!HEc#&A5v8i816hc>D@{DR?coO*v6i zBDfOqgKZ_!{#f3|b|<5}1zgZ2Hgove4i=*w;G& zq4R&$H$zMwKfd_`8%4zstX6uMEFv}QMI%ocBg~hF=G@Yx=ji(|{_BM_l8Q}lh69>VGye0Z5fJoKQtBJY(Z z?lV)e?n9;o>d^du#O5YdNgrUDPvC zapUIaOELW{jm>UT6X%E_zK?A2GdgzumX@88yPzOHZ;HH~xAHaJfIw^L^_Ebi&A*^6 z4Kj0OIz!=uD_}FcCP|ZmdixJE7`Y=6zEeR-pXPhtrCdIqUpub0n52s<*{~gux_U*p z#_suIg=s<~UAD`1&DCMECC>9(!AxjeTm1eqvUkB4mk#-dtr%C~%|Drky{8&kPMmML#PqWMg-Y5uvyhKi3^c#SS0-t>`O zWNhJ%6CGVyQ*F9wk_SpdZF0Vg-KZ9)4TEN@S=HV7snXU&*S zl~IqV_|c^LMSt={>sCc2YU2qkSxcb;trjGM+uI6(fr0AR8;?Ti9kpXK+yIS{Tjyjf zTU^k+o%UxS(Rz&HX%)xg5(Jp7v9ww|Pj46+kX(gxPZj^>E>Pv7*5JxIO^0r#R4 ziSLwtT<(*{-Ku?!h9NVXhLXNbCPH35)J(|SM-T6Hu%_J8N@3mJbzr|j@uRrj4vrXl#>|u=^^1UBY=6#9^ z65vL*0@5wdK+rdY?#sx0$XPsZWb}+4lek=sfhQUX8TQ(I9W~u?%Se85VB%j~Bfa(T zYjV=E5bKA?d9*rQlJ;zAwT?T(5`|^DSuiPt=kRg;q`uam1Py*0fJK)6on<#;5C0zT z4eJUoiQ=Pfl|=sOzV^H>Xv4wV&KQ#EDR`Iih8`X|UBU5eB-^9d32T{GaPojbzeTm7f2@DSeDcd$xKMxk3Y`H|YT}F~Zm# zTg)!dracwrGVLpjTIrC}0MklIbn)`nU)lX#)`z-JH!qIF4{>+0hIo4d+?A2Z0~&-F zNei>CB6ZxA;#4kRb2n-l2eSgk6603B<0#k86@rApuw-I4u1^E#-ByG^i@wl1J_?os ztSK+BIIG?|JkMt#*NFW<;YYuucLO ziExvu97Spi86%#kAT7ikzI+HIImhIU_AHPLMg8ccz<^#^u*3Fv8^x3w#tlBxak%SyQUlq$_~5Ef$wo7FW%tV2Go@rm@C-T|r;biJf%d zW||fz`(Z9zqv9+5^A4!q(m3*}w(*{|wXu{tb*($7ye4n=DlwMdH0#g#i+-|P{HHkj zoNkfR@Vw#AZ!s1-o=-0InYtZgTu)3jF?dNFB8Gx(WX_5{@ro%T?B7u`S-!QlJux~J z&5uT;aZ|lxVD0Knh`L$%@q@{uqP+USNphKGW~*C4rDtAk3% zRD0Jf!UjCp-E8f~TuE$LSuP(@@sn5ZefiOwE!&OB%5&fIw_P#(GdxCwgZ)zs;OF-MT={1q zEmZ(ePQlaH4()xx0iO)2w+6UG+ON532CbhzC*0iJ^MM{G2N$?^bgC8H<125hf zr`u0});HKahWd}ROG(nwhMzKHhr&8n5(*dtx!ebFKn*21mhh#p8x?r#>m;F@03i{P zX92k3(nDbxeAx>4Y>RRn2d~@}o+w}Wm_q@SDp&dkb=WEQ9~x%ue&G~wok;Y=sMV^( zsx?7OQFbp>!#n(#e8mBHol}Y=vxly;(?qhxZ0xr)T4atM2JCzGg4nS=bDKb52`A#BPbXYs9*_Hh%Cxn%aNc$h;ej$ z{8u|aOULf9ff^fFVmPFxV{PM_I8d&a1 zBk=|W_XMhyTO7+X`GG;%KM{<+_j>0b3nJg&qR)PnT))?Wo+=9uru!8{${Sw%fJgm( z|Cs=x>&gUhY25=Qn8%xRoOnMKyh7?g#d*8R7;_@)=Yx$_ zD+rdOXUuok53iv4=Bij)(!s7L+6&kVjsIh0p_SHl7BX*{5Q7a~N=|-&h-lfIv*uK$a$LL(i+p#Ws;}Nh< z?RniaV==O(8fnJ-zdP7fgk$y(Du}Q}sv_cans<8vb0TkZEET-xd@vT{TBko!;DqxF z6u|gtlCeuUt?_N%p^xqJ+pqlPIUj#bQqdK0u1d7Q7=Eq_5vBITR4^(=i`fuimec5; z?|tXy4jqc&$3%nOIk?$EBl6UHvI)0{O zO0J_vy?@i_+0obNm@Yo+@LvH_OR?)*VE2)z2Vuh-GK>Z9iymeC=FY$Dy6?2Oua zY~fU2J|9C>He=&rP6CRNU$<-Kd7Z6^$T5bwUZr{-k@V+UZ;g&@obd6I9e+_@v6)vN z^?#!JAyPf+vOPwh$OfCyzOnhk0_+YW#(Bkts14@~OU!?11>MoC5D3!T79Etq%&NO{ z4TIzSalgY|=1hol?v-fgM5E~Uu;+j}TF?YF36C%_dPXU#O&h1D75Xl@i~1R?P^5r4 z)30I{b@5rzVPOf9s;1nuZ4x$N;QwW! zEyWLHoGCR6D7U^QJ|A%;QMGM~Mc>JxhubXXs~|9gU258tYrx*%rLt96qM`rs$qCiG z15ejsfQ1T)HyEUWdzU!qsE`hf$E*)FhEZdkkI-#UV^hWHc^cosjB@P0S!8!$?RDjw))=KaH8|?xbi8_Xfw6p8t&ZXGgL$2s zO8bEx#`3QiluSh>er-(L^TjACT1bNa2se4mFL*OoZ3i*r-TuJ6h5GVzPX&TxIJx3R z{80spiAlu*n;6A?8k*5$)|YT*TGXvCPw0I?20 zS(KGqf=DV5x1w=KCP!HRD@jVo&q$+&*5;qAQ5;`vw=A6PvWQy$#8K{T#<3#6)pt5%dW$#KaZ$J zySjp}#PxM`gQ?wH8NgeK4MD|^v$D@RHI~vJIGKKX9ZH&Y2&K&0J&cX&(0U`Qh!7Uk zjHRp9YnA_s>-Y47x88g?vG0gRazf1?I^Aa&m7GG#ND0!AmS)4eF8?oDQ3xo9??pk4 z^xH}A1NOclh3<{Nf_Q&Zh9J+WAL(e0V2XGU1L{cgwKZ@D#Q<{^TB0iz7EvH%Un4Y8;I-zl^`Fq;=X<%(vMq$N<-0Fa(vOmpfrCtH=Z zUx{vJ%wFRC^Y%9!faHqsfl4nj5{)1!r9i8{j!_*uNusu%>MZ{W5$yNRCyK-s_mZwL zr#>djGa#e&8OoC5l@^qyy!kD%=+^>dKXN)Y8gEXGI1?#a=ogVID^Ivy6LsrGFWcM1 z>urD5+`v+v8miMAz2#qEx@`~rdXQeP;=#vGlVI{3q#8~`|fjutIx9TgIDObrd+ z0yrqhVOBw^0&2;7{RqXxE-sdAoU+C^NE1v&n_cG^Z#w|bA~qp}55L}#9(O$$j3pQZ zi8^)|nz~%FSrlaxY3qMjkHKIy`CxifA{FX0UKx#;vY9{_Fe-A6G${cUEvWuXE0@BI zNR_-4F_ynpIFrsNpW@R-H4Oel<5fZu4|n6pYmvzvgiM@{#v@|l>LlI&){^TtGX-KgrxKDPtKwU?I{ z494^6598=NekKs|VnZ-2dI&M}`nt8%5D!7EUML}`{P%a5T-&mvurh*-o{{u*V$(-f zg$o+>*siPsC588ILH&1K0k^28?O5XQLrPp;B2G1u2IENBoTR0Re#bvZ-nN^NWsYIr zcm`5;`hx`2Q5ZD$-)Zs$EVLabA{&Jdx)v&}VCAwaK?wc-qw7DQvF^h^aQrqCva?4v znTg2WqZAU^Gn+*A`dHZ`31yX0gvj1|g(SkAy|WUssr;_{`F_9W{LlaVe&^iJIi1tv zdEB4zzOL(ay@oq>ET`KnExR*)lUZ4*+X@b#>04fjE!Onf+z%STZoEPS$jhH{LxjVV zn+5CGb5}|l5W<%ROMy;H!`-}n{amzE?VEao%#$Nj?NVewKLbd+_Hu{wEwk{uyrJT3 zJJgpi=fLci*|V~><>(*5Wt~b!h@Ci|tcHc+T)E6s_FgG!XpV=nL&C=lHME*-mENmx zA2&(w`9X%RvB;~zA0@}5DrqgYOVb-d$B^hum-rKjYx(!zpAK)WRtxd+LVrkq;4Pw( zeG(Is|A#2e#Qh?sYlp1%B``q6xbFxy@89d}xy(sR++(1=JWzdf5&(BINJdRc{8@AB znaBLm+3?g9pPH~1FzcbV_K1MCkbj`(?^!I!;zyDp30VSs5f7vA(|FN`$A#ASJ3De;-me;Ma1b+mWO!zJbBuBOec)g8X1 z-V~L|a0eZ#VMr;F;JaNdu)ewV0C#qys6Rh($(Omgxf!(E`5lRftto)Gk~7qyAH zp8$7_36(F38!wKQ35ki~Bv8DRu^cXVy^Bm{Vks`d@?}Yu6vRp!Ql3=op7kypt9PCZ z^!GnE%9YxmF4q6h0ZTGM6o&Hoc)}))PO_ATiHXVH!69krH7R;_-GgiFK|@&}7w`dv zn)fM~{nl6^>D2yv4Ud-W35TQw5vmJ+$sWkyVbX61Bkib zaI*b30uW#Dqg@-;-;ujWL`?_(8VzR+n9d<+s=UqvwhToNZMdE|s|sc97d`LOvy-3y zAUK{{*RewM$t+XMW-@jEpoq< zl-Q4sxiXX>jDnt6$i9ll=2Ssun>AvboIKg#&eM8V-)xxrynXj zI}MP9=b{ybDuHyS2PSSR4UuZgDE(rw(zdDTX@0e%Y}r7g2XpYYZ5|{v^2}NY(;kW4 z5r=I7B)rn2)|qSl4mL1HTlTKFjH0~~*_DWTOO<)t_u+5dk)43DfKMeG?)9egOH>M1 z*~$cS?|~uu3qs{jDpW5B%=kLrJhNntVduBBebJ$OOOt^wB}l>7;zPO7idTe9en%l; z?1IYdji!%DBip2XN=5vF_qVK{6B$rj7z!)hdW{xJENd7Q+`cfLe>JJ*dq_^L)^EDX zmxDY4<$OZn4Z%oC>?ifN#cK5}kQ9-$8A2XQ#yjkU9xhehxgmPvz2Mt@EwVRMW4ldP zxcFD^GAEs7esyQ{(TNjaXJjY~*W;{VA|NWaCRgPdo-?G8EP&tOfT&vS5w|ZuZ&r zw`FCdvaenDnVtmit~L9EJA40jB4*>5)R*u-<*fZd|!SjEupRX?7_ik zYU*z4itB3U-)K@M&J$X@wS299c2tp0E_!+R>c7MkMfhgR-24Lx8+aK}&^RfSp08^m zB97{A>uYPDPMnY0Ergve##eM>2$P=3rar0-LLGKtw8SLURFv&$!+|G=)|ltajjNKB zclq{4JO;ga@Z-;46(VS0xMJAM0(MnSR;!)RfT}3_VRkI9*k^F&+4^FBh~B5?s!E6e zGE74FW-8rsD^dPjE^8N&yJExD_3s}gZ_8Vq{L-WwjY4%Wt%~0 zozwQX@^KQDzA|YvWfcEU6F|4>-FlW~f`Dy;T0FH7uTG0KCY%9?^GU{W%fpyCIa4C1 z_#7#`)9J$s_X{A^C~$3tDL^Y*lbYWb7Z{=a0VD6FIc!D{baDR=C%dH~+0C4IqE82> zqO|cxZ|7Tyx(J(6=y7Mw#_m;Z+;Tx{{Jl~p-e)8l3da1oxkW|~SR57GQGS!gSSYdh zWD9|+x6|C$fqu-9ezz-1|`}2&;F! z*Ed5R$e_FFJ#-gU$hpIww;6v<*LD??)ljKb+x_~1Z&>h_Bz_8MXhY2Moi%^Il+F9b z9|S^cZ(nXVF6ew6Ng6r4^~{-hgIiOWL(0)^lkVo!c6gsa-p@N^md zZ{n68Y8-rkTeD)9?=cMg@8qMOEiEm>-u+6T9^yk+b1e+0V)IxzTa_D71SRScHT5R4 zpaTMBYev(S3qh;VzPl_Vmx6vG5h60!cLJ+Lbj*Z-V+YYE2~GBWL3)++U3JPzk;}#syUQ?5xaC)5rhuSHOiVAy zOaH3&<*{qSG;W1$e5ND^a&ZQdl9Dgxd=;%fd&W_lQKjTkSF^cvJR1o*d@-_`|9&1% z`4*!kzs{UeZ6a4`H{aq8#nP*E=cL4^CW{A5WrgPII>Glxm!e<1G-XGtMolma82zHC zkG?{1(@@0CE!$`D;$y~0W5l)jGT8G7gV_)uIv8*S%o94T$}yA&3GZJfw^rRGvyLax zEfJXg{M>PmI5E8poI#KB(2;4Dw9`=Ae-B|26LpksMa^vwy^s3t;YXS13G+A)I=S3L zTkQ|j03IeKHuo3GvQ5ODpZZQnZvPrc|{G=KlBYp>~#7&V9Zgf&~T=uxUJp zeMM#NT?fRiJO$5?KflvjIktfn8btusVBX4O;6}Kz)&RkHwj4%d>mEOp@0Sd$C|)6> z4eXV+0leBh5CV4cew}ZcpBt6V`E7o1prcI6hy!})Wgo1+(c>tG-|9>b*{;Wb|CJa_ zH|_Q?RQUXRDinYPRwQ%?z>#K{GiY(I6stZ+!{vm)pI%E6dm`aN3c)Oq%R0Fali3R6 zrMHs&^!J&Ys_FUZtz>+v4MjaXHU8BD@u{K3!{a@>4?`S<5BxxchAxcnv~oM4&yqQ3 zJUl)7f-Hbe5!E^qL61`piGIut6BrG98aHU11bGF%k_joB02L&c9 zkKur_d-SI3CASW)kVPgbEMh#qB{q^vy8aQZ(z&8yngm&BX14Jp*P)Pl}Jc;WRI`50&EncF}^|}fd`Wju;SXGre z`5sH)ep2ZZkZzTjw=(lRKuY5@2$i8_P|p|HeOu$8Zo_sL7ASee#ZI43cS%S{5*)B3 zDXD18;z^_ak4!)_z~`EImm6q`5fI^ZbggUva|tl~&7S8A5O#{rd=dZ2RnuFKwd~peo)@|Qph{7R?l~TQAw_BsLOPa zJBTzqDnO**d$v0gEwOzswOlOK!9SSP-EzP2?GpEWC(2PKnV2@?2fA7NZZk76T4~w1 zPN=VA6TIm7kM=vnk_&t4ET^7#2CJGfzJAu$S~cNa=h_&cxid;h+GyG!hmVbAdQr6H z?-t_Pxp}bo$}W3YTpb>H>0YbyPs6E3uRz~P&z8InCUP2v4>eNduRMEE>-0|N!qqr$ z{SQW*#zMf_U34-(Gm2|#v>#^s(~JCx{Ilm8w8v0lQZKT!#L;tu-B46BT~vcQ7&?ul zk$kNyWS<72>B`W2+&2d}GH!r`Y7BN7JH^k2q1Nt>5r$sL|h4SRia0(!$eC6$qBR6_SR_>=h(JUO{+VMKziRP># zXfnNeK685NcV{(jaqT`o{ltv>?~Rzq#nfw=1|ktxwk_gEuk8Ivm#4}NL(|Mg#EgHiN?e>d#ih8bJHlv_~&397rQ}Ntd3hx zeSdjA=4tmVIeA1Zg(gg_+~WS(wGs_F>t)+dFJIJrQBVngR9K4AU?jsKOmw3xN%m!pMt#V)IK=e9o-$*E|--xU-u^Ght; zRLn1Zv}q5Wj#GMidvPuU-a+}|j7$h5R z&Yp=?c>he;sQG>!EBJ~?=OM-YK(*qHYf($~Y)4ubzVwZ#Th1mH!F zD{^MMBb^AjTT4=NCtso{y;VnMX23x{S4t-{30Qn3{7;!TH8Um1k>gI5ol#w_oV+}o ztu15=^cLWm0%nsVFYCOzzCX33h0z*TaCK8cb@SK&9S{JC{8>yJ@ILl#&9C?db=m#& z*jOasc)?YVc6`==!1u6m0)vKXnj`0kf{4s1$VufkL=eEI%<{6IWe+!GRgXAeP zo!!;1s_mlg+0?udTg;eZAwlv|hmTSpt%j~G=99kX#d}()nS5S4nVqAd*7d}0?v2T7 zZM~#@Hxr4t*`Pil!`u6US2({!-75gnDwT=^64NWoox7UEOzP7&3dybb@HZg zuh~C!I5|7tqt|wKESrMf1djdwfaijm09*=IoWbcMn;B*k1rVstbv7H*2hHq z<&ZJQF{QH)VP|`In9#B$(c@U1TVGE%I5;@LOqgVC@L@b0dmDCVE2m&> z@$q3FLNT+tAVC5|4VTb|@7x+^=n~C58)tmBs?jKQo$B>tehWitQ02q#%5XsU8Un^Y z=)-5?P~d97S%56m^rosGpQvEAZHV5T8(X{JM75!8-)u=zO&#dEZpysk{mMME#l~j! zLJD1@#>(V%4(~%gY}YXS*oa?d-Toy=i~dxFeYHwhHl2YT!czAMN8Yp(cd)lN&sbpR zh8V*L_q7&-z!i?i@4tvX49N{$X184;Cy7)Guum6OIAs7B1@1xu-@Iz~+<+j%%ss_H zI2*7~|LGN6q=#^xQM4-OMaliyQTm*nmHy@qY=t2s)N3Z`4(zgezGNW)M+vq55y6PS zuMMeWuMW@7Ccvd}G+MXhfP2J2L8hEe>+rkhld==$In=^-eK*nIS?qlDWK8eDTPhtU zQW(s7dU{@P?E{hIr&lBYafBi()K~bx)zL}d8U-%M+7E+7wi~vR?M5%?$Kvw^sM@-z zw{FLDMSXM5> zyE$xal~ZAX!uVP`(TKQ`PtW`57w_&nYAuhC=MfCw5lhivK~H?}sS_J@pmb+d(RCmS>MOM5jE6G>yf#$RIG!*@ zKGMVJ%Hr(u?FT^`qnXZuHxDi5Tj>cl0_-NU?KPZ7Ak#+`z-6i}-Yu#4m zZ{EM}d+g@T1I)L#Z{ONGJ1fJ;pPrtst)r8K=n=gsCx#xqA)2u1M(jn(pEq_`WtTwP z@Qx$yczQb|>e!)xf))F_NTIUov`La#`A(d!#{O569MgH3xncti&63+@^IMO3uwAX2 zYi#JUu7=c@so_4-1h&!!c~g!B3a5D_%wI3ix+j%^qRVID|4-$B{J+Z_GEC3@e`F4d zH4oJz>^*4y%247ebXnC-fy_|+G{FsH)VVWv{&$W5KMVp~FSP7sZ9kN@OhRuy?Oc!t zsq^IaF3=o`i-|&oy`&GX4>EaAlP438@nAad&NB}8v|*&H8uI$cz`q_3#VF%P1?B=; z>j5WZ-5Kk8c;F52XNI1s*%<5*+=!SXEG;T0;G=D7dMRNEaCXqrFC6&U4Akxt!!-Me zfUWD}ek9vYf9JijDbfP--?$k@mKT)Ct(GlsQN1wq(`nneQD2lk_+aQ~um53e)%({( zbV#ZKos+^71=cV4IBUy__A6obHPk&nxnndnSw_l7&efD;8A)#?wR%$XQKzJZ+0Y8x zI$Rhp!L6@+H#(JzT{hNbWtGCjKM<@lLra%zs3)#MnmT3VpRepdU3u|9FJ&aGqUrU< z(9PdOF-hlA4VTFqb8_wmv34H|Icv%|4qG5MGJuWxbgfaIDVDYWoHX(4V-L^%gNCTK z;)LaS_U%gwWA94$tbQK;O4$2dvk?(Z#@4HLrZZKfI#*Ft`+8dI=SjqN0kie~Uxsg7 zBy&QOFP-1qYC^|GPcWb9HpxVIw-HZq2#=7)6*BUfSH5h$d~KmFY8PA}|5rwV{D12x z9#+be;aZY`DgvI`y2D8)j1MVB;|G2QKvb`7XJ^c^Up8t`F^lX>+U5Le9S<(|Z&$9r z#5GflpK!+S6uy|ZCj<*B-`sO}I(Ug-D3$1O`CHE<&Prvso@k-PJM$fZ4^tzWlQ95)m zLp-mtk`lmoy#H)pppC8Kj{&$wNx8Lc<8N*Q#^(W}cYEQy1%4Ty`emlKyp+grN73kl z?&R9q+O$7(So{zx*L8GDn6?WOQE=`Iu<;Y*hb9M#PvZCey;Ro-MwC0C#l8c<~j#M_WY3SS&4~x?V~KqBh%RK=esFa`T+R? zukeTp5nef}p*<)9@LIZGjO?VVzSJ*@snAj6K9$R@3^vit#K>BT@+a@3TkHx z)l1vH_~mnaf0?^WE}~Nlj#NEu<#$QaiBDc1_ z3uTC~>GV|l+sy1$bTI?HF6OEo1KAEEXy}5xxL5nvFwaTp+DGs^SU%{a2HfN-6kkAS zVJXOHWMuUC1X)R2f_;)AkfOY{CZ7Vr*)vbh6^Y(dQB%7BDh6WHNhx7rRKYg~Se1UB za&66eYz!9=0&s{?FXa%~Y`+a&!)d(ywgh&ygn6*u>4sSp!w-h&@)Tt=HbM3~(x#8w z@!WH(?Wr7!KxdNB@{JZ&Z*eWjAyqXs$RgM?;ejsU#XNY@h?Z=_xg5v+Wi+<$Att0e zCN`z%PA(5wnM#ilB*B z>V?0AUXK!!xP>tzhgCKB7{QkLDa7Pa)0lebbXXrDKPSQ899?V2`;Jh0n$sVz#JY#3$t@o%Kknk$k7u_lHN zi+pmuM-E|sTa(q8@DCJ9sA)%W9M??#Rud>@BO)U3!Q~)-8oU$CZ+qU#>Eq*LGYa}D1!Uzmu!w*NldbGPSPEJk&QCj%)%SE^ZB#~iV zGaSBP81m35V(#BzZD*IM81xt5HRXyzVf5=K+Z1keTiN+F4iey@#k#7wlAR}q=WF|T*(@TnP2dFY;en_wt7gBVb`e&eZ9`2(LY*uzVS^f#H%hE+)w2hbuUg!?rfIkhS^MYIw{BpWl|82Hffvq{l z1ZFp-EP&He4mo&$Jl!m9{1Rn;{X-`5qzHGmVv+ptGqL%{v;`~UYb0mQYeY|u4beBH z4dm$XG=DO}I%!w8_h!IGT93Jv$ig-Q7pKkov#Y&&HPkfYFC1k5nIu;1YsR*1lHv}p zzDTVS6&sDO@aEqjFq6S{m0(mgFMCL;V{GpFDZaebm~&@5|IrClHW;j~jh7=+Tj@C3 zxiGPB9FTLgO>ta?f02}_MXS}63ddNj`DshTBl0vv(fWC~Qn znP~E@3~)86cjrVL9jovnIN9j?ZY4WfXSmU|H$s(#Ng5u4d2nD*wzOoWjZgsQc@Ufr zo#62S2@dc}FgI5c0Cfy_({ewCUSra4;VVET0RR`u3<0U^@fDPV;r(15GnBR9J`nf_ zny#cov0M})aL09Z2*G=+Po%7HKZXF&BVSTxj!nu9wS2)Qeyx*8s_7#aJ{M(}>zH^4 z)y;pw;9cjDsS%;FKGqAJ~r^c%oJDWMa-M4 zR!!IhxfJaK!o)vQs@?tnSgT&8-8Ik&zPI=%XqTR{nIwAPQ<>$T7Bwl^-{Rtvb-8Hy zvDYYh+J9&b%$-+GEib4ebOZw)f_5!Nr*MF2EVHa2Dek^=OVY9{4tdnaGV z#-2h`np{_$1pi*&K~k=FUf~wuobPIu2XXW#H&sfbG4dwWlO4j)XDQ(}e!C|D?2dA( zl*IVEBe#Y6y#|LDu2smi_)Pg2e1s)c6zW0>UW{5SD&-nAN`!L z{_cyBvAmW;UvDBg4tsooQ|X}GVu)--(U_ds**W5-`_jqiYWs%*+1{QS-Q&$Cy}MhL zy^kf=E}*DB@-wGP;1S)fRvX!9hu&x5xpdgO`x81AOQr%;Xs|Ju_mCWhqqMYCFjF>=9;6@xyl!^lL(9fGJA>7RWbj);C1;vM{6mr$_QO2mU*Hqn*){+-DB(xl`I9+MB$m$I&Dj8_p= zAv))#sKWO^CHFa=K7ZXTmzbD)YpQOyxTrS2vg~2lv!`Sf9rQP_unfC&sPI+0I5F2^w zoM)Tr)d)GE++DhK3DiESzP>k$ii>p~JXqc3Pzj8OWT}`H(84wiY&{JwtU03yM|BEk zO^O~LA75c*#X@P{yT?YDSn9qtDK_RMmfdAr={uU;Zd{(hTUmP=k+1!FqK&91&sC@ z=Dza?ht#okE^>3ca(Lm;Ow z*?Ucu^4F!r4*#%7t3ssAt~X1d@&PUNdy{#WM-D0qc-wgla#t87@${J=*xVp0CuH|o z`F1p_ypx*J&*sB!LIc$E{Bur@>4QUpRbk@1W265rUAVv7*gJ^pBov>1jDwo(Jp_bf z)XG3vXdbFQ=g*pHJ@5zQgApF; z7&aN`^%{sJw5qSFqJrV)d;Jq`OzhR~0U__3Dz>mZ_xXE(&MJkg)m!5-MHSy{Sf)00 zX5C$VPxL41JfYjsRb;d`?tv9$8&;i_1AZ^ouBpjKKd6w;4@XI`j)N6$dPREZuE15qbU~nJiUmSlZGHj(>=Hnbp^3b1}0ILc7Gdi9Ecgan2{z(2acVPqGcr$tO30pj~|a0?l;9*)UH8dS>} z$CU8Qu<+%Z%FhgaXH>l2YtF~t6U9=>?P>EsXM~81*>FIYoqMj=|0aszX#GG})=4SHD=SzU`@+%V4dcH0X+Zd`G(l~VVQqm~ zB2o7}B(!=tWH@+w)a?6R?qbS>ivyBAJ0x-O4o|L3z9z~sG1Fl^g~(+!CCR}0=+g`}hsC#?;n@Uq~tBa7mcJxIRt6ZtUYOBfv})*Oc?hh21MFXj6AUwxqf{Yd^)VbPlw|dMb^le zCp#x69)1{>fPesa*5miOr8mC=1AV`eHe+-1IhWWw0?*WDgv^@0uh6YwBgCEdzh5c& zBm{Bq5dLHBu2|M_ad2D|AON-EhB$aj_y8FUBe?|wW#%E7qs)z1LlcuspMt-D_GJ+^ z0l}ji{7a=w%+1VZ7bNXoW^#z~lx%^WjZ6qpil=?~@2e|OYr=)Tbjwt!(JRZ}f2r-b zGe!>JH4Q2%8}NKaT4dLLsohb?p*mrp%=iWGhfS4XeYTUYojY@k5 zHpUjiZ6d^MpJ!_UZOG(L5s65M(phy%*X6jFp+rT{q$gHwZ@=tnKpdx+%JJc&I#Ea( zBDC%y86F<--cgCfcL#P4B7A}vJ{a>IeC2)OKY#xwJ%7%IPjHDz zvPvzg5%qZOD=WfpSb!PLsiO0g|-2l*Io zFr>60i6MYVGG)dRdN|YG-%1z2hm7m{W$$1T!7e9gn;4?O@~s=TobO;kExG1;Eq^mV=E4 z(6L#)>jm91k7LG?L=zZoey?_-QB7cs21H$h(ybQM`BYg_w?=;OrW=2-cV2K|=m_A= zu=19;$Dx9lCNdOG>%XaJA%8peM>O$8$naNQvRiwlR8cG8A#O3J0zA)fXTKZRoMFoc znlhzloHV8-;k@9C*d(ZLV<21fQx^W;KC06~*yE;H10QWf`*Y6QT`OSW;~~+QbVYg) zvKPS-?++GuqxA-3;i%hhwDXYrRBKjoX`3RZGY6{7UCD>Uu$rgIo5Xmf-wDI|`{$47 z#jluCUfLRFfeY|7K?3H-puc_)nultV+X6?9ualE8pFe;0GOGohOB<%BK(|oqq)X-v zMz`^Cal}o(`}t<5y`3Pu!*-C~q`{+)^~Ctelk1o`X|P1bnU`CuLVlO%+}6tzZPC7d zvB^|t?4iP=%Se(I@g@0}uibCQK6${&GG)2j{{@*H3n-WNd8QbG?gsW~B(BpWK~uF} zfM`|X1gR?JL>Q(=z#WzwW{dHLG{W3V(-2aHITISExfIC$#+KsFuC3YY_<%aL=?8f& zP;X^=8uH%0MY^pIi6JrQ7>*9q4*51na>uIz<>chX}6cL=#bu0 z^?!{^7gQvidpAx7fs+Q!JrH;Exs>&VsEXHQy5?;bW@8k6%ju8@O z(Wo-Jhew{_70Hs398Trsizkbj`W2=(XVRi#T&xiK1$xv{=i!b;`JfTeDi<>_l!I^MefZh55 zFO$p!(S(NF;--nL{z7v{-=sC;Sqhr`zuQo`dvv4*OE5Hp7xq8}rWxp(qjefDX@{cG zxKa!gDV9f;JqXA13ks}Zyaz6QI{;`*xqhbIwv{48C*3|QI4C?b+S^HC3+wh2CAPffaN6Py8)K~fVk|1O6=93>5PznVN`5rT(*Qp9> zviaNF+YNxm1drWga9UknS;>L@FoastVjQPFzYM9W61|`>YJ9(+-KQ@%=Qc@S*Myr} z{}vsBn*XV%Vb4U(QfxohR4fvU|CYQ7Z=^+MxSyg5OTL~zRQz5T!R%?e<8Cu0+$qdg zSJ=0PY*Z*voj>KoOdz;VT|)!&)pS791!AfgFE89-8u+^FUn$2NtG&*|y{5LBPNt@( z8iJdUyy5RFJ`toW37|E>nx{lQTw;+msr-jE`;JvY;=4<4H>+^GrR2|onws&g)d)*k zz}lBu&;ORy1%Oe6!(>@&D-!8vpyjvao9F6!er`JKif4u*AR_7nx-+nCM;^JzfjuTr z8V1%I>gpr{prtSBrMP_gDIO8EMSCb-(bNuZ${miKs|yx6RE9Z74+2K@COWRek2P$E z$>g+ON$_(RLdHl~VxqfLcnx_|>AlV#0T10-T-qe z?yzp%*{?>3vE1~Sf!)H0i-3GWS(y+FS#Y9Vg{ZcoIa?zN`m3CONeBq!m6VhUrohjN z899e|b=YhRn#gNwKig;lf0C?mhWH*5v_o`aV&b=@x#{VjUS;-q!vuN5Q9#$ZFZ~1L z13eJtCAP)Q#r3Z935VQKc27ni4%2IYJBf^g?Te>xF9_viPulif=l(icKX(@?c;6jk zeTeInV@vtGW7PMpjrotK`ppR{?j}db(|g5Z5z_-rZ469wSmt=oaI(DzEC>V-0 zX{jIY%*RLFAB)JGbkyJ{B_MIDm-(*|j{Rm_&z+K_Yc>E!_1uP^Gbgs<)>^YQRL?S5qSTGZ!R zZqVM|+fQ>6ugt3`ma$#K@w4R*Du~~wnXO|nUBR$HrC}UI1w+Xr1M26Vx+tSUUB>aT z&&QIzx|+0?>sUPG>YAEekPDpj`ZZ?G4S&j;HoT4{g5&5|l!m{7C4}sa-7A;&{U{l- z*Tu=JVbo(`lT=loRu;4Jup7G!_AWF9pBo>@|7tIOs-k2X|Kj5pxhT*x{&$tIv9V34 z0fygS8-6M6D*_ybNFZ?qz~JEj=(e zsP}iOy0}G3>IkZK?%IYRHV)1(jPg({qdcL3paDxM(XC$R8@2vg9$vp>LSQK(FOP!u zSz0i&C~$7`z^}}JDMM-y1`!Iuty7 z-QvKxG4k%A#ic(=+z73d|5|=zUtj0iGOKZ!I3a0svf9~!oVfrcY2SfTZqD_!Hv0X= zuwjOuq}CTmaqh0%X4{Qg_K|m8J8u6-l9)O0Ts`9jnb7!?3bm=gaJ3fT$F|jl%u7U( zG8|u6-68vz-vH#+by%p<&VP+i@@GWqhC)|x$f-&uA=kz1W~J&2}!M5m^!o6IHN^x~ee@n%v` z%fC~nUGYPBDX%$>@(i9mu1yqPFyp~Z)>gKT-Cwv(-o&Kq{{gRe>tAEizZP{8T{{kh zO)F-eDXH-1qvh^_2VL&>n$jwNV(}sn{f<%pV`}x+k&#eXwb6jyCmlbJgCM%~@0RN< zGj`6kndtj3xUrw4wGpA9u16&#cp0C$lutM_M;AVFaq*0rZ3eZp$`tMg;a8eg`GUhz zIhATWx$SH!JE!y>W{es`KURz1ew9}}Y-&6A1|A?-y3^%Q;vgxGp8rr(LMFG?!NzuZdpj<|h&L3S z__#jtcofD$5&hTA=MC!NCkVvYfFuTr-SmeMuU-!qV->J0L5nX%@QN|x` zEHCd;U&3MQAfqCC^Xb7sjOo0MA@#Lu*9Z@nd6PK}Vrk!`UE0RFf-EISeT2Cg0|xo} z`W{24ib06(Omam*)WdWf9CEP(iu3T0s$s0>%7b8Lcq!>w)Z42jk&E7J7uHM@$Honz znRlU|hJ6Ye-6)`FkkD;4?0l$f=RDWKlqR&-Zrs<`_fo_r=0_mJ-obKWaEenJ0!hNz z9-bnC2IA9V8$_A>A5?vp#87Q@b&qk+iZYnp_j9RKq-@ojas$h#s29?^u~F(8n%Y%m zgSJX7Gr6o>T=+1`f4E`ey)|%)BSc)<1TsxE<_``?NAvIePq}H4mzNK=#V%{NXoKq= zZ}&Qo8ubyVi+?sAv?@9~mvC)S{z8^O2^`2ZS(Df0^!v2sck7`SZA3`BMR51Ovr2a9 zl}@4^ou*$cw!T;WykRlASkaj4&0B9Y2b|JeCy9k<%0L?|`CdsggYeC`KYpFxYb_s6 ziAc&y3GvZfy6GU(Tb{r)Fj-YzA!&(Byvz*hiCuag-!99n&plS|r_aCn6ZzL68CLS8 zuvYH&IF+Bjp}_@|l;G_{;wKS33U;FkuI5AZ9OY@{X>_mb$n(m(MqXfkN$j*`t%NQoA*syc z@aC3tp!m_k!}#kRXR!{gOiveq`$ML@t@hF3Yd?`Y1kk|EB?OnpSlAI`Nc}#rVHI`A zc+7I~Vn4Q&-&I*z*<(Ornq`O;u>f9-B&Ne=VPO#!7hhUN`jWwL#=@}XTj1yj)ElVv z$ET+aZvzvSv;o{styzIA9i_P0JGX9iz}qr2LVB`&cxig?5!)%h{wqmU@}@5*yH-gC z*;%>&N*9tc`K)|3`z=(?vIhn!2b3sMw)|IOMz`N6^f&qA!5iD<-WANF=TfB-HP7jf z5041~|EL+Hj!nz$!rtz3cM>3?N&|A|-@aj?05U(`dwpKwa0U~=r3T7&A_>AK5Q#iN zKUM~jgeX#3O1`7)E|xKfvDS^kfb)e@$_5`?NhoH)W(ACvVBMn_6 zT%aGm3{mnlPx}GDt&`C)1HK+akBGYHZXRZWnHkA7KXCx`fvn?cZ2s#vFPAha=3vv5 zT<3fek85_Jbo|D=fg2|zIkd&O;dJWNdd~l`DVGj1c%B=`SL~#N+%v<1gY8zZS=pu) zshXf^3rFhGnCebHKfPk-7|R7LUtFP!7$ZFRe7Q(WJlZq}iM^Nxb@4Z-abQ@>@s$fd z1EMAdYXE$ooR$`veuB!0>6Nf64IO}e@O6wj->?R*BPtlgt=B?cO1&>CqDYQvjDZ0i z0h2&9LxcR(+RO!(fUhpYjjDO&Q-}1*Lk0e|hEYMpb_j}?_Lx3D?Q^w$Mwn-?wyl9- zR`chLe8~g=oJj=l#eL285KpYE?Oc24dE>7A$2~-WHtq9D@bJZ@=RH@1Hi=9A8d9ho zsJ4`dYxK(VY~GO!@GsXcX?5|Dj*}fho<6VEGPEQf8NHnIv*cGA5t#pmFTA(dKXmy? zW3DRHNKpBwP9lP3?^SGCm!1P}pOs8FDW0m=^wTi5;$&mdu1k!#2{_Krn2v4?k$4_H zVO8G2$H!~8|E(}}o#V)zy8pN23GYf>4K~Ottk()_1WdR$tCW2v-0p7N_xG_6Mm&gD z)Dq&VF%L$r_|no6PP18=@9)>JbGLxVT})pBd$w}-m0OrV^744v1sQm;gkf3)EUyjR zyH0uhnwhC%-wtd6x)S#Dr!y*ms%+9QZw9PvU!>C>inN3rN^&cC9Fo5^nV`nQ$pg-D_7*t zCho$M4g_(s?%YNy2J=^<+g%!dM>%rA|!LwzH-^=3n;N|WJ?P7IrKtZo0okWncd+H5Co~H> zv2i0-F+T%# z?ZOCn!nt0`ShMj!M~btvD@~76$~IwxfN3HW%Ma^@(zg(r24pox3Plx$#~ z5O4Oj2x&fa>fM1j^1iQNm!quu-Vwx7ftLu^dUQ+X3u?q9I171-U~#Ta2?Ebw&lRO=ZJ)c0B9RISkrAzB+Lsj2uFOGxVE z=z0b?SYgc83k9uA@l>8S#JlXaUtozE_?0jVhwek9@>?&+wzZY(UVB6qH0`F-sEs_a zub%k0Tg4Ig&o*4r^1g*@4ONJLplVERAlRKoUth&S$#PLXr+lHZ@MFc4VgPcv>;SW#tOM9 zBQvvp!+rQR*=|NgMmdBTACV-iUFWGU7q$INTOM${W9d!Y%L`L!(W6@_X=#l!{um=R z9{%OM)BOB=3z%geZ6#9h9Af;~s1|s3Zuq-#$V6`WcJnyRCwAwDkke9?%(BoK(R(fD+-Unq3U697ceQ1n0a zRKX*n(U~44dutm+MwDl;Jn8}7-Vd+cFzunS;(OS32(n%qBkiXpxD)a8Y4i$^U+aB6 zl+AM*!O9}9ZPTGR4K|JdW_b9YK_FjFL&Ib|J2p5!bq*pg)R=v{wuY;hTc?iJvNZ^Y z6D@y|{^On261iJu--I8LA}Fc&D(Mcph3BCVVdj1GSRBEEV$a8sh|HApj!*qwKf1P` zRY-m*zqD)qES?l)U|AyRU?SP*v-!2I z+%xSDw^+PN5CzE~MMJaN^HI+4KhL{HlipjzaS7v3TzIRUioV#JF}CpHGvcjmbhYhM zLZf!-Sk4{OSx#OG-<^5f@8RFLrR+M=}o;VnXpe@b+P5f{+=ME$|AOV&IzY8BC zaB(TLiG*3jXX0j4&r|g46luS$lTn3MvovFIbPh8*sJv;KZeWqNd zR`4yvX5!Jz&%RHeg4-?Rp|dXI)`IiNUpw_7L=VN42D!ZZ4E6~4UP@cY^PnJ69Lu%HyluWt_zAFf8N zyl0QNI(yzVv1jG$QzYVl0rjbonL@PT9cb51vw(BE7p&j~oRb@3bzF8Jj z|E*iy?gq-?MK>lF?T6*f6#l0LF8H;j#4zrkR5Mp`mgD5vWk>-Fop{O2%a{-V8G0Nch9WaJ+B@7Kmk zK7yu8LnFF#ki@$jay0Ji>0!;gX2vr~jc)}$Z<`YTZ~hIjA-~+lUEg5zElNKla<%Q~ z3cayW`9BV!C=t{zy5#8-5>xG|(YUZxAuJSI7*3Li@GUn6waTHGm8A84+{K6pRrsIb z%u|-U|Er*FbdNuk9l5}B&XVc&!SK|A^Zz<*CxcYa9b-?LMCsoIk6`WNb^@k}y#U0` z%nA($+J4F8O(kWLjeFUCZ{0R2vAT*$Ox^m-V_Def)Y*^881Q8umw zP1qo7`!qPH5)>p0O)m(zAZ}VPr?m9T77~aaAGd-%_Tq2rM42ClvX}6~a`%+?nK7qX zGHt)skz@6Qw!td1dD(0mGGwAKQw?mfpRDDyjEj>rYcmCc9geL}d^!trVhU_T#mqY= z7-z%0Ei|b*l$5BpUT1Kh9C2}R@lHNSc=j;f#xOjb_cjHZ9A3}FGAsl8t*&c!&GiSw){uXI@}`Qa>LNU?xNZJBvp{CUL#Q2{w=D>iobOa z%Y>cwg}cvQOZsE!`-?Q_bbh1SMs{;KcDOh>ug zL_J#A+$3tA{p(w8nA#mH)Di_2fLQY?$Sm3%Vfuggdhcj1|37~CZSO5)Cws4u85t>& zB6}8z%fc2I)qC=07C##i`*WJTwxwO2Hmj81i(9RrTz*zvsp&kbUw)E;nj08u5R%f`JcPfpG-b48@cOo)7A>^L3o)*KK+;$Z^bLW{=V{vu9a~U^mW4Mi2 zENn@SHg{+eEam-5(Jxy`>BhZ2sIBFIE)bH;>F1 zzFn~{er72o#z!P|CGYEWX>{++cP{*l3ry{DB~(wJI#wkt3&?p%`3(O>#KzZjTtlyege4AklLZ|`VV=Xnwb9iT;hrApe-%LkeV7#` zB_;cTTrEw5c^rZUK%o}HH4xupVJ3X$SYT(H22Pdx)wgbU&U!J@kZ@LCLUc;R)oa&a znx?2_Ge%8?MVavLwy!AZ?3!PR1h>}nAasN)fKa~z`vNl1PEwP?xqe-ple>VV{3dpr zsA$@!jnWbopbV^Uas;<;>~9#0JZYD6K`L`x9_a7va*@;Ybxjl zU{oas>W_~0fKf?!VAA|UrF2b20i@8NNrMUQ9Ih!h$V7g>eg=d&g3$!=ZJTJ6E!h?= zo4L-z-=EL;LvjFSZ}HD%3WZ-!1HaliIdV?S@ewRo)Q+~e8l@fQF#kx!z4WD)k&H zxLG&rl`zjf<4%0U(k&Qq3F~MjsmG6+;QDv{H)42lys;FcRzZu_!sJ|kj(ZDcPIbaY z8U{-BR{Le8CN{$CrdOPOjNeYP(KuY8k(*#C9)lIlx|p_f_CN~x)pCtD3`;c761OU| zTchx6SdLoKb4k(G(Y6i3wBEV%b?+H5qP#yTNF?y<#%vRF@#Nw*XNt1oh=$@A3Tk2# z?};>j|Bj7ekmhe|ajuaAb@;2A=k)hz5)<_NUMJCYb9i5%^)^|WP{lugo&G;@|Ba}o zCMB4G)gB?HgNS)7=p8{`Mk~4}jg6W95C27Vh9@9S@aDYZkDxel_U_xGpPv^8F3m;W z2)-E|2S>{kpqhA?rO75XP$GDBmHOhscv^s=>uUTVM(3Q2;Rl5r#u)V+>-CJkX5c){ zw^m^m0TYABzRj`9RHdQg-eNCub8BTak2G8v=k$dckI2hUt;9S_=UR|Dhvo zF_*Np$yo#Uti~V-1f4-h#mc&}?z*j}9?KX?=l3n#^ji|-DYcw&ef}5WJwv4t%ZLZi z1(;~T!3v{vc*}kMUPr$kbemWhV6rS>sO5jpO%Xgy8Euzh@RKw`wPWIFy9qYCTS`d{ zGt8FUclJdncx{%K%WmRgwOwU$k!r*$Q@CQm&)jJ0zM&A}`khT+F{F&7qDZ^o&Ng!M zM?p{XNLFrbWtRv?Qxz2^o{A+ZG>7`t?)0RJZ{pUu&Jjmk!{Ll;gTh+n;*S!_QmR>> zQdf=pk292Iialqp!od)-xw~1V4opuGX-=^y+2S^@&HiB+_S}&T4_Nk?6S;!zd~+k- zvukv^^r4-_Jw(`Xwx!$kNuDOJoO2xFU_gKY!sy+O$m`%{fvON;7wV5of;C3@i6w3$ zRc-iMheDydQHc#7cG507*N03w;Kw8m^qicXJM@ZnVl(_#RQfUj#|%l@Sc=)Js8iuvdU3^I_V<_H$~Yk6siNQyMq?e|fJWgC z?&~jpz3KCzC>)3a9?8Ho5FVTw2@`l|R#(d!30E&+i@b_@kJYj!y`bxSzhkc8p0*PL-}Heh~S}_%3o+xXX3V_SDtYXQdrh?nM{9tAHrN%ABbn*kJDfpYk>_Z43#ZRw}vvvy60PWiwObH1@Z~# zz|=wA){~_a2Y78+7YQeDNDmnuM|BmHYj85O36gEj7`19;$ew|bAFxEm>#B8#>n8&b zg*G-eFc{cYT7jrpC~NeP&9Gi1MY4C5;ad^2w6RMFV=MipdsUWuCY+dGRPoK~gsVlR z2-CpVqm*f;ggc@(r5l{KJu?35{KTIjMP@dPT;2V-+i6C@3*+?e23*V)7{~CcML7yb z^f&fu{5ig;E_TpVwVgjNH(Y*52(f3DqFZ0z%Ko=_GTC`~2?1Vxj1A1t)$R+)dY=C8P9<5AjhU zzK2I;nAVhW^o*q)9&m$^OuvnNB2C;3(wzs?5t4O@Eekl+xjaufZU ztA$@gv~*biw|^86fv^Dq<|!`XPt21io#-xz&XvjvJm7nD&a9Kz0wUqd@G_*;|e)Hx{_i-Euy=U>vga7jJ zp790&#na^EFaYTwFAF7pu3W$FF-xd`bmwISLLWtpX-3&t*pcIQbWZAyaR4wL7)zI> z6gh5YdhD#|bp}b7!uG9l9zSDWHnP@Aq0t`kQ-kL<$Az0|f+?`nnv~dRQs}i=3JWr_ zC^Q{SrL{j?c~EH#!mN)UufPVDY6A&vdA#gp>{c6Y#l@$TJsNtFg9vpn#hLQy$i zEwA5aAbRL55fPDLqyI%#=q^C}e-_e;X!67WH}biN&Rs_ZGal#8odXhA9p)pcsHj+q zr-A|&X4e>-nDhY!3``7&76EBsg!c>T?xme)t|2W!&z{k~&doh{{yYvi{udH&brQ9j z6T($=p}fe)SZFab zJUTY$zA^YM#w;aDOhQ-As)T1P?(&>VWAG`qyi7aWY40!huOo^t(&D;vlQ2PjpF-j( zbA|m%Lz>gx$_MXmBni~mRx7*wzWg3}|5{a0LtX8yQ=X8Jc$Q2FS>sZ zeKN4vj+ATnazLT6dgOMy$Zv5_lW=YKQ4gnMp7e&r5kt;hjb`)=%6Ue0%dq$30}`nf zC9Y(8?JNvKKr_Q#I^1BENA*vBPnI*c#l6a{JVWqgooFx^8%I>ogI6XQ?=gvtKx`7t z0>f}ehL!71`mZKAce{ck%4nPYvOiO=foC&DOgV)$$RiR4<N;*R|n6#+uH@DzBTr{a^Rg#t(@(X4)*W zoYGH!*gYXcLxKrg1Axl|3L?UjP=PGWDn2107+yehB60q^cOBq3fTWxdNHj-gfXevT z+iUPlum{A&930qyZb!t%ssYAJO-(K8y=K|o*}3Dheg}e3XZBZ$4GV4Af+=sDUn5@a z0-M{fU%y_~)G+8W;*AcoTVL7zqp8Qk`lJOf{fX`-dAr^ z;~Fz$Jf9Vaki1*-mg`%9H$F6~ixg zDBw6`M>ZugKV`l;g#&+@+BSr>I=AD_cv-YvrR(1AQxZcdzOSQgZmB6=%xX`@Uz}6? z$yV~^b_(X*d2)*{8s@GSF_w2X*f~22XySHR5~z!=f7X-kthkqp>u@=|pxTa?PnfRz z>C=(%`$;@TNWYJ0KQBItE3_ z@&aftN`ZU>ZWssdXcy?kGl)MjcSYWMESJs08O;$fp>Ao(4p<9qq=*7hvJDm7=$9|q zohfpAIaF5}KXSz0z=Ea_#>$?t^h!lw7vWt47e-uz@f3vqiGW80H>m0DpQXe2IzZYV zWd>f}W%{c{BfNR(4`q>@ClzL`+_n5Wg(e1Yo|R~xJXWw zbCQlHD{Z&en5eFYT-^>Eb@wgqy9M@_Mav1M64>I$vaN!`yto|)Z8YTE`nS#*#!cs$XJYnZO93YGMP@1hU{5t!=Mk+z}y2B??uVSl4!W+sI>;bn%X zY8^-_VsxY~dY*wvtFFxxEh^AduHqAr;QWMY9ZJRgGMjh}yj?jFRp(AS zNq#!-{{DV3Xs_WsWrtf2@iEZWx%Lx|YXFQo5qRk6x<4Jx7M7Kj)zziELwX$D=qRP? z{rE?O<&nJ|Gv?(4NRMn9D;r`jho14as=J%9rX-0~?I)4((XLM0pfV|nr9If6S)hAuG$+*UH!OQt~MPEN7Ey$uz@@*qhZ zkh@mE1wum{S#u19R{gp&WdP=aVm-u@fueS1;|Yu0d**~LbO<{Pg~DLq{6S4u4dSrt z9MZo|rp#y)y_s+!Lr^nS;Sr5Plg8T8d#azFjTosXZ7}E`&8~~r(zqSQ_@k#BZ;*08Kc}SEFTyYM>4sRS2HBkJmlmMMfT)83u$G2Ch&mKbr(a9_!BxZ*uuD8 zJ2)mGxaL6P5(a!B^BUn-?~?;k|A0Tl1uu%NklkEsf4>!er>jr4cS>tt*A55QNiTKoyD7dk)(a9* ztiQwPPd3t|@VQ`dZQN>NQ|jANvIN!XI@9@SzKk_uE|L@{E%1u5jxpkuCRZ5e&W%HVK_H& zLk`-|@F8DF_K`}XqN7uvp_~>6Fpad0S3B&pC@J95v8W;!ll4G|kM9%9HCkp8q)%LI1{PiehxH-J;i7?(r)x0uHg)bAcE0?aQZ% z$?D?GjL9&BqG#9ooQpN|E_Er1n^}$bSbVdu4ls+#(T^OZpOl9sI>DlkUl>m=#XtH|&kZf^@J= zU3aQ65~M?SWz1ULCp zgqkU?TUi)<{@c17uxihl9yS=@9VQ!*-miEfSjSTa={z&ZA2&#oZY0Aep|0i&>)~#QK&;qqb zyC7IZgTE_TGykCQ6Bu=7{7&HlHjIO1LRv0KCPT=W}+^o6Ltf6t2^hw9w57hz3<5G={^^6PKvigoc28N<>RpVT)#VnznO-1(k_lYnMkp|$L0C;>FoSZ{|6hYl!OEgOqo}< zv*R^PhLE(i&CND|8rQ3vj>$0bNlBqIGuA1cs+7lPp@O2G$;1$3-*bVeK{S-J=EQd! ziyQY9er4n!II9q#iUfrbK(39+J_$)mN>2R)B99>5(ZgQhx6?=KTm;Z})jUzs=Fheeir`^Z0+HqP+`@3h8|TFaNkuufW_OLO_tstdF$ z*H?R-g1RM@lrhgp9<}bBWx*kD4%ZN#LJ;{dgsx6s#r@^kMY`VRwVyX!*)kr*k;j{0 zm+sVU$0$d+STC~~#^@H@iBa6~P`sJ(iGDSp+QKx@zeTWA(KdW}H2K^!8{NY6h9&kj z?X1s3-8m-9Ha(gwOG;lVbh4=8Jk-_kal1{s9!~F(Vx$f~#b+hVVDklgUD6_z88!~@ z|A-LHnVW}uek^<-E@GjnXE`E|L;CpPmpN5MOoGG2eUJpzj6Bz!>R{o0i_xlC^4xfhuGzGr?J#4BR_uN2GNI@ zMZTPcBE4ao4pm#O&vwm%Sm##2rm!#~Ob)hc)D;U6=+U4=C$Bz+jy7U0)30Y<7Sxt- zAbJ++WN7oSiU7dsd-#A+hnkWS6K19AK&CUZx+-#FSn~F*E9Hs8BiM1Ecm|o1{K{DY zrCqSg<`))jLZ;1OZvZ5@h3n=)+Pd{Gdl*;T)!U$*7XrNo!pOwLLcQjT4gX=8>ZcF) z7~XUd&Y=#KhYu^N!9|myb(h2uJ%=GIQ?5;KWdPo z3u(cyv1JuYa~?1Q`s8F~cvtkK%u+nC&Pw_LbNK2*VwXvJzqOWc1Vo<`5@r(;WhRos|*o_aAUJ0Lt0C3{5F(9X^xp^k>f=Lw1)3-#5?SsYd4J6Yow&qWZ=&z87-^W9Gg# zhL-1bv2We07?z)?J{t3zMd%Ok#vZO1C5L8ZS%lA&!Ou4L{BoT4J+6NGu&OE1OHD)L zda!jLb9DMGu~U7^(RBS0WIuF(Prl4=*9pzmI$Uk)f^>LE<_xJ{2$Zd0VJB#|5J3s2 zfn5eT3N7F$9P2Fl#c*`z#KU_=U?SUU@;CL7?|7W|+#jMVM7yTe#N^GLOWoevd9%FE z&sI!DuEcc}_|Qz>b3Yxm+#XaY9d2R}`QS=?T&wIWCgt;4U0?7Th ztP~j9y8i4IguB`fbVLw`#TT5UtN|x~o1G+J_%8v2#vveoQ2iBVOkmcCKK)>)_tJ=C zjfEi^b~Cwrs@wzw1a)I{%3EHb@Nb|#t6h@?kY+SCOm2T?kbp~lOapI2n3bO2&0ZLd z3;Hg6%n@MR%dXfW;K2qHe+QFNG@THl;^tOv(De&I>VXrYRb_jk>Dx}4ED^}5Ex-Sg zZe8BBLf3u`rKv1}-d6)}Ryax?AMe{3)&vq{?(fj+j&1iXt_=#&tgRd^%5$tpnO9hT z^*U_-t?}o>&a18ECkoa+J?}pij9&Cysxf5Ed^-L8c@**M{J&VbEcdJo;9wC!uJz$V z31HkNO8Vz(8Ue-{H!2;p%>R%GQGk~^mPwQX(O5IMX*0Pg=HFGvgoK6ZLg$_&yr;tL z3n0bE`BxZ{wvwUvh_z8oCe33WONP$e_UAB>j`kiS3&h`|Wud*@3zRFJ-g7u1-W zsRl7}(o3z&S;vn<_P@XHjtt=_T|T*3^YCtVm;4o_5m(A24>C+pT4!8ye9aRD?7CLl zib!mc?(!Pf+6}f5OxBgTUvF<=f&kWRZ*R|Mr9r0b`Y6v7I^vcvho#>Et;<*|q`9<+ zlA;I$V|C>7&%k=-_j>Uk`a&}{m zL77rsp+h41TXK85sy_lO1ttj4Uld9*XQk@Pa_OMBo!38Y&~%Vga|9n@FUENt_OUj| z#yBsm1i`!Jahcnmr{JuJ#&11;w-a(42~1{gV$*KEj9|Ep2hzCBqc?U`QvnQbc$zND4A5D!O;Y zg9^181uG!44(?SEnhR70=Hl>eRPYDMSs5z=-bb?6{Pk|!VAp;pkB<8jGX62mfS~O; zfgj=j&r%C<8XMZD(!uPtaea2GV23bB_(-*qlb=rrO>M{NQST`_TWID&08lIdCa;LM zipv_0p7m8dRk_mhHhI*kjtmfBBAF*`a6zF2tlk(nk#hRp&H)yC8BkZuKbxAwy(dVD z2CmpS&ORlefB!OvVHJXq_M$pB*U0uIZQF|G;k;gn4Ozcb2+w2i@V?jLQ6r?lTCyw? z23$5Kh~!M=-s!PBvw&AtJNQ3LvnWHq{B(hin0>6b>n;1}&+k!%7h+|MOd~Jjo!Q%W zBXScMMW|xeHd_*JeJ|sDNccq^;S1p@oHenrA(~_`Ql)Y@O=$aA9w_dc)1ZA2WF5=x zm%*te;d$Pt^KXcVCu?((_(`^s@C7P~&N#Xbz0O~YYor*_&!1%jNz=7`)0|Fp%kMq+ zn$-##Sf{`+3q(gye~!`X8t!Ipn_26-V|@(J3p+)2)x~Gab2i+)q44{flX*$9~Wv;cANR#oYUN8nCa_(4Liv}FH!(Y`$#x79ec*A=RE-) zWF7y#c}W-a%yZkT=kY^Ya$kF@u7<<(xY;jSPg=HK+dYu=IJ8XLJTBt5ZjMHsI4A9yt0#40uFEu=c9GRelCXgs z$O3GGh6q9+=*V&a8kT5OVSCYxo}NA&^nu3P-f%xqPm-Wv=_L;)Xe|LQ)1?JH#*0^> ze-Uq>z^&%A18tW8=P89;{((=OW*y+U>P1c}yk}=2bEXq0wFYszi@#oJ13T`G$I@mw zz1Fg~z1AX=Zt=vU%WdsAR7LN?MdIr1jY%#z0P2Zx*bxa~<~w)VEN5lX`QNrSUdxp+ zF^?trU0Gv19yH=0WS11c@SM!I9WPZ(^e5Y8(2#ITk*2y%Gfb_H-rY^pxMNN(_sQ~z#-iZlvV+*`=CMh z;EPPZ4Mwq)ccKv!V5m@ydt}296G7Y&k$(e|@VpDFX3!oFr#XK}Q|eWAYC%`{2$zLz zM)Ol6c$CZ0bEF?A{t za_h@{*fE;?Pgx%PSL)TPSHMkp@GU%HC&YF!o5BkMnqsB|ITirHhRaMl5woH{14!Tp z>>isz6q32$@`d%&=NBC4xUah%do|LHk zriDc4e`J_9F2r`kQ?8C?Lcws@*BWk6GBgV@r)N?o{Zc&&Qu>Yn&rJ{YuFnhZJF=SIHe2{cMys!%WNkDvynag&(|5NdQL%LJ z*qvdHWKY%ge#o6N?-fZ=Zi_&#8fQPPbaqh)XGoU<)czR`vjWL z1y{!=;m($3%vShYggxD%DH8wXRc(0a>%+pcIy4Nvt4a}zPZ!_PM42_fF!>t4b7bN7 z4dyJbx4s|M5|n#OJ*n~UHc~ngk&~-KbqQSwrK*UAz+n*T@&^efB-|;3s$l0BSsM58 zrWrUOSX=$Vc|SqQaUNUfn(n1xHCkmnypoUE9K;bdn5dV$`8C+tyiv{D1QG1QH9_WQ z&ZyaZFnHFTU1WX(xuJ_o8;Wh?aBY|C)6vNl@zihb*ToYhCwn)yKK;7;CtX_Nllw-` zmy3`l4CS!$+d6)r=bvAE_-4cZXm1^uBXXa*3sg)@B$~ua!QusM&!=L=D-}qbdnI4m zUQVY1j6P}h!>$2;Zx-7HhMSb?yGQN=L!YbQLLKc&ws}*mOh%quy4i- zv@p@!QNMZBzIi+2`i*x=d0$L$E1Ly8A=!%%qpGXg7Do0oF)_!BmlhkcSg#H_>O-Q> zK+k2pBump4l9~eowhQ*p5+0bnx1$nnI&zCp-ur5n1pDzh;0Z9_-rnLtBOT5;mPmPT z3;UEthatST%+1XUmB{V&WuZc94 z_ITLX+Rps#%fSQznBEGT%#8=Rxm+?nGcuT$uJAEufA!1}-wI^CaIV|!-R>DxRaHGD zqj$G=RtGjt+o zysh%vYH?Ij0a-1Wnqt*Vc^|irG@mbUX;NF`99`GhCMbcM4Y4JaFD7SP9`hy~@Cfiw zNGO@-$Y&`Y{CUYKuVlWSB(AVm+eoQa)^j=5aiUy5b;WZ&7fT(V`RfTHLo{ ziy2CuaB6}r7#%@Hj4>>=#6s_Qdq)SJ>FzM76ht8h`u9G>Xg_^Qb@ZrX@w073=r0Zd zh$&yA#`~1GT6{@alMf;~I#)9KDsH8{{BbxPeaOa=}?4Y*r zliP37Y}0960#2?ZcuD_uU(>*&qM~{_2vHr37wjLyMmNtwJn%@c`|oIVNy6Cj_goTI z|25??h`hY|{R9*OVA_^L+j<#ha;@j0SPFhSKO~moX>gHOodGEYB|nS4y0yG97+v7Z z=G644?T2Z2F9ADyxr3)1#2fW@yiOXOVF7VLX8}}4dA=t+ncmsj9tJQ{)VyVVwpi}= zXr)6qG))V>yEDTEc(gPU z2o~lG_jz+>1y0`WldVz3qpihNOweY}*X9m-cf$NeuxJSMD{HQu9H)q9^c|%KEpBS) zae1?l?+{*xm+N&nG!+nc%&w$lv3esv8G`&x9*i&4CYFGp2=xkbYPLcX_KwpVF#9M- z8MYp)akcAzLxx6K#<0lIAt2Ehl-PTp@vA3!iI{U@<>71G_ zxZdHVwN1nH=H6H!NeUi!LWxW(+Xtip^w_pV5 zwe$d&eJtaJ?~-A)PKTCPu3Y){&artQ&i#6mUrWu+2I1$(RBizg_LSnaa~}&TD6^M@ zH_dh5rhi(0sNOXvsAHzRN0>?QO64r>z_4EL^3j4sQTDG*!sf$Fy-%03?uYcvQDJa% z>b@}K@JqC;xLCKEbSIVbI5n`&Njc-Ud!Npea914aZud5C)+%4f&i~~|k&U$~v}@kr z*A>r%_35v108+5a5slx=bVEqsgT-cYM$g3%3*?Xts1!+QegeW zPRoPpQC(A0(;Oer=6`2%g~C0fM3qbx(lBAhhp=N;XD5GF+R@RGtXYSVv2hgW>trht zXDEMYXhI+gGk1Aw%RPqqA~lffQkW@_Hvb#w^6ZzNlanw=^|IyJlmg`eQVoIW6VH;8 z@Jta$APrzeWmQ3jB4B|k6P}_~0qn3S!80W6IJE625@mg*mh>$-O}#3Xag7^}CyNX& z#%_Z5Y#dpgFzNH)8@zWB2#u3An2w+S_{4mB03TC7|>x~NtCJN?sm&a z@VLh_@}8qxJY73a*s+jcR6szW@L~4XX_p^wMVx!@^&?Kzhk{B_x1!z%R91)%)tt*h zSbOC+wJud}beG4>_vfJme~^RA2H5v-Tw?XJHR#FUB|l5ZTo?sHNj!?Z$&{ zxHD#lk4nDoDjMPt58n1BJ=UOobs`(c*xT52)khIIS}tvWUSOF0=cHmqE?qtY2f@@7 z_(|WD)TGF7{F>Gvfd^T;{Q)seg~XWg1E1Wr4jo)wmQUp|esf4r_RS%q^m7KD%BRIFA-7LQ z*?j5(7+g1Fv4(ab6^p-L!FP+WM$stQ^>0ZCCP*fYY@O)Ue4IuAUWs|YFnXa+4InB{ z$KpBdg!P>}$H{qlej@CA{QNiH3`9;kj#h;Vi$=-5`m0of02b*D`j=;OB=o}VbOYms zmFp19bqAsoKW~EBlNP4+kw~_`?5|TYCf0$SbD%HHp~|{V=Md=Ybyx5C6O)kJUbcg- zNB0^QZx^(CGvQkd@w_~6b9-3kFeHq*)c@wXE39R#E^lsW0v~i*YR~;X@w?}Z8DTt9 zSO1|{WrHgfZN3ff*ODD3Cu!kIsq{IcI9^EJr%n*q;H^OqT$=-+LO#1U1s*@U-zzkaS%VOB z-hK#vCUbSCy{_`13C7}J3pegrV~XXJvV8uWMI(9!c1G>1N5z~c5P7XMsq3k`wTs1V zfEK9GIj}QI+P(B5qk*Y0b!VL8RxI`&Fb{&hUZ^?Z!TII)8%rDZ(xZ>%yM8E^Gy+26~~yQE%| zVx;BwSa<#6^}D?%vIMUd_;U89VPlF$rxYyf%J1pOjE{btAC8$Ac{b#4Zs4rAyJdm+ z($s(LRiGEY+cbLHEUW0fLD`qpIqaajYfkisMl2(T{><`=7B>)t+sr1rK#Nx1X2M2+ zjb8J(Y0`q+RIf{@hHXHgizP(o>C&e0JHiug0lI@C0;NX+lT=OH=yMo|<@uDBn)L2Eb25SgK0(!p!j$U4C$V33m51Pb4ZBhNbIwF z%uaajY<@RlCu!Z2e$jhp_#SE}!!(2NFBnV#GZ4b10=d8bf4pQfe%5*jpc|V7a;(p{ zI$XiSt)0$LE~kfMCCMnT>=qh9WS(vOVy7f&Z@u{(=F;pld_x(lyEnW}EQ*c7@9=12 zV~YO{;w_J+v$URr@K z`1bz(Ep)&Q7Z;ar_~k(N)fE;?ACR^)$hfd$45XRR`FfdJ=e@W8ZsupuBr-@7NOCf)+x8eb( z8P#~E77&=bv#Nx_*o{^RF-kizyJTS2c9pl!)SuH@D=5zP6m7W%--_0gQ&R;LrD%iv zYi@3lg&fJ?5)&)?{_F7l{r>o;PalC@0jPi*e9%VF(g_-3G(G4!bw!_l{S$A#E)vCo zs+8PU#?GRFn?SW-Fi=))X#OqW;^CP^M~K*@n9-HjU~@%rfX-lxJfxKYPQDB7qKlI1 zUjhw2Wc-?L8Jc3t7{#lMHS^n zoBlB5_UiBtgSF&B{7?`aa%8Wai{kkH{X2+g1Mru!*KQb1=4BSrfh8dn;P;nBMWj$} zSx?ZYp&Th4UG@#9*#@I) zF!*5411r6btgqV*mvg~LxZ75WI>DK|Ki z99kKMJ`n2A8>Xg3&n(q2}3HZWkVN(;u?wpIbistKV zn4pW?_`73bl2fwRK+)jTPG(~PibQdaDvLk&HsC8 zRs`ut01$Oq_`wHhX7eoCdLUw?hsz0@L{1x89fXYEqq2m)N(TcLjvTwZp@C^ozAGN< zY9xTu#4|-@z&bdkfRhj#_fv1z>#!X2+o?N+f_#64cds)dwRvj#sqD3Es!BE>#`e?5 z7p9Sy(-`ohAyprEUpHpix{Q;Dj=92_Jo{c*qSMU*bR9;*Is_h-k_}-i89UG!`IiU=t<_bFQving-kC_F zqt9#BJEta-uIQcVz2xxwH?u`9H$3^PSBcZLo0{Y!X`vd>pUxqhymLTM2=xOSQ7vb!O7t%^fY!Di(weZ*w#_6Dk{qGMz1cN)c@6C0lrO|2%C{^_ETkC!& zo45e;Nj|3fLL%+!82yhM(ARTnq7S z-5Z9u7zTecw)e6;$oZ*8FRV%2)eHLR9VJ2@!MkIWJlHuXGb&Y-}_>G+$TcE)C4 z%3fzoNtSne<@nfZX>@Mk>MN)z>?Z1cZYm2A%yLdRo1@|w? zFjLhTq;lY1pxenA)W}A&KU2-?y~}jm=rIqZm{8y3?|o2x78pHHWd)7~S*7H_KAgxM znL1OidT)P!HHSfF*@HH@=74Ci@70-!0PANulf^CX*T!={UYuq0pBX9i%YNA9sr+^~ zTEu|2ACMB#s~M~Wfs-W79_qT$0^ZkcNiZ*q9{<-msL3i*{%-;sn3+*SMY%xnOE9hF zi3beZr0i!Pb1*_s_YF{dR2$DDdThm00bvzaC(-BBq%N7rHJ*mc;#WHMpbf}t3`@s0 z@FJqGdJy)zHzwplr{32DkHoZ;xQitsx|vEBrW;=l9Ov@4FnwvE0r(@{XO9x|(rELc zr6-TYWxmewur{mkjOxvQ#RC&W1$_MY@zN~laj0l$ZXILm6U@J7x}aD^blC)%~<_#)AHtc+07O=6(It@)%zN zo`@9!{Zw9k!9;nRuqc~^-Kx?oa+b?Y_slPQHWv0441WDy=DWiwc_qf{tECIV<%5^Q zKkTse?Cr_L)Ukg20ik;-U{(#}tISY%tLi+{eAy|z%4{bkO>nTmXAG%x+WLWzgT#~|v816+u55isDrRk)J= z4&xk5503pPtM?4Jn4T1^9heo4e5Ud5-Op zeaJVn4cgSsxaPEt-``^7b~IhI9L^10Mf!B7^oLiAcZsJwlrqxc$?nlKt8s3 z>Vio<(F8TrUGTBl!H}T|+DPy#k-+?9FBz&iP*)efr_k}BXVCvjfPv13$oGLN)Oy*x z<%N`IG!wuZNuNq(CK5&P)4EJl(vuC062Y*~u*$nTYq zAG&S$IUe0m>g<;HCJXd~{_PQ`rv4W*KR;v~2NO?<|Nc9Fi$m9yVU{;PGplM1+T6m@jjEx+D%S-Ll@1j4~_eMS6MsQ7Q)@hX1&VV00_BKf1S z2f0UYH9B8e8K399GaiLMAfAvV-s|_)kUJWGX@A>b>13VC*Wj@pI_WkUy?Xy3|_p3mIQa{URc(+e3#d-X5Efs$;Q`XtS^Lz4p zv<2Nq+Z`oEA_M6sCbk+U=<(kO;KU0l$Efof$df&Hu}kJWI+;J@aI+cj1P^ssioS%X zAm4ChswYI|QN-nfvl;MaPY)@o-2YJM@L1*=X|h%$y|jDNl?s^&@6$U+wW}D+)DhjB zZ6X-sUngL%zy#EZ~=Twkoqx6Y3D4{Yf0OSc>NJ>Ufpi1CLR zcc~N8k24-W{zguFofh!vT4A(GyfZ{fE6VNtcA}e)w>5x7v3btKlh5W|TCUab4**fK zQ*B+D;p^Ni@i#G{qIKar%Y`x zXTQ2s;fby)-lHoCkGZcSFW4m{5)C$})kHNDz#Nx-a2Fy3i3JOkJ0cgNh|CLyq>kQG zveJY0kYHix$;^3XpiU9S2~v z!TSfpH|I0Y8SWgtH%WCGT@QXaXm%$0fOtaXf9pB}a%T|ehJKpi=svD66x2$KmTu)qB* z3&}I8L!Sq2BcO(WYXk!x1T4huCJa?53l!)Q_j>-ag#9VHEpRxV`w~j4g^aIFCy{>i zfiXB54LGExE>1M{(yo#twyzynbpoxZI+eDAH;z7Zv8ispi47U)1a&Q%iiRmhXfNYa z5ZNDNO1*kn0kp(i3fHE`M;cBF+DB%R=4XQaua9#7>#%Gj8J}im7LR>WSAELGSjPkL z!w?)oaq#>i?Ks>Oz^idT9g9(^X1Qf)G}k@6x%yhZdoN9gR-h;E1>KJlzc;X!dH0|F zrcbe4m`U>X=&4i1W}HjCpskI%uaKEM@nsKB>MhWTNz4fQ`>mU!Y~pWV$4F8v6L z+cRzh$3J6IKgxSDV_aPG$Qh)4Iqzz22n#Us3m5Y5TKuW|Qn!lakG=5R{PwD}v!jKt z+oizQ&ZV}_VU2I|$FxM&=@2q;oO5&HB$y=NPhzjHyU_VV`GaT5W?}q9m?BrH-#)r6 zXT24#FCY=w>_{D(es`@!ik?lm-$>qgR{4aB)@xfkCYT=L^)u*4W%YGatNV!qbO-GDC}?Z zN?gHL%Wi~MkhgiYX&DRiTG}b<*^e-n$Lx`&XDhhq67B}IS!qnCjxEst0}1laM{xiO z@Wdao<&h)o2SjyThjN>e;ZMtrn9QW_Nsz z;)<0^U0d2UrM7#gu(mD5x~3ZTPapSL=2vA6jEqzxN{uIfm@t=a-lTT=U}G3>;<7*A zmGN2GZSGoa*z4E=n7&xkjxY1@fRh*+b96mjWr$TrnM7!y;L}wv+?8Ym;fQ7R=r%c5 zKI+GSY5_Y(w@EIV&li16vn8{6>+K05W!9s;ioe@@&4Q5$1;YE9M2$EV^S@FW4%a`v zKfRUN8{kPj^4^{*q3Taz@dS&Z_R5UUYBd*ig?n>_b*@$ElD>2(`W!sbiXSXZP%Cvp#*bfzaZ~ zl|p=R8cv@4 zGT;yQ1N3>#QzWO|e`j&!{&4SNxm`2r4=CAH!b}|yi2up-OKS9DS-BRdEmPCu zv|c|6tGJjOGZlX7Xz*Eh$&KjlM@5740?~!U4IYJm_>Q@HyJ)oLSmv-#qge7v}6~q zNT`re^7{TCGw+#qy)Juqt8TJ7S5D1OOHZ9J`L)@_7vw7=Cgp;6mR{fATW-$61krs> zo8CV4BM)ZBZE(QHcpt|~-!L{N#xVGH3)um4NoefaP{QVQLV=+KmB1*pHQ%*8W~ix? z=7S}QasO=uPn8llUs<($WWe^ey z5#zA!y1-pNw!S>MH*t)Q-{TW08c>9X?63L<yeLoq zc@vPr$FEs1dv{J1(?5N}!npr+KNq8Z>*Gj4lu(Nwo^#;JeKYHStMMZhJv{;B8-mlQ z;qtj``~SkFfkB&Rxr)R_W=H(z(hx1XR(mHnrS)o{E8iRB=#05h8LMabF6(}Dufr3c zU5d-qo^$gIX1}E#9JA`wi<)AtJVK4KsP9Sz+MfcrRnMoBqE;? z?ciW^pIi(-QE(9KqTpEh%R>B=g4%?-zJ-VYGToECRMKZ|LN_$ z~oNwP~NGE)>GBO|*Kij1-{U(z-bl8`OglD(1&8D;ORDA~%)+qjQc zUDxOP{eADp@4kP3{XDLJF4g<}8qeqXJkR4i&f_?JTiC3{PC3k)gI?&%6y>$Gp4>o6 zsOY7|Nv=KKGgBB#{naN&Ii9LtSc3-l62Xicr zfqS2=yAbE9mn0m{dS+|I@Y(Kf)y-dj&uB*FBn;|p87QzWv~_x_*}OM5 zcBR~uz;r5D~K4K@7jo7)5puHE5qe_YJ_1ewm|Ks$$Q*iJiD$w>D|ZIOYT5&8sJ@hhV^*`VuS zm#;Ub{9v9yN@w%4zF5J_VZfkes_fzm-tujpLDBawTwdaNmnBL|uk}>GuI@-uR{=AH zVY^vL;Mkjc8TYts&r~!vVE)xNG?b2#^F3H1XZUsHiKZET%C)Xv=nf}Sx9%}}$+5{# zHQ*|$WAcQ~M7PBE*|Rt=Ln-N=a?{LvM()|yiQZC7SL2no;6jcVp;jfrlZq$lHL2^P z?1m)sdX`3WSJG`ZZ!|Dwqz!YtF43tsy^qoyR6uVK;`zv2=YGZ~ph{KerV6Q(4e@)- z=_y&}zc_B@>!(<|&m{5uDWD&i?&acIgY$;-z=2d(W41a{F94jG)GnuZB0e4>cEjKA zzT3IEASURx)=R7AFEg*EvLypv$Hvo*x81gn7~8k~2cya3r|q1$MuAsz_7LO-4ll&I3F$ zqg)&Or2ae%WC#e=zC=L43wus!wKe|bE6}nzy8jYVZB_{9drc0g_~pkxJnH;& zc;V($u_zmNZvM5#<{W!Jp)`^2S$m$%eav~B%~N?O?3wV64fkm%2knLng^O&jC73_i}*+)SrhPswR2531j9`{>@EaIkUnQRyB`KAat6pim%k0jw$a z9c0wIoeWn;#>KMoM!qssli%-=zBaLTejlW3b zsXlk&Ublv>{z-q>t>4m(?_Rr|sk}Tjz*iljL99@_wdQPBYeW4Yf3%Ddb7I>>(kk2a zCPk}{+iCWtOC;=&OpSd%beBkNBG*(KW)1;TpVrq8!^!K*0(f`EVkag>u+VJH!sD_~ zJUyOWoGCguo7;Syt=YDQeJ!y(w!>lb?xGix(p;}C5)Cr#Xzq1VR}b4YZ_S{gPL+yI z7)$xZ?A+fnn1J3E5K*--NpV|lG_AV*W$Eq?*S~y4y&p<(%P3W>$HAkHfe$$^EY&g~b(bV#r(2&4n=V@h&Y#Pek^x!RQ{dLh&A*r8-bFI@L z?m{`9Z#QyqaI`i!BY!bOu%a=m&~fG#>zy*eh%o-0l%{fzdHYE2I|B<#s(ib&ZAtycEox&6_DmKI7k&M{6h_FLyA~>WjLknEwKlC# z7xfi>`Msj@XRFS{#%L85G3xYKSrXy6%Ek%S>KAG_)8-p14g?G&QuPz%LIi#NI;Ddo zT;?A<@+Av3l^A)H_rtTn4@}E&oPk2BASYM*LNDq#GHq8O=4MDKPKdLzV=x`#spGE$nLz(}=XR;OwiJ+7~mm z1uX+PmGLf=s|?bX`4mnC{hEIoruWKu(B+j@mB=Y|q0~1sYfg#JWz?}W)48O)`<1QV z#0zj!zH?zaoSxUewVk%Yh(d5?;bKUt^5|J!gT|LE3LGPy_O`0^Azbw@A07Gw+)3ZU zyxhT^-q_F5t^|^eE1eulbILJa7dX`fRqkA{Vi}L4J-u04j?0OfsCe`1NOeH-XzrWs z`mWt}vQeuar1(6G*nM6xGbvl5@pSDues$i>4Mt%`4cX>52e+y9^NZCdY^XC*3Nn@0 z!4_LIFB1DnFYZalp>Yx3pd}?nI@*tmgLZ-Ct`)2|yTXeIqkuyyg5j(^HIXNu&Gv11 zFT0HRO9D@?cS&Jd{gRi;AI&?& zr#7hKtrD5h&-ZJ?8O;vH>nBR&q$rnqdoOyYN5-+TynJMILT!p}7;J411dy4Gn9&n0 zhi{7C`V5y~Ld!pn}xO#8Dt-*~FBX?GXJY5nS zS$j;1fmFvaEE9f1qmqO2R!Tv^tJ<XV^;g@_dci}`0&2^^Dnv4PQhyoojT)D#xz8N!&gS9E*GfktfEYg5q$T)IHwzr zV0gl?w@r{jNpE%MS*q1Z&objK=}$F>W{^qv zSn*EfeCr}r>Wg^4-nVOQBbo*tfn^=Ed*1Oc@o@2#nD_4g8c@Q*Y2?#I2yI*Fnt0rx zN$fW3&V*_%=9iY`(7K$@v5JfxnkX_zK84bT(p^DJ-rKjHy8G9_{h1#Z|5?3r?hl!x z#xJM09{H_UHLA^7Cr~VJd!tW{|DJ-uow^%v*O`+)@HWKf%Yb#IGyBI+m1UWGN6TI& z7HL!%pLJbvINVunLV8LGrlnlxhyuOC1@ph0!Ed{|^Sud+Lu%=piyxI0+f(9CDTeUW zmmOZJJHSB3u>>Ksp~0)`yvR_iC9QJjlkqwSreUW*HfCz~3vC{5zW3b&mfg}RiA=ZF zX&)(ZQu1<_@<^IkdH<1d&zxvkZ?g+Qp{%D}WBDoUkti))jo4Fn_Y+?T3Fj3U@n1#( zvHJa|opzrnl(O_k7wgIWdxw_JNGPHpM2wfE-o%%=V$g0 z2Ka=8wqx3{6e?;zpjqdJ-s|&Pr`Nr&PSGu~# zH|-{<*7M+>487eixMxGSVj+FncgE1@gitUeF1^*G?{0aB$-SKOJWmS+=$myNa`}IX z3HMwa?!tZ4M9Hix5VH*XF5sLyY$)6j9YdEhVEjx$K;m}lUC+`_g=;zuiG@f*b~5U! zg(LZdFjEwW9b?K-8v6RXPkAw_M9yPIm4F*6b5pX;eN4$-f>=$k{mavHpp*miLsw~? zV+YK80myn^s#^SH(@zG!7-5HAii`#rm?I- zk3)9w(Cun738O)W1^#I6&C*?`8#8z{cqhabO*-szY`++Ca`G+*jzcJvR1k%?&N7Q0 zmvGt1tl0Y-iaRa0~MOO7lo{CdE|f)s9!EzE3WVxi9$%^g(xc0Sa# z(%B#}rK%@Y;-y!$#I#-Y!5>fC_f)!>T(=F=g?W&d*>R;UR*`zdzMpH_w zvy@X0C>Mlve4>S6`i7KW^oif1_)r+#gx2Qh!N-m#x1Kw=>sY|;v;#${VualX z?;o8#V$6FJ3WYkCR?v%0x!-tUn%|WCYS6&*aV@)EU|-W{N4@}T;Ggs#HvatK?sok+ zhh0U)aOYi`qOp~f43Q$bHv2k>d%^3gYw4I5Aede@Hl|H}bC2$~sNbMZT)_r$FeRW< zbPsVIbhmL3pt|9~@qtu@`h2Xv8xw)gMD0^zr|xmbUib~%_k6k`J%x2ole_)yeEHmc z5?{6k5>LK1U00fAx4%!n<3Zz+Vt^+lz+B)7bdY;5_=R9aQW*2;sh z6}nS?dF>fJ=^j?@%>@`)b$j3QcB>|#du!v~Q*W;wJ;|^FG5BIrlGbDPH9FZA$+k=U zw;Grdp1DWHWvu^`jyRXbNnP=)J3b*i`1P{ghLl{}^%B$>p);)w_s7aatqt7DuQcrA z0De-l*XKfSspT>X8nm;QTMu!63LPMtbvgjd&z@p;% z`3-T#%7Hrwo^U1c)VM*V@@hBJ*F{oI(w42aiZ=Aj^%!37PCZGXL>IdWeJ^n9Icc#% zy0)=DC45;MWr^m6!z!zZ&4CFDDNzdBq;Y^%-Zc5S$v%gzQ>$PFpHAqi`{y=T9T$|W zT<-dP;`4b8jYl(``Y~cG4%Na-wuys3*FTQkJzQzc)qdKPZp) zB#?+#+u2r(xtT7~YE9@kl=#*-)2f^+yxNm=`mN}Xkh#Lui=F930v%!#@jR2~z8^l$ z%)No_B`=K{=j6l636Jn~AB?8l>^fCwc|OY`E^Uz!)BJhkZRWU&hQ__tbQ2m1Mnj5s z@=bj8Jitcq@KlbOHS1c_rNCcdXJhp4JC&CY4b;t~a(W=*=IJuVxE49;c8qF4BKGq3W7qt@kNJDD~&o?l03l=_6C7OmQ7qfr;`r@5nanyJ>B0tpeEct!-0vxJ5_a z#l{5n7SPqb{p0_xsGjZ~g(|J-%wO~0i0j!>9&Q^svPZsnGoAJ>i@&CMNu=2L{mU_4 z>D0>nft}5m6aU%QS6(rpwKTtfZhGjYj+jC6+@>-g=9LuPFC&M5J4Pz`9o02@-mDQM z;q3O8_@Xin)}}x)6UXzj`$>yjs12@XJcw3kM!f4D3K@zq%rY>ON#KNT8rbF{=V!z8 zMUi1eNB-8DbQh&|ZziErQ2)U&p{X8h#k4~=2Yi{HvvewKFm7)jnPxcMYcOjv=spbY zF-13UN;B&^X`>ZUN31AL5mFOad9r@;NP$(eB2xF9bW3_0^ZFC@(?d}}w?5wI;41I% z;ux46t)#allt!OEdD7jpX7iKuW6@<_Zc|5DUYaSe@Vd$ey_pE3+Iq#<0leJ%=GM^D z;JvG6-uKj)tUB}OxCt!3T=y+O;1{qxQgn&Mb@tD>gW;^H4G(QN{?MdVhXk%cd#)xz z>avZHoSKe`{Ypz5{}ju24J=9q5|FetvJ&1USW&l1)$*XBHk3>;zx!<_6ZqsjwQDKm zd+6+qZJ}>(3+Z`Bis!$*;^B36*qfF{!l8HHP@ri>wHtPfYWC~{~=y}+nVEtHLUMuS5*b9OF;^Nz@c%G2L0WfgiT4AJB3ca^97*gAev~7hB z`pslNN78$zqOx+~jq%YCvjTJPQ_+6I2QVgxHS2A-`YB-VmJvG=f2+&dzY0FSiN7t+ zgOS|QLYxOTx6~v;91Xmrq@$>XMWv!ISlPn(t_waEQXIFnY*c|V|& z{zJ*#>eV?|e#Q5^nJ_MxILtov#IKEkpcj+a(YrfTZ74n5G5=@Es%~YM80L*NnJds$ zd>Q=cWAdn{;+B#K?>;@ID9#fMEA-klScqVFGx`{T!0jW!kUJ9X)b8Y(D(4qdIhU%0 z^}cJvY47h-?Dj$yUmo!5<&heM5?v+1S6~46B6aK5t&Q9lqaP*4j_qlgG$nLG8?1$Q z1@HeYmb?D`dzC%KAyD4MJehjX*p-@yWq>Y*GDf3Z+7s{AXEncTb;t=$AqsEs%$Ynp z^8Fa&&q;?NAIIp%NfuewDcZxaEiEl)mL6j3HM-tsD3(i?=O2!pG#?cD2OXBt$=FB-*mwX*AEDJ-&3v zpgMo@T`RonO^<|&fPxskFo(Uf!@~if1#Z~P`$|@A@s4wLP#yV&~7bB@v2}-+FkKa9^6e_VszByy~ zFbj8R^ z|F$xH&G+AB31=^hiVsO;ZV(?c5D#y?2L3M+9+e80wuyAFm*PcZS}8e?76KiJBgdYf z{pR(?USp~9xniMWk7c2zt~6JMb~3g-Cm-JwAsVhb;Lp7-(V@il>&J=P$tp>aP1*cu z@~hZ9E(?lF__50k7<;z+aA{5i9t%!_I2)VkZEK%ttpAI8VH1Uqd<}YssA99n>p;`&%oxC0JGqJhs9}B zo9uG##TRN^F7UJQ$>_N^Pbf{JJ_(pMasok~a@=#9Ad52ZpR7>%1IGykJH6|s%{^{s zGS0J}Pv+#4Ouh=uZUL=5JE5)kD@t{2gwn{-=x&{*e<sd9lwDUfHPGB#- zc8Aj=g4{OJ4TX@ux<8->y;}XxyG`XL#ViMlyMxZIct+&SO!ufYT%-CYPMqjC{ z4gXZwagy^7yE_csfZY@;fK;ZcmT{NJNQWQ7V+%<|%kFYrN128C$-&DXAD`(Dl_cJD zb$ynWuH_s&dlLETG8$S61=WN`&lI%2lJpCAQfi`4(<=_rssHJ|<3RBJcsNeYh#2mF z_TVGWUDq29q`b2McJ-v zwG5gC+cF$!^q!I^FuKKcg63^okS*4Tu4jvSusL8&C%CpSS7SaGt5E#cm0dNw8RD;h z214K6IQw$*hWpWVQY#0wEhGMr6v?c5ql%43#fk$I8_-N4`CW?f$u!#`jmst`L^K5O zf7HLEC({oR`@Y?>g^?a4Kj`nz;LA8gc*vo!B3=6g3`-lXETVKEM{%W6h0g!?FL;EV2aNHo{4>bh zmTj48E#2nO=X?4UIYmYq!##ffJPE(c9m#5k&Sn#7cz2FytRVe@i#!wWGYb7x#@-EcAa}5Rg$tnyh9&@3oYniAfB>xH=ci!x! zdv4$9AaXh;6h5AYeII=(Zk7D`>kkx+yMNwz)eP_de7+qY1!$H2$)ToX+efmZgN89= zdI``|`7eCEe4-z>~Md%94kmr*oQ_31oIqHqVTm?pwF=W@R2nXQrQ|VLTCFQBpBC-Qr24H zgabT6LjjK}G`d#f;>E~cozWv=vaYTYXrhA0Hn~B@I?OyQJUrE!z0J=^%VA7LMn>fH zN$D+NIG)wUcsxBl17^l-2oc*yr*vu;3Dv@p=n1>f6gLT%IaG&3cp6P9dY%}QMfZ9rCI9T0)e+Rk8Y#MKD5whFOD_JT z58YdLRz+na{@ywY<_Y)k7f!1FefhtC|2|*jDo$aAyFFO+jmOx`N1$_q$cOpw$>G+` zfXv8m1-SsWmm7}TSWwAy7V=XRH-PG!0JP$dnWqF)F*JdWwh%s6oD|)GwAv~PMi$pX zEubEs>H^VP^wLooIE`eYu}OX?v_uT4(<2l&#LpB(ND`ha1lCqRlr2eH zFiM1s=e8d;k+(l@RQLX`5J5g+{+<7c2@n4F!Q}t-E7%9pw?NJbKXyWo4B?R$ z`~Ux7M2);*C~Dt_%87>766P&y@@$8+%f21>d?B^#!iUrnJ9;}NC_=!08c<(GDn9kS3exJS|-$JsG*VPr!!*{s|2t@O9B>o=Rx64}B09dm`7sq`Ti<|{h!(aj53uAbrLtD#h^6$8zKP#*pfys>$zA&?G#+QZ>{RXq5JW$aaB!5 zr*cV*j3^XDf8oLRfn-vNB*t&VE`QiY(9;3Twp1f`Tz-yZYyCcjKW>`@L(g*T_kd#20kMoi35!UAi&o#6ZnZ- zjM^E7>8%H^f0ZWdE0ki$SlwAkw?`7yJf|eSk}})p56;BieIAiikE>~}Lz{}WUB61+ z9Wo6HWnpgWcKPK5n>Wf9GoJi;tI@QSCW$C9aL&jRzOPcf znhf`{iuw4kxwLEU-}UIvoBQW(zAWY68L~7KZ~GNDMlQWhr-Q{h^gspS-L;%DKkaiV zQ@gf^ASfvk${BHF;eELDxbDrKyJx6~IZ}z)v6GHnJq&^RV|q`GAj-ar@?>rZ=9bM? z3%r|DCf9x={#`nKS4F=p?r)qiH|X;5ar-#i=}%rlQy!n-=+czWL+Vnu=aaEQkQAnl zV?Y0C8DRQoN>nM^qJu(d&$eui!7|xW#Yhu5dQ!$mBK_Yl2HZ=WMp#58Rf{4fqLGPz zFi-#eRJyEmEy~(|-{?k$uqFRNYF6r4+3Dk#oyW@e1ryuNI-c&FS(+>Rut1E9?{zdX%;OrMf+AhG09yv-Z#;jKrtV{ z8Ieq!5aDQ6NX1!GwF}4Eh|*LUT}Sf?Oi^v3kjbZMwq9J86KA7s;b5ltg9qH3D;8AQ zhk2r>T2`;F?v0*%GcrW|xshQUllxwp^|Ft;!p4} zr&@MPORKN>U&DY)=X5QCmvre&m~PZ>`?Mx=2kH7n$z24~Q0+Db=0B?`QGmd5g2@a` z+??I&srw};TYkLXOy;0m&jgz80@#IcC+U8HJsW}d?b@HNoy-Us2;|Q0sCmHl#F01; z4c+(T;4&$Yf@zRq6fQ$sPRDiG>-9EYa=KFZY1suh!%*C?&%y9e7cq#-{`-j22t4D2 zP58!5O_~#NFN67S=vMRv0MDW0`zql)@}of6A> z9Guk*vc(yg|CcqZab#ra{qO9w-R8?`)0TY5ZL{RrK!Y=$0%Pl>GyO1sqwSuHXq>D% zPO$z#$4k#Mfd@JICATHx>gGJV5sxw!qTsVj$r1B5wbojPIi3W*^dM}`xWK@&Xu^4EVTR%+ zf1ps!|4$FdFlo({Qf7xvBu?oBReZ(1;p@r=)}NpbA1rd{ zIgX!od}Q|-TfJ0AVjJ=B!RKxNS$-eWujMeaG`=(++o<^8Uuo^OnWe>n-|gpzvZAj3 zXNxbBVjxW(|5OLu1^T+WUYM<=p*T+dh?M)ggEUS*H3FS!75U))*8L3;^E5C8QGHVt zU@L9FoJ=&O{_|Gf%;0=^Z;>4>=g*=3$W3c=G0uNAoE7~hz7IZzFIkRt-j3!TlYB(@ z?F%^`rK`HNxMGES$p?z*+QTB8zDHQK54_lV8L(O6YV9&+{!BtX>Um^PS~O8Ud(K1`>HItvBozQ)kB4az+>F517K9{@B zw2nzopI<25$Mc~=mYGS;+}hbDOP4ywcGa0(tJl^?#CJYdxW#bK z66O%AK4dP}s=Uj|ux!MY2JM$n%(y=Z4ejU?dIjeHAS6thk!KZdYfMQ=(LgyssyoTm zZPnC`L7(Si-JMD5xqm)&94zGH^2(bn?eVP>a@enGWVTgsJ$ z3vZOp@6n*`olQP?)US>BKKpU7dC|tCeO6XPRDULJb9uO@PTkW+NhbN?+-Z&3YhUWJ z*nUr>+1M3~c0_pzKqBwt`H_~ilS<-KPpZu!gi-^maM=3f(6*ynCTQ6y2DER0o|rwE z*w|k8`qM^PQE%oM033-()(uoUvJZGC^BlonTbM1AoF?XrqM}X!Z#2&+!s4t=ua-yz zhOau6F7MW+z0UOM;x^nHZ~n+9$$4bREU|a*LxD}i z;(kBL?W@|FTE|<@n=Y7H22_{wU9B7p_j%D>N+cGH?u*w;Y`pUd<52Ymi@&xbGgAPQ zQRGJ7103i$#$LdhWySnvkEabxNfLKoGaGt!-A(Ig9~x?i_b1&lP|^VPmFsez@P_G7 zBT`;HyH=x$j~|uKoH_H>-fU;`0mib9oeT6Ow#eX-8T+fv_&+>c=pYwaIJ&sFdq|Sy zhrRu+ux91LrezWF?mGgL7?WH#Dfcv(a3}H;EleIr)HpkZ;$`Mf0>AQ3{znIv1Tx` zEIZz}XZro%HOzvy_KdD@8~^boH$BgOECov1aQ@SSoI)Uu>gEeYb#I z4Nbu^8zlz!dU0S@tNBY$Qy3f`EWFC&H1t|yzn`Go@ew$+_mo!+K%jk%F4jR$p7gcq z>*yq+=P=Tmej2r2TfTiN{G{GB>QN|e5r*h``rdD$=P`Up%lZ!cY5<;qsSfW`|4@52PkrNQ0+*?Iqw3^n6^CI#Hi?KLA0%N zb@}zIKM88*SnV-ew~XTU;N9$M3LAYIi^hkY1r#waft^RMJp4ILdiPQuh<$K9Pq#*&q7MtBn2FD#)e}odm>{lqFYPp5SWn5nANUR*SU@%y z^_P~qZ_~eP*o)g2mRZTPKJ>ET;<1A*dqpKD6XsMmgSh>7NIe zc=6AzKv@$^HDaIK8BpHdxKNK+bsv|jgUqURHPcSqdd!7?A2eI>y5ptjGYgzRw*YN^ zbXLv*AV8hX4N_`+UY<4}n!w6$Ywb00My%-Nbe4oN44Ul^K#X#dZ0fEeK(ADEL z1mxxF04*MInIk4&O>M*id-CrBOE`af`vWUc`%yJ*?O^r+*jdy;U#{~)I5Iw} zd`5;E#Puy09oJJR47y%teJHHI7^oL*d21=sSdt`E6%ZIGPp%x7H@DJ|>L3(%l@{4TH^Hgn z6c^Wn@=G(4f0j{;Gk*7A8H3?rjO*ViFvp{t5EJ}%eXt?^@yzz(sW!9Ja*J#;Xg>9f zdXRWp>&W!c36Yf)%6LD5X7gSju_*mQM=q=-bZWhCSX+Oy64p*epKSx@(W4rmk^{OW zq!Js~QC2H(UMgxh+DqhH*;9Pq_gKYcWn~plWM*cz!Tmk!T5A+en^R^@NwpBwJ=s-U zKi8L-8hMT7@!+OK{G}VYy10a{=7U%dI;zUbBw4e3z{W95r^vOiDb*m=VdDE$NN^4! z?ec6pSb7CGT|#`k?}u4tg7S`UOPYac{?G6o1*>y~I&YpVWvs>(TmjhwB85-jb(7xM z*m!Z&Xcp%6PN6I_(XU_ErB_4g#AS3d6BAQu7&b1Y$F}d|#(%vH3)5GGefFiXWt3a1WpEL8)3(*gss*^t;m=}9vAv1Wv0ZNgVG4qC^gW6$&=pB5>$OF<{(Wpuj zVP=WhSHI>!UPvduZXba*8(M#;T=dN(Q?)ZIEEh20JbVNY1xLr;sLW>ZYhD7UP-ywG zi^ETdE(`J%Dn!xZRO-IQPjA;Y0B4SGX*myuf)Wf0m%%?zqoL_55r>^o+FP{j#AQfx zpYkjJ^l2)$qsQtQy2E?Rp=**_JdIg1COKCYOx;du^*3r7bX>_rs1xE0^!2^S9t6ds zZH-gt(gcIlw0ZMpLsYfaNAwa8@i#BM^i@VUX4qq%I~o94StYI{)Ld@OSVOip_RWT2Tardx*>-_AgvQ?v}&CSg}&XHatrFL*9@@BVgRM%YD zPzr~kE9^l5JeoTVy;=*MvVAX$*pKeADq8mTOq=#Rz09ys>_{15JkB=kB_Lwe?FJ#P zI5410K`h@nlgdZo`Ee|cq&XAleu6(~Ru(vL_;;I96y1&S)Vyf*Nn z9|UkKQ3(LQBUt!45kYaa z5Sci*b&3GI+X@`gPx;X`>5DE{K!nrq5tKSqI^t^%C5IirAz{(dD z6=w=D0_PWVdbfi#>iTtY3&(v%2sAZn(G7fpK1rykeFZ`<0uC8z7Z<34_M=~nnGMc~ zFU?N`qq&DIiYRW2f({ql*w7Fc6C+Jpx|?V{4qu#ni7&?a;fL@0o;f|?E!4^QL}EFP zeKRa0iLo^oy?$0jkRJo!zQd&u^%T3@b^_D6U3N}WgZt#>aH!W0&%r`C9?k%YP@c-` zxyQ78jFWQyypHEm@uLMlnXkl^iSh@RM|TwXiKS}~Rqqt#4W!#tL$H>9r$w_2h3v^j563#6X zGvnA=bHgUa#<7U_iWc>n%<=K@r2{rcTNW#LQW@op$GeMNE9c0~U`^CtNpE%je3PG> z`&UDI@Zp;T4>z}%QMP&e_w4xrj01HGrfoI2x;Wv>_1J!V8*RZVAuJr3s{lj<2>B#-E?J zcAp~rjzCIJt`4J(Slo?C(=_5)!1iSQa90wxfOVvD-?$J|N8?Xfu^6(HtD_V8(ycXv zq%PtG;IbshNnHK-c#k$z_;<_zWs5>`9XyXS>2&l|?wA42@>NAt_=DVB)LTmb7<8@W zl@wYAj=#SJ+Cs2MFn1S4+TE&j=|ZV%xyaA(6$eg?Vo8n%zZ<)etEQq7S>m~Us9uSX zR0qmCiAt5qtApH&+#G;{&z(5oZt`;!F;=b)>P$VEO9xzkuERO{KEYSthhny-FSo6fK=j-eb&CU&_&g3g$ zp|D>?Lqj5aX0(H7w``3a>?$g%?l&|vY|OSakgG$>JEYMyqU<|Al%Se_^KJL~M*l@p z{`&q!WN_MpSQGWgaL64C6OY;X6ALyFY)XPFURz(;zChi&D=woU@L9G?PF;jjg~&3} zc`px+kpXa*!~E-!`1KTN5391J3ni}Qg!WHKn{{l=R_(}1i61&C0caMfOf!t%n_@n!#6!qpAeL@# zrS3B;S<31Vm3|Heuv`Llz;S`eA1OMgsHhHZ3jDHM)Dq%C+J{RK97`=j0r_ZJSfs#W zHv@ctZ+(4n5KsnP;wi#U(-nr*#(K#m;+4~u1U01V?&l64pduP(*}47iLOp#%iv1M`7+^9s(=0fCn zw{8gUs;H`JKmz&F-QE4wqI2UyVg}L{hfhrj)#9fLQEi3jj^Q9?Cu^mv#1k6uGN*rfC2U*I@NGm<>R_W&Udsr^R4P zT6EV4j!JddV*<5MS5^I*E(sRMUDHD}E6TKz_#{4%R{T#_phDem0ZTfAU1i>NG6zE~esp6fJt$NsI^7OgHRd?kw z9=iM>CeKv@=Odl#qw6>r3unK2`VCqvZsjeac8ncCP@ZTuQ zxz*}t&ptw$6H6c^ZBG0Gl6xlPxWmm)$;fa)DH!FP>hz%^)(RiB1o^G2Mhr-*=+6chif zEr0fjX9+=b=Oc(XUhMuMA?+91p>tgYK8^P$Vk?0|x z1KqjG&_~>u-C_6Cr)L813lNNq5kX_KvWO-|L$CUQBS7E2CnTmE@>8(0v%9?r<4Q)k zSitTwwk`ILY@D@p6`}FriajYO{dVB6<2rCasFcwfdu9T%r%qXb$G9TM*aUnn@RXmNqM{(u z#L{m!cD$^=iUncq|Lob1fo5>WIwFQaGV@}uM{qxigGuXjNUeK=! zMcn(2tq0IT=MWKTJG?tNDd|WlW4dLRgnw8V(jUZGDlfH^$?_R;e*k}tebaVhog=@W zFQ@oM@Rcqp6J@T3ZTf$T&+Ged?sM=91HDbZTuYAQ@Zoy@BR1B#y5ED%Qm(#ArNY6X z_i(XJk)nYQrV6a#6i(|h70^u=lY(Y)_4Eafn*cO}L~Ko?7yK_HTP~+YLk_icorBBAxKK7y!H9^mWSbLR2OXBK)JJd-H%zFnNqQGSlxC`=M@(s!YG#{AdUuI02Yhi`5UT0+P04 z<|Zp1iP<$`u6Odf3nzNurDL-ke0-n!pH(p$!q@6DdytnG;l+{N*d) zi={%l=hBzUbQe{0bcP&c5oY|r#z};@uPOFDUDIZ7505$&mr3Aezle?H77@|LanKBl z5yWD<4!lH(b~4!~OV#|*Jb5JQz@mVe_@kd001ht#)bGMbje|p`<|y-sU~#7`V5*bt zU5grMZR78XAfyXjBA$~pB3gGhd5CH{|Hf-It?dbV?dr&Sh9lchgDAW;yZcpWaO+r?d zM(H!RYV_P+9Su~d{3e(sxv+5qH&zs&@nGI+ACH55kXIz`@&ruO2Pup~K7j^BW3<*JDR^?W5&umIYQn eS}we8iqNrqasL?gkpcPVax!P7Q%+oP|9=2Wk$4^e diff --git a/search.json b/search.json index 4253aabb3..d7e6e177e 100644 --- a/search.json +++ b/search.json @@ -1 +1 @@ -[{"path":[]},{"path":"https://gavinsimpson.github.io/gratia/CODE_OF_CONDUCT.html","id":"our-pledge","dir":"","previous_headings":"","what":"Our Pledge","title":"Contributor Covenant Code of Conduct","text":"members, contributors, leaders pledge make participation community harassment-free experience everyone, regardless age, body size, visible invisible disability, ethnicity, sex characteristics, gender identity expression, level experience, education, socio-economic status, nationality, personal appearance, race, religion, sexual identity orientation. pledge act interact ways contribute open, welcoming, diverse, inclusive, healthy community.","code":""},{"path":"https://gavinsimpson.github.io/gratia/CODE_OF_CONDUCT.html","id":"our-standards","dir":"","previous_headings":"","what":"Our Standards","title":"Contributor Covenant Code of Conduct","text":"Examples behavior contributes positive environment community include: Demonstrating empathy kindness toward people respectful differing opinions, viewpoints, experiences Giving gracefully accepting constructive feedback Accepting responsibility apologizing affected mistakes, learning experience Focusing best just us individuals, overall community Examples unacceptable behavior include: use sexualized language imagery, sexual attention advances kind Trolling, insulting derogatory comments, personal political attacks Public private harassment Publishing others’ private information, physical email address, without explicit permission conduct reasonably considered inappropriate professional setting","code":""},{"path":"https://gavinsimpson.github.io/gratia/CODE_OF_CONDUCT.html","id":"enforcement-responsibilities","dir":"","previous_headings":"","what":"Enforcement Responsibilities","title":"Contributor Covenant Code of Conduct","text":"Community leaders responsible clarifying enforcing standards acceptable behavior take appropriate fair corrective action response behavior deem inappropriate, threatening, offensive, harmful. Community leaders right responsibility remove, edit, reject comments, commits, code, wiki edits, issues, contributions aligned Code Conduct, communicate reasons moderation decisions appropriate.","code":""},{"path":"https://gavinsimpson.github.io/gratia/CODE_OF_CONDUCT.html","id":"scope","dir":"","previous_headings":"","what":"Scope","title":"Contributor Covenant Code of Conduct","text":"Code Conduct applies within community spaces, also applies individual officially representing community public spaces. Examples representing community include using official e-mail address, posting via official social media account, acting appointed representative online offline event.","code":""},{"path":"https://gavinsimpson.github.io/gratia/CODE_OF_CONDUCT.html","id":"enforcement","dir":"","previous_headings":"","what":"Enforcement","title":"Contributor Covenant Code of Conduct","text":"Instances abusive, harassing, otherwise unacceptable behavior may reported package maintainer, Gavin Simpson (see email address CRAN package page) . complaints reviewed investigated promptly fairly. community leaders obligated respect privacy security reporter incident.","code":""},{"path":"https://gavinsimpson.github.io/gratia/CODE_OF_CONDUCT.html","id":"enforcement-guidelines","dir":"","previous_headings":"","what":"Enforcement Guidelines","title":"Contributor Covenant Code of Conduct","text":"Community leaders follow Community Impact Guidelines determining consequences action deem violation Code Conduct:","code":""},{"path":"https://gavinsimpson.github.io/gratia/CODE_OF_CONDUCT.html","id":"id_1-correction","dir":"","previous_headings":"Enforcement Guidelines","what":"1. Correction","title":"Contributor Covenant Code of Conduct","text":"Community Impact: Use inappropriate language behavior deemed unprofessional unwelcome community. Consequence: private, written warning community leaders, providing clarity around nature violation explanation behavior inappropriate. public apology may requested.","code":""},{"path":"https://gavinsimpson.github.io/gratia/CODE_OF_CONDUCT.html","id":"id_2-warning","dir":"","previous_headings":"Enforcement Guidelines","what":"2. Warning","title":"Contributor Covenant Code of Conduct","text":"Community Impact: violation single incident series actions. Consequence: warning consequences continued behavior. interaction people involved, including unsolicited interaction enforcing Code Conduct, specified period time. includes avoiding interactions community spaces well external channels like social media. Violating terms may lead temporary permanent ban.","code":""},{"path":"https://gavinsimpson.github.io/gratia/CODE_OF_CONDUCT.html","id":"id_3-temporary-ban","dir":"","previous_headings":"Enforcement Guidelines","what":"3. Temporary Ban","title":"Contributor Covenant Code of Conduct","text":"Community Impact: serious violation community standards, including sustained inappropriate behavior. Consequence: temporary ban sort interaction public communication community specified period time. public private interaction people involved, including unsolicited interaction enforcing Code Conduct, allowed period. Violating terms may lead permanent ban.","code":""},{"path":"https://gavinsimpson.github.io/gratia/CODE_OF_CONDUCT.html","id":"id_4-permanent-ban","dir":"","previous_headings":"Enforcement Guidelines","what":"4. Permanent Ban","title":"Contributor Covenant Code of Conduct","text":"Community Impact: Demonstrating pattern violation community standards, including sustained inappropriate behavior, harassment individual, aggression toward disparagement classes individuals. Consequence: permanent ban sort public interaction within community.","code":""},{"path":"https://gavinsimpson.github.io/gratia/CODE_OF_CONDUCT.html","id":"attribution","dir":"","previous_headings":"","what":"Attribution","title":"Contributor Covenant Code of Conduct","text":"Code Conduct adapted Contributor Covenant, version 2.0, available https://www.contributor-covenant.org/version/2/0/code_of_conduct.html. Community Impact Guidelines inspired Mozilla’s code conduct enforcement ladder. answers common questions code conduct, see FAQ https://www.contributor-covenant.org/faq. Translations available https://www.contributor-covenant.org/translations.","code":""},{"path":[]},{"path":"https://gavinsimpson.github.io/gratia/CONTRIBUTING.html","id":"something-isnt-working-right-or-generating-an-error","dir":"","previous_headings":"","what":"Something isn’t working right or generating an error","title":"Contributing","text":"something isn’t working, either might expect/want contrary documentation, probably bug missing feature. ’re getting error running {gratia}, ’s also likely bug, opportunity catch use-case wasn’t expecting. First, check issue hasn’t already fixed -development version Github. Install current development version {gratia} R-universe using issue remains, please file issue via Issues page. ’s OK report issue even ’re sure ’s problem, problem better described question, use Discussions page (see ). Feature requests welcome! problem {gratia} hit top TODO list quickly cen provide reproducible example demonstrating problem.","code":"install.packages(\"gratia\", repos = c( \"https://gavinsimpson.r-universe.dev\", \"https://cloud.r-project.org\" ))"},{"path":"https://gavinsimpson.github.io/gratia/CONTRIBUTING.html","id":"got-a-question-want-to-show-how-to-do-something-with-gratia","dir":"","previous_headings":"","what":"Got a question? Want to show how to do something with {gratia}?","title":"Contributing","text":"issue best described question, want know something {gratia}, cool example using {gratia} want share , please consider using Discussions page. ’re sure, can always ask issue Question (Use Q&category) , really bug, can easily create Issue discussion.","code":""},{"path":"https://gavinsimpson.github.io/gratia/CONTRIBUTING.html","id":"code-contributions","dir":"","previous_headings":"","what":"Code contributions","title":"Contributing","text":"Code contributions form Pull Requests always appreciated. suitable workflow: Fork repo Github account Clone version account machine account, e.g,. git clone https://github.com//gratia.git Make sure track progress upstream (.e., version gratis gavinsimpson/gratia) git remote add upstream https://github.com/gavinsimpson/gratia.git. making changes make sure pull changes upstream either git fetch upstream merge later git pull upstream fetch merge one step Make changes (bonus points making changes new feature branch) Push account Submit pull request home base gavinsimpson/gratia","code":""},{"path":"https://gavinsimpson.github.io/gratia/CONTRIBUTING.html","id":"development-tools--paradigm--ethos","dir":"","previous_headings":"Code contributions","what":"Development tools / paradigm / ethos","title":"Contributing","text":"Please note following contributing code: {gratia} tightly aligned tidyverse use {dplyr} related packages lot internally developing package. plan replacing code lower-level code using {vctrs}, right now development focus filling functionality package premature optimisation. original aim {gratia} provide {ggplot2} plotting smooths; please stick principle use plotting paradigm. aim {mgcv}-feature complete; {mgcv} can something terms plotting smooths, handling specialists smooths, etc, principle {gratia} support . aim general compatibility {mgcv}; {gratia} deviates {mgcv} things, needs good justification; example {gratia} deviates multivariate isotropic smooths fitted s() plotted, Dave Miller (@dill) argued convincingly way. Don’t add dependencies! Unless accompanied strong justification, want reduce dependencies increase number. ’m using {styler} style code, using 2 spaces indent. code written change, however. Respect 80 character line length limit. contribute functionality fix bug, please add test using {testthat} framework insure new things works correctly bug stays fixed. contributions must result new NOTES, WARNINGS, ERRORS running R CMD check ---cran; please check contributions Winbuilder example.","code":""},{"path":"https://gavinsimpson.github.io/gratia/CONTRIBUTING.html","id":"email","dir":"","previous_headings":"","what":"Email","title":"Contributing","text":"hate email! can email GMail address — ’s like can stop :-) — unless capture attention immediately label message {gratia}-related, quickly get swamped never seen . Even label , ’s guarantee ever get round replying; usually happens ’ll forget don’t remember check gratia label often. end result email , get response , tardy. infinitely better use Discussions Issues pages Github ask questions package report problems. Please don’t email work (academic) address (email address might find ) {gratia}. question GAMs, much better ask CrossValidated StackOverflow depending whether question statistical programming related. relates {gratia} can use Discussions page. Asking question public allows others reply public, contributes body knowledge easily available others. circumstances send email multiple addresses; quickest way get message trash.","code":""},{"path":"https://gavinsimpson.github.io/gratia/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"The MIT License (MIT)","title":"The MIT License (MIT)","text":"Copyright (c) 2013-2024 Gavin L. Simpson Permission hereby granted, free charge, person obtaining copy software associated documentation files (“Software”), deal Software without restriction, including without limitation rights use, copy, modify, merge, publish, distribute, sublicense, /sell copies Software, permit persons Software furnished , subject following conditions: copyright notice permission notice shall included copies substantial portions Software. SOFTWARE PROVIDED “”, WITHOUT WARRANTY KIND, EXPRESS IMPLIED, INCLUDING LIMITED WARRANTIES MERCHANTABILITY, FITNESS PARTICULAR PURPOSE NONINFRINGEMENT. EVENT SHALL AUTHORS COPYRIGHT HOLDERS LIABLE CLAIM, DAMAGES LIABILITY, WHETHER ACTION CONTRACT, TORT OTHERWISE, ARISING , CONNECTION SOFTWARE USE DEALINGS SOFTWARE.","code":""},{"path":"https://gavinsimpson.github.io/gratia/articles/custom-plotting.html","id":"background","dir":"Articles","previous_headings":"","what":"Background","title":"Customizing plots","text":"draw() function {gratia} envisaged ggplot-based alternative mgcv:::plot.gam(). , never intended allow sorts customization possible ggplot() packages use ggplot() plotting layer. largely due decision produce multiple separate ggplot() plots GAMs multiple smooths, subsequently combined single figure device, initially using {cowplot} recently {patchwork}. things way evident consider might represent smooths 3 4 variables (common might think; consider space-time models via te(x, y, time, d = c(2,1)) space-depth-time models [think ocean atmospheric data space depth (height), observed time] via te(x, y, depth, time, d = c(2, 1, 1))), require facets top produce small multiples, means can’t use facets plot separate smooths. Additional complications arise consider complex smooth types, splines sphere, might want us different coordinate systems geoms best represent underlying smooth. gone root combining multiple ggplot objects single figure, problem customizing plots quickly rears head. vignette presents solutions problem modifying adding plots produced draw() culminating example illustrating use {gratia}’s utility functions produce plots lower-lever components.","code":""},{"path":"https://gavinsimpson.github.io/gratia/articles/custom-plotting.html","id":"adding-layers-to-plots-with-the-operator","dir":"Articles","previous_headings":"","what":"Adding layers to plots with the & operator","title":"Customizing plots","text":"start simulating data fitting GAM four smooth functions default plot produced draw() want change theme plots, can’t append theme() layer p affects last plot patchwork1 One way apply theme plots patchwork & operator.","code":"library(\"gratia\") library(\"mgcv\") #> Loading required package: nlme #> This is mgcv 1.9-1. For overview type 'help(\"mgcv-package\")'. library(\"ggplot2\") library(\"dplyr\") #> #> Attaching package: 'dplyr' #> The following object is masked from 'package:nlme': #> #> collapse #> The following objects are masked from 'package:stats': #> #> filter, lag #> The following objects are masked from 'package:base': #> #> intersect, setdiff, setequal, union library(\"patchwork\") # simulate data n <- 400 eg1 <- data_sim(\"eg1\", n = n, seed = 1) # fit model m <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = eg1, method = \"REML\") p <- draw(m) p p + theme_bw() p & theme_bw()"},{"path":"https://gavinsimpson.github.io/gratia/articles/custom-plotting.html","id":"combining-individual-plots-produced-by-draw","dir":"Articles","previous_headings":"","what":"Combining individual plots produced by draw()","title":"Customizing plots","text":"draw() methods like draw.gam() return object created patchwork::wrap_plots(), result isn’t straightforward combine objects new patchwork avoid error, need use patchwork::plot_layout() set dimensions want achieved directly via draw() instructive know combine outputs draw() need arise, want create patchwork plots different models.","code":"p1 <- draw(m, select = \"s(x0)\") p2 <- draw(m, select = \"s(x1)\") p3 <- draw(m, select = \"s(x2)\") p1 + p2 + p3 #> Error in `wrap_dims()`: #> ! Need 3 panels, but together `nrow` and `ncol` only provide 1. #> ℹ Please increase `ncol` and/or `nrow`. p1 + p2 + p3 + plot_layout(ncol = 3) draw(m, select = c(\"s(x0)\", \"s(x1)\", \"s(x2)\"), ncol = 3)"},{"path":"https://gavinsimpson.github.io/gratia/articles/custom-plotting.html","id":"building-your-own-plot-by-hand","dir":"Articles","previous_headings":"","what":"Building your own plot by hand","title":"Customizing plots","text":"{gratia} provides high-level functions like draw() get good graphical overview fitted model, little option customisation — isn’t possible desirable allow possible customisation options fatures {ggplot2} single function. Think many arguments require! Instead, {gratia} also exports lower-level functions used draw() can create plot using whatever {ggplot2} functions make sense. next code blocks ’ll see plot created draw(m) can recreated hand using lower-level building blocks. main thing need evaluate smooths values covariates. done using smooth_estimates(). also need add credible interval evaluations, can done tidyverse-style via add_confint() default draw.gam() add partial residuals partial effects plots. achieve effect, need add partial residuals data used fit model. can done via add_partial_residuals() will2 add columns names \"s(x0)\". \"s(x1)\", etc. data. Now everything need recreate plots created draw.gam(). code block filter sm focus specific smooth, f(x2)f(x2) (\"s(x2)\"), add rug plot observed values x2, credible interval around estimated smooth, partial residuals point layer, estimated smooth line layer, annotation Assuming repeat steps smooths, creating plot objects p_sx0, p_sx1, p_sx2, p_sx3 (code shown), can complete plot creating patchwork desired number rows columns real benefit complete control data plotted can use power {ggplot2} map additional variables plot aesthetics. example, let’s assume factor variable original data want colour partial residuals according levels factor. Let’s create factor Now can modify plotting code map fac colour aesthetic plot partial residuals. save typing, ’ll reorder layers plot add partial residuals last can also simple model checking plotting smooth partial residuals coloured according one covariates (also plotting actual residuals covariates). code chunk , map covariate x1 colour size aesthetics (note deleted cex = 1.5 allow mapping size) resulting plot doesn’t show particular problems model way data simulated, hopefully illustrates can possible use low-level functions provided {gratia}.","code":"# evaluate the smooths sm <- smooth_estimates(m) |> add_confint() sm #> # A tibble: 400 × 11 #> .smooth .type .by .estimate .se x0 x1 x2 x3 .lower_ci #> #> 1 s(x0) TPRS NA -0.929 0.422 0.0131 NA NA NA -1.76 #> 2 s(x0) TPRS NA -0.881 0.396 0.0230 NA NA NA -1.66 #> 3 s(x0) TPRS NA -0.834 0.372 0.0329 NA NA NA -1.56 #> 4 s(x0) TPRS NA -0.786 0.348 0.0429 NA NA NA -1.47 #> 5 s(x0) TPRS NA -0.738 0.326 0.0528 NA NA NA -1.38 #> 6 s(x0) TPRS NA -0.690 0.305 0.0627 NA NA NA -1.29 #> 7 s(x0) TPRS NA -0.643 0.287 0.0727 NA NA NA -1.20 #> 8 s(x0) TPRS NA -0.595 0.270 0.0826 NA NA NA -1.12 #> 9 s(x0) TPRS NA -0.548 0.255 0.0925 NA NA NA -1.05 #> 10 s(x0) TPRS NA -0.501 0.242 0.102 NA NA NA -0.975 #> # ℹ 390 more rows #> # ℹ 1 more variable: .upper_ci # add partial residuals to data eg1 <- eg1 |> add_partial_residuals(m) names(eg1) #> [1] \"y\" \"x0\" \"x1\" \"x2\" \"x3\" \"f\" \"f0\" \"f1\" \"f2\" #> [10] \"f3\" \"s(x0)\" \"s(x1)\" \"s(x2)\" \"s(x3)\" p_sx2 <- sm |> filter(.smooth == \"s(x2)\") |> ggplot() + geom_rug(aes(x = x2), data = eg1, sides = \"b\", length = grid::unit(0.02, \"npc\") ) + geom_ribbon(aes(ymin = .lower_ci, ymax = .upper_ci, x = x2), alpha = 0.2 ) + geom_point(aes(x = x2, y = `s(x2)`), data = eg1, cex = 1.5, colour = \"steelblue3\" ) + geom_line(aes(x = x2, y = .estimate), lwd = 1.2) + labs(y = \"Partial effect\", title = \"s(x2)\") p_sx2 p_sx0 + p_sx1 + p_sx2 + p_sx3 + plot_layout(ncol = 2) set.seed(12) eg1 <- eg1 |> mutate(fac = sample(letters[1:4], n(), replace = TRUE)) plt <- sm |> filter(.smooth == \"s(x2)\") |> ggplot() + geom_rug(aes(x = x2), data = eg1, sides = \"b\", length = grid::unit(0.02, \"npc\") ) + geom_ribbon(aes(ymin = .lower_ci, ymax = .upper_ci, x = x2), alpha = 0.2 ) + geom_line(aes(x = x2, y = .estimate), lwd = 1.2) + labs(y = \"Partial effect\", title = \"s(x2)\") plt + geom_point( aes( x = x2, y = `s(x2)`, colour = fac ), # <-- map fac to colour aesthetic data = eg1, cex = 1.5 ) plt + geom_point( aes( x = x2, y = `s(x2)`, colour = x1, size = x1 ), # <-- map fac to colour aesthetic data = eg1, alpha = 0.3 ) + # <-- deleted cex!! scale_colour_viridis_c(option = \"plasma\")"},{"path":"https://gavinsimpson.github.io/gratia/articles/data-slices.html","id":"carbon-dioxide-uptake-in-grass-plants","dir":"Articles","previous_headings":"","what":"Carbon Dioxide Uptake in Grass Plants","title":"Data slices","text":"first example uses small data set experimental study cold tolerance grass Echinochloa crusgalli. data data frame CO2 provided {datasets} package ships R. One way model data allow different smooths combinations treatment type covariates can look fitted smooths using draw() might want compare model fitted values treatment types (origins), ignoring random effect component. want evaluate model range values covariate conc combinations factors. data slice covariate space, can create using data_slice(). create data slice conc Quebec type chilled treatment use Notice data_slice() filled something remaining covariates didn’t mention? case, data_slice() doesn’t know tt created, chosen modal level tt factor, correct choice case. Instead, need specify correct level explicitly tt created data slice, can predict model using combination covariate values specified slice. use predict.gam() , fitted_values() function {gratia} easier use, especially non-Gaussian models Notice excluded random effect term; even though specify something plant covariate can ignore term model using exclude argument. fitted_values() creates credible interval scale link function back-transforms response scale scale = \"response\", also default. Plotting fitted values data slice now requires simple {ggplot2} knowledge Next, let’s compare fitted effects treatment Mississippi origin plants , replaced automatically-generated tt variable correctly specified call fct_cross(), retaining levels type treatment factors. insures correct combinations corresponding treatment type factors also preserve original levels tt covariate created. can visualise fitted values data slice creating data slices, used helper functions specify covariate values slice. {gratia} provides several helper functions: evenly(x, n = 100) — creates n evenly spaced values range covariate, evenly(x, = 5 — creates evenly spaced values range covariate increments 5, evenly(x, ..., lower = 5, upper = 10) — either two uses evenly() shown use lower upper limits vector x. Arguments lower upper can used change one upper lower bounds. evenly(fct) — produces new factor containing level specified factor fct just , ref_level(fct) — creates new factor containing just reference level specified factor covariate fct, level(fct, \"level\") — creates factor requested \"level\" factor fct. cases involving factors, helper functions set levels factor match original model fit2. second argument data_slice() ... ... argument allows provide expressions create covariate values want data slice. Expressions passed ... evaluated within model frame fitted model (argument object) data (supplied). restricted either using helper functions provide {gratia}; R function used long makes sense context model frame, returns something can combined using tidyr::expand_grid().","code":"## data load and prep data(CO2, package = \"datasets\") plant <- CO2 |> as_tibble() |> rename(plant = Plant, type = Type, treatment = Treatment) |> mutate(plant = factor(plant, ordered = FALSE)) plant_ylab <- expression(CO[2] ~ uptake ~ (mu * mol ~ m^{-3})) plant_xlab <- expression(CO[2] ~ concentration ~ (mL ~ L^{-1})) plant |> ggplot(aes(x = conc, y = uptake, group = plant, colour = treatment)) + geom_point() + geom_line() + facet_wrap(~type) + labs(y = plant_ylab, x = plant_xlab, colour = \"Treatment\") plant <- plant |> mutate(tt = fct_cross(treatment, type)) m_plant <- gam(uptake ~ treatment * type + s(conc, by = tt, k = 6) + s(plant, bs = \"re\"), data = plant, method = \"REML\", family = Gamma(link = \"log\") ) overview(m_plant) #> #> Generalized Additive Model with 8 terms #> #> term type k edf statistic p.value #> #> 1 treatment parametric NA 1 1.59 0.2124864 #> 2 type parametric NA 1 11.2 0.0014830 #> 3 treatment:type parametric NA 1 7.45 0.0085489 #> 4 s(conc):ttnonchilled:Quebec TPRS 5 4.72 69.7 < 0.001 #> 5 s(conc):ttchilled:Quebec TPRS 5 4.71 86.5 < 0.001 #> 6 s(conc):ttnonchilled:Mississippi TPRS 5 4.62 74.1 < 0.001 #> 7 s(conc):ttchilled:Mississippi TPRS 5 4.39 25.3 < 0.001 #> 8 s(plant) Random effect 12 7.40 12.8 < 0.001 draw(m_plant, residuals = TRUE, scales = \"fixed\") ds1 <- data_slice(m_plant, conc = evenly(conc, n = 100), type = level(type, \"Quebec\"), treatment = level(treatment, \"chilled\") ) ds1 #> # A tibble: 100 × 5 #> conc type treatment tt plant #> #> 1 95 Quebec chilled nonchilled:Quebec Qn1 #> 2 104. Quebec chilled nonchilled:Quebec Qn1 #> 3 113. Quebec chilled nonchilled:Quebec Qn1 #> 4 122. Quebec chilled nonchilled:Quebec Qn1 #> 5 132. Quebec chilled nonchilled:Quebec Qn1 #> 6 141. Quebec chilled nonchilled:Quebec Qn1 #> 7 150. Quebec chilled nonchilled:Quebec Qn1 #> 8 159. Quebec chilled nonchilled:Quebec Qn1 #> 9 168. Quebec chilled nonchilled:Quebec Qn1 #> 10 177. Quebec chilled nonchilled:Quebec Qn1 #> # ℹ 90 more rows ds1 <- data_slice(m_plant, conc = evenly(conc, n = 100), treatment = level(treatment, \"chilled\"), type = level(type, \"Quebec\"), tt = level(tt, \"chilled:Quebec\") ) ds1 #> # A tibble: 100 × 5 #> conc treatment type tt plant #> #> 1 95 chilled Quebec chilled:Quebec Qn1 #> 2 104. chilled Quebec chilled:Quebec Qn1 #> 3 113. chilled Quebec chilled:Quebec Qn1 #> 4 122. chilled Quebec chilled:Quebec Qn1 #> 5 132. chilled Quebec chilled:Quebec Qn1 #> 6 141. chilled Quebec chilled:Quebec Qn1 #> 7 150. chilled Quebec chilled:Quebec Qn1 #> 8 159. chilled Quebec chilled:Quebec Qn1 #> 9 168. chilled Quebec chilled:Quebec Qn1 #> 10 177. chilled Quebec chilled:Quebec Qn1 #> # ℹ 90 more rows fv1 <- fitted_values(m_plant, data = ds1, scale = \"response\", exclude = \"s(plant)\") fv1 #> # A tibble: 100 × 10 #> .row conc treatment type tt plant .fitted .se .lower_ci .upper_ci #> #> 1 1 95 chilled Quebec chille… Qn1 13.0 0.0783 11.2 15.2 #> 2 2 104. chilled Quebec chille… Qn1 14.1 0.0757 12.1 16.3 #> 3 3 113. chilled Quebec chille… Qn1 15.2 0.0737 13.1 17.5 #> 4 4 122. chilled Quebec chille… Qn1 16.3 0.0722 14.2 18.8 #> 5 5 132. chilled Quebec chille… Qn1 17.6 0.0714 15.3 20.2 #> 6 6 141. chilled Quebec chille… Qn1 18.9 0.0711 16.4 21.7 #> 7 7 150. chilled Quebec chille… Qn1 20.2 0.0712 17.6 23.3 #> 8 8 159. chilled Quebec chille… Qn1 21.6 0.0716 18.8 24.9 #> 9 9 168. chilled Quebec chille… Qn1 23.0 0.0721 20.0 26.5 #> 10 10 177. chilled Quebec chille… Qn1 24.4 0.0726 21.2 28.1 #> # ℹ 90 more rows fv1 |> ggplot(aes(x = conc, y = .fitted)) + geom_point( data = plant |> filter(type == \"Quebec\", treatment == \"chilled\"), mapping = aes(y = uptake), alpha = 0.8, colour = \"steelblue\" ) + geom_ribbon(aes(ymin = .lower_ci, ymax = .upper_ci), alpha = 0.2) + geom_line() + labs( x = plant_xlab, y = plant_ylab, title = expression(Estimated ~ CO[2] ~ uptake), subtitle = \"Chilled plants of the Quebec type\" ) ds2 <- data_slice(m_plant, conc = evenly(conc, n = 100), treatment = evenly(treatment), type = level(type, \"Mississippi\") ) |> mutate(tt = fct_cross(treatment, type, keep_empty = TRUE)) ds2 #> # A tibble: 200 × 5 #> conc treatment type tt plant #> #> 1 95 nonchilled Mississippi nonchilled:Mississippi Qn1 #> 2 95 chilled Mississippi chilled:Mississippi Qn1 #> 3 104. nonchilled Mississippi nonchilled:Mississippi Qn1 #> 4 104. chilled Mississippi chilled:Mississippi Qn1 #> 5 113. nonchilled Mississippi nonchilled:Mississippi Qn1 #> 6 113. chilled Mississippi chilled:Mississippi Qn1 #> 7 122. nonchilled Mississippi nonchilled:Mississippi Qn1 #> 8 122. chilled Mississippi chilled:Mississippi Qn1 #> 9 132. nonchilled Mississippi nonchilled:Mississippi Qn1 #> 10 132. chilled Mississippi chilled:Mississippi Qn1 #> # ℹ 190 more rows fitted_values(m_plant, data = ds2, scale = \"response\", exclude = \"s(plant)\" ) |> ggplot(aes(x = conc, y = .fitted, group = treatment)) + geom_point( data = plant |> filter(type == \"Mississippi\"), mapping = aes(y = uptake, colour = treatment), alpha = 0.8 ) + geom_ribbon(aes(ymin = .lower_ci, ymax = .upper_ci, fill = treatment), alpha = 0.2 ) + geom_line(aes(colour = treatment)) + labs( x = plant_xlab, y = plant_ylab, title = expression(Estimated ~ CO[2] ~ uptake), subtitle = \"Comparison of treatment in plants of the Mississippi type\", colour = \"Treatment\", fill = \"Treatment\" ) args(gratia:::data_slice.gam) #> function (object, ..., data = NULL, envir = NULL) #> NULL"},{"path":"https://gavinsimpson.github.io/gratia/articles/data-slices.html","id":"slices-through-a-2d-smooth","dir":"Articles","previous_headings":"","what":"Slices through a 2D smooth","title":"Data slices","text":"second example, ’ll use bivariate example data set {mgcv} fit tensor product covariates x z aim example create univariate data slice 2D smooth user-specified values x holding z one fixed values. visualise effect smooth level, using smooth_estimates(), response level, using fitted_values().","code":"# simulate data from the bivariate surface df <- data_sim(\"eg2\", n = 1000, scale = 0.25, seed = 2) # fit the GAM m_biv <- gam(y ~ te(x, z), data = df, method = \"REML\")"},{"path":"https://gavinsimpson.github.io/gratia/articles/data-slices.html","id":"using-smooth_estimates","dir":"Articles","previous_headings":"Slices through a 2D smooth","what":"Using smooth_estimates()","title":"Data slices","text":"begin creating slice data space. also create label point nice axis label. evaluate smooth desired values add confidence interval can plot sm using {ggplot2} Note interval Marra Wood (2012) interval. doesn’t include uncertainty model constant term moment, unless smooth close linear shouldn’t make much difference. extends multiple slices asking several discrete z","code":"ds3 <- data_slice(m_biv, x = evenly(x, n = 100), z = quantile(z, probs = 0.25) ) z_val <- with(ds3, round(quantile(z, probs = 0.25), 2)) ylab <- bquote(hat(f)(x, .(z_val))) sm <- smooth_estimates(m_biv, select = \"te(x,z)\", data = ds3) |> add_confint() sm #> # A tibble: 100 × 9 #> .smooth .type .by .estimate .se x z .lower_ci .upper_ci #> #> 1 te(x,z) Tensor prod… NA 0.103 0.0583 6.63e-4 0.245 -0.0107 0.218 #> 2 te(x,z) Tensor prod… NA 0.122 0.0548 1.08e-2 0.245 0.0148 0.230 #> 3 te(x,z) Tensor prod… NA 0.141 0.0514 2.08e-2 0.245 0.0400 0.242 #> 4 te(x,z) Tensor prod… NA 0.159 0.0482 3.09e-2 0.245 0.0648 0.254 #> 5 te(x,z) Tensor prod… NA 0.177 0.0451 4.10e-2 0.245 0.0890 0.266 #> 6 te(x,z) Tensor prod… NA 0.195 0.0422 5.11e-2 0.245 0.113 0.278 #> 7 te(x,z) Tensor prod… NA 0.213 0.0396 6.12e-2 0.245 0.135 0.291 #> 8 te(x,z) Tensor prod… NA 0.230 0.0372 7.13e-2 0.245 0.157 0.303 #> 9 te(x,z) Tensor prod… NA 0.247 0.0351 8.14e-2 0.245 0.178 0.316 #> 10 te(x,z) Tensor prod… NA 0.263 0.0333 9.14e-2 0.245 0.198 0.328 #> # ℹ 90 more rows sm |> ggplot(aes(x = x, y = .estimate)) + geom_ribbon(aes(ymin = .lower_ci, ymax = .upper_ci), alpha = 0.2) + geom_line() + labs( title = \"Evaluation of smooth te(x,z) at fixed z\", y = ylab ) ds4 <- data_slice(m_biv, x = evenly(x, n = 100), z = round(quantile(z, probs = c(0.25, 0.5, 0.75)), 2) ) sm <- smooth_estimates(m_biv, select = \"te(x,z)\", data = ds4) |> add_confint() |> mutate(fz = factor(z)) sm |> ggplot(aes(x = x, y = .estimate, colour = fz, group = fz)) + geom_ribbon(aes(ymin = .lower_ci, ymax = .upper_ci, fill = fz, colour = NULL), alpha = 0.2 ) + geom_line() + labs( title = \"Evaluation of smooth te(x,z) at fixed z\", y = expression(hat(f)(x, z)), colour = \"z\", fill = \"z\" )"},{"path":"https://gavinsimpson.github.io/gratia/articles/data-slices.html","id":"using-fitted_samples","dir":"Articles","previous_headings":"Slices through a 2D smooth","what":"Using fitted_samples()","title":"Data slices","text":"want evaluate surface x fixed z conditional upon values covariates (model predicted fitted values) fitted_samples() tidy wrapper predict.gam(). single z multiple z difference now model constant included well uncertainty.","code":"fitted_values(m_biv, data = ds3) |> # default is response scale, not link ggplot(aes(x = x, y = .fitted)) + geom_ribbon(aes(ymin = .lower_ci, ymax = .upper_ci), alpha = 0.2) + geom_line() + labs( title = \"Fitted values from model\", y = expression(hat(y)) ) fitted_values(m_biv, data = ds4) |> mutate(fz = factor(z)) |> ggplot(aes(x = x, y = .fitted, colour = fz, group = fz)) + geom_ribbon(aes(ymin = .lower_ci, ymax = .upper_ci, fill = fz, colour = NULL), alpha = 0.2 ) + geom_line() + labs( title = \"Fitted values from model\", y = expression(hat(y)), colour = \"z\", fill = \"z\" )"},{"path":"https://gavinsimpson.github.io/gratia/articles/gratia.html","id":"plotting","dir":"Articles","previous_headings":"","what":"Plotting","title":"Getting started with gratia","text":"gratia provides draw() function produce plots using ggplot2 📦. draw estimated smooths GAM fitted , use intended reasonable overview estimated model, offers limited option modify resulting plot. want full control, can obtain data used create plot smooth_estimates() evaluate smooths unevenly spaced values range covariate(s). want evaluate selected smooths, can specify via smooth argument. takes smooth labels names smooths known mgcv. list labels smooths use evaluate f(x2)f(x_2) use can generate plot using ggplot2 package, example","code":"draw(m) sm <- smooth_estimates(m) sm #> # A tibble: 400 × 9 #> .smooth .type .by .estimate .se x0 x1 x2 x3 #> #> 1 s(x0) TPRS NA -1.32 0.390 0.000239 NA NA NA #> 2 s(x0) TPRS NA -1.24 0.365 0.0103 NA NA NA #> 3 s(x0) TPRS NA -1.17 0.340 0.0204 NA NA NA #> 4 s(x0) TPRS NA -1.09 0.318 0.0304 NA NA NA #> 5 s(x0) TPRS NA -1.02 0.297 0.0405 NA NA NA #> 6 s(x0) TPRS NA -0.947 0.279 0.0506 NA NA NA #> 7 s(x0) TPRS NA -0.875 0.263 0.0606 NA NA NA #> 8 s(x0) TPRS NA -0.803 0.249 0.0707 NA NA NA #> 9 s(x0) TPRS NA -0.732 0.237 0.0807 NA NA NA #> 10 s(x0) TPRS NA -0.662 0.228 0.0908 NA NA NA #> # ℹ 390 more rows smooths(m) #> [1] \"s(x0)\" \"s(x1)\" \"s(x2)\" \"s(x3)\" sm <- smooth_estimates(m, smooth = \"s(x2)\") #> Warning: The `smooth` argument of `smooth_estimates()` is deprecated as of gratia #> 0.8.9.9. #> ℹ Please use the `select` argument instead. #> This warning is displayed once every 8 hours. #> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was #> generated. sm #> # A tibble: 100 × 6 #> .smooth .type .by .estimate .se x2 #> #> 1 s(x2) TPRS NA -4.47 0.476 0.00359 #> 2 s(x2) TPRS NA -4.00 0.406 0.0136 #> 3 s(x2) TPRS NA -3.53 0.345 0.0237 #> 4 s(x2) TPRS NA -3.06 0.295 0.0338 #> 5 s(x2) TPRS NA -2.58 0.263 0.0438 #> 6 s(x2) TPRS NA -2.09 0.250 0.0539 #> 7 s(x2) TPRS NA -1.59 0.253 0.0639 #> 8 s(x2) TPRS NA -1.08 0.264 0.0740 #> 9 s(x2) TPRS NA -0.564 0.278 0.0841 #> 10 s(x2) TPRS NA -0.0364 0.289 0.0941 #> # ℹ 90 more rows library(\"ggplot2\") library(\"dplyr\") #> #> Attaching package: 'dplyr' #> The following object is masked from 'package:nlme': #> #> collapse #> The following objects are masked from 'package:stats': #> #> filter, lag #> The following objects are masked from 'package:base': #> #> intersect, setdiff, setequal, union sm |> add_confint() |> ggplot(aes(y = .estimate, x = x2)) + geom_ribbon(aes(ymin = .lower_ci, ymax = .upper_ci), alpha = 0.2, fill = \"forestgreen\" ) + geom_line(colour = \"forestgreen\", linewidth = 1.5) + labs( y = \"Partial effect\", title = expression(\"Partial effect of\" ~ f(x[2])), x = expression(x[2]) )"},{"path":"https://gavinsimpson.github.io/gratia/articles/gratia.html","id":"model-diagnostics","dir":"Articles","previous_headings":"","what":"Model diagnostics","title":"Getting started with gratia","text":"appraise() function provides standard diagnostic plots GAMs plots produced (left--right, top--bottom), quantile-quantile (QQ) plot deviance residuals, scatterplot deviance residuals linear predictor, histogram deviance residuals, scatterplot observed vs fitted values. Adding partial residuals partial effect plots produced draw() can also help diagnose problems model, oversmoothing","code":"appraise(m) draw(m, residuals = TRUE)"},{"path":"https://gavinsimpson.github.io/gratia/articles/gratia.html","id":"want-to-learn-more","dir":"Articles","previous_headings":"","what":"Want to learn more?","title":"Getting started with gratia","text":"gratia active development area development currently lacking documentation. find package, look help pages package look examples code help get going.","code":""},{"path":"https://gavinsimpson.github.io/gratia/articles/posterior-simulation.html","id":"what-are-we-simulating","dir":"Articles","previous_headings":"","what":"What are we simulating?","title":"Posterior Simulation","text":"Posterior simulation involves randomly sampling MVN(𝛃̂,𝐕b)\\text{MVN}(\\hat{\\boldsymbol{\\beta}}, \\mathbf{V}_{\\text{b}}) EF(μi,ϕ)\\text{EF}(\\mu_i, \\phi), . might simulate posterior distribution single estimated smooth function see uncertainty estimate function. simulate just subset βj⋅\\beta_{j \\cdot} associated fjf_j interest. Instead, might interested uncertainty expectation (expected value) model given values covariates, case can simulate 𝛃\\boldsymbol{\\beta} sample posterior 𝔼(yi)\\mathbb{E}(y_i), fitted values model. might want generate new values response variable via draws conditional distribution response, simulating new response data 𝕪*\\mathbb{y}^{\\ast}, either observed 𝐱\\mathbf{x} new values $^{}, yi*|𝛈,𝐱∼EF(μî,ϕ)y^{\\ast}_i | \\boldsymbol{\\eta}, \\mathbf{x} \\sim \\text{EF}(\\hat{\\mu_i}, \\phi). Finally, can combine posterior simulation distributions generate posterior draws new data 𝕪*\\mathbb{y}^{\\ast} also include uncertainty expected values. gratia functionality options following functions smooth_samples() generates draws posterior distribution single estimated smooth functions, fitted_samples() generates draws posterior distribution 𝔼(yi|𝐗i=xi)\\mathbb{E}(y_i | \\mathbf{X}_i = x_i), expected value responss, predicted_samples(), generates new response data given supplied values covariates yi*|𝐗i=xi*y^{\\ast}_i | \\mathbf{X}_i = x^{\\ast}_i posterior_samples(), generates draws posterior distribution model, including uncertainty estimated parameters model. simpler terms, fitted_samples() generates predictions “average” expected value response values covariates. predictions include uncertainty estimated values model coefficients. contrast, posterior_samples() generates predictions actual values response might expect observe (model correct) given values covariates. predicted values include variance sampling distribution (error term). predicted_samples() lies somewhere two; predicted values include variation sampling distribution, take model fixed, known. worth reminding posterior draws conditional upon selected values smoothing parameter(s) λj\\lambda_j. act wiggliness estimated smooths known, actual fact estimated (selected perhaps better description) wiglinesses data model fitting. estimated GAM fitted method argument \"REML\", \"ML\", version 𝐕b\\mathbf{V}_{\\text{b}} corrected selected smoothing parameters, 𝐕c\\mathbf{V}_{\\text{c}}, generally available. allows, extent, posterior simulation account additional source uncertainty chosen values 𝛌\\boldsymbol{\\lambda}. two additional functions available gratia posterior simulation: simulate(), derivative_samples(). gratia provides simulate() methods models estimated using gam(), bam(), gamm(), well fitted via scam() scam package. simulate() base R convention thing predicted_samples(), just non-tidy way (pejorative; returns simulated response values matrix, arguably useful math statistical computation.) derivative_samples() provides draws posterior distribution derivative response variable small change focal covariate value. derivative_samples() less general version fitted_samples(); achieve thing two separate calls fitted_samples(). ’ll reserve discussion derivative_samples() separate vignette focused estimating derivatives GAMs. following sections ’ll look four main posterior simulation functions turn.","code":""},{"path":"https://gavinsimpson.github.io/gratia/articles/posterior-simulation.html","id":"posterior-smooths-and-smooth_samples","dir":"Articles","previous_headings":"","what":"Posterior smooths and smooth_samples()","title":"Posterior Simulation","text":"can sample posterior distribution coefficients particular smooth β̂j\\hat{\\beta}_j given values smoothing parameters 𝛌̂\\hat{\\boldsymbol{\\lambda}}. generate posterior samples smooths sampling 𝛃j⋆∼N(β̂j,𝐕β̂j)\\boldsymbol{\\beta}_{j\\star} \\sim N(\\hat{\\beta}_j, \\mathbf{V}_{\\hat{\\beta}_j}) forming 𝐗𝛃̂j𝛃j⋆𝖳\\mathbf{X}_{\\hat{\\boldsymbol{\\beta}}_j} \\boldsymbol{\\beta}_{j\\star}^{\\mathsf{T}}. sampling can done using smooth_samples(). illustrate , ’ll simulate data Gu & Wabha’s 4 smooth example, fit GAM simulated data simulating posterior distribution estimated smooth, sampling coefficients particular smooth. model, coefficients smooth f(x0)f(x_0) stored elements 2 10 coefficients vector. sample posterior distribution coefficients use smooth_samples() choosing particular smooth ’re interested using select argument; want sample smooths posteriors smooths model, select can left default value. Typically ’re bothered particular values covariate evaluate posterior smooths; ask 100 evenly spaced values x0 using n_vals, can provide covariates values via data argument. number posterior smooths sampled controlled argument n; ask 100 samples. Objects returned smooth_samples() draw() method available draw posterior smooths can set n_samples randomly select many smooths draw (seed can provided via argument seed make set chosen smooths repeatable.) credible interval smooth contain smooths. standard 95% credible interval, sampled smooths exceed limits interval. Following Marra & Wood (2012), blue credible interval contain average 95% grey lines (posterior smooths) given value x0x_0. across function frequentist interpretation credible interval implies values x0x_0 coverage less 95% values greater 95%.","code":"ss_df <- data_sim(\"eg1\", seed = 42) m_ss <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = ss_df, method = \"REML\") s_x0 <- get_smooth(m_ss, \"s(x0)\") smooth_coef_indices(s_x0) #> [1] 2 3 4 5 6 7 8 9 10 sm_samp <- smooth_samples(m_ss, select = \"s(x0)\", n_vals = 100, n = 100, seed = 21) sm_samp |> draw(alpha = 0.3) # evaluate the fitted smooth over x0 and add on a credible interval sm_est <- smooth_estimates(m_ss, select = \"s(x0)\") |> add_confint() # plot the smooth, credible interval, and posterior smooths sm_est |> ggplot(aes(x = x0)) + geom_lineribbon(aes(ymin = .lower_ci, ymax = .upper_ci), orientation = \"vertical\", fill = \"#56B4E9\", alpha = 0.5 ) + geom_line( data = sm_samp, aes(y = .value, group = .draw), alpha = 0.2 ) + geom_line(aes(y = .estimate), linewidth = 1, colour = \"#E69F00\") + labs(y = smooth_label(s_x0))"},{"path":"https://gavinsimpson.github.io/gratia/articles/posterior-simulation.html","id":"posterior-fitted-values-via-fitted_samples","dir":"Articles","previous_headings":"","what":"Posterior fitted values via fitted_samples()","title":"Posterior Simulation","text":"Posterior fitted values draws posterior distribution mean expected value response. expectations returned use predict() estimated GAM, except fitted_samples() includes uncertainty estimated model coefficients, whereas predict() just uses estimated coefficients. example, using data_sim() simulate data example 6 Luo & Wahba (1997) sin(2⋅(4x−2))+2⋅exp(−256⋅(x−0.5)2) \\sin(2 \\cdot (4x - 2)) + 2 \\cdot \\exp(-256 \\cdot (x - 0.5)^2) data fit adaptive smoother Next create data slice 200 values interval (0,1) ’ll predict model generate posterior fitted values compute fitted values new data posterior fitted values drawn fitted_samples() using Gaussian approximation posterior. just take 10 draws posterior observation new_df merge posterior draws data Adding posterior fitted samples plot data, superimposing Bayesian credible interval fitted values see posterior draws largely contained credible interval. difference smooth_samples() now ’re including effects model terms. simple model single smooth identity link, difference model constant term uncertainty included samples.","code":"f <- function(x) { sin(2 * ((4 * x) - 2)) + (2 * exp(-256 * (x - 0.5)^2)) } df <- data_sim(\"lwf6\", dist = \"normal\", scale = 0.3, seed = 2) plt <- df |> ggplot(aes(x = x, y = y)) + geom_point(alpha = 0.5) + geom_function(fun = f) plt m <- gam(y ~ s(x, k = 25, bs = \"ad\"), data = df, method = \"REML\") new_df <- data_slice(m, x = evenly(x, lower = 0, upper = 1, n = 200)) |> mutate(.row = row_number()) fv <- fitted_values(m, data = new_df) fs <- fitted_samples(m, data = new_df, n = 10, seed = 4) |> left_join(new_df |> select(.row, x), by = join_by(.row == .row)) plt + geom_ribbon(data = fv, aes(y = .fitted, ymin = .lower_ci, ymax = .upper_ci), fill = \"red\", alpha = 0.3) + geom_line(data = fs, aes(group = .draw, x = x, y = .fitted), colour = \"yellow\", alpha = 0.4)"},{"path":[]},{"path":"https://gavinsimpson.github.io/gratia/articles/posterior-simulation.html","id":"prediction-intervals","dir":"Articles","previous_headings":"Additional examples","what":"Prediction intervals","title":"Posterior Simulation","text":"One use posterior simulation generate prediction intervals fitted model. Prediction intervals include two sources uncertainty; estimated model , plus sampling uncertainty error arises drawing observations conditional distribution response. example, Gaussian GAM, first source uncertainty comes uncertainty estimates βj\\beta_j, model coefficients. uncertainty mean expected value response. second source uncertainty stems error term, estimated variance response. two parameters define conditional distribution YiY_i. value covariate(s) 𝐗\\mathbf{X}, estimated model defines entire distribution response values might expect observe covariate values. illustrate, ’ll fit simple GAM single smooth function data simulate Gu & Wabha’s function f2f_2 using data_sim(). simulate 400 values Gaussian distribution variance σ2=1\\sigma^2 = 1. simulated data, true function generated shown GAM data contains single smooth function x consider new value covariate x, x*=0.5x^{\\ast} = 0.5, expected value response given model, 𝔼(y*|x=x*)\\mathbb{E}(y^{*} | x = x^{*}), ~2.92, obtain using predict() value mean Gaussian distribution , model correct description data, describes distribution values YY might take x=0.5x = 0.5. Gaussian distribution defined two parameters; mean, μ\\mu, describes middle distribution, variance, σ2\\sigma^2, describes spread distribution mean. fully describe Gaussian distribution response x=0.5x = 0.5, need estimate variance. didn’t model explicitly GAM, get estimate model’s scale parameter, ϕ\\phi. stored element scale model object can visualise distribution looks like magic ggdist package orange region shows expected density response values x*=0.5x^{\\ast} = 0.5 model predicts expect observe. region assumes uncertainty estimate mean variance. Prediction intervals take account variation expected value, plus uncertainty expected value. fitted_values() conveniently returns uncertainty us, default 95% credible interval .se column standard error (standard deviation) estimated value (.fitted), .lower_ci .upper_ci lower upper uncertainty bounds (95% level) estimated value respectively. GAMs fitted mgcv don’t corresponding estimate uncertainty scale parameter, ϕ\\phi, model estimated standard deviation σ̂\\hat{\\sigma}. pretty easy compute upper lower tail quantiles fitted Gaussian distribution range values x get prediction interval, ’d ignoring uncertainty model estimates mean. Posterior simulation provides simple convenient way generate prediction interval includes model uncertainty, works principle families available mgcv (although practice, families currently supported gratia). compute prediction interval x GAM, creating set data evenly range x observed data used fit model added variable .row used later match posterior simulated values row prediction data set ds. also compute fitted values new observations using fitted_values(). step isn’t required order posterior simulation gratia, ’ll use fitted values later show model estimated values uncertainty contrast prediction interval. use posterior_samples() generate new response data new x values ds use join add prediction data draw asked 10000 posterior draws new value x. Ideally ’d generate least three four times many draws get precise estimate prediction interval, keep number low vignette avoid excessive computation time. ’re also using smoothness parameter selection corrected version Bayesian covariance matrix; matrix adjusted account us knowing value smoothing parameter f(xi)f(x_i). ps tibble, n * nrow(ds) rows. .draw variable groups simulated values posterior draw, .row groups posterior draws value x. summarise posterior draws using {dplyr} need function compute quantiles posterior distribution value x (.row). following function simple wrapper around quantile() function base R, arranges output quantile() data frame. apply function set posterior draws, grouping .row summarise separately posterior distribution new value x. reframe() used summarise posterior using quantile_fun() function. ease use, pivot resulting summary long wide format add covariate values joining .row variable 95% prediction interval shown first 10 rows prediction data. column labelled .q50 median posterior distribution. can now use various objects produced plot fitted values model (uncertainties), well prediction intervals just generated. add observed data used fit model black points, summarise posterior samples (ps) using hexagonal binning (avoid plotting 2 million posterior samples) outermost pair blue lines plot prediction interval created. interval encloses, expected, almost observe data points. also encloses, design, posterior samples, indicated filled hexagonal bins, warmer colours indicating larger counts posterior draws.","code":"df <- data_sim(\"gwf2\", n = 400, scale = 1, dist = \"normal\", seed = 8) df |> ggplot(aes(x = x, y = y)) + geom_point() + geom_function(fun = gw_f2, colour = \"#0072B2\", linewidth = 1.5) m <- gam(y ~ s(x), data = df, method = \"REML\", family = gaussian()) mu <- predict(m, newdata = data.frame(x = 0.5)) mu #> 1 #> 2.919094 sigma <- m$scale sigma #> [1] 1.019426 df |> ggplot(aes(x = x, y = y)) + stat_halfeye(aes(ydist = dist_normal(mean = mu, sd = sigma)), x = 0.5, scale = 0.2, slab_fill = \"#E69F00\", slab_alpha = 0.7 ) + geom_point() + geom_function(fun = gw_f2, colour = \"#0072B2\", linewidth = 1.5) + geom_point(x = 0.5, y = mu, colour = \"red\") fitted_values(m, data = data.frame(x = 0.5)) #> # A tibble: 1 × 6 #> .row x .fitted .se .lower_ci .upper_ci #> #> 1 1 0.5 2.92 0.161 2.60 3.23 ds <- data_slice(m, x = evenly(x, n = 200)) |> mutate(.row = row_number()) fv <- fitted_values(m, data = ds) ps <- posterior_samples(m, n = 10000, data = ds, seed = 24, unconditional = TRUE) |> left_join(ds, by = join_by(.row == .row)) ps #> # A tibble: 2,000,000 × 4 #> .row .draw .response x #> #> 1 1 1 -1.34 0.00129 #> 2 2 1 -0.0495 0.00629 #> 3 3 1 0.0308 0.0113 #> 4 4 1 -0.783 0.0163 #> 5 5 1 0.861 0.0213 #> 6 6 1 0.475 0.0263 #> 7 7 1 0.858 0.0313 #> 8 8 1 0.143 0.0363 #> 9 9 1 -0.0344 0.0413 #> 10 10 1 1.04 0.0463 #> # ℹ 1,999,990 more rows quantile_fun <- function(x, probs = c(0.025, 0.5, 0.975), ...) { tibble::tibble( .value = quantile(x, probs = probs, ...), .q = probs * 100 ) } p_int <- ps |> group_by(.row) |> reframe(quantile_fun(.response)) |> pivot_wider( id_cols = .row, names_from = .q, values_from = .value, names_prefix = \".q\" ) |> left_join(ds, by = join_by(.row == .row)) p_int #> # A tibble: 200 × 5 #> .row .q2.5 .q50 .q97.5 x #> #> 1 1 -2.84 -0.847 1.25 0.00129 #> 2 2 -2.70 -0.651 1.41 0.00629 #> 3 3 -2.50 -0.434 1.62 0.0113 #> 4 4 -2.24 -0.207 1.83 0.0163 #> 5 5 -2.04 -0.0197 2.01 0.0213 #> 6 6 -1.81 0.191 2.25 0.0263 #> 7 7 -1.59 0.391 2.41 0.0313 #> 8 8 -1.38 0.594 2.60 0.0363 #> 9 9 -1.20 0.831 2.84 0.0413 #> 10 10 -0.935 1.06 3.04 0.0463 #> # ℹ 190 more rows fv |> ggplot(aes(x = x, y = .fitted)) + # summarise the posterior samples geom_hex( data = ps, aes(x = x, y = .response, fill = after_stat(count)), bins = 50, alpha = 0.7 ) + # add the lower and upper prediction intervals geom_line(data = p_int, aes(y = .q2.5), colour = \"#56B4E9\", linewidth = 1.5) + geom_line(data = p_int, aes(y = .q97.5), colour = \"#56B4E9\", linewidth = 1.5) + # add the lower and upper credible intervals geom_line(aes(y = .lower_ci), colour = \"#56B4E9\", linewidth = 1) + geom_line(aes(y = .upper_ci), colour = \"#56B4E9\", linewidth = 1) + # add the fitted model geom_line() + # add the observed data geom_point(data = df, aes(x = x, y = y)) + scale_fill_viridis_c(option = \"plasma\") + theme(legend.position = \"none\") + labs(y = \"Response\")"},{"path":"https://gavinsimpson.github.io/gratia/articles/posterior-simulation.html","id":"metropolis-hastings-sampler","dir":"Articles","previous_headings":"Additional examples","what":"Metropolis Hastings sampler","title":"Posterior Simulation","text":"cases, Gaussian approximation posterior distribution model coefficients can fail. Simon Wood shows example just failure ?gam.mh help page, Gaussian approximation basically useless binomial GAM large numbers zeroes. mgcv::gam.mh() implements simple Metropolis Hastings sampler, alternates proposals Gaussian t distribution approximation posterior random walk proposals based shrunken approximate posterior covariance matrix. section, rework Simon’s example failure Gaussian approximation ?gam.mh show use gratia generate posterior draws using Metropolis Hastings sampler provided gam.mh(). begin defining function simulate data example. use simulate data set plot Note zeroes large parts covariate space response zeroes. fit binomial (logistic) GAM data generate sample posterior distribution using default Gaussian approximation subsequently using simpler Metropolis Hastings sampler. method argument used select Metropolis Hastings sampler, specify two additional arguments: thin, controls many draws skipped retained sample, rw_scale, scaling factor posterior covariance matrix shrunk random walk proposals. leave two important arguments defaults: burnin = 1000, number samples discard prior sampling, t_df = 40, degrees freedom t proposals. degrees freedom t proposals large, ’re effectively Gaussian approximation default, alternating proposals random walk proposals. collected posterior draws, summarise set 50%, 80%, 95% intervals using ggdist::median_qi(), add data locations left join First plot intervals Gaussian approximation posterior, repeat plot using intervals derived Metropolis Hastings sampler, arranging two plots using patchwork Gaussian approximation-based intervals shown left figure, range x largely useless, covering entire range response, despite fact observed zeroes large parts covariate space. Contrast intervals ones obtained using Metropolis Hastings sampler; intervals much better reflect uncertainty estimated response function x data zeroes.","code":"ga_fail <- function(seed) { df <- tibble(y = c( rep(0, 89), 1, 0, 1, 0, 0, 1, rep(0, 13), 1, 0, 0, 1, rep(0, 10), 1, 0, 0, 1, 1, 0, 1, rep(0, 4), 1, rep(0, 3), 1, rep(0, 3), 1, rep(0, 10), 1, rep(0, 4), 1, 0, 1, 0, 0, rep(1, 4), 0, rep(1, 5), rep(0, 4), 1, 1, rep(0, 46) )) |> mutate( x = withr::with_seed( seed, sort(c(0:10 * 5, rnorm(length(y) - 11) * 20 + 100)) ), .row = row_number() ) |> relocate(.row, .before = 1L) df } df <- ga_fail(3) df |> ggplot(aes(x = x, y = y)) + geom_point() m_logit <- gam(y ~ s(x, k = 15), data = df, method = \"REML\", family = binomial) fs_ga <- fitted_samples(m_logit, n = 2000, seed = 2) fs_mh <- fitted_samples(m_logit, n = 2000, seed = 2, method = \"mh\", thin = 2, rw_scale = 0.4 ) excl_col <- c(\".draw\", \".parameter\", \".row\") int_ga <- fs_ga |> group_by(.row) |> median_qi(.width = c(0.5, 0.8, 0.95), .exclude = excl_col) |> left_join(df, by = join_by(.row == .row)) int_mh <- fs_mh |> group_by(.row) |> median_qi(.width = c(0.5, 0.8, 0.95), .exclude = excl_col) |> left_join(df, by = join_by(.row == .row)) plt_ga <- df |> ggplot(aes(x = x, y = y)) + geom_point() + geom_lineribbon( data = int_ga, aes(x = x, y = .fitted, ymin = .lower, ymax = .upper) ) + scale_fill_brewer() + labs(title = \"Gaussian approximation\") plt_mh <- df |> ggplot(aes(x = x, y = y)) + geom_point() + geom_lineribbon( data = int_mh, aes(x = x, y = .fitted, ymin = .lower, ymax = .upper) ) + scale_fill_brewer() + labs(title = \"Metropolis Hastings sampler\") plt_ga + plt_mh + plot_layout(guides = \"collect\")"},{"path":[]},{"path":"https://gavinsimpson.github.io/gratia/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Gavin L. Simpson. Author, maintainer, copyright holder. Henrik Singmann. Contributor.","code":""},{"path":"https://gavinsimpson.github.io/gratia/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Simpson G (????). gratia: Graceful ggplot-Based Graphics Functions GAMs Fitted using mgcv. R package version 0.9.2.9011, https://gavinsimpson.github.io/gratia/.","code":"@Manual{, title = {{gratia}: Graceful {ggplot}-Based Graphics and Other Functions for {GAM}s Fitted using {mgcv}}, author = {Gavin L. Simpson}, abstract = {Graceful ggplot-based graphics and utility functions for working with generalized additive models (GAMs) fitted using the mgcv package. Provides a reimplementation of the plot() method for GAMs that mgcv provides, as well as tidyverse-compatible representations of estimated smooths.}, note = {R package version 0.9.2.9011}, url = {https://gavinsimpson.github.io/gratia/}, }"},{"path":[]},{"path":"https://gavinsimpson.github.io/gratia/index.html","id":"overview","dir":"","previous_headings":"","what":"Overview","title":"An R package for working with generalized additive models","text":"Working GAMs within ‘tidyverse’ can tedious even difficult without good understanding GAMs model returned ‘mgcv’ model objects contain. ‘gratia’ designed help . ‘gratia’ provides ‘ggplot’-based graphics utility functions working generalized additive models (GAMs) fitted using ‘mgcv’ package, via reimplementation plot() method GAMs ‘mgcv’ provides, well ‘tidyverse’ compatible representations estimated smooths.","code":""},{"path":"https://gavinsimpson.github.io/gratia/index.html","id":"features","dir":"","previous_headings":"","what":"Features","title":"An R package for working with generalized additive models","text":"main features gratia currently ggplot2-based replacement mgcv:::plot.gam(): draw.gam(). example, classic four term additive example Gu & Wahba: Estimated smooths GAM bivariate smooth: Estimated smooths GAM Note specialist smoothers (bs %% c(\"mrf\",\"sw\", \"sf\")) currently supported, univariate, factor continuous -variable smooths, simple random effect smooths (bs = 're'), factor-smooth interaction smooths (bs = \"fs\"), constrained factor smooths (bs = \"sz\"), full soap film smooths (bs = \"\"), bivariate, trivariate, quadvariate TPRS tensor product smooths supported, Estimation derivatives fitted smoothers: derivatives(), Estimation point-wise across--function confidence intervals simultaneous intervals smooths: confint.gam(). Model diagnostics via appraise() Model diagnostics figure","code":""},{"path":"https://gavinsimpson.github.io/gratia/index.html","id":"installing-gratia","dir":"","previous_headings":"","what":"Installing gratia","title":"An R package for working with generalized additive models","text":"gratia now available CRAN, can installed however gratia active development may wish install development version github. easiest way via install_github() function package remotes. Make sure remotes installed, run install package. Alternatively, binary packages development version available rOpenSci’s R Universe service:","code":"install.packages(\"gratia\") remotes::install_github(\"gavinsimpson/gratia\") # Install gratia in R install.packages(\"gratia\", repos = c( \"https://gavinsimpson.r-universe.dev\", \"https://cloud.r-project.org\" ))"},{"path":"https://gavinsimpson.github.io/gratia/index.html","id":"history","dir":"","previous_headings":"","what":"History","title":"An R package for working with generalized additive models","text":"gratia grew earlier package, schoenberg, development earlier package tsgam, originally intended used GAMs fitted time series. developing tsgam however became clear package used generally name “tsgam” longer appropriate. avoid breaking blog posts written using tsgam decided copy git repo history new repo package name schoenberg. later date someone released another package called schoenberg CRAN, scuppered idea. Now ’m calling package gratia. Hopefully won’t change …","code":""},{"path":"https://gavinsimpson.github.io/gratia/index.html","id":"why-gratia","dir":"","previous_headings":"","what":"Why gratia?","title":"An R package for working with generalized additive models","text":"naming greta package, Nick Golding observed recent phenomena naming statistical modelling software, Stan Edward, individuals played prominent role development field. lead Nick name Tensor Flow-based package greta Grete Hermann. spirit, gratia named recognition contributions Grace Wahba, pioneering work penalised spline models foundation way GAMs estimated mgcv. wanted name package grace, explicitly recognise Grace’s contributions, unfortunately already package named Grace CRAN. looked elsewhere inspiration. English word “grace” derives Latin gratia, meaning “favor, charm, thanks” (according Merriam Webster). chair Grace Wabha currently holds named Isaac J Schoenberg, former University Madison-Wisconsin Professor Mathematics, 1946 paper provided first mathematical reference “splines”. (Hence previous name package.)","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/add_confint.html","id":null,"dir":"Reference","previous_headings":"","what":"Add a confidence interval to an existing object — add_confint","title":"Add a confidence interval to an existing object — add_confint","text":"Add confidence interval existing object","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/add_confint.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add a confidence interval to an existing object — add_confint","text":"","code":"add_confint(object, coverage = 0.95, ...) # S3 method for class 'smooth_estimates' add_confint(object, coverage = 0.95, ...) # S3 method for class 'parametric_effects' add_confint(object, coverage = 0.95, ...) # Default S3 method add_confint(object, coverage = 0.95, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/add_confint.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add a confidence interval to an existing object — add_confint","text":"object R object. coverage numeric; coverage interval. Must range 0 < coverage < 1. ... arguments passed methods.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/add_constant.html","id":null,"dir":"Reference","previous_headings":"","what":"Add a constant to estimated values — add_constant","title":"Add a constant to estimated values — add_constant","text":"Add constant estimated values","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/add_constant.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add a constant to estimated values — add_constant","text":"","code":"add_constant(object, constant = NULL, ...) # S3 method for class 'smooth_estimates' add_constant(object, constant = NULL, ...) # S3 method for class 'smooth_samples' add_constant(object, constant = NULL, ...) # S3 method for class 'mgcv_smooth' add_constant(object, constant = NULL, ...) # S3 method for class 'parametric_effects' add_constant(object, constant = NULL, ...) # S3 method for class 'tbl_df' add_constant(object, constant = NULL, column = NULL, ...) # S3 method for class 'evaluated_parametric_term' add_constant(object, constant = NULL, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/add_constant.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add a constant to estimated values — add_constant","text":"object object add constant . constant constant add. ... additional arguments passed methods. column character; \"tbl_df\" method, column add constant .","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/add_constant.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add a constant to estimated values — add_constant","text":"Returns object estimate shifted addition supplied constant.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/add_constant.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Add a constant to estimated values — add_constant","text":"Gavin L. Simpson","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/add_fitted.gam.html","id":null,"dir":"Reference","previous_headings":"","what":"Add fitted values from a GAM to a data frame — add_fitted.gam","title":"Add fitted values from a GAM to a data frame — add_fitted.gam","text":"Add fitted values GAM data frame","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/add_fitted.gam.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add fitted values from a GAM to a data frame — add_fitted.gam","text":"","code":"# S3 method for class 'gam' add_fitted(data, model, value = \".fitted\", type = \"response\", ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/add_fitted.gam.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add fitted values from a GAM to a data frame — add_fitted.gam","text":"data data frame containing values variables used fit model. Passed stats::predict() newdata. model fitted model stats::predict() method available. S3 method dispatch performed model argument. value character; name variable model predictions stored. type character; type predictions return. See mgcv::predict.gam() options. ... additional arguments passed mgcv::predict.gam().","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/add_fitted.gam.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add fitted values from a GAM to a data frame — add_fitted.gam","text":"data frame (tibble) formed data predictions model.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/add_fitted.gam.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add fitted values from a GAM to a data frame — add_fitted.gam","text":"","code":"load_mgcv() df <- data_sim(\"eg1\", seed = 1) df <- df[, c(\"y\", \"x0\", \"x1\", \"x2\", \"x3\")] m <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = df, method = \"REML\") # add fitted values to our data add_fitted(df, m) #> # A tibble: 400 x 6 #> y x0 x1 x2 x3 .fitted #> #> 1 3.34 0.266 0.659 0.859 0.367 5.90 #> 2 -0.0758 0.372 0.185 0.0344 0.741 3.15 #> 3 10.7 0.573 0.954 0.971 0.934 8.28 #> 4 8.73 0.908 0.898 0.745 0.673 8.65 #> 5 15.0 0.202 0.944 0.273 0.701 15.7 #> 6 7.67 0.898 0.724 0.677 0.848 8.38 #> 7 7.58 0.945 0.370 0.348 0.706 7.84 #> 8 8.51 0.661 0.781 0.947 0.859 6.74 #> 9 10.6 0.629 0.0111 0.339 0.446 9.14 #> 10 3.72 0.0618 0.940 0.0317 0.677 7.04 #> # i 390 more rows # with type = \"terms\" or \"iterms\" add_fitted(df, m, type = \"terms\") #> # A tibble: 400 x 10 #> y x0 x1 x2 x3 .constant `s(x0)` `s(x1)` `s(x2)` `s(x3)` #> #> 1 3.34 0.266 0.659 0.859 0.367 7.94 0.175 0.559 -2.81 0.0351 #> 2 -0.0758 0.372 0.185 0.0344 0.741 7.94 0.435 -1.92 -3.23 -0.0687 #> 3 10.7 0.573 0.954 0.971 0.934 7.94 0.593 3.35 -3.47 -0.122 #> 4 8.73 0.908 0.898 0.745 0.673 7.94 -0.812 2.77 -1.19 -0.0498 #> 5 15.0 0.202 0.944 0.273 0.701 7.94 -0.0589 3.23 4.63 -0.0576 #> 6 7.67 0.898 0.724 0.677 0.848 7.94 -0.745 1.15 0.146 -0.0981 #> 7 7.58 0.945 0.370 0.348 0.706 7.94 -1.07 -1.31 2.34 -0.0589 #> 8 8.51 0.661 0.781 0.947 0.859 7.94 0.434 1.67 -3.20 -0.101 #> 9 10.6 0.629 0.0111 0.339 0.446 7.94 0.512 -1.95 2.63 0.0132 #> 10 3.72 0.0618 0.940 0.0317 0.677 7.94 -0.695 3.20 -3.35 -0.0508 #> # i 390 more rows"},{"path":"https://gavinsimpson.github.io/gratia/reference/add_fitted.html","id":null,"dir":"Reference","previous_headings":"","what":"Add fitted values from a model to a data frame — add_fitted","title":"Add fitted values from a model to a data frame — add_fitted","text":"Add fitted values model data frame","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/add_fitted.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add fitted values from a model to a data frame — add_fitted","text":"","code":"add_fitted(data, model, value = \".value\", ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/add_fitted.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add fitted values from a model to a data frame — add_fitted","text":"data data frame containing values variables used fit model. Passed stats::predict() newdata. model fitted model stats::predict() method available. S3 method dispatch performed model argument. value character; name variable model predictions stored. ... additional arguments passed methods.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/add_fitted.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add fitted values from a model to a data frame — add_fitted","text":"data frame (tibble) formed data fitted values model.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/add_fitted_samples.html","id":null,"dir":"Reference","previous_headings":"","what":"Add posterior draws from a model to a data object — add_fitted_samples","title":"Add posterior draws from a model to a data object — add_fitted_samples","text":"Adds draws posterior distribution model data object using one fitted_samples(), predicted_samples(), posterior_samples().","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/add_fitted_samples.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add posterior draws from a model to a data object — add_fitted_samples","text":"","code":"add_fitted_samples(object, model, n = 1, seed = NULL, ...) add_predicted_samples(object, model, n = 1, seed = NULL, ...) add_posterior_samples(object, model, n = 1, seed = NULL, ...) add_smooth_samples(object, model, n = 1, seed = NULL, select = NULL, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/add_fitted_samples.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add posterior draws from a model to a data object — add_fitted_samples","text":"object data frame tibble posterior draws added. model fitted GAM (GAM-like) object posterior draw method exists. n integer; number posterior draws add. seed numeric; value seed random number generator. ... arguments passed posterior draw function, currently one fitted_samples(), predicted_samples(), posterior_samples(). n seed already specified arguments also passed posterior sampling function. select character; select smooth's posterior draw . default, NULL, means posteriors smooths model wil sampled individually. supplied, character vector requested smooth terms.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/add_fitted_samples.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add posterior draws from a model to a data object — add_fitted_samples","text":"","code":"load_mgcv() df <- data_sim(\"eg1\", n = 400, seed = 42) m <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = df, method = \"REML\") # add fitted samples (posterior draws of the expected value of the response) # note that there are 800 rows in the output: 400 data by `n = 2` samples. df |> add_fitted_samples(m, n = 2, seed = 84) #> # A tibble: 800 × 14 #> y x0 x1 x2 x3 f f0 f1 f2 f3 .row .draw #> #> 1 2.99 0.915 0.0227 0.909 0.402 1.62 0.529 1.05 0.0397 0 1 1 #> 2 2.99 0.915 0.0227 0.909 0.402 1.62 0.529 1.05 0.0397 0 1 2 #> 3 4.70 0.937 0.513 0.900 0.432 3.25 0.393 2.79 0.0630 0 2 1 #> 4 4.70 0.937 0.513 0.900 0.432 3.25 0.393 2.79 0.0630 0 2 2 #> 5 13.9 0.286 0.631 0.192 0.664 13.5 1.57 3.53 8.41 0 3 1 #> 6 13.9 0.286 0.631 0.192 0.664 13.5 1.57 3.53 8.41 0 3 2 #> 7 5.71 0.830 0.419 0.532 0.182 6.12 1.02 2.31 2.79 0 4 1 #> 8 5.71 0.830 0.419 0.532 0.182 6.12 1.02 2.31 2.79 0 4 2 #> 9 7.63 0.642 0.879 0.522 0.838 10.4 1.80 5.80 2.76 0 5 1 #> 10 7.63 0.642 0.879 0.522 0.838 10.4 1.80 5.80 2.76 0 5 2 #> # ℹ 790 more rows #> # ℹ 2 more variables: .parameter , .fitted # add posterior draws from smooth s(x2) df |> add_smooth_samples(m, n = 2, seed = 2, select= \"s(x2)\") #> # A tibble: 800 × 15 #> y x0 x1 x2 x3 f f0 f1 f2 f3 .row .smooth #> #> 1 2.99 0.915 0.0227 0.909 0.402 1.62 0.529 1.05 0.0397 0 1 s(x2) #> 2 2.99 0.915 0.0227 0.909 0.402 1.62 0.529 1.05 0.0397 0 1 s(x2) #> 3 4.70 0.937 0.513 0.900 0.432 3.25 0.393 2.79 0.0630 0 2 s(x2) #> 4 4.70 0.937 0.513 0.900 0.432 3.25 0.393 2.79 0.0630 0 2 s(x2) #> 5 13.9 0.286 0.631 0.192 0.664 13.5 1.57 3.53 8.41 0 3 s(x2) #> 6 13.9 0.286 0.631 0.192 0.664 13.5 1.57 3.53 8.41 0 3 s(x2) #> 7 5.71 0.830 0.419 0.532 0.182 6.12 1.02 2.31 2.79 0 4 s(x2) #> 8 5.71 0.830 0.419 0.532 0.182 6.12 1.02 2.31 2.79 0 4 s(x2) #> 9 7.63 0.642 0.879 0.522 0.838 10.4 1.80 5.80 2.76 0 5 s(x2) #> 10 7.63 0.642 0.879 0.522 0.838 10.4 1.80 5.80 2.76 0 5 s(x2) #> # ℹ 790 more rows #> # ℹ 3 more variables: .term , .draw , .value "},{"path":"https://gavinsimpson.github.io/gratia/reference/add_partial_residuals.html","id":null,"dir":"Reference","previous_headings":"","what":"Add partial residuals — add_partial_residuals","title":"Add partial residuals — add_partial_residuals","text":"Add partial residuals","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/add_partial_residuals.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add partial residuals — add_partial_residuals","text":"","code":"add_partial_residuals(data, model, ...) # S3 method for class 'gam' add_partial_residuals(data, model, select = NULL, partial_match = FALSE, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/add_partial_residuals.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add partial residuals — add_partial_residuals","text":"data data frame containing values variables used fit model. Passed stats::residuals() newdata. model fitted model stats::residuals() method available. S3 method dispatch performed model argument. ... arguments passed methods. select character, logical, numeric; smooths plot. NULL, default, model smooths drawn. Numeric select indexes smooths order specified formula stored object. Character select matches labels smooths shown example output summary(object). Logical select operates per numeric select order smooths stored. partial_match logical; smooths selected partial matches select? TRUE, select can single string match .","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/add_partial_residuals.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add partial residuals — add_partial_residuals","text":"","code":"load_mgcv() df <- data_sim(\"eg1\", seed = 1) df <- df[, c(\"y\", \"x0\", \"x1\", \"x2\", \"x3\")] m <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = df, method = \"REML\") ## add partial residuals add_partial_residuals(df, m) #> # A tibble: 400 x 9 #> y x0 x1 x2 x3 `s(x0)` `s(x1)` `s(x2)` `s(x3)` #> #> 1 3.34 0.266 0.659 0.859 0.367 -2.38 -2.00 -5.36 -2.52 #> 2 -0.0758 0.372 0.185 0.0344 0.741 -2.79 -5.15 -6.45 -3.29 #> 3 10.7 0.573 0.954 0.971 0.934 2.99 5.75 -1.07 2.28 #> 4 8.73 0.908 0.898 0.745 0.673 -0.734 2.84 -1.11 0.0287 #> 5 15.0 0.202 0.944 0.273 0.701 -0.752 2.54 3.94 -0.750 #> 6 7.67 0.898 0.724 0.677 0.848 -1.46 0.432 -0.567 -0.812 #> 7 7.58 0.945 0.370 0.348 0.706 -1.33 -1.57 2.08 -0.318 #> 8 8.51 0.661 0.781 0.947 0.859 2.21 3.44 -1.42 1.68 #> 9 10.6 0.629 0.0111 0.339 0.446 2.01 -0.445 4.13 1.51 #> 10 3.72 0.0618 0.940 0.0317 0.677 -4.02 -0.123 -6.67 -3.37 #> # i 390 more rows ## add partial residuals for selected smooths add_partial_residuals(df, m, select = \"s(x0)\") #> # A tibble: 400 x 6 #> y x0 x1 x2 x3 `s(x0)` #> #> 1 3.34 0.266 0.659 0.859 0.367 -2.38 #> 2 -0.0758 0.372 0.185 0.0344 0.741 -2.79 #> 3 10.7 0.573 0.954 0.971 0.934 2.99 #> 4 8.73 0.908 0.898 0.745 0.673 -0.734 #> 5 15.0 0.202 0.944 0.273 0.701 -0.752 #> 6 7.67 0.898 0.724 0.677 0.848 -1.46 #> 7 7.58 0.945 0.370 0.348 0.706 -1.33 #> 8 8.51 0.661 0.781 0.947 0.859 2.21 #> 9 10.6 0.629 0.0111 0.339 0.446 2.01 #> 10 3.72 0.0618 0.940 0.0317 0.677 -4.02 #> # i 390 more rows"},{"path":"https://gavinsimpson.github.io/gratia/reference/add_residuals.gam.html","id":null,"dir":"Reference","previous_headings":"","what":"Add residuals from a GAM to a data frame — add_residuals.gam","title":"Add residuals from a GAM to a data frame — add_residuals.gam","text":"Add residuals GAM data frame","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/add_residuals.gam.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add residuals from a GAM to a data frame — add_residuals.gam","text":"","code":"# S3 method for class 'gam' add_residuals(data, model, value = \".residual\", type = \"deviance\", ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/add_residuals.gam.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add residuals from a GAM to a data frame — add_residuals.gam","text":"data data frame containing values variables used fit model. Passed stats::predict() newdata. model fitted model stats::predict() method available. S3 method dispatch performed model argument. value character; name variable model predictions stored. type character; type residuals return. See mgcv::residuals.gam() options. ... additional arguments passed mgcv::residuals.gam().","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/add_residuals.gam.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add residuals from a GAM to a data frame — add_residuals.gam","text":"data frame (tibble) formed data residuals model.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/add_residuals.gam.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add residuals from a GAM to a data frame — add_residuals.gam","text":"","code":"load_mgcv() df <- data_sim(\"eg1\", seed = 1) df <- df[, c(\"y\", \"x0\", \"x1\", \"x2\", \"x3\")] m <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = df, method = \"REML\") ## add_residuals(df, m) #> # A tibble: 400 x 6 #> y x0 x1 x2 x3 .residual #> #> 1 3.34 0.266 0.659 0.859 0.367 -2.56 #> 2 -0.0758 0.372 0.185 0.0344 0.741 -3.22 #> 3 10.7 0.573 0.954 0.971 0.934 2.40 #> 4 8.73 0.908 0.898 0.745 0.673 0.0785 #> 5 15.0 0.202 0.944 0.273 0.701 -0.693 #> 6 7.67 0.898 0.724 0.677 0.848 -0.714 #> 7 7.58 0.945 0.370 0.348 0.706 -0.259 #> 8 8.51 0.661 0.781 0.947 0.859 1.78 #> 9 10.6 0.629 0.0111 0.339 0.446 1.50 #> 10 3.72 0.0618 0.940 0.0317 0.677 -3.32 #> # i 390 more rows"},{"path":"https://gavinsimpson.github.io/gratia/reference/add_residuals.html","id":null,"dir":"Reference","previous_headings":"","what":"Add residuals from a model to a data frame — add_residuals","title":"Add residuals from a model to a data frame — add_residuals","text":"Add residuals model data frame","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/add_residuals.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add residuals from a model to a data frame — add_residuals","text":"","code":"add_residuals(data, model, value = \".residual\", ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/add_residuals.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add residuals from a model to a data frame — add_residuals","text":"data data frame containing values variables used fit model. Passed stats::residuals() newdata. model fitted model stats::residuals() method available. S3 method dispatch performed model argument. value character; name variable model residuals stored. ... additional arguments passed methods.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/add_residuals.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add residuals from a model to a data frame — add_residuals","text":"data frame (tibble) formed data residuals model.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/add_sizer.html","id":null,"dir":"Reference","previous_headings":"","what":"Add indicators of significant change after SiZeR — add_sizer","title":"Add indicators of significant change after SiZeR — add_sizer","text":"Add indicators significant change SiZeR","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/add_sizer.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add indicators of significant change after SiZeR — add_sizer","text":"","code":"add_sizer(object, type = c(\"change\", \"sizer\"), ...) # S3 method for class 'derivatives' add_sizer(object, type = c(\"change\", \"sizer\"), ...) # S3 method for class 'smooth_estimates' add_sizer(object, type = c(\"change\", \"sizer\"), derivatives = NULL, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/add_sizer.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add indicators of significant change after SiZeR — add_sizer","text":"object R object. Currently supported methods classes \"derivatives\". type character; \"change\" adds single variable object indicating credible interval derivative excludes 0. \"sizer\" adds two variables indicating whether derivative postive negative. ... arguments passed methods derivatives object class \"derivatives\", resulting call derivatives().","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/add_sizer.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add indicators of significant change after SiZeR — add_sizer","text":"","code":"load_mgcv() df <- data_sim(\"eg1\", n = 400, dist = \"normal\", scale = 2, seed = 42) m <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = df, method = \"REML\") ## first derivatives of all smooths using central finite differences d <- derivatives(m, type = \"central\") |> add_sizer() # default adds a .change column names(d) #> [1] \".smooth\" \".by\" \".fs\" \".derivative\" \".se\" #> [6] \".crit\" \".lower_ci\" \".upper_ci\" \".change\" \"x0\" #> [11] \"x1\" \"x2\" \"x3\""},{"path":"https://gavinsimpson.github.io/gratia/reference/appraise.html","id":null,"dir":"Reference","previous_headings":"","what":"Model diagnostic plots — appraise","title":"Model diagnostic plots — appraise","text":"Model diagnostic plots","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/appraise.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Model diagnostic plots — appraise","text":"","code":"appraise(model, ...) # S3 method for class 'gam' appraise( model, method = c(\"uniform\", \"simulate\", \"normal\", \"direct\"), use_worm = FALSE, n_uniform = 10, n_simulate = 50, seed = NULL, type = c(\"deviance\", \"pearson\", \"response\"), n_bins = c(\"sturges\", \"scott\", \"fd\"), ncol = NULL, nrow = NULL, guides = \"keep\", level = 0.9, ci_col = \"black\", ci_alpha = 0.2, point_col = \"black\", point_alpha = 1, line_col = \"red\", ... ) # S3 method for class 'lm' appraise(model, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/appraise.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Model diagnostic plots — appraise","text":"model fitted model. Currently models inheriting class \"gam\", well classes \"glm\" \"lm\" calls stats::glm stats::lm supported. ... arguments passed patchwork::wrap_plots(). method character; method used generate theoretical quantiles. default \"uniform\", generates reference quantiles using random draws uniform distribution inverse cummulative distribution function (CDF) fitted values. reference quantiles averaged n_uniform draws. \"simulate\" generates reference quantiles simulating new response data model observed values covariates, residualised generate reference quantiles, using n_simulate simulated data sets. \"normal\" generates reference quantiles using standard normal distribution. \"uniform\" computationally efficient, \"simulate\" allows reference bands drawn QQ-plot. \"normal\" avoided used fall back random number generator (\"simulate\") inverse CDF (\"uniform\"``) available family` used model fitting. Note method = \"direct\" deprecated favour method = \"uniform\". use_worm logical; worm plot drawn place QQ plot? n_uniform numeric; number times randomize uniform quantiles direct computation method (method = \"direct\") QQ plots. n_simulate numeric; number data sets simulate estimated model using simulation method (method = \"simulate\") QQ plots. seed numeric; random number seed use method = \"simulate\" method = \"uniform\". type character; type residuals use. \"deviance\", \"response\", \"pearson\" residuals allowed. n_bins character numeric; either number bins string indicating calculate number bins. ncol, nrow numeric; numbers rows columns spread plots. guides character; one \"keep\" (default), \"collect\", \"auto\". Passed patchwork::plot_layout() level numeric; coverage level QQ plot reference intervals. Must strictly 0 < level < 1. used method = \"simulate\". ci_alpha, ci_col colour transparency used draw QQ plot reference interval method = \"simulate\". point_col, point_alpha colour transparency used draw points plots. See graphics::par() section Color Specification. passed individual plotting functions, therefore affects points plots. line_col colour specification 1:1 line QQ plot reference line residuals vs linear predictor plot.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/appraise.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Model diagnostic plots — appraise","text":"wording used mgcv::qq.gam() uses direct reference simulated residuals method (method = \"simulated\"). avoid confusion, method = \"direct\" deprecated favour method = \"uniform\".","code":""},{"path":[]},{"path":"https://gavinsimpson.github.io/gratia/reference/appraise.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Model diagnostic plots — appraise","text":"","code":"load_mgcv() ## simulate some data... dat <- data_sim(\"eg1\", n = 400, dist = \"normal\", scale = 2, seed = 2) mod <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = dat) ## run some basic model checks appraise(mod, point_col = \"steelblue\", point_alpha = 0.4) ## To change the theme for all panels use the & operator, for example to ## change the ggplot theme for all panels library(\"ggplot2\") appraise(mod, seed = 42, point_col = \"steelblue\", point_alpha = 0.4, line_col = \"black\" ) & theme_minimal()"},{"path":"https://gavinsimpson.github.io/gratia/reference/basis.html","id":null,"dir":"Reference","previous_headings":"","what":"Basis expansions for smooths — basis","title":"Basis expansions for smooths — basis","text":"Creates basis expansion definition smoother using syntax mgcv's smooths via mgcv::s()., mgcv::te(), mgcv::ti(), mgcv::t2(), fitted GAM(M).","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/basis.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Basis expansions for smooths — basis","text":"","code":"basis(object, ...) # S3 method for class 'gam' basis( object, select = NULL, term = deprecated(), data = NULL, n = 100, n_2d = 50, n_3d = 16, n_4d = 4, partial_match = FALSE, ... ) # S3 method for class 'scam' basis( object, select = NULL, term = deprecated(), data = NULL, n = 100, n_2d = 50, n_3d = 16, n_4d = 4, partial_match = FALSE, ... ) # S3 method for class 'gamm' basis( object, select = NULL, term = deprecated(), data = NULL, n = 100, n_2d = 50, n_3d = 16, n_4d = 4, partial_match = FALSE, ... ) # S3 method for class 'list' basis( object, select = NULL, term = deprecated(), data = NULL, n = 100, n_2d = 50, n_3d = 16, n_4d = 4, partial_match = FALSE, ... ) # Default S3 method basis( object, data, knots = NULL, constraints = FALSE, at = NULL, diagonalize = FALSE, ... )"},{"path":"https://gavinsimpson.github.io/gratia/reference/basis.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Basis expansions for smooths — basis","text":"object smooth specification, result call one mgcv::s()., mgcv::te(), mgcv::ti(), mgcv::t2(), fitted GAM(M) model. ... arguments passed mgcv::smoothCon(). select character; select smooths fitted model term argument renamed select data data frame containing variables used smooth. n numeric; number points range covariate evaluate smooth. n_2d numeric; number new observations dimension bivariate smooth. currently used; n used dimensions. n_3d numeric; number new observations generate third dimension 3D smooth. n_4d numeric; number new observations generate dimensions higher 2 (!) kD smooth (k >= 4). example, smooth 4D smooth, dimensions 3 4 get n_4d new observations. partial_match logical; case character select, select match partially smooths? partial_match = TRUE, select must single string, character vector length 1. knots list data frame named components containing knots locations. Names must match covariates basis required. See mgcv::smoothCon(). constraints logical; identifiability constraints applied smooth basis. See argument absorb.cons mgcv::smoothCon(). data frame containing values smooth covariate(s) basis evaluated. diagonalize logical; TRUE, reparameterises smooth associated penalty identity matrix. effect turning last diagonal elements penalty zero, highlights penalty null space.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/basis.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Basis expansions for smooths — basis","text":"tibble.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/basis.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Basis expansions for smooths — basis","text":"Gavin L. Simpson","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/basis.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Basis expansions for smooths — basis","text":"","code":"load_mgcv() df <- data_sim(\"eg4\", n = 400, seed = 42) bf <- basis(s(x0), data = df) bf <- basis(s(x2, by = fac, bs = \"bs\"), data = df, constraints = TRUE)"},{"path":"https://gavinsimpson.github.io/gratia/reference/basis_size.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract basis dimension of a smooth — basis_size","title":"Extract basis dimension of a smooth — basis_size","text":"Extract basis dimension smooth","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/basis_size.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract basis dimension of a smooth — basis_size","text":"","code":"basis_size(object, ...) # S3 method for class 'mgcv.smooth' basis_size(object, ...) # S3 method for class 'gam' basis_size(object, ...) # S3 method for class 'gamm' basis_size(object, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/basis_size.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract basis dimension of a smooth — basis_size","text":"object fitted GAM(M). Currently mgcv::gam() (anything inherits \"gam\" class, e.g. mgcv::bam()) mgcv::gamm() supported. ... Arguments passed methods.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/basis_size.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract basis dimension of a smooth — basis_size","text":"","code":"load_mgcv() df <- data_sim(\"eg1\", n = 200, seed = 1) m <- bam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = df) basis_size(m) #> s(x0) s(x1) s(x2) s(x3) #> 9 9 9 9"},{"path":"https://gavinsimpson.github.io/gratia/reference/bird_move.html","id":null,"dir":"Reference","previous_headings":"","what":"Simulated bird migration data — bird_move","title":"Simulated bird migration data — bird_move","text":"Data generated hypothetical study bird movement along migration corridor, sampled throughout year. dataset consists simulated sample records numbers observed locations 100 tagged individuals six species bird, ten locations along latitudinal gradient, one observation taken every four weeks. Counts simulated randomly species location week creating species-specific migration curve gave probability finding individual given species given location, simulated distribution individuals across sites using multinomial distribution, subsampling using binomial distribution simulation observation error (.e. every bird present location detected). data set (bird_move) consists variables count, latitude, week species.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/bird_move.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Simulated bird migration data — bird_move","text":"data frame","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/bird_move.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Simulated bird migration data — bird_move","text":"Pedersen EJ, Miller DL, Simpson GL, Ross N. 2018. Hierarchical generalized additive models: introduction mgcv. PeerJ Preprints 6:e27320v1 doi:10.7287/peerj.preprints.27320v1 .","code":""},{"path":[]},{"path":"https://gavinsimpson.github.io/gratia/reference/boundary.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract the boundary of a soap film smooth — boundary","text":"","code":"boundary(x, ...) # S3 method for class 'soap.film' boundary(x, ...) # S3 method for class 'gam' boundary(x, select, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/boundary.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract the boundary of a soap film smooth — boundary","text":"x R object. Currently objects inherit classes \"soap.film\" \"gam\". ... arguments passed methods. select character; label soap film smooth extract boundary.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/boundary.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract the boundary of a soap film smooth — boundary","text":"list lists data frames specifying loops define boundary soap film smooth.","code":""},{"path":[]},{"path":"https://gavinsimpson.github.io/gratia/reference/check_user_select_smooths.html","id":null,"dir":"Reference","previous_headings":"","what":"Select smooths based on user's choices — check_user_select_smooths","title":"Select smooths based on user's choices — check_user_select_smooths","text":"Given vector indexing smooths GAM, returns logical vector selecting requested smooths.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/check_user_select_smooths.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Select smooths based on user's choices — check_user_select_smooths","text":"","code":"check_user_select_smooths( smooths, select = NULL, partial_match = FALSE, model_name = NULL )"},{"path":"https://gavinsimpson.github.io/gratia/reference/check_user_select_smooths.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Select smooths based on user's choices — check_user_select_smooths","text":"smooths character; vector smooth labels. select numeric, logical, character vector selected smooths. partial_match logical; case character select, select match partially smooths? partial_match = TRUE, select must single string, character vector length 1. model_name character; model name used error messages.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/check_user_select_smooths.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Select smooths based on user's choices — check_user_select_smooths","text":"logical vector length length(smooths) indicating smooths selected.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/check_user_select_smooths.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Select smooths based on user's choices — check_user_select_smooths","text":"Gavin L. Simpson","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/coef.scam.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract coefficients from a fitted scam model. — coef.scam","title":"Extract coefficients from a fitted scam model. — coef.scam","text":"Extract coefficients fitted scam model.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/coef.scam.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract coefficients from a fitted scam model. — coef.scam","text":"","code":"# S3 method for class 'scam' coef(object, parametrized = TRUE, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/coef.scam.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract coefficients from a fitted scam model. — coef.scam","text":"object model object fitted scam() parametrized logical; extract parametrized coefficients, respect linear inequality constraints model. ... arguments.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/compare_smooths.html","id":null,"dir":"Reference","previous_headings":"","what":"Compare smooths across models — compare_smooths","title":"Compare smooths across models — compare_smooths","text":"Compare smooths across models","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/compare_smooths.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compare smooths across models — compare_smooths","text":"","code":"compare_smooths( model, ..., select = NULL, smooths = deprecated(), n = 100, data = NULL, unconditional = FALSE, overall_uncertainty = TRUE, partial_match = FALSE )"},{"path":"https://gavinsimpson.github.io/gratia/reference/compare_smooths.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compare smooths across models — compare_smooths","text":"model Primary model comparison. ... Additional models compare smooths model. select character; select smooths compare. default (NULL) means smooths model compared. Numeric select indexes smooths order specified formula stored model. Character select matches labels smooths shown example output summary(object). Logical select operates per numeric select order smooths stored. smooths Use select instead. n numeric; number points range covariate evaluate smooth. data data frame covariate values evaluate smooth. unconditional logical; confidence intervals include uncertainty due smoothness selection? TRUE, corrected Bayesian covariance matrix used. overall_uncertainty logical; uncertainty model constant term included standard error evaluate values smooth? partial_match logical; smooths selected partial matches select? TRUE, select can single string match .","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/compare_smooths.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compare smooths across models — compare_smooths","text":"","code":"load_mgcv() dat <- data_sim(\"eg1\", seed = 2) ## models to compare smooths across - artificially create differences m1 <- gam(y ~ s(x0, k = 5) + s(x1, k = 5) + s(x2, k = 5) + s(x3, k = 5), data = dat, method = \"REML\" ) m2 <- gam(y ~ s(x0, bs = \"ts\") + s(x1, bs = \"ts\") + s(x2, bs = \"ts\") + s(x3, bs = \"ts\"), data = dat, method = \"REML\") ## build comparisons comp <- compare_smooths(m1, m2) comp #> # A tibble: 8 x 5 #> .model .smooth .type .by data #> #> 1 m1 s(x0) TPRS NA #> 2 m2 s(x0) TPRS (shrink) NA #> 3 m1 s(x1) TPRS NA #> 4 m2 s(x1) TPRS (shrink) NA #> 5 m1 s(x2) TPRS NA #> 6 m2 s(x2) TPRS (shrink) NA #> 7 m1 s(x3) TPRS NA #> 8 m2 s(x3) TPRS (shrink) NA ## notice that the result is a nested tibble draw(comp)"},{"path":"https://gavinsimpson.github.io/gratia/reference/confint.fderiv.html","id":null,"dir":"Reference","previous_headings":"","what":"Point-wise and simultaneous confidence intervals for derivatives of smooths — confint.fderiv","title":"Point-wise and simultaneous confidence intervals for derivatives of smooths — confint.fderiv","text":"Calculates point-wise confidence simultaneous intervals first derivatives smooth terms fitted GAM.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/confint.fderiv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Point-wise and simultaneous confidence intervals for derivatives of smooths — confint.fderiv","text":"","code":"# S3 method for class 'fderiv' confint( object, parm, level = 0.95, type = c(\"confidence\", \"simultaneous\"), nsim = 10000, ncores = 1L, ... )"},{"path":"https://gavinsimpson.github.io/gratia/reference/confint.fderiv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Point-wise and simultaneous confidence intervals for derivatives of smooths — confint.fderiv","text":"object object class \"fderiv\" containing estimated derivatives. parm parameters (smooth terms) given intervals vector terms. missing, parameters considered. level numeric, 0 < level < 1; confidence level point-wise simultaneous interval. default 0.95 95% interval. type character; type interval compute. One \"confidence\" point-wise intervals, \"simultaneous\" simultaneous intervals. nsim integer; number simulations used computing simultaneous intervals. ncores number cores generating random variables multivariate normal distribution. Passed mvnfast::rmvn(). Parallelization take place OpenMP supported (appears work Windows current R). ... additional arguments methods","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/confint.fderiv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Point-wise and simultaneous confidence intervals for derivatives of smooths — confint.fderiv","text":"data frame components: term; factor indicating term row relates, lower; lower limit confidence simultaneous interval, est; estimated derivative upper; upper limit confidence simultaneous interval.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/confint.fderiv.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Point-wise and simultaneous confidence intervals for derivatives of smooths — confint.fderiv","text":"Gavin L. Simpson","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/confint.fderiv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Point-wise and simultaneous confidence intervals for derivatives of smooths — confint.fderiv","text":"","code":"load_mgcv() dat <- data_sim(\"eg1\", n = 1000, dist = \"normal\", scale = 2, seed = 2) mod <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = dat, method = \"REML\") # new data to evaluate the derivatives at, say over the middle 50% of range # of each covariate middle <- function(x, n = 25, coverage = 0.5) { v <- (1 - coverage) / 2 q <- quantile(x, prob = c(0 + v, 1 - v), type = 8) seq(q[1], q[2], length = n) } new_data <- sapply(dat[c(\"x0\", \"x1\", \"x2\", \"x3\")], middle) new_data <- data.frame(new_data) ## first derivatives of all smooths... fd <- fderiv(mod, newdata = new_data) #> Warning: `fderiv()` was deprecated in gratia 0.7.0. #> i Please use `derivatives()` instead. ## point-wise interval ci <- confint(fd, type = \"confidence\") ci #> # A tibble: 100 x 4 #> term lower est upper #> #> 1 s(x0) 1.7 4.1 6.6 #> 2 s(x0) 1.3 3.8 6.3 #> 3 s(x0) 0.99 3.5 6.0 #> 4 s(x0) 0.68 3.1 5.6 #> 5 s(x0) 0.37 2.8 5.2 #> 6 s(x0) 0.0049 2.4 4.8 #> 7 s(x0) -0.40 2.0 4.5 #> 8 s(x0) -0.79 1.7 4.2 #> 9 s(x0) -1.1 1.3 3.8 #> 10 s(x0) -1.4 0.99 3.4 #> # i 90 more rows ## simultaneous interval for smooth term of x2 x2_sint <- confint(fd, parm = \"x2\", type = \"simultaneous\", nsim = 10000, ncores = 2 ) # \\donttest{ x2_sint #> # A tibble: 25 x 4 #> term lower est upper #> #> 1 s(x2) -24. -15. -5.6 #> 2 s(x2) -35. -26. -16. #> 3 s(x2) -41. -33. -24. #> 4 s(x2) -44. -36. -29. #> 5 s(x2) -44. -36. -28. #> 6 s(x2) -42. -34. -25. #> 7 s(x2) -38. -30. -21. #> 8 s(x2) -33. -24. -16. #> 9 s(x2) -27. -19. -11. #> 10 s(x2) -22. -14. -5.8 #> # i 15 more rows # }"},{"path":"https://gavinsimpson.github.io/gratia/reference/confint.gam.html","id":null,"dir":"Reference","previous_headings":"","what":"Point-wise and simultaneous confidence intervals for smooths — confint.gam","title":"Point-wise and simultaneous confidence intervals for smooths — confint.gam","text":"Calculates point-wise confidence simultaneous intervals smooth terms fitted GAM.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/confint.gam.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Point-wise and simultaneous confidence intervals for smooths — confint.gam","text":"","code":"# S3 method for class 'gam' confint( object, parm, level = 0.95, data = newdata, n = 100, type = c(\"confidence\", \"simultaneous\"), nsim = 10000, shift = FALSE, transform = FALSE, unconditional = FALSE, ncores = 1, partial_match = FALSE, ..., newdata = NULL ) # S3 method for class 'gamm' confint(object, ...) # S3 method for class 'list' confint(object, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/confint.gam.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Point-wise and simultaneous confidence intervals for smooths — confint.gam","text":"object object class \"gam\" \"gamm\". parm parameters (smooth terms) given intervals vector terms. missing, parameters considered, although currently implemented. level numeric, 0 < level < 1; confidence level point-wise simultaneous interval. default 0.95 95% interval. data data frame; new values covariates used model fit. selected smooth(s) wil evaluated supplied values. n numeric; number points evaluate smooths . type character; type interval compute. One \"confidence\" point-wise intervals, \"simultaneous\" simultaneous intervals. nsim integer; number simulations used computing simultaneous intervals. shift logical; constant term add smooth? transform logical; smooth evaluated transformed scale? generalised models, involves applying inverse link function used fit model. Alternatively, name , actual, function can supplied transform smooth confidence interval. unconditional logical; TRUE (freq == FALSE) Bayesian smoothing parameter uncertainty corrected covariance matrix returned, available. ncores number cores generating random variables multivariate normal distribution. Passed mvnfast::rmvn(). Parallelization take place OpenMP supported (appears work Windows current R). partial_match logical; matching parm use partial match exact match? Can used length(parm) 1. ... additional arguments methods newdata DEPRECATED! data frame; containing new values covariates used model fit. selected smooth(s) wil evaluated supplied values.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/confint.gam.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Point-wise and simultaneous confidence intervals for smooths — confint.gam","text":"tibble components: .smooth; character indicating term row relates, .type; type smooth, .name variable smooth, NA otherwise, one vectors values smooth evaluated, named per variables smooth, zero variables containing values variable, .estimate; estimated value smooth, .se; standard error estimated value smooth, .crit; critical value 100 * level% confidence interval. .lower_ci; lower limit confidence simultaneous interval, .upper_ci; upper limit confidence simultaneous interval,","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/confint.gam.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Point-wise and simultaneous confidence intervals for smooths — confint.gam","text":"Gavin L. Simpson","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/confint.gam.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Point-wise and simultaneous confidence intervals for smooths — confint.gam","text":"","code":"load_mgcv() dat <- data_sim(\"eg1\", n = 1000, dist = \"normal\", scale = 2, seed = 2) mod <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = dat, method = \"REML\") # new data to evaluate the smooths at, say over the middle 50% of range # of each covariate middle <- function(x, n = 50, coverage = 0.5) { v <- (1 - coverage) / 2 q <- quantile(x, prob = c(0 + v, 1 - v), type = 8) seq(q[1], q[2], length = n) } new_data <- sapply(dat[c(\"x0\", \"x1\", \"x2\", \"x3\")], middle) new_data <- data.frame(new_data) ## point-wise interval for smooth of x2 ci <- confint(mod, parm = \"s(x2)\", type = \"confidence\", data = new_data) ci #> # A tibble: 50 x 9 #> .smooth .type .by x2 .estimate .se .crit .lower_ci .upper_ci #> #> 1 s(x2) TPRS NA 0.26 5.3 0.18 2.0 5.0 5.7 #> 2 s(x2) TPRS NA 0.27 5.1 0.18 2.0 4.8 5.5 #> 3 s(x2) TPRS NA 0.28 4.9 0.18 2.0 4.6 5.3 #> 4 s(x2) TPRS NA 0.29 4.6 0.18 2.0 4.3 5.0 #> 5 s(x2) TPRS NA 0.30 4.3 0.19 2.0 3.9 4.7 #> 6 s(x2) TPRS NA 0.32 4.0 0.19 2.0 3.6 4.3 #> 7 s(x2) TPRS NA 0.33 3.6 0.20 2.0 3.2 4.0 #> 8 s(x2) TPRS NA 0.34 3.2 0.20 2.0 2.9 3.6 #> 9 s(x2) TPRS NA 0.35 2.9 0.20 2.0 2.5 3.3 #> 10 s(x2) TPRS NA 0.36 2.5 0.19 2.0 2.1 2.9 #> # i 40 more rows"},{"path":"https://gavinsimpson.github.io/gratia/reference/data_combos.html","id":null,"dir":"Reference","previous_headings":"","what":"All combinations of factor levels plus typical values of continuous variables — data_combos","title":"All combinations of factor levels plus typical values of continuous variables — data_combos","text":"combinations factor levels plus typical values continuous variables","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/data_combos.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"All combinations of factor levels plus typical values of continuous variables — data_combos","text":"","code":"data_combos(object, ...) # S3 method for class 'gam' data_combos( object, vars = everything(), complete = TRUE, envir = environment(formula(object)), data = NULL, ... )"},{"path":"https://gavinsimpson.github.io/gratia/reference/data_combos.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"All combinations of factor levels plus typical values of continuous variables — data_combos","text":"object fitted model object. ... arguments passed methods. vars terms include exclude returned object. Uses tidyselect principles. complete logical; combinations factor levels returned? FALSE, combinations levels observed model retained. envir environment within recreate data used fit object. data optional data frame data used fit mdoel reconstruction data model work.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/data_sim.html","id":null,"dir":"Reference","previous_headings":"","what":"Simulate example data for fitting GAMs — data_sim","title":"Simulate example data for fitting GAMs — data_sim","text":"tidy reimplementation functions implemented mgcv::gamSim() can used fit GAMs. new feature sampling distribution can applied example types.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/data_sim.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Simulate example data for fitting GAMs — data_sim","text":"","code":"data_sim( model = \"eg1\", n = 400, scale = NULL, theta = 3, power = 1.5, dist = c(\"normal\", \"poisson\", \"binary\", \"negbin\", \"tweedie\", \"gamma\", \"ocat\", \"ordered categorical\"), n_cat = 4, cuts = c(-1, 0, 5), seed = NULL, gfam_families = c(\"binary\", \"tweedie\", \"normal\") )"},{"path":"https://gavinsimpson.github.io/gratia/reference/data_sim.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Simulate example data for fitting GAMs — data_sim","text":"model character; either \"egX\" X integer 1:7, name model. See Details possible options. n numeric; number observations simulate. scale numeric; level noise use. theta numeric; dispersion parameter \\(\\theta\\) use. default entirely arbitrary, chosen provide simulated data exhibits extra dispersion beyond assumed Poisson. power numeric; Tweedie power parameter. dist character; sampling distribution response variable. \"ordered categorical\" synonym \"ocat\". n_cat integer; number categories categorical response. Currently used distr %% c(\"ocat\", \"ordered categorical\"). cuts numeric; vector cut points latent variable, excluding end points -Inf Inf. Must one fewer number categories: length(cuts) == n_cat - 1. seed numeric; seed random number generator. Passed base::set.seed(). gfam_families character; vector distributions use generating data grouped families use family = gfam(). allowed distributions per dist.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/data_sim.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Simulate example data for fitting GAMs — data_sim","text":"data_sim() can simulate data several underlying models known true functions. available options currently : \"eg1\": four term additive true model. classic Gu & Wahba four univariate term test model. See gw_functions details underlying four functions. \"eg2\": bivariate smooth true model. \"eg3\": example containing continuous smooth (varying coefficient) true model. model \\(\\hat{y}_i = f_2(x_{1i})x_{2i}\\) function \\(f_2()\\) \\(f_2(x) = 0.2 * x^{11} * (10 * (1 - x))^6 + 10 * (10 * x)^3 * (1 - x)^{10}\\). \"eg4\": factor smooth true model. true model contains factor 3 levels, response nth level follows nth Gu & Wabha function (\\(n \\{1, 2, 3}\\)). \"eg5\": additive plus factor true model. response linear combination Gu & Wabha functions 2, 3, 4 (latter null function) plus factor term four levels. \"eg6\": additive plus random effect term true model. ´\"eg7\": version model \"eg1\"`, covariates correlated. \"gwf2\": model response Gu & Wabha's \\(f_2(x_i)\\) plus noise. \"lwf6\": model response Luo & Wabha's \"example 6\" function \\(sin(2(4x-2)) + 2 exp(-256(x-0.5)^2)\\) plus noise. \"gfam\": simulates data use GAMs family = gfam(families). See example mgcv::gfam(). model specified dist ignored gfam_families used specify distributions included simulated data. Can vector families allowed dist. \"ocat\" %% gfam_families (\"ordered categorical\"), 4 classes assumed, changed. Link functions used \"identity\" \"normal\", \"logit\" \"binary\", \"ocat\", \"ordered categorical\", \"exp\" elsewhere. random component providing noise sampling variation can follow one distributions, specified via argument dist \"normal\": Gaussian, \"poisson\": Poisson, \"binary\": Bernoulli, \"negbin\": Negative binomial, \"tweedie\": Tweedie, \"gamma\": gamma , \"ordered categorical\": ordered categorical arguments provide parameters distribution.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/data_sim.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Simulate example data for fitting GAMs — data_sim","text":"Gu, C., Wahba, G., (1993). Smoothing Spline ANOVA Component-Wise Bayesian \"Confidence Intervals.\" J. Comput. Graph. Stat. 2, 97–117. Luo, Z., Wahba, G., (1997). Hybrid adaptive splines. J. . Stat. Assoc. 92, 107–116.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/data_sim.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Simulate example data for fitting GAMs — data_sim","text":"","code":"data_sim(\"eg1\", n = 100, seed = 1) #> # A tibble: 100 x 10 #> y x0 x1 x2 x3 f f0 f1 f2 f3 #> #> 1 14.532 0.26551 0.65472 0.26751 0.67371 13.713 1.4814 3.7041 8.5277 0 #> 2 16.113 0.37212 0.35320 0.21865 0.094858 12.735 1.8408 2.0267 8.8680 0 #> 3 9.5835 0.57285 0.27026 0.51680 0.49260 6.4103 1.9478 1.7169 2.7456 0 #> 4 15.687 0.90821 0.99268 0.26895 0.46155 16.349 0.56879 7.2817 8.4980 0 #> 5 8.2216 0.20168 0.63349 0.18117 0.37522 12.792 1.1841 3.5501 8.0578 0 #> 6 9.9034 0.89839 0.21321 0.51858 0.99110 4.9081 0.62765 1.5318 2.7487 0 #> 7 5.9362 0.94468 0.12937 0.56278 0.17635 4.6020 0.34587 1.2953 2.9609 0 #> 8 10.839 0.66080 0.47812 0.12916 0.81344 9.7565 1.7502 2.6019 5.4045 0 #> 9 16.883 0.62911 0.92407 0.25637 0.068447 16.909 1.8377 6.3481 8.7237 0 #> 10 7.3603 0.061786 0.59876 0.71794 0.40045 6.3401 0.38578 3.3119 2.6424 0 #> # i 90 more rows # an ordered categorical response data_sim(\"eg1\", n = 100, dist = \"ocat\", n_cat = 4, cuts = c(-1, 0, 5)) #> # A tibble: 100 x 11 #> y x0 x1 x2 x3 f f0 f1 f2 #> #> 1 1 0.93708 0.21716 0.51711 0.44457 -3.5517 0.39280 1.5439 2.7461 #> 2 1 0.28614 0.21657 0.85193 0.060386 -4.7654 1.5653 1.5421 0.36166 #> 3 1 0.83045 0.38895 0.44280 0.32751 -1.7693 1.0157 2.1769 3.2727 #> 4 4 0.64175 0.94246 0.15788 0.87843 7.2150 1.8050 6.5858 7.0588 #> 5 3 0.51910 0.96261 0.44232 0.93060 3.8994 1.9964 6.8566 3.2808 #> 6 1 0.73659 0.73986 0.96773 0.39218 -2.3701 1.4725 4.3917 0.00015734 #> 7 1 0.13467 0.73325 0.48459 0.15885 -0.27657 0.82112 4.3340 2.8028 #> 8 3 0.65699 0.53576 0.25246 0.31995 5.2247 1.7616 2.9198 8.7777 #> 9 3 0.70506 0.0022730 0.25969 0.30697 3.0408 1.5991 1.0046 8.6716 #> 10 2 0.45774 0.60894 0.54202 0.10781 -0.036524 1.9824 3.3800 2.8356 #> # i 90 more rows #> # i 2 more variables: f3 , latent "},{"path":"https://gavinsimpson.github.io/gratia/reference/data_slice.html","id":null,"dir":"Reference","previous_headings":"","what":"Prepare a data slice through model covariates — data_slice","title":"Prepare a data slice through model covariates — data_slice","text":"Prepare data slice model covariates","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/data_slice.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prepare a data slice through model covariates — data_slice","text":"","code":"data_slice(object, ...) # Default S3 method data_slice(object, ...) # S3 method for class 'data.frame' data_slice(object, ...) # S3 method for class 'gam' data_slice(object, ..., data = NULL, envir = NULL) # S3 method for class 'gamm' data_slice(object, ...) # S3 method for class 'list' data_slice(object, ...) # S3 method for class 'scam' data_slice(object, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/data_slice.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prepare a data slice through model covariates — data_slice","text":"object R model object. ... User supplied variables defining data slice. Arguments passed via ... need named. data alternative data frame values containing variables needed fit model. NULL, default, data used fit model recovered using model.frame. User-supplied expressions passed ... evaluated data. envir environment within recreate data used fit object.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/data_slice.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Prepare a data slice through model covariates — data_slice","text":"data slice data set results one (covariates) varied systematically () range specified subset values interest, remaining covariates model held fixed, representative values. known reference grid package emmeans data grid marginaleffects package. GAMs, covariates specified via ... take representative values determined data used fit model follows: numeric covariates, value fitting data closest median value used, factor covariates, modal (frequently observed) level used, first level (sorted per vector returned base::levels() several levels observed number times. values already computed calling gam() bam() example can found var.summary component fitted model. Function typical_values() extract values interested. Convenience functions evenly(), ref_level(), level() provided help users specify data slices. ref_level(), level() also ensure factor covariates correct levels, needed mgcv::predict.gam() example. extended discussion data_slice() examples, see vignette(\"data-slices\", package = \"gratia\").","code":""},{"path":[]},{"path":"https://gavinsimpson.github.io/gratia/reference/data_slice.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Prepare a data slice through model covariates — data_slice","text":"","code":"load_mgcv() # simulate some Gaussian data df <- data_sim(\"eg1\", n = 50, seed = 2) # fit a GAM with 1 smooth and 1 linear term m <- gam(y ~ s(x2, k = 7) + x1, data = df, method = \"REML\") # Want to predict over f(x2) while holding `x1` at some value. # Default will use the observation closest to the median for unspecified # variables. ds <- data_slice(m, x2 = evenly(x2, n = 50)) ds #> # A tibble: 50 x 2 #> x2 x1 #> #> 1 0.0228 0.403 #> 2 0.0424 0.403 #> 3 0.0619 0.403 #> 4 0.0815 0.403 #> 5 0.101 0.403 #> 6 0.121 0.403 #> 7 0.140 0.403 #> 8 0.160 0.403 #> 9 0.179 0.403 #> 10 0.199 0.403 #> # i 40 more rows # for full control, specify the values you want ds <- data_slice(m, x2 = evenly(x2, n = 50), x1 = 0.3) # or provide an expression (function call) which will be evaluated in the # data frame passed to `data` or `model.frame(object)` ds <- data_slice(m, x2 = evenly(x2, n = 50), x1 = mean(x1))"},{"path":"https://gavinsimpson.github.io/gratia/reference/datagen.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate data over the range of variables used in smooths — datagen","title":"Generate data over the range of variables used in smooths — datagen","text":"smooth GAM, generate new data range variables involved smooth. function deprecated useful narrow use-case. Use data_slice() instead.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/datagen.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate data over the range of variables used in smooths — datagen","text":"","code":"datagen(x, ...) # S3 method for class 'mgcv.smooth' datagen(x, n = 100, data, ...) # S3 method for class 'fs.interaction' datagen(x, n = 100, data, ...) # S3 method for class 'gam' datagen(x, smooth = NULL, n = 200, ...) # S3 method for class 'gamm' datagen(x, ...) # S3 method for class 'list' datagen(x, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/datagen.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate data over the range of variables used in smooths — datagen","text":"x object new data required. Currently objects classes \"gam\", \"gamm\" supported, smooths mgcv inheriting class \"mgcv.smooth\". ... arguments passed methods n numeric; number data values generate per term smooth. data data frame; \"mgcv.smooth\" objects, data used fit GAM need supplied.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/datagen.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate data over the range of variables used in smooths — datagen","text":"data frame new values spread range observed values.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/datagen.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Generate data over the range of variables used in smooths — datagen","text":"Gavin L. Simpson","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/derivative_samples.html","id":null,"dir":"Reference","previous_headings":"","what":"Posterior expectations of derivatives from an estimated model — derivative_samples","title":"Posterior expectations of derivatives from an estimated model — derivative_samples","text":"Posterior expectations derivatives estimated model","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/derivative_samples.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Posterior expectations of derivatives from an estimated model — derivative_samples","text":"","code":"derivative_samples(object, ...) # Default S3 method derivative_samples(object, ...) # S3 method for class 'gamm' derivative_samples(object, ...) # S3 method for class 'gam' derivative_samples( object, focal = NULL, data = NULL, order = 1L, type = c(\"forward\", \"backward\", \"central\"), scale = c(\"response\", \"linear_predictor\"), method = c(\"gaussian\", \"mh\", \"inla\", \"user\"), n = 100, eps = 1e-07, n_sim = 10000, level = lifecycle::deprecated(), seed = NULL, envir = environment(formula(object)), draws = NULL, mvn_method = c(\"mvnfast\", \"mgcv\"), ... )"},{"path":"https://gavinsimpson.github.io/gratia/reference/derivative_samples.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Posterior expectations of derivatives from an estimated model — derivative_samples","text":"object R object compute derivatives ... arguments passed methods fitted_samples() focal character; name focal variable. response derivative response respect variable returned. variables involved model held constant values. can missing supplying data, case, focal variable identified one variable constant. data data frame containing values model covariates evaluate first derivatives smooths. supplied, one variable must held constant value. order numeric; order derivative. type character; type finite difference used. One \"forward\", \"backward\", \"central\". scale character; derivative estimated response linear predictor (link) scale? One \"response\" (default), \"linear predictor\". method character; method used draw samples posterior distribution. \"gaussian\" uses Gaussian (Laplace) approximation posterior. \"mh\" uses Metropolis Hastings sample alternates t proposals proposals based shrunken version posterior covariance matrix. \"inla\" uses variant Integrated Nested Laplace Approximation due Wood (2019), (currently implemented). \"user\" allows user-supplied posterior draws (currently implemented). n numeric; number points evaluate derivative (data supplied). eps numeric; finite difference. n_sim integer; number simulations used computing simultaneous intervals. level seed numeric; random seed simulations. envir environment within recreate data used fit object. draws matrix; user supplied posterior draws used method = \"user\". mvn_method character; one \"mvnfast\" \"mgcv\". default uses mvnfast::rmvn(), can considerably faster generate large numbers MVN random values mgcv::rmvn(), might work marginal fits, covariance matrix close singular.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/derivative_samples.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Posterior expectations of derivatives from an estimated model — derivative_samples","text":"tibble, currently following variables: .derivative: estimated partial derivative, additional columns containing covariate values derivative evaluated.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/derivative_samples.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Posterior expectations of derivatives from an estimated model — derivative_samples","text":"Gavin L. Simpson","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/derivative_samples.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Posterior expectations of derivatives from an estimated model — derivative_samples","text":"","code":"load_mgcv() df <- data_sim(\"eg1\", dist = \"negbin\", scale = 0.25, seed = 42) # fit the GAM (note: for execution time reasons using bam()) m <- bam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = df, family = nb(), method = \"fREML\") # data slice through data along x2 - all other covariates will be set to # typical values (value closest to median) ds <- data_slice(m, x2 = evenly(x2, n = 200)) # samples from posterior of derivatives fd_samp <- derivative_samples(m, data = ds, type = \"central\", focal = \"x2\", eps = 0.01, seed = 21, n_sim = 100 ) # plot the first 20 posterior draws if (requireNamespace(\"ggplot2\") && requireNamespace(\"dplyr\")) { library(\"ggplot2\") fd_samp |> dplyr::filter(.draw <= 20) |> ggplot(aes(x = x2, y = .derivative, group = .draw)) + geom_line(alpha = 0.5) }"},{"path":"https://gavinsimpson.github.io/gratia/reference/derivatives.html","id":null,"dir":"Reference","previous_headings":"","what":"Derivatives of estimated smooths via finite differences — derivatives","title":"Derivatives of estimated smooths via finite differences — derivatives","text":"Derivatives estimated smooths via finite differences","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/derivatives.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Derivatives of estimated smooths via finite differences — derivatives","text":"","code":"derivatives(object, ...) # Default S3 method derivatives(object, ...) # S3 method for class 'gamm' derivatives(object, ...) # S3 method for class 'gam' derivatives( object, select = NULL, term = deprecated(), data = newdata, order = 1L, type = c(\"forward\", \"backward\", \"central\"), n = 100, eps = 1e-07, interval = c(\"confidence\", \"simultaneous\"), n_sim = 10000, level = 0.95, unconditional = FALSE, frequentist = FALSE, offset = NULL, ncores = 1, partial_match = FALSE, ..., newdata = NULL )"},{"path":"https://gavinsimpson.github.io/gratia/reference/derivatives.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Derivatives of estimated smooths via finite differences — derivatives","text":"object R object compute derivatives . ... arguments passed methods. select character; select smooth's posterior draw . default (NULL) means posteriors smooths model wil sampled . supplied, character vector requested terms. Can partial match smooth term; see argument partial_match . term Use select instead. data data frame containing values model covariates evaluate first derivatives smooths. order numeric; order derivative. type character; type finite difference used. One \"forward\", \"backward\", \"central\". n numeric; number points evaluate derivative . eps numeric; finite difference. interval character; type interval compute. One \"confidence\" point-wise intervals, \"simultaneous\" simultaneous intervals. n_sim integer; number simulations used computing simultaneous intervals. level numeric; 0 < level < 1; confidence level point-wise simultaneous interval. default 0.95 95% interval. unconditional logical; use smoothness selection-corrected Bayesian covariance matrix? frequentist logical; use frequentist covariance matrix? offset numeric; value use offset term ncores number cores generating random variables multivariate normal distribution. Passed mvnfast::rmvn(). Parallelization take place OpenMP supported (appears work Windows current R). partial_match logical; smooths selected partial matches term? TRUE, term can single string match . newdata Deprecated: use data instead.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/derivatives.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Derivatives of estimated smooths via finite differences — derivatives","text":"tibble, currently following variables: smooth: smooth row refers , var: name variable involved smooth, data: values var derivative evaluated, derivative: estimated derivative, se: standard error estimated derivative, crit: critical value derivative ± (crit * se) gives upper lower bounds requested confidence simultaneous interval (given level), lower: lower bound confidence simultaneous interval, upper: upper bound confidence simultaneous interval.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/derivatives.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Derivatives of estimated smooths via finite differences — derivatives","text":"derivatives() ignore random effect smooths encounters object.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/derivatives.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Derivatives of estimated smooths via finite differences — derivatives","text":"Gavin L. Simpson","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/derivatives.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Derivatives of estimated smooths via finite differences — derivatives","text":"","code":"load_mgcv() dat <- data_sim(\"eg1\", n = 400, dist = \"normal\", scale = 2, seed = 42) mod <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = dat, method = \"REML\") ## first derivatives of all smooths using central finite differences derivatives(mod, type = \"central\") #> # A tibble: 400 x 12 #> .smooth .by .fs .derivative .se .crit .lower_ci .upper_ci x0 x1 #> #> 1 s(x0) NA NA 7.41 3.33 1.96 0.874 13.9 2.39e-4 NA #> 2 s(x0) NA NA 7.40 3.33 1.96 0.884 13.9 1.03e-2 NA #> 3 s(x0) NA NA 7.39 3.30 1.96 0.929 13.8 2.04e-2 NA #> 4 s(x0) NA NA 7.36 3.24 1.96 1.01 13.7 3.04e-2 NA #> 5 s(x0) NA NA 7.32 3.15 1.96 1.14 13.5 4.05e-2 NA #> 6 s(x0) NA NA 7.26 3.04 1.96 1.30 13.2 5.06e-2 NA #> 7 s(x0) NA NA 7.18 2.90 1.96 1.49 12.9 6.06e-2 NA #> 8 s(x0) NA NA 7.09 2.76 1.96 1.69 12.5 7.07e-2 NA #> 9 s(x0) NA NA 6.99 2.61 1.96 1.87 12.1 8.07e-2 NA #> 10 s(x0) NA NA 6.87 2.47 1.96 2.03 11.7 9.08e-2 NA #> # i 390 more rows #> # i 2 more variables: x2 , x3 ## derivatives for a selected smooth derivatives(mod, type = \"central\", select = \"s(x1)\") #> # A tibble: 100 x 9 #> .smooth .by .fs .derivative .se .crit .lower_ci .upper_ci x1 #> #> 1 s(x1) NA NA -0.907 3.12 1.96 -7.02 5.20 0.000405 #> 2 s(x1) NA NA -0.906 3.11 1.96 -7.01 5.20 0.0105 #> 3 s(x1) NA NA -0.898 3.10 1.96 -6.97 5.17 0.0205 #> 4 s(x1) NA NA -0.880 3.06 1.96 -6.88 5.12 0.0306 #> 5 s(x1) NA NA -0.849 3.00 1.96 -6.73 5.03 0.0406 #> 6 s(x1) NA NA -0.803 2.92 1.96 -6.52 4.92 0.0507 #> 7 s(x1) NA NA -0.740 2.81 1.96 -6.25 4.77 0.0607 #> 8 s(x1) NA NA -0.659 2.69 1.96 -5.93 4.61 0.0708 #> 9 s(x1) NA NA -0.557 2.56 1.96 -5.57 4.46 0.0809 #> 10 s(x1) NA NA -0.436 2.42 1.96 -5.19 4.32 0.0909 #> # i 90 more rows ## or via a partial match derivatives(mod, type = \"central\", select = \"x1\", partial_match = TRUE) #> # A tibble: 100 x 9 #> .smooth .by .fs .derivative .se .crit .lower_ci .upper_ci x1 #> #> 1 s(x1) NA NA -0.907 3.12 1.96 -7.02 5.20 0.000405 #> 2 s(x1) NA NA -0.906 3.11 1.96 -7.01 5.20 0.0105 #> 3 s(x1) NA NA -0.898 3.10 1.96 -6.97 5.17 0.0205 #> 4 s(x1) NA NA -0.880 3.06 1.96 -6.88 5.12 0.0306 #> 5 s(x1) NA NA -0.849 3.00 1.96 -6.73 5.03 0.0406 #> 6 s(x1) NA NA -0.803 2.92 1.96 -6.52 4.92 0.0507 #> 7 s(x1) NA NA -0.740 2.81 1.96 -6.25 4.77 0.0607 #> 8 s(x1) NA NA -0.659 2.69 1.96 -5.93 4.61 0.0708 #> 9 s(x1) NA NA -0.557 2.56 1.96 -5.57 4.46 0.0809 #> 10 s(x1) NA NA -0.436 2.42 1.96 -5.19 4.32 0.0909 #> # i 90 more rows"},{"path":"https://gavinsimpson.github.io/gratia/reference/difference_smooths.html","id":null,"dir":"Reference","previous_headings":"","what":"Differences of factor smooth interactions — difference_smooths","title":"Differences of factor smooth interactions — difference_smooths","text":"Estimates pairwise differences (comparisons) factor smooth interactions (smooths factor argument) pairs groups defined factor. group means can optionally included difference.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/difference_smooths.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Differences of factor smooth interactions — difference_smooths","text":"","code":"difference_smooths(model, ...) # S3 method for class 'gam' difference_smooths( model, select = NULL, smooth = deprecated(), n = 100, ci_level = 0.95, data = NULL, group_means = FALSE, partial_match = TRUE, unconditional = FALSE, frequentist = FALSE, ... )"},{"path":"https://gavinsimpson.github.io/gratia/reference/difference_smooths.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Differences of factor smooth interactions — difference_smooths","text":"model fitted model. ... arguments passed methods. currently used. select character, logical, numeric; smooths plot. NULL, default, model smooths drawn. Numeric select indexes smooths order specified formula stored object. Character select matches labels smooths shown example output summary(object). Logical select operates per numeric select order smooths stored. smooth Use select instead. n numeric; number points evaluate difference pairs smooths. ci_level numeric 0 1; coverage credible interval. data data frame locations evaluate difference smooths. group_means logical; group means included difference? partial_match logical; smooth match partially smooths? partial_match = TRUE, smooth must single string, character vector length 1. Unlike similar functions, default TRUE intention users matching factor-smooth labels. unconditional logical; account smoothness selection model? frequentist logical; use frequentist covariance matrix?","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/difference_smooths.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Differences of factor smooth interactions — difference_smooths","text":"","code":"load_mgcv() df <- data_sim(\"eg4\", seed = 42) m <- gam(y ~ fac + s(x2, by = fac) + s(x0), data = df, method = \"REML\") sm_dif <- difference_smooths(m, select = \"s(x2)\") sm_dif #> # A tibble: 300 x 9 #> .smooth .by .level_1 .level_2 .diff .se .lower_ci .upper_ci x2 #> #> 1 s(x2) fac 1 2 0.386 0.618 -0.824 1.60 0.00359 #> 2 s(x2) fac 1 2 0.479 0.574 -0.646 1.60 0.0136 #> 3 s(x2) fac 1 2 0.572 0.534 -0.474 1.62 0.0237 #> 4 s(x2) fac 1 2 0.665 0.497 -0.308 1.64 0.0338 #> 5 s(x2) fac 1 2 0.758 0.464 -0.151 1.67 0.0438 #> 6 s(x2) fac 1 2 0.850 0.435 -0.00342 1.70 0.0539 #> 7 s(x2) fac 1 2 0.941 0.412 0.134 1.75 0.0639 #> 8 s(x2) fac 1 2 1.03 0.393 0.262 1.80 0.0740 #> 9 s(x2) fac 1 2 1.12 0.378 0.380 1.86 0.0841 #> 10 s(x2) fac 1 2 1.21 0.367 0.489 1.93 0.0941 #> # i 290 more rows draw(sm_dif) # include the groups means for `fac` in the difference sm_dif2 <- difference_smooths(m, select = \"s(x2)\", group_means = TRUE) draw(sm_dif2)"},{"path":[]},{"path":"https://gavinsimpson.github.io/gratia/reference/dispersion.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Dispersion parameter for fitted model — dispersion","text":"","code":"dispersion(model, ...) # S3 method for class 'gam' dispersion(model, ...) # S3 method for class 'glm' dispersion(model, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/dispersion.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Dispersion parameter for fitted model — dispersion","text":"model fitted model. ... arguments passed methods.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.basis.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot basis functions — draw.basis","title":"Plot basis functions — draw.basis","text":"Plots basis functions using ggplot2","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.basis.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot basis functions — draw.basis","text":"","code":"# S3 method for class 'basis' draw( object, legend = FALSE, labeller = NULL, ylab = NULL, title = NULL, subtitle = NULL, caption = NULL, ncol = NULL, nrow = NULL, angle = NULL, guides = \"keep\", contour = FALSE, n_contour = 10, contour_col = \"black\", ... )"},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.basis.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot basis functions — draw.basis","text":"object object, result call basis(). legend logical; legend drawn indicate basis functions? labeller labeller function label facets. default use ggplot2::label_both(). ylab character expression; label y axis. supplied, suitable label generated object. title character expression; title plot. See ggplot2::labs(). subtitle character expression; subtitle plot. See ggplot2::labs(). caption character expression; plot caption. See ggplot2::labs(). ncol, nrow numeric; numbers rows columns spread plots angle numeric; angle x axis tick labels drawn passed angle argument ggplot2::guide_axis(). guides character; one \"keep\" (default), \"collect\", \"auto\". Passed patchwork::plot_layout() contour logical; contours draw plot using ggplot2::geom_contour(). n_contour numeric; number contour bins. result n_contour - 1 contour lines drawn. See ggplot2::geom_contour(). contour_col colour specification contour lines. ... arguments passed methods. used method.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.basis.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot basis functions — draw.basis","text":"patchwork object.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.basis.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot basis functions — draw.basis","text":"Gavin L. Simpson","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.basis.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot basis functions — draw.basis","text":"","code":"load_mgcv() df <- data_sim(\"eg1\", n = 400, seed = 42) m <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = df, method = \"REML\") bf <- basis(m) draw(bf) bf <- basis(m, \"s(x2)\") draw(bf)"},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.compare_smooths.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot comparisons of smooths — draw.compare_smooths","title":"Plot comparisons of smooths — draw.compare_smooths","text":"Plot comparisons smooths","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.compare_smooths.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot comparisons of smooths — draw.compare_smooths","text":"","code":"# S3 method for class 'compare_smooths' draw(object, ncol = NULL, nrow = NULL, guides = \"collect\", ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.compare_smooths.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot comparisons of smooths — draw.compare_smooths","text":"object class \"compare_smooths\", result call compare_smooths(). ncol, nrow numeric; numbers rows columns spread plots guides character; one \"keep\" (default), \"collect\", \"auto\". Passed patchwork::plot_layout() ... additional arguments passed patchwork::wrap_plots().","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.derivatives.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot derivatives of smooths — draw.derivatives","title":"Plot derivatives of smooths — draw.derivatives","text":"Plot derivatives smooths","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.derivatives.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot derivatives of smooths — draw.derivatives","text":"","code":"# S3 method for class 'derivatives' draw( object, select = NULL, scales = c(\"free\", \"fixed\"), add_change = FALSE, change_type = c(\"change\", \"sizer\"), alpha = 0.2, change_col = \"black\", decrease_col = \"#56B4E9\", increase_col = \"#E69F00\", lwd_change = 1.5, ncol = NULL, nrow = NULL, guides = \"keep\", angle = NULL, ... ) # S3 method for class 'partial_derivatives' draw( object, select = NULL, scales = c(\"free\", \"fixed\"), alpha = 0.2, ncol = NULL, nrow = NULL, guides = \"keep\", angle = NULL, ... )"},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.derivatives.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot derivatives of smooths — draw.derivatives","text":"object fitted GAM, result call mgcv::gam(). select character, logical, numeric; smooths plot. NULL, default, model smooths drawn. Numeric select indexes smooths order specified formula stored object. Character select matches labels smooths shown example output summary(object). Logical select operates per numeric select order smooths stored. scales character; univariate smooths plotted y-axis scale? scales = \"free\", default, univariate smooth y-axis scale. scales = \"fixed\", common y axis scale used univariate smooths. Currently affect y-axis scale plots parametric terms. add_change logical; periods significant change highlighted plot? change_type character; type change indicate. \"change\", differentiation made periods significant increase decrease. \"sizer\", periods increase decrease differentiated resulting plot. alpha numeric; alpha transparency confidence simultaneous interval. change_col, decrease_col, increase_col colour specifications use indicating periods change. col_change used change_type = \"change\", col_decrease col_increase used `change_type = \"sizer\"“. lwd_change numeric; linewidth use change indicators. ncol, nrow numeric; numbers rows columns spread plots guides character; one \"keep\" (default), \"collect\", \"auto\". Passed patchwork::plot_layout() angle numeric; angle x axis tick labels drawn passed angle argument ggplot2::guide_axis(). ... additional arguments passed patchwork::wrap_plots().","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.derivatives.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot derivatives of smooths — draw.derivatives","text":"","code":"load_mgcv() dat <- data_sim(\"eg1\", n = 800, dist = \"normal\", scale = 2, seed = 42) mod <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = dat, method = \"REML\") ## first derivative of all smooths df <- derivatives(mod, type = \"central\") draw(df) ## fixed axis scales draw(df, scales = \"fixed\")"},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.difference_smooth.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot differences of smooths — draw.difference_smooth","title":"Plot differences of smooths — draw.difference_smooth","text":"Plot differences smooths","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.difference_smooth.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot differences of smooths — draw.difference_smooth","text":"","code":"# S3 method for class 'difference_smooth' draw( object, select = NULL, rug = FALSE, ref_line = FALSE, contour = FALSE, contour_col = \"black\", n_contour = NULL, ci_alpha = 0.2, ci_col = \"black\", smooth_col = \"black\", line_col = \"red\", scales = c(\"free\", \"fixed\"), ncol = NULL, nrow = NULL, guides = \"keep\", xlab = NULL, ylab = NULL, title = NULL, subtitle = NULL, caption = NULL, angle = NULL, ... )"},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.difference_smooth.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot differences of smooths — draw.difference_smooth","text":"object fitted GAM, result call mgcv::gam(). select character, logical, numeric; smooths plot. NULL, default, model smooths drawn. Numeric select indexes smooths order specified formula stored object. Character select matches labels smooths shown example output summary(object). Logical select operates per numeric select order smooths stored. rug logical; ref_line logical; contour logical; contour lines added smooth surfaces? contour_col colour specification contour lines. n_contour numeric; number contour bins. result n_contour - 1 contour lines drawn. See ggplot2::geom_contour(). ci_alpha numeric; alpha transparency confidence simultaneous interval. ci_col colour specification confidence/credible intervals band. Affects fill interval. smooth_col colour specification smooth difference line. line_col colour specification drawing reference lines scales character; univariate smooths plotted y-axis scale? scales = \"free\", default, univariate smooth y-axis scale. scales = \"fixed\", common y axis scale used univariate smooths. Currently affect y-axis scale plots parametric terms. ncol, nrow numeric; numbers rows columns spread plots guides character; one \"keep\" (default), \"collect\", \"auto\". Passed patchwork::plot_layout() xlab, ylab, title, subtitle, caption character; labels annotate plots angle numeric; angle x axis tick labels drawn passed angle argument ggplot2::guide_axis(). ... additional arguments passed patchwork::wrap_plots().","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.difference_smooth.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot differences of smooths — draw.difference_smooth","text":"","code":"load_mgcv() # simulate some data; a factor smooth example df <- data_sim(\"eg4\", seed = 42) # fit GAM m <- gam(y ~ fac + s(x2, by = fac) + s(x0), data = df, method = \"REML\") # calculate the differences between pairs of smooths the f_j(x2) term diffs <- difference_smooths(m, select = \"s(x2)\") draw(diffs)"},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.evaluated_parametric_term.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot estimated parametric effects — draw.evaluated_parametric_term","title":"Plot estimated parametric effects — draw.evaluated_parametric_term","text":"Plots estimated univariate bivariate smooths using ggplot2.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.evaluated_parametric_term.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot estimated parametric effects — draw.evaluated_parametric_term","text":"","code":"# S3 method for class 'evaluated_parametric_term' draw( object, ci_level = 0.95, constant = NULL, fun = NULL, xlab, ylab, title = NULL, subtitle = NULL, caption = NULL, rug = TRUE, position = \"identity\", response_range = NULL, ... )"},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.evaluated_parametric_term.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot estimated parametric effects — draw.evaluated_parametric_term","text":"object object, result call evaluate_parametric_term(). ci_level numeric 0 1; coverage credible interval. constant numeric; constant add estimated values smooth. constant, supplied, added estimated value confidence band computed. fun function; function applied estimated values confidence interval plotting. Can function name function. Function fun applied adding constant, provided. xlab character expression; label x axis. supplied, suitable label generated object. ylab character expression; label y axis. supplied, suitable label generated object. title character expression; title plot. See ggplot2::labs(). subtitle character expression; subtitle plot. See ggplot2::labs(). caption character expression; plot caption. See ggplot2::labs(). rug evaluate_parametric_terms(), logical indicate rug plot drawn. position Position adjustment, either string, result call position adjustment function. response_range numeric; vector two values giving range response data guide. Used fix plots common scale/range. Ignored show set \"se\". ... arguments passed methods.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.evaluated_parametric_term.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot estimated parametric effects — draw.evaluated_parametric_term","text":"ggplot2::ggplot() object.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.evaluated_parametric_term.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot estimated parametric effects — draw.evaluated_parametric_term","text":"Gavin L. Simpson","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.gam.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot estimated smooths from a fitted GAM — draw.gam","title":"Plot estimated smooths from a fitted GAM — draw.gam","text":"Plots estimated smooths fitted GAM model similar way mgcv::plot.gam() instead using base graphics, ggplot2::ggplot() used instead.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.gam.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot estimated smooths from a fitted GAM — draw.gam","text":"","code":"# S3 method for class 'gam' draw( object, data = NULL, select = NULL, parametric = FALSE, terms = NULL, residuals = FALSE, scales = c(\"free\", \"fixed\"), ci_level = 0.95, n = 100, n_3d = 16, n_4d = 4, unconditional = FALSE, overall_uncertainty = TRUE, constant = NULL, fun = NULL, dist = 0.1, rug = TRUE, contour = TRUE, grouped_by = FALSE, ci_alpha = 0.2, ci_col = \"black\", smooth_col = \"black\", resid_col = \"steelblue3\", contour_col = \"black\", n_contour = NULL, partial_match = FALSE, discrete_colour = NULL, discrete_fill = NULL, continuous_colour = NULL, continuous_fill = NULL, position = \"identity\", angle = NULL, ncol = NULL, nrow = NULL, guides = \"keep\", widths = NULL, heights = NULL, crs = NULL, default_crs = NULL, lims_method = \"cross\", wrap = TRUE, caption = TRUE, envir = environment(formula(object)), ... )"},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.gam.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot estimated smooths from a fitted GAM — draw.gam","text":"object fitted GAM, result call mgcv::gam(). data optional data frame used supply data smooths evaluated plotted. usually needed, option need fine control exactly data used plotting. select character, logical, numeric; smooths plot. NULL, default, model smooths drawn. Numeric select indexes smooths order specified formula stored object. Character select matches labels smooths shown example output summary(object). Logical select operates per numeric select order smooths stored. parametric logical; plot parametric terms also? Note select used selecting smooths plot. terms argument used select parametric effects plotted. default, mgcv::plot.gam(), draw parametric effects. terms character; model parametric terms drawn? Default NULL plot parametric terms can drawn. residuals logical; partial residuals smooth drawn? Ignored anything simple univariate smooth. scales character; univariate smooths plotted y-axis scale? scales = \"free\", default, univariate smooth y-axis scale. scales = \"fixed\", common y axis scale used univariate smooths. Currently affect y-axis scale plots parametric terms. ci_level numeric 0 1; coverage credible interval. n numeric; number points range covariate evaluate smooth. n_3d numeric; number new observations generate third dimension 3D smooth. n_4d numeric; number new observations generate dimensions higher 2 (!) kD smooth (k >= 4). example, smooth 4D smooth, dimensions 3 4 get n_4d new observations. unconditional logical; confidence intervals include uncertainty due smoothness selection? TRUE, corrected Bayesian covariance matrix used. overall_uncertainty logical; uncertainty model constant term included standard error evaluate values smooth? constant numeric; constant add estimated values smooth. constant, supplied, added estimated value confidence band computed. fun function; function applied estimated values confidence interval plotting. Can function name function. Function fun applied adding constant, provided. dist numeric; greater 0, used determine location far data plotted plotting 2-D smooths. data scaled unit square deciding exclude, dist distance within unit square. See mgcv::exclude..far() details. rug logical; draw rug plot bottom plot 1-D smooths plot locations data higher dimensions. contour logical; contours draw plot using ggplot2::geom_contour(). grouped_by logical; factor smooths drawn one panel per level factor (FALSE, default), individual smooths combined single panel containing levels (TRUE)? ci_alpha numeric; alpha transparency confidence simultaneous interval. ci_col colour specification confidence/credible intervals band. Affects fill interval. smooth_col colour specification smooth line. resid_col colour specification partial residuals. contour_col colour specification contour lines. n_contour numeric; number contour bins. result n_contour - 1 contour lines drawn. See ggplot2::geom_contour(). partial_match logical; smooths selected partial matches select? TRUE, select can single string match . discrete_colour suitable colour scale used plotting discrete variables. discrete_fill suitable fill scale used plotting discrete variables. continuous_colour suitable colour scale used plotting continuous variables. continuous_fill suitable fill scale used plotting continuous variables. position Position adjustment, either string, result call position adjustment function. angle numeric; angle x axis tick labels drawn passed angle argument ggplot2::guide_axis(). ncol, nrow numeric; numbers rows columns spread plots guides character; one \"keep\" (default), \"collect\", \"auto\". Passed patchwork::plot_layout() widths, heights relative widths heights column row grid. get repeated match dimensions grid. 1 plot widths = NULL, value widths set internally widths = 1 accommodate plots smooths use fixed aspect ratio. crs coordinate reference system (CRS) use plot. data projected CRS. See ggplot2::coord_sf() details. default_crs coordinate reference system (CRS) use non-sf layers plot. left default NULL, CRS used 4326 (WGS84), appropriate spline---sphere smooths, parameterized terms latitude longitude coordinates. See ggplot2::coord_sf() details. lims_method character; affects axis limits determined. See ggplot2::coord_sf(). careful; testing examples, changing \"orthogonal\" example chlorophyll-example Simon Wood's GAM book quickly used RAM test system OS killed R. incorrect usage part; right now grid points SOS smooths evaluated (supplied user) can produce invalid coordinates corners tiles grid generated tile centres without respect spacing tiles. wrap logical; wrap plots patchwork? FALSE, list ggplot objects returned, 1 per term plotted. caption logical; show smooth type caption plot? envir environment look data within. ... additional arguments passed patchwork::wrap_plots().","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.gam.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot estimated smooths from a fitted GAM — draw.gam","text":"object returned created patchwork::wrap_plots().","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.gam.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Plot estimated smooths from a fitted GAM — draw.gam","text":"Internally, plots smooth created using ggplot2::ggplot() composed single plot using patchwork::wrap_plots(). result, possible use + add plots way one might typically work ggplot() plots. Instead, use & operator; see examples.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.gam.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot estimated smooths from a fitted GAM — draw.gam","text":"Gavin L. Simpson","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.gam.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot estimated smooths from a fitted GAM — draw.gam","text":"","code":"load_mgcv() # simulate some data df1 <- data_sim(\"eg1\", n = 400, dist = \"normal\", scale = 2, seed = 2) # fit GAM m1 <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = df1, method = \"REML\") # plot all smooths draw(m1) # can add partial residuals draw(m1, residuals = TRUE) df2 <- data_sim(\"eg2\", n = 1000, dist = \"normal\", scale = 1, seed = 2) m2 <- gam(y ~ s(x, z, k = 40), data = df2, method = \"REML\") draw(m2, contour = FALSE, n = 50) # See https://gavinsimpson.github.io/gratia/articles/custom-plotting.html # for more examples and for details on how to modify the theme of all the # plots produced by draw(). To modify all panels, for example to change the # theme, use the & operator"},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.gamlss.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot smooths of a GAMLSS model estimated by GJRM::gamlss — draw.gamlss","title":"Plot smooths of a GAMLSS model estimated by GJRM::gamlss — draw.gamlss","text":"Provides draw() method GAMLSS (distributional GAMs) fitted GJRM::gamlss().","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.gamlss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot smooths of a GAMLSS model estimated by GJRM::gamlss — draw.gamlss","text":"","code":"# S3 method for class 'gamlss' draw( object, scales = c(\"free\", \"fixed\"), ncol = NULL, nrow = NULL, guides = \"keep\", widths = NULL, heights = NULL, ... )"},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.gamlss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot smooths of a GAMLSS model estimated by GJRM::gamlss — draw.gamlss","text":"object model, fitted GJRM::gamlss() scales character; univariate smooths plotted y-axis scale? scales = \"free\", default, univariate smooth y-axis scale. scales = \"fixed\", common y axis scale used univariate smooths. Currently affect y-axis scale plots parametric terms. ncol, nrow numeric; numbers rows columns spread plots guides character; one \"keep\" (default), \"collect\", \"auto\". Passed patchwork::plot_layout() widths, heights relative widths heights column row grid. get repeated match dimensions grid. 1 plot widths = NULL, value widths set internally widths = 1 accommodate plots smooths use fixed aspect ratio. ... arguments passed draw.gam()","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.gamlss.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Plot smooths of a GAMLSS model estimated by GJRM::gamlss — draw.gamlss","text":"Plots smooths labelled linear predictor belong.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.gamlss.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot smooths of a GAMLSS model estimated by GJRM::gamlss — draw.gamlss","text":"","code":"if (require(\"GJRM\", quietly = TRUE)) { # follow example from ?GJRM::gamlss load_mgcv() suppressPackageStartupMessages(library(\"GJRM\")) set.seed(0) n <- 100 x1 <- round(runif(n)) x2 <- runif(n) x3 <- runif(n) f1 <- function(x) cos(pi * 2 * x) + sin(pi * x) y1 <- -1.55 + 2 * x1 + f1(x2) + rnorm(n) dataSim <- data.frame(y1, x1, x2, x3) eq_mu <- y1 ~ x1 + s(x2) eq_s <- ~ s(x3, k = 6) fl <- list(eq_mu, eq_s) m <- gamlss(fl, data = dataSim) draw(m) }"},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.html","id":null,"dir":"Reference","previous_headings":"","what":"Generic plotting via ggplot2 — draw","title":"Generic plotting via ggplot2 — draw","text":"Generic plotting via ggplot2","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generic plotting via ggplot2 — draw","text":"","code":"draw(object, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generic plotting via ggplot2 — draw","text":"object R object plot. ... arguments passed methods.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generic plotting via ggplot2 — draw","text":"ggplot2::ggplot() object.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generic plotting via ggplot2 — draw","text":"Generic function plotting R objects uses ggplot2 package.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Generic plotting via ggplot2 — draw","text":"Gavin L. Simpson","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.mgcv_smooth.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot basis functions — draw.mgcv_smooth","title":"Plot basis functions — draw.mgcv_smooth","text":"Plots basis functions using ggplot2","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.mgcv_smooth.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot basis functions — draw.mgcv_smooth","text":"","code":"# S3 method for class 'mgcv_smooth' draw( object, legend = FALSE, use_facets = TRUE, labeller = NULL, xlab, ylab, title = NULL, subtitle = NULL, caption = NULL, angle = NULL, ... )"},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.mgcv_smooth.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot basis functions — draw.mgcv_smooth","text":"object object, result call basis(). legend logical; legend drawn indicate basis functions? use_facets logical; factor smooths, use facets show basis functions level factor? FALSE, separate ggplot object created level combined using patchwork::wrap_plots(). Currently ignored. labeller labeller function label facets. default use ggplot2::label_both(). xlab character expression; label x axis. supplied, suitable label generated object. ylab character expression; label y axis. supplied, suitable label generated object. title character expression; title plot. See ggplot2::labs(). subtitle character expression; subtitle plot. See ggplot2::labs(). caption character expression; plot caption. See ggplot2::labs(). angle numeric; angle x axis tick labels drawn passed angle argument ggplot2::guide_axis(). ... arguments passed methods. used method.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.mgcv_smooth.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot basis functions — draw.mgcv_smooth","text":"ggplot2::ggplot() object.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.mgcv_smooth.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot basis functions — draw.mgcv_smooth","text":"Gavin L. Simpson","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.mgcv_smooth.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot basis functions — draw.mgcv_smooth","text":"","code":"load_mgcv() df <- data_sim(\"eg4\", n = 400, seed = 42) bf <- basis(s(x0), data = df) draw(bf) bf <- basis(s(x2, by = fac, bs = \"bs\"), data = df) draw(bf)"},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.pairwise_concurvity.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot concurvity measures — draw.pairwise_concurvity","title":"Plot concurvity measures — draw.pairwise_concurvity","text":"Plot concurvity measures","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.pairwise_concurvity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot concurvity measures — draw.pairwise_concurvity","text":"","code":"# S3 method for class 'pairwise_concurvity' draw( object, title = \"Smooth-wise concurvity\", subtitle = NULL, caption = NULL, x_lab = \"Term\", y_lab = \"With\", fill_lab = \"Concurvity\", continuous_colour = NULL, ... ) # S3 method for class 'overall_concurvity' draw( object, title = \"Overall concurvity\", subtitle = NULL, caption = NULL, y_lab = \"Concurvity\", x_lab = NULL, bar_col = \"steelblue\", bar_fill = \"steelblue\", ... )"},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.pairwise_concurvity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot concurvity measures — draw.pairwise_concurvity","text":"object object inheriting class \"concurvity\", usually result call model_concurvity() abbreviated form concrvity(). title character; plot title. subtitle character; plot subtitle. caption character; plot caption x_lab character; label x axis. y_lab character; label y axis. fill_lab character; label use fill guide. continuous_colour function; continuous colour (fill) scale use. ... arguments passed methods. bar_col colour specification bar colour. bar_fill colour specification bar fill","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.parametric_effects.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot estimated effects for model parametric terms — draw.parametric_effects","title":"Plot estimated effects for model parametric terms — draw.parametric_effects","text":"Plot estimated effects model parametric terms","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.parametric_effects.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot estimated effects for model parametric terms — draw.parametric_effects","text":"","code":"# S3 method for class 'parametric_effects' draw( object, scales = c(\"free\", \"fixed\"), ci_level = 0.95, ci_col = \"black\", ci_alpha = 0.2, line_col = \"black\", constant = NULL, fun = NULL, rug = TRUE, position = \"identity\", angle = NULL, ..., ncol = NULL, nrow = NULL, guides = \"keep\" )"},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.parametric_effects.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot estimated effects for model parametric terms — draw.parametric_effects","text":"object fitted GAM, result call mgcv::gam(). scales character; univariate smooths plotted y-axis scale? scales = \"free\", default, univariate smooth y-axis scale. scales = \"fixed\", common y axis scale used univariate smooths. Currently affect y-axis scale plots parametric terms. ci_level numeric 0 1; coverage credible interval. ci_col colour specification confidence/credible intervals band. Affects fill interval. ci_alpha numeric; alpha transparency confidence simultaneous interval. line_col colour specification used regression lines linear continuous terms. constant numeric; constant add estimated values smooth. constant, supplied, added estimated value confidence band computed. fun function; function applied estimated values confidence interval plotting. Can function name function. Function fun applied adding constant, provided. rug logical; draw rug plot bottom plot 1-D smooths plot locations data higher dimensions. position Position adjustment, either string, result call position adjustment function. angle numeric; angle x axis tick labels drawn passed angle argument ggplot2::guide_axis(). ... additional arguments passed patchwork::wrap_plots(). ncol, nrow numeric; numbers rows columns spread plots guides character; one \"keep\" (default), \"collect\", \"auto\". Passed patchwork::plot_layout()","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.penalty_df.html","id":null,"dir":"Reference","previous_headings":"","what":"Display penalty matrices of smooths using ggplot — draw.penalty_df","title":"Display penalty matrices of smooths using ggplot — draw.penalty_df","text":"Displays penalty matrices smooths heatmap using ggplot","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.penalty_df.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Display penalty matrices of smooths using ggplot — draw.penalty_df","text":"","code":"# S3 method for class 'penalty_df' draw( object, normalize = FALSE, as_matrix = TRUE, continuous_fill = NULL, xlab = NULL, ylab = NULL, title = NULL, subtitle = NULL, caption = NULL, ncol = NULL, nrow = NULL, guides = \"keep\", ... )"},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.penalty_df.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Display penalty matrices of smooths using ggplot — draw.penalty_df","text":"object fitted GAM, result call mgcv::gam(). normalize logical; normalize penalty range -1, 1? as_matrix logical; plotted penalty matrix oriented? TRUE row 1, column 1 penalty matrix draw upper left, whereas, FALSE drawn lower left plot. continuous_fill suitable fill scale used plotting continuous variables. xlab character expression; label x axis. supplied, axis label drawn. May vector, one per penalty. ylab character expression; label y axis. supplied, axis label drawn. May vector, one per penalty. title character expression; title plot. See ggplot2::labs(). May vector, one per penalty. subtitle character expression; subtitle plot. See ggplot2::labs(). May vector, one per penalty. caption character expression; plot caption. See ggplot2::labs(). May vector, one per penalty. ncol, nrow numeric; numbers rows columns spread plots. guides character; one \"keep\" (default), \"collect\", \"auto\". Passed patchwork::plot_layout() ... additional arguments passed patchwork::wrap_plots().","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.penalty_df.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Display penalty matrices of smooths using ggplot — draw.penalty_df","text":"","code":"load_mgcv() dat <- data_sim(\"eg4\", n = 400, seed = 42) m <- gam(y ~ s(x0) + s(x1, bs = \"cr\") + s(x2, bs = \"bs\", by = fac), data = dat, method = \"REML\" ) ## produce a multi-panel plot of all penalties draw(penalty(m)) # for a specific smooth draw(penalty(m, select = \"s(x2):fac1\"))"},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.rootogram.html","id":null,"dir":"Reference","previous_headings":"","what":"Draw a rootogram — draw.rootogram","title":"Draw a rootogram — draw.rootogram","text":"rootogram model diagnostic tool assesses goodness fit statistical model. observed values response compared expected fitted model. discrete, count responses, frequency count (0, 1, 2, etc) observed data expected conditional distribution response implied model compared. continuous variables, observed expected frequencies obtained grouping data bins. rootogram drawn using ggplot2::ggplot() graphics. design closely follows Kleiber & Zeileis (2016).","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.rootogram.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Draw a rootogram — draw.rootogram","text":"","code":"# S3 method for class 'rootogram' draw( object, type = c(\"hanging\", \"standing\", \"suspended\"), sqrt = TRUE, ref_line = TRUE, warn_limits = TRUE, fitted_colour = \"steelblue\", bar_colour = NA, bar_fill = \"grey\", ref_line_colour = \"black\", warn_line_colour = \"black\", ylab = NULL, xlab = NULL, ... )"},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.rootogram.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Draw a rootogram — draw.rootogram","text":"object R object plot. type character; type rootogram draw. sqrt logical; show observed fitted frequencies ref_line logical; draw reference line zero? warn_limits logical; draw Tukey's warning limit lines +/- 1? fitted_colour, bar_colour, bar_fill, ref_line_colour, warn_line_colour colours used draw respective element rootogram. xlab, ylab character; labels x y axis rootogram. May missing (NULL), case suitable labels used. ' ... arguments passed methods.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.rootogram.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Draw a rootogram — draw.rootogram","text":"'ggplot' object.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.rootogram.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Draw a rootogram — draw.rootogram","text":"Kleiber, C., Zeileis, ., (2016) Visualizing Count Data Regressions Using Rootograms. . Stat. 70, 296–303. doi:10.1080/00031305.2016.1173590","code":""},{"path":[]},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.rootogram.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Draw a rootogram — draw.rootogram","text":"","code":"load_mgcv() df <- data_sim(\"eg1\", n = 1000, dist = \"poisson\", scale = 0.1, seed = 6) # A poisson example m <- gam(y ~ s(x0, bs = \"cr\") + s(x1, bs = \"cr\") + s(x2, bs = \"cr\") + s(x3, bs = \"cr\"), family = poisson(), data = df, method = \"REML\") rg <- rootogram(m) # plot the rootogram draw(rg) # change the type of rootogram draw(rg, type = \"suspended\")"},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.smooth_estimates.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot the result of a call to smooth_estimates() — draw.smooth_estimates","title":"Plot the result of a call to smooth_estimates() — draw.smooth_estimates","text":"Plot result call smooth_estimates()","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.smooth_estimates.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot the result of a call to smooth_estimates() — draw.smooth_estimates","text":"","code":"# S3 method for class 'smooth_estimates' draw( object, constant = NULL, fun = NULL, contour = TRUE, grouped_by = FALSE, contour_col = \"black\", n_contour = NULL, ci_alpha = 0.2, ci_col = \"black\", smooth_col = \"black\", resid_col = \"steelblue3\", decrease_col = \"#56B4E9\", increase_col = \"#E69F00\", change_lwd = 1.75, partial_match = FALSE, discrete_colour = NULL, discrete_fill = NULL, continuous_colour = NULL, continuous_fill = NULL, angle = NULL, ylim = NULL, crs = NULL, default_crs = NULL, lims_method = \"cross\", caption = TRUE, ... )"},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.smooth_estimates.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot the result of a call to smooth_estimates() — draw.smooth_estimates","text":"object fitted GAM, result call mgcv::gam(). constant numeric; constant add estimated values smooth. constant, supplied, added estimated value confidence band computed. fun function; function applied estimated values confidence interval plotting. Can function name function. Function fun applied adding constant, provided. contour logical; contours draw plot using ggplot2::geom_contour(). grouped_by logical; factor smooths drawn one panel per level factor (FALSE, default), individual smooths combined single panel containing levels (TRUE)? contour_col colour specification contour lines. n_contour numeric; number contour bins. result n_contour - 1 contour lines drawn. See ggplot2::geom_contour(). ci_alpha numeric; alpha transparency confidence simultaneous interval. ci_col colour specification confidence/credible intervals band. Affects fill interval. smooth_col colour specification smooth line. resid_col colour specification partial residuals. decrease_col, increase_col colour specifications use indicating periods change. col_change used change_type = \"change\", col_decrease col_increase used `change_type = \"sizer\"“. change_lwd numeric; value set linewidth ggplot2::geom_line(), used represent periods change. partial_match logical; smooths selected partial matches select? TRUE, select can single string match . discrete_colour suitable colour scale used plotting discrete variables. discrete_fill suitable fill scale used plotting discrete variables. continuous_colour suitable colour scale used plotting continuous variables. continuous_fill suitable fill scale used plotting continuous variables. angle numeric; angle x axis tick labels drawn passed angle argument ggplot2::guide_axis(). ylim numeric; vector y axis limits use panels drawn. crs coordinate reference system (CRS) use plot. data projected CRS. See ggplot2::coord_sf() details. default_crs coordinate reference system (CRS) use non-sf layers plot. left default NULL, CRS used 4326 (WGS84), appropriate spline---sphere smooths, parameterized terms latitude longitude coordinates. See ggplot2::coord_sf() details. lims_method character; affects axis limits determined. See ggplot2::coord_sf(). careful; testing examples, changing \"orthogonal\" example chlorophyll-example Simon Wood's GAM book quickly used RAM test system OS killed R. incorrect usage part; right now grid points SOS smooths evaluated (supplied user) can produce invalid coordinates corners tiles grid generated tile centres without respect spacing tiles. caption logical; show smooth type caption plot? ... additional arguments passed patchwork::wrap_plots().","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.smooth_estimates.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot the result of a call to smooth_estimates() — draw.smooth_estimates","text":"","code":"load_mgcv() # example data df <- data_sim(\"eg1\", seed = 21) # fit GAM m <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = df, method = \"REML\") # plot all of the estimated smooths sm <- smooth_estimates(m) draw(sm) # evaluate smooth of `x2` sm <- smooth_estimates(m, select = \"s(x2)\") # plot it draw(sm) # customising some plot elements draw(sm, ci_col = \"steelblue\", smooth_col = \"forestgreen\", ci_alpha = 0.3) # Add a constant to the plotted smooth draw(sm, constant = coef(m)[1]) # Adding change indicators to smooths based on derivatives of the smooth d <- derivatives(m, n = 100) # n to match smooth_estimates() smooth_estimates(m) |> add_sizer(derivatives = d, type = \"sizer\") |> draw()"},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.smooth_samples.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot posterior smooths — draw.smooth_samples","title":"Plot posterior smooths — draw.smooth_samples","text":"Plot posterior smooths","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.smooth_samples.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot posterior smooths — draw.smooth_samples","text":"","code":"# S3 method for class 'smooth_samples' draw( object, select = NULL, n_samples = NULL, seed = NULL, xlab = NULL, ylab = NULL, title = NULL, subtitle = NULL, caption = NULL, alpha = 1, colour = \"black\", contour = FALSE, contour_col = \"black\", n_contour = NULL, scales = c(\"free\", \"fixed\"), rug = TRUE, partial_match = FALSE, angle = NULL, ncol = NULL, nrow = NULL, guides = \"keep\", ... )"},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.smooth_samples.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot posterior smooths — draw.smooth_samples","text":"object fitted GAM, result call mgcv::gam(). select character, logical, numeric; smooths plot. NULL, default, model smooths drawn. Numeric select indexes smooths order specified formula stored object. Character select matches labels smooths shown example output summary(object). Logical select operates per numeric select order smooths stored. n_samples numeric; NULL, sample n_samples posterior draws plotting. seed numeric; random seed used sampling draws. xlab character expression; label x axis. supplied, suitable label generated object. ylab character expression; label y axis. supplied, suitable label generated object. title character expression; title plot. See ggplot2::labs(). subtitle character expression; subtitle plot. See ggplot2::labs(). caption character expression; plot caption. See ggplot2::labs(). alpha numeric; alpha transparency confidence simultaneous interval. colour colour use draw posterior smooths. Passed ggplot2::geom_line() argument colour. contour logical; contour lines added smooth surfaces? contour_col colour specification contour lines. n_contour numeric; number contour bins. result n_contour - 1 contour lines drawn. See ggplot2::geom_contour(). scales character; univariate smooths plotted y-axis scale? scales = \"free\", default, univariate smooth y-axis scale. scales = \"fixed\", common y axis scale used univariate smooths. Currently affect y-axis scale plots parametric terms. rug logical; draw rug plot bottom plot 1-D smooths plot locations data higher dimensions. partial_match logical; smooths selected partial matches select? TRUE, select can single string match . angle numeric; angle x axis tick labels drawn passed angle argument ggplot2::guide_axis(). ncol, nrow numeric; numbers rows columns spread plots guides character; one \"keep\" (default), \"collect\", \"auto\". Passed patchwork::plot_layout() ... arguments passed patchwork::wrap_plots().","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.smooth_samples.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot posterior smooths — draw.smooth_samples","text":"Gavin L. Simpson","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.smooth_samples.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot posterior smooths — draw.smooth_samples","text":"","code":"load_mgcv() dat1 <- data_sim(\"eg1\", n = 400, dist = \"normal\", scale = 1, seed = 1) ## a single smooth GAM m1 <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = dat1, method = \"REML\") ## posterior smooths from m1 sm1 <- smooth_samples(m1, n = 15, seed = 23478) ## plot draw(sm1, alpha = 0.7) ## plot only 5 randomly smapled draws draw(sm1, n_samples = 5, alpha = 0.7) ## A factor-by smooth example dat2 <- data_sim(\"eg4\", n = 400, dist = \"normal\", scale = 1, seed = 1) ## a multi-smooth GAM with a factor-by smooth m2 <- gam(y ~ fac + s(x2, by = fac) + s(x0), data = dat2, method = \"REML\") ## posterior smooths from m1 sm2 <- smooth_samples(m2, n = 15, seed = 23478) ## plot, this time selecting only the factor-by smooth draw(sm2, select = \"s(x2)\", partial_match = TRUE, alpha = 0.7) # \\donttest{ ## A 2D smooth example dat3 <- data_sim(\"eg2\", n = 400, dist = \"normal\", scale = 1, seed = 1) ## fit a 2D smooth m3 <- gam(y ~ te(x, z), data = dat3, method = \"REML\") ## get samples sm3 <- smooth_samples(m3, n = 10) ## plot just 6 of the draws, with contour line overlays draw(sm3, n_samples = 6, contour = TRUE, seed = 42) # }"},{"path":"https://gavinsimpson.github.io/gratia/reference/draw_parametric_effect.html","id":null,"dir":"Reference","previous_headings":"","what":"Internal function to draw an individual parametric effect — draw_parametric_effect","title":"Internal function to draw an individual parametric effect — draw_parametric_effect","text":"Internal function draw individual parametric effect","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw_parametric_effect.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Internal function to draw an individual parametric effect — draw_parametric_effect","text":"","code":"draw_parametric_effect( object, ci_level = 0.95, ci_col = \"black\", ci_alpha = 0.2, line_col = \"black\", constant = NULL, fun = NULL, xlab = NULL, ylab = NULL, title = NULL, subtitle = NULL, caption = NULL, rug = TRUE, position = \"identity\", ylim = NULL, angle = NULL, factor_levels = NULL, ... )"},{"path":"https://gavinsimpson.github.io/gratia/reference/draw_parametric_effect.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Internal function to draw an individual parametric effect — draw_parametric_effect","text":"object fitted GAM, result call mgcv::gam(). ci_level numeric 0 1; coverage credible interval. ci_col colour specification confidence/credible intervals band. Affects fill interval. ci_alpha numeric; alpha transparency confidence simultaneous interval. constant numeric; constant add estimated values smooth. constant, supplied, added estimated value confidence band computed. fun function; function applied estimated values confidence interval plotting. Can function name function. Function fun applied adding constant, provided. xlab character expression; label x axis. supplied, suitable label generated object. ylab character expression; label y axis. supplied, suitable label generated object. title character expression; title plot. See ggplot2::labs(). subtitle character expression; subtitle plot. See ggplot2::labs(). caption character expression; plot caption. See ggplot2::labs(). rug logical; draw rug plot bottom plot 1-D smooths plot locations data higher dimensions. position Position adjustment, either string, result call position adjustment function. angle numeric; angle x axis tick labels drawn passed angle argument ggplot2::guide_axis(). factor_levels list; named list factor levels ... additional arguments passed patchwork::wrap_plots().","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/edf.html","id":null,"dir":"Reference","previous_headings":"","what":"Effective degrees of freedom for smooths and GAMs — edf","title":"Effective degrees of freedom for smooths and GAMs — edf","text":"Extracts effective degrees freedom (EDF) model smooth terms overall EDF fitted GAMs","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/edf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Effective degrees of freedom for smooths and GAMs — edf","text":"","code":"edf(object, ...) # S3 method for class 'gam' edf( object, select = NULL, smooth = deprecated(), type = c(\"default\", \"unconditional\", \"alternative\"), partial_match = FALSE, ... ) model_edf(object, ..., type = c(\"default\", \"unconditional\", \"alternative\"))"},{"path":"https://gavinsimpson.github.io/gratia/reference/edf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Effective degrees of freedom for smooths and GAMs — edf","text":"object fitted model extract smooth-specific EDFs. ... arguments passed methods. select character, logical, numeric; smooths plot. NULL, default, model smooths drawn. Numeric select indexes smooths order specified formula stored object. Character select matches labels smooths shown example output summary(object). Logical select operates per numeric select order smooths stored. smooth Use select instead. extracted. NULL, default, EDFs smooths returned. type character: type EDF return. \"default\" returns standard EDF; \"unconditional\" selects EDF corrected smoothness parameter selection, available; \"alternative\" returns alternative formulation EDF Wood (2017, pp. 252) partial_match logical; smooths selected partial matches select? TRUE, select can single string match .","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/edf.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Effective degrees of freedom for smooths and GAMs — edf","text":"Multiple formulations effective degrees freedom available. additional uncertainty due selection smoothness parameters can taken account computing EDF smooths. form EDF available type = \"unconditional\". Wood (2017; pp. 252) describes alternative EDF model $$\\mathrm{EDF} = 2\\mathrm{tr}(\\mathbf{F}) - \\mathrm{tr}(\\mathbf{FF}),$$ \\(\\mathrm{tr}\\) matrix trace \\(\\mathbf{F}\\) matrix mapping un-penalized coefficient estimates penalized coefficient estimates. trace \\(\\mathbf{F}\\) effectively average shrinkage coefficients multipled number coefficients (Wood, 2017). Smooth-specific EDFs obtained summing relevent elements \\(\\mathrm{diag}(2\\mathbf{F} - \\mathbf{FF})\\).","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/edf.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Effective degrees of freedom for smooths and GAMs — edf","text":"","code":"load_mgcv() df <- data_sim(\"eg1\", n = 400, seed = 42) m <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = df, method = \"REML\") # extract the EDFs for all smooths edf(m) #> # A tibble: 4 x 2 #> .smooth .edf #> #> 1 s(x0) 3.4248 #> 2 s(x1) 3.2213 #> 3 s(x2) 7.9049 #> 4 s(x3) 1.8847 # or selected smooths edf(m, select = c(\"s(x0)\", \"s(x2)\")) #> # A tibble: 2 x 2 #> .smooth .edf #> #> 1 s(x0) 3.4248 #> 2 s(x2) 7.9049 # accounting for smoothness parameter uncertainty edf(m, type = \"unconditional\") #> # A tibble: 4 x 2 #> .smooth .edf #> #> 1 s(x0) 3.7697 #> 2 s(x1) 3.8728 #> 3 s(x2) 8.0670 #> 4 s(x3) 2.8834 # over EDF of the model, including the intercept model_edf(m) #> # A tibble: 1 x 2 #> .model .edf #> #> 1 m 17.436 # can get model EDF for multiple models m2 <- gam(y ~ s(x0) + s(x1) + s(x3), data = df, method = \"REML\") model_edf(m, m2) #> # A tibble: 2 x 2 #> .model .edf #> #> 1 m 17.436 #> 2 m2 7.5777"},{"path":"https://gavinsimpson.github.io/gratia/reference/eval_smooth.html","id":null,"dir":"Reference","previous_headings":"","what":"S3 methods to evaluate individual smooths — eval_smooth","title":"S3 methods to evaluate individual smooths — eval_smooth","text":"S3 methods evaluate individual smooths","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/eval_smooth.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"S3 methods to evaluate individual smooths — eval_smooth","text":"","code":"eval_smooth(smooth, ...) # S3 method for class 'mgcv.smooth' eval_smooth( smooth, model, n = 100, n_3d = NULL, n_4d = NULL, data = NULL, unconditional = FALSE, overall_uncertainty = TRUE, dist = NULL, ... ) # S3 method for class 'soap.film' eval_smooth( smooth, model, n = 100, n_3d = NULL, n_4d = NULL, data = NULL, unconditional = FALSE, overall_uncertainty = TRUE, ... ) # S3 method for class 'scam_smooth' eval_smooth( smooth, model, n = 100, n_3d = NULL, n_4d = NULL, data = NULL, unconditional = FALSE, overall_uncertainty = TRUE, dist = NULL, ... ) # S3 method for class 'fs.interaction' eval_smooth( smooth, model, n = 100, data = NULL, unconditional = FALSE, overall_uncertainty = TRUE, ... ) # S3 method for class 'sz.interaction' eval_smooth( smooth, model, n = 100, data = NULL, unconditional = FALSE, overall_uncertainty = TRUE, ... ) # S3 method for class 'random.effect' eval_smooth( smooth, model, n = 100, data = NULL, unconditional = FALSE, overall_uncertainty = TRUE, ... ) # S3 method for class 'mrf.smooth' eval_smooth( smooth, model, n = 100, data = NULL, unconditional = FALSE, overall_uncertainty = TRUE, ... ) # S3 method for class 't2.smooth' eval_smooth( smooth, model, n = 100, n_3d = NULL, n_4d = NULL, data = NULL, unconditional = FALSE, overall_uncertainty = TRUE, dist = NULL, ... ) # S3 method for class 'tensor.smooth' eval_smooth( smooth, model, n = 100, n_3d = NULL, n_4d = NULL, data = NULL, unconditional = FALSE, overall_uncertainty = TRUE, dist = NULL, ... )"},{"path":"https://gavinsimpson.github.io/gratia/reference/eval_smooth.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"S3 methods to evaluate individual smooths — eval_smooth","text":"smooth currently object inherits class mgcv.smooth. ... arguments passed methods model fitted model; currently mgcv::gam() mgcv::bam() models suported. n numeric; number points range covariate evaluate smooth. n_3d, n_4d numeric; number points range last covariate 3D 4D smooth. default NULL achieves standard behaviour using n points range covariate, resulting n^d evaluation points, d dimension smooth. d > 2 can result many evaluation points slow performance. smooths d > 4, value n_4d used dimensions > 4, unless NULL, case default behaviour (using n dimensions) observed. data optional data frame values evaluate smooth . unconditional logical; confidence intervals include uncertainty due smoothness selection? TRUE, corrected Bayesian covariance matrix used. overall_uncertainty logical; uncertainty model constant term included standard error evaluate values smooth? dist numeric; greater 0, used determine location far data plotted plotting 2-D smooths. data scaled unit square deciding exclude, dist distance within unit square. See mgcv::exclude..far() details.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/evaluate_parametric_term.html","id":null,"dir":"Reference","previous_headings":"","what":"Evaluate parametric model terms — evaluate_parametric_term","title":"Evaluate parametric model terms — evaluate_parametric_term","text":"Returns values parametric model terms values factor terms grid covariate values linear parametric terms. function now deprecated favour parametric_effects().","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/evaluate_parametric_term.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Evaluate parametric model terms — evaluate_parametric_term","text":"","code":"evaluate_parametric_term(object, ...) # S3 method for class 'gam' evaluate_parametric_term(object, term, unconditional = FALSE, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/evaluate_parametric_term.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Evaluate parametric model terms — evaluate_parametric_term","text":"object object class \"gam\" \"gamm\". ... arguments passed methods. term character; parametric term whose effects evaluated unconditional logical; confidence intervals include uncertainty due smoothness selection? TRUE, corrected Bayesian covariance matrix used.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/evaluate_smooth.html","id":null,"dir":"Reference","previous_headings":"","what":"Evaluate a smooth — evaluate_smooth","title":"Evaluate a smooth — evaluate_smooth","text":"Evaluate smooth grid evenly spaced value range covariate associated smooth. Alternatively, set points smooth evaluated can supplied.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/evaluate_smooth.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Evaluate a smooth — evaluate_smooth","text":"","code":"evaluate_smooth(object, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/evaluate_smooth.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Evaluate a smooth — evaluate_smooth","text":"object object class \"gam\" \"gamm\". ... arguments passed methods.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/evaluate_smooth.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Evaluate a smooth — evaluate_smooth","text":"data frame, class \"evaluated_1d_smooth\" evaluated_2d_smooth, inherit classes \"evaluated_smooth\" \"data.frame\".","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/evaluate_smooth.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Evaluate a smooth — evaluate_smooth","text":"evaluate_smooth() deprecated favour smooth_estimates(), provides cleaner way evaluate smooth range covariate values. smooth_estimates() can handle much wider range models evaluate_smooth() capable smooth_estimates() much easier extend handle new smooth types. code uses evaluate_smooth() work simply changing function call smooth_estimates(). However, differences: newdata argument becomes data","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/evenly.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a sequence of evenly-spaced values — evenly","title":"Create a sequence of evenly-spaced values — evenly","text":"continuous vector x, evenly seq_min_max() create sequence n evenly-spaced values range lower – upper. default, lower defined min(x) upper max(x), excluding NAs. factor x, function returns levels(x).","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/evenly.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a sequence of evenly-spaced values — evenly","text":"","code":"evenly(x, n = 100, by = NULL, lower = NULL, upper = NULL) seq_min_max(x, n, by = NULL, lower = NULL, upper = NULL)"},{"path":"https://gavinsimpson.github.io/gratia/reference/evenly.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a sequence of evenly-spaced values — evenly","text":"x numeric; vector evenly-spaced values returned n numeric; number evenly-spaced values return. default 100 used convenience typically used evaluating smooth. numeric; increment sequence. specified, argument n ignored sequence returned min(x) max(x) increments . lower numeric; lower bound interval. upper numeric; upper bound interval.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/evenly.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a sequence of evenly-spaced values — evenly","text":"numeric vector length n.","code":""},{"path":[]},{"path":"https://gavinsimpson.github.io/gratia/reference/evenly.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a sequence of evenly-spaced values — evenly","text":"","code":"x <- rnorm(10) n <- 10L # 10 values evenly over the range of `x` evenly(x, n = n) #> [1] -0.83562861 -0.56552757 -0.29542652 -0.02532547 0.24477557 0.51487662 #> [7] 0.78497766 1.05507871 1.32517976 1.59528080 # evenly spaced values, incrementing by 0.2 evenly(x, by = 0.2) #> [1] -0.83562861 -0.63562861 -0.43562861 -0.23562861 -0.03562861 0.16437139 #> [7] 0.36437139 0.56437139 0.76437139 0.96437139 1.16437139 1.36437139 #> [13] 1.56437139 # evenly spaced values, incrementing by 0.2, starting at -2 evenly(x, by = 0.2, lower = -2) #> [1] -2.0 -1.8 -1.6 -1.4 -1.2 -1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 #> [16] 1.0 1.2 1.4"},{"path":"https://gavinsimpson.github.io/gratia/reference/factor_combos.html","id":null,"dir":"Reference","previous_headings":"","what":"All combinations of factor levels — factor_combos","title":"All combinations of factor levels — factor_combos","text":"combinations factor levels","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/factor_combos.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"All combinations of factor levels — factor_combos","text":"","code":"factor_combos(object, ...) # S3 method for class 'gam' factor_combos(object, vars = everything(), complete = TRUE, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/factor_combos.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"All combinations of factor levels — factor_combos","text":"object fitted model object. ... arguments passed methods. vars terms include exclude returned object. Uses tidyselect principles. complete logical; combinations factor levels returned? FALSE, combinations levels observed model retained.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/family.gam.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract family objects from models — family.gam","title":"Extract family objects from models — family.gam","text":"Provides stats::family() method range GAM objects.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/family.gam.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract family objects from models — family.gam","text":"","code":"# S3 method for class 'gam' family(object, ...) # S3 method for class 'gamm' family(object, ...) # S3 method for class 'bam' family(object, ...) # S3 method for class 'list' family(object, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/family.gam.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract family objects from models — family.gam","text":"object fitted model. Models fitted mgcv::gam(), mgcv::bam(), mgcv::gamm(), gamm4::gamm4() currently supported. ... arguments passed methods.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/family_name.html","id":null,"dir":"Reference","previous_headings":"","what":"Name of family used to fit model — family_name","title":"Name of family used to fit model — family_name","text":"Extracts name family used fit supplied model.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/family_name.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Name of family used to fit model — family_name","text":"","code":"family_name(object, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/family_name.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Name of family used to fit model — family_name","text":"object R object. ... arguments passed methods.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/family_name.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Name of family used to fit model — family_name","text":"character vector containing family name.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/family_type.html","id":null,"dir":"Reference","previous_headings":"","what":"Extracts the type of family in a consistent way — family_type","title":"Extracts the type of family in a consistent way — family_type","text":"Extracts type family consistent way","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/family_type.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extracts the type of family in a consistent way — family_type","text":"","code":"family_type(object, ...) # S3 method for class 'family' family_type(object, ...) # Default S3 method family_type(object, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/family_type.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extracts the type of family in a consistent way — family_type","text":"object R object. Currently family() objects anything family() method. ... arguments passed methods.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/fderiv.html","id":null,"dir":"Reference","previous_headings":"","what":"First derivatives of fitted GAM functions — fderiv","title":"First derivatives of fitted GAM functions — fderiv","text":"function deprecated limited first order forward finite differences derivatives , improved offer needed functionality without breaking backwards compatability papers blog posts already used fderiv(). replacement, derivatives(), now available recommended new analyses.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/fderiv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"First derivatives of fitted GAM functions — fderiv","text":"","code":"fderiv(model, ...) # S3 method for class 'gam' fderiv( model, newdata, term, n = 200, eps = 1e-07, unconditional = FALSE, offset = NULL, ... ) # S3 method for class 'gamm' fderiv(model, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/fderiv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"First derivatives of fitted GAM functions — fderiv","text":"model fitted GAM. Currently models fitted mgcv::gam() mgcv::gamm() supported. ... Arguments passed methods. newdata data frame containing values model covariates evaluate first derivatives smooths. term character; vector one terms derivatives required. missing, derivatives smooth terms returned. n integer; newdata missing original data can reconstructed model n controls number values range covariate populate newdata. eps numeric; value finite difference used approximate first derivative. unconditional logical; TRUE, smoothing parameter uncertainty corrected covariance matrix used, available, otherwise uncorrected Bayesian posterior covariance matrix used. offset numeric; value offset use generating predictions.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/fderiv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"First derivatives of fitted GAM functions — fderiv","text":"object class \"fderiv\" returned.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/fderiv.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"First derivatives of fitted GAM functions — fderiv","text":"Gavin L. Simpson","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/fderiv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"First derivatives of fitted GAM functions — fderiv","text":"","code":"load_mgcv() dat <- data_sim(\"eg1\", seed = 2) mod <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = dat, method = \"REML\") ## first derivatives of all smooths... fd <- fderiv(mod) ## now use --> fd <- derivatives(mod) ## ...and a selected smooth fd2 <- fderiv(mod, term = \"x1\") ## now use --> fd2 <- derivatives(mod, select = \"s(x1)\") ## Models with factors dat <- data_sim(\"eg4\", n = 400, dist = \"normal\", scale = 2, seed = 2) mod <- gam(y ~ s(x0) + s(x1) + fac, data = dat, method = \"REML\") ## first derivatives of all smooths... fd <- fderiv(mod) ## now use --> fd <- derivatives(mod) ## ...and a selected smooth fd2 <- fderiv(mod, term = \"x1\") ## now use --> fd2 <- derivatives(mod, select = \"s(x1)\")"},{"path":"https://gavinsimpson.github.io/gratia/reference/fitted_samples.html","id":null,"dir":"Reference","previous_headings":"","what":"Draw fitted values from the posterior distribution — fitted_samples","title":"Draw fitted values from the posterior distribution — fitted_samples","text":"Expectations (fitted values) response drawn posterior distribution fitted model using Gaussian approximation posterior simple Metropolis Hastings sampler.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/fitted_samples.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Draw fitted values from the posterior distribution — fitted_samples","text":"","code":"fitted_samples(model, ...) # S3 method for class 'gam' fitted_samples( model, n = 1, data = newdata, seed = NULL, scale = c(\"response\", \"linear_predictor\"), method = c(\"gaussian\", \"mh\", \"inla\", \"user\"), n_cores = 1, burnin = 1000, thin = 1, t_df = 40, rw_scale = 0.25, freq = FALSE, unconditional = FALSE, draws = NULL, mvn_method = c(\"mvnfast\", \"mgcv\"), ..., newdata = NULL, ncores = NULL )"},{"path":"https://gavinsimpson.github.io/gratia/reference/fitted_samples.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Draw fitted values from the posterior distribution — fitted_samples","text":"model fitted model supported types ... arguments passed methods. fitted_samples(), passed mgcv::predict.gam(). posterior_samples() passed fitted_samples(). predicted_samples() passed relevant simulate() method. n numeric; number posterior samples return. data data frame; new observations posterior draws model evaluated. supplied, data used fit model used data, available model. seed numeric; random seed simulations. scale character; scale fitted values returned ? \"linear predictor\" synonym \"link\" prefer terminology. method character; method used draw samples posterior distribution. \"gaussian\" uses Gaussian (Laplace) approximation posterior. \"mh\" uses Metropolis Hastings sampler alternates t proposals proposals based shrunken version posterior covariance matrix. \"inla\" uses variant Integrated Nested Laplace Approximation due Wood (2019), (currently implemented). \"user\" allows user-supplied posterior draws (currently implemented). n_cores number cores generating random variables multivariate normal distribution. Passed mvnfast::rmvn(). Parallelization take place OpenMP supported (appears work Windows current R). burnin numeric; number samples discard burnin draws. used method = \"mh\". thin numeric; number samples skip taking n draws. Results thin * n draws posterior taken. used method = \"mh\". t_df numeric; degrees freedom t distribution proposals. used method = \"mh\". rw_scale numeric; Factor scale posterior covariance matrix generating random walk proposals. Negative non finite skip random walk step. used method = \"mh\". freq logical; TRUE use frequentist covariance matrix parameter estimators, FALSE use Bayesian posterior covariance matrix parameters. unconditional logical; TRUE (freq == FALSE) Bayesian smoothing parameter uncertainty corrected covariance matrix used, available. draws matrix; user supplied posterior draws used method = \"user\". mvn_method character; one \"mvnfast\" \"mgcv\". default uses mvnfast::rmvn(), can considerably faster generate large numbers MVN random values mgcv::rmvn(), might work marginal fits, covariance matrix close singular. newdata Deprecated: use data instead. ncores Deprecated; use n_cores instead. number cores generating random variables multivariate normal distribution. Passed mvnfast::rmvn(). Parallelization take place OpenMP supported (appears work Windows current R).","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/fitted_samples.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Draw fitted values from the posterior distribution — fitted_samples","text":"tibble (data frame) 3 columns containing posterior predicted values long format. columns row (integer) row data posterior draw relates , draw (integer) index, range 1:n, indicating draw row relates , response (numeric) predicted response indicated row data.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/fitted_samples.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Draw fitted values from the posterior distribution — fitted_samples","text":"Models offset terms supplied via offset argument mgcv::gam() etc. ignored mgcv::predict.gam(). , kind offset term also ignored posterior_samples(). Offset terms included model formula supplied mgcv::gam() etc ignored posterior samples produced reflect offset term values. side effect requiring new data values provided posterior_samples() via data argument must include offset variable.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/fitted_samples.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Draw fitted values from the posterior distribution — fitted_samples","text":"Wood, S.N., (2020). Simplified integrated nested Laplace approximation. Biometrika 107, 223–230. doi:10.1093/biomet/asz044","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/fitted_samples.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Draw fitted values from the posterior distribution — fitted_samples","text":"Gavin L. Simpson","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/fitted_samples.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Draw fitted values from the posterior distribution — fitted_samples","text":"","code":"load_mgcv() dat <- data_sim(\"eg1\", n = 1000, dist = \"normal\", scale = 2, seed = 2) m1 <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = dat, method = \"REML\") fs <- fitted_samples(m1, n = 5, seed = 42) # \\donttest{ fs #> # A tibble: 5,000 x 4 #> .row .draw .parameter .fitted #> #> 1 1 1 location 6.34 #> 2 2 1 location 5.08 #> 3 3 1 location 6.84 #> 4 4 1 location 7.71 #> 5 5 1 location 9.23 #> 6 6 1 location 8.03 #> 7 7 1 location 6.19 #> 8 8 1 location 7.28 #> 9 9 1 location 14.0 #> 10 10 1 location 12.7 #> # i 4,990 more rows # } # can generate own set of draws and use them drws <- generate_draws(m1, n = 2, seed = 24) fs2 <- fitted_samples(m1, method = \"user\", draws = drws) # \\donttest{ fs2 #> # A tibble: 2,000 x 4 #> .row .draw .parameter .fitted #> #> 1 1 1 location 6.30 #> 2 2 1 location 5.12 #> 3 3 1 location 7.40 #> 4 4 1 location 7.42 #> 5 5 1 location 9.40 #> 6 6 1 location 8.04 #> 7 7 1 location 5.83 #> 8 8 1 location 7.30 #> 9 9 1 location 14.3 #> 10 10 1 location 13.1 #> # i 1,990 more rows # }"},{"path":"https://gavinsimpson.github.io/gratia/reference/fitted_values.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate fitted values from a estimated GAM — fitted_values","title":"Generate fitted values from a estimated GAM — fitted_values","text":"Generate fitted values estimated GAM","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/fitted_values.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate fitted values from a estimated GAM — fitted_values","text":"","code":"fitted_values(object, ...) # S3 method for class 'gam' fitted_values( object, data = NULL, scale = c(\"response\", \"link\", \"linear predictor\"), ci_level = 0.95, ... ) # S3 method for class 'gamm' fitted_values(object, ...) # S3 method for class 'scam' fitted_values(object, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/fitted_values.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate fitted values from a estimated GAM — fitted_values","text":"object fitted model. Currently models fitted mgcv::gam() mgcv::bam() supported. ... arguments passed mgcv::predict.gam(). Note type, newdata, se.fit already used passed mgcv::predict.gam(). data optional data frame covariate values fitted values returned. scale character; scale fitted values returned ? \"linear predictor\" synonym \"link\" prefer terminology. ci_level numeric; value 0 1 indicating coverage credible interval.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/fitted_values.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate fitted values from a estimated GAM — fitted_values","text":"tibble (data frame) whose first m columns contain either data used fit model (data NULL), variables supplied data. Four columns added: fitted: fitted values specified scale, se: standard error fitted values (always link scale), lower, upper: limits credible interval fitted values, specified scale. Models fitted certain families include additional variables mgcv::ocat() models: scale = \"repsonse\", returned object contain row column category column, indicate row data row returned object belongs. Additionally, nrow(data) * n_categories rows returned object; row predicted probability single category response.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/fitted_values.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Generate fitted values from a estimated GAM — fitted_values","text":"families, regardless scale fitted values returned, se component returned object link (linear predictor) scale, response scale. exception mgcv::ocat() family, se response scale scale = \"response\".","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/fitted_values.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate fitted values from a estimated GAM — fitted_values","text":"","code":"load_mgcv() sim_df <- data_sim(\"eg1\", n = 400, dist = \"normal\", scale = 2, seed = 2) m <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = sim_df, method = \"REML\") fv <- fitted_values(m) fv #> # A tibble: 400 x 9 #> .row x0 x1 x2 x3 .fitted .se .lower_ci #> #> 1 1 0.184882 0.617142 0.415244 0.132410 8.73875 0.354677 8.04360 #> 2 2 0.702374 0.569064 0.531439 0.365331 7.62581 0.337779 6.96378 #> 3 3 0.573326 0.153970 0.00324621 0.454532 3.12106 0.591862 1.96103 #> 4 4 0.168052 0.0348332 0.252100 0.537114 11.1124 0.402378 10.3237 #> 5 5 0.943839 0.997953 0.155229 0.185495 14.0533 0.452947 13.1655 #> 6 6 0.943475 0.835574 0.878840 0.449276 6.13080 0.364521 5.41635 #> 7 7 0.129159 0.586562 0.203511 0.256527 12.4838 0.355808 11.7864 #> 8 8 0.833449 0.339117 0.583528 0.618458 6.25215 0.344700 5.57655 #> 9 9 0.468019 0.166883 0.804473 0.880744 4.21463 0.372003 3.48552 #> 10 10 0.549984 0.807410 0.264717 0.317747 15.5283 0.369999 14.8031 #> # i 390 more rows #> # i 1 more variable: .upper_ci "},{"path":"https://gavinsimpson.github.io/gratia/reference/fix_offset.html","id":null,"dir":"Reference","previous_headings":"","what":"Fix the names of a data frame containing an offset variable. — fix_offset","title":"Fix the names of a data frame containing an offset variable. — fix_offset","text":"Identifies variable, , model offset, fixed name offset(foo(var)) converted var, possibly sets values variable offset_val.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/fix_offset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fix the names of a data frame containing an offset variable. — fix_offset","text":"","code":"fix_offset(model, newdata, offset_val = NULL)"},{"path":"https://gavinsimpson.github.io/gratia/reference/fix_offset.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fix the names of a data frame containing an offset variable. — fix_offset","text":"model fitted GAM. newdata data frame; new values predict . offset_val numeric, optional; provided, offset variable newdata set constant value returning newdata","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/fix_offset.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fix the names of a data frame containing an offset variable. — fix_offset","text":"original newdata returned fixed names possibly modified offset variable.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/fix_offset.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fix the names of a data frame containing an offset variable. — fix_offset","text":"Gavin L. Simpson","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/fix_offset.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fix the names of a data frame containing an offset variable. — fix_offset","text":"","code":"load_mgcv() df <- data_sim(\"eg1\", n = 400, dist = \"normal\", seed = 2) m <- gam(y ~ s(x0) + s(x1) + offset(x2), data = df, method = \"REML\") names(model.frame(m)) #> [1] \"y\" \"offset(x2)\" \"x0\" \"x1\" names(fix_offset(m, model.frame(m), offset_val = 1L)) #> [1] \"y\" \"x2\" \"x0\" \"x1\""},{"path":"https://gavinsimpson.github.io/gratia/reference/fixef.gam.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract fixed effects estimates from a fitted GAM — fixef.gam","title":"Extract fixed effects estimates from a fitted GAM — fixef.gam","text":"Extract fixed effects estimates fitted GAM","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/fixef.gam.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract fixed effects estimates from a fitted GAM — fixef.gam","text":"","code":"# S3 method for class 'gam' fixef(object, ...) # S3 method for class 'gamm' fixef(object, ...) # S3 method for class 'lm' fixef(object, ...) # S3 method for class 'glm' fixef(object, ...) fixed_effects(object, ...) # Default S3 method fixed_effects(object, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/fixef.gam.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract fixed effects estimates from a fitted GAM — fixef.gam","text":"object fitted GAM ... arguments passed methods","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/fixef.gam.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract fixed effects estimates from a fitted GAM — fixef.gam","text":"","code":"load_mgcv() # run example if lme4 is available if (require(\"lme4\")) { data(sleepstudy, package = \"lme4\") m <- gam( Reaction ~ Days + s(Subject, bs = \"re\") + s(Days, Subject, bs = \"re\"), data = sleepstudy, method = \"REML\" ) fixef(m) } #> Loading required package: lme4 #> Loading required package: Matrix #> (Intercept) Days #> 251.40510 10.46729"},{"path":"https://gavinsimpson.github.io/gratia/reference/fixef.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract fixed effects estimates — fixef","title":"Extract fixed effects estimates — fixef","text":"Extract fixed effects estimates","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/fixef.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract fixed effects estimates — fixef","text":"object fitted GAM ... arguments passed methods","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/gaussian_draws.html","id":null,"dir":"Reference","previous_headings":"","what":"Posterior samples using a simple Metropolis Hastings sampler — gaussian_draws","title":"Posterior samples using a simple Metropolis Hastings sampler — gaussian_draws","text":"Posterior samples using simple Metropolis Hastings sampler","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/gaussian_draws.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Posterior samples using a simple Metropolis Hastings sampler — gaussian_draws","text":"","code":"gaussian_draws(model, ...) # S3 method for class 'gam' gaussian_draws( model, n, n_cores = 1L, index = NULL, frequentist = FALSE, unconditional = FALSE, mvn_method = \"mvnfast\", ... ) # S3 method for class 'scam' gaussian_draws( model, n, n_cores = 1L, index = NULL, frequentist = FALSE, parametrized = TRUE, mvn_method = \"mvnfast\", ... )"},{"path":"https://gavinsimpson.github.io/gratia/reference/gaussian_draws.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Posterior samples using a simple Metropolis Hastings sampler — gaussian_draws","text":"model fitted R model. Currently models fitted mgcv::gam() mgcv::bam(), return object inherits objects supported. , \"inherits\" used loose fashion; models fitted scam::scam() support even though models strictly inherit class \"gam\" far inherits() concerned. ... arguments passed methods. n numeric; number posterior draws take. n_cores integer; number CPU cores use generating multivariate normal distributed random values. used mvn_method = \"mvnfast\" method = \"gaussian\". index numeric; vector indices coefficients use. Can used subset mean vector covariance matrix extracted model. frequentist logical; TRUE, frequentist covariance matrix parameter estimates used. FALSE, Bayesian posterior covariance matrix parameters used. See mgcv::vcov.gam(). unconditional logical; TRUE Bayesian smoothing parameter uncertainty corrected covariance matrix used, available model. See mgcv::vcov.gam(). mvn_method character; one \"mvnfast\" \"mgcv\". default uses mvnfast::rmvn(), can considerably faster generate large numbers MVN random values mgcv::rmvn(), might work marginal fits, covariance matrix close singular. parametrized logical; use parametrized coefficients covariance matrix, respect linear inequality constraints model. scam::scam() model fits.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/get_by_smooth.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract an factor-by smooth by name — get_by_smooth","title":"Extract an factor-by smooth by name — get_by_smooth","text":"Extract factor-smooth name","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/get_by_smooth.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract an factor-by smooth by name — get_by_smooth","text":"","code":"get_by_smooth(object, term, level)"},{"path":"https://gavinsimpson.github.io/gratia/reference/get_by_smooth.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract an factor-by smooth by name — get_by_smooth","text":"object fitted GAM model object. term character; name smooth term extract. level character; level factor exrtact smooth .","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/get_by_smooth.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract an factor-by smooth by name — get_by_smooth","text":"single smooth object, list smooths several match named term.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/get_smooth.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract an mgcv smooth by name — get_smooth","title":"Extract an mgcv smooth by name — get_smooth","text":"Extract mgcv smooth name","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/get_smooth.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract an mgcv smooth by name — get_smooth","text":"","code":"get_smooth(object, term)"},{"path":"https://gavinsimpson.github.io/gratia/reference/get_smooth.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract an mgcv smooth by name — get_smooth","text":"object fitted GAM model object. term character; name smooth term extract","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/get_smooth.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract an mgcv smooth by name — get_smooth","text":"single smooth object, list smooths several match named term.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/get_smooths_by_id.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract an mgcv smooth given its position in the model object — get_smooths_by_id","title":"Extract an mgcv smooth given its position in the model object — get_smooths_by_id","text":"Extract mgcv smooth given position model object","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/get_smooths_by_id.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract an mgcv smooth given its position in the model object — get_smooths_by_id","text":"","code":"get_smooths_by_id(object, id) # S3 method for class 'gam' get_smooths_by_id(object, id) # S3 method for class 'scam' get_smooths_by_id(object, id) # S3 method for class 'gamm' get_smooths_by_id(object, id) # S3 method for class 'gamm4' get_smooths_by_id(object, id) # S3 method for class 'list' get_smooths_by_id(object, id)"},{"path":"https://gavinsimpson.github.io/gratia/reference/get_smooths_by_id.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract an mgcv smooth given its position in the model object — get_smooths_by_id","text":"object fitted GAM model object. id numeric; position smooth model object.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/gratia-package.html","id":null,"dir":"Reference","previous_headings":"","what":"gratia: Graceful 'ggplot'-Based Graphics and Other Functions for GAMs Fitted Using 'mgcv' — gratia-package","title":"gratia: Graceful 'ggplot'-Based Graphics and Other Functions for GAMs Fitted Using 'mgcv' — gratia-package","text":"Graceful 'ggplot'-based graphics utility functions working generalized additive models (GAMs) fitted using 'mgcv' package. Provides reimplementation plot() method GAMs 'mgcv' provides, well 'tidyverse' compatible representations estimated smooths.","code":""},{"path":[]},{"path":"https://gavinsimpson.github.io/gratia/reference/gratia-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"gratia: Graceful 'ggplot'-Based Graphics and Other Functions for GAMs Fitted Using 'mgcv' — gratia-package","text":"Maintainer: Gavin L. Simpson ucfagls@gmail.com (ORCID) [copyright holder] contributors: Henrik Singmann (ORCID) [contributor]","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/gss_vocab.html","id":null,"dir":"Reference","previous_headings":"","what":"Data from the General Social Survey (GSS) from the National Opinion Research Center of the University of Chicago — gss_vocab","title":"Data from the General Social Survey (GSS) from the National Opinion Research Center of the University of Chicago — gss_vocab","text":"subset data carData::GSSvocab dataset carData package, containing observations 2016 .","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/gss_vocab.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data from the General Social Survey (GSS) from the National Opinion Research Center of the University of Chicago — gss_vocab","text":"data frame 1858 rows 3 variables: vocab: numeric; number words 10 correct vocabulary test. nativeBorn: factor; respondent born US? factor levels yes. ageGroup: factor; grouped age respondent levels 18-29 30-39, 40-49, 50-59, 60+.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/gw_functions.html","id":null,"dir":"Reference","previous_headings":"","what":"Gu and Wabha test functions — gw_f0","title":"Gu and Wabha test functions — gw_f0","text":"Gu Wabha test functions","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/gw_functions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Gu and Wabha test functions — gw_f0","text":"","code":"gw_f0(x, ...) gw_f1(x, ...) gw_f2(x, ...) gw_f3(x, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/gw_functions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Gu and Wabha test functions — gw_f0","text":"x numeric; vector points evaluate function , interval (0,1) ... arguments passed methods, ignored.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/gw_functions.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Gu and Wabha test functions — gw_f0","text":"","code":"x <- seq(0, 1, length = 6) gw_f0(x) #> [1] 0.000e+00 1.176e+00 1.902e+00 1.902e+00 1.176e+00 2.449e-16 gw_f1(x) #> [1] 1.000 1.492 2.226 3.320 4.953 7.389 gw_f2(x) #> [1] 0.000 8.591 4.261 3.199 1.100 0.000 gw_f3(x) # should be constant 0 #> [1] 0 0 0 0 0 0"},{"path":"https://gavinsimpson.github.io/gratia/reference/has_theta.html","id":null,"dir":"Reference","previous_headings":"","what":"Are additional parameters available for a GAM? — has_theta","title":"Are additional parameters available for a GAM? — has_theta","text":"additional parameters available GAM?","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/has_theta.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Are additional parameters available for a GAM? — has_theta","text":"","code":"has_theta(object)"},{"path":"https://gavinsimpson.github.io/gratia/reference/has_theta.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Are additional parameters available for a GAM? — has_theta","text":"object R object, either family() object object whose class family() method.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/has_theta.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Are additional parameters available for a GAM? — has_theta","text":"logical; TRUE additional parameters available, FALSE otherwise.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/has_theta.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Are additional parameters available for a GAM? — has_theta","text":"","code":"load_mgcv() df <- data_sim(\"eg1\", dist = \"poisson\", seed = 42, scale = 1 / 5) m <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = df, method = \"REML\", family = nb() ) has_theta(m) #> [1] TRUE p <- theta(m)"},{"path":"https://gavinsimpson.github.io/gratia/reference/is_by_smooth.html","id":null,"dir":"Reference","previous_headings":"","what":"Tests for by variable smooths — is_by_smooth","title":"Tests for by variable smooths — is_by_smooth","text":"Functions check smooth -variable one test type -variable smooth factor-smooth continous-smooth interaction.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/is_by_smooth.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tests for by variable smooths — is_by_smooth","text":"","code":"is_by_smooth(smooth) is_factor_by_smooth(smooth) is_continuous_by_smooth(smooth) by_variable(smooth) by_level(smooth)"},{"path":"https://gavinsimpson.github.io/gratia/reference/is_by_smooth.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tests for by variable smooths — is_by_smooth","text":"smooth object class \"mgcv.smooth\"","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/is_by_smooth.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tests for by variable smooths — is_by_smooth","text":"logical vector.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/is_by_smooth.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tests for by variable smooths — is_by_smooth","text":"Gavin L. Simpson","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/is_factor_term.html","id":null,"dir":"Reference","previous_headings":"","what":"Is a model term a factor (categorical)? — is_factor_term","title":"Is a model term a factor (categorical)? — is_factor_term","text":"Given name (term label) term model, identify term factor term numeric. useful considering interactions, terms like fac1:fac2 num1:fac1 may requested user. terms type fac1:fac2 function return TRUE.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/is_factor_term.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Is a model term a factor (categorical)? — is_factor_term","text":"","code":"is_factor_term(object, term, ...) # S3 method for class 'terms' is_factor_term(object, term, ...) # S3 method for class 'gam' is_factor_term(object, term, ...) # S3 method for class 'bam' is_factor_term(object, term, ...) # S3 method for class 'gamm' is_factor_term(object, term, ...) # S3 method for class 'list' is_factor_term(object, term, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/is_factor_term.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Is a model term a factor (categorical)? — is_factor_term","text":"object R object method dispatch performed term character; name model term, sense attr(terms(object), \"term.labels\"). Currently checked see term exists model. ... arguments passed methods.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/is_factor_term.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Is a model term a factor (categorical)? — is_factor_term","text":"logical: TRUE variables involved term factors, otherwise FALSE.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/is_mgcv_smooth.html","id":null,"dir":"Reference","previous_headings":"","what":"Check if objects are smooths or are a particular type of smooth — is_mgcv_smooth","title":"Check if objects are smooths or are a particular type of smooth — is_mgcv_smooth","text":"Check objects smooths particular type smooth","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/is_mgcv_smooth.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check if objects are smooths or are a particular type of smooth — is_mgcv_smooth","text":"","code":"is_mgcv_smooth(smooth) stop_if_not_mgcv_smooth(smooth) check_is_mgcv_smooth(smooth) is_mrf_smooth(smooth)"},{"path":"https://gavinsimpson.github.io/gratia/reference/is_mgcv_smooth.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check if objects are smooths or are a particular type of smooth — is_mgcv_smooth","text":"smooth R object, typically list","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/is_mgcv_smooth.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Check if objects are smooths or are a particular type of smooth — is_mgcv_smooth","text":"Check smooth inherits class \"mgcv.smooth\". stop_if_not_mgcv_smooth() wrapper around is_mgcv_smooth(), useful programming checking supplied object one mgcv's smooths, throwing consistent error . check_is_mgcv_smooth() similar stop_if_not_mgcv_smooth() returns result is_mgcv_smooth() invisibly.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/is_offset.html","id":null,"dir":"Reference","previous_headings":"","what":"Is a model term an offset? — is_offset","title":"Is a model term an offset? — is_offset","text":"Given character vector model terms, checks see , , model offset.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/is_offset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Is a model term an offset? — is_offset","text":"","code":"is_offset(terms)"},{"path":"https://gavinsimpson.github.io/gratia/reference/is_offset.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Is a model term an offset? — is_offset","text":"terms character vector model terms.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/is_offset.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Is a model term an offset? — is_offset","text":"logical vector length terms.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/is_offset.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Is a model term an offset? — is_offset","text":"Gavin L. Simpson","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/is_offset.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Is a model term an offset? — is_offset","text":"","code":"load_mgcv() df <- data_sim(\"eg1\", n = 400, dist = \"normal\") m <- gam(y ~ s(x0) + s(x1) + offset(x0), data = df, method = \"REML\") nm <- names(model.frame(m)) nm #> [1] \"y\" \"offset(x0)\" \"x0\" \"x1\" is_offset(nm) #> [1] FALSE TRUE FALSE FALSE"},{"path":"https://gavinsimpson.github.io/gratia/reference/link.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract link and inverse link functions from models — link","title":"Extract link and inverse link functions from models — link","text":"Returns link inverse estimated model, provides simple way extract functions complex models multiple links, location scale models.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/link.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract link and inverse link functions from models — link","text":"","code":"link(object, ...) # S3 method for class 'family' link(object, parameter = NULL, which_eta = NULL, ...) # S3 method for class 'gam' link(object, parameter = NULL, which_eta = NULL, ...) # S3 method for class 'bam' link(object, parameter = NULL, which_eta = NULL, ...) # S3 method for class 'gamm' link(object, ...) # S3 method for class 'glm' link(object, ...) # S3 method for class 'list' link(object, ...) inv_link(object, ...) # S3 method for class 'family' inv_link(object, parameter = NULL, which_eta = NULL, ...) # S3 method for class 'gam' inv_link(object, parameter = NULL, which_eta = NULL, ...) # S3 method for class 'bam' inv_link(object, parameter = NULL, which_eta = NULL, ...) # S3 method for class 'gamm' inv_link(object, ...) # S3 method for class 'list' inv_link(object, ...) # S3 method for class 'glm' inv_link(object, ...) extract_link(family, ...) # S3 method for class 'family' extract_link(family, inverse = FALSE, ...) # S3 method for class 'general.family' extract_link(family, parameter, inverse = FALSE, which_eta = NULL, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/link.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract link and inverse link functions from models — link","text":"object family object fitted model extract family object. Models fitted stats::glm(), mgcv::gam(), mgcv::bam(), mgcv::gamm(), gamm4::gamm4() currently supported. ... arguments passed methods. parameter character; parameter distribution. Usually \"location\" \"scale\" \"shape\" may provided location scale models. options include \"mu\" synonym \"location\", \"sigma\" scale parameter mgcv::gaulss(), \"pi\" zero-inflation term mgcv::ziplss(), \"power\" mgcv::twlss() power parameter, \"xi\", shape parameter mgcv::gevlss(), \"epsilon\" \"skewness\" skewness \"delta\" \"kurtosis\" kurtosis parameter mgcv::shash(), \"phi\" scale parameter mgcv::gammals() & mgcv::twlss(). which_eta numeric; linear predictor extract families mgcv::mvn() mgcv::multinom(). family family object, result call family(). inverse logical; return inverse link function?","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/link.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Extract link and inverse link functions from models — link","text":"Gavin L. Simpson","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/link.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract link and inverse link functions from models — link","text":"","code":"load_mgcv() link(gaussian()) #> function (mu) #> mu #> link(nb()) #> function (mu) #> log(mu) #> inv_link(nb()) #> function (eta) #> pmax(exp(eta), .Machine$double.eps) #> dat <- data_sim(\"eg1\", seed = 4234) mod <- gam(list(y ~ s(x0) + s(x1) + s(x2) + s(x3), ~1), data = dat, family = gaulss ) link(mod, parameter = \"scale\") #> function (mu) #> log(1/mu - 0.01) #> inv_link(mod, parameter = \"scale\") #> function (eta) #> 1/(exp(eta) + 0.01) #> ## Works with `family` objects too link(shash(), parameter = \"skewness\") #> function (mu) #> mu #> "},{"path":"https://gavinsimpson.github.io/gratia/reference/load_mgcv.html","id":null,"dir":"Reference","previous_headings":"","what":"Load mgcv quietly — load_mgcv","title":"Load mgcv quietly — load_mgcv","text":"Simple function loads mgcv package whilst suppressing startup messages prints console.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/load_mgcv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Load mgcv quietly — load_mgcv","text":"","code":"load_mgcv()"},{"path":"https://gavinsimpson.github.io/gratia/reference/load_mgcv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Load mgcv quietly — load_mgcv","text":"Returns logical vectors invisibly, indicating whether package loaded .","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/lp_matrix.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the linear prediction matrix of a fitted GAM — lp_matrix","title":"Return the linear prediction matrix of a fitted GAM — lp_matrix","text":"lp_matrix() wrapper predict(..., type = \"lpmatrix\") returning linear predictor matrix model training data (data = NULL), user-specified data values supplied via data.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/lp_matrix.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the linear prediction matrix of a fitted GAM — lp_matrix","text":"","code":"lp_matrix(model, ...) # S3 method for class 'gam' lp_matrix(model, data = NULL, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/lp_matrix.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the linear prediction matrix of a fitted GAM — lp_matrix","text":"model fitted model ... arguments passed methods predict methods including mgcv::predict.gam() mgcv::predict.bam() data data frame values return linear prediction matrix.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/lp_matrix.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the linear prediction matrix of a fitted GAM — lp_matrix","text":"linear prediction matrix returned matrix. object returned class \"lp_matrix\", inherits classes \"matrix\" \"array\". special class allows printing matrix controlled, printing matrix tibble.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/lp_matrix.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Return the linear prediction matrix of a fitted GAM — lp_matrix","text":"linear prediction matrix \\(\\mathbf{X}_p\\) matrix maps values parameters \\(\\hat{\\mathbf{\\beta}}_p\\) values linear predictor model \\(\\hat{\\eta}_p = \\mathbf{X}_p \\hat{\\mathbf{\\beta}}_p\\). \\(\\mathbf{X}_p\\) model matrix spline covariates replaced values basis functions evaluated respective covariates. Parametric covariates also included.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/lp_matrix.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Return the linear prediction matrix of a fitted GAM — lp_matrix","text":"","code":"load_mgcv() df <- data_sim(\"eg1\", seed = 1) m <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = df) # linear prediction matrix for observed data xp <- lp_matrix(m) ## IGNORE_RDIFF_BEGIN xp #> Linear prediction matrix (400 x 37) #> `(Intercept)` `s(x0).1` `s(x0).2` `s(x0).3` `s(x0).4` `s(x0).5` `s(x0).6` #> #> 1 1 0.961 0.227 0.706 -0.135 0.457 -0.146 #> 2 1 0.651 -0.241 0.0684 -0.308 0.394 -0.00994 #> 3 1 -0.385 -0.549 0.0660 -0.204 -0.416 -0.247 #> 4 1 -1.27 0.156 -1.53 0.222 -1.60 0.198 #> 5 1 1.05 0.420 1.11 0.214 0.893 0.0351 #> # i 395 more rows ## IGNORE_RDIFF_END # the object `xp` *is* a matrix class(xp) #> [1] \"lp_matrix\" \"matrix\" \"array\" # but we print like a tibble to avoid spamming the R console # linear predictor matrix for new data set ds <- data_slice(m, x2 = evenly(x2)) xp <- lp_matrix(m, data = ds) ## IGNORE_RDIFF_BEGIN xp #> Linear prediction matrix (100 x 37) #> `(Intercept)` `s(x0).1` `s(x0).2` `s(x0).3` `s(x0).4` `s(x0).5` `s(x0).6` #> #> 1 1 0.170 -0.542 -0.0371 -0.0534 0.144 -0.353 #> 2 1 0.170 -0.542 -0.0371 -0.0534 0.144 -0.353 #> 3 1 0.170 -0.542 -0.0371 -0.0534 0.144 -0.353 #> 4 1 0.170 -0.542 -0.0371 -0.0534 0.144 -0.353 #> 5 1 0.170 -0.542 -0.0371 -0.0534 0.144 -0.353 #> # i 95 more rows ## IGNORE_RDIFF_END"},{"path":"https://gavinsimpson.github.io/gratia/reference/lss_parameters.html","id":null,"dir":"Reference","previous_headings":"","what":"General names of LSS parameters for each GAM family — lss_parameters","title":"General names of LSS parameters for each GAM family — lss_parameters","text":"General names LSS parameters GAM family","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/lss_parameters.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"General names of LSS parameters for each GAM family — lss_parameters","text":"","code":"lss_parameters(object)"},{"path":"https://gavinsimpson.github.io/gratia/reference/mh_draws.html","id":null,"dir":"Reference","previous_headings":"","what":"Posterior samples using a Gaussian approximation to the posterior distribution — mh_draws","title":"Posterior samples using a Gaussian approximation to the posterior distribution — mh_draws","text":"Posterior samples using Gaussian approximation posterior distribution","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/mh_draws.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Posterior samples using a Gaussian approximation to the posterior distribution — mh_draws","text":"","code":"mh_draws(model, ...) # S3 method for class 'gam' mh_draws( model, n, burnin = 1000, thin = 1, t_df = 40, rw_scale = 0.25, index = NULL, ... )"},{"path":"https://gavinsimpson.github.io/gratia/reference/mh_draws.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Posterior samples using a Gaussian approximation to the posterior distribution — mh_draws","text":"model fitted R model. Currently models fitted mgcv::gam() mgcv::bam(), return object inherits objects supported. , \"inherits\" used loose fashion; models fitted scam::scam() support even though models strictly inherit class \"gam\" far inherits() concerned. ... arguments passed methods. n numeric; number posterior draws take. burnin numeric; length initial burn period discard. See mgcv::gam.mh(). thin numeric; retain thin samples. See mgcv::gam.mh(). t_df numeric; degrees freedom static multivariate t proposal. See mgcv::gam.mh(). rw_scale numeric; factor scale posterior covariance matrix generating random walk proposals. See mgcv::gam.mh(). index numeric; vector indices coefficients use. Can used subset mean vector covariance matrix extracted model.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/model_concurvity.html","id":null,"dir":"Reference","previous_headings":"","what":"Concurvity of an estimated GAM — model_concurvity","title":"Concurvity of an estimated GAM — model_concurvity","text":"Concurvity estimated GAM","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/model_concurvity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Concurvity of an estimated GAM — model_concurvity","text":"","code":"model_concurvity(model, ...) # S3 method for class 'gam' model_concurvity( model, terms = everything(), type = c(\"all\", \"estimate\", \"observed\", \"worst\"), pairwise = FALSE, ... ) concrvity( model, terms = everything(), type = c(\"all\", \"estimate\", \"observed\", \"worst\"), pairwise = FALSE, ... )"},{"path":"https://gavinsimpson.github.io/gratia/reference/model_concurvity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Concurvity of an estimated GAM — model_concurvity","text":"model fitted GAM. Currently objects class \"gam\" supported ... arguents passed methods. terms currently ignored type character; pairwise logical; extract pairwise concurvity model terms?","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/model_concurvity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Concurvity of an estimated GAM — model_concurvity","text":"","code":"## simulate data with concurvity... library(\"tibble\") load_mgcv() set.seed(8) n <- 200 df <- tibble( t = sort(runif(n)), x = gw_f2(t) + rnorm(n) * 3, y = sin(4 * pi * t) + exp(x / 20) + rnorm(n) * 0.3 ) ## fit model m <- gam(y ~ s(t, k = 15) + s(x, k = 15), data = df, method = \"REML\") ## overall concurvity o_conc <- concrvity(m) draw(o_conc) ## pairwise concurvity p_conc <- concrvity(m, pairwise = TRUE) draw(p_conc)"},{"path":"https://gavinsimpson.github.io/gratia/reference/model_constant.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract the model constant term — model_constant","title":"Extract the model constant term — model_constant","text":"Extracts model constant term(s), model intercept, fitted model object.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/model_constant.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract the model constant term — model_constant","text":"","code":"model_constant(model, ...) # S3 method for class 'gam' model_constant(model, lp = NULL, ...) # S3 method for class 'gamlss' model_constant(model, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/model_constant.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract the model constant term — model_constant","text":"model fitted model coef() method exists. ... arguments passed methods. lp numeric; linear predictors extract constant terms .","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/model_constant.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract the model constant term — model_constant","text":"","code":"load_mgcv() # simulate a small example df <- data_sim(\"eg1\", seed = 42) # fit the GAM m <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = df, method = \"REML\") # extract the estimate of the constant term model_constant(m) #> [1] 7.495 #> attr(,\"par_names\") #> [1] \"location\" # same as coef(m)[1L] coef(m)[1L] #> (Intercept) #> 7.495"},{"path":"https://gavinsimpson.github.io/gratia/reference/model_vars.html","id":null,"dir":"Reference","previous_headings":"","what":"List the variables involved in a model fitted with a formula — model_vars","title":"List the variables involved in a model fitted with a formula — model_vars","text":"List variables involved model fitted formula","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/model_vars.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"List the variables involved in a model fitted with a formula — model_vars","text":"","code":"model_vars(model, ...) # S3 method for class 'gam' model_vars(model, ...) # Default S3 method model_vars(model, ...) # S3 method for class 'bam' model_vars(model, ...) # S3 method for class 'gamm' model_vars(model, ...) # S3 method for class 'gamm4' model_vars(model, ...) # S3 method for class 'list' model_vars(model, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/model_vars.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"List the variables involved in a model fitted with a formula — model_vars","text":"model fitted model object $pred.formula, $terms component \"terms\" attribute ... Arguments passed methods. Currently ignored.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/model_vars.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"List the variables involved in a model fitted with a formula — model_vars","text":"","code":"load_mgcv() # simulate some Gaussian data df <- data_sim(\"eg1\", n = 50, seed = 2) # fit a GAM with 1 smooth and 1 linear term m1 <- gam(y ~ s(x2, k = 7) + x1, data = df, method = \"REML\") model_vars(m1) #> [1] \"x1\" \"x2\" # fit a lm with two linear terms m2 <- lm(y ~ x2 + x1, data = df) model_vars(m2) #> [1] \"x2\" \"x1\""},{"path":"https://gavinsimpson.github.io/gratia/reference/n_eta.html","id":null,"dir":"Reference","previous_headings":"","what":"The Number of linear predictors in model — n_eta","title":"The Number of linear predictors in model — n_eta","text":"Extracts number linear predictors fitted model.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/n_eta.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"The Number of linear predictors in model — n_eta","text":"","code":"n_eta(model, ...) # S3 method for class 'gam' n_eta(model, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/n_eta.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"The Number of linear predictors in model — n_eta","text":"model fitted model. Currently, models inheriting class \"gam\" supported. ... arguments passed methods.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/n_eta.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"The Number of linear predictors in model — n_eta","text":"integer vector length 1 containing number linear predictors model.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/n_smooths.html","id":null,"dir":"Reference","previous_headings":"","what":"How many smooths in a fitted model — n_smooths","title":"How many smooths in a fitted model — n_smooths","text":"many smooths fitted model","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/n_smooths.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"How many smooths in a fitted model — n_smooths","text":"","code":"n_smooths(object) # Default S3 method n_smooths(object) # S3 method for class 'gam' n_smooths(object) # S3 method for class 'gamm' n_smooths(object) # S3 method for class 'bam' n_smooths(object)"},{"path":"https://gavinsimpson.github.io/gratia/reference/n_smooths.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"How many smooths in a fitted model — n_smooths","text":"object fitted GAM related model. Typically result call mgcv::gam(), mgcv::bam(), mgcv::gamm().","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/nb_theta.html","id":null,"dir":"Reference","previous_headings":"","what":"Negative binomial parameter theta — nb_theta","title":"Negative binomial parameter theta — nb_theta","text":"Negative binomial parameter theta","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/nb_theta.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Negative binomial parameter theta — nb_theta","text":"","code":"nb_theta(model) # S3 method for class 'gam' nb_theta(model)"},{"path":"https://gavinsimpson.github.io/gratia/reference/nb_theta.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Negative binomial parameter theta — nb_theta","text":"model fitted model.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/nb_theta.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Negative binomial parameter theta — nb_theta","text":"numeric vector length 1 containing estimated value theta.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/nb_theta.html","id":"methods-by-class-","dir":"Reference","previous_headings":"","what":"Methods (by class)","title":"Negative binomial parameter theta — nb_theta","text":"nb_theta(gam): Method class \"gam\"","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/nb_theta.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Negative binomial parameter theta — nb_theta","text":"","code":"load_mgcv() df <- data_sim(\"eg1\", n = 500, dist = \"poisson\", scale = 0.1, seed = 6) m <- gam(y ~ s(x0, bs = \"cr\") + s(x1, bs = \"cr\") + s(x2, bs = \"cr\") + s(x3, bs = \"cr\"), family = nb, data = df, method = \"REML\") ## IGNORE_RDIFF_BEGIN nb_theta(m) #> [1] 239333.8 ## IGNORE_RDIFF_END"},{"path":"https://gavinsimpson.github.io/gratia/reference/nested_partial_residuals.html","id":null,"dir":"Reference","previous_headings":"","what":"Partial residuals in nested form — nested_partial_residuals","title":"Partial residuals in nested form — nested_partial_residuals","text":"Computes partial residuals smooth terms, formats long/tidy format, nests partial_residual column result nested data frame one row per smooth.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/nested_partial_residuals.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Partial residuals in nested form — nested_partial_residuals","text":"","code":"nested_partial_residuals(object, terms = NULL, data = NULL)"},{"path":"https://gavinsimpson.github.io/gratia/reference/nested_partial_residuals.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Partial residuals in nested form — nested_partial_residuals","text":"object fitted GAM model terms vector terms include partial residuals . Passed argument terms mgcv::predict.gam()]. data optional data frame","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/nested_partial_residuals.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Partial residuals in nested form — nested_partial_residuals","text":"nested tibble (data frame) one row per smooth term. Contains two columns: smooth - label indicating smooth term partial_residual - list column containing tibble (data frame) 1 column partial_residual containing requested partial residuals indicated smooth.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/nested_rug_values.html","id":null,"dir":"Reference","previous_headings":"","what":"Values for rug plot in nested form — nested_rug_values","title":"Values for rug plot in nested form — nested_rug_values","text":"Extracts original data smooth terms, formats long/tidy format, nests data column(s) result nested data frame one row per smooth.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/nested_rug_values.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Values for rug plot in nested form — nested_rug_values","text":"","code":"nested_rug_values(object, terms = NULL, data = NULL)"},{"path":"https://gavinsimpson.github.io/gratia/reference/nested_rug_values.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Values for rug plot in nested form — nested_rug_values","text":"object fitted GAM model terms vector terms include original data . Passed argument terms mgcv::predict.gam()]. data optional data frame","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/nested_rug_values.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Values for rug plot in nested form — nested_rug_values","text":"nested tibble (data frame) one row per smooth term.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/null_deviance.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract the null deviance of a fitted model — null_deviance","title":"Extract the null deviance of a fitted model — null_deviance","text":"Extract null deviance fitted model","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/null_deviance.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract the null deviance of a fitted model — null_deviance","text":"","code":"null_deviance(model, ...) # Default S3 method null_deviance(model, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/null_deviance.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract the null deviance of a fitted model — null_deviance","text":"model fitted model ... arguments passed methods","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/observed_fitted_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot of fitted against observed response values — observed_fitted_plot","title":"Plot of fitted against observed response values — observed_fitted_plot","text":"Plot fitted observed response values","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/observed_fitted_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot of fitted against observed response values — observed_fitted_plot","text":"","code":"observed_fitted_plot( model, ylab = NULL, xlab = NULL, title = NULL, subtitle = NULL, caption = NULL, point_col = \"black\", point_alpha = 1 )"},{"path":"https://gavinsimpson.github.io/gratia/reference/observed_fitted_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot of fitted against observed response values — observed_fitted_plot","text":"model fitted model. Currently class \"gam\". ylab character expression; label y axis. supplied, suitable label generated. xlab character expression; label y axis. supplied, suitable label generated. title character expression; title plot. See ggplot2::labs(). subtitle character expression; subtitle plot. See ggplot2::labs(). caption character expression; plot caption. See ggplot2::labs(). point_col colour used draw points plots. See graphics::par() section Color Specification. passed individual plotting functions, therefore affects points plots. point_alpha numeric; alpha transparency points plots.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/overview.html","id":null,"dir":"Reference","previous_headings":"","what":"Provides an overview of a model and the terms in that model — overview","title":"Provides an overview of a model and the terms in that model — overview","text":"Provides overview model terms model","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/overview.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Provides an overview of a model and the terms in that model — overview","text":"","code":"overview(model, ...) # S3 method for class 'gam' overview( model, parametric = TRUE, random_effects = TRUE, dispersion = NULL, frequentist = FALSE, accuracy = 0.001, stars = FALSE, ... )"},{"path":"https://gavinsimpson.github.io/gratia/reference/overview.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Provides an overview of a model and the terms in that model — overview","text":"model fitted model object overview. ... arguments passed methods. parametric logical; include model parametric terms overview? random_effects tests fully penalized smooth terms (zero-dimensional null space, e.g. random effects) computationally expensive large data sets producing p values can take long time. random_effects = FALSE, tests expensive terms skipped. dispersion numeric; known value dispersion parameter. default NULL implies estimated value default value (1 Poisson distribution example) specified used instead. frequentist logical; default Bayesian estimated covariance matrix parameter estimates used calculate p values parametric terms. frequentist = FALSE, frequentist covariance matrix parameter estimates used. accuracy numeric; accuracy report p values, p values value displayed \"< accuracy\". stars logical; significance stars added output?","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/overview.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Provides an overview of a model and the terms in that model — overview","text":"","code":"load_mgcv() df <- data_sim(n = 400, seed = 2) m <- gam(y ~ x3 + s(x0) + s(x1, bs = \"bs\") + s(x2, bs = \"ts\"), data = df, method = \"REML\" ) overview(m) #> #> Generalized Additive Model with 4 terms #> #> term type k edf statistic p.value #> #> 1 x3 parametric NA 1 4.28 0.03926 #> 2 s(x0) TPRS 9 3.02 6.25 < 0.001 #> 3 s(x1) B spline 9 2.81 71.0 < 0.001 #> 4 s(x2) TPRS (shrink) 9 7.91 83.8 < 0.001"},{"path":"https://gavinsimpson.github.io/gratia/reference/parametric_effects.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimated values for parametric model terms — parametric_effects","title":"Estimated values for parametric model terms — parametric_effects","text":"Estimated values parametric model terms","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/parametric_effects.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimated values for parametric model terms — parametric_effects","text":"","code":"parametric_effects(object, ...) # S3 method for class 'gam' parametric_effects( object, terms = NULL, data = NULL, unconditional = FALSE, unnest = TRUE, ci_level = 0.95, envir = environment(formula(object)), transform = FALSE, ... )"},{"path":"https://gavinsimpson.github.io/gratia/reference/parametric_effects.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimated values for parametric model terms — parametric_effects","text":"object fitted model object. ... arguments passed methods. terms character; model parametric terms drawn? Default NULL plot parametric terms can drawn. data optional data frame may may used? FIXME! unconditional logical; confidence intervals include uncertainty due smoothness selection? TRUE, corrected Bayesian covariance matrix used. unnest logical; unnest parametric effect objects? ci_level numeric; coverage required confidence interval. Currently ignored. envir environment look data within. transform logical; TRUE, parametric effect plotted transformed scale result effect straight line. FALSE, effect plotted raw data (.e. log10(x), poly(z), x-axis plot x z respectively.)","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/parametric_terms.html","id":null,"dir":"Reference","previous_headings":"","what":"Names of any parametric terms in a GAM — parametric_terms","title":"Names of any parametric terms in a GAM — parametric_terms","text":"Names parametric terms GAM","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/parametric_terms.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Names of any parametric terms in a GAM — parametric_terms","text":"","code":"parametric_terms(model, ...) # Default S3 method parametric_terms(model, ...) # S3 method for class 'gam' parametric_terms(model, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/parametric_terms.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Names of any parametric terms in a GAM — parametric_terms","text":"model fitted model. ... arguments passed methods.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/partial_derivatives.html","id":null,"dir":"Reference","previous_headings":"","what":"Partial derivatives of estimated multivariate smooths via finite differences — partial_derivatives","title":"Partial derivatives of estimated multivariate smooths via finite differences — partial_derivatives","text":"Partial derivatives estimated multivariate smooths via finite differences","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/partial_derivatives.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Partial derivatives of estimated multivariate smooths via finite differences — partial_derivatives","text":"","code":"partial_derivatives(object, ...) # Default S3 method partial_derivatives(object, ...) # S3 method for class 'gamm' partial_derivatives(object, ...) # S3 method for class 'gam' partial_derivatives( object, select = NULL, term = deprecated(), focal = NULL, data = newdata, order = 1L, type = c(\"forward\", \"backward\", \"central\"), n = 100, eps = 1e-07, interval = c(\"confidence\", \"simultaneous\"), n_sim = 10000, level = 0.95, unconditional = FALSE, frequentist = FALSE, offset = NULL, ncores = 1, partial_match = FALSE, seed = NULL, ..., newdata = NULL )"},{"path":"https://gavinsimpson.github.io/gratia/reference/partial_derivatives.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Partial derivatives of estimated multivariate smooths via finite differences — partial_derivatives","text":"object R object compute derivatives . ... arguments passed methods. select character; vector one smooth terms derivatives required. missing, derivatives smooth terms returned. Can partial match smooth term; see argument partial_match . term Use select instead. focal character; name focal variable. partial derivative estimated smooth respect variable returned. variables involved smooth held constant. can missing supplying data, case, focal variable identified one variable constant. data data frame containing values model covariates evaluate first derivatives smooths. supplied, one variable must held constant value. order numeric; order derivative. type character; type finite difference used. One \"forward\", \"backward\", \"central\". n numeric; number points evaluate derivative . eps numeric; finite difference. interval character; type interval compute. One \"confidence\" point-wise intervals, \"simultaneous\" simultaneous intervals. n_sim integer; number simulations used computing simultaneous intervals. level numeric; 0 < level < 1; confidence level point-wise simultaneous interval. default 0.95 95% interval. unconditional logical; use smoothness selection-corrected Bayesian covariance matrix? frequentist logical; use frequentist covariance matrix? offset numeric; value use offset term ncores number cores generating random variables multivariate normal distribution. Passed mvnfast::rmvn(). Parallelization take place OpenMP supported (appears work Windows current R). partial_match logical; smooths selected partial matches term? TRUE, term can single string match . seed numeric; RNG seed use. newdata Deprecated: use data instead.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/partial_derivatives.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Partial derivatives of estimated multivariate smooths via finite differences — partial_derivatives","text":"tibble, currently following variables: .smooth: smooth row refers , .partial_deriv: estimated partial derivative, .se: standard error estimated partial derivative, .crit: critical value derivative ± (crit * se) gives upper lower bounds requested confidence simultaneous interval (given level), .lower_ci: lower bound confidence simultaneous interval, .upper_ci: upper bound confidence simultaneous interval.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/partial_derivatives.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Partial derivatives of estimated multivariate smooths via finite differences — partial_derivatives","text":"partial_derivatives() ignore random effect smooths encounters object.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/partial_derivatives.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Partial derivatives of estimated multivariate smooths via finite differences — partial_derivatives","text":"Gavin L. Simpson","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/partial_derivatives.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Partial derivatives of estimated multivariate smooths via finite differences — partial_derivatives","text":"","code":"library(\"ggplot2\") library(\"patchwork\") load_mgcv() df <- data_sim(\"eg2\", n = 2000, dist = \"normal\", scale = 0.5, seed = 42) # fit the GAM (note: for execution time reasons, k is set articifially low) m <- gam(y ~ te(x, z, k = c(5, 5)), data = df, method = \"REML\") # data slice through te(x,z) holding z == 0.4 ds <- data_slice(m, x = evenly(x, n = 100), z = 0.4) # evaluate te(x,z) at values of x & z sm <- smooth_estimates(m, select = \"te(x,z)\", data = ds) |> add_confint() # partial derivatives pd_x <- partial_derivatives(m, data = ds, type = \"central\", focal = \"x\") # draw te(x,z) p1 <- draw(m, rug = FALSE) & geom_hline(yintercept = 0.4, linewidth = 1) p1 # draw te(x,z) along slice cap <- expression(z == 0.4) p2 <- sm |> ggplot(aes(x = x, y = .estimate)) + geom_ribbon(aes(ymin = .lower_ci, ymax = .upper_ci), alpha = 0.2) + geom_line() + labs( x = \"x\", y = \"Partial effect\", title = \"te(x,z)\", caption = cap ) p2 # draw partial derivs p3 <- pd_x |> draw() + labs(caption = cap) p3 # draw all three panels p1 + p2 + p3 + plot_layout(ncol = 3)"},{"path":"https://gavinsimpson.github.io/gratia/reference/partial_residuals.html","id":null,"dir":"Reference","previous_headings":"","what":"Partial residuals — partial_residuals","title":"Partial residuals — partial_residuals","text":"Partial residuals","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/partial_residuals.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Partial residuals — partial_residuals","text":"","code":"partial_residuals(object, ...) # S3 method for class 'gam' partial_residuals(object, select = NULL, partial_match = FALSE, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/partial_residuals.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Partial residuals — partial_residuals","text":"object R object, typically model. Currently objects class \"gam\" (inherit class) supported. ... arguments passed methods. select character, logical, numeric; smooths plot. NULL, default, model smooths drawn. Numeric select indexes smooths order specified formula stored object. Character select matches labels smooths shown example output summary(object). Logical select operates per numeric select order smooths stored. partial_match logical; smooths selected partial matches select? TRUE, select can single string match .","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/partial_residuals.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Partial residuals — partial_residuals","text":"","code":"## load mgcv load_mgcv() ## example data - Gu & Wabha four term model df <- data_sim(\"eg1\", n = 400, seed = 42) ## fit the model m <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = df, method = \"REML\") ## extract partial residuals partial_residuals(m) #> # A tibble: 400 x 4 #> `s(x0)` `s(x1)` `s(x2)` `s(x3)` #> #> 1 -0.3527 -1.321 -2.180 0.6077 #> 2 -0.1233 0.5013 -1.775 0.9613 #> 3 1.429 1.515 5.609 0.9910 #> 4 -1.110 -1.700 -0.8882 -0.6593 #> 5 -2.120 -0.01378 -2.733 -3.012 #> 6 1.254 -1.224 3.915 0.07275 #> 7 -0.5220 3.023 -0.8197 -1.019 #> 8 1.398 0.2184 7.055 1.897 #> 9 2.797 0.4969 7.329 2.498 #> 10 1.151 -0.2267 0.7202 0.7437 #> # i 390 more rows ## and for a select term partial_residuals(m, select = \"s(x2)\") #> # A tibble: 400 x 1 #> `s(x2)` #> #> 1 -2.180 #> 2 -1.775 #> 3 5.609 #> 4 -0.8882 #> 5 -2.733 #> 6 3.915 #> 7 -0.8197 #> 8 7.055 #> 9 7.329 #> 10 0.7202 #> # i 390 more rows ## or with partial matching partial_residuals(m, select = \"x\", partial_match = TRUE) # returns all #> # A tibble: 400 x 4 #> `s(x0)` `s(x1)` `s(x2)` `s(x3)` #> #> 1 -0.3527 -1.321 -2.180 0.6077 #> 2 -0.1233 0.5013 -1.775 0.9613 #> 3 1.429 1.515 5.609 0.9910 #> 4 -1.110 -1.700 -0.8882 -0.6593 #> 5 -2.120 -0.01378 -2.733 -3.012 #> 6 1.254 -1.224 3.915 0.07275 #> 7 -0.5220 3.023 -0.8197 -1.019 #> 8 1.398 0.2184 7.055 1.897 #> 9 2.797 0.4969 7.329 2.498 #> 10 1.151 -0.2267 0.7202 0.7437 #> # i 390 more rows"},{"path":"https://gavinsimpson.github.io/gratia/reference/penalty.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract and tidy penalty matrices — penalty","title":"Extract and tidy penalty matrices — penalty","text":"Extract tidy penalty matrices","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/penalty.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract and tidy penalty matrices — penalty","text":"","code":"penalty(object, ...) # Default S3 method penalty( object, rescale = FALSE, data, knots = NULL, constraints = FALSE, diagonalize = FALSE, ... ) # S3 method for class 'gam' penalty( object, select = NULL, smooth = deprecated(), rescale = FALSE, partial_match = FALSE, ... ) # S3 method for class 'mgcv.smooth' penalty(object, rescale = FALSE, ...) # S3 method for class 'tensor.smooth' penalty(object, margins = FALSE, ...) # S3 method for class 't2.smooth' penalty(object, margins = FALSE, ...) # S3 method for class 're.smooth.spec' penalty(object, data, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/penalty.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract and tidy penalty matrices — penalty","text":"object fitted GAM smooth. ... additional arguments passed methods. rescale logical; default, mgcv scale penalty matrix better performance mgcv::gamm(). rescale TRUE, scaling undone put penalty matrix back original scale. data data frame; data frame values terms mentioned smooth specification. knots list data frame named components containing knots locations. Names must match covariates basis required. See mgcv::smoothCon(). constraints logical; identifiability constraints applied smooth basis. See argument absorb.cons mgcv::smoothCon(). diagonalize logical; TRUE, reparameterises smooth associated penalty identity matrix. effect turning last diagonal elements penalty zero, highlights penalty null space. select character, logical, numeric; smooths extract penalties . NULL, default, penalties model smooths drawn. Numeric select indexes smooths order specified formula stored object. Character select matches labels smooths shown example output summary(object). Logical select operates per numeric select order smooths stored. smooth Use select instead. partial_match logical; smooths selected partial matches select? TRUE, select can single string match . margins logical; extract penalty matrices tensor product marginal smooths tensor product?","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/penalty.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract and tidy penalty matrices — penalty","text":"'tibble' (data frame) class penalty_df inheriting tbl_df, following components: .smooth - character; label mgcv uses refer smooth, .type - character; type smooth, .penalty - character; label specific penalty. smooths multiple penalty matrices, penalty component identifies particular penalty matrix uses labelling mgcv uses internally, .row - character; label form fn n integer nth basis function, referencing columns penalty matrix, .col - character; label form fn n integer nth basis function, referencing columns penalty matrix, .value - double; value penalty matrix combination row col,","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/penalty.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Extract and tidy penalty matrices — penalty","text":"print() method uses base::zapsmall() turn small numbers 0s display purposes ; underlying values penalty matrix matrices changed. smooths subject eigendecomposition (e.g. default thin plate regression splines, bs = \"tp\"), signs eigenvectors defined can expect differences across systems penalties smooths system-, OS-, CPU architecture- specific.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/penalty.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Extract and tidy penalty matrices — penalty","text":"Gavin L. Simpson","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/penalty.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract and tidy penalty matrices — penalty","text":"","code":"load_mgcv() dat <- data_sim(\"eg4\", n = 400, seed = 42) m <- gam( y ~ s(x0, bs = \"cr\") + s(x1, bs = \"cr\") + s(x2, by = fac, bs = \"cr\"), data = dat, method = \"REML\" ) # penalties for all smooths penalty(m) #> # A tibble: 405 x 6 #> .smooth .type .penalty .row .col .value #> #> 1 s(x0) CRS s(x0) F1 F1 0.783 #> 2 s(x0) CRS s(x0) F1 F2 -0.635 #> 3 s(x0) CRS s(x0) F1 F3 0.265 #> 4 s(x0) CRS s(x0) F1 F4 -0.0203 #> 5 s(x0) CRS s(x0) F1 F5 0.0441 #> 6 s(x0) CRS s(x0) F1 F6 0.0378 #> 7 s(x0) CRS s(x0) F1 F7 0.0482 #> 8 s(x0) CRS s(x0) F1 F8 0.0216 #> 9 s(x0) CRS s(x0) F1 F9 0.0247 #> 10 s(x0) CRS s(x0) F2 F1 -0.635 #> # i 395 more rows # for a specific smooth penalty(m, select = \"s(x2):fac1\") #> # A tibble: 81 x 6 #> .smooth .type .penalty .row .col .value #> #> 1 s(x2):fac1 CRS s(x2):fac1 F1 F1 1.66 #> 2 s(x2):fac1 CRS s(x2):fac1 F1 F2 -0.755 #> 3 s(x2):fac1 CRS s(x2):fac1 F1 F3 0.430 #> 4 s(x2):fac1 CRS s(x2):fac1 F1 F4 0.0846 #> 5 s(x2):fac1 CRS s(x2):fac1 F1 F5 0.192 #> 6 s(x2):fac1 CRS s(x2):fac1 F1 F6 0.152 #> 7 s(x2):fac1 CRS s(x2):fac1 F1 F7 0.188 #> 8 s(x2):fac1 CRS s(x2):fac1 F1 F8 0.164 #> 9 s(x2):fac1 CRS s(x2):fac1 F1 F9 0.0597 #> 10 s(x2):fac1 CRS s(x2):fac1 F2 F1 -0.755 #> # i 71 more rows"},{"path":"https://gavinsimpson.github.io/gratia/reference/post_draws.html","id":null,"dir":"Reference","previous_headings":"","what":"Low-level Functions to generate draws from the posterior distribution of model coefficients — post_draws","title":"Low-level Functions to generate draws from the posterior distribution of model coefficients — post_draws","text":"Low-level Functions generate draws posterior distribution model coefficients Generate posterior draws fitted model","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/post_draws.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Low-level Functions to generate draws from the posterior distribution of model coefficients — post_draws","text":"","code":"post_draws(model, ...) # Default S3 method post_draws( model, n, method = c(\"gaussian\", \"mh\", \"inla\", \"user\"), mu = NULL, sigma = NULL, n_cores = 1L, burnin = 1000, thin = 1, t_df = 40, rw_scale = 0.25, index = NULL, frequentist = FALSE, unconditional = FALSE, parametrized = TRUE, mvn_method = c(\"mvnfast\", \"mgcv\"), draws = NULL, seed = NULL, ... ) generate_draws(model, ...) # S3 method for class 'gam' generate_draws( model, n, method = c(\"gaussian\", \"mh\", \"inla\"), mu = NULL, sigma = NULL, n_cores = 1L, burnin = 1000, thin = 1, t_df = 40, rw_scale = 0.25, index = NULL, frequentist = FALSE, unconditional = FALSE, mvn_method = c(\"mvnfast\", \"mgcv\"), seed = NULL, ... )"},{"path":"https://gavinsimpson.github.io/gratia/reference/post_draws.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Low-level Functions to generate draws from the posterior distribution of model coefficients — post_draws","text":"model fitted R model. Currently models fitted mgcv::gam() mgcv::bam(), return object inherits objects supported. , \"inherits\" used loose fashion; models fitted scam::scam() support even though models strictly inherit class \"gam\" far inherits() concerned. ... arguments passed methods. n numeric; number posterior draws take. method character; algorithm use sample posterior. Currently implemented methods : \"gaussian\" \"mh\". \"gaussian\" calls gaussian_draws() uses Gaussian approximation posterior distribution. \"mh\" uses simple Metropolis Hasting sampler alternates static proposals based Gaussian approximation posterior, random walk proposals. Note, setting t_df low value result heavier-tailed statistic proposals. See mgcv::gam.mh() details. mu numeric; user-supplied mean vector (vector model coefficients). Currently ignored. sigma matrix; user-supplied covariance matrix mu. Currently ignored. n_cores integer; number CPU cores use generating multivariate normal distributed random values. used mvn_method = \"mvnfast\" method = \"gaussian\". burnin numeric; length initial burn period discard. See mgcv::gam.mh(). thin numeric; retain thin samples. See mgcv::gam.mh(). t_df numeric; degrees freedom static multivariate t proposal. See mgcv::gam.mh(). rw_scale numeric; factor scale posterior covariance matrix generating random walk proposals. See mgcv::gam.mh(). index numeric; vector indices coefficients use. Can used subset mean vector covariance matrix extracted model. frequentist logical; TRUE, frequentist covariance matrix parameter estimates used. FALSE, Bayesian posterior covariance matrix parameters used. See mgcv::vcov.gam(). unconditional logical; TRUE Bayesian smoothing parameter uncertainty corrected covariance matrix used, available model. See mgcv::vcov.gam(). parametrized logical; use parametrized coefficients covariance matrix, respect linear inequality constraints model. scam::scam() model fits. mvn_method character; one \"mvnfast\" \"mgcv\". default uses mvnfast::rmvn(), can considerably faster generate large numbers MVN random values mgcv::rmvn(), might work marginal fits, covariance matrix close singular. draws matrix; user supplied posterior draws used method = \"user\". seed numeric; random seed use. NULL, random seed generated without affecting current state R's RNG.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/post_link_funs.html","id":null,"dir":"Reference","previous_headings":"","what":"A list of transformation functions named for LSS parameters in a GAMLSS — post_link_funs","title":"A list of transformation functions named for LSS parameters in a GAMLSS — post_link_funs","text":"list transformation functions named LSS parameters GAMLSS","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/post_link_funs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A list of transformation functions named for LSS parameters in a GAMLSS — post_link_funs","text":"","code":"post_link_funs( location = identity_fun, scale = identity_fun, shape = identity_fun, skewness = identity_fun, kurtosis = identity_fun, power = identity_fun, pi = identity_fun )"},{"path":"https://gavinsimpson.github.io/gratia/reference/posterior_samples.html","id":null,"dir":"Reference","previous_headings":"","what":"Draw samples from the posterior distribution of an estimated model — posterior_samples","title":"Draw samples from the posterior distribution of an estimated model — posterior_samples","text":"Draw samples posterior distribution estimated model","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/posterior_samples.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Draw samples from the posterior distribution of an estimated model — posterior_samples","text":"","code":"posterior_samples(model, ...) # S3 method for class 'gam' posterior_samples( model, n = 1, data = newdata, seed = NULL, method = c(\"gaussian\", \"mh\", \"inla\", \"user\"), n_cores = 1, burnin = 1000, thin = 1, t_df = 40, rw_scale = 0.25, freq = FALSE, unconditional = FALSE, weights = NULL, draws = NULL, mvn_method = c(\"mvnfast\", \"mgcv\"), ..., newdata = NULL, ncores = NULL )"},{"path":"https://gavinsimpson.github.io/gratia/reference/posterior_samples.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Draw samples from the posterior distribution of an estimated model — posterior_samples","text":"model fitted model supported types ... arguments passed methods. fitted_samples(), passed mgcv::predict.gam(). posterior_samples() passed fitted_samples(). predicted_samples() passed relevant simulate() method. n numeric; number posterior samples return. data data frame; new observations posterior draws model evaluated. supplied, data used fit model used data, available model. seed numeric; random seed simulations. method character; method used draw samples posterior distribution. \"gaussian\" uses Gaussian (Laplace) approximation posterior. \"mh\" uses Metropolis Hastings sampler alternates t proposals proposals based shrunken version posterior covariance matrix. \"inla\" uses variant Integrated Nested Laplace Approximation due Wood (2019), (currently implemented). \"user\" allows user-supplied posterior draws (currently implemented). n_cores number cores generating random variables multivariate normal distribution. Passed mvnfast::rmvn(). Parallelization take place OpenMP supported (appears work Windows current R). burnin numeric; number samples discard burnin draws. used method = \"mh\". thin numeric; number samples skip taking n draws. Results thin * n draws posterior taken. used method = \"mh\". t_df numeric; degrees freedom t distribution proposals. used method = \"mh\". rw_scale numeric; Factor scale posterior covariance matrix generating random walk proposals. Negative non finite skip random walk step. used method = \"mh\". freq logical; TRUE use frequentist covariance matrix parameter estimators, FALSE use Bayesian posterior covariance matrix parameters. unconditional logical; TRUE (freq == FALSE) Bayesian smoothing parameter uncertainty corrected covariance matrix used, available. weights numeric; vector prior weights. data null defaults object[[\"prior.weights\"]], otherwise vector ones. draws matrix; user supplied posterior draws used method = \"user\". mvn_method character; one \"mvnfast\" \"mgcv\". default uses mvnfast::rmvn(), can considerably faster generate large numbers MVN random values mgcv::rmvn(), might work marginal fits, covariance matrix close singular. newdata Deprecated: use data instead. ncores Deprecated; use n_cores instead. number cores generating random variables multivariate normal distribution. Passed mvnfast::rmvn(). Parallelization take place OpenMP supported (appears work Windows current R).","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/posterior_samples.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Draw samples from the posterior distribution of an estimated model — posterior_samples","text":"tibble (data frame) 3 columns containing posterior predicted values long format. columns row (integer) row data posterior draw relates , draw (integer) index, range 1:n, indicating draw row relates , response (numeric) predicted response indicated row data.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/posterior_samples.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Draw samples from the posterior distribution of an estimated model — posterior_samples","text":"Models offset terms supplied via offset argument mgcv::gam() etc. ignored mgcv::predict.gam(). , kind offset term also ignored posterior_samples(). Offset terms included model formula supplied mgcv::gam() etc ignored posterior samples produced reflect offset term values. side effect requiring new data values provided posterior_samples() via data argument must include offset variable.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/posterior_samples.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Draw samples from the posterior distribution of an estimated model — posterior_samples","text":"Wood, S.N., (2020). Simplified integrated nested Laplace approximation. Biometrika 107, 223–230. doi:10.1093/biomet/asz044","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/posterior_samples.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Draw samples from the posterior distribution of an estimated model — posterior_samples","text":"Gavin L. Simpson","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/predicted_samples.html","id":null,"dir":"Reference","previous_headings":"","what":"Draw new response values from the conditional distribution of the response — predicted_samples","title":"Draw new response values from the conditional distribution of the response — predicted_samples","text":"Predicted values response (new response data) drawn fitted model, created via simulate() (e.g. simulate.gam()) returned tidy, long, format. predicted values include uncertainty estimated model; simply draws conditional distribution response.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/predicted_samples.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Draw new response values from the conditional distribution of the response — predicted_samples","text":"","code":"predicted_samples(model, ...) # S3 method for class 'gam' predicted_samples( model, n = 1, data = newdata, seed = NULL, weights = NULL, ..., newdata = NULL )"},{"path":"https://gavinsimpson.github.io/gratia/reference/predicted_samples.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Draw new response values from the conditional distribution of the response — predicted_samples","text":"model fitted model supported types ... arguments passed methods. fitted_samples(), passed mgcv::predict.gam(). posterior_samples() passed fitted_samples(). predicted_samples() passed relevant simulate() method. n numeric; number posterior samples return. data data frame; new observations posterior draws model evaluated. supplied, data used fit model used data, available model. seed numeric; random seed simulations. weights numeric; vector prior weights. data null defaults object[[\"prior.weights\"]], otherwise vector ones. newdata Deprecated: use data instead.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/predicted_samples.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Draw new response values from the conditional distribution of the response — predicted_samples","text":"tibble (data frame) 3 columns containing posterior predicted values long format. columns row (integer) row data posterior draw relates , draw (integer) index, range 1:n, indicating draw row relates , response (numeric) predicted response indicated row data.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/predicted_samples.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Draw new response values from the conditional distribution of the response — predicted_samples","text":"Gavin L. Simpson","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/predicted_samples.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Draw new response values from the conditional distribution of the response — predicted_samples","text":"","code":"load_mgcv() dat <- data_sim(\"eg1\", n = 1000, dist = \"normal\", scale = 2, seed = 2) m <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = dat, method = \"REML\") predicted_samples(m, n = 5, seed = 42) #> # A tibble: 5,000 x 3 #> .row .draw .response #> #> 1 1 1 8.93 #> 2 2 1 4.23 #> 3 3 1 7.71 #> 4 4 1 8.51 #> 5 5 1 10.1 #> 6 6 1 8.20 #> 7 7 1 8.95 #> 8 8 1 7.20 #> 9 9 1 18.1 #> 10 10 1 12.7 #> # i 4,990 more rows ## Can pass arguments to predict.gam() newd <- data.frame( x0 = runif(10), x1 = runif(10), x2 = runif(10), x3 = runif(10) ) ## Exclude s(x2) predicted_samples(m, n = 5, newd, exclude = \"s(x2)\", seed = 25) #> # A tibble: 50 x 3 #> .row .draw .response #> #> 1 1 1 9.42 #> 2 2 1 6.97 #> 3 3 1 8.10 #> 4 4 1 9.95 #> 5 5 1 6.75 #> 6 6 1 10.3 #> 7 7 1 10.8 #> 8 8 1 10.5 #> 9 9 1 8.43 #> 10 10 1 12.2 #> # i 40 more rows ## Exclude s(x1) predicted_samples(m, n = 5, newd, exclude = \"s(x1)\", seed = 25) #> # A tibble: 50 x 3 #> .row .draw .response #> #> 1 1 1 6.05 #> 2 2 1 5.28 #> 3 3 1 5.96 #> 4 4 1 13.7 #> 5 5 1 4.36 #> 6 6 1 5.11 #> 7 7 1 12.5 #> 8 8 1 5.66 #> 9 9 1 12.6 #> 10 10 1 8.38 #> # i 40 more rows ## Select which terms --- result should be the same as previous ## but note that we have to include any parametric terms, including the ## constant term predicted_samples(m, n = 5, newd, seed = 25, terms = c(\"Intercept\", \"s(x0)\", \"s(x2)\", \"s(x3)\") ) #> # A tibble: 50 x 3 #> .row .draw .response #> #> 1 1 1 -1.94 #> 2 2 1 -2.71 #> 3 3 1 -2.03 #> 4 4 1 5.73 #> 5 5 1 -3.63 #> 6 6 1 -2.87 #> 7 7 1 4.48 #> 8 8 1 -2.33 #> 9 9 1 4.65 #> 10 10 1 0.395 #> # i 40 more rows"},{"path":"https://gavinsimpson.github.io/gratia/reference/qq_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Quantile-quantile plot of model residuals — qq_plot","title":"Quantile-quantile plot of model residuals — qq_plot","text":"Quantile-quantile plots (QQ-plots) GAMs using reference quantiles Augustin et al (2012).","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/qq_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Quantile-quantile plot of model residuals — qq_plot","text":"","code":"qq_plot(model, ...) # Default S3 method qq_plot(model, ...) # S3 method for class 'gam' qq_plot( model, method = c(\"uniform\", \"simulate\", \"normal\", \"direct\"), type = c(\"deviance\", \"response\", \"pearson\"), n_uniform = 10, n_simulate = 50, seed = NULL, level = 0.9, ylab = NULL, xlab = NULL, title = NULL, subtitle = NULL, caption = NULL, ci_col = \"black\", ci_alpha = 0.2, point_col = \"black\", point_alpha = 1, line_col = \"red\", ... ) # S3 method for class 'glm' qq_plot(model, ...) # S3 method for class 'lm' qq_plot(model, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/qq_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Quantile-quantile plot of model residuals — qq_plot","text":"model fitted model. Currently models inheriting class \"gam\", well classes \"glm\" \"lm\" calls stats::glm stats::lm supported. ... arguments passed ot methods. method character; method used generate theoretical quantiles. default \"uniform\", generates reference quantiles using random draws uniform distribution inverse cummulative distribution function (CDF) fitted values. reference quantiles averaged n_uniform draws. \"simulate\" generates reference quantiles simulating new response data model observed values covariates, residualised generate reference quantiles, using n_simulate simulated data sets. \"normal\" generates reference quantiles using standard normal distribution. \"uniform\" computationally efficient, \"simulate\" allows reference bands drawn QQ-plot. \"normal\" avoided used fall back random number generator (\"simulate\") inverse CDF available family used model fitting (`\"uniform\"“). Note method = \"direct\" deprecated favour method = \"uniform\". type character; type residuals use. \"deviance\", \"response\", \"pearson\" residuals allowed. n_uniform numeric; number times randomize uniform quantiles direct computation method (method = \"uniform\"). n_simulate numeric; number data sets simulate estimated model using simulation method (method = \"simulate\"). seed numeric; random number seed use method = \"simulate\" method = \"uniform\". level numeric; coverage level reference intervals. Must strictly 0 < level < 1. used method = \"simulate\". ylab character expression; label y axis. supplied, suitable label generated. xlab character expression; label y axis. supplied, suitable label generated. title character expression; title plot. See ggplot2::labs(). May vector, one per penalty. subtitle character expression; subtitle plot. See ggplot2::labs(). May vector, one per penalty. caption character expression; plot caption. See ggplot2::labs(). May vector, one per penalty. ci_col fill colour reference interval method = \"simulate\". ci_alpha alpha transparency reference interval method = \"simulate\". point_col colour points QQ plot. point_alpha alpha transparency points QQ plot. line_col colour used draw reference line.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/qq_plot.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Quantile-quantile plot of model residuals — qq_plot","text":"wording used mgcv::qq.gam() uses direct reference simulated residuals method (method = \"simulated\"). avoid confusion, method = \"direct\" deprecated favour method = \"uniform\".","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/qq_plot.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Quantile-quantile plot of model residuals — qq_plot","text":"underlying methodology used method \"simulate\" \"uniform\" described Augustin et al (2012): Augustin, N.H., Sauleau, E.-., Wood, S.N., (2012) quantile quantile plots generalized linear models. Computational Statatistics Data Analysis 56, 2404-2409 doi:10.1016/j.csda.2012.01.026 .","code":""},{"path":[]},{"path":"https://gavinsimpson.github.io/gratia/reference/qq_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Quantile-quantile plot of model residuals — qq_plot","text":"","code":"load_mgcv() ## simulate binomial data... dat <- data_sim(\"eg1\", n = 200, dist = \"binary\", scale = .33, seed = 0) p <- binomial()$linkinv(dat$f) # binomial p n <- sample(c(1, 3), 200, replace = TRUE) # binomial n dat <- transform(dat, y = rbinom(n, n, p), n = n) m <- gam(y / n ~ s(x0) + s(x1) + s(x2) + s(x3), family = binomial, data = dat, weights = n, method = \"REML\" ) ## Q-Q plot; default using direct randomization of uniform quantiles qq_plot(m) ## Alternatively use simulate new data from the model, which ## allows construction of reference intervals for the Q-Q plot qq_plot(m, method = \"simulate\", seed = 42, point_col = \"steelblue\", point_alpha = 0.4 ) ## ... or use the usual normality assumption qq_plot(m, method = \"normal\")"},{"path":"https://gavinsimpson.github.io/gratia/reference/ref_level.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the reference or specific level of a factor — ref_level","title":"Return the reference or specific level of a factor — ref_level","text":"Extracts reference specific level supplied factor, returning factor levels one supplied.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/ref_level.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the reference or specific level of a factor — ref_level","text":"","code":"ref_level(fct) level(fct, level)"},{"path":"https://gavinsimpson.github.io/gratia/reference/ref_level.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the reference or specific level of a factor — ref_level","text":"fct factor; factor reference specific level extracted. level character; specific level extract case level().","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/ref_level.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the reference or specific level of a factor — ref_level","text":"length 1 factor levels supplied factor fct.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/ref_level.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Return the reference or specific level of a factor — ref_level","text":"","code":"f <- factor(sample(letters[1:5], 100, replace = TRUE)) # the reference level ref_level(f) #> [1] a #> Levels: a b c d e # a specific level level(f, level = \"b\") #> [1] b #> Levels: a b c d e # note that the levels will always match the input factor identical(levels(f), levels(ref_level(f))) #> [1] TRUE identical(levels(f), levels(level(f, \"c\"))) #> [1] TRUE"},{"path":"https://gavinsimpson.github.io/gratia/reference/ref_sims.html","id":null,"dir":"Reference","previous_headings":"","what":"Reference simulation data — ref_sims","title":"Reference simulation data — ref_sims","text":"set reference objects testing data_sim().","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/ref_sims.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Reference simulation data — ref_sims","text":"named list simulated data sets created data_sim().","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/reorder_fs_smooth_terms.html","id":null,"dir":"Reference","previous_headings":"","what":"Reorder random factor smooth terms to place factor last — reorder_fs_smooth_terms","title":"Reorder random factor smooth terms to place factor last — reorder_fs_smooth_terms","text":"Reorder random factor smooth terms place factor last","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/reorder_fs_smooth_terms.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reorder random factor smooth terms to place factor last — reorder_fs_smooth_terms","text":"","code":"reorder_fs_smooth_terms(smooth)"},{"path":"https://gavinsimpson.github.io/gratia/reference/reorder_fs_smooth_terms.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reorder random factor smooth terms to place factor last — reorder_fs_smooth_terms","text":"smooth mgcv smooth object","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/reorder_tensor_smooth_terms.html","id":null,"dir":"Reference","previous_headings":"","what":"Reorder tensor product terms for nicer plotting — reorder_tensor_smooth_terms","title":"Reorder tensor product terms for nicer plotting — reorder_tensor_smooth_terms","text":"tensor product smooth 3 terms contains 2d marginal smooth, get nicer output smooth_estimates() hence nicer plot draw.smooth_estimates() method reorder terms smooth vary terms 2d marginal first, terms vary slowly generate data evaluate smooth . results automatically generated data focuses (first one) 2d marginal smooth, end result smooth_estimates() shows 2d smooth changes terms involved smooth.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/reorder_tensor_smooth_terms.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reorder tensor product terms for nicer plotting — reorder_tensor_smooth_terms","text":"","code":"reorder_tensor_smooth_terms(smooth)"},{"path":"https://gavinsimpson.github.io/gratia/reference/reorder_tensor_smooth_terms.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reorder tensor product terms for nicer plotting — reorder_tensor_smooth_terms","text":"smooth mgcv smooth object","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/rep_first_factor_value.html","id":null,"dir":"Reference","previous_headings":"","what":"Repeat the first level of a factor n times — rep_first_factor_value","title":"Repeat the first level of a factor n times — rep_first_factor_value","text":"Function repeat first level factor n times return vector factor original levels intact","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/rep_first_factor_value.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Repeat the first level of a factor n times — rep_first_factor_value","text":"","code":"rep_first_factor_value(f, n)"},{"path":"https://gavinsimpson.github.io/gratia/reference/rep_first_factor_value.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Repeat the first level of a factor n times — rep_first_factor_value","text":"f factor n numeric; number times repeat first level f","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/rep_first_factor_value.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Repeat the first level of a factor n times — rep_first_factor_value","text":"factor length n levels f, whose elements first level f.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/residuals_hist_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Histogram of model residuals — residuals_hist_plot","title":"Histogram of model residuals — residuals_hist_plot","text":"Histogram model residuals","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/residuals_hist_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Histogram of model residuals — residuals_hist_plot","text":"","code":"residuals_hist_plot( model, type = c(\"deviance\", \"pearson\", \"response\"), n_bins = c(\"sturges\", \"scott\", \"fd\"), ylab = NULL, xlab = NULL, title = NULL, subtitle = NULL, caption = NULL )"},{"path":"https://gavinsimpson.github.io/gratia/reference/residuals_hist_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Histogram of model residuals — residuals_hist_plot","text":"model fitted model. Currently class \"gam\". type character; type residuals use. \"deviance\", \"response\", \"pearson\" residuals allowed. n_bins character numeric; either number bins string indicating calculate number bins. ylab character expression; label y axis. supplied, suitable label generated. xlab character expression; label y axis. supplied, suitable label generated. title character expression; title plot. See ggplot2::labs(). subtitle character expression; subtitle plot. See ggplot2::labs(). caption character expression; plot caption. See ggplot2::labs().","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/residuals_linpred_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot of residuals versus linear predictor values — residuals_linpred_plot","title":"Plot of residuals versus linear predictor values — residuals_linpred_plot","text":"Plot residuals versus linear predictor values","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/residuals_linpred_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot of residuals versus linear predictor values — residuals_linpred_plot","text":"","code":"residuals_linpred_plot( model, type = c(\"deviance\", \"pearson\", \"response\"), ylab = NULL, xlab = NULL, title = NULL, subtitle = NULL, caption = NULL, point_col = \"black\", point_alpha = 1, line_col = \"red\" )"},{"path":"https://gavinsimpson.github.io/gratia/reference/residuals_linpred_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot of residuals versus linear predictor values — residuals_linpred_plot","text":"model fitted model. Currently class \"gam\". type character; type residuals use. \"deviance\", \"response\", \"pearson\" residuals allowed. ylab character expression; label y axis. supplied, suitable label generated. xlab character expression; label y axis. supplied, suitable label generated. title character expression; title plot. See ggplot2::labs(). subtitle character expression; subtitle plot. See ggplot2::labs(). caption character expression; plot caption. See ggplot2::labs(). point_col colour used draw points plots. See graphics::par() section Color Specification. passed individual plotting functions, therefore affects points plots. point_alpha numeric; alpha transparency points plots. line_col colour specification 1:1 line.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/response_derivatives.html","id":null,"dir":"Reference","previous_headings":"","what":"Derivatives on the response scale from an estimated GAM — response_derivatives","title":"Derivatives on the response scale from an estimated GAM — response_derivatives","text":"Derivatives response scale estimated GAM","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/response_derivatives.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Derivatives on the response scale from an estimated GAM — response_derivatives","text":"","code":"response_derivatives(object, ...) # Default S3 method response_derivatives(object, ...) # S3 method for class 'gamm' response_derivatives(object, ...) # S3 method for class 'gam' response_derivatives( object, focal = NULL, data = NULL, order = 1L, type = c(\"forward\", \"backward\", \"central\"), scale = c(\"response\", \"linear_predictor\"), method = c(\"gaussian\", \"mh\", \"inla\", \"user\"), n = 100, eps = 1e-07, n_sim = 10000, level = 0.95, seed = NULL, mvn_method = c(\"mvnfast\", \"mgcv\"), ... )"},{"path":"https://gavinsimpson.github.io/gratia/reference/response_derivatives.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Derivatives on the response scale from an estimated GAM — response_derivatives","text":"object R object compute derivatives . ... arguments passed methods fitted_samples() focal character; name focal variable. response derivative response respect variable returned. variables involved model held constant values. can missing supplying data, case, focal variable identified one variable constant. data data frame containing values model covariates evaluate first derivatives smooths. supplied, one variable must held constant value. order numeric; order derivative. type character; type finite difference used. One \"forward\", \"backward\", \"central\". scale character; derivative estimated response linear predictor (link) scale? One \"response\" (default), \"linear predictor\". method character; method used draw samples posterior distribution. \"gaussian\" uses Gaussian (Laplace) approximation posterior. \"mh\" uses Metropolis Hastings sample alternates t proposals proposals based shrunken version posterior covariance matrix. \"inla\" uses variant Integrated Nested Laplace Approximation due Wood (2019), (currently implemented). \"user\" allows user-supplied posterior draws (currently implemented). n numeric; number points evaluate derivative (data supplied). eps numeric; finite difference. n_sim integer; number simulations used computing simultaneous intervals. level numeric; 0 < level < 1; coverage level credible interval. default 0.95 95% interval. seed numeric; random seed simulations. mvn_method character; one \"mvnfast\" \"mgcv\". default uses mvnfast::rmvn(), can considerably faster generate large numbers MVN random values mgcv::rmvn(), might work marginal fits, covariance matrix close singular.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/response_derivatives.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Derivatives on the response scale from an estimated GAM — response_derivatives","text":"tibble, currently following variables: .row: integer, indexing row data row output represents .focal: name variable partial derivative evaluated, .derivative: estimated partial derivative, .lower_ci: lower bound confidence simultaneous interval, .upper_ci: upper bound confidence simultaneous interval, additional columns containing covariate values derivative evaluated.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/response_derivatives.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Derivatives on the response scale from an estimated GAM — response_derivatives","text":"Gavin L. Simpson","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/response_derivatives.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Derivatives on the response scale from an estimated GAM — response_derivatives","text":"","code":"library(\"ggplot2\") library(\"patchwork\") load_mgcv() df <- data_sim(\"eg1\", dist = \"negbin\", scale = 0.25, seed = 42) # fit the GAM (note: for execution time reasons using bam()) m <- bam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = df, family = nb(), method = \"fREML\" ) # data slice through data along x2 - all other covariates will be set to # typical values (value closest to median) ds <- data_slice(m, x2 = evenly(x2, n = 100)) # fitted values along x2 fv <- fitted_values(m, data = ds) # response derivatives - ideally n_sim = >10000 y_d <- response_derivatives(m, data = ds, type = \"central\", focal = \"x2\", eps = 0.01, seed = 21, n_sim = 1000 ) # draw fitted values along x2 p1 <- fv |> ggplot(aes(x = x2, y = .fitted)) + geom_ribbon(aes(ymin = .lower_ci, ymax = .upper_ci, y = NULL), alpha = 0.2 ) + geom_line() + labs( title = \"Estimated count as a function of x2\", y = \"Estimated count\" ) # draw response derivatives p2 <- y_d |> ggplot(aes(x = x2, y = .derivative)) + geom_ribbon(aes(ymin = .lower_ci, ymax = .upper_ci), alpha = 0.2) + geom_line() + labs( title = \"Estimated 1st derivative of estimated count\", y = \"First derivative\" ) # draw both panels p1 + p2 + plot_layout(nrow = 2)"},{"path":"https://gavinsimpson.github.io/gratia/reference/rootogram.html","id":null,"dir":"Reference","previous_headings":"","what":"Rootograms to assess goodness of model fit — rootogram","title":"Rootograms to assess goodness of model fit — rootogram","text":"rootogram model diagnostic tool assesses goodness fit statistical model. observed values response compared expected fitted model. discrete, count responses, frequency count (0, 1, 2, etc) observed data expected conditional distribution response implied model compared. continuous variables, observed expected frequencies obtained grouping data bins. rootogram drawn using ggplot2::ggplot() graphics. design closely follows Kleiber & Zeileis (2016).","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/rootogram.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rootograms to assess goodness of model fit — rootogram","text":"","code":"rootogram(object, ...) # S3 method for class 'gam' rootogram(object, max_count = NULL, breaks = \"Sturges\", ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/rootogram.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rootograms to assess goodness of model fit — rootogram","text":"object R object ... arguments passed methods max_count integer; largest count consider breaks continuous responses, group response. Can anything acceptable breaks argument graphics::hist.default()","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/rootogram.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Rootograms to assess goodness of model fit — rootogram","text":"Kleiber, C., Zeileis, ., (2016) Visualizing Count Data Regressions Using Rootograms. . Stat. 70, 296–303. doi:10.1080/00031305.2016.1173590","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/rootogram.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rootograms to assess goodness of model fit — rootogram","text":"","code":"load_mgcv() df <- data_sim(\"eg1\", n = 1000, dist = \"poisson\", scale = 0.1, seed = 6) # A poisson example m <- gam(y ~ s(x0, bs = \"cr\") + s(x1, bs = \"cr\") + s(x2, bs = \"cr\") + s(x3, bs = \"cr\"), family = poisson(), data = df, method = \"REML\") rg <- rootogram(m) rg #> # A tibble: 21 x 3 #> .bin .observed .fitted #> #> 1 0 113 116.640 #> 2 1 236 227.869 #> 3 2 230 239.168 #> 4 3 200 181.679 #> 5 4 94 113.432 #> 6 5 68 62.4881 #> 7 6 27 31.6795 #> 8 7 22 15.1323 #> 9 8 4 6.88637 #> 10 9 3 2.99628 #> # i 11 more rows draw(rg) # plot the rootogram # A Gaussian example df <- data_sim(\"eg1\", dist = \"normal\", seed = 2) m <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = df, method = \"REML\") draw(rootogram(m, breaks = \"FD\"), type = \"suspended\")"},{"path":"https://gavinsimpson.github.io/gratia/reference/seq_min_max_eps.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a sequence of evenly-spaced values adjusted to accommodate a small adjustment — seq_min_max_eps","title":"Create a sequence of evenly-spaced values adjusted to accommodate a small adjustment — seq_min_max_eps","text":"Creates sequence n evenly-spaced values range min(x) – max(x), minimum maximum adjusted always contained within range x x may shifted forwards backwards amount related eps. particularly useful computing derivatives via finite differences without adjustment may predicting values outside range data hence conmstraints penalty.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/seq_min_max_eps.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a sequence of evenly-spaced values adjusted to accommodate a small adjustment — seq_min_max_eps","text":"","code":"seq_min_max_eps(x, n, order, type = c(\"forward\", \"backward\", \"central\"), eps)"},{"path":"https://gavinsimpson.github.io/gratia/reference/seq_min_max_eps.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a sequence of evenly-spaced values adjusted to accommodate a small adjustment — seq_min_max_eps","text":"x numeric; vector evenly-spaced values returned n numeric; number evenly-spaced values return order integer; order derivative. Either 1 2 first second order derivatives type character; type finite difference used. One \"forward\", \"backward\", \"central\" eps numeric; finite difference","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/seq_min_max_eps.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a sequence of evenly-spaced values adjusted to accommodate a small adjustment — seq_min_max_eps","text":"numeric vector length n.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/shift_values.html","id":null,"dir":"Reference","previous_headings":"","what":"Shift numeric values in a data frame by an amount eps — shift_values","title":"Shift numeric values in a data frame by an amount eps — shift_values","text":"Shift numeric values data frame amount eps","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/shift_values.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Shift numeric values in a data frame by an amount eps — shift_values","text":"","code":"shift_values(df, h, i, FUN = `+`, focal = NULL)"},{"path":"https://gavinsimpson.github.io/gratia/reference/shift_values.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Shift numeric values in a data frame by an amount eps — shift_values","text":"df data frame tibble. h numeric; amount shift values df . logical; vector indexing columns df included shift. FUN function; function applut shift. Typically + -. focal character; focal variable computing partial derivatives. allows shifting focal variable eps.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/simulate.html","id":null,"dir":"Reference","previous_headings":"","what":"Simulate from the posterior distribution of a GAM — simulate.gam","title":"Simulate from the posterior distribution of a GAM — simulate.gam","text":"Simulations posterior distribution fitted GAM model involve computing predicted values observation data simulated data required, generating random draws probability distribution used fitting model.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/simulate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Simulate from the posterior distribution of a GAM — simulate.gam","text":"","code":"# S3 method for class 'gam' simulate( object, nsim = 1, seed = NULL, data = newdata, weights = NULL, ..., newdata = NULL ) # S3 method for class 'gamm' simulate( object, nsim = 1, seed = NULL, data = newdata, weights = NULL, ..., newdata = NULL ) # S3 method for class 'scam' simulate( object, nsim = 1, seed = NULL, data = newdata, weights = NULL, ..., newdata = NULL )"},{"path":"https://gavinsimpson.github.io/gratia/reference/simulate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Simulate from the posterior distribution of a GAM — simulate.gam","text":"object fitted GAM, typically result call mgcv::gam` mgcv::gamm(). nsim numeric; number posterior simulations return. seed numeric; random seed simulations. data data frame; new observations posterior draws model evaluated. supplied, data used fit model used newdata, available object. weights numeric; vector prior weights. newdata null defaults object[[\"prior.weights\"]], otherwise vector ones. ... arguments passed methods. simulate.gam() simulate.scam() pass ... predict.gam(). can pass additional arguments terms, exclude, select model terms included predictions. may useful, example, excluding effects random effect terms. newdata Deprecated. Use data instead.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/simulate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Simulate from the posterior distribution of a GAM — simulate.gam","text":"(Currently) matrix nsim columns.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/simulate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Simulate from the posterior distribution of a GAM — simulate.gam","text":"simulate.gam() function, family component fitted model must contain, updateable contain, required random number generator. See mgcv::fix.family.rd().","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/simulate.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Simulate from the posterior distribution of a GAM — simulate.gam","text":"Gavin L. Simpson","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/simulate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Simulate from the posterior distribution of a GAM — simulate.gam","text":"","code":"load_mgcv() dat <- data_sim(\"eg1\", n = 400, dist = \"normal\", scale = 2, seed = 2) m1 <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = dat, method = \"REML\") sims <- simulate(m1, nsim = 5, seed = 42) head(sims) #> [,1] [,2] [,3] [,4] [,5] #> [1,] 11.445470 11.374304 10.098681 7.264881 8.796630 #> [2,] 6.510912 5.909584 9.057362 7.698084 11.444781 #> [3,] 3.837995 3.230610 3.550240 3.759380 4.774581 #> [4,] 12.361830 11.209226 10.714215 11.861957 10.746417 #> [5,] 14.851461 12.911440 11.356984 15.783913 15.106270 #> [6,] 5.921276 4.158963 5.520856 7.973614 9.654888"},{"path":"https://gavinsimpson.github.io/gratia/reference/smallAges.html","id":null,"dir":"Reference","previous_headings":"","what":"Lead-210 age-depth measurements for Small Water — smallAges","title":"Lead-210 age-depth measurements for Small Water — smallAges","text":"dataset containing lead-210 based age depth measurements SMALL1 core Small Water.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/smallAges.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Lead-210 age-depth measurements for Small Water — smallAges","text":"data frame 12 rows 7 variables.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/smallAges.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Lead-210 age-depth measurements for Small Water — smallAges","text":"Simpson, G.L. (Unpublished data).","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/smallAges.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Lead-210 age-depth measurements for Small Water — smallAges","text":"variables follows: Depth Drymass Date Age Error SedAccRate SedPerCentChange","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/smooth_coef_indices.html","id":null,"dir":"Reference","previous_headings":"","what":"Indices of the parametric terms for a particular smooth — smooth_coef_indices","title":"Indices of the parametric terms for a particular smooth — smooth_coef_indices","text":"Returns vector indices parametric terms represent supplied smooth. Useful extracting model coefficients columns covariance matrix.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/smooth_coef_indices.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Indices of the parametric terms for a particular smooth — smooth_coef_indices","text":"","code":"smooth_coef_indices(smooth)"},{"path":"https://gavinsimpson.github.io/gratia/reference/smooth_coef_indices.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Indices of the parametric terms for a particular smooth — smooth_coef_indices","text":"smooth object inherits class mgcv.smooth","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/smooth_coef_indices.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Indices of the parametric terms for a particular smooth — smooth_coef_indices","text":"numeric vector indices.","code":""},{"path":[]},{"path":"https://gavinsimpson.github.io/gratia/reference/smooth_coef_indices.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Indices of the parametric terms for a particular smooth — smooth_coef_indices","text":"Gavin L. Simpson","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/smooth_coefs.html","id":null,"dir":"Reference","previous_headings":"","what":"Coefficients for a particular smooth — smooth_coefs","title":"Coefficients for a particular smooth — smooth_coefs","text":"Returns vector model coefficients parametric terms represent supplied smooth.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/smooth_coefs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Coefficients for a particular smooth — smooth_coefs","text":"","code":"smooth_coefs(object, ...) # S3 method for class 'gam' smooth_coefs(object, select, term = deprecated(), ...) # S3 method for class 'bam' smooth_coefs(object, select, term = deprecated(), ...) # S3 method for class 'gamm' smooth_coefs(object, select, term = deprecated(), ...) # S3 method for class 'gamm4' smooth_coefs(object, select, term = deprecated(), ...) # S3 method for class 'list' smooth_coefs(object, select, term = deprecated(), ...) # S3 method for class 'mgcv.smooth' smooth_coefs(object, model, ...) # S3 method for class 'scam' smooth_coefs(object, select, term = deprecated(), ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/smooth_coefs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Coefficients for a particular smooth — smooth_coefs","text":"object fitted GAM(M) object, , \"mgcv.smooth\" method, object inherits class mgcv.smooth. ... arguments passed methods. select character; label smooth whose coefficients returned. term Use select instead. model fitted GAM(M) object.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/smooth_coefs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Coefficients for a particular smooth — smooth_coefs","text":"numeric vector model coefficients.","code":""},{"path":[]},{"path":"https://gavinsimpson.github.io/gratia/reference/smooth_coefs.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Coefficients for a particular smooth — smooth_coefs","text":"Gavin L. Simpson","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/smooth_coefs.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Coefficients for a particular smooth — smooth_coefs","text":"","code":"load_mgcv() df <- data_sim(\"eg1\", seed = 2) m <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = df, method = \"REML\") ## IGNORE_RDIFF_BEGIN smooth_coefs(m, select = \"s(x2)\") #> s(x2).1 s(x2).2 s(x2).3 s(x2).4 s(x2).5 s(x2).6 s(x2).7 s(x2).8 #> -6.533373 9.694277 2.194078 -1.967280 -2.374874 1.207638 -1.572586 9.269744 #> s(x2).9 #> 5.622738 ## IGNORE_RDIFF_END"},{"path":"https://gavinsimpson.github.io/gratia/reference/smooth_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate regular data over the covariates of a smooth — smooth_data","title":"Generate regular data over the covariates of a smooth — smooth_data","text":"Generate regular data covariates smooth","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/smooth_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate regular data over the covariates of a smooth — smooth_data","text":"","code":"smooth_data( model, id, n = 100, n_2d = NULL, n_3d = NULL, n_4d = NULL, offset = NULL, include_all = FALSE, var_order = NULL )"},{"path":"https://gavinsimpson.github.io/gratia/reference/smooth_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate regular data over the covariates of a smooth — smooth_data","text":"model fitted model id number ID smooth within model process. n numeric; number new observations generate. n_2d numeric; number new observations generate second dimension 2D smooth. Currently ignored. n_3d numeric; number new observations generate third dimension 3D smooth. n_4d numeric; number new observations generate dimensions higher 2 (!) kD smooth (k >= 4). example, smooth 4D smooth, dimensions 3 4 get n_4d new observations. offset numeric; value model offset use. include_all logical; include covariates involved smooth? FALSE, covariates involved smooth included returned data frame. TRUE, representative value included covariates model actually used smooth. can useful want pass returned data frame mgcv::PredictMat(). var_order character; order terms smooth processed. useful tensor products least one 2d marginal smooth.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/smooth_data.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate regular data over the covariates of a smooth — smooth_data","text":"","code":"load_mgcv() df <- data_sim(\"eg1\", seed = 42) m <- bam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = df) # generate data over range of x1 for smooth s(x1) smooth_data(m, id = 2) #> # A tibble: 100 x 1 #> x1 #> #> 1 0.0004050 #> 2 0.01046 #> 3 0.02052 #> 4 0.03057 #> 5 0.04063 #> 6 0.05069 #> 7 0.06074 #> 8 0.07080 #> 9 0.08086 #> 10 0.09091 #> # i 90 more rows # generate data over range of x1 for smooth s(x1), with typical value for # other covariates in the model smooth_data(m, id = 2, include_all = TRUE) #> # A tibble: 100 x 4 #> x1 x0 x2 x3 #> #> 1 0.0004050 0.4883 0.4708 0.4879 #> 2 0.01046 0.4883 0.4708 0.4879 #> 3 0.02052 0.4883 0.4708 0.4879 #> 4 0.03057 0.4883 0.4708 0.4879 #> 5 0.04063 0.4883 0.4708 0.4879 #> 6 0.05069 0.4883 0.4708 0.4879 #> 7 0.06074 0.4883 0.4708 0.4879 #> 8 0.07080 0.4883 0.4708 0.4879 #> 9 0.08086 0.4883 0.4708 0.4879 #> 10 0.09091 0.4883 0.4708 0.4879 #> # i 90 more rows"},{"path":"https://gavinsimpson.github.io/gratia/reference/smooth_dim.html","id":null,"dir":"Reference","previous_headings":"","what":"Dimension of a smooth — smooth_dim","title":"Dimension of a smooth — smooth_dim","text":"Extracts dimension estimated smooth.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/smooth_dim.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Dimension of a smooth — smooth_dim","text":"","code":"smooth_dim(object) # S3 method for class 'gam' smooth_dim(object) # S3 method for class 'gamm' smooth_dim(object) # S3 method for class 'mgcv.smooth' smooth_dim(object)"},{"path":"https://gavinsimpson.github.io/gratia/reference/smooth_dim.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Dimension of a smooth — smooth_dim","text":"object R object. See Details list supported objects.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/smooth_dim.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Dimension of a smooth — smooth_dim","text":"numeric vector dimensions smooth.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/smooth_dim.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Dimension of a smooth — smooth_dim","text":"generic function methods objects class \"gam\", \"gamm\", \"mgcv.smooth\".","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/smooth_dim.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Dimension of a smooth — smooth_dim","text":"Gavin L. Simpson","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/smooth_estimates.html","id":null,"dir":"Reference","previous_headings":"","what":"Evaluate smooths at covariate values — smooth_estimates","title":"Evaluate smooths at covariate values — smooth_estimates","text":"Evaluate smooth grid evenly spaced value range covariate associated smooth. Alternatively, set points smooth evaluated can supplied. smooth_estimates() new implementation evaluate_smooth(), replaces function, removed package.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/smooth_estimates.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Evaluate smooths at covariate values — smooth_estimates","text":"","code":"smooth_estimates(object, ...) # S3 method for class 'gam' smooth_estimates( object, select = NULL, smooth = deprecated(), n = 100, n_3d = 16, n_4d = 4, data = NULL, unconditional = FALSE, overall_uncertainty = TRUE, dist = NULL, unnest = TRUE, partial_match = FALSE, ... )"},{"path":"https://gavinsimpson.github.io/gratia/reference/smooth_estimates.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Evaluate smooths at covariate values — smooth_estimates","text":"object object class \"gam\" \"gamm\". ... arguments passed methods. select character; select smooth's posterior draw . default (NULL) means posteriors smooths model wil sampled . supplied, character vector requested terms. smooth Use select instead. n numeric; number points range covariate evaluate smooth. n_3d, n_4d numeric; number points range last covariate 3D 4D smooth. default NULL achieves standard behaviour using n points range covariate, resulting n^d evaluation points, d dimension smooth. d > 2 can result many evaluation points slow performance. smooths d > 4, value n_4d used dimensions > 4, unless NULL, case default behaviour (using n dimensions) observed. data data frame covariate values evaluate smooth. unconditional logical; confidence intervals include uncertainty due smoothness selection? TRUE, corrected Bayesian covariance matrix used. overall_uncertainty logical; uncertainty model constant term included standard error evaluate values smooth? dist numeric; greater 0, used determine location far data plotted plotting 2-D smooths. data scaled unit square deciding exclude, dist distance within unit square. See mgcv::exclude..far() details. unnest logical; unnest smooth objects? partial_match logical; case character select, select match partially smooths? partial_match = TRUE, select must single string, character vector length 1.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/smooth_estimates.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Evaluate smooths at covariate values — smooth_estimates","text":"data frame (tibble), class \"smooth_estimates\".","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/smooth_estimates.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Evaluate smooths at covariate values — smooth_estimates","text":"","code":"load_mgcv() dat <- data_sim(\"eg1\", n = 400, dist = \"normal\", scale = 2, seed = 2) m1 <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = dat, method = \"REML\") ## evaluate all smooths smooth_estimates(m1) #> # A tibble: 400 x 9 #> .smooth .type .by .estimate .se x0 x1 x2 x3 #> #> 1 s(x0) TPRS NA -0.966542 0.316118 0.00710904 NA NA NA #> 2 s(x0) TPRS NA -0.925391 0.297170 0.0171157 NA NA NA #> 3 s(x0) TPRS NA -0.884233 0.279256 0.0271224 NA NA NA #> 4 s(x0) TPRS NA -0.843050 0.262594 0.0371291 NA NA NA #> 5 s(x0) TPRS NA -0.801824 0.247376 0.0471358 NA NA NA #> 6 s(x0) TPRS NA -0.760536 0.233728 0.0571425 NA NA NA #> 7 s(x0) TPRS NA -0.719175 0.221701 0.0671492 NA NA NA #> 8 s(x0) TPRS NA -0.677736 0.211261 0.0771559 NA NA NA #> 9 s(x0) TPRS NA -0.636220 0.202303 0.0871626 NA NA NA #> 10 s(x0) TPRS NA -0.594641 0.194685 0.0971693 NA NA NA #> # i 390 more rows ## or selected smooths smooth_estimates(m1, select = c(\"s(x0)\", \"s(x1)\")) #> # A tibble: 200 x 7 #> .smooth .type .by .estimate .se x0 x1 #> #> 1 s(x0) TPRS NA -0.966542 0.316118 0.00710904 NA #> 2 s(x0) TPRS NA -0.925391 0.297170 0.0171157 NA #> 3 s(x0) TPRS NA -0.884233 0.279256 0.0271224 NA #> 4 s(x0) TPRS NA -0.843050 0.262594 0.0371291 NA #> 5 s(x0) TPRS NA -0.801824 0.247376 0.0471358 NA #> 6 s(x0) TPRS NA -0.760536 0.233728 0.0571425 NA #> 7 s(x0) TPRS NA -0.719175 0.221701 0.0671492 NA #> 8 s(x0) TPRS NA -0.677736 0.211261 0.0771559 NA #> 9 s(x0) TPRS NA -0.636220 0.202303 0.0871626 NA #> 10 s(x0) TPRS NA -0.594641 0.194685 0.0971693 NA #> # i 190 more rows"},{"path":"https://gavinsimpson.github.io/gratia/reference/smooth_label.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract the label for a smooth used by 'mgcv' — smooth_label","title":"Extract the label for a smooth used by 'mgcv' — smooth_label","text":"label 'mgcv' uses smooths useful many contexts, including selecting smooths labelling plots. smooth_label() extracts label 'mgcv' smooth object, .e. object inherits class \"mgcv.smooth\". typically found $smooth component GAM fitted mgcv::gam() mgcv::bam(), related functions.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/smooth_label.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract the label for a smooth used by 'mgcv' — smooth_label","text":"","code":"smooth_label(object, ...) # S3 method for class 'gam' smooth_label(object, id, ...) # S3 method for class 'mgcv.smooth' smooth_label(object, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/smooth_label.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract the label for a smooth used by 'mgcv' — smooth_label","text":"object R object. Currently, methods class \"gam\" mgcv smooth objects inheriting class \"mgcv.smooth\" supported. ... arguments passed methods. id numeric; indices smooths whose labels extracted. missing, labels smooths model returned.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/smooth_label.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract the label for a smooth used by 'mgcv' — smooth_label","text":"character vector.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/smooth_label.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract the label for a smooth used by 'mgcv' — smooth_label","text":"","code":"load_mgcv() df <- data_sim(\"gwf2\", n = 100) m <- gam(y ~ s(x), data = df, method = \"REML\") # extract the smooth sm <- get_smooths_by_id(m, id = 1)[[1]] # extract the label smooth_label(sm) #> [1] \"s(x)\" # or directly on the fitted GAM smooth_label(m$smooth[[1]]) #> [1] \"s(x)\" # or extract labels by idex/position smooth_label(m, id = 1) #> [1] \"s(x)\""},{"path":"https://gavinsimpson.github.io/gratia/reference/smooth_samples.html","id":null,"dir":"Reference","previous_headings":"","what":"Posterior draws for individual smooths — smooth_samples","title":"Posterior draws for individual smooths — smooth_samples","text":"Returns draws posterior distributions smooth functions GAM. Useful, example, visualising uncertainty individual estimated functions.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/smooth_samples.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Posterior draws for individual smooths — smooth_samples","text":"","code":"smooth_samples(model, ...) # S3 method for class 'gam' smooth_samples( model, select = NULL, term = deprecated(), n = 1, data = newdata, method = c(\"gaussian\", \"mh\", \"inla\", \"user\"), seed = NULL, freq = FALSE, unconditional = FALSE, n_cores = 1L, n_vals = 200, burnin = 1000, thin = 1, t_df = 40, rw_scale = 0.25, rng_per_smooth = FALSE, draws = NULL, partial_match = NULL, mvn_method = c(\"mvnfast\", \"mgcv\"), ..., newdata = NULL, ncores = NULL )"},{"path":"https://gavinsimpson.github.io/gratia/reference/smooth_samples.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Posterior draws for individual smooths — smooth_samples","text":"model fitted model supported types ... arguments passed methods. fitted_samples(), passed mgcv::predict.gam(). posterior_samples() passed fitted_samples(). predicted_samples() passed relevant simulate() method. select character; select smooth's posterior draw . default (NULL) means posteriors smooths model wil sampled . supplied, character vector requested terms. term Use select instead. n numeric; number posterior samples return. data data frame; new observations posterior draws model evaluated. supplied, data used fit model used data, available model. method character; method used draw samples posterior distribution. \"gaussian\" uses Gaussian (Laplace) approximation posterior. \"mh\" uses Metropolis Hastings sampler alternates t proposals proposals based shrunken version posterior covariance matrix. \"inla\" uses variant Integrated Nested Laplace Approximation due Wood (2019), (currently implemented). \"user\" allows user-supplied posterior draws (currently implemented). seed numeric; random seed simulations. freq logical; TRUE use frequentist covariance matrix parameter estimators, FALSE use Bayesian posterior covariance matrix parameters. unconditional logical; TRUE (freq == FALSE) Bayesian smoothing parameter uncertainty corrected covariance matrix used, available. n_cores number cores generating random variables multivariate normal distribution. Passed mvnfast::rmvn(). Parallelization take place OpenMP supported (appears work Windows current R). n_vals numeric; many locations evaluate smooth data supplied burnin numeric; number samples discard burnin draws. used method = \"mh\". thin numeric; number samples skip taking n draws. Results thin * n draws posterior taken. used method = \"mh\". t_df numeric; degrees freedom t distribution proposals. used method = \"mh\". rw_scale numeric; Factor scale posterior covariance matrix generating random walk proposals. Negative non finite skip random walk step. used method = \"mh\". rng_per_smooth logical; TRUE, behaviour gratia version 0.8.1 earlier used, whereby separate call random number generator (RNG) performed smooth. FALSE, single call RNG performed model parameters draws matrix; user supplied posterior draws used method = \"user\". partial_match logical; smooths selected partial matches select? TRUE, select can single string match . mvn_method character; one \"mvnfast\" \"mgcv\". default uses mvnfast::rmvn(), can considerably faster generate large numbers MVN random values mgcv::rmvn(), might work marginal fits, covariance matrix close singular. newdata Deprecated: use data instead. ncores Deprecated; use n_cores instead. number cores generating random variables multivariate normal distribution. Passed mvnfast::rmvn(). Parallelization take place OpenMP supported (appears work Windows current R).","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/smooth_samples.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Posterior draws for individual smooths — smooth_samples","text":"tibble additional classes \"smooth_samples\" `\"posterior_samples\". \"gam\" method, columns currently returned (order) : .smooth; character vector. Indicates smooth function particular draw, .term; character vector. Similar smooth, contain full label smooth, differentiate factor-smooths example. .; character vector. smooth involves term, variable named , NA_character_ otherwise. .row; integer. vector values seq_len(n_vals), repeated n > 1L. Indexes row data particular draw. .draw; integer. vector integer values indexing particular posterior draw row belongs . .value; numeric. value smooth function posterior draw covariate combination. xxx; numeric. series one columns containing data required smooth, named per variables involved respective smooth. Additional columns present case factor smooths, contain level factor named by_variable particular posterior draw.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/smooth_samples.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"Posterior draws for individual smooths — smooth_samples","text":"set variables returned order tibble subject change future versions. rely position.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/smooth_samples.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Posterior draws for individual smooths — smooth_samples","text":"Gavin L. Simpson","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/smooth_samples.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Posterior draws for individual smooths — smooth_samples","text":"","code":"load_mgcv() dat <- data_sim(\"eg1\", n = 400, seed = 2) m1 <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = dat, method = \"REML\") sms <- smooth_samples(m1, select = \"s(x0)\", n = 5, seed = 42) # \\donttest{ sms #> # A tibble: 1,000 x 8 #> .smooth .term .type .by .row .draw .value x0 #> #> 1 s(x0) s(x0) TPRS NA 1 1 -0.357 0.00711 #> 2 s(x0) s(x0) TPRS NA 1 2 -0.465 0.00711 #> 3 s(x0) s(x0) TPRS NA 1 3 -0.720 0.00711 #> 4 s(x0) s(x0) TPRS NA 1 4 -1.27 0.00711 #> 5 s(x0) s(x0) TPRS NA 1 5 -1.18 0.00711 #> 6 s(x0) s(x0) TPRS NA 2 1 -0.365 0.0121 #> 7 s(x0) s(x0) TPRS NA 2 2 -0.464 0.0121 #> 8 s(x0) s(x0) TPRS NA 2 3 -0.708 0.0121 #> 9 s(x0) s(x0) TPRS NA 2 4 -1.24 0.0121 #> 10 s(x0) s(x0) TPRS NA 2 5 -1.16 0.0121 #> # i 990 more rows # } ## A factor by example (with a spurious covariate x0) dat <- data_sim(\"eg4\", n = 1000, seed = 2) ## fit model... m2 <- gam(y ~ fac + s(x2, by = fac) + s(x0), data = dat) sms <- smooth_samples(m2, n = 5, seed = 42) draw(sms)"},{"path":[]},{"path":"https://gavinsimpson.github.io/gratia/reference/smooth_terms.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"List the variables involved in smooths — smooth_terms","text":"","code":"smooth_terms(object, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/smooth_terms.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"List the variables involved in smooths — smooth_terms","text":"object R object result call mgcv::gam(), mgcv::bam(), mgcv::gamm(), inherits classes \"gam\" \"mgcv.smooth\", \"fs.interaction\". ... arguments passed methods. Currently unused.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/smooth_type.html","id":null,"dir":"Reference","previous_headings":"","what":"Determine the type of smooth and return it n a human readable form — smooth_type","title":"Determine the type of smooth and return it n a human readable form — smooth_type","text":"Determine type smooth return n human readable form","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/smooth_type.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Determine the type of smooth and return it n a human readable form — smooth_type","text":"","code":"smooth_type(smooth) # Default S3 method smooth_type(smooth) # S3 method for class 'tprs.smooth' smooth_type(smooth) # S3 method for class 'ts.smooth' smooth_type(smooth) # S3 method for class 'cr.smooth' smooth_type(smooth) # S3 method for class 'cs.smooth' smooth_type(smooth) # S3 method for class 'cyclic.smooth' smooth_type(smooth) # S3 method for class 'pspline.smooth' smooth_type(smooth) # S3 method for class 'cpspline.smooth' smooth_type(smooth) # S3 method for class 'Bspline.smooth' smooth_type(smooth) # S3 method for class 'duchon.spline' smooth_type(smooth) # S3 method for class 'fs.interaction' smooth_type(smooth) # S3 method for class 'sz.interaction' smooth_type(smooth) # S3 method for class 'gp.smooth' smooth_type(smooth) # S3 method for class 'mrf.smooth' smooth_type(smooth) # S3 method for class 'random.effect' smooth_type(smooth) # S3 method for class 'sw' smooth_type(smooth) # S3 method for class 'sf' smooth_type(smooth) # S3 method for class 'soap.film' smooth_type(smooth) # S3 method for class 't2.smooth' smooth_type(smooth) # S3 method for class 'sos.smooth' smooth_type(smooth) # S3 method for class 'tensor.smooth' smooth_type(smooth) # S3 method for class 'mpi.smooth' smooth_type(smooth) # S3 method for class 'mpd.smooth' smooth_type(smooth) # S3 method for class 'cx.smooth' smooth_type(smooth) # S3 method for class 'cv.smooth' smooth_type(smooth) # S3 method for class 'micx.smooth' smooth_type(smooth) # S3 method for class 'micv.smooth' smooth_type(smooth) # S3 method for class 'mdcx.smooth' smooth_type(smooth) # S3 method for class 'mdcv.smooth' smooth_type(smooth) # S3 method for class 'miso.smooth' smooth_type(smooth) # S3 method for class 'mifo.smooth' smooth_type(smooth)"},{"path":"https://gavinsimpson.github.io/gratia/reference/smooth_type.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Determine the type of smooth and return it n a human readable form — smooth_type","text":"smooth object inheriting class mgcv.smooth.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/smooths.html","id":null,"dir":"Reference","previous_headings":"","what":"Names of smooths in a GAM — smooths","title":"Names of smooths in a GAM — smooths","text":"Names smooths GAM","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/smooths.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Names of smooths in a GAM — smooths","text":"","code":"smooths(object) # Default S3 method smooths(object) # S3 method for class 'gamm' smooths(object)"},{"path":"https://gavinsimpson.github.io/gratia/reference/smooths.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Names of smooths in a GAM — smooths","text":"object fitted GAM related model. Typically result call mgcv::gam(), mgcv::bam(), mgcv::gamm().","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/spline_values.html","id":null,"dir":"Reference","previous_headings":"","what":"Evaluate a spline at provided covariate values — spline_values","title":"Evaluate a spline at provided covariate values — spline_values","text":"Evaluate spline provided covariate values","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/spline_values.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Evaluate a spline at provided covariate values — spline_values","text":"","code":"spline_values( smooth, data, model, unconditional, overall_uncertainty = TRUE, frequentist = FALSE )"},{"path":"https://gavinsimpson.github.io/gratia/reference/spline_values.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Evaluate a spline at provided covariate values — spline_values","text":"smooth currently object inherits class mgcv.smooth. data data frame values evaluate smooth . model fitted model; currently mgcv::gam() mgcv::bam() models suported. unconditional logical; confidence intervals include uncertainty due smoothness selection? TRUE, corrected Bayesian covariance matrix used. overall_uncertainty logical; uncertainty model constant term included standard error evaluate values smooth? frequentist logical; use frequentist covariance matrix?","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/spline_values2.html","id":null,"dir":"Reference","previous_headings":"","what":"Evaluate a spline at provided covariate values — spline_values2","title":"Evaluate a spline at provided covariate values — spline_values2","text":"function spline_values2() renamed spline_values() version 0.9.0. allowed following removal evaluate_smooth(), function using spline_values(). spline_values2() renamed spline_values().","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/spline_values2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Evaluate a spline at provided covariate values — spline_values2","text":"","code":"spline_values2( smooth, data, model, unconditional, overall_uncertainty = TRUE, frequentist = FALSE )"},{"path":"https://gavinsimpson.github.io/gratia/reference/spline_values2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Evaluate a spline at provided covariate values — spline_values2","text":"smooth currently object inherits class mgcv.smooth. data optional data frame values evaluate smooth . model fitted model; currently mgcv::gam() mgcv::bam() models suported. unconditional logical; confidence intervals include uncertainty due smoothness selection? TRUE, corrected Bayesian covariance matrix used. overall_uncertainty logical; uncertainty model constant term included standard error evaluate values smooth? frequentist logical; use frequentist covariance matrix?","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/term_names.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract names of all variables needed to fit a GAM or a smooth — term_names","title":"Extract names of all variables needed to fit a GAM or a smooth — term_names","text":"Extract names variables needed fit GAM smooth","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/term_names.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract names of all variables needed to fit a GAM or a smooth — term_names","text":"","code":"term_names(object, ...) # S3 method for class 'gam' term_names(object, ...) # S3 method for class 'mgcv.smooth' term_names(object, ...) # S3 method for class 'gamm' term_names(object, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/term_names.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract names of all variables needed to fit a GAM or a smooth — term_names","text":"object fitted GAM object (inheriting class \"gam\" mgcv::smooth.construct smooth object, inheriting class \"mgcv.smooth\". ... arguments passed methods. currently used.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/term_names.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract names of all variables needed to fit a GAM or a smooth — term_names","text":"vector variable names required terms model","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/term_variables.html","id":null,"dir":"Reference","previous_headings":"","what":"Names of variables involved in a specified model term — term_variables","title":"Names of variables involved in a specified model term — term_variables","text":"Given name (term label) term model, returns names variables involved term.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/term_variables.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Names of variables involved in a specified model term — term_variables","text":"","code":"term_variables(object, term, ...) # S3 method for class 'terms' term_variables(object, term, ...) # S3 method for class 'gam' term_variables(object, term, ...) # S3 method for class 'bam' term_variables(object, term, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/term_variables.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Names of variables involved in a specified model term — term_variables","text":"object R object method dispatch performed term character; name model term, sense attr(terms(object), \"term.labels\"). Currently checked see term exists model. ... arguments passed methods.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/term_variables.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Names of variables involved in a specified model term — term_variables","text":"character vector variable names.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/theta.html","id":null,"dir":"Reference","previous_headings":"","what":"General extractor for additional parameters in mgcv models — theta","title":"General extractor for additional parameters in mgcv models — theta","text":"General extractor additional parameters mgcv models","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/theta.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"General extractor for additional parameters in mgcv models — theta","text":"","code":"theta(object, ...) # S3 method for class 'gam' theta(object, transform = TRUE, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/theta.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"General extractor for additional parameters in mgcv models — theta","text":"object fitted model ... arguments passed methods. transform logical; transform natural scale parameter","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/theta.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"General extractor for additional parameters in mgcv models — theta","text":"Returns numeric vector additional parameters","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/theta.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"General extractor for additional parameters in mgcv models — theta","text":"","code":"load_mgcv() df <- data_sim(\"eg1\", dist = \"poisson\", seed = 42, scale = 1 / 5) m <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = df, method = \"REML\", family = nb() ) p <- theta(m)"},{"path":"https://gavinsimpson.github.io/gratia/reference/tidy_basis.html","id":null,"dir":"Reference","previous_headings":"","what":"A tidy basis representation of a smooth object — tidy_basis","title":"A tidy basis representation of a smooth object — tidy_basis","text":"Takes object class mgcv.smooth returns tidy representation basis.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/tidy_basis.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A tidy basis representation of a smooth object — tidy_basis","text":"","code":"tidy_basis(smooth, data = NULL, at = NULL, coefs = NULL, p_ident = NULL)"},{"path":"https://gavinsimpson.github.io/gratia/reference/tidy_basis.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A tidy basis representation of a smooth object — tidy_basis","text":"smooth smooth object inheriting class \"mgcv.smooth\". Typically, objects returned part fitted GAM GAMM $smooth component model object $gam$smooth component model fitted mgcv::gamm() gamm4::gamm4(). data data frame containing variables used smooth. data frame containing values smooth covariate(s) basis evaluated. coefs numeric; optional vector coefficients smooth p_ident logical vector; used handling scam::scam() smooths.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/tidy_basis.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A tidy basis representation of a smooth object — tidy_basis","text":"tibble.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/tidy_basis.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"A tidy basis representation of a smooth object — tidy_basis","text":"Gavin L. Simpson","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/tidy_basis.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A tidy basis representation of a smooth object — tidy_basis","text":"","code":"load_mgcv() df <- data_sim(\"eg1\", n = 400, seed = 42) # fit model m <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = df, method = \"REML\") # tidy representaition of a basis for a smooth definition # extract the smooth sm <- get_smooth(m, \"s(x2)\") # get the tidy basis - need to pass where we want it to be evaluated bf <- tidy_basis(sm, at = df) # can weight the basis by the model coefficients for this smooth bf <- tidy_basis(sm, at = df, coefs = smooth_coefs(sm, model = m))"},{"path":"https://gavinsimpson.github.io/gratia/reference/to_na.html","id":null,"dir":"Reference","previous_headings":"","what":"Sets the elements of vector to NA — to_na","title":"Sets the elements of vector to NA — to_na","text":"Given vector indexing elements x, sets selected elements x NA.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/to_na.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sets the elements of vector to NA — to_na","text":"","code":"to_na(x, i)"},{"path":"https://gavinsimpson.github.io/gratia/reference/to_na.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sets the elements of vector to NA — to_na","text":"x vector values vector values used subset x","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/to_na.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Sets the elements of vector to NA — to_na","text":"Returns x possibly elements set NA","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/too_far.html","id":null,"dir":"Reference","previous_headings":"","what":"Exclude values that lie too far from the support of data — too_far","title":"Exclude values that lie too far from the support of data — too_far","text":"Identifies pairs covariate values lie far original data. function currently basic wrapper around mgcv::exclude..far().","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/too_far.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Exclude values that lie too far from the support of data — too_far","text":"","code":"too_far(x, y, ref_1, ref_2, dist = NULL)"},{"path":"https://gavinsimpson.github.io/gratia/reference/too_far.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Exclude values that lie too far from the support of data — too_far","text":"x, y numeric; vector values covariates compare observed data ref_1, ref_2 numeric; vectors covariate values represent reference x1 x2` compared dist supplied, numeric vector length 1 representing distance data beyond observation excluded. example, want exclude values lie observation 10% range observed data, use 0.1.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/too_far.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Exclude values that lie too far from the support of data — too_far","text":"Returns logical vector length x1.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/too_far_to_na.html","id":null,"dir":"Reference","previous_headings":"","what":"Set rows of data to NA if the lie too far from a reference set of values — too_far_to_na","title":"Set rows of data to NA if the lie too far from a reference set of values — too_far_to_na","text":"Set rows data NA lie far reference set values","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/too_far_to_na.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set rows of data to NA if the lie too far from a reference set of values — too_far_to_na","text":"","code":"too_far_to_na(smooth, input, reference, cols, dist = NULL)"},{"path":"https://gavinsimpson.github.io/gratia/reference/too_far_to_na.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set rows of data to NA if the lie too far from a reference set of values — too_far_to_na","text":"smooth mgcv smooth object input data frame containing input observations columns set NA reference data frame containing reference values cols character vector columns whose elements set NA data lies far reference set dist numeric, distance reference set beyond elements input set NA","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/transform_fun.html","id":null,"dir":"Reference","previous_headings":"","what":"Transform estimated values and confidence intervals by applying a function — transform_fun","title":"Transform estimated values and confidence intervals by applying a function — transform_fun","text":"Transform estimated values confidence intervals applying function","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/transform_fun.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transform estimated values and confidence intervals by applying a function — transform_fun","text":"","code":"transform_fun(object, fun = NULL, ...) # S3 method for class 'smooth_estimates' transform_fun(object, fun = NULL, constant = NULL, ...) # S3 method for class 'smooth_samples' transform_fun(object, fun = NULL, constant = NULL, ...) # S3 method for class 'mgcv_smooth' transform_fun(object, fun = NULL, constant = NULL, ...) # S3 method for class 'evaluated_parametric_term' transform_fun(object, fun = NULL, constant = NULL, ...) # S3 method for class 'parametric_effects' transform_fun(object, fun = NULL, constant = NULL, ...) # S3 method for class 'tbl_df' transform_fun(object, fun = NULL, column = NULL, constant = NULL, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/transform_fun.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Transform estimated values and confidence intervals by applying a function — transform_fun","text":"object object apply transform function . fun function apply. ... additional arguments passed methods. constant numeric; constant apply transformation. column character; \"tbl_df\" method, column transform.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/transform_fun.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Transform estimated values and confidence intervals by applying a function — transform_fun","text":"Returns object estimate upper lower values confidence interval transformed via function.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/transform_fun.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Transform estimated values and confidence intervals by applying a function — transform_fun","text":"Gavin L. Simpson","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/typical_values.html","id":null,"dir":"Reference","previous_headings":"","what":"Typical values of model covariates — typical_values","title":"Typical values of model covariates — typical_values","text":"Typical values model covariates","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/typical_values.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Typical values of model covariates — typical_values","text":"","code":"typical_values(object, ...) # S3 method for class 'gam' typical_values( object, vars = everything(), envir = environment(formula(object)), data = NULL, ... ) # S3 method for class 'data.frame' typical_values(object, vars = everything(), ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/typical_values.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Typical values of model covariates — typical_values","text":"object fitted GAM(M) model. ... arguments passed methods. vars terms include exclude returned object. Uses tidyselect principles. envir environment within recreate data used fit object. data optional data frame data used fit model reconstruction data model work.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/user_draws.html","id":null,"dir":"Reference","previous_headings":"","what":"Handle user-supplied posterior draws — user_draws","title":"Handle user-supplied posterior draws — user_draws","text":"Handle user-supplied posterior draws","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/user_draws.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Handle user-supplied posterior draws — user_draws","text":"","code":"user_draws(model, draws, ...) # S3 method for class 'gam' user_draws(model, draws, index = NULL, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/user_draws.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Handle user-supplied posterior draws — user_draws","text":"model fitted R model. Currently models fitted mgcv::gam() mgcv::bam(), return object inherits objects supported. , \"inherits\" used loose fashion; models fitted scam::scam() support even though models strictly inherit class \"gam\" far inherits() concerned. draws matrix; user supplied posterior draws used method = \"user\". ... arguments passed methods. index vector index (subset) columns draws.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/user_draws.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Handle user-supplied posterior draws — user_draws","text":"supplied draws must matrix (currently), 1 column per model coefficient, 1 row per posterior draw. \"gam\" method argument index, can used subset (select) coefficients (columns) draws. index can valid way selecting (indexing) columns matrix. index useful set posterior draws entire model (say mgcv::gam.mh()) wish use draws individual smooth, via smooth_samples().","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/variance_comp.html","id":null,"dir":"Reference","previous_headings":"","what":"Variance components of smooths from smoothness estimates — variance_comp","title":"Variance components of smooths from smoothness estimates — variance_comp","text":"wrapper mgcv::gam.vcomp() returns smoothing parameters expressed variance components.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/variance_comp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Variance components of smooths from smoothness estimates — variance_comp","text":"","code":"variance_comp(object, ...) # S3 method for class 'gam' variance_comp(object, rescale = TRUE, coverage = 0.95, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/variance_comp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Variance components of smooths from smoothness estimates — variance_comp","text":"object R object. Currently models fitted mgcv::gam() mgcv::bam() supported. ... arguments passed methods rescale logical; numerical stability reasons penalty matrices smooths rescaled fitting. rescale = TRUE, rescaling undone, resulting variance components original scale. needed comparing mixed model software, lmer(). coverage numeric; value 0 1 indicating (approximate) coverage confidence interval returned.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/variance_comp.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Variance components of smooths from smoothness estimates — variance_comp","text":"function wrapper mgcv::gam.vcomp() performs three additional services suppresses annoying text output mgcv::gam.vcomp() prints terminal, returns variance smooth well standard deviation, returns variance components tibble.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/vars_from_label.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns names of variables from a smooth label — vars_from_label","title":"Returns names of variables from a smooth label — vars_from_label","text":"Returns names variables smooth label","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/vars_from_label.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns names of variables from a smooth label — vars_from_label","text":"","code":"vars_from_label(label)"},{"path":"https://gavinsimpson.github.io/gratia/reference/vars_from_label.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns names of variables from a smooth label — vars_from_label","text":"label character; length 1 character vector containing label smooth.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/vars_from_label.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Returns names of variables from a smooth label — vars_from_label","text":"","code":"vars_from_label(\"s(x1)\") #> [1] \"x1\" vars_from_label(\"t2(x1,x2,x3)\") #> [1] \"x1\" \"x2\" \"x3\""},{"path":"https://gavinsimpson.github.io/gratia/reference/which_smooths.html","id":null,"dir":"Reference","previous_headings":"","what":"Identify a smooth term by its label — which_smooths","title":"Identify a smooth term by its label — which_smooths","text":"Identify smooth term label","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/which_smooths.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Identify a smooth term by its label — which_smooths","text":"","code":"which_smooths(object, ...) # Default S3 method which_smooths(object, ...) # S3 method for class 'gam' which_smooths(object, terms, ...) # S3 method for class 'bam' which_smooths(object, terms, ...) # S3 method for class 'gamm' which_smooths(object, terms, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/which_smooths.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Identify a smooth term by its label — which_smooths","text":"object fitted GAM. ... arguments passed methods. terms character; one (partial) term labels identify required smooths.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/worm_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Worm plot of model residuals — worm_plot","title":"Worm plot of model residuals — worm_plot","text":"Worm plot model residuals","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/worm_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Worm plot of model residuals — worm_plot","text":"","code":"worm_plot(model, ...) # S3 method for class 'gam' worm_plot( model, method = c(\"uniform\", \"simulate\", \"normal\", \"direct\"), type = c(\"deviance\", \"response\", \"pearson\"), n_uniform = 10, n_simulate = 50, level = 0.9, ylab = NULL, xlab = NULL, title = NULL, subtitle = NULL, caption = NULL, ci_col = \"black\", ci_alpha = 0.2, point_col = \"black\", point_alpha = 1, line_col = \"red\", ... ) # S3 method for class 'glm' worm_plot(model, ...) # S3 method for class 'lm' worm_plot(model, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/worm_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Worm plot of model residuals — worm_plot","text":"model fitted model. Currently models inheriting class \"gam\", well classes \"glm\" \"lm\" calls stats::glm stats::lm supported. ... arguments passed ot methods. method character; method used generate theoretical quantiles. default \"uniform\", generates reference quantiles using random draws uniform distribution inverse cummulative distribution function (CDF) fitted values. reference quantiles averaged n_uniform draws. \"simulate\" generates reference quantiles simulating new response data model observed values covariates, residualised generate reference quantiles, using n_simulate simulated data sets. \"normal\" generates reference quantiles using standard normal distribution. \"uniform\" computationally efficient, \"simulate\" allows reference bands drawn QQ-plot. \"normal\" avoided used fall back random number generator (\"simulate\") inverse CDF available family used model fitting (`\"uniform\"“). Note method = \"direct\" deprecated favour method = \"uniform\". type character; type residuals use. \"deviance\", \"response\", \"pearson\" residuals allowed. n_uniform numeric; number times randomize uniform quantiles direct computation method (method = \"uniform\"). n_simulate numeric; number data sets simulate estimated model using simulation method (method = \"simulate\"). level numeric; coverage level reference intervals. Must strictly 0 < level < 1. used method = \"simulate\". ylab character expression; label y axis. supplied, suitable label generated. xlab character expression; label y axis. supplied, suitable label generated. title character expression; title plot. See ggplot2::labs(). May vector, one per penalty. subtitle character expression; subtitle plot. See ggplot2::labs(). May vector, one per penalty. caption character expression; plot caption. See ggplot2::labs(). May vector, one per penalty. ci_col fill colour reference interval method = \"simulate\". ci_alpha alpha transparency reference interval method = \"simulate\". point_col colour points QQ plot. point_alpha alpha transparency points QQ plot. line_col colour used draw reference line.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/worm_plot.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Worm plot of model residuals — worm_plot","text":"wording used mgcv::qq.gam() uses direct reference simulated residuals method (method = \"simulated\"). avoid confusion, method = \"direct\" deprecated favour method = \"uniform\".","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/worm_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Worm plot of model residuals — worm_plot","text":"","code":"load_mgcv() ## simulate binomial data... dat <- data_sim(\"eg1\", n = 200, dist = \"binary\", scale = .33, seed = 0) p <- binomial()$linkinv(dat$f) # binomial p n <- sample(c(1, 3), 200, replace = TRUE) # binomial n dat <- transform(dat, y = rbinom(n, n, p), n = n) m <- gam(y / n ~ s(x0) + s(x1) + s(x2) + s(x3), family = binomial, data = dat, weights = n, method = \"REML\" ) ## Worm plot; default using direct randomization of uniform quantiles ## Note no reference bands are drawn with this method. worm_plot(m) ## Alternatively use simulate new data from the model, which ## allows construction of reference intervals for the Q-Q plot worm_plot(m, method = \"simulate\", point_col = \"steelblue\", point_alpha = 0.4 ) ## ... or use the usual normality assumption worm_plot(m, method = \"normal\")"},{"path":"https://gavinsimpson.github.io/gratia/reference/zooplankton.html","id":null,"dir":"Reference","previous_headings":"","what":"Madison lakes zooplankton data — zooplankton","title":"Madison lakes zooplankton data — zooplankton","text":"Madison lake zooplankton data long-term study seasonal dynamics zooplankton, collected Richard Lathrop. data collected chain lakes Wisconsin (Mendota, Monona, Kegnonsa, Waubesa) approximately bi-weekly 1976 1994. consist samples zooplankton communities, taken deepest point lake via vertical tow. data provided Wisconsin Department Natural Resources collection processing fully described Lathrop (2000).","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/zooplankton.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Madison lakes zooplankton data — zooplankton","text":"data frame","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/zooplankton.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Madison lakes zooplankton data — zooplankton","text":"Pedersen EJ, Miller DL, Simpson GL, Ross N. 2018. Hierarchical generalized additive models: introduction mgcv. PeerJ Preprints 6:e27320v1 doi:10.7287/peerj.preprints.27320v1 .","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/zooplankton.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Madison lakes zooplankton data — zooplankton","text":"record consists counts given zooplankton taxon taken subsample single vertical net tow, scaled account relative volume subsample versus whole net sample area net tow rounded nearest 1000 give estimated population density per m2 taxon point time sampled lake.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/zooplankton.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Madison lakes zooplankton data — zooplankton","text":"Lathrop RC. (2000). Madison Wisonsin Lakes Zooplankton 1976–1994. Environmental Data Initiative.","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"gratia-092","dir":"Changelog","previous_headings":"","what":"gratia 0.9.2","title":"gratia 0.9.2","text":"CRAN release: 2024-06-25","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"breaking-changes-0-9-2","dir":"Changelog","previous_headings":"","what":"Breaking changes","title":"gratia 0.9.2","text":"parametric_effects() slightly escaped great renaming happened 0.9.0. Columns type term gain prefix .. now rectified two columns now .type .term.","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"user-visible-changes-0-9-2","dir":"Changelog","previous_headings":"","what":"User visible changes","title":"gratia 0.9.2","text":"Plots random effects now labelled smooth label. Previously, title taken fro variable involved smooth, doesn’t work terms like s(subject, continuous_var, bs = \"re\") random slopes, previsouly title \"subject\". Now terms title \"s(subject,continuous_var)\". Simple random intercept terms, s(subject, bs = \"re\"), now titled \"s(subject)\". #287 vignettes custom-plotting.Rmd, posterior-simulation.Rmd moved vignettes/articles thus longer available package vignettes. Instead, accessible Articles package website: https://gavinsimpson.github.io/gratia/","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"new-features-0-9-2","dir":"Changelog","previous_headings":"","what":"New features","title":"gratia 0.9.2","text":"fitted_samples() now works gam() models multiple linear predictors, currently location parameter supported. parameter indicated new variable .parameter returned object.","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"bug-fixes-0-9-2","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"gratia 0.9.2","text":"partial_residuals() computing partial residuals deviance residuals. compatibility mgcv::plot.gam(), partial residuals now computed working residuals. Reported @wStockhausen #273 appraise() passing ci_col argument qq_plot() worm_plot(). Reported Sate Ahmed. Couldn’t pass mvn_method posterior sampling functions user facing functions fitted_samples(), posterior_samples(), smooth_samples(), derivative_samples(), repsonse_derivatives(). Reported @stefgehrig #279 fitted_values() works quantile GAMs fitted qgam(). confint.gam() applying shift estimate upper lower interval. #280 reported @TIMAVID & @rbentham parametric_effects() draw.parametric_effects() forget levels factors (intentionally), lead problems ordered factors ordering levels preserved. Now, parametric_effects() returns named list factor levels attribute \"factor_levels\" containing required information order levels preserved plotting. #284 Reported @mhpob parametric_effects() fail parametric terms model interaction terms (don’t currently handle). #282","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"gratia-090","dir":"Changelog","previous_headings":"","what":"gratia 0.9.0","title":"gratia 0.9.0","text":"CRAN release: 2024-03-27","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"breaking-changes-0-9-0","dir":"Changelog","previous_headings":"","what":"Breaking changes","title":"gratia 0.9.0","text":"Many functions now return objects different named variables. order avoid clashes variable names used user’s models data, period (.) now used prefix generated variable names. functions whose names changed : smooth_estimates(), fitted_values(), fitted_samples(), posterior_samples(), derivatives(), partial_derivatives(), derivative_samples(). addition, add_confint() also adds newly-named variables. derivatives() partial_derivatives() now work like smooth_estimates(); place var data columns, gratia now stores data variables derivatives evaluated columns object actual variable names. way spline---sphere (SOS) smooths (bs = \"sos\") plotted changed use ggplot2::coord_sf() instead previously-used ggplot2::coord_map(). changed made result coord_map() soft-deprecated (“superseded”) minor versions ggplot2 now already, changes guides system version 3.5.0 ggplot2. axes plots created coord_map() never really worked correctly changing angle tick labels never worked. coord_map() superseded, didn’t receive updates guides system side effect changes, code plotted SOS smooths producing warning release ggplot2 version 3.5.0. projection settings used draw SOS smooths previously controlled via arguments projection orientation. arguments affect ggplot2::coord_sf(), Instead projection used controlled new argument crs, takes PROJ string detailing projection use integer refers known coordinate reference system (CRS). default projection used +proj=ortho +lat_0=20 +lon_0=XX XX mean longitude coordinates data points.","code":"1. `est` is now `.estimate`, 2. `lower` and `upper` are now `.lower_ci` and `.upper_ci`, 3. `draw` and `row` and now `.draw` and `.row` respectively, 4. `fitted`, `se`, `crit` are now `.fitted`, `.se`, `.crit`, respectively 5. `smooth`, `by`, and `type` in `smooth_estimates()` are now `.smooth`, `.by`, `.type`, respectively."},{"path":[]},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"defunct-0-9-0","dir":"Changelog","previous_headings":"Breaking changes > Defunct and deprecated functions and arguments","what":"Defunct","title":"gratia 0.9.0","text":"evaluate_smooth() deprecated gratia version 0.7.0. function ’s methods removed package. Use smooth_estimates() instead.","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"deprecated-functions-0-9-0","dir":"Changelog","previous_headings":"Breaking changes > Defunct and deprecated functions and arguments","what":"Deprecated functions","title":"gratia 0.9.0","text":"following functions deprecated version 0.9.0 gratia. eventually removed package part clean ahead eventual 1.0.0 release. functions become defunct version 0.11.0 1.0.0, whichever released soonest. evaluate_parametric_term() deprecated. Use parametric_effects() instead. datagen() deprecated. never really originally designed , replaced data_slice().","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"deprecated-arguments-0-9-0","dir":"Changelog","previous_headings":"Breaking changes > Defunct and deprecated functions and arguments","what":"Deprecated arguments","title":"gratia 0.9.0","text":"make functions package consistent, arguments select, term, smooth used thing hence latter two deprecated favour select. deprecated argument used, warning issued value assigned argument assigned select function continue.","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"user-visible-changes-0-9-0","dir":"Changelog","previous_headings":"","what":"User visible changes","title":"gratia 0.9.0","text":"smooth_samples() now uses single call RNG generate draws posterior smooths. Previous version 0.9.0, smooth_samples() separate call mvnfast::rmvn() smooth. result, result call smooth_samples() model multiple smooths now produce different results generated previously. regain old behaviour, add rng_per_smooth = TRUE smooth_samples() call. Note, however, using per-smooth RNG calls method = \"mh\" inefficient , method, posterior draws coefficients model sampled . , use rng_per_smooth = TRUE method = \"gaussian\". output smooth_estimates() draw() method changed tensor product smooths involve one 2D marginal smooths. Now, covariate values supplied via data argument, smooth_estimates() identifies one marginals 2d surface allows covariates involved surface vary fastest, ahead terms marginals. change made provides better default nothing provided data. also affects draw.gam(). fitted_values() now level support location, scale, shape families. Supported families mgcv::gaulss(), mgcv::gammals(), mgcv::gumbls(), mgcv::gevlss(), mgcv::shash(), mgcv::twlss(), mgcv::ziplss(). gratia now requires dplyr versions >= 1.1.0 tidyselect >= 1.2.0. new vignette Posterior Simulation available, describes posterior simulation fitted GAMs using {gratia}.","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"new-features-0-9-0","dir":"Changelog","previous_headings":"","what":"New features","title":"gratia 0.9.0","text":"Soap film smooths using basis bs = \"\" now handled draw(), smooth_estimates() etc. #8 response_derivatives() new function computing derivatives response respect (continuous) focal variable. First second order derivatives can computed using forward, backward, central finite differences. uncertainty estimated derivative determined using posterior sampling via fitted_samples(), hence can derived Gaussian approximation posterior using Metropolis Hastings sampler (see .) derivative_samples() work horse function behind response_derivatives(), computes returns posterior draws derivatives additive combination model terms. Requested @jonathanmellor #237 data_sim() can now simulate response data gamma, Tweedie ordered categorical distributions. data_sim() gains two new example models \"gwf2\", simulating data Gu & Wabha’s f2 function, \"lwf6\", example function 6 Luo & Wabha (1997 JASA 92(437), 107-116). data_sim() can also simulate data use GAMs fitted using family = gfam() grouped families different types data response handled. #266 part #265 fitted_samples() smooth_samples() can now use Metropolis Hastings sampler mgcv::gam.mh(), instead Gaussian approximation, sample posterior distribution model specific smooths respectively. posterior_samples() new function family fitted_samples() smooth_samples(). posterior_samples() returns draws posterior distribution response, combining uncertainty estimated expected value response dispersion response distribution. difference posterior_samples() predicted_samples() latter includes variation due drawing samples conditional distribution response (uncertainty expected values ignored), former includes sources uncertainty. fitted_samples() can new use matrix user-supplied posterior draws. Related #120 add_fitted_samples(), add_predicted_samples(), add_posterior_samples(), add_smooth_samples() new utility functions add respective draws posterior distribution existing data object covariate values object: obj |> add_posterior_draws(model). #50 basis_size() new function extract basis dimension (number basis functions) smooths. Methods available objects inherit classes \"gam\", \"gamm\", \"mgcv.smooth\" (individual smooths). data_slice() gains method data frames tibbles. typical_values() gains method data frames tibbles. fitted_values() now works models fitted using mgcv::ocat() family. predicted probability category returned, alongside Wald interval created using standard error (SE) estimated probability. SE estimated probabilities transformed logit (linear predictor) scale, Wald credible interval formed, back-transformed response (probability) scale. fitted_values() now works GAMMs fitted using mgcv::gamm(). Fitted (predicted) values use GAM part model, thus exclude random effects. link() inv_link() work models fitted using cnorm() family. worm plot can now drawn place QQ plot appraise() via new argument use_worm = TRUE. #62 smooths() now works models fitted mgcv::gamm(). overview() now returns basis dimension smooth gains argument stars TRUE add significance stars output plus legend printed tibble footer. Part wish @noamross #214 New add_constant() transform_fun() methods smooth_samples(). evenly() gains arguments lower upper modify lower / upper bound interval evenly spaced values generated. add_sizer() new function add information whether derivative smooth significantly changing (credible interval excludes 0). Currently, methods derivatives() smooth_estimates() objects implemented. Part request @asanders11 #117 draw.derivatives() gains arguments add_change change_type allow derivatives smooths plotted indicators credible interval derivative excludes 0. Options allow periods decrease increase differentiated via change_type = \"sizer\" instead default change_type = \"change\", emphasises either type change way. Part wish @asanders11 #117 draw.gam() can now group factor smooths given factor single panel, rather plotting smooths level separate panels. achieved via new argument grouped_by. Requested @RPanczak #89 draw.smooth_estimates() can now also group factor smooths given factor single panel. underlying plotting code used draw_smooth_estimates() univariate smooths can now add change indicators plots smooths change indicators added object created smooth_estimates() using add_sizer(). See example ?draw.smooth_estimates. smooth_estimates() can, evaluating 3D 4D tensor product smooth, identify one 2D smooths marginal tensor product. users provide covariate values evaluate smooths, smooth_estimates() focus 2D marginal smooth (first one involved tensor product), instead following ordering terms definition tensor product. #191 example, te(z, x, y, bs = c(cr, ds), d = c(1, 2)), second marginal smooth 2D Duchon spline covariates x y. Previously, smooth_estimates() generated n values z x n_3d values y, evaluated tensor product combinations generated values. ignore structure implicit tensor product, likely want know surface estimated Duchon spline x y smoothly varies z. Previously smooth_estimates() generate surfaces z x, varying y. Now, smooth_estimates() correctly identifies one marginal smooths tensor product 2D surface focus surface varying terms tensor product. improved behaviour needed bam() models always possible obvious thing reorder smooths defining tensor product te(x, y, z, bs = c(ds, cr), d = c(2, 1)). discrete = TRUE used bam() terms tensor product may get rearranged model setup maximum efficiency (See Details ?mgcv::bam). Additionally, draw.gam() now also works way. New function null_deviance() extracts null deviance fitted model. draw(), smooth_estimates(), fitted_values(), data_slice(), smooth_samples() now work models fitted scam::scam(). matters, current support extends univariate smooths. generate_draws() new low-level function generating posterior draws fitted model coefficients. generate_daws() S3 generic function extensible users. Currently provides simple interface simple Gaussian approximation sampler (gaussian_draws()) simple Metropolis Hasting sample (mh_draws()) available via mgcv::gam.mh(). #211 smooth_label() new function extracting labels ‘mgcv’ creates smooths smooth object . penalty() default method works s(), te(), t2(), ti(), create smooth specification. transform_fun() gains argument constant allow addition constant value objects (e.g. estimate confidence interval). enables single obj |> transform_fun(fun = exp, constant = 5) instead separate calls add_constant() transform_fun(). Part discussion #79 model_constant() new function simply extracts first coefficient estimated model.","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"bug-fixes-0-9-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"gratia 0.9.0","text":"link(), inv_link(), related family functions ocat() weren’t correctly identifying family name result throw error even passed object correct family. link() inv_link() now work correctly betar() family fitted GAM. print() method lp_matrix() now converts matrix data frame conversion tibble. makes sense results typical behaviour columns printed object doubles. Constrained factor smooths (bs = \"sz\") factor first variable mentioned smooth (.e. s(x, f, bs = \"sz\") continuous x factor f) now plotable draw(). #208 parametric_effects() unable handle special parametric terms like poly(x) log(x) formulas. Reported @fhui28 #212 parametric_effects() now works better location, scale, shape models. Reported @pboesu #45 parametric_effects now works missing values one variables used fitted GAM. #219 response_derivatives() incorrectly using .data tidyselect selectors. typical_values() handle logical variables GAM fit mgcv stores numerics var.summary. affected evenly() data_slice(). #222 parametric_effects() fail two ordered factors model. Reported @dsmi31 #221 Continuous smooths evaluated median value variable instead value 1. #224 fitted_samples() (hence posterior_samples()) now handles models offset terms formula. Offset terms supplied via offset argument ignored mgcv:::predict.gam() hence ignored also gratia. Reported @jonathonmellor #231 #233 smooth_estimates() fail \"fs\" smooth multivariate base smoother used factor last variable specified definition smooth: s(x1, x2, f, bs = \"fs\", xt = list(bs = \"ds\")) work, s(f, x1, x2, bs = \"fs\", xt = list(bs = \"ds\")) (ordering variables places factor last) emit obscure error. ordering terms involved smooth now doesn’t matter. Reported @chrisaak #249. draw.gam() fail plotting multivariate base smoother used \"sz\" smooth. Now, use case identified message printed indicating (currently) gratia doesn’t know plot smooth. Reported @chrisaak #249. draw.gam() fail plotting multivariate base smoother used \"fs\" smooth. Now, use case identified message printed indicating (currently) gratia doesn’t know plot smooth. Reported @chrisaak #249. derivative_samples() fail order = 2 computing forward finite differences, regardless type order = 1. Partly reported @samlipworth #251. draw() method penalty() normalizing penalty range 0–1, claimed documented -1–1 argument normalize = TRUE. now fixed. smooth_samples() failing data supplied contained variables used smooth sampled. Hence generally fail unless single smooth sampled model contained single smooth. function never intended retain variables data written way fail relocating data columns end posterior sampling object. #255 draw.gam() draw.smooth_estimates() fail plotting univariate tensor product smooth (e.g. te(x), ti(x), t2()). Reported @wStockhausen #260 plot.smooth() printing factor level subtitles ordered factor smooths.","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"gratia-082","dir":"Changelog","previous_headings":"","what":"gratia 0.8.2","title":"gratia 0.8.2","text":"CRAN release: 2024-01-09 Small fixes CRAN.","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"gratia-081","dir":"Changelog","previous_headings":"","what":"gratia 0.8.1","title":"gratia 0.8.1","text":"CRAN release: 2023-02-02","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"user-visible-changes-0-8-1","dir":"Changelog","previous_headings":"","what":"User visible changes","title":"gratia 0.8.1","text":"smooth_samples() now returns objects variables involved smooths correct name. Previously variables named .x1, .x2, etc. Fixing #126 improving compatibility compare_smooths() smooth_estimates() allowed variables named correctly. gratia now depends version 1.8-41 later mgcv package.","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"new-features-0-8-1","dir":"Changelog","previous_headings":"","what":"New features","title":"gratia 0.8.1","text":"draw.gam() can now handle tensor products include marginal random effect smooth. Beware plotting smooths many levels, however, separate surface plot produced level.","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"bug-fixes-0-8-1","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"gratia 0.8.1","text":"Additional fixes changes dplyr 1.1.0. smooth_samples() now works sampling posteriors multiple smooths different dimension. #126 reported @Aariq","code":""},{"path":[]},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"user-visible-changes-0-8-0","dir":"Changelog","previous_headings":"","what":"User visible changes","title":"gratia 0.8.0","text":"{gratia} now depends R version 4.1 later. new vignette “Data slices” supplied {gratia}. Functions {gratia} harmonised use argument named data instead newdata passing new data evaluate features smooths. message printed newdata used now . Existing code need changed data takes value newdata. Note due way ... handled R, R script uses data argument, run versions gratia prior 8.0 (released; 0.7.3.8 using development version) user-supplied data silently ignored. , scripts using data check installed version gratia >= 0.8 package developers update depend versions >= 0.8 using gratia (>= 0.8) DESCRIPTION. order plots smooths changed draw.gam() match order smooths specified model formula. See Bug Fixes detail #154.","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"new-features-0-8-0","dir":"Changelog","previous_headings":"","what":"New features","title":"gratia 0.8.0","text":"Added basic support GAMLSS (distributional GAMs) fitted gamlss() function package GJRM. Support currently restricted draw() method. difference_smooths() can now include group means difference, many users expected. include group means use group_means = TRUE function call, e.g. difference_smooths(model, smooth = \"s(x)\", group_means = TRUE). Note: function still differs plot_diff() package itsadug, essentially computes differences model predictions. main practical difference effects beyond factor smooth, including random effects, may included plot_diff(). implements main wish #108 (@dinga92) #143 (@mbolyanatz) despite protestations complicated cases (isn’t; complexity just cancels .) data_slice() totally revised. Now, user provides values variables want slice variables model specified held typical values (.e. value observation closest median numeric variables, modal factor level.) Data slices now produced passing name = value pairs variables values want appear slice. example value pair can expression looked (evaluated) data argument model frame fitted model (default). example, resulting slice data frame 100 observations, comprising x1, vector 100 values spread evenly range x1, constant value mean x2 x2 variable, constant factor level, model class fac, fac variable model. partial_derivatives() new function computing partial derivatives multivariate smooths (e.g. s(x,z), te(x,z)) respect one margins smooth. Multivariate smooths dimension handled, one dimensions allowed vary. Partial derivatives estimated using method finite differences, forward, backward, central finite differences available. Requested @noamross #101 overview() provides simple overview model terms fitted GAMs. new bs = \"sz\" basis released mgcv version 1.18-41 now supported smooth_estimates(), draw.gam(), draw.smooth_estimates() basis unique plotting method. #202 basis() now method fitted GAM(M)s can extract estimated basis model plot , using estimated coefficients smooth weight basis. #137 also new draw.basis() method plotting results call basis(). method can now also handle bivariate bases. tidy_basis() lower level function heavy lifting basis(), now exported. tidy_basis() returns tidy representation basis supplied object inheriting class \"mgcv.smooth\". objects returned $smooth component fitted GAM(M) model. lp_matrix() new utility function quickly return linear predictor matrix estimated model. wrapper predict(..., type = \"lpmatrix\") evenly() synonym seq_min_max() preferred going forward. Gains argument produce sequences covariate increment units . ref_level() level() new utility functions extracting reference specific level factor respectively. useful specifying covariate values condition data slice. model_vars() new, public facing way returning vector variables used model. difference_smooths() now use user-supplied data points evaluate pair smooths. Also note argument newdata renamed data. #175 draw() method difference_smooths() now uses better labels plot titles avoid long labels even modest factor levels. derivatives() now works factor-smooth interaction (\"fs\") smooths. draw() methods now allow angle tick labels x axis plots rotated using argument angle. Requested @tamas-ferenci #87 draw.gam() related functions (draw.parametric_effects(), draw.smooth_estimates()) now add basis plot using caption. #155 smooth_coefs() new utility function extracting coefficients particular smooth fitted model. smooth_coef_indices() associated function returns indices (positions) vector model coefficients (returned coef(gam_model)) coefficients pertain stated smooth. draw.gam() now better handles patchworks plots one plots fixed aspect ratios. #190","code":"m <- gam(y ~ s(x1) + x2 + fac) data_slice(model, x1 = evenly(x1, n = 100), x2 = mean(x2))"},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"bug-fixes-0-8-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"gratia 0.8.0","text":"draw.posterior_smooths now plots posterior samples fixed aspect ratio smooth isotropic. #148 derivatives() now ignores random effect smooths (derivatives don’t make sense anyway). #168 confint.gam(...., method = \"simultaneous\") now works factor smooths parm passed full name specific smooth s(x)faclevel. order plots produced gratia::draw.gam() matches order smooths entered model formula. Recent changes internals gratia::draw.gam() switch smooth_estimates() undertaken lead change behaviour resulting use dplyr::group_split(), ’s coercion internally character vector factor. factor now created explicitly, levels set correct order. #154 Setting dist argument set response smooth values NA lay far support data multivariate smooths, lead incorrect scale response guide. now fixed. #193 Argument fun draw.gam() applied parametric terms. Reported @grasshoppermouse #195 draw.gam() adding uncertainty linear predictors smooths overall_uncertainty = TRUE used. Now draw.gam() includes uncertainty linear predictors smooth takes part. #158 partial_derivatives() works provided single data point evaluate derivative. #199 transform_fun.smooth_estimates() addressing wrong variable names trying transform confidence interval. #201 data_slice() doesn’t fail error used model contains offset term. #198 confint.gam() longer uses evaluate_smooth(), soft deprecated. #167 qq_plot() worm_plot() compute wrong deviance residuals used generate theoretical quantiles exotic families (distributions) available mgcv. also affected appraise() QQ plot; residuals shown plots deviance residuals shown y-axis QQ plot correct. generation reference intervals/quantiles affected.","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"gratia-073","dir":"Changelog","previous_headings":"","what":"gratia 0.7.3","title":"gratia 0.7.3","text":"CRAN release: 2022-05-09","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"user-visible-changes-0-7-3","dir":"Changelog","previous_headings":"","what":"User visible changes","title":"gratia 0.7.3","text":"Plots smooths now use “Partial effect” y-axis label place “Effect”, better indicate displayed.","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"new-features-0-7-3","dir":"Changelog","previous_headings":"","what":"New features","title":"gratia 0.7.3","text":"confint.fderiv() confint.gam() now return results tibble instead common--garden data frame. latter mostly already . Examples confint.fderiv() confint.gam() reworked, part remove inconsistent output examples run M1 macs.","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"bug-fixes-0-7-3","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"gratia 0.7.3","text":"compare_smooths() failed passed non-standard model “names” like compare_smooths(m_gam, m_gamm$gam) compare_smooths(l[[1]], l[[2]]) even evaluated objects valid GAM(M) models. Reported Andrew Irwin #150","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"gratia-072","dir":"Changelog","previous_headings":"","what":"gratia 0.7.2","title":"gratia 0.7.2","text":"CRAN release: 2022-03-17","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"new-features-0-7-2","dir":"Changelog","previous_headings":"","what":"New features","title":"gratia 0.7.2","text":"draw.gam() draw.smooth_estimates() can now handle splines sphere (s(lat, long, bs = \"sos\")) special plotting methods using ggplot2::coord_map() handle projection spherical coordinates. orthographic projection used default, essentially arbitrary (northern hemisphere-centric) default orientation view. fitted_values() insures data (hence returned object) tibble rather common garden data frame.","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"bug-fixes-0-7-2","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"gratia 0.7.2","text":"draw.posterior_smooths() redundantly plotting duplicate data rug plot. Now unique set covariate values used drawing rug. data_sim() passing scale argument bivariate example setting (\"eg2\"). draw() methods gamm() gamm4::gamm4() fits passing arguments draw.gam(). draw.smooth_estimates() produce subtitle data continuous smooth factor smooth. Now subtitle contains name continuous variable.","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"gratia-071","dir":"Changelog","previous_headings":"","what":"gratia 0.7.1","title":"gratia 0.7.1","text":"Due issue size package source tarball, wasn’t discovered submission CRAN, 0.7.1 never released.","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"new-features-0-7-1","dir":"Changelog","previous_headings":"","what":"New features","title":"gratia 0.7.1","text":"draw.gam() draw.smooth_estimates(): {gratia} can now handle smooths 3 4 covariates plotting. smooths 3 covariates, third covariate handled ggplot2::facet_wrap() set (default n = 16) small multiples drawn, 2d surface evaluated specified value third covariate. smooths 4 covariates, ggplot2::facet_grid() used draw small multiples, default producing 4 rows 4 columns plots specific values third fourth covariates. number small multiples produced controlled new arguments n_3d (default = n_3d = 16) n_4d (default n_4d = 4, yielding n_4d * n_4d = 16 facets) respectively. affects plotting; smooth_estimates() able handle smooths number covariates . handling higher-dimensional smooths, actually drawing plots default device can slow, especially default value n = 100 (3D 4D smooths result 160,000 data points plotted). recommended reduce n smaller value: n = 50 reasonable compromise resolution speed. model_concurvity() returns concurvity measures mgcv::concurvity() estimated GAMs tidy format. synonym concrvity() also provided. draw() method provided produces bar plot heatmap concurvity values depending whether overall concurvity smooth pairwise concurvity smooth model requested. draw.gam() gains argument resid_col = \"steelblue3\" allows colour partial residuals (plotted) changed.","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"bug-fixes-0-7-1","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"gratia 0.7.1","text":"model_edf() using type argument. result ever returned default EDF type. add_constant() methods weren’t applying constant required variables. draw.gam(), draw.parametric_effects() now actually work model parametric effects. #142 Reported @Nelson-Gon parametric_effects() fail model parametric terms predict.gam() returns empty arrays passed exclude = character(0).","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"gratia-070","dir":"Changelog","previous_headings":"","what":"gratia 0.7.0","title":"gratia 0.7.0","text":"CRAN release: 2022-02-07","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"major-changes-0-7-0","dir":"Changelog","previous_headings":"","what":"Major changes","title":"gratia 0.7.0","text":"draw.gam() now uses smooth_estimates() internally consequently uses draw() method underlying plotting code. simplified code compared evaluate_smooth() methods, allow future development addition features easily evaluate_smooth() retained. Similarly, evaluate_parametric_terms() now deprecated favour parametric_effects(), also used internally draw.gam() parametric terms present model (parametric = TRUE). lot code reused differences plots result change minimal, corner cases may missed. File Issue notice something changed think shouldn’t. draw.gam() now plots 2D isotropic smooths (TPRS Duchon splines) equally-scaled x y coordinates using coord_equal(ratio = 1). Alignment plots little different now plotting models multiple smooths. See Issue #81.","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"deprecated-functions-0-7-0","dir":"Changelog","previous_headings":"Major changes","what":"Deprecated functions","title":"gratia 0.7.0","text":"version 0.7.0, following functions considered deprecated use discouraged: fderiv() soft-deprecated favour derivatives(), evaluate_smooth() soft-deprecated favour smooth_estimates(), evaluate_parametric_term() soft-deprecated favour parametric_effects(). first call one functions generate warning, pointing newer, alternative, function. safe ignore warnings, deprecated functions longer receive updates thus risk removed package future date. newer alternatives can handle types models smooths, especially case smooth_estimates().","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"new-features-0-7-0","dir":"Changelog","previous_headings":"","what":"New features","title":"gratia 0.7.0","text":"fitted_values() provides tidy wrapper around predict.gam() generating fitted values model. New covariate values can provided via argument data. credible interval fitted values returned, values can link (linear predictor) response scale. Note function returns expected values response. Hence, “fitted values” used instead “predictions” case new covariate values differentiate values case generating new response values fitted model. rootogram() draw() method produce rootograms diagnostic plots fitted models. Currently models fitted poisson(), nb(), negbin(), gaussian() families. New helper functions typical_values(), factor_combos() data_combos() quickly creating data sets producing predictions fitted models covariatess fixed come typical representative values. typical_values() new helper function return typical values covariates fitted model. returns value observation closest median numerical covariates modal level factor preserving levels factor. typical_values() useful preparing data slices scenarios fitted values estimated model required. factor_combos() extracts returns combinations levels factors found data used fit model. Unlike typical_values(), factor_combos() returns combinations factor levels observed data, just modal level. Optionally, combinations factor levels can returned, just observed data. data_combos() combines returns factor data factor_combos() plus typical values numerical covariates. useful want generate predictions model combination factor terms holding continuous covariates median values. nb_theta() new extractor function returns theta parameter fitted negative binomial GAM (families nb() negbin()). Additionally, theta() has_theta() provide additional functionality. theta() experimental function extracting additional parameters model family. has_theta() useful checking additional parameters available family model. edf() extracts effective degrees freedom (EDF) fitted model specific smooth model. Various forms EDF can extracted. model_edf() returns EDF overall model. supplied multiple models, EDFs model returned comparison. draw.gam() can now show “rug” plot bivariate smooth drawing small points high transparency smooth surface data coordinates. addition, rugs plots factor smooths now show locations covariate values specific level factor levels. better reflects data used estimate smooth, even though basis smooth set using covariate locations. draw.gam() draw.smooth_estimates() now allow aspects plot changed: fill (colour) alpha attributes credible interval, line colour smooth can now specified using arguments ci_col, ci_alpha, smooth_col respectively. Partial residuals can now plotted factor smooths. allow , partial residuals filtered residuals associated particular level’s smooth drawn plot smooth. smooth_estimates() uses check_user_select_smooths() handle user-specified selection smooth terms. flexible previously, allows easier selection smooths evaluate. fixef() now imported (re-exported) nlme package, methods models fitted gam() gamm(), extract fixed effects estimates fitted models. fixed_effects() alias fixef(). draw() method smooth_samples() can now handle 2D smooths. Additionally, number posterior draws plot can now specified plotting using new argument n_samples, result n_samples draws selected random set draws plotting. New argument seed allows selection draws repeatable.","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"bug-fixes-0-7-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"gratia 0.7.0","text":"smooth_estimates() filtering user-supplied data level specific smooth used factor smooths. result smooth evaluated rows user-supplied data, therefore result nrow(user_data) * nlevels(by_variable) rows returned object instead nrow(user_data) rows. add_confint() method smooth_estimates() upper lower intervals reversed. #107 Reported @Aariq draw.gam() smooth_estimates() ignoring dist argument allows covariate values lie far support data excluded returning estimated values smooth plotting . #111 Reported @Aariq smooth_samples() factor GAM return samples first factor level . Reported @rroyaute discussion #121 smooth_samples() fail model contained random effect “smooths”. now ignored message running smooth_samples(). Reported @isabellaghement #121 link(), inv_link() failing models fitted family = scat(). Reported @Aariq #130","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"gratia-060","dir":"Changelog","previous_headings":"","what":"gratia 0.6.0","title":"gratia 0.6.0","text":"CRAN release: 2021-04-18","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"major-changes-0-6-0","dir":"Changelog","previous_headings":"","what":"Major changes","title":"gratia 0.6.0","text":"{cowplot} package replaced {patchwork} package producing multi-panel figures draw() appraise(). shouldn’t affect code used {gratia} , passed additional arguments cowplot::plot_grid() used align axis arguments draw() appraise(), ’ll need adapt code accordingly. Typically, can simply delete align axis arguments {patchwork} just work align plots nicely. arguments passed via ... cowplot::plot_grid() just ignored patchwork::wrap_plots() unless passed arguments match arguments patchwork::wrap_plots().","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"new-features-0-6-0","dir":"Changelog","previous_headings":"","what":"New features","title":"gratia 0.6.0","text":"{patchwork} package now used multi-panel figures. , {gratia} longer Imports {cowplot} package. Worm plot diagnostic plots available via new function worm_plot(). Worm plots detrended Q-Q plots, deviation Q-Q reference line emphasized deviations around line occupy full height plot. worm_plot() methods available models classes \"gam\", \"glm\", \"lm\". (#62) Smooths can now compared across models using compare_smooths(), comparisons visualised associated draw() method. (#85 @dill) feature bit experimental; returned object uses nested lists may change future users find confusing. reference line qq_plot() method = \"normal\" previously drawn line intercept 0 slope 1, match methods. inconsistent stats::qqplot() drew line 1st 3rd quartiles. qq_plot() method = \"normal\" now uses robust reference line. Reference lines methods remain drawn slope 1 intercept 0. qq_plot() method = \"normal\" now draws point-wise reference band using standard error order statistic. draw() method penalty() now plots penalty matrix heatmaps -logical orientation, match matrices might written printed R console. link(), inv_link() now work models fitted gumbls() shash() families. (#84) extract_link() lower level utility function related link() inv_link(), now exported.","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"user-visible-changes-0-6-0","dir":"Changelog","previous_headings":"","what":"User visible changes","title":"gratia 0.6.0","text":"default method name generating reference quantiles qq_plot() changed \"direct\" \"uniform\", avoid confusion mgcv::qq.gam() help page description methods. Accordingly using method = \"direct\" deprecated message effect displayed used. way smooths/terms selected derivatives() switched use mechanism draw.gam()’s select argument. get partial match term, now need also specify partial_match = TRUE call derivatives().","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"bug-fixes-0-6-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"gratia 0.6.0","text":"transform_fun() copy paste bug definition generic. (#96 @Aariq) derivatives() user-supplied newdata fail factor smooths interval = \"simultaneous\" introduce rows derivative == 0 interval = \"confidence\" didn’t subset rows newdata specific level factor computing derivatives. (#102 @sambweber) evaluate_smooth() can now handle random effect smooths defined using ordered factor. (#99 @StefanoMezzini)","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"gratia-051","dir":"Changelog","previous_headings":"","what":"gratia 0.5.1","title":"gratia 0.5.1","text":"CRAN release: 2021-01-23","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"new-features-0-5-1","dir":"Changelog","previous_headings":"","what":"New features","title":"gratia 0.5.1","text":"smooth_estimates() can now handle bivariate multivariate thinplate regression spline smooths, e.g.  s(x, z, ), tensor product smooths (te(), t2(), & ti()), e.g. te(x, z, ) factor smooth interactions, e.g. s(x, f, bs = \"fs\") random effect smooths, e.g. s(f, bs = \"re\") penalty() provides tidy representation penalty matrices smooths. tidy representation suitable plotting ggplot(). draw() method provided, represents penalty matrix heatmap.","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"user-visible-changes-0-5-1","dir":"Changelog","previous_headings":"","what":"User visible changes","title":"gratia 0.5.1","text":"newdata argument smooth_estimates() changed data originally intended.","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"gratia-050","dir":"Changelog","previous_headings":"","what":"gratia 0.5.0","title":"gratia 0.5.0","text":"CRAN release: 2021-01-10","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"new-features-0-5-0","dir":"Changelog","previous_headings":"","what":"New features","title":"gratia 0.5.0","text":"Partial residuals models can computed partial_residuals(). partial residuals weighted residuals model added contribution smooth term (returned predict(model, type = \"terms\"). Wish #76 (@noamross) Also, new function add_partial_residuals() can used add partial residuals data frames. Users can now control extent colour fill scales used plotting smooths draw() methods use . useful change fill scale plotting 2D smooths, change discrete colour scale used plotting random factor smooths (bs = \"fs\"). user can pass scales via arguments discrete_colour continuous_fill. effects certain smooths can excluded data simulated model using simulate.gam() predicted_samples() passing exclude terms predict.gam(). allows excluding random effects, example, model predicted values used simulate new data conditional distribution. See example predicted_samples(). Wish #74 (@hgoldspiel) draw.gam() related functions gain arguments constant fun allow user-defined constants transformations smooth estimates confidence intervals applied. Part wish Wish #79. confint.gam() now works 2D smooths also. smooth_estimates() early version code replace (likely supersede) evaluate_smooth(). smooth_estimates() can currently handle 1D smooths standard types.","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"user-visible-changes-0-5-0","dir":"Changelog","previous_headings":"","what":"User visible changes","title":"gratia 0.5.0","text":"meaning parm confint.gam changed. argument now requires smooth label match smooth. vector labels can provided, partial matching smooth label works single parm value. default behaviour remains unchanged however; parm NULL smooths evaluated returned confidence intervals. data_class() longer exported; ever intended internal function.","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"bug-fixes-0-5-0","dir":"Changelog","previous_headings":"","what":"Bug Fixes","title":"gratia 0.5.0","text":"confint.gam() failing tensor product smooth due matching issues. Reported @tamas-ferenci #88 also fixes #80 related issue selecting specific smooth. vdiffr package now used conditionally package tests. Reported Brian Ripley #93","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"gratia-041","dir":"Changelog","previous_headings":"","what":"gratia 0.4.1","title":"gratia 0.4.1","text":"CRAN release: 2020-05-30","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"user-visible-changes-0-4-1","dir":"Changelog","previous_headings":"","what":"User visible changes","title":"gratia 0.4.1","text":"draw.gam() scales = \"fixed\" now applies terms can plotted, including 2d smooths. Reported @StefanoMezzini #73","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"bug-fixes-0-4-1","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"gratia 0.4.1","text":"dplyr::combine() deprecated. Switch vctrs::vec_c(). draw.gam() scales = \"fixed\" wasn’t using fixed scales 2d smooths model. Reported @StefanoMezzini #73","code":""},{"path":[]},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"new-features-0-4-0","dir":"Changelog","previous_headings":"","what":"New features","title":"gratia 0.4.0","text":"draw.gam() can include partial residuals drawing univariate smooths. Use residuals = TRUE add partial residuals univariate smooth drawn. feature available smooths one variable, smooths, factor-smooth interactions (bs = \"fs\"). coverage credible confidence intervals drawn draw.gam() can specified via argument ci_level. default arbitrarily 0.95 reason (rough) compatibility plot.gam(). change effect making intervals slightly narrower previous versions gratia; intervals drawn ± 2 × standard error. default intervals now drawn ± ~1.96 × standard error. New function difference_smooths() computing differences factor smooth interactions. Methods available gam(), bam(), gamm() gamm4::gamm4(). Also draw() method, can handle differences 1D 2D smooths currently (handling 3D 4D smooths planned). New functions add_fitted() add_residuals() add fitted values (expectations) model residuals existing data frame. Currently methods available objects fitted gam() bam(). data_sim() tidy reimplementation mgcv::gamSim() added ability use sampling distributions Gaussian models implemented. Currently Gaussian, Poisson, Bernoulli sampling distributions available. smooth_samples() can handle continuous variable smooths varying coefficient models. link() inv_link() now work families available mgcv, including location, scale, shape families, specialised families described ?mgcv::family.mgcv. evaluate_smooth(), data_slice(), family(), link(), inv_link() methods models fitted using gamm4() gamm4 package. data_slice() can generate data 1-d slice (single variable varying). colour points, reference lines, simulation band appraise() can now specified via arguments point_col, point_alpha, ci_col ci_alpha line_col passed qq_plot(), observed_fitted_plot(), residuals_linpred_plot(), residuals_hist_plot(), also now take new arguments applicable. Added utility functions is_factor_term() term_variables() working models. is_factor_term() identifies named term factor using information terms() object fitted model. term_variables() returns character vector variable names involved model term. strictly working parametric terms models. appraise() now works models fitted glm() lm(), underlying functions calls, especially qq_plot. appraise() also works models fitted family gaulss(). location scale models models fitted extended family functions supported upcoming releases.","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"user-visible-changes-0-4-0","dir":"Changelog","previous_headings":"","what":"User visible changes","title":"gratia 0.4.0","text":"datagen() now internal function longer exported. Use data_slice() instead. evaluate_parametric_term() now much stricter can evaluate main effect terms, .e. whose order, stored terms object model 1.","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"bug-fixes-0-4-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"gratia 0.4.0","text":"draw() method derivatives() getting x-axis label factor smooths correctly, instead using NA second subsequent levels factor. datagen() method class \"gam\" couldn’t possibly worked anything simplest models fail even simple factor smooths. issues fixed, behaviour datagen() changed, function now intended use users. Fixed issue models terms form factor1:factor2 incorrectly identified numeric parametric terms. #68","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"gratia-031","dir":"Changelog","previous_headings":"","what":"gratia 0.3.1","title":"gratia 0.3.1","text":"CRAN release: 2020-03-29","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"new-features-0-3-1","dir":"Changelog","previous_headings":"","what":"New features","title":"gratia 0.3.1","text":"New functions link() inv_link() access link function inverse fitted models family functions. Methods classes: \"glm\", \"gam\", \"bam\", \"gamm\" currently. #58 Adds explicit family() methods objects classes \"gam\", \"bam\", \"gamm\". derivatives() now handles non-numeric creating shifted data finite differences. Fixes problem stringsAsFactors = FALSE default R-devel. #64","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"bug-fixes-0-3-1","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"gratia 0.3.1","text":"Updated gratia work tibble versions >= 3.0","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"gratia-030","dir":"Changelog","previous_headings":"","what":"gratia 0.3.0","title":"gratia 0.3.0","text":"CRAN release: 2020-01-19","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"new-features-0-3-0","dir":"Changelog","previous_headings":"","what":"New features","title":"gratia 0.3.0","text":"gratia now uses mvnfast package random draws multivariate normal distribution (mvnfast::rmvn()). Contributed Henrik Singmann #28 New function basis() generating tidy representations basis expansions mgcv-like definition smooth, e.g. s(), te(), ti(), t2(). basic smooth types also simple draw() method plotting basis. basis() simple wrapper around mgcv::smoothCon() post processing basis model matrix tidy format. #42 New function smooth_samples() draw samples entire smooth functions posterior distribution. Also draw() method plotting posterior samples.","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"bug-fixes-0-3-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"gratia 0.3.0","text":"draw.gam() produce empty plots panels parametric terms 2 parametric terms model. Reported @sklayn #39. derivatives() now works factor smooths, including ordered factor smooths. function also now works correctly complex models multiple covariates/smooths. #47 derivatives() also now handles 'fs' smooths. Reported @tomand-uio #57. evaluate_parametric_term() hence draw.gam() fail ziplss() model ) gratia didn’t handle parametric terms models multiple linear predictors correctly, ii) gratia didn’t convert naming convention mgcv terms higher linear predictors. Reported @pboesu #45","code":""}] +[{"path":[]},{"path":"https://gavinsimpson.github.io/gratia/CODE_OF_CONDUCT.html","id":"our-pledge","dir":"","previous_headings":"","what":"Our Pledge","title":"Contributor Covenant Code of Conduct","text":"members, contributors, leaders pledge make participation community harassment-free experience everyone, regardless age, body size, visible invisible disability, ethnicity, sex characteristics, gender identity expression, level experience, education, socio-economic status, nationality, personal appearance, race, religion, sexual identity orientation. pledge act interact ways contribute open, welcoming, diverse, inclusive, healthy community.","code":""},{"path":"https://gavinsimpson.github.io/gratia/CODE_OF_CONDUCT.html","id":"our-standards","dir":"","previous_headings":"","what":"Our Standards","title":"Contributor Covenant Code of Conduct","text":"Examples behavior contributes positive environment community include: Demonstrating empathy kindness toward people respectful differing opinions, viewpoints, experiences Giving gracefully accepting constructive feedback Accepting responsibility apologizing affected mistakes, learning experience Focusing best just us individuals, overall community Examples unacceptable behavior include: use sexualized language imagery, sexual attention advances kind Trolling, insulting derogatory comments, personal political attacks Public private harassment Publishing others’ private information, physical email address, without explicit permission conduct reasonably considered inappropriate professional setting","code":""},{"path":"https://gavinsimpson.github.io/gratia/CODE_OF_CONDUCT.html","id":"enforcement-responsibilities","dir":"","previous_headings":"","what":"Enforcement Responsibilities","title":"Contributor Covenant Code of Conduct","text":"Community leaders responsible clarifying enforcing standards acceptable behavior take appropriate fair corrective action response behavior deem inappropriate, threatening, offensive, harmful. Community leaders right responsibility remove, edit, reject comments, commits, code, wiki edits, issues, contributions aligned Code Conduct, communicate reasons moderation decisions appropriate.","code":""},{"path":"https://gavinsimpson.github.io/gratia/CODE_OF_CONDUCT.html","id":"scope","dir":"","previous_headings":"","what":"Scope","title":"Contributor Covenant Code of Conduct","text":"Code Conduct applies within community spaces, also applies individual officially representing community public spaces. Examples representing community include using official e-mail address, posting via official social media account, acting appointed representative online offline event.","code":""},{"path":"https://gavinsimpson.github.io/gratia/CODE_OF_CONDUCT.html","id":"enforcement","dir":"","previous_headings":"","what":"Enforcement","title":"Contributor Covenant Code of Conduct","text":"Instances abusive, harassing, otherwise unacceptable behavior may reported package maintainer, Gavin Simpson (see email address CRAN package page) . complaints reviewed investigated promptly fairly. community leaders obligated respect privacy security reporter incident.","code":""},{"path":"https://gavinsimpson.github.io/gratia/CODE_OF_CONDUCT.html","id":"enforcement-guidelines","dir":"","previous_headings":"","what":"Enforcement Guidelines","title":"Contributor Covenant Code of Conduct","text":"Community leaders follow Community Impact Guidelines determining consequences action deem violation Code Conduct:","code":""},{"path":"https://gavinsimpson.github.io/gratia/CODE_OF_CONDUCT.html","id":"id_1-correction","dir":"","previous_headings":"Enforcement Guidelines","what":"1. Correction","title":"Contributor Covenant Code of Conduct","text":"Community Impact: Use inappropriate language behavior deemed unprofessional unwelcome community. Consequence: private, written warning community leaders, providing clarity around nature violation explanation behavior inappropriate. public apology may requested.","code":""},{"path":"https://gavinsimpson.github.io/gratia/CODE_OF_CONDUCT.html","id":"id_2-warning","dir":"","previous_headings":"Enforcement Guidelines","what":"2. Warning","title":"Contributor Covenant Code of Conduct","text":"Community Impact: violation single incident series actions. Consequence: warning consequences continued behavior. interaction people involved, including unsolicited interaction enforcing Code Conduct, specified period time. includes avoiding interactions community spaces well external channels like social media. Violating terms may lead temporary permanent ban.","code":""},{"path":"https://gavinsimpson.github.io/gratia/CODE_OF_CONDUCT.html","id":"id_3-temporary-ban","dir":"","previous_headings":"Enforcement Guidelines","what":"3. Temporary Ban","title":"Contributor Covenant Code of Conduct","text":"Community Impact: serious violation community standards, including sustained inappropriate behavior. Consequence: temporary ban sort interaction public communication community specified period time. public private interaction people involved, including unsolicited interaction enforcing Code Conduct, allowed period. Violating terms may lead permanent ban.","code":""},{"path":"https://gavinsimpson.github.io/gratia/CODE_OF_CONDUCT.html","id":"id_4-permanent-ban","dir":"","previous_headings":"Enforcement Guidelines","what":"4. Permanent Ban","title":"Contributor Covenant Code of Conduct","text":"Community Impact: Demonstrating pattern violation community standards, including sustained inappropriate behavior, harassment individual, aggression toward disparagement classes individuals. Consequence: permanent ban sort public interaction within community.","code":""},{"path":"https://gavinsimpson.github.io/gratia/CODE_OF_CONDUCT.html","id":"attribution","dir":"","previous_headings":"","what":"Attribution","title":"Contributor Covenant Code of Conduct","text":"Code Conduct adapted Contributor Covenant, version 2.0, available https://www.contributor-covenant.org/version/2/0/code_of_conduct.html. Community Impact Guidelines inspired Mozilla’s code conduct enforcement ladder. answers common questions code conduct, see FAQ https://www.contributor-covenant.org/faq. Translations available https://www.contributor-covenant.org/translations.","code":""},{"path":[]},{"path":"https://gavinsimpson.github.io/gratia/CONTRIBUTING.html","id":"something-isnt-working-right-or-generating-an-error","dir":"","previous_headings":"","what":"Something isn’t working right or generating an error","title":"Contributing","text":"something isn’t working, either might expect/want contrary documentation, probably bug missing feature. ’re getting error running {gratia}, ’s also likely bug, opportunity catch use-case wasn’t expecting. First, check issue hasn’t already fixed -development version Github. Install current development version {gratia} R-universe using issue remains, please file issue via Issues page. ’s OK report issue even ’re sure ’s problem, problem better described question, use Discussions page (see ). Feature requests welcome! problem {gratia} hit top TODO list quickly cen provide reproducible example demonstrating problem.","code":"install.packages(\"gratia\", repos = c( \"https://gavinsimpson.r-universe.dev\", \"https://cloud.r-project.org\" ))"},{"path":"https://gavinsimpson.github.io/gratia/CONTRIBUTING.html","id":"got-a-question-want-to-show-how-to-do-something-with-gratia","dir":"","previous_headings":"","what":"Got a question? Want to show how to do something with {gratia}?","title":"Contributing","text":"issue best described question, want know something {gratia}, cool example using {gratia} want share , please consider using Discussions page. ’re sure, can always ask issue Question (Use Q&category) , really bug, can easily create Issue discussion.","code":""},{"path":"https://gavinsimpson.github.io/gratia/CONTRIBUTING.html","id":"code-contributions","dir":"","previous_headings":"","what":"Code contributions","title":"Contributing","text":"Code contributions form Pull Requests always appreciated. suitable workflow: Fork repo Github account Clone version account machine account, e.g,. git clone https://github.com//gratia.git Make sure track progress upstream (.e., version gratis gavinsimpson/gratia) git remote add upstream https://github.com/gavinsimpson/gratia.git. making changes make sure pull changes upstream either git fetch upstream merge later git pull upstream fetch merge one step Make changes (bonus points making changes new feature branch) Push account Submit pull request home base gavinsimpson/gratia","code":""},{"path":"https://gavinsimpson.github.io/gratia/CONTRIBUTING.html","id":"development-tools--paradigm--ethos","dir":"","previous_headings":"Code contributions","what":"Development tools / paradigm / ethos","title":"Contributing","text":"Please note following contributing code: {gratia} tightly aligned tidyverse use {dplyr} related packages lot internally developing package. plan replacing code lower-level code using {vctrs}, right now development focus filling functionality package premature optimisation. original aim {gratia} provide {ggplot2} plotting smooths; please stick principle use plotting paradigm. aim {mgcv}-feature complete; {mgcv} can something terms plotting smooths, handling specialists smooths, etc, principle {gratia} support . aim general compatibility {mgcv}; {gratia} deviates {mgcv} things, needs good justification; example {gratia} deviates multivariate isotropic smooths fitted s() plotted, Dave Miller (@dill) argued convincingly way. Don’t add dependencies! Unless accompanied strong justification, want reduce dependencies increase number. ’m using {styler} style code, using 2 spaces indent. code written change, however. Respect 80 character line length limit. contribute functionality fix bug, please add test using {testthat} framework insure new things works correctly bug stays fixed. contributions must result new NOTES, WARNINGS, ERRORS running R CMD check ---cran; please check contributions Winbuilder example.","code":""},{"path":"https://gavinsimpson.github.io/gratia/CONTRIBUTING.html","id":"email","dir":"","previous_headings":"","what":"Email","title":"Contributing","text":"hate email! can email GMail address — ’s like can stop :-) — unless capture attention immediately label message {gratia}-related, quickly get swamped never seen . Even label , ’s guarantee ever get round replying; usually happens ’ll forget don’t remember check gratia label often. end result email , get response , tardy. infinitely better use Discussions Issues pages Github ask questions package report problems. Please don’t email work (academic) address (email address might find ) {gratia}. question GAMs, much better ask CrossValidated StackOverflow depending whether question statistical programming related. relates {gratia} can use Discussions page. Asking question public allows others reply public, contributes body knowledge easily available others. circumstances send email multiple addresses; quickest way get message trash.","code":""},{"path":"https://gavinsimpson.github.io/gratia/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"The MIT License (MIT)","title":"The MIT License (MIT)","text":"Copyright (c) 2013-2024 Gavin L. Simpson Permission hereby granted, free charge, person obtaining copy software associated documentation files (“Software”), deal Software without restriction, including without limitation rights use, copy, modify, merge, publish, distribute, sublicense, /sell copies Software, permit persons Software furnished , subject following conditions: copyright notice permission notice shall included copies substantial portions Software. SOFTWARE PROVIDED “”, WITHOUT WARRANTY KIND, EXPRESS IMPLIED, INCLUDING LIMITED WARRANTIES MERCHANTABILITY, FITNESS PARTICULAR PURPOSE NONINFRINGEMENT. EVENT SHALL AUTHORS COPYRIGHT HOLDERS LIABLE CLAIM, DAMAGES LIABILITY, WHETHER ACTION CONTRACT, TORT OTHERWISE, ARISING , CONNECTION SOFTWARE USE DEALINGS SOFTWARE.","code":""},{"path":"https://gavinsimpson.github.io/gratia/articles/custom-plotting.html","id":"background","dir":"Articles","previous_headings":"","what":"Background","title":"Customizing plots","text":"draw() function {gratia} envisaged ggplot-based alternative mgcv:::plot.gam(). , never intended allow sorts customization possible ggplot() packages use ggplot() plotting layer. largely due decision produce multiple separate ggplot() plots GAMs multiple smooths, subsequently combined single figure device, initially using {cowplot} recently {patchwork}. things way evident consider might represent smooths 3 4 variables (common might think; consider space-time models via te(x, y, time, d = c(2,1)) space-depth-time models [think ocean atmospheric data space depth (height), observed time] via te(x, y, depth, time, d = c(2, 1, 1))), require facets top produce small multiples, means can’t use facets plot separate smooths. Additional complications arise consider complex smooth types, splines sphere, might want us different coordinate systems geoms best represent underlying smooth. gone root combining multiple ggplot objects single figure, problem customizing plots quickly rears head. vignette presents solutions problem modifying adding plots produced draw() culminating example illustrating use {gratia}’s utility functions produce plots lower-lever components.","code":""},{"path":"https://gavinsimpson.github.io/gratia/articles/custom-plotting.html","id":"adding-layers-to-plots-with-the-operator","dir":"Articles","previous_headings":"","what":"Adding layers to plots with the & operator","title":"Customizing plots","text":"start simulating data fitting GAM four smooth functions default plot produced draw() want change theme plots, can’t append theme() layer p affects last plot patchwork1 One way apply theme plots patchwork & operator.","code":"library(\"gratia\") library(\"mgcv\") #> Loading required package: nlme #> This is mgcv 1.9-1. For overview type 'help(\"mgcv-package\")'. library(\"ggplot2\") library(\"dplyr\") #> #> Attaching package: 'dplyr' #> The following object is masked from 'package:nlme': #> #> collapse #> The following objects are masked from 'package:stats': #> #> filter, lag #> The following objects are masked from 'package:base': #> #> intersect, setdiff, setequal, union library(\"patchwork\") # simulate data n <- 400 eg1 <- data_sim(\"eg1\", n = n, seed = 1) # fit model m <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = eg1, method = \"REML\") p <- draw(m) p p + theme_bw() p & theme_bw()"},{"path":"https://gavinsimpson.github.io/gratia/articles/custom-plotting.html","id":"combining-individual-plots-produced-by-draw","dir":"Articles","previous_headings":"","what":"Combining individual plots produced by draw()","title":"Customizing plots","text":"draw() methods like draw.gam() return object created patchwork::wrap_plots(), result isn’t straightforward combine objects new patchwork avoid error, need use patchwork::plot_layout() set dimensions want achieved directly via draw() instructive know combine outputs draw() need arise, want create patchwork plots different models.","code":"p1 <- draw(m, select = \"s(x0)\") p2 <- draw(m, select = \"s(x1)\") p3 <- draw(m, select = \"s(x2)\") p1 + p2 + p3 #> Error in `wrap_dims()`: #> ! Need 3 panels, but together `nrow` and `ncol` only provide 1. #> ℹ Please increase `ncol` and/or `nrow`. p1 + p2 + p3 + plot_layout(ncol = 3) draw(m, select = c(\"s(x0)\", \"s(x1)\", \"s(x2)\"), ncol = 3)"},{"path":"https://gavinsimpson.github.io/gratia/articles/custom-plotting.html","id":"building-your-own-plot-by-hand","dir":"Articles","previous_headings":"","what":"Building your own plot by hand","title":"Customizing plots","text":"{gratia} provides high-level functions like draw() get good graphical overview fitted model, little option customisation — isn’t possible desirable allow possible customisation options fatures {ggplot2} single function. Think many arguments require! Instead, {gratia} also exports lower-level functions used draw() can create plot using whatever {ggplot2} functions make sense. next code blocks ’ll see plot created draw(m) can recreated hand using lower-level building blocks. main thing need evaluate smooths values covariates. done using smooth_estimates(). also need add credible interval evaluations, can done tidyverse-style via add_confint() default draw.gam() add partial residuals partial effects plots. achieve effect, need add partial residuals data used fit model. can done via add_partial_residuals() will2 add columns names \"s(x0)\". \"s(x1)\", etc. data. Now everything need recreate plots created draw.gam(). code block filter sm focus specific smooth, f(x2)f(x2) (\"s(x2)\"), add rug plot observed values x2, credible interval around estimated smooth, partial residuals point layer, estimated smooth line layer, annotation Assuming repeat steps smooths, creating plot objects p_sx0, p_sx1, p_sx2, p_sx3 (code shown), can complete plot creating patchwork desired number rows columns real benefit complete control data plotted can use power {ggplot2} map additional variables plot aesthetics. example, let’s assume factor variable original data want colour partial residuals according levels factor. Let’s create factor Now can modify plotting code map fac colour aesthetic plot partial residuals. save typing, ’ll reorder layers plot add partial residuals last can also simple model checking plotting smooth partial residuals coloured according one covariates (also plotting actual residuals covariates). code chunk , map covariate x1 colour size aesthetics (note deleted cex = 1.5 allow mapping size) resulting plot doesn’t show particular problems model way data simulated, hopefully illustrates can possible use low-level functions provided {gratia}.","code":"# evaluate the smooths sm <- smooth_estimates(m) |> add_confint() sm #> # A tibble: 400 × 11 #> .smooth .type .by .estimate .se x0 x1 x2 x3 .lower_ci #> #> 1 s(x0) TPRS NA -0.929 0.422 0.0131 NA NA NA -1.76 #> 2 s(x0) TPRS NA -0.881 0.396 0.0230 NA NA NA -1.66 #> 3 s(x0) TPRS NA -0.834 0.372 0.0329 NA NA NA -1.56 #> 4 s(x0) TPRS NA -0.786 0.348 0.0429 NA NA NA -1.47 #> 5 s(x0) TPRS NA -0.738 0.326 0.0528 NA NA NA -1.38 #> 6 s(x0) TPRS NA -0.690 0.305 0.0627 NA NA NA -1.29 #> 7 s(x0) TPRS NA -0.643 0.287 0.0727 NA NA NA -1.20 #> 8 s(x0) TPRS NA -0.595 0.270 0.0826 NA NA NA -1.12 #> 9 s(x0) TPRS NA -0.548 0.255 0.0925 NA NA NA -1.05 #> 10 s(x0) TPRS NA -0.501 0.242 0.102 NA NA NA -0.975 #> # ℹ 390 more rows #> # ℹ 1 more variable: .upper_ci # add partial residuals to data eg1 <- eg1 |> add_partial_residuals(m) names(eg1) #> [1] \"y\" \"x0\" \"x1\" \"x2\" \"x3\" \"f\" \"f0\" \"f1\" \"f2\" #> [10] \"f3\" \"s(x0)\" \"s(x1)\" \"s(x2)\" \"s(x3)\" p_sx2 <- sm |> filter(.smooth == \"s(x2)\") |> ggplot() + geom_rug(aes(x = x2), data = eg1, sides = \"b\", length = grid::unit(0.02, \"npc\") ) + geom_ribbon(aes(ymin = .lower_ci, ymax = .upper_ci, x = x2), alpha = 0.2 ) + geom_point(aes(x = x2, y = `s(x2)`), data = eg1, cex = 1.5, colour = \"steelblue3\" ) + geom_line(aes(x = x2, y = .estimate), lwd = 1.2) + labs(y = \"Partial effect\", title = \"s(x2)\") p_sx2 p_sx0 + p_sx1 + p_sx2 + p_sx3 + plot_layout(ncol = 2) set.seed(12) eg1 <- eg1 |> mutate(fac = sample(letters[1:4], n(), replace = TRUE)) plt <- sm |> filter(.smooth == \"s(x2)\") |> ggplot() + geom_rug(aes(x = x2), data = eg1, sides = \"b\", length = grid::unit(0.02, \"npc\") ) + geom_ribbon(aes(ymin = .lower_ci, ymax = .upper_ci, x = x2), alpha = 0.2 ) + geom_line(aes(x = x2, y = .estimate), lwd = 1.2) + labs(y = \"Partial effect\", title = \"s(x2)\") plt + geom_point( aes( x = x2, y = `s(x2)`, colour = fac ), # <-- map fac to colour aesthetic data = eg1, cex = 1.5 ) plt + geom_point( aes( x = x2, y = `s(x2)`, colour = x1, size = x1 ), # <-- map fac to colour aesthetic data = eg1, alpha = 0.3 ) + # <-- deleted cex!! scale_colour_viridis_c(option = \"plasma\")"},{"path":"https://gavinsimpson.github.io/gratia/articles/data-slices.html","id":"carbon-dioxide-uptake-in-grass-plants","dir":"Articles","previous_headings":"","what":"Carbon Dioxide Uptake in Grass Plants","title":"Data slices","text":"first example uses small data set experimental study cold tolerance grass Echinochloa crusgalli. data data frame CO2 provided {datasets} package ships R. One way model data allow different smooths combinations treatment type covariates can look fitted smooths using draw() might want compare model fitted values treatment types (origins), ignoring random effect component. want evaluate model range values covariate conc combinations factors. data slice covariate space, can create using data_slice(). create data slice conc Quebec type chilled treatment use Notice data_slice() filled something remaining covariates didn’t mention? case, data_slice() doesn’t know tt created, chosen modal level tt factor, correct choice case. Instead, need specify correct level explicitly tt created data slice, can predict model using combination covariate values specified slice. use predict.gam() , fitted_values() function {gratia} easier use, especially non-Gaussian models Notice excluded random effect term; even though specify something plant covariate can ignore term model using exclude argument. fitted_values() creates credible interval scale link function back-transforms response scale scale = \"response\", also default. Plotting fitted values data slice now requires simple {ggplot2} knowledge Next, let’s compare fitted effects treatment Mississippi origin plants , replaced automatically-generated tt variable correctly specified call fct_cross(), retaining levels type treatment factors. insures correct combinations corresponding treatment type factors also preserve original levels tt covariate created. can visualise fitted values data slice creating data slices, used helper functions specify covariate values slice. {gratia} provides several helper functions: evenly(x, n = 100) — creates n evenly spaced values range covariate, evenly(x, = 5 — creates evenly spaced values range covariate increments 5, evenly(x, ..., lower = 5, upper = 10) — either two uses evenly() shown use lower upper limits vector x. Arguments lower upper can used change one upper lower bounds. evenly(fct) — produces new factor containing level specified factor fct just , ref_level(fct) — creates new factor containing just reference level specified factor covariate fct, level(fct, \"level\") — creates factor requested \"level\" factor fct. cases involving factors, helper functions set levels factor match original model fit2. second argument data_slice() ... ... argument allows provide expressions create covariate values want data slice. Expressions passed ... evaluated within model frame fitted model (argument object) data (supplied). restricted either using helper functions provide {gratia}; R function used long makes sense context model frame, returns something can combined using tidyr::expand_grid().","code":"## data load and prep data(CO2, package = \"datasets\") plant <- CO2 |> as_tibble() |> rename(plant = Plant, type = Type, treatment = Treatment) |> mutate(plant = factor(plant, ordered = FALSE)) plant_ylab <- expression(CO[2] ~ uptake ~ (mu * mol ~ m^{-3})) plant_xlab <- expression(CO[2] ~ concentration ~ (mL ~ L^{-1})) plant |> ggplot(aes(x = conc, y = uptake, group = plant, colour = treatment)) + geom_point() + geom_line() + facet_wrap(~type) + labs(y = plant_ylab, x = plant_xlab, colour = \"Treatment\") plant <- plant |> mutate(tt = fct_cross(treatment, type)) m_plant <- gam(uptake ~ treatment * type + s(conc, by = tt, k = 6) + s(plant, bs = \"re\"), data = plant, method = \"REML\", family = Gamma(link = \"log\") ) overview(m_plant) #> #> Generalized Additive Model with 8 terms #> #> term type k edf statistic p.value #> #> 1 treatment parametric NA 1 1.59 0.2124864 #> 2 type parametric NA 1 11.2 0.0014830 #> 3 treatment:type parametric NA 1 7.45 0.0085489 #> 4 s(conc):ttnonchilled:Quebec TPRS 5 4.72 69.7 < 0.001 #> 5 s(conc):ttchilled:Quebec TPRS 5 4.71 86.5 < 0.001 #> 6 s(conc):ttnonchilled:Mississippi TPRS 5 4.62 74.1 < 0.001 #> 7 s(conc):ttchilled:Mississippi TPRS 5 4.39 25.3 < 0.001 #> 8 s(plant) Random effect 12 7.40 12.8 < 0.001 draw(m_plant, residuals = TRUE, scales = \"fixed\") ds1 <- data_slice(m_plant, conc = evenly(conc, n = 100), type = level(type, \"Quebec\"), treatment = level(treatment, \"chilled\") ) ds1 #> # A tibble: 100 × 5 #> conc type treatment tt plant #> #> 1 95 Quebec chilled nonchilled:Quebec Qn1 #> 2 104. Quebec chilled nonchilled:Quebec Qn1 #> 3 113. Quebec chilled nonchilled:Quebec Qn1 #> 4 122. Quebec chilled nonchilled:Quebec Qn1 #> 5 132. Quebec chilled nonchilled:Quebec Qn1 #> 6 141. Quebec chilled nonchilled:Quebec Qn1 #> 7 150. Quebec chilled nonchilled:Quebec Qn1 #> 8 159. Quebec chilled nonchilled:Quebec Qn1 #> 9 168. Quebec chilled nonchilled:Quebec Qn1 #> 10 177. Quebec chilled nonchilled:Quebec Qn1 #> # ℹ 90 more rows ds1 <- data_slice(m_plant, conc = evenly(conc, n = 100), treatment = level(treatment, \"chilled\"), type = level(type, \"Quebec\"), tt = level(tt, \"chilled:Quebec\") ) ds1 #> # A tibble: 100 × 5 #> conc treatment type tt plant #> #> 1 95 chilled Quebec chilled:Quebec Qn1 #> 2 104. chilled Quebec chilled:Quebec Qn1 #> 3 113. chilled Quebec chilled:Quebec Qn1 #> 4 122. chilled Quebec chilled:Quebec Qn1 #> 5 132. chilled Quebec chilled:Quebec Qn1 #> 6 141. chilled Quebec chilled:Quebec Qn1 #> 7 150. chilled Quebec chilled:Quebec Qn1 #> 8 159. chilled Quebec chilled:Quebec Qn1 #> 9 168. chilled Quebec chilled:Quebec Qn1 #> 10 177. chilled Quebec chilled:Quebec Qn1 #> # ℹ 90 more rows fv1 <- fitted_values(m_plant, data = ds1, scale = \"response\", exclude = \"s(plant)\") fv1 #> # A tibble: 100 × 10 #> .row conc treatment type tt plant .fitted .se .lower_ci .upper_ci #> #> 1 1 95 chilled Quebec chille… Qn1 13.0 0.0783 11.2 15.2 #> 2 2 104. chilled Quebec chille… Qn1 14.1 0.0757 12.1 16.3 #> 3 3 113. chilled Quebec chille… Qn1 15.2 0.0737 13.1 17.5 #> 4 4 122. chilled Quebec chille… Qn1 16.3 0.0722 14.2 18.8 #> 5 5 132. chilled Quebec chille… Qn1 17.6 0.0714 15.3 20.2 #> 6 6 141. chilled Quebec chille… Qn1 18.9 0.0711 16.4 21.7 #> 7 7 150. chilled Quebec chille… Qn1 20.2 0.0712 17.6 23.3 #> 8 8 159. chilled Quebec chille… Qn1 21.6 0.0716 18.8 24.9 #> 9 9 168. chilled Quebec chille… Qn1 23.0 0.0721 20.0 26.5 #> 10 10 177. chilled Quebec chille… Qn1 24.4 0.0726 21.2 28.1 #> # ℹ 90 more rows fv1 |> ggplot(aes(x = conc, y = .fitted)) + geom_point( data = plant |> filter(type == \"Quebec\", treatment == \"chilled\"), mapping = aes(y = uptake), alpha = 0.8, colour = \"steelblue\" ) + geom_ribbon(aes(ymin = .lower_ci, ymax = .upper_ci), alpha = 0.2) + geom_line() + labs( x = plant_xlab, y = plant_ylab, title = expression(Estimated ~ CO[2] ~ uptake), subtitle = \"Chilled plants of the Quebec type\" ) ds2 <- data_slice(m_plant, conc = evenly(conc, n = 100), treatment = evenly(treatment), type = level(type, \"Mississippi\") ) |> mutate(tt = fct_cross(treatment, type, keep_empty = TRUE)) ds2 #> # A tibble: 200 × 5 #> conc treatment type tt plant #> #> 1 95 nonchilled Mississippi nonchilled:Mississippi Qn1 #> 2 95 chilled Mississippi chilled:Mississippi Qn1 #> 3 104. nonchilled Mississippi nonchilled:Mississippi Qn1 #> 4 104. chilled Mississippi chilled:Mississippi Qn1 #> 5 113. nonchilled Mississippi nonchilled:Mississippi Qn1 #> 6 113. chilled Mississippi chilled:Mississippi Qn1 #> 7 122. nonchilled Mississippi nonchilled:Mississippi Qn1 #> 8 122. chilled Mississippi chilled:Mississippi Qn1 #> 9 132. nonchilled Mississippi nonchilled:Mississippi Qn1 #> 10 132. chilled Mississippi chilled:Mississippi Qn1 #> # ℹ 190 more rows fitted_values(m_plant, data = ds2, scale = \"response\", exclude = \"s(plant)\" ) |> ggplot(aes(x = conc, y = .fitted, group = treatment)) + geom_point( data = plant |> filter(type == \"Mississippi\"), mapping = aes(y = uptake, colour = treatment), alpha = 0.8 ) + geom_ribbon(aes(ymin = .lower_ci, ymax = .upper_ci, fill = treatment), alpha = 0.2 ) + geom_line(aes(colour = treatment)) + labs( x = plant_xlab, y = plant_ylab, title = expression(Estimated ~ CO[2] ~ uptake), subtitle = \"Comparison of treatment in plants of the Mississippi type\", colour = \"Treatment\", fill = \"Treatment\" ) args(gratia:::data_slice.gam) #> function (object, ..., data = NULL, envir = NULL) #> NULL"},{"path":"https://gavinsimpson.github.io/gratia/articles/data-slices.html","id":"slices-through-a-2d-smooth","dir":"Articles","previous_headings":"","what":"Slices through a 2D smooth","title":"Data slices","text":"second example, ’ll use bivariate example data set {mgcv} fit tensor product covariates x z aim example create univariate data slice 2D smooth user-specified values x holding z one fixed values. visualise effect smooth level, using smooth_estimates(), response level, using fitted_values().","code":"# simulate data from the bivariate surface df <- data_sim(\"eg2\", n = 1000, scale = 0.25, seed = 2) # fit the GAM m_biv <- gam(y ~ te(x, z), data = df, method = \"REML\")"},{"path":"https://gavinsimpson.github.io/gratia/articles/data-slices.html","id":"using-smooth_estimates","dir":"Articles","previous_headings":"Slices through a 2D smooth","what":"Using smooth_estimates()","title":"Data slices","text":"begin creating slice data space. also create label point nice axis label. evaluate smooth desired values add confidence interval can plot sm using {ggplot2} Note interval Marra Wood (2012) interval. doesn’t include uncertainty model constant term moment, unless smooth close linear shouldn’t make much difference. extends multiple slices asking several discrete z","code":"ds3 <- data_slice(m_biv, x = evenly(x, n = 100), z = quantile(z, probs = 0.25) ) z_val <- with(ds3, round(quantile(z, probs = 0.25), 2)) ylab <- bquote(hat(f)(x, .(z_val))) sm <- smooth_estimates(m_biv, select = \"te(x,z)\", data = ds3) |> add_confint() sm #> # A tibble: 100 × 9 #> .smooth .type .by .estimate .se x z .lower_ci .upper_ci #> #> 1 te(x,z) Tensor prod… NA 0.103 0.0583 6.63e-4 0.245 -0.0107 0.218 #> 2 te(x,z) Tensor prod… NA 0.122 0.0548 1.08e-2 0.245 0.0148 0.230 #> 3 te(x,z) Tensor prod… NA 0.141 0.0514 2.08e-2 0.245 0.0400 0.242 #> 4 te(x,z) Tensor prod… NA 0.159 0.0482 3.09e-2 0.245 0.0648 0.254 #> 5 te(x,z) Tensor prod… NA 0.177 0.0451 4.10e-2 0.245 0.0890 0.266 #> 6 te(x,z) Tensor prod… NA 0.195 0.0422 5.11e-2 0.245 0.113 0.278 #> 7 te(x,z) Tensor prod… NA 0.213 0.0396 6.12e-2 0.245 0.135 0.291 #> 8 te(x,z) Tensor prod… NA 0.230 0.0372 7.13e-2 0.245 0.157 0.303 #> 9 te(x,z) Tensor prod… NA 0.247 0.0351 8.14e-2 0.245 0.178 0.316 #> 10 te(x,z) Tensor prod… NA 0.263 0.0333 9.14e-2 0.245 0.198 0.328 #> # ℹ 90 more rows sm |> ggplot(aes(x = x, y = .estimate)) + geom_ribbon(aes(ymin = .lower_ci, ymax = .upper_ci), alpha = 0.2) + geom_line() + labs( title = \"Evaluation of smooth te(x,z) at fixed z\", y = ylab ) ds4 <- data_slice(m_biv, x = evenly(x, n = 100), z = round(quantile(z, probs = c(0.25, 0.5, 0.75)), 2) ) sm <- smooth_estimates(m_biv, select = \"te(x,z)\", data = ds4) |> add_confint() |> mutate(fz = factor(z)) sm |> ggplot(aes(x = x, y = .estimate, colour = fz, group = fz)) + geom_ribbon(aes(ymin = .lower_ci, ymax = .upper_ci, fill = fz, colour = NULL), alpha = 0.2 ) + geom_line() + labs( title = \"Evaluation of smooth te(x,z) at fixed z\", y = expression(hat(f)(x, z)), colour = \"z\", fill = \"z\" )"},{"path":"https://gavinsimpson.github.io/gratia/articles/data-slices.html","id":"using-fitted_samples","dir":"Articles","previous_headings":"Slices through a 2D smooth","what":"Using fitted_samples()","title":"Data slices","text":"want evaluate surface x fixed z conditional upon values covariates (model predicted fitted values) fitted_samples() tidy wrapper predict.gam(). single z multiple z difference now model constant included well uncertainty.","code":"fitted_values(m_biv, data = ds3) |> # default is response scale, not link ggplot(aes(x = x, y = .fitted)) + geom_ribbon(aes(ymin = .lower_ci, ymax = .upper_ci), alpha = 0.2) + geom_line() + labs( title = \"Fitted values from model\", y = expression(hat(y)) ) fitted_values(m_biv, data = ds4) |> mutate(fz = factor(z)) |> ggplot(aes(x = x, y = .fitted, colour = fz, group = fz)) + geom_ribbon(aes(ymin = .lower_ci, ymax = .upper_ci, fill = fz, colour = NULL), alpha = 0.2 ) + geom_line() + labs( title = \"Fitted values from model\", y = expression(hat(y)), colour = \"z\", fill = \"z\" )"},{"path":"https://gavinsimpson.github.io/gratia/articles/gratia.html","id":"plotting","dir":"Articles","previous_headings":"","what":"Plotting","title":"Getting started with gratia","text":"gratia provides draw() function produce plots using ggplot2 📦. draw estimated smooths GAM fitted , use intended reasonable overview estimated model, offers limited option modify resulting plot. want full control, can obtain data used create plot smooth_estimates() evaluate smooths unevenly spaced values range covariate(s). want evaluate selected smooths, can specify via smooth argument. takes smooth labels names smooths known mgcv. list labels smooths use evaluate f(x2)f(x_2) use can generate plot using ggplot2 package, example","code":"draw(m) sm <- smooth_estimates(m) sm #> # A tibble: 400 × 9 #> .smooth .type .by .estimate .se x0 x1 x2 x3 #> #> 1 s(x0) TPRS NA -1.32 0.390 0.000239 NA NA NA #> 2 s(x0) TPRS NA -1.24 0.365 0.0103 NA NA NA #> 3 s(x0) TPRS NA -1.17 0.340 0.0204 NA NA NA #> 4 s(x0) TPRS NA -1.09 0.318 0.0304 NA NA NA #> 5 s(x0) TPRS NA -1.02 0.297 0.0405 NA NA NA #> 6 s(x0) TPRS NA -0.947 0.279 0.0506 NA NA NA #> 7 s(x0) TPRS NA -0.875 0.263 0.0606 NA NA NA #> 8 s(x0) TPRS NA -0.803 0.249 0.0707 NA NA NA #> 9 s(x0) TPRS NA -0.732 0.237 0.0807 NA NA NA #> 10 s(x0) TPRS NA -0.662 0.228 0.0908 NA NA NA #> # ℹ 390 more rows smooths(m) #> [1] \"s(x0)\" \"s(x1)\" \"s(x2)\" \"s(x3)\" sm <- smooth_estimates(m, smooth = \"s(x2)\") #> Warning: The `smooth` argument of `smooth_estimates()` is deprecated as of gratia #> 0.8.9.9. #> ℹ Please use the `select` argument instead. #> This warning is displayed once every 8 hours. #> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was #> generated. sm #> # A tibble: 100 × 6 #> .smooth .type .by .estimate .se x2 #> #> 1 s(x2) TPRS NA -4.47 0.476 0.00359 #> 2 s(x2) TPRS NA -4.00 0.406 0.0136 #> 3 s(x2) TPRS NA -3.53 0.345 0.0237 #> 4 s(x2) TPRS NA -3.06 0.295 0.0338 #> 5 s(x2) TPRS NA -2.58 0.263 0.0438 #> 6 s(x2) TPRS NA -2.09 0.250 0.0539 #> 7 s(x2) TPRS NA -1.59 0.253 0.0639 #> 8 s(x2) TPRS NA -1.08 0.264 0.0740 #> 9 s(x2) TPRS NA -0.564 0.278 0.0841 #> 10 s(x2) TPRS NA -0.0364 0.289 0.0941 #> # ℹ 90 more rows library(\"ggplot2\") library(\"dplyr\") #> #> Attaching package: 'dplyr' #> The following object is masked from 'package:nlme': #> #> collapse #> The following objects are masked from 'package:stats': #> #> filter, lag #> The following objects are masked from 'package:base': #> #> intersect, setdiff, setequal, union sm |> add_confint() |> ggplot(aes(y = .estimate, x = x2)) + geom_ribbon(aes(ymin = .lower_ci, ymax = .upper_ci), alpha = 0.2, fill = \"forestgreen\" ) + geom_line(colour = \"forestgreen\", linewidth = 1.5) + labs( y = \"Partial effect\", title = expression(\"Partial effect of\" ~ f(x[2])), x = expression(x[2]) )"},{"path":"https://gavinsimpson.github.io/gratia/articles/gratia.html","id":"model-diagnostics","dir":"Articles","previous_headings":"","what":"Model diagnostics","title":"Getting started with gratia","text":"appraise() function provides standard diagnostic plots GAMs plots produced (left--right, top--bottom), quantile-quantile (QQ) plot deviance residuals, scatterplot deviance residuals linear predictor, histogram deviance residuals, scatterplot observed vs fitted values. Adding partial residuals partial effect plots produced draw() can also help diagnose problems model, oversmoothing","code":"appraise(m) draw(m, residuals = TRUE)"},{"path":"https://gavinsimpson.github.io/gratia/articles/gratia.html","id":"want-to-learn-more","dir":"Articles","previous_headings":"","what":"Want to learn more?","title":"Getting started with gratia","text":"gratia active development area development currently lacking documentation. find package, look help pages package look examples code help get going.","code":""},{"path":"https://gavinsimpson.github.io/gratia/articles/posterior-simulation.html","id":"what-are-we-simulating","dir":"Articles","previous_headings":"","what":"What are we simulating?","title":"Posterior Simulation","text":"Posterior simulation involves randomly sampling MVN(𝛃̂,𝐕b)\\text{MVN}(\\hat{\\boldsymbol{\\beta}}, \\mathbf{V}_{\\text{b}}) EF(μi,ϕ)\\text{EF}(\\mu_i, \\phi), . might simulate posterior distribution single estimated smooth function see uncertainty estimate function. simulate just subset βj⋅\\beta_{j \\cdot} associated fjf_j interest. Instead, might interested uncertainty expectation (expected value) model given values covariates, case can simulate 𝛃\\boldsymbol{\\beta} sample posterior 𝔼(yi)\\mathbb{E}(y_i), fitted values model. might want generate new values response variable via draws conditional distribution response, simulating new response data 𝕪*\\mathbb{y}^{\\ast}, either observed 𝐱\\mathbf{x} new values $^{}, yi*|𝛈,𝐱∼EF(μî,ϕ)y^{\\ast}_i | \\boldsymbol{\\eta}, \\mathbf{x} \\sim \\text{EF}(\\hat{\\mu_i}, \\phi). Finally, can combine posterior simulation distributions generate posterior draws new data 𝕪*\\mathbb{y}^{\\ast} also include uncertainty expected values. gratia functionality options following functions smooth_samples() generates draws posterior distribution single estimated smooth functions, fitted_samples() generates draws posterior distribution 𝔼(yi|𝐗i=xi)\\mathbb{E}(y_i | \\mathbf{X}_i = x_i), expected value responss, predicted_samples(), generates new response data given supplied values covariates yi*|𝐗i=xi*y^{\\ast}_i | \\mathbf{X}_i = x^{\\ast}_i posterior_samples(), generates draws posterior distribution model, including uncertainty estimated parameters model. simpler terms, fitted_samples() generates predictions “average” expected value response values covariates. predictions include uncertainty estimated values model coefficients. contrast, posterior_samples() generates predictions actual values response might expect observe (model correct) given values covariates. predicted values include variance sampling distribution (error term). predicted_samples() lies somewhere two; predicted values include variation sampling distribution, take model fixed, known. worth reminding posterior draws conditional upon selected values smoothing parameter(s) λj\\lambda_j. act wiggliness estimated smooths known, actual fact estimated (selected perhaps better description) wiglinesses data model fitting. estimated GAM fitted method argument \"REML\", \"ML\", version 𝐕b\\mathbf{V}_{\\text{b}} corrected selected smoothing parameters, 𝐕c\\mathbf{V}_{\\text{c}}, generally available. allows, extent, posterior simulation account additional source uncertainty chosen values 𝛌\\boldsymbol{\\lambda}. two additional functions available gratia posterior simulation: simulate(), derivative_samples(). gratia provides simulate() methods models estimated using gam(), bam(), gamm(), well fitted via scam() scam package. simulate() base R convention thing predicted_samples(), just non-tidy way (pejorative; returns simulated response values matrix, arguably useful math statistical computation.) derivative_samples() provides draws posterior distribution derivative response variable small change focal covariate value. derivative_samples() less general version fitted_samples(); achieve thing two separate calls fitted_samples(). ’ll reserve discussion derivative_samples() separate vignette focused estimating derivatives GAMs. following sections ’ll look four main posterior simulation functions turn.","code":""},{"path":"https://gavinsimpson.github.io/gratia/articles/posterior-simulation.html","id":"posterior-smooths-and-smooth_samples","dir":"Articles","previous_headings":"","what":"Posterior smooths and smooth_samples()","title":"Posterior Simulation","text":"can sample posterior distribution coefficients particular smooth β̂j\\hat{\\beta}_j given values smoothing parameters 𝛌̂\\hat{\\boldsymbol{\\lambda}}. generate posterior samples smooths sampling 𝛃j⋆∼N(β̂j,𝐕β̂j)\\boldsymbol{\\beta}_{j\\star} \\sim N(\\hat{\\beta}_j, \\mathbf{V}_{\\hat{\\beta}_j}) forming 𝐗𝛃̂j𝛃j⋆𝖳\\mathbf{X}_{\\hat{\\boldsymbol{\\beta}}_j} \\boldsymbol{\\beta}_{j\\star}^{\\mathsf{T}}. sampling can done using smooth_samples(). illustrate , ’ll simulate data Gu & Wabha’s 4 smooth example, fit GAM simulated data simulating posterior distribution estimated smooth, sampling coefficients particular smooth. model, coefficients smooth f(x0)f(x_0) stored elements 2 10 coefficients vector. sample posterior distribution coefficients use smooth_samples() choosing particular smooth ’re interested using select argument; want sample smooths posteriors smooths model, select can left default value. Typically ’re bothered particular values covariate evaluate posterior smooths; ask 100 evenly spaced values x0 using n_vals, can provide covariates values via data argument. number posterior smooths sampled controlled argument n; ask 100 samples. Objects returned smooth_samples() draw() method available draw posterior smooths can set n_samples randomly select many smooths draw (seed can provided via argument seed make set chosen smooths repeatable.) credible interval smooth contain smooths. standard 95% credible interval, sampled smooths exceed limits interval. Following Marra & Wood (2012), blue credible interval contain average 95% grey lines (posterior smooths) given value x0x_0. across function frequentist interpretation credible interval implies values x0x_0 coverage less 95% values greater 95%.","code":"ss_df <- data_sim(\"eg1\", seed = 42) m_ss <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = ss_df, method = \"REML\") s_x0 <- get_smooth(m_ss, \"s(x0)\") smooth_coef_indices(s_x0) #> [1] 2 3 4 5 6 7 8 9 10 sm_samp <- smooth_samples(m_ss, select = \"s(x0)\", n_vals = 100, n = 100, seed = 21) sm_samp |> draw(alpha = 0.3) # evaluate the fitted smooth over x0 and add on a credible interval sm_est <- smooth_estimates(m_ss, select = \"s(x0)\") |> add_confint() # plot the smooth, credible interval, and posterior smooths sm_est |> ggplot(aes(x = x0)) + geom_lineribbon(aes(ymin = .lower_ci, ymax = .upper_ci), orientation = \"vertical\", fill = \"#56B4E9\", alpha = 0.5 ) + geom_line( data = sm_samp, aes(y = .value, group = .draw), alpha = 0.2 ) + geom_line(aes(y = .estimate), linewidth = 1, colour = \"#E69F00\") + labs(y = smooth_label(s_x0))"},{"path":"https://gavinsimpson.github.io/gratia/articles/posterior-simulation.html","id":"posterior-fitted-values-via-fitted_samples","dir":"Articles","previous_headings":"","what":"Posterior fitted values via fitted_samples()","title":"Posterior Simulation","text":"Posterior fitted values draws posterior distribution mean expected value response. expectations returned use predict() estimated GAM, except fitted_samples() includes uncertainty estimated model coefficients, whereas predict() just uses estimated coefficients. example, using data_sim() simulate data example 6 Luo & Wahba (1997) sin(2⋅(4x−2))+2⋅exp(−256⋅(x−0.5)2) \\sin(2 \\cdot (4x - 2)) + 2 \\cdot \\exp(-256 \\cdot (x - 0.5)^2) data fit adaptive smoother Next create data slice 200 values interval (0,1) ’ll predict model generate posterior fitted values compute fitted values new data posterior fitted values drawn fitted_samples() using Gaussian approximation posterior. just take 10 draws posterior observation new_df merge posterior draws data Adding posterior fitted samples plot data, superimposing Bayesian credible interval fitted values see posterior draws largely contained credible interval. difference smooth_samples() now ’re including effects model terms. simple model single smooth identity link, difference model constant term uncertainty included samples.","code":"f <- function(x) { sin(2 * ((4 * x) - 2)) + (2 * exp(-256 * (x - 0.5)^2)) } df <- data_sim(\"lwf6\", dist = \"normal\", scale = 0.3, seed = 2) plt <- df |> ggplot(aes(x = x, y = y)) + geom_point(alpha = 0.5) + geom_function(fun = f) plt m <- gam(y ~ s(x, k = 25, bs = \"ad\"), data = df, method = \"REML\") new_df <- data_slice(m, x = evenly(x, lower = 0, upper = 1, n = 200)) |> mutate(.row = row_number()) fv <- fitted_values(m, data = new_df) fs <- fitted_samples(m, data = new_df, n = 10, seed = 4) |> left_join(new_df |> select(.row, x), by = join_by(.row == .row)) plt + geom_ribbon(data = fv, aes(y = .fitted, ymin = .lower_ci, ymax = .upper_ci), fill = \"red\", alpha = 0.3) + geom_line(data = fs, aes(group = .draw, x = x, y = .fitted), colour = \"yellow\", alpha = 0.4)"},{"path":[]},{"path":"https://gavinsimpson.github.io/gratia/articles/posterior-simulation.html","id":"prediction-intervals","dir":"Articles","previous_headings":"Additional examples","what":"Prediction intervals","title":"Posterior Simulation","text":"One use posterior simulation generate prediction intervals fitted model. Prediction intervals include two sources uncertainty; estimated model , plus sampling uncertainty error arises drawing observations conditional distribution response. example, Gaussian GAM, first source uncertainty comes uncertainty estimates βj\\beta_j, model coefficients. uncertainty mean expected value response. second source uncertainty stems error term, estimated variance response. two parameters define conditional distribution YiY_i. value covariate(s) 𝐗\\mathbf{X}, estimated model defines entire distribution response values might expect observe covariate values. illustrate, ’ll fit simple GAM single smooth function data simulate Gu & Wabha’s function f2f_2 using data_sim(). simulate 400 values Gaussian distribution variance σ2=1\\sigma^2 = 1. simulated data, true function generated shown GAM data contains single smooth function x consider new value covariate x, x*=0.5x^{\\ast} = 0.5, expected value response given model, 𝔼(y*|x=x*)\\mathbb{E}(y^{*} | x = x^{*}), ~2.92, obtain using predict() value mean Gaussian distribution , model correct description data, describes distribution values YY might take x=0.5x = 0.5. Gaussian distribution defined two parameters; mean, μ\\mu, describes middle distribution, variance, σ2\\sigma^2, describes spread distribution mean. fully describe Gaussian distribution response x=0.5x = 0.5, need estimate variance. didn’t model explicitly GAM, get estimate model’s scale parameter, ϕ\\phi. stored element scale model object can visualise distribution looks like magic ggdist package orange region shows expected density response values x*=0.5x^{\\ast} = 0.5 model predicts expect observe. region assumes uncertainty estimate mean variance. Prediction intervals take account variation expected value, plus uncertainty expected value. fitted_values() conveniently returns uncertainty us, default 95% credible interval .se column standard error (standard deviation) estimated value (.fitted), .lower_ci .upper_ci lower upper uncertainty bounds (95% level) estimated value respectively. GAMs fitted mgcv don’t corresponding estimate uncertainty scale parameter, ϕ\\phi, model estimated standard deviation σ̂\\hat{\\sigma}. pretty easy compute upper lower tail quantiles fitted Gaussian distribution range values x get prediction interval, ’d ignoring uncertainty model estimates mean. Posterior simulation provides simple convenient way generate prediction interval includes model uncertainty, works principle families available mgcv (although practice, families currently supported gratia). compute prediction interval x GAM, creating set data evenly range x observed data used fit model added variable .row used later match posterior simulated values row prediction data set ds. also compute fitted values new observations using fitted_values(). step isn’t required order posterior simulation gratia, ’ll use fitted values later show model estimated values uncertainty contrast prediction interval. use posterior_samples() generate new response data new x values ds use join add prediction data draw asked 10000 posterior draws new value x. Ideally ’d generate least three four times many draws get precise estimate prediction interval, keep number low vignette avoid excessive computation time. ’re also using smoothness parameter selection corrected version Bayesian covariance matrix; matrix adjusted account us knowing value smoothing parameter f(xi)f(x_i). ps tibble, n * nrow(ds) rows. .draw variable groups simulated values posterior draw, .row groups posterior draws value x. summarise posterior draws using {dplyr} need function compute quantiles posterior distribution value x (.row). following function simple wrapper around quantile() function base R, arranges output quantile() data frame. apply function set posterior draws, grouping .row summarise separately posterior distribution new value x. reframe() used summarise posterior using quantile_fun() function. ease use, pivot resulting summary long wide format add covariate values joining .row variable 95% prediction interval shown first 10 rows prediction data. column labelled .q50 median posterior distribution. can now use various objects produced plot fitted values model (uncertainties), well prediction intervals just generated. add observed data used fit model black points, summarise posterior samples (ps) using hexagonal binning (avoid plotting 2 million posterior samples) outermost pair blue lines plot prediction interval created. interval encloses, expected, almost observe data points. also encloses, design, posterior samples, indicated filled hexagonal bins, warmer colours indicating larger counts posterior draws.","code":"df <- data_sim(\"gwf2\", n = 400, scale = 1, dist = \"normal\", seed = 8) df |> ggplot(aes(x = x, y = y)) + geom_point() + geom_function(fun = gw_f2, colour = \"#0072B2\", linewidth = 1.5) m <- gam(y ~ s(x), data = df, method = \"REML\", family = gaussian()) mu <- predict(m, newdata = data.frame(x = 0.5)) mu #> 1 #> 2.919094 sigma <- m$scale sigma #> [1] 1.019426 df |> ggplot(aes(x = x, y = y)) + stat_halfeye(aes(ydist = dist_normal(mean = mu, sd = sigma)), x = 0.5, scale = 0.2, slab_fill = \"#E69F00\", slab_alpha = 0.7 ) + geom_point() + geom_function(fun = gw_f2, colour = \"#0072B2\", linewidth = 1.5) + geom_point(x = 0.5, y = mu, colour = \"red\") fitted_values(m, data = data.frame(x = 0.5)) #> # A tibble: 1 × 6 #> .row x .fitted .se .lower_ci .upper_ci #> #> 1 1 0.5 2.92 0.161 2.60 3.23 ds <- data_slice(m, x = evenly(x, n = 200)) |> mutate(.row = row_number()) fv <- fitted_values(m, data = ds) ps <- posterior_samples(m, n = 10000, data = ds, seed = 24, unconditional = TRUE) |> left_join(ds, by = join_by(.row == .row)) ps #> # A tibble: 2,000,000 × 4 #> .row .draw .response x #> #> 1 1 1 -1.34 0.00129 #> 2 2 1 -0.0495 0.00629 #> 3 3 1 0.0308 0.0113 #> 4 4 1 -0.783 0.0163 #> 5 5 1 0.861 0.0213 #> 6 6 1 0.475 0.0263 #> 7 7 1 0.858 0.0313 #> 8 8 1 0.143 0.0363 #> 9 9 1 -0.0344 0.0413 #> 10 10 1 1.04 0.0463 #> # ℹ 1,999,990 more rows quantile_fun <- function(x, probs = c(0.025, 0.5, 0.975), ...) { tibble::tibble( .value = quantile(x, probs = probs, ...), .q = probs * 100 ) } p_int <- ps |> group_by(.row) |> reframe(quantile_fun(.response)) |> pivot_wider( id_cols = .row, names_from = .q, values_from = .value, names_prefix = \".q\" ) |> left_join(ds, by = join_by(.row == .row)) p_int #> # A tibble: 200 × 5 #> .row .q2.5 .q50 .q97.5 x #> #> 1 1 -2.84 -0.847 1.25 0.00129 #> 2 2 -2.70 -0.651 1.41 0.00629 #> 3 3 -2.50 -0.434 1.62 0.0113 #> 4 4 -2.24 -0.207 1.83 0.0163 #> 5 5 -2.04 -0.0197 2.01 0.0213 #> 6 6 -1.81 0.191 2.25 0.0263 #> 7 7 -1.59 0.391 2.41 0.0313 #> 8 8 -1.38 0.594 2.60 0.0363 #> 9 9 -1.20 0.831 2.84 0.0413 #> 10 10 -0.935 1.06 3.04 0.0463 #> # ℹ 190 more rows fv |> ggplot(aes(x = x, y = .fitted)) + # summarise the posterior samples geom_hex( data = ps, aes(x = x, y = .response, fill = after_stat(count)), bins = 50, alpha = 0.7 ) + # add the lower and upper prediction intervals geom_line(data = p_int, aes(y = .q2.5), colour = \"#56B4E9\", linewidth = 1.5) + geom_line(data = p_int, aes(y = .q97.5), colour = \"#56B4E9\", linewidth = 1.5) + # add the lower and upper credible intervals geom_line(aes(y = .lower_ci), colour = \"#56B4E9\", linewidth = 1) + geom_line(aes(y = .upper_ci), colour = \"#56B4E9\", linewidth = 1) + # add the fitted model geom_line() + # add the observed data geom_point(data = df, aes(x = x, y = y)) + scale_fill_viridis_c(option = \"plasma\") + theme(legend.position = \"none\") + labs(y = \"Response\")"},{"path":"https://gavinsimpson.github.io/gratia/articles/posterior-simulation.html","id":"metropolis-hastings-sampler","dir":"Articles","previous_headings":"Additional examples","what":"Metropolis Hastings sampler","title":"Posterior Simulation","text":"cases, Gaussian approximation posterior distribution model coefficients can fail. Simon Wood shows example just failure ?gam.mh help page, Gaussian approximation basically useless binomial GAM large numbers zeroes. mgcv::gam.mh() implements simple Metropolis Hastings sampler, alternates proposals Gaussian t distribution approximation posterior random walk proposals based shrunken approximate posterior covariance matrix. section, rework Simon’s example failure Gaussian approximation ?gam.mh show use gratia generate posterior draws using Metropolis Hastings sampler provided gam.mh(). begin defining function simulate data example. use simulate data set plot Note zeroes large parts covariate space response zeroes. fit binomial (logistic) GAM data generate sample posterior distribution using default Gaussian approximation subsequently using simpler Metropolis Hastings sampler. method argument used select Metropolis Hastings sampler, specify two additional arguments: thin, controls many draws skipped retained sample, rw_scale, scaling factor posterior covariance matrix shrunk random walk proposals. leave two important arguments defaults: burnin = 1000, number samples discard prior sampling, t_df = 40, degrees freedom t proposals. degrees freedom t proposals large, ’re effectively Gaussian approximation default, alternating proposals random walk proposals. collected posterior draws, summarise set 50%, 80%, 95% intervals using ggdist::median_qi(), add data locations left join First plot intervals Gaussian approximation posterior, repeat plot using intervals derived Metropolis Hastings sampler, arranging two plots using patchwork Gaussian approximation-based intervals shown left figure, range x largely useless, covering entire range response, despite fact observed zeroes large parts covariate space. Contrast intervals ones obtained using Metropolis Hastings sampler; intervals much better reflect uncertainty estimated response function x data zeroes.","code":"ga_fail <- function(seed) { df <- tibble(y = c( rep(0, 89), 1, 0, 1, 0, 0, 1, rep(0, 13), 1, 0, 0, 1, rep(0, 10), 1, 0, 0, 1, 1, 0, 1, rep(0, 4), 1, rep(0, 3), 1, rep(0, 3), 1, rep(0, 10), 1, rep(0, 4), 1, 0, 1, 0, 0, rep(1, 4), 0, rep(1, 5), rep(0, 4), 1, 1, rep(0, 46) )) |> mutate( x = withr::with_seed( seed, sort(c(0:10 * 5, rnorm(length(y) - 11) * 20 + 100)) ), .row = row_number() ) |> relocate(.row, .before = 1L) df } df <- ga_fail(3) df |> ggplot(aes(x = x, y = y)) + geom_point() m_logit <- gam(y ~ s(x, k = 15), data = df, method = \"REML\", family = binomial) fs_ga <- fitted_samples(m_logit, n = 2000, seed = 2) fs_mh <- fitted_samples(m_logit, n = 2000, seed = 2, method = \"mh\", thin = 2, rw_scale = 0.4 ) excl_col <- c(\".draw\", \".parameter\", \".row\") int_ga <- fs_ga |> group_by(.row) |> median_qi(.width = c(0.5, 0.8, 0.95), .exclude = excl_col) |> left_join(df, by = join_by(.row == .row)) int_mh <- fs_mh |> group_by(.row) |> median_qi(.width = c(0.5, 0.8, 0.95), .exclude = excl_col) |> left_join(df, by = join_by(.row == .row)) plt_ga <- df |> ggplot(aes(x = x, y = y)) + geom_point() + geom_lineribbon( data = int_ga, aes(x = x, y = .fitted, ymin = .lower, ymax = .upper) ) + scale_fill_brewer() + labs(title = \"Gaussian approximation\") plt_mh <- df |> ggplot(aes(x = x, y = y)) + geom_point() + geom_lineribbon( data = int_mh, aes(x = x, y = .fitted, ymin = .lower, ymax = .upper) ) + scale_fill_brewer() + labs(title = \"Metropolis Hastings sampler\") plt_ga + plt_mh + plot_layout(guides = \"collect\")"},{"path":[]},{"path":"https://gavinsimpson.github.io/gratia/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Gavin L. Simpson. Author, maintainer, copyright holder. Henrik Singmann. Contributor.","code":""},{"path":"https://gavinsimpson.github.io/gratia/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Simpson G (????). gratia: Graceful ggplot-Based Graphics Functions GAMs Fitted using mgcv. R package version 0.9.2.9011, https://gavinsimpson.github.io/gratia/.","code":"@Manual{, title = {{gratia}: Graceful {ggplot}-Based Graphics and Other Functions for {GAM}s Fitted using {mgcv}}, author = {Gavin L. Simpson}, abstract = {Graceful ggplot-based graphics and utility functions for working with generalized additive models (GAMs) fitted using the mgcv package. Provides a reimplementation of the plot() method for GAMs that mgcv provides, as well as tidyverse-compatible representations of estimated smooths.}, note = {R package version 0.9.2.9011}, url = {https://gavinsimpson.github.io/gratia/}, }"},{"path":[]},{"path":"https://gavinsimpson.github.io/gratia/index.html","id":"overview","dir":"","previous_headings":"","what":"Overview","title":"An R package for working with generalized additive models","text":"Working GAMs within ‘tidyverse’ can tedious even difficult without good understanding GAMs model returned ‘mgcv’ model objects contain. ‘gratia’ designed help . ‘gratia’ provides ‘ggplot’-based graphics utility functions working generalized additive models (GAMs) fitted using ‘mgcv’ package, via reimplementation plot() method GAMs ‘mgcv’ provides, well ‘tidyverse’ compatible representations estimated smooths.","code":""},{"path":"https://gavinsimpson.github.io/gratia/index.html","id":"features","dir":"","previous_headings":"","what":"Features","title":"An R package for working with generalized additive models","text":"main features gratia currently ggplot2-based replacement mgcv:::plot.gam(): draw.gam(). example, classic four term additive example Gu & Wahba: Estimated smooths GAM bivariate smooth: Estimated smooths GAM Note specialist smoothers (bs %% c(\"mrf\",\"sw\", \"sf\")) currently supported, univariate, factor continuous -variable smooths, simple random effect smooths (bs = 're'), factor-smooth interaction smooths (bs = \"fs\"), constrained factor smooths (bs = \"sz\"), full soap film smooths (bs = \"\"), bivariate, trivariate, quadvariate TPRS tensor product smooths supported, Estimation derivatives fitted smoothers: derivatives(), Estimation point-wise across--function confidence intervals simultaneous intervals smooths: confint.gam(). Model diagnostics via appraise() Model diagnostics figure","code":""},{"path":"https://gavinsimpson.github.io/gratia/index.html","id":"installing-gratia","dir":"","previous_headings":"","what":"Installing gratia","title":"An R package for working with generalized additive models","text":"gratia now available CRAN, can installed however gratia active development may wish install development version github. easiest way via install_github() function package remotes. Make sure remotes installed, run install package. Alternatively, binary packages development version available rOpenSci’s R Universe service:","code":"install.packages(\"gratia\") remotes::install_github(\"gavinsimpson/gratia\") # Install gratia in R install.packages(\"gratia\", repos = c( \"https://gavinsimpson.r-universe.dev\", \"https://cloud.r-project.org\" ))"},{"path":"https://gavinsimpson.github.io/gratia/index.html","id":"history","dir":"","previous_headings":"","what":"History","title":"An R package for working with generalized additive models","text":"gratia grew earlier package, schoenberg, development earlier package tsgam, originally intended used GAMs fitted time series. developing tsgam however became clear package used generally name “tsgam” longer appropriate. avoid breaking blog posts written using tsgam decided copy git repo history new repo package name schoenberg. later date someone released another package called schoenberg CRAN, scuppered idea. Now ’m calling package gratia. Hopefully won’t change …","code":""},{"path":"https://gavinsimpson.github.io/gratia/index.html","id":"why-gratia","dir":"","previous_headings":"","what":"Why gratia?","title":"An R package for working with generalized additive models","text":"naming greta package, Nick Golding observed recent phenomena naming statistical modelling software, Stan Edward, individuals played prominent role development field. lead Nick name Tensor Flow-based package greta Grete Hermann. spirit, gratia named recognition contributions Grace Wahba, pioneering work penalised spline models foundation way GAMs estimated mgcv. wanted name package grace, explicitly recognise Grace’s contributions, unfortunately already package named Grace CRAN. looked elsewhere inspiration. English word “grace” derives Latin gratia, meaning “favor, charm, thanks” (according Merriam Webster). chair Grace Wabha currently holds named Isaac J Schoenberg, former University Madison-Wisconsin Professor Mathematics, 1946 paper provided first mathematical reference “splines”. (Hence previous name package.)","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/add_confint.html","id":null,"dir":"Reference","previous_headings":"","what":"Add a confidence interval to an existing object — add_confint","title":"Add a confidence interval to an existing object — add_confint","text":"Add confidence interval existing object","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/add_confint.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add a confidence interval to an existing object — add_confint","text":"","code":"add_confint(object, coverage = 0.95, ...) # S3 method for class 'smooth_estimates' add_confint(object, coverage = 0.95, ...) # S3 method for class 'parametric_effects' add_confint(object, coverage = 0.95, ...) # Default S3 method add_confint(object, coverage = 0.95, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/add_confint.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add a confidence interval to an existing object — add_confint","text":"object R object. coverage numeric; coverage interval. Must range 0 < coverage < 1. ... arguments passed methods.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/add_constant.html","id":null,"dir":"Reference","previous_headings":"","what":"Add a constant to estimated values — add_constant","title":"Add a constant to estimated values — add_constant","text":"Add constant estimated values","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/add_constant.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add a constant to estimated values — add_constant","text":"","code":"add_constant(object, constant = NULL, ...) # S3 method for class 'smooth_estimates' add_constant(object, constant = NULL, ...) # S3 method for class 'smooth_samples' add_constant(object, constant = NULL, ...) # S3 method for class 'mgcv_smooth' add_constant(object, constant = NULL, ...) # S3 method for class 'parametric_effects' add_constant(object, constant = NULL, ...) # S3 method for class 'tbl_df' add_constant(object, constant = NULL, column = NULL, ...) # S3 method for class 'evaluated_parametric_term' add_constant(object, constant = NULL, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/add_constant.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add a constant to estimated values — add_constant","text":"object object add constant . constant constant add. ... additional arguments passed methods. column character; \"tbl_df\" method, column add constant .","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/add_constant.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add a constant to estimated values — add_constant","text":"Returns object estimate shifted addition supplied constant.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/add_constant.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Add a constant to estimated values — add_constant","text":"Gavin L. Simpson","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/add_fitted.gam.html","id":null,"dir":"Reference","previous_headings":"","what":"Add fitted values from a GAM to a data frame — add_fitted.gam","title":"Add fitted values from a GAM to a data frame — add_fitted.gam","text":"Add fitted values GAM data frame","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/add_fitted.gam.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add fitted values from a GAM to a data frame — add_fitted.gam","text":"","code":"# S3 method for class 'gam' add_fitted(data, model, value = \".fitted\", type = \"response\", ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/add_fitted.gam.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add fitted values from a GAM to a data frame — add_fitted.gam","text":"data data frame containing values variables used fit model. Passed stats::predict() newdata. model fitted model stats::predict() method available. S3 method dispatch performed model argument. value character; name variable model predictions stored. type character; type predictions return. See mgcv::predict.gam() options. ... additional arguments passed mgcv::predict.gam().","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/add_fitted.gam.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add fitted values from a GAM to a data frame — add_fitted.gam","text":"data frame (tibble) formed data predictions model.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/add_fitted.gam.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add fitted values from a GAM to a data frame — add_fitted.gam","text":"","code":"load_mgcv() df <- data_sim(\"eg1\", seed = 1) df <- df[, c(\"y\", \"x0\", \"x1\", \"x2\", \"x3\")] m <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = df, method = \"REML\") # add fitted values to our data add_fitted(df, m) #> # A tibble: 400 x 6 #> y x0 x1 x2 x3 .fitted #> #> 1 3.34 0.266 0.659 0.859 0.367 5.90 #> 2 -0.0758 0.372 0.185 0.0344 0.741 3.15 #> 3 10.7 0.573 0.954 0.971 0.934 8.28 #> 4 8.73 0.908 0.898 0.745 0.673 8.65 #> 5 15.0 0.202 0.944 0.273 0.701 15.7 #> 6 7.67 0.898 0.724 0.677 0.848 8.38 #> 7 7.58 0.945 0.370 0.348 0.706 7.84 #> 8 8.51 0.661 0.781 0.947 0.859 6.74 #> 9 10.6 0.629 0.0111 0.339 0.446 9.14 #> 10 3.72 0.0618 0.940 0.0317 0.677 7.04 #> # i 390 more rows # with type = \"terms\" or \"iterms\" add_fitted(df, m, type = \"terms\") #> # A tibble: 400 x 10 #> y x0 x1 x2 x3 .constant `s(x0)` `s(x1)` `s(x2)` `s(x3)` #> #> 1 3.34 0.266 0.659 0.859 0.367 7.94 0.175 0.559 -2.81 0.0351 #> 2 -0.0758 0.372 0.185 0.0344 0.741 7.94 0.435 -1.92 -3.23 -0.0687 #> 3 10.7 0.573 0.954 0.971 0.934 7.94 0.593 3.35 -3.47 -0.122 #> 4 8.73 0.908 0.898 0.745 0.673 7.94 -0.812 2.77 -1.19 -0.0498 #> 5 15.0 0.202 0.944 0.273 0.701 7.94 -0.0589 3.23 4.63 -0.0576 #> 6 7.67 0.898 0.724 0.677 0.848 7.94 -0.745 1.15 0.146 -0.0981 #> 7 7.58 0.945 0.370 0.348 0.706 7.94 -1.07 -1.31 2.34 -0.0589 #> 8 8.51 0.661 0.781 0.947 0.859 7.94 0.434 1.67 -3.20 -0.101 #> 9 10.6 0.629 0.0111 0.339 0.446 7.94 0.512 -1.95 2.63 0.0132 #> 10 3.72 0.0618 0.940 0.0317 0.677 7.94 -0.695 3.20 -3.35 -0.0508 #> # i 390 more rows"},{"path":"https://gavinsimpson.github.io/gratia/reference/add_fitted.html","id":null,"dir":"Reference","previous_headings":"","what":"Add fitted values from a model to a data frame — add_fitted","title":"Add fitted values from a model to a data frame — add_fitted","text":"Add fitted values model data frame","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/add_fitted.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add fitted values from a model to a data frame — add_fitted","text":"","code":"add_fitted(data, model, value = \".value\", ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/add_fitted.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add fitted values from a model to a data frame — add_fitted","text":"data data frame containing values variables used fit model. Passed stats::predict() newdata. model fitted model stats::predict() method available. S3 method dispatch performed model argument. value character; name variable model predictions stored. ... additional arguments passed methods.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/add_fitted.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add fitted values from a model to a data frame — add_fitted","text":"data frame (tibble) formed data fitted values model.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/add_fitted_samples.html","id":null,"dir":"Reference","previous_headings":"","what":"Add posterior draws from a model to a data object — add_fitted_samples","title":"Add posterior draws from a model to a data object — add_fitted_samples","text":"Adds draws posterior distribution model data object using one fitted_samples(), predicted_samples(), posterior_samples().","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/add_fitted_samples.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add posterior draws from a model to a data object — add_fitted_samples","text":"","code":"add_fitted_samples(object, model, n = 1, seed = NULL, ...) add_predicted_samples(object, model, n = 1, seed = NULL, ...) add_posterior_samples(object, model, n = 1, seed = NULL, ...) add_smooth_samples(object, model, n = 1, seed = NULL, select = NULL, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/add_fitted_samples.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add posterior draws from a model to a data object — add_fitted_samples","text":"object data frame tibble posterior draws added. model fitted GAM (GAM-like) object posterior draw method exists. n integer; number posterior draws add. seed numeric; value seed random number generator. ... arguments passed posterior draw function, currently one fitted_samples(), predicted_samples(), posterior_samples(). n seed already specified arguments also passed posterior sampling function. select character; select smooth's posterior draw . default, NULL, means posteriors smooths model wil sampled individually. supplied, character vector requested smooth terms.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/add_fitted_samples.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add posterior draws from a model to a data object — add_fitted_samples","text":"","code":"load_mgcv() df <- data_sim(\"eg1\", n = 400, seed = 42) m <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = df, method = \"REML\") # add fitted samples (posterior draws of the expected value of the response) # note that there are 800 rows in the output: 400 data by `n = 2` samples. df |> add_fitted_samples(m, n = 2, seed = 84) #> # A tibble: 800 × 14 #> y x0 x1 x2 x3 f f0 f1 f2 f3 .row .draw #> #> 1 2.99 0.915 0.0227 0.909 0.402 1.62 0.529 1.05 0.0397 0 1 1 #> 2 2.99 0.915 0.0227 0.909 0.402 1.62 0.529 1.05 0.0397 0 1 2 #> 3 4.70 0.937 0.513 0.900 0.432 3.25 0.393 2.79 0.0630 0 2 1 #> 4 4.70 0.937 0.513 0.900 0.432 3.25 0.393 2.79 0.0630 0 2 2 #> 5 13.9 0.286 0.631 0.192 0.664 13.5 1.57 3.53 8.41 0 3 1 #> 6 13.9 0.286 0.631 0.192 0.664 13.5 1.57 3.53 8.41 0 3 2 #> 7 5.71 0.830 0.419 0.532 0.182 6.12 1.02 2.31 2.79 0 4 1 #> 8 5.71 0.830 0.419 0.532 0.182 6.12 1.02 2.31 2.79 0 4 2 #> 9 7.63 0.642 0.879 0.522 0.838 10.4 1.80 5.80 2.76 0 5 1 #> 10 7.63 0.642 0.879 0.522 0.838 10.4 1.80 5.80 2.76 0 5 2 #> # ℹ 790 more rows #> # ℹ 2 more variables: .parameter , .fitted # add posterior draws from smooth s(x2) df |> add_smooth_samples(m, n = 2, seed = 2, select= \"s(x2)\") #> # A tibble: 800 × 15 #> y x0 x1 x2 x3 f f0 f1 f2 f3 .row .smooth #> #> 1 2.99 0.915 0.0227 0.909 0.402 1.62 0.529 1.05 0.0397 0 1 s(x2) #> 2 2.99 0.915 0.0227 0.909 0.402 1.62 0.529 1.05 0.0397 0 1 s(x2) #> 3 4.70 0.937 0.513 0.900 0.432 3.25 0.393 2.79 0.0630 0 2 s(x2) #> 4 4.70 0.937 0.513 0.900 0.432 3.25 0.393 2.79 0.0630 0 2 s(x2) #> 5 13.9 0.286 0.631 0.192 0.664 13.5 1.57 3.53 8.41 0 3 s(x2) #> 6 13.9 0.286 0.631 0.192 0.664 13.5 1.57 3.53 8.41 0 3 s(x2) #> 7 5.71 0.830 0.419 0.532 0.182 6.12 1.02 2.31 2.79 0 4 s(x2) #> 8 5.71 0.830 0.419 0.532 0.182 6.12 1.02 2.31 2.79 0 4 s(x2) #> 9 7.63 0.642 0.879 0.522 0.838 10.4 1.80 5.80 2.76 0 5 s(x2) #> 10 7.63 0.642 0.879 0.522 0.838 10.4 1.80 5.80 2.76 0 5 s(x2) #> # ℹ 790 more rows #> # ℹ 3 more variables: .term , .draw , .value "},{"path":"https://gavinsimpson.github.io/gratia/reference/add_partial_residuals.html","id":null,"dir":"Reference","previous_headings":"","what":"Add partial residuals — add_partial_residuals","title":"Add partial residuals — add_partial_residuals","text":"Add partial residuals","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/add_partial_residuals.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add partial residuals — add_partial_residuals","text":"","code":"add_partial_residuals(data, model, ...) # S3 method for class 'gam' add_partial_residuals(data, model, select = NULL, partial_match = FALSE, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/add_partial_residuals.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add partial residuals — add_partial_residuals","text":"data data frame containing values variables used fit model. Passed stats::residuals() newdata. model fitted model stats::residuals() method available. S3 method dispatch performed model argument. ... arguments passed methods. select character, logical, numeric; smooths plot. NULL, default, model smooths drawn. Numeric select indexes smooths order specified formula stored object. Character select matches labels smooths shown example output summary(object). Logical select operates per numeric select order smooths stored. partial_match logical; smooths selected partial matches select? TRUE, select can single string match .","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/add_partial_residuals.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add partial residuals — add_partial_residuals","text":"","code":"load_mgcv() df <- data_sim(\"eg1\", seed = 1) df <- df[, c(\"y\", \"x0\", \"x1\", \"x2\", \"x3\")] m <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = df, method = \"REML\") ## add partial residuals add_partial_residuals(df, m) #> # A tibble: 400 x 9 #> y x0 x1 x2 x3 `s(x0)` `s(x1)` `s(x2)` `s(x3)` #> #> 1 3.34 0.266 0.659 0.859 0.367 -2.38 -2.00 -5.36 -2.52 #> 2 -0.0758 0.372 0.185 0.0344 0.741 -2.79 -5.15 -6.45 -3.29 #> 3 10.7 0.573 0.954 0.971 0.934 2.99 5.75 -1.07 2.28 #> 4 8.73 0.908 0.898 0.745 0.673 -0.734 2.84 -1.11 0.0287 #> 5 15.0 0.202 0.944 0.273 0.701 -0.752 2.54 3.94 -0.750 #> 6 7.67 0.898 0.724 0.677 0.848 -1.46 0.432 -0.567 -0.812 #> 7 7.58 0.945 0.370 0.348 0.706 -1.33 -1.57 2.08 -0.318 #> 8 8.51 0.661 0.781 0.947 0.859 2.21 3.44 -1.42 1.68 #> 9 10.6 0.629 0.0111 0.339 0.446 2.01 -0.445 4.13 1.51 #> 10 3.72 0.0618 0.940 0.0317 0.677 -4.02 -0.123 -6.67 -3.37 #> # i 390 more rows ## add partial residuals for selected smooths add_partial_residuals(df, m, select = \"s(x0)\") #> # A tibble: 400 x 6 #> y x0 x1 x2 x3 `s(x0)` #> #> 1 3.34 0.266 0.659 0.859 0.367 -2.38 #> 2 -0.0758 0.372 0.185 0.0344 0.741 -2.79 #> 3 10.7 0.573 0.954 0.971 0.934 2.99 #> 4 8.73 0.908 0.898 0.745 0.673 -0.734 #> 5 15.0 0.202 0.944 0.273 0.701 -0.752 #> 6 7.67 0.898 0.724 0.677 0.848 -1.46 #> 7 7.58 0.945 0.370 0.348 0.706 -1.33 #> 8 8.51 0.661 0.781 0.947 0.859 2.21 #> 9 10.6 0.629 0.0111 0.339 0.446 2.01 #> 10 3.72 0.0618 0.940 0.0317 0.677 -4.02 #> # i 390 more rows"},{"path":"https://gavinsimpson.github.io/gratia/reference/add_residuals.gam.html","id":null,"dir":"Reference","previous_headings":"","what":"Add residuals from a GAM to a data frame — add_residuals.gam","title":"Add residuals from a GAM to a data frame — add_residuals.gam","text":"Add residuals GAM data frame","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/add_residuals.gam.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add residuals from a GAM to a data frame — add_residuals.gam","text":"","code":"# S3 method for class 'gam' add_residuals(data, model, value = \".residual\", type = \"deviance\", ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/add_residuals.gam.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add residuals from a GAM to a data frame — add_residuals.gam","text":"data data frame containing values variables used fit model. Passed stats::predict() newdata. model fitted model stats::predict() method available. S3 method dispatch performed model argument. value character; name variable model predictions stored. type character; type residuals return. See mgcv::residuals.gam() options. ... additional arguments passed mgcv::residuals.gam().","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/add_residuals.gam.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add residuals from a GAM to a data frame — add_residuals.gam","text":"data frame (tibble) formed data residuals model.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/add_residuals.gam.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add residuals from a GAM to a data frame — add_residuals.gam","text":"","code":"load_mgcv() df <- data_sim(\"eg1\", seed = 1) df <- df[, c(\"y\", \"x0\", \"x1\", \"x2\", \"x3\")] m <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = df, method = \"REML\") ## add_residuals(df, m) #> # A tibble: 400 x 6 #> y x0 x1 x2 x3 .residual #> #> 1 3.34 0.266 0.659 0.859 0.367 -2.56 #> 2 -0.0758 0.372 0.185 0.0344 0.741 -3.22 #> 3 10.7 0.573 0.954 0.971 0.934 2.40 #> 4 8.73 0.908 0.898 0.745 0.673 0.0785 #> 5 15.0 0.202 0.944 0.273 0.701 -0.693 #> 6 7.67 0.898 0.724 0.677 0.848 -0.714 #> 7 7.58 0.945 0.370 0.348 0.706 -0.259 #> 8 8.51 0.661 0.781 0.947 0.859 1.78 #> 9 10.6 0.629 0.0111 0.339 0.446 1.50 #> 10 3.72 0.0618 0.940 0.0317 0.677 -3.32 #> # i 390 more rows"},{"path":"https://gavinsimpson.github.io/gratia/reference/add_residuals.html","id":null,"dir":"Reference","previous_headings":"","what":"Add residuals from a model to a data frame — add_residuals","title":"Add residuals from a model to a data frame — add_residuals","text":"Add residuals model data frame","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/add_residuals.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add residuals from a model to a data frame — add_residuals","text":"","code":"add_residuals(data, model, value = \".residual\", ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/add_residuals.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add residuals from a model to a data frame — add_residuals","text":"data data frame containing values variables used fit model. Passed stats::residuals() newdata. model fitted model stats::residuals() method available. S3 method dispatch performed model argument. value character; name variable model residuals stored. ... additional arguments passed methods.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/add_residuals.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add residuals from a model to a data frame — add_residuals","text":"data frame (tibble) formed data residuals model.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/add_sizer.html","id":null,"dir":"Reference","previous_headings":"","what":"Add indicators of significant change after SiZeR — add_sizer","title":"Add indicators of significant change after SiZeR — add_sizer","text":"Add indicators significant change SiZeR","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/add_sizer.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add indicators of significant change after SiZeR — add_sizer","text":"","code":"add_sizer(object, type = c(\"change\", \"sizer\"), ...) # S3 method for class 'derivatives' add_sizer(object, type = c(\"change\", \"sizer\"), ...) # S3 method for class 'smooth_estimates' add_sizer(object, type = c(\"change\", \"sizer\"), derivatives = NULL, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/add_sizer.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add indicators of significant change after SiZeR — add_sizer","text":"object R object. Currently supported methods classes \"derivatives\". type character; \"change\" adds single variable object indicating credible interval derivative excludes 0. \"sizer\" adds two variables indicating whether derivative postive negative. ... arguments passed methods derivatives object class \"derivatives\", resulting call derivatives().","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/add_sizer.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add indicators of significant change after SiZeR — add_sizer","text":"","code":"load_mgcv() df <- data_sim(\"eg1\", n = 400, dist = \"normal\", scale = 2, seed = 42) m <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = df, method = \"REML\") ## first derivatives of all smooths using central finite differences d <- derivatives(m, type = \"central\") |> add_sizer() # default adds a .change column names(d) #> [1] \".smooth\" \".by\" \".fs\" \".derivative\" \".se\" #> [6] \".crit\" \".lower_ci\" \".upper_ci\" \".change\" \"x0\" #> [11] \"x1\" \"x2\" \"x3\""},{"path":"https://gavinsimpson.github.io/gratia/reference/appraise.html","id":null,"dir":"Reference","previous_headings":"","what":"Model diagnostic plots — appraise","title":"Model diagnostic plots — appraise","text":"Model diagnostic plots","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/appraise.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Model diagnostic plots — appraise","text":"","code":"appraise(model, ...) # S3 method for class 'gam' appraise( model, method = c(\"uniform\", \"simulate\", \"normal\", \"direct\"), use_worm = FALSE, n_uniform = 10, n_simulate = 50, seed = NULL, type = c(\"deviance\", \"pearson\", \"response\"), n_bins = c(\"sturges\", \"scott\", \"fd\"), ncol = NULL, nrow = NULL, guides = \"keep\", level = 0.9, ci_col = \"black\", ci_alpha = 0.2, point_col = \"black\", point_alpha = 1, line_col = \"red\", ... ) # S3 method for class 'lm' appraise(model, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/appraise.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Model diagnostic plots — appraise","text":"model fitted model. Currently models inheriting class \"gam\", well classes \"glm\" \"lm\" calls stats::glm stats::lm supported. ... arguments passed patchwork::wrap_plots(). method character; method used generate theoretical quantiles. default \"uniform\", generates reference quantiles using random draws uniform distribution inverse cummulative distribution function (CDF) fitted values. reference quantiles averaged n_uniform draws. \"simulate\" generates reference quantiles simulating new response data model observed values covariates, residualised generate reference quantiles, using n_simulate simulated data sets. \"normal\" generates reference quantiles using standard normal distribution. \"uniform\" computationally efficient, \"simulate\" allows reference bands drawn QQ-plot. \"normal\" avoided used fall back random number generator (\"simulate\") inverse CDF (\"uniform\"``) available family` used model fitting. Note method = \"direct\" deprecated favour method = \"uniform\". use_worm logical; worm plot drawn place QQ plot? n_uniform numeric; number times randomize uniform quantiles direct computation method (method = \"direct\") QQ plots. n_simulate numeric; number data sets simulate estimated model using simulation method (method = \"simulate\") QQ plots. seed numeric; random number seed use method = \"simulate\" method = \"uniform\". type character; type residuals use. \"deviance\", \"response\", \"pearson\" residuals allowed. n_bins character numeric; either number bins string indicating calculate number bins. ncol, nrow numeric; numbers rows columns spread plots. guides character; one \"keep\" (default), \"collect\", \"auto\". Passed patchwork::plot_layout() level numeric; coverage level QQ plot reference intervals. Must strictly 0 < level < 1. used method = \"simulate\". ci_alpha, ci_col colour transparency used draw QQ plot reference interval method = \"simulate\". point_col, point_alpha colour transparency used draw points plots. See graphics::par() section Color Specification. passed individual plotting functions, therefore affects points plots. line_col colour specification 1:1 line QQ plot reference line residuals vs linear predictor plot.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/appraise.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Model diagnostic plots — appraise","text":"wording used mgcv::qq.gam() uses direct reference simulated residuals method (method = \"simulated\"). avoid confusion, method = \"direct\" deprecated favour method = \"uniform\".","code":""},{"path":[]},{"path":"https://gavinsimpson.github.io/gratia/reference/appraise.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Model diagnostic plots — appraise","text":"","code":"load_mgcv() ## simulate some data... dat <- data_sim(\"eg1\", n = 400, dist = \"normal\", scale = 2, seed = 2) mod <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = dat) ## run some basic model checks appraise(mod, point_col = \"steelblue\", point_alpha = 0.4) ## To change the theme for all panels use the & operator, for example to ## change the ggplot theme for all panels library(\"ggplot2\") appraise(mod, seed = 42, point_col = \"steelblue\", point_alpha = 0.4, line_col = \"black\" ) & theme_minimal()"},{"path":"https://gavinsimpson.github.io/gratia/reference/basis.html","id":null,"dir":"Reference","previous_headings":"","what":"Basis expansions for smooths — basis","title":"Basis expansions for smooths — basis","text":"Creates basis expansion definition smoother using syntax mgcv's smooths via mgcv::s()., mgcv::te(), mgcv::ti(), mgcv::t2(), fitted GAM(M).","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/basis.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Basis expansions for smooths — basis","text":"","code":"basis(object, ...) # S3 method for class 'gam' basis( object, select = NULL, term = deprecated(), data = NULL, n = 100, n_2d = 50, n_3d = 16, n_4d = 4, partial_match = FALSE, ... ) # S3 method for class 'scam' basis( object, select = NULL, term = deprecated(), data = NULL, n = 100, n_2d = 50, n_3d = 16, n_4d = 4, partial_match = FALSE, ... ) # S3 method for class 'gamm' basis( object, select = NULL, term = deprecated(), data = NULL, n = 100, n_2d = 50, n_3d = 16, n_4d = 4, partial_match = FALSE, ... ) # S3 method for class 'list' basis( object, select = NULL, term = deprecated(), data = NULL, n = 100, n_2d = 50, n_3d = 16, n_4d = 4, partial_match = FALSE, ... ) # Default S3 method basis( object, data, knots = NULL, constraints = FALSE, at = NULL, diagonalize = FALSE, ... )"},{"path":"https://gavinsimpson.github.io/gratia/reference/basis.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Basis expansions for smooths — basis","text":"object smooth specification, result call one mgcv::s()., mgcv::te(), mgcv::ti(), mgcv::t2(), fitted GAM(M) model. ... arguments passed mgcv::smoothCon(). select character; select smooths fitted model term argument renamed select data data frame containing variables used smooth. n numeric; number points range covariate evaluate smooth. n_2d numeric; number new observations dimension bivariate smooth. currently used; n used dimensions. n_3d numeric; number new observations generate third dimension 3D smooth. n_4d numeric; number new observations generate dimensions higher 2 (!) kD smooth (k >= 4). example, smooth 4D smooth, dimensions 3 4 get n_4d new observations. partial_match logical; case character select, select match partially smooths? partial_match = TRUE, select must single string, character vector length 1. knots list data frame named components containing knots locations. Names must match covariates basis required. See mgcv::smoothCon(). constraints logical; identifiability constraints applied smooth basis. See argument absorb.cons mgcv::smoothCon(). data frame containing values smooth covariate(s) basis evaluated. diagonalize logical; TRUE, reparameterises smooth associated penalty identity matrix. effect turning last diagonal elements penalty zero, highlights penalty null space.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/basis.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Basis expansions for smooths — basis","text":"tibble.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/basis.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Basis expansions for smooths — basis","text":"Gavin L. Simpson","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/basis.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Basis expansions for smooths — basis","text":"","code":"load_mgcv() df <- data_sim(\"eg4\", n = 400, seed = 42) bf <- basis(s(x0), data = df) bf <- basis(s(x2, by = fac, bs = \"bs\"), data = df, constraints = TRUE)"},{"path":"https://gavinsimpson.github.io/gratia/reference/basis_size.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract basis dimension of a smooth — basis_size","title":"Extract basis dimension of a smooth — basis_size","text":"Extract basis dimension smooth","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/basis_size.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract basis dimension of a smooth — basis_size","text":"","code":"basis_size(object, ...) # S3 method for class 'mgcv.smooth' basis_size(object, ...) # S3 method for class 'gam' basis_size(object, ...) # S3 method for class 'gamm' basis_size(object, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/basis_size.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract basis dimension of a smooth — basis_size","text":"object fitted GAM(M). Currently mgcv::gam() (anything inherits \"gam\" class, e.g. mgcv::bam()) mgcv::gamm() supported. ... Arguments passed methods.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/basis_size.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract basis dimension of a smooth — basis_size","text":"","code":"load_mgcv() df <- data_sim(\"eg1\", n = 200, seed = 1) m <- bam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = df) basis_size(m) #> s(x0) s(x1) s(x2) s(x3) #> 9 9 9 9"},{"path":"https://gavinsimpson.github.io/gratia/reference/bird_move.html","id":null,"dir":"Reference","previous_headings":"","what":"Simulated bird migration data — bird_move","title":"Simulated bird migration data — bird_move","text":"Data generated hypothetical study bird movement along migration corridor, sampled throughout year. dataset consists simulated sample records numbers observed locations 100 tagged individuals six species bird, ten locations along latitudinal gradient, one observation taken every four weeks. Counts simulated randomly species location week creating species-specific migration curve gave probability finding individual given species given location, simulated distribution individuals across sites using multinomial distribution, subsampling using binomial distribution simulation observation error (.e. every bird present location detected). data set (bird_move) consists variables count, latitude, week species.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/bird_move.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Simulated bird migration data — bird_move","text":"data frame","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/bird_move.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Simulated bird migration data — bird_move","text":"Pedersen EJ, Miller DL, Simpson GL, Ross N. 2018. Hierarchical generalized additive models: introduction mgcv. PeerJ Preprints 6:e27320v1 doi:10.7287/peerj.preprints.27320v1 .","code":""},{"path":[]},{"path":"https://gavinsimpson.github.io/gratia/reference/boundary.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract the boundary of a soap film smooth — boundary","text":"","code":"boundary(x, ...) # S3 method for class 'soap.film' boundary(x, ...) # S3 method for class 'gam' boundary(x, select, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/boundary.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract the boundary of a soap film smooth — boundary","text":"x R object. Currently objects inherit classes \"soap.film\" \"gam\". ... arguments passed methods. select character; label soap film smooth extract boundary.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/boundary.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract the boundary of a soap film smooth — boundary","text":"list lists data frames specifying loops define boundary soap film smooth.","code":""},{"path":[]},{"path":"https://gavinsimpson.github.io/gratia/reference/check_user_select_smooths.html","id":null,"dir":"Reference","previous_headings":"","what":"Select smooths based on user's choices — check_user_select_smooths","title":"Select smooths based on user's choices — check_user_select_smooths","text":"Given vector indexing smooths GAM, returns logical vector selecting requested smooths.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/check_user_select_smooths.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Select smooths based on user's choices — check_user_select_smooths","text":"","code":"check_user_select_smooths( smooths, select = NULL, partial_match = FALSE, model_name = NULL )"},{"path":"https://gavinsimpson.github.io/gratia/reference/check_user_select_smooths.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Select smooths based on user's choices — check_user_select_smooths","text":"smooths character; vector smooth labels. select numeric, logical, character vector selected smooths. partial_match logical; case character select, select match partially smooths? partial_match = TRUE, select must single string, character vector length 1. model_name character; model name used error messages.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/check_user_select_smooths.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Select smooths based on user's choices — check_user_select_smooths","text":"logical vector length length(smooths) indicating smooths selected.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/check_user_select_smooths.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Select smooths based on user's choices — check_user_select_smooths","text":"Gavin L. Simpson","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/coef.scam.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract coefficients from a fitted scam model. — coef.scam","title":"Extract coefficients from a fitted scam model. — coef.scam","text":"Extract coefficients fitted scam model.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/coef.scam.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract coefficients from a fitted scam model. — coef.scam","text":"","code":"# S3 method for class 'scam' coef(object, parametrized = TRUE, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/coef.scam.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract coefficients from a fitted scam model. — coef.scam","text":"object model object fitted scam() parametrized logical; extract parametrized coefficients, respect linear inequality constraints model. ... arguments.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/compare_smooths.html","id":null,"dir":"Reference","previous_headings":"","what":"Compare smooths across models — compare_smooths","title":"Compare smooths across models — compare_smooths","text":"Compare smooths across models","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/compare_smooths.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compare smooths across models — compare_smooths","text":"","code":"compare_smooths( model, ..., select = NULL, smooths = deprecated(), n = 100, data = NULL, unconditional = FALSE, overall_uncertainty = TRUE, partial_match = FALSE )"},{"path":"https://gavinsimpson.github.io/gratia/reference/compare_smooths.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compare smooths across models — compare_smooths","text":"model Primary model comparison. ... Additional models compare smooths model. select character; select smooths compare. default (NULL) means smooths model compared. Numeric select indexes smooths order specified formula stored model. Character select matches labels smooths shown example output summary(object). Logical select operates per numeric select order smooths stored. smooths Use select instead. n numeric; number points range covariate evaluate smooth. data data frame covariate values evaluate smooth. unconditional logical; confidence intervals include uncertainty due smoothness selection? TRUE, corrected Bayesian covariance matrix used. overall_uncertainty logical; uncertainty model constant term included standard error evaluate values smooth? partial_match logical; smooths selected partial matches select? TRUE, select can single string match .","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/compare_smooths.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compare smooths across models — compare_smooths","text":"","code":"load_mgcv() dat <- data_sim(\"eg1\", seed = 2) ## models to compare smooths across - artificially create differences m1 <- gam(y ~ s(x0, k = 5) + s(x1, k = 5) + s(x2, k = 5) + s(x3, k = 5), data = dat, method = \"REML\" ) m2 <- gam(y ~ s(x0, bs = \"ts\") + s(x1, bs = \"ts\") + s(x2, bs = \"ts\") + s(x3, bs = \"ts\"), data = dat, method = \"REML\") ## build comparisons comp <- compare_smooths(m1, m2) comp #> # A tibble: 8 x 5 #> .model .smooth .type .by data #> #> 1 m1 s(x0) TPRS NA #> 2 m2 s(x0) TPRS (shrink) NA #> 3 m1 s(x1) TPRS NA #> 4 m2 s(x1) TPRS (shrink) NA #> 5 m1 s(x2) TPRS NA #> 6 m2 s(x2) TPRS (shrink) NA #> 7 m1 s(x3) TPRS NA #> 8 m2 s(x3) TPRS (shrink) NA ## notice that the result is a nested tibble draw(comp)"},{"path":"https://gavinsimpson.github.io/gratia/reference/confint.fderiv.html","id":null,"dir":"Reference","previous_headings":"","what":"Point-wise and simultaneous confidence intervals for derivatives of smooths — confint.fderiv","title":"Point-wise and simultaneous confidence intervals for derivatives of smooths — confint.fderiv","text":"Calculates point-wise confidence simultaneous intervals first derivatives smooth terms fitted GAM.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/confint.fderiv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Point-wise and simultaneous confidence intervals for derivatives of smooths — confint.fderiv","text":"","code":"# S3 method for class 'fderiv' confint( object, parm, level = 0.95, type = c(\"confidence\", \"simultaneous\"), nsim = 10000, ncores = 1L, ... )"},{"path":"https://gavinsimpson.github.io/gratia/reference/confint.fderiv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Point-wise and simultaneous confidence intervals for derivatives of smooths — confint.fderiv","text":"object object class \"fderiv\" containing estimated derivatives. parm parameters (smooth terms) given intervals vector terms. missing, parameters considered. level numeric, 0 < level < 1; confidence level point-wise simultaneous interval. default 0.95 95% interval. type character; type interval compute. One \"confidence\" point-wise intervals, \"simultaneous\" simultaneous intervals. nsim integer; number simulations used computing simultaneous intervals. ncores number cores generating random variables multivariate normal distribution. Passed mvnfast::rmvn(). Parallelization take place OpenMP supported (appears work Windows current R). ... additional arguments methods","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/confint.fderiv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Point-wise and simultaneous confidence intervals for derivatives of smooths — confint.fderiv","text":"data frame components: term; factor indicating term row relates, lower; lower limit confidence simultaneous interval, est; estimated derivative upper; upper limit confidence simultaneous interval.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/confint.fderiv.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Point-wise and simultaneous confidence intervals for derivatives of smooths — confint.fderiv","text":"Gavin L. Simpson","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/confint.fderiv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Point-wise and simultaneous confidence intervals for derivatives of smooths — confint.fderiv","text":"","code":"load_mgcv() dat <- data_sim(\"eg1\", n = 1000, dist = \"normal\", scale = 2, seed = 2) mod <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = dat, method = \"REML\") # new data to evaluate the derivatives at, say over the middle 50% of range # of each covariate middle <- function(x, n = 25, coverage = 0.5) { v <- (1 - coverage) / 2 q <- quantile(x, prob = c(0 + v, 1 - v), type = 8) seq(q[1], q[2], length = n) } new_data <- sapply(dat[c(\"x0\", \"x1\", \"x2\", \"x3\")], middle) new_data <- data.frame(new_data) ## first derivatives of all smooths... fd <- fderiv(mod, newdata = new_data) #> Warning: `fderiv()` was deprecated in gratia 0.7.0. #> i Please use `derivatives()` instead. ## point-wise interval ci <- confint(fd, type = \"confidence\") ci #> # A tibble: 100 x 4 #> term lower est upper #> #> 1 s(x0) 1.7 4.1 6.6 #> 2 s(x0) 1.3 3.8 6.3 #> 3 s(x0) 0.99 3.5 6.0 #> 4 s(x0) 0.68 3.1 5.6 #> 5 s(x0) 0.37 2.8 5.2 #> 6 s(x0) 0.0049 2.4 4.8 #> 7 s(x0) -0.40 2.0 4.5 #> 8 s(x0) -0.79 1.7 4.2 #> 9 s(x0) -1.1 1.3 3.8 #> 10 s(x0) -1.4 0.99 3.4 #> # i 90 more rows ## simultaneous interval for smooth term of x2 x2_sint <- confint(fd, parm = \"x2\", type = \"simultaneous\", nsim = 10000, ncores = 2 ) # \\donttest{ x2_sint #> # A tibble: 25 x 4 #> term lower est upper #> #> 1 s(x2) -24. -15. -5.6 #> 2 s(x2) -35. -26. -16. #> 3 s(x2) -41. -33. -24. #> 4 s(x2) -44. -36. -29. #> 5 s(x2) -44. -36. -28. #> 6 s(x2) -42. -34. -25. #> 7 s(x2) -38. -30. -21. #> 8 s(x2) -33. -24. -16. #> 9 s(x2) -27. -19. -11. #> 10 s(x2) -22. -14. -5.8 #> # i 15 more rows # }"},{"path":"https://gavinsimpson.github.io/gratia/reference/confint.gam.html","id":null,"dir":"Reference","previous_headings":"","what":"Point-wise and simultaneous confidence intervals for smooths — confint.gam","title":"Point-wise and simultaneous confidence intervals for smooths — confint.gam","text":"Calculates point-wise confidence simultaneous intervals smooth terms fitted GAM.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/confint.gam.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Point-wise and simultaneous confidence intervals for smooths — confint.gam","text":"","code":"# S3 method for class 'gam' confint( object, parm, level = 0.95, data = newdata, n = 100, type = c(\"confidence\", \"simultaneous\"), nsim = 10000, shift = FALSE, transform = FALSE, unconditional = FALSE, ncores = 1, partial_match = FALSE, ..., newdata = NULL ) # S3 method for class 'gamm' confint(object, ...) # S3 method for class 'list' confint(object, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/confint.gam.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Point-wise and simultaneous confidence intervals for smooths — confint.gam","text":"object object class \"gam\" \"gamm\". parm parameters (smooth terms) given intervals vector terms. missing, parameters considered, although currently implemented. level numeric, 0 < level < 1; confidence level point-wise simultaneous interval. default 0.95 95% interval. data data frame; new values covariates used model fit. selected smooth(s) wil evaluated supplied values. n numeric; number points evaluate smooths . type character; type interval compute. One \"confidence\" point-wise intervals, \"simultaneous\" simultaneous intervals. nsim integer; number simulations used computing simultaneous intervals. shift logical; constant term add smooth? transform logical; smooth evaluated transformed scale? generalised models, involves applying inverse link function used fit model. Alternatively, name , actual, function can supplied transform smooth confidence interval. unconditional logical; TRUE (freq == FALSE) Bayesian smoothing parameter uncertainty corrected covariance matrix returned, available. ncores number cores generating random variables multivariate normal distribution. Passed mvnfast::rmvn(). Parallelization take place OpenMP supported (appears work Windows current R). partial_match logical; matching parm use partial match exact match? Can used length(parm) 1. ... additional arguments methods newdata DEPRECATED! data frame; containing new values covariates used model fit. selected smooth(s) wil evaluated supplied values.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/confint.gam.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Point-wise and simultaneous confidence intervals for smooths — confint.gam","text":"tibble components: .smooth; character indicating term row relates, .type; type smooth, .name variable smooth, NA otherwise, one vectors values smooth evaluated, named per variables smooth, zero variables containing values variable, .estimate; estimated value smooth, .se; standard error estimated value smooth, .crit; critical value 100 * level% confidence interval. .lower_ci; lower limit confidence simultaneous interval, .upper_ci; upper limit confidence simultaneous interval,","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/confint.gam.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Point-wise and simultaneous confidence intervals for smooths — confint.gam","text":"Gavin L. Simpson","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/confint.gam.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Point-wise and simultaneous confidence intervals for smooths — confint.gam","text":"","code":"load_mgcv() dat <- data_sim(\"eg1\", n = 1000, dist = \"normal\", scale = 2, seed = 2) mod <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = dat, method = \"REML\") # new data to evaluate the smooths at, say over the middle 50% of range # of each covariate middle <- function(x, n = 50, coverage = 0.5) { v <- (1 - coverage) / 2 q <- quantile(x, prob = c(0 + v, 1 - v), type = 8) seq(q[1], q[2], length = n) } new_data <- sapply(dat[c(\"x0\", \"x1\", \"x2\", \"x3\")], middle) new_data <- data.frame(new_data) ## point-wise interval for smooth of x2 ci <- confint(mod, parm = \"s(x2)\", type = \"confidence\", data = new_data) ci #> # A tibble: 50 x 9 #> .smooth .type .by x2 .estimate .se .crit .lower_ci .upper_ci #> #> 1 s(x2) TPRS NA 0.26 5.3 0.18 2.0 5.0 5.7 #> 2 s(x2) TPRS NA 0.27 5.1 0.18 2.0 4.8 5.5 #> 3 s(x2) TPRS NA 0.28 4.9 0.18 2.0 4.6 5.3 #> 4 s(x2) TPRS NA 0.29 4.6 0.18 2.0 4.3 5.0 #> 5 s(x2) TPRS NA 0.30 4.3 0.19 2.0 3.9 4.7 #> 6 s(x2) TPRS NA 0.32 4.0 0.19 2.0 3.6 4.3 #> 7 s(x2) TPRS NA 0.33 3.6 0.20 2.0 3.2 4.0 #> 8 s(x2) TPRS NA 0.34 3.2 0.20 2.0 2.9 3.6 #> 9 s(x2) TPRS NA 0.35 2.9 0.20 2.0 2.5 3.3 #> 10 s(x2) TPRS NA 0.36 2.5 0.19 2.0 2.1 2.9 #> # i 40 more rows"},{"path":"https://gavinsimpson.github.io/gratia/reference/data_combos.html","id":null,"dir":"Reference","previous_headings":"","what":"All combinations of factor levels plus typical values of continuous variables — data_combos","title":"All combinations of factor levels plus typical values of continuous variables — data_combos","text":"combinations factor levels plus typical values continuous variables","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/data_combos.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"All combinations of factor levels plus typical values of continuous variables — data_combos","text":"","code":"data_combos(object, ...) # S3 method for class 'gam' data_combos( object, vars = everything(), complete = TRUE, envir = environment(formula(object)), data = NULL, ... )"},{"path":"https://gavinsimpson.github.io/gratia/reference/data_combos.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"All combinations of factor levels plus typical values of continuous variables — data_combos","text":"object fitted model object. ... arguments passed methods. vars terms include exclude returned object. Uses tidyselect principles. complete logical; combinations factor levels returned? FALSE, combinations levels observed model retained. envir environment within recreate data used fit object. data optional data frame data used fit mdoel reconstruction data model work.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/data_sim.html","id":null,"dir":"Reference","previous_headings":"","what":"Simulate example data for fitting GAMs — data_sim","title":"Simulate example data for fitting GAMs — data_sim","text":"tidy reimplementation functions implemented mgcv::gamSim() can used fit GAMs. new feature sampling distribution can applied example types.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/data_sim.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Simulate example data for fitting GAMs — data_sim","text":"","code":"data_sim( model = \"eg1\", n = 400, scale = NULL, theta = 3, power = 1.5, dist = c(\"normal\", \"poisson\", \"binary\", \"negbin\", \"tweedie\", \"gamma\", \"ocat\", \"ordered categorical\"), n_cat = 4, cuts = c(-1, 0, 5), seed = NULL, gfam_families = c(\"binary\", \"tweedie\", \"normal\") )"},{"path":"https://gavinsimpson.github.io/gratia/reference/data_sim.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Simulate example data for fitting GAMs — data_sim","text":"model character; either \"egX\" X integer 1:7, name model. See Details possible options. n numeric; number observations simulate. scale numeric; level noise use. theta numeric; dispersion parameter \\(\\theta\\) use. default entirely arbitrary, chosen provide simulated data exhibits extra dispersion beyond assumed Poisson. power numeric; Tweedie power parameter. dist character; sampling distribution response variable. \"ordered categorical\" synonym \"ocat\". n_cat integer; number categories categorical response. Currently used distr %% c(\"ocat\", \"ordered categorical\"). cuts numeric; vector cut points latent variable, excluding end points -Inf Inf. Must one fewer number categories: length(cuts) == n_cat - 1. seed numeric; seed random number generator. Passed base::set.seed(). gfam_families character; vector distributions use generating data grouped families use family = gfam(). allowed distributions per dist.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/data_sim.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Simulate example data for fitting GAMs — data_sim","text":"data_sim() can simulate data several underlying models known true functions. available options currently : \"eg1\": four term additive true model. classic Gu & Wahba four univariate term test model. See gw_functions details underlying four functions. \"eg2\": bivariate smooth true model. \"eg3\": example containing continuous smooth (varying coefficient) true model. model \\(\\hat{y}_i = f_2(x_{1i})x_{2i}\\) function \\(f_2()\\) \\(f_2(x) = 0.2 * x^{11} * (10 * (1 - x))^6 + 10 * (10 * x)^3 * (1 - x)^{10}\\). \"eg4\": factor smooth true model. true model contains factor 3 levels, response nth level follows nth Gu & Wabha function (\\(n \\{1, 2, 3}\\)). \"eg5\": additive plus factor true model. response linear combination Gu & Wabha functions 2, 3, 4 (latter null function) plus factor term four levels. \"eg6\": additive plus random effect term true model. ´\"eg7\": version model \"eg1\"`, covariates correlated. \"gwf2\": model response Gu & Wabha's \\(f_2(x_i)\\) plus noise. \"lwf6\": model response Luo & Wabha's \"example 6\" function \\(sin(2(4x-2)) + 2 exp(-256(x-0.5)^2)\\) plus noise. \"gfam\": simulates data use GAMs family = gfam(families). See example mgcv::gfam(). model specified dist ignored gfam_families used specify distributions included simulated data. Can vector families allowed dist. \"ocat\" %% gfam_families (\"ordered categorical\"), 4 classes assumed, changed. Link functions used \"identity\" \"normal\", \"logit\" \"binary\", \"ocat\", \"ordered categorical\", \"exp\" elsewhere. random component providing noise sampling variation can follow one distributions, specified via argument dist \"normal\": Gaussian, \"poisson\": Poisson, \"binary\": Bernoulli, \"negbin\": Negative binomial, \"tweedie\": Tweedie, \"gamma\": gamma , \"ordered categorical\": ordered categorical arguments provide parameters distribution.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/data_sim.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Simulate example data for fitting GAMs — data_sim","text":"Gu, C., Wahba, G., (1993). Smoothing Spline ANOVA Component-Wise Bayesian \"Confidence Intervals.\" J. Comput. Graph. Stat. 2, 97–117. Luo, Z., Wahba, G., (1997). Hybrid adaptive splines. J. . Stat. Assoc. 92, 107–116.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/data_sim.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Simulate example data for fitting GAMs — data_sim","text":"","code":"data_sim(\"eg1\", n = 100, seed = 1) #> # A tibble: 100 x 10 #> y x0 x1 x2 x3 f f0 f1 f2 f3 #> #> 1 14.532 0.26551 0.65472 0.26751 0.67371 13.713 1.4814 3.7041 8.5277 0 #> 2 16.113 0.37212 0.35320 0.21865 0.094858 12.735 1.8408 2.0267 8.8680 0 #> 3 9.5835 0.57285 0.27026 0.51680 0.49260 6.4103 1.9478 1.7169 2.7456 0 #> 4 15.687 0.90821 0.99268 0.26895 0.46155 16.349 0.56879 7.2817 8.4980 0 #> 5 8.2216 0.20168 0.63349 0.18117 0.37522 12.792 1.1841 3.5501 8.0578 0 #> 6 9.9034 0.89839 0.21321 0.51858 0.99110 4.9081 0.62765 1.5318 2.7487 0 #> 7 5.9362 0.94468 0.12937 0.56278 0.17635 4.6020 0.34587 1.2953 2.9609 0 #> 8 10.839 0.66080 0.47812 0.12916 0.81344 9.7565 1.7502 2.6019 5.4045 0 #> 9 16.883 0.62911 0.92407 0.25637 0.068447 16.909 1.8377 6.3481 8.7237 0 #> 10 7.3603 0.061786 0.59876 0.71794 0.40045 6.3401 0.38578 3.3119 2.6424 0 #> # i 90 more rows # an ordered categorical response data_sim(\"eg1\", n = 100, dist = \"ocat\", n_cat = 4, cuts = c(-1, 0, 5)) #> # A tibble: 100 x 11 #> y x0 x1 x2 x3 f f0 f1 f2 #> #> 1 1 0.93708 0.21716 0.51711 0.44457 -3.5517 0.39280 1.5439 2.7461 #> 2 1 0.28614 0.21657 0.85193 0.060386 -4.7654 1.5653 1.5421 0.36166 #> 3 1 0.83045 0.38895 0.44280 0.32751 -1.7693 1.0157 2.1769 3.2727 #> 4 4 0.64175 0.94246 0.15788 0.87843 7.2150 1.8050 6.5858 7.0588 #> 5 3 0.51910 0.96261 0.44232 0.93060 3.8994 1.9964 6.8566 3.2808 #> 6 1 0.73659 0.73986 0.96773 0.39218 -2.3701 1.4725 4.3917 0.00015734 #> 7 1 0.13467 0.73325 0.48459 0.15885 -0.27657 0.82112 4.3340 2.8028 #> 8 3 0.65699 0.53576 0.25246 0.31995 5.2247 1.7616 2.9198 8.7777 #> 9 3 0.70506 0.0022730 0.25969 0.30697 3.0408 1.5991 1.0046 8.6716 #> 10 2 0.45774 0.60894 0.54202 0.10781 -0.036524 1.9824 3.3800 2.8356 #> # i 90 more rows #> # i 2 more variables: f3 , latent "},{"path":"https://gavinsimpson.github.io/gratia/reference/data_slice.html","id":null,"dir":"Reference","previous_headings":"","what":"Prepare a data slice through model covariates — data_slice","title":"Prepare a data slice through model covariates — data_slice","text":"Prepare data slice model covariates","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/data_slice.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prepare a data slice through model covariates — data_slice","text":"","code":"data_slice(object, ...) # Default S3 method data_slice(object, ...) # S3 method for class 'data.frame' data_slice(object, ...) # S3 method for class 'gam' data_slice(object, ..., data = NULL, envir = NULL) # S3 method for class 'gamm' data_slice(object, ...) # S3 method for class 'list' data_slice(object, ...) # S3 method for class 'scam' data_slice(object, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/data_slice.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prepare a data slice through model covariates — data_slice","text":"object R model object. ... User supplied variables defining data slice. Arguments passed via ... need named. data alternative data frame values containing variables needed fit model. NULL, default, data used fit model recovered using model.frame. User-supplied expressions passed ... evaluated data. envir environment within recreate data used fit object.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/data_slice.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Prepare a data slice through model covariates — data_slice","text":"data slice data set results one (covariates) varied systematically () range specified subset values interest, remaining covariates model held fixed, representative values. known reference grid package emmeans data grid marginaleffects package. GAMs, covariates specified via ... take representative values determined data used fit model follows: numeric covariates, value fitting data closest median value used, factor covariates, modal (frequently observed) level used, first level (sorted per vector returned base::levels() several levels observed number times. values already computed calling gam() bam() example can found var.summary component fitted model. Function typical_values() extract values interested. Convenience functions evenly(), ref_level(), level() provided help users specify data slices. ref_level(), level() also ensure factor covariates correct levels, needed mgcv::predict.gam() example. extended discussion data_slice() examples, see vignette(\"data-slices\", package = \"gratia\").","code":""},{"path":[]},{"path":"https://gavinsimpson.github.io/gratia/reference/data_slice.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Prepare a data slice through model covariates — data_slice","text":"","code":"load_mgcv() # simulate some Gaussian data df <- data_sim(\"eg1\", n = 50, seed = 2) # fit a GAM with 1 smooth and 1 linear term m <- gam(y ~ s(x2, k = 7) + x1, data = df, method = \"REML\") # Want to predict over f(x2) while holding `x1` at some value. # Default will use the observation closest to the median for unspecified # variables. ds <- data_slice(m, x2 = evenly(x2, n = 50)) ds #> # A tibble: 50 x 2 #> x2 x1 #> #> 1 0.0228 0.403 #> 2 0.0424 0.403 #> 3 0.0619 0.403 #> 4 0.0815 0.403 #> 5 0.101 0.403 #> 6 0.121 0.403 #> 7 0.140 0.403 #> 8 0.160 0.403 #> 9 0.179 0.403 #> 10 0.199 0.403 #> # i 40 more rows # for full control, specify the values you want ds <- data_slice(m, x2 = evenly(x2, n = 50), x1 = 0.3) # or provide an expression (function call) which will be evaluated in the # data frame passed to `data` or `model.frame(object)` ds <- data_slice(m, x2 = evenly(x2, n = 50), x1 = mean(x1))"},{"path":"https://gavinsimpson.github.io/gratia/reference/datagen.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate data over the range of variables used in smooths — datagen","title":"Generate data over the range of variables used in smooths — datagen","text":"smooth GAM, generate new data range variables involved smooth. function deprecated useful narrow use-case. Use data_slice() instead.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/datagen.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate data over the range of variables used in smooths — datagen","text":"","code":"datagen(x, ...) # S3 method for class 'mgcv.smooth' datagen(x, n = 100, data, ...) # S3 method for class 'fs.interaction' datagen(x, n = 100, data, ...) # S3 method for class 'gam' datagen(x, smooth = NULL, n = 200, ...) # S3 method for class 'gamm' datagen(x, ...) # S3 method for class 'list' datagen(x, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/datagen.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate data over the range of variables used in smooths — datagen","text":"x object new data required. Currently objects classes \"gam\", \"gamm\" supported, smooths mgcv inheriting class \"mgcv.smooth\". ... arguments passed methods n numeric; number data values generate per term smooth. data data frame; \"mgcv.smooth\" objects, data used fit GAM need supplied.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/datagen.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate data over the range of variables used in smooths — datagen","text":"data frame new values spread range observed values.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/datagen.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Generate data over the range of variables used in smooths — datagen","text":"Gavin L. Simpson","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/derivative_samples.html","id":null,"dir":"Reference","previous_headings":"","what":"Posterior expectations of derivatives from an estimated model — derivative_samples","title":"Posterior expectations of derivatives from an estimated model — derivative_samples","text":"Posterior expectations derivatives estimated model","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/derivative_samples.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Posterior expectations of derivatives from an estimated model — derivative_samples","text":"","code":"derivative_samples(object, ...) # Default S3 method derivative_samples(object, ...) # S3 method for class 'gamm' derivative_samples(object, ...) # S3 method for class 'gam' derivative_samples( object, focal = NULL, data = NULL, order = 1L, type = c(\"forward\", \"backward\", \"central\"), scale = c(\"response\", \"linear_predictor\"), method = c(\"gaussian\", \"mh\", \"inla\", \"user\"), n = 100, eps = 1e-07, n_sim = 10000, level = lifecycle::deprecated(), seed = NULL, envir = environment(formula(object)), draws = NULL, mvn_method = c(\"mvnfast\", \"mgcv\"), ... )"},{"path":"https://gavinsimpson.github.io/gratia/reference/derivative_samples.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Posterior expectations of derivatives from an estimated model — derivative_samples","text":"object R object compute derivatives ... arguments passed methods fitted_samples() focal character; name focal variable. response derivative response respect variable returned. variables involved model held constant values. can missing supplying data, case, focal variable identified one variable constant. data data frame containing values model covariates evaluate first derivatives smooths. supplied, one variable must held constant value. order numeric; order derivative. type character; type finite difference used. One \"forward\", \"backward\", \"central\". scale character; derivative estimated response linear predictor (link) scale? One \"response\" (default), \"linear predictor\". method character; method used draw samples posterior distribution. \"gaussian\" uses Gaussian (Laplace) approximation posterior. \"mh\" uses Metropolis Hastings sample alternates t proposals proposals based shrunken version posterior covariance matrix. \"inla\" uses variant Integrated Nested Laplace Approximation due Wood (2019), (currently implemented). \"user\" allows user-supplied posterior draws (currently implemented). n numeric; number points evaluate derivative (data supplied). eps numeric; finite difference. n_sim integer; number simulations used computing simultaneous intervals. level seed numeric; random seed simulations. envir environment within recreate data used fit object. draws matrix; user supplied posterior draws used method = \"user\". mvn_method character; one \"mvnfast\" \"mgcv\". default uses mvnfast::rmvn(), can considerably faster generate large numbers MVN random values mgcv::rmvn(), might work marginal fits, covariance matrix close singular.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/derivative_samples.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Posterior expectations of derivatives from an estimated model — derivative_samples","text":"tibble, currently following variables: .derivative: estimated partial derivative, additional columns containing covariate values derivative evaluated.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/derivative_samples.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Posterior expectations of derivatives from an estimated model — derivative_samples","text":"Gavin L. Simpson","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/derivative_samples.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Posterior expectations of derivatives from an estimated model — derivative_samples","text":"","code":"load_mgcv() df <- data_sim(\"eg1\", dist = \"negbin\", scale = 0.25, seed = 42) # fit the GAM (note: for execution time reasons using bam()) m <- bam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = df, family = nb(), method = \"fREML\") # data slice through data along x2 - all other covariates will be set to # typical values (value closest to median) ds <- data_slice(m, x2 = evenly(x2, n = 200)) # samples from posterior of derivatives fd_samp <- derivative_samples(m, data = ds, type = \"central\", focal = \"x2\", eps = 0.01, seed = 21, n_sim = 100 ) # plot the first 20 posterior draws if (requireNamespace(\"ggplot2\") && requireNamespace(\"dplyr\")) { library(\"ggplot2\") fd_samp |> dplyr::filter(.draw <= 20) |> ggplot(aes(x = x2, y = .derivative, group = .draw)) + geom_line(alpha = 0.5) }"},{"path":"https://gavinsimpson.github.io/gratia/reference/derivatives.html","id":null,"dir":"Reference","previous_headings":"","what":"Derivatives of estimated smooths via finite differences — derivatives","title":"Derivatives of estimated smooths via finite differences — derivatives","text":"Derivatives estimated smooths via finite differences","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/derivatives.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Derivatives of estimated smooths via finite differences — derivatives","text":"","code":"derivatives(object, ...) # Default S3 method derivatives(object, ...) # S3 method for class 'gamm' derivatives(object, ...) # S3 method for class 'gam' derivatives( object, select = NULL, term = deprecated(), data = newdata, order = 1L, type = c(\"forward\", \"backward\", \"central\"), n = 100, eps = 1e-07, interval = c(\"confidence\", \"simultaneous\"), n_sim = 10000, level = 0.95, unconditional = FALSE, frequentist = FALSE, offset = NULL, ncores = 1, partial_match = FALSE, ..., newdata = NULL )"},{"path":"https://gavinsimpson.github.io/gratia/reference/derivatives.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Derivatives of estimated smooths via finite differences — derivatives","text":"object R object compute derivatives . ... arguments passed methods. select character; select smooth's posterior draw . default (NULL) means posteriors smooths model wil sampled . supplied, character vector requested terms. Can partial match smooth term; see argument partial_match . term Use select instead. data data frame containing values model covariates evaluate first derivatives smooths. order numeric; order derivative. type character; type finite difference used. One \"forward\", \"backward\", \"central\". n numeric; number points evaluate derivative . eps numeric; finite difference. interval character; type interval compute. One \"confidence\" point-wise intervals, \"simultaneous\" simultaneous intervals. n_sim integer; number simulations used computing simultaneous intervals. level numeric; 0 < level < 1; confidence level point-wise simultaneous interval. default 0.95 95% interval. unconditional logical; use smoothness selection-corrected Bayesian covariance matrix? frequentist logical; use frequentist covariance matrix? offset numeric; value use offset term ncores number cores generating random variables multivariate normal distribution. Passed mvnfast::rmvn(). Parallelization take place OpenMP supported (appears work Windows current R). partial_match logical; smooths selected partial matches term? TRUE, term can single string match . newdata Deprecated: use data instead.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/derivatives.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Derivatives of estimated smooths via finite differences — derivatives","text":"tibble, currently following variables: smooth: smooth row refers , var: name variable involved smooth, data: values var derivative evaluated, derivative: estimated derivative, se: standard error estimated derivative, crit: critical value derivative ± (crit * se) gives upper lower bounds requested confidence simultaneous interval (given level), lower: lower bound confidence simultaneous interval, upper: upper bound confidence simultaneous interval.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/derivatives.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Derivatives of estimated smooths via finite differences — derivatives","text":"derivatives() ignore random effect smooths encounters object.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/derivatives.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Derivatives of estimated smooths via finite differences — derivatives","text":"Gavin L. Simpson","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/derivatives.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Derivatives of estimated smooths via finite differences — derivatives","text":"","code":"load_mgcv() dat <- data_sim(\"eg1\", n = 400, dist = \"normal\", scale = 2, seed = 42) mod <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = dat, method = \"REML\") ## first derivatives of all smooths using central finite differences derivatives(mod, type = \"central\") #> # A tibble: 400 x 12 #> .smooth .by .fs .derivative .se .crit .lower_ci .upper_ci x0 x1 #> #> 1 s(x0) NA NA 7.41 3.33 1.96 0.874 13.9 2.39e-4 NA #> 2 s(x0) NA NA 7.40 3.33 1.96 0.884 13.9 1.03e-2 NA #> 3 s(x0) NA NA 7.39 3.30 1.96 0.929 13.8 2.04e-2 NA #> 4 s(x0) NA NA 7.36 3.24 1.96 1.01 13.7 3.04e-2 NA #> 5 s(x0) NA NA 7.32 3.15 1.96 1.14 13.5 4.05e-2 NA #> 6 s(x0) NA NA 7.26 3.04 1.96 1.30 13.2 5.06e-2 NA #> 7 s(x0) NA NA 7.18 2.90 1.96 1.49 12.9 6.06e-2 NA #> 8 s(x0) NA NA 7.09 2.76 1.96 1.69 12.5 7.07e-2 NA #> 9 s(x0) NA NA 6.99 2.61 1.96 1.87 12.1 8.07e-2 NA #> 10 s(x0) NA NA 6.87 2.47 1.96 2.03 11.7 9.08e-2 NA #> # i 390 more rows #> # i 2 more variables: x2 , x3 ## derivatives for a selected smooth derivatives(mod, type = \"central\", select = \"s(x1)\") #> # A tibble: 100 x 9 #> .smooth .by .fs .derivative .se .crit .lower_ci .upper_ci x1 #> #> 1 s(x1) NA NA -0.907 3.12 1.96 -7.02 5.20 0.000405 #> 2 s(x1) NA NA -0.906 3.11 1.96 -7.01 5.20 0.0105 #> 3 s(x1) NA NA -0.898 3.10 1.96 -6.97 5.17 0.0205 #> 4 s(x1) NA NA -0.880 3.06 1.96 -6.88 5.12 0.0306 #> 5 s(x1) NA NA -0.849 3.00 1.96 -6.73 5.03 0.0406 #> 6 s(x1) NA NA -0.803 2.92 1.96 -6.52 4.92 0.0507 #> 7 s(x1) NA NA -0.740 2.81 1.96 -6.25 4.77 0.0607 #> 8 s(x1) NA NA -0.659 2.69 1.96 -5.93 4.61 0.0708 #> 9 s(x1) NA NA -0.557 2.56 1.96 -5.57 4.46 0.0809 #> 10 s(x1) NA NA -0.436 2.42 1.96 -5.19 4.32 0.0909 #> # i 90 more rows ## or via a partial match derivatives(mod, type = \"central\", select = \"x1\", partial_match = TRUE) #> # A tibble: 100 x 9 #> .smooth .by .fs .derivative .se .crit .lower_ci .upper_ci x1 #> #> 1 s(x1) NA NA -0.907 3.12 1.96 -7.02 5.20 0.000405 #> 2 s(x1) NA NA -0.906 3.11 1.96 -7.01 5.20 0.0105 #> 3 s(x1) NA NA -0.898 3.10 1.96 -6.97 5.17 0.0205 #> 4 s(x1) NA NA -0.880 3.06 1.96 -6.88 5.12 0.0306 #> 5 s(x1) NA NA -0.849 3.00 1.96 -6.73 5.03 0.0406 #> 6 s(x1) NA NA -0.803 2.92 1.96 -6.52 4.92 0.0507 #> 7 s(x1) NA NA -0.740 2.81 1.96 -6.25 4.77 0.0607 #> 8 s(x1) NA NA -0.659 2.69 1.96 -5.93 4.61 0.0708 #> 9 s(x1) NA NA -0.557 2.56 1.96 -5.57 4.46 0.0809 #> 10 s(x1) NA NA -0.436 2.42 1.96 -5.19 4.32 0.0909 #> # i 90 more rows"},{"path":"https://gavinsimpson.github.io/gratia/reference/difference_smooths.html","id":null,"dir":"Reference","previous_headings":"","what":"Differences of factor smooth interactions — difference_smooths","title":"Differences of factor smooth interactions — difference_smooths","text":"Estimates pairwise differences (comparisons) factor smooth interactions (smooths factor argument) pairs groups defined factor. group means can optionally included difference.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/difference_smooths.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Differences of factor smooth interactions — difference_smooths","text":"","code":"difference_smooths(model, ...) # S3 method for class 'gam' difference_smooths( model, select = NULL, smooth = deprecated(), n = 100, ci_level = 0.95, data = NULL, group_means = FALSE, partial_match = TRUE, unconditional = FALSE, frequentist = FALSE, ... )"},{"path":"https://gavinsimpson.github.io/gratia/reference/difference_smooths.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Differences of factor smooth interactions — difference_smooths","text":"model fitted model. ... arguments passed methods. currently used. select character, logical, numeric; smooths plot. NULL, default, model smooths drawn. Numeric select indexes smooths order specified formula stored object. Character select matches labels smooths shown example output summary(object). Logical select operates per numeric select order smooths stored. smooth Use select instead. n numeric; number points evaluate difference pairs smooths. ci_level numeric 0 1; coverage credible interval. data data frame locations evaluate difference smooths. group_means logical; group means included difference? partial_match logical; smooth match partially smooths? partial_match = TRUE, smooth must single string, character vector length 1. Unlike similar functions, default TRUE intention users matching factor-smooth labels. unconditional logical; account smoothness selection model? frequentist logical; use frequentist covariance matrix?","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/difference_smooths.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Differences of factor smooth interactions — difference_smooths","text":"","code":"load_mgcv() df <- data_sim(\"eg4\", seed = 42) m <- gam(y ~ fac + s(x2, by = fac) + s(x0), data = df, method = \"REML\") sm_dif <- difference_smooths(m, select = \"s(x2)\") sm_dif #> # A tibble: 300 x 9 #> .smooth .by .level_1 .level_2 .diff .se .lower_ci .upper_ci x2 #> #> 1 s(x2) fac 1 2 0.386 0.618 -0.824 1.60 0.00359 #> 2 s(x2) fac 1 2 0.479 0.574 -0.646 1.60 0.0136 #> 3 s(x2) fac 1 2 0.572 0.534 -0.474 1.62 0.0237 #> 4 s(x2) fac 1 2 0.665 0.497 -0.308 1.64 0.0338 #> 5 s(x2) fac 1 2 0.758 0.464 -0.151 1.67 0.0438 #> 6 s(x2) fac 1 2 0.850 0.435 -0.00342 1.70 0.0539 #> 7 s(x2) fac 1 2 0.941 0.412 0.134 1.75 0.0639 #> 8 s(x2) fac 1 2 1.03 0.393 0.262 1.80 0.0740 #> 9 s(x2) fac 1 2 1.12 0.378 0.380 1.86 0.0841 #> 10 s(x2) fac 1 2 1.21 0.367 0.489 1.93 0.0941 #> # i 290 more rows draw(sm_dif) # include the groups means for `fac` in the difference sm_dif2 <- difference_smooths(m, select = \"s(x2)\", group_means = TRUE) draw(sm_dif2)"},{"path":[]},{"path":"https://gavinsimpson.github.io/gratia/reference/dispersion.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Dispersion parameter for fitted model — dispersion","text":"","code":"dispersion(model, ...) # S3 method for class 'gam' dispersion(model, ...) # S3 method for class 'glm' dispersion(model, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/dispersion.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Dispersion parameter for fitted model — dispersion","text":"model fitted model. ... arguments passed methods.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.basis.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot basis functions — draw.basis","title":"Plot basis functions — draw.basis","text":"Plots basis functions using ggplot2","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.basis.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot basis functions — draw.basis","text":"","code":"# S3 method for class 'basis' draw( object, legend = FALSE, labeller = NULL, ylab = NULL, title = NULL, subtitle = NULL, caption = NULL, ncol = NULL, nrow = NULL, angle = NULL, guides = \"keep\", contour = FALSE, n_contour = 10, contour_col = \"black\", ... )"},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.basis.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot basis functions — draw.basis","text":"object object, result call basis(). legend logical; legend drawn indicate basis functions? labeller labeller function label facets. default use ggplot2::label_both(). ylab character expression; label y axis. supplied, suitable label generated object. title character expression; title plot. See ggplot2::labs(). subtitle character expression; subtitle plot. See ggplot2::labs(). caption character expression; plot caption. See ggplot2::labs(). ncol, nrow numeric; numbers rows columns spread plots angle numeric; angle x axis tick labels drawn passed angle argument ggplot2::guide_axis(). guides character; one \"keep\" (default), \"collect\", \"auto\". Passed patchwork::plot_layout() contour logical; contours draw plot using ggplot2::geom_contour(). n_contour numeric; number contour bins. result n_contour - 1 contour lines drawn. See ggplot2::geom_contour(). contour_col colour specification contour lines. ... arguments passed methods. used method.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.basis.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot basis functions — draw.basis","text":"patchwork object.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.basis.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot basis functions — draw.basis","text":"Gavin L. Simpson","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.basis.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot basis functions — draw.basis","text":"","code":"load_mgcv() df <- data_sim(\"eg1\", n = 400, seed = 42) m <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = df, method = \"REML\") bf <- basis(m) draw(bf) bf <- basis(m, \"s(x2)\") draw(bf)"},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.compare_smooths.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot comparisons of smooths — draw.compare_smooths","title":"Plot comparisons of smooths — draw.compare_smooths","text":"Plot comparisons smooths","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.compare_smooths.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot comparisons of smooths — draw.compare_smooths","text":"","code":"# S3 method for class 'compare_smooths' draw(object, ncol = NULL, nrow = NULL, guides = \"collect\", ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.compare_smooths.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot comparisons of smooths — draw.compare_smooths","text":"object class \"compare_smooths\", result call compare_smooths(). ncol, nrow numeric; numbers rows columns spread plots guides character; one \"keep\" (default), \"collect\", \"auto\". Passed patchwork::plot_layout() ... additional arguments passed patchwork::wrap_plots().","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.derivatives.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot derivatives of smooths — draw.derivatives","title":"Plot derivatives of smooths — draw.derivatives","text":"Plot derivatives smooths","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.derivatives.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot derivatives of smooths — draw.derivatives","text":"","code":"# S3 method for class 'derivatives' draw( object, select = NULL, scales = c(\"free\", \"fixed\"), add_change = FALSE, change_type = c(\"change\", \"sizer\"), alpha = 0.2, change_col = \"black\", decrease_col = \"#56B4E9\", increase_col = \"#E69F00\", lwd_change = 1.5, ncol = NULL, nrow = NULL, guides = \"keep\", angle = NULL, ... ) # S3 method for class 'partial_derivatives' draw( object, select = NULL, scales = c(\"free\", \"fixed\"), alpha = 0.2, ncol = NULL, nrow = NULL, guides = \"keep\", angle = NULL, ... )"},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.derivatives.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot derivatives of smooths — draw.derivatives","text":"object fitted GAM, result call mgcv::gam(). select character, logical, numeric; smooths plot. NULL, default, model smooths drawn. Numeric select indexes smooths order specified formula stored object. Character select matches labels smooths shown example output summary(object). Logical select operates per numeric select order smooths stored. scales character; univariate smooths plotted y-axis scale? scales = \"free\", default, univariate smooth y-axis scale. scales = \"fixed\", common y axis scale used univariate smooths. Currently affect y-axis scale plots parametric terms. add_change logical; periods significant change highlighted plot? change_type character; type change indicate. \"change\", differentiation made periods significant increase decrease. \"sizer\", periods increase decrease differentiated resulting plot. alpha numeric; alpha transparency confidence simultaneous interval. change_col, decrease_col, increase_col colour specifications use indicating periods change. col_change used change_type = \"change\", col_decrease col_increase used `change_type = \"sizer\"“. lwd_change numeric; linewidth use change indicators. ncol, nrow numeric; numbers rows columns spread plots guides character; one \"keep\" (default), \"collect\", \"auto\". Passed patchwork::plot_layout() angle numeric; angle x axis tick labels drawn passed angle argument ggplot2::guide_axis(). ... additional arguments passed patchwork::wrap_plots().","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.derivatives.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot derivatives of smooths — draw.derivatives","text":"","code":"load_mgcv() dat <- data_sim(\"eg1\", n = 800, dist = \"normal\", scale = 2, seed = 42) mod <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = dat, method = \"REML\") ## first derivative of all smooths df <- derivatives(mod, type = \"central\") draw(df) ## fixed axis scales draw(df, scales = \"fixed\")"},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.difference_smooth.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot differences of smooths — draw.difference_smooth","title":"Plot differences of smooths — draw.difference_smooth","text":"Plot differences smooths","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.difference_smooth.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot differences of smooths — draw.difference_smooth","text":"","code":"# S3 method for class 'difference_smooth' draw( object, select = NULL, rug = FALSE, ref_line = FALSE, contour = FALSE, contour_col = \"black\", n_contour = NULL, ci_alpha = 0.2, ci_col = \"black\", smooth_col = \"black\", line_col = \"red\", scales = c(\"free\", \"fixed\"), ncol = NULL, nrow = NULL, guides = \"keep\", xlab = NULL, ylab = NULL, title = NULL, subtitle = NULL, caption = NULL, angle = NULL, ... )"},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.difference_smooth.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot differences of smooths — draw.difference_smooth","text":"object fitted GAM, result call mgcv::gam(). select character, logical, numeric; smooths plot. NULL, default, model smooths drawn. Numeric select indexes smooths order specified formula stored object. Character select matches labels smooths shown example output summary(object). Logical select operates per numeric select order smooths stored. rug logical; ref_line logical; contour logical; contour lines added smooth surfaces? contour_col colour specification contour lines. n_contour numeric; number contour bins. result n_contour - 1 contour lines drawn. See ggplot2::geom_contour(). ci_alpha numeric; alpha transparency confidence simultaneous interval. ci_col colour specification confidence/credible intervals band. Affects fill interval. smooth_col colour specification smooth difference line. line_col colour specification drawing reference lines scales character; univariate smooths plotted y-axis scale? scales = \"free\", default, univariate smooth y-axis scale. scales = \"fixed\", common y axis scale used univariate smooths. Currently affect y-axis scale plots parametric terms. ncol, nrow numeric; numbers rows columns spread plots guides character; one \"keep\" (default), \"collect\", \"auto\". Passed patchwork::plot_layout() xlab, ylab, title, subtitle, caption character; labels annotate plots angle numeric; angle x axis tick labels drawn passed angle argument ggplot2::guide_axis(). ... additional arguments passed patchwork::wrap_plots().","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.difference_smooth.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot differences of smooths — draw.difference_smooth","text":"","code":"load_mgcv() # simulate some data; a factor smooth example df <- data_sim(\"eg4\", seed = 42) # fit GAM m <- gam(y ~ fac + s(x2, by = fac) + s(x0), data = df, method = \"REML\") # calculate the differences between pairs of smooths the f_j(x2) term diffs <- difference_smooths(m, select = \"s(x2)\") draw(diffs)"},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.evaluated_parametric_term.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot estimated parametric effects — draw.evaluated_parametric_term","title":"Plot estimated parametric effects — draw.evaluated_parametric_term","text":"Plots estimated univariate bivariate smooths using ggplot2.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.evaluated_parametric_term.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot estimated parametric effects — draw.evaluated_parametric_term","text":"","code":"# S3 method for class 'evaluated_parametric_term' draw( object, ci_level = 0.95, constant = NULL, fun = NULL, xlab, ylab, title = NULL, subtitle = NULL, caption = NULL, rug = TRUE, position = \"identity\", response_range = NULL, ... )"},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.evaluated_parametric_term.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot estimated parametric effects — draw.evaluated_parametric_term","text":"object object, result call evaluate_parametric_term(). ci_level numeric 0 1; coverage credible interval. constant numeric; constant add estimated values smooth. constant, supplied, added estimated value confidence band computed. fun function; function applied estimated values confidence interval plotting. Can function name function. Function fun applied adding constant, provided. xlab character expression; label x axis. supplied, suitable label generated object. ylab character expression; label y axis. supplied, suitable label generated object. title character expression; title plot. See ggplot2::labs(). subtitle character expression; subtitle plot. See ggplot2::labs(). caption character expression; plot caption. See ggplot2::labs(). rug evaluate_parametric_terms(), logical indicate rug plot drawn. position Position adjustment, either string, result call position adjustment function. response_range numeric; vector two values giving range response data guide. Used fix plots common scale/range. Ignored show set \"se\". ... arguments passed methods.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.evaluated_parametric_term.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot estimated parametric effects — draw.evaluated_parametric_term","text":"ggplot2::ggplot() object.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.evaluated_parametric_term.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot estimated parametric effects — draw.evaluated_parametric_term","text":"Gavin L. Simpson","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.gam.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot estimated smooths from a fitted GAM — draw.gam","title":"Plot estimated smooths from a fitted GAM — draw.gam","text":"Plots estimated smooths fitted GAM model similar way mgcv::plot.gam() instead using base graphics, ggplot2::ggplot() used instead.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.gam.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot estimated smooths from a fitted GAM — draw.gam","text":"","code":"# S3 method for class 'gam' draw( object, data = NULL, select = NULL, parametric = FALSE, terms = NULL, residuals = FALSE, scales = c(\"free\", \"fixed\"), ci_level = 0.95, n = 100, n_3d = 16, n_4d = 4, unconditional = FALSE, overall_uncertainty = TRUE, constant = NULL, fun = NULL, dist = 0.1, rug = TRUE, contour = TRUE, grouped_by = FALSE, ci_alpha = 0.2, ci_col = \"black\", smooth_col = \"black\", resid_col = \"steelblue3\", contour_col = \"black\", n_contour = NULL, partial_match = FALSE, discrete_colour = NULL, discrete_fill = NULL, continuous_colour = NULL, continuous_fill = NULL, position = \"identity\", angle = NULL, ncol = NULL, nrow = NULL, guides = \"keep\", widths = NULL, heights = NULL, crs = NULL, default_crs = NULL, lims_method = \"cross\", wrap = TRUE, caption = TRUE, envir = environment(formula(object)), ... )"},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.gam.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot estimated smooths from a fitted GAM — draw.gam","text":"object fitted GAM, result call mgcv::gam(). data optional data frame used supply data smooths evaluated plotted. usually needed, option need fine control exactly data used plotting. select character, logical, numeric; smooths plot. NULL, default, model smooths drawn. Numeric select indexes smooths order specified formula stored object. Character select matches labels smooths shown example output summary(object). Logical select operates per numeric select order smooths stored. parametric logical; plot parametric terms also? Note select used selecting smooths plot. terms argument used select parametric effects plotted. default, mgcv::plot.gam(), draw parametric effects. terms character; model parametric terms drawn? Default NULL plot parametric terms can drawn. residuals logical; partial residuals smooth drawn? Ignored anything simple univariate smooth. scales character; univariate smooths plotted y-axis scale? scales = \"free\", default, univariate smooth y-axis scale. scales = \"fixed\", common y axis scale used univariate smooths. Currently affect y-axis scale plots parametric terms. ci_level numeric 0 1; coverage credible interval. n numeric; number points range covariate evaluate smooth. n_3d numeric; number new observations generate third dimension 3D smooth. n_4d numeric; number new observations generate dimensions higher 2 (!) kD smooth (k >= 4). example, smooth 4D smooth, dimensions 3 4 get n_4d new observations. unconditional logical; confidence intervals include uncertainty due smoothness selection? TRUE, corrected Bayesian covariance matrix used. overall_uncertainty logical; uncertainty model constant term included standard error evaluate values smooth? constant numeric; constant add estimated values smooth. constant, supplied, added estimated value confidence band computed. fun function; function applied estimated values confidence interval plotting. Can function name function. Function fun applied adding constant, provided. dist numeric; greater 0, used determine location far data plotted plotting 2-D smooths. data scaled unit square deciding exclude, dist distance within unit square. See mgcv::exclude..far() details. rug logical; draw rug plot bottom plot 1-D smooths plot locations data higher dimensions. contour logical; contours draw plot using ggplot2::geom_contour(). grouped_by logical; factor smooths drawn one panel per level factor (FALSE, default), individual smooths combined single panel containing levels (TRUE)? ci_alpha numeric; alpha transparency confidence simultaneous interval. ci_col colour specification confidence/credible intervals band. Affects fill interval. smooth_col colour specification smooth line. resid_col colour specification partial residuals. contour_col colour specification contour lines. n_contour numeric; number contour bins. result n_contour - 1 contour lines drawn. See ggplot2::geom_contour(). partial_match logical; smooths selected partial matches select? TRUE, select can single string match . discrete_colour suitable colour scale used plotting discrete variables. discrete_fill suitable fill scale used plotting discrete variables. continuous_colour suitable colour scale used plotting continuous variables. continuous_fill suitable fill scale used plotting continuous variables. position Position adjustment, either string, result call position adjustment function. angle numeric; angle x axis tick labels drawn passed angle argument ggplot2::guide_axis(). ncol, nrow numeric; numbers rows columns spread plots guides character; one \"keep\" (default), \"collect\", \"auto\". Passed patchwork::plot_layout() widths, heights relative widths heights column row grid. get repeated match dimensions grid. 1 plot widths = NULL, value widths set internally widths = 1 accommodate plots smooths use fixed aspect ratio. crs coordinate reference system (CRS) use plot. data projected CRS. See ggplot2::coord_sf() details. default_crs coordinate reference system (CRS) use non-sf layers plot. left default NULL, CRS used 4326 (WGS84), appropriate spline---sphere smooths, parameterized terms latitude longitude coordinates. See ggplot2::coord_sf() details. lims_method character; affects axis limits determined. See ggplot2::coord_sf(). careful; testing examples, changing \"orthogonal\" example chlorophyll-example Simon Wood's GAM book quickly used RAM test system OS killed R. incorrect usage part; right now grid points SOS smooths evaluated (supplied user) can produce invalid coordinates corners tiles grid generated tile centres without respect spacing tiles. wrap logical; wrap plots patchwork? FALSE, list ggplot objects returned, 1 per term plotted. caption logical; show smooth type caption plot? envir environment look data within. ... additional arguments passed patchwork::wrap_plots().","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.gam.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot estimated smooths from a fitted GAM — draw.gam","text":"object returned created patchwork::wrap_plots().","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.gam.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Plot estimated smooths from a fitted GAM — draw.gam","text":"Internally, plots smooth created using ggplot2::ggplot() composed single plot using patchwork::wrap_plots(). result, possible use + add plots way one might typically work ggplot() plots. Instead, use & operator; see examples.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.gam.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot estimated smooths from a fitted GAM — draw.gam","text":"Gavin L. Simpson","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.gam.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot estimated smooths from a fitted GAM — draw.gam","text":"","code":"load_mgcv() # simulate some data df1 <- data_sim(\"eg1\", n = 400, dist = \"normal\", scale = 2, seed = 2) # fit GAM m1 <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = df1, method = \"REML\") # plot all smooths draw(m1) # can add partial residuals draw(m1, residuals = TRUE) df2 <- data_sim(\"eg2\", n = 1000, dist = \"normal\", scale = 1, seed = 2) m2 <- gam(y ~ s(x, z, k = 40), data = df2, method = \"REML\") draw(m2, contour = FALSE, n = 50) # See https://gavinsimpson.github.io/gratia/articles/custom-plotting.html # for more examples and for details on how to modify the theme of all the # plots produced by draw(). To modify all panels, for example to change the # theme, use the & operator"},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.gamlss.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot smooths of a GAMLSS model estimated by GJRM::gamlss — draw.gamlss","title":"Plot smooths of a GAMLSS model estimated by GJRM::gamlss — draw.gamlss","text":"Provides draw() method GAMLSS (distributional GAMs) fitted GJRM::gamlss().","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.gamlss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot smooths of a GAMLSS model estimated by GJRM::gamlss — draw.gamlss","text":"","code":"# S3 method for class 'gamlss' draw( object, scales = c(\"free\", \"fixed\"), ncol = NULL, nrow = NULL, guides = \"keep\", widths = NULL, heights = NULL, ... )"},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.gamlss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot smooths of a GAMLSS model estimated by GJRM::gamlss — draw.gamlss","text":"object model, fitted GJRM::gamlss() scales character; univariate smooths plotted y-axis scale? scales = \"free\", default, univariate smooth y-axis scale. scales = \"fixed\", common y axis scale used univariate smooths. Currently affect y-axis scale plots parametric terms. ncol, nrow numeric; numbers rows columns spread plots guides character; one \"keep\" (default), \"collect\", \"auto\". Passed patchwork::plot_layout() widths, heights relative widths heights column row grid. get repeated match dimensions grid. 1 plot widths = NULL, value widths set internally widths = 1 accommodate plots smooths use fixed aspect ratio. ... arguments passed draw.gam()","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.gamlss.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Plot smooths of a GAMLSS model estimated by GJRM::gamlss — draw.gamlss","text":"Plots smooths labelled linear predictor belong.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.gamlss.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot smooths of a GAMLSS model estimated by GJRM::gamlss — draw.gamlss","text":"","code":"if (require(\"GJRM\", quietly = TRUE)) { # follow example from ?GJRM::gamlss load_mgcv() suppressPackageStartupMessages(library(\"GJRM\")) set.seed(0) n <- 100 x1 <- round(runif(n)) x2 <- runif(n) x3 <- runif(n) f1 <- function(x) cos(pi * 2 * x) + sin(pi * x) y1 <- -1.55 + 2 * x1 + f1(x2) + rnorm(n) dataSim <- data.frame(y1, x1, x2, x3) eq_mu <- y1 ~ x1 + s(x2) eq_s <- ~ s(x3, k = 6) fl <- list(eq_mu, eq_s) m <- gamlss(fl, data = dataSim) draw(m) }"},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.html","id":null,"dir":"Reference","previous_headings":"","what":"Generic plotting via ggplot2 — draw","title":"Generic plotting via ggplot2 — draw","text":"Generic plotting via ggplot2","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generic plotting via ggplot2 — draw","text":"","code":"draw(object, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generic plotting via ggplot2 — draw","text":"object R object plot. ... arguments passed methods.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generic plotting via ggplot2 — draw","text":"ggplot2::ggplot() object.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generic plotting via ggplot2 — draw","text":"Generic function plotting R objects uses ggplot2 package.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Generic plotting via ggplot2 — draw","text":"Gavin L. Simpson","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.mgcv_smooth.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot basis functions — draw.mgcv_smooth","title":"Plot basis functions — draw.mgcv_smooth","text":"Plots basis functions using ggplot2","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.mgcv_smooth.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot basis functions — draw.mgcv_smooth","text":"","code":"# S3 method for class 'mgcv_smooth' draw( object, legend = FALSE, use_facets = TRUE, labeller = NULL, xlab, ylab, title = NULL, subtitle = NULL, caption = NULL, angle = NULL, ... )"},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.mgcv_smooth.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot basis functions — draw.mgcv_smooth","text":"object object, result call basis(). legend logical; legend drawn indicate basis functions? use_facets logical; factor smooths, use facets show basis functions level factor? FALSE, separate ggplot object created level combined using patchwork::wrap_plots(). Currently ignored. labeller labeller function label facets. default use ggplot2::label_both(). xlab character expression; label x axis. supplied, suitable label generated object. ylab character expression; label y axis. supplied, suitable label generated object. title character expression; title plot. See ggplot2::labs(). subtitle character expression; subtitle plot. See ggplot2::labs(). caption character expression; plot caption. See ggplot2::labs(). angle numeric; angle x axis tick labels drawn passed angle argument ggplot2::guide_axis(). ... arguments passed methods. used method.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.mgcv_smooth.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot basis functions — draw.mgcv_smooth","text":"ggplot2::ggplot() object.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.mgcv_smooth.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot basis functions — draw.mgcv_smooth","text":"Gavin L. Simpson","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.mgcv_smooth.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot basis functions — draw.mgcv_smooth","text":"","code":"load_mgcv() df <- data_sim(\"eg4\", n = 400, seed = 42) bf <- basis(s(x0), data = df) draw(bf) bf <- basis(s(x2, by = fac, bs = \"bs\"), data = df) draw(bf)"},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.pairwise_concurvity.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot concurvity measures — draw.pairwise_concurvity","title":"Plot concurvity measures — draw.pairwise_concurvity","text":"Plot concurvity measures","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.pairwise_concurvity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot concurvity measures — draw.pairwise_concurvity","text":"","code":"# S3 method for class 'pairwise_concurvity' draw( object, title = \"Smooth-wise concurvity\", subtitle = NULL, caption = NULL, x_lab = \"Term\", y_lab = \"With\", fill_lab = \"Concurvity\", continuous_colour = NULL, ... ) # S3 method for class 'overall_concurvity' draw( object, title = \"Overall concurvity\", subtitle = NULL, caption = NULL, y_lab = \"Concurvity\", x_lab = NULL, bar_col = \"steelblue\", bar_fill = \"steelblue\", ... )"},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.pairwise_concurvity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot concurvity measures — draw.pairwise_concurvity","text":"object object inheriting class \"concurvity\", usually result call model_concurvity() abbreviated form concrvity(). title character; plot title. subtitle character; plot subtitle. caption character; plot caption x_lab character; label x axis. y_lab character; label y axis. fill_lab character; label use fill guide. continuous_colour function; continuous colour (fill) scale use. ... arguments passed methods. bar_col colour specification bar colour. bar_fill colour specification bar fill","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.parametric_effects.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot estimated effects for model parametric terms — draw.parametric_effects","title":"Plot estimated effects for model parametric terms — draw.parametric_effects","text":"Plot estimated effects model parametric terms","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.parametric_effects.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot estimated effects for model parametric terms — draw.parametric_effects","text":"","code":"# S3 method for class 'parametric_effects' draw( object, scales = c(\"free\", \"fixed\"), ci_level = 0.95, ci_col = \"black\", ci_alpha = 0.2, line_col = \"black\", constant = NULL, fun = NULL, rug = TRUE, position = \"identity\", angle = NULL, ..., ncol = NULL, nrow = NULL, guides = \"keep\" )"},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.parametric_effects.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot estimated effects for model parametric terms — draw.parametric_effects","text":"object fitted GAM, result call mgcv::gam(). scales character; univariate smooths plotted y-axis scale? scales = \"free\", default, univariate smooth y-axis scale. scales = \"fixed\", common y axis scale used univariate smooths. Currently affect y-axis scale plots parametric terms. ci_level numeric 0 1; coverage credible interval. ci_col colour specification confidence/credible intervals band. Affects fill interval. ci_alpha numeric; alpha transparency confidence simultaneous interval. line_col colour specification used regression lines linear continuous terms. constant numeric; constant add estimated values smooth. constant, supplied, added estimated value confidence band computed. fun function; function applied estimated values confidence interval plotting. Can function name function. Function fun applied adding constant, provided. rug logical; draw rug plot bottom plot 1-D smooths plot locations data higher dimensions. position Position adjustment, either string, result call position adjustment function. angle numeric; angle x axis tick labels drawn passed angle argument ggplot2::guide_axis(). ... additional arguments passed patchwork::wrap_plots(). ncol, nrow numeric; numbers rows columns spread plots guides character; one \"keep\" (default), \"collect\", \"auto\". Passed patchwork::plot_layout()","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.penalty_df.html","id":null,"dir":"Reference","previous_headings":"","what":"Display penalty matrices of smooths using ggplot — draw.penalty_df","title":"Display penalty matrices of smooths using ggplot — draw.penalty_df","text":"Displays penalty matrices smooths heatmap using ggplot","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.penalty_df.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Display penalty matrices of smooths using ggplot — draw.penalty_df","text":"","code":"# S3 method for class 'penalty_df' draw( object, normalize = FALSE, as_matrix = TRUE, continuous_fill = NULL, xlab = NULL, ylab = NULL, title = NULL, subtitle = NULL, caption = NULL, ncol = NULL, nrow = NULL, guides = \"keep\", ... )"},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.penalty_df.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Display penalty matrices of smooths using ggplot — draw.penalty_df","text":"object fitted GAM, result call mgcv::gam(). normalize logical; normalize penalty range -1, 1? as_matrix logical; plotted penalty matrix oriented? TRUE row 1, column 1 penalty matrix draw upper left, whereas, FALSE drawn lower left plot. continuous_fill suitable fill scale used plotting continuous variables. xlab character expression; label x axis. supplied, axis label drawn. May vector, one per penalty. ylab character expression; label y axis. supplied, axis label drawn. May vector, one per penalty. title character expression; title plot. See ggplot2::labs(). May vector, one per penalty. subtitle character expression; subtitle plot. See ggplot2::labs(). May vector, one per penalty. caption character expression; plot caption. See ggplot2::labs(). May vector, one per penalty. ncol, nrow numeric; numbers rows columns spread plots. guides character; one \"keep\" (default), \"collect\", \"auto\". Passed patchwork::plot_layout() ... additional arguments passed patchwork::wrap_plots().","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.penalty_df.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Display penalty matrices of smooths using ggplot — draw.penalty_df","text":"","code":"load_mgcv() dat <- data_sim(\"eg4\", n = 400, seed = 42) m <- gam(y ~ s(x0) + s(x1, bs = \"cr\") + s(x2, bs = \"bs\", by = fac), data = dat, method = \"REML\" ) ## produce a multi-panel plot of all penalties draw(penalty(m)) # for a specific smooth draw(penalty(m, select = \"s(x2):fac1\"))"},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.rootogram.html","id":null,"dir":"Reference","previous_headings":"","what":"Draw a rootogram — draw.rootogram","title":"Draw a rootogram — draw.rootogram","text":"rootogram model diagnostic tool assesses goodness fit statistical model. observed values response compared expected fitted model. discrete, count responses, frequency count (0, 1, 2, etc) observed data expected conditional distribution response implied model compared. continuous variables, observed expected frequencies obtained grouping data bins. rootogram drawn using ggplot2::ggplot() graphics. design closely follows Kleiber & Zeileis (2016).","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.rootogram.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Draw a rootogram — draw.rootogram","text":"","code":"# S3 method for class 'rootogram' draw( object, type = c(\"hanging\", \"standing\", \"suspended\"), sqrt = TRUE, ref_line = TRUE, warn_limits = TRUE, fitted_colour = \"steelblue\", bar_colour = NA, bar_fill = \"grey\", ref_line_colour = \"black\", warn_line_colour = \"black\", ylab = NULL, xlab = NULL, ... )"},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.rootogram.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Draw a rootogram — draw.rootogram","text":"object R object plot. type character; type rootogram draw. sqrt logical; show observed fitted frequencies ref_line logical; draw reference line zero? warn_limits logical; draw Tukey's warning limit lines +/- 1? fitted_colour, bar_colour, bar_fill, ref_line_colour, warn_line_colour colours used draw respective element rootogram. xlab, ylab character; labels x y axis rootogram. May missing (NULL), case suitable labels used. ' ... arguments passed methods.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.rootogram.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Draw a rootogram — draw.rootogram","text":"'ggplot' object.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.rootogram.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Draw a rootogram — draw.rootogram","text":"Kleiber, C., Zeileis, ., (2016) Visualizing Count Data Regressions Using Rootograms. . Stat. 70, 296–303. doi:10.1080/00031305.2016.1173590","code":""},{"path":[]},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.rootogram.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Draw a rootogram — draw.rootogram","text":"","code":"load_mgcv() df <- data_sim(\"eg1\", n = 1000, dist = \"poisson\", scale = 0.1, seed = 6) # A poisson example m <- gam(y ~ s(x0, bs = \"cr\") + s(x1, bs = \"cr\") + s(x2, bs = \"cr\") + s(x3, bs = \"cr\"), family = poisson(), data = df, method = \"REML\") rg <- rootogram(m) # plot the rootogram draw(rg) # change the type of rootogram draw(rg, type = \"suspended\")"},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.smooth_estimates.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot the result of a call to smooth_estimates() — draw.smooth_estimates","title":"Plot the result of a call to smooth_estimates() — draw.smooth_estimates","text":"Plot result call smooth_estimates()","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.smooth_estimates.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot the result of a call to smooth_estimates() — draw.smooth_estimates","text":"","code":"# S3 method for class 'smooth_estimates' draw( object, constant = NULL, fun = NULL, contour = TRUE, grouped_by = FALSE, contour_col = \"black\", n_contour = NULL, ci_alpha = 0.2, ci_col = \"black\", smooth_col = \"black\", resid_col = \"steelblue3\", decrease_col = \"#56B4E9\", increase_col = \"#E69F00\", change_lwd = 1.75, partial_match = FALSE, discrete_colour = NULL, discrete_fill = NULL, continuous_colour = NULL, continuous_fill = NULL, angle = NULL, ylim = NULL, crs = NULL, default_crs = NULL, lims_method = \"cross\", caption = TRUE, ... )"},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.smooth_estimates.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot the result of a call to smooth_estimates() — draw.smooth_estimates","text":"object fitted GAM, result call mgcv::gam(). constant numeric; constant add estimated values smooth. constant, supplied, added estimated value confidence band computed. fun function; function applied estimated values confidence interval plotting. Can function name function. Function fun applied adding constant, provided. contour logical; contours draw plot using ggplot2::geom_contour(). grouped_by logical; factor smooths drawn one panel per level factor (FALSE, default), individual smooths combined single panel containing levels (TRUE)? contour_col colour specification contour lines. n_contour numeric; number contour bins. result n_contour - 1 contour lines drawn. See ggplot2::geom_contour(). ci_alpha numeric; alpha transparency confidence simultaneous interval. ci_col colour specification confidence/credible intervals band. Affects fill interval. smooth_col colour specification smooth line. resid_col colour specification partial residuals. decrease_col, increase_col colour specifications use indicating periods change. col_change used change_type = \"change\", col_decrease col_increase used `change_type = \"sizer\"“. change_lwd numeric; value set linewidth ggplot2::geom_line(), used represent periods change. partial_match logical; smooths selected partial matches select? TRUE, select can single string match . discrete_colour suitable colour scale used plotting discrete variables. discrete_fill suitable fill scale used plotting discrete variables. continuous_colour suitable colour scale used plotting continuous variables. continuous_fill suitable fill scale used plotting continuous variables. angle numeric; angle x axis tick labels drawn passed angle argument ggplot2::guide_axis(). ylim numeric; vector y axis limits use panels drawn. crs coordinate reference system (CRS) use plot. data projected CRS. See ggplot2::coord_sf() details. default_crs coordinate reference system (CRS) use non-sf layers plot. left default NULL, CRS used 4326 (WGS84), appropriate spline---sphere smooths, parameterized terms latitude longitude coordinates. See ggplot2::coord_sf() details. lims_method character; affects axis limits determined. See ggplot2::coord_sf(). careful; testing examples, changing \"orthogonal\" example chlorophyll-example Simon Wood's GAM book quickly used RAM test system OS killed R. incorrect usage part; right now grid points SOS smooths evaluated (supplied user) can produce invalid coordinates corners tiles grid generated tile centres without respect spacing tiles. caption logical; show smooth type caption plot? ... additional arguments passed patchwork::wrap_plots().","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.smooth_estimates.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot the result of a call to smooth_estimates() — draw.smooth_estimates","text":"","code":"load_mgcv() # example data df <- data_sim(\"eg1\", seed = 21) # fit GAM m <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = df, method = \"REML\") # plot all of the estimated smooths sm <- smooth_estimates(m) draw(sm) # evaluate smooth of `x2` sm <- smooth_estimates(m, select = \"s(x2)\") # plot it draw(sm) # customising some plot elements draw(sm, ci_col = \"steelblue\", smooth_col = \"forestgreen\", ci_alpha = 0.3) # Add a constant to the plotted smooth draw(sm, constant = coef(m)[1]) # Adding change indicators to smooths based on derivatives of the smooth d <- derivatives(m, n = 100) # n to match smooth_estimates() smooth_estimates(m) |> add_sizer(derivatives = d, type = \"sizer\") |> draw()"},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.smooth_samples.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot posterior smooths — draw.smooth_samples","title":"Plot posterior smooths — draw.smooth_samples","text":"Plot posterior smooths","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.smooth_samples.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot posterior smooths — draw.smooth_samples","text":"","code":"# S3 method for class 'smooth_samples' draw( object, select = NULL, n_samples = NULL, seed = NULL, xlab = NULL, ylab = NULL, title = NULL, subtitle = NULL, caption = NULL, alpha = 1, colour = \"black\", contour = FALSE, contour_col = \"black\", n_contour = NULL, scales = c(\"free\", \"fixed\"), rug = TRUE, partial_match = FALSE, angle = NULL, ncol = NULL, nrow = NULL, guides = \"keep\", ... )"},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.smooth_samples.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot posterior smooths — draw.smooth_samples","text":"object fitted GAM, result call mgcv::gam(). select character, logical, numeric; smooths plot. NULL, default, model smooths drawn. Numeric select indexes smooths order specified formula stored object. Character select matches labels smooths shown example output summary(object). Logical select operates per numeric select order smooths stored. n_samples numeric; NULL, sample n_samples posterior draws plotting. seed numeric; random seed used sampling draws. xlab character expression; label x axis. supplied, suitable label generated object. ylab character expression; label y axis. supplied, suitable label generated object. title character expression; title plot. See ggplot2::labs(). subtitle character expression; subtitle plot. See ggplot2::labs(). caption character expression; plot caption. See ggplot2::labs(). alpha numeric; alpha transparency confidence simultaneous interval. colour colour use draw posterior smooths. Passed ggplot2::geom_line() argument colour. contour logical; contour lines added smooth surfaces? contour_col colour specification contour lines. n_contour numeric; number contour bins. result n_contour - 1 contour lines drawn. See ggplot2::geom_contour(). scales character; univariate smooths plotted y-axis scale? scales = \"free\", default, univariate smooth y-axis scale. scales = \"fixed\", common y axis scale used univariate smooths. Currently affect y-axis scale plots parametric terms. rug logical; draw rug plot bottom plot 1-D smooths plot locations data higher dimensions. partial_match logical; smooths selected partial matches select? TRUE, select can single string match . angle numeric; angle x axis tick labels drawn passed angle argument ggplot2::guide_axis(). ncol, nrow numeric; numbers rows columns spread plots guides character; one \"keep\" (default), \"collect\", \"auto\". Passed patchwork::plot_layout() ... arguments passed patchwork::wrap_plots().","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.smooth_samples.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot posterior smooths — draw.smooth_samples","text":"Gavin L. Simpson","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw.smooth_samples.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot posterior smooths — draw.smooth_samples","text":"","code":"load_mgcv() dat1 <- data_sim(\"eg1\", n = 400, dist = \"normal\", scale = 1, seed = 1) ## a single smooth GAM m1 <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = dat1, method = \"REML\") ## posterior smooths from m1 sm1 <- smooth_samples(m1, n = 15, seed = 23478) ## plot draw(sm1, alpha = 0.7) ## plot only 5 randomly smapled draws draw(sm1, n_samples = 5, alpha = 0.7) ## A factor-by smooth example dat2 <- data_sim(\"eg4\", n = 400, dist = \"normal\", scale = 1, seed = 1) ## a multi-smooth GAM with a factor-by smooth m2 <- gam(y ~ fac + s(x2, by = fac) + s(x0), data = dat2, method = \"REML\") ## posterior smooths from m1 sm2 <- smooth_samples(m2, n = 15, seed = 23478) ## plot, this time selecting only the factor-by smooth draw(sm2, select = \"s(x2)\", partial_match = TRUE, alpha = 0.7) # \\donttest{ ## A 2D smooth example dat3 <- data_sim(\"eg2\", n = 400, dist = \"normal\", scale = 1, seed = 1) ## fit a 2D smooth m3 <- gam(y ~ te(x, z), data = dat3, method = \"REML\") ## get samples sm3 <- smooth_samples(m3, n = 10) ## plot just 6 of the draws, with contour line overlays draw(sm3, n_samples = 6, contour = TRUE, seed = 42) # }"},{"path":"https://gavinsimpson.github.io/gratia/reference/draw_parametric_effect.html","id":null,"dir":"Reference","previous_headings":"","what":"Internal function to draw an individual parametric effect — draw_parametric_effect","title":"Internal function to draw an individual parametric effect — draw_parametric_effect","text":"Internal function draw individual parametric effect","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/draw_parametric_effect.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Internal function to draw an individual parametric effect — draw_parametric_effect","text":"","code":"draw_parametric_effect( object, ci_level = 0.95, ci_col = \"black\", ci_alpha = 0.2, line_col = \"black\", constant = NULL, fun = NULL, xlab = NULL, ylab = NULL, title = NULL, subtitle = NULL, caption = NULL, rug = TRUE, position = \"identity\", ylim = NULL, angle = NULL, factor_levels = NULL, ... )"},{"path":"https://gavinsimpson.github.io/gratia/reference/draw_parametric_effect.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Internal function to draw an individual parametric effect — draw_parametric_effect","text":"object fitted GAM, result call mgcv::gam(). ci_level numeric 0 1; coverage credible interval. ci_col colour specification confidence/credible intervals band. Affects fill interval. ci_alpha numeric; alpha transparency confidence simultaneous interval. constant numeric; constant add estimated values smooth. constant, supplied, added estimated value confidence band computed. fun function; function applied estimated values confidence interval plotting. Can function name function. Function fun applied adding constant, provided. xlab character expression; label x axis. supplied, suitable label generated object. ylab character expression; label y axis. supplied, suitable label generated object. title character expression; title plot. See ggplot2::labs(). subtitle character expression; subtitle plot. See ggplot2::labs(). caption character expression; plot caption. See ggplot2::labs(). rug logical; draw rug plot bottom plot 1-D smooths plot locations data higher dimensions. position Position adjustment, either string, result call position adjustment function. angle numeric; angle x axis tick labels drawn passed angle argument ggplot2::guide_axis(). factor_levels list; named list factor levels ... additional arguments passed patchwork::wrap_plots().","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/edf.html","id":null,"dir":"Reference","previous_headings":"","what":"Effective degrees of freedom for smooths and GAMs — edf","title":"Effective degrees of freedom for smooths and GAMs — edf","text":"Extracts effective degrees freedom (EDF) model smooth terms overall EDF fitted GAMs","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/edf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Effective degrees of freedom for smooths and GAMs — edf","text":"","code":"edf(object, ...) # S3 method for class 'gam' edf( object, select = NULL, smooth = deprecated(), type = c(\"default\", \"unconditional\", \"alternative\"), partial_match = FALSE, ... ) model_edf(object, ..., type = c(\"default\", \"unconditional\", \"alternative\"))"},{"path":"https://gavinsimpson.github.io/gratia/reference/edf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Effective degrees of freedom for smooths and GAMs — edf","text":"object fitted model extract smooth-specific EDFs. ... arguments passed methods. select character, logical, numeric; smooths plot. NULL, default, model smooths drawn. Numeric select indexes smooths order specified formula stored object. Character select matches labels smooths shown example output summary(object). Logical select operates per numeric select order smooths stored. smooth Use select instead. extracted. NULL, default, EDFs smooths returned. type character: type EDF return. \"default\" returns standard EDF; \"unconditional\" selects EDF corrected smoothness parameter selection, available; \"alternative\" returns alternative formulation EDF Wood (2017, pp. 252) partial_match logical; smooths selected partial matches select? TRUE, select can single string match .","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/edf.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Effective degrees of freedom for smooths and GAMs — edf","text":"Multiple formulations effective degrees freedom available. additional uncertainty due selection smoothness parameters can taken account computing EDF smooths. form EDF available type = \"unconditional\". Wood (2017; pp. 252) describes alternative EDF model $$\\mathrm{EDF} = 2\\mathrm{tr}(\\mathbf{F}) - \\mathrm{tr}(\\mathbf{FF}),$$ \\(\\mathrm{tr}\\) matrix trace \\(\\mathbf{F}\\) matrix mapping un-penalized coefficient estimates penalized coefficient estimates. trace \\(\\mathbf{F}\\) effectively average shrinkage coefficients multipled number coefficients (Wood, 2017). Smooth-specific EDFs obtained summing relevent elements \\(\\mathrm{diag}(2\\mathbf{F} - \\mathbf{FF})\\).","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/edf.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Effective degrees of freedom for smooths and GAMs — edf","text":"","code":"load_mgcv() df <- data_sim(\"eg1\", n = 400, seed = 42) m <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = df, method = \"REML\") # extract the EDFs for all smooths edf(m) #> # A tibble: 4 x 2 #> .smooth .edf #> #> 1 s(x0) 3.4248 #> 2 s(x1) 3.2213 #> 3 s(x2) 7.9049 #> 4 s(x3) 1.8847 # or selected smooths edf(m, select = c(\"s(x0)\", \"s(x2)\")) #> # A tibble: 2 x 2 #> .smooth .edf #> #> 1 s(x0) 3.4248 #> 2 s(x2) 7.9049 # accounting for smoothness parameter uncertainty edf(m, type = \"unconditional\") #> # A tibble: 4 x 2 #> .smooth .edf #> #> 1 s(x0) 3.7697 #> 2 s(x1) 3.8728 #> 3 s(x2) 8.0670 #> 4 s(x3) 2.8834 # over EDF of the model, including the intercept model_edf(m) #> # A tibble: 1 x 2 #> .model .edf #> #> 1 m 17.436 # can get model EDF for multiple models m2 <- gam(y ~ s(x0) + s(x1) + s(x3), data = df, method = \"REML\") model_edf(m, m2) #> # A tibble: 2 x 2 #> .model .edf #> #> 1 m 17.436 #> 2 m2 7.5777"},{"path":"https://gavinsimpson.github.io/gratia/reference/eval_smooth.html","id":null,"dir":"Reference","previous_headings":"","what":"S3 methods to evaluate individual smooths — eval_smooth","title":"S3 methods to evaluate individual smooths — eval_smooth","text":"S3 methods evaluate individual smooths","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/eval_smooth.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"S3 methods to evaluate individual smooths — eval_smooth","text":"","code":"eval_smooth(smooth, ...) # S3 method for class 'mgcv.smooth' eval_smooth( smooth, model, n = 100, n_3d = NULL, n_4d = NULL, data = NULL, unconditional = FALSE, overall_uncertainty = TRUE, dist = NULL, ... ) # S3 method for class 'soap.film' eval_smooth( smooth, model, n = 100, n_3d = NULL, n_4d = NULL, data = NULL, unconditional = FALSE, overall_uncertainty = TRUE, ... ) # S3 method for class 'scam_smooth' eval_smooth( smooth, model, n = 100, n_3d = NULL, n_4d = NULL, data = NULL, unconditional = FALSE, overall_uncertainty = TRUE, dist = NULL, ... ) # S3 method for class 'fs.interaction' eval_smooth( smooth, model, n = 100, data = NULL, unconditional = FALSE, overall_uncertainty = TRUE, ... ) # S3 method for class 'sz.interaction' eval_smooth( smooth, model, n = 100, data = NULL, unconditional = FALSE, overall_uncertainty = TRUE, ... ) # S3 method for class 'random.effect' eval_smooth( smooth, model, n = 100, data = NULL, unconditional = FALSE, overall_uncertainty = TRUE, ... ) # S3 method for class 'mrf.smooth' eval_smooth( smooth, model, n = 100, data = NULL, unconditional = FALSE, overall_uncertainty = TRUE, ... ) # S3 method for class 't2.smooth' eval_smooth( smooth, model, n = 100, n_3d = NULL, n_4d = NULL, data = NULL, unconditional = FALSE, overall_uncertainty = TRUE, dist = NULL, ... ) # S3 method for class 'tensor.smooth' eval_smooth( smooth, model, n = 100, n_3d = NULL, n_4d = NULL, data = NULL, unconditional = FALSE, overall_uncertainty = TRUE, dist = NULL, ... )"},{"path":"https://gavinsimpson.github.io/gratia/reference/eval_smooth.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"S3 methods to evaluate individual smooths — eval_smooth","text":"smooth currently object inherits class mgcv.smooth. ... arguments passed methods model fitted model; currently mgcv::gam() mgcv::bam() models suported. n numeric; number points range covariate evaluate smooth. n_3d, n_4d numeric; number points range last covariate 3D 4D smooth. default NULL achieves standard behaviour using n points range covariate, resulting n^d evaluation points, d dimension smooth. d > 2 can result many evaluation points slow performance. smooths d > 4, value n_4d used dimensions > 4, unless NULL, case default behaviour (using n dimensions) observed. data optional data frame values evaluate smooth . unconditional logical; confidence intervals include uncertainty due smoothness selection? TRUE, corrected Bayesian covariance matrix used. overall_uncertainty logical; uncertainty model constant term included standard error evaluate values smooth? dist numeric; greater 0, used determine location far data plotted plotting 2-D smooths. data scaled unit square deciding exclude, dist distance within unit square. See mgcv::exclude..far() details.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/evaluate_parametric_term.html","id":null,"dir":"Reference","previous_headings":"","what":"Evaluate parametric model terms — evaluate_parametric_term","title":"Evaluate parametric model terms — evaluate_parametric_term","text":"Returns values parametric model terms values factor terms grid covariate values linear parametric terms. function now deprecated favour parametric_effects().","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/evaluate_parametric_term.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Evaluate parametric model terms — evaluate_parametric_term","text":"","code":"evaluate_parametric_term(object, ...) # S3 method for class 'gam' evaluate_parametric_term(object, term, unconditional = FALSE, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/evaluate_parametric_term.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Evaluate parametric model terms — evaluate_parametric_term","text":"object object class \"gam\" \"gamm\". ... arguments passed methods. term character; parametric term whose effects evaluated unconditional logical; confidence intervals include uncertainty due smoothness selection? TRUE, corrected Bayesian covariance matrix used.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/evaluate_smooth.html","id":null,"dir":"Reference","previous_headings":"","what":"Evaluate a smooth — evaluate_smooth","title":"Evaluate a smooth — evaluate_smooth","text":"Evaluate smooth grid evenly spaced value range covariate associated smooth. Alternatively, set points smooth evaluated can supplied.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/evaluate_smooth.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Evaluate a smooth — evaluate_smooth","text":"","code":"evaluate_smooth(object, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/evaluate_smooth.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Evaluate a smooth — evaluate_smooth","text":"object object class \"gam\" \"gamm\". ... arguments passed methods.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/evaluate_smooth.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Evaluate a smooth — evaluate_smooth","text":"data frame, class \"evaluated_1d_smooth\" evaluated_2d_smooth, inherit classes \"evaluated_smooth\" \"data.frame\".","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/evaluate_smooth.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Evaluate a smooth — evaluate_smooth","text":"evaluate_smooth() deprecated favour smooth_estimates(), provides cleaner way evaluate smooth range covariate values. smooth_estimates() can handle much wider range models evaluate_smooth() capable smooth_estimates() much easier extend handle new smooth types. code uses evaluate_smooth() work simply changing function call smooth_estimates(). However, differences: newdata argument becomes data","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/evenly.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a sequence of evenly-spaced values — evenly","title":"Create a sequence of evenly-spaced values — evenly","text":"continuous vector x, evenly seq_min_max() create sequence n evenly-spaced values range lower – upper. default, lower defined min(x) upper max(x), excluding NAs. factor x, function returns levels(x).","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/evenly.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a sequence of evenly-spaced values — evenly","text":"","code":"evenly(x, n = 100, by = NULL, lower = NULL, upper = NULL) seq_min_max(x, n, by = NULL, lower = NULL, upper = NULL)"},{"path":"https://gavinsimpson.github.io/gratia/reference/evenly.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a sequence of evenly-spaced values — evenly","text":"x numeric; vector evenly-spaced values returned n numeric; number evenly-spaced values return. default 100 used convenience typically used evaluating smooth. numeric; increment sequence. specified, argument n ignored sequence returned min(x) max(x) increments . lower numeric; lower bound interval. upper numeric; upper bound interval.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/evenly.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a sequence of evenly-spaced values — evenly","text":"numeric vector length n.","code":""},{"path":[]},{"path":"https://gavinsimpson.github.io/gratia/reference/evenly.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a sequence of evenly-spaced values — evenly","text":"","code":"x <- rnorm(10) n <- 10L # 10 values evenly over the range of `x` evenly(x, n = n) #> [1] -0.83562861 -0.56552757 -0.29542652 -0.02532547 0.24477557 0.51487662 #> [7] 0.78497766 1.05507871 1.32517976 1.59528080 # evenly spaced values, incrementing by 0.2 evenly(x, by = 0.2) #> [1] -0.83562861 -0.63562861 -0.43562861 -0.23562861 -0.03562861 0.16437139 #> [7] 0.36437139 0.56437139 0.76437139 0.96437139 1.16437139 1.36437139 #> [13] 1.56437139 # evenly spaced values, incrementing by 0.2, starting at -2 evenly(x, by = 0.2, lower = -2) #> [1] -2.0 -1.8 -1.6 -1.4 -1.2 -1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 #> [16] 1.0 1.2 1.4"},{"path":"https://gavinsimpson.github.io/gratia/reference/factor_combos.html","id":null,"dir":"Reference","previous_headings":"","what":"All combinations of factor levels — factor_combos","title":"All combinations of factor levels — factor_combos","text":"combinations factor levels","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/factor_combos.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"All combinations of factor levels — factor_combos","text":"","code":"factor_combos(object, ...) # S3 method for class 'gam' factor_combos(object, vars = everything(), complete = TRUE, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/factor_combos.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"All combinations of factor levels — factor_combos","text":"object fitted model object. ... arguments passed methods. vars terms include exclude returned object. Uses tidyselect principles. complete logical; combinations factor levels returned? FALSE, combinations levels observed model retained.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/family.gam.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract family objects from models — family.gam","title":"Extract family objects from models — family.gam","text":"Provides stats::family() method range GAM objects.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/family.gam.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract family objects from models — family.gam","text":"","code":"# S3 method for class 'gam' family(object, ...) # S3 method for class 'gamm' family(object, ...) # S3 method for class 'bam' family(object, ...) # S3 method for class 'list' family(object, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/family.gam.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract family objects from models — family.gam","text":"object fitted model. Models fitted mgcv::gam(), mgcv::bam(), mgcv::gamm(), gamm4::gamm4() currently supported. ... arguments passed methods.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/family_name.html","id":null,"dir":"Reference","previous_headings":"","what":"Name of family used to fit model — family_name","title":"Name of family used to fit model — family_name","text":"Extracts name family used fit supplied model.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/family_name.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Name of family used to fit model — family_name","text":"","code":"family_name(object, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/family_name.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Name of family used to fit model — family_name","text":"object R object. ... arguments passed methods.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/family_name.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Name of family used to fit model — family_name","text":"character vector containing family name.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/family_type.html","id":null,"dir":"Reference","previous_headings":"","what":"Extracts the type of family in a consistent way — family_type","title":"Extracts the type of family in a consistent way — family_type","text":"Extracts type family consistent way","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/family_type.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extracts the type of family in a consistent way — family_type","text":"","code":"family_type(object, ...) # S3 method for class 'family' family_type(object, ...) # Default S3 method family_type(object, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/family_type.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extracts the type of family in a consistent way — family_type","text":"object R object. Currently family() objects anything family() method. ... arguments passed methods.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/fderiv.html","id":null,"dir":"Reference","previous_headings":"","what":"First derivatives of fitted GAM functions — fderiv","title":"First derivatives of fitted GAM functions — fderiv","text":"function deprecated limited first order forward finite differences derivatives , improved offer needed functionality without breaking backwards compatability papers blog posts already used fderiv(). replacement, derivatives(), now available recommended new analyses.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/fderiv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"First derivatives of fitted GAM functions — fderiv","text":"","code":"fderiv(model, ...) # S3 method for class 'gam' fderiv( model, newdata, term, n = 200, eps = 1e-07, unconditional = FALSE, offset = NULL, ... ) # S3 method for class 'gamm' fderiv(model, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/fderiv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"First derivatives of fitted GAM functions — fderiv","text":"model fitted GAM. Currently models fitted mgcv::gam() mgcv::gamm() supported. ... Arguments passed methods. newdata data frame containing values model covariates evaluate first derivatives smooths. term character; vector one terms derivatives required. missing, derivatives smooth terms returned. n integer; newdata missing original data can reconstructed model n controls number values range covariate populate newdata. eps numeric; value finite difference used approximate first derivative. unconditional logical; TRUE, smoothing parameter uncertainty corrected covariance matrix used, available, otherwise uncorrected Bayesian posterior covariance matrix used. offset numeric; value offset use generating predictions.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/fderiv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"First derivatives of fitted GAM functions — fderiv","text":"object class \"fderiv\" returned.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/fderiv.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"First derivatives of fitted GAM functions — fderiv","text":"Gavin L. Simpson","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/fderiv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"First derivatives of fitted GAM functions — fderiv","text":"","code":"load_mgcv() dat <- data_sim(\"eg1\", seed = 2) mod <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = dat, method = \"REML\") ## first derivatives of all smooths... fd <- fderiv(mod) ## now use --> fd <- derivatives(mod) ## ...and a selected smooth fd2 <- fderiv(mod, term = \"x1\") ## now use --> fd2 <- derivatives(mod, select = \"s(x1)\") ## Models with factors dat <- data_sim(\"eg4\", n = 400, dist = \"normal\", scale = 2, seed = 2) mod <- gam(y ~ s(x0) + s(x1) + fac, data = dat, method = \"REML\") ## first derivatives of all smooths... fd <- fderiv(mod) ## now use --> fd <- derivatives(mod) ## ...and a selected smooth fd2 <- fderiv(mod, term = \"x1\") ## now use --> fd2 <- derivatives(mod, select = \"s(x1)\")"},{"path":"https://gavinsimpson.github.io/gratia/reference/fitted_samples.html","id":null,"dir":"Reference","previous_headings":"","what":"Draw fitted values from the posterior distribution — fitted_samples","title":"Draw fitted values from the posterior distribution — fitted_samples","text":"Expectations (fitted values) response drawn posterior distribution fitted model using Gaussian approximation posterior simple Metropolis Hastings sampler.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/fitted_samples.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Draw fitted values from the posterior distribution — fitted_samples","text":"","code":"fitted_samples(model, ...) # S3 method for class 'gam' fitted_samples( model, n = 1, data = newdata, seed = NULL, scale = c(\"response\", \"linear_predictor\"), method = c(\"gaussian\", \"mh\", \"inla\", \"user\"), n_cores = 1, burnin = 1000, thin = 1, t_df = 40, rw_scale = 0.25, freq = FALSE, unconditional = FALSE, draws = NULL, mvn_method = c(\"mvnfast\", \"mgcv\"), ..., newdata = NULL, ncores = NULL )"},{"path":"https://gavinsimpson.github.io/gratia/reference/fitted_samples.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Draw fitted values from the posterior distribution — fitted_samples","text":"model fitted model supported types ... arguments passed methods. fitted_samples(), passed mgcv::predict.gam(). posterior_samples() passed fitted_samples(). predicted_samples() passed relevant simulate() method. n numeric; number posterior samples return. data data frame; new observations posterior draws model evaluated. supplied, data used fit model used data, available model. seed numeric; random seed simulations. scale character; scale fitted values returned ? \"linear predictor\" synonym \"link\" prefer terminology. method character; method used draw samples posterior distribution. \"gaussian\" uses Gaussian (Laplace) approximation posterior. \"mh\" uses Metropolis Hastings sampler alternates t proposals proposals based shrunken version posterior covariance matrix. \"inla\" uses variant Integrated Nested Laplace Approximation due Wood (2019), (currently implemented). \"user\" allows user-supplied posterior draws (currently implemented). n_cores number cores generating random variables multivariate normal distribution. Passed mvnfast::rmvn(). Parallelization take place OpenMP supported (appears work Windows current R). burnin numeric; number samples discard burnin draws. used method = \"mh\". thin numeric; number samples skip taking n draws. Results thin * n draws posterior taken. used method = \"mh\". t_df numeric; degrees freedom t distribution proposals. used method = \"mh\". rw_scale numeric; Factor scale posterior covariance matrix generating random walk proposals. Negative non finite skip random walk step. used method = \"mh\". freq logical; TRUE use frequentist covariance matrix parameter estimators, FALSE use Bayesian posterior covariance matrix parameters. unconditional logical; TRUE (freq == FALSE) Bayesian smoothing parameter uncertainty corrected covariance matrix used, available. draws matrix; user supplied posterior draws used method = \"user\". mvn_method character; one \"mvnfast\" \"mgcv\". default uses mvnfast::rmvn(), can considerably faster generate large numbers MVN random values mgcv::rmvn(), might work marginal fits, covariance matrix close singular. newdata Deprecated: use data instead. ncores Deprecated; use n_cores instead. number cores generating random variables multivariate normal distribution. Passed mvnfast::rmvn(). Parallelization take place OpenMP supported (appears work Windows current R).","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/fitted_samples.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Draw fitted values from the posterior distribution — fitted_samples","text":"tibble (data frame) 3 columns containing posterior predicted values long format. columns row (integer) row data posterior draw relates , draw (integer) index, range 1:n, indicating draw row relates , response (numeric) predicted response indicated row data.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/fitted_samples.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Draw fitted values from the posterior distribution — fitted_samples","text":"Models offset terms supplied via offset argument mgcv::gam() etc. ignored mgcv::predict.gam(). , kind offset term also ignored posterior_samples(). Offset terms included model formula supplied mgcv::gam() etc ignored posterior samples produced reflect offset term values. side effect requiring new data values provided posterior_samples() via data argument must include offset variable.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/fitted_samples.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Draw fitted values from the posterior distribution — fitted_samples","text":"Wood, S.N., (2020). Simplified integrated nested Laplace approximation. Biometrika 107, 223–230. doi:10.1093/biomet/asz044","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/fitted_samples.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Draw fitted values from the posterior distribution — fitted_samples","text":"Gavin L. Simpson","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/fitted_samples.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Draw fitted values from the posterior distribution — fitted_samples","text":"","code":"load_mgcv() dat <- data_sim(\"eg1\", n = 1000, dist = \"normal\", scale = 2, seed = 2) m1 <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = dat, method = \"REML\") fs <- fitted_samples(m1, n = 5, seed = 42) # \\donttest{ fs #> # A tibble: 5,000 x 4 #> .row .draw .parameter .fitted #> #> 1 1 1 location 6.34 #> 2 2 1 location 5.08 #> 3 3 1 location 6.84 #> 4 4 1 location 7.71 #> 5 5 1 location 9.23 #> 6 6 1 location 8.03 #> 7 7 1 location 6.19 #> 8 8 1 location 7.28 #> 9 9 1 location 14.0 #> 10 10 1 location 12.7 #> # i 4,990 more rows # } # can generate own set of draws and use them drws <- generate_draws(m1, n = 2, seed = 24) fs2 <- fitted_samples(m1, method = \"user\", draws = drws) # \\donttest{ fs2 #> # A tibble: 2,000 x 4 #> .row .draw .parameter .fitted #> #> 1 1 1 location 6.30 #> 2 2 1 location 5.12 #> 3 3 1 location 7.40 #> 4 4 1 location 7.42 #> 5 5 1 location 9.40 #> 6 6 1 location 8.04 #> 7 7 1 location 5.83 #> 8 8 1 location 7.30 #> 9 9 1 location 14.3 #> 10 10 1 location 13.1 #> # i 1,990 more rows # }"},{"path":"https://gavinsimpson.github.io/gratia/reference/fitted_values.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate fitted values from a estimated GAM — fitted_values","title":"Generate fitted values from a estimated GAM — fitted_values","text":"Generate fitted values estimated GAM","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/fitted_values.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate fitted values from a estimated GAM — fitted_values","text":"","code":"fitted_values(object, ...) # S3 method for class 'gam' fitted_values( object, data = NULL, scale = c(\"response\", \"link\", \"linear predictor\"), ci_level = 0.95, ... ) # S3 method for class 'gamm' fitted_values(object, ...) # S3 method for class 'scam' fitted_values(object, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/fitted_values.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate fitted values from a estimated GAM — fitted_values","text":"object fitted model. Currently models fitted mgcv::gam() mgcv::bam() supported. ... arguments passed mgcv::predict.gam(). Note type, newdata, se.fit already used passed mgcv::predict.gam(). data optional data frame covariate values fitted values returned. scale character; scale fitted values returned ? \"linear predictor\" synonym \"link\" prefer terminology. ci_level numeric; value 0 1 indicating coverage credible interval.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/fitted_values.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate fitted values from a estimated GAM — fitted_values","text":"tibble (data frame) whose first m columns contain either data used fit model (data NULL), variables supplied data. Four columns added: fitted: fitted values specified scale, se: standard error fitted values (always link scale), lower, upper: limits credible interval fitted values, specified scale. Models fitted certain families include additional variables mgcv::ocat() models: scale = \"repsonse\", returned object contain row column category column, indicate row data row returned object belongs. Additionally, nrow(data) * n_categories rows returned object; row predicted probability single category response.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/fitted_values.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Generate fitted values from a estimated GAM — fitted_values","text":"families, regardless scale fitted values returned, se component returned object link (linear predictor) scale, response scale. exception mgcv::ocat() family, se response scale scale = \"response\".","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/fitted_values.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate fitted values from a estimated GAM — fitted_values","text":"","code":"load_mgcv() sim_df <- data_sim(\"eg1\", n = 400, dist = \"normal\", scale = 2, seed = 2) m <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = sim_df, method = \"REML\") fv <- fitted_values(m) fv #> # A tibble: 400 x 9 #> .row x0 x1 x2 x3 .fitted .se .lower_ci #> #> 1 1 0.184882 0.617142 0.415244 0.132410 8.73875 0.354677 8.04360 #> 2 2 0.702374 0.569064 0.531439 0.365331 7.62581 0.337779 6.96378 #> 3 3 0.573326 0.153970 0.00324621 0.454532 3.12106 0.591862 1.96103 #> 4 4 0.168052 0.0348332 0.252100 0.537114 11.1124 0.402378 10.3237 #> 5 5 0.943839 0.997953 0.155229 0.185495 14.0533 0.452947 13.1655 #> 6 6 0.943475 0.835574 0.878840 0.449276 6.13080 0.364521 5.41635 #> 7 7 0.129159 0.586562 0.203511 0.256527 12.4838 0.355808 11.7864 #> 8 8 0.833449 0.339117 0.583528 0.618458 6.25215 0.344700 5.57655 #> 9 9 0.468019 0.166883 0.804473 0.880744 4.21463 0.372003 3.48552 #> 10 10 0.549984 0.807410 0.264717 0.317747 15.5283 0.369999 14.8031 #> # i 390 more rows #> # i 1 more variable: .upper_ci "},{"path":"https://gavinsimpson.github.io/gratia/reference/fix_offset.html","id":null,"dir":"Reference","previous_headings":"","what":"Fix the names of a data frame containing an offset variable. — fix_offset","title":"Fix the names of a data frame containing an offset variable. — fix_offset","text":"Identifies variable, , model offset, fixed name offset(foo(var)) converted var, possibly sets values variable offset_val.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/fix_offset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fix the names of a data frame containing an offset variable. — fix_offset","text":"","code":"fix_offset(model, newdata, offset_val = NULL)"},{"path":"https://gavinsimpson.github.io/gratia/reference/fix_offset.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fix the names of a data frame containing an offset variable. — fix_offset","text":"model fitted GAM. newdata data frame; new values predict . offset_val numeric, optional; provided, offset variable newdata set constant value returning newdata","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/fix_offset.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fix the names of a data frame containing an offset variable. — fix_offset","text":"original newdata returned fixed names possibly modified offset variable.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/fix_offset.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fix the names of a data frame containing an offset variable. — fix_offset","text":"Gavin L. Simpson","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/fix_offset.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fix the names of a data frame containing an offset variable. — fix_offset","text":"","code":"load_mgcv() df <- data_sim(\"eg1\", n = 400, dist = \"normal\", seed = 2) m <- gam(y ~ s(x0) + s(x1) + offset(x2), data = df, method = \"REML\") names(model.frame(m)) #> [1] \"y\" \"offset(x2)\" \"x0\" \"x1\" names(fix_offset(m, model.frame(m), offset_val = 1L)) #> [1] \"y\" \"x2\" \"x0\" \"x1\""},{"path":"https://gavinsimpson.github.io/gratia/reference/fixef.gam.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract fixed effects estimates from a fitted GAM — fixef.gam","title":"Extract fixed effects estimates from a fitted GAM — fixef.gam","text":"Extract fixed effects estimates fitted GAM","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/fixef.gam.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract fixed effects estimates from a fitted GAM — fixef.gam","text":"","code":"# S3 method for class 'gam' fixef(object, ...) # S3 method for class 'gamm' fixef(object, ...) # S3 method for class 'lm' fixef(object, ...) # S3 method for class 'glm' fixef(object, ...) fixed_effects(object, ...) # Default S3 method fixed_effects(object, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/fixef.gam.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract fixed effects estimates from a fitted GAM — fixef.gam","text":"object fitted GAM ... arguments passed methods","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/fixef.gam.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract fixed effects estimates from a fitted GAM — fixef.gam","text":"","code":"load_mgcv() # run example if lme4 is available if (require(\"lme4\")) { data(sleepstudy, package = \"lme4\") m <- gam( Reaction ~ Days + s(Subject, bs = \"re\") + s(Days, Subject, bs = \"re\"), data = sleepstudy, method = \"REML\" ) fixef(m) } #> Loading required package: lme4 #> Loading required package: Matrix #> (Intercept) Days #> 251.40510 10.46729"},{"path":"https://gavinsimpson.github.io/gratia/reference/fixef.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract fixed effects estimates — fixef","title":"Extract fixed effects estimates — fixef","text":"Extract fixed effects estimates","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/fixef.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract fixed effects estimates — fixef","text":"object fitted GAM ... arguments passed methods","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/gaussian_draws.html","id":null,"dir":"Reference","previous_headings":"","what":"Posterior samples using a simple Metropolis Hastings sampler — gaussian_draws","title":"Posterior samples using a simple Metropolis Hastings sampler — gaussian_draws","text":"Posterior samples using simple Metropolis Hastings sampler","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/gaussian_draws.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Posterior samples using a simple Metropolis Hastings sampler — gaussian_draws","text":"","code":"gaussian_draws(model, ...) # S3 method for class 'gam' gaussian_draws( model, n, n_cores = 1L, index = NULL, frequentist = FALSE, unconditional = FALSE, mvn_method = \"mvnfast\", ... ) # S3 method for class 'scam' gaussian_draws( model, n, n_cores = 1L, index = NULL, frequentist = FALSE, parametrized = TRUE, mvn_method = \"mvnfast\", ... )"},{"path":"https://gavinsimpson.github.io/gratia/reference/gaussian_draws.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Posterior samples using a simple Metropolis Hastings sampler — gaussian_draws","text":"model fitted R model. Currently models fitted mgcv::gam() mgcv::bam(), return object inherits objects supported. , \"inherits\" used loose fashion; models fitted scam::scam() support even though models strictly inherit class \"gam\" far inherits() concerned. ... arguments passed methods. n numeric; number posterior draws take. n_cores integer; number CPU cores use generating multivariate normal distributed random values. used mvn_method = \"mvnfast\" method = \"gaussian\". index numeric; vector indices coefficients use. Can used subset mean vector covariance matrix extracted model. frequentist logical; TRUE, frequentist covariance matrix parameter estimates used. FALSE, Bayesian posterior covariance matrix parameters used. See mgcv::vcov.gam(). unconditional logical; TRUE Bayesian smoothing parameter uncertainty corrected covariance matrix used, available model. See mgcv::vcov.gam(). mvn_method character; one \"mvnfast\" \"mgcv\". default uses mvnfast::rmvn(), can considerably faster generate large numbers MVN random values mgcv::rmvn(), might work marginal fits, covariance matrix close singular. parametrized logical; use parametrized coefficients covariance matrix, respect linear inequality constraints model. scam::scam() model fits.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/get_by_smooth.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract an factor-by smooth by name — get_by_smooth","title":"Extract an factor-by smooth by name — get_by_smooth","text":"Extract factor-smooth name","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/get_by_smooth.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract an factor-by smooth by name — get_by_smooth","text":"","code":"get_by_smooth(object, term, level)"},{"path":"https://gavinsimpson.github.io/gratia/reference/get_by_smooth.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract an factor-by smooth by name — get_by_smooth","text":"object fitted GAM model object. term character; name smooth term extract. level character; level factor exrtact smooth .","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/get_by_smooth.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract an factor-by smooth by name — get_by_smooth","text":"single smooth object, list smooths several match named term.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/get_smooth.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract an mgcv smooth by name — get_smooth","title":"Extract an mgcv smooth by name — get_smooth","text":"Extract mgcv smooth name","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/get_smooth.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract an mgcv smooth by name — get_smooth","text":"","code":"get_smooth(object, term)"},{"path":"https://gavinsimpson.github.io/gratia/reference/get_smooth.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract an mgcv smooth by name — get_smooth","text":"object fitted GAM model object. term character; name smooth term extract","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/get_smooth.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract an mgcv smooth by name — get_smooth","text":"single smooth object, list smooths several match named term.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/get_smooths_by_id.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract an mgcv smooth given its position in the model object — get_smooths_by_id","title":"Extract an mgcv smooth given its position in the model object — get_smooths_by_id","text":"Extract mgcv smooth given position model object","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/get_smooths_by_id.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract an mgcv smooth given its position in the model object — get_smooths_by_id","text":"","code":"get_smooths_by_id(object, id) # S3 method for class 'gam' get_smooths_by_id(object, id) # S3 method for class 'scam' get_smooths_by_id(object, id) # S3 method for class 'gamm' get_smooths_by_id(object, id) # S3 method for class 'gamm4' get_smooths_by_id(object, id) # S3 method for class 'list' get_smooths_by_id(object, id)"},{"path":"https://gavinsimpson.github.io/gratia/reference/get_smooths_by_id.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract an mgcv smooth given its position in the model object — get_smooths_by_id","text":"object fitted GAM model object. id numeric; position smooth model object.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/gratia-package.html","id":null,"dir":"Reference","previous_headings":"","what":"gratia: Graceful 'ggplot'-Based Graphics and Other Functions for GAMs Fitted Using 'mgcv' — gratia-package","title":"gratia: Graceful 'ggplot'-Based Graphics and Other Functions for GAMs Fitted Using 'mgcv' — gratia-package","text":"Graceful 'ggplot'-based graphics utility functions working generalized additive models (GAMs) fitted using 'mgcv' package. Provides reimplementation plot() method GAMs 'mgcv' provides, well 'tidyverse' compatible representations estimated smooths.","code":""},{"path":[]},{"path":"https://gavinsimpson.github.io/gratia/reference/gratia-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"gratia: Graceful 'ggplot'-Based Graphics and Other Functions for GAMs Fitted Using 'mgcv' — gratia-package","text":"Maintainer: Gavin L. Simpson ucfagls@gmail.com (ORCID) [copyright holder] contributors: Henrik Singmann (ORCID) [contributor]","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/gss_vocab.html","id":null,"dir":"Reference","previous_headings":"","what":"Data from the General Social Survey (GSS) from the National Opinion Research Center of the University of Chicago — gss_vocab","title":"Data from the General Social Survey (GSS) from the National Opinion Research Center of the University of Chicago — gss_vocab","text":"subset data carData::GSSvocab dataset carData package, containing observations 2016 .","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/gss_vocab.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data from the General Social Survey (GSS) from the National Opinion Research Center of the University of Chicago — gss_vocab","text":"data frame 1858 rows 3 variables: vocab: numeric; number words 10 correct vocabulary test. nativeBorn: factor; respondent born US? factor levels yes. ageGroup: factor; grouped age respondent levels 18-29 30-39, 40-49, 50-59, 60+.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/gw_functions.html","id":null,"dir":"Reference","previous_headings":"","what":"Gu and Wabha test functions — gw_f0","title":"Gu and Wabha test functions — gw_f0","text":"Gu Wabha test functions","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/gw_functions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Gu and Wabha test functions — gw_f0","text":"","code":"gw_f0(x, ...) gw_f1(x, ...) gw_f2(x, ...) gw_f3(x, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/gw_functions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Gu and Wabha test functions — gw_f0","text":"x numeric; vector points evaluate function , interval (0,1) ... arguments passed methods, ignored.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/gw_functions.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Gu and Wabha test functions — gw_f0","text":"","code":"x <- seq(0, 1, length = 6) gw_f0(x) #> [1] 0.000e+00 1.176e+00 1.902e+00 1.902e+00 1.176e+00 2.449e-16 gw_f1(x) #> [1] 1.000 1.492 2.226 3.320 4.953 7.389 gw_f2(x) #> [1] 0.000 8.591 4.261 3.199 1.100 0.000 gw_f3(x) # should be constant 0 #> [1] 0 0 0 0 0 0"},{"path":"https://gavinsimpson.github.io/gratia/reference/has_theta.html","id":null,"dir":"Reference","previous_headings":"","what":"Are additional parameters available for a GAM? — has_theta","title":"Are additional parameters available for a GAM? — has_theta","text":"additional parameters available GAM?","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/has_theta.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Are additional parameters available for a GAM? — has_theta","text":"","code":"has_theta(object)"},{"path":"https://gavinsimpson.github.io/gratia/reference/has_theta.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Are additional parameters available for a GAM? — has_theta","text":"object R object, either family() object object whose class family() method.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/has_theta.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Are additional parameters available for a GAM? — has_theta","text":"logical; TRUE additional parameters available, FALSE otherwise.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/has_theta.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Are additional parameters available for a GAM? — has_theta","text":"","code":"load_mgcv() df <- data_sim(\"eg1\", dist = \"poisson\", seed = 42, scale = 1 / 5) m <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = df, method = \"REML\", family = nb() ) has_theta(m) #> [1] TRUE p <- theta(m)"},{"path":"https://gavinsimpson.github.io/gratia/reference/is_by_smooth.html","id":null,"dir":"Reference","previous_headings":"","what":"Tests for by variable smooths — is_by_smooth","title":"Tests for by variable smooths — is_by_smooth","text":"Functions check smooth -variable one test type -variable smooth factor-smooth continous-smooth interaction.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/is_by_smooth.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tests for by variable smooths — is_by_smooth","text":"","code":"is_by_smooth(smooth) is_factor_by_smooth(smooth) is_continuous_by_smooth(smooth) by_variable(smooth) by_level(smooth)"},{"path":"https://gavinsimpson.github.io/gratia/reference/is_by_smooth.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tests for by variable smooths — is_by_smooth","text":"smooth object class \"mgcv.smooth\"","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/is_by_smooth.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tests for by variable smooths — is_by_smooth","text":"logical vector.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/is_by_smooth.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tests for by variable smooths — is_by_smooth","text":"Gavin L. Simpson","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/is_factor_term.html","id":null,"dir":"Reference","previous_headings":"","what":"Is a model term a factor (categorical)? — is_factor_term","title":"Is a model term a factor (categorical)? — is_factor_term","text":"Given name (term label) term model, identify term factor term numeric. useful considering interactions, terms like fac1:fac2 num1:fac1 may requested user. terms type fac1:fac2 function return TRUE.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/is_factor_term.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Is a model term a factor (categorical)? — is_factor_term","text":"","code":"is_factor_term(object, term, ...) # S3 method for class 'terms' is_factor_term(object, term, ...) # S3 method for class 'gam' is_factor_term(object, term, ...) # S3 method for class 'bam' is_factor_term(object, term, ...) # S3 method for class 'gamm' is_factor_term(object, term, ...) # S3 method for class 'list' is_factor_term(object, term, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/is_factor_term.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Is a model term a factor (categorical)? — is_factor_term","text":"object R object method dispatch performed term character; name model term, sense attr(terms(object), \"term.labels\"). Currently checked see term exists model. ... arguments passed methods.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/is_factor_term.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Is a model term a factor (categorical)? — is_factor_term","text":"logical: TRUE variables involved term factors, otherwise FALSE.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/is_mgcv_smooth.html","id":null,"dir":"Reference","previous_headings":"","what":"Check if objects are smooths or are a particular type of smooth — is_mgcv_smooth","title":"Check if objects are smooths or are a particular type of smooth — is_mgcv_smooth","text":"Check objects smooths particular type smooth","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/is_mgcv_smooth.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check if objects are smooths or are a particular type of smooth — is_mgcv_smooth","text":"","code":"is_mgcv_smooth(smooth) stop_if_not_mgcv_smooth(smooth) check_is_mgcv_smooth(smooth) is_mrf_smooth(smooth)"},{"path":"https://gavinsimpson.github.io/gratia/reference/is_mgcv_smooth.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check if objects are smooths or are a particular type of smooth — is_mgcv_smooth","text":"smooth R object, typically list","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/is_mgcv_smooth.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Check if objects are smooths or are a particular type of smooth — is_mgcv_smooth","text":"Check smooth inherits class \"mgcv.smooth\". stop_if_not_mgcv_smooth() wrapper around is_mgcv_smooth(), useful programming checking supplied object one mgcv's smooths, throwing consistent error . check_is_mgcv_smooth() similar stop_if_not_mgcv_smooth() returns result is_mgcv_smooth() invisibly.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/is_offset.html","id":null,"dir":"Reference","previous_headings":"","what":"Is a model term an offset? — is_offset","title":"Is a model term an offset? — is_offset","text":"Given character vector model terms, checks see , , model offset.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/is_offset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Is a model term an offset? — is_offset","text":"","code":"is_offset(terms)"},{"path":"https://gavinsimpson.github.io/gratia/reference/is_offset.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Is a model term an offset? — is_offset","text":"terms character vector model terms.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/is_offset.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Is a model term an offset? — is_offset","text":"logical vector length terms.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/is_offset.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Is a model term an offset? — is_offset","text":"Gavin L. Simpson","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/is_offset.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Is a model term an offset? — is_offset","text":"","code":"load_mgcv() df <- data_sim(\"eg1\", n = 400, dist = \"normal\") m <- gam(y ~ s(x0) + s(x1) + offset(x0), data = df, method = \"REML\") nm <- names(model.frame(m)) nm #> [1] \"y\" \"offset(x0)\" \"x0\" \"x1\" is_offset(nm) #> [1] FALSE TRUE FALSE FALSE"},{"path":"https://gavinsimpson.github.io/gratia/reference/link.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract link and inverse link functions from models — link","title":"Extract link and inverse link functions from models — link","text":"Returns link inverse estimated model, provides simple way extract functions complex models multiple links, location scale models.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/link.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract link and inverse link functions from models — link","text":"","code":"link(object, ...) # S3 method for class 'family' link(object, parameter = NULL, which_eta = NULL, ...) # S3 method for class 'gam' link(object, parameter = NULL, which_eta = NULL, ...) # S3 method for class 'bam' link(object, parameter = NULL, which_eta = NULL, ...) # S3 method for class 'gamm' link(object, ...) # S3 method for class 'glm' link(object, ...) # S3 method for class 'list' link(object, ...) inv_link(object, ...) # S3 method for class 'family' inv_link(object, parameter = NULL, which_eta = NULL, ...) # S3 method for class 'gam' inv_link(object, parameter = NULL, which_eta = NULL, ...) # S3 method for class 'bam' inv_link(object, parameter = NULL, which_eta = NULL, ...) # S3 method for class 'gamm' inv_link(object, ...) # S3 method for class 'list' inv_link(object, ...) # S3 method for class 'glm' inv_link(object, ...) extract_link(family, ...) # S3 method for class 'family' extract_link(family, inverse = FALSE, ...) # S3 method for class 'general.family' extract_link(family, parameter, inverse = FALSE, which_eta = NULL, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/link.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract link and inverse link functions from models — link","text":"object family object fitted model extract family object. Models fitted stats::glm(), mgcv::gam(), mgcv::bam(), mgcv::gamm(), gamm4::gamm4() currently supported. ... arguments passed methods. parameter character; parameter distribution. Usually \"location\" \"scale\" \"shape\" may provided location scale models. options include \"mu\" synonym \"location\", \"sigma\" scale parameter mgcv::gaulss(), \"pi\" zero-inflation term mgcv::ziplss(), \"power\" mgcv::twlss() power parameter, \"xi\", shape parameter mgcv::gevlss(), \"epsilon\" \"skewness\" skewness \"delta\" \"kurtosis\" kurtosis parameter mgcv::shash(), \"phi\" scale parameter mgcv::gammals() & mgcv::twlss(). which_eta numeric; linear predictor extract families mgcv::mvn() mgcv::multinom(). family family object, result call family(). inverse logical; return inverse link function?","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/link.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Extract link and inverse link functions from models — link","text":"Gavin L. Simpson","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/link.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract link and inverse link functions from models — link","text":"","code":"load_mgcv() link(gaussian()) #> function (mu) #> mu #> link(nb()) #> function (mu) #> log(mu) #> inv_link(nb()) #> function (eta) #> pmax(exp(eta), .Machine$double.eps) #> dat <- data_sim(\"eg1\", seed = 4234) mod <- gam(list(y ~ s(x0) + s(x1) + s(x2) + s(x3), ~1), data = dat, family = gaulss ) link(mod, parameter = \"scale\") #> function (mu) #> log(1/mu - 0.01) #> inv_link(mod, parameter = \"scale\") #> function (eta) #> 1/(exp(eta) + 0.01) #> ## Works with `family` objects too link(shash(), parameter = \"skewness\") #> function (mu) #> mu #> "},{"path":"https://gavinsimpson.github.io/gratia/reference/load_mgcv.html","id":null,"dir":"Reference","previous_headings":"","what":"Load mgcv quietly — load_mgcv","title":"Load mgcv quietly — load_mgcv","text":"Simple function loads mgcv package whilst suppressing startup messages prints console.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/load_mgcv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Load mgcv quietly — load_mgcv","text":"","code":"load_mgcv()"},{"path":"https://gavinsimpson.github.io/gratia/reference/load_mgcv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Load mgcv quietly — load_mgcv","text":"Returns logical vectors invisibly, indicating whether package loaded .","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/lp_matrix.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the linear prediction matrix of a fitted GAM — lp_matrix","title":"Return the linear prediction matrix of a fitted GAM — lp_matrix","text":"lp_matrix() wrapper predict(..., type = \"lpmatrix\") returning linear predictor matrix model training data (data = NULL), user-specified data values supplied via data.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/lp_matrix.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the linear prediction matrix of a fitted GAM — lp_matrix","text":"","code":"lp_matrix(model, ...) # S3 method for class 'gam' lp_matrix(model, data = NULL, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/lp_matrix.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the linear prediction matrix of a fitted GAM — lp_matrix","text":"model fitted model ... arguments passed methods predict methods including mgcv::predict.gam() mgcv::predict.bam() data data frame values return linear prediction matrix.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/lp_matrix.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the linear prediction matrix of a fitted GAM — lp_matrix","text":"linear prediction matrix returned matrix. object returned class \"lp_matrix\", inherits classes \"matrix\" \"array\". special class allows printing matrix controlled, printing matrix tibble.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/lp_matrix.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Return the linear prediction matrix of a fitted GAM — lp_matrix","text":"linear prediction matrix \\(\\mathbf{X}_p\\) matrix maps values parameters \\(\\hat{\\mathbf{\\beta}}_p\\) values linear predictor model \\(\\hat{\\eta}_p = \\mathbf{X}_p \\hat{\\mathbf{\\beta}}_p\\). \\(\\mathbf{X}_p\\) model matrix spline covariates replaced values basis functions evaluated respective covariates. Parametric covariates also included.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/lp_matrix.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Return the linear prediction matrix of a fitted GAM — lp_matrix","text":"","code":"load_mgcv() df <- data_sim(\"eg1\", seed = 1) m <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = df) # linear prediction matrix for observed data xp <- lp_matrix(m) ## IGNORE_RDIFF_BEGIN xp #> Linear prediction matrix (400 x 37) #> `(Intercept)` `s(x0).1` `s(x0).2` `s(x0).3` `s(x0).4` `s(x0).5` `s(x0).6` #> #> 1 1 0.961 0.227 0.706 -0.135 0.457 -0.146 #> 2 1 0.651 -0.241 0.0684 -0.308 0.394 -0.00994 #> 3 1 -0.385 -0.549 0.0660 -0.204 -0.416 -0.247 #> 4 1 -1.27 0.156 -1.53 0.222 -1.60 0.198 #> 5 1 1.05 0.420 1.11 0.214 0.893 0.0351 #> # i 395 more rows ## IGNORE_RDIFF_END # the object `xp` *is* a matrix class(xp) #> [1] \"lp_matrix\" \"matrix\" \"array\" # but we print like a tibble to avoid spamming the R console # linear predictor matrix for new data set ds <- data_slice(m, x2 = evenly(x2)) xp <- lp_matrix(m, data = ds) ## IGNORE_RDIFF_BEGIN xp #> Linear prediction matrix (100 x 37) #> `(Intercept)` `s(x0).1` `s(x0).2` `s(x0).3` `s(x0).4` `s(x0).5` `s(x0).6` #> #> 1 1 0.170 -0.542 -0.0371 -0.0534 0.144 -0.353 #> 2 1 0.170 -0.542 -0.0371 -0.0534 0.144 -0.353 #> 3 1 0.170 -0.542 -0.0371 -0.0534 0.144 -0.353 #> 4 1 0.170 -0.542 -0.0371 -0.0534 0.144 -0.353 #> 5 1 0.170 -0.542 -0.0371 -0.0534 0.144 -0.353 #> # i 95 more rows ## IGNORE_RDIFF_END"},{"path":"https://gavinsimpson.github.io/gratia/reference/lss_parameters.html","id":null,"dir":"Reference","previous_headings":"","what":"General names of LSS parameters for each GAM family — lss_parameters","title":"General names of LSS parameters for each GAM family — lss_parameters","text":"General names LSS parameters GAM family","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/lss_parameters.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"General names of LSS parameters for each GAM family — lss_parameters","text":"","code":"lss_parameters(object)"},{"path":"https://gavinsimpson.github.io/gratia/reference/mh_draws.html","id":null,"dir":"Reference","previous_headings":"","what":"Posterior samples using a Gaussian approximation to the posterior distribution — mh_draws","title":"Posterior samples using a Gaussian approximation to the posterior distribution — mh_draws","text":"Posterior samples using Gaussian approximation posterior distribution","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/mh_draws.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Posterior samples using a Gaussian approximation to the posterior distribution — mh_draws","text":"","code":"mh_draws(model, ...) # S3 method for class 'gam' mh_draws( model, n, burnin = 1000, thin = 1, t_df = 40, rw_scale = 0.25, index = NULL, ... )"},{"path":"https://gavinsimpson.github.io/gratia/reference/mh_draws.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Posterior samples using a Gaussian approximation to the posterior distribution — mh_draws","text":"model fitted R model. Currently models fitted mgcv::gam() mgcv::bam(), return object inherits objects supported. , \"inherits\" used loose fashion; models fitted scam::scam() support even though models strictly inherit class \"gam\" far inherits() concerned. ... arguments passed methods. n numeric; number posterior draws take. burnin numeric; length initial burn period discard. See mgcv::gam.mh(). thin numeric; retain thin samples. See mgcv::gam.mh(). t_df numeric; degrees freedom static multivariate t proposal. See mgcv::gam.mh(). rw_scale numeric; factor scale posterior covariance matrix generating random walk proposals. See mgcv::gam.mh(). index numeric; vector indices coefficients use. Can used subset mean vector covariance matrix extracted model.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/model_concurvity.html","id":null,"dir":"Reference","previous_headings":"","what":"Concurvity of an estimated GAM — model_concurvity","title":"Concurvity of an estimated GAM — model_concurvity","text":"Concurvity estimated GAM","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/model_concurvity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Concurvity of an estimated GAM — model_concurvity","text":"","code":"model_concurvity(model, ...) # S3 method for class 'gam' model_concurvity( model, terms = everything(), type = c(\"all\", \"estimate\", \"observed\", \"worst\"), pairwise = FALSE, ... ) concrvity( model, terms = everything(), type = c(\"all\", \"estimate\", \"observed\", \"worst\"), pairwise = FALSE, ... )"},{"path":"https://gavinsimpson.github.io/gratia/reference/model_concurvity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Concurvity of an estimated GAM — model_concurvity","text":"model fitted GAM. Currently objects class \"gam\" supported ... arguents passed methods. terms currently ignored type character; pairwise logical; extract pairwise concurvity model terms?","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/model_concurvity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Concurvity of an estimated GAM — model_concurvity","text":"","code":"## simulate data with concurvity... library(\"tibble\") load_mgcv() set.seed(8) n <- 200 df <- tibble( t = sort(runif(n)), x = gw_f2(t) + rnorm(n) * 3, y = sin(4 * pi * t) + exp(x / 20) + rnorm(n) * 0.3 ) ## fit model m <- gam(y ~ s(t, k = 15) + s(x, k = 15), data = df, method = \"REML\") ## overall concurvity o_conc <- concrvity(m) draw(o_conc) ## pairwise concurvity p_conc <- concrvity(m, pairwise = TRUE) draw(p_conc)"},{"path":"https://gavinsimpson.github.io/gratia/reference/model_constant.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract the model constant term — model_constant","title":"Extract the model constant term — model_constant","text":"Extracts model constant term(s), model intercept, fitted model object.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/model_constant.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract the model constant term — model_constant","text":"","code":"model_constant(model, ...) # S3 method for class 'gam' model_constant(model, lp = NULL, ...) # S3 method for class 'gamlss' model_constant(model, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/model_constant.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract the model constant term — model_constant","text":"model fitted model coef() method exists. ... arguments passed methods. lp numeric; linear predictors extract constant terms .","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/model_constant.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract the model constant term — model_constant","text":"","code":"load_mgcv() # simulate a small example df <- data_sim(\"eg1\", seed = 42) # fit the GAM m <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = df, method = \"REML\") # extract the estimate of the constant term model_constant(m) #> [1] 7.495 #> attr(,\"par_names\") #> [1] \"location\" # same as coef(m)[1L] coef(m)[1L] #> (Intercept) #> 7.495"},{"path":"https://gavinsimpson.github.io/gratia/reference/model_vars.html","id":null,"dir":"Reference","previous_headings":"","what":"List the variables involved in a model fitted with a formula — model_vars","title":"List the variables involved in a model fitted with a formula — model_vars","text":"List variables involved model fitted formula","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/model_vars.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"List the variables involved in a model fitted with a formula — model_vars","text":"","code":"model_vars(model, ...) # S3 method for class 'gam' model_vars(model, ...) # Default S3 method model_vars(model, ...) # S3 method for class 'bam' model_vars(model, ...) # S3 method for class 'gamm' model_vars(model, ...) # S3 method for class 'gamm4' model_vars(model, ...) # S3 method for class 'list' model_vars(model, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/model_vars.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"List the variables involved in a model fitted with a formula — model_vars","text":"model fitted model object $pred.formula, $terms component \"terms\" attribute ... Arguments passed methods. Currently ignored.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/model_vars.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"List the variables involved in a model fitted with a formula — model_vars","text":"","code":"load_mgcv() # simulate some Gaussian data df <- data_sim(\"eg1\", n = 50, seed = 2) # fit a GAM with 1 smooth and 1 linear term m1 <- gam(y ~ s(x2, k = 7) + x1, data = df, method = \"REML\") model_vars(m1) #> [1] \"x1\" \"x2\" # fit a lm with two linear terms m2 <- lm(y ~ x2 + x1, data = df) model_vars(m2) #> [1] \"x2\" \"x1\""},{"path":"https://gavinsimpson.github.io/gratia/reference/n_eta.html","id":null,"dir":"Reference","previous_headings":"","what":"The Number of linear predictors in model — n_eta","title":"The Number of linear predictors in model — n_eta","text":"Extracts number linear predictors fitted model.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/n_eta.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"The Number of linear predictors in model — n_eta","text":"","code":"n_eta(model, ...) # S3 method for class 'gam' n_eta(model, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/n_eta.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"The Number of linear predictors in model — n_eta","text":"model fitted model. Currently, models inheriting class \"gam\" supported. ... arguments passed methods.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/n_eta.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"The Number of linear predictors in model — n_eta","text":"integer vector length 1 containing number linear predictors model.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/n_smooths.html","id":null,"dir":"Reference","previous_headings":"","what":"How many smooths in a fitted model — n_smooths","title":"How many smooths in a fitted model — n_smooths","text":"many smooths fitted model","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/n_smooths.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"How many smooths in a fitted model — n_smooths","text":"","code":"n_smooths(object) # Default S3 method n_smooths(object) # S3 method for class 'gam' n_smooths(object) # S3 method for class 'gamm' n_smooths(object) # S3 method for class 'bam' n_smooths(object)"},{"path":"https://gavinsimpson.github.io/gratia/reference/n_smooths.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"How many smooths in a fitted model — n_smooths","text":"object fitted GAM related model. Typically result call mgcv::gam(), mgcv::bam(), mgcv::gamm().","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/nb_theta.html","id":null,"dir":"Reference","previous_headings":"","what":"Negative binomial parameter theta — nb_theta","title":"Negative binomial parameter theta — nb_theta","text":"Negative binomial parameter theta","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/nb_theta.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Negative binomial parameter theta — nb_theta","text":"","code":"nb_theta(model) # S3 method for class 'gam' nb_theta(model)"},{"path":"https://gavinsimpson.github.io/gratia/reference/nb_theta.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Negative binomial parameter theta — nb_theta","text":"model fitted model.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/nb_theta.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Negative binomial parameter theta — nb_theta","text":"numeric vector length 1 containing estimated value theta.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/nb_theta.html","id":"methods-by-class-","dir":"Reference","previous_headings":"","what":"Methods (by class)","title":"Negative binomial parameter theta — nb_theta","text":"nb_theta(gam): Method class \"gam\"","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/nb_theta.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Negative binomial parameter theta — nb_theta","text":"","code":"load_mgcv() df <- data_sim(\"eg1\", n = 500, dist = \"poisson\", scale = 0.1, seed = 6) m <- gam(y ~ s(x0, bs = \"cr\") + s(x1, bs = \"cr\") + s(x2, bs = \"cr\") + s(x3, bs = \"cr\"), family = nb, data = df, method = \"REML\") ## IGNORE_RDIFF_BEGIN nb_theta(m) #> [1] 239333.8 ## IGNORE_RDIFF_END"},{"path":"https://gavinsimpson.github.io/gratia/reference/nested_partial_residuals.html","id":null,"dir":"Reference","previous_headings":"","what":"Partial residuals in nested form — nested_partial_residuals","title":"Partial residuals in nested form — nested_partial_residuals","text":"Computes partial residuals smooth terms, formats long/tidy format, nests partial_residual column result nested data frame one row per smooth.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/nested_partial_residuals.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Partial residuals in nested form — nested_partial_residuals","text":"","code":"nested_partial_residuals(object, terms = NULL, data = NULL)"},{"path":"https://gavinsimpson.github.io/gratia/reference/nested_partial_residuals.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Partial residuals in nested form — nested_partial_residuals","text":"object fitted GAM model terms vector terms include partial residuals . Passed argument terms mgcv::predict.gam()]. data optional data frame","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/nested_partial_residuals.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Partial residuals in nested form — nested_partial_residuals","text":"nested tibble (data frame) one row per smooth term. Contains two columns: smooth - label indicating smooth term partial_residual - list column containing tibble (data frame) 1 column partial_residual containing requested partial residuals indicated smooth.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/nested_rug_values.html","id":null,"dir":"Reference","previous_headings":"","what":"Values for rug plot in nested form — nested_rug_values","title":"Values for rug plot in nested form — nested_rug_values","text":"Extracts original data smooth terms, formats long/tidy format, nests data column(s) result nested data frame one row per smooth.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/nested_rug_values.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Values for rug plot in nested form — nested_rug_values","text":"","code":"nested_rug_values(object, terms = NULL, data = NULL)"},{"path":"https://gavinsimpson.github.io/gratia/reference/nested_rug_values.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Values for rug plot in nested form — nested_rug_values","text":"object fitted GAM model terms vector terms include original data . Passed argument terms mgcv::predict.gam()]. data optional data frame","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/nested_rug_values.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Values for rug plot in nested form — nested_rug_values","text":"nested tibble (data frame) one row per smooth term.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/null_deviance.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract the null deviance of a fitted model — null_deviance","title":"Extract the null deviance of a fitted model — null_deviance","text":"Extract null deviance fitted model","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/null_deviance.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract the null deviance of a fitted model — null_deviance","text":"","code":"null_deviance(model, ...) # Default S3 method null_deviance(model, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/null_deviance.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract the null deviance of a fitted model — null_deviance","text":"model fitted model ... arguments passed methods","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/observed_fitted_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot of fitted against observed response values — observed_fitted_plot","title":"Plot of fitted against observed response values — observed_fitted_plot","text":"Plot fitted observed response values","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/observed_fitted_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot of fitted against observed response values — observed_fitted_plot","text":"","code":"observed_fitted_plot( model, ylab = NULL, xlab = NULL, title = NULL, subtitle = NULL, caption = NULL, point_col = \"black\", point_alpha = 1 )"},{"path":"https://gavinsimpson.github.io/gratia/reference/observed_fitted_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot of fitted against observed response values — observed_fitted_plot","text":"model fitted model. Currently class \"gam\". ylab character expression; label y axis. supplied, suitable label generated. xlab character expression; label y axis. supplied, suitable label generated. title character expression; title plot. See ggplot2::labs(). subtitle character expression; subtitle plot. See ggplot2::labs(). caption character expression; plot caption. See ggplot2::labs(). point_col colour used draw points plots. See graphics::par() section Color Specification. passed individual plotting functions, therefore affects points plots. point_alpha numeric; alpha transparency points plots.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/overview.html","id":null,"dir":"Reference","previous_headings":"","what":"Provides an overview of a model and the terms in that model — overview","title":"Provides an overview of a model and the terms in that model — overview","text":"Provides overview model terms model","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/overview.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Provides an overview of a model and the terms in that model — overview","text":"","code":"overview(model, ...) # S3 method for class 'gam' overview( model, parametric = TRUE, random_effects = TRUE, dispersion = NULL, frequentist = FALSE, accuracy = 0.001, stars = FALSE, ... )"},{"path":"https://gavinsimpson.github.io/gratia/reference/overview.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Provides an overview of a model and the terms in that model — overview","text":"model fitted model object overview. ... arguments passed methods. parametric logical; include model parametric terms overview? random_effects tests fully penalized smooth terms (zero-dimensional null space, e.g. random effects) computationally expensive large data sets producing p values can take long time. random_effects = FALSE, tests expensive terms skipped. dispersion numeric; known value dispersion parameter. default NULL implies estimated value default value (1 Poisson distribution example) specified used instead. frequentist logical; default Bayesian estimated covariance matrix parameter estimates used calculate p values parametric terms. frequentist = FALSE, frequentist covariance matrix parameter estimates used. accuracy numeric; accuracy report p values, p values value displayed \"< accuracy\". stars logical; significance stars added output?","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/overview.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Provides an overview of a model and the terms in that model — overview","text":"","code":"load_mgcv() df <- data_sim(n = 400, seed = 2) m <- gam(y ~ x3 + s(x0) + s(x1, bs = \"bs\") + s(x2, bs = \"ts\"), data = df, method = \"REML\" ) overview(m) #> #> Generalized Additive Model with 4 terms #> #> term type k edf statistic p.value #> #> 1 x3 parametric NA 1 4.28 0.03926 #> 2 s(x0) TPRS 9 3.02 6.25 < 0.001 #> 3 s(x1) B spline 9 2.81 71.0 < 0.001 #> 4 s(x2) TPRS (shrink) 9 7.91 83.8 < 0.001"},{"path":"https://gavinsimpson.github.io/gratia/reference/parametric_effects.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimated values for parametric model terms — parametric_effects","title":"Estimated values for parametric model terms — parametric_effects","text":"Estimated values parametric model terms","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/parametric_effects.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimated values for parametric model terms — parametric_effects","text":"","code":"parametric_effects(object, ...) # S3 method for class 'gam' parametric_effects( object, terms = NULL, data = NULL, unconditional = FALSE, unnest = TRUE, ci_level = 0.95, envir = environment(formula(object)), transform = FALSE, ... )"},{"path":"https://gavinsimpson.github.io/gratia/reference/parametric_effects.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimated values for parametric model terms — parametric_effects","text":"object fitted model object. ... arguments passed methods. terms character; model parametric terms drawn? Default NULL plot parametric terms can drawn. data optional data frame may may used? FIXME! unconditional logical; confidence intervals include uncertainty due smoothness selection? TRUE, corrected Bayesian covariance matrix used. unnest logical; unnest parametric effect objects? ci_level numeric; coverage required confidence interval. Currently ignored. envir environment look data within. transform logical; TRUE, parametric effect plotted transformed scale result effect straight line. FALSE, effect plotted raw data (.e. log10(x), poly(z), x-axis plot x z respectively.)","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/parametric_terms.html","id":null,"dir":"Reference","previous_headings":"","what":"Names of any parametric terms in a GAM — parametric_terms","title":"Names of any parametric terms in a GAM — parametric_terms","text":"Names parametric terms GAM","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/parametric_terms.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Names of any parametric terms in a GAM — parametric_terms","text":"","code":"parametric_terms(model, ...) # Default S3 method parametric_terms(model, ...) # S3 method for class 'gam' parametric_terms(model, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/parametric_terms.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Names of any parametric terms in a GAM — parametric_terms","text":"model fitted model. ... arguments passed methods.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/partial_derivatives.html","id":null,"dir":"Reference","previous_headings":"","what":"Partial derivatives of estimated multivariate smooths via finite differences — partial_derivatives","title":"Partial derivatives of estimated multivariate smooths via finite differences — partial_derivatives","text":"Partial derivatives estimated multivariate smooths via finite differences","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/partial_derivatives.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Partial derivatives of estimated multivariate smooths via finite differences — partial_derivatives","text":"","code":"partial_derivatives(object, ...) # Default S3 method partial_derivatives(object, ...) # S3 method for class 'gamm' partial_derivatives(object, ...) # S3 method for class 'gam' partial_derivatives( object, select = NULL, term = deprecated(), focal = NULL, data = newdata, order = 1L, type = c(\"forward\", \"backward\", \"central\"), n = 100, eps = 1e-07, interval = c(\"confidence\", \"simultaneous\"), n_sim = 10000, level = 0.95, unconditional = FALSE, frequentist = FALSE, offset = NULL, ncores = 1, partial_match = FALSE, seed = NULL, ..., newdata = NULL )"},{"path":"https://gavinsimpson.github.io/gratia/reference/partial_derivatives.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Partial derivatives of estimated multivariate smooths via finite differences — partial_derivatives","text":"object R object compute derivatives . ... arguments passed methods. select character; vector one smooth terms derivatives required. missing, derivatives smooth terms returned. Can partial match smooth term; see argument partial_match . term Use select instead. focal character; name focal variable. partial derivative estimated smooth respect variable returned. variables involved smooth held constant. can missing supplying data, case, focal variable identified one variable constant. data data frame containing values model covariates evaluate first derivatives smooths. supplied, one variable must held constant value. order numeric; order derivative. type character; type finite difference used. One \"forward\", \"backward\", \"central\". n numeric; number points evaluate derivative . eps numeric; finite difference. interval character; type interval compute. One \"confidence\" point-wise intervals, \"simultaneous\" simultaneous intervals. n_sim integer; number simulations used computing simultaneous intervals. level numeric; 0 < level < 1; confidence level point-wise simultaneous interval. default 0.95 95% interval. unconditional logical; use smoothness selection-corrected Bayesian covariance matrix? frequentist logical; use frequentist covariance matrix? offset numeric; value use offset term ncores number cores generating random variables multivariate normal distribution. Passed mvnfast::rmvn(). Parallelization take place OpenMP supported (appears work Windows current R). partial_match logical; smooths selected partial matches term? TRUE, term can single string match . seed numeric; RNG seed use. newdata Deprecated: use data instead.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/partial_derivatives.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Partial derivatives of estimated multivariate smooths via finite differences — partial_derivatives","text":"tibble, currently following variables: .smooth: smooth row refers , .partial_deriv: estimated partial derivative, .se: standard error estimated partial derivative, .crit: critical value derivative ± (crit * se) gives upper lower bounds requested confidence simultaneous interval (given level), .lower_ci: lower bound confidence simultaneous interval, .upper_ci: upper bound confidence simultaneous interval.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/partial_derivatives.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Partial derivatives of estimated multivariate smooths via finite differences — partial_derivatives","text":"partial_derivatives() ignore random effect smooths encounters object.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/partial_derivatives.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Partial derivatives of estimated multivariate smooths via finite differences — partial_derivatives","text":"Gavin L. Simpson","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/partial_derivatives.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Partial derivatives of estimated multivariate smooths via finite differences — partial_derivatives","text":"","code":"library(\"ggplot2\") library(\"patchwork\") load_mgcv() df <- data_sim(\"eg2\", n = 2000, dist = \"normal\", scale = 0.5, seed = 42) # fit the GAM (note: for execution time reasons, k is set articifially low) m <- gam(y ~ te(x, z, k = c(5, 5)), data = df, method = \"REML\") # data slice through te(x,z) holding z == 0.4 ds <- data_slice(m, x = evenly(x, n = 100), z = 0.4) # evaluate te(x,z) at values of x & z sm <- smooth_estimates(m, select = \"te(x,z)\", data = ds) |> add_confint() # partial derivatives pd_x <- partial_derivatives(m, data = ds, type = \"central\", focal = \"x\") # draw te(x,z) p1 <- draw(m, rug = FALSE) & geom_hline(yintercept = 0.4, linewidth = 1) p1 # draw te(x,z) along slice cap <- expression(z == 0.4) p2 <- sm |> ggplot(aes(x = x, y = .estimate)) + geom_ribbon(aes(ymin = .lower_ci, ymax = .upper_ci), alpha = 0.2) + geom_line() + labs( x = \"x\", y = \"Partial effect\", title = \"te(x,z)\", caption = cap ) p2 # draw partial derivs p3 <- pd_x |> draw() + labs(caption = cap) p3 # draw all three panels p1 + p2 + p3 + plot_layout(ncol = 3)"},{"path":"https://gavinsimpson.github.io/gratia/reference/partial_residuals.html","id":null,"dir":"Reference","previous_headings":"","what":"Partial residuals — partial_residuals","title":"Partial residuals — partial_residuals","text":"Partial residuals","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/partial_residuals.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Partial residuals — partial_residuals","text":"","code":"partial_residuals(object, ...) # S3 method for class 'gam' partial_residuals(object, select = NULL, partial_match = FALSE, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/partial_residuals.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Partial residuals — partial_residuals","text":"object R object, typically model. Currently objects class \"gam\" (inherit class) supported. ... arguments passed methods. select character, logical, numeric; smooths plot. NULL, default, model smooths drawn. Numeric select indexes smooths order specified formula stored object. Character select matches labels smooths shown example output summary(object). Logical select operates per numeric select order smooths stored. partial_match logical; smooths selected partial matches select? TRUE, select can single string match .","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/partial_residuals.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Partial residuals — partial_residuals","text":"","code":"## load mgcv load_mgcv() ## example data - Gu & Wabha four term model df <- data_sim(\"eg1\", n = 400, seed = 42) ## fit the model m <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = df, method = \"REML\") ## extract partial residuals partial_residuals(m) #> # A tibble: 400 x 4 #> `s(x0)` `s(x1)` `s(x2)` `s(x3)` #> #> 1 -0.3527 -1.321 -2.180 0.6077 #> 2 -0.1233 0.5013 -1.775 0.9613 #> 3 1.429 1.515 5.609 0.9910 #> 4 -1.110 -1.700 -0.8882 -0.6593 #> 5 -2.120 -0.01378 -2.733 -3.012 #> 6 1.254 -1.224 3.915 0.07275 #> 7 -0.5220 3.023 -0.8197 -1.019 #> 8 1.398 0.2184 7.055 1.897 #> 9 2.797 0.4969 7.329 2.498 #> 10 1.151 -0.2267 0.7202 0.7437 #> # i 390 more rows ## and for a select term partial_residuals(m, select = \"s(x2)\") #> # A tibble: 400 x 1 #> `s(x2)` #> #> 1 -2.180 #> 2 -1.775 #> 3 5.609 #> 4 -0.8882 #> 5 -2.733 #> 6 3.915 #> 7 -0.8197 #> 8 7.055 #> 9 7.329 #> 10 0.7202 #> # i 390 more rows ## or with partial matching partial_residuals(m, select = \"x\", partial_match = TRUE) # returns all #> # A tibble: 400 x 4 #> `s(x0)` `s(x1)` `s(x2)` `s(x3)` #> #> 1 -0.3527 -1.321 -2.180 0.6077 #> 2 -0.1233 0.5013 -1.775 0.9613 #> 3 1.429 1.515 5.609 0.9910 #> 4 -1.110 -1.700 -0.8882 -0.6593 #> 5 -2.120 -0.01378 -2.733 -3.012 #> 6 1.254 -1.224 3.915 0.07275 #> 7 -0.5220 3.023 -0.8197 -1.019 #> 8 1.398 0.2184 7.055 1.897 #> 9 2.797 0.4969 7.329 2.498 #> 10 1.151 -0.2267 0.7202 0.7437 #> # i 390 more rows"},{"path":"https://gavinsimpson.github.io/gratia/reference/penalty.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract and tidy penalty matrices — penalty","title":"Extract and tidy penalty matrices — penalty","text":"Extract tidy penalty matrices","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/penalty.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract and tidy penalty matrices — penalty","text":"","code":"penalty(object, ...) # Default S3 method penalty( object, rescale = FALSE, data, knots = NULL, constraints = FALSE, diagonalize = FALSE, ... ) # S3 method for class 'gam' penalty( object, select = NULL, smooth = deprecated(), rescale = FALSE, partial_match = FALSE, ... ) # S3 method for class 'mgcv.smooth' penalty(object, rescale = FALSE, ...) # S3 method for class 'tensor.smooth' penalty(object, margins = FALSE, ...) # S3 method for class 't2.smooth' penalty(object, margins = FALSE, ...) # S3 method for class 're.smooth.spec' penalty(object, data, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/penalty.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract and tidy penalty matrices — penalty","text":"object fitted GAM smooth. ... additional arguments passed methods. rescale logical; default, mgcv scale penalty matrix better performance mgcv::gamm(). rescale TRUE, scaling undone put penalty matrix back original scale. data data frame; data frame values terms mentioned smooth specification. knots list data frame named components containing knots locations. Names must match covariates basis required. See mgcv::smoothCon(). constraints logical; identifiability constraints applied smooth basis. See argument absorb.cons mgcv::smoothCon(). diagonalize logical; TRUE, reparameterises smooth associated penalty identity matrix. effect turning last diagonal elements penalty zero, highlights penalty null space. select character, logical, numeric; smooths extract penalties . NULL, default, penalties model smooths drawn. Numeric select indexes smooths order specified formula stored object. Character select matches labels smooths shown example output summary(object). Logical select operates per numeric select order smooths stored. smooth Use select instead. partial_match logical; smooths selected partial matches select? TRUE, select can single string match . margins logical; extract penalty matrices tensor product marginal smooths tensor product?","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/penalty.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract and tidy penalty matrices — penalty","text":"'tibble' (data frame) class penalty_df inheriting tbl_df, following components: .smooth - character; label mgcv uses refer smooth, .type - character; type smooth, .penalty - character; label specific penalty. smooths multiple penalty matrices, penalty component identifies particular penalty matrix uses labelling mgcv uses internally, .row - character; label form fn n integer nth basis function, referencing columns penalty matrix, .col - character; label form fn n integer nth basis function, referencing columns penalty matrix, .value - double; value penalty matrix combination row col,","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/penalty.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Extract and tidy penalty matrices — penalty","text":"print() method uses base::zapsmall() turn small numbers 0s display purposes ; underlying values penalty matrix matrices changed. smooths subject eigendecomposition (e.g. default thin plate regression splines, bs = \"tp\"), signs eigenvectors defined can expect differences across systems penalties smooths system-, OS-, CPU architecture- specific.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/penalty.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Extract and tidy penalty matrices — penalty","text":"Gavin L. Simpson","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/penalty.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract and tidy penalty matrices — penalty","text":"","code":"load_mgcv() dat <- data_sim(\"eg4\", n = 400, seed = 42) m <- gam( y ~ s(x0, bs = \"cr\") + s(x1, bs = \"cr\") + s(x2, by = fac, bs = \"cr\"), data = dat, method = \"REML\" ) # penalties for all smooths penalty(m) #> # A tibble: 405 x 6 #> .smooth .type .penalty .row .col .value #> #> 1 s(x0) CRS s(x0) F1 F1 0.783 #> 2 s(x0) CRS s(x0) F1 F2 -0.635 #> 3 s(x0) CRS s(x0) F1 F3 0.265 #> 4 s(x0) CRS s(x0) F1 F4 -0.0203 #> 5 s(x0) CRS s(x0) F1 F5 0.0441 #> 6 s(x0) CRS s(x0) F1 F6 0.0378 #> 7 s(x0) CRS s(x0) F1 F7 0.0482 #> 8 s(x0) CRS s(x0) F1 F8 0.0216 #> 9 s(x0) CRS s(x0) F1 F9 0.0247 #> 10 s(x0) CRS s(x0) F2 F1 -0.635 #> # i 395 more rows # for a specific smooth penalty(m, select = \"s(x2):fac1\") #> # A tibble: 81 x 6 #> .smooth .type .penalty .row .col .value #> #> 1 s(x2):fac1 CRS s(x2):fac1 F1 F1 1.66 #> 2 s(x2):fac1 CRS s(x2):fac1 F1 F2 -0.755 #> 3 s(x2):fac1 CRS s(x2):fac1 F1 F3 0.430 #> 4 s(x2):fac1 CRS s(x2):fac1 F1 F4 0.0846 #> 5 s(x2):fac1 CRS s(x2):fac1 F1 F5 0.192 #> 6 s(x2):fac1 CRS s(x2):fac1 F1 F6 0.152 #> 7 s(x2):fac1 CRS s(x2):fac1 F1 F7 0.188 #> 8 s(x2):fac1 CRS s(x2):fac1 F1 F8 0.164 #> 9 s(x2):fac1 CRS s(x2):fac1 F1 F9 0.0597 #> 10 s(x2):fac1 CRS s(x2):fac1 F2 F1 -0.755 #> # i 71 more rows"},{"path":"https://gavinsimpson.github.io/gratia/reference/post_draws.html","id":null,"dir":"Reference","previous_headings":"","what":"Low-level Functions to generate draws from the posterior distribution of model coefficients — post_draws","title":"Low-level Functions to generate draws from the posterior distribution of model coefficients — post_draws","text":"Low-level Functions generate draws posterior distribution model coefficients Generate posterior draws fitted model","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/post_draws.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Low-level Functions to generate draws from the posterior distribution of model coefficients — post_draws","text":"","code":"post_draws(model, ...) # Default S3 method post_draws( model, n, method = c(\"gaussian\", \"mh\", \"inla\", \"user\"), mu = NULL, sigma = NULL, n_cores = 1L, burnin = 1000, thin = 1, t_df = 40, rw_scale = 0.25, index = NULL, frequentist = FALSE, unconditional = FALSE, parametrized = TRUE, mvn_method = c(\"mvnfast\", \"mgcv\"), draws = NULL, seed = NULL, ... ) generate_draws(model, ...) # S3 method for class 'gam' generate_draws( model, n, method = c(\"gaussian\", \"mh\", \"inla\"), mu = NULL, sigma = NULL, n_cores = 1L, burnin = 1000, thin = 1, t_df = 40, rw_scale = 0.25, index = NULL, frequentist = FALSE, unconditional = FALSE, mvn_method = c(\"mvnfast\", \"mgcv\"), seed = NULL, ... )"},{"path":"https://gavinsimpson.github.io/gratia/reference/post_draws.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Low-level Functions to generate draws from the posterior distribution of model coefficients — post_draws","text":"model fitted R model. Currently models fitted mgcv::gam() mgcv::bam(), return object inherits objects supported. , \"inherits\" used loose fashion; models fitted scam::scam() support even though models strictly inherit class \"gam\" far inherits() concerned. ... arguments passed methods. n numeric; number posterior draws take. method character; algorithm use sample posterior. Currently implemented methods : \"gaussian\" \"mh\". \"gaussian\" calls gaussian_draws() uses Gaussian approximation posterior distribution. \"mh\" uses simple Metropolis Hasting sampler alternates static proposals based Gaussian approximation posterior, random walk proposals. Note, setting t_df low value result heavier-tailed statistic proposals. See mgcv::gam.mh() details. mu numeric; user-supplied mean vector (vector model coefficients). Currently ignored. sigma matrix; user-supplied covariance matrix mu. Currently ignored. n_cores integer; number CPU cores use generating multivariate normal distributed random values. used mvn_method = \"mvnfast\" method = \"gaussian\". burnin numeric; length initial burn period discard. See mgcv::gam.mh(). thin numeric; retain thin samples. See mgcv::gam.mh(). t_df numeric; degrees freedom static multivariate t proposal. See mgcv::gam.mh(). rw_scale numeric; factor scale posterior covariance matrix generating random walk proposals. See mgcv::gam.mh(). index numeric; vector indices coefficients use. Can used subset mean vector covariance matrix extracted model. frequentist logical; TRUE, frequentist covariance matrix parameter estimates used. FALSE, Bayesian posterior covariance matrix parameters used. See mgcv::vcov.gam(). unconditional logical; TRUE Bayesian smoothing parameter uncertainty corrected covariance matrix used, available model. See mgcv::vcov.gam(). parametrized logical; use parametrized coefficients covariance matrix, respect linear inequality constraints model. scam::scam() model fits. mvn_method character; one \"mvnfast\" \"mgcv\". default uses mvnfast::rmvn(), can considerably faster generate large numbers MVN random values mgcv::rmvn(), might work marginal fits, covariance matrix close singular. draws matrix; user supplied posterior draws used method = \"user\". seed numeric; random seed use. NULL, random seed generated without affecting current state R's RNG.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/post_link_funs.html","id":null,"dir":"Reference","previous_headings":"","what":"A list of transformation functions named for LSS parameters in a GAMLSS — post_link_funs","title":"A list of transformation functions named for LSS parameters in a GAMLSS — post_link_funs","text":"list transformation functions named LSS parameters GAMLSS","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/post_link_funs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A list of transformation functions named for LSS parameters in a GAMLSS — post_link_funs","text":"","code":"post_link_funs( location = identity_fun, scale = identity_fun, shape = identity_fun, skewness = identity_fun, kurtosis = identity_fun, power = identity_fun, pi = identity_fun )"},{"path":"https://gavinsimpson.github.io/gratia/reference/posterior_samples.html","id":null,"dir":"Reference","previous_headings":"","what":"Draw samples from the posterior distribution of an estimated model — posterior_samples","title":"Draw samples from the posterior distribution of an estimated model — posterior_samples","text":"Draw samples posterior distribution estimated model","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/posterior_samples.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Draw samples from the posterior distribution of an estimated model — posterior_samples","text":"","code":"posterior_samples(model, ...) # S3 method for class 'gam' posterior_samples( model, n = 1, data = newdata, seed = NULL, method = c(\"gaussian\", \"mh\", \"inla\", \"user\"), n_cores = 1, burnin = 1000, thin = 1, t_df = 40, rw_scale = 0.25, freq = FALSE, unconditional = FALSE, weights = NULL, draws = NULL, mvn_method = c(\"mvnfast\", \"mgcv\"), ..., newdata = NULL, ncores = NULL )"},{"path":"https://gavinsimpson.github.io/gratia/reference/posterior_samples.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Draw samples from the posterior distribution of an estimated model — posterior_samples","text":"model fitted model supported types ... arguments passed methods. fitted_samples(), passed mgcv::predict.gam(). posterior_samples() passed fitted_samples(). predicted_samples() passed relevant simulate() method. n numeric; number posterior samples return. data data frame; new observations posterior draws model evaluated. supplied, data used fit model used data, available model. seed numeric; random seed simulations. method character; method used draw samples posterior distribution. \"gaussian\" uses Gaussian (Laplace) approximation posterior. \"mh\" uses Metropolis Hastings sampler alternates t proposals proposals based shrunken version posterior covariance matrix. \"inla\" uses variant Integrated Nested Laplace Approximation due Wood (2019), (currently implemented). \"user\" allows user-supplied posterior draws (currently implemented). n_cores number cores generating random variables multivariate normal distribution. Passed mvnfast::rmvn(). Parallelization take place OpenMP supported (appears work Windows current R). burnin numeric; number samples discard burnin draws. used method = \"mh\". thin numeric; number samples skip taking n draws. Results thin * n draws posterior taken. used method = \"mh\". t_df numeric; degrees freedom t distribution proposals. used method = \"mh\". rw_scale numeric; Factor scale posterior covariance matrix generating random walk proposals. Negative non finite skip random walk step. used method = \"mh\". freq logical; TRUE use frequentist covariance matrix parameter estimators, FALSE use Bayesian posterior covariance matrix parameters. unconditional logical; TRUE (freq == FALSE) Bayesian smoothing parameter uncertainty corrected covariance matrix used, available. weights numeric; vector prior weights. data null defaults object[[\"prior.weights\"]], otherwise vector ones. draws matrix; user supplied posterior draws used method = \"user\". mvn_method character; one \"mvnfast\" \"mgcv\". default uses mvnfast::rmvn(), can considerably faster generate large numbers MVN random values mgcv::rmvn(), might work marginal fits, covariance matrix close singular. newdata Deprecated: use data instead. ncores Deprecated; use n_cores instead. number cores generating random variables multivariate normal distribution. Passed mvnfast::rmvn(). Parallelization take place OpenMP supported (appears work Windows current R).","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/posterior_samples.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Draw samples from the posterior distribution of an estimated model — posterior_samples","text":"tibble (data frame) 3 columns containing posterior predicted values long format. columns row (integer) row data posterior draw relates , draw (integer) index, range 1:n, indicating draw row relates , response (numeric) predicted response indicated row data.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/posterior_samples.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Draw samples from the posterior distribution of an estimated model — posterior_samples","text":"Models offset terms supplied via offset argument mgcv::gam() etc. ignored mgcv::predict.gam(). , kind offset term also ignored posterior_samples(). Offset terms included model formula supplied mgcv::gam() etc ignored posterior samples produced reflect offset term values. side effect requiring new data values provided posterior_samples() via data argument must include offset variable.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/posterior_samples.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Draw samples from the posterior distribution of an estimated model — posterior_samples","text":"Wood, S.N., (2020). Simplified integrated nested Laplace approximation. Biometrika 107, 223–230. doi:10.1093/biomet/asz044","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/posterior_samples.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Draw samples from the posterior distribution of an estimated model — posterior_samples","text":"Gavin L. Simpson","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/predicted_samples.html","id":null,"dir":"Reference","previous_headings":"","what":"Draw new response values from the conditional distribution of the response — predicted_samples","title":"Draw new response values from the conditional distribution of the response — predicted_samples","text":"Predicted values response (new response data) drawn fitted model, created via simulate() (e.g. simulate.gam()) returned tidy, long, format. predicted values include uncertainty estimated model; simply draws conditional distribution response.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/predicted_samples.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Draw new response values from the conditional distribution of the response — predicted_samples","text":"","code":"predicted_samples(model, ...) # S3 method for class 'gam' predicted_samples( model, n = 1, data = newdata, seed = NULL, weights = NULL, ..., newdata = NULL )"},{"path":"https://gavinsimpson.github.io/gratia/reference/predicted_samples.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Draw new response values from the conditional distribution of the response — predicted_samples","text":"model fitted model supported types ... arguments passed methods. fitted_samples(), passed mgcv::predict.gam(). posterior_samples() passed fitted_samples(). predicted_samples() passed relevant simulate() method. n numeric; number posterior samples return. data data frame; new observations posterior draws model evaluated. supplied, data used fit model used data, available model. seed numeric; random seed simulations. weights numeric; vector prior weights. data null defaults object[[\"prior.weights\"]], otherwise vector ones. newdata Deprecated: use data instead.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/predicted_samples.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Draw new response values from the conditional distribution of the response — predicted_samples","text":"tibble (data frame) 3 columns containing posterior predicted values long format. columns row (integer) row data posterior draw relates , draw (integer) index, range 1:n, indicating draw row relates , response (numeric) predicted response indicated row data.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/predicted_samples.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Draw new response values from the conditional distribution of the response — predicted_samples","text":"Gavin L. Simpson","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/predicted_samples.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Draw new response values from the conditional distribution of the response — predicted_samples","text":"","code":"load_mgcv() dat <- data_sim(\"eg1\", n = 1000, dist = \"normal\", scale = 2, seed = 2) m <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = dat, method = \"REML\") predicted_samples(m, n = 5, seed = 42) #> # A tibble: 5,000 x 3 #> .row .draw .response #> #> 1 1 1 8.93 #> 2 2 1 4.23 #> 3 3 1 7.71 #> 4 4 1 8.51 #> 5 5 1 10.1 #> 6 6 1 8.20 #> 7 7 1 8.95 #> 8 8 1 7.20 #> 9 9 1 18.1 #> 10 10 1 12.7 #> # i 4,990 more rows ## Can pass arguments to predict.gam() newd <- data.frame( x0 = runif(10), x1 = runif(10), x2 = runif(10), x3 = runif(10) ) ## Exclude s(x2) predicted_samples(m, n = 5, newd, exclude = \"s(x2)\", seed = 25) #> # A tibble: 50 x 3 #> .row .draw .response #> #> 1 1 1 9.42 #> 2 2 1 6.97 #> 3 3 1 8.10 #> 4 4 1 9.95 #> 5 5 1 6.75 #> 6 6 1 10.3 #> 7 7 1 10.8 #> 8 8 1 10.5 #> 9 9 1 8.43 #> 10 10 1 12.2 #> # i 40 more rows ## Exclude s(x1) predicted_samples(m, n = 5, newd, exclude = \"s(x1)\", seed = 25) #> # A tibble: 50 x 3 #> .row .draw .response #> #> 1 1 1 6.05 #> 2 2 1 5.28 #> 3 3 1 5.96 #> 4 4 1 13.7 #> 5 5 1 4.36 #> 6 6 1 5.11 #> 7 7 1 12.5 #> 8 8 1 5.66 #> 9 9 1 12.6 #> 10 10 1 8.38 #> # i 40 more rows ## Select which terms --- result should be the same as previous ## but note that we have to include any parametric terms, including the ## constant term predicted_samples(m, n = 5, newd, seed = 25, terms = c(\"Intercept\", \"s(x0)\", \"s(x2)\", \"s(x3)\") ) #> # A tibble: 50 x 3 #> .row .draw .response #> #> 1 1 1 -1.94 #> 2 2 1 -2.71 #> 3 3 1 -2.03 #> 4 4 1 5.73 #> 5 5 1 -3.63 #> 6 6 1 -2.87 #> 7 7 1 4.48 #> 8 8 1 -2.33 #> 9 9 1 4.65 #> 10 10 1 0.395 #> # i 40 more rows"},{"path":"https://gavinsimpson.github.io/gratia/reference/qq_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Quantile-quantile plot of model residuals — qq_plot","title":"Quantile-quantile plot of model residuals — qq_plot","text":"Quantile-quantile plots (QQ-plots) GAMs using reference quantiles Augustin et al (2012).","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/qq_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Quantile-quantile plot of model residuals — qq_plot","text":"","code":"qq_plot(model, ...) # Default S3 method qq_plot(model, ...) # S3 method for class 'gam' qq_plot( model, method = c(\"uniform\", \"simulate\", \"normal\", \"direct\"), type = c(\"deviance\", \"response\", \"pearson\"), n_uniform = 10, n_simulate = 50, seed = NULL, level = 0.9, ylab = NULL, xlab = NULL, title = NULL, subtitle = NULL, caption = NULL, ci_col = \"black\", ci_alpha = 0.2, point_col = \"black\", point_alpha = 1, line_col = \"red\", ... ) # S3 method for class 'glm' qq_plot(model, ...) # S3 method for class 'lm' qq_plot(model, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/qq_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Quantile-quantile plot of model residuals — qq_plot","text":"model fitted model. Currently models inheriting class \"gam\", well classes \"glm\" \"lm\" calls stats::glm stats::lm supported. ... arguments passed ot methods. method character; method used generate theoretical quantiles. default \"uniform\", generates reference quantiles using random draws uniform distribution inverse cummulative distribution function (CDF) fitted values. reference quantiles averaged n_uniform draws. \"simulate\" generates reference quantiles simulating new response data model observed values covariates, residualised generate reference quantiles, using n_simulate simulated data sets. \"normal\" generates reference quantiles using standard normal distribution. \"uniform\" computationally efficient, \"simulate\" allows reference bands drawn QQ-plot. \"normal\" avoided used fall back random number generator (\"simulate\") inverse CDF available family used model fitting (`\"uniform\"“). Note method = \"direct\" deprecated favour method = \"uniform\". type character; type residuals use. \"deviance\", \"response\", \"pearson\" residuals allowed. n_uniform numeric; number times randomize uniform quantiles direct computation method (method = \"uniform\"). n_simulate numeric; number data sets simulate estimated model using simulation method (method = \"simulate\"). seed numeric; random number seed use method = \"simulate\" method = \"uniform\". level numeric; coverage level reference intervals. Must strictly 0 < level < 1. used method = \"simulate\". ylab character expression; label y axis. supplied, suitable label generated. xlab character expression; label y axis. supplied, suitable label generated. title character expression; title plot. See ggplot2::labs(). May vector, one per penalty. subtitle character expression; subtitle plot. See ggplot2::labs(). May vector, one per penalty. caption character expression; plot caption. See ggplot2::labs(). May vector, one per penalty. ci_col fill colour reference interval method = \"simulate\". ci_alpha alpha transparency reference interval method = \"simulate\". point_col colour points QQ plot. point_alpha alpha transparency points QQ plot. line_col colour used draw reference line.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/qq_plot.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Quantile-quantile plot of model residuals — qq_plot","text":"wording used mgcv::qq.gam() uses direct reference simulated residuals method (method = \"simulated\"). avoid confusion, method = \"direct\" deprecated favour method = \"uniform\".","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/qq_plot.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Quantile-quantile plot of model residuals — qq_plot","text":"underlying methodology used method \"simulate\" \"uniform\" described Augustin et al (2012): Augustin, N.H., Sauleau, E.-., Wood, S.N., (2012) quantile quantile plots generalized linear models. Computational Statatistics Data Analysis 56, 2404-2409 doi:10.1016/j.csda.2012.01.026 .","code":""},{"path":[]},{"path":"https://gavinsimpson.github.io/gratia/reference/qq_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Quantile-quantile plot of model residuals — qq_plot","text":"","code":"load_mgcv() ## simulate binomial data... dat <- data_sim(\"eg1\", n = 200, dist = \"binary\", scale = .33, seed = 0) p <- binomial()$linkinv(dat$f) # binomial p n <- sample(c(1, 3), 200, replace = TRUE) # binomial n dat <- transform(dat, y = rbinom(n, n, p), n = n) m <- gam(y / n ~ s(x0) + s(x1) + s(x2) + s(x3), family = binomial, data = dat, weights = n, method = \"REML\" ) ## Q-Q plot; default using direct randomization of uniform quantiles qq_plot(m) ## Alternatively use simulate new data from the model, which ## allows construction of reference intervals for the Q-Q plot qq_plot(m, method = \"simulate\", seed = 42, point_col = \"steelblue\", point_alpha = 0.4 ) ## ... or use the usual normality assumption qq_plot(m, method = \"normal\")"},{"path":"https://gavinsimpson.github.io/gratia/reference/ref_level.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the reference or specific level of a factor — ref_level","title":"Return the reference or specific level of a factor — ref_level","text":"Extracts reference specific level supplied factor, returning factor levels one supplied.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/ref_level.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the reference or specific level of a factor — ref_level","text":"","code":"ref_level(fct) level(fct, level)"},{"path":"https://gavinsimpson.github.io/gratia/reference/ref_level.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the reference or specific level of a factor — ref_level","text":"fct factor; factor reference specific level extracted. level character; specific level extract case level().","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/ref_level.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the reference or specific level of a factor — ref_level","text":"length 1 factor levels supplied factor fct.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/ref_level.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Return the reference or specific level of a factor — ref_level","text":"","code":"f <- factor(sample(letters[1:5], 100, replace = TRUE)) # the reference level ref_level(f) #> [1] a #> Levels: a b c d e # a specific level level(f, level = \"b\") #> [1] b #> Levels: a b c d e # note that the levels will always match the input factor identical(levels(f), levels(ref_level(f))) #> [1] TRUE identical(levels(f), levels(level(f, \"c\"))) #> [1] TRUE"},{"path":"https://gavinsimpson.github.io/gratia/reference/ref_sims.html","id":null,"dir":"Reference","previous_headings":"","what":"Reference simulation data — ref_sims","title":"Reference simulation data — ref_sims","text":"set reference objects testing data_sim().","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/ref_sims.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Reference simulation data — ref_sims","text":"named list simulated data sets created data_sim().","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/reorder_fs_smooth_terms.html","id":null,"dir":"Reference","previous_headings":"","what":"Reorder random factor smooth terms to place factor last — reorder_fs_smooth_terms","title":"Reorder random factor smooth terms to place factor last — reorder_fs_smooth_terms","text":"Reorder random factor smooth terms place factor last","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/reorder_fs_smooth_terms.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reorder random factor smooth terms to place factor last — reorder_fs_smooth_terms","text":"","code":"reorder_fs_smooth_terms(smooth)"},{"path":"https://gavinsimpson.github.io/gratia/reference/reorder_fs_smooth_terms.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reorder random factor smooth terms to place factor last — reorder_fs_smooth_terms","text":"smooth mgcv smooth object","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/reorder_tensor_smooth_terms.html","id":null,"dir":"Reference","previous_headings":"","what":"Reorder tensor product terms for nicer plotting — reorder_tensor_smooth_terms","title":"Reorder tensor product terms for nicer plotting — reorder_tensor_smooth_terms","text":"tensor product smooth 3 terms contains 2d marginal smooth, get nicer output smooth_estimates() hence nicer plot draw.smooth_estimates() method reorder terms smooth vary terms 2d marginal first, terms vary slowly generate data evaluate smooth . results automatically generated data focuses (first one) 2d marginal smooth, end result smooth_estimates() shows 2d smooth changes terms involved smooth.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/reorder_tensor_smooth_terms.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reorder tensor product terms for nicer plotting — reorder_tensor_smooth_terms","text":"","code":"reorder_tensor_smooth_terms(smooth)"},{"path":"https://gavinsimpson.github.io/gratia/reference/reorder_tensor_smooth_terms.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reorder tensor product terms for nicer plotting — reorder_tensor_smooth_terms","text":"smooth mgcv smooth object","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/rep_first_factor_value.html","id":null,"dir":"Reference","previous_headings":"","what":"Repeat the first level of a factor n times — rep_first_factor_value","title":"Repeat the first level of a factor n times — rep_first_factor_value","text":"Function repeat first level factor n times return vector factor original levels intact","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/rep_first_factor_value.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Repeat the first level of a factor n times — rep_first_factor_value","text":"","code":"rep_first_factor_value(f, n)"},{"path":"https://gavinsimpson.github.io/gratia/reference/rep_first_factor_value.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Repeat the first level of a factor n times — rep_first_factor_value","text":"f factor n numeric; number times repeat first level f","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/rep_first_factor_value.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Repeat the first level of a factor n times — rep_first_factor_value","text":"factor length n levels f, whose elements first level f.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/residuals_hist_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Histogram of model residuals — residuals_hist_plot","title":"Histogram of model residuals — residuals_hist_plot","text":"Histogram model residuals","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/residuals_hist_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Histogram of model residuals — residuals_hist_plot","text":"","code":"residuals_hist_plot( model, type = c(\"deviance\", \"pearson\", \"response\"), n_bins = c(\"sturges\", \"scott\", \"fd\"), ylab = NULL, xlab = NULL, title = NULL, subtitle = NULL, caption = NULL )"},{"path":"https://gavinsimpson.github.io/gratia/reference/residuals_hist_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Histogram of model residuals — residuals_hist_plot","text":"model fitted model. Currently class \"gam\". type character; type residuals use. \"deviance\", \"response\", \"pearson\" residuals allowed. n_bins character numeric; either number bins string indicating calculate number bins. ylab character expression; label y axis. supplied, suitable label generated. xlab character expression; label y axis. supplied, suitable label generated. title character expression; title plot. See ggplot2::labs(). subtitle character expression; subtitle plot. See ggplot2::labs(). caption character expression; plot caption. See ggplot2::labs().","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/residuals_linpred_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot of residuals versus linear predictor values — residuals_linpred_plot","title":"Plot of residuals versus linear predictor values — residuals_linpred_plot","text":"Plot residuals versus linear predictor values","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/residuals_linpred_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot of residuals versus linear predictor values — residuals_linpred_plot","text":"","code":"residuals_linpred_plot( model, type = c(\"deviance\", \"pearson\", \"response\"), ylab = NULL, xlab = NULL, title = NULL, subtitle = NULL, caption = NULL, point_col = \"black\", point_alpha = 1, line_col = \"red\" )"},{"path":"https://gavinsimpson.github.io/gratia/reference/residuals_linpred_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot of residuals versus linear predictor values — residuals_linpred_plot","text":"model fitted model. Currently class \"gam\". type character; type residuals use. \"deviance\", \"response\", \"pearson\" residuals allowed. ylab character expression; label y axis. supplied, suitable label generated. xlab character expression; label y axis. supplied, suitable label generated. title character expression; title plot. See ggplot2::labs(). subtitle character expression; subtitle plot. See ggplot2::labs(). caption character expression; plot caption. See ggplot2::labs(). point_col colour used draw points plots. See graphics::par() section Color Specification. passed individual plotting functions, therefore affects points plots. point_alpha numeric; alpha transparency points plots. line_col colour specification 1:1 line.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/response_derivatives.html","id":null,"dir":"Reference","previous_headings":"","what":"Derivatives on the response scale from an estimated GAM — response_derivatives","title":"Derivatives on the response scale from an estimated GAM — response_derivatives","text":"Derivatives response scale estimated GAM","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/response_derivatives.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Derivatives on the response scale from an estimated GAM — response_derivatives","text":"","code":"response_derivatives(object, ...) # Default S3 method response_derivatives(object, ...) # S3 method for class 'gamm' response_derivatives(object, ...) # S3 method for class 'gam' response_derivatives( object, focal = NULL, data = NULL, order = 1L, type = c(\"forward\", \"backward\", \"central\"), scale = c(\"response\", \"linear_predictor\"), method = c(\"gaussian\", \"mh\", \"inla\", \"user\"), n = 100, eps = 1e-07, n_sim = 10000, level = 0.95, seed = NULL, mvn_method = c(\"mvnfast\", \"mgcv\"), ... )"},{"path":"https://gavinsimpson.github.io/gratia/reference/response_derivatives.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Derivatives on the response scale from an estimated GAM — response_derivatives","text":"object R object compute derivatives . ... arguments passed methods fitted_samples() focal character; name focal variable. response derivative response respect variable returned. variables involved model held constant values. can missing supplying data, case, focal variable identified one variable constant. data data frame containing values model covariates evaluate first derivatives smooths. supplied, one variable must held constant value. order numeric; order derivative. type character; type finite difference used. One \"forward\", \"backward\", \"central\". scale character; derivative estimated response linear predictor (link) scale? One \"response\" (default), \"linear predictor\". method character; method used draw samples posterior distribution. \"gaussian\" uses Gaussian (Laplace) approximation posterior. \"mh\" uses Metropolis Hastings sample alternates t proposals proposals based shrunken version posterior covariance matrix. \"inla\" uses variant Integrated Nested Laplace Approximation due Wood (2019), (currently implemented). \"user\" allows user-supplied posterior draws (currently implemented). n numeric; number points evaluate derivative (data supplied). eps numeric; finite difference. n_sim integer; number simulations used computing simultaneous intervals. level numeric; 0 < level < 1; coverage level credible interval. default 0.95 95% interval. seed numeric; random seed simulations. mvn_method character; one \"mvnfast\" \"mgcv\". default uses mvnfast::rmvn(), can considerably faster generate large numbers MVN random values mgcv::rmvn(), might work marginal fits, covariance matrix close singular.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/response_derivatives.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Derivatives on the response scale from an estimated GAM — response_derivatives","text":"tibble, currently following variables: .row: integer, indexing row data row output represents .focal: name variable partial derivative evaluated, .derivative: estimated partial derivative, .lower_ci: lower bound confidence simultaneous interval, .upper_ci: upper bound confidence simultaneous interval, additional columns containing covariate values derivative evaluated.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/response_derivatives.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Derivatives on the response scale from an estimated GAM — response_derivatives","text":"Gavin L. Simpson","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/response_derivatives.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Derivatives on the response scale from an estimated GAM — response_derivatives","text":"","code":"library(\"ggplot2\") library(\"patchwork\") load_mgcv() df <- data_sim(\"eg1\", dist = \"negbin\", scale = 0.25, seed = 42) # fit the GAM (note: for execution time reasons using bam()) m <- bam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = df, family = nb(), method = \"fREML\" ) # data slice through data along x2 - all other covariates will be set to # typical values (value closest to median) ds <- data_slice(m, x2 = evenly(x2, n = 100)) # fitted values along x2 fv <- fitted_values(m, data = ds) # response derivatives - ideally n_sim = >10000 y_d <- response_derivatives(m, data = ds, type = \"central\", focal = \"x2\", eps = 0.01, seed = 21, n_sim = 1000 ) # draw fitted values along x2 p1 <- fv |> ggplot(aes(x = x2, y = .fitted)) + geom_ribbon(aes(ymin = .lower_ci, ymax = .upper_ci, y = NULL), alpha = 0.2 ) + geom_line() + labs( title = \"Estimated count as a function of x2\", y = \"Estimated count\" ) # draw response derivatives p2 <- y_d |> ggplot(aes(x = x2, y = .derivative)) + geom_ribbon(aes(ymin = .lower_ci, ymax = .upper_ci), alpha = 0.2) + geom_line() + labs( title = \"Estimated 1st derivative of estimated count\", y = \"First derivative\" ) # draw both panels p1 + p2 + plot_layout(nrow = 2)"},{"path":"https://gavinsimpson.github.io/gratia/reference/rootogram.html","id":null,"dir":"Reference","previous_headings":"","what":"Rootograms to assess goodness of model fit — rootogram","title":"Rootograms to assess goodness of model fit — rootogram","text":"rootogram model diagnostic tool assesses goodness fit statistical model. observed values response compared expected fitted model. discrete, count responses, frequency count (0, 1, 2, etc) observed data expected conditional distribution response implied model compared. continuous variables, observed expected frequencies obtained grouping data bins. rootogram drawn using ggplot2::ggplot() graphics. design closely follows Kleiber & Zeileis (2016).","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/rootogram.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rootograms to assess goodness of model fit — rootogram","text":"","code":"rootogram(object, ...) # S3 method for class 'gam' rootogram(object, max_count = NULL, breaks = \"Sturges\", ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/rootogram.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rootograms to assess goodness of model fit — rootogram","text":"object R object ... arguments passed methods max_count integer; largest count consider breaks continuous responses, group response. Can anything acceptable breaks argument graphics::hist.default()","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/rootogram.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Rootograms to assess goodness of model fit — rootogram","text":"Kleiber, C., Zeileis, ., (2016) Visualizing Count Data Regressions Using Rootograms. . Stat. 70, 296–303. doi:10.1080/00031305.2016.1173590","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/rootogram.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rootograms to assess goodness of model fit — rootogram","text":"","code":"load_mgcv() df <- data_sim(\"eg1\", n = 1000, dist = \"poisson\", scale = 0.1, seed = 6) # A poisson example m <- gam(y ~ s(x0, bs = \"cr\") + s(x1, bs = \"cr\") + s(x2, bs = \"cr\") + s(x3, bs = \"cr\"), family = poisson(), data = df, method = \"REML\") rg <- rootogram(m) rg #> # A tibble: 21 x 3 #> .bin .observed .fitted #> #> 1 0 113 116.640 #> 2 1 236 227.869 #> 3 2 230 239.168 #> 4 3 200 181.679 #> 5 4 94 113.432 #> 6 5 68 62.4881 #> 7 6 27 31.6795 #> 8 7 22 15.1323 #> 9 8 4 6.88637 #> 10 9 3 2.99628 #> # i 11 more rows draw(rg) # plot the rootogram # A Gaussian example df <- data_sim(\"eg1\", dist = \"normal\", seed = 2) m <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = df, method = \"REML\") draw(rootogram(m, breaks = \"FD\"), type = \"suspended\")"},{"path":"https://gavinsimpson.github.io/gratia/reference/seq_min_max_eps.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a sequence of evenly-spaced values adjusted to accommodate a small adjustment — seq_min_max_eps","title":"Create a sequence of evenly-spaced values adjusted to accommodate a small adjustment — seq_min_max_eps","text":"Creates sequence n evenly-spaced values range min(x) – max(x), minimum maximum adjusted always contained within range x x may shifted forwards backwards amount related eps. particularly useful computing derivatives via finite differences without adjustment may predicting values outside range data hence conmstraints penalty.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/seq_min_max_eps.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a sequence of evenly-spaced values adjusted to accommodate a small adjustment — seq_min_max_eps","text":"","code":"seq_min_max_eps(x, n, order, type = c(\"forward\", \"backward\", \"central\"), eps)"},{"path":"https://gavinsimpson.github.io/gratia/reference/seq_min_max_eps.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a sequence of evenly-spaced values adjusted to accommodate a small adjustment — seq_min_max_eps","text":"x numeric; vector evenly-spaced values returned n numeric; number evenly-spaced values return order integer; order derivative. Either 1 2 first second order derivatives type character; type finite difference used. One \"forward\", \"backward\", \"central\" eps numeric; finite difference","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/seq_min_max_eps.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a sequence of evenly-spaced values adjusted to accommodate a small adjustment — seq_min_max_eps","text":"numeric vector length n.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/shift_values.html","id":null,"dir":"Reference","previous_headings":"","what":"Shift numeric values in a data frame by an amount eps — shift_values","title":"Shift numeric values in a data frame by an amount eps — shift_values","text":"Shift numeric values data frame amount eps","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/shift_values.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Shift numeric values in a data frame by an amount eps — shift_values","text":"","code":"shift_values(df, h, i, FUN = `+`, focal = NULL)"},{"path":"https://gavinsimpson.github.io/gratia/reference/shift_values.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Shift numeric values in a data frame by an amount eps — shift_values","text":"df data frame tibble. h numeric; amount shift values df . logical; vector indexing columns df included shift. FUN function; function applut shift. Typically + -. focal character; focal variable computing partial derivatives. allows shifting focal variable eps.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/simulate.html","id":null,"dir":"Reference","previous_headings":"","what":"Simulate from the posterior distribution of a GAM — simulate.gam","title":"Simulate from the posterior distribution of a GAM — simulate.gam","text":"Simulations posterior distribution fitted GAM model involve computing predicted values observation data simulated data required, generating random draws probability distribution used fitting model.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/simulate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Simulate from the posterior distribution of a GAM — simulate.gam","text":"","code":"# S3 method for class 'gam' simulate( object, nsim = 1, seed = NULL, data = newdata, weights = NULL, ..., newdata = NULL ) # S3 method for class 'gamm' simulate( object, nsim = 1, seed = NULL, data = newdata, weights = NULL, ..., newdata = NULL ) # S3 method for class 'scam' simulate( object, nsim = 1, seed = NULL, data = newdata, weights = NULL, ..., newdata = NULL )"},{"path":"https://gavinsimpson.github.io/gratia/reference/simulate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Simulate from the posterior distribution of a GAM — simulate.gam","text":"object fitted GAM, typically result call mgcv::gam` mgcv::gamm(). nsim numeric; number posterior simulations return. seed numeric; random seed simulations. data data frame; new observations posterior draws model evaluated. supplied, data used fit model used newdata, available object. weights numeric; vector prior weights. newdata null defaults object[[\"prior.weights\"]], otherwise vector ones. ... arguments passed methods. simulate.gam() simulate.scam() pass ... predict.gam(). can pass additional arguments terms, exclude, select model terms included predictions. may useful, example, excluding effects random effect terms. newdata Deprecated. Use data instead.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/simulate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Simulate from the posterior distribution of a GAM — simulate.gam","text":"(Currently) matrix nsim columns.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/simulate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Simulate from the posterior distribution of a GAM — simulate.gam","text":"simulate.gam() function, family component fitted model must contain, updateable contain, required random number generator. See mgcv::fix.family.rd().","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/simulate.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Simulate from the posterior distribution of a GAM — simulate.gam","text":"Gavin L. Simpson","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/simulate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Simulate from the posterior distribution of a GAM — simulate.gam","text":"","code":"load_mgcv() dat <- data_sim(\"eg1\", n = 400, dist = \"normal\", scale = 2, seed = 2) m1 <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = dat, method = \"REML\") sims <- simulate(m1, nsim = 5, seed = 42) head(sims) #> [,1] [,2] [,3] [,4] [,5] #> [1,] 11.445470 11.374304 10.098681 7.264881 8.796630 #> [2,] 6.510912 5.909584 9.057362 7.698084 11.444781 #> [3,] 3.837995 3.230610 3.550240 3.759380 4.774581 #> [4,] 12.361830 11.209226 10.714215 11.861957 10.746417 #> [5,] 14.851461 12.911440 11.356984 15.783913 15.106270 #> [6,] 5.921276 4.158963 5.520856 7.973614 9.654888"},{"path":"https://gavinsimpson.github.io/gratia/reference/smallAges.html","id":null,"dir":"Reference","previous_headings":"","what":"Lead-210 age-depth measurements for Small Water — smallAges","title":"Lead-210 age-depth measurements for Small Water — smallAges","text":"dataset containing lead-210 based age depth measurements SMALL1 core Small Water.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/smallAges.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Lead-210 age-depth measurements for Small Water — smallAges","text":"data frame 12 rows 7 variables.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/smallAges.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Lead-210 age-depth measurements for Small Water — smallAges","text":"Simpson, G.L. (Unpublished data).","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/smallAges.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Lead-210 age-depth measurements for Small Water — smallAges","text":"variables follows: Depth Drymass Date Age Error SedAccRate SedPerCentChange","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/smooth_coef_indices.html","id":null,"dir":"Reference","previous_headings":"","what":"Indices of the parametric terms for a particular smooth — smooth_coef_indices","title":"Indices of the parametric terms for a particular smooth — smooth_coef_indices","text":"Returns vector indices parametric terms represent supplied smooth. Useful extracting model coefficients columns covariance matrix.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/smooth_coef_indices.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Indices of the parametric terms for a particular smooth — smooth_coef_indices","text":"","code":"smooth_coef_indices(smooth)"},{"path":"https://gavinsimpson.github.io/gratia/reference/smooth_coef_indices.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Indices of the parametric terms for a particular smooth — smooth_coef_indices","text":"smooth object inherits class mgcv.smooth","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/smooth_coef_indices.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Indices of the parametric terms for a particular smooth — smooth_coef_indices","text":"numeric vector indices.","code":""},{"path":[]},{"path":"https://gavinsimpson.github.io/gratia/reference/smooth_coef_indices.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Indices of the parametric terms for a particular smooth — smooth_coef_indices","text":"Gavin L. Simpson","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/smooth_coefs.html","id":null,"dir":"Reference","previous_headings":"","what":"Coefficients for a particular smooth — smooth_coefs","title":"Coefficients for a particular smooth — smooth_coefs","text":"Returns vector model coefficients parametric terms represent supplied smooth.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/smooth_coefs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Coefficients for a particular smooth — smooth_coefs","text":"","code":"smooth_coefs(object, ...) # S3 method for class 'gam' smooth_coefs(object, select, term = deprecated(), ...) # S3 method for class 'bam' smooth_coefs(object, select, term = deprecated(), ...) # S3 method for class 'gamm' smooth_coefs(object, select, term = deprecated(), ...) # S3 method for class 'gamm4' smooth_coefs(object, select, term = deprecated(), ...) # S3 method for class 'list' smooth_coefs(object, select, term = deprecated(), ...) # S3 method for class 'mgcv.smooth' smooth_coefs(object, model, ...) # S3 method for class 'scam' smooth_coefs(object, select, term = deprecated(), ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/smooth_coefs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Coefficients for a particular smooth — smooth_coefs","text":"object fitted GAM(M) object, , \"mgcv.smooth\" method, object inherits class mgcv.smooth. ... arguments passed methods. select character; label smooth whose coefficients returned. term Use select instead. model fitted GAM(M) object.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/smooth_coefs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Coefficients for a particular smooth — smooth_coefs","text":"numeric vector model coefficients.","code":""},{"path":[]},{"path":"https://gavinsimpson.github.io/gratia/reference/smooth_coefs.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Coefficients for a particular smooth — smooth_coefs","text":"Gavin L. Simpson","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/smooth_coefs.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Coefficients for a particular smooth — smooth_coefs","text":"","code":"load_mgcv() df <- data_sim(\"eg1\", seed = 2) m <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = df, method = \"REML\") ## IGNORE_RDIFF_BEGIN smooth_coefs(m, select = \"s(x2)\") #> s(x2).1 s(x2).2 s(x2).3 s(x2).4 s(x2).5 s(x2).6 s(x2).7 s(x2).8 #> -6.533373 9.694277 2.194078 -1.967280 -2.374874 1.207638 -1.572586 9.269744 #> s(x2).9 #> 5.622738 ## IGNORE_RDIFF_END"},{"path":"https://gavinsimpson.github.io/gratia/reference/smooth_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate regular data over the covariates of a smooth — smooth_data","title":"Generate regular data over the covariates of a smooth — smooth_data","text":"Generate regular data covariates smooth","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/smooth_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate regular data over the covariates of a smooth — smooth_data","text":"","code":"smooth_data( model, id, n = 100, n_2d = NULL, n_3d = NULL, n_4d = NULL, offset = NULL, include_all = FALSE, var_order = NULL )"},{"path":"https://gavinsimpson.github.io/gratia/reference/smooth_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate regular data over the covariates of a smooth — smooth_data","text":"model fitted model id number ID smooth within model process. n numeric; number new observations generate. n_2d numeric; number new observations generate second dimension 2D smooth. Currently ignored. n_3d numeric; number new observations generate third dimension 3D smooth. n_4d numeric; number new observations generate dimensions higher 2 (!) kD smooth (k >= 4). example, smooth 4D smooth, dimensions 3 4 get n_4d new observations. offset numeric; value model offset use. include_all logical; include covariates involved smooth? FALSE, covariates involved smooth included returned data frame. TRUE, representative value included covariates model actually used smooth. can useful want pass returned data frame mgcv::PredictMat(). var_order character; order terms smooth processed. useful tensor products least one 2d marginal smooth.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/smooth_data.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate regular data over the covariates of a smooth — smooth_data","text":"","code":"load_mgcv() df <- data_sim(\"eg1\", seed = 42) m <- bam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = df) # generate data over range of x1 for smooth s(x1) smooth_data(m, id = 2) #> # A tibble: 100 x 1 #> x1 #> #> 1 0.0004050 #> 2 0.01046 #> 3 0.02052 #> 4 0.03057 #> 5 0.04063 #> 6 0.05069 #> 7 0.06074 #> 8 0.07080 #> 9 0.08086 #> 10 0.09091 #> # i 90 more rows # generate data over range of x1 for smooth s(x1), with typical value for # other covariates in the model smooth_data(m, id = 2, include_all = TRUE) #> # A tibble: 100 x 4 #> x1 x0 x2 x3 #> #> 1 0.0004050 0.4883 0.4708 0.4879 #> 2 0.01046 0.4883 0.4708 0.4879 #> 3 0.02052 0.4883 0.4708 0.4879 #> 4 0.03057 0.4883 0.4708 0.4879 #> 5 0.04063 0.4883 0.4708 0.4879 #> 6 0.05069 0.4883 0.4708 0.4879 #> 7 0.06074 0.4883 0.4708 0.4879 #> 8 0.07080 0.4883 0.4708 0.4879 #> 9 0.08086 0.4883 0.4708 0.4879 #> 10 0.09091 0.4883 0.4708 0.4879 #> # i 90 more rows"},{"path":"https://gavinsimpson.github.io/gratia/reference/smooth_dim.html","id":null,"dir":"Reference","previous_headings":"","what":"Dimension of a smooth — smooth_dim","title":"Dimension of a smooth — smooth_dim","text":"Extracts dimension estimated smooth.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/smooth_dim.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Dimension of a smooth — smooth_dim","text":"","code":"smooth_dim(object) # S3 method for class 'gam' smooth_dim(object) # S3 method for class 'gamm' smooth_dim(object) # S3 method for class 'mgcv.smooth' smooth_dim(object)"},{"path":"https://gavinsimpson.github.io/gratia/reference/smooth_dim.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Dimension of a smooth — smooth_dim","text":"object R object. See Details list supported objects.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/smooth_dim.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Dimension of a smooth — smooth_dim","text":"numeric vector dimensions smooth.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/smooth_dim.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Dimension of a smooth — smooth_dim","text":"generic function methods objects class \"gam\", \"gamm\", \"mgcv.smooth\".","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/smooth_dim.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Dimension of a smooth — smooth_dim","text":"Gavin L. Simpson","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/smooth_estimates.html","id":null,"dir":"Reference","previous_headings":"","what":"Evaluate smooths at covariate values — smooth_estimates","title":"Evaluate smooths at covariate values — smooth_estimates","text":"Evaluate smooth grid evenly spaced value range covariate associated smooth. Alternatively, set points smooth evaluated can supplied. smooth_estimates() new implementation evaluate_smooth(), replaces function, removed package.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/smooth_estimates.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Evaluate smooths at covariate values — smooth_estimates","text":"","code":"smooth_estimates(object, ...) # S3 method for class 'gam' smooth_estimates( object, select = NULL, smooth = deprecated(), n = 100, n_3d = 16, n_4d = 4, data = NULL, unconditional = FALSE, overall_uncertainty = TRUE, dist = NULL, unnest = TRUE, partial_match = FALSE, ... )"},{"path":"https://gavinsimpson.github.io/gratia/reference/smooth_estimates.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Evaluate smooths at covariate values — smooth_estimates","text":"object object class \"gam\" \"gamm\". ... arguments passed methods. select character; select smooth's posterior draw . default (NULL) means posteriors smooths model wil sampled . supplied, character vector requested terms. smooth Use select instead. n numeric; number points range covariate evaluate smooth. n_3d, n_4d numeric; number points range last covariate 3D 4D smooth. default NULL achieves standard behaviour using n points range covariate, resulting n^d evaluation points, d dimension smooth. d > 2 can result many evaluation points slow performance. smooths d > 4, value n_4d used dimensions > 4, unless NULL, case default behaviour (using n dimensions) observed. data data frame covariate values evaluate smooth. unconditional logical; confidence intervals include uncertainty due smoothness selection? TRUE, corrected Bayesian covariance matrix used. overall_uncertainty logical; uncertainty model constant term included standard error evaluate values smooth? dist numeric; greater 0, used determine location far data plotted plotting 2-D smooths. data scaled unit square deciding exclude, dist distance within unit square. See mgcv::exclude..far() details. unnest logical; unnest smooth objects? partial_match logical; case character select, select match partially smooths? partial_match = TRUE, select must single string, character vector length 1.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/smooth_estimates.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Evaluate smooths at covariate values — smooth_estimates","text":"data frame (tibble), class \"smooth_estimates\".","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/smooth_estimates.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Evaluate smooths at covariate values — smooth_estimates","text":"","code":"load_mgcv() dat <- data_sim(\"eg1\", n = 400, dist = \"normal\", scale = 2, seed = 2) m1 <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = dat, method = \"REML\") ## evaluate all smooths smooth_estimates(m1) #> # A tibble: 400 x 9 #> .smooth .type .by .estimate .se x0 x1 x2 x3 #> #> 1 s(x0) TPRS NA -0.966542 0.316118 0.00710904 NA NA NA #> 2 s(x0) TPRS NA -0.925391 0.297170 0.0171157 NA NA NA #> 3 s(x0) TPRS NA -0.884233 0.279256 0.0271224 NA NA NA #> 4 s(x0) TPRS NA -0.843050 0.262594 0.0371291 NA NA NA #> 5 s(x0) TPRS NA -0.801824 0.247376 0.0471358 NA NA NA #> 6 s(x0) TPRS NA -0.760536 0.233728 0.0571425 NA NA NA #> 7 s(x0) TPRS NA -0.719175 0.221701 0.0671492 NA NA NA #> 8 s(x0) TPRS NA -0.677736 0.211261 0.0771559 NA NA NA #> 9 s(x0) TPRS NA -0.636220 0.202303 0.0871626 NA NA NA #> 10 s(x0) TPRS NA -0.594641 0.194685 0.0971693 NA NA NA #> # i 390 more rows ## or selected smooths smooth_estimates(m1, select = c(\"s(x0)\", \"s(x1)\")) #> # A tibble: 200 x 7 #> .smooth .type .by .estimate .se x0 x1 #> #> 1 s(x0) TPRS NA -0.966542 0.316118 0.00710904 NA #> 2 s(x0) TPRS NA -0.925391 0.297170 0.0171157 NA #> 3 s(x0) TPRS NA -0.884233 0.279256 0.0271224 NA #> 4 s(x0) TPRS NA -0.843050 0.262594 0.0371291 NA #> 5 s(x0) TPRS NA -0.801824 0.247376 0.0471358 NA #> 6 s(x0) TPRS NA -0.760536 0.233728 0.0571425 NA #> 7 s(x0) TPRS NA -0.719175 0.221701 0.0671492 NA #> 8 s(x0) TPRS NA -0.677736 0.211261 0.0771559 NA #> 9 s(x0) TPRS NA -0.636220 0.202303 0.0871626 NA #> 10 s(x0) TPRS NA -0.594641 0.194685 0.0971693 NA #> # i 190 more rows"},{"path":"https://gavinsimpson.github.io/gratia/reference/smooth_label.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract the label for a smooth used by 'mgcv' — smooth_label","title":"Extract the label for a smooth used by 'mgcv' — smooth_label","text":"label 'mgcv' uses smooths useful many contexts, including selecting smooths labelling plots. smooth_label() extracts label 'mgcv' smooth object, .e. object inherits class \"mgcv.smooth\". typically found $smooth component GAM fitted mgcv::gam() mgcv::bam(), related functions.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/smooth_label.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract the label for a smooth used by 'mgcv' — smooth_label","text":"","code":"smooth_label(object, ...) # S3 method for class 'gam' smooth_label(object, id, ...) # S3 method for class 'mgcv.smooth' smooth_label(object, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/smooth_label.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract the label for a smooth used by 'mgcv' — smooth_label","text":"object R object. Currently, methods class \"gam\" mgcv smooth objects inheriting class \"mgcv.smooth\" supported. ... arguments passed methods. id numeric; indices smooths whose labels extracted. missing, labels smooths model returned.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/smooth_label.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract the label for a smooth used by 'mgcv' — smooth_label","text":"character vector.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/smooth_label.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract the label for a smooth used by 'mgcv' — smooth_label","text":"","code":"load_mgcv() df <- data_sim(\"gwf2\", n = 100) m <- gam(y ~ s(x), data = df, method = \"REML\") # extract the smooth sm <- get_smooths_by_id(m, id = 1)[[1]] # extract the label smooth_label(sm) #> [1] \"s(x)\" # or directly on the fitted GAM smooth_label(m$smooth[[1]]) #> [1] \"s(x)\" # or extract labels by idex/position smooth_label(m, id = 1) #> [1] \"s(x)\""},{"path":"https://gavinsimpson.github.io/gratia/reference/smooth_samples.html","id":null,"dir":"Reference","previous_headings":"","what":"Posterior draws for individual smooths — smooth_samples","title":"Posterior draws for individual smooths — smooth_samples","text":"Returns draws posterior distributions smooth functions GAM. Useful, example, visualising uncertainty individual estimated functions.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/smooth_samples.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Posterior draws for individual smooths — smooth_samples","text":"","code":"smooth_samples(model, ...) # S3 method for class 'gam' smooth_samples( model, select = NULL, term = deprecated(), n = 1, data = newdata, method = c(\"gaussian\", \"mh\", \"inla\", \"user\"), seed = NULL, freq = FALSE, unconditional = FALSE, n_cores = 1L, n_vals = 200, burnin = 1000, thin = 1, t_df = 40, rw_scale = 0.25, rng_per_smooth = FALSE, draws = NULL, partial_match = NULL, mvn_method = c(\"mvnfast\", \"mgcv\"), ..., newdata = NULL, ncores = NULL )"},{"path":"https://gavinsimpson.github.io/gratia/reference/smooth_samples.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Posterior draws for individual smooths — smooth_samples","text":"model fitted model supported types ... arguments passed methods. fitted_samples(), passed mgcv::predict.gam(). posterior_samples() passed fitted_samples(). predicted_samples() passed relevant simulate() method. select character; select smooth's posterior draw . default (NULL) means posteriors smooths model wil sampled . supplied, character vector requested terms. term Use select instead. n numeric; number posterior samples return. data data frame; new observations posterior draws model evaluated. supplied, data used fit model used data, available model. method character; method used draw samples posterior distribution. \"gaussian\" uses Gaussian (Laplace) approximation posterior. \"mh\" uses Metropolis Hastings sampler alternates t proposals proposals based shrunken version posterior covariance matrix. \"inla\" uses variant Integrated Nested Laplace Approximation due Wood (2019), (currently implemented). \"user\" allows user-supplied posterior draws (currently implemented). seed numeric; random seed simulations. freq logical; TRUE use frequentist covariance matrix parameter estimators, FALSE use Bayesian posterior covariance matrix parameters. unconditional logical; TRUE (freq == FALSE) Bayesian smoothing parameter uncertainty corrected covariance matrix used, available. n_cores number cores generating random variables multivariate normal distribution. Passed mvnfast::rmvn(). Parallelization take place OpenMP supported (appears work Windows current R). n_vals numeric; many locations evaluate smooth data supplied burnin numeric; number samples discard burnin draws. used method = \"mh\". thin numeric; number samples skip taking n draws. Results thin * n draws posterior taken. used method = \"mh\". t_df numeric; degrees freedom t distribution proposals. used method = \"mh\". rw_scale numeric; Factor scale posterior covariance matrix generating random walk proposals. Negative non finite skip random walk step. used method = \"mh\". rng_per_smooth logical; TRUE, behaviour gratia version 0.8.1 earlier used, whereby separate call random number generator (RNG) performed smooth. FALSE, single call RNG performed model parameters draws matrix; user supplied posterior draws used method = \"user\". partial_match logical; smooths selected partial matches select? TRUE, select can single string match . mvn_method character; one \"mvnfast\" \"mgcv\". default uses mvnfast::rmvn(), can considerably faster generate large numbers MVN random values mgcv::rmvn(), might work marginal fits, covariance matrix close singular. newdata Deprecated: use data instead. ncores Deprecated; use n_cores instead. number cores generating random variables multivariate normal distribution. Passed mvnfast::rmvn(). Parallelization take place OpenMP supported (appears work Windows current R).","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/smooth_samples.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Posterior draws for individual smooths — smooth_samples","text":"tibble additional classes \"smooth_samples\" `\"posterior_samples\". \"gam\" method, columns currently returned (order) : .smooth; character vector. Indicates smooth function particular draw, .term; character vector. Similar smooth, contain full label smooth, differentiate factor-smooths example. .; character vector. smooth involves term, variable named , NA_character_ otherwise. .row; integer. vector values seq_len(n_vals), repeated n > 1L. Indexes row data particular draw. .draw; integer. vector integer values indexing particular posterior draw row belongs . .value; numeric. value smooth function posterior draw covariate combination. xxx; numeric. series one columns containing data required smooth, named per variables involved respective smooth. Additional columns present case factor smooths, contain level factor named by_variable particular posterior draw.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/smooth_samples.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"Posterior draws for individual smooths — smooth_samples","text":"set variables returned order tibble subject change future versions. rely position.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/smooth_samples.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Posterior draws for individual smooths — smooth_samples","text":"Gavin L. Simpson","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/smooth_samples.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Posterior draws for individual smooths — smooth_samples","text":"","code":"load_mgcv() dat <- data_sim(\"eg1\", n = 400, seed = 2) m1 <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = dat, method = \"REML\") sms <- smooth_samples(m1, select = \"s(x0)\", n = 5, seed = 42) # \\donttest{ sms #> # A tibble: 1,000 x 8 #> .smooth .term .type .by .row .draw .value x0 #> #> 1 s(x0) s(x0) TPRS NA 1 1 -0.357 0.00711 #> 2 s(x0) s(x0) TPRS NA 1 2 -0.465 0.00711 #> 3 s(x0) s(x0) TPRS NA 1 3 -0.720 0.00711 #> 4 s(x0) s(x0) TPRS NA 1 4 -1.27 0.00711 #> 5 s(x0) s(x0) TPRS NA 1 5 -1.18 0.00711 #> 6 s(x0) s(x0) TPRS NA 2 1 -0.365 0.0121 #> 7 s(x0) s(x0) TPRS NA 2 2 -0.464 0.0121 #> 8 s(x0) s(x0) TPRS NA 2 3 -0.708 0.0121 #> 9 s(x0) s(x0) TPRS NA 2 4 -1.24 0.0121 #> 10 s(x0) s(x0) TPRS NA 2 5 -1.16 0.0121 #> # i 990 more rows # } ## A factor by example (with a spurious covariate x0) dat <- data_sim(\"eg4\", n = 1000, seed = 2) ## fit model... m2 <- gam(y ~ fac + s(x2, by = fac) + s(x0), data = dat) sms <- smooth_samples(m2, n = 5, seed = 42) draw(sms)"},{"path":[]},{"path":"https://gavinsimpson.github.io/gratia/reference/smooth_terms.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"List the variables involved in smooths — smooth_terms","text":"","code":"smooth_terms(object, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/smooth_terms.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"List the variables involved in smooths — smooth_terms","text":"object R object result call mgcv::gam(), mgcv::bam(), mgcv::gamm(), inherits classes \"gam\" \"mgcv.smooth\", \"fs.interaction\". ... arguments passed methods. Currently unused.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/smooth_type.html","id":null,"dir":"Reference","previous_headings":"","what":"Determine the type of smooth and return it n a human readable form — smooth_type","title":"Determine the type of smooth and return it n a human readable form — smooth_type","text":"Determine type smooth return n human readable form","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/smooth_type.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Determine the type of smooth and return it n a human readable form — smooth_type","text":"","code":"smooth_type(smooth) # Default S3 method smooth_type(smooth) # S3 method for class 'tprs.smooth' smooth_type(smooth) # S3 method for class 'ts.smooth' smooth_type(smooth) # S3 method for class 'cr.smooth' smooth_type(smooth) # S3 method for class 'cs.smooth' smooth_type(smooth) # S3 method for class 'cyclic.smooth' smooth_type(smooth) # S3 method for class 'pspline.smooth' smooth_type(smooth) # S3 method for class 'cpspline.smooth' smooth_type(smooth) # S3 method for class 'Bspline.smooth' smooth_type(smooth) # S3 method for class 'duchon.spline' smooth_type(smooth) # S3 method for class 'fs.interaction' smooth_type(smooth) # S3 method for class 'sz.interaction' smooth_type(smooth) # S3 method for class 'gp.smooth' smooth_type(smooth) # S3 method for class 'mrf.smooth' smooth_type(smooth) # S3 method for class 'random.effect' smooth_type(smooth) # S3 method for class 'sw' smooth_type(smooth) # S3 method for class 'sf' smooth_type(smooth) # S3 method for class 'soap.film' smooth_type(smooth) # S3 method for class 't2.smooth' smooth_type(smooth) # S3 method for class 'sos.smooth' smooth_type(smooth) # S3 method for class 'tensor.smooth' smooth_type(smooth) # S3 method for class 'mpi.smooth' smooth_type(smooth) # S3 method for class 'mpd.smooth' smooth_type(smooth) # S3 method for class 'cx.smooth' smooth_type(smooth) # S3 method for class 'cv.smooth' smooth_type(smooth) # S3 method for class 'micx.smooth' smooth_type(smooth) # S3 method for class 'micv.smooth' smooth_type(smooth) # S3 method for class 'mdcx.smooth' smooth_type(smooth) # S3 method for class 'mdcv.smooth' smooth_type(smooth) # S3 method for class 'miso.smooth' smooth_type(smooth) # S3 method for class 'mifo.smooth' smooth_type(smooth)"},{"path":"https://gavinsimpson.github.io/gratia/reference/smooth_type.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Determine the type of smooth and return it n a human readable form — smooth_type","text":"smooth object inheriting class mgcv.smooth.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/smooths.html","id":null,"dir":"Reference","previous_headings":"","what":"Names of smooths in a GAM — smooths","title":"Names of smooths in a GAM — smooths","text":"Names smooths GAM","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/smooths.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Names of smooths in a GAM — smooths","text":"","code":"smooths(object) # Default S3 method smooths(object) # S3 method for class 'gamm' smooths(object)"},{"path":"https://gavinsimpson.github.io/gratia/reference/smooths.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Names of smooths in a GAM — smooths","text":"object fitted GAM related model. Typically result call mgcv::gam(), mgcv::bam(), mgcv::gamm().","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/spline_values.html","id":null,"dir":"Reference","previous_headings":"","what":"Evaluate a spline at provided covariate values — spline_values","title":"Evaluate a spline at provided covariate values — spline_values","text":"Evaluate spline provided covariate values","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/spline_values.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Evaluate a spline at provided covariate values — spline_values","text":"","code":"spline_values( smooth, data, model, unconditional, overall_uncertainty = TRUE, frequentist = FALSE )"},{"path":"https://gavinsimpson.github.io/gratia/reference/spline_values.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Evaluate a spline at provided covariate values — spline_values","text":"smooth currently object inherits class mgcv.smooth. data data frame values evaluate smooth . model fitted model; currently mgcv::gam() mgcv::bam() models suported. unconditional logical; confidence intervals include uncertainty due smoothness selection? TRUE, corrected Bayesian covariance matrix used. overall_uncertainty logical; uncertainty model constant term included standard error evaluate values smooth? frequentist logical; use frequentist covariance matrix?","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/spline_values2.html","id":null,"dir":"Reference","previous_headings":"","what":"Evaluate a spline at provided covariate values — spline_values2","title":"Evaluate a spline at provided covariate values — spline_values2","text":"function spline_values2() renamed spline_values() version 0.9.0. allowed following removal evaluate_smooth(), function using spline_values(). spline_values2() renamed spline_values().","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/spline_values2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Evaluate a spline at provided covariate values — spline_values2","text":"","code":"spline_values2( smooth, data, model, unconditional, overall_uncertainty = TRUE, frequentist = FALSE )"},{"path":"https://gavinsimpson.github.io/gratia/reference/spline_values2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Evaluate a spline at provided covariate values — spline_values2","text":"smooth currently object inherits class mgcv.smooth. data optional data frame values evaluate smooth . model fitted model; currently mgcv::gam() mgcv::bam() models suported. unconditional logical; confidence intervals include uncertainty due smoothness selection? TRUE, corrected Bayesian covariance matrix used. overall_uncertainty logical; uncertainty model constant term included standard error evaluate values smooth? frequentist logical; use frequentist covariance matrix?","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/term_names.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract names of all variables needed to fit a GAM or a smooth — term_names","title":"Extract names of all variables needed to fit a GAM or a smooth — term_names","text":"Extract names variables needed fit GAM smooth","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/term_names.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract names of all variables needed to fit a GAM or a smooth — term_names","text":"","code":"term_names(object, ...) # S3 method for class 'gam' term_names(object, ...) # S3 method for class 'mgcv.smooth' term_names(object, ...) # S3 method for class 'gamm' term_names(object, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/term_names.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract names of all variables needed to fit a GAM or a smooth — term_names","text":"object fitted GAM object (inheriting class \"gam\" mgcv::smooth.construct smooth object, inheriting class \"mgcv.smooth\". ... arguments passed methods. currently used.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/term_names.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract names of all variables needed to fit a GAM or a smooth — term_names","text":"vector variable names required terms model","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/term_variables.html","id":null,"dir":"Reference","previous_headings":"","what":"Names of variables involved in a specified model term — term_variables","title":"Names of variables involved in a specified model term — term_variables","text":"Given name (term label) term model, returns names variables involved term.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/term_variables.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Names of variables involved in a specified model term — term_variables","text":"","code":"term_variables(object, term, ...) # S3 method for class 'terms' term_variables(object, term, ...) # S3 method for class 'gam' term_variables(object, term, ...) # S3 method for class 'bam' term_variables(object, term, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/term_variables.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Names of variables involved in a specified model term — term_variables","text":"object R object method dispatch performed term character; name model term, sense attr(terms(object), \"term.labels\"). Currently checked see term exists model. ... arguments passed methods.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/term_variables.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Names of variables involved in a specified model term — term_variables","text":"character vector variable names.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/theta.html","id":null,"dir":"Reference","previous_headings":"","what":"General extractor for additional parameters in mgcv models — theta","title":"General extractor for additional parameters in mgcv models — theta","text":"General extractor additional parameters mgcv models","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/theta.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"General extractor for additional parameters in mgcv models — theta","text":"","code":"theta(object, ...) # S3 method for class 'gam' theta(object, transform = TRUE, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/theta.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"General extractor for additional parameters in mgcv models — theta","text":"object fitted model ... arguments passed methods. transform logical; transform natural scale parameter","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/theta.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"General extractor for additional parameters in mgcv models — theta","text":"Returns numeric vector additional parameters","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/theta.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"General extractor for additional parameters in mgcv models — theta","text":"","code":"load_mgcv() df <- data_sim(\"eg1\", dist = \"poisson\", seed = 42, scale = 1 / 5) m <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = df, method = \"REML\", family = nb() ) p <- theta(m)"},{"path":"https://gavinsimpson.github.io/gratia/reference/tidy_basis.html","id":null,"dir":"Reference","previous_headings":"","what":"A tidy basis representation of a smooth object — tidy_basis","title":"A tidy basis representation of a smooth object — tidy_basis","text":"Takes object class mgcv.smooth returns tidy representation basis.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/tidy_basis.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A tidy basis representation of a smooth object — tidy_basis","text":"","code":"tidy_basis(smooth, data = NULL, at = NULL, coefs = NULL, p_ident = NULL)"},{"path":"https://gavinsimpson.github.io/gratia/reference/tidy_basis.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A tidy basis representation of a smooth object — tidy_basis","text":"smooth smooth object inheriting class \"mgcv.smooth\". Typically, objects returned part fitted GAM GAMM $smooth component model object $gam$smooth component model fitted mgcv::gamm() gamm4::gamm4(). data data frame containing variables used smooth. data frame containing values smooth covariate(s) basis evaluated. coefs numeric; optional vector coefficients smooth p_ident logical vector; used handling scam::scam() smooths.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/tidy_basis.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A tidy basis representation of a smooth object — tidy_basis","text":"tibble.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/tidy_basis.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"A tidy basis representation of a smooth object — tidy_basis","text":"Gavin L. Simpson","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/tidy_basis.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A tidy basis representation of a smooth object — tidy_basis","text":"","code":"load_mgcv() df <- data_sim(\"eg1\", n = 400, seed = 42) # fit model m <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = df, method = \"REML\") # tidy representaition of a basis for a smooth definition # extract the smooth sm <- get_smooth(m, \"s(x2)\") # get the tidy basis - need to pass where we want it to be evaluated bf <- tidy_basis(sm, at = df) # can weight the basis by the model coefficients for this smooth bf <- tidy_basis(sm, at = df, coefs = smooth_coefs(sm, model = m))"},{"path":"https://gavinsimpson.github.io/gratia/reference/to_na.html","id":null,"dir":"Reference","previous_headings":"","what":"Sets the elements of vector to NA — to_na","title":"Sets the elements of vector to NA — to_na","text":"Given vector indexing elements x, sets selected elements x NA.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/to_na.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sets the elements of vector to NA — to_na","text":"","code":"to_na(x, i)"},{"path":"https://gavinsimpson.github.io/gratia/reference/to_na.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sets the elements of vector to NA — to_na","text":"x vector values vector values used subset x","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/to_na.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Sets the elements of vector to NA — to_na","text":"Returns x possibly elements set NA","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/too_far.html","id":null,"dir":"Reference","previous_headings":"","what":"Exclude values that lie too far from the support of data — too_far","title":"Exclude values that lie too far from the support of data — too_far","text":"Identifies pairs covariate values lie far original data. function currently basic wrapper around mgcv::exclude..far().","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/too_far.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Exclude values that lie too far from the support of data — too_far","text":"","code":"too_far(x, y, ref_1, ref_2, dist = NULL)"},{"path":"https://gavinsimpson.github.io/gratia/reference/too_far.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Exclude values that lie too far from the support of data — too_far","text":"x, y numeric; vector values covariates compare observed data ref_1, ref_2 numeric; vectors covariate values represent reference x1 x2` compared dist supplied, numeric vector length 1 representing distance data beyond observation excluded. example, want exclude values lie observation 10% range observed data, use 0.1.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/too_far.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Exclude values that lie too far from the support of data — too_far","text":"Returns logical vector length x1.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/too_far_to_na.html","id":null,"dir":"Reference","previous_headings":"","what":"Set rows of data to NA if the lie too far from a reference set of values — too_far_to_na","title":"Set rows of data to NA if the lie too far from a reference set of values — too_far_to_na","text":"Set rows data NA lie far reference set values","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/too_far_to_na.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set rows of data to NA if the lie too far from a reference set of values — too_far_to_na","text":"","code":"too_far_to_na(smooth, input, reference, cols, dist = NULL)"},{"path":"https://gavinsimpson.github.io/gratia/reference/too_far_to_na.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set rows of data to NA if the lie too far from a reference set of values — too_far_to_na","text":"smooth mgcv smooth object input data frame containing input observations columns set NA reference data frame containing reference values cols character vector columns whose elements set NA data lies far reference set dist numeric, distance reference set beyond elements input set NA","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/transform_fun.html","id":null,"dir":"Reference","previous_headings":"","what":"Transform estimated values and confidence intervals by applying a function — transform_fun","title":"Transform estimated values and confidence intervals by applying a function — transform_fun","text":"Transform estimated values confidence intervals applying function","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/transform_fun.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transform estimated values and confidence intervals by applying a function — transform_fun","text":"","code":"transform_fun(object, fun = NULL, ...) # S3 method for class 'smooth_estimates' transform_fun(object, fun = NULL, constant = NULL, ...) # S3 method for class 'smooth_samples' transform_fun(object, fun = NULL, constant = NULL, ...) # S3 method for class 'mgcv_smooth' transform_fun(object, fun = NULL, constant = NULL, ...) # S3 method for class 'evaluated_parametric_term' transform_fun(object, fun = NULL, constant = NULL, ...) # S3 method for class 'parametric_effects' transform_fun(object, fun = NULL, constant = NULL, ...) # S3 method for class 'tbl_df' transform_fun(object, fun = NULL, column = NULL, constant = NULL, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/transform_fun.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Transform estimated values and confidence intervals by applying a function — transform_fun","text":"object object apply transform function . fun function apply. ... additional arguments passed methods. constant numeric; constant apply transformation. column character; \"tbl_df\" method, column transform.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/transform_fun.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Transform estimated values and confidence intervals by applying a function — transform_fun","text":"Returns object estimate upper lower values confidence interval transformed via function.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/transform_fun.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Transform estimated values and confidence intervals by applying a function — transform_fun","text":"Gavin L. Simpson","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/typical_values.html","id":null,"dir":"Reference","previous_headings":"","what":"Typical values of model covariates — typical_values","title":"Typical values of model covariates — typical_values","text":"Typical values model covariates","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/typical_values.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Typical values of model covariates — typical_values","text":"","code":"typical_values(object, ...) # S3 method for class 'gam' typical_values( object, vars = everything(), envir = environment(formula(object)), data = NULL, ... ) # S3 method for class 'data.frame' typical_values(object, vars = everything(), ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/typical_values.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Typical values of model covariates — typical_values","text":"object fitted GAM(M) model. ... arguments passed methods. vars terms include exclude returned object. Uses tidyselect principles. envir environment within recreate data used fit object. data optional data frame data used fit model reconstruction data model work.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/user_draws.html","id":null,"dir":"Reference","previous_headings":"","what":"Handle user-supplied posterior draws — user_draws","title":"Handle user-supplied posterior draws — user_draws","text":"Handle user-supplied posterior draws","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/user_draws.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Handle user-supplied posterior draws — user_draws","text":"","code":"user_draws(model, draws, ...) # S3 method for class 'gam' user_draws(model, draws, index = NULL, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/user_draws.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Handle user-supplied posterior draws — user_draws","text":"model fitted R model. Currently models fitted mgcv::gam() mgcv::bam(), return object inherits objects supported. , \"inherits\" used loose fashion; models fitted scam::scam() support even though models strictly inherit class \"gam\" far inherits() concerned. draws matrix; user supplied posterior draws used method = \"user\". ... arguments passed methods. index vector index (subset) columns draws.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/user_draws.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Handle user-supplied posterior draws — user_draws","text":"supplied draws must matrix (currently), 1 column per model coefficient, 1 row per posterior draw. \"gam\" method argument index, can used subset (select) coefficients (columns) draws. index can valid way selecting (indexing) columns matrix. index useful set posterior draws entire model (say mgcv::gam.mh()) wish use draws individual smooth, via smooth_samples().","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/variance_comp.html","id":null,"dir":"Reference","previous_headings":"","what":"Variance components of smooths from smoothness estimates — variance_comp","title":"Variance components of smooths from smoothness estimates — variance_comp","text":"wrapper mgcv::gam.vcomp() returns smoothing parameters expressed variance components.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/variance_comp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Variance components of smooths from smoothness estimates — variance_comp","text":"","code":"variance_comp(object, ...) # S3 method for class 'gam' variance_comp(object, rescale = TRUE, coverage = 0.95, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/variance_comp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Variance components of smooths from smoothness estimates — variance_comp","text":"object R object. Currently models fitted mgcv::gam() mgcv::bam() supported. ... arguments passed methods rescale logical; numerical stability reasons penalty matrices smooths rescaled fitting. rescale = TRUE, rescaling undone, resulting variance components original scale. needed comparing mixed model software, lmer(). coverage numeric; value 0 1 indicating (approximate) coverage confidence interval returned.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/variance_comp.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Variance components of smooths from smoothness estimates — variance_comp","text":"function wrapper mgcv::gam.vcomp() performs three additional services suppresses annoying text output mgcv::gam.vcomp() prints terminal, returns variance smooth well standard deviation, returns variance components tibble.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/vars_from_label.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns names of variables from a smooth label — vars_from_label","title":"Returns names of variables from a smooth label — vars_from_label","text":"Returns names variables smooth label","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/vars_from_label.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns names of variables from a smooth label — vars_from_label","text":"","code":"vars_from_label(label)"},{"path":"https://gavinsimpson.github.io/gratia/reference/vars_from_label.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns names of variables from a smooth label — vars_from_label","text":"label character; length 1 character vector containing label smooth.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/vars_from_label.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Returns names of variables from a smooth label — vars_from_label","text":"","code":"vars_from_label(\"s(x1)\") #> [1] \"x1\" vars_from_label(\"t2(x1,x2,x3)\") #> [1] \"x1\" \"x2\" \"x3\""},{"path":"https://gavinsimpson.github.io/gratia/reference/which_smooths.html","id":null,"dir":"Reference","previous_headings":"","what":"Identify a smooth term by its label — which_smooths","title":"Identify a smooth term by its label — which_smooths","text":"Identify smooth term label","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/which_smooths.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Identify a smooth term by its label — which_smooths","text":"","code":"which_smooths(object, ...) # Default S3 method which_smooths(object, ...) # S3 method for class 'gam' which_smooths(object, terms, ...) # S3 method for class 'bam' which_smooths(object, terms, ...) # S3 method for class 'gamm' which_smooths(object, terms, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/which_smooths.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Identify a smooth term by its label — which_smooths","text":"object fitted GAM. ... arguments passed methods. terms character; one (partial) term labels identify required smooths.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/worm_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Worm plot of model residuals — worm_plot","title":"Worm plot of model residuals — worm_plot","text":"Worm plot model residuals","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/worm_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Worm plot of model residuals — worm_plot","text":"","code":"worm_plot(model, ...) # S3 method for class 'gam' worm_plot( model, method = c(\"uniform\", \"simulate\", \"normal\", \"direct\"), type = c(\"deviance\", \"response\", \"pearson\"), n_uniform = 10, n_simulate = 50, level = 0.9, ylab = NULL, xlab = NULL, title = NULL, subtitle = NULL, caption = NULL, ci_col = \"black\", ci_alpha = 0.2, point_col = \"black\", point_alpha = 1, line_col = \"red\", ... ) # S3 method for class 'glm' worm_plot(model, ...) # S3 method for class 'lm' worm_plot(model, ...)"},{"path":"https://gavinsimpson.github.io/gratia/reference/worm_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Worm plot of model residuals — worm_plot","text":"model fitted model. Currently models inheriting class \"gam\", well classes \"glm\" \"lm\" calls stats::glm stats::lm supported. ... arguments passed ot methods. method character; method used generate theoretical quantiles. default \"uniform\", generates reference quantiles using random draws uniform distribution inverse cummulative distribution function (CDF) fitted values. reference quantiles averaged n_uniform draws. \"simulate\" generates reference quantiles simulating new response data model observed values covariates, residualised generate reference quantiles, using n_simulate simulated data sets. \"normal\" generates reference quantiles using standard normal distribution. \"uniform\" computationally efficient, \"simulate\" allows reference bands drawn QQ-plot. \"normal\" avoided used fall back random number generator (\"simulate\") inverse CDF available family used model fitting (`\"uniform\"“). Note method = \"direct\" deprecated favour method = \"uniform\". type character; type residuals use. \"deviance\", \"response\", \"pearson\" residuals allowed. n_uniform numeric; number times randomize uniform quantiles direct computation method (method = \"uniform\"). n_simulate numeric; number data sets simulate estimated model using simulation method (method = \"simulate\"). level numeric; coverage level reference intervals. Must strictly 0 < level < 1. used method = \"simulate\". ylab character expression; label y axis. supplied, suitable label generated. xlab character expression; label y axis. supplied, suitable label generated. title character expression; title plot. See ggplot2::labs(). May vector, one per penalty. subtitle character expression; subtitle plot. See ggplot2::labs(). May vector, one per penalty. caption character expression; plot caption. See ggplot2::labs(). May vector, one per penalty. ci_col fill colour reference interval method = \"simulate\". ci_alpha alpha transparency reference interval method = \"simulate\". point_col colour points QQ plot. point_alpha alpha transparency points QQ plot. line_col colour used draw reference line.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/worm_plot.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Worm plot of model residuals — worm_plot","text":"wording used mgcv::qq.gam() uses direct reference simulated residuals method (method = \"simulated\"). avoid confusion, method = \"direct\" deprecated favour method = \"uniform\".","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/worm_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Worm plot of model residuals — worm_plot","text":"","code":"load_mgcv() ## simulate binomial data... dat <- data_sim(\"eg1\", n = 200, dist = \"binary\", scale = .33, seed = 0) p <- binomial()$linkinv(dat$f) # binomial p n <- sample(c(1, 3), 200, replace = TRUE) # binomial n dat <- transform(dat, y = rbinom(n, n, p), n = n) m <- gam(y / n ~ s(x0) + s(x1) + s(x2) + s(x3), family = binomial, data = dat, weights = n, method = \"REML\" ) ## Worm plot; default using direct randomization of uniform quantiles ## Note no reference bands are drawn with this method. worm_plot(m) ## Alternatively use simulate new data from the model, which ## allows construction of reference intervals for the Q-Q plot worm_plot(m, method = \"simulate\", point_col = \"steelblue\", point_alpha = 0.4 ) ## ... or use the usual normality assumption worm_plot(m, method = \"normal\")"},{"path":"https://gavinsimpson.github.io/gratia/reference/zooplankton.html","id":null,"dir":"Reference","previous_headings":"","what":"Madison lakes zooplankton data — zooplankton","title":"Madison lakes zooplankton data — zooplankton","text":"Madison lake zooplankton data long-term study seasonal dynamics zooplankton, collected Richard Lathrop. data collected chain lakes Wisconsin (Mendota, Monona, Kegnonsa, Waubesa) approximately bi-weekly 1976 1994. consist samples zooplankton communities, taken deepest point lake via vertical tow. data provided Wisconsin Department Natural Resources collection processing fully described Lathrop (2000).","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/zooplankton.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Madison lakes zooplankton data — zooplankton","text":"data frame","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/zooplankton.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Madison lakes zooplankton data — zooplankton","text":"Pedersen EJ, Miller DL, Simpson GL, Ross N. 2018. Hierarchical generalized additive models: introduction mgcv. PeerJ Preprints 6:e27320v1 doi:10.7287/peerj.preprints.27320v1 .","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/zooplankton.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Madison lakes zooplankton data — zooplankton","text":"record consists counts given zooplankton taxon taken subsample single vertical net tow, scaled account relative volume subsample versus whole net sample area net tow rounded nearest 1000 give estimated population density per m2 taxon point time sampled lake.","code":""},{"path":"https://gavinsimpson.github.io/gratia/reference/zooplankton.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Madison lakes zooplankton data — zooplankton","text":"Lathrop RC. (2000). Madison Wisonsin Lakes Zooplankton 1976–1994. Environmental Data Initiative.","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"gratia-092","dir":"Changelog","previous_headings":"","what":"gratia 0.9.2","title":"gratia 0.9.2","text":"CRAN release: 2024-06-25","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"breaking-changes-0-9-2","dir":"Changelog","previous_headings":"","what":"Breaking changes","title":"gratia 0.9.2","text":"parametric_effects() slightly escaped great renaming happened 0.9.0. Columns type term gain prefix .. now rectified two columns now .type .term.","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"user-visible-changes-0-9-2","dir":"Changelog","previous_headings":"","what":"User visible changes","title":"gratia 0.9.2","text":"Plots random effects now labelled smooth label. Previously, title taken fro variable involved smooth, doesn’t work terms like s(subject, continuous_var, bs = \"re\") random slopes, previsouly title \"subject\". Now terms title \"s(subject,continuous_var)\". Simple random intercept terms, s(subject, bs = \"re\"), now titled \"s(subject)\". #287 vignettes custom-plotting.Rmd, posterior-simulation.Rmd moved vignettes/articles thus longer available package vignettes. Instead, accessible Articles package website: https://gavinsimpson.github.io/gratia/","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"new-features-0-9-2","dir":"Changelog","previous_headings":"","what":"New features","title":"gratia 0.9.2","text":"fitted_samples() now works gam() models multiple linear predictors, currently location parameter supported. parameter indicated new variable .parameter returned object.","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"bug-fixes-0-9-2","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"gratia 0.9.2","text":"partial_residuals() computing partial residuals deviance residuals. compatibility mgcv::plot.gam(), partial residuals now computed working residuals. Reported @wStockhausen #273 appraise() passing ci_col argument qq_plot() worm_plot(). Reported Sate Ahmed. Couldn’t pass mvn_method posterior sampling functions user facing functions fitted_samples(), posterior_samples(), smooth_samples(), derivative_samples(), repsonse_derivatives(). Reported @stefgehrig #279 fitted_values() works quantile GAMs fitted qgam(). confint.gam() applying shift estimate upper lower interval. #280 reported @TIMAVID & @rbentham parametric_effects() draw.parametric_effects() forget levels factors (intentionally), lead problems ordered factors ordering levels preserved. Now, parametric_effects() returns named list factor levels attribute \"factor_levels\" containing required information order levels preserved plotting. #284 Reported @mhpob parametric_effects() fail parametric terms model interaction terms (don’t currently handle). #282","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"gratia-090","dir":"Changelog","previous_headings":"","what":"gratia 0.9.0","title":"gratia 0.9.0","text":"CRAN release: 2024-03-27","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"breaking-changes-0-9-0","dir":"Changelog","previous_headings":"","what":"Breaking changes","title":"gratia 0.9.0","text":"Many functions now return objects different named variables. order avoid clashes variable names used user’s models data, period (.) now used prefix generated variable names. functions whose names changed : smooth_estimates(), fitted_values(), fitted_samples(), posterior_samples(), derivatives(), partial_derivatives(), derivative_samples(). addition, add_confint() also adds newly-named variables. derivatives() partial_derivatives() now work like smooth_estimates(); place var data columns, gratia now stores data variables derivatives evaluated columns object actual variable names. way spline---sphere (SOS) smooths (bs = \"sos\") plotted changed use ggplot2::coord_sf() instead previously-used ggplot2::coord_map(). changed made result coord_map() soft-deprecated (“superseded”) minor versions ggplot2 now already, changes guides system version 3.5.0 ggplot2. axes plots created coord_map() never really worked correctly changing angle tick labels never worked. coord_map() superseded, didn’t receive updates guides system side effect changes, code plotted SOS smooths producing warning release ggplot2 version 3.5.0. projection settings used draw SOS smooths previously controlled via arguments projection orientation. arguments affect ggplot2::coord_sf(), Instead projection used controlled new argument crs, takes PROJ string detailing projection use integer refers known coordinate reference system (CRS). default projection used +proj=ortho +lat_0=20 +lon_0=XX XX mean longitude coordinates data points.","code":"1. `est` is now `.estimate`, 2. `lower` and `upper` are now `.lower_ci` and `.upper_ci`, 3. `draw` and `row` and now `.draw` and `.row` respectively, 4. `fitted`, `se`, `crit` are now `.fitted`, `.se`, `.crit`, respectively 5. `smooth`, `by`, and `type` in `smooth_estimates()` are now `.smooth`, `.by`, `.type`, respectively."},{"path":[]},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"defunct-0-9-0","dir":"Changelog","previous_headings":"Breaking changes > Defunct and deprecated functions and arguments","what":"Defunct","title":"gratia 0.9.0","text":"evaluate_smooth() deprecated gratia version 0.7.0. function ’s methods removed package. Use smooth_estimates() instead.","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"deprecated-functions-0-9-0","dir":"Changelog","previous_headings":"Breaking changes > Defunct and deprecated functions and arguments","what":"Deprecated functions","title":"gratia 0.9.0","text":"following functions deprecated version 0.9.0 gratia. eventually removed package part clean ahead eventual 1.0.0 release. functions become defunct version 0.11.0 1.0.0, whichever released soonest. evaluate_parametric_term() deprecated. Use parametric_effects() instead. datagen() deprecated. never really originally designed , replaced data_slice().","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"deprecated-arguments-0-9-0","dir":"Changelog","previous_headings":"Breaking changes > Defunct and deprecated functions and arguments","what":"Deprecated arguments","title":"gratia 0.9.0","text":"make functions package consistent, arguments select, term, smooth used thing hence latter two deprecated favour select. deprecated argument used, warning issued value assigned argument assigned select function continue.","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"user-visible-changes-0-9-0","dir":"Changelog","previous_headings":"","what":"User visible changes","title":"gratia 0.9.0","text":"smooth_samples() now uses single call RNG generate draws posterior smooths. Previous version 0.9.0, smooth_samples() separate call mvnfast::rmvn() smooth. result, result call smooth_samples() model multiple smooths now produce different results generated previously. regain old behaviour, add rng_per_smooth = TRUE smooth_samples() call. Note, however, using per-smooth RNG calls method = \"mh\" inefficient , method, posterior draws coefficients model sampled . , use rng_per_smooth = TRUE method = \"gaussian\". output smooth_estimates() draw() method changed tensor product smooths involve one 2D marginal smooths. Now, covariate values supplied via data argument, smooth_estimates() identifies one marginals 2d surface allows covariates involved surface vary fastest, ahead terms marginals. change made provides better default nothing provided data. also affects draw.gam(). fitted_values() now level support location, scale, shape families. Supported families mgcv::gaulss(), mgcv::gammals(), mgcv::gumbls(), mgcv::gevlss(), mgcv::shash(), mgcv::twlss(), mgcv::ziplss(). gratia now requires dplyr versions >= 1.1.0 tidyselect >= 1.2.0. new vignette Posterior Simulation available, describes posterior simulation fitted GAMs using {gratia}.","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"new-features-0-9-0","dir":"Changelog","previous_headings":"","what":"New features","title":"gratia 0.9.0","text":"Soap film smooths using basis bs = \"\" now handled draw(), smooth_estimates() etc. #8 response_derivatives() new function computing derivatives response respect (continuous) focal variable. First second order derivatives can computed using forward, backward, central finite differences. uncertainty estimated derivative determined using posterior sampling via fitted_samples(), hence can derived Gaussian approximation posterior using Metropolis Hastings sampler (see .) derivative_samples() work horse function behind response_derivatives(), computes returns posterior draws derivatives additive combination model terms. Requested @jonathanmellor #237 data_sim() can now simulate response data gamma, Tweedie ordered categorical distributions. data_sim() gains two new example models \"gwf2\", simulating data Gu & Wabha’s f2 function, \"lwf6\", example function 6 Luo & Wabha (1997 JASA 92(437), 107-116). data_sim() can also simulate data use GAMs fitted using family = gfam() grouped families different types data response handled. #266 part #265 fitted_samples() smooth_samples() can now use Metropolis Hastings sampler mgcv::gam.mh(), instead Gaussian approximation, sample posterior distribution model specific smooths respectively. posterior_samples() new function family fitted_samples() smooth_samples(). posterior_samples() returns draws posterior distribution response, combining uncertainty estimated expected value response dispersion response distribution. difference posterior_samples() predicted_samples() latter includes variation due drawing samples conditional distribution response (uncertainty expected values ignored), former includes sources uncertainty. fitted_samples() can new use matrix user-supplied posterior draws. Related #120 add_fitted_samples(), add_predicted_samples(), add_posterior_samples(), add_smooth_samples() new utility functions add respective draws posterior distribution existing data object covariate values object: obj |> add_posterior_draws(model). #50 basis_size() new function extract basis dimension (number basis functions) smooths. Methods available objects inherit classes \"gam\", \"gamm\", \"mgcv.smooth\" (individual smooths). data_slice() gains method data frames tibbles. typical_values() gains method data frames tibbles. fitted_values() now works models fitted using mgcv::ocat() family. predicted probability category returned, alongside Wald interval created using standard error (SE) estimated probability. SE estimated probabilities transformed logit (linear predictor) scale, Wald credible interval formed, back-transformed response (probability) scale. fitted_values() now works GAMMs fitted using mgcv::gamm(). Fitted (predicted) values use GAM part model, thus exclude random effects. link() inv_link() work models fitted using cnorm() family. worm plot can now drawn place QQ plot appraise() via new argument use_worm = TRUE. #62 smooths() now works models fitted mgcv::gamm(). overview() now returns basis dimension smooth gains argument stars TRUE add significance stars output plus legend printed tibble footer. Part wish @noamross #214 New add_constant() transform_fun() methods smooth_samples(). evenly() gains arguments lower upper modify lower / upper bound interval evenly spaced values generated. add_sizer() new function add information whether derivative smooth significantly changing (credible interval excludes 0). Currently, methods derivatives() smooth_estimates() objects implemented. Part request @asanders11 #117 draw.derivatives() gains arguments add_change change_type allow derivatives smooths plotted indicators credible interval derivative excludes 0. Options allow periods decrease increase differentiated via change_type = \"sizer\" instead default change_type = \"change\", emphasises either type change way. Part wish @asanders11 #117 draw.gam() can now group factor smooths given factor single panel, rather plotting smooths level separate panels. achieved via new argument grouped_by. Requested @RPanczak #89 draw.smooth_estimates() can now also group factor smooths given factor single panel. underlying plotting code used draw_smooth_estimates() univariate smooths can now add change indicators plots smooths change indicators added object created smooth_estimates() using add_sizer(). See example ?draw.smooth_estimates. smooth_estimates() can, evaluating 3D 4D tensor product smooth, identify one 2D smooths marginal tensor product. users provide covariate values evaluate smooths, smooth_estimates() focus 2D marginal smooth (first one involved tensor product), instead following ordering terms definition tensor product. #191 example, te(z, x, y, bs = c(cr, ds), d = c(1, 2)), second marginal smooth 2D Duchon spline covariates x y. Previously, smooth_estimates() generated n values z x n_3d values y, evaluated tensor product combinations generated values. ignore structure implicit tensor product, likely want know surface estimated Duchon spline x y smoothly varies z. Previously smooth_estimates() generate surfaces z x, varying y. Now, smooth_estimates() correctly identifies one marginal smooths tensor product 2D surface focus surface varying terms tensor product. improved behaviour needed bam() models always possible obvious thing reorder smooths defining tensor product te(x, y, z, bs = c(ds, cr), d = c(2, 1)). discrete = TRUE used bam() terms tensor product may get rearranged model setup maximum efficiency (See Details ?mgcv::bam). Additionally, draw.gam() now also works way. New function null_deviance() extracts null deviance fitted model. draw(), smooth_estimates(), fitted_values(), data_slice(), smooth_samples() now work models fitted scam::scam(). matters, current support extends univariate smooths. generate_draws() new low-level function generating posterior draws fitted model coefficients. generate_daws() S3 generic function extensible users. Currently provides simple interface simple Gaussian approximation sampler (gaussian_draws()) simple Metropolis Hasting sample (mh_draws()) available via mgcv::gam.mh(). #211 smooth_label() new function extracting labels ‘mgcv’ creates smooths smooth object . penalty() default method works s(), te(), t2(), ti(), create smooth specification. transform_fun() gains argument constant allow addition constant value objects (e.g. estimate confidence interval). enables single obj |> transform_fun(fun = exp, constant = 5) instead separate calls add_constant() transform_fun(). Part discussion #79 model_constant() new function simply extracts first coefficient estimated model.","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"bug-fixes-0-9-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"gratia 0.9.0","text":"link(), inv_link(), related family functions ocat() weren’t correctly identifying family name result throw error even passed object correct family. link() inv_link() now work correctly betar() family fitted GAM. print() method lp_matrix() now converts matrix data frame conversion tibble. makes sense results typical behaviour columns printed object doubles. Constrained factor smooths (bs = \"sz\") factor first variable mentioned smooth (.e. s(x, f, bs = \"sz\") continuous x factor f) now plotable draw(). #208 parametric_effects() unable handle special parametric terms like poly(x) log(x) formulas. Reported @fhui28 #212 parametric_effects() now works better location, scale, shape models. Reported @pboesu #45 parametric_effects now works missing values one variables used fitted GAM. #219 response_derivatives() incorrectly using .data tidyselect selectors. typical_values() handle logical variables GAM fit mgcv stores numerics var.summary. affected evenly() data_slice(). #222 parametric_effects() fail two ordered factors model. Reported @dsmi31 #221 Continuous smooths evaluated median value variable instead value 1. #224 fitted_samples() (hence posterior_samples()) now handles models offset terms formula. Offset terms supplied via offset argument ignored mgcv:::predict.gam() hence ignored also gratia. Reported @jonathonmellor #231 #233 smooth_estimates() fail \"fs\" smooth multivariate base smoother used factor last variable specified definition smooth: s(x1, x2, f, bs = \"fs\", xt = list(bs = \"ds\")) work, s(f, x1, x2, bs = \"fs\", xt = list(bs = \"ds\")) (ordering variables places factor last) emit obscure error. ordering terms involved smooth now doesn’t matter. Reported @chrisaak #249. draw.gam() fail plotting multivariate base smoother used \"sz\" smooth. Now, use case identified message printed indicating (currently) gratia doesn’t know plot smooth. Reported @chrisaak #249. draw.gam() fail plotting multivariate base smoother used \"fs\" smooth. Now, use case identified message printed indicating (currently) gratia doesn’t know plot smooth. Reported @chrisaak #249. derivative_samples() fail order = 2 computing forward finite differences, regardless type order = 1. Partly reported @samlipworth #251. draw() method penalty() normalizing penalty range 0–1, claimed documented -1–1 argument normalize = TRUE. now fixed. smooth_samples() failing data supplied contained variables used smooth sampled. Hence generally fail unless single smooth sampled model contained single smooth. function never intended retain variables data written way fail relocating data columns end posterior sampling object. #255 draw.gam() draw.smooth_estimates() fail plotting univariate tensor product smooth (e.g. te(x), ti(x), t2()). Reported @wStockhausen #260 plot.smooth() printing factor level subtitles ordered factor smooths.","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"gratia-082","dir":"Changelog","previous_headings":"","what":"gratia 0.8.2","title":"gratia 0.8.2","text":"CRAN release: 2024-01-09 Small fixes CRAN.","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"gratia-081","dir":"Changelog","previous_headings":"","what":"gratia 0.8.1","title":"gratia 0.8.1","text":"CRAN release: 2023-02-02","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"user-visible-changes-0-8-1","dir":"Changelog","previous_headings":"","what":"User visible changes","title":"gratia 0.8.1","text":"smooth_samples() now returns objects variables involved smooths correct name. Previously variables named .x1, .x2, etc. Fixing #126 improving compatibility compare_smooths() smooth_estimates() allowed variables named correctly. gratia now depends version 1.8-41 later mgcv package.","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"new-features-0-8-1","dir":"Changelog","previous_headings":"","what":"New features","title":"gratia 0.8.1","text":"draw.gam() can now handle tensor products include marginal random effect smooth. Beware plotting smooths many levels, however, separate surface plot produced level.","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"bug-fixes-0-8-1","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"gratia 0.8.1","text":"Additional fixes changes dplyr 1.1.0. smooth_samples() now works sampling posteriors multiple smooths different dimension. #126 reported @Aariq","code":""},{"path":[]},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"user-visible-changes-0-8-0","dir":"Changelog","previous_headings":"","what":"User visible changes","title":"gratia 0.8.0","text":"{gratia} now depends R version 4.1 later. new vignette “Data slices” supplied {gratia}. Functions {gratia} harmonised use argument named data instead newdata passing new data evaluate features smooths. message printed newdata used now . Existing code need changed data takes value newdata. Note due way ... handled R, R script uses data argument, run versions gratia prior 8.0 (released; 0.7.3.8 using development version) user-supplied data silently ignored. , scripts using data check installed version gratia >= 0.8 package developers update depend versions >= 0.8 using gratia (>= 0.8) DESCRIPTION. order plots smooths changed draw.gam() match order smooths specified model formula. See Bug Fixes detail #154.","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"new-features-0-8-0","dir":"Changelog","previous_headings":"","what":"New features","title":"gratia 0.8.0","text":"Added basic support GAMLSS (distributional GAMs) fitted gamlss() function package GJRM. Support currently restricted draw() method. difference_smooths() can now include group means difference, many users expected. include group means use group_means = TRUE function call, e.g. difference_smooths(model, smooth = \"s(x)\", group_means = TRUE). Note: function still differs plot_diff() package itsadug, essentially computes differences model predictions. main practical difference effects beyond factor smooth, including random effects, may included plot_diff(). implements main wish #108 (@dinga92) #143 (@mbolyanatz) despite protestations complicated cases (isn’t; complexity just cancels .) data_slice() totally revised. Now, user provides values variables want slice variables model specified held typical values (.e. value observation closest median numeric variables, modal factor level.) Data slices now produced passing name = value pairs variables values want appear slice. example value pair can expression looked (evaluated) data argument model frame fitted model (default). example, resulting slice data frame 100 observations, comprising x1, vector 100 values spread evenly range x1, constant value mean x2 x2 variable, constant factor level, model class fac, fac variable model. partial_derivatives() new function computing partial derivatives multivariate smooths (e.g. s(x,z), te(x,z)) respect one margins smooth. Multivariate smooths dimension handled, one dimensions allowed vary. Partial derivatives estimated using method finite differences, forward, backward, central finite differences available. Requested @noamross #101 overview() provides simple overview model terms fitted GAMs. new bs = \"sz\" basis released mgcv version 1.18-41 now supported smooth_estimates(), draw.gam(), draw.smooth_estimates() basis unique plotting method. #202 basis() now method fitted GAM(M)s can extract estimated basis model plot , using estimated coefficients smooth weight basis. #137 also new draw.basis() method plotting results call basis(). method can now also handle bivariate bases. tidy_basis() lower level function heavy lifting basis(), now exported. tidy_basis() returns tidy representation basis supplied object inheriting class \"mgcv.smooth\". objects returned $smooth component fitted GAM(M) model. lp_matrix() new utility function quickly return linear predictor matrix estimated model. wrapper predict(..., type = \"lpmatrix\") evenly() synonym seq_min_max() preferred going forward. Gains argument produce sequences covariate increment units . ref_level() level() new utility functions extracting reference specific level factor respectively. useful specifying covariate values condition data slice. model_vars() new, public facing way returning vector variables used model. difference_smooths() now use user-supplied data points evaluate pair smooths. Also note argument newdata renamed data. #175 draw() method difference_smooths() now uses better labels plot titles avoid long labels even modest factor levels. derivatives() now works factor-smooth interaction (\"fs\") smooths. draw() methods now allow angle tick labels x axis plots rotated using argument angle. Requested @tamas-ferenci #87 draw.gam() related functions (draw.parametric_effects(), draw.smooth_estimates()) now add basis plot using caption. #155 smooth_coefs() new utility function extracting coefficients particular smooth fitted model. smooth_coef_indices() associated function returns indices (positions) vector model coefficients (returned coef(gam_model)) coefficients pertain stated smooth. draw.gam() now better handles patchworks plots one plots fixed aspect ratios. #190","code":"m <- gam(y ~ s(x1) + x2 + fac) data_slice(model, x1 = evenly(x1, n = 100), x2 = mean(x2))"},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"bug-fixes-0-8-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"gratia 0.8.0","text":"draw.posterior_smooths now plots posterior samples fixed aspect ratio smooth isotropic. #148 derivatives() now ignores random effect smooths (derivatives don’t make sense anyway). #168 confint.gam(...., method = \"simultaneous\") now works factor smooths parm passed full name specific smooth s(x)faclevel. order plots produced gratia::draw.gam() matches order smooths entered model formula. Recent changes internals gratia::draw.gam() switch smooth_estimates() undertaken lead change behaviour resulting use dplyr::group_split(), ’s coercion internally character vector factor. factor now created explicitly, levels set correct order. #154 Setting dist argument set response smooth values NA lay far support data multivariate smooths, lead incorrect scale response guide. now fixed. #193 Argument fun draw.gam() applied parametric terms. Reported @grasshoppermouse #195 draw.gam() adding uncertainty linear predictors smooths overall_uncertainty = TRUE used. Now draw.gam() includes uncertainty linear predictors smooth takes part. #158 partial_derivatives() works provided single data point evaluate derivative. #199 transform_fun.smooth_estimates() addressing wrong variable names trying transform confidence interval. #201 data_slice() doesn’t fail error used model contains offset term. #198 confint.gam() longer uses evaluate_smooth(), soft deprecated. #167 qq_plot() worm_plot() compute wrong deviance residuals used generate theoretical quantiles exotic families (distributions) available mgcv. also affected appraise() QQ plot; residuals shown plots deviance residuals shown y-axis QQ plot correct. generation reference intervals/quantiles affected.","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"gratia-073","dir":"Changelog","previous_headings":"","what":"gratia 0.7.3","title":"gratia 0.7.3","text":"CRAN release: 2022-05-09","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"user-visible-changes-0-7-3","dir":"Changelog","previous_headings":"","what":"User visible changes","title":"gratia 0.7.3","text":"Plots smooths now use “Partial effect” y-axis label place “Effect”, better indicate displayed.","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"new-features-0-7-3","dir":"Changelog","previous_headings":"","what":"New features","title":"gratia 0.7.3","text":"confint.fderiv() confint.gam() now return results tibble instead common--garden data frame. latter mostly already . Examples confint.fderiv() confint.gam() reworked, part remove inconsistent output examples run M1 macs.","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"bug-fixes-0-7-3","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"gratia 0.7.3","text":"compare_smooths() failed passed non-standard model “names” like compare_smooths(m_gam, m_gamm$gam) compare_smooths(l[[1]], l[[2]]) even evaluated objects valid GAM(M) models. Reported Andrew Irwin #150","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"gratia-072","dir":"Changelog","previous_headings":"","what":"gratia 0.7.2","title":"gratia 0.7.2","text":"CRAN release: 2022-03-17","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"new-features-0-7-2","dir":"Changelog","previous_headings":"","what":"New features","title":"gratia 0.7.2","text":"draw.gam() draw.smooth_estimates() can now handle splines sphere (s(lat, long, bs = \"sos\")) special plotting methods using ggplot2::coord_map() handle projection spherical coordinates. orthographic projection used default, essentially arbitrary (northern hemisphere-centric) default orientation view. fitted_values() insures data (hence returned object) tibble rather common garden data frame.","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"bug-fixes-0-7-2","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"gratia 0.7.2","text":"draw.posterior_smooths() redundantly plotting duplicate data rug plot. Now unique set covariate values used drawing rug. data_sim() passing scale argument bivariate example setting (\"eg2\"). draw() methods gamm() gamm4::gamm4() fits passing arguments draw.gam(). draw.smooth_estimates() produce subtitle data continuous smooth factor smooth. Now subtitle contains name continuous variable.","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"gratia-071","dir":"Changelog","previous_headings":"","what":"gratia 0.7.1","title":"gratia 0.7.1","text":"Due issue size package source tarball, wasn’t discovered submission CRAN, 0.7.1 never released.","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"new-features-0-7-1","dir":"Changelog","previous_headings":"","what":"New features","title":"gratia 0.7.1","text":"draw.gam() draw.smooth_estimates(): {gratia} can now handle smooths 3 4 covariates plotting. smooths 3 covariates, third covariate handled ggplot2::facet_wrap() set (default n = 16) small multiples drawn, 2d surface evaluated specified value third covariate. smooths 4 covariates, ggplot2::facet_grid() used draw small multiples, default producing 4 rows 4 columns plots specific values third fourth covariates. number small multiples produced controlled new arguments n_3d (default = n_3d = 16) n_4d (default n_4d = 4, yielding n_4d * n_4d = 16 facets) respectively. affects plotting; smooth_estimates() able handle smooths number covariates . handling higher-dimensional smooths, actually drawing plots default device can slow, especially default value n = 100 (3D 4D smooths result 160,000 data points plotted). recommended reduce n smaller value: n = 50 reasonable compromise resolution speed. model_concurvity() returns concurvity measures mgcv::concurvity() estimated GAMs tidy format. synonym concrvity() also provided. draw() method provided produces bar plot heatmap concurvity values depending whether overall concurvity smooth pairwise concurvity smooth model requested. draw.gam() gains argument resid_col = \"steelblue3\" allows colour partial residuals (plotted) changed.","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"bug-fixes-0-7-1","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"gratia 0.7.1","text":"model_edf() using type argument. result ever returned default EDF type. add_constant() methods weren’t applying constant required variables. draw.gam(), draw.parametric_effects() now actually work model parametric effects. #142 Reported @Nelson-Gon parametric_effects() fail model parametric terms predict.gam() returns empty arrays passed exclude = character(0).","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"gratia-070","dir":"Changelog","previous_headings":"","what":"gratia 0.7.0","title":"gratia 0.7.0","text":"CRAN release: 2022-02-07","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"major-changes-0-7-0","dir":"Changelog","previous_headings":"","what":"Major changes","title":"gratia 0.7.0","text":"draw.gam() now uses smooth_estimates() internally consequently uses draw() method underlying plotting code. simplified code compared evaluate_smooth() methods, allow future development addition features easily evaluate_smooth() retained. Similarly, evaluate_parametric_terms() now deprecated favour parametric_effects(), also used internally draw.gam() parametric terms present model (parametric = TRUE). lot code reused differences plots result change minimal, corner cases may missed. File Issue notice something changed think shouldn’t. draw.gam() now plots 2D isotropic smooths (TPRS Duchon splines) equally-scaled x y coordinates using coord_equal(ratio = 1). Alignment plots little different now plotting models multiple smooths. See Issue #81.","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"deprecated-functions-0-7-0","dir":"Changelog","previous_headings":"Major changes","what":"Deprecated functions","title":"gratia 0.7.0","text":"version 0.7.0, following functions considered deprecated use discouraged: fderiv() soft-deprecated favour derivatives(), evaluate_smooth() soft-deprecated favour smooth_estimates(), evaluate_parametric_term() soft-deprecated favour parametric_effects(). first call one functions generate warning, pointing newer, alternative, function. safe ignore warnings, deprecated functions longer receive updates thus risk removed package future date. newer alternatives can handle types models smooths, especially case smooth_estimates().","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"new-features-0-7-0","dir":"Changelog","previous_headings":"","what":"New features","title":"gratia 0.7.0","text":"fitted_values() provides tidy wrapper around predict.gam() generating fitted values model. New covariate values can provided via argument data. credible interval fitted values returned, values can link (linear predictor) response scale. Note function returns expected values response. Hence, “fitted values” used instead “predictions” case new covariate values differentiate values case generating new response values fitted model. rootogram() draw() method produce rootograms diagnostic plots fitted models. Currently models fitted poisson(), nb(), negbin(), gaussian() families. New helper functions typical_values(), factor_combos() data_combos() quickly creating data sets producing predictions fitted models covariatess fixed come typical representative values. typical_values() new helper function return typical values covariates fitted model. returns value observation closest median numerical covariates modal level factor preserving levels factor. typical_values() useful preparing data slices scenarios fitted values estimated model required. factor_combos() extracts returns combinations levels factors found data used fit model. Unlike typical_values(), factor_combos() returns combinations factor levels observed data, just modal level. Optionally, combinations factor levels can returned, just observed data. data_combos() combines returns factor data factor_combos() plus typical values numerical covariates. useful want generate predictions model combination factor terms holding continuous covariates median values. nb_theta() new extractor function returns theta parameter fitted negative binomial GAM (families nb() negbin()). Additionally, theta() has_theta() provide additional functionality. theta() experimental function extracting additional parameters model family. has_theta() useful checking additional parameters available family model. edf() extracts effective degrees freedom (EDF) fitted model specific smooth model. Various forms EDF can extracted. model_edf() returns EDF overall model. supplied multiple models, EDFs model returned comparison. draw.gam() can now show “rug” plot bivariate smooth drawing small points high transparency smooth surface data coordinates. addition, rugs plots factor smooths now show locations covariate values specific level factor levels. better reflects data used estimate smooth, even though basis smooth set using covariate locations. draw.gam() draw.smooth_estimates() now allow aspects plot changed: fill (colour) alpha attributes credible interval, line colour smooth can now specified using arguments ci_col, ci_alpha, smooth_col respectively. Partial residuals can now plotted factor smooths. allow , partial residuals filtered residuals associated particular level’s smooth drawn plot smooth. smooth_estimates() uses check_user_select_smooths() handle user-specified selection smooth terms. flexible previously, allows easier selection smooths evaluate. fixef() now imported (re-exported) nlme package, methods models fitted gam() gamm(), extract fixed effects estimates fitted models. fixed_effects() alias fixef(). draw() method smooth_samples() can now handle 2D smooths. Additionally, number posterior draws plot can now specified plotting using new argument n_samples, result n_samples draws selected random set draws plotting. New argument seed allows selection draws repeatable.","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"bug-fixes-0-7-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"gratia 0.7.0","text":"smooth_estimates() filtering user-supplied data level specific smooth used factor smooths. result smooth evaluated rows user-supplied data, therefore result nrow(user_data) * nlevels(by_variable) rows returned object instead nrow(user_data) rows. add_confint() method smooth_estimates() upper lower intervals reversed. #107 Reported @Aariq draw.gam() smooth_estimates() ignoring dist argument allows covariate values lie far support data excluded returning estimated values smooth plotting . #111 Reported @Aariq smooth_samples() factor GAM return samples first factor level . Reported @rroyaute discussion #121 smooth_samples() fail model contained random effect “smooths”. now ignored message running smooth_samples(). Reported @isabellaghement #121 link(), inv_link() failing models fitted family = scat(). Reported @Aariq #130","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"gratia-060","dir":"Changelog","previous_headings":"","what":"gratia 0.6.0","title":"gratia 0.6.0","text":"CRAN release: 2021-04-18","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"major-changes-0-6-0","dir":"Changelog","previous_headings":"","what":"Major changes","title":"gratia 0.6.0","text":"{cowplot} package replaced {patchwork} package producing multi-panel figures draw() appraise(). shouldn’t affect code used {gratia} , passed additional arguments cowplot::plot_grid() used align axis arguments draw() appraise(), ’ll need adapt code accordingly. Typically, can simply delete align axis arguments {patchwork} just work align plots nicely. arguments passed via ... cowplot::plot_grid() just ignored patchwork::wrap_plots() unless passed arguments match arguments patchwork::wrap_plots().","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"new-features-0-6-0","dir":"Changelog","previous_headings":"","what":"New features","title":"gratia 0.6.0","text":"{patchwork} package now used multi-panel figures. , {gratia} longer Imports {cowplot} package. Worm plot diagnostic plots available via new function worm_plot(). Worm plots detrended Q-Q plots, deviation Q-Q reference line emphasized deviations around line occupy full height plot. worm_plot() methods available models classes \"gam\", \"glm\", \"lm\". (#62) Smooths can now compared across models using compare_smooths(), comparisons visualised associated draw() method. (#85 @dill) feature bit experimental; returned object uses nested lists may change future users find confusing. reference line qq_plot() method = \"normal\" previously drawn line intercept 0 slope 1, match methods. inconsistent stats::qqplot() drew line 1st 3rd quartiles. qq_plot() method = \"normal\" now uses robust reference line. Reference lines methods remain drawn slope 1 intercept 0. qq_plot() method = \"normal\" now draws point-wise reference band using standard error order statistic. draw() method penalty() now plots penalty matrix heatmaps -logical orientation, match matrices might written printed R console. link(), inv_link() now work models fitted gumbls() shash() families. (#84) extract_link() lower level utility function related link() inv_link(), now exported.","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"user-visible-changes-0-6-0","dir":"Changelog","previous_headings":"","what":"User visible changes","title":"gratia 0.6.0","text":"default method name generating reference quantiles qq_plot() changed \"direct\" \"uniform\", avoid confusion mgcv::qq.gam() help page description methods. Accordingly using method = \"direct\" deprecated message effect displayed used. way smooths/terms selected derivatives() switched use mechanism draw.gam()’s select argument. get partial match term, now need also specify partial_match = TRUE call derivatives().","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"bug-fixes-0-6-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"gratia 0.6.0","text":"transform_fun() copy paste bug definition generic. (#96 @Aariq) derivatives() user-supplied newdata fail factor smooths interval = \"simultaneous\" introduce rows derivative == 0 interval = \"confidence\" didn’t subset rows newdata specific level factor computing derivatives. (#102 @sambweber) evaluate_smooth() can now handle random effect smooths defined using ordered factor. (#99 @StefanoMezzini)","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"gratia-051","dir":"Changelog","previous_headings":"","what":"gratia 0.5.1","title":"gratia 0.5.1","text":"CRAN release: 2021-01-23","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"new-features-0-5-1","dir":"Changelog","previous_headings":"","what":"New features","title":"gratia 0.5.1","text":"smooth_estimates() can now handle bivariate multivariate thinplate regression spline smooths, e.g.  s(x, z, ), tensor product smooths (te(), t2(), & ti()), e.g. te(x, z, ) factor smooth interactions, e.g. s(x, f, bs = \"fs\") random effect smooths, e.g. s(f, bs = \"re\") penalty() provides tidy representation penalty matrices smooths. tidy representation suitable plotting ggplot(). draw() method provided, represents penalty matrix heatmap.","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"user-visible-changes-0-5-1","dir":"Changelog","previous_headings":"","what":"User visible changes","title":"gratia 0.5.1","text":"newdata argument smooth_estimates() changed data originally intended.","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"gratia-050","dir":"Changelog","previous_headings":"","what":"gratia 0.5.0","title":"gratia 0.5.0","text":"CRAN release: 2021-01-10","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"new-features-0-5-0","dir":"Changelog","previous_headings":"","what":"New features","title":"gratia 0.5.0","text":"Partial residuals models can computed partial_residuals(). partial residuals weighted residuals model added contribution smooth term (returned predict(model, type = \"terms\"). Wish #76 (@noamross) Also, new function add_partial_residuals() can used add partial residuals data frames. Users can now control extent colour fill scales used plotting smooths draw() methods use . useful change fill scale plotting 2D smooths, change discrete colour scale used plotting random factor smooths (bs = \"fs\"). user can pass scales via arguments discrete_colour continuous_fill. effects certain smooths can excluded data simulated model using simulate.gam() predicted_samples() passing exclude terms predict.gam(). allows excluding random effects, example, model predicted values used simulate new data conditional distribution. See example predicted_samples(). Wish #74 (@hgoldspiel) draw.gam() related functions gain arguments constant fun allow user-defined constants transformations smooth estimates confidence intervals applied. Part wish Wish #79. confint.gam() now works 2D smooths also. smooth_estimates() early version code replace (likely supersede) evaluate_smooth(). smooth_estimates() can currently handle 1D smooths standard types.","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"user-visible-changes-0-5-0","dir":"Changelog","previous_headings":"","what":"User visible changes","title":"gratia 0.5.0","text":"meaning parm confint.gam changed. argument now requires smooth label match smooth. vector labels can provided, partial matching smooth label works single parm value. default behaviour remains unchanged however; parm NULL smooths evaluated returned confidence intervals. data_class() longer exported; ever intended internal function.","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"bug-fixes-0-5-0","dir":"Changelog","previous_headings":"","what":"Bug Fixes","title":"gratia 0.5.0","text":"confint.gam() failing tensor product smooth due matching issues. Reported @tamas-ferenci #88 also fixes #80 related issue selecting specific smooth. vdiffr package now used conditionally package tests. Reported Brian Ripley #93","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"gratia-041","dir":"Changelog","previous_headings":"","what":"gratia 0.4.1","title":"gratia 0.4.1","text":"CRAN release: 2020-05-30","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"user-visible-changes-0-4-1","dir":"Changelog","previous_headings":"","what":"User visible changes","title":"gratia 0.4.1","text":"draw.gam() scales = \"fixed\" now applies terms can plotted, including 2d smooths. Reported @StefanoMezzini #73","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"bug-fixes-0-4-1","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"gratia 0.4.1","text":"dplyr::combine() deprecated. Switch vctrs::vec_c(). draw.gam() scales = \"fixed\" wasn’t using fixed scales 2d smooths model. Reported @StefanoMezzini #73","code":""},{"path":[]},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"new-features-0-4-0","dir":"Changelog","previous_headings":"","what":"New features","title":"gratia 0.4.0","text":"draw.gam() can include partial residuals drawing univariate smooths. Use residuals = TRUE add partial residuals univariate smooth drawn. feature available smooths one variable, smooths, factor-smooth interactions (bs = \"fs\"). coverage credible confidence intervals drawn draw.gam() can specified via argument ci_level. default arbitrarily 0.95 reason (rough) compatibility plot.gam(). change effect making intervals slightly narrower previous versions gratia; intervals drawn ± 2 × standard error. default intervals now drawn ± ~1.96 × standard error. New function difference_smooths() computing differences factor smooth interactions. Methods available gam(), bam(), gamm() gamm4::gamm4(). Also draw() method, can handle differences 1D 2D smooths currently (handling 3D 4D smooths planned). New functions add_fitted() add_residuals() add fitted values (expectations) model residuals existing data frame. Currently methods available objects fitted gam() bam(). data_sim() tidy reimplementation mgcv::gamSim() added ability use sampling distributions Gaussian models implemented. Currently Gaussian, Poisson, Bernoulli sampling distributions available. smooth_samples() can handle continuous variable smooths varying coefficient models. link() inv_link() now work families available mgcv, including location, scale, shape families, specialised families described ?mgcv::family.mgcv. evaluate_smooth(), data_slice(), family(), link(), inv_link() methods models fitted using gamm4() gamm4 package. data_slice() can generate data 1-d slice (single variable varying). colour points, reference lines, simulation band appraise() can now specified via arguments point_col, point_alpha, ci_col ci_alpha line_col passed qq_plot(), observed_fitted_plot(), residuals_linpred_plot(), residuals_hist_plot(), also now take new arguments applicable. Added utility functions is_factor_term() term_variables() working models. is_factor_term() identifies named term factor using information terms() object fitted model. term_variables() returns character vector variable names involved model term. strictly working parametric terms models. appraise() now works models fitted glm() lm(), underlying functions calls, especially qq_plot. appraise() also works models fitted family gaulss(). location scale models models fitted extended family functions supported upcoming releases.","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"user-visible-changes-0-4-0","dir":"Changelog","previous_headings":"","what":"User visible changes","title":"gratia 0.4.0","text":"datagen() now internal function longer exported. Use data_slice() instead. evaluate_parametric_term() now much stricter can evaluate main effect terms, .e. whose order, stored terms object model 1.","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"bug-fixes-0-4-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"gratia 0.4.0","text":"draw() method derivatives() getting x-axis label factor smooths correctly, instead using NA second subsequent levels factor. datagen() method class \"gam\" couldn’t possibly worked anything simplest models fail even simple factor smooths. issues fixed, behaviour datagen() changed, function now intended use users. Fixed issue models terms form factor1:factor2 incorrectly identified numeric parametric terms. #68","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"gratia-031","dir":"Changelog","previous_headings":"","what":"gratia 0.3.1","title":"gratia 0.3.1","text":"CRAN release: 2020-03-29","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"new-features-0-3-1","dir":"Changelog","previous_headings":"","what":"New features","title":"gratia 0.3.1","text":"New functions link() inv_link() access link function inverse fitted models family functions. Methods classes: \"glm\", \"gam\", \"bam\", \"gamm\" currently. #58 Adds explicit family() methods objects classes \"gam\", \"bam\", \"gamm\". derivatives() now handles non-numeric creating shifted data finite differences. Fixes problem stringsAsFactors = FALSE default R-devel. #64","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"bug-fixes-0-3-1","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"gratia 0.3.1","text":"Updated gratia work tibble versions >= 3.0","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"gratia-030","dir":"Changelog","previous_headings":"","what":"gratia 0.3.0","title":"gratia 0.3.0","text":"CRAN release: 2020-01-19","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"new-features-0-3-0","dir":"Changelog","previous_headings":"","what":"New features","title":"gratia 0.3.0","text":"gratia now uses mvnfast package random draws multivariate normal distribution (mvnfast::rmvn()). Contributed Henrik Singmann #28 New function basis() generating tidy representations basis expansions mgcv-like definition smooth, e.g. s(), te(), ti(), t2(). basic smooth types also simple draw() method plotting basis. basis() simple wrapper around mgcv::smoothCon() post processing basis model matrix tidy format. #42 New function smooth_samples() draw samples entire smooth functions posterior distribution. Also draw() method plotting posterior samples.","code":""},{"path":"https://gavinsimpson.github.io/gratia/news/index.html","id":"bug-fixes-0-3-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"gratia 0.3.0","text":"draw.gam() produce empty plots panels parametric terms 2 parametric terms model. Reported @sklayn #39. derivatives() now works factor smooths, including ordered factor smooths. function also now works correctly complex models multiple covariates/smooths. #47 derivatives() also now handles 'fs' smooths. Reported @tomand-uio #57. evaluate_parametric_term() hence draw.gam() fail ziplss() model ) gratia didn’t handle parametric terms models multiple linear predictors correctly, ii) gratia didn’t convert naming convention mgcv terms higher linear predictors. Reported @pboesu #45","code":""}]