From 4334de65f0b29a1e08d49a8c002005c36f359594 Mon Sep 17 00:00:00 2001
From: mbalazs98 <49280623+mbalazs98@users.noreply.github.com>
Date: Mon, 27 Nov 2023 15:06:29 +0000
Subject: [PATCH] Delay learning and new data generation (#96)

Quick_Start_random.ipynb contains code for sampling the noise offsets in every iteration. Quick_Start_Delay_DCLS.ipynb contains code for delay learning using dilated convolutions with learnable spacings.
---
 Quick_Start_Delay_DCLS.ipynb | 2905 ++++++++++++++++++++++++++++++++++
 Quick_Start_random.ipynb     | 1090 +++++++++++++
 2 files changed, 3995 insertions(+)
 create mode 100644 Quick_Start_Delay_DCLS.ipynb
 create mode 100644 Quick_Start_random.ipynb

diff --git a/Quick_Start_Delay_DCLS.ipynb b/Quick_Start_Delay_DCLS.ipynb
new file mode 100644
index 0000000..e2ce31b
--- /dev/null
+++ b/Quick_Start_Delay_DCLS.ipynb
@@ -0,0 +1,2905 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "id": "7a19cd62-7df3-40ac-b7e7-25821f94177c",
+      "metadata": {
+        "id": "7a19cd62-7df3-40ac-b7e7-25821f94177c"
+      },
+      "source": [
+        "# Quick Start Notebook"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "eb1a636b-f95e-4034-99da-ae958d253cb4",
+      "metadata": {
+        "tags": [],
+        "id": "eb1a636b-f95e-4034-99da-ae958d253cb4"
+      },
+      "source": [
+        "## Imports"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "7c2677e0-f786-4222-b36b-716b2cf8d86e",
+      "metadata": {
+        "id": "7c2677e0-f786-4222-b36b-716b2cf8d86e"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "import matplotlib.colors\n",
+        "import matplotlib.cm\n",
+        "\n",
+        "import torch\n",
+        "import torch.nn as nn\n",
+        "\n",
+        "from tqdm.auto import tqdm as pbar\n",
+        "\n",
+        "dtype = torch.float\n",
+        "\n",
+        "if torch.cuda.is_available():\n",
+        "    device = torch.device(\"cuda\")\n",
+        "else:\n",
+        "    device = torch.device(\"cpu\")\n",
+        "\n",
+        "from typing import Optional, Tuple\n",
+        "import math"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "0f6ca855-9ec7-43c1-a0a3-3bee04e601e4",
+      "metadata": {
+        "id": "0f6ca855-9ec7-43c1-a0a3-3bee04e601e4"
+      },
+      "source": [
+        "## Hyperparameters"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "57bffae9-6755-4c52-ae31-91e8cee36c28",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "57bffae9-6755-4c52-ae31-91e8cee36c28",
+        "outputId": "59ce3e96-b23e-4bbe-8577-2d6ff43a53bf"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Number of classes = 12\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Constants\n",
+        "SECONDS = 1\n",
+        "MS = 1e-3\n",
+        "HZ = 1\n",
+        "\n",
+        "DT = 1 * MS            # large time step to make simulations run faster\n",
+        "ANF_PER_EAR = 100    # repeats of each ear with independent noise\n",
+        "\n",
+        "DURATION = .1 * SECONDS # stimulus duration\n",
+        "DURATION_STEPS = int(np.round(DURATION / DT))\n",
+        "INPUT_SIZE = 2 * ANF_PER_EAR\n",
+        "\n",
+        "# Training\n",
+        "LR = 0.001\n",
+        "N_EPOCHS = 150\n",
+        "batch_size = 64\n",
+        "n_training_batches = 64\n",
+        "n_testing_batches = 32\n",
+        "num_samples = batch_size*n_training_batches\n",
+        "\n",
+        "# classes at 15 degree increments\n",
+        "NUM_CLASSES = 180 // 15\n",
+        "print(f'Number of classes = {NUM_CLASSES}')\n",
+        "\n",
+        "# Network\n",
+        "NUM_HIDDEN = 30 # number of hidden units\n",
+        "TAU = 5 # membrane time constant\n",
+        "\n",
+        "max_delay = 300//10\n",
+        "max_delay = max_delay if max_delay%2==1 else max_delay+1 # to make kernel_size an odd number\n",
+        "left_padding = max_delay-1\n",
+        "right_padding = (max_delay-1) // 2"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "0c55ffa2-b4c8-473b-8907-54138075df3d",
+      "metadata": {
+        "jp-MarkdownHeadingCollapsed": true,
+        "tags": [],
+        "id": "0c55ffa2-b4c8-473b-8907-54138075df3d"
+      },
+      "source": [
+        "## Functions"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "3f838d0c-4ac1-4296-a2d2-abb278644142",
+      "metadata": {
+        "tags": [],
+        "id": "3f838d0c-4ac1-4296-a2d2-abb278644142"
+      },
+      "source": [
+        "### Stimulus"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "5d168c57-5ae0-49ae-b510-555c612bdf44",
+      "metadata": {
+        "id": "5d168c57-5ae0-49ae-b510-555c612bdf44"
+      },
+      "outputs": [],
+      "source": [
+        "def input_signal(ipd, poisson, with_delays = False):\n",
+        "    \"\"\"\n",
+        "    Generate an input signal (spike array) from array of true IPDs\n",
+        "    \"\"\"\n",
+        "    envelope_power = 2   # higher values make sharper envelopes, easier\n",
+        "    rate_max = 600 * HZ   # maximum Poisson firing rate\n",
+        "    stimulus_frequency = 20 * HZ\n",
+        "\n",
+        "    num_samples = len(ipd)\n",
+        "    times = np.arange(DURATION_STEPS) * DT # array of times\n",
+        "    phi = 2*np.pi*(stimulus_frequency * times + np.random.rand()) # array of phases corresponding to those times with random offset\n",
+        "    # each point in the array will have a different phase based on which ear it is\n",
+        "    theta = np.zeros((num_samples, DURATION_STEPS, 2*ANF_PER_EAR))\n",
+        "    if with_delays:\n",
+        "      # for each ear, we have anf_per_ear different phase delays from to pi/2 so\n",
+        "      # that the differences between the two ears can cover the full range from -pi/2 to pi/2\n",
+        "      phase_delays = np.linspace(0, np.pi/2, ANF_PER_EAR)\n",
+        "    else:\n",
+        "      phase_delays = np.zeros((ANF_PER_EAR))\n",
+        "    # now we set up these theta to implement that. Some numpy vectorisation logic here which looks a little weird,\n",
+        "    # but implements the idea in the text above.\n",
+        "    theta[:, :, :ANF_PER_EAR] = phi[np.newaxis, :, np.newaxis]+phase_delays[np.newaxis, np.newaxis, :]\n",
+        "    theta[:, :, ANF_PER_EAR:] = phi[np.newaxis, :, np.newaxis]+phase_delays[np.newaxis, np.newaxis, :]+ipd[:, np.newaxis, np.newaxis]\n",
+        "    # now generate Poisson spikes at the given firing rate as in the previous notebook\n",
+        "    if poisson is None:\n",
+        "      poisson = np.random.rand(num_samples, DURATION_STEPS, 2*ANF_PER_EAR)\n",
+        "    spikes = poisson<rate_max*DT*(0.5*(1+np.sin(theta)))**envelope_power\n",
+        "    return spikes\n",
+        "\n",
+        "def random_ipd_input_signal(num_samples, tensor=True):\n",
+        "    \"\"\"\n",
+        "    Generate the training data\n",
+        "    Returns true IPDs from U(-pi/2, pi/2) and corresponding spike arrays\n",
+        "    \"\"\"\n",
+        "    ipd = np.random.rand(num_samples)*np.pi-np.pi/2 # uniformly random in (-pi/2, pi/2)\n",
+        "    poisson = np.random.rand(num_samples, DURATION_STEPS, 2*ANF_PER_EAR)\n",
+        "    if tensor:\n",
+        "        ipd = torch.tensor(ipd, device=device, dtype=dtype)\n",
+        "\n",
+        "    return ipd, poisson\n",
+        "\n",
+        "def spikes_from_fixed_idp_input_signal(ipd, poisson=None, tensor=True, with_delays=False):\n",
+        "    spikes = input_signal(ipd, poisson, with_delays)\n",
+        "    if tensor:\n",
+        "        spikes = torch.tensor(spikes, device=device, dtype=dtype)\n",
+        "    return spikes\n",
+        "\n",
+        "def show_examples(shown=8):\n",
+        "    ipd = np.linspace(-np.pi/2, np.pi/2, shown)\n",
+        "    spikes = spikes_from_fixed_idp_input_signal(ipd, shown).cpu()\n",
+        "\n",
+        "    plt.figure(figsize=(10, 4), dpi=100)\n",
+        "    for i in range(shown):\n",
+        "        plt.subplot(2, shown // 2, i+1)\n",
+        "        plt.imshow(spikes[i, :, :].T, aspect='auto', interpolation='nearest', cmap=plt.cm.gray_r)\n",
+        "        plt.title(f'True IPD = {int(ipd[i]*180/np.pi)} deg')\n",
+        "        if i>=4:\n",
+        "            plt.xlabel('Time (steps)')\n",
+        "        if i%4==0:\n",
+        "            plt.ylabel('Input neuron index')\n",
+        "    plt.tight_layout()\n",
+        "\n",
+        "def data_generator(ipds, spikes):\n",
+        "    perm = torch.randperm(spikes.shape[0])\n",
+        "    spikes = spikes[perm, :, :]\n",
+        "    ipds = ipds[perm]\n",
+        "    n, _, _ = spikes.shape\n",
+        "    n_batch = n//batch_size\n",
+        "    for i in range(n_batch):\n",
+        "        x_local = spikes[i*batch_size:(i+1)*batch_size, :, :]\n",
+        "        y_local = ipds[i*batch_size:(i+1)*batch_size]\n",
+        "        yield x_local, y_local\n",
+        "\n",
+        "def discretise(ipds):\n",
+        "    return ((ipds+np.pi/2) * NUM_CLASSES / np.pi).long() # assumes input is tensor\n",
+        "\n",
+        "def continuise(ipd_indices): # convert indices back to IPD midpoints\n",
+        "    return (ipd_indices+0.5) / NUM_CLASSES * np.pi - np.pi / 2"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Without delays"
+      ],
+      "metadata": {
+        "id": "ofHEbonPJZKd"
+      },
+      "id": "ofHEbonPJZKd"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Plot a few just to show how it looks\n",
+        "ipds, poisson = random_ipd_input_signal(8, False)\n",
+        "spikes = spikes_from_fixed_idp_input_signal(ipds, poisson, True, False)\n",
+        "spikes = spikes.cpu()\n",
+        "plt.figure(figsize=(10, 4), dpi=100)\n",
+        "for i in range(8):\n",
+        "    plt.subplot(2, 4, i+1)\n",
+        "    plt.imshow(spikes[i, :, :].T, aspect='auto', interpolation='nearest', cmap=plt.cm.gray_r)\n",
+        "    plt.title(f'True IPD = {int(ipds[i]*180/np.pi)} deg')\n",
+        "    if i>=4:\n",
+        "        plt.xlabel('Time (steps)')\n",
+        "    if i%4==0:\n",
+        "        plt.ylabel('Input neuron index')\n",
+        "plt.tight_layout()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 407
+        },
+        "id": "KHi-FCvrtlq3",
+        "outputId": "bd089388-1b77-4cbb-a413-d0f5352bd707"
+      },
+      "id": "KHi-FCvrtlq3",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x400 with 8 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "With delays"
+      ],
+      "metadata": {
+        "id": "vGh0aVwWJems"
+      },
+      "id": "vGh0aVwWJems"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Plot a few just to show how it looks\n",
+        "ipds, poisson = random_ipd_input_signal(8, False)\n",
+        "spikes = spikes_from_fixed_idp_input_signal(ipds, poisson, True, True)\n",
+        "spikes = spikes.cpu()\n",
+        "plt.figure(figsize=(10, 4), dpi=100)\n",
+        "for i in range(8):\n",
+        "    plt.subplot(2, 4, i+1)\n",
+        "    plt.imshow(spikes[i, :, :].T, aspect='auto', interpolation='nearest', cmap=plt.cm.gray_r)\n",
+        "    plt.title(f'True IPD = {int(ipds[i]*180/np.pi)} deg')\n",
+        "    if i>=4:\n",
+        "        plt.xlabel('Time (steps)')\n",
+        "    if i%4==0:\n",
+        "        plt.ylabel('Input neuron index')\n",
+        "plt.tight_layout()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 407
+        },
+        "id": "YUtR5CxgvT-t",
+        "outputId": "ab6d7e97-43ac-4d29-bdde-b30f812cb4a5"
+      },
+      "id": "YUtR5CxgvT-t",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x400 with 8 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "5bd60321-1802-471e-87c9-083282a1df73",
+      "metadata": {
+        "id": "5bd60321-1802-471e-87c9-083282a1df73"
+      },
+      "source": [
+        "### SNN"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "84e6ae7e-8695-43d6-8db1-9f5c01024df9",
+      "metadata": {
+        "id": "84e6ae7e-8695-43d6-8db1-9f5c01024df9"
+      },
+      "outputs": [],
+      "source": [
+        "def sigmoid(x, beta):\n",
+        "    return 1 / (1 + torch.exp(-beta*x))\n",
+        "\n",
+        "def sigmoid_deriv(x, beta):\n",
+        "    s = sigmoid(x, beta)\n",
+        "    return beta * s * (1 - s)\n",
+        "\n",
+        "class SurrGradSpike(torch.autograd.Function):\n",
+        "    @staticmethod\n",
+        "    def forward(ctx, inp):\n",
+        "        ctx.save_for_backward(inp)\n",
+        "        out = torch.zeros_like(inp)\n",
+        "        out[inp > 0] = 1.0\n",
+        "        return out\n",
+        "\n",
+        "    @staticmethod\n",
+        "    def backward(ctx, grad_output):\n",
+        "        inp, = ctx.saved_tensors\n",
+        "        sigmoid_derivative = sigmoid_deriv(inp, beta=5)\n",
+        "        grad = grad_output*sigmoid_derivative\n",
+        "        return grad\n",
+        "\n",
+        "spike_fn  = SurrGradSpike.apply\n",
+        "\n",
+        "def membrane_only(input_spikes, tau):\n",
+        "    \"\"\"\n",
+        "    :param input_spikes: has shape (batch_size, duration_steps, input_size)\n",
+        "    :param weights: has shape  (input_size, num_classes\n",
+        "    :param tau:\n",
+        "    :return:\n",
+        "    \"\"\"\n",
+        "    batch_size = input_spikes.shape[0]\n",
+        "    assert len(input_spikes.shape) == 3\n",
+        "\n",
+        "    v = torch.zeros((batch_size, NUM_CLASSES), device=device, dtype=dtype)\n",
+        "    v_rec = [v]\n",
+        "    h = input_spikes\n",
+        "    alpha = np.exp(-DT / tau)\n",
+        "    for t in range(DURATION_STEPS - 1):\n",
+        "        v = alpha*v + h[:, t, :]\n",
+        "        v_rec.append(v)\n",
+        "    v_rec = torch.stack(v_rec, dim=1)  # (batch_size, duration_steps, num_classes)\n",
+        "    return v_rec\n",
+        "\n",
+        "def layer1(input_spikes, tau):\n",
+        "\n",
+        "\n",
+        "    batch_size = input_spikes.shape[0]\n",
+        "\n",
+        "    # First layer: input to hidden\n",
+        "    v = torch.zeros((batch_size, NUM_HIDDEN), device=device, dtype=dtype)\n",
+        "    s = torch.zeros((batch_size, NUM_HIDDEN), device=device, dtype=dtype)\n",
+        "    s_rec = [s]\n",
+        "    h = input_spikes\n",
+        "    alpha = np.exp(-DT / tau)\n",
+        "\n",
+        "    for t in range(DURATION_STEPS - 1):\n",
+        "        new_v = (alpha*v + h[:, t, :])*(1-s) # multiply by 0 after a spike\n",
+        "        s = spike_fn(v-1) # threshold of 1\n",
+        "        v = new_v\n",
+        "        s_rec.append(s)\n",
+        "    s_rec = torch.stack(s_rec, dim=1)\n",
+        "    return s_rec\n",
+        "\n",
+        "def layer2(s_rec, tau):\n",
+        "    \"\"\"Second layer: hidden to output\"\"\"\n",
+        "\n",
+        "    v_rec = membrane_only(s_rec, tau=tau)\n",
+        "    return v_rec\n",
+        "\n",
+        "def snn(input_spikes, w1, w2, tau=5*MS):\n",
+        "    \"\"\"Run the simulation\"\"\"\n",
+        "    x = input_spikes.permute(0,2,1)\n",
+        "    x = nn.functional.pad(x, (left_padding, right_padding), 'constant', 0)\n",
+        "    x = w1(x)\n",
+        "    x = x.permute(0,2,1)\n",
+        "    s_rec = layer1(x, tau)\n",
+        "    x = s_rec.permute(0,2,1)\n",
+        "    x = nn.functional.pad(x, (left_padding, right_padding), 'constant', 0)\n",
+        "    x = w2(x)\n",
+        "    x = x.permute(0,2,1)\n",
+        "    v_rec = layer2(x, tau)\n",
+        "    # Return recorded membrane potential of output\n",
+        "    return v_rec"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Code from https://github.com/K-H-Ismail/Dilated-Convolution-with-Learnable-Spacings-PyTorch"
+      ],
+      "metadata": {
+        "id": "R5zrvF-ssRxl"
+      },
+      "id": "R5zrvF-ssRxl"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "class _DclsNd(nn.modules.Module):\n",
+        "\n",
+        "    __constants__ = ['stride', 'padding', 'dilated_kernel_size', 'groups',\n",
+        "                     'padding_mode', 'output_padding', 'in_channels',\n",
+        "                     'out_channels', 'kernel_count', 'version']\n",
+        "    __annotations__ = {'bias': Optional[torch.Tensor]}\n",
+        "\n",
+        "    def _conv_forward(self, input: torch.Tensor, weight: torch.Tensor, bias: Optional[torch.Tensor]) -> torch.Tensor:\n",
+        "        ...\n",
+        "\n",
+        "    _in_channels: int\n",
+        "    out_channels: int\n",
+        "    kernel_count: int\n",
+        "    stride: Tuple[int, ...]\n",
+        "    padding: Tuple[int, ...]\n",
+        "    dilated_kernel_size: Tuple[int, ...]\n",
+        "    transposed: bool\n",
+        "    output_padding: Tuple[int, ...]\n",
+        "    groups: int\n",
+        "    padding_mode: str\n",
+        "    weight: torch.Tensor\n",
+        "    bias: Optional[torch.Tensor]\n",
+        "\n",
+        "    def __init__(self,\n",
+        "                 in_channels: int,\n",
+        "                 out_channels: int,\n",
+        "                 kernel_count: int,\n",
+        "                 stride: Tuple[int, ...],\n",
+        "                 padding: Tuple[int, ...],\n",
+        "                 dilated_kernel_size: Tuple[int, ...],\n",
+        "                 transposed: bool,\n",
+        "                 output_padding: Tuple[int, ...],\n",
+        "                 groups: int,\n",
+        "                 bias: bool,\n",
+        "                 padding_mode: str,\n",
+        "                 version: str) -> None:\n",
+        "        super(_DclsNd, self).__init__()\n",
+        "        if in_channels % groups != 0:\n",
+        "            raise ValueError('in_channels must be divisible by groups')\n",
+        "        if out_channels % groups != 0:\n",
+        "            raise ValueError('out_channels must be divisible by groups')\n",
+        "        valid_padding_modes = {'zeros', 'reflect', 'replicate', 'circular'}\n",
+        "        if padding_mode not in valid_padding_modes:\n",
+        "            raise ValueError(\"padding_mode must be one of {}, but got padding_mode='{}'\".format(\n",
+        "                valid_padding_modes, padding_mode))\n",
+        "        self.in_channels = in_channels\n",
+        "        self.out_channels = out_channels\n",
+        "        self.kernel_count = kernel_count\n",
+        "        self.stride = stride\n",
+        "        self.padding = padding\n",
+        "        self.dilated_kernel_size = dilated_kernel_size\n",
+        "        self.transposed = transposed\n",
+        "        self.output_padding = output_padding\n",
+        "        self.groups = groups\n",
+        "        self.padding_mode = padding_mode\n",
+        "        self.version = version\n",
+        "        # `_reversed_padding_repeated_twice` is the padding to be passed to\n",
+        "        # `F.pad` if needed (e.g., for non-zero padding types that are\n",
+        "        # implemented as two ops: padding + conv). `F.pad` accepts paddings in\n",
+        "        # reverse order than the dimension.\n",
+        "        self._reversed_padding_repeated_twice = nn.modules.utils._reverse_repeat_tuple(self.padding, 2)\n",
+        "        if transposed:\n",
+        "            self.weight = nn.parameter.Parameter(torch.Tensor(\n",
+        "                in_channels, out_channels // groups, kernel_count))\n",
+        "            self.P = nn.parameter.Parameter(torch.Tensor(len(dilated_kernel_size), in_channels,\n",
+        "                                            out_channels // groups, kernel_count))\n",
+        "            if version in ['gauss', 'max']:\n",
+        "                self.SIG = nn.parameter.Parameter(torch.Tensor(len(dilated_kernel_size), in_channels,\n",
+        "                                                out_channels // groups, kernel_count))\n",
+        "            else:\n",
+        "                self.register_parameter('SIG', None)\n",
+        "        else:\n",
+        "            self.weight = nn.parameter.Parameter(torch.Tensor(\n",
+        "                out_channels, in_channels // groups, kernel_count))\n",
+        "            self.P = nn.parameter.Parameter(torch.Tensor(len(dilated_kernel_size),\n",
+        "                                            out_channels, in_channels // groups, kernel_count))\n",
+        "            if version in ['gauss', 'max']:\n",
+        "                self.SIG = nn.parameter.Parameter(torch.Tensor(len(dilated_kernel_size), out_channels,\n",
+        "                    in_channels // groups, kernel_count))\n",
+        "            else:\n",
+        "                self.register_parameter('SIG', None)\n",
+        "        if bias:\n",
+        "            self.bias = nn.parameter.Parameter(torch.empty(out_channels))\n",
+        "        else:\n",
+        "            self.register_parameter('bias', None)\n",
+        "\n",
+        "        self.reset_parameters()\n",
+        "\n",
+        "    def reset_parameters(self) -> None:\n",
+        "        nn.init.kaiming_uniform_(self.weight, a=math.sqrt(5))\n",
+        "        if self.bias is not None:\n",
+        "            fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self.weight)\n",
+        "            bound = 1 / math.sqrt(fan_in)\n",
+        "            nn.init.uniform_(self.bias, -bound, bound)\n",
+        "        with torch.no_grad():\n",
+        "            for i in range(len(self.dilated_kernel_size)):\n",
+        "                lim = self.dilated_kernel_size[i] // 2\n",
+        "                nn.init.normal_(self.P.select(0,i), 0, 0.5).clamp(-lim, lim)\n",
+        "            if self.SIG is not None:\n",
+        "                if self.version == 'gauss':\n",
+        "                    nn.init.constant_(self.SIG, 0.23)\n",
+        "                else:\n",
+        "                    nn.init.constant_(self.SIG, 0.0)\n",
+        "\n",
+        "    def clamp_parameters(self) -> None:\n",
+        "        for i in range(len(self.dilated_kernel_size)):\n",
+        "            with torch.no_grad():\n",
+        "                lim = self.dilated_kernel_size[i] // 2\n",
+        "                self.P.select(0,i).clamp_(-lim, lim)\n",
+        "\n",
+        "    def extra_repr(self):\n",
+        "        s = ('{in_channels}, {out_channels}, kernel_count={kernel_count} (previous kernel_size)'\n",
+        "             ', stride={stride}, version={version}')\n",
+        "        if self.padding != (0,) * len(self.padding):\n",
+        "            s += ', padding={padding}'\n",
+        "        if self.dilated_kernel_size != (1,) * len(self.dilated_kernel_size):\n",
+        "            s += ', dilated_kernel_size={dilated_kernel_size} (learnable)'\n",
+        "        if self.output_padding != (0,) * len(self.output_padding):\n",
+        "            s += ', output_padding={output_padding}'\n",
+        "        if self.groups != 1:\n",
+        "            s += ', groups={groups}'\n",
+        "        if self.bias is None:\n",
+        "            s += ', bias=False'\n",
+        "        if self.padding_mode != 'zeros':\n",
+        "            s += ', padding_mode={padding_mode}'\n",
+        "        return s.format(**self.__dict__)\n",
+        "\n",
+        "    def __setstate__(self, state):\n",
+        "        super(_DclsNd, self).__setstate__(state)\n",
+        "        if not hasattr(self, 'padding_mode'):\n",
+        "            self.padding_mode = 'zeros'"
+      ],
+      "metadata": {
+        "id": "e7izOPzj-M0X"
+      },
+      "id": "e7izOPzj-M0X",
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "class ConstructKernel1d(nn.modules.Module):\n",
+        "    def __init__(self, out_channels, in_channels, groups, kernel_count, dilated_kernel_size, version):\n",
+        "        super().__init__()\n",
+        "        self.version = version\n",
+        "        self.out_channels = out_channels\n",
+        "        self.in_channels = in_channels\n",
+        "        self.groups = groups\n",
+        "        self.dilated_kernel_size = dilated_kernel_size\n",
+        "        self.kernel_count = kernel_count\n",
+        "        self.IDX = None\n",
+        "        self.lim = None\n",
+        "\n",
+        "    def __init_tmp_variables__(self, device):\n",
+        "        if self.IDX is None or self.lim is None:\n",
+        "            I = nn.parameter.Parameter(torch.arange(0, self.dilated_kernel_size[0]), requires_grad=False).to(device)\n",
+        "            IDX = I.unsqueeze(0)\n",
+        "            IDX = IDX.expand(self.out_channels, self.in_channels//self.groups, self.kernel_count, -1, -1).permute(4,3,0,1,2)\n",
+        "            self.IDX = IDX\n",
+        "            lim = torch.tensor(self.dilated_kernel_size).to(device)\n",
+        "            self.lim = lim.expand(self.out_channels, self.in_channels//self.groups, self.kernel_count, -1).permute(3,0,1,2)\n",
+        "        else:\n",
+        "            pass\n",
+        "\n",
+        "    def forward_vmax(self, W, P, SIG):\n",
+        "        P = P + self.lim // 2\n",
+        "        SIG = SIG.abs() + 1.0\n",
+        "        X = (self.IDX - P)\n",
+        "        X = ((SIG - X.abs()).relu()).prod(1)\n",
+        "        X  = X / (X.sum(0) + 1e-7) # normalization\n",
+        "        K = (X * W).sum(-1)\n",
+        "        K = K.permute(1,2,0)\n",
+        "        return K\n",
+        "\n",
+        "    def forward_vgauss(self, W, P, SIG):\n",
+        "        P = P + self.lim // 2\n",
+        "        SIG = SIG.abs() + 0.27\n",
+        "        X = ((self.IDX - P) / SIG).norm(2, dim=1)\n",
+        "        X = (-0.5 * X**2).exp()\n",
+        "        X  = X / (X.sum(0) + 1e-7) # normalization\n",
+        "        K = (X * W).sum(-1)\n",
+        "        K = K.permute(1,2,0)\n",
+        "        return K\n",
+        "\n",
+        "    def forward(self, W, P, SIG):\n",
+        "        self.__init_tmp_variables__(W.device)\n",
+        "        if self.version == 'max':\n",
+        "            return self.forward_vmax(W, torch.clamp(torch.round(P), -(self.dilated_kernel_size[0] // 2), (self.dilated_kernel_size[0]// 2)), torch.zeros_like(SIG))\n",
+        "        elif self.version == 'gauss':\n",
+        "            return self.forward_vgauss(W, P, SIG)\n",
+        "        else:\n",
+        "            raise\n",
+        "\n",
+        "    def extra_repr(self):\n",
+        "        s = ('{in_channels}, {out_channels}, kernel_count={kernel_count}, version={version}')\n",
+        "        if self.dilated_kernel_size != (1,) * len(self.dilated_kernel_size):\n",
+        "            s += ', dilated_kernel_size={dilated_kernel_size}'\n",
+        "        if self.groups != 1:\n",
+        "            s += ', groups={groups}'\n",
+        "        return s.format(**self.__dict__)"
+      ],
+      "metadata": {
+        "id": "WDl1TIATKSTC"
+      },
+      "id": "WDl1TIATKSTC",
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "class Dcls1d(_DclsNd):\n",
+        "    __doc__ = r\"\"\"\n",
+        "\n",
+        "    Shape:\n",
+        "        - Input: :math:`(N, C_{in}, L_{in})` or :math:`(C_{in}, L_{in})`\n",
+        "        - Output: :math:`(N, C_{out}, L_{out})` or :math:`(C_{out}, L_{out})`, where\n",
+        "\n",
+        "          .. math::\n",
+        "              L_{out} = \\left\\lfloor\\frac{L_{in} + 2 \\times \\text{padding} - \\text{dilation}\n",
+        "                        \\times (\\text{kernel\\_size} - 1) - 1}{\\text{stride}} + 1\\right\\rfloor\n",
+        "\n",
+        "    Attributes:\n",
+        "        weight (Tensor): the learnable weights of the module of shape\n",
+        "            :math:`(\\text{out\\_channels},\n",
+        "            \\frac{\\text{in\\_channels}}{\\text{groups}}, \\text{kernel\\_size})`.\n",
+        "            The values of these weights are sampled from\n",
+        "            :math:`\\mathcal{U}(-\\sqrt{k}, \\sqrt{k})` where\n",
+        "            :math:`k = \\frac{groups}{C_\\text{in} * \\text{kernel\\_size}}`\n",
+        "        bias (Tensor):   the learnable bias of the module of shape\n",
+        "            (out_channels). If :attr:`bias` is ``True``, then the values of these weights are\n",
+        "            sampled from :math:`\\mathcal{U}(-\\sqrt{k}, \\sqrt{k})` where\n",
+        "            :math:`k = \\frac{groups}{C_\\text{in} * \\text{kernel\\_size}}`\n",
+        "\n",
+        "    Examples::\n",
+        "\n",
+        "        >>> m = nn.Conv1d(16, 33, 3, stride=2)\n",
+        "        >>> input = torch.randn(20, 16, 50)\n",
+        "        >>> output = m(input)\n",
+        "\n",
+        "    .. _cross-correlation:\n",
+        "        https://en.wikipedia.org/wiki/Cross-correlation\n",
+        "\n",
+        "    .. _link:\n",
+        "        https://github.com/vdumoulin/conv_arithmetic/blob/master/README.md\n",
+        "    \"\"\"\n",
+        "\n",
+        "    def __init__(\n",
+        "        self,\n",
+        "        in_channels: int,\n",
+        "        out_channels: int,\n",
+        "        kernel_count: int,\n",
+        "        stride: nn.common_types._size_1_t = 1,\n",
+        "        padding: nn.common_types._size_1_t = 0,\n",
+        "        dilated_kernel_size: nn.common_types._size_1_t = 1,\n",
+        "        groups: int = 1,\n",
+        "        bias: bool = True,\n",
+        "        padding_mode: str = 'zeros',  # TODO: refine this type\n",
+        "        version: str = 'v1'\n",
+        "    ):\n",
+        "        stride_ = nn.modules.utils._single(stride)\n",
+        "        padding_ = nn.modules.utils._single(padding)\n",
+        "        dilated_kernel_size_ = nn.modules.utils._single(dilated_kernel_size)\n",
+        "        super(Dcls1d, self).__init__(\n",
+        "            in_channels, out_channels, kernel_count, stride_, padding_, dilated_kernel_size_,\n",
+        "            False, nn.modules.utils._single(0), groups, bias, padding_mode, version)\n",
+        "\n",
+        "        self.DCK = ConstructKernel1d(self.out_channels,\n",
+        "                                     self.in_channels,\n",
+        "                                     self.groups,\n",
+        "                                     self.kernel_count,\n",
+        "                                     self.dilated_kernel_size,\n",
+        "                                     self.version)\n",
+        "    def extra_repr(self):\n",
+        "        s = super(Dcls1d, self).extra_repr()\n",
+        "        return s.format(**self.__dict__)\n",
+        "\n",
+        "    def _conv_forward(self, input: torch.Tensor, weight: torch.Tensor, bias: Optional[torch.Tensor], P: torch.Tensor, SIG: Optional[torch.Tensor]):\n",
+        "            if self.padding_mode != 'zeros':\n",
+        "                return nn.functional.conv1d(nn.functional.pad(input, self._reversed_padding_repeated_twice, mode=self.padding_mode),\n",
+        "                                self.DCK(weight, P, SIG), bias,\n",
+        "                                self.stride, nn.modules.utils._single(0), nn.modules.utils._single(1), self.groups)\n",
+        "            return nn.functional.conv1d(input, self.DCK(weight, P, SIG), bias,\n",
+        "                                   self.stride, self.padding, nn.modules.utils._single(1), self.groups)\n",
+        "\n",
+        "    def forward(self, input: torch.Tensor) -> torch.Tensor:\n",
+        "            return self._conv_forward(input, self.weight, self.bias, self.P, self.SIG)"
+      ],
+      "metadata": {
+        "id": "ZBNH9d1vI38H"
+      },
+      "id": "ZBNH9d1vI38H",
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "3e088884-247b-4d38-a6bf-15c8cd9e1f82",
+      "metadata": {
+        "id": "3e088884-247b-4d38-a6bf-15c8cd9e1f82"
+      },
+      "outputs": [],
+      "source": [
+        "def init_weight_matrices():\n",
+        "    \"\"\"Weights initialisation\"\"\"\n",
+        "\n",
+        "    w1 = Dcls1d(INPUT_SIZE, NUM_HIDDEN, kernel_count=1, groups = 1, dilated_kernel_size = max_delay, bias=False, version='gauss')\n",
+        "    w2 = Dcls1d(NUM_HIDDEN, NUM_CLASSES, kernel_count=1, groups = 1, dilated_kernel_size = max_delay, bias=False, version='gauss')\n",
+        "\n",
+        "    nn.init.kaiming_uniform_(w1.weight, nonlinearity='relu')\n",
+        "    nn.init.kaiming_uniform_(w2.weight, nonlinearity='relu')\n",
+        "\n",
+        "    torch.nn.init.constant_(w1.SIG, max_delay // 2 )\n",
+        "    w1.SIG.requires_grad = False\n",
+        "    torch.nn.init.constant_(w2.SIG, max_delay // 2 )\n",
+        "    w2.SIG.requires_grad = False\n",
+        "\n",
+        "    return w1, w2"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "c215cfc6-d04d-4ad4-8314-a7c627939ff7",
+      "metadata": {
+        "id": "c215cfc6-d04d-4ad4-8314-a7c627939ff7"
+      },
+      "source": [
+        "### Training"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "a8c7c066-4bc4-4604-8a40-6989b57f19b9",
+      "metadata": {
+        "id": "a8c7c066-4bc4-4604-8a40-6989b57f19b9"
+      },
+      "outputs": [],
+      "source": [
+        "def train(w1, w2, ipds, poisson, ipds_validation, poisson_validation, lr=0.001, n_epochs=150, tau=5*MS):\n",
+        "    \"\"\"\n",
+        "    :param lr: learning rate\n",
+        "    :return:\n",
+        "    \"\"\"\n",
+        "    # Optimiser and loss function\n",
+        "    positions = []\n",
+        "    weights = []\n",
+        "    positions.append(w1.P)\n",
+        "    weights.append(w1.weight)\n",
+        "    positions.append(w2.P)\n",
+        "    weights.append(w2.weight)\n",
+        "\n",
+        "    optimizers = []\n",
+        "    optimizers.append(torch.optim.Adam([{'params':weights, 'lr':lr}]))\n",
+        "    optimizers.append(torch.optim.Adam(positions, lr = lr * 100))\n",
+        "\n",
+        "    schedulers = []\n",
+        "    schedulers.append(torch.optim.lr_scheduler.OneCycleLR(optimizers[0], max_lr=5*lr, total_steps=n_epochs))\n",
+        "    schedulers.append(torch.optim.lr_scheduler.CosineAnnealingLR(optimizers[1], T_max=n_epochs))\n",
+        "\n",
+        "    log_softmax_fn = nn.LogSoftmax(dim=1)\n",
+        "    loss_fn = nn.NLLLoss()\n",
+        "\n",
+        "    loss_hist = []\n",
+        "    val_loss_hist = []\n",
+        "\n",
+        "    best_loss = 1e10\n",
+        "    val_loss_best_loss = 1e10\n",
+        "\n",
+        "    for e in pbar(range(n_epochs)):\n",
+        "        local_loss = []\n",
+        "        spikes = spikes_from_fixed_idp_input_signal(ipds, poisson)\n",
+        "        for x_local, y_local in data_generator(discretise(torch.tensor(ipds, device=device, dtype=dtype)), spikes):\n",
+        "            # Run network\n",
+        "            output = snn(x_local, w1, w2, tau=tau)\n",
+        "            # Compute cross entropy loss\n",
+        "            m = torch.sum(output, 1)*0.01  # Sum time dimension\n",
+        "\n",
+        "            reg = 0\n",
+        "            loss = loss_fn(log_softmax_fn(m), y_local) + reg\n",
+        "            local_loss.append(loss.item())\n",
+        "\n",
+        "            # Update gradients\n",
+        "            for opt in optimizers: opt.zero_grad()\n",
+        "            loss.backward()\n",
+        "            for opt in optimizers: opt.step()\n",
+        "\n",
+        "            w1.clamp_parameters()\n",
+        "            w2.clamp_parameters()\n",
+        "\n",
+        "        loss_hist.append(np.mean(local_loss))\n",
+        "\n",
+        "        for scheduler in schedulers: scheduler.step()\n",
+        "\n",
+        "        alpha = 0\n",
+        "        sig = w2.SIG[0,0,0,0].detach().cpu().item()\n",
+        "        if e < ((1*n_epochs)//4)  and sig > 0.23:\n",
+        "          alpha = (0.23/(max_delay // 2  ))**(1/(((1*n_epochs)//4)))\n",
+        "          w1.SIG *= alpha\n",
+        "          w2.SIG *= alpha\n",
+        "\n",
+        "        val_local_loss = []\n",
+        "\n",
+        "\n",
+        "        with torch.no_grad():\n",
+        "\n",
+        "          w1.version = 'max'\n",
+        "          w1.DCK.version = 'max'\n",
+        "          w2.version = 'max'\n",
+        "          w2.DCK.version = 'max'\n",
+        "          w1.clamp_parameters()\n",
+        "          w2.clamp_parameters()\n",
+        "          spikes_validation = spikes_from_fixed_idp_input_signal(ipds_validation, poisson_validation)\n",
+        "          for x_local, y_local in data_generator(discretise(torch.tensor(ipds_validation, device=device, dtype=dtype)), spikes_validation):\n",
+        "              # Run network\n",
+        "              output = snn(x_local, w1, w2, tau=tau)\n",
+        "              # Compute cross entropy loss\n",
+        "              m = torch.sum(output, 1)*0.01  # Sum time dimension\n",
+        "\n",
+        "              val_loss = loss_fn(log_softmax_fn(m), y_local)\n",
+        "              val_local_loss.append(val_loss.item())\n",
+        "\n",
+        "              w1.clamp_parameters()\n",
+        "              w2.clamp_parameters()\n",
+        "\n",
+        "        w1.version = 'gauss'\n",
+        "        w1.DCK.version = 'gauss'\n",
+        "        w2.version = 'gauss'\n",
+        "        w2.DCK.version = 'gauss'\n",
+        "\n",
+        "        val_loss_hist.append(np.mean(val_local_loss))\n",
+        "\n",
+        "        if np.mean(val_local_loss) < val_loss_best_loss:\n",
+        "            val_loss_best_loss = np.mean(val_local_loss)\n",
+        "            best_weights = w1, w2\n",
+        "\n",
+        "        #Early Stopping :\n",
+        "        if torch.tensor(val_loss_hist[-10:]).argmin() == 0  and e>10:\n",
+        "            print('Early Stop !')\n",
+        "            return best_weights\n",
+        "\n",
+        "    # Plot the loss function over time\n",
+        "    plt.plot(loss_hist)\n",
+        "    plt.xlabel('Epoch')\n",
+        "    plt.ylabel('Loss')\n",
+        "    plt.tight_layout()\n",
+        "\n",
+        "    plt.plot(val_loss_hist)\n",
+        "    plt.xlabel('Epoch')\n",
+        "    plt.ylabel('Loss')\n",
+        "    plt.tight_layout()\n",
+        "\n",
+        "    return w1, w2"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "263eb0bc-edf4-4e8d-80d5-55b98889b0c6",
+      "metadata": {
+        "id": "263eb0bc-edf4-4e8d-80d5-55b98889b0c6"
+      },
+      "source": [
+        "### Testing"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "6877e75a-3e5c-4a95-9c2c-7fb3caf608a6",
+      "metadata": {
+        "id": "6877e75a-3e5c-4a95-9c2c-7fb3caf608a6"
+      },
+      "outputs": [],
+      "source": [
+        "def test_accuracy(ipds, poisson, run):\n",
+        "    accs = []\n",
+        "    ipd_true = []\n",
+        "    ipd_est = []\n",
+        "    confusion = np.zeros((NUM_CLASSES, NUM_CLASSES))\n",
+        "    spikes = spikes_from_fixed_idp_input_signal(ipds, poisson)\n",
+        "    for x_local, y_local in data_generator((torch.tensor(ipds, device=device, dtype=dtype)), spikes):\n",
+        "        y_local_orig = y_local\n",
+        "        y_local = discretise(y_local)\n",
+        "        output = run(x_local)\n",
+        "        m = torch.sum(output, 1)  # Sum time dimension\n",
+        "        _, am = torch.max(m, 1)  # argmax over output units\n",
+        "        tmp = np.mean((y_local == am).detach().cpu().numpy())  # compare to labels\n",
+        "        for i, j in zip(y_local.detach().cpu().numpy(), am.detach().cpu().numpy()):\n",
+        "            confusion[j, i] += 1\n",
+        "        ipd_true.append(y_local_orig.cpu().data.numpy())\n",
+        "        ipd_est.append(continuise(am.detach().cpu().numpy()))\n",
+        "        accs.append(tmp)\n",
+        "\n",
+        "    ipd_true = np.hstack(ipd_true)\n",
+        "    ipd_est = np.hstack(ipd_est)\n",
+        "\n",
+        "    return ipd_true, ipd_est, confusion, accs\n",
+        "\n",
+        "def report_accuracy(ipd_true, ipd_est, confusion, accs, label):\n",
+        "\n",
+        "    abs_errors_deg = abs(ipd_true-ipd_est)*180/np.pi\n",
+        "\n",
+        "    print()\n",
+        "    print(f\"{label} classifier accuracy: {100*np.mean(accs):.1f}%\")\n",
+        "    print(f\"{label} absolute error: {np.mean(abs_errors_deg):.1f} deg\")\n",
+        "\n",
+        "    plt.figure(figsize=(10, 4), dpi=100)\n",
+        "    plt.subplot(121)\n",
+        "    plt.hist(ipd_true * 180 / np.pi, bins=NUM_CLASSES, label='True')\n",
+        "    plt.hist(ipd_est * 180 / np.pi, bins=NUM_CLASSES, label='Estimated')\n",
+        "    plt.xlabel(\"IPD\")\n",
+        "    plt.yticks([])\n",
+        "    plt.legend(loc='best')\n",
+        "    plt.title(label)\n",
+        "    plt.subplot(122)\n",
+        "    confusion /= np.sum(confusion, axis=0)[np.newaxis, :]\n",
+        "    plt.imshow(confusion, interpolation='nearest', aspect='equal', origin='lower', extent=(-90, 90, -90, 90))\n",
+        "    plt.xlabel('True IPD')\n",
+        "    plt.ylabel('Estimated IPD')\n",
+        "    plt.title('Confusion matrix')\n",
+        "    plt.tight_layout()\n",
+        "\n",
+        "def analyse_accuracy(ipds, poisson, run, label):\n",
+        "    ipd_true, ipd_est, confusion, accs = test_accuracy(ipds, poisson, run)\n",
+        "    report_accuracy(ipd_true, ipd_est, confusion, accs, label)\n",
+        "    return 100*np.mean(accs)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "1d4de8bd-0ea7-4f5b-a2d5-209f37755916",
+      "metadata": {
+        "id": "1d4de8bd-0ea7-4f5b-a2d5-209f37755916"
+      },
+      "source": [
+        "## Train Network"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "1c95d56c-7e14-4d17-9b96-acd3daa342cb",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 66,
+          "referenced_widgets": [
+            "2be91d6aeb8c40efbd8f2472456eee74",
+            "0a3db85c9fae456fbe6f1f537f801f2b",
+            "bde4fb5180a84ed982df0a9c9aa8d1cd",
+            "41880cd2dc9841a7ac7474137de49b7e",
+            "92fc62f466bf490d9a20650a29bf2eb4",
+            "cebd768a58b24ee8b0359b2f59f1c2c1",
+            "c696cce569e742358dac32c66fc96f89",
+            "4994abd2b7254bbab9fe40ef3cdde241",
+            "0dcc45d10b0a45abaab5c388e630a0f0",
+            "dda0ec4bf6914a40b82c86a8c1dd80b9",
+            "150e7dfa7ca7408581172e34307cd371"
+          ]
+        },
+        "id": "1c95d56c-7e14-4d17-9b96-acd3daa342cb",
+        "outputId": "58a4f122-7b72-432f-d982-a9f18ca21142"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "  0%|          | 0/150 [00:00<?, ?it/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "2be91d6aeb8c40efbd8f2472456eee74"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Early Stop !\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Generate the training data\n",
+        "w1, w2 = init_weight_matrices()\n",
+        "\n",
+        "ipds_training, poisson_training = random_ipd_input_signal(num_samples, False)\n",
+        "ipds_validation, poisson_validation = random_ipd_input_signal(num_samples, False)\n",
+        "\n",
+        "# Train network\n",
+        "w1_trained, w2_trained = train(w1=w1, w2=w2,  ipds=ipds_training, poisson=poisson_training, poisson_validation=poisson_validation, ipds_validation=ipds_validation, lr=LR, n_epochs=N_EPOCHS, tau=TAU*MS)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "with torch.no_grad():\n",
+        "  w1_trained.version = 'max'\n",
+        "  w1_trained.DCK.version = 'max'\n",
+        "  w2_trained.version = 'max'\n",
+        "  w2_trained.DCK.version = 'max'\n",
+        "  # Analyse\n",
+        "  print(f\"Chance accuracy level: {100 * 1 / NUM_CLASSES:.1f}%\")\n",
+        "  run_func = lambda x: snn(x, w1_trained, w2_trained)\n",
+        "  analyse_accuracy(ipds_training, poisson_training, run_func, 'Train')\n",
+        "\n",
+        "  ipds_test, poisson_test = random_ipd_input_signal(batch_size*n_testing_batches, False)\n",
+        "  analyse_accuracy(ipds_test, poisson_test, run_func, 'Test')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 919
+        },
+        "id": "eCrjeU8MAD5X",
+        "outputId": "771057d8-a3b9-4da1-cd73-67c10522a897"
+      },
+      "id": "eCrjeU8MAD5X",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Chance accuracy level: 8.3%\n",
+            "\n",
+            "Train classifier accuracy: 89.9%\n",
+            "Train absolute error: 4.1 deg\n",
+            "\n",
+            "Test classifier accuracy: 88.2%\n",
+            "Test absolute error: 4.2 deg\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x400 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x400 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "w1_delay = w1_trained.P.squeeze().detach().round_()"
+      ],
+      "metadata": {
+        "id": "XQMQmCXSpWrX"
+      },
+      "id": "XQMQmCXSpWrX",
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(\"Minimum: \", torch.min(w1_delay), \"Maximum: \", torch.max(w1_delay), \"Mean: \", torch.mean(w1_delay), \"STD: \", torch.std(w1_delay))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Gpv9w-L4pgL3",
+        "outputId": "0018bd70-5b04-4c99-f9ee-0a1ac2176534"
+      },
+      "id": "Gpv9w-L4pgL3",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Minimum:  tensor(-15.) Maximum:  tensor(15.) Mean:  tensor(6.5355) STD:  tensor(7.2640)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plt.imshow(w1_delay.numpy())"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 156
+        },
+        "id": "YHrNpz0Mo70w",
+        "outputId": "9cc275e8-3d93-4da3-9ff4-148435d9f05d"
+      },
+      "id": "YHrNpz0Mo70w",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.image.AxesImage at 0x78290aa8f7c0>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 37
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "w2_delay = w2_trained.P.squeeze().detach().round_()"
+      ],
+      "metadata": {
+        "id": "fqluNgHxpTnp"
+      },
+      "id": "fqluNgHxpTnp",
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(\"Minimum: \", torch.min(w2_delay), \"Maximum: \", torch.max(w2_delay), \"Mean: \", torch.mean(w2_delay), \"STD: \", torch.std(w2_delay))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "mx7ClPwPqJa4",
+        "outputId": "62c897d1-3e33-42df-fb89-aabe544ebe5b"
+      },
+      "id": "mx7ClPwPqJa4",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Minimum:  tensor(-15.) Maximum:  tensor(15.) Mean:  tensor(4.8556) STD:  tensor(10.8649)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plt.imshow(w2_delay.numpy())"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 276
+        },
+        "id": "I4L8SQZYqRFc",
+        "outputId": "b3982169-d15d-4280-ceab-48d089856dd1"
+      },
+      "id": "I4L8SQZYqRFc",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.image.AxesImage at 0x7828f3988df0>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 41
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Now go from 30ms max delay to 25ms"
+      ],
+      "metadata": {
+        "id": "0m0I33MTqgrh"
+      },
+      "id": "0m0I33MTqgrh"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "max_delay = 250//10\n",
+        "max_delay = max_delay if max_delay%2==1 else max_delay+1 # to make kernel_size an odd number\n",
+        "left_padding = max_delay-1\n",
+        "right_padding = (max_delay-1) // 2"
+      ],
+      "metadata": {
+        "id": "ZApVLstnqjxV"
+      },
+      "id": "ZApVLstnqjxV",
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Generate the training data\n",
+        "w1, w2 = init_weight_matrices()\n",
+        "\n",
+        "# Train network\n",
+        "w1_trained, w2_trained = train(w1=w1, w2=w2,  ipds=ipds_training, poisson=poisson_training, poisson_validation=poisson_validation, ipds_validation=ipds_validation, lr=LR, n_epochs=N_EPOCHS, tau=TAU*MS)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 66,
+          "referenced_widgets": [
+            "0c3fb48840154960ae015898de492589",
+            "797f0b4551ba48c5965900ba44e2cf1a",
+            "ae0247b682844004bc731c7db2ee86a3",
+            "ecd75a34908d4994aa6fa66f702db50b",
+            "16f7d5f4f90b4bcf9388c71bf4aea000",
+            "1aea9c2c21c5482ebdb047ad9dcf012f",
+            "0efc5174d77240909d1b376ba0ebd1e9",
+            "fb55c37aeb89401ea39811c4a773dabc",
+            "fd52e99313534b578d95ad5bcb1471db",
+            "cf6b433f179847baac723d797c797129",
+            "fe6b3818b57f4b3e82095284a6846018"
+          ]
+        },
+        "id": "UUfsGTEdqqAi",
+        "outputId": "7033fbcb-5370-4c08-ed2d-b0b8b4afb3c5"
+      },
+      "id": "UUfsGTEdqqAi",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "  0%|          | 0/150 [00:00<?, ?it/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "0c3fb48840154960ae015898de492589"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Early Stop !\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "with torch.no_grad():\n",
+        "  w1_trained.version = 'max'\n",
+        "  w1_trained.DCK.version = 'max'\n",
+        "  w2_trained.version = 'max'\n",
+        "  w2_trained.DCK.version = 'max'\n",
+        "  # Analyse\n",
+        "  print(f\"Chance accuracy level: {100 * 1 / NUM_CLASSES:.1f}%\")\n",
+        "  run_func = lambda x: snn(x, w1_trained, w2_trained)\n",
+        "  analyse_accuracy(ipds_training, poisson_training, run_func, 'Train')\n",
+        "\n",
+        "  analyse_accuracy(ipds_test, poisson_test, run_func, 'Test')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 919
+        },
+        "id": "vQCSgpZPqtFy",
+        "outputId": "6d00a4b3-c3b5-4c9d-f9c4-24e6c937b820"
+      },
+      "id": "vQCSgpZPqtFy",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Chance accuracy level: 8.3%\n",
+            "\n",
+            "Train classifier accuracy: 80.3%\n",
+            "Train absolute error: 4.8 deg\n",
+            "\n",
+            "Test classifier accuracy: 81.8%\n",
+            "Test absolute error: 4.6 deg\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x400 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x400 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "w1_delay = w1_trained.P.squeeze().detach().round_()"
+      ],
+      "metadata": {
+        "id": "g77uHCMB5JYZ"
+      },
+      "id": "g77uHCMB5JYZ",
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(\"Minimum: \", torch.min(w1_delay), \"Maximum: \", torch.max(w1_delay), \"Mean: \", torch.mean(w1_delay), \"STD: \", torch.std(w1_delay))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "a0hpUJHX5M6G",
+        "outputId": "6f7ec3ac-0ed0-4931-8450-ea54b14533c0"
+      },
+      "id": "a0hpUJHX5M6G",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Minimum:  tensor(-12.) Maximum:  tensor(12.) Mean:  tensor(3.8682) STD:  tensor(6.5771)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plt.imshow(w1_delay.numpy())"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 156
+        },
+        "id": "kcR46_om5OIn",
+        "outputId": "2b6a2b4a-7f15-4461-a94b-5bcb1e9d612b"
+      },
+      "id": "kcR46_om5OIn",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.image.AxesImage at 0x78290ab58070>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 47
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "w2_delay = w2_trained.P.squeeze().detach().round_()"
+      ],
+      "metadata": {
+        "id": "3ChmAS4e5RVO"
+      },
+      "id": "3ChmAS4e5RVO",
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(\"Minimum: \", torch.min(w2_delay), \"Maximum: \", torch.max(w2_delay), \"Mean: \", torch.mean(w2_delay), \"STD: \", torch.std(w2_delay))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "9E-2T3g35VAY",
+        "outputId": "752b2039-743c-460e-8f63-d05e093d2dfc"
+      },
+      "id": "9E-2T3g35VAY",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Minimum:  tensor(-12.) Maximum:  tensor(12.) Mean:  tensor(4.1833) STD:  tensor(8.6819)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plt.imshow(w2_delay.numpy())"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 276
+        },
+        "id": "1JyRTxw65Z3-",
+        "outputId": "88e0ddc6-fa17-4e1e-d539-48c7859398e4"
+      },
+      "id": "1JyRTxw65Z3-",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.image.AxesImage at 0x7828f3b0cc40>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 50
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Train accuracy is slightly lower than test..Seems like 25 is too small , let's see 35"
+      ],
+      "metadata": {
+        "id": "QkszZEeP5bT4"
+      },
+      "id": "QkszZEeP5bT4"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "max_delay = 350//10\n",
+        "max_delay = max_delay if max_delay%2==1 else max_delay+1 # to make kernel_size an odd number\n",
+        "left_padding = max_delay-1\n",
+        "right_padding = (max_delay-1) // 2"
+      ],
+      "metadata": {
+        "id": "C_APwmIl5dvO"
+      },
+      "id": "C_APwmIl5dvO",
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Generate the training data\n",
+        "w1, w2 = init_weight_matrices()\n",
+        "\n",
+        "# Train network\n",
+        "w1_trained, w2_trained = train(w1=w1, w2=w2,  ipds=ipds_training, poisson=poisson_training, poisson_validation=poisson_validation, ipds_validation=ipds_validation, lr=LR, n_epochs=N_EPOCHS, tau=TAU*MS)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 66,
+          "referenced_widgets": [
+            "b1cc5a31cca4428aa63ebf9c4cc0960a",
+            "b13993ac98e2451e95d900382d95a63e",
+            "1cdffc066ec14378a741cd9787f5bdf7",
+            "a26a910f86d241a5a12447c506e0f6e8",
+            "dfeee453fa564f7c8e1b37afbb45ae81",
+            "2f796248bb7e43f88514342013d018d9",
+            "2b0f9f43eb7c4bbdb893ad4ef123f4f1",
+            "92b311893fa445eb943c0345950eec49",
+            "79604c2ddd234117a7df94b67d513106",
+            "ece91ff079dc45ab882b9bf495ad9f10",
+            "bef9cd112bf94285ae6f5f35673ea215"
+          ]
+        },
+        "id": "4zsj2k7o5iL-",
+        "outputId": "ebad45a3-7f88-4466-9a77-a22853cf8eeb"
+      },
+      "id": "4zsj2k7o5iL-",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "  0%|          | 0/150 [00:00<?, ?it/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "b1cc5a31cca4428aa63ebf9c4cc0960a"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Early Stop !\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "with torch.no_grad():\n",
+        "  w1_trained.version = 'max'\n",
+        "  w1_trained.DCK.version = 'max'\n",
+        "  w2_trained.version = 'max'\n",
+        "  w2_trained.DCK.version = 'max'\n",
+        "  # Analyse\n",
+        "  print(f\"Chance accuracy level: {100 * 1 / NUM_CLASSES:.1f}%\")\n",
+        "  run_func = lambda x: snn(x, w1_trained, w2_trained)\n",
+        "  analyse_accuracy(ipds_training, poisson_training, run_func, 'Train')\n",
+        "\n",
+        "  analyse_accuracy(ipds_test, poisson_test, run_func, 'Test')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 919
+        },
+        "id": "PF4PgisO5lOB",
+        "outputId": "0a30f09d-66a5-44a9-8d1e-b2157d2c014b"
+      },
+      "id": "PF4PgisO5lOB",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Chance accuracy level: 8.3%\n",
+            "\n",
+            "Train classifier accuracy: 85.1%\n",
+            "Train absolute error: 4.4 deg\n",
+            "\n",
+            "Test classifier accuracy: 84.4%\n",
+            "Test absolute error: 4.4 deg\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x400 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x400 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "w1_delay = w1_trained.P.squeeze().detach().round_()"
+      ],
+      "metadata": {
+        "id": "GmLBy0FvLi2p"
+      },
+      "id": "GmLBy0FvLi2p",
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(\"Minimum: \", torch.min(w1_delay), \"Maximum: \", torch.max(w1_delay), \"Mean: \", torch.mean(w1_delay), \"STD: \", torch.std(w1_delay))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "sj3qEiZELmEV",
+        "outputId": "d90a8f69-3866-41e1-c96e-04b41a34ec7a"
+      },
+      "id": "sj3qEiZELmEV",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Minimum:  tensor(-17.) Maximum:  tensor(17.) Mean:  tensor(5.5645) STD:  tensor(9.3541)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plt.imshow(w1_delay.numpy())"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 156
+        },
+        "id": "QL1XQjtkLnJm",
+        "outputId": "6c18521e-4877-42bd-d3fc-9950aa8379f8"
+      },
+      "id": "QL1XQjtkLnJm",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.image.AxesImage at 0x7829089299c0>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 56
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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I2KaVVnhm+yjDxONYs8256ubBGDAIbown2U0iYu3STQK0kWgjMRaqQcLdjXVcobnSn2ZrVN7/92LgX96b4kavSeRmTAUDDILrnQm6g5Bmdch9k2s4UvPycIln9TE2xlVWO3WMEQRehudo0lyR5g6u0lyrTzIVDEiNwzD3GOUeq506ceJyK6xzqTRDyU04W9liwe/sL1gSV2gqMsYVOReHMzx9axmtJSaXCGGp+jEPVuPiHoaazCpcodnKamymVV5uL5BoxVKlw9GoTdlJmS31SI3D1rjCzX6j+I7hAIllbVjnYnuGRjDmRHWHihMz5fSZdTpcGM9zcWeafqtMJbagFCqxbO1VMFZwPNphyW2TWcXIeMTGxZWaUGX0M5+1YR1jBa+JOaSwxfyyAmsFiVZYK1gf1PgSS5S8lHO1LUpOQncY4m85xHmJjx65m81yjZeHS1zvT9LPAp7zjjLhDHh9vMC10RSj1EXUBcZVaCP5UuqxmU/x3PAYnSxkpT/BzqiYm9dK0/RMj6qMiWTGStbkmeEJennArWEDYwUb/So32k2MESSJi8klr3lz/G50J1tZFbPn4w6g3wt5Jj5GZhXD1MMaARbY/82oImvYTzyejpeJZEJsXAySrazGWlInVBnH/BaBzHi6e4zXtme5HdaZ9bvMux1+t30nL20tMFkeAjDv7jE0HrH1eLZ3jCx2ELlgs1Xj4/EZpsqLdKeL72pGDtoTWGHZS0PWkgab1ABouCNueTt4QhPJBE9oXugs4fUkVlpuxw1u5WNaOmQlm0QjCUSGK3Iu7Mzg71qsA3tDj924xC2nSVkd4cZoEutAVhYYr5ibVkAWCYRVyJ7kH7ceo+aMKat4f7yPqaoYiaEkE7SVfKm3zM3dJo6jGVddQicjMWe4OJ4HQO/rH14ZLqKRlFXCotfGFRqNxFhBZh0+pEMyqxjkPolx2UlLrPbrZFohhUVJw1i7XEznqMgxBom2snimxqdvAm6Nm7SSMk9mx3llsIAjDFVnjCMNnSxkNynhSMO03ydUGa7QKGFwhabhDFEYrvYnyUcOfUJeH82TWcWT3RO82pqj7KcMJn0a7oirvSkG/YCbosGHGvfyur9FIDMCkfGl0THWujXGY4/PBqfYyyLKTsKkM8CXGSWZHNy/zCo0ks+PTvAZJDtZhRvjSaQwNJ0BI7PBS71FvF2FzAR79TIr0pLlxbzEiiKzM5aopBjHwsBnN06SGAe5nylOjcPmuMIw87nVbsDAwUpLX4QMHR8VfzmwkLnA5BJjBI40aGm+5ljhDz348DyPBx54gE9+8pN8y7d8C1CQSD/5yU/ywz/8w1/zz9l7dZLnKy6PVy+xltS5fXUab0cRdMHrWWQOZhzRdeDyO3O+7d7neW17lvCDNcKh4cIHIt557yUimVBXI1p5lX/+1NdRe9ljNGcJz3Xw3ZzddhnTd1mfq7IY7DEyHr/69JsoX3XQAWTlotSjZkc0qyOcsmbCG7AzmCF/psHcZc3uHSFr98M4dgmeLVPaMLz6Fsv3P/Z5XkiWiD5dZun1mKQu2XnrPKNZwfLsbU6Ud3ju6lEaT3sM58p0Zla5L7rJc8NlLvVnmPIHvLNxgUBk/O0vfRvlJyJkbpEpWAc67xjzQ3c9xUD7bCTFAnRHtM7dwSqfbJ9j6+OLOGP4/PsUbzl1hWuJx7XuJLu9EvLlCpVbFmss2sCoJuBbN/jLxz/N//Pq+xl/cAa3WBsRwNajmh97+29xI5ni137lcaZfzLj9UBX5NkvNHxclIyxPrh5DXykDoCOLdSz+tqKxYUkagvabiiDnRNTiGyov8293HyP/zSmmr6W89k3LnHrbNjmSVlphmHs8fWMZ50aAzAQqAaHfKA9Z1o7CxGNDpoMBL7+0TPNlyWhGkN89QCmLfL5C47Jm+4ji+gNjpLQEL0ZM3tC07ivhvfcmvsz54PP3EV13cWLwexZhilJfJiDoGhqtDO1Lrp+Z4EIDnCH4nWIMlmJLWVusjOipBuuLkolvH/H+2otcS6e5lhQZgGD/JPz5F89y6ucTrBKM5jzSsuDye6b4S4ut/Y2gCD4+3b+DFzuLXN6cInqyjDO0PPtYncm7hlScmNPNTQY64N889VbqL7ls3KH5ga/7NSbUgL/0ie9n+knF7UXBrfsaTFUHLC7uMRUMeXb3KN6H6yzuGPydGGYm8fdyqp8PGDZCPvu4JppP2cnKbMZVJJa5oMuU1+f39s6yfXEKZyCorIDfMyRVSdIQGBfyclFGK60Lqjdz9uYVF74dHpu6Rnq7xLFPx6Q1h8+17uMT9fMHZZ+rgeGFiUVcN2c88snj/WXp7hQAN3b5H175bvrDANZCVFqUUoWGF2capMZhJujT9IaUVcKHb9/J6PNTyBT6ZzOac126l5osfkqjEkN32SNpCHajWX6m9h5kJpi4BKXtDAj4J+q9SKXJRh6kEqEFVhUTwfjFmG5v1vh/9d6PEBYhDUJAthkRbUh0AOmJMWEpJb5WpXod+n6dn52dwXiW6jXJxJWU0USN/8/5BXTF4PQkbl9gHHCjYhOovhhQXZEk9Sq/fHQR7UEkIK0XJdPLO9OsdJrs7VSQPQfjGQgMQtqDkqOz7jNxyaA9wedWTlB338oXto7TenUaqSGvGKxnqL7mMvfUDqbkM5qtcMNOseJM8IQ6js4l+JbBkSLogWLj6h2TCC2pXbI88dTDGBfiZvH9R3MGZ36ElBYhLMYI9EqZ8i1BVoary2XwNRdaHn5bkJUt+dEY5RjEtYjyLdC+IK2CDizpQkZzqkd7rc7Eswp3bBnMS9LGvl7CFmuiXoip14e8vjPDK9tFgBx4Ga40DBKP3iDEGnEQVHorAZMva6yE7nFFWrf4bUG4XfzcS5VibGuP4nffks7kyCjH7vqEOxLtu/zG8AGErwkvBEy+mhPXFR+8dwZdy3F3XKJdQeqE/IfX34FxQJc1IsqxY4dg08FPYOtLEZ9Klop/D4tAN6tZ8nJRIn7P8Yu4QvMbL91PdMUjL1nSpgbX8oXmcapRzPaFKY58Pkemht3dgO6MT1Y1OFMxOpdEqw7htiWPiucgU5C/NMGL/QbjCcVopigjBzsWJ7ZEZYFTEfu6WAcsOOOi/APgDATCKHRFUfVjrB5/zXv8H0nZ5Ud+5Ef4/u//fh588EHe9KY38ZM/+ZMMh0N+8Ad/8Gv+GW5X0Iv9IsLVPmoocQfgdS3hnkGNDUFrjHUkrYcipLAkscv0aorbTVgfhzSdARUZM+t0AFB7LvUrGcK49I/65L7EDF2cvmJc90iMwzD38XYVlVVDWhWMjSDPIGs4JJlDbtV+bdni9SBai+kvlMh1kdIqbRiq10fs3l2iomKkMEQtg3dtm/yueeKmIGlYGv6IioohUUQtgw4VxkoimWCsoJ/6VN2YCTUgkgl536V2I9sPPgw6UOzGDpNun0BmDHSRParKMSWRM8h9ShsWd2joxD6uyNFWMoh90rFLfbfgnwhjEZnBnS7+/3G3TZo7VG/meJ0U60qMkuzELnf7t5GY4ntfbRMcn6GX+ghhSbWDFIZ44FHZLSZ2HhUTN2hDsGcwSpLkxemmomLmnRwpLKUNTXBpA++tywcnnH7uM8h8TN8l2BXIDFRskRqcscVJDFnVId+vF7tdSWU1w7gufSNBacIdS+VKF+3V6Y0cjLL4u5by6pjuiTL+ftlJdVVxr0YWr5sjLFhZcCqCnRh5awsRBmTlOWQqCTqWsJUhc4NIDTI3WAFIgVEljBVMqTGbMj0Yz2/wENyuwnn1BnguJbOE0/DYjQtOkSs0VZGgRZFNGKQ+2dAj2jJ4PU176BTfVWhmnS49mSJHispaznBJMaV6LDg91EBSvRmjvYD20KPvFfMoEJpB5lFezwm2YwBMyUdYCPYsCOjHPiPtMdYuo9zD2S+HRTIlzRVuT+J1oXozwd8cksyWGGYe2oMkK553adNQvtIB6vSTYlypWODujJCJT7SxXxcPio3FpIpYBMSuLTb7VGADQzQxwnM0g2FAZ6eM7DtELYFM9vdWCzqQbA+LYDezkrHj0e6Umb5hUKlluKgAcEaC6GobMU7IyvNYR6GSgjMkc/B7GjU2eD3LYMdHe+bLPClhsWqfY+MWwYdIJWbsFe/Zp8GEW5LyqiErCdKqzzB2iNqCqKXRrgArMa6gtpIRXtrCnakTT1RIKwq/Y/G7hrQsGC4Wm3vYNoRXtvHrZYSukkWC8bQkqRfBcZo4ZJlCdhyCHYnxJHlYPEf270+wI/AGmjwQpAOPm6Mm27tVyrcFMoekrtChJGwZxF4PmZdQcQUbKyyKN86xwrNkjkXkApkVwZjxLBgor0H9hRa4DuPFCnlUpIcGQfDle2ihtCsIdwwyFcQTCpMLgm1BZdUQNwWdmkfmGerrgualMXmgiJsOeSjoeC69IMJrKSZeG6J6MSptMEhUcS37AUKWFc87Tl3GfR+swA0zXC8nHnnYrlfcm33uQ7gFtRe2sI7CqilGI0nUMpRXi/mRVl2MJ8gDSR4WpQakQ15SuIP9A1Eu0B0H6xbrSOlyG2+qzHAhIs1cvI7A7RfPxR0UXIm05pCXFTIBrwsyKdZIv6uxSpAHAqsE40lJWnUYEdJeLOFKjbPtUl0xJLWCvGd8GNmIeOzh70r8Vh+RaPwFDx0KjCcPglGZgju0aK8IdJ0cKjcTvJUWwZFJVBogDERbGWqUM54LMI4CAUJb2I9rjQNWFQcAkYG14AiN/a+Z+QD4ru/6LlqtFn/v7/09Njc3uffee/nYxz72+0iofxDShsWmDr+ze57cSk48eIvMKDrjgN3EY9z38VcriFzgzvfp5QHzE11ufMsMInM5f/oGTTWgrkbMqxEVmXL2oRVem1gAm8LIYTR2EFGOKWecnNnhzeWrjIzPR4/eQScOiWc1cydauNKwMygxHPmYmuC4v01Fxbz49gWuni3hTg24c2qb7VKF1h0zjCfKmCNjZtwuZytbvPzICfqLRzFeMUG0X6SrY+Pi1mP2TpcZzxruKd/mrNfCq2lOhVt0dcRvtu+nm4Wocs76Y/sZgJhiIEcDnu0dQ4ovP/CP793JR+zdXN+dgHmB0IqFWpcJOSw2tH6AjRV7D2TsPVQMSiEFXjjmz84X9eBvXHqV/+PPPEieOghpEVLzyPI1mjLlhLdN+o4ul5en0PWUSuIxSryD1HtUjckfSchSh7zjIRNJspAT1xKkNITKsDUoc7veoKUld0TrfOg770G88wjhcpdh7tOKy7y+MUOeOPgTY9Q7U7SRpFqS54qsFeLtOiSLKffVV5lxu3zi2Flag5DRouaxY9cpOykffehu4maT0ZLmkTuvUnJSnqgdY/fBiKkjLR4pXwXgifPHuTk1gdrwqV92i1PQKcinMsQowumfKEhVi0X2qz0ISXo+YuwRriu8PsQTkExrnIkhX9d4jSkpmHU6tNwKFRWz5HQIhCY422H7u+7ESkgaAu2DTS1/78I3E7o58+UuJSel6Q559+xFLlem+WJ0jDxV3HH0NiejbWpqTF2NkMIgZ2LaZyN0aPiptXcROSm6oll/S0has0hPf9W8Ol1v8cWHZnF7LjIv0q6j2YJkWC7FPDy7SsMdMukOOBNtERuXG+NJbo6a1MOY7Pweo2HAZhgS7HqM5iFZSiGXuC0HZyxo3S/YeGsDGxiaRvDZrVOI40Mu/nAVoQUyLnZGtTji3OwWBsE4dzFWUHJTIic9IFsn2mGcuOS5i61ncHQMwtLdLRWn/XpGxU+InJS7K2vcEaxxdW6S9cWjyAyCo13eu3iRp4JjXGzOIlKBKWuEn+BHGTO1PsYKdvol0sQFMcYRFuUYZus9al5Ma1xip1sQ+0I/w3dz6sGY6bBPJ4147dYcpu8yPp1g7ksxRmDHLmjB+GxMcpdFOZpaeYyrNLfuqSPfuYCVYMIcpCUpZ4RhitaSbOxiteT2tIT3zBVz1M+LBzgo0uZ2MuWtx65RchI+oc4wdkuIesrJ+RZKGm7sTBD3fIaBg/YdrALppawNakw0BsTviDFA1ctwpGFtZoLx9HHyANTDe7x99vZXjZtEF+TO2/06rf17US3FuEqzGUyj3WmsA+NpQR5azPKIh46sAjDKPTKtuBLOkJeKQFXXc3AMyaREWElStzhTY3w/Z3CkhjABxitO5zqwhGc6vHPpCp+vHOf2aBJnVGY0Z8kbGSLUlKtjym7O/Y0WR8I2L3cXeH00hxCGs/Nb3FVdZ2Q8+llANwt4bXOOuOczmrdsPz6HdaB7AvJGRtcIhPYRmcDbK0oUo3lD+WgHRxpKgLWCwTBgMHCRYc7x+R0a/ogvNY8yWJzGeJZkKgfPkM8ZYscWxNT9w9fkVJ8TjR0u704xfKmJMxL0ToKd0NiRQ7BZENrzskUHFnzDSq9J6GZ4p3pszfnYVCFGRbDFfiYinjZsvK2GUTC4I2VmtsO8H3OkvEduJVempuiOA4a9ALnjkVUENz7gA4v7Yo68CBiFAFxILSLTWNfi1mN8P2c89tADtyDY5m/UIwXX2pPYcfw17/F/ZITTH/7hH/6/VGb5T5E3M0Tm8vz6IssTbf7Bsd/kLk+g921JXkgdfnrjXWyNK5TclG4Wcqa+xfvf8QqRTFlyd5lWfWoyOSAt/dPlX2d9qcIv77yZTz57FzIRuEeGHGnu8ebJGzwatIit5c6lDV4189xzdI2/f/SDjIzL/3TlO1jdm8BYwbK7wx3+Bsfv3aKvQ3ompKtD1koNfvdMie5MwLn5LWadDm4p5/UH51g7U2N3p4La9DBBESxkVjFVH7B+wqM6OeT+8Aan3RInnDGEq3xy7POL1x6k34ko1cZMv2mXOHfYG0RoLSmHCZc600RuylzUA+DFrQU67RKkEjljsJ7lVLVFU8VoJLbnITR83Ztf5i9NfYaKzJhUxQDuG83ICv5s4xn+5mPPAyD3j3UGQ2YlrhjxE+d/ldZdVX63fSdfvLmMzhRm7IARHD2+zQ8d+QIbWYOfv/Imhnsh77zzIv944Xe5lIX8rcvfwVa7ynZSYdeE3Bvc5D++9V+iEXxheIYvdY+yPqhiV0r4sWD+sR3+2Yn/eFCPHBmXn9t5jKfWlznbaPNY+RIVGXP+yG1eNEscmW3zPVNP01QDuA++tLDEI5Pr/NWZT9KUOevTPh0dMaGGzKsUDfyN5Y+zujDBL9x8E73uDMaHBx67yJ+b/SxD4zMyPoHMuN/fZFJ6bOmUm3mVi8kcP3P5bXRaZU4f3+CvHPkU06rPOS+lJiNmzYCOt0tFxswrS1mGfOuxl/nldz8AgOtqFCA2yoyfmWTgW9YmJxFRzvvOvc4PVZ+AKmSzBVmubwJ6JsDbr6kDLM/scvWkCxZeuXAEpMWpZMiHhvhWoPVXq1PuLq/x3L2LRXkjUZBJTp7Y5J8e/zUqMuNiNkkrrzLl9Fh29tjUZV7sLXK1PcnJ5g7fMvcSW1mV3wzuYa8dsbC0y7ctvsj18RQffu48clvRvKfFPzn7a1xPp/mJi+/i1voE7zh7ib/15t/llWSOf3jpfXT7Ed9y+mX+l6kv0tI5Hx+dpqtD7g9XuMPtcj2P+HD3XjaSGhvdKokRlOsj/u4dH+GI0+ZfbT/OlzaXiPyUmleU/e4PV3h7kPHS5FX+zcISMhO8beEm39f4It/deIbspCS2Dq8li2xnVe4Kb/N4sE0kXbS1GAy/MVjk399+FFdqvmn2Ze7013gxPsIzlWNfQUROeKR8lXeHO7yauvyFve+lN3S5/8RN/uGRD3IxneLHXv9mOu0Sbz55g78+/3uURE5FFqyGnTMu27rM9XSaD22dZ5D6fM/SM3xv9RrPpwH/fPW9dJKQ71l6hu+qXKOtNa9nk2xmdf7VjcfYWmky2RzwQ9Ofoylj2mmJZ/UR7pzb4H9e+jCB0Pzz8Ov40uYSWU0RTxSZNeVq2v0Sd8+t88Pzn6QqEobWJbMOn507y8eXzlJyU/780ud4NFj/z67LHx8t87Hdu/FVzoPVFaacHv/We4xL3gIoS3l6SCOMecfsFb6j9iU0gk1dpa9Dfiu4jxejBaQV+MJirSBtyoLXU9Ucn+xQcRNePeLRcwOMUygbRaD5juVX+SvNp7i/tMJP2ncxij3magMmwwHHSrs8Xr1IXY2oigRfaH5VPsjF9RkE8M7Ji3xP9TVcBJF0uZmn/PX8v+NSMkM6B7t1hXUNM0t7LO7zqs6XbrGRNfiFqw8x2I14yx1X+cmlDzOylg/27+RW0mQjrtEal5mJevz3M5/juDPgd5pn+L3lOxjlHruj0gHx/ZH6NUbG4+Z4ksxK3t94ifdGXX6hucQ/WP0AVknO3X+THz/6QT41PMe/euUxsoGHcA1CGZSy7PRKeF7Ou45e5rHqZT6+dxefeO0cJBL2lShyKmY8bQnClL988hneW36NzBbjXmE5Oj+mJj3+8e59/IdnH0G4hm+/+wW+rvoqm3mN2+kEFRXzcHSVKZnwm/17+K2181T9mO+d+yJnvU2eiY/xbO8YrbjM5dYUyX6w3GuXMGP1Ne/x/9XVLv8l1CaHBNWC6BeojIvpHH2zR0XGRCJnV9fxVU7JTUm1YpyXyI2k6lQpqwRjJW1VZsndZVb1cYUksYrMFl/ZKotxoRwmzEQ9Jp0BrpBkViMp6pT9zOeVZKGQzWbFBJbC7pNCJU8NTnF9OElqVEFCzLwi7bdP4AxEQSpypEYIixelJDPg+JpYu2zEVdLcAWVJUod/u/02fi/c487wNme9LVp6ikY0xgLz1R5LpQ5j7VLzYzKjkPuM6TfkuQC+mxNWEpLYxeYS6xSKkEhYZtweqpFgtGCsXV5Mllh2d5hUQ1wUCo2LxRMCX7hkVnMj13SMT0tXWcsaDHTAxeEse0nE5rCCMXKfzARY6I4Dnh8uM9YulTBGykLNsq4Vu7pM2UvoRwVp11jJyPr0TEBsi/u7EHaItcPObJV8XJD+fr79CL7MqTlfriceb7SZCXuspFMEMqPmxcxMdTlW3aUixwRCczTYpdcImPL6rGRNWjIhEBkTasjQejyfFhI2JQzLXotj1TZfXJzEOoalcI+6HFMSKVoNCEROTSoi6ZHpjM28xk5efH+ExXdyJtQAV2gupB4pMDITxMZDW8nrFGQ1V2iOTRXSWU9pjBW83itSm9oHvzmmWoqRWF5LZ/GExhXFqddFU5VFKc/9itexIFKJGhaBRq4smaNxXU2zMqTsptTUqJhXasR8tccg8Elyh1zL/eA0IxCCkkjpi4y+DnllP7AOVMZ0ecC0P6CmCiLQUqPDlqM5Xtth0dstSLK1hDQPcKThSjLLrXSCJC3Y9WujGl8cH+VWOokU4Diadlri6aREK6/y+mieYe7TVEMm5Ii+CYF9Ka8sSiBaS24k0/R1yFZcIU6KbElblcitYjOvs2tuYRCYyGAsTPoDKtKwpR2uZVP0dcitZIJ2VmLO7SBFC20tr2aCzbzJ7XSCmlcQD7t5xIqcpJtHB+Oul/sMtcclZ466HHE5nS1moWswVrCpS/RNSLM0QltBqDLW8wZNNaAmBwRCoBGMbDFfF6IuQ88jkBkdk9PW5UJqn7kkxkUhGFnF9WSGjbRWnHwnYqpBzJV0lkgmDDIfKS2j3ONKOoMrNLtJiSxXaF2odP5TeGg0govJPBtZnSvDacZZsTZeiucIZEpFxkzIMRpBW0fE1mUrL7hl2grWkgZ7eYl+6iOswCrDZHnIQqlLRcVs6zIZilZeZWh8JEXmSFtBniuMARFoNCDDnCQvSI+elzNu5AfXKpRFCYMSoiDTehm5kYwzly1bIVA5o7KPEoaWqR6U6muVomzXVANcBLE1dPKEtg5ZiLp0JwM6w5ARIcI1xb2VRaawb0JGb7BrBYxyl+u5x8j4bKQ1tpIKvSxgnLsMMp/NvEYgMkbGx5EGRxo8J98nxwu6eURXh2wnZVLt8GqwREkmXIunsb4mLwlyI7mSzrCZ1IrahmOKjMZ/IhwJVcaEGlB1xgjHYDPBgbjECizFXNnLSmzmlWKN1cWhRbNLfZ8k7ZYylGOK/WN/vr1BFVjNJmjLhJV4gu44IDOSV8eLxNblWjzNbhIxzD2MEV++Pi2KX18j/sQGH//L2d9hHNZo6zKrcZN/cfWdjBKPhVqX5UqxeEssVTfm8mCKdreE42huhE1cZQicHF/lnKltUZ94grpMuZwtcCOZZi8NEaHGBnDX5AZf33yZZXeHQDj00aRGYTLFza0J/t/992KtYDwsBqIjDJHMuZhO8YtfeJT6qxIdCrIIrAu2XpC+HKGZUmM0klFelCbOzmzzpsYKe3nEF7aOc7PdKGq2jiHdjHj5o3dzqW/4hccEDzx4hYY34v3zrxQnZ3ePWaeL2g84NIJnx8e5MJxnJy2x0m2Sacmp5g7H53Z4vTfHy3oBIWDa6zGlfN5dfp3sbsV2WuHp1jJP33ofd8+v86OLH2FGpSigJAUugpFN2dKGf7zx9byyPU93r4SzUej6S+sWr28ZHJOYs8lByg8LnbUqH1y7H1nJePeZi9xTvs1OVuFftt4OwOnqNmdrWywHOwytx1rW5GOtO9lLIt4+fYXvbjzNsO5xZWqWrazGv37+MT750TejfYgnLbpkePP9l/n7Rz/IK8kCv7L5EOPc5fGpK/ypyS9SkglNGaOE5ZsrL/HNlZd4Nj7Cv15/G7mRfPvs8zwaXufJ0Sl+8daDSGH5y8c+yzdEq9TnPsn5apFufjC6TiRySiqlJhUKQSiKDeOVZI5f3HyY3XHEaOBDLg+ed0uH/LPb72Wl0+T0RIs3168z0AHPd5YYZj6PTl7n7x/7LTyKSR9bxf9qP8DF3hJqMuGv3vUZ7vTX+HD3PD+z8g6UNFTcoqzwvolXeHu0graQ7StpktxBxBKvIynfBKmhv+wRT0vcuQHfvPAKi16bZbcFwJ3+GsFCsQFUVUwgMuadLjPKR1tLcz9I+fTgHJ/YOIMrDY/PXOHdjdepqpiKHLPg7nHmyDoGSV2OmFJjFpw91k7VuTXfpB1H/ItL7yBJXNK9AJEKLl1e4MdufgDpGsrlmEqU8MzGEZ68vVyU0vZJpiuLTW5NTuyPcUmoUlylQVnikccvXnsQJQ17u2Vk1yX1DaOyj+vnfCY4i7aCbh7SXOigpOWeaJUp5fNkPMlPXHs3g9jf3/gEvaWAd0ZX6VvB/3Tlu7l5c4rJuS7vXbyAEoYXe4t8NjmF7+QEKiPVDtf6k4xTl+fkIr8sHijUPkYS1ce04xL/fvsxSk7C+2ZfI5IpK/Ekv7j1MPNhlx9oPsG8k3MlneHJ/kmqTsy76q9TUWM6usQHB+d4ebBYePqkigvTc1wvXeLzo9P826uPME5c7l+4zbfNv8hKPMG/u/Uoo8xlnBaB+3qvys9kb8dYwVa7Sj50QVmkq78qADFW0DMBfRPyM1ffRnutXmx2qvj18/038Svu/RxrtHmkeZ3YuDzdXqY9jqj4CQ1/xCD1ebU1V3ArOgFqJNGh5n2zr/NY6RKvJYt8pHt+35/HJzeKWDss1LoMM4+dQQlrFY3GgIqfkmhFZxTSNhHT1QHTsxvsxiVWW41iLFjJ0FikMEyGQyywsdlA7Lm0ZivMBV3KTsILnSU2+lUWKl2++9hzTDl97g2KOX0lL/PJ/p0AvL/5En9ueo8Pde/nt1fuAqDmxZSclHZWYiup0kkLdSXA1fYk/5t8H7mRrPYajFO3INFqSXcc8CHnPmb8Hq20zCgvytCRmxXBcRLxZHacXhKwsVtD55LX12f5BfchhLBEjTGiCVv9Cv/syteR5Aqji33hP4eKiplSQybcIV6QkdrCkwfAZBKbSfJE8WTrOBtJjWHu0UsL24Wym+BJTW4lZ+e3cIRmJynz6fwcE96AObdDV0f83PZdbA/LtLslzK5PT1l+afdBHFdjLVhTDCi7Lz+2bwQe/3cIPs57u3T8Eet5g52sQrtbQvc8blsQwhKonIZfGPTkWqJjB7Ov/1bKMFIuShRKjL4JcIWho0u08xKpcRCOQUrLpDfgiNOmLlO0dcksaCMLMlusGMTRf/b6RsbH31bUbqSkVUVSl2SRKEiWxXM+CBQKPkRh/vRo6QrX0mk+o0+RxEVAIySIRNK4PMZd36N3dIGVU01MVfCu+g5HnDZNFdOUoPZTh9paNvNd1pwGnSxknLpoU/h3HPF3aQUVlFuoyV2hkUiaMuF8eItbzgSfy0+S7gVs1Kr0TUBFZkTCooAMC9bS1gErvQk6O2XcbZfyTXBHlurNGKcTk1bqDI3YZ9cDAuRY4owEKTDlDbg3uMln83PcGjYIVMbZyhZNZ0hFxmTWoatDNoZV+uOAZNJhRqVAyqy6RkuH/Kv0bdSvZ2SRRGaKrFKk9e7xAlq6w+44oh/7RDMp9/sdMmvp70+IWQVl6fN6mrI5qJDlivZUqTAmysu09ipFAHDUpSoDTrlj3PLr+8TfDAAXiIR34ImQWc2uLrM9LDOIfWz25VU9EJbMOqz2GuztVFj3UwaVgL0s4na/zjD2YBLOe+DgooQksRlzYY8LniWMEh4IVrjLy/iosGz3yihliEOHyC1OXcXJ2WLsl8eW0AUJ0BsahLbIRCGTIiP1RuBREkXAEYicZbeFwjKlxlT2g02Ji0ajKBb4bh6y0y3juhp/NueEt30wpl2hacqUkjxgZJLamDPRJqHKeDpZZtCJCj+BTCCMQA4EoqPQJYMpJThK0+2HZF2/MALUxQlqdxixWaniy5yySvBljudohGuwRjAcFSRCMXBQI4HQEu04pFawMa5yI5imlwdUgwRf5VTkGAdFX4e0eyXSkVvoRIFOGhJbSccEbOxV8TYdetWASbdfzPHcY2cUUQ0SgjAjt5JR4hKPPawWWC0RjqFUiQm9vJD0jqtMBgMeq+4UAVnSYHNY3Q80HSBnaHx2kvIBcbipRnR0iY20zl4akWcKm0nG2qVvPHayCr1+iBk7+Edy7g5W6eqQnUGJeOzhuDlKGfJcsdWpYI0gHzmIVBZGgfseidZItLX7Zl6SofHo9kp4LVXw0UpFhilOFLGw3JKGU5UyQ+2zNSjT60dkFUnJSUlyh8EwIBu7kBZzQCjDotfmuDPiSpqzk5RJjSLW7oE1QOSkGCsKTx9pKHkZk+GAvSRirx+hc4WrNLNBYZOw4VYLebRxaO2XQKUwBac2lbhDQTp26eUhmoJ8vNct0QxHnPE3mHW6VESORtDREbfGTTyZM1Xpcd6D14MtysEpjBUETuFhk+Q+vSwoiNS5glwQjz3WBjW0kfSGQfH6PlJp2YlL5FYS58V3lcLiqyJ7k2iHOHUZJF5R6kwUGQ4ZIMKcyck+ntLsDUN6vXD/XlrEV1QwBGBMEegW65IhkBmuqw+uxVKQaDGAFvQTj1ZcZpy79BMfawV9x8eVhrKXMBUU0vzMSpIsLDyTPIu2xX1sd8rogYNKBAiBsR7pfoCKU6iqhLJF1uUrFFZfK/7EBh//rvMAOi2RGIftuEKlFJN4OZGfkWgHawWOLE6ikZeRNUaU/JTlWhtHGG71G7SHEd0k5LVkgYoc8+pwgVvDJknuMD3Rw5WGhjNiaD1W4kl+LZlmNyvRGpb2H75B7tfSdKqwWtBJQp6Nj3A7bRIvZmznHknTYGcKmVgUpoReRqByfntwF4lxWSp1aPojTkbb+ynznNDNGHg5gZdR8lN2vZxb76ngDiKGSxode+y4ZW6nxSlwLS/UErF1aeflg437K6G15KXteS53ptBGUimPcZThwnCOnxWabh6xntQZa5d6OEYsWeZKPV6Jl1hRRUlDW0nfBGynVTp5RHccIJRFh5akIUnr0FsOsU5AWrOoIEdKgwwyhKD4s7RMhjGBzFjNJtjOKvSSgNx9Q6suuJVO8MpokX4W4CuNDRJe7i7w4+l7CzMrtziBzyzucfNbGgg3p1TrU/VT3tW8AEAgMmp+jKDgz9zOHTom5Fo6fVDGAViJJ6n4CdqVvNA9wlrS4HJvutiEhOW5wTIlmZBadZB+dIXG23fM9ESRio2ti7aSlwdLqP20ahzmGEfiKU1bu3RMVMgvlSHOHW6MJkmMQ+RmSGHZyyM+PS5TlyMWnQEKirJclCOATw3u4LK3y0ZcJfAyyn7Kmfo2VWdMbF0+NVpEiqKk1zdh4bURaca+Ia0XVra6lCPCHM/JeX5wlBvuFJEszM4yW5itGSsPfDwimdJ0hmiK9PDIeFwfTCL2F5Mb40lceYopp8+8s0eG4tnxBH0TEIicSCZoJAMdEMmU+VKXeNohzRWj2MNoic4lOlUIp9gk++OAIEwpRQmRn7JU6VBSKSUnoawSas6YY/42ntDUnDHXGlM4+4Z0/Szg0507Ka0J4imBWhzh+xmvXV/g9f5RbCVncrqHExquJrM8q64QyIyvP/k6/azw/ujGAXVvzEvJArF1OTG9w6qXc35mnTv8NTyhCaYzdpoV+jqgnwd0RDG2AYJSSilIKfsJD07cYs7rHtzbsoqZVn2C/bJYZiSpLsa9BOpqxFzQRQrLpwfncIVm0ulzT3SLmjMiPeoQa4ezpU0UlkglRKWEsbRc7kzxM/k7DvyHgjDlrpkN7qqs89nWKW68uIAaCxynUMzoisULcrQWcL1EsCN47b453MWcZW+HuxbXueRNk+4FeFsOKhZEmxZvaNl4e40HzqxQUWOOhS32shLPto/y8s0FpLI06wMqUwmhk1F2E2rumI6O+Ox4iY6OOBK20UhyU5jytdOIXhriSc2xRhuDYHcccbE1g5SGSpRgLFy9Nc31vQVMPef00U1KbsKHrtzNr++86WBOW2VpzHdZPttmwh9xrrSBRjBdmiTJHGr+mMw6jIxPC+hbTUcX96yfBfyHnbfwKzLHWMn5iXU8mXMq3GLK6ZHZQlX2xf5Jtj63QP2KYfuBiIWlIoNirGCcunhOTujmuKoIqr4SVS/mTbUVms6AoSn8SEbGY2ehTGocelnAKHeJnMKYEuA3rpxHrQVkdc3JUxtMBEMcYXCl5kJ7htblSWINH4vuQFuJQfCBY68UvlB5wHifoB1rB0cajpV2mXF7B+NSYnFljsLy2mCel3fm8Zyc98xd5Fy4Rl+HdHVEZhXH67tMRkM8WbgvGyvopQGZUTT8ERP+sHCFTUqkRlH1YpreiHSQ8nNf4x7/Jzb4+MjtOwirDq4s0tOT5SESS2oU2b6ZEWmA2o/iSm6x6D1WKxaa/5g9SKtXppf6XBrN4sucy71ptgdlqkHCqXqLqpNQc0bE1uXl0RIfWz1HnLpkaZEZUY4mCDKsFYy0j9WKfuLx/OAoiXGZWujQqYacnN7lfTOvFWZoeZmR9minJT61c5bISbmnssaM22XCGaAoTL0iNyXwilr6YqlDVpNsT1RItIMYRoyGAV1Hs5bUAQ42inZW4lqv4JkslfdYDDoH98wYwd5Wlc5IQT3l9NIWZTdhpT/Bpc4MmZHEmYOrDMdru9zbuI2xgkuj2UJxsD94W3GZzV6lYN7v3wsTGNKawASW6TMtTtVbrA3rbHSqSGmpR+ODZ3Aiau07lhpWsyY7SZnhfmr4DZfAW+MmN3pFYOWrokR2c6/B6yvzSE/TrA0peSlvmbnOQyduHBgXBTLjuDMAygQip+mPkMKSWcWarrGaTvBcf5l+7tNJQuLcxd/PkhkruNqZ5KV4nvwrTi4XOjN00hBf5YQq+6pxmBl14G4b5+6BY64AvP3xoV2JJ3M6JqSnA6QoTgRJ5rA2qhU8IScjcDK6Wcjn+meY9no8Gl2hJHJcYQiiwqn1mb1lXnXmaScR0f6p8KHqDSbUgEvxHF8YncaXGdNen8wW30GWcoIwZanRwVOa3XHEIPbxHM1r3bmCf6S+XM8GSI3iVq/BMPbwHE3JT3GkOXg+w9Q98GhYHdYZ5h7HSzvUSyM6OuIze2fYHFaJ3JS6NyZUGYvhHmUVsxztUnFjxtqlnZTItGKQeQxiH20kSeySZYrZZo8z9W2ORTv86dqXmFEeTyUhz4+XmXG6POCvUZGC894mo6rCFYaKsKxrj0+JO6jcztGBQ6k6JHIz9NMNpl/I2DvtMXrcpewXNWuAo94O/8PkZ8gQ/E7/bi4M5whVysujJaSwPDpxHTlhuSNc425vD1cI7vCKDMiLSZ0nhqfR+yd2IaAaxZxrbrEQdPjT9ac56fps6DGreYTCUpHpAScrzQtO2BuoyDGzfpedrMKz7aMk2uF9s6/xuHeFZXeHebdDbFxm3WJuRzKlEY0RwrLdrrK+0UB5hjBKKAcJ3zT5Et9Z3mYzqbF9ewmvY4knC6WICQVRkDBOPKIbMPlinxvNMsFbMubViPdNvcqJ8gwfvnYn3qUKwa5l8sltzI1VOqce5KFglWUn4j3hZQyG7x9NsnpjiTyyzC+u80jzOg9G13nEH7OlU361dx8XhvPM+l1OBF/uYq6RvGoX2InLlN2E0+VtXKH56PAOhtslZCljbl+pM3pukpmnM3bu8bjr/DpLQZsLnzzFmd/aI52M6JzwSJqC8/eu8y+XPsWWTriYNmjrMotRh1QrGt6oMBWzHkNdZJjbuoy2gkHu88L6IvHAY3lxhx9ceoJpp89xt01TQiAUvnCJrcdLl+6l8sEXGM4+wNGoMDwbZD5dFTARjpjyBwCMtYv5CnJGzR3z9eVXOe0GjG3KyOoDwivA7TxhTZfx0FRkSsf4/Jq5l2hN0Pclj05eP3BIDWTGT+XvYrA5jduHtcoEnxRnuH9ylf/H1GdoSo+beU7LfHWW/g3ybSAMgQD1Fa6jP5mV2dk+iwpyTh7b4ttLezybtPnd/t1kVnFXpTBvvC9c4dGgz8hqvhhPsZnXOe1tcsbt0TGSJ8fHaesSD0fXeNjPGPT5bz/4GI58xgRFTSxIOT3RouIkjLVLrB0GmX/AJp6v9jhS2sMRmovjuYMU2mKzQ8MfUXVilDDU/TGJdpgIhiwGHXyZsxJPcmk0SycLmS4PiHOXtZ06ZuygyoZ6GBdeFH5KpiUVP2V13CDWLv1RQJ44aCupyOKk74qc2HGJ9q27Q5Wy6O0y4QyIjcdKNkU7LzMX9QhUjkHQSspILHV/jKRI13X9lIlwxKzfpaZG3EqaXO1PFc6hXnFNg8zn9XSOWDuUgwTfzRm5mrSicBzD7qhE3/GLSRIOSI0qvDOsoJOG9LKAspsw6Q3RVnB7WKcbB0Xas1pIEOPcIdOKHVNB7CkwUPNjTkQ7KGEPXAXnS10qboyxkpvjCUKVcjxsFc6eUpNrxTCFC71ZVt2ijjtf6gLFyd9YyUa/8BfQuaDvBmS5Iqm7RPtE0aqMCUSOtz+JIpFzotRi1y2zl0U80T+NRlJ3R1SdMSWVMtYujtSUnRRtBbEuemp0RyFp30MrSehkLEV7X+FqwH52QDDIy+zEJQAmg+HByXvg+ORGUvELX5amN6JjIjLrMFvqI4RlKhoyF3ZRwqJtwdEYa5eL/RluOU2202pxqunOkCbFZl/1xjS9Ebtxie44oOSmVOWYKadHx40wiAO7dm0lRyp7xLlDLYg5Wm4X5QonYRD6BCqj4hRW9G8822Y44mRlh8xKrmRTjPsBsWMYjnyEtEhZpMPzfbKi62rq/pilaA9jBV8aFu6fFSfBKXcK3wFhCit5mRKInEl3QFklZFYVp6F9x0QlLJmRqP0gJ9WK6/0iOOhWXCoyY2jqjLRPX4bsGh9Nysi4xLbwkclETt8EyGpGfykknrQ093uQjGcN7bMuo1lLxS0ciatOzIzbxRU5LeMX2SsEvsxppyUujWdwlebO2gZH/F1Sq1jVPoXLRWH3b5BMuz1G2iPNHPTAISk7B86XLRPh5QNaOqSlC3LlzWSSgfZZGU1Q8YtT8Ud753naGRIbl8wWQe0bjr3dPGJLlxlaj74uCNgV41MSBTHbVRpHGvKxg+w66FrO5PSQqbDY/Db0mJ20VJhGachDSJsa6xl6/QidKlRN0DtRIqsZdnX5wDnUlzmVKKEzVUZ7guDcBGGzRFozrOQ1hnbIStakoyNa4zJZxWAig6fy/UxpyJbusqVDunlYrNHGPciGXR1PM8g9cqMouwm5kTy/t1TI5/OCqByEBbdHCktaMwwWnAMDscyq4rUTVYxTeGs4Q+imAbEtskuBzAhsdhDwGSvp6Iih8dnI6nTzwhW17KRFP5nYQXZc2o2Iri6RWYdX4kUS43I62OAhf63IVkxJqudOYBx4cvsY5f3+PI1gTD/12RxUcJVmrtQjclJCv7iGCXdI2wSs5CP6xmVoQ4bGZzWbILOKWbfLrOruq4HK7OoynqdJmmBCw25aZtWZ4JS/yYQcMxv0eLFuMY5ABPpgDrWNgyGlbaKDzA4UJHojJK7ID0qpwAHXLLMKFeR4Xk47L3M5W+W5+AzP7h0l0+qgh9NoymPWeY7MunR0RGJceiagbYas5VWeGyyzHRcHwVn1OuP8q6X9fxD+xAYf+VaEQ4hKBHvzGW858UUejq7S1mU6OuLT3bNcurSAGklOv7nFX5/+JL/Tv5uffvFxzNjhPedf5X+c/xhD69HKq8TWpeEMGVQC5twOdwertHWZv/XqtzG6XCc41eUf3PVbZNbhR3e/FX/DIV3WPDR5k1m/yxl/g2nV55f3HuYjl+8kG7uotoMbC7ZrZSacAU01oOoV0SaAEhYXS0kW6dbfGR7jIzt30/BGfMfks8yqHj+z9U4+c+UUUTnhm5Zf5USwTUWNqcsRgcyYkGMyK/nl9Ye48eICzCb8hfOf46i3w7+4/i42Lk8hJ1LedeoiS8Eex/wWC84ev7H3AL/9zH2ITHLHm1/hR2Y+Ttf4rGSTrGcN/tWFt5DdLCHmY95/5lWkNVxbnca97eGc6/F3zn+EKdWnpSsMjc/PuO9g6+o8CDhb2+Jba8+zXqpxpTKLK3KOe9tUZcwv7D7K7109SylKuP/MTR4OVnhltEicuGRJyN5KA6EFJ+++zY8uf6RoSqbL9HTApfY0cUdhpSKJFbFnWG9UMXWJEoZIZgSiOEEAHHUcfqD+NC3j87+ufIAL1+aZmuvyd05/lGVnlxSJsZLYFhMmsw67lTIj4/HB9fOkF6tYBcfv2uUvNr9A17hs6mKs7Oblgtke17jRmsD3M94zfYHHSpdYzxtcSQpVwbK3Q12OWPuK175v7ikqsvDiKLhEgsQqhtblJ9bewwsXlhGp5JmBROSFQZL1LMms5fH6Zc766/z9zjczvF1hbUEwu9zlvDdm2blKxzgEQlPZL4mc9ddZm2wcyG+/cqFR+wqb9bzBj65+K+ZSmd7pAX9x6bMAfPr6KdzbHjIXiAyQkEeWzLVF40HXklQz3lRb4QOVl/nZ9mP86uv3EgQZP3z2M7w1uspqXudKMosUhroaEYiMWafDklOUzTILqZU8HS/zTP94oXQwisQ4PLt6hPatSW7OTHB35Tb3Bre4lMxxa9xkb19hUpLJvtzZO/humVU8fGyF21N1Jp2cmjcmUDnf8/gTzH1dh7Wkwau9eRyhuSe6xdvDdV5PK3yoez9j7SKFJVQpT20eZfjCBMaD6LGUB0o32MrqPDs4DnBgQncy2OKt0RUqcsyv9R4gvO3SrwTU97/jx7r3HKS2c6O40pvi+nNLeHuC5K4x33j2Fa72p/ilT78FryvJT4945Nj1g6ZqxgpWRhN8gjsPUujaCpLQxXhFeazkpsSeQ7/lUrsKe3c4fNv8i9wdrHI5neU/dB7k1a05oqFF5pZ4Kee+Mytc2JrFXijjGMHgzgTx2IiztS6vjpe4Kmfp64Kg9tDMLXbqu0XLgIfrbCUuM7VtfrH1CHtpyAuXj6L2XPRkytSZXUpeSslJ2UyrDLTPq2qRkfZYGU0wyj1CldF0hrwyWOAzz5/D7Sj8c12+aflVnmsfYeXJJbyeIH1wwJ++41lGxmMjrhJrlyN3bdA9EbAQxoyNx1rS4Pj5NTaPVRjeqjL/eYM3gGvtSV5aDPHQ1GWMolD2AXSzgIvjOcba5bMrJ0l2QhqLXb73+LPsOGWe3TlN9Yqk61Z5eW6RbhbwwpOnKa0Kuvem/NmHvkA3Dxm8ZcSlMxW8PRh+eJZ2A46+/SaPTlznP7z2MMGzJXo1CN6SczRqczxsccrbpG9CPj24g24e0ski+rnPlfYkgxcncEaC+uOb/KPTv0Err/L5/mnaacRCrUv7oYyKsDy3s8hrziw/uPQEbwv6vLN2gefuXaI3DjhaHtLwR4y1x+8O7iQQOV0dMtrP8AAHxoCuzOnmEa20MOKru2MimdLJIk7M7CCE5enuMV4dzvPE7eNkF6qoscDrgkosP39+jhv3T1B2UjyZozC08xLXnWleGyzwiefuxGsrXj09x7UTU5hhDPzW17TH/4kNPmQicKxAjSHJBTNuh+NOSl226agBzznLqJHEGUgklqOOV7iD9l3UoEjRnnOhbQbExkVZg1aCSKbMuh3mVdEzZjTyCVqC8aLHEWfvqz4fYNbvctxrcbe3zaIT8gl3UPReGBcOiSoR5PteCh6amswoSYEvJJHwkQiUkGhrSK3D9qjo9regupx2Bb7KMSOHxC0avE05PRacDvPqyzXEvjUMUg+3K4lriimnzxGnTaYLx8m0rJjyBpz0tzjvr3HS9Xk2aKNGEpUISk7COS9iT48IxCZSGPJM4XUk44aDwhS9QGOF2xdoI7nb22FOhWzoHfpGUffHbO0rahvOiCVlUHRIrSIQGSfcPSpSFJtD3yt6zWCoyGIDtEZgM4nbl6i06Ldzxi28SdZFyq4o4SiNyAEJIi2sI1PjHHQq/fJptLjfkfQ4Jj0qesg4d3HaLsOGx5TqcdTRZORoa+nbGFfnZNYhkCmZdSi5aeHUaC11d8Qxt8y2HhLbogwXy+LkZhDkmUKpon/CcSdF0WZoCtfYU26LptQMrceF8Tw1Z8Qpb5slZYikiy9KaGsY25S+KTYrNVCosSBoCVRqSeqCrAraCKacHkvOCINADSVp4hDJhJoMgTEuOa4Q1GS4T4IdUZfJl8ctlpI0fJnxAsZ2SROH0p5gmDhMqeK+Y4vxK/I3LJOL17RfKLe0BaMFTWfAslMEA7rnMdaCuhpx1vWBDru6WNhKMsEVeaF+URFq/zlpa1jXu1x2ZtEUvW4S4xSdUbuCOHTZSOvMu3sMdFD0mch9trMqvshJrENsvrpstxB2WAg7jLVHO41wpeZt5Ys8Ho74QhxwfTRJbiQTasC0KnFF5GwlVYa5x6Q/oOrExKmLvysKh8jcoySLbM1OWj6QybrCcNTfYUrmTDgD0KJob6DFflZEHCgKpLBILDujiHBTEG0bRsvOgUQ83JKE25adae+AhPlGZ91YO7SzEtoKhnnBZevrgJ4JiY2LI3RhppoVDpVCC477W5x0e1xOZ9lMasVYsWCUQJUyTpR3uNaeRA8LMm+pFvPw3C18mbOXRwfeOQBTXp+jQSGZPlMpTridrJA0bw0qeBsu4bagW1IsVjpEToYjDIkpXJ97eUhiHEa5R7ofXMbGpZsGeHsKvy2I4yIjnFtJ0Bb47aIB4F3hbVp5hXZaiAGWynucrOqDIDo3ilPVFqeqLX5vfBasj0oKe4K2LhOJhEhmB5kqZ99lc5j7dLOApOfjtRXDCZ+aGhEbF5kKvL5FjSTdLKA1LhOtCxpXMsYzLtdGU3gyZ3Fqj245YDBqUt4wyLx4Zg1nSJ4qoi2D0JJMK0KVFf44bofV3LCdVtiMq3STkF7q096pMLECft/QOl/Mmdi67CRlOmlI6GQcrbXppwGb/QpxWpR+fFHM2aOVPdpeRNlNCPbJrNv7dgFjXTh0H6wDwlBWCY40tNPSQfl37LuU9/kpU+GA3EoGmU87iRh2QiptgTO0lLYNzsgwmndZG9apeWMa++XVXg6JcdiKK7gdRbAr6HUKLtWfCJOx/1+xeO86TqmYhEfKe0yoAV1jCQTMq4TlYAddz7HS4Xp3gn+4cy+D3Ofhe66SasVmXOEHVr6eipMwF3RRokhl9XKfs6Utlp09FJY7FzZ41c4jpeHPvPQDGCPJYodsIWd2qssdwRolkfKR4Tl2sgpbaZU7j60XJCpbaLhdqfm3648RqIylcI+qU5RgIlkEE4+Hq0yrYkIPU49NW+VXOw8x7fVopxGVmQGBm3N5OM1OVt5vKy7JjWKoC9nWRDgietttat6YC+N5biRTHK/vsv5QznJ1l7eVLzKhhryULPA7gyYv9ZZgLiG3RbBwOx+QWXCFYNbp8six67xanmM6SNiIC+3+6VPrjI+53D+5ytBIrpqEz47OcCOZouGPOPK2W9T8MY+XLxJJl93M5bnRMXyRQ3SdaTUgtwoZ5Qhp+dju3bw2XmR13GC22YMmhMsZvpPzjdMvU5EOLZ3z7PgYt9MmAOl0Dq6hVIsJvYyHGjc56xUy0dgqhtbBNTnGjg7GyshaHpm8wRfOS87UtwlETstYfqd/Ny/2F4vGefut39+AJ3OqD7YInJwj/i6XsyErWZ0Xxsv0dUA7KzHYP8G97eRVau6YWadL2xQy57oaMjQ+H+zdS1eHDPf9HzK/4GFE0sXhqw13XCF498QF0gcdxrlLNy5KS6NWGWenMAqLrctw38NApZBmir4J2NN9fqF3jk/unOVEucX3NL9IU6Z8frzMa+PFr/qc3MiD5nRvLD4mVaTVwmHxl3YfoeqMuXfpNrtThR28NpLMSDqDCJ066FghxgohYCurcS2/wRF/lzOn1whUzmZe48OjhKp0uNMrDKl61i9KBNiDwCOzmsRmrGazXOzPUPNiHqtdoa5GPD+9xI22j400T20f40o4xalKi3c3X+d22uQLOycYpD5VP6bsJsTapROHB12ccy1xHU0tKEp7/27rMX7djdlLI7ZHFQIn2ycefzkV7EjNcrDLorfLxekZnj9ZxrqWk5UWE3LI+bDo6qsRBw3jTnmbVKTDsrPHA+ducGlqmrqT8+nNQiWR7jfuOj3R4pH6dZajXT76+B3sDAOalREvdJYKWfaiJmlKKvN9jkZtenlAOy4Cjvmwy6OVq2jkAUHx1eE8X2ofIdEOvdgn1wpxZkDruMOJ2RZ1OcIAp71N6rUhw2Men3n0LCKVeI7mye1jpJlDvFy4VlalYXVYP9ig3yhRQLFZOdJQcRIeqt5gydullVdZTxtcC6b41EwNqxzKcwPeNXkRheF22mSgfRb8PZbcNru6zBP5SeIkYmNcpRWXya1k/k3raCOZDAesxg1q3piNt/QYZ4q75jdYzxp0ddGB3FjBzr5CYy7q8bbGZepqxHrWYCcrMz/RZfOBWVQqcBzDR/fuPuiMrTBMun2+cbpFV0fsZEULhYfO3mBwwudcdZNlr4UShnwiYzjvkVdyRvuBY/+EJq266NDw2Uun8KOM++Zv86bJm3wouZu9XoW8ZAkzjxf7R5COpX9EktYtZ0o9ms6QQBScMW0l/Tygm4TcaDXJWyEyE/SPQU8KTs+2mJIjAi9DNwVtXeYzO2e42JqhFo1509wt6u6IQKQ8m1ieHx/nVr9BqhV31jZ4uHKN9azBheEc/Szg9qBOPy5aXShpcZRmodxlwh+ym0TsxSGuNBwv77Ac7B70PEutKuwn8ojkiMOKN4EZOwx2HNRYYc4NeN/sa9TU+OBwMTI+Q+MTNVLMo4JeEjCpNKPcI0+/dsXLn9jg428vf5TT9RFzymNks8KkyvgsqYQ5p8yy1yKqjxkRstMp82vjezkztc0/OPJB6tLwpy5+L7dfmkM3M84c26DqxWyPKvTiYoF8vHyBikz3CVctPnrjDtQnGjjaMrxHU53vc1dzk7PuDkPr8Nn2aV7bmuXM1DbfOfssE86A406bijT8b62388EX7wULr9bnCP0UVxk8pZkvdzk7v8m0gtg6jNNCl//pzVP4qmASn55okRvF6qDBdT1JexARD3xsLhCxwkrL2++/wI/Pf5TX0wn+9cZb2Usi3jZ1lb84+xmaasRJR5Kh+dXOUT61dhpHaY7O7BI6Rfrzdh7uKxNypuSI759+gs5EiVfHi3xh5wSu1Pzg4hM8Fq4ysoKO8WjpCp9qn+Xa3gTvXLjCP174GDXp4aBQwqWty7zSncdTuiDuuruFnDNMMUby4sYCL4l5pqsDTtVaLIZ7fHftWY677v7PCFjXQ14eLrEyKDrhVqcHVIKEc40tprw+j5cvcsIJGdiEm3nhcAqQiS+bEGkE76m+ygOllQODsbYO+PDmXVy/Ogu+xi+lKGVwlMFRmlPNHf7O6Y9SkQUr/ko2wevxAk/vLTPKPbpJQJorHpxZ5Qcnv0AkMzIrae+nqSdkEXx8tnWK250apSClGY4OFnRfuL9vTCsE31C+xFujqwcZitgK/tq17+Ta7hHsvuX+yDqMMweZCGxSdOLsmC6/tXGemy8s8NrCHHfff5tld4fPd8/w/PbCwWdYKxgnhdmdlAbHMYURUCrJKgZyyefWjtOMxvzQkS/wddEKrhBEwqVtUv7N3pt4pTfP9b0J9tZrBckxrXAlm2DJ3eXPLX6eofG4HM9xdTTD22oXeSRI0NZyIx/TNx5qn9emrSGxGSOrWU8brPYa5JUep/xNjjsDnmzcZn2m8D24vdngtmhw7M5d3htd5+MYrm89TNbz2G3GTFSGDBOPbreQ8JJIZCox5Rw7K3CU5vL6DLrvIsKcUjWmFsYHpoJvwBWGRW+Xe/111ptXuXW8gas0d0TrNFXMcRnTkCkGQ0sn9I1kQllCEXLUSflrC59gc6bGL268mZcuHQEjCjMoZWGixVujy8iS5X21l4mNy3/cfRNPrS1jLZQW+jjScPf0OqfCLdbSBpeYIdeKpaDNO6Pb+5k6Qd+4fHr3NJduziIEBwT49528wAcazxOIjKaMySycdHvc5fWZmBpQeSBmO6nw6tYc6+tNnCBncqmDkoZMS7YH5T9w3Z0ojfieqad4e5CxrXe46YXMe3M8P7NIR5a5f3qTbyhdILaST9hzGFvjuNfi0WCdVe3z8mCRdhKxMyrRGwUsNLr8oxO/wV1exi/0TvA72/cwFQz4n+/+KFOqx/V0+iCIyfZLUHtxSHcYUvES7g9XWFIJz8oEzRx3NTcY3+OS5AolLF/aWjq49pKX8UNHn+AbSje4mJX43OAsNWfMd048zSlnQAbE+51xS80x8bSDKOWM86IUN3Fsj3jRId4sU7oQkNR9lk7u8WcaT9E5GvJ7gzsQAsapy4W9GaTSjJZyZCVjPuzSdAaU9rOQGYp+5tNLffLtkOrVIvhPzo6pVUe8deIqU8oyRcpx5yZdY/nMzhmG2yXUrOH9zZc47u5wMZ3hydEpXhvMs9svuGenwoIg+vm4zzOdY2yNK6zv1tBdr5C8OhbhfrlbeTcN6Y0CfDen6sSc9Dc54uxxzpPENudpp8tqNsGCv8d2s0oni3h9b4Z+7PN1S5f4rupLBKKgDhhgPXdY0zXu9Nf4843niITi57pn+PW1+4jT3z+m/kv4Ext8bOVVWuNpYlvUsd6QPfbdPdpmwFo2j+/mpGGO42qktBgrWdcVuqboDWEdC9KSfYXWXAhLJw357OAckUrYyYoyiO/mJHUQRoBfbFDGClo6ZNeUuLQzzXitzA1H054oo4SlJWMSm9DNwkLrbiHPJbmjkAJQmlQrLqazaLbYTqtIacgyh+2dKlZLGhN96s0iLftGGlSI4rrRErcrwcBKv8lKXmYzrx3o5AfaZy1vMLQeir39Fu6CkpcihMWVBYFrJy9zLZumoyPWkgYayZzXoamKNvOu1AdKCAOMjMNmXqOVV4vMjirSq6u5y67IMBQb/2p6tCiLiOLeQ7G4O8pglSHyMjylKblpYfFrXNomoKRH+4uA5Fo2VZj5fIWCRAq7n8YusZnX2NBvBEQl4n3CExRZgpHxSa0itl7BbpceSpjCUVFYRKCRXuH2KUVxIpCiyA6sZQ0qMiCQGZ7QJMY96CcihcVVRaakZwJS1MFnXYgXeKm3SC8LaA1LWCvQRpBop6jtptMotoitOrA1jvY9WjZ1jc28TkWOOeUWpN26Pyav54RRSleXWMvruMrQq1pkmNPKq9yU+43gQoPvFrLBTVFjrPeb0u0b670BKR2S2GPcDoox7dj9niYaYyTD1OPz3TNs5TWOejs85K/Rtw4D7TPKPXw3x2vE+H5xktvNy8TWK+6RdQ5SvO28zIV0G41gJZthaDx2zYBNXShFjHWJbcTz3SV2NquMU5f2fJllZ8C012e23iPVin5c+BDcHDX5uc4D3E4aVMpjYi9nttZnLupxe1Cn0ypDJhGlHNXMcaUtPG60j93yiXYk41nB5PwuS+U96rLIkO3qMle6UwD0ayEullmny8n6Do7UTKjBwb1zRdGs0BOCQBgkAoNlZDWvxEtcGc8Qa4egUTwTzy28SBb2lWexVaxmE/R1gCs1c7Ueo8ylMwwZZ4UCaCOtc2Uwzc3bkxBLrk5P49YEUoDa93CZCgaU6+OCo+JlBE7OcrDLrBrQ0iU+OzpFbN3CSVlmXBjP8+LuYpE1k4byvv3AfLlLbhUrew1Gw0LiPFPtk2nF7Y0m9B1spAmqCQM350oyy4S8zuVsgZdHR9iIawzH3oGBVCAKL6CtrMrtuM6012Td3aFvAurumDTqsTsqMe77bKoKL8ZHSFnj5eESN/caJFUH1TBUZVy4piZVulnAar9Omis8RzNZGTIT9KmIDFcIAlFkkuvOiJlyn3HusjcKGSfFWK1HY2peTF2NiISiLmPm3A6xdXlpfJSXgHl3j7PeFoHIWKh1uTbnMlkbslxu40hNNyjUcS/3A7BO0XBRZlSkLkQLnsEaGAwDhmOfrO/jDBS5hOuDycJ1uKSZV31i47E5rLLbKeP2JG7fIowgux3QKXv8XnSOmhoRyYRpp09qFQaBiHJqYUxdjnAxPNk/xZObx5DC0qwUqq6mMyBH0zNl1oc1uuMAz8vRTYNSBt8t7A+0LXrxDBOPJPbIUofPbZ3kYjTD480rnHavkFnD68kCrw0WqDpjGu4IX+aMqy6jyGXB76AtaCxv2KimSPo6pE/Iri6jEbw8WKQ7DkiT/4yd7n8Bf2KDj9/evZcXXj1NcDkgq1oq59pMlYaU9utduZVMlwdMlAoJpbGC1Ch+sfUIAEnuwFRC4OfEuVMYh1FsbivtJq9dXQAB8wttTtR2WKh12XjYYK2gpAxKGrpZwGeHZ7k6miZ/usHRFzI2H27yheZJZoM+rzkL+DLntd1ZnF7R+S8vO2SOwXM0vpMzzHx+YePNxYaaeURexs4woPJMSLhr2HpzA29yE09CvE+GS4Mi3T0clahdAa9vuFmd46fCdx3cn4obc20wybXBJIHKmQ76uPtE14cmb9LLAzbGNXIjeaGzxCtinlfX5/CfLxfEjUc7vO/oBRLjMBP28WRhfnQ9q7KSTfLi8MgBOW+6NGA7qfCzrceBQlaWG8Ug9xmkPnhF9gEgVCnlICFyMx6bvMai1+ZyPMul3gypcfgQ91NRMTtZmZ2kzCh32RpVSHMH38mpBjHaSK52JgFIjUMrryKFQWHQSG4lE+wkZXaTEqu9GrlWNKIxVT+m6sYshB0AGv6IY4stHFE8TygM5HIr6aYhH9o4j+/kPFC/xTF/m24e0k0CtJHU/BjfKXwAXhgvA1+WO//qcw+y8FFFHgr23mKozvWLRlOJT6oVP68fIXJSWnGZ3WFE2U95ZPoGM26PL3aOcWlnmmZpxPcuPs2y1+KuyjrmToEjDBdGc1wazVIPxiRn96gECV/sneAltUToZEws71EPx1wfT7EmGwyyQlI7EY64p7aGL3Nuxw320pDnrx1l5kmJ0LD5VsvyiW3Gmcsg9un0Ij7z2j082TnP6FTKn3ngKXyR8/LeApv9Cgu1Lu+au3zAz3hltMRWUmFrVEVJw1KpKC++PFziC51T5FbSS4t7Z/aDYEcYKl6MsYKXnj/B7NPQW67xzLHjLDu7PBxd48zyBkPjs5XVaOclfvHlh7j08mmGS4bve/vneVPpGhNySE0m/B+dh7l1YRZnJDh9bpW/sfR7fKRznt948X6cXZe5pw3VlzdZ+4ZZvuPdz/PO6BLzjgBCnhsdY+3FOayAi5NzfFP5Go+Gq9y9UJSMvrIE8QZ8IUGafYvzlItZif/94tsYrVZoHG/zQ+eepKbGLLutA9v7FMlKNskvbbyJrUGFR2Zv8D8e/RgvjJf52Rcfw7Z9LuWKQepzc3WSo78u8HfGfKp+hmjhc8Q2J7aFwuM99de4r3yLQBZcgpJIOer0mFEeT4xn+SfPvhd67oHBn+pLwpZAe1B/fJPvO/I0kUyYcAasphP801vvwbsaEp+G959+tbDO/ugcc1/osXO+wu7Dglam+AXxMB8J7+bS5jRmpbTf5RmUKhR2gZC0jOTZ3aOs7DTZmqyyM1Gh5ox4U+k6QSXl5dY3E171SUoe/yJ/J7XSmK3VBuFtl6uLJXrzRSZ2NW7yYmuBvV6EvBkicmg+uM33H3mKJXeXeUcQCJcJNaTj9KirEQv+Ht084jdvnWe8F+JODXjb9FWOeLvc4W0RipCTTs5s+RpXspA/9/yfQV+s4N3Z5afP/yJTashfOfIp2gtlZp0uR529Qollix5KfzP/71i/sIQVUFNjZlRRWqqUx/SHAeZ2hDsUlEfg9i1ZyeEVjnCxNsPwpM+9U7fZzGusrUwSbDhUblqqKwky1cx+IcMKwc6DC/zTc/OYsmZyvkvZL0zx7jq6zrnqJsfdHpmFD33xAY5+SLP1kMef/1Mf4a3RZSoyY0tbXhgtc/PGFCKX3HHnLd479TpNZ8CSW5TM/sHFr6ezWgddmPjZRNB/okTSneUn3n2Eb33Ha3SN4ueuPMLgRo36yTZ/4eQXmFADvrH64oHRYte49IWltJ9tbukKN9NJro6m+fTV0+jul8eg+b8D56MTh6gtn/o1w3hS0pkpFw6eXkDgFMYuDX+EIwyDvOhToY3k9rCOoTiJul6O4+iDwEMArjSkqSpq7EB/wsdUJVU3JmoUGZNuGhLnDqlx2EhrrI1qhDuWcLWHd2aCfhoQqPxAPz6MvWKCiiJrUdTdCnMYbSQ7oxKZlihpcZXGmqJ9crSZoUYerijkT54sHm7kFt4iQ2lxhwa/k+P2PFb7dQInZyoc4AhDO4kYpD6+kxNrB08Vp6/pfX+CDQpHvlHmkWhF1g6YvFlswpt3BQx1wakpqcKASiMZWo92XmY7rpAaVcgV3ZhYO6yN6hhbnPDfuKdvwFhJZp19T4uckpNyzP8/ufvPKMvS6zwTfL7vO+6e62/c8C4z0rtKU94CBVSh4EEQdKCTRIpUUzOcFiXKdatHzZZEUSJbEsUlLoqi1PQGhPdAAYVCeV+Vld5HZHhzvTv+mx/nVpBSr6Xhv8Ho/sm1qjIjrjvn7LP3u59ni33WFjtR2l3yotRnU5dZtr0cTT+T5hPiIa9i2K2JE4kfGmkL1nfZdAookaTZEmDLz6cVv+/QbGXR8V9kFvxouAI57OaUhxwQQyQkiHTVOBaEsaIfpmu3tWyWqpmhF9u7DBkx/DwiLdkKUhLqILYItcTYMSm8vUlSdNk5lbax05VJiGLJxrDD1upl8Lo2vUzIeqGAImGrn6fTyqRrcnGW0aRNUQ2Yd+v4iUEzzBAlKi0oc90UJR5kaAkHQySM59JCsRM59IbcGyk0jgqZtFq40v+vgmd2M0FGGqRg0m2z7eXoDGziSJLdEeRWEsKiyZpXImv4DCKTKJY4KmS/swnAdW+cQWTRDFx2+i6mSqg66TptM8iw3i8QxioFaSWCKFZEsUSphLaddqCspsTd9AkKFs3IxdMGJTlgyujQT9JA3UZURPcMytciYttg0mxyxtrBERJbGFTNTgpR1DCXbfDeTMw1fxNCidETZHYCksUVzN4402aDBdPcFSO2ogxmZ0g2DV0CrXGFYNw0iLVmMw7oa0WgY0I97AJqnRJBSVXi21GBXjODU5OE82qXoHnIjCjKDCvRgJUok7pnunmabRcm4KjVYDsukEQSwxNEA4N6P4NsGWRWmoj1GlF3D6GOCXVCT1sEKCaMJtNGI11fViGWEOSEhSsturGD3LKxG3/BbjD6kNlJCLMCUyaccm7vjloVqcjNGECgU+5JP7Gw2hp5a43M7AFEkJKid9pZ2p5NuJMht5mGVf2SJnbE8DgVu+ORoGOx47ps5tLg46jbpiS91N/jpZ3kfi2DPzAx6wZWE4LSEHo17N52+jZx1yTTSBXtkG5xVaSHI9LP0Bx6srLSp6R61GRq1hVBWjZOmk1mzVpKaRYSGwNTKBwRMahnGFmC2liWZuxSkh57zDp7zDoVGTE5DEeHw8++bPdZTXl92DLEIA3VGyoleagBmJ30T2OQnveNtiLEZsfPEiPwExPpSYw+GAON8iJkN0CsbkIUkR8/TJA3CYqSmp2j69pMlVtMuy2qZgdHCEKtMesS9+Iq9r45Zs06xy1BPU5Hc93YRg4UIoaK3ePuzE0qymNGmayodYBd3xOACiC7EeOueuyczuDrtEvXaWfIbEu6Uw6OCCipHvPGgIqy2Yx91qJMesM6/FG9xKYTO9R8F71tk9mRRFlN5OrhosBf7fE9W3y8d+wKv9uborOdJTFA7ph0egWK+7f40NS5XbJiqBVf3z7O7XoZ/ZduXuKhv+GdPw2VcKi6xf7sNuedKc576Zz87ollnqic4+3+HM9s7qcXmAx8iyhUtAcOTS9Dc+Cgi4LOwRK9Gc191VsAfHHxBK1GFrfgsffuZbKmz95sbdi6StuEfmKy6pfoxjbLvTIbnTxCaZoHJL0pG/tgiw9W3gagOZQ3pRdyxVO5Q9xc3otdU0TZhHbfwSl0ubN4m6rR4Vx/hsXuCEGi2BmkF8EokXRjm0bgUhu4eKGRtggDA1kI2fooKCPmrulVpuwmRTVg1qrhiJCS7OPKkNfiBc5uTCEEfGDPRe7O3aQe5XZDoXnl7ULETBHTTywu9SZ5qbmXqt3jkdHrFI0+02aDrAg5nVlkcrzBZlTkuzsHaXoZFoo7PDiSKu3DxCDUiivdcW53yhRsj7vnligbfWasGtNmg9WwzLOtQzSCDBu9As1+hqI74D0HrmLLiDdr02w38qhKmztyK+TVgM9vnuZ6rcp0scUHJs4D8OfLZ1jfKDNS7fDB2QvEWvKVpWN8afsk0onJ5lJi6nKjRBxLMnZIMeOl2ZzCFlNWh3jSp3HXGImRrv90mi5InWLmNegkPQCr1Q4Pzt6iYvW4M5uCworGgLPZGap2lxPOCqOqw7OdQzy7sY+SM+DR0auUjR6f3zjF9fVRLDtiptIkb3rMuE0m7BZrfonz9UkGoUmciHRjwhXMW9tkpc8rnb1cbIyTL/VZ+REHpRLes/cGD5eu8lTjMDeWx9CBpHfcY3BKs39yi/eXz+HIgDm7zk6Y43JnnF+98DiWEfOBuYs8UrzM0xxhuVmiG5q8ujIPwN5qjcfHL2PKiDBJteuhVqn4UKQo9gTBHx0ost3OMJjQjFvptk1b29Qil1hLQtIZPnaCXzRRIfzm1Uf4XP40fmykfBChOXDHMq4R8EjhCo24Tz3KIb105Ll9RwZz4U56U4JfuvhhfrvQ5hMTb/Cwe4M9zg7O3TWCyOBiY5yf7v4IWdOnbKVjjUhLYi04llvnXdnLeNrkW51T3B6UaQYu9YGLBqan6sjpdKvi3y4+zmimy09NPMdxq0Y9MYYcEYltRJhWihj/7mCeC/1ppJEQuwlioOis5REKbvxIGaHLOOUuv7CadjYjnW7w3V24xSnnNjfCUT7V3EOM5OPF17nPSS+KiZsQBZLEhMTUaFPTPh6jMjEPj6zQ0xaOiChJWDDrvP/IRd6ozvDwaJoXykqfP/rA3TQPHgLA6ILuSYK+wjcTRC4kuK+PYSRU3QGuGXJf5RY7ccxiNEqjlUU1DaKqYm9mh3GzRUl6lGTEh2fP8+VHj9P3Tai70DFJTM1gApKRtJPjiJj1QZFg00V5Mt2ykpCz0uCArxVrkY8S0H+HPEycbiUpg07fwd5WdPMZQp0i4zdji77ucTOs8HJvH7cH6TmrP5F6R37j9nvJGKmYLtGCh6o3+MnSa/QSye82HuBSe4JrW6MEIwlJMV0tbSSDVKZWy6EHBsKCoADhdEJSGCroh3qAKJHUY5dxs8WRU0ts7s+zvl6kvp4FnQNRBp1uTKkQRAJ0TDxPsegZrNpF1kcLPOBeIy8DRu/e5EpmhmTU4zPbd/JSt8nD+SsctrY54S5z4eQkcSK5t3gLV4YsRwWe7o/TijMcHtli88xgt/M7iExuL1RIOjYHD67Q0wbbcR5lJERZjVIp9qCTZPhC4wzNMMM+d4eT7m16ic3b/VmaYYaSOaBqdtiX22HtYJH2jMNYrs9MvknYC1j+K17jv2eLj4/lz/PK1BHObexF+QK7lpZdmcMhP144ixoGYFqJ5uvbx/Fqw+rsnceQQZ9InRYfZsy8W+f9xbcpmz22+1mk0DxROccnsg28xOTzvTsY9CwSz4BIEJom/Z5NEkrsPLT3KMR0n0dzl9iIivz+5gPkrpno+z1+dd+nmVAxOWFiCoWvQzwd09eaa2GRZpzlq8kdLNXLKJUQ7hsQAt83f4kPuDsoIegnG8ToXcLegr3F3z0wiV+2SLJx6pTIC+7PXhtSPtMRyLaXY2VQJI7TE2g/sugGNq1+hjBUBC0b2VcU9jb59RN/yrTqci0cYTUss8/a4h7b+6+ecze2Gazm0JZm7lCdT2Qb3I5WuWhVUWgWzDolyXCd2OJqGPCtzSPc3Kxy/96b/HT1LVwRpRcT4IzRpJjxeDNY5wsrJ9msFzhTXeaThbO7wLBAa35L38+V+igT2YC/XnmBg+ZfOFVeElv8ce9eNjt5uj2HyDOoZPv8/Pi3mVAxf7P/CdZuVfEKBndnbqZt+uheeptZuu6AB9xrKDS/038Q67aFVzT4sdIrdBKTP156gMkXBK29FuGZ1JPRr7nIvsJ3Etq5DE4m4GBhixmrzuR4k51j46mPBI1omGhDk6h0riu91GdSmd3kH018g6IU5IdB3QPmm9yfvZYK3VQaTlv3imwul+mNWZycu80hs8ZnkjPINQcvH9PN9VMQmlPn/uw1nuEw324eJOibKDvNswDMGQ1cGdEKM2zWihydWefXTn+GKaUIdUKM5upgArrpPPvROy7xo6MvMqG67DcNJJIH7POEaP6nzvchXi0ycDXMwbsza9zyx3hO78UbWLBhowaCdr7HDxdfJy//73c874QnPa24sXeU5739ZIop9Osd10YzdlFC7wb1lB0TFCxkCMGbZRaNEmYvXblvH4n4ncf/M/fbA1pJQD2BWphF+gKhoXU0wiz5hC0b8UqFW2aFz71bMzHTZMHa4n89/DWWgiq/efZd7NxyiG2Ic/FuSA+pWZwaITfr0YpcPn/zDnpbWYyWwm4I/JLm+973Ej9Wfol/vvIh3nxtP4u5mDsKq5RkuqLdG2aPMmaIbUbUfZfn2wfYGOSRShM5CappYDUF3mTMR554haPuGn+wfB/feusoyNTwapgx2X0+J5xlloIqX1k6RhAazB2rcZ+zjCNCtJPaUJNsjHQixkY6fHjmPFWjQ8Xo7ioGitKiLCX/aPxbNEcNsiKiKAVTqsbv3fVfqJ/O8a9uvZ/Np6dRAcRdRWJK5NEBf+fYU4yoLnk52DUsb8cZFoMqUdsi00wv4gv2FqNGm5KMyAvJT5Ze4ePFN/hu7xD/5uXHUR1J7CbExZhypZsaoNFs97M4m+nIOnY0sa3JDldBPZ2iwgF6u9m/OLWy6gF+36S4A/5oCm0LtGIjLrARw9PtI3z11tFdf5Y3ESMiwY3z0wgNIkq7Ms3jGR7LXWAjLvKZS6dQNzOEeQ0jAfliWpjWE1gelJE7FiISJEb6PAsLTT40f4HtIMfTtw4Q9KwUbpa4jKo2vzT/BRSaJ/cc5cX6AmVrwJnCEqaI+Tfn3ot4K49IwGhLtJTQMIgFXI0U1yYnOGyt888PfJ5gv+LpzhE+c/UUr8Tz2EcjDlvbnHJW2DO/A0BJejgiZjkc4fMbp0i04P3jFzg1cXuYl0lHKM19Dl5iYomYfmKyFeVRRkyY0VgqNTk3IpevXj1GsmNzdX+N6p4OjSjLU6sHabZdjkxv8MHRbYqZAWNzbRItOZ5Z5qS1Q7+T/BUpH9/DxYcjRIrEzsVoUw4hBOnjZuSmroAhnKoXWWDHCKUxzBghNaYZY6qYWKfmQUMlaYrXm6YbO0znWhgyoZ/YXA59bgdVokiCAKvgo1Qa3jFVjB8aDEomCIEyYm4EY+koQUBspz6dxbBCohvsNWNcYdFIPNYig5422Y4KNGOXrOEzW27SDWy2WzniWLId5HgtsMiKAFeAEqCIMEVEoCuMVLo0VBYF/1VnB1JHxISdfvhrVkgQKUYyfWbcJjt+NqWTKoUQmihrMJbrDpHDBjECU0S0E4e3g3DYSYpxhCanfEQlwDDiXYW6EqlLJdQGy1GBNVID6qgc4GmLcbdNu2yTMwJWoxJZ6ZMVAVIkuMKnKhQW0RBPnd45WkLgac3NsEAzcVn3ikMUt821cBTYxhExtoCNaIpOYOMFJkImKCdCyYS1qIinu2nGR6at5Y24iKf76X/TaTr9ZjCGKVIy4CCnKTs+jtDEIoJiSH/Mwa8mzJdT58aNjo32JOxSPzWdyGElqKTz4XICcQriQmpEJsbN+kRRWuyJMMWwv+rNklcD8tLDIsYU1q6mfCXK4OkU2S7d1MVyIxgj0ClhMM4mYL/DOpC0oky6lhhl0udkJBRyA0ayfYqWx1v+LJKEfmRhWlE6nklMOiLAERJHKObsGvZ4nyRJGTMbUYlOkmEjTkPPnk5pj53QIcxpYkez7hd5yRtlPSimunAzInAs0BDEile92WEXMj2d7DF32G/GdHTMzbBCLc6RUSHV8TZKJnxx8yTfVEdxjXDX5TJtN4lJBYyxnd4RWi1ACIIieNWE7FiPUdXDlQ59HSJ12ra3mgKrA4M9CfNjdTacPL0wDxpu1Sr8+/gxDhS2eV/pHFNmg/GRFmueAl+iugqEJs4lYCXD/JjElBHVXI8okgSORT+ThjI3/TzP9g8SJAZ6JEAZCd/aOMxbmRmOFda5J3sDU6TEVccKKdkDxq027dAh6pmoliKxEwazGpUPWR6U8RMzlWVWPJRKyDl+auWW6eez6I3QrmchkKz6ZXx9k82wiLFtYrUEfjVlssRJSmKdNuu7UsvVuMhiP2XljClwZZiuEWu9y/N1RMi422FpPL1AJ5kEzIQ9xQ6OCDBFNFyzjAm1wtNmKhgseXhhhn2FDhXVJSsCOomih+asP8vb/VluDyqYbkA4rjEzIdlMwFyxSVYEmAIms202J6popcGJUVbClJuSj1OHUVpYS536jBwRpZA96VGtdqjvsVBlf7hWO8Ko0aYgPcpGn6lSm5bn0BBZooGB8BRGT6AFRKUI6aaOrZcG+2hFLq7r0xk3MXMBI6Uuo26Pkkrpz5aMSJzhzC8XYlgxk4U2RSPNHE6W27Qch81+jt9Yeg/T2SZ/bex5Zo02y16Fy1vjVHJ9TuWXyao+UaRwOhDmIByJUst6z0AO0vVxV/pIkfCdzhHONmfoRRbF3AAlE5RIWAxLODKkJNNM1UaUp5NkeK2zlxtbVaTU9Ks2oyrVkjgiIQE8EZIM1+CDIQogY4f42ZhCxqNqdujGNknNxl1VNMZdSipF1UexJB6KNEeNDkokOCLlJpVkH0eI3Z/9V3l8zxYfBWlxNLfG5Zmx3RkypJmK391+mE0vz/XtKmFgkHF9JiaajLo97ikvUjZ6jKguJdVnMajyQmsf3dDmSnOMN7am2Veu8eMTL+EKn2e6h/lm7Rgr3RKBZyKNhCf2XeKx4oUUGzwMwj2T289WK0c24/OptbsYRCbCivEmJWak+N31B5lxm/yt6jMULbgcZvlK61RqpoxSIc+Zwm1+fOQFrgUT/Pr191Bv5nhjfZZ/2PwEZWfAu6rXGDdbNGOXfmxjyoif2fcciRZ8cfMkV9fGiRJJP7HxdZd95jajRpub9hi9yMKLDT40eo6H3RtcDkb5knmaQWxyILvFvL1DoA0ueikTomJ0KSiP84NZXqgvIIXm8eolTjjL7Hc2+ckTL+PIkGP2Gl0dE2vIioCtxOaLjdPc7pVZyO1wMrtMVvr88OgrmKMxF/1pvlQ/jSlj9me2KKo+yrnNpEpzLbYRoYxkNxy7GOX4laUPsN4uEIQGUahY1UX+i3poNzw6YzU435tiq14gGhjkKn0mCh0yRsif79wNwE7fRTip4fjp9mFMEdPop63adjfDH67fh6PSEYpxIObO6gquEDhK897DV3i5OMd9Y+v83MRTNBOXf+Z/mE2/jMpGu4K+G60qi50KporZd3htFz8fJ5K7Rpf5eOU1NqISf7ZxN+udArWeyy+9/WGkTHDtlG/y/TNv8dOlC6xEmj9v3M2qV0p39+fWiRLJZ9dO76LHR+Ybw5ONph9aXGxPsuKVaAYujhXiWCEfmT/PRwtv8kL/AP/h+rvpeRblXJ/5kTquEfBU7wgV1eNh9wb7DIuP5a5w9M7VlCvQPsxnNs/Q8F22OzmS4QjnnePMOpZeBF7fmOGNjZl0Vc/xyVghTZUQhoruwOaXL70frQW+b6ATyUcPvc0/G3+JVgL/ZeMhbrfLPDh+k//z6Kf4o537efX/OkXxZsiNIxadfTE6FzE/XcM1A3Qk8UsauyGoXA4RiWbpk5pfvv+zTJkN9g/N1Y5QJDJmuVdm/PUAsx0weETzqwufZjUucn7/LJd7E7zyxRNELzk8+d5Z3vdD57jXWWb24Kfp7M/wKzc/QP0bU4hI0FkAkQ0xVfq9HDU6/NTcczAHyXDYveRX+bNrZ3j27UNMz9f4hTu/zYvNBS7+6RHE1YDf//gCd773FiXVYzLTxhAJdxaXeDx7kU/ru3hj6SjZNU3r8QH//M7P82p3gU+/fheyo5g9vsE/PPEN8tJjwmgC8MZgL99pHeHZ1QUKb1soX/Py/j1sjrzAMzv7mX4mwlnvs3Nnge6cQadkc4e9zILpsRErmonDp+t386W3T4LQfOTE23y49BaODMmKgBjBdpynE2e4r3ST2YcaSKHZa29TUn1i0k6qp83UrSRCtrXJVpwnrwb82JHX6B+yuD93nbvtFj2d8IY/xlpY5tcvvAfj5Tx+VXPm4SvcU1ykarQZMbpDqeIASwh+eupZ3iwt48qAcbOFI8JdUm9WREwpjS0MEmLi4ZZdqAWu8Pm1I3/OxoEi170JXqgv8DbTPDZ6iYnMIu/KXeKx/HnqcY7f3XyQKztjNNcLOFuCyIWTD9ziZye/yxcbZ/jNc49gmjHv33OJu4/cIq8GjMgepoh3O7gzTpPMRBfHCvn4/Nscz6zQS2y8xGTU6HDP3pvEWvKLT/0I+/4s4vyhaV7821vsKb7GN28epvjlHPX5Atc+NsZduR7Jjk3lkk/thM333/U6Dxeu8lvL7+LK0gTVQo+D5ha2iPnD7zzMwmd81h/I8BM//iT3ujd4oXeAP9m5jxO5VT5ZeJsY+JPmMV7c2svyygilNy2iDCzNV9hbUYTEhFrQ15pO4rAdFTBFvNtt3FfZYcWMuHv0Ng+712hFLpW3JGPPbnK1Osape1dYVQWCSEHXxJIR9zprKGA7MQi1pCQDFJL/evzw3398zxYfEokrA1wzJDGi3TVUPza43q5S77kMdlxEJAiMmJFsato7nllhQrXIywB32Ca87YywLfOsdEq0Oy7dXJdR1cYREe3IYa1XTHMdgURLzbTd5Iy9RStRrKo223GB24VyysRPJPWBm34QgDYS4kix1Ep9L81KGuLcjgrc6FZ3VzcBZDHhqBmT6K2UzR8L+l2bfsemlc0wmRmn4wzthInJqNXhpHObrPR52jqETtI74Fqco6K6OCJmVPTZlh6GjDG0ZMJsctDM4uk6JbOPLS2OZ1Y4Za9xIyzzTPcwoVbYMj3Iu7HNWjsNiy3mRiiqHkpojmRSs6dCU49jWu8w/eMcNzsjrDTT8OmY1aFqdFiwtphQPovhKDU/pSdmZEDHcNhjbZOQqrSjRO4qspsJaSq8VaRbc1OyqdQMtM2yLGGbEd3QpuemeZmobyD8NBCaGZoWb/fKRInED02ETB0q9SBt5b8jj0sCxWqriGnEWCpOL6AqoJNolID97haDMZMzhdvMG33ySUDWCsBMkLtbMiL9jmhBNddjf2GbREtqvkuQGBzJrvGQ47EZL/Fqbi9RIrndKOM1HBAwsGOUkbA5XsARqVp9eVBmuVOi7Ayo2H26oZ0WYZEi6wS7mz9eZBDEiiiRuyFdQ6VW3YPOBnfaFhf8XjqT7hkYw+2XREt2wvwQMa9QQjJp5BhTCbVkk+c7B6h7WbbaOQbb6bGUfqlBjgTsndgh0YLlnRLhwMTJBTiF9GTsWGkAz/PNv2DS9BUiEdyaGaGvQzqJw0avQL2VxZ6MuN+O+bbdJr8S455dJsztwS8rwkhQK7l4tpF2PpzUyGp2I0SU4ORifjBXG5KCjd1AqEkafjabPqrZxzAcjliSqaTGhGqTUx5vtU5gv3Ed9+ixlO8gBXcZMbbw+cNck5Y/hQx1GnQUaej5HWvwrFmjJH1cEZOXgjeMBr/v3Yu1bRDNSu7O3GTRG2FpI3091iML6d0gaUdnYJpDE3BEXnkoL91cs52QD7vbeInJZ3v3YtfSgPLdzhJ5GVORBjGay75PM8ww8EwKXY3hadqeQzMxaPkOhR0PtdXA7OWRYVooFKVPWTp0kgF9YupBFnMjHb+s7S8SlhRKJ4SoIUjNItCKCaPFrFknK30OmDWKUrAUmVwOJgFSurB4x7ViYIqYk27a0p83GrjSpBf71OMc62EJbyfD9K2YdqKoWj3ucm8yIgeMDtfXzWFBd8CsMZrv4IiYioxQQDORtBIbiR5Sgk1inZCgU2gdCUoITloD7rVDviZivu4fJUwk/WGIviI9Zg1JU9WYcNos2yWaUiNDEDHsy+3waMbj+V6XsOkQOSl87j3uyu44OUGwGSf0EomrAvIZn0qmz/3Za5yxOlwJbS4Hk+SlxwlrA1OA7EvM165RMg+xE+aQgN+1ya0GBDmbwXBJQYYCoxeBtjmZvc27nC2+ma9x06lSsL1dRYezKVGvXMRdOMO8tcNxq8NzPcFar8ik3cIUApM0hL/dzqGaBk49DR33Ihs1DAcnCCR6qJrI4IgANYQulq0BQdZg2m4wKiNc5WN1NXp1A+WP4Q4LlSSREAkMmVCRVrqSzgBfgzlcvw7/Ryg+Qh3zJ7fvpvWtCbSRpq21qTFbErMLgzHNwl2rjGa6vHxlgc23p1mcHWPuvjpzVo3fWXyQjaURChMdfu7gM9yTu8nB7AYbfpHXa3P8xJd/DrTg6MklfmTmVX77+kOUXzMRiclvBe/iqflDnKqs8MnSy0wbbZyRkO1iPmVLxDbnO1O8+dYRitcSYssgdhxuTo7w8uh+7rOv8+fbd3HhyYPIKPVlaAN+c0+Zs3tmWe8X2FgcwWwqrJbAbmgS5fBK9g5iC+x76vzi4Se50J/m51//JEHbRvQVxkBQX3H4pbM/hFYwcnybj8+e5Vtbh1l9ehYZwJ98LOZDe55mLSry4tZeBqHBIXeDe501rvhT/PmV0ySx5IkDl5goNZmzazwyfYO1QZHPvnmGL6/fS1hOcKfSTEmvkUF4CukJ1EAgQ4HVBNvTXD6Wo3TnACkm+IPb99L3LQ6MbPPIyDUWvSqfffs0qmby8p17OLTvU1wI5lm6PEF2WfHV23fx5fET6EgiegoVCDIbkux6QmIIgnyGvgk3xRg3JCgfRlsakYBfLnI9XySxNWFOpzyXd743TsSp/Aqu9HndnCHwJKqlCC/bRDF4SdrS/0p2ms9W7iXJxjx8xxU+Wn2Tz27fyW889xgA03t2ePeRqzxzfT/JkyPIUCMtQWLA7dM2Pzz9Gq70WQlG6CcWE0aLVhIQajjhrlA1u5woOXRmHeqBy8XtcXw/Zc20koBr4Thvr00RbLvs9CRGX+BXYg6fWGYy0+bZW/toLlURIRgDkb5uD2QAvTnNqYeuciy/zgFrAzB5o7eH4isO7naCVyqxlC3TPhTx8w99i+POMqPDTaqVqMtr/gS9ZJRpu0FxYsCf9c5QuGJgdjVmT6NCzeZdDnv2pwXsnlydXmzxyrW9tJ6bBAm96YQ4H1OebPPAwhXWBgXeOreA2ZC8eXWe7+v/BBkztfLOzTc45q7i65D7s9f50x+/k/CJPWgnRjoBUmriWNLzLcbHW2RnA5Y2R9j2MhieZrq8svvcvzuYpxM7nHCWmTf6HClu8tTjM5jdPN5gwM/cfi973Rrvy59j1GjTOpjgfOAIgzHN760/wLO5Hd5TuMhha5uK1aN1OE5hbrZG99INq31WamP9SusUK16JB4o3+FjuCgXpMTdeZ1lUsFTMnzfuZt0rsvaBmI3793Lo9BIHhqC9fu4GzYxLRXVZi4a5hbmYyFVM5btsxgFjRodjpxepDVzuqS6xGhWoxzne7s/STyyqZpf7SzeoWD2edvbTDQwmbZ9fW38ffmiw+LEcMsgTHepzYHKLM+Vl8jIh1OmoclQNmHRahKX0QjbhdBiRvd0NGICS9IkRvDrYw3cah8mokI9U3uSwtY0rIk7YqymnRsSYAqZVd+hR0ZjDi9fFYJzPtGfYCXOca0zRHDhgJWzdaRLlYl7bnmWlX2LGbTLr1Jmx6rwrs0RFWunFTvhsxhaf7hynH9ucdNP30RGaThLQIuANv8KNYIxW5LLml4ZY97Tg2hrkqfdchNCc70wRakU3ttkJcgxik7Vukb5vYeYDWscE2DFbfp7fb0/Tjy0OHVxFCs2iN8Lv6TsoqgFjRhqKfqe7UVQDPjpzbqgaaGELi2bicqE/TXmoVKiICHuuS/Mjx2jPS8pGn1ZiMjHZYP3+cQYzEfcVbnCHvcr4sS2W+uMEoxHfahzllj/GqNXhBw6/SU75vOrN0Yqz9BdCtn7qDN15+NONe/hO5jBnd6apt7LkLQ9vROOIdCykVAJzPbYnU8DgSrfET9x6P8fy63ys8CYxBq/1FrjRr5IzAvJDEvep/G2yRX+X5Ht35ib/4WMeOyfvgIUev77zbuqBSxxJtBuTN/xh5lJQkgaejnnNr/Bafy/9Tgx87a90jf/eLT6IWbs+ypFPr6CzGdpHSkSOoHCrj7m4xfZj8/zgR1/niewV3vvG32P2W122T2W5dGyCbsam/ew4h7/SYOV9ZfYc3+G9mT79zDa+Tvjwxl/n8H9sQhCy+mtFfubAMr9rxJSfayBbXWQ0y+LaLDsnXX5+5DlmjByHzSbQxNcRfR3yaaPL9VuHqXzpYvqEpSA8ucBbj8+QlK/x+tIc+7/YRIQx3nSeMKtobed4un4EGUhyaxKjB8XFEPdqDeEFJM0WwjC4PHaIB04t8VZvjvy3shRvBPTHFd5I6osoPX8b7fss/cwhvvP4QW5cmuLwp7YQ3T6vnJqHPcOOwnoZfMX6TJGskFwbjCEv5DBCuDI+xvtKsMfa4aizyovWAW68cpDxP79MeHwPG/cVEQksvOVj395GdPskjWb6Wk0TYRiI7z/M0sEyXmjQf61KZkvz9qMm/3z2i3yVY5Retxl9o8cld4a1PTZXvQmKlxTVCwMSQxJbijAnaS0oYhuq50Ls77yNMAxktQJKodsdklYHYZkI10UYCp3NoB2bqJyhvdchygj8UtpOjaqKM5lFRlWP/2w8iBcI3DXB2Gs9jLaH6Hvg+aA1OgxJ5iZZ/JURPjHX4Fcuj3LkN1okGZP6Lyl+ceKbPH3pINOfXSRpNJHjoyT5DNcnytyducWoCrhp1ncDZs0kxSqfcJY5bK8xpfpMGTbnA80/8j7BalQkRlKLBTf9McKVLLkVSXExJnejTe10iT3313l/6W2eevsIMy8nKC/BagXIIEZtNIg3t+l832mOfGCDT5ZeZVxJwORya5yJZ+voi9cp2DbCNFA/cpSpRxvcbbdwZXpHuBJl+GbzOGGieLx8nhPZdT6rTlK8FOBs9BCrWyTNFr2JeziY3eCQvU5Wph6kl8/uZ/pPryNMk40PztGdNSguePzvE0/xml/h5y7sxWqD2bFoXZ1gY1Tz/ne/wWOlC+wxaoQ64V67wSsP/kcSrflsd4Fv1I7R8F2WayWi0OS+qSV+pPoS3xw5wafa96C6insK2yghWYkyfG7rNJ3AIZ6UjGYvczq3xOsPzdDqZYibDs+9eoTze5o8ePQqE0aLsQM7bMoRtJFw7sYMl5wJOASqeJ4Rs8fcwU1aA4fmVh7ZTbtqC2adZmLx3NY+ltcqBAcNnsheoSh97qkuMeL0aPguz6zvp5QZ8E8f+CIn7BXGVcCkcomIycpUUdBKbFbjYuramGvTqzjM5RqsxWlh8n/MfQEgDUpGRc725vjKjWOEgcFHjpzjE4U3eDx7mV8c+w6dRPG/3f4oz14+QLbo8cB7zzNmd/ho8Q3usmNirQlR+DoiKyRZAZNWC1VKA5wTdouK8nBEumasELjSwEDxbN/kjbUZDCPmYHaDMdVhXA3Yb6R+qoEOCHVCRRrYwviv8Plf6IzzqZun8QYWYdNGBBJZ8SmdbOGFBpsbJTaDCpcrY4wVU9rxfc4SrrRwsSgDK1HMN7eO0vAy5Oc87rVrJAg6iaajDb7dPsqrO/O0PZtWyyUJ08yOCAXa0gg3HefebI3QDDKstoq01/KIRCCKAZYTUcwPmJzaxBAxNT/L5zZPs5Db4SemX8JLTJ6sH+X12lya08m0MYfr+RLNXflbfDx/gayQ5IZepVqc41pnjFGnSz9nMioj7pq+zbMPHcYoeVSMHm1tc7q6yvN3WRws17kvc4v9psFfm3+Jr7vH6IQOZzenOcs0f/PAC/xs6TrnA83v1R5ky8+zsHeTzpSNHZi8vTSNjmbBS+m+twtlQg1ZITBFmk+8Y3yNn554hl5i87+c+zirb01y5eAYp48tAnChM8n1epWMFZK3fMYyHT4w/jZ322nXUwmH01bAn97/22zfk+cztbv42vWjxLFEJwLlRmSNYThcSHLCwdYxl/1Jvrh8gqAb8P/3xcc3+lMQC8LpCkHRpLVHEbkQZjO447N0ZwTrYYmzwQTkI+pHswwmUi5EogWJCd5kjqCgcUSIKRRqOM+eyTdZO70PFUCUtPlMr0y775CZdjBKDpErQGgGvsVzg9lhgM6jLDMoIVBakFceXlWQ7JslzpqEeZPujEExsnjeS0ORnX0p36I3oYiy0J9MyIz1CQODPg5qIFCBgdktIiKNmK6QGBJtJ5wNJlgdpAVXUDLoT0h6U5rIkeRvlJH9gLCgmXTb3Kj4tO4YweyXyeda7MQ9OnGad2AY7oTU0hlnUuZDc5DZ9bIUjT5L3giJKWC0wmDcYjCegBZ0pyzQZWRYRIZjkKT/XgtBf0KwP9+k4bksqbQrIQSYaKbMBp15jQxccEK+0z3Kq/V5zJ5GDiK8aZfehCTMCfrTMdrSdGYNnAN7U7uqY6KVRM+WSQyJiNNdeQCv6uCVFbEFYV6QWOCNJsTliNmRdG4ck7I6tEitsVHeRBuScL5AbMvdi/pg3KHs1IiIGct1aR6fJTHBUn0Wo3I6ypkaQWYzJJaJVgoZwFveHCNGl8WgSitymbSazJk1YgSdOJMm72XAtXDAYjhKGCsMIx5K/FIBnzY1sQ1eSaJmcgxGBRWzR0n1yY32aB4oYfQVmW2FCjRqLIOMJmnvUdSCHOf8SWJrA9MImHTbXDg9R370JGbLR3Q8wrwYBtcEa5FPX0e87e/bRdmHJQPnHSKtIUgyJnJqFDlawS9DRaWuio2olNpPDU14YIrEVnRnIZgOWcjXcIVJRXVxx3r0/PzuMZwUUyT9cjBCL7HZiNMNjNWwTD+x2AoK5A2fIDYwzZhAC663q/yxvj8VYeVDEjfCEDFXwx61ZISi6WHIBD8xWQxL9BObcbeLY0RsaIGnUpBfJ0nDvFkrgHzqGhJSo4yYQWyyEZZoR84u9Vg6EYkGW6XbHBtRiZ1OFtkwWe6UuBmlG2tv1GdZrpUwzRjHjJBoAm3Q0xabMXSSFLLU19ZQl15kIyzSCLO4VojOpsK6XmKjRJL6oUj17/1kyN3J+PhGws1uld+VDzBpNbk7cxNIx0LKjtEabrSqrJsFBrHJy06NcbPFCXsVE42v1ZBE6eA46aZDJ3a4Fo5Qi3Ksh+XU2GunhcaqX8Yw0pB+K3K5HVWAOlnp0Us0L3jzrIUlDtibuyMGSIOufmJiGzGJHRFlFdqWZNyAvO0TD9H+wpdEoUGsBYPYZDEqEtPmWjjCYjCaBmljAyU0njaoJ6RUZJ26jdqRs5uvUsYwKtsyMNuSsJhglCNsOyRjhhgyIWOFdLIRaIGdCcnYAZYR4UcGgVBYKt7lKnmJST3OstIpsdXM4RUMXCPAVhEZFWLImOveOPUol+ZApI8SCbf8UQrWgMJQHBgCGRUishEZN/WgAJSMPpVsH9cIuBGO0NMtWnEGS8WYcdqV0kArzrAW+VwLZjnXmKLt2VTdPvtLO9xsjdBbyyMDQeLG6HI6Gr4ZFTFFzPVOlVbL5Zoa5dvuMfzEIAwVsa1JtOCKP4kpUvbRSLaPEqn+A2A1KlNR68TD8YynFctRhWacyiRdJ+VfxcPcpS0j+knItvZ52ZtiO8pzoTuNpWLEX6Is/397CK3/2x2K/98+2u02xWKRmd/4JYwki9ETBNWID5w5x0F3g1bk7rbULjfGCCLFfLHBXLbBSr/E+fVJQt8g6ZqoviS7r8WfnvrPHLHc9KKcaJaiAi/0DrDml3jy5iGidReSdA4Hw515BdqNsYsepdyAv7v/SZ5wN9L/rzXnQpeffe0niG/lEHN9Ht13lURLrjTH6Hj27pZN1gm4b3yRWafOlJlCg3ra4qI3zU6Y50s3jhNfypOYEE/6mE56ELlWSKPjktzKIsOU+vc/LzzFZ7fPcOGrh7DaIN5X45ePfp7FoMpT9cP0I4v7Kre4I3Ob77YP8/lLJ9Gx4KdOv8D/o/wGf9g+wr978z0kPQNMjVAJhhWTdX28wIQLedw1TeNEwo889CIAn7l6inDDRWdizHyA1oJouIp87OAKvzT/Bd7y5vjlr38fheuS6LEm37nrPwHw1GCKpaDK1zeOcfPWONaWwcy3A+z1NoufGOXoE1eZcDocz67gyJD/cP3dNM+PIEMw+sMV3BN9njhwiQvNCZbPTqY22BPpitv13ihvLM2RRIJHD13l+6uvpYE91aOZWPy/Ln2SnUtVjJ5I7aUm6AdafGDPRc41p7iyNIE0E3725HN8svgmr3pTfLl+Mt2eIjWonl2ewbzgYvYgvxJjtWO2zlio+xoYKqbvpcCukVKXA6U0B/IOLr7ez9DpZhBSk8342GbE+6Yu8yPFV3lhsMAvv/4B2LbRZjo6yk90+JfHP8dJa4ezQZWL3jQXu1M8e2M/8UCxZ36b+6u32A7yXGqMo4HvmznLE7kLrEVF3ujvYcUv8/VXTlK8pGieDvjce/4D80bM77WP8kpzL0udMmtrFZQd87dPfpcfL57jJ67+MM3fncUcJLTnFEFJkznZ4Hfu+H1Crfj3649xpT5Kp+cQtmxULuKHjr7OI/krzBsNDpoOXe3zoldiIyruHsvbUZ6XG3tp+O4uhKzWzGGdc7E60L53wCdPvEYzdDnfmKQ1cOheLlO8Dt0ZwdFHr3G0sEGiU3ZI0RgwY9WRJFz1Jln1ShTNAXN2HSkSVv0yzTDdKpu0WoRacbEzyeYgn/6MOIXmTWTbFM0B64Mii40yWovdDZPpbIuTheWU3vjUKQrXoX4y4b33nGe1X2TxW3soLCXUjwvGTm1ScgYs5HYoGB6D2GSQWFgyYo+zgysDrvQnuNFNse7GUHdwMLfFkcwazdjlUm+KQWxSMvsUDC/lpCTp2OBLb50kf9GiP51w3/2X2ePWWOyPsD3IsVQro6/mMHopOEx5mtZhzSff/Txzdo0b3hj1IIufpNtj8Bf6hrdXpjHfzoIA78iAsWp71zNlyISc6WPJiL1ujdPuEucHM/zn5x/BvW3gHRvww8dex/1L1u2tIM/WXyo6ISXGGjJmsT3C6oVxzI7EnwmYmq6TMwP2FXYwRMyXz99B9oKNN6IZuWM7dWHlNzmYSc+1sU4D9k/uHOFmbQTLSPHjXmTQeGWc0tWE1n7J3CO3mcq2dn9/rAXBELaXUamBd7lXYrlRQkrNoeoWU5nWLotmdVDipbcO4qwpBjMR03t3KDkD9ue3KRt9PnvrJP7bJUQsdq8PmSNN/uaBF9JRjLmNIyI+17qTpzcOUM30+JGJV5g1a1zwZ7jhjdGLbLb8HEFspBk9maQ5r05+V0x4PL/Gs9v7ufXyLMoX7H33Iv/r/Jf5jfXHuPCFwxh96D3U5YMHLqYZOq3Y8bO89tZ+souKxIIwp9PryUiIlQ2wrIhCxsM1Q06VV1KWT5RjzUtze8ZQrRENbcTvmG7DWFG0B4zZXUIt2fZyeLHJAyM3+XD+LM/2D/Jvn34/zprCO+hx174ldN/js4//Hq1Wi0Kh8N+91n/Pdj6sbYO4CsF4SGW8zUfLb3LSqu36KZ/q7+HVtTkGfYuHp27yt0ae4RvOUd5aniHpGwg3QhQT5ssNHJEQ6wRPpy28eaPNXZWzLEWab944hLsm8cuaaNpHSJ3++1AiAom/k2HLM1mZG6HvrKKEwESQFQHTlRaLvsH980v8g/EnWY4K/KPa99PcyVGqdjk1tcyM0+QTpddYMCJsYeBKi37SZo/RoJZkuDFe5fV6BpmJODq7wajT5VJ9nO16niSUiHxCJDV3j97mB3M1eskFXh87QGJK5nNdpowWFdVlYWKLkKGELHYZJFaK4pfpCSdG40qfbM6jLxzijgm+QWgntCOJjiXK1QzGBdZ4n/cXU/DZ5clxrhhjlLMDFoo7aYjRy+JFJg9UbrJgRNTMGtpKD0ghNKHWVJTNh91t/Mw6Lzf2Ym4b2DWB8tNPMChqPj72JhNGkz1GCwm8OLaPb89mCTxF3EzXZO/bs8j/PvEU3yjM8Uu1DxH0LD48e4W/W32R72YnWWxVGAQmZwpLvC/To68D6rEm1MNAsExXN4MSRBnNE7PX+fujz/Fkdo7f1Q8AUDZ6dBLFPnObvzX2NLUky2d37uJGu4oyYrypmLArMbsyhQJp6LTToiLxFESSutSsmukdZi+wCGNJs5mFmk1iJ9iTIblheNXXipiUABraCbIQks95zJcbZEV6Uj9p7XDS2uENe531foFOYPPjMy/z1wtrfKWf45X1j9Hv2yyNVKlnXKaMFg9VzlJPIl6em6PbGMEtDYazfcFWUGClW6LRdREDRZykd8KBTlefU8CTZDCuiccDjpZrOCLG0wY1L0u742I7IZXZBhPZDh8tvsl9jiLWDhExJop77QaxXUcN2SxXQpsX6ws0Bw5eYBL4JvGOTfFmSu1tHbZwZEhGBZgqRsoEqyUo3vQJXZtRp8vxzArnBzMpXdcRHM+s4IiQc8ks214OQyRUsl1c6ePKgK6Vcgx2wtwuRbNoDYh0WvwAqfY9VrR9hzA0kDKhYPmMOD2yhk8jzFLzs6i+wOwnqJ5k08uz3cuR2dbkl33a8xlMFWOIhF5kE2lFM8jQDDK4RkBO+RSNPs3QTbXyMmYq2yerAkyRunkaUZadIEs3TCmwGZU6WsbsNt3YQfQVhaUYrRQr3RK2jEi0oGgPgDJWU2C3NJmdBLsZEhYcbg/SjsZyv0zNy5I1fUadLomWLPdKdAKbaMdhZDFBS/ArNtsqTyE3oFQcIEVC088Qa0lGhdSsHBt+EWfDoLCU4FdtFveOkDWC1B1FiiXPG+lqaEaFmCLeVbxLoZGhQPlALBCkELUtL7V3G+sW5asRrT0GyQmBa4Sprj0sDpUKGi9Js1Jag5KarBkghaYZgznQiAhsIyKrUodUoiWWTCiZKafDliksbNvLEQYGQmqiRO2uDg8Si3boYLQlTl0TFhWD0MA20i0fV/n0+jalZRBRemFPDEFn3k7ZJiplvHjaxJEhlUy6/PDORl9W+hTVgF5ks9wp0fctKtk+I05v152Vbs6ZbPhFdvouVjsFkRky4ajpUTQHGH0wuxrLSkWIq0GZC+1JNnoFjLbEbmoSA8yuIHagWxI4dhrMb3RdBlaIWYkZN9O19oZyGcQWNT+LF5sMotS6nmhBFMtUNWL6zDr1lKOSGGn2DU1Pm6wHRZw1RWExwZs0yRs+whz8la/x37PFx8H7F1kLx2k1XXoDm99cfZSC6dEOHTqBja0i7hhfI2sEHM6s00psJowW791/hU7oULH6FIwB+5w0PLYa9/mt2gO8uLN3F7cNMD3SIn5vB9cMqDo9uqHN2atzmI2UHBg7Gj1QfG3zGLf9Cqeyt3nCvYmnDbY6OdixedOd5rczD6Vadd9CGAm9gcXZzWnW80Xuy11nXG0REuLHETGaitTkZZfHRi7BEWj4Ltc2R7kUTJD4Km1V5iKOHrvNZKbNvfmbrMd9NsMiIky/mFdXxvkn6vvYk63xsfIbTKjO7vt3eTCVGj4DwdXeGNdzDiXV54cW3qQVZbjVG6Hhu3iRkaqYgfJUuu54b2WRPUaXUMO95UWKlset9ggv3FrANGMe3XONO7LL1KMcv7T1CGuDItpK6E1Jkq7Dz936BJGWrHfyeIGJ17dIyjGJLdm8y8XwMjDlEWrFNX+Cz9Xvoh64nNuaJG6biESQWGk34J354h5zm0cXrtEKM9ydu4UaQqrqzRxx1+Cz+dPshHnGzRannNt42sRSMYmdQDVkZLRFzkpPkL/XuoMX6wtcvz0GWvD74X08V9zPXrfGPdkbKDT3Fm9yLLfG5miB1ZkS7dDh+r4q9b5FdaTOu8duA7Dh5elHFvWBy1qjSMYOuHviNlN2i2+ah1lvj6YcENJOyuuNOW70Rlnploi2HayOJJDgWxE3dkb4u60fRElNxgzJGCFFe8D9I7coGn1Kqs9LPrzU3U97M4fsKb5lHORae5Ss6TORST//0WwP80zCXaPLlCSYQjJtN9hX3GEi26ZRdokSydfXjvDl5ePUdvJYc5BYMHZyk/tGF6mHWf7+rU9gyIQDhW3urNzmreYM19bTbmN72gFCPtMr84frqcvmI9Wz7DO3GFcDxpXFlDHgQ6PnWC+V2Ary7Pg56hMut8ZHiALFwel1TBHTjhyWayWCvgWHPG4dBtPucqU5znKvzMHCFg+Wr7MelPjtlUcIE8Wx0jofGDvPelDi85unSRC8e+QqJzNLPNM9zNeXjjAYWMQ9M0Vw2zFmLkAIiEKFjiTaU6iuJMlo8mObPFi+nramRUTZ7LHxQIGtozkOTWzzwdFzXMxO8bWFEbRyGMyH3FNdomL0KKo+jgx5qzfHtpcj0YKq2WHKbFAsDjiWW9ulASda8mxjP5e2T5FzfO4dW2Is32HcbDFqtNmOCrzZnacZZpg6uE19Km1973Sz7HSzyOHqdcYOEY+k4dZt3yQKDSy7zc1WlcX2CI1+Bt8zma42eWTkGgDXWqNsN/KIUsDGewwwEqYnG8zkm2wPctxsVIgihde30KFE7U34ROU18sUBVx4ZY/VEEdeIudYYpZQZ8KGJcyxYW3yzeYIXN2dJhltYkE5nk0TSarlkawKjC32lOVLeJGv4jJkdQq04W52jP2bgjWkeHrvN6dwS5/ozvFhfYDzT5pHiVbLSpzLeZWckz/nOFK/emk+L/r0+q/s0dOD6kwtcFzDYE1Cqdun2HOKGjTYS7jx6i0dHrnDTqKaffSA525nlLLPMz+3wM/PPkjVmWWzupXzVR0ubeqFEp+Rxf/UWh+x1Hth7kxf0PrQG2wkxjJj3T99kwdqiHuf4VO0eNgcFDuS3+NjYW+TVgGmjgSliXmzv57sr+/A9M83ERIK2m2UlU6aQ7/Pg5C0mrDZVs0NJ9Ziym3w7c4g4kfzA+Gu40mTKbtGf1BhdQRQqnmvup2D43F1aYieb49OVKkHNYDCqsQ60MFSC6Zl0uw7ZnMdCtQbAM5v7eTI6zGS+zR3FVSasNnfmuzgy5HcWH6LxZtqlC0ZiRCain+8waTZYCUZ4dW0u1Qscjvjh4us8mL/Kk/cfZvtwlgNT2xzMbhLj/5Wv8d+zxcdv7f0y/2f/XXy5d5xgYHLuyiwkAqOlMLuCwVzIP3noS9yXuUUtSc2zo0abnxt9GlvEjCpNUTrDO2FYjly+vHQM/3zaOpMRRFnNY+99k/9l/Fv0hwbby/4UZ6/NYjfSAKNWmkRIrt8eY3FzhJ09Od6VuZnSDNsOmW3JwMjxdXUEAN8zEUoT9i3CpsNgYLE2WeaYtYWvU0iNKWBc2RgoPpi7wlFnlW93jvEHFx/C2VQktiax0pn5L8x+k4ccj7XIZy222QrzyAhkCOZtm/ONPdyaq/ADlVeZVcnuato3zB6qozAGsNwtc7k8yR5zh5+vvIlCcDFMzZvnBzM8s70fU8b8/Ny3eSzTGboUcvSTgHdlL3PIWec/9B5F3cgQZDUHj27w08Xb/GrtKF++fCLt0Ngx8VSM7pqcf2UBYwC5ZSj0NMlBSXyohy4K2iUDEsH+iZQ7csMb45vXDxO27NSF4Ik0C5FNEE5MbkgBnTf6/I3qc6nzQvUAQSt2SRoWVlNyU46z0cqzd6TOxHSa+zBVDFbC7HiDX9n/GfIy4D/VHuZLq3ewtlPCvm0jYljrjbKaK7M4NcK++U0mjBbvdq9RGvJy0iAfbMzZdBKHEdlnXIV4Gm5GRWpxjj9ev4/NlTJxQfBg4RrvcRdZ8cqsGSMpbZd0rntzZyRdTe0rMpsqJUrakjBr4PdMWDIw+tDPpp2aeMHj/3n/t7nLCnjFd3h1sMAbjVmsLQOrLQgGOa6tZ8HQaCdGOTHv2neNn5l5lmmjQVFaxFqzx9qmm3XIKY9Ro52SPp99L4XLBtaIxp8JcYsDfn7vd/hEboe/t34fz75+jCSjeeLRC/zN4k3+iVZcujRDM0opjrFu8MWd01x5Zi9RVpN5ICQuCRx7lTnDYFoZfH/uJiGa5chkOargCp/ZhRaOSLgYVrniTdGLbPx6BtWTnLn3Gv9q9gs82T/Ir731OGHPZPaOBu9xr/CZ6AyXrk4jPMWhB7b4wfxl/rxzmD9duYs4kJwuLXPSavPNxMS7VsRuCMxeOpLwy4r+tAIJZkui+gIVgvIgKEL5rgHvca/gD03Eh6113nPoEgpNXobkpWDB2uLJfYdpWVmm52o8mr9ESfbJy7RbtRaUeSueITEkFdVlzqhzwlpnRGk8rdmOLWpxlt9v3kv0ZomNyYjp2bd4wL3GqBpQkfCyn3CtfRct3+En9rzMXytc4886e/gXL38IWibaTmFo01N1fvngZzlgdHfdN3/SmeNfv/0+gq6F8BQiFLRzA044KfD6T6O7CJs2pak2P3jyTSpGykIyRcxntu/k5uIYYqAwmxLlCbZGc+wxm1Rkk4cP/Akh8G+2380XL9yBlzc4PL/Oo5kuT7Vj6uvFXcT47kOnzhO7pjH7KYzvVP52Ckc0moTa4NPVUwyqJYLRkMdKFzhpbfBmd56r26MEI4rRSpt9ZoN3OVvkpM2vonlj+RB2XzD36BJ/f/7r/Pybn2T+P8XIVo/Vj8zQPFrA2TAYvZwQugarc0VmJ2pk1B50IJFdA3dNYjU1K26Jhw8vYYqIb9fvx7mwQtGaw6+YeFEGU8QcMHf4a2PP81DpOpaIWLC2KEmPvIjIS0E9hudXFujtuIyfavPJfHpjspMENBOD841JgotFVCCwvHTbLnIVUcakPQdPHDnH3XZaHEjg8cw6f6dyDiUEBgolTGasOsF4SJQ1EJHi/PYkR6ub/NDINbzE5EuV44T5PGJPj395x+ewRMz/+8pH2b5dhpzHHcXVdJR38yTWqkn7oMO7R64yY9W4z1llUmX4rUQy+kZaPNaPGvgjEm/SZMJosRkV6a/myK4obk1VGJ8zqKoGv3LkM9Ti3HD7KaIfxvxVH9+zxQeAIgVTSSvGyKYH+EBkUt2xFbMeljgnp3i7P8v13ijzbp0PFc8yqnqEOl05S4aRFoWm4g5YquQxW4rMdro6ujHI7xYe14IJVoIKdsGnN2OQ2AkiGyENTT43IGsHTGeaKJHuutMxsRuaoChRMjXZunb6PJtdF69jI1XCeljiQjDGmOowZQwwAU9HqOEQqSB8Js0mxtiAgeHsHsCG0Hyqdg+vZXbYa2+xx9whSAyMrsDqaLwxUKMeU4U2FjEhmpXIx9MRW0GBxNLEiSBv+ZRUH0ekYwGJJC9CJowmG2aRvOljyHg4Xw3xdEJHC/qJwQV/etfpEoxHqGxERXVJSFjyKrDmoGKI8wnaGlIixyP8voHVNBAxRI7GzQQYKibJpgG/uWyDrPQpG31GSl2aRoLXsklFPqSOlFByqzfCdweTODLYpYRuxxm2Y7jQncLeUtgNkIGJ3yxwNVRsjReYMJpMum2a4w62SmexjgzpRTYT2Ta1rkukHECgMzGZvE81k56Ms9LHFRpbKN70s3yzfRwpdMqQMVqYIsGVKS8hKwI86aeSw1xIzvUpKA9HCCpWD7OQUjAn8h3ylsd2M4faSjMlfjVBK40cCRgtd2hYLlEti5aCoJyiqMfLHQrCJ0Hzne4Rvrp8jE7fJqjGhKX0uUsnRhkxGSckY4VUrB4xglqS5U0/JdTGWjJj1VkLS3yjfoJtP4fIxPSnFVEhxs755DMpxdLXaes8MUGbKfPCFmY657cShNRcGkzzvNGiH5mEpQRtJzR8l8uDKUqqz14zPZl6OsHT8NJgH9/eOUzZGvCe8iVGVXqX7yUmQaIgARJBy89wLSyzFRaQKkFYCTnlk5UJk2aT7GgfzzOxZchylHqQKqUufmgQJorzQT4Nx437DLImUTOVzvlljTGahkGjKIOIQStBotJV+IwKUEJjD4/JGEEtzuLp9OTrCj9dta00uBULCrbHRW+aqtHmDnuVigpRIiFMZPp6ho/lqMjFIENMSk7tDUOlkZsi3W8NRnFExKjRZtRosxpWyJo+QmjyMm1hmyJCWjGxrTAKAbmsRzXT283XlKS3O64bLXZp2za9jkMyMLCMePfiUHIG1Es+4/kOM1Zt95xgiZhZt8GFao9+3yaKnDT0nUje8GaYMFqMqy6OSBgzO4xUulQy/SGkzCdKFJgJINMCRAOZGDMTEpoW3qhF1BdIM2ErLKTrsmEJPzGxjIjGZIyRCznbn6MdOwDMlpuMZzo0E5fVOMQUXVwSKkaXcDQk8hSuEbARlVAqYTCVwyw5DMY1mdE+A+HSCgwSE6bMNDTdiyyQpITZUYFfAtf1ORdUue5N4BcF4b5J+uMGYVajMzFFo09WJvRCm9v+CK4MWLC2cERMXyvqkWI1LJMMfU6NwOVSCI6IMYfqiDG3w9LoKEQC6Q1Ht+88tOCKN4VJzB6zybiShDphM04/z4pS5LBxZIiZCwgBx4pQMpVKroXlXYx+bKcjmaz0U31Drk2rksExI272q/QjKw3QZzSG0Kz6JaRI8OzU7Jy3fRrl1JMUO2nn2VYRBekxbrQwxwb0dYZxI+IFL48jQxSaMdXZtY476i+/uP/+43u2+KjH6TxOGQmjhS4/Nf88s2aNT9Xu4dWNOYTQfH3tKFFynNq5UYpX4c2DUP5An3uz1+lpj+0kRiEwhcCVIZ+YfpMr5Qm+cu4ElSc1ItG8fWaaF8b2cq43w0vbe0i04LG9V9l/bBM/MfGSdI53xFllwmhRkgGuSNessouKsVfbhLkCBcdnJtfkkfJV9pjbfKN1gmfW9wPw3PY+XlXzfGj8HMcLaQt0M47wtMIRMK5C3pO9ysSdKd30rd4cV9tj3Niq8tIfneb1rib8SJM/OPV/0QwyFJYSsushjfvhV+76XCoskz61WPC17gneaM+x0i1BKSAWcEdxlZNWGuBqJSCJGVWCKcMnZpnbhRGiRLIRlXjWg+VwhCv9CepBlos743S6GRbGd/iHD32VitHloLnFZhzy/MoCM0+l6ebGYROvInHvaPBPjnyV6/44v2W+C3/DRM8NODm+yqjV5WT2NmNGB0mCJWImjCYnDyzhJRa/t/4A525OQyCRfYVI4M1r81zcmGC60uJnZp9lymzwTPcw5ztTvHxlgYXnfOzVFkgBQrDxrhHe3jdLqdjjJ8eepzDh8e/WH+dLn34AkcDMY7f5hfkn+X3xAC8v5RFaMztb46GxGxzMbHDY2iYvNEVpYQuDX1n6ALU/mCN24MVPrPIzs8+Sl2lxYaJxZUiAx5n8beK9gorVZ8poAHBP9ia9/TYZFXAqmwKZ3rj5A0y+pulOSw594iofrJ4jLz3ycsBZb44/tO+h37e5a/42H6meZVS1GVcha7Hg959/kH2fighOO7z/x1/h4fxVanGOfmJRUn32mDtIkbAWltkMS6wEZc41pzBkwo9Pvsjj7m3+17XDvPjlOxAJzL17jR+99xVWgzJXuuNkVLoldDNKZ83G2AAnEzA6ZB6MmW3y1R5RpPjMrZN8QZ6gmPG458w1vMhkqVnm6vYotbks08Z3UWh62qaZuPzbNx9j9Cs2tyqSZx9aYLbaZDTTZczu0A4chE5ZJtfXR/nl+ENEiSSb8SlkPQ5kNhlXNk9krzNxR5NOnGEpqPLH9fsom31+8cA30+OsfZBfW36CuWyDf3ZfusL6jfoxbrSq7Cvu8ETlAl5i8h9vPsz2Wim9SCYCmQuZtpsAOCKhJBM2Y5Ovtk5ytT3GvZVFfqj4GhXl8Ut7v0Bvj8WXmqf5vev3Usn2+acLX+LgcP2j69lDPbtBgOLPdu7l2Vv7MMyY6XKLrJEW4eb+DoYWfPvmQb7NQbIZn2LGo2h5HCpsUjF6FJTHzQj6iU2l2GOQCXjv3FUeL57npj/OZ7fvpB9ZjDsdSmafMavNL+77JoFWPNk4zvV2lbLd5/XeXhwZcv/ILR6q3mCvvc0ZexlHJKjhjc4nSy/zSP4yy8EI/+XWA+zs5BGBwb+5/hhlZ8BHJ89yzF7lLvcmZw4vEmhFJ87wVH8P7cgmW/QIAkXYtiERzE7V+b7ps6z6Jb5VPUS3b5Nzfb67eQAvMqi3siSJZH68xrsffJXlQZk/unQXcaT4wKGL/PLCZ1mNSrzS28ericHD+Sucsrc44Szzt+/7Dq0o1TF8ZvMMUmiW36fQUnHPXVf40bGXqMc5lvwq/cSiHmR5un6IjV4BacUoN+BdZ25wKn+bS70p/tWND9D1LbqHQm7MOCS5kEzJYzLf46i9yriyueJP8oVbJyhkPOb37jChepzzp3i9t4d1Ly0ChRNzqznCr4r3M+00+UjpLaaMDj86/jJ3Fm/jJWbqzYotXljfQ2O9gAglf3DjHvLOSX5q/nl+LL/Ordjjc+3TdGKHx/IXOG33GFVtjk+t0xgCHCHNLn21fgIvNtFaEJZiRt0B5tB/86OTL7FZLfHd+gFevrIAscDMBVgLHkomPLuxj0pmksP2OuOqybHSOl+4axIRCbQTI8yEqWyLBdNj3lhk5M5PsRGWeKG1n3986eOMZnv8/Ny3OWntYAmBLSRt63+A4qOTpHc1UibkLJ9T9jIHTcHl3ArrxSKdwGa7k8PzTLIbgtJNj6Dk0IhcQm3QT2ySoQvAFAkmCfPWNqaI+IZzBKOfIPyYeJChHuXY9AvstHIolTA7U+fx7CV62qAe5zBFxAGzRUUaeBr6WtOMXYwByI6HDAuYKiZr+By21zhq9riZ2eQNezYNtnkOiYadSp4YTaI1PW0Mi4+0Qh4Vgnc5W4Skq8FRori1M0JxMSKzPuDqg9kUu50ozL5G9SMMGx52VgHwNHS0wapfYrFdoR+YKDNBGTF55eGIdBUs1GljIS9Til9WBDgypK8tWsP13Nv+CDe7Vdq+k+7Ud03EhOY92auUJHQSTTMxGPQtnO1B6rzxDGQoyDs+j7mbzJo1/qB8D/2+opj1mHTa6cqgc5sJBduJZjvOkBcRoyoNnH43W+OyM04kDBgoSASia+D7kg2V0J7OUEm6bAV5ljslZMvA2mnC5jY6jCCOyRyv0ItTwdMBs8FeM4clI/LLGhVoeqHFrNFkzOmkYCkNE9k2BzMbTBkNHKFRQhASg4bVVpHp813CgsVON0ugUzKkRMLwe2URUzG67HVrlI3ero9i1Giz393ElcFw/huhI4lTC+lXbU4VVvhYdpEQTTBkNEwXWzTsTIrldm8jAYVkO5HYOwrr3HXs+UPcl7vB92WbLEZrrMY5StJjfghb6yQOy2GFepBlo5PfncOPqSyD2MTdSP9exenxkexVzpojtKIMamj76CcmigTTinDMaLgSmnZAXCukpwX9nkMSSnKTAcfza+yEOa7Xqng9i20vx3acxxTRbgA62bEpXulgT7i0jmRYN9KuZsXqo7VAS52OOH3FVjuHacTkHJ+cGZBXHgaKUWVzt12jk2iWwwrLgzI5w+eUvYaJ5mmdWndHnS4PZ5ZwhaATO9gy5lhulYczS3QSxZdyJ2lks+nv1eBkUgdRSoGNdu9Y1wZFVlpFZrNFfK0oipAjVoBJzHeUT7fjoEm7JLZIqZ//7ergppcnqjmEZsKmEZN3fJTQVHJ9er5Fs5aDUBD6Bn3PIigo7ikPGDdbSBLqcXo+c80QJRP2OducsXboxBnWukW6fhqe7dkWRWPAAXMbieaWmyoXDBnTiRx8aXAgk44UR4ZdDFOANRR0OkZEVe0wIntM5Y7THdhEkaTWyNF3LXaqeXqWxajqMKV8OonkhcEC62GJKFHDjq9FKDUMO0PHnWWqRptblRF2Mjm8yKA1cBj4Zir/iwWZ6ZB3FS7zFEd5tbkP1ZMkhwR3WApFg6+HWdqhQ93N0Ulq5GXAw+5V2onD58M72RnkMFSMHPcwrYh7ioucsbcI9RZ9Z4lakuHT9btZ7FYYDCnIlhVzX+EGH8vd4Lcjlyebh4gjhV30UJV09dw2Iwq2hyVSfkojzNJrOykML0m3GZuxy+qgRDPIIIRGGQlBpFjrFofn5LQjsceoMZFtEaBoxi6dJMP1TpWGmUdr6HQzDHwzNTSTdpyXvBHaoUPNzZHoLkokFC2PRKedtTiRqUHXzxLGCilTN46lYrzEJJYee8wd5ow6b5hzyLYBCahSQjXXox+aKXhNxbQTh1AnVM0uVtkjjlLfkSDdFJKAKw3utWt41g5vdudp1PIEkUGsJdnh2M+VFkL+D1B8fLlzkoEyma80KFgeX+3cwbPKIx4Gy24ORnlhsIfIkHROe7SPKKpTO7y7cIkDw5YvpNhXVwg6ScK3Wsd4cWMvQmiufdIFQ3P3oevMWDVumqOpgC2SvNzYQz3Kss/Z4sHMDWpJhr+z+HGubI8RRSoNLSUCfSagebKMsD28nTId3+bRYpkFo4spYkYz3d0UcaIFL9b28npzjslMix+rvrhrpvW0Hs7M0hPBqNFmIbPN7bEy104vYO3No+wBf9S4j/VegdYBRXcii1QdPt05xpTZ4G5nLSXsDXfXldRYdjpm+dbWYS50JzmY3eID+bdxRMzLvSmWwwqvt+Z5bWkOHQuKxf7uGlvXs1OwjBZgpTvhoZZsx/Bk7wg3B6NMVFtc+dk00zA6scO+bJcPjJ3HEQZTyudDey9wrTrKyeIq92evUVJ9RpXGFibLUYYXewcwRUxxOHceMXs8tv8KYaIYxGk7/srOGO1aljiWPNM4yNvmLJda47T6GZJczOrjFdSggl9JV8zk3h7/c+VtjlkblGQa2jiWW+eFO46ifEEuNPhPtYdphRmOH1/CkhEPl68za9ZYHeLnu7HNYneEpp+hW3NZf8giyMMjUxc55ayQFxGtYZu1JNP2eF6u7BYYWRHhDV04B+wNrvkT/IPLn6A+VJBvnZb0ZxKmrAYhmj9uH+ObW0fJmT53V5YoqgFF1eep/gwV1eWolRZFycEeW99/iPqJhCmzQUTM84M9PN8+QKIl/rDdb4oEQ6bF8Mfmz5FXHsesDSDDh0fOcuEjE/ihQS+0+YerH9w9VkrmYNe7sekX8G8WGDgJz0wdZlq9nMrMZEIh43GoukXeTPM4V3tjmCLhPXNXkUKz4+f4d7cfS/+u6Q1/eMDy+4pEWY1Z7VFwPeayDY5lV5mymxTtAf3IomL3qFh9bBlRMXrYMmRUtbkRDXjLn+JPN+6h7mXphyZhlG6zNAsWjohZ6ZfoNF1eZ4Zf4v3YKuJGu0rDy6RbHNnLmCS8u3qFKbfFpeY4S2sjDDoOf3D9Hj7vnOTu0dv89crzLEYjvLU8A0su551JzLGEemLx6zsPcLNbJYgVeyZrTLptRmQfJWyOOqu8e+Y6ZbPPCWeZCeXznuoVwiOKbmhR72TZ9kxGij1m801kXkN1i0QLrtVGaTVdupY9NLQafLd9mOVBGUgLZIA3OnOc702x2BlhdauETgT7Kzs8WL7Oql/mHy99nEQLDuU3uau4tBt07ScWzzf2s94vECcpGTRnBnx08ix3Oou8NNjHUzuHiRJJwfK4d2aJy40xNjZKhKHCkSEjspdmjtrztEKH1W4RLzCZLTV5dOoaS/0KL9fSAuLmzgh/5txLRoUcyG9zIL/NG/VZlrbLWFbM/IE18pbH3aWlXWmdiAUiSpUJr/iCt7z9XGxM0PEtgkRxKTtFxeix197CEjEH3Q1KRp8Vr8RlOU6cCD6/epJvbB5FySTlwUQGt+tl/J6VijkTgQ/cDka4Hq5ya1AlaKd20NmxOnvydV5ZnWNwuUrNGuEfDz7OvmKNizvj6IFBYCb0EwtfK+pRjm0vhylj3jt/lYLhoYbvd055w+LR2T2+NqIi324epeZn2ezkESrBdkOOjKdbjgv2Fu3E43ZU5UprjF5gsV3KAxucHczz9NnDqK5Cj/kUCgMmC20eHrmOLUPOZWdYH6Sf7b+89UGyZsBHx89yX+YmecMjcWMQcHpqhUfKV3dJ3aaMUGjOBgU6sUMxl24I7SnWqVh9dvwsP3j5R7FVxEK+Rlb5vFmbRocp9+Md71E9CViJQ7rh/wDFx3Nb+5gb8zhWXKcdOXx3+wCRlrx/4gIfzJ3nVWOe13dmCCKD9x09x4dLbzGmuhw0BbZw8XWEryNMIckIi4QBb+zM0rwwgtzT4x+974vpbrYMUSSUzD5CpAntqztjLLXK+FMGP5i7TieJOfv6PsZeARVojEFCd8rgzp99i3899RT/dPNhvvDyGbYGJmtzZfrOynBlrpvu/hvphfTtlWm45XJuLORD7zrLjAONuE89ST/Cd8A9I7LPfnuDftni5h0jdLo2thXx1OoB+p6NtzeCBFyV8LWtY5worfGAs0ZeGn+p+Eir9yiR3Fgd5Xp/kmvzo9x/8Bp56fFk4xhvbU9R3yyQu2IhYuiMOzSKcarpNTQIEEaCtFMgzzvAn6d2DnNjZ4SHZm/y24f/iKKMKUkDU6jdgNS4kvxPlefolxWjKmFEZgCFEllinbAalnmlsQcA1wiwZMQDxRt8vPjGsBtiEOqE/81+lK/3jpLEknNbk6mV1rcIAwMzFxA/0McwI35wKFgryYAZw8YWmd3v0vHMMhPHt2gNHPzQ5MnFQxwZ2+Tvznxz1/qohObsYJ6v3j6asjmWMtgNgTGi6d01oFzs8dHKm9xhObSSAfU4RgmoSgtXWswAsY6IiNmOwdcMg4o1zvbn6b44yujNhNY+Se+4z0i1w5TRINCar20c59bLs0QzPn/rwae53x7wtX6V77SOMJ/ZYY/ZxBGaRxeu8R0OcHRim2nVJdQGr3X38tzyAr5nkTTTLEl2qsNsqcmZ8jI/W36FqspgDt+Pj2Y3edfp32EpyvCLV3+QZ88exip7HJnYJGcElFSfGSNHzc9SuAmxo3jj6CzHMitshfmUA2H5/MDY65yw1/hy5w6+vnGU0UyXvzX+HY6bml9YexcvXDqEFhqjFGBaEfniAPuhDkom5Kx0TXOvu8MJZxmLmI8W3to1sUJaQOWH7eW12OJaOMKTjeO8/co+zLYkqCQkhYhlO6AZuxSkx3Y/i2iYdHsFnqodTtfMh2DB67ZPs+owoXo8kb3I49lL/K56gMXbo4i+wl8tEQ0E3ziZ5T2FiywGVfRtl/Il2JguIg9ranGWL18+gVp0EAe6/PChN5izalRVCNgct2rkKy+TFSH7TU1GuHwgd54Fe4s3+/P8cf1uoraFLvSZd+uMWW0ecK9hipj/I/oozfUCnmOmrA+teKM+y+JKlVxpwPvmLpMzfL65dpiNjRL4CtVRYGqKpsd73Cv8zuAhLry5BxFD5YEe319+bff9XI3K/OH2PXSWihh9gdUS7GQ1X3s4Jjvh85WNEyy+NkPsaB699zwfrLxNL7qbjeVK2hWQIUXp83Z3hqfOH4ZAogYSEkHvdJ8fKr3Cq/ZeXtb7MPqCwbbLC3ovo4Uuf23uRfZY29zoVgkHY2QzAX9j5nlO2Gt4Wu2uRJOAjGCrl+Pl/n4udKdY3SkR+Yq+Z3PDGWEy38EeDamoLgfsDc5kFrloT9MNbXYGOZZvjWLtKBKD1EKrQXkCK0xX7uNcii5YGZS5bE+y3CshOwpta/YXdnh/+RwvLO2l+pZGS2i2x3mhPIpI87IkjqIf2/S0SSNyaQ4yjOc6/PjIC5y2JI3EoxYLfK3YinPUkiyOSEV+21GBVzfmaHczoAVCaYrZAR8dO7sbYq0nCcvhCKv1ImFgsDmddlEudKeovGHgNDQ7Jx06e2Cy0OaJ3HmqKuSwvc7tcIQvbp7kxpszaAX7H9nm3e41CoaHdCOUEfP+kXP8WH6LZHisdROfpwYTnB3MM4hNqm6PguXxg6OvccDa4heu/xCbT0+jDbiyZ4JM3sfrWxClHKtYS2I09cRgLSrSCRNg8690jf+eLT42Gnl2wipn1RRhqAj76Yn1u0ZIUQ14vTPP5lYRPTB4KztDYcipL6oB5jCRPGF02IjynPdm2QwLtD2bqBBjJoKvbx8jZ/oESepzWO0W8QcmOhZ4WhD4Bq9tz/Er8gF2/BxaQn9CYnY1dhNiOyX17cQxrTCTzsl8yaI3wkV7nEuDKW52R+gGNhuNPJFvQNPCDgRxx+C/rD3MK8W13ddbMXrc614nLwMuBFNcGkxxsT1Bb9tFdRVeQRHnJKFnYLTTPISXt2hlHc41p/i15BEMmfBGfZZGL1WumypGQOpP8SXb9QL/cf3dOCrkWnOUgW+l+YoIGDpPRCTQmQSn4KM1+A0HMVDcLIzwVnWOepzl8voY8brLs3ofGRXiyr8ADt3hLvNAZhlPCy4GEzRjlwmjxYRq09Y2Zwfz7EQ5rnXHqA1c4kSSaIEUmmC43/+O8MhLTJ5e3o9YcYhtTaeskGaCTtJQWyL+IlpviwhXRPS0wdkAIMQRMY6IebF7gtWVCmKg0LkIw4651azwO+oRSuaA+cwOo0aHl5p7aWwUkD2F2xBYLU3kCvxI0B3Y/N7Ggzw9vGhMmc2/UI0TsxaV07CyDDnqrFKSfZqJSzPOcr4zhdUGq52gPImOBO2ew+9vPUjV7rLSLKZQO0/xe1sP8oxbY80rse3l2Amy3Bqk629v16aII8Vys8S/3nycjAr5zvIB+ptZRCLSsB/Q3cxxqeayPJJSPMtmH0jBT35i0E8sWmGGetcFqQn6JudXJ7nuVKmYPbZz19jo5UmMVD++1izwLfcoy90yG8082YxFbSyHpxWXexMsLo1y267wb8QTLLg7PH3rAJkVRWKDZycIofF6Fm1fId2I8vQmVafL2+1pXmvM4xghs5kGOeXjyBBbhiRaEiMIE4MVv8yWl+NabRQZpNkQ6Qm0VNQ7Wc56cynjw7PTMF8gIEk3W7TSIDT1nstz3UNUhl1JJRLebkxjbJsYXvp90hIC3+DN/p5UBd8WWN0Ytm3+tHU3tSBH3DExfYE/MLnVH6EbpaTSiuqyEU2xHqSq89eNThpclj4lmb7/cd3G2VFs5/IsFSps+nmWvTTMfXNnBNWVBJbFte4YDdtls5VHNkwGVjqicmVAnEgIJQhNXIjA0FxvV/mPxiO8vDWPGr6WG60q33KP796Jt6IMrh3Qq/rE2zZqS4AWXNsc5fPiNLc2R7A6AhnCxcY4WeMQt5ojCE8SC4ML3Wny0qMVZlBuRGJJYicNJyZa8N3eYVb9Ek7Zw1M2hBJ/y2VlYPLt3BGmM5P0I4tcacB4vkNeDZBoXvf28HpnnkuNCcQQ8thsuzxTO8DOIEsy/E67js90oY2jQq73x7FlhbLZJ6c8urHDjNvENUJW3DKJmXYApSfS4Kyr0WaC0O+IE1OwVkr/TcnM+i/dsDt2yGAkpSpLH6ymZFgHE2iTZ3b204ozXGpP0PdNasrlud4hNqINrvsHuD4YQ6KxZYgU6THnJwY3O1Uaq0VUVxHnY2QuxA8NzvVmWAvKdGObfmJxvTNK0LfQkeBsa5rPWnWutUZJTEE4vKeKfUWYqF2/zjlvhtdb8zS8DMlogJCalX6JT7Xu5PntBdi2CTMJnSSDEpJB4tFMIjZjizf781zvjdIOHFq+gx8bvNJbYDmssNXJIaP0/RE9gwGg7Bh7rEc5O2ApqPKyDNKMk1b0k7/6tsv3LOH0wN/7ZcrrFoVbfbQQJI4ithVbZ0y8IwOo2VTeFpg9TWdOMhhLjYVWW6AlqHsafGLvWb54+zjxN6sYA03tnohD+9e4ujxO6SUbq5OugCk/oTth0D6Q0k1JBEKDXRfkbyeEGUHtgZD52R1ub1SwbzjEjmbizAb3ji7yreVD9C6VSQyNu7/FVKHNcqNEfzuL2VBMvhCTWe/T3pejtSBRAWTXE4yBZjAi8SuCwVjCfQ9c5mRhma+snWBpuYqxbTL6psZqx7T2mXRnNWZXkLudyr/qRwTRXg+xaTPylsAcJLT2KgZjmjgfU5psI2VCfbWEVUsldrnlBATUjwiCiQjVNMiupicsb1QT5TTWdI+P7j/Hjp/j+W+eoHxJ0zgsyd+5Q6dvk/96jvLlPkHZol81EAnYnRgZapbfq/gbjz2Nl5h8a+0QzW6GQtZjItfhVr2CfLpEdjOhtSDpLwQQSYymSgug9DqBGggyW6nF06nH2A0fb9Rh57hBmNdE+QSdjREqQZoJphnz0f3n+ETpVd7y5vnM2hl6ocVEts2I3eOb546x8Ecaox+y+q4cvX0h9oZB+XJ6d7NzGtRMn/h2lupb6e81vAQRadp7TJpHNDIQjLytya6HbN1pw/1NTBXT9yziSCFvZqhc1AyqkvJHVvng5Hleb81zaXuc9kaeuS+Du9imdmeZ+vF0xTN/O/2zsyddHVctRemSwOxr6kcF8b4BybbD6GsCq5fQXFD0pxLsuqRyOcboJ0SuJLYEXkXS2ZugBYy9BqWLbeKshV+2SEyRFjdSIBKNDDVRRrJ9RsDMAHkrw/irMYkSbN0liaZ8aJk4W2mRG9vpRVxGAhFCUNI8/PB5niif5x8/8wPs/8MIESYEJYvEEtiNEHOnjzeZY/kxi2g0JH/BYuwNj+Z+m/1/4wqPVy7yL578GAufCwlzBo0DBlEWwmy6Zi19gdWUw2NaY3U0oStSpYGRrprLGLyKxjrQxjJimht5jKaBjNLvECIdxcWZJJUP2glCpdtzSiXoG1mmnk87hbVjJl5VE+VjjGJA1DGZeFpRvNalvT9H47BEi/TzkjF4VU08kW6lJKFKC79IIEKR+kRSthtH71rk1/Z8hv9Ue4jv/vv7KF/usXF/js4pD90zyC0aGL208EGka7/efg/DihHXXfJL0NoP3//Eixx0NvjN64/QuFVGlANOzKU3L+df3UvpiiC2BUEetPHfvG4rQTox795/jYdLV/n1K+9BfqWMMYDIhdgSw44uxBb0pgRRTqeQq74gtsGfCbDzPuV8n/lCg4wKqZg9bBnxzOZ+VpdHMPM+P3n0FY44a/z9l36A8a9ZxJagOycIc5rSsRo/ufdlRo02h60NYgQ//tpP4zydJ7HAL6eQLDG8EYptiEoRIhPz/iMX+ZGRl/hO5yh/fPkuwoGJNBOkijk4sc0/mPsaioR/sfRhLi9PQNvE2VAkJrinazwwucibtWnWb4yi7ZhP3vkKP1R6lV/5/3D339GWXud5J/jbe3/55HNzqhwBFFAAiESCGQwSFanclmypJattBbclt9xtLY9n5J6W7Lbs9tiS21kt2RIl0iIlUqRIiQEEQIIgcq4cblXdfM898Ttf3Lv/2AcX1Fhelnqme3nmW4trEYXCufd8ab/7fZ/n96x9E88+cRLtGR64/zwfnHqZz+zeydcvH4K+S/28ItzV9ncqbbzFcEWQ16xr5I1xBtj3V3jdpX7Npsr2j0FZ0fhbimAH3JF9f8jCsHmfz/j2sdUdFTYtNrhp9VhFJBjPGbRrgWY4BpkI3L6c2HQN2jM0TnT4yJ2/hsLwoaf+Kur5GuOTKX/z/s8C8MsvvA/nku3gVm+WjKckb/3R5/iVpad4IU354mR8/ofnbkNs+ejA2vUBez9rgdtV+Dv2C5a+vbeit+zwP53+fbaKGv/u1kPsDCucnN7iweYV8mHO/3D/l/9/m3Dq9wy11QTx7Dmk5+K2W5goIFqcIZn28TuSaKvAHRWUvg8InBiirRKjBOtHK2wu1el2Kxy4mOOMCjp3e6xUupzX8zSuFvibMao3gniMun2Z0ZIPkY1cxkCwa2i+2iNvhXTeCfdNXyfOXXZ3PIyCce5ydTTFOLXCIowgjn3WqRP3A5y+wusKoqt9uHSNoHk7vSM+MoPG+QFqY4/w4AzDlRBRSm4Om0z7Q7YHFZxdl6AjqGwkOL2U8UwDlQjUGIJeicwMztglyyVBT9J+uYvsDimCRZt3EghLjHTszSRK8HqG5msWQTyab5FNC2s5lN9w4jU4Tslhf9tanVNB0Mnxeh7dfkQxcpnfLHCvb+PshASbIaLQyN0+JkmpnDrOjaRFph129mqUPZfd1CHNHYabVQ6dzwmvdMijWeJlicgl7sgSEGUKKjV4A039yhg1SJFxAuOEsJzBOVJDu4IyFBgNRgiMFmhtu1ADHbCV11nr10lTG0M/DH17Li/fxIxivLtPMCps27lxvg9CMFqsM6wERHuCoFPgJCVC25pcZi5ispg1zw/hpQs0G2e5ebyCcDWMHEQqaFyF1rM7BIeabL2zys50jY1Rnf5ehNNVqCRDlCWysIu4OxI0LyW4ewlJu0kaFIiOQ/NKirc1Ipmaorvk4Hclrdf7qJ0+WWWJdErg70H9pR3Y66MPzJLORBS+2F/Awp0C88oFlO9TqVYQUkLgYxyFKDXkBbpdY+9UC+OUyERQuTbECMFwqcFIWf6J9gyiFHg9UNmbXSbjCHpZwEj7yKHCu7aBiRNczwUhMHkOaYrvLCJzD7TtIvmXt4gai0gMU84Qtyfxnr+M32yg3QXSuiRtCvKaQo0h2jKo1BDsFbjdlGQuJJlyLEl3cr84Y0HcC0ncEpHbnbgobXHyxn0vtECOBWKgQEAZaErXUNsV+NsJxrHhfGWgEaWg6Hk4A4U7LhFZgd8tqdyyRU8ZvFn8FLGD0eAMlC2GCgv/w9gUZgRsDGu4WOKu39M4G128bgUGLl5PUlvVeIOSpKnIagKVCszIIU8VlZHAHen9c6+ERk4ug3JLjtesXuT84CiNyynJlEMRKkoJXk9Az+K2i8BQ1CXT/pC3hlf5SHXIDi1kYQh3Jzt/aYtTWUDQERTxhNlhGyTIvkOaS7IwZSXco+XEHPR3CETOYxzD3XbIgTvDVR4Ju/wttyToFDYzyHXIa4LspOK24CY1aSPjB8Yl2QuYv5yTNhVZXYJncAYCbwBZHYqGQCrDjDfglDviGZWQDT3kwKH0NKV0GU15HHKGREKwFPW4XmkRJwqhFcJY6N79tctspVXW1DQIcGWJLyyh1n53JrkmHm0vZna6z45TBSKcxBbsMrcbk9JXuANbCKeeRmiBM5TIDGqrhua5AXkzIKv75DVFtG6o3Spw4hJv0+r8VOIjJOhUojqO1aPdMNSvZ4xnXNKWxEjwJu9G7UIZmv3BpMgFRalsjAeCtBcwvWGID0neGl1GG0EZO9TXwO9rgt0C7bh085DclHR1yGraZjVuYboefkdS1Ay5MG9+n9R2woyabAozMAUEbsHDQY/X8iG9JKC3V2G3WiE3ipI/e7bLf7HFR9ISdG4LqLTPYhQUvkQ7MDwgMO2MxHfY0Q4ydxjPa/R0ikkVg56DUYYzx27yweZLhLdl/P4P3UWZKJrTPW6Mmghl6B51cefquOMaMrcdCO0atGNQh2NmGkP2Toece0vVvtCjMY+uHWecuYj5BGEEvWHAK/GC3dk/tE1WKlY7LYbDAOlq9GLCqOFw85vaeL0WvZOG6dNbdPoRa26DcKfOcEkwXi4xQYYaBzy1dZCylBRTOSPfYfPeEJWEjOcgnSkoA8UgcRClITk95ttve5kvto6zudXE79XpHZGkcznV2RHfcuBVIpnx29m9dHWd0lMUkRWwjQ5oG9xVy4lXsO3NvoscS4adiH+3+gBJ7pC1NNtnPUbHMz50/HU6WcTX+yepHD+0v0PRLoxnGxSRYfrYNndWb7KaTqELgRrZl2GsDCjDzp0uzpE5uncU3H7bDduWLGyeQFI4jDOX7V5E/1wFb1DBHRi8oWa0oBjeN6ZeG1OMLMNAOpqokuKqkq9tHuLZnRVqXsrbl68gMVwaTNMZR5SRpvvAkr3OMwKUYXi45PJcHe0apk5s85apDS4fnmb1VAsTOwQbDu4ARsua9vEOSe5w3TQJ772HtC2QI2GD71yDiQyjZYedB2cYzwgOttdY9vZ41VtAOppiNufq90gwLVDWwlbUHLbTAGfkMzykabdGxFHG5e+tIPIm0XKPB+bWea09x81xC69XI6/ZF9F4znD5L81ixOyb7eCZgntPX0UKw0v9E8xU7oXJ9EE7gtGCJGtaeF0RaVBgnBz6Pk7VsPFwk9KDwR0ZM3M9esPQjtxygXalXRQVaMeQtUvONNa4N7jOA/ef52t/8wRiMg5BC6INQfWmJmnbztz8VJ+tMzMYtWI7F4MWn1JncW/vc+4XTtvvYMBIg7cw5MjMLp1xxOZ6E5EonL6LO/DIq4Z8PkP5JSrIUG5BCARGoI1g7GiKmqIoJWlhV2nhaYSj0XqywwTLDVKa0bJk+94qZSAo7+/zzuXrrI0a3Ow2SGoem/cG+EfbxPMGuTJCCMgTB1NK2jN97pm1TrNB4ZMULue3ZhmvVTCepr4woBEmHKjt8b91H+Bm3GTtnYKdO5fJGhoTlGSVgs2DlpsSBDHVIEVpiSgUWkvyhmL7jCKMUi4OZlhP6hxu7rJydo/FsM/ZioVZvfKORS6fmEHnBSax2gmR212ybuYsLuwxE464PbwJwDfNv8JvfXvIMHPteTFWoxGt2uIsa9quiWhm+7kvvipQUnOmuca7a68D0C0jumXEdDhifSWh3RwxpYa4QvGdp17kP/zQ3ehEoXoGUUJdlTw1Osayt8vbwmvMiJT67JDO6RZ5BMlCAX5J6bmUgSSdLbjr9HWWoy6lkfx6705e7K+gghLtaA4t7nK6ucmdlRs0pMJF8aH2ixwMd3luZoUXw2WkMrx1+gpng5voaYlzxgY7JtrlD4ZnmPP7nH3Q4g9ClfPs4CBNd8x3HXienYUaX24eY7MfkXcCgs03eRpGGorpnPr0CM8pqXgZQhg2b6ux9Y5oslrbuI7imGGgSvK+T+XyNCqFwT0J7zt+jiuDaS4lC4Bk5wHN7rsMXjhiodXHkZqrG9NkHc92sIISIcAUdsTqqJLHx8eoyJS7T17j4vQMJxs9/nBwBoC7jt9gY7HG5k4d75pP6UEwavCLO2foFwG9PMQRJeHikHHdx/ELmmFGViji3QiRSkQrY37aphN3NuuIRHGq0qerCzwED85dY63e4L7mdd4aXSQp//8AMpa3NL0ZGByaZHRMqvB8NqPejClqkqRhdSAL0z1ONrcYly6dNMKRmr+08FU+FA15JPwaPzPzGB3t8Ktb7+bpjQNIZRgetGMaUUi7+3cMxrG7vXcfusxfmH6Smkxoy4zrRZ2/ee672L7RwqlnrMzskZWKtVtt5MBh+q5d/vWR3+VG4fLjox9kvB3hthJWprtoI+gtBGSl4oPLl/mR6cd5OV3mF/kgw92Q5kqX9y5eYSut8dyNZbqdCm5QUJ8akdUdBjUfSoEMCjyvJAtdRqX93m87dpm/P/8kvx6t8Usb34LbVeRzKfVWzF1zt/jh5lPUpODC3BxfSQ9jpgTjQ/b8hl6JkhZ+dW97lVh7fOq1M8hhAB2XjcEsRhlMsyCe1dx35Do/OfMlUqP4R28tOdeZoz8KSPs+Xi3jZ898gQ9UzhMbxUB75EZhcokT20W6cBxQhtFtKUJpHjhynb88/2WacswRpyCSLrHOGRnNU8kivzz9Pts52fNxepJ8PuOHzzzFYX+LX7/5Vq5cn0UqQysao6Tm2rVZvA2H7WMxP3fwsyyqAb9UfJDru21MWLJzxkVoSdYqQRlai3t858GXWHC7nA1WWVQZyQIMTjrcKJr8gysfZHWjzYH5Dt+2+BK5UTzeOsbmsEa8XcO75WGUIJ8qkGFBsmjI64qykXN7Y50Vb5emN0YoQ7vZ5+dPfpaz/hq/07uXz6zdTj/x6XtVRC6JFoYcbHSYDYa8447ztNUQT5RIoXmmcYR/L++zIrX1AH9XEh8o+QsPPsmxYJMn+0e51J/hbOsm/+3MY0jg++//i6xHs9Y9MOlgTN+2xXvmrnE83ORd0QXr4Hrl++hdblHUSnpvKXGCnEcOX+be2jWe6h/h684BstQlUx4ykTa/x9cEzYR7o6uc8Vz+14OfobfyKRIj2CgrDHTAP7n+CJdeWkZHBXcfuMnhyi5PS83NdgshDDv9Cr3xAb7n6PP8tfue4Xzu8882381OUuV7F57he6s3uV4UfGL5bjayOtdGU2yNqniqpB3YdNB3tc/zYHiFl9Mlfn/7LL00ZBy65FriSo0/SddMC5ukmpeKvJi4gSbprbtKsxeEiKjgb972JX6kcY0/GE3x75yHGOQ+3UZAVjg8OLfGD8x+jUR7fKl3mo2kxiNTr/OD9cv4wkWjKY3hFxv38tv5vbTqMX/v9O/ygD/if+3ezseu30PgFLzjwVepqIwXdpfY2G0wN9Xj/3bs09zh7e7bFV/KFP9s89100gpvbV/hgcolnhsf4mPX7yHNHT58+EW+u/EsYDU8ntC878Qq1ZMufxhP879cecQKqxOXspAcWdjlJw48ajNIpHUefXftFX7srnPWLj45fnHnXn7TedCyIJoJtSDnncuX+KmZR1EYrhUNBjrkiLvDadelpxM+H0d0ygqLUY9kyWEp6tGWCa6I+PmZJ/npdzzBM+k8/+O5b6bbq6Ck4aXBEnHF413RNdrS4faZDb52rI4JSprTQ3y3YNutkfkezcU+f335j1lx+vybzlv59NoZ4tzF83PcSskPLD/Nj9RvIBGoiaD6W6M+H4pe5SuVC/y69zYkhkdqr3CnF7CirvFAcI1tHfHxvbfw5Z0TvHP6An979gm6WvMPth7hlc4C7194nR9vWtdeb/orJMbw77v38h+unbUwu9RqA+emBpxubzLrD3hv7VWWnD6u0ATCcCWv82+3HmYnqfId8y/wfbXLPJXU+YUD30pnUOE7j7zKX5r6Kp+LbufS6ixaC95z9jX+9oIdmbzxHv0nwSM85y7jOJpqYN1lo9QjzxWu0jzZO8q0N+THFx/jjkO7fHl8kN9cewApDH91+Uu8O+jzydEc/2LhHYwyj91RxEcv3U09Sliq9vBUyenZTZgFT5aEKmdQ+Lwm5hnHHu84dom/tfBZNsoKv1z/IDe6TQ5GHXoTvck3N18iabicdLc44Qb0v6FD+p87/ostPphN0YGwkcywr1oPKhmeU1pv9yQ0zZGaXm7Dmw5WO1RVyrzTRQmJNprU2Hjmeb/PgUaXHS9nUxiyxMG75eJ3BXlFkNc0JippujHNCV2woz262uYrvBHSNs5dskLZGW8Je0nIc2mbvg5ohgnx9Jip2oiFqE+mJ4FWGexkFb4aH2c9a1AJMwYtyXxtwKFgl1Dl3Gw2GQQ+jTChHcQkpcNuWCEtFINOhXLLRzFpv3n2dzmfl/TKEFnNyQFKwaAbcS2c4uJMi6ayYjffK3BUSegWSGFjlsEGMg1Ln1HhI5WhCG2HwjhW3i08jXQ0niypSI1nNAtBn06lQqkFWeLiupYlUpECZUrA0kiFM5lbVktq05a0mWUOxggkZp+8KMUIX7gWQKJzmmrEVBiT5g7dTFFmLtIt2czsDDEvFcLVeH7ObDTAkyU3Ky2KiiLwCrplhYrImPGGLLZ6bKoacWy5IXIqo1YdM1cdTkKxFLeKJgNtbdylkQzKkMVKj2JW0g5G7ORVukXEpa1p0k6IyIWd93qGaCqmWRkzSHzimk8tSveDwwC8CSujRJAZScOJWa512XMjkrFHkSua0ZjZYEjNSeiWEZlRKDHBFWsbEZ+HimHdJZ1AsQAy4zDv91ENw6FgFxcogbqfcKv6DQo6VxO6OZl2rLMAg8LGju+FGlnJaTZHhG7BqPB4ZbTM5tgmbdoHzFgmyqR7JaUmNw6psQt8TUgCYYARdWED2i6EGhkVLIZ9DgW7vO7NI4RBSHsvRn5GpFJyDCPj0c0iOuOIP9q9nevpNK4oCWTOoWAXKQxVJ0VPhutSGLbyOi/LJTpFlcWwx7Q/Ylh4NqxMlviTpM5UKwqtyL4h3TVyMhxp7eNbSlMJMhbdPXzhMqWGLEY9+nlAy48ptLKj0KJOaSTzfo+Kk3I1neEXt6eoqpQ7whs0VUyqHVr1mJmKdSJ1dEEgCuarNnMn14q+8WkGY5iCA7U9anKMAqQQKCHYKBs8u7Zix7bCEj3PDRfY3qpDLtlbiajJktzAyDgTe3VOJjLW8ha7gwpZ4uB4JWElo+XH+6TUx+PjrGdN7ghv8kh0k0hIXKGQCAKZ2/ebMLTrMYvVHitBh0CYCRcoQWGoiAKJh5r8N4EouBk3ubg2y26zwvZsxDFT8kxa5bHhKbazGqFbQGPEfHXArD+g7YzIDQx0YQPzthRFKOl7Ia5f4PkFYi7nYHOPtoqJhL3mhZYETkGjleCrglS7vJSVbJR1zieLSKF5V3Se055ko2hwbm8WKQy7U1Ug5mLh8judB9jOqryyvcBgaAuWRbdrn1VZsFztsuB2CYRDbkqeTqe4lbfZKyIONLqMCo/NQZUsc2gGYw6EHaadIRWZIjGURpBg2S9VJyNxU3Kj2Cw1nbJqBcOTezgQJTPOgPb0gLjqczTapq0UudEoU1KSUxhJnjk4TsZ8ZYAjSy5mM+SZQ6kls/6AObe/fx/V5JiFsI9GMNI+N8ucjaLBKPOIU49x7KEnNllXaiI342C1Q9Mdcz1u82pnHj1JeW5EYw6FuzQlbJeajWGN7m6VzZk6rtBslyEf27mPW3GDe1o3eEftPONx/mdY3CevlT/z3/y/+Pi5uz9HvSZwRbGPJc6N4vnhAa4MpnFlSduPkUJzpTfNa5vzHJne5UfnH+OQ02NaKSCkowsejY+RGJe3VS7wXY1n9y2jryXL/MrHv5npl1I6p3yy2xLmmkNOhevMqZyXsxaPDk6zndkXsVPNMcB2p2YdJIlCFIKNWy3+jv42WsGYd89e4MDyzv7OdTNv8gfpGeLU47kbyzx14TBuUHDb4gaLiz3urV7jwfAqiVE8VL1Eol2LWlYjciPp6pBbeYu/+6nv4dhHh/SOVdj+lpSFqR6dtMLfu/VNFEZycmmTOPe48eo8tauS9eV5fiV4L3PBgEHhs9LsMhcMuKd+HVeUXEum2c0r9PKQl/cWyUpFEGZk81bAGfkZxgji1KUoFBUnpSYkCLi3cpWGGvOcWmE09gm9nNIIRtpQkYJpR7HrbxDWEsY1h0OHt/ipg19it6zyextn2YktOOjR3mkOBrscc5+n8Q26k6Ycc2fjlk34debYdmoopXni1mGUsA94VE1ZavT4tpkXaKoYR5a8WpunHY352vAoLSfmvupVvrX5Ak/FR/lUdAYDPLJwnocql7iVt7iQzLOR1Xk0Pckw9wlUTs1NqaiMb5t+gSOLW3xpeBufXruD9d0Grc+FtF4fsnV/jdHbRsy1BvzQgae4J7xGp6yyUTRItGtx+vES2ghm60OqXsqlZJ5Y2xTMH577Cqv5FJ/3T7OXRpxt3eTB6mXW8hZf7Jyil4ZU3JSqa9HV7TCm7ieU9T6FkYROznrSoF8EPFy/wNnmGgqbPzPQiqWox635BpGfMRcNLUCrdLncnwbgiLdFbhwWKn2G8z5H2zt82+yLdMuIX7/0IN3NGjgG5VvbtZoUO2+4jKQ0bBQNrhabRKKkKSUugkUlKZXmSGWHZ5orNGtj3t98mbP+Fs8NDvB6soSKCk7PbLIUdAlEwdeSGZ6ND3NubY686zO6Osu16yfZOy35b77nM3yg8hpxxRZNl/NZHu2eYi8L+ezabfTGASent/hrS59nXo2ItUNi7CtNijcCzuyN1dcB26UtXt/ATyfGZaR9ApFzj78FVDno9Hl/8xUA5lWPSOZ8NT7KZ3fvoKIyvmf6aY64Hb7zmR+n/ZtVsorkXz1c0Froc6S1y3cffB5XlFxM57mczdJQI35s8TGuZLP8hxv30E983rNyke8/8JR13WDYLF0iUxCIlI/v3EPzd2osr8acf/9RVt/SZG+9ztTXrZD21aMLzM37bJcp5/ImI+0zKENi7fN763fBqzX8HLi7z32LqxyPtmiqmI2iwT985n1Erwb89l0xzfs/wkFnjymV4wtJol17bf2Sb1t+mW+pv4iLpjS2oJ1XKVKl1KRCCYkrJE0ZM3J8Xrm6xNInHfoHQ544cJJT7nP83Kv/FfJTbeJZwYn3X+YdyxeZc3vMO10CkTPQLjd0yM1zcxz/wwHpdMDuHSFp07B03xr/7aEvMK96HHGgRO074A7Vd/nBmSepyJSP7d3P763fxZUbM7S/5qEdwaPfe5J/cvh3+UznDP3H5zASHp89ybdVnucfrX2Ai79x0hoNlKAl4drCYf72oRXcRspfvP0pvr/1FHNqjEPE67nmf3j5w8TX6yyd3uTnj36Gvg74+Pa9bIzqvKW9yrfVn8cVmtxIBsZlu6yxXdQZaZ+ak+CIkivjGf5VMs2NcYu9QUSRKcalR2kEZ/0b/N3Tn6Q0ktu9LaoiohAlSueM0GzFNdj20Ss53zP/DDU55he63wLbPkVtzIcaL3DIGTLSks3SZVYN+L7pp4iNz4vxAb7QvY3X9+bZXmsiUonTl3iJIK953Gj7hI2Eb51/iQ9WXucnOt/P6NFZ8qrhofe/wg9Mf41DbpeWDNgtq+y9Os3URXihuUR7Bb44WuGZ376TqVdSPnnvQT56572Y8Rj4f/6Z1vj/YouPB4LrTIeCQAhKY4gNJBOwy41Ri8jJaHsjpDBcE4Y8s5bZQ06Po251/3MSI+iUFXLtMBsOudN7A/pSMqPO80/VN6PiAqE9gsDGIdvMA0mOYj1tsJdOdrF+bmPBcwmFtI0QAWSSTrdKUZNMLwy4N7hBYhQj45EbByVtZHI+8nC3XfK6onow5Vi4xZK7R0OWNChpyw000JaSlooojWZohsyrIbIEubqJP30Qz8+Zr/TpZSHXkxaRm7MQ9cg8h5t6HndgcAeCW/26LSpUQdVNmfaHHPc28CZR0lIYxqWNUc4LZZHaQUbg5dS9lMJIilKitcQV2u7OENRlQtsZUnNTPK/Am7S4J+kORNKSU5Wyavu5aMADwRprpc8TwTHi3I6NullI0wnJJ7qF8htyeHxZEKqc0M3x/MKKSmMfAwRBju/m1LyEWWdgQ7zCPTq1CpGT0S/sNZ6p9LnLG5KYVV6sLaMR3Btd5QF/jxdFzrnxAqPCZ2NUpzcO9r93K4hpqyEn3YKnZEZ3HFD0ParrOerKGs4dJ6hWbNvynvAa9/suPd1lp9xluwzZKWqMShuTXvcTApWTGodOUaXtD1lxrE33tXART5ZMu0NqcowUDbbHVbpxSC1wbYS1MAQqRzo2idmVJaURpNqBApoy5rATMNQpa6Wgq318WVALUtphzPGaTXW+PJymk1vCaTzJFwlUQTVIWQz7nPFvsl3WSHMHp+tQVjTa0fbnOtYdUpaScnKfpNploD2QGYGxlF5/0sivqYQgyGkECbNqwLT0CNUbClBDwx0z69luwHZRZzurkY9d1EhSvalpPrtJVp2311AZNDmlyRiZLgCZdtgbhcTdkN1qhRkZc9DxSExBbhL+NMzRQI+pS9vdqogcX5RorFjPRVMRktyUuAJmVN8Gijk5dRnwskrYHlfJvDE1OWZZuYwHAfVn19C1Cv0jbfbcGsPqkGWvQ2kEq9k0celxKhxzwNmjW0aMc4dx7FNVKXd6JakpuFI4jIxHqQW5KNmM61SvjVAXVgnP3kavF+F0HaIdK1YepPba5QYGZWiTnYsqw9KnM4pw+1ZkWwjDrD+g4cQ2lt64iF2P2qomXvLZKmo0ZUwgxiA1ubEjKSFh2dvlpKvo6ZLd0hZvNaEJJtb21OTk3+hNHTlUVkcUQZXdvMJIG/Z2qxx7fYzMQzxVcMTfoq2GTMkxJYJYu+yWVdyBwFnfQ+gmbt9BOzaL6gF/g0BIXOGS60kmlTDUnZRT3h6BEAzygFudBu66R/u1BO1JVntNRlqyk1QJdg1aCXayKqnJuTFo0ryY4XXG5M2AMlAgHErfIc1tN/aIm+ALey76xme4U6G6Jhkc9jno7BEbh7YX088C2s6IOWUxA5ulR2JcumWFzbxho0HQ+LKgm0f08oDdpEJZWEBlbiQ5kkgW3KN2cIUgEA4FlqhqWbmCXEvkxIK84u7SlAlKWvedNoIZGTOnfK7onL6xhfSS06erffaKiGuDKTojq9+QqcAZC1RidXo6UWShM2H7+GRaEWwbZC5ouyPu8bu4QqKxFl13IAg6Jd3Eal86RZXGtYLg+as02idIZn30Gy/zP8PxX2zxcSGb5sI4IjEuNZlw1N3e9zSD5dpfG03hiJLTrY39bI72hGqZmxKNJhKGe8Jr5MbBFyV7ZYwrJFUZMK9KTr3tKi/PL+NVRizXB1TdlGDCrRhpn+2kSl4q7phep+GOuRk3ud5rU2qB7xYoYZgORxyodKzYb7TC88MD+7/nqPDppXYxlH2H6nVIphzWT9TZCat8Nj3DJ0obmfzdjWdZVIZI2os7NhnXC8FGUWPm7CYX/8ZRylDjG8HlzjR5qSgKydAr0EbgCM3c7VuMj7sEWGDa1rBKK7Jt13HpEhsfJcYc9bY45G2jxFEudmcohERJgxAGYwTDSYHgKE3g5Uih6emSrnb4re0HeH13nqloxAMLq8z5fU7567Sl3RGlJqerQ0aDAHdP0U1DAiFYUSnfPvUC2w0bShTJlCk1pK3swz40ORul4hP9e/jNP3oHwY4gXimJFoeEfsFUJcZXBQcqeyz6XTbSBv/g2gcoteTeqVW+e/5ZiwCf7GyPOD0i6XPc3eU7Zp5DG8kpb5tIetRkQsuJyY0icHJix0VN1JubcY2f/PpfoBi57Cs6hWH1/Q7yXSdQx4f88OHnOOjtsKJSwN2/3pHMuTu6xonAJ9a+3VHCPluiU1bplFUS7dJyLMnzjzZP82+234o21r0jpGGl2eXtUxfpFdG+B//C2hxm00fOJ/zIHU9yLNjgRj7Fv8xczsfzfGX9MFnhMFWJmQ5HLEdd7q1cI5IpZ6IbNiPEGXLU3aarQ7QRthVb2t+xJse8ZWmVc+EcSe6QTEZqp2c3ORh1yI2iMIpQZrSdIVtljXNZlU5RJTeKQRmQG4UrSr73yPNIDF8bH+XpscGTBXefvAbY8K3n8xUqKqPmJnSyiLCekDgee6d88so8/aPQKyKeSducSxe4lkxzM27y+tYcWWqzLKRf0k98/sXuO5h2h0w7A5oqpq9DdvLa/oIK1smQa1twN5yYSGZcS6Z5vT9viY6VXabcEf0ioJNXqKiMh+sXWHF3uZm1cYQm0w6/172XJ9whTpBz69tWMA5kLb2P4v+IvB9PFkSOfWZe6i3xL5J3kBUOo7ENmnytP8+/dE+QG8VWVt8/Z1IY+pnP8B01/DtvY3gI686pavaOW0u7ST3+cec2ekXI6rhNYSQNd0yocgIvZ2fOTHg9ihf3lrgVNNkrrBPhyJ232Dxc5US9z9V0ls1JMB3AC51lyCWlFvzmrQd5pmFBUVLYjcBhf5umitku6qxnDYalz5XhNMPMxyjD2jvqpFOG7azK50Ynqbdibj7SIq9q6LX538q3MRf2WfR7b77DtUe2mHPrOw5QRFjjQFQwLlx+o3c3uVHs5ZG9t4qAxWqPfuHzP2++l9xIXtpZtJvOdsmN9wVoB2a8nI/23kKpJZ2zNiLh9c4cf9m8l+4oRL/DR+Q+xrEOl7yuMVMpXpDzQn+Zf1iGHPa3uS24xbVsmvZ8j46qM+OU/OvdhymRdLKIQOXcSpt8ZnTC6vrKiES7rI7brI/r5Frt642UMBaOmLtIVYKQvL43zz8176XpxpwK1wlkzvV0mo2sTt1JbBGLZCYc0T/eY74+4Jn4CK4omY5GDI4EzNaGfDE+yXMq5mbWplPYCI7cWEL0hd4s3dii34P5EVoL8gVFZiZOSKUJ/ZzHeie4mbUZZR6941iBfh7x+XiZK+ksF+NZ1uM640MZa9MOrVrMP9+7l2f2DtI56ZBHJ+gdlZTtFJO+yXz6zx1/ruLjl37pl/j4xz/OuXPnCMOQt771rfz9v//3OXny5P7fSZKEv/E3/ga//du/TZqmfOADH+Cf/bN/xtzc3J/nR3Exm6drpthM6ywEPWaafabEeP/fp6XD9ihASc37p1/jRxrXJnRN26XIjd1ZVITkLq+PxmLMOxpq0hAaS938t0d+l/iw4bl0nk/tnkUKgztJtoy1z14SooThocZl3hZe5unKQT6vbsOVJffUVll095h3uhx0Yq4XEX/nyndwfauNlBrHsYAlR1menNuX1K/nqMShM4rYrVd4aXeRzc0mU9MDPlB7mZb35gszMSXX8hm6ZcTPHvk8tWNjnhwd5yPn77WZEJN1MfccDFDxM37s0Ff4gdoqnxzN8Yuvf5DxKMBRdsecascG5Ymcg84eM8qwW1RRwl4/JTVKakotGWd2QfKcEs+xOpFO6bJatHhq9RD6egVug59Y+RJLTpdjbklDRqQmJ9Y53XIKPXCJuoJB5hMIxbQK+I5KF+j+iWv9hlisq+FG0eRLGyc48okY55WrrP2lO9DLmkaYcE/7BnNun3vCa9zmDfjXe/fwhy/fjRMLhu/b5C/U1pHfkOmthO2AHXVdDk3C3pSoAFCTIxqT4iNyMobKx5H2eu2OIpp/HDL9XJfubQ12zgqKdsG73/4y72qe47i3wV0ek/ut+ie+S00U3OPt7OeDaGCkDdeLOl0dcSWd5cp4hlBlHPA7tBnx0Rv3Mv24R14RDA9pylrJlD/im6uvcDGf4Vo8RZx7OJcDZp7XbN0Tcs/913go6PJPO3fz+Y1TrG60qb4QIEtYe9Dl5NFNjoTb3BfcoCEFkXDxhbNP/l0rh1b1n1kBc4mgKW1x+NZGjaf7h3lq7SChl/PI1Ou8M7q4r7lIjOJG0aZTVnlhdIAXO0skhUN3GFEWkm898TL/w/TTXC8Mv7zxfq4N2tw/fZ2fXPoi17IZPrr2FnbiiIqXU/VSSi1pV2OKKKHjVtid9whaCf0i4Ln4EF/aPsG1zSmKxEHtOaAFZTvHr6XEic8fXzuJUppDrT2Woy47aYXVfou8fHOWp6TBU1Zo2vASIifj9Z05+leaYOCV6UXCSrrf3fH8nHTZ4XRlnVtpE1eVpKXDE5tHyApFJcwo3tOlKBQmdqGwLrFX9yKUXzI/1aPiZlxYnSO46lMGhmI5xQ9zrnXb7IzvRhtBWjjob9gsFqUiu29Iqu3OFi0wUcHwyETfNvb46LW70VqSFTbT42Brj6WoS9XL2JxPMYVElZLVTottv8L2uMpUMOLnDn6Wh4MRTyQVPrZ7P708sELdwmWzW7OQr0xy4fICF9w5lGd1Vb5bcHxqiml/xPneLDe2W5SFRA9ca2+OSpL7Rnh+QSet8NjeCQ629hg9PGKUeezs1djeqhPVE6aqsaUvK/tOmV/YY9iyYXxTbrG/SH967Q6GqUevZ9/nxxa3Odu+ycXBDF+9eoQyVVYHaMBrJRw6sYunSsaFyxc2TuI7BXfddp1x4XJpbYatS1PQyFl8cANXlWSlzUcJ3ZyWH5Nph8t7U7y6Oc/B9h69aVv03DmzTq+xx864yueun0ZJzVKjR91LuJU0WU8aaARx4VFoyeawSm8QWQRAbu8/N8yphJndIDoarQ0bezXWdhtEUcqtmSYVJ+O5rSX2dmt4UcaJuW3qbsJi1ON4bYtx6fHCYJlCK+bCAQsrVtfxZPcohZHsJRHjwiUpHOLUQ2tBkVvXVKWScHRmm8DJaXsxoczo5BU2xzVyrXhpd5FnihWywkEcHRF5Bb084LH+SZ7aOMDe9RbG1xw5vMmB6h7rcZ3fX72TceYyOpoTr0ioJ9TqY3Sc/qnr+Z92/LmKjy9/+cv85E/+JPfddx9FUfDzP//zvP/97+e1116jUrEv9Z/5mZ/h05/+NB/72MdoNBr81E/9FB/+8If5yle+8uf5UXzyxp2MRIMk9qjVx1RVypzb48vbx7l4c9ba5lIJEh5tnOS4v0G3rHApnSM3imO+DVHaLaucTxbIjWLO7e0LMG0EsCGQEYHIeWZ0mGc2VgCY8QbkXODL3ZOs3WwjHMPFmTlO+Wu8PFrmubVlK3ZdhEEUsO3U2HAG+xHXvp9TllYoBJBLDUbgpiAzjTcy7N5s8GhynKwT4O0odkct/tX0O3m6doOaGlOTCdezI3x++xTDzOeh2au8t/YqgI1vLgU41qYnJJSlfRl1ygqbZcb1bJpBp4IYKbqT3bsUhufdg0Qy40vaPiwv7C1za3VqonJPqUQppRFoLTEGkszuMjuNiBJBYlyyoUe0J9i51eBfVt9J0xuzFHZpqLFtZRvJ+eEcMpVoBZudOn9v5z6m3QFHvC3qMqGro0nho4lkisLwyvg2LsfT3Fxvc7I/wmS2ivYnrJIbcYsdVeXKeAZf5jy+cZRgS+Ik8MSNI/z9YI+WM7IdHawuoVtGtNWQJXePQOQwaco/MTrLJ2+dYZBM4scTB6eSM90akGUOUW4QeYnfLwk3XbLE5WvTB9nLQhpuwpzfp6pSbgtv0VZDXk5O8MLgAE035n31V1hxeryYLvHs6BDj0mMjqZGULpvDGr1BSBhm3L+wSsVJEX2XoKtRqSCvSoqx4CvXDpPrbyLTit2kwijzUInAGWvckeLJ0TFKLnM1nmaUeZhS7O/mtJm0pNMmX3EOEcicblkh1h7DMmArq9HJKjy3ukKxE/Bcucw/5v34qmA9rjPMfNLCsVlHpeQz22d42j/MnN/ngL9LbhxW0zb9ImB93GCY+owSj2Q7RI0lr8/PszZdsl3W2E0r9BOfr+8c5OJghkEecLPTJM8cxn7OyHcn97Ql3ZaFjWVPY5entg8Rujk3O02K2LE03omlt0wlmetaEasyKKUptMQRJVnp0B2GFMWbhXwQ5ERVO6Yd5D6D3Ld8ngnYTo8VYx1gEoWMJXHg8Uo0z6jwWI/rbPWrFIUiTx1MIZFuiXJLjJaYyRhWFAKRCbSn2BB1XLdEjKxWoxQQVVOmqyPSUjFMPZQ0+JPCvjsO9seKZiKyV0ojXE0pmCy2UIxcOkndPveORkrDbmAX6J1hBYYuohCUpSDxFFnqkmQuO17EF6Lb6eqrvDA6yPnuLEnhkOSOfVeNXUQpMMrgVHPCKCVNXZKRR6pcXtdzBG5BfxSQj2yxJVPrFCyFwfMLQi/Hk8W+fX4vDskKZ59SmqUuHRFhDPu5UWUpMaVASEjdAintmM+RmjjxbKilFlz3W4wLl704pOx7lsQsbQSELiWBKnBkyfWBRR0opyQMcktQHri4sSSP5P753uzVSMcurl/QD320lvQHITpVXAe+zHEClTPlxyyGfTpJhfHIQ7klQTtn2huxGrdYH9TRhklIoWA4CihH7r7dGQN5Iem/kQeWWyT9GxvHkRasV+r4qmA09jGJovAUhbYBchvjGto0SEqHQeajjSBwCjxpi+FRZkd2SeZSFBJdqv17Zd8gIT1uqAau0uz6FSvWLR2GmYeZ2NSlwBKxfYM/Of9vPJciE2AktzoN+knAOHPJUociV8iRQmaCIrTbvjcSd/8sx/9HhNPt7W1mZ2f58pe/zDve8Q56vR4zMzP81m/9Ft/93d8NwLlz5zh9+jRPPvkkDz744H/2M98gnD70gV8gTB3UMGN0sMrNRwyqmRG+EDH9YmZhTWmJcSW33h4g7uwz3oqYelahMth6W8ntp25wfm2O8NkImUHv9oLp5a59gHZCCxKbHzHXGHDt2ixTT9lioXO3prbUJz7fZPHxkiKS7H73iO8+8QK/9dJ9zH/GQzuwdb8hWLQuDmMEjlOyWO9T8xIud6bpblftSym3gUmN84LpF2OEMZSBg5ECNS5QcUZRD9g7EZDXBMmUIZsq8XcUc18vcUcFl7/P4Wff/jleGS3y+fOn0CMXWc0JQisMLXKFkIYzi2ucbdzkE9fvRP/hNN7Q0DsqSOcKcDUqLNCZovqyT221xBkbvF5OESk2HvBIDmY200VpTCmRXQvAqd2xy6/e/hFeSlf4X37nO5h9pkBMbh0jBXnFcliKQFgQkwtZzRJj/T1BuGPI6oLuXTlBMyHZCfG3LJMlr1vuhLcr8XcFlU1N80tXML0+N3/qHlY+dI1+GrC+00CPHaoXXepXS5zE4PVz0JBOuWRVyWheMrwtQ7olzvWAcEswnjWIE0NC/00l9vB8i5XP5ziDHO0rtCPYO+XTuz/BZJL5Lzo0X+uD1ojSoD2H+GCFrGqpmyozpA3J9oMl9fkB49ebzD2tGc1KZr93lQ8vPM/f+/oHmfuch8os50AYg9svcQcZo5WImx/QRNMx7pcbLH5uCxxF3o7QrgUMIQWDJZedhwpUpaD2ZMjsM0N6RyM231MQNhL0BLKWxx7Ojl1MzXLCTLtvuxqxT5EpvOs+wY7AHRgqW4W14DqW3WF5IHZRd+ISlWm27g4wb++Spg7RV+xsd++Ew/BMCtJgYscuVq5GBCX0XWa+bsF/qx9U/Oh7vsReEfG51VMMeyGVVwOmX8woKoruUUVRgSI0lJG2LhrXVgFi5ODEApkJ3KHVL+RVKCODyC1xUxhLeSx9e3/pSIOnuf3ILR5sX+XL28e58uISzvjNLlg2l3PX8Rs4suTc9hxxP8DkNnaAUqDGFqgU7AjqqyWlJ9g7LUmnSty+xO9YZ5vMQGhD2hYk05YW/AbfxO/Y+9xIyKsWRvYGpGs8r/nOh7/OA7XL/PbG/by4ukwYZdy3uErNSfjc5dOYyxWQk+/kgDM7Zr7dpzsOGGxXEYkkXFf4HUNeF8QLGu3b7y5cjeh4VK/buISsbs+RKC04zAgoKgbtTzJUJjoCC6azYCkMFLWSd93zOo+0XuNfrz7MzWcXcRKBM7SQqSK0VFSE2ccfZIs5K0u7+84JXxY8evMYo6sNa9evlghHY8YKkUpULAm3BCqb5K14lpxZ+hZ3kDdKRKXADB3CWw4qewMWZ4FXpTcpsn1LAM1nc+4+fh2Al54+Su2ytFCuwP5+orT/Gx7UHL/jpu30fH2e2nXIa4K0ab+PTCesGjFB7bc073ybzbn5levv5tazixQ1zTff/wLvaJzjH116H92nZ4GJA1FZvYTMQJSWybMPcJvcJyqZ0FuDCcE1MuQzxT6wUI4FZbPgxOENQifntbV5iu3AXsf8DdKoxcXLROL15T4U841CeiIX2ee1yExY7D6gHUsr1r7BRCXSK2m3RtQDa8PWRuCrgpVKl7oz5o9vnCR5vYlK7T0gc/s85nVLwI02BCo19E5AeKIL44RXv/8f/J9POO317Pyu3bb5BM8++yx5nvPII4/s/51Tp05x4MCB/2TxkaYpafpmq6bft+mNldc3UeMSE4+p6MP4O3Uy4xOtG8LLu4iihDQD3yM6sURnJyK66TD9Qg8ZZwwOzrC2WKfc8WleLlBjTTLt0a1HFF2Pyg1bHY5kxFopcbcdqrescHK44jKoRFR2BJUrXcp6wNrAp5tH0HWpXRlhXEXvSMQ4CmwlqwUiLFDNLrPBkJtu0/5ZKVBjOSEgglECmZR4N/cQaQ55jslzvGqFFtPkVYfhkkOcOERbhuprW5j+APd9J4i1nc/rQlqGg7BjkaxQdscI7CYVNoIGg2HI1J7G75eMp12KSGFcSZlL5FjSvFxQe3aSLWMMbqOKd3qKNFYWnOVhX8qZwIkFae5SMqmSU3BHBe5egtzuWqplvYLxHIqqR15zyWuKvRMS7Vmyauu1AelUQDznkhQh4ZpD5ZZ9eaQtGwQVbhvCnZKgk0NRgOtilPWfG0DHDmqgqF3XNJ/ZwDgKE3ogJc4gpQK4oxpp20V7DrVrUF/N6I9cOo2INHwTgFNfE4SvrqG7PUvPDX2CmRm6Y7WfkWJchRzmsNNFKUmlLAkjDzVIEb0h5UyTwYEGfVWltQq1l7ZwD7XZHNTYm6kgt3xaL+wikgyjJAiB6A/R/QGV4jCqXyeJPMKxQaQZZAJXa5ASMYwxoxiZHWT3rIf2S9u00QYnNaiOyKEL6AAAh1tJREFUyziXFp3t2tl2GdndijAwTHzikQ/bPs5YUL8C1ZspXi9D3djCGAPtBrriI7ICMc7sMzVOMEVBuHyCoRaUhaJ5JSf6+jWMOky84GIccBKLEC8igRagxhK/V+Jvx7i9BteTNqPCJ0lcTKKINgzhM1dgus24PQPiDXymxDi2CEGKCXsHnDGEWzZGIJ6VpI7NHRLafj+ZT3a+CEwhMGpiHZe5bW+PBc5och0FUEg8VeAITZ4rTDLhBykbHS4L+994fUO4maB9RTwXYKTE7QuCXVtAysLYToknyGviDQIAYKmm7nASyFjYBbWIBEUIOIaVoMNRd5tAFehcorWg5iRMuSOMFhMmDnbxmMjbQidn7LgMJjtKlVr6s5G2QEPIfUS3MxY4sbGdFk/sE0tVYj/T69tzrl27gNvsm8niqCyQDgmHwl3u8W/Q8BLWM3BiCDoGZ2xIG/ZcG2k/x0zsub5TECibqu3LgqJQOCOBdgVFtUQqQ6ntu9CJBf6epYXmkaCIbNdOlBOKrC/RnkRmNorCGdvz6owNRShIW/ZnW9yrscAtaTtfKhaEuxrt2o2QkezHCwiNxeEbgTcQRNsFaaYwcnKu8jfyrSbXWUs0gqayG0yVWIR9xUmZVQPyUuJNJCz5pPMoJvgFWU4KjTdeOeKN+9reP7l+o/gTFInt/sjMblLRb95U+diScGU5QfYDMgDtKnu/dv/kz3jjMAqKir1PZSpw+/bZ0c7kZ1YNuTL74mxXlvtdi0Dl+LLY735gbNEddGwcybiUGFdM8PtmQoCdQP3+HJ2P/8PFh9aav/7X/zpve9vbuOOOOwDY2NjA8zyazeaf+Ltzc3NsbGz8qZ/zS7/0S/zCL/zCf/z5zRqd982ydxrKRsGRI7eouSmvjo7gpHOI0qAyQ+kLOneVvP2uczzunUB/yUEOEkrfMFsdEs97bJ+tWbz4XXu8b+U8f3zjJFxuWVT5hkMxrOCOBf2DDmUgcM70+NDB1/lE735EkiE9h7CheX/zZf5o5hTpVIDQBicBtedi5hNOLm/S9mPO1G7Z8Y4Tc6E2y2q/Rf+ZGcItGBw2dB5WGKMgiRCFfRhtNWxzMwDGSwWV+RH91GH37lmEnuPOM1e4O7zGudE80QWfoGPYO+MRTfXo7bVoPuUhc9j5UIWHD1zg4uwM6wsHKD1B7aameUWze9ph8ZFbFFoyfnYBMxiQnznCrXeEaA/CbZh+RrJ3Gu5++DINN2EzqTHKPR6avsqMGnPKX6f29i0uH24RrjZpna9RBJLOGSim80k7OsXzSg40uzT9MRdOz3D+oRrCMTTbHWa8nOKgHRM5SjPt5iip2YtDtmKfciNiqX4MfzfDSHh9Y44oyDhz8gbaCK50DxNtTTFc8ti6D0ytmCzMAlVNWZnZo9SSzXQeWbjkdfsAaqPsDk9bcumFv3ZwH7dtXIMcg7/pIArBcAnimRpFVCNvTNvx8mSX6A6reL0pu+uqGcgF3Ts03dtnEY2Mv3joJe4KV2FpTPdMm9IX7J2Coq5BTyO0QNcLHjp5jqWwyxebx3n97ALOQFJZFTiJYbQ4RTJXIpoZ9xy6RORkPDE8jdBV4kWYuWOLVjDm/K052PKhUbB8fAspDKvn5uDFkHoM4Y5GlprSFcTzLru3+4yWqwgDtav23xeBJKsJtDfJ96kYjDBwq4rXlfi7I0wcE08rjt59g4Y/pp8F5FpxY6cJGxHaM9z4kAEnRAUxT9w4YgMhOwFqLEkbgviBo8QzDrsPFFSnR+jMwRQK1ytYbgzwVcFav86oH5AbwejEpD09uWYIQFn+TKWR0I7GjHOH/iBCSE3TGxPJjIY35mpTU/oCHRqMo/GaKUnp4smChVafuDomLxRp7lAUkizwyDJJXlXktQraheHBEho52dAlbdtwMaMEBigrJdSKNwXJRjCsOYxnpd2dV8r9ZGikwY1yHts9zqvDJW4Om7ihFYhqI8mNYqY1YO2IHUEJab+j55bsJSFFKfFqGaYiGLUkwzst+tyOXA3NWkwzTNgZVuhMV+2mJypw3BIhDaU0pGOX+tMBzSs5u7e5yHt7+G7OYBhSZHYkgBa4UU6sPS7m03iqIJ0vSLVgeEoj1J8el16ppeSlIpPWQgrQqIzZWAxQQcGhmT0aXsLasE53EJLlip0FOx56Y/5gfE00FRO6Ba62BVUauQxDD1FI1FCiEknpG4pGsX8fIKDajpn2RxYmdyBlV/q26Hhjl9/KcMOcZsVqfUojWV/QGOGQTBvkyhApjXWiGEE5cHF6ijLQvN6Z418U72KUeWRHxwRRRiQzujriaGuXZ+6sYQxIx3bB9NhBJPJPnJ83mEkYYamhBnQjI6hmeG7BYnWEIzQ39pqMewFBPWUu6lN1Mm7NNthVVSszmGhIVD0jDDPioU++6dtIgFoJ/jdcH2HfVUIa8pokb1lMAq4l/nphznwtxpU2tDDXiplwyMGoA2DXiNKlFqQMZnPylrTPQCHQvu24CS1IWxKhBeVCykI0xnyDLvM/d/wfLj5+8id/kldeeYUnnnji/+hHAPC3/tbf4md/9mf3/7nf77OyskLe9Nl9Z8qn3/kreEKTGUlP+/y14ffTiWcQha34tAt33X6ZX1z6DH8lrZB684iiRHuwUuniqZLLSiME/PCxp/jBxstsZ1VeLFp4Q4MsQXcERQSjZdvy+7Fjz/BzU6/x2eXTkOWINGeukfGhaMj/a2aPuLlod59j8PcE+lDBjy8/xozqU5MZLpoz/k1GDY9PV8/y6XiG6lpJ/6GMx97xT6lJxUBbUNDFvMXFbJ5ryTRfvHWc0djn3oV13j11nnmnx33BGg2p0BP71e9gaF0oqV4fMVypUXUz6DvMf2EL0R9y7p2LvCe6yStT1/mduRWEEbS+MkQ89zrj1n380NKT1FXC363+EGW3x3DF56FvfQkpDM/+2p3MPL7OaGmRH5t/jDPe3v51UULgIqi5ff7J6Y/QPRHxD69/gGvBCnld8zPv/Sw/0jgPsE9OVBPRZXwgZ2A0ndLl68lh9ooKD0SXeYtv9TexecNeZo9/s3c/v7P7LqI1hZFQrEUky4afWPoSi06P7zj+EwyveuyeEfzjb/513hN2iE1JYgwjLenogBv5FH9799sZDSOMY3fKshCT+0agTw75pXt+j6PuNpEsCIThJ658D7c+ehhZGHonDXom4/jSFj+68jiByLmSzdIrIi7H01zszjBKPcrNKnKsOHbmJv/o6MeoyXLfHn50bocbxw+QTml+5L2P8i21F2nInLZSKMQE8CQZznyFwV2azwxP8j9//YPQc3n3Ay/zy0t/NImrhk4ZsH6qwWWzSLgw5McOfYWmivn5re/A2ZHEDfivVr6OK0p++asfZunRAXKYwNYuwnXpve0QgwOKwV0p//zh30Aj+auP/yDlS74NVDto0fU/c/xxPlA5z3977bu58pkjtvuwM6CMY5Jpwd85/EkOOjEDLUmN4m8738GFK4fIWyV/++2f4sPVK/z0jW/ma0+eQuY2ylxoQdaAnTtdxrOab7/neR6sXubp4WFe7S3Q9mPe0bpA2xny1cFxXu/O0wpi7q7fwJc5T3SOcXF3htDLma0MiZyMB5tXOBus8tz4EB+/eZa0cGh7IyoypeWNEY2MMlRUmmNqYYIrtYX9Accb2zTdmE5WYX1cJy0d+tWArFCMWx69WReUYXpmQDuM2UtC+qMAISD0LeTwP7XDM0bgqZLFas/SInOfQRYwyj1e25innBRbYZBT9TPrwjGKw/UOVTejMHLfJRGnHoM4wHFKWrWYwCl4YOYa91auciFZ4Mvbx8lLxdmpm9wWrbGT17i+2KbQlsvjy2I/g+WleIU/euqtVF7bYvf0En/11GMsuXt8unMXq8MWo9xjlHr4bsG4dLmUzhOonNZ8n9DLef/COU4G65xPFnh1sEChJXFhNQOZVuSlIi0t9EwKx/JlFg0NP+GtU1eYdgZcrs1yvdFGCut+AthJquwlIXPRkPfNvEZTxTw1OMKF/iymLtCzttvaGUXEiUfgWdebq0rSwiHXkrloyJxnO+YnVjZZa7zZ7vedkofmr3JbtMZ63uTyaIZMO6iFmFHVZ25pj+868DyRzIi1R6pdXugt89rmPCJXbO3U2dxsUGvFnDmwRt0bE6mUbhlxe22dxumEwkhGE8Hp+qjO3iBCSkMlyHBUSdWzri5tJOPCFpgnG5vcFq3hipKaGpMbhz8Mz/Cys8B0dcRy0KWqUoZTHpthnXHhMkxtYXesvcPBqMPFwSyvOfMYIzgy2+FQtbP/vVPtsDpo0RsHOKrEVRpHauq+LcCm/BErwR6Jdnmpt0RnHNFwx9xbuUpuHF6NlxjmPg0/IZ6xeTSuY7sj48wlzRykNPvPQ91LafhjcvN/ktvljeOnfuqn+IM/+AMee+wxlpeX9/98fn6eLMvodrt/ovuxubnJ/Pz8n/pZvu/j+/5/9Oe7pwPmZns0pWZgBE8nB1jPWwzHPkYaygo23dTXVN2US7m9QHnLReQNhIZLE6jSfGNA6OS0nSGJMSwEfb5yUJNOicm8UdsZWKXEDXP2iogvjQPL9Jhvkdd9Gt5NlJC0/JjdaYksDPGCoWhoGl7O14dHmPWsE2NGxpM49zFzbp+sZYhnJJVqQlPaU36lqLJd1LmSzXB1PMOw8JiO7A7mcGWXeadHIDOu5HVcUTCvYqaVouYmJG2Jk4TkTc2B6h6X2jOMj7RxhjWiaoovJIHMJy1HSKcDoiMHKSqCC8kCkcwoKgJ14ijxnORwtIMvcx49cif+A/PEK8W+MPd8XmerrHHA6XDETUgMvJyscDWdoZcGdtYZlTTUiFB4KCH/o2u5plMu5i26OuJW1mJY+CShSyg8hiblYh7S1RErTpcVpa0LpWrIGoJ0SiPmEhZbPZoypiIK5mZ67J2YpZyzVMfnVEyiXXIUgchpyjFNFSMnbcMiMuRzud1R9h3UWBIGOTNOn7bKiSYe+0OVDhcXD1kPvacxhaSf+VxK5glkTqeokGqHqpNxvLnN1rjG+c2q7aogaMiS2qRQRMBUMOJSW6OrJXt5xLlsHik0nihpypi7/RENGVJiSIygphJmZ3v0KiEHQvsi6Wj42vggO4UVpQXzI5abPWpqTCAzfD8njQwqLGg7Q+oyIVnK2bmzipNUCLpNjBTEM5IiAKShU1qHjnQ0eQR5Q7M43aMdxqymU3yG04wLl/GCtvdJNkd4dIq0bXhidJKLru01l0YyzHy0Z8DTNFVMXQYcinZ5cjYljx3cPQeRQtYwlNUS1cgYFT6X0jkcqTlS26XpxETSjl71ZDEDmHN7RNKC1uLYJy+UdUO4rrVIT1qFWalIC8XauEGoFtlNK1YQ7pbkuWJAYCmy4WCffNrJKvTygGHmk2uJNlYsF4YZQWgJqO0wpuKm7MQR6dBHOJq5xoDZaMAw94lzDyEMFTfDEXofbFYYxdqwQWkEU2HMgeoe49LFVSXj3GWUeozHHtoIVn3LLDq/M0u/U8ENcw7P7hI6Oau5FeZKqWn4CTUvYc7t05QxLWdkXRqlw7Q7ZEoNUZMmemocdrMqO1kVXxZUZEpDjcnqkC+2yBpmYnEfshzsoY1gfVxnkNj38Bvi/uWgSqdu2TkH/R0W3T36OmTbr5Fqh0AVZFrRy0IregaS0lrWHVnSmCx0cemxQ43NtMb2uErFzThSHRCqnK1xjX4cYIzgpXCZumO1B0dqu/vvj9IIdv0Kvczaw/NS7RcepZY4smTaHZAbxVq/zvBGHdHMODDfoeXHtNx4f3c/zH1GuU2iphD0RiHP9w7gq4Jcq8n95zBdG5FrySj1KAqFq0oGuY8UGm0kEs1mVueVzjy+Kjne2KbipOyMq2SJi+MVNMMxDW9MyxvT9kZ085ALvVmyUrGR1HFFSd1JOOxvoYSh7Y1YqPdp+zEtd0QkM+Z8y8PZSap0Rlas20vDSegdzLQG+5iF3bRC1U2Z8/v7z1Iz8JGY/d97XLjspRENN2HW7ZMbRTeKqDr2+XspPmCpz+Mmw9y6PUdjDykt2VhNMO+NMLHOUFUiJ92rvTSiSN8Uef/njj9X8WGM4ad/+qf5xCc+waOPPsrhw4f/xL+/9957cV2XL3zhC3zXd30XAOfPn2d1dZWHHnroz/Oj+M4ffpSHZ2/gCsELySK/9PI3keyGqIHCMYJ8OudHH3ic08EaXxse5ZdvfIBbnQbqtINz0EElcPP5RcSBEf/dXZ/nhGfHPlfyOu+onePOD92gNGKfRJoba0PtlBU+detOfu/cXZTbAesPS/IaPNSw+oj7mtd59p6DCGV454mL3FNb5Q+3bucjzzyAquT85TNf4ZHqq7RlxoxyeGt0kY/eeQ9b8w0+uHQVVyjO5yX/09Vv4fpuiyz2rNColfJDt3+du6JV5lWPOTXmxWyeX7r+CHtJyA8d+jp/pXmFs5VVfv+ee+gfdbjtjmv85dlHmfP7/OZ3PYhIfb7n0AtUhU8kM4xrZ6Rb97pwzxxZS/O7F88ihCFd1lz73jnKM0O+qfYScypj5ds7XPngLCeDdVZUyrZ2+OUbH+Dc2hwPHrrG/7j0B6yVEf/otfeSXq1RRhoxnVFvjKnIjLHJcFEWlT45SqP53Ogk/371AYqJo0EKw5Lf5b3hRa4Xgl9a/RBr/TofPvQiP956lnmnh1lOGFY9Tt12gx9deoIpNWTZGRMIwS+f/BiXD89yIVng368+wCj19nHxp2c2+bnFzzKjBihlH4p8Lue/f/APaaqYf3Xj7Vxdm7YjIZkQiTdtqN/dfpr0fQ7r4zqvXl1EdVw2sil+be8hlFNSryREbs5909f50enHeDld5hcufjteVzBIfSpCEgmPmAxpDPc3r3Lz9iZZqXh84yiPmWPs9SPKnkc0O+Jf3v3veNDX3CwcXkqX8ETJ3z3x+/v35Mt5xBPDk/zaKw9RxA4PnL7C37vr43iipCbHjLTPgWaX11ZCjs7tcru3waIy/PI7f4eX7l8hLj36RcBeFnHj8kHUug9a8PHte5DCIJUhWSiZO7rD3zn2Kbo64u+88G2UV6u4Rwf8lUf+mEhm7BUV4tLjia2j/POvvBsAWc1x3NLa+VoF1bbFeCsh+d7m0xx+yzbPDw/y6afvwttVtE7v8nMnPsfNbIrfvXmWr60d5IMHX+enZ75IbByu5dN0y4ittMbuKKLhJdzm32JGpvxKEiGuheQOrNUiCEqO1HZ4e3SBWNvuQDp2eWm8xKvOAkIYPD9Ha0nSDcjHEu9QwYennyMQOb+x+VbOd2ZIc5dk7IEwBEGOq0pOTm/xgalXUUJzNZ1hL494dX2BygWPvGY4emKHD08/w9dHR3m6c5BA5dzVsKNWixvP+drwKJ984S0E25L8oW3+u5XP4lEyMh4j7fPLVz7A2uU6sRPy6p4NIWs+63P82RE7d0Us/vAVHm5c5F/Hb0N3PYrpknvbqxwKdjjkbjPvDNBI9uqW33E6WOOouw0uyNDQ1QH/bP09vLK5QG864P7KZZa9XcanE26JCHHSus7mVcoHai/TrwT8QfcsV3emUFJze3SL90WrnPLWuTO6QSBzzngbTCtFRVwjEBmx9vfhZi90l+kNQxynJHAKMtdhyh9xtLpjeRPDWeLC43qnRbwbEU3FvHP6Aituh8c2jpJeq5GIGn/sT2FczfvPvsLfmPv8n1gPciPJjeTR+CS/cfkBRmMfpazbx5Gat0cX2ShqZC+2OPGpPjcfqfO+u85xW3iLraJOrD22sxo3+g3ixMfsebhDSdmr8vSVU3Zk4tlYialDe/zFw0/hioKdosagDHhxb4krm9PshBXuatwikDlPrh0if7LN9rTm3e+7wAdqL/NiZwnnlk/ecrjz+C3uq15lxd1lXsV8ZXyIr946zHAvYt1p8pyzQqMW800rr7PgdbmrcoO3VK8SyJy6TJBoVtxdkqrLo73TnLu+gEkUlxOPW2GD5WaXHzrwFFIYPrZ2L6/eWmB5Zo8HGldtlk/DFvWx9unrgLW8xW9du4+d7TqVwxlng+vURM6D4WUS4/CR3Yf47dfuRRcS6VhjfTF0UT2HwjOYeUE1Srh9eoOHm5fIjeJaMs2o9Hmls8D6boMyTv7Ma/yfq/j4yZ/8SX7rt36L3//936dWq+3rOBqNBmEY0mg0+NEf/VF+9md/lna7Tb1e56d/+qd56KGH/kxOl288vq/xAg1PkhjYzJukWxHB5qSqEiC8kocrF7jbH/Hs6BC3eg2K3KFsGMrACq28rmA86zLvdFlx+lzOW2yXdZacPd5Z6eILF3dCs+vpMTcLuJxP8e/H96M3A1RunSd53RDJjKFOiGRGtTnGcwoealzm7eElvqRO4G06FBXF+skGI+NRs0krRDLnUKODNoJFv0tiCrbLKjf2mmSbESq2QrFEedRUwiFnl5rMCQQk2rVzwIHPtfkpYnOOQOS4zZRceRyqdFhUKXeEN5lf7hCnHof9bYrJAMNIm9JbRhN1szSUncDO/kLNeElzqN2jKTPa0uNbKuuUlTVyo0kMdMqIG90mZivg5nST2Ci6OiLeiaitScZzAtFObBqn0ORGU2LITYlEoibjh/W8ydZeDa0lUmqk0uzkljr4xs8YdCM2FqxV2RUlnp8zDhVHqru8NVjbvy9yY7jNTbjLu8nvC8PHeneT9fz9+e/NoMlABwQit8A0B9wo4+3RJdqy5DOVLreCBpFj24MaSE1BTklbZbytcZFL/hyvqQVkKgBJYTwKx9DVgiSwj8xJVzMyW2Csqj0rFPnku8e6JDE2D2O52qWTRlzbaZONXeSOR7QjiU2F3bKKZsDIuHTKKvNOj7N+l4b0OJ+XXMmnuZ600VsBbiwoTkrOeFvkCLraQwlN3U0I9jM8Suoy4p3hOmd9e84yI1kra1ztfhedPdu2vTVs7GvTjK+ZCmPOeHuslSPSgU99TRCvOLw9usCiM6Y2wWn/lXGbra15RCnIGpIssE4LGZQE7iTIzeQsqpJ3RZcokXxa3oXQgqloxAeiDZ5XA349eYBRLyTVDvMKujrjolHE2meYW5HqG+1pJWxnwxkLtGswygo197LI3oulZRqYUpCXNtZdupogtNdXpBJ3IMkKhxnVJxAFSekwjAPKQlGOlXWYeAXS1bS8mDPBDbSRbBc1ekVIkSuivhXqObJkXvVpqDFS2IWv4cS0nSGBsMWHK0rcocDfg1ILjjhDKkICKSPTI3RyKxYtwEjLq4i2NM6lNcLlIzhCM+UMLfRuMsZvuTYxNpgIaiWaqkpsB2gCRfRFSSA0ucgY5j7jkcdeLWKgQ3LjEFZS0imPdsUuEKWBmswIREFVvSn6D0ROVbi0ZWY7sCKnJm13MBI5FZnZmAMk7mR8UkyspHmpKBzbjWiosQVwZSG9JCCJPUQiyVKXQBTU1JisUKhYWC3WALQv0UZy1AlRE+qs/gZm7bmsZ8PdEgftapRTUmiJi0YJjdcDeXUNd1hn2dvliLtDot3JSEWRZK7tJiVy3+Ekc7umlL4Vso4XXRbdPSoy3V+8X5UL5LE7eQfZNWMU+zQ3DGbS7W2rhLxUuANBGUqqTsqiu8eMGjOtFHWVWPt3JjGJvY97QCevUFMJTS9m3un+iXWwIlLqJHiywKTWLFAqh7GBvK5YdPcssbpUFLG13rqiIJIpR9wOM1LQ0UNuFHVi7ZPmDiZWjHLPZvVITYUMsCPAN2zMpadtjtNI4Q6FdebkiqJUhCrnkLtNYtx9gF1pBEXioMd/9pLiz2W1FeJPT6z7tV/7NX74h38YeBMy9pGPfORPQMb+U2OX//fjDavtkV//W0zNGmYrQ86tz1L/QoVou6QIJIUvGBwWPPjBlzlTvcUXd05yeXuaLHXQfStkkpmd8Ze+QdcKECAHDmosKJcTvu+OZzkabPFgeJVjrsPvDBb4xxfeyzD2KSZefgqByCRGGdxWShhmDPohctujrGi+6d6XeH/zFf7mM9/F3EcDsqpk70MjHjx4jc24ztawSuDl3Dt9k3m/x1raZC1usNprEj8/RbA7UVaX1oY6PJUR1FPmm32O1Xc4351l/fl5vL5gPF8ip1NMKdCJvcD1mSEHW7Zt+kZHYS4YMO0Pebm7yOsXl5CxwutIa1s0b+pkRksa084JawlHp3cptOTc9QXkrktZK6lMxwhhbHs4l8zO9HnnwiVuxC2e+fIp6ldgcBjc030WGn3++sE/5gF/l9/oneEj196C5xTcN7PKgtfj8d1jnFubo4wdvE3XjiluH/JdJ15gO6vxxOoRssThw7e/wP999kn+ff8o//CT30a4JUjuG/EDtz1DXHqsJ9bvvj6q0x2FxHshlUseMoPRAY2aG79JCRXQqI9ohgkLUZ8HGlfJjeJjN+5mc7NJa2rAuxbtAvnFG8cZbFdxqxmzrYFti7/YonIL+kdh6ew6hZbcujaN21U0z+zwP5/6XZ4eH+bXfucDTL1SsvYOwfe966vE2uNLN44zHARUawnztQF7ScjO1TbOQOLvCfyOYXAQ/uvv+DzfWX+BThmwqyuMtM9m3iTWHrfSJltpjde25yieaeGOJtdsLmVhpsd/d/SPmFc9frf7Fp7rrHC2dZP/fvZxAL739b/AzZfnEQsJ33HqRaoq5as7R6yYsxfibHiI3Do8RAnJiYT/5u7HcUXJF3dO2r839smGHkhDrRVTC1I6gwpJN4DyDVU+mEqJX0sJ/Zwzs2ssB13ur17h4WCTj/Rv41/8uw/RvFiy9h74nrc+xU5a5fFrR8hHHtIvcVwbEOm6BcYIRjdrhGuKMjSk01ZEFzXHtCpj4syl14/QuUTEDjIR6OmMtxy9TsuLGeQBceFxYXsG/WoddwT+rsEbGronJK23bRC5OZdvzUD3zfeEDgzzp7a4a2ptv0XdySp8ffUAeddSesMtO6KNl0tMtcCLcuqVBG1s0GFZSpKx5VLg2t858nO+88CL/LX2i4DVNo204Td7b+HzG6fYGVYYr9ZQiXVlGGnQgUG2U1y3ZLHV2x/Z7CYV8lKxF4ekmYPaJw/bAqfUtvmttaQoJOlegBwpm9btWcutuydxRoK0bWBpjOOUuK6d409XR/ujju2kyrhw93UgjtLMVIZU3ZTdpEJnFNEIE75n+VmOelv8wsVvoffVOfKa4eT917i9sc6tcZP1uE5nFNG72UCOBWYuZX7GJqm2JnqvV9YWKNcj6zLJBcY1vPftL/K/LH6ZS4XmE7172M0r++vD47eOUjzexolheMDG2r8h6qUUOLsuzkhQ3jbkV97yEQ653f3Mn9/YfRt/+NwZnJ5D4yJE2yXxjGK4bJ1TKp08DzMatTDGcct93cbmpWlaL0vSlqD57g0emLnGJ752HyufNWQ1ydb9YFoZlVcDZl7IGC669L95yLHZHW71GvT7NjRvZaqLK0tev7BE5YpL1jKEp7q0KzGllvsJzOMJX8l3cwK3YGOnQfBaiMxhPKcpm4UVYheTwu2NBOtGwfRcn6loxF9efpz3hBt8bHiMf3HpYYZxQLYboEYKHWhMpUS6JdWa3UDWvJTZcEBceJzbnmM88vDPh8w+n5NHks7tinSqxJkZc2J+m0JLOuOINHcYDgP0wEXHCTd/9u/8f99q+2epU4Ig4Fd/9Vf51V/91T/PR/9HR+vTFeIDEa/PzhBuCWae7iI3O+ipJkUzABHylatHuNiaoRuH+0AvWcutYjlR6FwSbChmviDxejnubh/RH9F96wqfCO7i8PQutaUxi2qdP+rcQfnFKaqJoXubxp+P0aVN/zNjB//5Cv5WhOvaCjltSM4fm+VwOIe5EVF77DyiUad/dJGvlkeRNwLqF6E3L5j63nP8cPMZ/tq17+S1J4/g9gRTl0r8XjlhLQj0tqCy7lB6LusHa1xbmUF2HVqXwO9rpl41yMwlnnPYPWvQlZLBap3XLzQpZjLefuoibS/m6e0DbHWOWcZMVKCVwbvm0bhaIDODMy4pQkUy5SCjjLgf8Nq1Q7h9ybE/HqGeexVOHmbnniZZQ1DellOfHZLmDp+/eYLBMCTcFvi9klGu8N2cipPhUqKBj988i/7UFMNA8Mm7W7SmBoxTb6L0VtQvQ7BXskOVj4u7EALyXCGVIVIZvnDZKWo0LkPjSspavcKnKncwTj2SnRA5llRuSurrmnZq8PcStCtJp1yCMGNws87847ZIdv7rLr9+4rd4Lp3lt7ceYDupsr1Xw4wVe7s1Pl+eZDz2aH4uYul5SzNdfyAEDVNXoLqW0z/s8h1LL7CT1/jo83M0z8F2s8VzBw/xYn+Fyi1D9WKP2nKbTx48w3jk0XosYGWtYPMtLfbuLxjEAcGmwt+ztmO/X5JXHS6M5rgYTjElRxxxOryWzfPpzTvYHlUYpx5Z5qA7Ps1dgzcw1K8b1Nhh6555bh1ocdzd5oDfoV8PJqm2grVSsP2lRU78y3P0332cz/3gKQ619pgKRhyo7vGF3ilmntW4Q01eUxS+oPR9Pj13B4vVHj+w8HXOHLrFDzz7oyx+FFRq6B9qMmwI8gOa9okOWku6GzVkrDDCJveWRvDU6iG+pgX5ScU3RXskxqFxRdN46ibxzAF+b/pOAIrEhVIQvhzSvFSSR5Lhit1dtVcNtZsZKtM4/RQduFz+qx6/cs+/5dnkIP/uxoOs79WpPeEx+9gmax+c55sfeJlHoitcLyI2iia/2P8g4csl0XqK6iXI0RhvMMuGO8eWB34yYTFMbJV5TTAXDXh/8xW+2DvNl28eYzgIiF4OaW4am3hdt3bDhccF7lCyc6ZK76zl63jXfNyBYHrDUL2V0T3u8chfeYafnnqCpnSoyoDU5GS6IEfwTbWXeLhygU/37uLj1x9AjQXzD9/i7x/7D/zGzsN84VP34vXA/fYOv7r8Bf7DcJH/x1Pfhtj1iNYk9R1ree0v2c6mvyvxBtbK749tB9BvWItvsGtoXM1BG8YzLnkFKuugngssy6Jm7aPX7w/4+cOfZruo8/NPfpjgkr9vGc0UXK5ad5fMrOV0dbZEL0nu8HbpxwGtcyWjeUX9bQn3V67wm4MHuHpzBnoujQsSZ2zozMH3rzzL5WSGz14+TTry7AJaKyy0LLG2ayUMscl5OT3ARy/dTdwP9teF8IrPgS8PkHHG+jun6PsO0bpk9tmU0hOs/kDKD971NQ76O8w7A1wMx1xDKAQfkxnurkO4LWi/OsK9sgEPHmTvtIVVqtSyR2rXJOp8ROkJegu2c9x+RTD7+DbpUoMrJ9q84maWS7QTE2wbwi0X4yjC69voi1eZPn6YwcEZXt0JabzucOjVlN3ba5z+S+d4T+N1fubK91O/rkn6gm6lTj+qIPsOztDiDdwhoCGpwDA0RANBZU0jNBShpIwklVWHhSdiZFayc7bKaElgYpfuzhQ7tSavTC3znnCDJ7rHSL46TZCAikB7EF2RNC+XGOnQP9ik1xTEb+3wr4/9Dj2t+O/LD3MxnyHYNYSPnaNSrVD6hxiNFFm/wqt79pqIfFL8MGGaZf+x5u8/dfwXm+0icsuAKOolWapI5iJ8JUjmIsZTDuNZgZxgy6XUOJOd0xuHdrS1a+U+g2WF21KEdQe3X2E8JXBd62PulhVulJKdpIJKDDIH4xnqlYQ0d0hSl1wLsoYDxvrQ37BYWpGSQ9EsGL/lCGUoSdslUZgRRx5ZQ1FUbcHW04pR7jPpkJK0JXlF7nvsjZy0/RwLrBGORoea8YxDEQmiLQhGuYXWOAbhlxhpKHyNG9q49HHpErk5teqYQkvy3CEXFjgUTyuEBlk4lJ6FNjnSoDxNWbFtzf7hkLo5yWg5IF4Q5DWD10hpV2ILMtOSLMhJWwaZKbKGZj5IiZyMgQ7ZKAe4qqQ78eELx36u69jd7VjDcCUkbSnS2ZLpSsI4c0l2bEz9y71FXq0/R68IiecEGI90qmQusFY+ObYjqiKE4ZKFKTnTEu0IstmcQ7UBo7bPcDkCAwf8hNhYKqsUBiVsYm8ZlBgD/U4FUkkRQrxSYzwt0VFu+S+LLkXoUjRLekXEsPQp6pp4XqFqNrcjVDnJtGB8sEbagkaQ2gC86RBwSNuag2FMUdrxoSgMRQRFqEhbdo69XdRJpEdXpvuEXCkgHbuYPQ+ZSpIpQVYXuCNwYkleN/SKiFtF3UZ3q5TNvM6v929jPWtSBpCePcxgSRF9A3WyMBIvyugf8nHGap/rUNRstLYnC7aLGhfFLEppBssuKoe0ac95WdHMVEbkWjGohpTYkVY7GpNrSVEoCiPpFwGX8oK49BnNS/zbFkhmBIFvRwbFG2wFZXkURfQmECkdKPyBg8o0RgpKX+L5CTWZM+92OVrfQQjDzlSVYrZOXoWmivGE4FbR4tWxFcD3jiiSVkhlw8PbC0ib1jllHEM6Za3VopwQSQNNJ6nw+e7tPLV5kOG1BjIVZDVjOR2BoaxoZCpQqcTzBVnTMNUcWqpwosjrCu1ItOORtAW3xk2eGK/s60ASU2V3koEz7/ZsYq7KMJ6h9A0NL2FGphyLNvn0YkFeUxyp7RJJjylnSK0xZmAEMS55TVoQmWuBZumUJmuBO5CEm7bDWQRWaJ0g0I6H0MZudN5gerj2/xdVQ+kZokrC5WyOYRngVzKSOQcVS9zBBBg4VSKignLPknZlLnhpuMyM08dRmt4RRdY0xIXH68kio9wKdHWlYLTiIgqYn+mx4u1SIlhq99gLQmv1HU00YhO+yVZS5bW8wqVkjjTx4A0Ro7GArc5tVWRpSKbtuzptGjonfbQHs9N7HPR3mHH6BKJECSYjYMmS3yWfyzHKJV4IqMVTxDMKZhKUoxkHPjKRuD1JsCMoA8hbViQdz0UkB5vEMw5hY8hipcflyjJFxUG7ksGKQxEKwqkpoqU68bxLspgTTo0ZjmoI4xMvGsalx5V0FpEoZG4wUiKaGZVqyiiuoVKJcWA8Z+zaNulQYqB0J2wQx4C0Iu690yGihKwp7IU3tggVb6DdheRAuMfjKwXOUCFT+3lFKBjNuxhhn0GhrbDXhoMalqIu/WbAxkqF1n0n0J4kbU7gbb6BoLSFY6kwBkxYoioF4v8svPr/lYectL/uuu06q70m67Rxhx7DQwX1RWuhrTolAqgHKTJ8U+gihWE2GtDyYnqHQtZONxjnLqudKmbo4jTHnGnv0PASLo7nWM+aXNtp0xgBAtxGytvnL3MraXK936IoFWq6vx+6VmqL6V2KesTa4+1nzrN6qI0ShreEQypOxoVoho1WAz/MiUuPr4yPsj6oocaWp9C5P8eN8v0xAUx8+7CfpyBaBnXAkBeK3lNtgh3rXaeeM9UeMlMZMhcOGJcu/SygnwWcbmywMtfhVtri9e48w9xj+7aS3UMe0i1xfYswjpT1d1fDlMa8PXfdkyHruUM9HHCm1iVUObP+gJpKGJQBgyKgm4W8eqdhEPsstPvc075BqHIupXNcz6aZiwZs399DAm0vx1Elc9Uh08EQV2jCU28u3IHMeWL7KPLRJpUNzQvmKL8ovxltBCvvXkUjuKuyx+Fohy9vH+dmt4k3gMFdKW85ds2KJrHK+hOVLQ54O6zOTPP1pUMUWnJn4xZfHR9mu6hRcVIIwLQF/arPxq0W9Vc8hIb+Uc3efRqvMmSlOcB3CvyTFkh1AHi2ewBtBMdP3aI4KSeLQsZysEd5f5/rRyJai7vcP3udVDucr88xSD0ebG/zUPMKT/pHeIEpVGrHOOZoTBhmZNrhyf5RtJEURpJrmzNjQsHe+TZTLwtGCwL/oV0W63124gqDsc90lHA5nmErrzHtDjkU7PIHG2e4/pUV1FiQnUooH+pw0E9YjrpIDJ0sYjetcHbpFo1DYzLt8NL2Av1BxHy7z+2NdaQwPLpzkk4SUQtSyu/aJisUo0FEmSpWlnZ5/+zrJNrlCXWUzjhiudblTH2NfhHwFIfoJz43Ri3+7e7b6OQV9Hv2uPmQx2yrw8nmFrtphXP5LJkWpC3DQEuSKcOxe1dZinp8uX2cvBYiSonKHLQLJ2dXqUnBXd4OR+b/iM2ZKj9x/3/FTbdOenrMimOdQR/fvpevXzpEszXi/d/3NUokn3z5LvxrkeVDRNaZ9cidr/Gt7ecZ6JBOUeVaMsUnXr6b9acXqF2F48/2Secitn885r85+SZKYDVt86lLd9DthBw8ssVfPmgt2KPjNr362eEhXtxdpMxcvnb1MF+9cPRNDgjshwa+9fgVfnz+UaoqQbZScsflUHWXOeXx4dorzL+7R1+HvC28DITc5u7wY8e/SqeoMO0OqMkxX+kf5/PnT2EKycOnLvItUy/+7+29d5hkZ33n+zn5VK7q6pynJ2pGkySNBkkoC0kkiWSDjA2YZLDAOO1l7bteHPbaXLNr9trri+1djO0LBpNzsHJAeYImavJM93Tuqq548nnf+8dpNdbCAl4LSYj6PE8/T/ep01XvW+97znnD7/f98pm5Szn04Lokhqw3hnxIvtzimsET+ELnm8e3wHQKhlyuXXuCouEwYNbJah5HnEH+afoSdEXwsjVP0buxyV3zGzl7qg8lHXHb1ie4KneM//vMzcw8OojqK9x58ALuTa9nuGeZS990lGZk88TCCP+4eAm6HlPIO5T6XW7cfZRhs8p6c44JPeASa44r08epiRQfOXczx44PJeJcK9uAR+b7+W/K9Uw2SoiaieaoKwrBEAyEbLnhGAXD5dGFMRaXc5Qnmtz4yqfoNRpssaYZXFnxyKgKKspqyv+rcgfJXp7U9av2JeSHitS3B7x/53106w0EKrFU+cS5y1nY20ecltxw0WFuLh3kb/qu4mT3MCIf8a4Nj3N19iiPrxmlsSaP262w8eUnuL77KZbCHJUwQ8lwuDh9hqLmMLetwHxYZD7M80R1lP2LQ9hzGroTEqU0Xr35ILuyZ/iQ+2qMU2naI4JrX3qQPqvBPx7YhX0sMc3zu5IBe5gTKKmI3FCNG29+ihiVLxzZiXrORomTlakVP0tSisk7Sg9zycvOcMQd4r8/diWpMybtYYF3qYOINdRJG6OhQKxyOsyTUz1e27WXG4sWj/Ss5Yldo9Rdm9psHtXRUHp9xnqWcUKDxbkC+Cpj44u8afhxpOPyvh/zGf+CHXw8LZ/bl2rgxTrHugtEKY1Ur8PW3lm8WGfZTxMLFU0V6Ktpbiq6Iui3G4zbFRxh0m83cGODs6kyS06anBVgaok1eS1M48ZJOlWy7wqWHdJrNnGFyZyWx1AF/ZkGBSPJ6XZjY3VvuBHZjKaWuSg/iZDqqrOnmzMIYw1bj2jHFpN+Gc81scJk2StXbjNarNEOTdzQWM3WgMTgTVUkaSNkIF0nEhpP5EoIMxEwUtTEJbHbbrEpM8dsUGDZSxNJFUuN6NPr+MIgpYeJ62XWw1vxXujNtBIvCS+FGxpkzIAeu4WlRVxQmCOlhaTVgC69veJ/I1acLcPkdyS9uRZ1I6I/01i1Rm/GNr7QMdWI4WKNUGg4oUEs1FUr7KLhsC09SVF1qMZZKnE2sYxflmRmfOylFKeWy3SlXXaUzlPSHdKaT1oNMNQYzQfNlViZgBvKRzGVKHGLJUnzTKs+GTVALUl8qZNWg5WIfJtoJY1OUwW2nizz2tWVVanNgqHBKhkjoGQ7pLSQ9ekFuo0mR5xB9leGURTJSHaZouFSNhLFQ1sNGSnVWDRDxotV+le0BrJlH1eYDFo1cpqLpSVBeYpMVrUGuxpYekQQayzLNEGsE0l1Vf/A0GI0b0WBsWgwmG+wo3ieWbuQpJEqgnZsEkqVku6Q01zqvk3+FBiuINoR8abhx3GElQT2Cp1AJEFmfXaT64tHVvvaGau82o6OMKl6aRYbWfqLDS7pPYMbmzyqjlFvpshbHlnNw1BisoaPGxkUDI9uo4mmCDJGgB/pOKHJiVZvYlFeqkIJ8uaK86oeouuCSJeIlCDMqsT5iC2FWdan5tmfH6SesVCEQiQTqfGy1UYFMopKTlewlQZ9hSaT5Sz5nIutJDoxM61CEo9VcHlj16NklIiDw4Oc9voT6elIAU2yIzfJLRmH5XiJc5FGWh3ji84lpGcVclMBytFTpIIxiumQV2UPE5JYoJ+1ShzsHmRKK7KxOM8Wc4a0GmEg0RTo0Rv0mE2ONvt5fHotxnISzCqfXomWiejV7FCeWpzGFwaqKoj1lawsYrpUnZvS08RINBTqwkUAY+Yig8Yym805xvTktn2PsR6ATZl5bkrPcbQwzZPptckE2BIYZsxIfpk3lx4hQGVv1wjTDZPBrgav7tpHv16nX/NJKwqt2OaO+kZ0TXBlz0kuTZ/iVK6Hc6kydjrgJdmTXJ9y+GSmzgyDqKGCVtOJXQ2zL+bl+QMcD/q5b3ItbjWFWfDJF1oMpBvckD3MRkNgKBo6iYnkgAaubNObanJMSZRqFQFIBc8xmWyUEr0Mb0V5dGVVxMgE/HLvAwxpLZrhzTzi2Izll3lz8TH6NHU12BsSQ0dNAURAqMR0qXB9+jj9eo1v9m7BbabJldtstqfp1xqk1QgNyV25C5jX+xCWYFv2PFemZtnTNcmpvm7yaZ9hs0KP5pK1fdy0QliQ3NL7JG/OzeLIgKaI0YC0qmGgIcx5YuZ42Ctyz9x6lhtpUi5oQSJMttZeZIs5g2lGIJOsmx25ScbNRT5r7kSNWAmIXRFOMxM/o4Fck18qPYJA4duZC/CkvaqEqkTQii1a0qdL07g2tUiP1uDj1uWJ0mlKsK6nihsZzMz1oUiFKFZpS5M0PiN6MmkpF1psTU9x3Bvg894OfJkikwroTTdZ9tMsKgUUqVC0XHalzhDF37Ow+FG8YAcflW0q1oLGg1/aibBA6Y6JczHRyRz79mzGHYi5fvch1qSWeGR5DacrZVzHRJmzQIHCbpdf6HqEjy9exf13bkNzFVK7l3jt2AG+PbOZJ+/YlCzNXzXJO0YewNoYcndmA7FUWFNa5rTbzVS7xGIzg5QKbmhg6hHpFRfEip/h4QPrMZe+l9ccFgW3XL6HVxf3sc4uczLTx1mnzLcObUGrGOTOqpSOBzRGDMpXNbmh5ygfP3450f5i0mFWVByd0cSDRkiFWZKl+HjE4/x1FpoL+UdS+DLFAztK1DYkOdoXFmcIpcaXn9rOV869hNiWyFLSEbQ5E7Ou0DRgOd1NnBZsuPA8rxl6ku/Mb+bRxzaixGCtaTJSqjHbyNNYzIIq6ept0JNpr0b2V9w0C/v7SC0o7NuZ5YaLjxKj8t3q2lWTJSFUmm0b83AaaxnO5ns4nh9HGPCpQuIxQ6iihEkbj5xsY8zWKB/qZzns5lxBMrW2SD7jYWgxphZzdrKH4RMxZj1ieXuK6aDEwfog+46NowQq3eNVdvacZ9/iMLUnu1EEbLzyDB8YvoOPz13FE/dtwmwoKwG+UG5I7GpMlEqWNC09omy3uTA3g0DhUHOQqp/GW3GKbPsm5472YzRUojGPK9adAiAQiUqrribR9qoi6LfqCKnyj6cuwT9QRIkVUKE1omBWFSr3DhDmJPqGJt25NhuLC+zITbIU5thTG6XiprGXFNJn6uhOlkltDadyaxC6TGY+/QFv2L6XtfYCadXHVkPcwKA8HWDWfCpP5fmYdRVpK6CccvBjnZNn+jAWDSbVIe7RtyJsyYYLznND/1MMGDXWW3NU4yyPm2MskqVkOVycOctpv5fK6R3kT2qcTa3hzzLjKLGC2Uj2/+9dN8BTF/RSsl12dJ2nS2/zyZO7mP/uYHIjtZKtATVIHiJBUTC8bY7RgSlmnQKVdpo12RbrU/OkVZ/qTIHBRyVeSaG2SSKKyZbR+Ujn683tfHzPFaj1JGhZD6HRTPGAsw5bDZk600P5qELVKLB/4xgbzDlu7DvKucICd57cSO7eNLGlceDCYShO8bHlnfz3716N1tKw2wpBDsKsRiqTRnF96ncNct3Z38TqcpnoqaCvTArGuhLxvXudjSxHGe6a3UjNSbG5d44bykc5o5VJT+pkz0uqWyB/QQVNTUz6pFQ4M93Nrx/5JYymSv5sIpn+jcVd/PO6TYx2LfOekfvo0Rr8/plbOfXUYOJ9YyYKo8VSm55Mm5lGnngmjRYo/O3CtfwP/VrQJNIWhIZC+oyBXdE5tGYdv3bRmyjaLkXbJb/xPOPZKtU4i0BFo0pODbl7aSPKYwWEhI/PXsXfF15C5OkobQ1PgZN+P8eNCnumR+h7IsbPa1Rv9tg2NMuRmX7e9fivoDsKmVlJ1oeFyxS2TRynFqT4pT1vJ/ANrl13nPf03oO2ogsRo7I1N010ocbhxX7CJ0oYLQgcm8qcheZBppJMyhobI0rDdTaVF7CVkBiFrbnziCGFeTfHa594N2GgE7YNFF9bVTxGkyjFRLtlY88Cbxl4mFiqXLnmFGd7uphv5Hjv3W9ZVdFVhEJ6SmPgWES7T+PR7Wu4PH2CPdVR9JMpfJnm98+9AWkJUud1cssSYSl8fXEbgdQ50BrhaL2PMNbwIp1YKCxXcihVY3VwpcaQmRWYi20KZ3Q++vgN/F15N7oeE17ZQPEM/sudr0BzVQrHoXDWpzlsrsb6oUuIVI6cG+BVU7cjPJ3ikwYDZ0KEriB0hchW+Lp/GV/ovXT1u1BDsCsqugOpaZ2T3jCar9B9UJKZ8zkzmCajBGSUiLk4S1tYCBLNqD6jweb+OZYKWfozDdZkKtT9FJljJplZyQExzkf0m1FcB/jkj/WMf8EOPlKba+j32/R/7jjRxhFOvtFCy4XkH9fo+845lq4dZcN1c7wu9yQHmkO0ayn0BYPywaRjV7Zn2GUp/H67yMTn6qiVBicu7OLdpT18Y3oLY1+powYR53fmuC41w8tSs/xR3yNURcBfVS9j7/II860sbtNGCgXfNVBUyOcccoZH1UnR84hGed8yiheiOB7OlgHqu1JcY4csm2dYtM/yBfUiDj65md59LsZCC6bngQ2M5yrcmj3EnzevY/y7PpoXo0bJPvf01Rn8fo0oVvHDxF10/eACA+sa3HtkIxOfa6FMziLVLZwo9jDatcwt3fsJpMZdB3cx/v+dw1/fx8xLbWJLUnpKkpvyELpKlNZwyxrZnT7vL53g3soG+h+SGI5gOs4zuVYhOJNjYE/y0KhsLVPty6PbEdmMR72WZvBxQf7AIqcL/RQvdfCkwZlqF635LBgC1YphwWL4Xgf98BmUrhJxOYcwNYKiQWzqqKFEjQRG00c/OklUb5CPYjLnS7j9KebDLEuFDOiJYVP6nE7ucKLiai6tYc7Pc/D8ECPfULCWA85f28MDF1rER3Os+8clEJITG3vYPtHgfKvI+NcdjHOLSecSApnLEJWzKF1JMKyhxvTaTS5Jn6Ym0nxjegtzsyV0OyKd9mk3bfofUigcWWb2mi4OFQYShcoVRcunV95sJaJHT1ZAnKeKrP/oU9DfzZmf78EdCint1+l7uI47lGGykKGqSvr6Grwue5SDQYmHK2uot1PkliTi+GmM0zrDR/IopkncX8Lvsli42GL00ipXpk9SESlqcZow0rCn6siZeUrrt1BVizQLgsZQizhWyT5lUjoZYdYjzLkmQV+O6r9L8/r8XoqqoFtLMRvPkTeTLbiS6bLLnsKTBrlTGgN3V1HbLrLRhDhGej5SSnIv3855rYdKv8O7h+7julSVv2m/lHXfaqKEgrBkIwwFa8FFW6zRvGSIwcvqvLPvfhajPJU4i60EjBpVaiKNPWtQuPMo6QvHWd5mki64GGrMZFTiS+e2sf5vIoyz09ReOkZtnUbQMHi0MQFA5oxO95MNgkKeI84gRa3NG/L7GO1KcU1lkNxegdQUTt7SA8CXp7ax8X+0Ub2I2avLtEckQVZFSafBcRn56gLSNljeWuD4jgxxIWbrhikuyM8lWwz1cU7Wumk+2Et6TrL3epO39z9IShsjNyUoHWpQ3VrgzWuewFIT2fLlMMPnD1/BxBcaqC0fqnUQMYWTI7RGs5zbmOe7t65nfWqe6XtHuOAfphCFDK21BaKUjtttM5Uvo8SQXvFt6d3roj81SfvydUy9IULVJYX7LUr3n6WwdZiZYIDz5ZjLLzrGa7v30hYm1ThLM05hKBGhbHN8tpex7zpoXkR7NIOfT+GXFNweSajrnPXKnLB6CCczZO87RmrjCOVfXuJ3R7/Obfvez8aPHEO2HTAMFNumtmEdL8md5JuVbaS/k6NvMuTO125h19VnsJXvqWBelDrLK7KH+HPjOh5pl8jOCMRCYnqoRhLDiYksFWd3wLvXPUiP3lxZ6VLYYU8ybi7xn0/dSOqbeboXYlJzLmqtjRILCCMwdJz13XhdOQ5cnOPQjVOss+Z5e+/95PoDbtvzDsa/ILGqHqoTJIrWzTZxZZnMlnUcfVUvUz1dnF3qongCrGaEPe+vmoEGBYPY1Dg4M0grtDg+2Y912kINwWwmAdvrDjto+4+i9veydMUAQU4hO+OjzFfJKQrl+4t45W7Mq5b43Qu+zUeOv4zS/1CwTswhGk1E2yF37Q4qLxP0lFos1zNEro513qLv8QhrOcA4O000O49WyENvGVSF8kNB8h1EETIMUdJpWjsGafdpmE3ITimYbUFhzxzx1DSpSy8lrfqklZjFKM9MWKKgOfToDfqMGteWE12dtBqQVn2eVIYoH4nI7ptGaKM8mppABM+BvPpPmqzls9yr4G8fpzVkIrMhlh3g9qVobx+kPaTgC4PpOIuKxEiFhCWFxkQSRDORarIsXLKGz9zaHGZvmkyqRVNILC3G60+jBQJLb9IUEkORxIQ0hcpZp8zkcgmATCH5MnVVoCiSUjp5z5wVUOlVaE/kV42IGqM6Od2jJX2OhSn2ums40hwgKEB93MbsMTFHCtTWGlhqRFWYZPMuyxtLGC1JZj5CDQVRVjKca6KsqPIJqeCEJk8t96K0NKSpo+ZyxKlE7tnSI2pxmhgVvyxo7Rii3a/hlwXCFLQHdKSa7BtGKYUgD2k9YFl42FqI06uhBSpRV8RQvsVkj0VzNPFHCMsh6YKLpolkhm/GtAZt1LiboJyIYalSkLEC2it+EqYV0c7pNEdtcnI8aVBFIUppON16YrSlgFQUNF8n07UezRdEsUwC44zEZVTXISwJtHyA16uyfFE3htOFMGDf4jDxsoXqC4glQpNkrYDloqC1sQQSUladmVgjbQRUN6RJl4aS2Y2E2FYIsomniZL1MbVEG6Um0ixGeWqtNGpdJ9YFpbSLBJrDNkpcwCtDfiVmxtIjVCSN0Obx2hgFw2NXPtnrjUoRwfY1+CUDvyfGLPr4RYOwZOMXNMgGdGWcxPJeqDjSwtYibDMkyClo/X2gqchMCmFoq07IUoEYBU9qPOmOcczpx3NM3HEbs5TG6VMJi8mefznrEMQa9WyOIKMSmyZBvoRX1EgbVaoiycTwpMuisDDViLTtsxyk+Kf6xRxoDqFGINIGccFGjJdRQoGx2EL1AyIrMU0MA51anKYpFshmPWobi6hRInIn1eQ7T9VaCF0howfkFR/0BrYa0BYWe91x5sM8YU7QvmI97T4NqcWEgc5ku8RjxloczyJXMlHDbvy8Spxa0XZpF4EkENztTxPmoaQ7ZFSfQKq0hE9fusnk9j6kClsyZ4mlYDhXY2rbOnRf4peSLR63T6X2kqEkAM9SEJpCc1xB9rtk0gFpPSASKhnNJ6d71IIUdTNZDle1mLTqM2jVuX+dSmwUiPIxJ91e9BX3Lzc2CYsx1QvzaIHEbHWhCEmQ1QhTiTRAWgvIqD5eX0xr6wBBTqU1rBLbEOQlcSZGa2vYS8l32xqxsbIT1NfopLJtFAWCnI3oKeKVdMKCQGYiqn6aR1prKekOgyvWCce8QRxhEno6QUlDEQaNEY2gBEJLAhsh0d6xlZA4I5AjffglC8fJ8JCzHqOhIF0PxTSJLlyD32URDIT06k3G0lX2jUFkm6S6GxS1Ns04xUFnmEDoXJU/xnqjzqBVx+2RIJLMGM1PAixFXiNKQS7rMmgsI1DZ7yVBxb16kx6tQX+mwdG+fqSugUxh6SrC0ogyGrGl0hrQCPIgej269WaiDqxE2EpMKePSGswTZjWsZRPNi1ALadRyEWcgg+N5PNxalwRgjyk4no5d0tD91IrTbrIaGTgG880cMlAR5orbbm4lBlRJk89txi/qNNYoSUKBtMhlxgkzK75KBgSRzkm/Dz80CIo6en8JtZhDCyNaXToijGh7ZmIiGiXvU1uno7same5hrGoffk7H60pMU9PzIWY9ILZ1wqyO1BW8opY43uYUwhxonoZdKWMaOlFG0hRJ2MABZ4RJt4vN2VlGjAqh1Dnl9bDkZzHUGFONqPs2zoROZA/TmIBs2UHxXM7/mM/4F+zgYyRXI3/1OWq7U+iqYIMRoKmC+Po60XUqE0bAjF/ki+El6GrMhUMzlNa4bLlihqzmUdTaPOT1sCG7wJHb+okilRsGz3Iw6Kc33WTfNQMoAjblGjwZJBokKoJzQQ8PnZjAPmHjrfd4645HVqV7Q6kRCp1QanSZDo9cqzN/SRrTjMhYAT22y+b0DE8GKf7bzPU8figJOMtsr6O9xMPQI2w9ZJ3hUzIcHnA28Po1T9J6h8UTlVEW7xrCXpLItW3ePXI/odSYCrtYDjN89uhFWIfS5JvgDKVR+tO0R2Ou7Z1GV2MOtEcAuOSy4zi7THrUiM16gJAq59cVqbs2lhFRtHxSesiadIVvt8fotVocujYJpn3d+FGuzB/n7FA3hy8YAqDHbFLQXc55ZU41usnbHuGrGgip8LKu2UTgRtHZ3j3NbKaFrSWpt8vlNE8VeplrWaTOmBSPC/yCQnVXYirWnW0zlKljqRFdZhshFb647yJKTyRBoHZFYjYU6sMRt248QLxRxb3CoBnaTB1eh3tvD101ieZHCEsjLAl29MywXFjm3HgJKRU2d8/zpfpFbCrMM/n2JMCzFVh4sUYsVMIVY7sLcw3KVptA6Oxpr2HKKRGezZI/o1BL6VzddwJLiXiia5QlN8uAmmyzGGpMXyqRiX5weg3u8SJRPka/KOaK/Amu2f4Ue/uGsYwm13fPkNddvhxuZyFI4fVIXrLuDJcWz2ArIY9641SjLD12C1URHJgoUb1qJMlIWHmA624ykxJWku1yKuzhb45fgX+0gKbBuVdIpKFSHKqyrbjMYLrOtswUTWHz18tXUVNtgpIgPdQml/LYXJrjIWc9QiaBdqHUKBouF/VO89D5cQ7ftw7dUUj5ksbaDMubVKzty3i+gf5kL6nFRJ9GcyVBw2Bve5yi5vDq8UPsffsIoUhksINYY+7hXob8LtyyyqBdo08LGVQiDBzucAf4myNX4C/b9G9eZOc15zjd6qZyapioanO4NcwRcwDp6sxepqFGeYJiIlsPcPJsX7Ks3C2YeamGub7OS7PHGNEbzMUZToRpXtO7j9kPnMVQYq7OPEVLxvz7kW9y329tYtov8c9nNhEvpVHW1dn5c6dJaQGGkmhgDJtVJswFHGnx3eZ65v0cu/LnuCl7hEdSY3yofwQl1CkX2vRrbd5Q2MPwG6vMhwXuWdzAt49sRlElVipE12O2bznH5stmgcSDwxUmd57ciHIqTTQYsMZaZJM5y1uvfIA9W0cpW20uyk2S09zV+9Tn5y/h+L0TSBUWb/HYOTrFkBqT0kKWgxT7N+UQeonmBFyw4yy2FnLg/BBHjw4zvn6e/7b+M7SlzgfOvIm5c2W0lsrCTpUoI7ngJad5WfdRPnf+ImYO9oECed2jX2swsmaRqZcPIFVwT3fx/8zeQO8JkGEEG8Y5fbvKtesO897iEXZby6w3HmTN6xapRlkuTE2xxVzgjvZGvnp4G9QN4t0Kt2TmuTZ3hD1XjDJVK9I4ViJ3GqKMkhgdZgW3DJ3iSnuJ7zhD/MXJ6/AjjV/Z8CC/mD/OW/of4m9uNJht5pg+3kVqLkN7JGbDhefpttv0WQ2yus9aa55d9iS2ksiWxSjcPHiEz95qUW3ZqOdtzEaSNRNbyaAiWkrx2fol9A3WuO71BwCohBnakcV3n1pHYb+J0MGYNWlWDRRLEvRFWAWPN23cywZ7jsUox1KYQ1ME6ZV0x9NuN7NugYV2lsb5EkqgQsPmH49fgtuwiXfoqBcUnjZ+RpgStWbgNpNHtgJo4y1efeN+CrrDweYQC26OPrvNpuwcy2GaLz55Efa5HN5owDVbnqIZWjz58HqyZxUaGyNu3b2XZmhzz9gW0uf7iNe4HPRGWI4yfP7YDqKlFLULU7w2v48TocE/n9mEN5tZMa0DsiEvfdMhNmQWGDBq9Bs1/GbEz/2Yz/gX7OCjoHusL03SpbdpxjaTbhe+0BlK1RgwayxHGY61+vAiA1sPyRo+a9JL3Jo7QEFVeMLv4oTfj6pItg3MJDr3usN0WErSDruS4J60HlCL0wRSxxcGk34XNA3MBriRmnga6MmIO0ZhMcpzzu/GUiPWFZdoZSz6Ug0mUok/Slr1mQlLTDZKpKaT0WTPRIvtXdMMWcuMGFXawuKU18tZr5vN6Rm2WVNYasTncoPobYWUHdKv1xLJd2kkDwdfw1pemRFYKkIDacVkdB83Nln00gBsLcyw1ponlPrqKLbbalEPU+R0jyGrlsjJC53JoBuBwmjXMqYWc1H2HFvNOfq1Ov16nRh11S/CEwYzeiHJLMku0G20KGgOKgJDiegxk2wWS03smLuMNqYW0QhsjreGiSbVJGW4y2F9eZGthRkuTp+hrLXYbgaoqOwdG2Hh1BC6C1ZNooYSZUWKvVtvsNZcoC0sHj4+QXpOonsCqSe23dIS9FkNuq0Wo5nl1QDexSBH0XC4pfdJVARLUZ56nKIVWVTDDLFUsNQ4sZRG0o4sWpGF5if21wBD5jJlrUW67FOP0swHec47RXQlJqd7ZDUf3zewFxUCX2PRy9LMplibXqQw4mKpEaNWBUOJ6Squo95tExZjBuw6PXqTZmxTjbI04yR3XlcFcVbg9OpJOqqV2MKbjWQpV+hJMNlilKdVSVM8r+D1gramRT7jMV6oMpiqM2Qts96aoxZnSGc8nJSNUgq4bOgsxRW/i+Uw8atpRClURZDXPfK6RxjoZKdB95LttzCj4PdGvGH8ENUww7eq25CajlRWTPt8lTkvz2m7lwGzxi19VTxpMO2XqIVp7il0E9kawlCIZZJ6XFQU8qqNkCp+JYVZ0Ri4oMF/6L2Pr6TXcvDUcCJOFWhITUVRIehZSXswJIomkIGGWteT1ayMQOQFo4UmZdUhp0jOCoPFKM9ac4E35mbR0XClWFHKjbm4dIKzkcPRWj8nWhYbyov8Ru9dFFQFbUUH1lJ00qrJbLTMPmeMQOjkNJcx3WTOXAIrRhp64h8iVQpqzOuypwmRHGkNcKo5iFTADTVUM2ZwqM7buh7GWIl9aEqdE40ezsyk0K0IWwlJKxE35A5xSeY0PVqTzUZMSjFxZUAoBU/lBzmmrgFFYbyvwtv7H6QSZznj92CoReJShNdjEnUFjGeSbKA9no5Z0agNpb7nx9FMYy5pCB2CkkAUQ17WfZTb8kfYXxhhRvaDSFQ9A1SKtstsWSb28G0VWiqGI1Bsi6hgceHwDO/qvZce1cdQdLo1hTfkjq/e1zWSwGoaBuayyqKXxZcReUWyrTBN3vB46HwBFDVZqc0IyEb0Gk3yqk2MynI9QxyoOMKkoKYYN6pcXJpk0u7i/noGL7ZJD7d41/ADDOnLK4HzkrQSUVzxe3Jkklo6ai5xcf95Zp08x4IBpKETpwRKOk6yk5o6akvDHo14ffEJbCWmIlI0hc2xai9OJokv0x0F6SsERYHMCHJpn+tzh7nEDBAkAcS+FNQEeFKjmk4Ueh9rTfCF5o5E0C9WcZsWhApBIdH0EKZM0sIDBd1RQSgIK0mzLmZd3lJ6hGHN4HjmCNNxgR6tyXo9ZCZWeGhgDfNON0NDVX6l917m4gJ7imsQhoFWCHl1Mcn4eqh/DV6QIZVO7m+VIEvYtDDrKg3fxl7ZUnYbNvbi92IcPVVyQ+kIt2anEj8roBn8YOfjH8QLdvBxQWaa3lRETnVxhEVa81kOM3z51DaikzmUKBGFQYLUkyXCR4wt/G36alBZiZJWVrNmpCZ53JpIYhKWDfKTKsKExYksXVqLzy9dwgP7N6H6KrIQ4lzuYkiF/2vvK0ilfd627lFuyB7hE7Mv5eB961fTmaQmObaxxQWbZwmlxqemX8JcM0fzfJ5sO0l7mto/yLl0P9IUqHaECDS0mo4aKHwTQJKIFJ2MMdqCil3iFxd+JVFt0SSKAroV417XIjifYfQ7MWbVw+vK8XXnYjRXwa4mwZRPpdYT25Jw1OedO79Lt9HkMzMXszxZIj3Q4pfWP4aG5HNT21icLaD4GlpTJc4ILszPcHXqHJ+sXM6379uJGiqEXRFqOqK/p87VfScp6C7DZpWi1iajBBRVlwAVLZ0s2T1NIHX6rTqt2OaYOYTVEOi+Qn1PgYOFPPWLU7xy7X6KakCMCgheN7ifr98Qc3q+G/W7aexlQWZfir+auok4LdH63GQGOWUiNWiOaMxcK1EyEReMzmGpEY8vj3Hk0CjECpdcfIK39n+XL1cu5lOPXIbWVhNRHJlEg2uBQpSWrLl0ilsGniS3YkhXzWX5/EsVJi8ssqt7gbzqMhOW+OsjV+LPpsmONnjV2GFKRpv11jxpxefzYieFMzGxqXDQXMe+rnEUX0VzVKJyyHsuvY+XZo7xqpFDPJSaYMlJ88XHLuHL/qVMbJ3m9tF7uLt+AXc/vBWzqsJYQO7mucSsK9SRK8qHrVhFFQpfObYtkf0+bWI1BG6fyrbBGQZTde6eWs/jc2vpHq4xsWGRprBxjxcZfChmeX2KRzJjdGfbbCgsMGIvc94rcrTSj6lHvGb4SS5KnWVxbZY7o03IhknuuEaqkkTmr7PnUW2Bv1NnemOBYycGKR4wsJdUDi5s4klrE15/RGmwTtu1kKcymHWFvjMCe65NVyT54uev5NPdV7Bx+yT/fuybPNjYQHpKx16SzDs5bEVjMiiTP2iSnhPMXSXYve0kupKsNrUjk8cPrCV70kTzwWxKhA6VixS6R+qr7p6OTLbRqnGGYpxlMW6QWZGKT6sG1djnSKxRi0tc2XOStfklNqTnqAiLQIb0aJKcauLJiPNRi6XYIFwxvQukjicjzgYDpE9YdB+IqLb6edX59yezwkBNApm7XTZsPs90vUD8RBGrDnef3sm3erclGTihghIrpOZUuuYljXaah9avQ1UERdWhrLYJpcajvo2GZFB36VEVWrGF2VCwK5KZu0Z4f+HtKGNtPrDtHjbYs6jbJKcnykzXC3zj8e0oUsHo8shsTwQDX/Xg7SiqJJ91SV8yT9OzaDsWGTuJTTkWpnhiboSuw4kE/GcbL+VTxctITet0nZNEKahvkMSFiIVdBs3RrQgNwnvX8ovmrxGWYoyiR7nQ5o2je1hvzTGhVxnTdXbY57hkx0lmWgVOVHq49Pyvks14XDpwjozuY1RVuo76RCkNd1YnyFvcMbSJNxT24gmDvnKdINLJqR7LscO3mtv5h0cvR/FVciMN1r7kPJty8/TrNSoiw384/BrqZ4vYQy1et+5JCrqzep8SUmVtepFYKpxaGiV7VkFqKlLXCbPA9gYT3RVe0nUGW4k5Efbwfx17BUuLOTRTYFzUxKtbFPeZpCoimRRqJk5vN+/xfpGhUp0z82XknI3IxoyvWaAn1UJXBLoaU/Uz6HqMzAQUcy5dKYeTcz1kDxvorqSyUzA0vsT0TBfZI8kWZ+XyiJ3rz1EyXT5bv5is5rHDnmSTscTnGzt5y9GX4DctUmdNChVYnunnF47dngTJdjukblpmfa7Gw+31nHPLBFMZspMqzW6bPqNOt9FkblOOhdEc1/YfRwAzYQlryqR0XCBXVmPS8xp/2Po5PlSIuOLCE/zB0NcJ5Itg8LHFmqbXjEmrEY7QKWoO01qJ+FiOdf+wCFKCoSM1BWkkMyPVj1DrbYgF6BpS14jLWZrjaWJTXdXgNxxBaskjKOjUrrTp1ZocXupn7OsSiJl7a8CvX3g3f33iSvhOF3EqzbHBPt5SOMiB80Os+/v5JChpqJsoZ3HOyJK+MGAhzHP85ACpSYOsm9wUlRiy0xJFKAhNQ+g6ui9JzwXo7RBtsY6YWwDDQC3kwdCxZ7NEj1vEtobXpRNmFNybG3x0+2f5w9yrMT6XQzs+SZ8YITedxqxFWCfmkO02rNRx6ZZN+Dt08qpL9XyRnsdUqltzsB5URbB0skzPXgUtlOiuwCupHN/RS6Zb5d6pdWz4eAWl6dDeNojTazNzmc6OibsY1auk1XDFtVeSVhJhmh51Bk8mNuue1IlXZo2eNPgH8zLMhkQNBPaSSmxrnB0q07/BJ6eoCCkRSH65cIx3F0/yX3s388l9L0Obk/Q/6qAvNIjLWWobMkSpJCtIaOAMSH77mm+xK3WaE0E/M2GRc8slBu5X0ALB4uYsN6cc/ruXZeyrktT52mr/UqIkIC3sLzB3QY4da87RszKbVWnyqszn8aSkKnRmogIHnBHUvTlG94dM3lxgx+ZzrDUWmdAj0mqSKp07XkeJJVY9T5DVMNoCo+WzvNGiebHNRsNlsPgYbyjs4c8XruPJz3RRPN7m2Lv7GZ+oUA0yDN4nyZ5e5vhv23zjwk/RFDGHgjJtYdGjN8grPh9bvIYHvrKT/ILEbAqMdiINdEP5KKNGha88sZOehzUq27uorM3iCyORk/76XoyrtnNmJIdbtuhLN1mXXqAWpFmay6PaMT3jTa6xQ8o997IuvcC++ij7Zy8gOyNBlUyYC4xoLW7NTqGhcHX7Ldh3dWFVQ1KnlpDNFs0r1zF/aRdmTWHkOzWUc7OgqaCoWDMwvj9GMU1O3r6GB8sb2V8ZIjslycyHzLdTWIrBebdEzz4P69QCczf0859HvsrTc66p2OK2x95P3x4XzYlQa21ENkVle47t3TOsSy8A0JY6tTjDcpihorlUhU6oRPRpOpZiUBMhe91xAF6eO8BgMaAqNKaiIo4S0qM1sRSDugiYj00W48TNFRLHUF8KzgdlSsdjMvc/ReZ4D+HjeZRQoC82UWLBsfcO8Ks7v8BnrN2cPZWneHAZqSmgKChhjOJ4EMVI2wTLRI1KHLxskJLhsDN9jnGtxkyU4wlnglBqvDRznB6zRTOysWqSzFxE954WaqXB9GvH6LmowXWpGV6RnsdQNG479QpmPpe4fLffmARt/tmB6xn6nEGUUtF/uc4frvsKD7Y3cOfcJgwtxhEmJ4J+GnM5Nu2roTg+5X0ppKWh+DFqEOEN5Vi+UCPf3SY37NOVcjg8NcDa/1eg7T0GG8ZpT+RpjGb5yqu3c1VvhlzOY4MiuNCI+E8jX2UmyvHLd76Dsa/C8oY0ldclVvF2VcHcexLT0MnkssSlHMcu7uap8R48abC2sISQKjnNoyoED1TW0fuAjhpJsu9s8ZfjXyIEQgmngl7i+7vY9NU5Fq/s46u3XEhXOkl/N7SYdblFLs+doBnbWFWF0skAJZKosaAxajN20ywfGv46Kony6mm/l/bD3Qw9FTN9veRXrrqfexY3UrtzlOKBKkrTQTablAf7mHW6mSnkGdkbYD9wADaMc+Z1g5ztjVEzIVYqTIzxVqTyt3fPsCt/ho81r6Jw0sCq+lQutnnF4GH+vr6b4mnQnAj3FSF/NPpVHnDW8ZnzuwAYX7PENYbg4eoE5c9nSC0EKNIFQRLH0nDxRgoo/77B32/4R+52xvnq4g5OL5fJTqkUzkY01xv06E369RpbhxJ/o5zqEaMwGxbJTsqk/+oq0tAglvTdH4Cq8tB7N1Ib+Gd0+RMylnsumYzK1EIwlZhYqrSFST1OE6Uk3kiSfiosFakkwisoK7PZvgwr/tgAK4OO5D2DfKKmqDvairKiQsqI8KSBbUQ4PTpI0HWRmJOZIbVeiE2JGxscCpIIIm+shOblcQZtgoxCVIhXnT+1XIjfoxG5iYqc7oE5mchZe2UNv6gQCIUwZaGFJnavjTlQQuoqYU5PlqXNZCshNhTCbKIuqaqSqbCMocXU16XJptbh9hh4RQUzqwL9aF6UPFRjiV9UCIWGJw2UTITbqxNnY6pRBlsNoRTQXGOjOwpmTRLmEivpE1Fi/d1eV0Jz87QGdIKCAmZyow2lnmxBqD4mMbYSEaNQEyk8adCMUzREClOJGDGSrYZU3qMxmkONVvYvDdB0j6eCEp402OOsoRVZbM9MssM+TzO2CYqSdr9GmE5h9lqJvkks0V2FMJPEQYh0jJBqYvzkDHGoMYjTsrByCmqsYQuV46GHrsbUJwyCXJHUUojeDBBWEoTlduukzCaVFZt5W3HwpMLj3igzYXHVAMsXOmFOJlshdkQtzrCoeuTUKmnpY9sh7TV5FAGtQY0oraC3FYx2olDapbcxFJW6UDkblZP4k2EVqWUxcm0WVj6/3a8CeTTD5UyoMh2X+E5tK83IZmt2mo32DG5sEGUSie0wraBGKkEp6YMaAjUT0R40EPlwxVDLwO1WYPtG3B4DzVOIWjrLXpqldA4/0nnaaW4mKHE8PMtk1E0rthEo+GVBc1TDLLiJSZeqkZECA4ViymNpQCXImAijF8PpwulVk9W3LLTW5EhlzGSyIEHqKlFGJ0ol8QWHmoPUnBRWCvyiju8bfMvJUfEz1NZb2OUhSj01MoqKqigIKbGVOHFstjXCrE44nibMqFAIiKRKPUpxPOxFSJV/XtrMiUoP2/um2WZNYaiCx3ybhrBxRLIFayvBavrnXJzlSXcUSw3p046QU0PilSVlQ4noNZuoisSTBo94PZxxu3HLKplNY7gDKdp9WpK2PpxKghF1yWcXL2XfzDB99RjF9fHHy7QHDbQAUosBSiiIMjpxSsXtUdFijRmviKHE1OI082GBw60BIqGR1TxipvBjndawQpg2cLuLGO08zqBkMcpzOlpiQg/Iqja2FhGbidGm55qccPuQQsXtSvro2nSTHq3NUphlcqaMogsmckuMmhWwYvy+DGqYot1vEqUUsjMRqVMV1FAgdZWs7a+mXi6WM7RGeyg4EzTX5WiMaXhlSSrSOdXuYVsqD1YNX0acDruYDMuggF/QiGxohRa1ME1kgxwfRJo6YcHCL+qk8y3yqkdZazGaWiaUGjNhkbuERTs0aQ0nMt+2UHnAGyKUOm1hJcHY3ZLlS3pprIE1K5b1kIhRlnSHouowYNZwhmKqbRPdkVgNgdel0Gu1KKgxvky2Syw1xBuMqcUa6b46Y+YSQ+l+ZrtV7OE8Ui0g1QHCrEaUSe53YU4jNTaEM5TB743J9LZJWwE5K1FubgeJIlgzspgOSomSdE5DEYlhZj1OoWkCp0dHC3WKmQY5NU6UcjON1S3m5dgha/icGVIJUxZmW6D5Es1VMRSIbY1qM8s32xtYCnP02i28vMHR0RKxqSPTAffUL6BoOHQbTdJqQFlr0b+i+dEaVdC9ElJVkuetWFEEV0HaEY+4a1G9NrDwYz3j/1XGcs8FTxvLveLb70BJpwhiDVuLKFuJsNMj02O401lYiXlAIdGMiBSkKTDyPpomCTwd4WvolWTWh4TKrpiJdXNU22lq1QyKJrl6wwluKh3im9WtPHB4A8QKfSPLjBeqSSf4FzMdgMlakeZsDlRJvq9Fd7ZNSg/JGx6qIklpiYLnopdlwckxs1Bk5HM6maeWmL+2j9qVHqYd0VdokjYC6r5Nw7WT/zVDNFXQcG0810RKBREnn5vLu/TmWkRCXckfTwImI6ESBDpB00z8EVwV1VeIB3xeteUgZaPNk/UhZtt5LC0ma/rYWsja7BLD5jIP1yZ49Mw4SBgfqDCRqzDVLjK1XCSOVRQFFEWSsoLVGcN4pkrRcBJdCyS+0Jn1CzRDi3qQouamKKZc3jT4OJusGf6pupu7JzcQxyrxSn3KhTYjuRpHF/swvlMgsyCYuVLh4l0ncCKTyVqRMNTJpT0Ktsep6R567rSw6jHVC3TaYxFW2eWVaw+T0kI+ffASrOMphCGJMolBV++aCtu7p1cVROfdHKcfGKN0VNIcVfG2O+QyHhd0zzOerpDWAgqay6Tfxecf20X6nI6zNuDGbYcRUuWx2VFaTZtyV4vtKwGk43aFgtbmyws72X96FFRJqdQiZwXUXZu2Y9FTavKnGz7PJVbMJ+rjiZKkKijbbVJaSG4lzmLWK/D43Ai+b9BTaDGaW+boUh/+EytGWlt9Llp7joqXYXopaZ9s1qOQ8rD0iNxKH4yEhkAhq/sMpWr4QueB6bXUKlnUho49n0g4R5vbTPQtUXEyVGsZVFWytn+RsWyVZpio2T5tWKgqknW5RXZlz5BRfXKqi6HEfK22kzunNq6aq0mZDJQ1TSCEQhjoCKESNw30hkZUjLhsy0nG0xXumVvP3GQXSpBIWquRgt8VQyHEzgRs7puj127xmtIerk/5RMQrWyDw6vtvp3yvRWMtbL/6OGPpKrNegUZor6reLrlZFu8ZpOdAyPSVOh9+3acoay3+48lbmZrsZmikwq9N3EVZa5FTPUwEn63t4suntmEZER/YeDfXpU8TS/Ckmrh+rgiOfal+Md+augDXN3GXUyiBSmG4ztXDJzFWMlt8ofO1/dvpetwgVREUHp1GNpucfd8WXv7aRzjndLHn2DhKW0dmI4xMgK6LRN1YkQSRThhqiFglWpEYz5UcyhmHjBEwlK6tigKmtYB6lGLWK5DSQt7W8wBX2Cq/NrOLu76wC92F5kRiTikiFRmoGJmQD+74DrflJrnuwC9g/b9dRGmV8K0VfmXiAf7H2SuoPdpHZEsmdk2xq3yOz37rpaz/y0mi4TIn3mNw+YZTXFY8xcsyT7HXG+ZD+19NuJiid6LCLcMHmfGL3Du1Ds8zeNPmPfxBz5PsDyL+aPLVTDUKVBfzaFWdOCsoDdaxjIj5092kzmtEGUnQH2JkQm7b/ATvKD1KKJMVrWqc5s/O38jhqQHyOZetvTNoiuTQ0gDLjXRiwBmpaKbgorFJduTP02fUWW8lTuwzYYmGSDFqVNhoVHCkxr3OBib9MvfPrWX+VDfkQv7oJV/hDdk5ZiKf01EBTxpUoiyeNNlkzbDZaPOg18dvPvzzaDMWxrom142dYNIpcfDoKHpdS/xwfAV3KOI9L72HyzInECvKcwe9Eb4wvZO6a6NrAk0VLFZy2EdTqBG0N/tsGJ1jtpGneT6PVCVvvvxhfq9nL4uxz5GVCdzTHPWGuG9pPUtOhsqJMvZ8slWjO0l4gtub3B97Ni7x+xu+Rk51mQrL1OI0nzm/i/NPDoAqUQddclmXNcUqu4rniKXKcpTGFzrtyKIdm6hIMnqAqghONnqYrhaI2z6n3/bHz76x3HPJgpNFxBmiWF1xbxSJG2LGRQ4qaJpYsXKXtH0TP9TJpnw2lBax1IhFP0vDt5nUy8RTVrIc3uVyWfcZJjMlntL7ACgYLsFK9kqpt0kYa+iqYNlL05dusCt7Dk8YPLQ0wUIri6ZIioMNbDNkV88k43aFs16Z061ubC1ka26aPqPOfKrAdLqIG+pAEcX1EYZCsdimL9vi6u4TDBjLzEcFlsIchhKT0zxURXDG7WGyXcKPdZq+RRgnHbXqpOhKu1zbf4KC7jLtF1nysziRyVIhQyRUmq5F4BuU804SyCd0BlMNypZDM7KYd3KJ9Hh6iutS58hpLpPNEm6oI6TCVLtIWg94ydA5ACp+Bi/W8SKDmptC12LSeoAvdFRFYCgCNzaYd3M4oUndtWm1bWKhrAS86kykFqkNpAiETiOwiUUyGFhwcrSqaSaOB1iTVdITA5zd0IWtRwzmG9haxPrcAutT83xa7KJpDCZbLgao2ZCUFeILA18YyIaJvSjxuxT84RAjlUjXn22WWZtf4pXFJ6lEWf5TzzDhlIbfJdk2PE2PnViXN6IUjjBxYpMpt4Q9m5iihXmDRS9LWg/pzzVp2z4pI6Tip2lHJpYa4egmZcthbGgJS4tYl1uiaDjMegWmnQIjmRpdqoeKxVKUY6GRpZRxuW7wKdaa8xzxhjnm9KOrMdt6ZxFSoRakmGkXqC9nKE8n2yvOoMHCQC4Jns4nAaMThQoTmSUWgyxnm2UEChvzC0ykFmnGNpWVgNL15UVa+QbHZ/pQp1MQgRdoeJGBkKBpAkWV1H2bc3QRCo0w1rD0iE2FWQbMOpYa0hYWnjDwVANDiSnpDlt7ZxAr6rqqIln0siy5GXRVULRcdDVmslGiWssyWK7zW4Pf4QIDjjQGqCz3AhBlJFITicT2gkU4JPj5vse52Jompyq0pEooBU0hqYk0mi4IcgpBd8g7++9nk7nMP9Qu4ZHqGvxYJxA2lXaa9JwkfaKKvbmPSpxFUwTztRzmvM5SIYOQKhqSUCb6vfN+HncpjWfHLEY5wpWpmbkSdKcCKEnAb62WuK1mu9ukzJCrB07y5tIjWEpMThX4Er5hXUj+LJjLPtLzQdPwemN+o+d+DgVlFpwclVaanlybwUydqp9msloiDPRkwLEimKX6yT2gKTP4vsF4d5WXlw4yolcZ0UPKaoo73BT/ZflGIqGy2JUHWhhKjDBBxMl7xA0TNRNS6GnRlXEo6y18GbHcTLP21DJxzuZsKwmotPUIvytGZmJu6jvCy7OH+FTpMoiSFVbVEHSZbcpaiy4VRowKa3oqzFh5bhg8xjtLe3nI6+POMxuIG2ayqkFMU9jMtXPUm2kUVRIXYhQrJhIqhDpkQ9wBkLmIsaEK3akWW1LnySkqKNCFwKBFw7eRVROR9XhV+UlMJebA4gBiyUKJFPRQIc4Kdmw9z693HcSXETUh8KRKU7MJpLaiiqyQQ/Dq7FG8jIKhxHyhlSab8unRGliKQUhALc5gqwFXps7SpT29CagmsTldLSoSrhk5w2/13sVD3hiHpwYQjkqclgSGJNef6AhtNRw8KXAkLBg1LC0ZbLY9kyjUEIFGkJcoElRD4EUGuhajFAN0TdBtNImlxFQU+vUmntSYiwrU4jRZzePy8mkWcjm+WcvihzZqCFE6UZA1mgpmTWF5ME1OdRnRHTYaLhoK37YuZGkp2RJsZywaUmHBDJlOFek1m7yy8CR9WovFFTkCWw0Y0WtoSP4ofCWnq/2I9veUun8UL9jBR8NNYWga2orM+O7SWbr1JnvMcY7pvRhqIvGsKhLHNnEjA10RNEIbU424ID/HGmuRI12DPNg1QSxUXjd2mGtzR7iHzTzhjiJEYsq1wZwnowaUxhyWozSHawMstLIULJcxc4lAahw0B1nWU/ihThDq6JpgIrXIS1KnMJSYOS+PrYUMm1XWGgsM6sussdIIqXLXRd14pVGcAYnqWThWwLBZZYs5w3fCEgdrgxRNl+u7jiYSzXqTdalsMssSOgJlJc03SfetRWlqUZouo824XWF/c5iDk4MIX2NifIHd5bNYakRa8/GFwZHGADOtPAXb44LiPKYacW9tE9+pbsGLDfKWR95KsixUJF5scLaVzHpjoSKkwnCuxrb+aUKpcbAxyMlGNxsKC1yaO4WmSGr5RC66GmWorlhgn3Z7OOt1M2Qt84qug8yEJb48vZ1lJ8Xargo7i1OczVe4/+3rEK0eMAKc5SyFvMP1/ccYs5aYCUoccQbJGAFnLw+puhrSDFFiFUOPuSR3hh69QXVHmkND/eStgK35ZXRFcL5VZKGVpeqmObLcjx8nOe7NcYh6QkwtxlIj1qcW6DNqyZaCsAB4fL3LYsomysccmhlA1wUZO9FUWWplOOd0oRsxtXKKbrtFj9niFQOHSKsBI0aFjOrztXgni+1BdFXgSw0VJZmlWiFBrPHF2Z3YWsR4tsIFmZln9P+nt0uesMY40BhHc1X6tszzxpEnABJpbkWQUz1ymkuY0rk4ZyUDEy2RXPeESdtKfEfmwwKt2KLh28z1JSrAG0bmubH36Ir4VRpXmJxtddHwbQwtJm0EqIpksl1ixi2slk1IBS82iITKpsI8N3Udphpl+c7iZhbaWTRVoCkSP9aYahQQIhHLU1eOe1KnJV2OzvXRvU/SHlDRr6+wvmuJaKW/rc0uscWcw1DgPWdew74ja1AiBdVNHsJxOSR4aZOtvYuYSkxTaKyxFqEr0aTIah71KM0XXredo5d1oVgeHz10fTJpSXsYFyVL739x5lpyps/bhx/kcnuaU41uyo8n2znntnTTVfieS+ex0OK3jv8cs3Mlil0tLlt7hoye6HrkNI96nOLvqlck8SBCR0iVgZ46U2/NE3sWWmUdSgw7dpzEVBTSqk9vOtnGubg8ye7cKT47vwtlT57csqT2koCXXXSQA5VBKvt6MVoKUdtAmAZTmiA37tKnBczFOqdDyd/OvZSZO0dQBPzjq3ezeeSrnGmXMWuJ2Zi60+HKNafJ6R5lo00lzPB/PPF6ooUUXYcUFGcZLRZk7+7lY8dfjhYopIMk4PSv527iY9pNDO6ViGYLqfbSV65zXeEoH5u8hv/z4DCap2BVki2e7+gX8JbSIwTye9kRJ5vdfL7VTy1Oc0nPFNVCmsfOjqNNmwTdsGvDFKOpKstDaephioLhsik1S07z2GAkS/kxklBKNEXhlYMH2Zepsz67wFpjEQ3Jz4/v46mefk7UepieKoOEz53ZueINVcA9n0vM2oLER6Zw0RKf3/oJulSdukjiC3emz6FtEBQ0lxG9jiN0Pl3bxaeO7EpMEX0tUURVEh0UNRUxMbDE2nVLNCOLD07dynSrgKxY6J5C3OuzYXCBipPhnf/8DjTne30qLkXsXHeOnu4WDz2xka4nVZwBBeOSZfpzTS4szjCRWuSLMzvxHupGd+H/cW7gyyM7Er+VagbVELxxyx7eVHqMv69czhf3XAyxQs/oMuvGz9KKkuveCQ0WF/IoLR3mU/zS1351VbZekUlZuq9cImWEbMvWyBkeJxs9fPvkBaTskOaYzQZ7jkcaazlYGaA73ebdQ/ex1qgkIotakiDx4/KCHXz4rgGWjmaG5Ayfl2aOMaEnS/2xVNAUSUZLBh9ubODGJu3IZNbJo6sGO9PneH12CSd/kpnu+xAoDGqSvGpzKqgkrqGxSloNWGd4rDNmuTI1y0yk8x+ar6HZSuEVDcaNJWIUCkYi9e2HyVJoYGhMmAvsshQqYp7HtTHyus+IUWGd4QEeUCOvetyzbR2L3VmkLVB9HS/SGTcW2WLqfF1qTC6XcLIm5d4Wm8xFYrkEJLOttAKaotAWEk+qHAt7+fziJTRCmzXlRXanT3La7UGZszE9mNha4Q9693E+cnncH2ImLLHoZlicL6D2S7ZlpjCUmL86dRVL54tYXS4XDZ0nowdJLIFMVk/mGzmEUND1GF0VDNk13lvaR1UIPth4DfP1HBcU57kydZacqvB0jLMnJW2hcjrq4r9PX810s8BrR5/klsw8B4IlPhVcQquWIt/vcmt+H7liSNeQSojkfedu4Ym963CtiGtzR7jEDPhEw+Kh6gRZw+e2ix5DVSR3zmxkfraIqcXsss+xwTDZPvI1qkM6oVTxpE4lzvJX/jWcXywRuxrL9SSeh1xMNOpRzLuYaoSlRmy1p7jQbLIYK5yLSthqyMXjk0x2lVhaziHOp/F1kENtzFxEs5WCGRvfkJxTJK2sxdr+Jd6Yf5LcSiaFisqDukOzlcLQBJ5MLjVLDUkZIU3P4tjZAYgU9AtiXlPcm8zClGg1DRJgnTVPIDRagcVbRx/mLflpVBTEyjlLceJ2mlYkPZqFirp6DJ52qlRYNDPURDJone0roGiSG3uP8ouFg6ufNRPp/OfwJuaaObKWJGv7CKkw7+Rwg2SwEUXJ9krQMiFU6drq8Kq+85wMNf7RvYTF+QJW1qecbxNEGrV6BhEkKaaGGaGpYiUNXBLNpCk9dB4uH+Zlo4d5T+nR1bLYikpetZmNHQ48vpYL/mYJxfEQ1WXUfI4jvzfKH7z0q2RUH0OJaAqTcWORcWORLtVjTNcxFI1f7zqI2Cn4jenrefiL21EEFG86z6+O3svfz17OkcfWMGsLZvpKDGfrzNdzrLlvjqgnz8zr85S09GqZ5rwstfv6WfOoz5nXFXnnls8xojfo05LP+q/VzXxpaluyDeoZIOEVmw/zR1s+SYjkRJiiLSyG9AYGCrYSMpiqY2sRl+dOcktmmW8ZPgMPe1hnFqld3sNHB+/jI9YO/unBPlKLSVaP1BRqxRQ51aNHszgSpDnkjfDEqTE2fq2KEsfs2TrKif4y55tF7GqiWbF5cI4/HvzWqtHat9tjfPeeS+j7wjFQlKRHOR79X22DphGOdNNck0L3JPl9c8QzcxDHiChCaiobiovckFriN04McsF/OoH0A9RiAZlN8dREF80tBp4wVt3GZxp5vmZtp99ucEPxMIYS8cTUCKl5hTCr8bLSYa5LnyetaBiKhoq62td9KQllohTuSdCQvDH/JL+YfxJNUVABDYX3lw5jdD3FPxSG+C+tG3CbFq2nSvjNLgqnBeP3TyFddzUu8OyvbsK7UMFQNAKZXKc7rBlelams3M8UHBlyx+wmcvemsWqS/Ok2at1JkhoMjdrmAuX3tXlX/338l8mbeOrwCKqnYDVUFAm5Qpu3DT3EfztzLev/Pz8JyDUMFNOgcc160r8VclnxFPtnNtP7xaMsv3wjN/7cAX6+8AR9mqCkpnhgeT3RwYjUdJvUUo7K4CDWsmR8MiTMmRwYHuKP+2zOOV303a8hFYWxrVU+Mf7PNEXAfKwyF2f5WP5ajld6iB4vMf75RDFath1kFDH56zv4r9d8hhE9iXvzpMb/0Xo96rEsji15ND1OrZjiwckJ4hNZ5sslzvX0sNaoYKgx6OKne/DxdAiKcH1iMyKOIkIjwGvGOIbEdwLito8EQj0RbImEJIpFoiTr+KAKvFaIi4IrFMIVsxtX09FUCFoBseMjYwWvFeLo3/vC/DgidnzitkfYDmg3BTEKUdsnbvvEPghHEhPgNGPaAnwnImwHBFqIZ0Y4xvfeLwh8pOcRBxogQMbElo/XjGkE4LVCorZPpPg4zZiW+b1UpRCJqipogCdFcnEEIWE7IAxVPCvCi2Oito90PYQHUdvDbSl4kcD3IrwoImwFCCepj98OiRCEbR/R9ogtn9jxCdSQUCS265HrE7VNpARFT4bFYTvAbUE7FoTtgKjtE7QCnLRAU743kn+6nEGYfFbY9vFaIW1T4gbJdytcIylLK8JSBUKzkluM4yEcn6jtETZDXEshaAUErYDQDAAXFIF0XYRrIxwPpxnjmgphLIlEYqQXSEkUBytt5iFcHbWd7MMLPXFdEsbK9xGGtNUYx5S4QuCEMX4cIB0PXBfh6igOCF0SOx6R6iMcD8UFEcnkeyDAb4V4ukBRk2VLkPitEOF4xLpPuyloBEl9hOMReyDaOkTqyncboSgxihKj/Qsn1NAPUFwXJYyRjourPr2suZJNFEtaQoIicbTkcx2xcmyFWCq4IsKLQ6JWgHA9VFUSt3089X/u+x5ROyCKfEKZyMdHjk8UyJXBh4qIVERbQqSsXCMSN4yT93Y8YtUn0nziKEa0NUSoQRQTRxGR6dNuxrR0gXA9osglDjzCVkD4va3rJEhcETRjgWz7xF4L6XkIr4VqKkjHR7Q8AiWkrYrVgNHkOo9xdYNIUXj6FicdN2k3QXIttJLrWbY9ZCxxmoKGLojaHnHgIHwdvxXRaH7venTaMbLtIVwX4ZgErQBPF7iaQohC0A4IWyFRECN8gZQQtX1E20DIGCXy0ERMpIU0VZV2JAhaIWEYENo+rlSIWj6x7xIFDsL18Fsqcdsj9j1iXyLiZPAhnBC3GdMwFVqupO3Hyf3Ja0McE7cNmg1J7PiovovQFaTrEraS+wpA4IZIxyN0Gii6DoaRZMutqJ8LP00cKCi+IPJaRF7re+0TeUQtH7elINrJe8ggQkvpSEMi2hlaTYEbRAjHQ7gQtQP8VIgXRnhKiFDi5DVPR7iCsBXgxRJFVRDK91ZMQMEREEhByPf6dkoBFHW1PooCQlGIFIWo7SFcD+FKpCsQjpJ8httCOu3VdxaOR70haRiCVihoC0FaEzh6cn25UuJKsXrdClcQu22k20LqOgQawjWJ2z7Byr1ctHyUQEF4iRRE7PiEreR+JDyN2G2iRCZKpCFcl9jxCPSA2PMI/VbS1m2PSAtxNQ1ThbAdEPtu0jc9A9FWk7o5PkLXCVoBjWZybxauB2ryuX5LIxAKcRwSx8lzAjf5bpJrqoVw2sgoJPY8olZArEs8IfBlcn8TjoeUEq8Z4mkRUcuHto6wY7xWREusfD+uh/D8pH/8GKGkL7iA0/PnzzMyMvJ8F6NDhw4dOnTo8L/B1NQUw8PDP/ScF9zgQwjBsWPH2Lx5M1NTUz8yYvbFRKPRYGRkpFPvnxE69e7U+2eBTr1/duotpaTZbDI4OIiqqj/03BfctouqqgwNJb4i+Xz+Z6bR/iWdev9s0an3zxadev9s8bNW70Kh8KNPYtWzsEOHDh06dOjQ4bmhM/jo0KFDhw4dOjynvCAHH5Zl8aEPfQjLsp7vojyndOrdqffPAp16d+r9s8DPar1/XF5wAacdOnTo0KFDhxc3L8iVjw4dOnTo0KHDi5fO4KNDhw4dOnTo8JzSGXx06NChQ4cOHZ5TOoOPDh06dOjQocNzygty8PGXf/mXjI+PY9s2u3fv5rHHHnu+i/Ss8id/8ifs2rWLXC5Hb28vr3nNazh27NgzzrnmmmtQFOUZP+95z3uepxI/O/z+7//+99Vp06ZNq697nsftt99OuVwmm83y+te/nvn5+eexxM8O4+Pj31dvRVG4/fbbgRdPW99///28+tWvZnBwEEVR+PKXv/yM16WU/Mf/+B8ZGBgglUpxww03cOLEiWecU61WefOb30w+n6dYLPKOd7yDVqvFC5kfVu8wDPngBz/I1q1byWQyDA4O8pa3vIWZmWe6GP+gPvLhD3/4Oa7Jv44f1d5ve9vbvq9ON9988zPOebG1N/ADr3VFUfjIRz6yes5PY3s/27zgBh//9E//xG/+5m/yoQ99iL1797J9+3ZuuukmFhYWnu+iPWvcd9993H777TzyyCPccccdhGHIjTfeSLvdfsZ573rXu5idnV39+dM//dPnqcTPHlu2bHlGnR588MHV137jN36Dr33ta3zuc5/jvvvuY2Zmhte97nXPY2mfHR5//PFn1PmOO+4A4Od+7udWz3kxtHW73Wb79u385V/+5Q98/U//9E/58z//c/7qr/6KRx99lEwmw0033YTneavnvPnNb+bw4cPccccdfP3rX+f+++/n3e9+93NVhf8tfli9Hcdh7969/N7v/R579+7li1/8IseOHeOWW275vnP/8A//8Bl94P3vf/9zUfz/bX5UewPcfPPNz6jTpz/96We8/mJrb+AZ9Z2dneVv//ZvURSF17/+9c8476etvZ915AuMSy+9VN5+++2rf8dxLAcHB+Wf/MmfPI+l+smysLAgAXnfffetHrv66qvlBz7wgeevUD8BPvShD8nt27f/wNdqtZo0DEN+7nOfWz129OhRCciHH374OSrhc8MHPvABuXbtWimEkFK+ONsakF/60pdW/xZCyP7+fvmRj3xk9VitVpOWZclPf/rTUkopjxw5IgH5+OOPr57zrW99SyqKIqenp5+zsv9b+J/r/YN47LHHJCDPnTu3emxsbEx+9KMf/ckW7ifID6r3W9/6Vnnrrbf+L//nZ6W9b731Vnndddc949hPe3s/G7ygVj6CIGDPnj3ccMMNq8dUVeWGG27g4Ycffh5L9pOlXq8D0NXV9Yzjn/rUp+ju7ubCCy/kd37nd3Ac5/ko3rPKiRMnGBwcZGJigje/+c1MTk4CsGfPHsIwfEbbb9q0idHR0RdV2wdBwCc/+Une/va3oyjK6vEXY1v/S86cOcPc3Nwz2rdQKLB79+7V9n344YcpFotccsklq+fccMMNqKrKo48++pyX+SdFvV5HURSKxeIzjn/4wx+mXC6zc+dOPvKRjxBF0fNTwGeRe++9l97eXjZu3Mh73/teKpXK6ms/C+09Pz/PN77xDd7xjnd832svxvb+1/CCMpZbWloijmP6+vqecbyvr4+nnnrqeSrVTxYhBL/+67/OFVdcwYUXXrh6/Bd+4RcYGxtjcHCQAwcO8MEPfpBjx47xxS9+8Xks7b+N3bt383d/93ds3LiR2dlZ/uAP/oArr7ySQ4cOMTc3h2ma33dD7uvrY25u7vkp8E+AL3/5y9RqNd72tretHnsxtvX/zNNt+IOu7adfm5ubo7e39xmv67pOV1fXi6YPeJ7HBz/4QW677bZnmI392q/9GhdddBFdXV089NBD/M7v/A6zs7P82Z/92fNY2n8bN998M6973etYs2YNp06d4nd/93d5+ctfzsMPP4ymaT8T7f33f//35HK579s+fjG297+WF9Tg42eR22+/nUOHDj0j9gF4xr7n1q1bGRgY4Prrr+fUqVOsXbv2uS7ms8LLX/7y1d+3bdvG7t27GRsb47Of/SypVOp5LNlzx8c//nFe/vKXMzg4uHrsxdjWHb6fMAz5+Z//eaSUfOxjH3vGa7/5m7+5+vu2bdswTZNf+ZVf4U/+5E9+auW53/SmN63+vnXrVrZt28batWu59957uf7665/Hkj13/O3f/i1vfvObsW37GcdfjO39r+UFte3S3d2Npmnfl+EwPz9Pf3//81Sqnxzve9/7+PrXv84999zD8PDwDz139+7dAJw8efK5KNpzQrFYZMOGDZw8eZL+/n6CIKBWqz3jnBdT2587d44777yTd77znT/0vBdjWz/dhj/s2u7v7/++wPIoiqhWqz/1feDpgce5c+e44447fqTF+u7du4miiLNnzz43BXwOmJiYoLu7e7Vfv5jbG+CBBx7g2LFjP/J6hxdne/8oXlCDD9M0ufjii7nrrrtWjwkhuOuuu7jsssuex5I9u0gped/73seXvvQl7r77btasWfMj/2f//v0ADAwM/IRL99zRarU4deoUAwMDXHzxxRiG8Yy2P3bsGJOTky+atv/EJz5Bb28vr3zlK3/oeS/Gtl6zZg39/f3PaN9Go8Gjjz662r6XXXYZtVqNPXv2rJ5z9913I4RYHZD9NPL0wOPEiRPceeedlMvlH/k/+/fvR1XV79uW+Gnm/PnzVCqV1X79Ym3vp/n4xz/OxRdfzPbt23/kuS/G9v6RPN8Rr/8zn/nMZ6RlWfLv/u7v5JEjR+S73/1uWSwW5dzc3PNdtGeN9773vbJQKMh7771Xzs7Orv44jiOllPLkyZPyD//wD+UTTzwhz5w5I7/yla/IiYkJedVVVz3PJf+38Vu/9Vvy3nvvlWfOnJHf/e535Q033CC7u7vlwsKClFLK97znPXJ0dFTefffd8oknnpCXXXaZvOyyy57nUj87xHEsR0dH5Qc/+MFnHH8xtXWz2ZT79u2T+/btk4D8sz/7M7lv377VrI4Pf/jDslgsyq985SvywIED8tZbb5Vr1qyRruuuvsfNN98sd+7cKR999FH54IMPyvXr18vbbrvt+arSj8UPq3cQBPKWW26Rw8PDcv/+/c+43n3fl1JK+dBDD8mPfvSjcv/+/fLUqVPyk5/8pOzp6ZFvectbnuea/XB+WL2bzab87d/+bfnwww/LM2fOyDvvvFNedNFFcv369dLzvNX3eLG199PU63WZTqflxz72se/7/5/W9n62ecENPqSU8i/+4i/k6OioNE1TXnrppfKRRx55vov0rAL8wJ9PfOITUkopJycn5VVXXSW7urqkZVly3bp18t/9u38n6/X681vwfyNvfOMb5cDAgDRNUw4NDck3vvGN8uTJk6uvu64rf/VXf1WWSiWZTqfla1/7Wjk7O/s8lvjZ4zvf+Y4E5LFjx55x/MXU1vfcc88P7NdvfetbpZRJuu3v/d7vyb6+PmlZlrz++uu/7/uoVCrytttuk9lsVubzefnLv/zLstlsPg+1+fH5YfU+c+bM//J6v+eee6SUUu7Zs0fu3r1bFgoFadu2vOCCC+Qf//EfP+Mh/ULkh9XbcRx54403yp6eHmkYhhwbG5Pvete7vm8S+WJr76f567/+a5lKpWStVvu+//9pbe9nG0VKKX+iSysdOnTo0KFDhw7/ghdUzEeHDh06dOjQ4cVPZ/DRoUOHDh06dHhO6Qw+OnTo0KFDhw7PKZ3BR4cOHTp06NDhOaUz+OjQoUOHDh06PKd0Bh8dOnTo0KFDh+eUzuCjQ4cOHTp06PCc0hl8dOjQoUOHDh2eUzqDjw4dOnTo0KHDc0pn8NGhQ4cOHTp0eE7pDD46dOjQoUOHDs8pncFHhw4dOnTo0OE55f8HQ/1HMYYm1bQAAAAASUVORK5CYII=\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "w2_delay = w2_trained.P.squeeze().detach().round_()"
+      ],
+      "metadata": {
+        "id": "gmV1lHplLyKZ"
+      },
+      "id": "gmV1lHplLyKZ",
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(\"Minimum: \", torch.min(w2_delay), \"Maximum: \", torch.max(w2_delay), \"Mean: \", torch.mean(w2_delay), \"STD: \", torch.std(w2_delay))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "-unzAurEL08g",
+        "outputId": "ca11d3fd-5ec2-44a1-871f-d9bd907dccb3"
+      },
+      "id": "-unzAurEL08g",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Minimum:  tensor(-17.) Maximum:  tensor(17.) Mean:  tensor(6.9472) STD:  tensor(12.1914)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plt.imshow(w2_delay.numpy())"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 276
+        },
+        "id": "wxW8TfcFL8AP",
+        "outputId": "140e0954-f727-469e-9cbf-965c1baae727"
+      },
+      "id": "wxW8TfcFL8AP",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.image.AxesImage at 0x78290ab5bd00>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 59
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "35 seems like too much"
+      ],
+      "metadata": {
+        "id": "UXudXEBxPZIm"
+      },
+      "id": "UXudXEBxPZIm"
+    }
+  ],
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+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
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+    },
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+            "box_style": "",
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diff --git a/Quick_Start_random.ipynb b/Quick_Start_random.ipynb
new file mode 100644
index 0000000..eed13ac
--- /dev/null
+++ b/Quick_Start_random.ipynb
@@ -0,0 +1,1090 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "id": "7a19cd62-7df3-40ac-b7e7-25821f94177c",
+      "metadata": {
+        "id": "7a19cd62-7df3-40ac-b7e7-25821f94177c"
+      },
+      "source": [
+        "# Quick Start Notebook"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "eb1a636b-f95e-4034-99da-ae958d253cb4",
+      "metadata": {
+        "tags": [],
+        "id": "eb1a636b-f95e-4034-99da-ae958d253cb4"
+      },
+      "source": [
+        "## Imports"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "7c2677e0-f786-4222-b36b-716b2cf8d86e",
+      "metadata": {
+        "id": "7c2677e0-f786-4222-b36b-716b2cf8d86e"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "import matplotlib.colors\n",
+        "import matplotlib.cm\n",
+        "\n",
+        "import torch\n",
+        "import torch.nn as nn\n",
+        "\n",
+        "from tqdm.auto import tqdm as pbar\n",
+        "\n",
+        "dtype = torch.float\n",
+        "\n",
+        "if torch.cuda.is_available():\n",
+        "    device = torch.device(\"cuda\")\n",
+        "else:\n",
+        "    device = torch.device(\"cpu\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "0f6ca855-9ec7-43c1-a0a3-3bee04e601e4",
+      "metadata": {
+        "id": "0f6ca855-9ec7-43c1-a0a3-3bee04e601e4"
+      },
+      "source": [
+        "## Hyperparameters"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "57bffae9-6755-4c52-ae31-91e8cee36c28",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "57bffae9-6755-4c52-ae31-91e8cee36c28",
+        "outputId": "4e9cbfc7-8d45-47bf-e4b5-83b467d296d9"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Number of classes = 12\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Constants\n",
+        "SECONDS = 1\n",
+        "MS = 1e-3\n",
+        "HZ = 1\n",
+        "\n",
+        "DT = 1 * MS            # large time step to make simulations run faster\n",
+        "ANF_PER_EAR = 100    # repeats of each ear with independent noise\n",
+        "\n",
+        "DURATION = .1 * SECONDS # stimulus duration\n",
+        "DURATION_STEPS = int(np.round(DURATION / DT))\n",
+        "INPUT_SIZE = 2 * ANF_PER_EAR\n",
+        "\n",
+        "# Training\n",
+        "LR = 0.005\n",
+        "N_EPOCHS = 150\n",
+        "batch_size = 64\n",
+        "n_training_batches = 64\n",
+        "n_testing_batches = 32\n",
+        "num_samples = batch_size*n_training_batches\n",
+        "\n",
+        "# classes at 15 degree increments\n",
+        "NUM_CLASSES = 180 // 15\n",
+        "print(f'Number of classes = {NUM_CLASSES}')\n",
+        "\n",
+        "# Network\n",
+        "NUM_HIDDEN = 30 # number of hidden units\n",
+        "TAU = 5 # membrane time constant\n",
+        "IE_RATIO = 0.5 # ratio of inhibitory:excitatory units (used if DALES_LAW = True). 0 = all excitatory, 1 = all inhibitory\n",
+        "DALES_LAW = False # When True, units will be only excitatory or inhibitory. When False, units will use both (like a normal ANN)\n",
+        "if DALES_LAW:\n",
+        "    print('Using Dales Law')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "0c55ffa2-b4c8-473b-8907-54138075df3d",
+      "metadata": {
+        "jp-MarkdownHeadingCollapsed": true,
+        "tags": [],
+        "id": "0c55ffa2-b4c8-473b-8907-54138075df3d"
+      },
+      "source": [
+        "## Functions"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "3f838d0c-4ac1-4296-a2d2-abb278644142",
+      "metadata": {
+        "tags": [],
+        "id": "3f838d0c-4ac1-4296-a2d2-abb278644142"
+      },
+      "source": [
+        "### Stimulus"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "input_signal will be called in every iteration to resample for the random offset. The Poisson spikes are only generated once."
+      ],
+      "metadata": {
+        "id": "ZtJWro0TrxJJ"
+      },
+      "id": "ZtJWro0TrxJJ"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "5d168c57-5ae0-49ae-b510-555c612bdf44",
+      "metadata": {
+        "id": "5d168c57-5ae0-49ae-b510-555c612bdf44"
+      },
+      "outputs": [],
+      "source": [
+        "def input_signal(ipd, poisson):\n",
+        "    \"\"\"\n",
+        "    Generate an input signal (spike array) from array of true IPDs\n",
+        "    \"\"\"\n",
+        "    envelope_power = 2   # higher values make sharper envelopes, easier\n",
+        "    rate_max = 600 * HZ   # maximum Poisson firing rate\n",
+        "    stimulus_frequency = 20 * HZ\n",
+        "\n",
+        "    num_samples = len(ipd)\n",
+        "    times = np.arange(DURATION_STEPS) * DT # array of times\n",
+        "    phi = 2*np.pi*(stimulus_frequency * times + np.random.rand()) # array of phases corresponding to those times with random offset\n",
+        "    # each point in the array will have a different phase based on which ear it is\n",
+        "    # and its delay\n",
+        "    theta = np.zeros((num_samples, DURATION_STEPS, 2*ANF_PER_EAR))\n",
+        "    # for each ear, we have anf_per_ear different phase delays from to pi/2 so\n",
+        "    # that the differences between the two ears can cover the full range from -pi/2 to pi/2\n",
+        "    phase_delays = np.linspace(0, np.pi/2, ANF_PER_EAR)\n",
+        "    # now we set up these theta to implement that. Some numpy vectorisation logic here which looks a little weird,\n",
+        "    # but implements the idea in the text above.\n",
+        "    theta[:, :, :ANF_PER_EAR] = phi[np.newaxis, :, np.newaxis]+phase_delays[np.newaxis, np.newaxis, :]\n",
+        "    theta[:, :, ANF_PER_EAR:] = phi[np.newaxis, :, np.newaxis]+phase_delays[np.newaxis, np.newaxis, :]+ipd[:, np.newaxis, np.newaxis]\n",
+        "    # now generate Poisson spikes at the given firing rate as in the previous notebook\n",
+        "    if poisson is None:\n",
+        "      poisson = np.random.rand(num_samples, DURATION_STEPS, 2*ANF_PER_EAR)\n",
+        "    spikes = poisson<rate_max*DT*(0.5*(1+np.sin(theta)))**envelope_power\n",
+        "    return spikes\n",
+        "\n",
+        "def random_ipd_input_signal(num_samples, tensor=True):\n",
+        "    \"\"\"\n",
+        "    Generate the training data\n",
+        "    Returns true IPDs from U(-pi/2, pi/2) and corresponding spike arrays\n",
+        "    \"\"\"\n",
+        "    ipd = np.random.rand(num_samples)*np.pi-np.pi/2 # uniformly random in (-pi/2, pi/2)\n",
+        "    poisson = np.random.rand(num_samples, DURATION_STEPS, 2*ANF_PER_EAR)\n",
+        "    if tensor:\n",
+        "        ipd = torch.tensor(ipd, device=device, dtype=dtype)\n",
+        "\n",
+        "    return ipd, poisson\n",
+        "\n",
+        "def spikes_from_fixed_idp_input_signal(ipd, poisson=None, tensor=True):\n",
+        "    spikes = input_signal(ipd, poisson)\n",
+        "    if tensor:\n",
+        "        spikes = torch.tensor(spikes, device=device, dtype=dtype)\n",
+        "    return spikes\n",
+        "\n",
+        "def show_examples(shown=8):\n",
+        "    ipd = np.linspace(-np.pi/2, np.pi/2, shown)\n",
+        "    spikes = spikes_from_fixed_idp_input_signal(ipd, shown).cpu()\n",
+        "\n",
+        "    plt.figure(figsize=(10, 4), dpi=100)\n",
+        "    for i in range(shown):\n",
+        "        plt.subplot(2, shown // 2, i+1)\n",
+        "        plt.imshow(spikes[i, :, :].T, aspect='auto', interpolation='nearest', cmap=plt.cm.gray_r)\n",
+        "        plt.title(f'True IPD = {int(ipd[i]*180/np.pi)} deg')\n",
+        "        if i>=4:\n",
+        "            plt.xlabel('Time (steps)')\n",
+        "        if i%4==0:\n",
+        "            plt.ylabel('Input neuron index')\n",
+        "    plt.tight_layout()\n",
+        "\n",
+        "def data_generator(ipds, spikes):\n",
+        "    perm = torch.randperm(spikes.shape[0])\n",
+        "    spikes = spikes[perm, :, :]\n",
+        "    ipds = ipds[perm]\n",
+        "    n, _, _ = spikes.shape\n",
+        "    n_batch = n//batch_size\n",
+        "    for i in range(n_batch):\n",
+        "        x_local = spikes[i*batch_size:(i+1)*batch_size, :, :]\n",
+        "        y_local = ipds[i*batch_size:(i+1)*batch_size]\n",
+        "        yield x_local, y_local\n",
+        "\n",
+        "def discretise(ipds):\n",
+        "    return ((ipds+np.pi/2) * NUM_CLASSES / np.pi).long() # assumes input is tensor\n",
+        "\n",
+        "def continuise(ipd_indices): # convert indices back to IPD midpoints\n",
+        "    return (ipd_indices+0.5) / NUM_CLASSES * np.pi - np.pi / 2"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "5bd60321-1802-471e-87c9-083282a1df73",
+      "metadata": {
+        "id": "5bd60321-1802-471e-87c9-083282a1df73"
+      },
+      "source": [
+        "### SNN"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "84e6ae7e-8695-43d6-8db1-9f5c01024df9",
+      "metadata": {
+        "id": "84e6ae7e-8695-43d6-8db1-9f5c01024df9"
+      },
+      "outputs": [],
+      "source": [
+        "def sigmoid(x, beta):\n",
+        "    return 1 / (1 + torch.exp(-beta*x))\n",
+        "\n",
+        "def sigmoid_deriv(x, beta):\n",
+        "    s = sigmoid(x, beta)\n",
+        "    return beta * s * (1 - s)\n",
+        "\n",
+        "class SurrGradSpike(torch.autograd.Function):\n",
+        "    @staticmethod\n",
+        "    def forward(ctx, inp):\n",
+        "        ctx.save_for_backward(inp)\n",
+        "        out = torch.zeros_like(inp)\n",
+        "        out[inp > 0] = 1.0\n",
+        "        return out\n",
+        "\n",
+        "    @staticmethod\n",
+        "    def backward(ctx, grad_output):\n",
+        "        inp, = ctx.saved_tensors\n",
+        "        sigmoid_derivative = sigmoid_deriv(inp, beta=5)\n",
+        "        grad = grad_output*sigmoid_derivative\n",
+        "        return grad\n",
+        "\n",
+        "spike_fn  = SurrGradSpike.apply\n",
+        "\n",
+        "def membrane_only(input_spikes, weights, tau):\n",
+        "    \"\"\"\n",
+        "    :param input_spikes: has shape (batch_size, duration_steps, input_size)\n",
+        "    :param weights: has shape  (input_size, num_classes\n",
+        "    :param tau:\n",
+        "    :return:\n",
+        "    \"\"\"\n",
+        "    batch_size = input_spikes.shape[0]\n",
+        "    assert len(input_spikes.shape) == 3\n",
+        "\n",
+        "    v = torch.zeros((batch_size, NUM_CLASSES), device=device, dtype=dtype)\n",
+        "    v_rec = [v]\n",
+        "    h = torch.einsum(\"abc,cd->abd\", (input_spikes, weights))\n",
+        "    alpha = np.exp(-DT / tau)\n",
+        "    for t in range(DURATION_STEPS - 1):\n",
+        "        v = alpha*v + h[:, t, :]\n",
+        "        v_rec.append(v)\n",
+        "    v_rec = torch.stack(v_rec, dim=1)  # (batch_size, duration_steps, num_classes)\n",
+        "    return v_rec\n",
+        "\n",
+        "def layer1(input_spikes, w1, tau, sign1):\n",
+        "\n",
+        "    if DALES_LAW:\n",
+        "        w1 = get_signed_weights(w1, sign1)\n",
+        "\n",
+        "    batch_size = input_spikes.shape[0]\n",
+        "\n",
+        "    # First layer: input to hidden\n",
+        "    v = torch.zeros((batch_size, NUM_HIDDEN), device=device, dtype=dtype)\n",
+        "    s = torch.zeros((batch_size, NUM_HIDDEN), device=device, dtype=dtype)\n",
+        "    s_rec = [s]\n",
+        "    h = torch.einsum(\"abc,cd->abd\", (input_spikes, w1))\n",
+        "    alpha = np.exp(-DT / tau)\n",
+        "\n",
+        "    for t in range(DURATION_STEPS - 1):\n",
+        "        new_v = (alpha*v + h[:, t, :])*(1-s) # multiply by 0 after a spike\n",
+        "        s = spike_fn(v-1) # threshold of 1\n",
+        "        v = new_v\n",
+        "        s_rec.append(s)\n",
+        "    s_rec = torch.stack(s_rec, dim=1)\n",
+        "    return s_rec\n",
+        "\n",
+        "def layer2(s_rec, w2, tau, sign2):\n",
+        "    \"\"\"Second layer: hidden to output\"\"\"\n",
+        "    if DALES_LAW:\n",
+        "        w2 = get_signed_weights(w2, sign2)\n",
+        "\n",
+        "    v_rec = membrane_only(s_rec, w2, tau=tau)\n",
+        "    return v_rec\n",
+        "\n",
+        "def snn(input_spikes, w1, w2, signs, tau=5*MS):\n",
+        "    \"\"\"Run the simulation\"\"\"\n",
+        "\n",
+        "    s_rec = layer1(input_spikes, w1, tau, signs[0])\n",
+        "    v_rec = layer2(s_rec, w2, tau, signs[1])\n",
+        "\n",
+        "    # Return recorded membrane potential of output\n",
+        "    return v_rec"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "b706d488-162c-4e70-86cd-340da7d49115",
+      "metadata": {
+        "tags": [],
+        "id": "b706d488-162c-4e70-86cd-340da7d49115"
+      },
+      "source": [
+        "### Dale's Law"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "3e088884-247b-4d38-a6bf-15c8cd9e1f82",
+      "metadata": {
+        "id": "3e088884-247b-4d38-a6bf-15c8cd9e1f82"
+      },
+      "outputs": [],
+      "source": [
+        "def get_dales_mask(nb_inputs, nb_out, ie_ratio) :\n",
+        "\n",
+        "    d_mask = torch.ones(nb_inputs, nb_out)\n",
+        "    #inhib_units = np.random.choice(nb_inputs, int(nb_inputs*ie_ratio), replace=False)\n",
+        "    inhib_units = torch.arange(ie_ratio*nb_inputs, dtype=int)\n",
+        "    d_mask[inhib_units, :] = -1\n",
+        "    return d_mask\n",
+        "\n",
+        "def init_weight_matrices(ie_ratio = 0.1):\n",
+        "    \"\"\"Weights and uniform weight initialisation\"\"\"\n",
+        "\n",
+        "    # Input to hidden layer\n",
+        "    w1 = nn.Parameter(torch.empty((INPUT_SIZE, NUM_HIDDEN), device=device, dtype=dtype, requires_grad=True))\n",
+        "    fan_in, _ = nn.init._calculate_fan_in_and_fan_out(w1)\n",
+        "    bound = 1 / np.sqrt(fan_in)\n",
+        "    nn.init.uniform_(w1, -bound, bound)\n",
+        "\n",
+        "    # Hidden layer to output\n",
+        "    w2 = nn.Parameter(torch.empty((NUM_HIDDEN, NUM_CLASSES), device=device, dtype=dtype, requires_grad=True))\n",
+        "    fan_in, _ = nn.init._calculate_fan_in_and_fan_out(w2)\n",
+        "    bound = 1 / np.sqrt(fan_in)\n",
+        "    nn.init.uniform_(w2, -bound, bound)\n",
+        "\n",
+        "    #Get fixed signs for the weight, 90% excitatory\n",
+        "    signs = [get_dales_mask(*w.shape, ie_ratio).to(w.device) for w in (w1, w2)]\n",
+        "\n",
+        "    return w1, w2, signs\n",
+        "\n",
+        "def get_signed_weights(w, sign):\n",
+        "    \"\"\"Get the signed value of the weight\"\"\"\n",
+        "    # Note abs is in principle not differentiable.\n",
+        "    # In practice, pytorch will set the derivative to 0 when the values are 0.\n",
+        "    # (see https://discuss.pytorch.org/t/how-does-autograd-deal-with-non-differentiable-opponents-such-as-abs-and-max/34538)\n",
+        "    # This has the adverse effect that, during training, if a synapse reaches 0,\n",
+        "    # it is \"culled\" and can not be recovered.\n",
+        "    # It should be possible to cheat here and either \"wiggle\" 0-valued synapses,\n",
+        "    # or to override abs gradient to return a very small random number.\n",
+        "\n",
+        "    #TODO try ReLu or other activation\n",
+        "    #TODO reproduce paper https://www.biorxiv.org/content/10.1101/2020.11.02.364968v2.full\n",
+        "\n",
+        "   # return torch.max(w, 0)*sign\n",
+        "    return torch.abs(w)*sign"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "c215cfc6-d04d-4ad4-8314-a7c627939ff7",
+      "metadata": {
+        "id": "c215cfc6-d04d-4ad4-8314-a7c627939ff7"
+      },
+      "source": [
+        "### Training"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "a8c7c066-4bc4-4604-8a40-6989b57f19b9",
+      "metadata": {
+        "id": "a8c7c066-4bc4-4604-8a40-6989b57f19b9"
+      },
+      "outputs": [],
+      "source": [
+        "def train(w1, w2, signs, ipds, poisson,  ipds_validation, poisson_validation, lr=0.01, n_epochs=30, tau=5*MS):\n",
+        "    \"\"\"\n",
+        "    :param lr: learning rate\n",
+        "    :return:\n",
+        "    \"\"\"\n",
+        "    # Optimiser and loss function\n",
+        "    optimizer = torch.optim.Adam([w1, w2], lr=lr)\n",
+        "    log_softmax_fn = nn.LogSoftmax(dim=1)\n",
+        "    loss_fn = nn.NLLLoss()\n",
+        "\n",
+        "    loss_hist = []\n",
+        "    val_loss_hist = []\n",
+        "\n",
+        "    best_loss = 1e10\n",
+        "    val_loss_best_loss = 1e10\n",
+        "\n",
+        "    for e in pbar(range(n_epochs)):\n",
+        "        local_loss = []\n",
+        "        spikes = spikes_from_fixed_idp_input_signal(ipds, poisson)\n",
+        "        for x_local, y_local in data_generator(discretise(torch.tensor(ipds, device=device, dtype=dtype)), spikes):\n",
+        "            # Run network\n",
+        "            output = snn(x_local, w1, w2, signs, tau=tau)\n",
+        "            # Compute cross entropy loss\n",
+        "            m = torch.sum(output, 1)*0.01  # Sum time dimension\n",
+        "\n",
+        "            reg = 0\n",
+        "            loss = loss_fn(log_softmax_fn(m), y_local) + reg\n",
+        "            local_loss.append(loss.item())\n",
+        "\n",
+        "            # Update gradients\n",
+        "            optimizer.zero_grad()\n",
+        "            loss.backward()\n",
+        "            optimizer.step()\n",
+        "\n",
+        "        loss_hist.append(np.mean(local_loss))\n",
+        "\n",
+        "        val_local_loss = []\n",
+        "        spikes_validation = spikes_from_fixed_idp_input_signal(ipds_validation, poisson_validation)\n",
+        "        for x_local, y_local in data_generator(discretise(torch.tensor(ipds_validation, device=device, dtype=dtype)), spikes_validation):\n",
+        "            # Run network\n",
+        "            output = snn(x_local, w1, w2, signs, tau=tau)\n",
+        "            # Compute cross entropy loss\n",
+        "            m = torch.sum(output, 1)*0.01  # Sum time dimension\n",
+        "\n",
+        "            val_loss = loss_fn(log_softmax_fn(m), y_local)\n",
+        "            val_local_loss.append(val_loss.item())\n",
+        "\n",
+        "        val_loss_hist.append(np.mean(val_local_loss))\n",
+        "\n",
+        "        if np.mean(val_local_loss) < val_loss_best_loss:\n",
+        "            val_loss_best_loss = np.mean(val_local_loss)\n",
+        "            if DALES_LAW:\n",
+        "                best_weights = get_signed_weights(w1, signs[0]), get_signed_weights(w2, signs[1]), signs\n",
+        "            else:\n",
+        "                best_weights = w1, w2, signs\n",
+        "\n",
+        "        #Early Stopping :\n",
+        "        if torch.tensor(val_loss_hist[-10:]).argmin() == 0  and e>10:\n",
+        "            print('Early Stop !')\n",
+        "            return best_weights\n",
+        "\n",
+        "    # Plot the loss function over time\n",
+        "    plt.plot(loss_hist)\n",
+        "    plt.xlabel('Epoch')\n",
+        "    plt.ylabel('Loss')\n",
+        "    plt.tight_layout()\n",
+        "\n",
+        "    plt.plot(val_loss_hist)\n",
+        "    plt.xlabel('Epoch')\n",
+        "    plt.ylabel('Loss')\n",
+        "    plt.tight_layout()\n",
+        "\n",
+        "    if DALES_LAW:\n",
+        "        return get_signed_weights(w1, signs[0]), get_signed_weights(w2, signs[1]), signs\n",
+        "    else:\n",
+        "        return w1, w2, signs"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "263eb0bc-edf4-4e8d-80d5-55b98889b0c6",
+      "metadata": {
+        "id": "263eb0bc-edf4-4e8d-80d5-55b98889b0c6"
+      },
+      "source": [
+        "### Testing"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "6877e75a-3e5c-4a95-9c2c-7fb3caf608a6",
+      "metadata": {
+        "id": "6877e75a-3e5c-4a95-9c2c-7fb3caf608a6"
+      },
+      "outputs": [],
+      "source": [
+        "def test_accuracy(ipds, poisson, run):\n",
+        "    accs = []\n",
+        "    ipd_true = []\n",
+        "    ipd_est = []\n",
+        "    confusion = np.zeros((NUM_CLASSES, NUM_CLASSES))\n",
+        "    spikes = spikes_from_fixed_idp_input_signal(ipds, poisson)\n",
+        "    for x_local, y_local in data_generator((torch.tensor(ipds, device=device, dtype=dtype)), spikes):\n",
+        "        y_local_orig = y_local\n",
+        "        y_local = discretise(y_local)\n",
+        "        output = run(x_local)\n",
+        "        m = torch.sum(output, 1)  # Sum time dimension\n",
+        "        _, am = torch.max(m, 1)  # argmax over output units\n",
+        "        tmp = np.mean((y_local == am).detach().cpu().numpy())  # compare to labels\n",
+        "        for i, j in zip(y_local.detach().cpu().numpy(), am.detach().cpu().numpy()):\n",
+        "            confusion[j, i] += 1\n",
+        "        ipd_true.append(y_local_orig.cpu().data.numpy())\n",
+        "        ipd_est.append(continuise(am.detach().cpu().numpy()))\n",
+        "        accs.append(tmp)\n",
+        "\n",
+        "    ipd_true = np.hstack(ipd_true)\n",
+        "    ipd_est = np.hstack(ipd_est)\n",
+        "\n",
+        "    return ipd_true, ipd_est, confusion, accs\n",
+        "\n",
+        "def report_accuracy(ipd_true, ipd_est, confusion, accs, label):\n",
+        "\n",
+        "    abs_errors_deg = abs(ipd_true-ipd_est)*180/np.pi\n",
+        "\n",
+        "    print()\n",
+        "    print(f\"{label} classifier accuracy: {100*np.mean(accs):.1f}%\")\n",
+        "    print(f\"{label} absolute error: {np.mean(abs_errors_deg):.1f} deg\")\n",
+        "\n",
+        "    plt.figure(figsize=(10, 4), dpi=100)\n",
+        "    plt.subplot(121)\n",
+        "    plt.hist(ipd_true * 180 / np.pi, bins=NUM_CLASSES, label='True')\n",
+        "    plt.hist(ipd_est * 180 / np.pi, bins=NUM_CLASSES, label='Estimated')\n",
+        "    plt.xlabel(\"IPD\")\n",
+        "    plt.yticks([])\n",
+        "    plt.legend(loc='best')\n",
+        "    plt.title(label)\n",
+        "    plt.subplot(122)\n",
+        "    confusion /= np.sum(confusion, axis=0)[np.newaxis, :]\n",
+        "    plt.imshow(confusion, interpolation='nearest', aspect='equal', origin='lower', extent=(-90, 90, -90, 90))\n",
+        "    plt.xlabel('True IPD')\n",
+        "    plt.ylabel('Estimated IPD')\n",
+        "    plt.title('Confusion matrix')\n",
+        "    plt.tight_layout()\n",
+        "\n",
+        "def analyse_accuracy(ipds, poisson, run, label):\n",
+        "    ipd_true, ipd_est, confusion, accs = test_accuracy(ipds, poisson, run)\n",
+        "    report_accuracy(ipd_true, ipd_est, confusion, accs, label)\n",
+        "    return 100*np.mean(accs)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "1d4de8bd-0ea7-4f5b-a2d5-209f37755916",
+      "metadata": {
+        "id": "1d4de8bd-0ea7-4f5b-a2d5-209f37755916"
+      },
+      "source": [
+        "## Train Network"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Needs lower lr and a few more epochs, but generally it achieves higher accuracy, and it is much more robust to noise."
+      ],
+      "metadata": {
+        "id": "fQn9fIfQsHP0"
+      },
+      "id": "fQn9fIfQsHP0"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "1c95d56c-7e14-4d17-9b96-acd3daa342cb",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 66,
+          "referenced_widgets": [
+            "56c83dddb0914a1dbd8e792c781b42d0",
+            "02dbbe0092cc41fe91d54a150a597d93",
+            "174725ea15e74d4a9a4e1dfd28d7a972",
+            "3e5cecf7581d432fba73c85122d2b75d",
+            "5f6a893dcc9b4c128a034eb13789d122",
+            "6d6505b02e574f72bb583b11052aba77",
+            "a1cdf6b0b5364cbfba3622e315ee40fb",
+            "1e815907c26b4478ae0422cc06e2f712",
+            "eea2e7968b83445da4d5a91d550d06aa",
+            "d322343d9f2948109257d4009abcaacb",
+            "7d8bcc3dcdf64ac881226cd97e11bc56"
+          ]
+        },
+        "id": "1c95d56c-7e14-4d17-9b96-acd3daa342cb",
+        "outputId": "33fe5e27-7dec-4421-a8eb-c1382c870e76"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "  0%|          | 0/150 [00:00<?, ?it/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "56c83dddb0914a1dbd8e792c781b42d0"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Early Stop !\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Generate the training data\n",
+        "w1, w2, signs = init_weight_matrices(ie_ratio=IE_RATIO)\n",
+        "\n",
+        "ipds_training, poisson_training = random_ipd_input_signal(num_samples, False)\n",
+        "ipds_validation, poisson_validation = random_ipd_input_signal(num_samples, False)\n",
+        "\n",
+        "# Train network\n",
+        "w1_trained, w2_trained, signs = train(w1, w2, signs, ipds_training, poisson_training, ipds_validation, poisson_validation, lr=LR, n_epochs=N_EPOCHS, tau=TAU*MS)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "b3bf0fa2-31f0-41e7-91bb-284396da2e68",
+      "metadata": {
+        "id": "b3bf0fa2-31f0-41e7-91bb-284396da2e68",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 936
+        },
+        "outputId": "bcbb98d1-ba86-42e7-9c10-d97ca3e32606"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Chance accuracy level: 8.3%\n",
+            "\n",
+            "Train classifier accuracy: 85.0%\n",
+            "Train absolute error: 4.5 deg\n",
+            "\n",
+            "Test classifier accuracy: 85.3%\n",
+            "Test absolute error: 4.4 deg\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "85.302734375"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 9
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x400 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x400 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# Analyse\n",
+        "print(f\"Chance accuracy level: {100 * 1 / NUM_CLASSES:.1f}%\")\n",
+        "run_func = lambda x: snn(x, w1_trained, w2_trained, signs)\n",
+        "analyse_accuracy(ipds_training, poisson_training, run_func, 'Train')\n",
+        "\n",
+        "ipds_test, poisson_test = random_ipd_input_signal(batch_size*n_testing_batches, False)\n",
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