diff --git a/com.ibm.wala.cast.python.ml.test/source/com/ibm/wala/cast/python/ml/test/TestTensorflowModel.java b/com.ibm.wala.cast.python.ml.test/source/com/ibm/wala/cast/python/ml/test/TestTensorflowModel.java
index 95ed16e89..c30a8e189 100644
--- a/com.ibm.wala.cast.python.ml.test/source/com/ibm/wala/cast/python/ml/test/TestTensorflowModel.java
+++ b/com.ibm.wala.cast.python.ml.test/source/com/ibm/wala/cast/python/ml/test/TestTensorflowModel.java
@@ -199,12 +199,10 @@ public void testTf2()
     testTf2("tf2_testing_decorator8.py", "returned", 1, 3, 2);
     testTf2("tf2_testing_decorator9.py", "returned", 1, 3, 2);
     testTf2("tf2_testing_decorator10.py", "returned", 1, 3, 2);
-    testTf2(
-        "tf2_test_dataset.py",
-        "add",
-        0,
-        0); // NOTE: Change to testTf2("tf2_test_dataset.py", "add", 2, 3, 2, 3) once
-    // https://github.com/wala/ML/issues/89 is fixed.
+    // FIXME: Test tf2_test_dataset.py really has three tensors in its dataset. We are currently
+    // treating it as one. But, in the literal case, it should be possible to model it like the list
+    // tests below.
+    testTf2("tf2_test_dataset.py", "add", 2, 2, 2, 3);
     testTf2("tf2_test_tensor_list.py", "add", 2, 3, 2, 3);
     testTf2("tf2_test_tensor_list2.py", "add", 0, 2);
     testTf2("tf2_test_tensor_list3.py", "add", 0, 2);
diff --git a/com.ibm.wala.cast.python.ml/data/tensorflow.xml b/com.ibm.wala.cast.python.ml/data/tensorflow.xml
index 1d2ccbc0d..b5860ff3a 100644
--- a/com.ibm.wala.cast.python.ml/data/tensorflow.xml
+++ b/com.ibm.wala.cast.python.ml/data/tensorflow.xml
@@ -41,6 +41,10 @@
 
         <new def="nn" class="Lobject" />
         <putfield class="LRoot" field="nn" fieldType="LRoot" ref="x" value="nn" />
+        <new def="data" class="Lobject" />
+        <putfield class="LRoot" field="data" fieldType="LRoot" ref="x" value="data" />
+        <new def="Dataset" class="Lobject" />
+        <putfield class="LRoot" field="Dataset" fieldType="LRoot" ref="data" value="Dataset" />
         <new def="random" class="Lobject" />
         <putfield class="LRoot" field="random" fieldType="LRoot" ref="x" value="random" />
         <new def="sparse" class="Lobject" />
@@ -122,6 +126,9 @@
         <new def="array_ops" class="Lobject" />
         <putfield class="LRoot" field="array_ops" fieldType="LRoot" ref="ops" value="array_ops" />
 
+        <new def="data_ops" class="Lobject" />
+        <putfield class="LRoot" field="data_ops" fieldType="LRoot" ref="ops" value="data_ops" />
+
         <new def="random_ops" class="Lobject" />
         <putfield class="LRoot" field="random_ops" fieldType="LRoot" ref="ops" value="random_ops" />
 
@@ -167,6 +174,10 @@
         <putfield class="LRoot" field="ones" fieldType="LRoot" ref="x" value="ones" />
         <putfield class="LRoot" field="ones" fieldType="LRoot" ref="array_ops" value="ones" />
 
+        <new def="from_tensor_slices" class="Ltensorflow/functions/from_tensor_slices" />
+        <putfield class="LRoot" field="from_tensor_slices" fieldType="LRoot" ref="Dataset" value="from_tensor_slices" />
+        <putfield class="LRoot" field="from_tensor_slices" fieldType="LRoot" ref="data_ops" value="from_tensor_slices" />
+
         <new def="zeros" class="Ltensorflow/functions/zeros" />
         <putfield class="LRoot" field="zeros" fieldType="LRoot" ref="x" value="zeros" />
         <putfield class="LRoot" field="zeros" fieldType="LRoot" ref="array_ops" value="zeros" />
@@ -399,6 +410,18 @@
         </method>
       </class>
 
+      <class name="from_tensor_slices" allocatable="true">
+        <!-- "read_dataset" means that this function reads a tensor iterable. -->
+        <method name="read_dataset" descriptor="()LRoot;">
+          <new def="x" class="Ltensorflow/python/ops/data_ops/from_tensor_slices" />
+          <return value="x" />
+        </method>
+        <method name="do" descriptor="()LRoot;" numArgs="2" paramNames="tensors name">
+          <call class="LRoot" name="read_dataset" descriptor="()LRoot;" type="virtual" arg0="arg0" def="x" />
+          <return value="x" />
+        </method>
+      </class>
+
       <class name="Variable" allocatable="true">
         <method name="read_data" descriptor="()LRoot;">
           <new def="x" class="Ltensorflow/python/ops/variables/Variable" />
diff --git a/com.ibm.wala.cast.python.ml/source/com/ibm/wala/cast/python/ml/client/PythonTensorAnalysisEngine.java b/com.ibm.wala.cast.python.ml/source/com/ibm/wala/cast/python/ml/client/PythonTensorAnalysisEngine.java
index 97901bcef..bbca9c2e5 100644
--- a/com.ibm.wala.cast.python.ml/source/com/ibm/wala/cast/python/ml/client/PythonTensorAnalysisEngine.java
+++ b/com.ibm.wala.cast.python.ml/source/com/ibm/wala/cast/python/ml/client/PythonTensorAnalysisEngine.java
@@ -1,5 +1,8 @@
 package com.ibm.wala.cast.python.ml.client;
 
+import static com.ibm.wala.cast.types.AstMethodReference.fnReference;
+
+import com.ibm.wala.cast.ir.ssa.EachElementGetInstruction;
 import com.ibm.wala.cast.lsp.AnalysisError;
 import com.ibm.wala.cast.python.client.PythonAnalysisEngine;
 import com.ibm.wala.cast.python.ml.analysis.TensorTypeAnalysis;
@@ -7,9 +10,15 @@
 import com.ibm.wala.cast.python.types.PythonTypes;
 import com.ibm.wala.cast.types.AstMethodReference;
 import com.ibm.wala.classLoader.CallSiteReference;
+import com.ibm.wala.classLoader.IClass;
+import com.ibm.wala.classLoader.IMethod;
 import com.ibm.wala.ipa.callgraph.AnalysisOptions;
 import com.ibm.wala.ipa.callgraph.CGNode;
+import com.ibm.wala.ipa.callgraph.CallGraph;
+import com.ibm.wala.ipa.callgraph.propagation.AllocationSiteInNode;
+import com.ibm.wala.ipa.callgraph.propagation.InstanceKey;
 import com.ibm.wala.ipa.callgraph.propagation.LocalPointerKey;
+import com.ibm.wala.ipa.callgraph.propagation.PointerAnalysis;
 import com.ibm.wala.ipa.callgraph.propagation.PointerKey;
 import com.ibm.wala.ipa.callgraph.propagation.PointsToSetVariable;
 import com.ibm.wala.ipa.callgraph.propagation.PropagationCallGraphBuilder;
@@ -26,6 +35,7 @@
 import com.ibm.wala.util.collections.HashSetFactory;
 import com.ibm.wala.util.graph.Graph;
 import com.ibm.wala.util.graph.impl.SlowSparseNumberedGraph;
+import com.ibm.wala.util.intset.OrdinalSet;
 import java.util.Iterator;
 import java.util.Map;
 import java.util.Set;
@@ -33,6 +43,14 @@
 
 public class PythonTensorAnalysisEngine extends PythonAnalysisEngine<TensorTypeAnalysis> {
 
+  /** A "fake" function name in the summaries that indicates that an API produces a new tensor. */
+  private static final String TENSOR_GENERATOR_SYNTHETIC_FUNCTION_NAME = "read_data";
+
+  /**
+   * A "fake" function name in the summaries that indicates that an API produces a tensor iterable.
+   */
+  private static final String TENSOR_ITERABLE_SYNTHETIC_FUNCTION_NAME = "read_dataset";
+
   private static final Logger logger = Logger.getLogger(PythonTensorAnalysisEngine.class.getName());
 
   private static final MethodReference conv2d =
@@ -69,7 +87,10 @@ public class PythonTensorAnalysisEngine extends PythonAnalysisEngine<TensorTypeA
 
   private final Map<PointerKey, AnalysisError> errorLog = HashMapFactory.make();
 
-  private static Set<PointsToSetVariable> getDataflowSources(Graph<PointsToSetVariable> dataflow) {
+  private static Set<PointsToSetVariable> getDataflowSources(
+      Graph<PointsToSetVariable> dataflow,
+      CallGraph callGraph,
+      PointerAnalysis<InstanceKey> pointerAnalysis) {
     Set<PointsToSetVariable> sources = HashSetFactory.make();
     for (PointsToSetVariable src : dataflow) {
       PointerKey k = src.getPointerKey();
@@ -81,12 +102,63 @@ private static Set<PointsToSetVariable> getDataflowSources(Graph<PointsToSetVari
         SSAInstruction inst = du.getDef(vn);
 
         if (inst instanceof SSAAbstractInvokeInstruction) {
+          // We potentially have a function call that generates a tensor.
           SSAAbstractInvokeInstruction ni = (SSAAbstractInvokeInstruction) inst;
 
-          if (ni.getCallSite().getDeclaredTarget().getName().toString().equals("read_data")
+          if (ni.getCallSite()
+                  .getDeclaredTarget()
+                  .getName()
+                  .toString()
+                  .equals(TENSOR_GENERATOR_SYNTHETIC_FUNCTION_NAME)
               && ni.getException() != vn) {
             sources.add(src);
-            logger.info("Added dataflow source " + src + ".");
+            logger.info("Added dataflow source from tensor generator: " + src + ".");
+          }
+        } else if (inst instanceof EachElementGetInstruction) {
+          // We are potentially pulling a tensor out of a tensor iterable.
+          EachElementGetInstruction eachElementGetInstruction = (EachElementGetInstruction) inst;
+
+          // Find the potential tensor iterable creation site.
+          SSAInstruction iterableDef = du.getDef(eachElementGetInstruction.getUse(0));
+
+          if (iterableDef instanceof SSAAbstractInvokeInstruction) {
+            SSAAbstractInvokeInstruction iterableGenInvocationInstruction =
+                (SSAAbstractInvokeInstruction) iterableDef;
+
+            // What function are we calling?
+            int use = iterableGenInvocationInstruction.getUse(0);
+            PointerKey pointerKeyForLocal =
+                pointerAnalysis.getHeapModel().getPointerKeyForLocal(kk.getNode(), use);
+            OrdinalSet<InstanceKey> pointsToSet =
+                pointerAnalysis.getPointsToSet(pointerKeyForLocal);
+
+            for (InstanceKey ik : pointsToSet) {
+              if (ik instanceof AllocationSiteInNode) {
+                AllocationSiteInNode asin = (AllocationSiteInNode) ik;
+                IClass concreteType = asin.getConcreteType();
+                TypeReference reference = concreteType.getReference();
+                MethodReference methodReference = fnReference(reference);
+
+                // Get the nodes this method calls.
+                Set<CGNode> iterableNodes = callGraph.getNodes(methodReference);
+
+                for (CGNode itNode : iterableNodes)
+                  for (Iterator<CGNode> succNodes = callGraph.getSuccNodes(itNode);
+                      succNodes.hasNext(); ) {
+                    CGNode callee = succNodes.next();
+                    IMethod calledMethod = callee.getMethod();
+
+                    // Does this method call the sythetic "marker?"
+                    if (calledMethod
+                        .getName()
+                        .toString()
+                        .equals(TENSOR_ITERABLE_SYNTHETIC_FUNCTION_NAME)) {
+                      sources.add(src);
+                      logger.info("Added dataflow source from tensor iterable: " + src + ".");
+                    }
+                  }
+              }
+            }
           }
         }
       }
@@ -165,7 +237,8 @@ public TensorTypeAnalysis performAnalysis(PropagationCallGraphBuilder builder)
         SlowSparseNumberedGraph.duplicate(
             builder.getPropagationSystem().getFlowGraphIncludingImplicitConstraints());
 
-    Set<PointsToSetVariable> sources = getDataflowSources(dataflow);
+    Set<PointsToSetVariable> sources =
+        getDataflowSources(dataflow, builder.getCallGraph(), builder.getPointerAnalysis());
 
     TensorType mnistData = TensorType.mnistInput();
     Map<PointsToSetVariable, TensorType> init = HashMapFactory.make();