diff --git a/edu.cuny.hunter.hybridize.core/tensorflow.xml b/edu.cuny.hunter.hybridize.core/tensorflow.xml
index 01b98e231..bfb90ab43 100644
--- a/edu.cuny.hunter.hybridize.core/tensorflow.xml
+++ b/edu.cuny.hunter.hybridize.core/tensorflow.xml
@@ -6,44 +6,34 @@
     <class name="tensorflow" allocatable="true">
       <method name="import" static="true" descriptor="()Ltensorflow;">
         <new def="x" class="Ltensorflow" />
-
         <new def="train" class="Lobject" />
         <putfield class="LRoot" field="train" fieldType="LRoot" ref="x" value="train" />
-
         <new def="function" class="Ltensorflow/class/function" />
         <putfield class="LRoot" field="function" fieldType="LRoot" ref="x" value="function" />
-
         <new def="AdamOptimizer" class="Ltensorflow/functions/AdamOptimizer" />
         <putfield class="LRoot" field="AdamOptimizer" fieldType="LRoot" ref="train" value="AdamOptimizer" />
-
         <new def="shuffle_batch" class="Ltensorflow/functions/shuffle_batch" />
         <putfield class="LRoot" field="shuffle_batch" fieldType="LRoot" ref="train" value="shuffle_batch" />
-
         <new def="InteractiveSession" class="Ltensorflow/functions/InteractiveSession" />
         <putfield class="LRoot" field="InteractiveSession" fieldType="LRoot" ref="x" value="InteractiveSession" />
         <putfield class="LRoot" field="Session" fieldType="LRoot" ref="x" value="InteractiveSession" />
-
         <new def="parse_single_example" class="Ltensorflow/functions/parse_single_example" />
         <putfield class="LRoot" field="parse_single_example" fieldType="LRoot" ref="x" value="parse_single_example" />
-
         <new def="FixedLenFeature" class="Ltensorflow/functions/FixedLenFeature" />
         <putfield class="LRoot" field="FixedLenFeature" fieldType="LRoot" ref="x" value="FixedLenFeature" />
-
         <new def="pass_through" class="Ltensorflow/functions/pass_through" />
         <putfield class="LRoot" field="cast" fieldType="LRoot" ref="x" value="pass_through" />
         <putfield class="LRoot" field="decode_raw" fieldType="LRoot" ref="x" value="pass_through" />
-
         <new def="estimator" class="Lobject" />
         <putfield class="LRoot" field="estimator" fieldType="LRoot" ref="x" value="estimator" />
-
         <new def="data" class="Lobject" />
         <putfield class="LRoot" field="data" fieldType="LRoot" ref="x" value="data" />
-
         <new def="distribute" class="Lobject" />
         <putfield class="LRoot" field="distribute" fieldType="LRoot" ref="x" value="distribute" />
-
         <new def="nn" class="Lobject" />
         <putfield class="LRoot" field="nn" fieldType="LRoot" ref="x" value="nn" />
+        <new def="math" class="Lobject" />
+        <putfield class="LRoot" field="math" fieldType="LRoot" ref="x" value="math" />
         <new def="random" class="Lobject" />
         <putfield class="LRoot" field="random" fieldType="LRoot" ref="x" value="random" />
         <new def="sparse" class="Lobject" />
@@ -55,269 +45,196 @@
         <new def="layers" class="Lobject" />
         <putfield class="LRoot" field="layers" fieldType="LRoot" ref="x" value="layers" />
         <putfield class="LRoot" field="layers" fieldType="LRoot" ref="keras" value="layers" />
-
         <new def="app" class="Lobject" />
         <putfield class="LRoot" field="app" fieldType="LRoot" ref="x" value="app" />
         <new def="run" class="Ltensorflow/app/run" />
         <putfield class="LRoot" field="run" fieldType="LRoot" ref="app" value="run" />
-
         <new def="Estimator" class="Ltensorflow/estimator/Estimator" />
         <putfield class="LRoot" field="Estimator" fieldType="LRoot" ref="estimator" value="Estimator" />
-
         <new def="Dataset" class="Ltensorflow/data/Dataset" />
         <putfield class="LRoot" field="Dataset" fieldType="LRoot" ref="data" value="Dataset" />
-
         <new def="MirroredStrategy" class="Ltensorflow/distribute/MirroredStrategy" />
         <putfield class="LRoot" field="MirroredStrategy" fieldType="LRoot" ref="distribute" value="MirroredStrategy" />
-
         <new def="inputs" class="Lobject" />
         <putfield class="LRoot" field="inputs" fieldType="LRoot" ref="estimator" value="inputs" />
-
         <new def="numpy_input_fn" class="Ltensorflow/estimator/numpy_input_fn" />
         <putfield class="LRoot" field="numpy_input_fn" fieldType="LRoot" ref="inputs" value="numpy_input_fn" />
-
         <new def="from_tensor_slices" class="Ltensorflow/data/Dataset/from_tensor_slices" />
         <putfield class="LRoot" field="from_tensor_slices" fieldType="LRoot" ref="Dataset" value="from_tensor_slices" />
-
         <new def="reshape" class="Ltensorflow/functions/reshape" />
         <putfield class="LRoot" field="reshape" fieldType="LRoot" ref="x" value="reshape" />
-
         <new def="conv2d" class="Ltensorflow/functions/conv2d" />
         <putfield class="LRoot" field="conv2d" fieldType="LRoot" ref="x" value="conv2d" />
         <putfield class="LRoot" field="conv2d" fieldType="LRoot" ref="nn" value="conv2d" />
         <putfield class="LRoot" field="conv2d" fieldType="LRoot" ref="layers" value="conv2d" />
-
         <new def="conv3d" class="Ltensorflow/functions/conv3d" />
         <putfield class="LRoot" field="conv3d" fieldType="LRoot" ref="nn" value="conv3d" />
-
         <new def="softmax" class="Ltensorflow/functions/softmax" />
         <putfield class="LRoot" field="softmax" fieldType="LRoot" ref="nn" value="softmax" />
-
-        <new def="sigmoid" class="Ltensorflow/functions/sigmoid" />
+        <new def="sigmoid" class="Ltensorflow/math/sigmoid" />
         <putfield class="LRoot" field="sigmoid" fieldType="LRoot" ref="nn" value="sigmoid" />
-
+        <putfield class="LRoot" field="sigmoid" fieldType="LRoot" ref="math" value="sigmoid" />
+        <new def="add" class="Ltensorflow/math/add" />
+        <putfield class="LRoot" field="add" fieldType="LRoot" ref="x" value="add" />
+        <putfield class="LRoot" field="add" fieldType="LRoot" ref="math" value="add" />
         <new def="placeholder" class="Ltensorflow/functions/placeholder" />
         <putfield class="LRoot" field="placeholder" fieldType="LRoot" ref="x" value="placeholder" />
-
         <new def="examples" class="Lobject" />
         <putfield class="LRoot" field="examples" fieldType="LRoot" ref="x" value="examples" />
-
         <new def="tutorials" class="Lobject" />
         <putfield class="LRoot" field="tutorials" fieldType="LRoot" ref="examples" value="tutorials" />
-
         <new def="mnist" class="Lobject" />
         <putfield class="LRoot" field="mnist" fieldType="LRoot" ref="tutorials" value="mnist" />
-
         <new def="id" class="Ltensorflow/examples/tutorials/mnist/input_data" />
         <putfield class="LRoot" field="input_data" fieldType="LRoot" ref="mnist" value="id" />
-
         <new def="rds" class="Ltensorflow/examples/tutorials/mnist/read_data_sets" />
         <putfield class="LRoot" field="read_data_sets" fieldType="LRoot" ref="id" value="rds" />
-
         <new def="python" class="Lobject" />
         <putfield class="LRoot" field="python" fieldType="LRoot" ref="x" value="python" />
-
         <new def="framework" class="Lobject" />
         <putfield class="LRoot" field="framework" fieldType="LRoot" ref="python" value="framework" />
-
         <new def="ops" class="Lobject" />
         <putfield class="LRoot" field="ops" fieldType="LRoot" ref="python" value="ops" />
         <putfield class="LRoot" field="ops" fieldType="LRoot" ref="framework" value="ops" />
-
         <new def="ragged" class="Lobject" />
         <putfield class="LRoot" field="ragged" fieldType="LRoot" ref="x" value="ragged" />
         <putfield class="LRoot" field="ragged" fieldType="LRoot" ref="ops" value="ragged" />
-
         <new def="experimental" class="Lobject" />
         <putfield class="LRoot" field="experimental" fieldType="LRoot" ref="x" value="experimental" />
-
         <new def="numpy" class="Lobject" />
         <putfield class="LRoot" field="numpy" fieldType="LRoot" ref="experimental" value="numpy" />
-
         <new def="array_ops" class="Lobject" />
         <putfield class="LRoot" field="array_ops" fieldType="LRoot" ref="ops" value="array_ops" />
-
         <new def="random_ops" class="Lobject" />
         <putfield class="LRoot" field="random_ops" fieldType="LRoot" ref="ops" value="random_ops" />
-
         <new def="math_ops" class="Lobject" />
         <putfield class="LRoot" field="math_ops" fieldType="LRoot" ref="ops" value="math_ops" />
-
         <new def="linalg_ops" class="Lobject" />
         <putfield class="LRoot" field="linalg_ops" fieldType="LRoot" ref="ops" value="linalg_ops" />
-
         <new def="sparse_ops" class="Lobject" />
         <putfield class="LRoot" field="sparse_ops" fieldType="LRoot" ref="ops" value="sparse_ops" />
-
         <new def="variables" class="Lobject" />
         <putfield class="LRoot" field="variables" fieldType="LRoot" ref="ops" value="variables" />
-
         <new def="sparse_tensor" class="Lobject" />
         <putfield class="LRoot" field="sparse_tensor" fieldType="LRoot" ref="framework" value="sparse_tensor" />
-
         <new def="constant_op" class="Lobject" />
         <putfield class="LRoot" field="constant_op" fieldType="LRoot" ref="framework" value="constant_op" />
-
         <new def="ragged_factory_ops" class="Lobject" />
         <putfield class="LRoot" field="ragged_factory_ops" fieldType="LRoot" ref="ragged" value="ragged_factory_ops" />
-
         <new def="ragged_math_ops" class="Lobject" />
         <putfield class="LRoot" field="ragged_math_ops" fieldType="LRoot" ref="ragged" value="ragged_math_ops" />
-
         <new def="ragged_tensor" class="Lobject" />
         <putfield class="LRoot" field="ragged_tensor" fieldType="LRoot" ref="ragged" value="ragged_tensor" />
-
         <new def="ndarray" class="Ltensorflow/functions/ndarray" />
         <putfield class="LRoot" field="ndarray" fieldType="LRoot" ref="numpy" value="ndarray" />
-
         <new def="Input" class="Ltensorflow/functions/Input" />
         <putfield class="LRoot" field="Input" fieldType="LRoot" ref="keras" value="Input" />
         <putfield class="LRoot" field="Input" fieldType="LRoot" ref="layers" value="Input" />
-
         <new def="Dense" class="Ltensorflow/keras/layers/Dense" />
         <putfield class="LRoot" field="Dense" fieldType="LRoot" ref="layers" value="Dense" />
-
         <new def="Variable" class="Ltensorflow/functions/Variable" />
         <putfield class="LRoot" field="Variable" fieldType="LRoot" ref="x" value="Variable" />
         <putfield class="LRoot" field="Variable" fieldType="LRoot" ref="variables" value="Variable" />
-
         <new def="ones" class="Ltensorflow/functions/ones" />
         <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="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" />
-
         <new def="fill" class="Ltensorflow/functions/fill" />
         <putfield class="LRoot" field="fill" fieldType="LRoot" ref="x" value="fill" />
         <putfield class="LRoot" field="fill" fieldType="LRoot" ref="array_ops" value="fill" />
-
         <new def="zeros_like" class="Ltensorflow/functions/zeros_like" />
         <putfield class="LRoot" field="zeros_like" fieldType="LRoot" ref="x" value="zeros_like" />
         <putfield class="LRoot" field="zeros_like" fieldType="LRoot" ref="array_ops" value="zeros_like" />
-
         <new def="one_hot" class="Ltensorflow/functions/one_hot" />
         <putfield class="LRoot" field="one_hot" fieldType="LRoot" ref="x" value="one_hot" />
         <putfield class="LRoot" field="one_hot" fieldType="LRoot" ref="array_ops" value="one_hot" />
-
         <new def="uniform" class="Ltensorflow/functions/uniform" />
         <putfield class="LRoot" field="uniform" fieldType="LRoot" ref="random" value="uniform" />
         <putfield class="LRoot" field="random_uniform" fieldType="LRoot" ref="random_ops" value="uniform" />
-
         <new def="gamma" class="Ltensorflow/functions/gamma" />
         <putfield class="LRoot" field="gamma" fieldType="LRoot" ref="random" value="gamma" />
         <putfield class="LRoot" field="random_gamma" fieldType="LRoot" ref="random_ops" value="gamma" />
-
         <new def="normal" class="Ltensorflow/functions/normal" />
         <putfield class="LRoot" field="normal" fieldType="LRoot" ref="random" value="normal" />
         <putfield class="LRoot" field="random_normal" fieldType="LRoot" ref="random_ops" value="normal" />
-
         <new def="poisson" class="Ltensorflow/functions/poisson" />
         <putfield class="LRoot" field="poisson" fieldType="LRoot" ref="random" value="poisson" />
         <putfield class="LRoot" field="random_poisson_v2" fieldType="LRoot" ref="random_ops" value="poisson" />
-
         <new def="truncated_normal" class="Ltensorflow/functions/truncated_normal" />
         <putfield class="LRoot" field="truncated_normal" fieldType="LRoot" ref="random" value="truncated_normal" />
         <putfield class="LRoot" field="truncated_normal" fieldType="LRoot" ref="random_ops" value="truncated_normal" />
-
         <new def="range" class="Ltensorflow/functions/range" />
         <putfield class="LRoot" field="range" fieldType="LRoot" ref="x" value="range" />
         <putfield class="LRoot" field="range" fieldType="LRoot" ref="math_ops" value="range" />
-
         <new def="ragged_range" class="Ltensorflow/functions/ragged_range" />
         <putfield class="LRoot" field="range" fieldType="LRoot" ref="ragged" value="ragged_range" />
         <putfield class="LRoot" field="range" fieldType="LRoot" ref="ragged_math_ops" value="ragged_range" />
-
         <new def="eye" class="Ltensorflow/functions/eye" />
         <putfield class="LRoot" field="eye" fieldType="LRoot" ref="x" value="eye" />
         <putfield class="LRoot" field="eye" fieldType="LRoot" ref="linalg" value="eye" />
         <putfield class="LRoot" field="eye" fieldType="LRoot" ref="linalg_ops" value="eye" />
-
         <new def="constant" class="Ltensorflow/functions/constant" />
         <putfield class="LRoot" field="constant" fieldType="LRoot" ref="x" value="constant" />
         <putfield class="LRoot" field="constant" fieldType="LRoot" ref="constant_op" value="constant" />
-
         <new def="ragged_constant" class="Ltensorflow/functions/ragged_constant" />
         <putfield class="LRoot" field="constant" fieldType="LRoot" ref="ragged" value="ragged_constant" />
         <putfield class="LRoot" field="constant" fieldType="LRoot" ref="ragged_factory_ops" value="ragged_constant" />
-
         <new def="SparseTensor" class="Ltensorflow/functions/SparseTensor" />
         <putfield class="LRoot" field="SparseTensor" fieldType="LRoot" ref="x" value="SparseTensor" />
         <putfield class="LRoot" field="SparseTensor" fieldType="LRoot" ref="sparse" value="SparseTensor" />
         <putfield class="LRoot" field="SparseTensor" fieldType="LRoot" ref="sparse_tensor" value="SparseTensor" />
-
         <new def="sparse_eye" class="Ltensorflow/functions/sparse_eye" />
         <putfield class="LRoot" field="eye" fieldType="LRoot" ref="sparse" value="sparse_eye" />
         <putfield class="LRoot" field="sparse_eye" fieldType="LRoot" ref="sparse_ops" value="sparse_eye" />
-
         <new def="convert_to_tensor" class="Ltensorflow/functions/convert_to_tensor" />
         <putfield class="LRoot" field="convert_to_tensor" fieldType="LRoot" ref="x" value="convert_to_tensor" />
         <putfield class="LRoot" field="convert_to_tensor" fieldType="LRoot" ref="ops" value="convert_to_tensor" />
-
         <new def="Tensor" class="Ltensorflow/functions/Tensor" />
         <putfield class="LRoot" field="Tensor" fieldType="LRoot" ref="x" value="Tensor" />
         <putfield class="LRoot" field="Tensor" fieldType="LRoot" ref="ops" value="Tensor" />
-
         <new def="RaggedTensor" class="Lobject" />
         <putfield class="LRoot" field="RaggedTensor" fieldType="LRoot" ref="x" value="RaggedTensor" />
         <putfield class="LRoot" field="RaggedTensor" fieldType="LRoot" ref="ragged_tensor" value="RaggedTensor" />
-
         <new def="from_nested_row_lengths" class="Ltensorflow/functions/from_nested_row_lengths" />
         <putfield class="LRoot" field="from_nested_row_lengths" fieldType="LRoot" ref="RaggedTensor" value="from_nested_row_lengths" />
-
         <new def="from_nested_row_splits" class="Ltensorflow/functions/from_nested_row_splits" />
         <putfield class="LRoot" field="from_nested_row_splits" fieldType="LRoot" ref="RaggedTensor" value="from_nested_row_splits" />
-
         <new def="from_nested_value_rowids" class="Ltensorflow/functions/from_nested_value_rowids" />
         <putfield class="LRoot" field="from_nested_value_rowids" fieldType="LRoot" ref="RaggedTensor" value="from_nested_value_rowids" />
-
         <new def="from_row_lengths" class="Ltensorflow/functions/from_row_lengths" />
         <putfield class="LRoot" field="from_row_lengths" fieldType="LRoot" ref="RaggedTensor" value="from_row_lengths" />
-
         <new def="from_row_limits" class="Ltensorflow/functions/from_row_limits" />
         <putfield class="LRoot" field="from_row_limits" fieldType="LRoot" ref="RaggedTensor" value="from_row_limits" />
-
         <new def="from_row_splits" class="Ltensorflow/functions/from_row_splits" />
         <putfield class="LRoot" field="from_row_splits" fieldType="LRoot" ref="RaggedTensor" value="from_row_splits" />
-
         <new def="from_row_starts" class="Ltensorflow/functions/from_row_starts" />
         <putfield class="LRoot" field="from_row_starts" fieldType="LRoot" ref="RaggedTensor" value="from_row_starts" />
-
         <new def="from_value_rowids" class="Ltensorflow/functions/from_value_rowids" />
         <putfield class="LRoot" field="from_value_rowids" fieldType="LRoot" ref="RaggedTensor" value="from_value_rowids" />
-
         <return value="x" />
       </method>
     </class>
-
     <class name="keras" allocatable="true">
       <method name="import" static="true" descriptor="()Lkeras;">
         <new def="x" class="Lkeras" />
-
         <new def="engine" class="Lobject" />
         <putfield class="LRoot" field="engine" fieldType="LRoot" ref="x" value="engine" />
-
         <new def="input_layer" class="Lobject" />
         <putfield class="LRoot" field="input_layer" fieldType="LRoot" ref="engine" value="input_layer" />
-
         <new def="Input" class="Lkeras/engine/input_layer/Input" />
         <putfield class="LRoot" field="Input" fieldType="LRoot" ref="input_layer" value="Input" />
         <return value="x" />
       </method>
     </class>
-
     <package name="tensorflow/class">
-
       <class name="Function" allocatable="true">
         <method name="do" descriptor="()LRoot;" numArgs="2" paramNames="self test">
-
           <putfield class="LRoot" field="params" fieldType="LRoot" ref="test" value="self" />
-
           <return value="test" />
         </method>
       </class>
-
       <class name="function" allocatable="true">
         <!-- These parameters are from TensorFlow v.2.9 https://www.tensorflow.org/versions/r2.9/api_docs/python/tf/function -->
         <method name="do" descriptor="()LRoot;" numArgs="10" paramNames="func input_signature autograph jit_compile reduce_retracing experimental_implements experimental_autograph_options experimental_relax_shapes experimental_compile experimental_follow_type_hints">
@@ -336,28 +253,41 @@
         </method>
       </class>
     </package>
-
     <package name="tensorflow/objects">
       <class name="feature" allocatable="true" />
     </package>
-
     <package name="keras/objects">
       <class name="feature" allocatable="true" />
     </package>
-
+    <package name="tensorflow/math">
+      <class name="sigmoid" allocatable="true">
+        <!-- https://www.tensorflow.org/versions/r2.9/api_docs/python/tf/nn/sigmoid -->
+        <method name="do" descriptor="()LRoot;" numArgs="3" paramNames="self x name">
+          <return value="x" />
+        </method>
+      </class>
+      <class name="add" allocatable="true">
+        <!-- https://www.tensorflow.org/versions/r2.9/api_docs/python/tf/add -->
+        <method name="read_data" descriptor="()LRoot;">
+          <new def="x" class="Ltensorflow/math/add" />
+          <return value="x" />
+        </method>
+        <method name="do" descriptor="()LRoot;" numArgs="4" paramNames="self x y name">
+          <!-- Even though tf.add() isn't a tensor "generator," it can convert its non-tensor arguments to tensors. -->
+          <call class="LRoot" name="read_data" descriptor="()LRoot;" type="virtual" arg0="arg0" def="xx" />
+          <return value="xx" />
+        </method>
+      </class>
+    </package>
     <package name="tensorflow/functions">
-
       <class name="AdamOptimizer" allocatable="true">
         <method name="do" descriptor="()LRoot;">
           <new def="opt" class="Lobject" />
-
           <new def="minimize" class="Ltensorflow/functions/minimize" />
           <putfield class="LRoot" field="minimize" fieldType="LRoot" ref="opt" value="minimize" />
-
           <return value="opt" />
         </method>
       </class>
-
       <class name="minimize" allocatable="true">
         <method name="do" descriptor="()LRoot;">
           <new def="v" class="Lobject" />
@@ -366,13 +296,11 @@
           <return value="v" />
         </method>
       </class>
-
       <class name="shuffle_batch" allocatable="true">
         <method name="do" descriptor="()LRoot;" numArgs="3" paramNames="self data">
           <return value="data" />
         </method>
       </class>
-
       <class name="InteractiveSession" allocatable="true">
         <method name="do" descriptor="()LRoot;">
           <new def="v" class="Lobject" />
@@ -381,31 +309,26 @@
           <return value="v" />
         </method>
       </class>
-
       <class name="Runner" allocatable="true">
         <method name="do" descriptor="()LRoot;" numArgs="3" paramNames="self graph feed_dict">
           <return value="self" />
         </method>
       </class>
-
       <class name="set_shape" allocatable="true">
         <method name="do" descriptor="()LRoot;" numArgs="2" paramNames="self shape">
           <return value="self" />
         </method>
       </class>
-
       <class name="reshape" allocatable="true">
         <method name="copy_data" descriptor="()LRoot;">
           <new def="x" class="Ltensorflow/examples/tutorials/mnist/dataset" />
           <return value="x" />
         </method>
-
         <method name="do" descriptor="()LRoot;" numArgs="3">
           <call class="LRoot" name="copy_data" descriptor="()LRoot;" type="virtual" arg0="arg0" def="x" />
           <return value="x" />
         </method>
       </class>
-
       <class name="ones" allocatable="true">
         <method name="read_data" descriptor="()LRoot;">
           <new def="x" class="Ltensorflow/python/ops/array_ops/ones" />
@@ -416,7 +339,6 @@
           <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" />
@@ -427,7 +349,6 @@
           <return value="x" />
         </method>
       </class>
-
       <class name="constant" allocatable="true">
         <method name="read_data" descriptor="()LRoot;">
           <new def="x" class="Ltensorflow/python/framework/constant_op/constant" />
@@ -438,7 +359,6 @@
           <return value="x" />
         </method>
       </class>
-
       <class name="zeros" allocatable="true">
         <method name="read_data" descriptor="()LRoot;">
           <new def="x" class="Ltensorflow/python/ops/array_ops/zeros" />
@@ -449,7 +369,6 @@
           <return value="x" />
         </method>
       </class>
-
       <class name="SparseTensor" allocatable="true">
         <method name="read_data" descriptor="()LRoot;">
           <new def="x" class="Ltensorflow/python/framework/sparse_tensor/SparseTensor" />
@@ -460,7 +379,6 @@
           <return value="x" />
         </method>
       </class>
-
       <class name="sparse_eye" allocatable="true">
         <method name="read_data" descriptor="()LRoot;">
           <new def="x" class="Ltensorflow/python/framework/sparse_tensor/sparse_eye" />
@@ -471,7 +389,6 @@
           <return value="x" />
         </method>
       </class>
-
       <class name="fill" allocatable="true">
         <method name="read_data" descriptor="()LRoot;">
           <new def="x" class="Ltensorflow/python/ops/array_ops/fill" />
@@ -482,7 +399,6 @@
           <return value="x" />
         </method>
       </class>
-
       <class name="zeros_like" allocatable="true">
         <method name="read_data" descriptor="()LRoot;">
           <new def="x" class="Ltensorflow/python/ops/array_ops/zeros_like" />
@@ -493,7 +409,6 @@
           <return value="x" />
         </method>
       </class>
-
       <class name="one_hot" allocatable="true">
         <method name="read_data" descriptor="()LRoot;">
           <new def="x" class="Ltensorflow/python/ops/array_ops/one_hot" />
@@ -504,7 +419,6 @@
           <return value="x" />
         </method>
       </class>
-
       <class name="convert_to_tensor" allocatable="true">
         <method name="read_data" descriptor="()LRoot;">
           <new def="x" class="Ltensorflow/python/framework/ops/convert_to_tensor" />
@@ -515,7 +429,6 @@
           <return value="x" />
         </method>
       </class>
-
       <class name="range" allocatable="true">
         <method name="read_data" descriptor="()LRoot;">
           <new def="x" class="Ltensorflow/python/ops/math_ops/range" />
@@ -526,7 +439,6 @@
           <return value="x" />
         </method>
       </class>
-
       <class name="Tensor" allocatable="true">
         <method name="read_data" descriptor="()LRoot;">
           <new def="x" class="Ltensorflow/python/framework/ops/Tensor" />
@@ -537,7 +449,6 @@
           <return value="x" />
         </method>
       </class>
-
       <class name="ndarray" allocatable="true">
         <method name="read_data" descriptor="()LRoot;">
           <new def="x" class="Ltensorflow/python/framework/ops/ndarray" />
@@ -548,7 +459,6 @@
           <return value="x" />
         </method>
       </class>
-
       <class name="ragged_range" allocatable="true">
         <method name="read_data" descriptor="()LRoot;">
           <new def="x" class="Ltensorflow/python/ops/ragged/ragged_math_ops/range" />
@@ -559,7 +469,6 @@
           <return value="x" />
         </method>
       </class>
-
       <class name="ragged_constant" allocatable="true">
         <method name="read_data" descriptor="()LRoot;">
           <new def="x" class="Ltensorflow/python/ops/ragged/ragged_factory_ops/constant" />
@@ -570,7 +479,6 @@
           <return value="x" />
         </method>
       </class>
-
       <class name="eye" allocatable="true">
         <method name="read_data" descriptor="()LRoot;">
           <new def="x" class="Ltensorflow/python/ops/linalg_ops/eye" />
@@ -581,7 +489,6 @@
           <return value="x" />
         </method>
       </class>
-
       <class name="uniform" allocatable="true">
         <method name="read_data" descriptor="()LRoot;">
           <new def="x" class="Ltensorflow/python/ops/random_ops/uniform" />
@@ -592,7 +499,6 @@
           <return value="x" />
         </method>
       </class>
-
       <class name="gamma" allocatable="true">
         <method name="read_data" descriptor="()LRoot;">
           <new def="x" class="Ltensorflow/python/ops/random_ops/gamma" />
@@ -603,7 +509,6 @@
           <return value="x" />
         </method>
       </class>
-
       <class name="normal" allocatable="true">
         <method name="read_data" descriptor="()LRoot;">
           <new def="x" class="Ltensorflow/python/ops/random_ops/normal" />
@@ -614,7 +519,6 @@
           <return value="x" />
         </method>
       </class>
-
       <class name="poisson" allocatable="true">
         <method name="read_data" descriptor="()LRoot;">
           <new def="x" class="Ltensorflow/python/ops/random_ops/poisson" />
@@ -625,7 +529,6 @@
           <return value="x" />
         </method>
       </class>
-
       <class name="truncated_normal" allocatable="true">
         <method name="read_data" descriptor="()LRoot;">
           <new def="x" class="Ltensorflow/python/ops/random_ops/truncated_normal" />
@@ -636,7 +539,6 @@
           <return value="x" />
         </method>
       </class>
-
       <class name="Input" allocatable="true">
         <method name="read_data" descriptor="()LRoot;">
           <new def="x" class="Ltensorflow/functions/Input" />
@@ -647,7 +549,6 @@
           <return value="x" />
         </method>
       </class>
-
       <class name="from_nested_row_lengths" allocatable="true">
         <method name="read_data" descriptor="()LRoot;">
           <new def="x" class="Ltensorflow/python/ops/ragged/ragged_tensor/from_nested_row_lengths" />
@@ -658,7 +559,6 @@
           <return value="x" />
         </method>
       </class>
-
       <class name="from_nested_row_splits" allocatable="true">
         <method name="read_data" descriptor="()LRoot;">
           <new def="x" class="Ltensorflow/python/ops/ragged/ragged_tensor/from_nested_row_splits" />
@@ -669,7 +569,6 @@
           <return value="x" />
         </method>
       </class>
-
       <class name="from_nested_value_rowids" allocatable="true">
         <method name="read_data" descriptor="()LRoot;">
           <new def="x" class="Ltensorflow/python/ops/ragged/ragged_tensor/from_nested_value_rowids" />
@@ -680,7 +579,6 @@
           <return value="x" />
         </method>
       </class>
-
       <class name="from_row_lengths" allocatable="true">
         <method name="read_data" descriptor="()LRoot;">
           <new def="x" class="Ltensorflow/python/ops/ragged/ragged_tensor/from_row_lengths" />
@@ -691,7 +589,6 @@
           <return value="x" />
         </method>
       </class>
-
       <class name="from_row_limits" allocatable="true">
         <method name="read_data" descriptor="()LRoot;">
           <new def="x" class="Ltensorflow/python/ops/ragged/ragged_tensor/from_row_limits" />
@@ -702,7 +599,6 @@
           <return value="x" />
         </method>
       </class>
-
       <class name="from_row_splits" allocatable="true">
         <method name="read_data" descriptor="()LRoot;">
           <new def="x" class="Ltensorflow/python/ops/ragged/ragged_tensor/from_row_splits" />
@@ -713,7 +609,6 @@
           <return value="x" />
         </method>
       </class>
-
       <class name="from_row_starts" allocatable="true">
         <method name="read_data" descriptor="()LRoot;">
           <new def="x" class="Ltensorflow/python/ops/ragged/ragged_tensor/from_row_starts" />
@@ -724,7 +619,6 @@
           <return value="x" />
         </method>
       </class>
-
       <class name="from_value_rowids" allocatable="true">
         <method name="read_data" descriptor="()LRoot;">
           <new def="x" class="Ltensorflow/python/ops/ragged/ragged_tensor/from_value_rowids" />
@@ -735,64 +629,47 @@
           <return value="x" />
         </method>
       </class>
-
       <class name="placeholder" allocatable="true">
         <method name="do" descriptor="()LRoot;" numArgs="3" paramNames="dtype shape name">
           <new def="x" class="Lobject" />
           <return value="x" />
         </method>
       </class>
-
       <class name="pass_through" allocatable="true">
         <method name="do" descriptor="()LRoot;" numArgs="3" paramNames="self data features">
           <return value="data" />
         </method>
       </class>
-
       <class name="parse_single_example" allocatable="true">
         <method name="do" descriptor="()LRoot;" numArgs="3" paramNames="self data features">
           <return value="features" />
         </method>
       </class>
-
       <class name="FixedLenFeature" allocatable="true">
         <method name="do" descriptor="()LRoot;" numArgs="3" paramNames="self dims type">
           <new def="x" class="Ltensorflow/objects/feature" />
-
           <new def="y" class="Ltensorflow/functions/set_shape" />
           <putfield class="LRoot" field="set_shape" fieldType="LRoot" ref="x" value="y" />
-
           <return value="x" />
         </method>
       </class>
-
       <class name="conv2d" allocatable="true">
         <method name="do" descriptor="()LRoot;" numArgs="3" paramNames="self x y">
           <return value="x" />
         </method>
       </class>
-
       <class name="conv3d" allocatable="true">
         <method name="do" descriptor="()LRoot;" numArgs="3" paramNames="self x y">
           <return value="x" />
         </method>
       </class>
-
       <class name="softmax" allocatable="true">
         <!-- https://www.tensorflow.org/versions/r2.9/api_docs/python/tf/nn/softmax -->
         <method name="do" descriptor="()LRoot;" numArgs="4" paramNames="self logits axis name">
           <return value="logits" />
         </method>
       </class>
-
-      <class name="sigmoid" allocatable="true">
-        <!-- https://www.tensorflow.org/versions/r2.9/api_docs/python/tf/nn/sigmoid -->
-        <method name="do" descriptor="()LRoot;" numArgs="3" paramNames="self x name">
-          <return value="x" />
-        </method>
-      </class>
     </package>
-
     <package name="tensorflow/estimator">
       <class name="Estimator" allocatable="true">
         <method name="do" descriptor="()LRoot;" numArgs="2" paramNames="self model">
@@ -802,7 +679,6 @@
           <return value="arg0" />
         </method>
       </class>
-
       <class name="numpy_input_fn" allocatable="true">
         <method name="do" descriptor="()LRoot;" numArgs="5" paramNames="self x y batch_size shuffle">
           <new def="xx" class="Lobject" />
@@ -812,7 +688,6 @@
         </method>
       </class>
     </package>
-
     <package name="tensorflow/keras/layers">
       <class name="Dense" allocatable="true">
         <!-- https://www.tensorflow.org/versions/r2.9/api_docs/python/tf/keras/layers/Dense -->
@@ -839,7 +714,6 @@
         </method>
       </class>
     </package>
-
     <package name="tensorflow/data">
       <class name="Dataset" allocatable="true">
         <!-- "read_dataset" means that this function reads a tensor iterable. -->
@@ -861,7 +735,6 @@
           <return value="x" />
         </method>
       </class>
-
       <class name="shuffle" allocatable="true">
         <!-- https://www.tensorflow.org/versions/r2.9/api_docs/python/tf/data/Dataset#shuffle -->
         <method name="do" descriptor="()LRoot;" numArgs="5" paramNames="self buffer_size seed reshuffle_each_iteration name">
@@ -871,7 +744,6 @@
           <return value="xx" />
         </method>
       </class>
-
       <class name="batch" allocatable="true">
         <!-- https://www.tensorflow.org/versions/r2.9/api_docs/python/tf/data/Dataset#batch -->
         <method name="do" descriptor="()LRoot;" numArgs="6" paramNames="self batch_size drop_remainder num_parallel_calls deterministic name">
@@ -881,7 +753,6 @@
           <return value="xx" />
         </method>
       </class>
-
       <class name="repeat" allocatable="true">
         <!-- https://www.tensorflow.org/versions/r2.9/api_docs/python/tf/data/Dataset#repeat -->
         <method name="do" descriptor="()LRoot;" numArgs="6" paramNames="self count name">
@@ -891,7 +762,6 @@
           <return value="xx" />
         </method>
       </class>
-
       <class name="prefetch" allocatable="true">
         <!-- https://www.tensorflow.org/versions/r2.9/api_docs/python/tf/data/Dataset#prefetch -->
         <method name="do" descriptor="()LRoot;" numArgs="3" paramNames="self buffer_size name">
@@ -901,7 +771,6 @@
           <return value="xx" />
         </method>
       </class>
-
       <class name="take" allocatable="true">
         <!-- https://www.tensorflow.org/versions/r2.9/api_docs/python/tf/data/Dataset#take -->
         <method name="do" descriptor="()LRoot;" numArgs="3" paramNames="self count name">
@@ -912,7 +781,6 @@
         </method>
       </class>
     </package>
-
     <package name="tensorflow/data/Dataset">
       <class name="from_tensor_slices" allocatable="true">
         <method name="do" descriptor="()LRoot;" numArgs="2" paramNames="tensors name">
@@ -922,7 +790,6 @@
         </method>
       </class>
     </package>
-
     <package name="tensorflow/estimator/train">
       <class name="train" allocatable="true">
         <method name="do" descriptor="()LRoot;" numArgs="3">
@@ -933,7 +800,6 @@
         </method>
       </class>
     </package>
-
     <package name="tensorflow/distribute">
       <class name="MirroredStrategy" allocatable="true">
         <method name="do" descriptor="()LRoot;" numArgs="3" paramNames="self devices cross_device_ops">
@@ -943,7 +809,6 @@
         </method>
       </class>
     </package>
-
     <package name="tensorflow/distribute/run">
       <class name="run" allocatable="true">
         <method name="do" descriptor="()LRoot;" numArgs="3">
@@ -953,7 +818,6 @@
         </method>
       </class>
     </package>
-
     <package name="tensorflow/app">
       <class name="run" allocatable="true">
         <method name="do" descriptor="()LRoot;" numArgs="3" paramNames="self main argv">
@@ -962,30 +826,22 @@
         </method>
       </class>
     </package>
-
     <package name="tensorflow/examples/tutorials/mnist">
       <class name="read_data_sets" allocatable="true">
         <method name="read_data" descriptor="()LRoot;">
           <new def="x" class="Ltensorflow/examples/tutorials/mnist/dataset" />
           <return value="x" />
         </method>
-
         <method name="do" descriptor="()LRoot;">
           <new def="test" class="Lobject" />
-
           <call class="LRoot" name="read_data" descriptor="()LRoot;" type="virtual" arg0="arg0" def="x" />
-
           <putfield class="LRoot" field="images" fieldType="LRoot" ref="test" value="x" />
-
           <call class="LRoot" name="read_data" descriptor="()LRoot;" type="virtual" arg0="arg0" def="y" />
-
           <new def="training" class="Lobject" />
           <putfield class="LRoot" field="images" fieldType="LRoot" ref="training" value="y" />
-
           <new def="data" class="Lobject" />
           <putfield class="LRoot" field="test" fieldType="LRoot" ref="data" value="test" />
           <putfield class="LRoot" field="train" fieldType="LRoot" ref="data" value="training" />
-
           <return value="data" />
         </method>
       </class>
@@ -994,7 +850,6 @@
       <class name="dataset" allocatable="true">
       </class>
     </package>
-
     <package name="tensorflow/examples/tutorials">
       <class name="mnist" allocatable="true">
         <method name="import" static="true" descriptor="()Ltensorflow/examples/tutorials/mnist;">
@@ -1010,7 +865,6 @@
         </method>
       </class>
     </package>
-
     <package name="keras/engine/input_layer">
       <class name="Input" allocatable="true">
         <method name="read_data" descriptor="()LRoot;">
@@ -1023,6 +877,5 @@
         </method>
       </class>
     </package>
-
   </classloader>
 </summary-spec>
diff --git a/edu.cuny.hunter.hybridize.tests/resources/HybridizeFunction/testAutoEncoder/in/A.py b/edu.cuny.hunter.hybridize.tests/resources/HybridizeFunction/testAutoEncoder/in/A.py
new file mode 100644
index 000000000..9e99da203
--- /dev/null
+++ b/edu.cuny.hunter.hybridize.tests/resources/HybridizeFunction/testAutoEncoder/in/A.py
@@ -0,0 +1,188 @@
+# From https://github.com/aymericdamien/TensorFlow-Examples/blob/6dcbe14649163814e72a22a999f20c5e247ce988/tensorflow_v2/notebooks/3_NeuralNetworks/autoencoder.ipynb.
+
+# %%
+# """
+# # Auto-Encoder Example
+
+# Build a 2 layers auto-encoder with TensorFlow v2 to compress images to a lower latent space and then reconstruct them.
+
+# - Author: Aymeric Damien
+# - Project: https://github.com/aymericdamien/TensorFlow-Examples/
+# """
+
+# %%
+# """
+# ## Auto-Encoder Overview
+
+# <img src="http://kvfrans.com/content/images/2016/08/autoenc.jpg" alt="ae" style="width: 800px;"/>
+
+# References:
+# - [Gradient-based learning applied to document recognition](http://yann.lecun.com/exdb/publis/pdf/lecun-01a.pdf). Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. Proceedings of the IEEE, 86(11):2278-2324, November 1998.
+
+# ## MNIST Dataset Overview
+
+# This example is using MNIST handwritten digits. The dataset contains 60,000 examples for training and 10,000 examples for testing. The digits have been size-normalized and centered in a fixed-size image (28x28 pixels) with values from 0 to 255.
+
+# In this example, each image will be converted to float32, normalized to [0, 1] and flattened to a 1-D array of 784 features (28*28).
+
+# ![MNIST Dataset](http://neuralnetworksanddeeplearning.com/images/mnist_100_digits.png)
+
+# More info: http://yann.lecun.com/exdb/mnist/
+# """
+
+# %%
+from __future__ import absolute_import, division, print_function
+
+import tensorflow as tf
+print("TensorFlow version:", tf.__version__)
+assert(tf.__version__ == "2.9.3")
+import numpy as np
+
+# %%
+# MNIST Dataset parameters.
+num_features = 784  # data features (img shape: 28*28).
+
+# Training parameters.
+learning_rate = 0.01
+training_steps = 1
+batch_size = 256
+display_step = 1000
+
+# Network Parameters
+num_hidden_1 = 128  # 1st layer num features.
+num_hidden_2 = 64  # 2nd layer num features (the latent dim).
+
+# %%
+# Prepare MNIST data.
+from tensorflow.keras.datasets import mnist
+(x_train, y_train), (x_test, y_test) = mnist.load_data()
+# Convert to float32.
+x_train, x_test = x_train.astype(np.float32), x_test.astype(np.float32)
+# Flatten images to 1-D vector of 784 features (28*28).
+x_train, x_test = x_train.reshape([-1, num_features]), x_test.reshape([-1, num_features])
+# Normalize images value from [0, 255] to [0, 1].
+x_train, x_test = x_train / 255., x_test / 255.
+
+# %%
+# Use tf.data API to shuffle and batch data.
+train_data = tf.data.Dataset.from_tensor_slices((x_train, y_train))
+train_data = train_data.repeat().shuffle(10000).batch(batch_size).prefetch(1)
+
+test_data = tf.data.Dataset.from_tensor_slices((x_test, y_test))
+test_data = test_data.repeat().batch(batch_size).prefetch(1)
+
+# %%
+# Store layers weight & bias
+
+# A random value generator to initialize weights.
+random_normal = tf.initializers.RandomNormal()
+
+weights = {
+    'encoder_h1': tf.Variable(random_normal([num_features, num_hidden_1])),
+    'encoder_h2': tf.Variable(random_normal([num_hidden_1, num_hidden_2])),
+    'decoder_h1': tf.Variable(random_normal([num_hidden_2, num_hidden_1])),
+    'decoder_h2': tf.Variable(random_normal([num_hidden_1, num_features])),
+}
+biases = {
+    'encoder_b1': tf.Variable(random_normal([num_hidden_1])),
+    'encoder_b2': tf.Variable(random_normal([num_hidden_2])),
+    'decoder_b1': tf.Variable(random_normal([num_hidden_1])),
+    'decoder_b2': tf.Variable(random_normal([num_features])),
+}
+
+
+# %%
+# Building the encoder.
+def encoder(x):
+    # Encoder Hidden layer with sigmoid activation.
+    layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(x, weights['encoder_h1']),
+                                   biases['encoder_b1']))
+    # Encoder Hidden layer with sigmoid activation.
+    layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1, weights['encoder_h2']),
+                                   biases['encoder_b2']))
+    return layer_2
+
+
+# Building the decoder.
+def decoder(x):
+    # Decoder Hidden layer with sigmoid activation.
+    layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(x, weights['decoder_h1']),
+                                   biases['decoder_b1']))
+    # Decoder Hidden layer with sigmoid activation.
+    layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1, weights['decoder_h2']),
+                                   biases['decoder_b2']))
+    return layer_2
+
+
+# %%
+# Mean square loss between original images and reconstructed ones.
+def mean_square(reconstructed, original):
+    return tf.reduce_mean(tf.pow(original - reconstructed, 2))
+
+
+# Adam optimizer.
+optimizer = tf.optimizers.Adam(learning_rate=learning_rate)
+
+
+# %%
+# Optimization process.
+def run_optimization(x):
+    # Wrap computation inside a GradientTape for automatic differentiation.
+    with tf.GradientTape() as g:
+        reconstructed_image = decoder(encoder(x))
+        loss = mean_square(reconstructed_image, x)
+
+    # Variables to update, i.e. trainable variables.
+    trainable_variables = list(weights.values()) + list(biases.values())
+
+    # Compute gradients.
+    gradients = g.gradient(loss, trainable_variables)
+
+    # Update W and b following gradients.
+    optimizer.apply_gradients(zip(gradients, trainable_variables))
+
+    return loss
+
+
+# %%
+# Run training for the given number of steps.
+for step, (batch_x, _) in enumerate(train_data.take(training_steps + 1)):
+
+    # Run the optimization.
+    loss = run_optimization(batch_x)
+
+    if step % display_step == 0:
+        print("step: %i, loss: %f" % (step, loss))
+
+# %%
+# Testing and Visualization.
+import matplotlib.pyplot as plt
+
+# %%
+# Encode and decode images from test set and visualize their reconstruction.
+n = 4
+canvas_orig = np.empty((28 * n, 28 * n))
+canvas_recon = np.empty((28 * n, 28 * n))
+for i, (batch_x, _) in enumerate(test_data.take(n)):
+    # Encode and decode the digit image.
+    reconstructed_images = decoder(encoder(batch_x))
+    # Display original images.
+    for j in range(n):
+        # Draw the generated digits.
+        img = batch_x[j].numpy().reshape([28, 28])
+        canvas_orig[i * 28:(i + 1) * 28, j * 28:(j + 1) * 28] = img
+    # Display reconstructed images.
+    for j in range(n):
+        # Draw the generated digits.
+        reconstr_img = reconstructed_images[j].numpy().reshape([28, 28])
+        canvas_recon[i * 28:(i + 1) * 28, j * 28:(j + 1) * 28] = reconstr_img
+
+# print("Original Images")
+# plt.figure(figsize=(n, n))
+# plt.imshow(canvas_orig, origin="upper", cmap="gray")
+# plt.show()
+#
+# print("Reconstructed Images")
+# plt.figure(figsize=(n, n))
+# plt.imshow(canvas_recon, origin="upper", cmap="gray")
+# plt.show()
diff --git a/edu.cuny.hunter.hybridize.tests/resources/HybridizeFunction/testAutoEncoder/in/requirements.txt b/edu.cuny.hunter.hybridize.tests/resources/HybridizeFunction/testAutoEncoder/in/requirements.txt
new file mode 100644
index 000000000..b154f958f
--- /dev/null
+++ b/edu.cuny.hunter.hybridize.tests/resources/HybridizeFunction/testAutoEncoder/in/requirements.txt
@@ -0,0 +1 @@
+tensorflow==2.9.3
diff --git a/edu.cuny.hunter.hybridize.tests/test cases/edu/cuny/hunter/hybridize/tests/HybridizeFunctionRefactoringTest.java b/edu.cuny.hunter.hybridize.tests/test cases/edu/cuny/hunter/hybridize/tests/HybridizeFunctionRefactoringTest.java
index aa7611038..18970c31d 100644
--- a/edu.cuny.hunter.hybridize.tests/test cases/edu/cuny/hunter/hybridize/tests/HybridizeFunctionRefactoringTest.java	
+++ b/edu.cuny.hunter.hybridize.tests/test cases/edu/cuny/hunter/hybridize/tests/HybridizeFunctionRefactoringTest.java	
@@ -6267,4 +6267,23 @@ public void testNeuralNetwork() throws Exception {
 			}
 		}
 	}
+
+	@Test
+	public void testAutoEncoder() throws Exception {
+		Set<Function> functions = getFunctions();
+
+		for (Function function : functions) {
+			switch (function.getIdentifier()) {
+			case "encoder":
+			case "mean_square":
+			case "run_optimization":
+			case "decoder":
+				testFunction(function, false, true, false, false, false, CONVERT_EAGER_FUNCTION_TO_HYBRID, P1,
+						singleton(CONVERT_TO_HYBRID), RefactoringStatus.OK);
+				break;
+			default:
+				throw new IllegalStateException("Not expecting: " + function.getIdentifier() + ".");
+			}
+		}
+	}
 }