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tensorflow.xml
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tensorflow.xml
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<?xml version="1.0" ?>
<!DOCTYPE summary-spec>
<!-- TensorFlow model -->
<summary-spec>
<classloader name="PythonLoader">
<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" />
<putfield class="LRoot" field="sparse" fieldType="LRoot" ref="x" value="sparse" />
<new def="linalg" class="Lobject" />
<putfield class="LRoot" field="linalg" fieldType="LRoot" ref="x" value="linalg" />
<new def="keras" class="Lobject" />
<putfield class="LRoot" field="keras" fieldType="LRoot" ref="x" value="keras" />
<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="models" class="Lobject" />
<putfield class="LRoot" field="models" fieldType="LRoot" ref="keras" value="models" />
<new def="preprocessing" class="Lobject" />
<putfield class="LRoot" field="preprocessing" fieldType="LRoot" ref="keras" value="preprocessing" />
<new def="image" class="Lobject" />
<putfield class="LRoot" field="image" fieldType="LRoot" ref="preprocessing" value="image" />
<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="GradientTape" class="Ltensorflow/GradientTape" />
<putfield class="LRoot" field="GradientTape" fieldType="LRoot" ref="x" value="GradientTape" />
<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="sample_from_datasets" class="Ltensorflow/data/Dataset/sample_from_datasets" />
<putfield class="LRoot" field="sample_from_datasets" fieldType="LRoot" ref="Dataset" value="sample_from_datasets" />
<new def="list_files" class="Ltensorflow/data/Dataset/list_files" />
<putfield class="LRoot" field="list_files" fieldType="LRoot" ref="Dataset" value="list_files" />
<new def="choose_from_datasets" class="Ltensorflow/data/Dataset/choose_from_datasets" />
<putfield class="LRoot" field="choose_from_datasets" fieldType="LRoot" ref="Dataset" value="choose_from_datasets" />
<new def="from_generator" class="Ltensorflow/data/Dataset/from_generator" />
<putfield class="LRoot" field="from_generator" fieldType="LRoot" ref="Dataset" value="from_generator" />
<new def="zip" class="Ltensorflow/data/Dataset/zip" />
<putfield class="LRoot" field="zip" fieldType="LRoot" ref="Dataset" value="zip" />
<new def="dsrange" class="Ltensorflow/data/Dataset/range" />
<putfield class="LRoot" field="range" fieldType="LRoot" ref="Dataset" value="dsrange" />
<new def="dsrandom" class="Ltensorflow/data/Dataset/random" />
<putfield class="LRoot" field="random" fieldType="LRoot" ref="Dataset" value="dsrandom" />
<new def="from_tensors" class="Ltensorflow/data/Dataset/from_tensors" />
<putfield class="LRoot" field="from_tensors" fieldType="LRoot" ref="Dataset" value="from_tensors" />
<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" />
<!-- https://www.tensorflow.org/versions/r2.9/api_docs/python/tf/nn/relu -->
<putfield class="LRoot" field="relu" fieldType="LRoot" ref="nn" value="pass_through" />
<new def="sparse_softmax_cross_entropy_with_logits" class="Ltensorflow/functions/sparse_softmax_cross_entropy_with_logits" />
<putfield class="LRoot" field="sparse_softmax_cross_entropy_with_logits" fieldType="LRoot" ref="nn" value="sparse_softmax_cross_entropy_with_logits" />
<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="multiply" class="Ltensorflow/math/multiply" />
<putfield class="LRoot" field="multiply" fieldType="LRoot" ref="x" value="multiply" />
<putfield class="LRoot" field="multiply" fieldType="LRoot" ref="math" value="multiply" />
<new def="reduce_mean" class="Ltensorflow/math/reduce_mean" />
<putfield class="LRoot" field="reduce_mean" fieldType="LRoot" ref="x" value="reduce_mean" />
<putfield class="LRoot" field="reduce_mean" fieldType="LRoot" ref="math" value="reduce_mean" />
<new def="placeholder" class="Ltensorflow/functions/placeholder" />
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<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" />
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<new def="ragged_math_ops" class="Lobject" />
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<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="Model" class="Ltensorflow/keras/models/Model" />
<putfield class="LRoot" field="Model" fieldType="LRoot" ref="keras" value="Model" />
<putfield class="LRoot" field="Model" fieldType="LRoot" ref="models" value="Model" />
<new def="ImageDataGenerator" class="Ltensorflow/keras/preprocessing/image/ImageDataGenerator" />
<putfield class="LRoot" field="ImageDataGenerator" fieldType="LRoot" ref="image" value="ImageDataGenerator" />
<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">
<new def="params" class="Ltensorflow/class/Function" />
<putfield class="LRoot" field="func" fieldType="LRoot" ref="params" value="func" />
<putfield class="LRoot" field="input_signature" fieldType="LRoot" ref="params" value="input_signature" />
<putfield class="LRoot" field="autograph" fieldType="LRoot" ref="params" value="autograph" />
<putfield class="LRoot" field="jit_compile" fieldType="LRoot" ref="params" value="jit_compile" />
<putfield class="LRoot" field="reduce_retracing" fieldType="LRoot" ref="params" value="reduce_retracing" />
<putfield class="LRoot" field="experimental_implements" fieldType="LRoot" ref="params" value="experimental_implements" />
<putfield class="LRoot" field="experimental_autograph_options" fieldType="LRoot" ref="params" value="experimental_autograph_options" />
<putfield class="LRoot" field="experimental_relax_shapes" fieldType="LRoot" ref="params" value="experimental_relax_shapes" />
<putfield class="LRoot" field="experimental_compile" fieldType="LRoot" ref="params" value="experimental_compile" />
<putfield class="LRoot" field="experimental_follow_type_hints" fieldType="LRoot" ref="params" value="experimental_follow_type_hints" />
<return value="params" />
</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>
<class name="multiply" allocatable="true">
<!-- https://www.tensorflow.org/versions/r2.9/api_docs/python/tf/multiply -->
<method name="read_data" descriptor="()LRoot;">
<new def="x" class="Ltensorflow/math/multiply" />
<return value="x" />
</method>
<method name="do" descriptor="()LRoot;" numArgs="4" paramNames="self x y name">
<!-- Even though tf.multiply() 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>
<class name="reduce_mean" allocatable="true">
<!-- https://www.tensorflow.org/versions/r2.9/api_docs/python/tf/reduce_mean -->
<method name="do" descriptor="()LRoot;" numArgs="5" paramNames="self input_tensor axis keepdims name">
<return value="input_tensor" />
</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" />
<new def="f" class="Ltensorflow/functions/Runner" />
<putfield class="LRoot" field="run" fieldType="LRoot" ref="v" value="f" />
<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" />
<new def="f" class="Ltensorflow/functions/Runner" />
<putfield class="LRoot" field="run" fieldType="LRoot" ref="v" value="f" />
<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" />
<return value="x" />
</method>
<method name="do" descriptor="()LRoot;" numArgs="3" paramNames="shape dtype name">
<call class="LRoot" name="read_data" 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" />
<return value="x" />
</method>
<method name="do" descriptor="()LRoot;" numArgs="12" paramNames="initial_value trainable validate_shape caching_device name variable_def dtype import_scope constraint synchronization aggregation shape">
<call class="LRoot" name="read_data" descriptor="()LRoot;" type="virtual" arg0="arg0" def="x" />
<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" />
<return value="x" />
</method>
<method name="do" descriptor="()LRoot;" numArgs="4" paramNames="value dtype shape name">
<call class="LRoot" name="read_data" descriptor="()LRoot;" type="virtual" arg0="arg0" def="x" />
<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" />
<return value="x" />
</method>
<method name="do" descriptor="()LRoot;" numArgs="3" paramNames="shape dtype name">
<call class="LRoot" name="read_data" descriptor="()LRoot;" type="virtual" arg0="arg0" def="x" />
<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" />
<return value="x" />
</method>
<method name="do" descriptor="()LRoot;" numArgs="3" paramNames="indices values dense_shape">
<call class="LRoot" name="read_data" descriptor="()LRoot;" type="virtual" arg0="arg0" def="x" />
<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" />
<return value="x" />
</method>
<method name="do" descriptor="()LRoot;" numArgs="4" paramNames="num_rows num_columns dtype name">
<call class="LRoot" name="read_data" descriptor="()LRoot;" type="virtual" arg0="arg0" def="x" />
<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" />
<return value="x" />
</method>
<method name="do" descriptor="()LRoot;" numArgs="3" paramNames="dims value name">
<call class="LRoot" name="read_data" descriptor="()LRoot;" type="virtual" arg0="arg0" def="x" />
<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" />
<return value="x" />
</method>
<method name="do" descriptor="()LRoot;" numArgs="3" paramNames="input dtype name">
<call class="LRoot" name="read_data" descriptor="()LRoot;" type="virtual" arg0="arg0" def="x" />
<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" />
<return value="x" />
</method>
<method name="do" descriptor="()LRoot;" numArgs="7" paramNames="indices depth on_value off_value axis dtype name">
<call class="LRoot" name="read_data" descriptor="()LRoot;" type="virtual" arg0="arg0" def="x" />
<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" />
<return value="x" />
</method>
<method name="do" descriptor="()LRoot;" numArgs="4" paramNames="value dtype dtype_hint name">
<call class="LRoot" name="read_data" descriptor="()LRoot;" type="virtual" arg0="arg0" def="x" />
<return value="x" />
</method>
</class>
<class name="range" allocatable="true">
<method name="read_data" descriptor="()LRoot;">
<new def="x" class="Llist" />
<new def="z" class="Ltensorflow/functions/constant" />
<call class="Ltensorflow/functions/constant" name="do" descriptor="()LRoot;" type="virtual" arg0="z" arg1="1" def="y" />
<putfield class="LRoot" field="0" fieldType="LRoot" ref="x" value="y" />
<return value="x" />
</method>
<method name="do" descriptor="()LRoot;" numArgs="4" paramNames="limit delta dtype name">
<call class="LRoot" name="read_data" descriptor="()LRoot;" type="virtual" arg0="arg0" def="x" />
<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" />
<return value="x" />
</method>
<method name="do" descriptor="()LRoot;" numArgs="3" paramNames="op value_index dtype">
<call class="LRoot" name="read_data" descriptor="()LRoot;" type="virtual" arg0="arg0" def="x" />
<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" />
<return value="x" />
</method>
<method name="do" descriptor="()LRoot;" numArgs="3" paramNames="op value_index dtype">
<call class="LRoot" name="read_data" descriptor="()LRoot;" type="virtual" arg0="arg0" def="x" />
<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" />
<return value="x" />
</method>
<method name="do" descriptor="()LRoot;" numArgs="6" paramNames="starts limits deltas dtype name row_splits_dtype">
<call class="LRoot" name="read_data" descriptor="()LRoot;" type="virtual" arg0="arg0" def="x" />
<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" />
<return value="x" />
</method>
<method name="do" descriptor="()LRoot;" numArgs="6" paramNames="pylist dtype ragged_rank inner_shape name row_splits_dtype">
<call class="LRoot" name="read_data" descriptor="()LRoot;" type="virtual" arg0="arg0" def="x" />
<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" />
<return value="x" />
</method>
<method name="do" descriptor="()LRoot;" numArgs="5" paramNames="num_rows num_columns batch_shape dtype name">
<call class="LRoot" name="read_data" descriptor="()LRoot;" type="virtual" arg0="arg0" def="x" />
<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" />
<return value="x" />
</method>
<method name="do" descriptor="()LRoot;" numArgs="6" paramNames="shape minval maxval dtype seed name">
<call class="LRoot" name="read_data" descriptor="()LRoot;" type="virtual" arg0="arg0" def="x" />
<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" />
<return value="x" />
</method>
<method name="do" descriptor="()LRoot;" numArgs="6" paramNames="shape alpha beta dtype seed name">
<call class="LRoot" name="read_data" descriptor="()LRoot;" type="virtual" arg0="arg0" def="x" />
<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" />
<return value="x" />
</method>
<method name="do" descriptor="()LRoot;" numArgs="6" paramNames="shape mean stddev dtype seed name">
<call class="LRoot" name="read_data" descriptor="()LRoot;" type="virtual" arg0="arg0" def="x" />
<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" />
<return value="x" />
</method>
<method name="do" descriptor="()LRoot;" numArgs="5" paramNames="shape lam dtype seed name">
<call class="LRoot" name="read_data" descriptor="()LRoot;" type="virtual" arg0="arg0" def="x" />
<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" />
<return value="x" />
</method>
<method name="do" descriptor="()LRoot;" numArgs="6" paramNames="shape mean stddev dtype seed name">
<call class="LRoot" name="read_data" descriptor="()LRoot;" type="virtual" arg0="arg0" def="x" />
<return value="x" />
</method>
</class>
<class name="Input" allocatable="true">
<method name="read_data" descriptor="()LRoot;">
<new def="x" class="Ltensorflow/functions/Input" />
<return value="x" />
</method>
<method name="do" descriptor="()LRoot;" numArgs="8" paramNames="shape batch_size name dtype sparse tensor ragged type_spec">
<call class="LRoot" name="read_data" descriptor="()LRoot;" type="virtual" arg0="arg0" def="x" />
<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" />
<return value="x" />
</method>
<method name="do" descriptor="()LRoot;" numArgs="4" paramNames="flat_values nested_row_lengths name validate">
<call class="LRoot" name="read_data" descriptor="()LRoot;" type="virtual" arg0="arg0" def="x" />
<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" />
<return value="x" />
</method>
<method name="do" descriptor="()LRoot;" numArgs="4" paramNames="flat_values nested_row_splits name validate">
<call class="LRoot" name="read_data" descriptor="()LRoot;" type="virtual" arg0="arg0" def="x" />
<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" />
<return value="x" />
</method>
<method name="do" descriptor="()LRoot;" numArgs="5" paramNames="flat_values nested_value_rowids nested_nrows name validate">
<call class="LRoot" name="read_data" descriptor="()LRoot;" type="virtual" arg0="arg0" def="x" />
<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" />
<return value="x" />
</method>
<method name="do" descriptor="()LRoot;" numArgs="4" paramNames="values row_lengths name validate">
<call class="LRoot" name="read_data" descriptor="()LRoot;" type="virtual" arg0="arg0" def="x" />
<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" />
<return value="x" />
</method>
<method name="do" descriptor="()LRoot;" numArgs="4" paramNames="values row_limits name validate">
<call class="LRoot" name="read_data" descriptor="()LRoot;" type="virtual" arg0="arg0" def="x" />
<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" />
<return value="x" />
</method>
<method name="do" descriptor="()LRoot;" numArgs="4" paramNames="values row_splits name validate">
<call class="LRoot" name="read_data" descriptor="()LRoot;" type="virtual" arg0="arg0" def="x" />
<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" />
<return value="x" />
</method>
<method name="do" descriptor="()LRoot;" numArgs="4" paramNames="values row_starts name validate">
<call class="LRoot" name="read_data" descriptor="()LRoot;" type="virtual" arg0="arg0" def="x" />
<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" />
<return value="x" />
</method>
<method name="do" descriptor="()LRoot;" numArgs="5" paramNames="values value_rowids nrows name validate">
<call class="LRoot" name="read_data" descriptor="()LRoot;" type="virtual" arg0="arg0" def="x" />
<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="sparse_softmax_cross_entropy_with_logits" allocatable="true">
<!-- https://www.tensorflow.org/versions/r2.9/api_docs/python/tf/nn/sparse_softmax_cross_entropy_with_logits -->
<method name="do" descriptor="()LRoot;" numArgs="4" paramNames="self labels logits name">
<return value="labels" />
</method>
</class>
</package>
<package name="tensorflow/estimator">
<class name="Estimator" allocatable="true">
<method name="do" descriptor="()LRoot;" numArgs="2" paramNames="self model">
<new def="x" class="Ltensorflow/estimator/train/train" />
<putfield class="LRoot" field="train" fieldType="LRoot" ref="self" value="x" />
<putfield class="LRoot" field="$callback" fieldType="LRoot" ref="x" value="model" />
<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" />
<putfield class="LRoot" field="data" fieldType="LRoot" ref="xx" value="2" />
<putfield class="LRoot" field="labels" fieldType="LRoot" ref="xx" value="3" />
<return value="xx" />
</method>
</class>
</package>
<package name="tensorflow/keras/preprocessing/image">
<class name="ImageDataGenerator" allocatable="true">
<!-- https://www.tensorflow.org/versions/r2.9/api_docs/python/tf/keras/preprocessing/image/ImageDataGenerator -->
<method name="read_dataset" descriptor="()LRoot;">
<new def="flow_from_directory" class="Ltensorflow/keras/preprocessing/image/flow_from_directory" />
<putfield class="LRoot" field="flow_from_directory" fieldType="LRoot" ref="arg0" value="flow_from_directory" />
<return value="arg0" />
</method>
<method name="do" descriptor="()LRoot;" numArgs="24"
paramNames="self featurewise_center samplewise_center featurewise_std_normalization samplewise_std_normalization zca_whitening zca_epsilon rotation_range width_shift_range height_shift_range brightness_range shear_range zoom_range channel_shift_range fill_mode cval horizontal_flip vertical_flip rescale preprocessing_function data_format validation_split interpolation_order dtype">
<!-- NOTE: Workaround for https://github.com/wala/ML/issues/127. This ctor doesn't really return a dataset but rather the instance methods do. It shouldn't be a problem since you can't iterate over an `ImageDataGenerator`. -->
<call class="LRoot" name="read_dataset" descriptor="()LRoot;" type="virtual" arg0="arg0" def="x" />
<return value="x" />
</method>
</class>
<class name="flow_from_directory" allocatable="true">
<!-- https://www.tensorflow.org/versions/r2.9/api_docs/python/tf/keras/preprocessing/image/ImageDataGenerator#flow_from_directory -->
<method name="do" descriptor="()LRoot;" numArgs="16" paramNames="self directory target_size color_mode classes class_mode batch_size shuffle seed save_to_dir save_prefix save_format follow_links subset interpolation keep_aspect_ratio">
<new def="x" class="Ltensorflow/data/Dataset" />
<call class="Ltensorflow/data/Dataset" name="read_dataset" descriptor="()LRoot;" type="virtual" arg0="x" def="xx" />
<return value="xx" />
</method>
</class>
</package>
<package name="tensorflow/keras/models">
<class name="Model" allocatable="true">
<method name="read_data" descriptor="()LRoot;">
<new def="x" class="Ltensorflow/keras/Model/attribute" />
<return value="x" />
</method>
<!-- https://www.tensorflow.org/versions/r2.9/api_docs/python/tf/keras/models/Model -->
<method name="do" descriptor="()LRoot;" numArgs="4" paramNames="self inputs outputs name">
<new def="__call__" class="Ltensorflow/keras/models/__call__" />
<putfield class="LRoot" field="__call__" fieldType="LRoot" ref="arg0" value="__call__" />
<new def="call" class="Ltensorflow/keras/models/call" />
<putfield class="LRoot" field="call" fieldType="LRoot" ref="arg0" value="call" />
<new def="x" class="Llist" />
<call class="LRoot" name="read_data" descriptor="()LRoot;" type="virtual" arg0="arg0" def="xx" />
<putfield class="LRoot" field="0" fieldType="LRoot" ref="x" value="xx" />
<!-- https://www.tensorflow.org/guide/keras/transfer_learning#freezing_layers_understanding_the_trainable_attribute -->
<putfield class="LRoot" field="trainable_weights" fieldType="LRoot" ref="arg0" value="x" />
<putfield class="LRoot" field="weights" fieldType="LRoot" ref="arg0" value="x" />
<putfield class="LRoot" field="non_trainable_weights" fieldType="LRoot" ref="arg0" value="x" />
<return value="arg0" />
</method>
</class>
<class name="__call__" allocatable="true">
<!-- https://www.tensorflow.org/versions/r2.9/api_docs/python/tf/keras/Model#call -->
<method name="do" descriptor="()LRoot;" numArgs="4" paramNames="self inputs training mask">
<return value="inputs" />
</method>
</class>
<!-- FIXME: Workaround for https://github.com/wala/ML/issues/106. -->
<class name="call" allocatable="true">
<!-- https://www.tensorflow.org/versions/r2.9/api_docs/python/tf/keras/Model#call -->
<method name="do" descriptor="()LRoot;" numArgs="4" paramNames="self inputs training mask">
<return value="inputs" />
</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 -->
<method name="do" descriptor="()LRoot;" numArgs="11" paramNames="self units activation use_bias kernel_initializer bias_initializer kernel_regularizer bias_regularizer activity_regularizer kernel_constraint bias_constraint">
<new def="__call__" class="Ltensorflow/keras/layers/__call__" />
<putfield class="LRoot" field="__call__" fieldType="LRoot" ref="arg0" value="__call__" />
<new def="call" class="Ltensorflow/keras/layers/call" />
<putfield class="LRoot" field="call" fieldType="LRoot" ref="arg0" value="call" />
<return value="arg0" />
</method>
</class>
<!-- FIXME: These methods must be called explicitly. The implicit cases blocked on https://github.com/wala/ML/issues/127. -->
<class name="__call__" allocatable="true">
<!-- https://github.com/keras-team/keras/blob/07e13740fd181fc3ddec7d9a594d8a08666645f6/keras/layers/core/dense.py#L166-L240 -->
<method name="do" descriptor="()LRoot;" numArgs="2" paramNames="self inputs">
<return value="inputs" />
</method>
</class>
<!-- FIXME: Workaround for https://github.com/wala/ML/issues/106. -->
<class name="call" allocatable="true">
<!-- https://github.com/keras-team/keras/blob/07e13740fd181fc3ddec7d9a594d8a08666645f6/keras/layers/core/dense.py#L166-L240 -->
<method name="do" descriptor="()LRoot;" numArgs="2" paramNames="self inputs">
<return value="inputs" />
</method>
</class>
</package>
<package name="tensorflow">
<class name="GradientTape" allocatable="true">
<!-- https://www.tensorflow.org/versions/r2.9/api_docs/python/tf/GradientTape -->
<method name="do" descriptor="()LRoot;" numArgs="3" paramNames="self persistent watch_accessed_variables">
<new def="gradient" class="Ltensorflow/gradient" />
<putfield class="LRoot" field="gradient" fieldType="LRoot" ref="arg0" value="gradient" />
<return value="arg0" />
</method>
</class>
<class name="gradient" allocatable="true">
<!-- https://www.tensorflow.org/versions/r2.9/api_docs/python/tf/GradientTape#gradient -->
<method name="do" descriptor="()LRoot;" numArgs="5" paramNames="self target sources output_gradients unconnected_gradients">
<return value="sources" />
</method>
</class>
</package>
<package name="tensorflow/data">
<class name="Dataset" allocatable="true">
<!-- "read_dataset" means that this function reads a tensor iterable. -->
<method name="read_dataset" descriptor="()LRoot;">
<new def="shuffle" class="Ltensorflow/data/shuffle" />
<putfield class="LRoot" field="shuffle" fieldType="LRoot" ref="arg0" value="shuffle" />
<new def="batch" class="Ltensorflow/data/batch" />
<putfield class="LRoot" field="batch" fieldType="LRoot" ref="arg0" value="batch" />
<new def="repeat" class="Ltensorflow/data/repeat" />
<putfield class="LRoot" field="repeat" fieldType="LRoot" ref="arg0" value="repeat" />
<new def="prefetch" class="Ltensorflow/data/prefetch" />
<putfield class="LRoot" field="prefetch" fieldType="LRoot" ref="arg0" value="prefetch" />
<new def="take" class="Ltensorflow/data/take" />
<putfield class="LRoot" field="take" fieldType="LRoot" ref="arg0" value="take" />
<return value="arg0" />
<new def="map" class="Ltensorflow/data/map" />
<putfield class="LRoot" field="map" fieldType="LRoot" ref="arg0" value="map" />
<return value="arg0" />
<new def="filter" class="Ltensorflow/data/filter" />
<putfield class="LRoot" field="filter" fieldType="LRoot" ref="arg0" value="filter" />
<new def="concatenate" class="Ltensorflow/data/concatenate" />
<putfield class="LRoot" field="concatenate" fieldType="LRoot" ref="arg0" value="concatenate" />
<new def="reduce" class="Ltensorflow/data/reduce" />
<putfield class="LRoot" field="reduce" fieldType="LRoot" ref="arg0" value="reduce" />
<new def="enumerate" class="Ltensorflow/data/enumerate" />
<putfield class="LRoot" field="enumerate" fieldType="LRoot" ref="arg0" value="enumerate" />
<return value="arg0" />
</method>
<method name="do" descriptor="()LRoot;" numArgs="2" paramNames="self variant_tensor">
<call class="LRoot" name="read_dataset" descriptor="()LRoot;" type="virtual" arg0="arg0" def="x" />
<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">
<!-- FIXME: Workaround for https://github.com/wala/ML/issues/127. This method (shuffle) doesn't really return a "new" dataset but rather a modified version of the receiver. But, the receiver isn't available without a trampoline AFAIK. -->
<new def="x" class="Ltensorflow/data/Dataset" />
<call class="Ltensorflow/data/Dataset" name="read_dataset" descriptor="()LRoot;" type="virtual" arg0="x" def="xx" />
<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">
<!-- FIXME: Workaround for https://github.com/wala/ML/issues/127. -->
<new def="x" class="Ltensorflow/data/Dataset" />
<call class="Ltensorflow/data/Dataset" name="read_dataset" descriptor="()LRoot;" type="virtual" arg0="x" def="xx" />
<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">
<!-- FIXME: Workaround for https://github.com/wala/ML/issues/127. -->
<new def="x" class="Ltensorflow/data/Dataset" />
<call class="Ltensorflow/data/Dataset" name="read_dataset" descriptor="()LRoot;" type="virtual" arg0="x" def="xx" />
<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">
<!-- FIXME: Workaround for https://github.com/wala/ML/issues/127. -->
<new def="x" class="Ltensorflow/data/Dataset" />
<call class="Ltensorflow/data/Dataset" name="read_dataset" descriptor="()LRoot;" type="virtual" arg0="x" def="xx" />
<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">
<!-- FIXME: Workaround for https://github.com/wala/ML/issues/127. -->
<new def="x" class="Ltensorflow/data/Dataset" />
<call class="Ltensorflow/data/Dataset" name="read_dataset" descriptor="()LRoot;" type="virtual" arg0="x" def="xx" />
<return value="xx" />
</method>
</class>
<class name="map" allocatable="true">
<!-- https://www.tensorflow.org/versions/r2.9/api_docs/python/tf/data/Dataset#map -->
<method name="do" descriptor="()LRoot;" numArgs="5" paramNames="self map_func num_parallel_calls deterministic name">
<!-- FIXME: Workaround for https://github.com/wala/ML/issues/127. -->
<new def="x" class="Ltensorflow/data/Dataset" />
<call class="Ltensorflow/data/Dataset" name="read_dataset" descriptor="()LRoot;" type="virtual" arg0="x" def="xx" />
<return value="xx" />
</method>
</class>
<class name="filter" allocatable="true">
<!-- https://www.tensorflow.org/versions/r2.9/api_docs/python/tf/data/Dataset#filter -->
<method name="do" descriptor="()LRoot;" numArgs="3" paramNames="self predicate name">
<!-- FIXME: Workaround for https://github.com/wala/ML/issues/127. -->
<new def="x" class="Ltensorflow/data/Dataset" />
<call class="Ltensorflow/data/Dataset" name="read_dataset" descriptor="()LRoot;" type="virtual" arg0="x" def="xx" />
<return value="xx" />
</method>
</class>
<class name="concatenate" allocatable="true">
<!-- https://www.tensorflow.org/versions/r2.9/api_docs/python/tf/data/Dataset#concatenate -->
<method name="do" descriptor="()LRoot;" numArgs="3" paramNames="self predicate name">
<!-- FIXME: Workaround for https://github.com/wala/ML/issues/127. -->
<new def="x" class="Ltensorflow/data/Dataset" />
<call class="Ltensorflow/data/Dataset" name="read_dataset" descriptor="()LRoot;" type="virtual" arg0="x" def="xx" />
<return value="xx" />
</method>
</class>
<class name="reduce" allocatable="true">
<!-- https://www.tensorflow.org/versions/r2.9/api_docs/python/tf/data/Dataset#reduce -->
<method name="do" descriptor="()LRoot;" numArgs="4" paramNames="self initial_state reduce_func name">
<call class="LRoot" name="read_data" descriptor="()LRoot;" type="virtual" arg0="arg0" def="xx" />
<return value="xx" />
</method>
</class>
<class name="enumerate" allocatable="true">
<!-- https://www.tensorflow.org/versions/r2.9/api_docs/python/tf/data/Dataset#enumerate -->
<method name="do" descriptor="()LRoot;" numArgs="3" paramNames="self predicate name">
<!-- FIXME: Workaround for https://github.com/wala/ML/issues/127. -->
<new def="x" class="Ltensorflow/data/Dataset" />
<call class="Ltensorflow/data/Dataset" name="read_dataset" descriptor="()LRoot;" type="virtual" arg0="x" def="xx" />
<return value="xx" />
</method>
</class>
</package>
<package name="tensorflow/data/Dataset">
<class name="from_tensors" allocatable="true">
<!-- https://www.tensorflow.org/versions/r2.9/api_docs/python/tf/data/Dataset#from_tensors -->
<method name="do" descriptor="()LRoot;" numArgs="2" paramNames="tensors name">
<!-- FIXME: We should encode the tensors argument here. See https://github.com/wala/ML/issues/164. -->
<new def="x" class="Ltensorflow/data/Dataset" />
<call class="Ltensorflow/data/Dataset" name="read_dataset" descriptor="()LRoot;" type="virtual" arg0="x" def="xx" />
<return value="xx" />
</method>
</class>
<class name="from_tensor_slices" allocatable="true">
<!-- https://www.tensorflow.org/versions/r2.9/api_docs/python/tf/data/Dataset#from_tensor_slices -->
<method name="do" descriptor="()LRoot;" numArgs="2" paramNames="tensors name">
<new def="x" class="Ltensorflow/data/Dataset" />
<call class="Ltensorflow/data/Dataset" name="read_dataset" descriptor="()LRoot;" type="virtual" arg0="x" def="xx" />
<return value="xx" />
</method>
</class>
<class name="sample_from_datasets" allocatable="true">
<!-- https://www.tensorflow.org/versions/r2.9/api_docs/python/tf/data/Dataset#sample_from_datasets -->