kse-01/data.csv

1		name	file	line	type	comment
2	0	UserInputError	tensorflow/configure.py	74	class
3	1	is_windows	tensorflow/configure.py	78	function
4	2	is_linux	tensorflow/configure.py	82	function
5	3	is_macos	tensorflow/configure.py	86	function
6	4	is_ppc64le	tensorflow/configure.py	90	function
7	5	is_cygwin	tensorflow/configure.py	94	function
8	6	get_input	tensorflow/configure.py	98	function
9	7	symlink_force	tensorflow/configure.py	109	function	Force symlink, equivalent of 'ln -sf'. Args: target: items to link to. link_name: name of the link.
10	8	sed_in_place	tensorflow/configure.py	126	function	Replace old string with new string in file. Args: filename: string for filename. old: string to replace. new: new string to replace to.
11	9	write_to_bazelrc	tensorflow/configure.py	141	function
12	10	write_action_env_to_bazelrc	tensorflow/configure.py	146	function
13	11	run_shell	tensorflow/configure.py	150	function
14	12	cygpath	tensorflow/configure.py	163	function	Convert path from posix to windows.
15	13	get_python_path	tensorflow/configure.py	168	function	Get the python site package paths.
16	14	get_python_major_version	tensorflow/configure.py	198	function	Get the python major version.
17	15	setup_python	tensorflow/configure.py	203	function	Setup python related env variables.
18	16	reset_tf_configure_bazelrc	tensorflow/configure.py	273	function	Reset file that contains customized config settings.
19	17	cleanup_makefile	tensorflow/configure.py	278	function	Delete any leftover BUILD files from the Makefile build. These files could interfere with Bazel parsing.
20	18	get_var	tensorflow/configure.py	292	function	Get boolean input from user. If var_name is not set in env, ask user to enable query_item or not. If the response is empty, use the default. Args: environ_cp: copy of the os.environ. var_name: string for name of environment variable, e.g. "TF_NEED_CUDA". query_item: string for feature related to the variable, e.g. "CUDA for Nvidia GPUs". enabled_by_default: boolean for default behavior. question: optional string for how to ask for user input. yes_reply: optional string for reply when feature is enabled. no_reply: optional string for reply when feature is disabled. Returns: boolean value of the variable. Raises: UserInputError: if an environment variable is set, but it cannot be interpreted as a boolean indicator, assume that the user has made a scripting error, and will continue to provide invalid input. Raise the error to avoid infinitely looping.
21	19	set_build_var	tensorflow/configure.py	377	function	Set if query_item will be enabled for the build. Ask user if query_item will be enabled. Default is used if no input is given. Set subprocess environment variable and write to .bazelrc if enabled. Args: environ_cp: copy of the os.environ. var_name: string for name of environment variable, e.g. "TF_NEED_CUDA". query_item: string for feature related to the variable, e.g. "CUDA for Nvidia GPUs". option_name: string for option to define in .bazelrc. enabled_by_default: boolean for default behavior. bazel_config_name: Name for Bazel --config argument to enable build feature.
22	20	set_action_env_var	tensorflow/configure.py	411	function	Set boolean action_env variable. Ask user if query_item will be enabled. Default is used if no input is given. Set environment variable and write to .bazelrc. Args: environ_cp: copy of the os.environ. var_name: string for name of environment variable, e.g. "TF_NEED_CUDA". query_item: string for feature related to the variable, e.g. "CUDA for Nvidia GPUs". enabled_by_default: boolean for default behavior. question: optional string for how to ask for user input. yes_reply: optional string for reply when feature is enabled. no_reply: optional string for reply when feature is disabled. bazel_config_name: adding config to .bazelrc instead of action_env.
23	21	convert_version_to_int	tensorflow/configure.py	446	function	Convert a version number to a integer that can be used to compare. Version strings of the form X.YZ and X.Y.Z-xxxxx are supported. The 'xxxxx' part, for instance 'homebrew' on OS/X, is ignored. Args: version: a version to be converted Returns: An integer if converted successfully, otherwise return None.
24	22	check_bazel_version	tensorflow/configure.py	471	function	Check installed bazel version is between min_version and max_version. Args: min_version: string for minimum bazel version (must exist!). max_version: string for maximum bazel version (must exist!). Returns: The bazel version detected.
25	23	set_cc_opt_flags	tensorflow/configure.py	518	function	Set up architecture-dependent optimization flags. Also append CC optimization flags to bazel.rc.. Args: environ_cp: copy of the os.environ.
26	24	set_tf_cuda_clang	tensorflow/configure.py	546	function	set TF_CUDA_CLANG action_env. Args: environ_cp: copy of the os.environ.
27	25	set_tf_download_clang	tensorflow/configure.py	566	function	Set TF_DOWNLOAD_CLANG action_env.
28	26	get_from_env_or_user_or_default	tensorflow/configure.py	582	function	Get var_name either from env, or user or default. If var_name has been set as environment variable, use the preset value, else ask for user input. If no input is provided, the default is used. Args: environ_cp: copy of the os.environ. var_name: string for name of environment variable, e.g. "TF_NEED_CUDA". ask_for_var: string for how to ask for user input. var_default: default value string. Returns: string value for var_name
29	27	set_clang_cuda_compiler_path	tensorflow/configure.py	607	function	Set CLANG_CUDA_COMPILER_PATH.
30	28	prompt_loop_or_load_from_env	tensorflow/configure.py	630	function	Loop over user prompts for an ENV param until receiving a valid response. For the env param var_name, read from the environment or verify user input until receiving valid input. When done, set var_name in the environ_cp to its new value. Args: environ_cp: (Dict) copy of the os.environ. var_name: (String) string for name of environment variable, e.g. "TF_MYVAR". var_default: (String) default value string. ask_for_var: (String) string for how to ask for user input. check_success: (Function) function that takes one argument and returns a boolean. Should return True if the value provided is considered valid. May contain a complex error message if error_msg does not provide enough information. In that case, set suppress_default_error to True. error_msg: (String) String with one and only one '%s'. Formatted with each invalid response upon check_success(input) failure. suppress_default_error: (Bool) Suppress the above error message in favor of one from the check_success function. resolve_symlinks: (Bool) Translate symbolic links into the real filepath. n_ask_attempts: (Integer) Number of times to query for valid input before raising an error and quitting. Returns: [String] The value of var_name after querying for input. Raises: UserInputError: if a query has been attempted n_ask_attempts times without success, assume that the user has made a scripting error, and will continue to provide invalid input. Raise the error to avoid infinitely looping.
31	29	create_android_ndk_rule	tensorflow/configure.py	696	function	Set ANDROID_NDK_HOME and write Android NDK WORKSPACE rule.
32	30	create_android_sdk_rule	tensorflow/configure.py	724	function	Set Android variables and write Android SDK WORKSPACE rule.
33	31	get_ndk_api_level	tensorflow/configure.py	788	function	Gets the appropriate NDK API level to use for the provided Android NDK path.
34	32	set_gcc_host_compiler_path	tensorflow/configure.py	836	function	Set GCC_HOST_COMPILER_PATH.
35	33	reformat_version_sequence	tensorflow/configure.py	858	function	Reformat the version string to have the given number of sequences. For example: Given (7, 2) -> 7.0 (7.0.1, 2) -> 7.0 (5, 1) -> 5 (5.0.3.2, 1) -> 5 Args: version_str: String, the version string. sequence_count: int, an integer. Returns: string, reformatted version string.
36	34	set_tf_cuda_paths	tensorflow/configure.py	881	function	Set TF_CUDA_PATHS.
37	35	set_tf_cuda_version	tensorflow/configure.py	892	function	Set TF_CUDA_VERSION.
38	36	set_tf_cudnn_version	tensorflow/configure.py	904	function	Set TF_CUDNN_VERSION.
39	37	is_cuda_compatible	tensorflow/configure.py	916	function	Check compatibility between given library and cudnn/cudart libraries.
40	38	set_tf_tensorrt_version	tensorflow/configure.py	945	function	Set TF_TENSORRT_VERSION.
41	39	set_tf_nccl_version	tensorflow/configure.py	962	function	Set TF_NCCL_VERSION.
42	40	get_native_cuda_compute_capabilities	tensorflow/configure.py	979	function	Get native cuda compute capabilities. Args: environ_cp: copy of the os.environ. Returns: string of native cuda compute capabilities, separated by comma.
43	41	set_tf_cuda_compute_capabilities	tensorflow/configure.py	1003	function	Set TF_CUDA_COMPUTE_CAPABILITIES.
44	42	set_other_cuda_vars	tensorflow/configure.py	1074	function	Set other CUDA related variables.
45	43	set_host_cxx_compiler	tensorflow/configure.py	1083	function	Set HOST_CXX_COMPILER.
46	44	set_host_c_compiler	tensorflow/configure.py	1100	function	Set HOST_C_COMPILER.
47	45	set_computecpp_toolkit_path	tensorflow/configure.py	1117	function	Set COMPUTECPP_TOOLKIT_PATH.
48	46	set_trisycl_include_dir	tensorflow/configure.py	1149	function	Set TRISYCL_INCLUDE_DIR.
49	47	system_specific_test_config	tensorflow/configure.py	1173	function	Add default build and test flags required for TF tests to bazelrc.
50	48	set_system_libs_flag	tensorflow/configure.py	1216	function
51	49	is_reduced_optimize_huge_functions_available	tensorflow/configure.py	1233	function	Check to see if the system supports /d2ReducedOptimizeHugeFunctions. The above compiler flag is a new compiler flag introduced to the Visual Studio compiler in version 16.4 (available in Visual Studio 2019, Preview edition only, as of 2019-11-19). TensorFlow needs this flag to massively reduce compile times, but until 16.4 is officially released, we can't depend on it. See also https://groups.google.com/a/tensorflow.org/d/topic/build/SsW98Eo7l3o/discussion Because it's very annoying to check this manually (to check the MSVC installed versions, you need to use the registry, and it's not clear if Bazel will be using that install version anyway), we expect enviroments who know they may use this flag to export TF_VC_VERSION=16.4 TODO(angerson, gunan): Remove this function when TensorFlow's minimum VS version is upgraded to 16.4. Arguments: environ_cp: Environment of the current execution Returns: boolean, whether or not /d2ReducedOptimizeHugeFunctions is available on this machine.
52	50	set_windows_build_flags	tensorflow/configure.py	1262	function	Set Windows specific build options.
53	51	config_info_line	tensorflow/configure.py	1283	function	Helper function to print formatted help text for Bazel config options.
54	52	configure_ios	tensorflow/configure.py	1288	function	Configures TensorFlow for iOS builds. This function will only be executed if `is_macos()` is true.
55	53	validate_cuda_config	tensorflow/configure.py	1305	function	Run find_cuda_config.py and return cuda_toolkit_path, or None.
56	54	main	tensorflow/configure.py	1365	function
57	55	_running_from_pip_package	tensorflow/tensorflow/api_template.__init__.py	132	function
58	56	_running_from_pip_package	tensorflow/tensorflow/api_template_v1.__init__.py	142	function
59	57	_LazyLoader	tensorflow/tensorflow/virtual_root_template_v1.__init__.py	33	class	Lazily import a module so that we can forward it.
60	58	_forward_module	tensorflow/tensorflow/virtual_root_template_v1.__init__.py	63	function
61	59	_LazyLoader	tensorflow/tensorflow/virtual_root_template_v2.__init__.py	33	class	Lazily import a module so that we can forward it.
62	60	_forward_module	tensorflow/tensorflow/virtual_root_template_v2.__init__.py	63	function
63	61	VarsAndArithmeticObjectGraph	tensorflow/tensorflow/cc/saved_model/testdata/generate_saved_models.py	37	class	Three vars (one in a sub-module) and compute method.
64	62	ReferencesParent	tensorflow/tensorflow/cc/saved_model/testdata/generate_saved_models.py	55	class
65	63	CyclicModule	tensorflow/tensorflow/cc/saved_model/testdata/generate_saved_models.py	64	class
66	64	main	tensorflow/tensorflow/cc/saved_model/testdata/generate_saved_models.py	77	function
67	65	tfadd	tensorflow/tensorflow/compiler/aot/tests/make_test_graphs.py	48	function
68	66	tfadd_with_ckpt	tensorflow/tensorflow/compiler/aot/tests/make_test_graphs.py	54	function
69	67	tfadd_with_ckpt_saver	tensorflow/tensorflow/compiler/aot/tests/make_test_graphs.py	69	function
70	68	tfassert_eq	tensorflow/tensorflow/compiler/aot/tests/make_test_graphs.py	88	function
71	69	tfcond	tensorflow/tensorflow/compiler/aot/tests/make_test_graphs.py	96	function
72	70	tfgather	tensorflow/tensorflow/compiler/aot/tests/make_test_graphs.py	104	function
73	71	tfmatmul	tensorflow/tensorflow/compiler/aot/tests/make_test_graphs.py	110	function
74	72	tfmatmulandadd	tensorflow/tensorflow/compiler/aot/tests/make_test_graphs.py	116	function
75	73	tffunction	tensorflow/tensorflow/compiler/aot/tests/make_test_graphs.py	124	function
76	74	tfsplits	tensorflow/tensorflow/compiler/aot/tests/make_test_graphs.py	135	function	A more complex graph, including splits.
77	75	tftop_k	tensorflow/tensorflow/compiler/aot/tests/make_test_graphs.py	152	function
78	76	tfvariable_readonly	tensorflow/tensorflow/compiler/aot/tests/make_test_graphs.py	158	function
79	77	tfvariable	tensorflow/tensorflow/compiler/aot/tests/make_test_graphs.py	169	function
80	78	tfvariable_sequential_updates	tensorflow/tensorflow/compiler/aot/tests/make_test_graphs.py	177	function
81	79	export_debug_info	tensorflow/tensorflow/compiler/aot/tests/make_test_graphs.py	189	function	Exports debug information from a graph. Args: exported_graph: A Graph that has been created by tracing a saveable view. Returns: Corresponding GraphDebugInfo with traces for all ops in exported_graph.
82	80	write_graph	tensorflow/tensorflow/compiler/aot/tests/make_test_graphs.py	204	function	Build a graph using build_graph and write it out.
83	81	main	tensorflow/tensorflow/compiler/aot/tests/make_test_graphs.py	223	function
84	82	_XlaClusterOutputGrad	tensorflow/tensorflow/compiler/jit/ops/xla_ops_grad.py	25	function
85	83	TestGraphDebugInfo	tensorflow/tensorflow/compiler/mlir/lite/tests/debuginfo/concrete_function_error.py	32	class	Test stack trace can be displayed.
86	84	main	tensorflow/tensorflow/compiler/mlir/lite/tests/debuginfo/concrete_function_error.py	64	function
87	85	TestModule	tensorflow/tensorflow/compiler/mlir/lite/tests/debuginfo/saved_model_error.py	32	class	The test model has unsupported op.
88	86	TestGraphDebugInfo	tensorflow/tensorflow/compiler/mlir/lite/tests/debuginfo/saved_model_error.py	41	class	Test stack trace can be displayed.
89	87	main	tensorflow/tensorflow/compiler/mlir/lite/tests/debuginfo/saved_model_error.py	73	function	test driver method writes the error message to stdout.
90	88	TestModule	tensorflow/tensorflow/compiler/mlir/tensorflow/tests/tf_saved_model/basic.py	38	class
91	89	Test	tensorflow/tensorflow/compiler/mlir/tensorflow/tests/tf_saved_model/basic_v1.py	49	function
92	90	TestModule	tensorflow/tensorflow/compiler/mlir/tensorflow/tests/tf_saved_model/call_to_exported.py	27	class
93	91	do_test	tensorflow/tensorflow/compiler/mlir/tensorflow/tests/tf_saved_model/common.py	43	function	Runs test. 1. Performs absl and tf "main"-like initialization that must run before almost anything else. 2. Converts `tf.Module` to SavedModel 3. Converts SavedModel to MLIR 4. Prints the textual MLIR to stdout (it is expected that the caller will have FileCheck checks in its file to check this output). This is only for use by the MLIR SavedModel importer tests. Args: create_module_fn: A callable taking no arguments, which returns the `tf.Module` to be converted and printed. exported_names: A set of exported names for the MLIR converter (default is "export all"). show_debug_info: If true, shows debug locations in the resulting MLIR.
94	92	set_tf_options	tensorflow/tensorflow/compiler/mlir/tensorflow/tests/tf_saved_model/common_v1.py	38	function
95	93	do_test	tensorflow/tensorflow/compiler/mlir/tensorflow/tests/tf_saved_model/common_v1.py	49	function	Runs test. 1. Performs absl and tf "main"-like initialization that must run before almost anything else. 2. Converts signature_def_map to SavedModel V1 3. Converts SavedModel V1 to MLIR 4. Prints the textual MLIR to stdout (it is expected that the caller will have FileCheck checks in its file to check this output). This is only for use by the MLIR SavedModel importer tests. Args: create_signature: A functor that return signature_def_map, init_op and assets_collection. signature_def_map is a map from string key to signature_def. The key will be used as function name in the resulting MLIR. canonicalize: If true, canonicalizer will be run on the resulting MLIR. show_debug_info: If true, shows debug locations in the resulting MLIR.
96	94	Test	tensorflow/tensorflow/compiler/mlir/tensorflow/tests/tf_saved_model/control_flow_duplicate_v1.py	42	function
97	95	Test	tensorflow/tensorflow/compiler/mlir/tensorflow/tests/tf_saved_model/control_flow_upgrade_legacy_v1.py	34	function
98	96	ReferencesParent	tensorflow/tensorflow/compiler/mlir/tensorflow/tests/tf_saved_model/cyclic_object_graph.py	27	class
99	97	TestModule	tensorflow/tensorflow/compiler/mlir/tensorflow/tests/tf_saved_model/cyclic_object_graph.py	38	class
100	98	Child	tensorflow/tensorflow/compiler/mlir/tensorflow/tests/tf_saved_model/dag_object_graph.py	27	class
101	99	TestModule	tensorflow/tensorflow/compiler/mlir/tensorflow/tests/tf_saved_model/dag_object_graph.py	37	class
102	100	TestModule	tensorflow/tensorflow/compiler/mlir/tensorflow/tests/tf_saved_model/debug_info.py	27	class
103	101	plus	tensorflow/tensorflow/compiler/mlir/tensorflow/tests/tf_saved_model/defun_export.py	29	function
104	102	test_defun	tensorflow/tensorflow/compiler/mlir/tensorflow/tests/tf_saved_model/defun_export.py	33	function
105	103	Test	tensorflow/tensorflow/compiler/mlir/tensorflow/tests/tf_saved_model/duplicate_method_names_v1.py	37	function
106	104	TestModule	tensorflow/tensorflow/compiler/mlir/tensorflow/tests/tf_saved_model/exported_python_args.py	27	class
107	105	write_vocabulary_file	tensorflow/tensorflow/compiler/mlir/tensorflow/tests/tf_saved_model/hash_table_asset_v1.py	39	function	Write temporary vocab file for module construction.
108	106	test	tensorflow/tensorflow/compiler/mlir/tensorflow/tests/tf_saved_model/hash_table_asset_v1.py	49	function
109	107	Test	tensorflow/tensorflow/compiler/mlir/tensorflow/tests/tf_saved_model/hash_table_v1.py	60	function
110	108	mnist_model	tensorflow/tensorflow/compiler/mlir/tensorflow/tests/tf_saved_model/keras.py	27	function	Creates a MNIST model.
111	109	TestModule	tensorflow/tensorflow/compiler/mlir/tensorflow/tests/tf_saved_model/keras.py	36	class
112	110	Test	tensorflow/tensorflow/compiler/mlir/tensorflow/tests/tf_saved_model/multi_arguments_results_v1.py	52	function
113	111	Test	tensorflow/tensorflow/compiler/mlir/tensorflow/tests/tf_saved_model/multi_variables_v1.py	39	function
114	112	TestModule	tensorflow/tensorflow/compiler/mlir/tensorflow/tests/tf_saved_model/partially_shaped_variables.py	27	class
115	113	Test	tensorflow/tensorflow/compiler/mlir/tensorflow/tests/tf_saved_model/remove_init_variable_v1.py	50	function
116	114	TestModule	tensorflow/tensorflow/compiler/mlir/tensorflow/tests/tf_saved_model/shapes_for_arguments.py	27	class
117	115	Test	tensorflow/tensorflow/compiler/mlir/tensorflow/tests/tf_saved_model/shared_variable_v1.py	41	function
118	116	TestModule	tensorflow/tensorflow/compiler/mlir/tensorflow/tests/tf_saved_model/structured_input.py	27	class
119	117	AdadeltaOptimizerTest	tensorflow/tensorflow/compiler/tests/adadelta_test.py	31	class
120	118	AdagradDAOptimizerTest	tensorflow/tensorflow/compiler/tests/adagrad_da_test.py	32	class
121	119	AdagradOptimizerTest	tensorflow/tensorflow/compiler/tests/adagrad_test.py	31	class
122	120	adam_update_numpy	tensorflow/tensorflow/compiler/tests/adam_test.py	34	function
123	121	AdamOptimizerTest	tensorflow/tensorflow/compiler/tests/adam_test.py	52	class
124	122	XlaAddNTest	tensorflow/tensorflow/compiler/tests/add_n_test.py	30	class
125	123	ArgMinMaxTest	tensorflow/tensorflow/compiler/tests/argminmax_test.py	30	class
126	124	BinaryOpsTest	tensorflow/tensorflow/compiler/tests/binary_ops_test.py	39	class	Test cases for binary operators.
127	125	BucketizationOpTest	tensorflow/tensorflow/compiler/tests/bucketize_op_test.py	30	class
128	126	CaseTest	tensorflow/tensorflow/compiler/tests/case_test.py	31	class
129	127	CategoricalTest	tensorflow/tensorflow/compiler/tests/categorical_op_test.py	36	class	Test cases for random-number generating operators.
130	128	CholeskyOpTest	tensorflow/tensorflow/compiler/tests/cholesky_op_test.py	35	class
131	129	ClusteringTest	tensorflow/tensorflow/compiler/tests/clustering_test.py	35	class
132	130	ComplexNumbersDivisionTest	tensorflow/tensorflow/compiler/tests/complex_div_test.py	35	class	Test cases for complex numbers division operators.
133	131	ConcatTest	tensorflow/tensorflow/compiler/tests/concat_ops_test.py	34	class
134	132	ConcatOffsetTest	tensorflow/tensorflow/compiler/tests/concat_ops_test.py	335	class
135	133	PackTest	tensorflow/tensorflow/compiler/tests/concat_ops_test.py	349	class
136	134	CondTest	tensorflow/tensorflow/compiler/tests/cond_test.py	39	class
137	135	Conv2DTest	tensorflow/tensorflow/compiler/tests/conv2d_test.py	42	class
138	136	Conv2DBackpropInputTest	tensorflow/tensorflow/compiler/tests/conv2d_test.py	236	class
139	137	Conv2DBackpropFilterTest	tensorflow/tensorflow/compiler/tests/conv2d_test.py	534	class
140	138	Conv3DBackpropFilterV2GradTest	tensorflow/tensorflow/compiler/tests/conv3d_test.py	36	class
141	139	Conv3DTransposeTest	tensorflow/tensorflow/compiler/tests/conv3d_test.py	69	class
142	140	ConvolutionNodeNameTest	tensorflow/tensorflow/compiler/tests/conv_node_name_test.py	35	class	Verify convolution node name match. Verify convolution node names on TPU and CPU match with dilation > 1.
143	141	XlaDataFormatDimMapTest	tensorflow/tensorflow/compiler/tests/data_format_ops_test.py	30	class
144	142	XlaPermuteOpTest	tensorflow/tensorflow/compiler/tests/data_format_ops_test.py	67	class
145	143	GetRunMetadataLabels	tensorflow/tensorflow/compiler/tests/dense_layer_test.py	36	function	Returns all labels in run_metadata.
146	144	InLabels	tensorflow/tensorflow/compiler/tests/dense_layer_test.py	45	function	Returns true iff one of the labels contains substr.
147	145	DenseLayerTest	tensorflow/tensorflow/compiler/tests/dense_layer_test.py	50	class
148	146	ReferenceDepthwiseConv2D	tensorflow/tensorflow/compiler/tests/depthwise_conv_op_test.py	35	function
149	147	ConfigsToTest	tensorflow/tensorflow/compiler/tests/depthwise_conv_op_test.py	64	function	Iterator for different convolution shapes, strides and paddings. Yields: Tuple (input_size, filter_size, out_size, stride, padding), the depthwise convolution parameters.
150	148	ConfigsWithDilationsToTest	tensorflow/tensorflow/compiler/tests/depthwise_conv_op_test.py	91	function	Iterator for different convolution shapes, strides and paddings. Yields: Tuple (input_size, filter_size, out_size, stride, dilation, padding), the depthwise convolution parameters.
151	149	CheckGradConfigsToTest	tensorflow/tensorflow/compiler/tests/depthwise_conv_op_test.py	117	function	Iterator for different convolution shapes, strides and paddings. compute_gradient_error() is very expensive. So the configs should be relatively small. Yields: Tuple (input_size, filter_size, out_size, stride, padding), the depthwise convolution parameters.
152	150	DepthwiseConv2DTest	tensorflow/tensorflow/compiler/tests/depthwise_conv_op_test.py	144	class
153	151	DynamicUpdateSliceOpsTest	tensorflow/tensorflow/compiler/tests/dynamic_slice_ops_test.py	30	class
154	152	DynamicStitchTest	tensorflow/tensorflow/compiler/tests/dynamic_stitch_test.py	30	class
155	153	EagerTest	tensorflow/tensorflow/compiler/tests/eager_test.py	47	class
156	154	EagerFunctionTest	tensorflow/tensorflow/compiler/tests/eager_test.py	301	class
157	155	ExcessivePaddingTest	tensorflow/tensorflow/compiler/tests/eager_test.py	721	class	Test that eager execution works with TPU flattened tensors. Tensors that would normally be excessively padded when written to TPU memory are reshaped to 1-D flat tensors. This test case verifies that such tensors work with eager execution. The flattening currently only happens on TPU, but tests should work fine with all backends as flattening is transparent.
158	156	multiple_tpus	tensorflow/tensorflow/compiler/tests/eager_test.py	772	function
159	157	MultiDeviceTest	tensorflow/tensorflow/compiler/tests/eager_test.py	777	class	Test running TPU computation on more than one core.
160	158	EinsumOpTest	tensorflow/tensorflow/compiler/tests/einsum_op_test.py	30	class	Test cases for einsum op.
161	159	EnsureShapeOpTest	tensorflow/tensorflow/compiler/tests/ensure_shape_op_test.py	29	class
162	160	ExtractImagePatches	tensorflow/tensorflow/compiler/tests/extract_image_patches_op_test.py	29	class	Functional tests for ExtractImagePatches op.
163	161	FakeQuantWithMinMaxArgsTest	tensorflow/tensorflow/compiler/tests/fake_quant_ops_test.py	27	class	Test cases for FakeQuantWithMinMaxArgs operation.
164	162	FakeQuantWithMinMaxArgsGradientTest	tensorflow/tensorflow/compiler/tests/fake_quant_ops_test.py	125	class	Test cases for FakeQuantWithMinMaxArgsGradient operation.
165	163	FakeQuantWithMinMaxVarsTest	tensorflow/tensorflow/compiler/tests/fake_quant_ops_test.py	226	class	Test cases for FakeQuantWithMinMaxVars operation.
166	164	FakeQuantWithMinMaxVarsGradientTest	tensorflow/tensorflow/compiler/tests/fake_quant_ops_test.py	331	class	Test cases for FakeQuantWithMinMaxVarsGradient operation.
167	165	pick_10	tensorflow/tensorflow/compiler/tests/fft_test.py	38	function
168	166	to_32bit	tensorflow/tensorflow/compiler/tests/fft_test.py	45	function
169	167	FFTTest	tensorflow/tensorflow/compiler/tests/fft_test.py	60	class
170	168	FIFOQueueTest	tensorflow/tensorflow/compiler/tests/fifo_queue_test.py	31	class
171	169	FtrlOptimizerTest	tensorflow/tensorflow/compiler/tests/ftrl_test.py	32	class
172	170	FunctionTest	tensorflow/tensorflow/compiler/tests/function_test.py	31	class
173	171	FusedBatchNormTest	tensorflow/tensorflow/compiler/tests/fused_batchnorm_test.py	45	class
174	172	GatherNdTest	tensorflow/tensorflow/compiler/tests/gather_nd_op_test.py	30	class
175	173	GatherTest	tensorflow/tensorflow/compiler/tests/gather_test.py	34	class
176	174	GatherBenchmark	tensorflow/tensorflow/compiler/tests/gather_test.py	158	class	Microbenchmarks for the gather op.
177	175	_generate_numpy_random_rgb	tensorflow/tensorflow/compiler/tests/image_ops_test.py	40	function
178	176	RGBToHSVTest	tensorflow/tensorflow/compiler/tests/image_ops_test.py	47	class
179	177	AdjustContrastTest	tensorflow/tensorflow/compiler/tests/image_ops_test.py	110	class
180	178	AdjustHueTest	tensorflow/tensorflow/compiler/tests/image_ops_test.py	174	class
181	179	AdjustSaturationTest	tensorflow/tensorflow/compiler/tests/image_ops_test.py	309	class
182	180	ResizeNearestNeighborTest	tensorflow/tensorflow/compiler/tests/image_ops_test.py	409	class
183	181	ResizeBilinearTest	tensorflow/tensorflow/compiler/tests/image_ops_test.py	548	class
184	182	ResizeBilinearGradTest	tensorflow/tensorflow/compiler/tests/image_ops_test.py	631	class
185	183	ResizeBilinearNonAlignCornersTest	tensorflow/tensorflow/compiler/tests/image_ops_test.py	713	class
186	184	NonMaxSuppressionTest	tensorflow/tensorflow/compiler/tests/image_ops_test.py	776	class
187	185	BatchedNonMaxSuppressionCorrectnessTest	tensorflow/tensorflow/compiler/tests/image_ops_test.py	985	class
188	186	NoRewriteSessionConfig	tensorflow/tensorflow/compiler/tests/jit_test.py	46	function
189	187	CompiledKernel	tensorflow/tensorflow/compiler/tests/jit_test.py	56	function	Execute 'fn' as a compiled XLA kernel, with 'inputs'.
190	188	RunMetadataLabels	tensorflow/tensorflow/compiler/tests/jit_test.py	68	function	Returns all labels in run_metadata.
191	189	InLabels	tensorflow/tensorflow/compiler/tests/jit_test.py	77	function	Returns true iff one of the labels contains substr.
192	190	MetadataHasXlaRunOp	tensorflow/tensorflow/compiler/tests/jit_test.py	82	function	Returns true if there are XlaRun kernels in run_metadata's timeline.
193	191	JitLaunchTest	tensorflow/tensorflow/compiler/tests/jit_test.py	89	class
194	192	XlaCompilationTest	tensorflow/tensorflow/compiler/tests/jit_test.py	279	class	Tests for auto-compilation on CPU/GPU devices.
195	193	ElementWiseFusionTest	tensorflow/tensorflow/compiler/tests/jit_test.py	480	class
196	194	LazyCompilationTest	tensorflow/tensorflow/compiler/tests/jit_test.py	520	class
197	195	ListDiffTest	tensorflow/tensorflow/compiler/tests/listdiff_op_test.py	31	class
198	196	LRNTest	tensorflow/tensorflow/compiler/tests/lrn_ops_test.py	39	class
199	197	Clip	tensorflow/tensorflow/compiler/tests/lstm.py	38	function	Clips x to the range [-1., 1.].
200	198	LSTMCellWeightsShape	tensorflow/tensorflow/compiler/tests/lstm.py	43	function	Returns the shape of the weights for a single LSTM cell.
201	199	LSTMCell	tensorflow/tensorflow/compiler/tests/lstm.py	50	function	Unrolls a single LSTM cell with clipped activations forward by one step. Args: weights: Weight matrix with shape LSTMCellWeightsShape. m_prev: Previous m states with shape [batch_size, num_nodes]. c_prev: Previous c states with shape [batch_size, num_nodes]. x: Input with shape [batch_size, num_inputs]. pad: Padding with shape [batch_size, 1]. Each padding value is either 0 or 1, where 1 indicates padding; i.e. the input is shorter than the sequence length, and the (m, c) states should simply be passed through from the previous states. Returns: The next (m, c) states, each with shape [batch_size, num_nodes].
202	200	LSTMLayer	tensorflow/tensorflow/compiler/tests/lstm.py	88	function	Unrolls a layer of LSTM cells forward by the sequence length. The sequence length is determined by the length of x_seq and pad_seq, which must be the same. Args: cell_name: Base name of each cell. weights: Weight matrix with shape LSTMCellWeightsShape. m: Initial m states with shape [batch_size, num_nodes]. c: Initial c states with shape [batch_size, num_nodes]. x_seq: List of inputs, each with shape [batch_size, num_inputs]. The length of the list is the sequence length. pad_seq: List of paddings, each with shape [batch_size, 1]. The length of the list is the sequence length. Each padding value is either 0 or 1, where 1 indicates padding; i.e. the input is shorter than the sequence length. Returns: List of per-sequence-step outputs, each with shape [batch_size, num_nodes]. Raises: ValueError: If len(x_seq) != len(pad_seq).
203	201	RandomVar	tensorflow/tensorflow/compiler/tests/lstm.py	121	function	Returns a variable of the given shape initialized to random values.
204	202	RandomInputs	tensorflow/tensorflow/compiler/tests/lstm.py	127	function	Returns randomly initialized (x_seq, pad_seq) sequences.
205	203	BuildLSTMLayer	tensorflow/tensorflow/compiler/tests/lstm.py	140	function	Builds a single LSTM layer with random weights and inputs. Args: batch_size: Inputs are fed in batches of this size. seq_length: The sequence length to unroll the LSTM layer. num_inputs: Dimension of inputs that are fed into each LSTM cell. num_nodes: The number of nodes in each LSTM cell. Returns: (out_seq, weights) pair. The out_seq is a list of per-sequence-step outputs, each with shape [batch_size, num_nodes]. The weights are a list of weight variables that may be trained.
206	204	_DumpGraph	tensorflow/tensorflow/compiler/tests/lstm_test.py	40	function
207	205	_Sigmoid	tensorflow/tensorflow/compiler/tests/lstm_test.py	47	function
208	206	_Clip	tensorflow/tensorflow/compiler/tests/lstm_test.py	51	function
209	207	LSTMTest	tensorflow/tensorflow/compiler/tests/lstm_test.py	55	class
210	208	LSTMBenchmark	tensorflow/tensorflow/compiler/tests/lstm_test.py	238	class	Mcro-benchmarks for a single layer of LSTM cells.
211	209	ManipOpsTest	tensorflow/tensorflow/compiler/tests/manip_ops_test.py	30	class	Test cases for manip ops.
212	210	MatrixBandPartTest	tensorflow/tensorflow/compiler/tests/matrix_band_part_test.py	30	class
213	211	zip_to_first_list_length	tensorflow/tensorflow/compiler/tests/matrix_diag_ops_test.py	32	function
214	212	repack_diagonals	tensorflow/tensorflow/compiler/tests/matrix_diag_ops_test.py	40	function
215	213	repack_diagonals_in_tests	tensorflow/tensorflow/compiler/tests/matrix_diag_ops_test.py	77	function
216	214	square_cases	tensorflow/tensorflow/compiler/tests/matrix_diag_ops_test.py	95	function
217	215	tall_cases	tensorflow/tensorflow/compiler/tests/matrix_diag_ops_test.py	173	function
218	216	fat_cases	tensorflow/tensorflow/compiler/tests/matrix_diag_ops_test.py	261	function
219	217	all_tests	tensorflow/tensorflow/compiler/tests/matrix_diag_ops_test.py	329	function
220	218	MatrixDiagTest	tensorflow/tensorflow/compiler/tests/matrix_diag_ops_test.py	333	class
221	219	MatrixSetDiagTest	tensorflow/tensorflow/compiler/tests/matrix_diag_ops_test.py	519	class
222	220	MatrixDiagPartTest	tensorflow/tensorflow/compiler/tests/matrix_diag_ops_test.py	652	class
223	221	InverseOpTest	tensorflow/tensorflow/compiler/tests/matrix_inverse_op_test.py	31	class
224	222	MatrixSolveOpTest	tensorflow/tensorflow/compiler/tests/matrix_solve_op_test.py	30	class
225	223	MakePlaceholder	tensorflow/tensorflow/compiler/tests/matrix_triangular_solve_op_test.py	36	function
226	224	MatrixTriangularSolveOpTest	tensorflow/tensorflow/compiler/tests/matrix_triangular_solve_op_test.py	40	class
227	225	MomentumOptimizerTest	tensorflow/tensorflow/compiler/tests/momentum_test.py	33	class
228	226	NAryOpsTest	tensorflow/tensorflow/compiler/tests/nary_ops_test.py	32	class
229	227	NullaryOpsTest	tensorflow/tensorflow/compiler/tests/nullary_ops_test.py	29	class
230	228	PlaceholderTest	tensorflow/tensorflow/compiler/tests/placeholder_test.py	28	class
231	229	_AvgPoolGrad	tensorflow/tensorflow/compiler/tests/pooling_ops_3d_test.py	35	function
232	230	Pooling3DTest	tensorflow/tensorflow/compiler/tests/pooling_ops_3d_test.py	45	class
233	231	NHWCToNCHW	tensorflow/tensorflow/compiler/tests/pooling_ops_test.py	33	function	Convert the input from NHWC format to NCHW. Args: input_tensor: a 4-D tensor, or a 4-element array representing the same. Returns: the converted tensor or a shape array
234	232	NCHWToNHWC	tensorflow/tensorflow/compiler/tests/pooling_ops_test.py	48	function	Convert the input from NCHW format to NHWC. Args: input_tensor: a 4-D tensor, or a 4-element array representing the same. Returns: the converted tensor or a shape array
235	233	GetTestConfigs	tensorflow/tensorflow/compiler/tests/pooling_ops_test.py	63	function	Get all the valid tests configs to run. Returns: all the valid test configs
236	234	PoolingTest	tensorflow/tensorflow/compiler/tests/pooling_ops_test.py	73	class
237	235	PoolGradTest	tensorflow/tensorflow/compiler/tests/pooling_ops_test.py	292	class
238	236	ProximalAdagradOptimizerTest	tensorflow/tensorflow/compiler/tests/proximal_adagrad_test.py	32	class
239	237	ProximalGradientDescentOptimizerTest	tensorflow/tensorflow/compiler/tests/proximal_gradient_descent_test.py	32	class
240	238	QrOpTest	tensorflow/tensorflow/compiler/tests/qr_op_test.py	33	class
241	239	QuantizedOpsTest	tensorflow/tensorflow/compiler/tests/quantized_ops_test.py	36	class
242	240	DequantizedOpsTest	tensorflow/tensorflow/compiler/tests/quantized_ops_test.py	53	class
243	241	RandomOpsTest	tensorflow/tensorflow/compiler/tests/random_ops_test.py	34	class	Test cases for random-number generating operators.
244	242	ReduceOpsTest	tensorflow/tensorflow/compiler/tests/reduce_ops_test.py	37	class
245	243	ReduceOpPrecisionTest	tensorflow/tensorflow/compiler/tests/reduce_ops_test.py	183	class
246	244	ReduceWindowTest	tensorflow/tensorflow/compiler/tests/reduce_window_test.py	31	class	Test cases for xla.reduce_window.
247	245	ReshapeTest	tensorflow/tensorflow/compiler/tests/reshape_op_test.py	30	class
248	246	ReverseOpsTest	tensorflow/tensorflow/compiler/tests/reverse_ops_test.py	32	class
249	247	ReverseSequenceTest	tensorflow/tensorflow/compiler/tests/reverse_sequence_op_test.py	29	class
250	248	RmspropTest	tensorflow/tensorflow/compiler/tests/rmsprop_test.py	31	class
251	249	numpy_reverse	tensorflow/tensorflow/compiler/tests/scan_ops_test.py	32	function
252	250	handle_options	tensorflow/tensorflow/compiler/tests/scan_ops_test.py	43	function	Adds tf options to numpy scan ops.
253	251	CumsumTest	tensorflow/tensorflow/compiler/tests/scan_ops_test.py	72	class
254	252	CumprodTest	tensorflow/tensorflow/compiler/tests/scan_ops_test.py	150	class
255	253	_AsType	tensorflow/tensorflow/compiler/tests/scatter_nd_op_test.py	31	function
256	254	_FlatInnerDims	tensorflow/tensorflow/compiler/tests/scatter_nd_op_test.py	35	function
257	255	_FlatOuterDims	tensorflow/tensorflow/compiler/tests/scatter_nd_op_test.py	42	function
258	256	_NumpyScatterNd	tensorflow/tensorflow/compiler/tests/scatter_nd_op_test.py	49	function
259	257	_NumpyUpdate	tensorflow/tensorflow/compiler/tests/scatter_nd_op_test.py	66	function
260	258	ScatterNdTest	tensorflow/tensorflow/compiler/tests/scatter_nd_op_test.py	71	class
261	259	ScatterNdTensorTest	tensorflow/tensorflow/compiler/tests/scatter_nd_op_test.py	193	class
262	260	SearchSorteddOpTest	tensorflow/tensorflow/compiler/tests/searchsorted_op_test.py	28	class
263	261	SegmentReductionOpsTest	tensorflow/tensorflow/compiler/tests/segment_reduction_ops_test.py	32	class	Test cases for segment reduction ops.
264	262	SelfAdjointEigOpTest	tensorflow/tensorflow/compiler/tests/self_adjoint_eig_op_test.py	32	class
265	263	SliceTest	tensorflow/tensorflow/compiler/tests/slice_ops_test.py	29	class
266	264	StridedSliceTest	tensorflow/tensorflow/compiler/tests/slice_ops_test.py	127	class
267	265	XlaSortOpTest	tensorflow/tensorflow/compiler/tests/sort_ops_test.py	32	class
268	266	space_to_batch_direct	tensorflow/tensorflow/compiler/tests/spacetobatch_op_test.py	30	function	Direct Python implementation of space-to-batch conversion. This is used for tests only. Args: input_array: N-D array block_shape: 1-D array of shape [num_block_dims]. paddings: 2-D array of shape [num_block_dims, 2]. Returns: Converted tensor.
269	267	SpaceToBatchTest	tensorflow/tensorflow/compiler/tests/spacetobatch_op_test.py	71	class	Tests input-output pairs for the SpaceToBatch and BatchToSpace ops.
270	268	SpaceToBatchNDTest	tensorflow/tensorflow/compiler/tests/spacetobatch_op_test.py	152	class	Tests input-output pairs for the SpaceToBatchND and BatchToSpaceND ops.
271	269	_SparseToDense	tensorflow/tensorflow/compiler/tests/sparse_to_dense_op_test.py	31	function
272	270	SparseToDenseTest	tensorflow/tensorflow/compiler/tests/sparse_to_dense_op_test.py	46	class
273	271	_igamma	tensorflow/tensorflow/compiler/tests/special_math_test.py	48	function
274	272	_igammac	tensorflow/tensorflow/compiler/tests/special_math_test.py	53	function
275	273	implicit_reparameterization_grad	tensorflow/tensorflow/compiler/tests/special_math_test.py	58	function
276	274	_log1p	tensorflow/tensorflow/compiler/tests/special_math_test.py	65	function
277	275	Log1pTest	tensorflow/tensorflow/compiler/tests/special_math_test.py	69	class
278	276	IgammaTest	tensorflow/tensorflow/compiler/tests/special_math_test.py	139	class
279	277	IgammacTest	tensorflow/tensorflow/compiler/tests/special_math_test.py	324	class
280	278	StackOpTest	tensorflow/tensorflow/compiler/tests/stack_ops_test.py	32	class
281	279	xla_device	tensorflow/tensorflow/compiler/tests/stateful_random_ops_test.py	41	function
282	280	xla_device_name	tensorflow/tensorflow/compiler/tests/stateful_random_ops_test.py	55	function
283	281	StatefulRandomOpsTest	tensorflow/tensorflow/compiler/tests/stateful_random_ops_test.py	64	class	Test cases for stateful random-number generator operators.
284	282	StatelessRandomOpsTest	tensorflow/tensorflow/compiler/tests/stateless_random_ops_test.py	33	class	Test cases for stateless random-number generator operators.
285	283	StatelessRandomOpsBenchmark	tensorflow/tensorflow/compiler/tests/stateless_random_ops_test.py	136	class	Microbenchmarks for the stateless random ops.
286	284	SvdOpTest	tensorflow/tensorflow/compiler/tests/svd_op_test.py	33	class
287	285	_make_converter	tensorflow/tensorflow/compiler/tests/tensor_array_ops_test.py	42	function
288	286	TensorArrayTest	tensorflow/tensorflow/compiler/tests/tensor_array_ops_test.py	53	class
289	287	ListOpsTest	tensorflow/tensorflow/compiler/tests/tensor_list_ops_test.py	34	class
290	288	TernaryOpsTest	tensorflow/tensorflow/compiler/tests/ternary_ops_test.py	34	class
291	289	ConvertBetweenDataFormats	tensorflow/tensorflow/compiler/tests/test_utils.py	26	function	Converts 4D tensor between data formats.
292	290	PermuteDimsBetweenDataFormats	tensorflow/tensorflow/compiler/tests/test_utils.py	47	function	Get new shape for converting between data formats.
293	291	RunWithWarmup	tensorflow/tensorflow/compiler/tests/test_utils.py	71	function	Runs a graph a few times to ensure that its clusters are compiled.
294	292	_tfconst	tensorflow/tensorflow/compiler/tests/tridiagonal_solve_ops_test.py	39	function
295	293	_tf_ones	tensorflow/tensorflow/compiler/tests/tridiagonal_solve_ops_test.py	43	function
296	294	TridiagonalSolveOpsTest	tensorflow/tensorflow/compiler/tests/tridiagonal_solve_ops_test.py	47	class	Test for tri-diagonal matrix related ops.
297	295	nhwc_to_format	tensorflow/tensorflow/compiler/tests/unary_ops_test.py	37	function	Converts a numpy array from NHWC format to `data_format`.
298	296	UnaryOpsTest	tensorflow/tensorflow/compiler/tests/unary_ops_test.py	48	class	Test cases for unary operators.
299	297	UnstackOpTest	tensorflow/tensorflow/compiler/tests/unstack_test.py	29	class
300	298	VariableOpsTest	tensorflow/tensorflow/compiler/tests/variable_ops_test.py	40	class	Test cases for resource variable operators.
301	299	StridedSliceAssignChecker	tensorflow/tensorflow/compiler/tests/variable_ops_test.py	422	class	Compares the results of a slice assignment using Tensorflow and numpy.
302	300	SliceAssignTest	tensorflow/tensorflow/compiler/tests/variable_ops_test.py	451	class
303	301	WhileTest	tensorflow/tensorflow/compiler/tests/while_test.py	39	class
304	302	is_compile_on_demand	tensorflow/tensorflow/compiler/tests/while_test.py	260	function
305	303	XlaDeviceGpuTest	tensorflow/tensorflow/compiler/tests/xla_device_gpu_test.py	28	class
306	304	XlaDeviceTest	tensorflow/tensorflow/compiler/tests/xla_device_test.py	32	class
307	305	XlaOpsNumericalTest	tensorflow/tensorflow/compiler/tests/xla_ops_test.py	37	class
308	306	XlaOpsShapeInferenceTest	tensorflow/tensorflow/compiler/tests/xla_ops_test.py	366	class
309	307	parse_disabled_manifest	tensorflow/tensorflow/compiler/tests/xla_test.py	55	function
310	308	XLATestCase	tensorflow/tensorflow/compiler/tests/xla_test.py	81	class	XLA test cases are parameterized test cases.
311	309	Benchmark	tensorflow/tensorflow/compiler/tests/xla_test.py	250	function	Build a graph and run benchmarks against it, with or without XLA. Args: tf_bench: An instance of tf.test.Benchmark, used to run the benchmark. builder_fn: A function that builds a graph when invoked, and returns (name, fetches), where name is the name of the test, and fetches is a list of tensors to fetch as output. use_xla_jit: If true compile with the XLA JIT, otherwise use regular TF. device: The tensorflow device to run on, e.g. "cpu", "gpu". separate_compiled_gradients: If true put each gradient subgraph into a separate compilation scope. This gives fine-grained control over which portions of the graph will be compiled as a single unit. Compiling gradients separately may yield better performance for some graphs. The scope is named based on the scope of the forward computation as well as the name of the gradients. As a result, the gradients will be compiled in a scope that is separate from both the forward computation, and from other gradients.
312	310	XlaTestCaseTestCase	tensorflow/tensorflow/compiler/tests/xla_test_test.py	25	class
313	311	_unary_op	tensorflow/tensorflow/compiler/tf2xla/python/xla.py	70	function	Wrapper that restricts `fn` to have the correct signature.
314	312	_broadcasting_binary_op	tensorflow/tensorflow/compiler/tf2xla/python/xla.py	119	function	Wraps a binary Tensorflow operator and performs XLA-style broadcasting.
315	313	_shift_right_logical_helper	tensorflow/tensorflow/compiler/tf2xla/python/xla.py	152	function	Performs an integer right logical shift irrespective of input type.
316	314	_shift_right_arithmetic_helper	tensorflow/tensorflow/compiler/tf2xla/python/xla.py	167	function	Performs an integer right arithmetic shift irrespective of input type.
317	315	_binary_op	tensorflow/tensorflow/compiler/tf2xla/python/xla.py	211	function	Wrapper that restricts `fn` to have the correct signature.
318	316	broadcast	tensorflow/tensorflow/compiler/tf2xla/python/xla.py	226	function
319	317	clamp	tensorflow/tensorflow/compiler/tf2xla/python/xla.py	234	function
320	318	conv	tensorflow/tensorflow/compiler/tf2xla/python/xla.py	241	function	Wraps the XLA ConvGeneralDilated operator. ConvGeneralDilated is the most general form of XLA convolution and is documented at https://www.tensorflow.org/performance/xla/operation_semantics#conv_convolution Args: lhs: the input tensor rhs: the kernel tensor window_strides: the inter-window strides padding: the padding to apply at the start and end of each input dimensions lhs_dilation: dilation to apply between input elements rhs_dilation: dilation to apply between kernel elements dimension_numbers: a `ConvolutionDimensionNumbers` proto. feature_group_count: number of feature groups for grouped convolution. precision_config: a `xla.PrecisionConfig` proto. name: an optional name for the operator Returns: A tensor representing the output of the convolution.
321	319	dot	tensorflow/tensorflow/compiler/tf2xla/python/xla.py	291	function
322	320	dot_general	tensorflow/tensorflow/compiler/tf2xla/python/xla.py	295	function
323	321	self_adjoint_eig	tensorflow/tensorflow/compiler/tf2xla/python/xla.py	307	function
324	322	svd	tensorflow/tensorflow/compiler/tf2xla/python/xla.py	311	function
325	323	random_normal	tensorflow/tensorflow/compiler/tf2xla/python/xla.py	327	function
326	324	random_uniform	tensorflow/tensorflow/compiler/tf2xla/python/xla.py	333	function
327	325	reduce_window	tensorflow/tensorflow/compiler/tf2xla/python/xla.py	343	function	Wraps the XLA ReduceWindow operator. ReduceWindow is documented at https://www.tensorflow.org/performance/xla/operation_semantics#reducewindow . Args: operand: the input tensor init: a scalar tensor representing the initial value for the reduction reducer: a reduction function that combines a pair of scalars. window_dimensions: shape of the window, as a list of integers window_strides: inter-window strides, as a list of integers. Optional; if omitted, defaults to strides of 1. padding: padding to apply to 'operand'. List of (low, high) pairs of integers that specify the padding to apply before and after each dimension. Optional; if omitted, defaults to no padding. name: the operator name, or None. Returns: A tensor that represents the output of the reduce_window operator.
328	326	reshape	tensorflow/tensorflow/compiler/tf2xla/python/xla.py	391	function
329	327	select	tensorflow/tensorflow/compiler/tf2xla/python/xla.py	398	function
330	328	slice	tensorflow/tensorflow/compiler/tf2xla/python/xla.py	406	function
331	329	_sharding_grad	tensorflow/tensorflow/compiler/tf2xla/python/xla.py	418	function
332	330	_spmd_full_to_shard_shape_grad	tensorflow/tensorflow/compiler/tf2xla/python/xla.py	431	function
333	331	_spmd_shard_to_full_shape_grad	tensorflow/tensorflow/compiler/tf2xla/python/xla.py	440	function
334	332	gather	tensorflow/tensorflow/compiler/tf2xla/python/xla.py	452	function
335	333	scatter	tensorflow/tensorflow/compiler/tf2xla/python/xla.py	463	function
336	334	Sharding	tensorflow/tensorflow/compiler/xla/experimental/xla_sharding/xla_sharding.py	28	class	A class to support adding sharding attributes to Ops. Use the factory constructors and then call apply_to_tensor: Sharding.replicate().apply_to_tensor(tensor)
337	335	replicate	tensorflow/tensorflow/compiler/xla/experimental/xla_sharding/xla_sharding.py	179	function
338	336	assign_device	tensorflow/tensorflow/compiler/xla/experimental/xla_sharding/xla_sharding.py	188	function	Returns a tensor that has AssignDevice sharding attribute.
339	337	tile	tensorflow/tensorflow/compiler/xla/experimental/xla_sharding/xla_sharding.py	202	function	Returns a tensor that has tiled sharding. Args: tensor: A tf.Tensor to shard. tile_assignment: An np.ndarray describing the topology of the tiling and which device will compute which part of the topology. assign_tuple_sharding: If the sharding type should be a tuple. use_sharding_op: If true, adds a sharding op to set the sharding.
340	338	split	tensorflow/tensorflow/compiler/xla/experimental/xla_sharding/xla_sharding.py	224	function	Returns a tensor that is split along the given dimension. Args: tensor: A tf.Tensor to split. split_dimension: The dimension to split. num_devices: The number of devices to partition the dimension. assign_tuple_sharding: If the sharding type should be a tuple. use_sharding_op: If true, adds a sharding op to set the sharding. input_shape: The full shape of the input tensor.
341	339	get_op_sharding	tensorflow/tensorflow/compiler/xla/experimental/xla_sharding/xla_sharding.py	248	function	Returns sharding attribute of an op. Args: op: a TensorFlow op. Returns: The attribute representing XLA sharding on this op.
342	340	auto_to_manual_spmd_partition	tensorflow/tensorflow/compiler/xla/experimental/xla_sharding/xla_sharding.py	260	function	Switches from automatic SPMD partitioning to manual partitioning. Converts a full-shaped tensor (to be automatically partitioned by SPMD partitioner) to a shard-shaped tensor to be consumed by manually partitioned ops. Args: tensor: A tf.Tensor in full shape. manual_sharding: a serialized string of OpSharding to be used in manual partitioning. Returns: A shard-shaped tensor to be consumed by manually partitioned ops.
343	341	manual_to_auto_spmd_partition	tensorflow/tensorflow/compiler/xla/experimental/xla_sharding/xla_sharding.py	279	function	Switches from manual partitioning to automatic SPMD partitioning. Converts a shard-shaped tensor (manually partitioned in SPMD-style) to a full-shaped tensor to be partitioned automatically by the SPMD partitioner. Args: tensor: A tf.Tensor in shard shape. manual_sharding: a serialized string of OpSharding to be used in manual partitioning. full_shape: the shape of tensor before partitioning. Returns: A full-shaped tensor to be partitioned automatically by the SPMD partitioner.
344	342	numpy_assert_allclose	tensorflow/tensorflow/compiler/xla/python/bfloat16_test.py	35	function
345	343	Bfloat16Test	tensorflow/tensorflow/compiler/xla/python/bfloat16_test.py	53	class	Tests the non-numpy Python methods of the bfloat16 type.
346	344	Bfloat16NumPyTest	tensorflow/tensorflow/compiler/xla/python/bfloat16_test.py	251	class	Tests the NumPy integration of the bfloat16 type.
347	345	_interpreter_backend_factory	tensorflow/tensorflow/compiler/xla/python/xla_client.py	58	function
348	346	_cpu_backend_factory	tensorflow/tensorflow/compiler/xla/python/xla_client.py	62	function
349	347	_gpu_backend_factory	tensorflow/tensorflow/compiler/xla/python/xla_client.py	66	function	Returns a GPU backend. BFC allocator is used by default.
350	348	register_local_backend_factory	tensorflow/tensorflow/compiler/xla/python/xla_client.py	101	function
351	349	_get_local_backends	tensorflow/tensorflow/compiler/xla/python/xla_client.py	108	function	Instantiates all known local backends.
352	350	get_local_backend	tensorflow/tensorflow/compiler/xla/python/xla_client.py	131	function	Returns a local backend. Args: name: the backend name. If `None`, a default local backend is returned, typically `gpu` if one is present, or `cpu` if not. If a string, the named backend is returned or an exception raised. Returns: A LocalBackend object.
353	351	OpMetadata	tensorflow/tensorflow/compiler/xla/python/xla_client.py	152	class	Python representation of a xla.OpMetadata protobuf.
354	352	CurrentSourceInfoMetadata	tensorflow/tensorflow/compiler/xla/python/xla_client.py	163	function	Helper for use in source mapping that returns an OpMetadata object.
355	353	dtype_to_etype	tensorflow/tensorflow/compiler/xla/python/xla_client.py	206	function	Convenience function for reading DTYPE_TO_XLA_ELEMENT_TYPE.
356	354	shape_from_pyval	tensorflow/tensorflow/compiler/xla/python/xla_client.py	272	function	Returns a Shape that describes a tuple-tree of Numpy arrays.
357	355	execute_with_python_values	tensorflow/tensorflow/compiler/xla/python/xla_client.py	334	function	Execute on one replica with Python values as arguments and output.
358	356	execute_with_python_values_replicated	tensorflow/tensorflow/compiler/xla/python/xla_client.py	345	function	Execute on many replicas with Python values as arguments and output. Arguments: executable: the program to run. arguments: a list of lists of Python values indexed by `[replica][arg_num]` to pass as inputs. backend: the backend we are targeting. Returns: A list of python values, one per replica.
359	357	PaddingType	tensorflow/tensorflow/compiler/xla/python/xla_client.py	374	class
360	358	window_padding_type_to_pad_values	tensorflow/tensorflow/compiler/xla/python/xla_client.py	379	function	Maps PaddingType or string to pad values (list of pairs of ints).
361	359	register_custom_call_target	tensorflow/tensorflow/compiler/xla/python/xla_client.py	418	function	Registers a custom call target. Args: name: bytes containing the name of the function. fn: a PyCapsule object containing the function pointer. platform: the target platform.
362	360	PaddingConfigDimension	tensorflow/tensorflow/compiler/xla/python/xla_client.py	433	class	Python representation of a xla.PaddingConfigDimension protobuf.
363	361	PaddingConfig	tensorflow/tensorflow/compiler/xla/python/xla_client.py	443	class	Python representation of a xla.PaddingConfig protobuf.
364	362	make_padding_config	tensorflow/tensorflow/compiler/xla/python/xla_client.py	451	function	Create PaddingConfig proto from list of triples of integers. Args: padding_config: either a PaddingConfig or a list of integer triples (edge_padding_low, edge_padding_high, interior_padding) representing the configuration of the padding operation. Returns: A `PaddingConfig` object.
365	363	DotDimensionNumbers	tensorflow/tensorflow/compiler/xla/python/xla_client.py	476	class	Python representation of a xla.DotDimensionNumbers protobuf.
366	364	make_dot_dimension_numbers	tensorflow/tensorflow/compiler/xla/python/xla_client.py	488	function	Builds a DotDimensionNumbers object from a specification. Args: dimension_numbers: either a `DotDimensionNumbers` or a nested tuple `((lhs_contract, rhs_contract), (lhs_batch, rhs_batch))` of lists of integers representing the dimensions to treat as contracting dimensions and batch dimensions on each input operand. Returns: A `DotDimensionNumbers` object.
367	365	ConvolutionDimensionNumbers	tensorflow/tensorflow/compiler/xla/python/xla_client.py	516	class	Python representation of a xla.ConvolutionDimensionNumbers protobuf.
368	366	make_convolution_dimension_numbers	tensorflow/tensorflow/compiler/xla/python/xla_client.py	536	function	Builds a ConvolutionDimensionNumbers object from a specification. Args: dimension_numbers: optional, either a ConvolutionDimensionNumbers object or a tuple (lhs_spec, rhs_spec, out_spec). Each element is a string of length N+2 identifying by position: (1) batch dimensions in lhs, rhs, and the output with the character 'N', (2) feature dimensions in lhs and the output with the character 'C', (3) input and output feature dimensions in rhs with the characters 'I' and 'O' respectively, and (4) spatial dimension correspondences between lhs, rhs, and the output using any distinct characters. For example, to indicate dimension numbers consistent with the Conv operation with two spatial dimensions, one could use ('NCHW', 'OIHW', 'NCHW'). As another example, to indicate dimension numbers consistent with the TensorFlow Conv2D operation, one could use ('NHWC', 'HWIO', 'NHWC'). When using the latter form of convolution dimension specification, window strides are associated with spatial dimension character labels according to the order in which the labels appear in the rhs_spec string, so that window_strides[0] is matched with the dimension corresponding to the first character appearing in rhs_spec that is not 'I' or 'O'. By default, use the same dimension numbering as Conv and ConvWithGeneralPadding. num_spatial_dimensions: the number of spatial dimensions. Returns: A `ConvolutionDimensionNumbers` object.
369	367	OpSharding	tensorflow/tensorflow/compiler/xla/python/xla_client.py	600	class	Python representation of a xla.OpSharding protobuf.
370	368	PrecisionConfig	tensorflow/tensorflow/compiler/xla/python/xla_client.py	614	class	Python representation of a xla.PrecisionConfig protobuf.
371	369	GatherDimensionNumbers	tensorflow/tensorflow/compiler/xla/python/xla_client.py	624	class	Python representation of a xla.GatherDimensionNumbers protobuf.
372	370	ScatterDimensionNumbers	tensorflow/tensorflow/compiler/xla/python/xla_client.py	636	class	Python representation of a xla.ScatterDimensionNumbers protobuf.
373	371	ReplicaGroup	tensorflow/tensorflow/compiler/xla/python/xla_client.py	648	class	Python representation of a xla.ReplicaGroup protobuf.
374	372	_make_replica_group_proto	tensorflow/tensorflow/compiler/xla/python/xla_client.py	656	function
375	373	make_replica_groups	tensorflow/tensorflow/compiler/xla/python/xla_client.py	662	function
376	374	tracebacks	tensorflow/tensorflow/compiler/xla/python/xla_client.py	677	function	Context manager that enables or disables traceback collection.
377	375	heap_profile	tensorflow/tensorflow/compiler/xla/python/xla_client.py	687	function	Returns a gzipped pprof protocol buffer containing a heap profile.
378	376	TestFactory	tensorflow/tensorflow/compiler/xla/python/xla_client_test.py	56	function
379	377	InstantiateTests	tensorflow/tensorflow/compiler/xla/python/xla_client_test.py	2103	function
380	378	TpuBackend	tensorflow/tensorflow/compiler/xla/python/tpu_driver/client/tpu_client.py	29	class	XLA backend implemented using the Tpu driver API.
381	379	ConvertLiteralToNumpyArray	tensorflow/tensorflow/compiler/xla/python_api/xla_literal.py	28	function	Converts a XLA literal to a Numpy array.
382	380	_ConvertNumpyArrayToLiteral	tensorflow/tensorflow/compiler/xla/python_api/xla_literal.py	64	function	Converts a Numpy array to a XLA literal.
383	381	ConvertNumpyArrayToLiteral	tensorflow/tensorflow/compiler/xla/python_api/xla_literal.py	85	function	Converts a Numpy array or a nested tuple thereof to an XLA literal.
384	382	Shape	tensorflow/tensorflow/compiler/xla/python_api/xla_shape.py	29	class	Wraps a xla_data_pb2.ShapeProto message with a convenient Python type. Provides direct access to the underlying xla_data_pb2.ShapeProto message in the message attribute, along with accessor wrappers to the message's fields. Avoid direct access to .message unless interacting directly with protobuf APIs like CopyFrom. In other words, prefer hauling the shape around in a Shape, and only access .message when strictly required by the protobuf API.
385	383	_CreateShapeFromNumpy	tensorflow/tensorflow/compiler/xla/python_api/xla_shape.py	103	function	Create a Shape from a given Numpy array. Args: ndarray: Numpy array. Returns: A Shape object.
386	384	CreateShapeFromNumpy	tensorflow/tensorflow/compiler/xla/python_api/xla_shape.py	129	function	Create a Shape from a Numpy array or a nested tuple structure thereof. Args: value: Numpy array or (possibly nested) tuple structure that bottoms out in Numpy arrays. Returns: A Shape object.
387	385	CreateShapeFromDtypeAndTuple	tensorflow/tensorflow/compiler/xla/python_api/xla_shape.py	147	function	Create a shape from a Numpy dtype and a sequence of nonnegative integers. Args: dtype: a numpy dtype, e.g. np.dtype('int32'). shape_tuple: a sequence of nonnegative integers. Returns: A Shape object.
388	386	RamFilesystemTest	tensorflow/tensorflow/core/platform/ram_file_system_test.py	38	class
389	387	AddOneTest	tensorflow/tensorflow/examples/adding_an_op/cuda_op_test.py	25	class
390	388	FactTest	tensorflow/tensorflow/examples/adding_an_op/fact_test.py	25	class
391	389	ZeroOut1Test	tensorflow/tensorflow/examples/adding_an_op/zero_out_1_test.py	29	class
392	390	ZeroOut2Test	tensorflow/tensorflow/examples/adding_an_op/zero_out_2_test.py	30	class
393	391	ZeroOut3Test	tensorflow/tensorflow/examples/adding_an_op/zero_out_3_test.py	27	class
394	392	_zero_out_grad	tensorflow/tensorflow/examples/adding_an_op/zero_out_grad_2.py	28	function	The gradients for `zero_out`. Args: op: The `zero_out` `Operation` that we are differentiating, which we can use to find the inputs and outputs of the original op. grad: Gradient with respect to the output of the `zero_out` op. Returns: Gradients with respect to the input of `zero_out`.
395	393	load_graph	tensorflow/tensorflow/examples/label_image/label_image.py	26	function
396	394	read_tensor_from_image_file	tensorflow/tensorflow/examples/label_image/label_image.py	38	function
397	395	load_labels	tensorflow/tensorflow/examples/label_image/label_image.py	65	function
398	396	main	tensorflow/tensorflow/examples/saved_model/integration_tests/deploy_mnist_cnn.py	47	function
399	397	MaybeDistributionScope	tensorflow/tensorflow/examples/saved_model/integration_tests/distribution_strategy_utils.py	48	class	Provides a context allowing no distribution strategy.
400	398	make_feature_extractor	tensorflow/tensorflow/examples/saved_model/integration_tests/export_mnist_cnn.py	56	function	Returns a Keras Model to compute a feature vector from MNIST images.
401	399	set_feature_extractor_hparams	tensorflow/tensorflow/examples/saved_model/integration_tests/export_mnist_cnn.py	72	function
402	400	make_classifier	tensorflow/tensorflow/examples/saved_model/integration_tests/export_mnist_cnn.py	76	function	Returns a Keras Model to classify MNIST using feature_extractor.
403	401	wrap_keras_model_for_export	tensorflow/tensorflow/examples/saved_model/integration_tests/export_mnist_cnn.py	87	function	Wraps `model` for saving and loading as SavedModel.
404	402	_get_traced_loss	tensorflow/tensorflow/examples/saved_model/integration_tests/export_mnist_cnn.py	144	function	Returns tf.function for model.losses[i] with a trace for zero args. The intended usage is [_get_traced_loss(model, i) for i in range(len(model.losses))] This is better than [tf.function(lambda: model.losses[i], input_signature=[]) for i ...] because it avoids capturing a loop index in a lambda, and removes any chance of deferring the trace. Args: model: a Keras Model. i: an integer between from 0 up to but to len(model.losses).
405	403	main	tensorflow/tensorflow/examples/saved_model/integration_tests/export_mnist_cnn.py	163	function
406	404	main	tensorflow/tensorflow/examples/saved_model/integration_tests/export_rnn_cell.py	32	function
407	405	write_vocabulary_file	tensorflow/tensorflow/examples/saved_model/integration_tests/export_simple_text_embedding.py	34	function	Write temporary vocab file for module construction.
408	406	TextEmbeddingModel	tensorflow/tensorflow/examples/saved_model/integration_tests/export_simple_text_embedding.py	44	class	Text embedding model. A text embeddings model that takes a sentences on input and outputs the sentence embedding.
409	407	main	tensorflow/tensorflow/examples/saved_model/integration_tests/export_simple_text_embedding.py	96	function
410	408	TextRnnModel	tensorflow/tensorflow/examples/saved_model/integration_tests/export_text_rnn_model.py	31	class	Text RNN model. A full generative text RNN model that can train and decode sentences from a starting word.
411	409	main	tensorflow/tensorflow/examples/saved_model/integration_tests/export_text_rnn_model.py	170	function
412	410	TestCase	tensorflow/tensorflow/examples/saved_model/integration_tests/integration_scripts.py	42	class	Base class to write SavedModel integration tests.
413	411	MaybeRunScriptInstead	tensorflow/tensorflow/examples/saved_model/integration_tests/integration_scripts.py	62	function
414	412	_load_random_data	tensorflow/tensorflow/examples/saved_model/integration_tests/mnist_util.py	28	function
415	413	load_reshaped_data	tensorflow/tensorflow/examples/saved_model/integration_tests/mnist_util.py	34	function	Returns MNIST or Fashion MNIST or fake train and test data.
416	414	_prepare_image	tensorflow/tensorflow/examples/saved_model/integration_tests/mnist_util.py	44	function	Converts images to [n,h,w,c] format in range [0,1].
417	415	_prepare_label	tensorflow/tensorflow/examples/saved_model/integration_tests/mnist_util.py	49	function	Conerts labels to one-hot encoding.
418	416	SavedModelTest	tensorflow/tensorflow/examples/saved_model/integration_tests/saved_model_test.py	32	class
419	417	make_feature_extractor	tensorflow/tensorflow/examples/saved_model/integration_tests/use_mnist_cnn.py	72	function	Load a pre-trained feature extractor and wrap it for use in Keras.
420	418	make_classifier	tensorflow/tensorflow/examples/saved_model/integration_tests/use_mnist_cnn.py	100	function	Returns a Keras Model to classify MNIST using feature_extractor.
421	419	main	tensorflow/tensorflow/examples/saved_model/integration_tests/use_mnist_cnn.py	112	function
422	420	train	tensorflow/tensorflow/examples/saved_model/integration_tests/use_model_in_sequential_keras.py	35	function	Build a Keras model and train with mock data.
423	421	main	tensorflow/tensorflow/examples/saved_model/integration_tests/use_model_in_sequential_keras.py	67	function
424	422	main	tensorflow/tensorflow/examples/saved_model/integration_tests/use_rnn_cell.py	33	function
425	423	train	tensorflow/tensorflow/examples/saved_model/integration_tests/use_text_embedding_in_dataset.py	34	function	Build a Keras model and train with mock data.
426	424	main	tensorflow/tensorflow/examples/saved_model/integration_tests/use_text_embedding_in_dataset.py	65	function
427	425	main	tensorflow/tensorflow/examples/saved_model/integration_tests/use_text_rnn_model.py	32	function
428	426	StreamingAccuracyStats	tensorflow/tensorflow/examples/speech_commands/accuracy_utils.py	24	class	Get streaming accuracy statistics every time a new command is founded. Attributes: _how_many_gt: How many ground truths. _how_many_gt_matched: How many ground truths have been matched. _how_many_fp: How many commands have been fired as false positive. _how_many_c: How many commands have been fired correctly. _how_many_w: How many commands have been fired wrongly. _gt_occurrence: A list to record which commands and when it occurs in the input audio stream. _previous_c: A variable to record the last status of _how_many_c. _previous_w: A variable to record the last status of _how_many_w. _previous_fp: A variable to record the last status of _how_many_fp.
429	427	create_inference_graph	tensorflow/tensorflow/examples/speech_commands/freeze.py	63	function	Creates an audio model with the nodes needed for inference. Uses the supplied arguments to create a model, and inserts the input and output nodes that are needed to use the graph for inference. Args: wanted_words: Comma-separated list of the words we're trying to recognize. sample_rate: How many samples per second are in the input audio files. clip_duration_ms: How many samples to analyze for the audio pattern. clip_stride_ms: How often to run recognition. Useful for models with cache. window_size_ms: Time slice duration to estimate frequencies from. window_stride_ms: How far apart time slices should be. feature_bin_count: Number of frequency bands to analyze. model_architecture: Name of the kind of model to generate. preprocess: How the spectrogram is processed to produce features, for example 'mfcc', 'average', or 'micro'. Returns: Input and output tensor objects. Raises: Exception: If the preprocessing mode isn't recognized.
430	428	save_graph_def	tensorflow/tensorflow/examples/speech_commands/freeze.py	161	function	Writes a graph def file out to disk. Args: file_name: Where to save the file. frozen_graph_def: GraphDef proto object to save.
431	429	save_saved_model	tensorflow/tensorflow/examples/speech_commands/freeze.py	176	function	Writes a SavedModel out to disk. Args: file_name: Where to save the file. sess: TensorFlow session containing the graph. input_tensor: Tensor object defining the input's properties. output_tensor: Tensor object defining the output's properties.
432	430	main	tensorflow/tensorflow/examples/speech_commands/freeze.py	211	function
433	431	FreezeTest	tensorflow/tensorflow/examples/speech_commands/freeze_test.py	30	class
434	432	mix_in_audio_sample	tensorflow/tensorflow/examples/speech_commands/generate_streaming_test_wav.py	55	function	Mixes the sample data into the main track at the specified offset. Args: track_data: Numpy array holding main audio data. Modified in-place. track_offset: Where to mix the sample into the main track. sample_data: Numpy array of audio data to mix into the main track. sample_offset: Where to start in the audio sample. clip_duration: How long the sample segment is. sample_volume: Loudness to mix the sample in at. ramp_in: Length in samples of volume increase stage. ramp_out: Length in samples of volume decrease stage.
435	433	main	tensorflow/tensorflow/examples/speech_commands/generate_streaming_test_wav.py	86	function
436	434	GenerateStreamingTestWavTest	tensorflow/tensorflow/examples/speech_commands/generate_streaming_test_wav_test.py	27	class
437	435	prepare_words_list	tensorflow/tensorflow/examples/speech_commands/input_data.py	58	function	Prepends common tokens to the custom word list. Args: wanted_words: List of strings containing the custom words. Returns: List with the standard silence and unknown tokens added.
438	436	which_set	tensorflow/tensorflow/examples/speech_commands/input_data.py	70	function	Determines which data partition the file should belong to. We want to keep files in the same training, validation, or testing sets even if new ones are added over time. This makes it less likely that testing samples will accidentally be reused in training when long runs are restarted for example. To keep this stability, a hash of the filename is taken and used to determine which set it should belong to. This determination only depends on the name and the set proportions, so it won't change as other files are added. It's also useful to associate particular files as related (for example words spoken by the same person), so anything after '_nohash_' in a filename is ignored for set determination. This ensures that 'bobby_nohash_0.wav' and 'bobby_nohash_1.wav' are always in the same set, for example. Args: filename: File path of the data sample. validation_percentage: How much of the data set to use for validation. testing_percentage: How much of the data set to use for testing. Returns: String, one of 'training', 'validation', or 'testing'.
439	437	load_wav_file	tensorflow/tensorflow/examples/speech_commands/input_data.py	118	function	Loads an audio file and returns a float PCM-encoded array of samples. Args: filename: Path to the .wav file to load. Returns: Numpy array holding the sample data as floats between -1.0 and 1.0.
440	438	save_wav_file	tensorflow/tensorflow/examples/speech_commands/input_data.py	136	function	Saves audio sample data to a .wav audio file. Args: filename: Path to save the file to. wav_data: 2D array of float PCM-encoded audio data. sample_rate: Samples per second to encode in the file.
441	439	get_features_range	tensorflow/tensorflow/examples/speech_commands/input_data.py	160	function	Returns the expected min/max for generated features. Args: model_settings: Information about the current model being trained. Returns: Min/max float pair holding the range of features. Raises: Exception: If preprocessing mode isn't recognized.
442	440	AudioProcessor	tensorflow/tensorflow/examples/speech_commands/input_data.py	190	class	Handles loading, partitioning, and preparing audio training data.
443	441	InputDataTest	tensorflow/tensorflow/examples/speech_commands/input_data_test.py	33	class
444	442	load_graph	tensorflow/tensorflow/examples/speech_commands/label_wav.py	43	function	Unpersists graph from file as default graph.
445	443	load_labels	tensorflow/tensorflow/examples/speech_commands/label_wav.py	51	function	Read in labels, one label per line.
446	444	run_graph	tensorflow/tensorflow/examples/speech_commands/label_wav.py	56	function	Runs the audio data through the graph and prints predictions.
447	445	label_wav	tensorflow/tensorflow/examples/speech_commands/label_wav.py	77	function	Loads the model and labels, and runs the inference to print predictions.
448	446	main	tensorflow/tensorflow/examples/speech_commands/label_wav.py	98	function	Entry point for script, converts flags to arguments.
449	447	load_graph	tensorflow/tensorflow/examples/speech_commands/label_wav_dir.py	44	function	Unpersists graph from file as default graph.
450	448	load_labels	tensorflow/tensorflow/examples/speech_commands/label_wav_dir.py	52	function	Read in labels, one label per line.
451	449	run_graph	tensorflow/tensorflow/examples/speech_commands/label_wav_dir.py	57	function	Runs the audio data through the graph and prints predictions.
452	450	label_wav	tensorflow/tensorflow/examples/speech_commands/label_wav_dir.py	85	function	Loads the model and labels, and runs the inference to print predictions.
453	451	main	tensorflow/tensorflow/examples/speech_commands/label_wav_dir.py	101	function	Entry point for script, converts flags to arguments.
454	452	LabelWavTest	tensorflow/tensorflow/examples/speech_commands/label_wav_test.py	29	class
455	453	_next_power_of_two	tensorflow/tensorflow/examples/speech_commands/models.py	27	function	Calculates the smallest enclosing power of two for an input. Args: x: Positive float or integer number. Returns: Next largest power of two integer.
456	454	prepare_model_settings	tensorflow/tensorflow/examples/speech_commands/models.py	39	function	Calculates common settings needed for all models. Args: label_count: How many classes are to be recognized. sample_rate: Number of audio samples per second. clip_duration_ms: Length of each audio clip to be analyzed. window_size_ms: Duration of frequency analysis window. window_stride_ms: How far to move in time between frequency windows. feature_bin_count: Number of frequency bins to use for analysis. preprocess: How the spectrogram is processed to produce features. Returns: Dictionary containing common settings. Raises: ValueError: If the preprocessing mode isn't recognized.
457	455	create_model	tensorflow/tensorflow/examples/speech_commands/models.py	95	function	Builds a model of the requested architecture compatible with the settings. There are many possible ways of deriving predictions from a spectrogram input, so this function provides an abstract interface for creating different kinds of models in a black-box way. You need to pass in a TensorFlow node as the 'fingerprint' input, and this should output a batch of 1D features that describe the audio. Typically this will be derived from a spectrogram that's been run through an MFCC, but in theory it can be any feature vector of the size specified in model_settings['fingerprint_size']. The function will build the graph it needs in the current TensorFlow graph, and return the tensorflow output that will contain the 'logits' input to the softmax prediction process. If training flag is on, it will also return a placeholder node that can be used to control the dropout amount. See the implementations below for the possible model architectures that can be requested. Args: fingerprint_input: TensorFlow node that will output audio feature vectors. model_settings: Dictionary of information about the model. model_architecture: String specifying which kind of model to create. is_training: Whether the model is going to be used for training. runtime_settings: Dictionary of information about the runtime. Returns: TensorFlow node outputting logits results, and optionally a dropout placeholder. Raises: Exception: If the architecture type isn't recognized.
458	456	load_variables_from_checkpoint	tensorflow/tensorflow/examples/speech_commands/models.py	153	function	Utility function to centralize checkpoint restoration. Args: sess: TensorFlow session. start_checkpoint: Path to saved checkpoint on disk.
459	457	create_single_fc_model	tensorflow/tensorflow/examples/speech_commands/models.py	164	function	Builds a model with a single hidden fully-connected layer. This is a very simple model with just one matmul and bias layer. As you'd expect, it doesn't produce very accurate results, but it is very fast and simple, so it's useful for sanity testing. Here's the layout of the graph: (fingerprint_input) v [MatMul]<-(weights) v [BiasAdd]<-(bias) v Args: fingerprint_input: TensorFlow node that will output audio feature vectors. model_settings: Dictionary of information about the model. is_training: Whether the model is going to be used for training. Returns: TensorFlow node outputting logits results, and optionally a dropout placeholder.
460	458	create_conv_model	tensorflow/tensorflow/examples/speech_commands/models.py	207	function	Builds a standard convolutional model. This is roughly the network labeled as 'cnn-trad-fpool3' in the 'Convolutional Neural Networks for Small-footprint Keyword Spotting' paper: http://www.isca-speech.org/archive/interspeech_2015/papers/i15_1478.pdf Here's the layout of the graph: (fingerprint_input) v [Conv2D]<-(weights) v [BiasAdd]<-(bias) v [Relu] v [MaxPool] v [Conv2D]<-(weights) v [BiasAdd]<-(bias) v [Relu] v [MaxPool] v [MatMul]<-(weights) v [BiasAdd]<-(bias) v This produces fairly good quality results, but can involve a large number of weight parameters and computations. For a cheaper alternative from the same paper with slightly less accuracy, see 'low_latency_conv' below. During training, dropout nodes are introduced after each relu, controlled by a placeholder. Args: fingerprint_input: TensorFlow node that will output audio feature vectors. model_settings: Dictionary of information about the model. is_training: Whether the model is going to be used for training. Returns: TensorFlow node outputting logits results, and optionally a dropout placeholder.
461	459	create_low_latency_conv_model	tensorflow/tensorflow/examples/speech_commands/models.py	333	function	Builds a convolutional model with low compute requirements. This is roughly the network labeled as 'cnn-one-fstride4' in the 'Convolutional Neural Networks for Small-footprint Keyword Spotting' paper: http://www.isca-speech.org/archive/interspeech_2015/papers/i15_1478.pdf Here's the layout of the graph: (fingerprint_input) v [Conv2D]<-(weights) v [BiasAdd]<-(bias) v [Relu] v [MatMul]<-(weights) v [BiasAdd]<-(bias) v [MatMul]<-(weights) v [BiasAdd]<-(bias) v [MatMul]<-(weights) v [BiasAdd]<-(bias) v This produces slightly lower quality results than the 'conv' model, but needs fewer weight parameters and computations. During training, dropout nodes are introduced after the relu, controlled by a placeholder. Args: fingerprint_input: TensorFlow node that will output audio feature vectors. model_settings: Dictionary of information about the model. is_training: Whether the model is going to be used for training. Returns: TensorFlow node outputting logits results, and optionally a dropout placeholder.
462	460	create_low_latency_svdf_model	tensorflow/tensorflow/examples/speech_commands/models.py	462	function	Builds an SVDF model with low compute requirements. This is based in the topology presented in the 'Compressing Deep Neural Networks using a Rank-Constrained Topology' paper: https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/43813.pdf Here's the layout of the graph: (fingerprint_input) v [SVDF]<-(weights) v [BiasAdd]<-(bias) v [Relu] v [MatMul]<-(weights) v [BiasAdd]<-(bias) v [MatMul]<-(weights) v [BiasAdd]<-(bias) v [MatMul]<-(weights) v [BiasAdd]<-(bias) v This model produces lower recognition accuracy than the 'conv' model above, but requires fewer weight parameters and, significantly fewer computations. During training, dropout nodes are introduced after the relu, controlled by a placeholder. Args: fingerprint_input: TensorFlow node that will output audio feature vectors. The node is expected to produce a 2D Tensor of shape: [batch, model_settings['fingerprint_width'] * model_settings['spectrogram_length']] with the features corresponding to the same time slot arranged contiguously, and the oldest slot at index [:, 0], and newest at [:, -1]. model_settings: Dictionary of information about the model. is_training: Whether the model is going to be used for training. runtime_settings: Dictionary of information about the runtime. Returns: TensorFlow node outputting logits results, and optionally a dropout placeholder. Raises: ValueError: If the inputs tensor is incorrectly shaped.
463	461	create_tiny_conv_model	tensorflow/tensorflow/examples/speech_commands/models.py	673	function	Builds a convolutional model aimed at microcontrollers. Devices like DSPs and microcontrollers can have very small amounts of memory and limited processing power. This model is designed to use less than 20KB of working RAM, and fit within 32KB of read-only (flash) memory. Here's the layout of the graph: (fingerprint_input) v [Conv2D]<-(weights) v [BiasAdd]<-(bias) v [Relu] v [MatMul]<-(weights) v [BiasAdd]<-(bias) v This doesn't produce particularly accurate results, but it's designed to be used as the first stage of a pipeline, running on a low-energy piece of hardware that can always be on, and then wake higher-power chips when a possible utterance has been found, so that more accurate analysis can be done. During training, a dropout node is introduced after the relu, controlled by a placeholder. Args: fingerprint_input: TensorFlow node that will output audio feature vectors. model_settings: Dictionary of information about the model. is_training: Whether the model is going to be used for training. Returns: TensorFlow node outputting logits results, and optionally a dropout placeholder.
464	462	create_tiny_embedding_conv_model	tensorflow/tensorflow/examples/speech_commands/models.py	765	function	Builds a convolutional model aimed at microcontrollers. Devices like DSPs and microcontrollers can have very small amounts of memory and limited processing power. This model is designed to use less than 20KB of working RAM, and fit within 32KB of read-only (flash) memory. Here's the layout of the graph: (fingerprint_input) v [Conv2D]<-(weights) v [BiasAdd]<-(bias) v [Relu] v [Conv2D]<-(weights) v [BiasAdd]<-(bias) v [Relu] v [Conv2D]<-(weights) v [BiasAdd]<-(bias) v [Relu] v [MatMul]<-(weights) v [BiasAdd]<-(bias) v This doesn't produce particularly accurate results, but it's designed to be used as the first stage of a pipeline, running on a low-energy piece of hardware that can always be on, and then wake higher-power chips when a possible utterance has been found, so that more accurate analysis can be done. During training, a dropout node is introduced after the relu, controlled by a placeholder. Args: fingerprint_input: TensorFlow node that will output audio feature vectors. model_settings: Dictionary of information about the model. is_training: Whether the model is going to be used for training. Returns: TensorFlow node outputting logits results, and optionally a dropout placeholder.
465	463	ModelsTest	tensorflow/tensorflow/examples/speech_commands/models_test.py	28	class
466	464	RecognizeResult	tensorflow/tensorflow/examples/speech_commands/recognize_commands.py	25	class	Save recognition result temporarily. Attributes: founded_command: A string indicating the word just founded. Default value is '_silence_' score: An float representing the confidence of founded word. Default value is zero. is_new_command: A boolean indicating if the founded command is a new one against the last one. Default value is False.
467	465	RecognizeCommands	tensorflow/tensorflow/examples/speech_commands/recognize_commands.py	67	class	Smooth the inference results by using average window. Maintain a slide window over the audio stream, which adds new result(a pair of the 1.confidences of all classes and 2.the start timestamp of input audio clip) directly the inference produces one and removes the most previous one and other abnormal values. Then it smooth the results in the window to get the most reliable command in this period. Attributes: _label: A list containing commands at corresponding lines. _average_window_duration: The length of average window. _detection_threshold: A confidence threshold for filtering out unreliable command. _suppression_ms: Milliseconds every two reliable founded commands should apart. _minimum_count: An integer count indicating the minimum results the average window should cover. _previous_results: A deque to store previous results. _label_count: The length of label list. _previous_top_label: Last founded command. Initial value is '_silence_'. _previous_top_time: The timestamp of _previous results. Default is -np.inf.
468	466	load_graph	tensorflow/tensorflow/examples/speech_commands/test_streaming_accuracy.py	80	function	Read a tensorflow model, and creates a default graph object.
469	467	read_label_file	tensorflow/tensorflow/examples/speech_commands/test_streaming_accuracy.py	92	function	Load a list of label.
470	468	read_wav_file	tensorflow/tensorflow/examples/speech_commands/test_streaming_accuracy.py	101	function	Load a wav file and return sample_rate and numpy data of float64 type.
471	469	main	tensorflow/tensorflow/examples/speech_commands/test_streaming_accuracy.py	111	function
472	470	main	tensorflow/tensorflow/examples/speech_commands/train.py	88	function
473	471	verbosity_arg	tensorflow/tensorflow/examples/speech_commands/train.py	480	function	Parses verbosity argument. Args: value: A member of tf.logging. Raises: ArgumentTypeError: Not an expected value.
474	472	requires_contrib	tensorflow/tensorflow/examples/speech_commands/train_test.py	32	function
475	473	DictStruct	tensorflow/tensorflow/examples/speech_commands/train_test.py	44	class
476	474	TrainTest	tensorflow/tensorflow/examples/speech_commands/train_test.py	50	class
477	475	wav_to_features	tensorflow/tensorflow/examples/speech_commands/wav_to_features.py	47	function	Converts an audio file into its corresponding feature map. Args: sample_rate: Expected sample rate of the wavs. clip_duration_ms: Expected duration in milliseconds of the wavs. window_size_ms: How long each spectrogram timeslice is. window_stride_ms: How far to move in time between spectrogram timeslices. feature_bin_count: How many bins to use for the feature fingerprint. quantize: Whether to train the model for eight-bit deployment. preprocess: Spectrogram processing mode; "mfcc", "average" or "micro". input_wav: Path to the audio WAV file to read. output_c_file: Where to save the generated C source file.
478	476	main	tensorflow/tensorflow/examples/speech_commands/wav_to_features.py	125	function
479	477	WavToFeaturesTest	tensorflow/tensorflow/examples/speech_commands/wav_to_features_test.py	30	class
480	478	create_model	tensorflow/tensorflow/examples/tf2_showcase/mnist.py	69	function	Model to recognize digits in the MNIST dataset. Network structure is equivalent to: https://github.com/tensorflow/tensorflow/blob/r1.5/tensorflow/examples/tutorials/mnist/mnist_deep.py and https://github.com/tensorflow/models/blob/master/tutorials/image/mnist/convolutional.py But uses the tf.keras API. Returns: A tf.keras.Model.
481	479	mnist_datasets	tensorflow/tensorflow/examples/tf2_showcase/mnist.py	115	function
482	480	loss	tensorflow/tensorflow/examples/tf2_showcase/mnist.py	125	function
483	481	compute_accuracy	tensorflow/tensorflow/examples/tf2_showcase/mnist.py	131	function
484	482	train	tensorflow/tensorflow/examples/tf2_showcase/mnist.py	140	function	Trains model on `dataset` using `optimizer`.
485	483	test	tensorflow/tensorflow/examples/tf2_showcase/mnist.py	166	function	Perform an evaluation of `model` on the examples from `dataset`.
486	484	train_and_export	tensorflow/tensorflow/examples/tf2_showcase/mnist.py	184	function	Run MNIST training and eval loop in eager mode. Args: flags_obj: An object containing parsed flag values.
487	485	import_and_eval	tensorflow/tensorflow/examples/tf2_showcase/mnist.py	237	function
488	486	apply_clean	tensorflow/tensorflow/examples/tf2_showcase/mnist.py	247	function
489	487	main	tensorflow/tensorflow/examples/tf2_showcase/mnist.py	254	function
490	488	placeholder_inputs	tensorflow/tensorflow/examples/tutorials/mnist/fully_connected_feed.py	37	function	Generate placeholder variables to represent the input tensors. These placeholders are used as inputs by the rest of the model building code and will be fed from the downloaded data in the .run() loop, below. Args: batch_size: The batch size will be baked into both placeholders. Returns: images_placeholder: Images placeholder. labels_placeholder: Labels placeholder.
491	489	fill_feed_dict	tensorflow/tensorflow/examples/tutorials/mnist/fully_connected_feed.py	59	function	Fills the feed_dict for training the given step. A feed_dict takes the form of: feed_dict = { <placeholder>: <tensor of values to be passed for placeholder>, .... } Args: data_set: The set of images and labels, from input_data.read_data_sets() images_pl: The images placeholder, from placeholder_inputs(). labels_pl: The labels placeholder, from placeholder_inputs(). Returns: feed_dict: The feed dictionary mapping from placeholders to values.
492	490	do_eval	tensorflow/tensorflow/examples/tutorials/mnist/fully_connected_feed.py	87	function	Runs one evaluation against the full epoch of data. Args: sess: The session in which the model has been trained. eval_correct: The Tensor that returns the number of correct predictions. images_placeholder: The images placeholder. labels_placeholder: The labels placeholder. data_set: The set of images and labels to evaluate, from input_data.read_data_sets().
493	491	run_training	tensorflow/tensorflow/examples/tutorials/mnist/fully_connected_feed.py	116	function	Train MNIST for a number of steps.
494	492	main	tensorflow/tensorflow/examples/tutorials/mnist/fully_connected_feed.py	218	function
495	493	_read32	tensorflow/tensorflow/examples/tutorials/mnist/input_data.py	43	function
496	494	_extract_images	tensorflow/tensorflow/examples/tutorials/mnist/input_data.py	49	function	Extract the images into a 4D uint8 numpy array [index, y, x, depth]. Args: f: A file object that can be passed into a gzip reader. Returns: data: A 4D uint8 numpy array [index, y, x, depth]. Raises: ValueError: If the bytestream does not start with 2051.
497	495	_dense_to_one_hot	tensorflow/tensorflow/examples/tutorials/mnist/input_data.py	78	function	Convert class labels from scalars to one-hot vectors.
498	496	_extract_labels	tensorflow/tensorflow/examples/tutorials/mnist/input_data.py	88	function	Extract the labels into a 1D uint8 numpy array [index]. Args: f: A file object that can be passed into a gzip reader. one_hot: Does one hot encoding for the result. num_classes: Number of classes for the one hot encoding. Returns: labels: a 1D uint8 numpy array. Raises: ValueError: If the bystream doesn't start with 2049.
499	497	_DataSet	tensorflow/tensorflow/examples/tutorials/mnist/input_data.py	116	class	Container class for a _DataSet (deprecated). THIS CLASS IS DEPRECATED.
500	498	_maybe_download	tensorflow/tensorflow/examples/tutorials/mnist/input_data.py	242	function	Download the data from source url, unless it's already here. Args: filename: string, name of the file in the directory. work_directory: string, path to working directory. source_url: url to download from if file doesn't exist. Returns: Path to resulting file.
501	499	read_data_sets	tensorflow/tensorflow/examples/tutorials/mnist/input_data.py	266	function
502	500	inference	tensorflow/tensorflow/examples/tutorials/mnist/mnist.py	45	function	Build the MNIST model up to where it may be used for inference. Args: images: Images placeholder, from inputs(). hidden1_units: Size of the first hidden layer. hidden2_units: Size of the second hidden layer. Returns: softmax_linear: Output tensor with the computed logits.
503	501	loss	tensorflow/tensorflow/examples/tutorials/mnist/mnist.py	86	function	Calculates the loss from the logits and the labels. Args: logits: Logits tensor, float - [batch_size, NUM_CLASSES]. labels: Labels tensor, int32 - [batch_size]. Returns: loss: Loss tensor of type float.
504	502	training	tensorflow/tensorflow/examples/tutorials/mnist/mnist.py	101	function	Sets up the training Ops. Creates a summarizer to track the loss over time in TensorBoard. Creates an optimizer and applies the gradients to all trainable variables. The Op returned by this function is what must be passed to the `sess.run()` call to cause the model to train. Args: loss: Loss tensor, from loss(). learning_rate: The learning rate to use for gradient descent. Returns: train_op: The Op for training.
505	503	evaluation	tensorflow/tensorflow/examples/tutorials/mnist/mnist.py	130	function	Evaluate the quality of the logits at predicting the label. Args: logits: Logits tensor, float - [batch_size, NUM_CLASSES]. labels: Labels tensor, int32 - [batch_size], with values in the range [0, NUM_CLASSES). Returns: A scalar int32 tensor with the number of examples (out of batch_size) that were predicted correctly.
506	504	main	tensorflow/tensorflow/examples/tutorials/mnist/mnist_softmax_xla.py	34	function
507	505	train	tensorflow/tensorflow/examples/tutorials/mnist/mnist_with_summaries.py	38	function
508	506	main	tensorflow/tensorflow/examples/tutorials/mnist/mnist_with_summaries.py	185	function
509	507	_hash_file	tensorflow/tensorflow/examples/tutorials/word2vec/word2vec_basic.py	41	function
510	508	word2vec_basic	tensorflow/tensorflow/examples/tutorials/word2vec/word2vec_basic.py	49	function	Example of building, training and visualizing a word2vec model.
511	509	main	tensorflow/tensorflow/examples/tutorials/word2vec/word2vec_basic.py	360	function
512	510	suppress_exception	tensorflow/tensorflow/lite/examples/experimental_new_converter/stack_trace_example.py	37	function
513	511	TestModule	tensorflow/tensorflow/lite/examples/experimental_new_converter/stack_trace_example.py	46	class	The test model has unsupported op.
514	512	test_from_saved_model	tensorflow/tensorflow/lite/examples/experimental_new_converter/stack_trace_example.py	57	function	displaying stack trace when converting saved model.
515	513	test_from_concrete_function	tensorflow/tensorflow/lite/examples/experimental_new_converter/stack_trace_example.py	71	function	displaying stack trace when converting concrete function.
516	514	main	tensorflow/tensorflow/lite/examples/experimental_new_converter/stack_trace_example.py	83	function
517	515	load_labels	tensorflow/tensorflow/lite/examples/python/label_image.py	29	function
518	516	_convert_bytes_to_cc_source	tensorflow/tensorflow/lite/experimental/acceleration/compatibility/convert_binary_to_cc_source.py	35	function	Returns strings representing a C++ constant array containing `data`. Args: data: Byte array that will be converted into a C++ constant. array_name: String to use as the variable name for the constant array. max_line_width: The longest line length, for formatting purposes. include_guard: Name to use for the include guard macro definition. include_path: Optional path to include in the source file. use_tensorflow_license: Whether to include the standard TensorFlow Apache2 license in the generated files. Returns: Text that can be compiled as a C++ source file to link in the data as a literal array of values. Text that can be used as a C++ header file to reference the literal array.
519	517	main	tensorflow/tensorflow/lite/experimental/acceleration/compatibility/convert_binary_to_cc_source.py	155	function
520	518	BidirectionalSequenceLstmTest	tensorflow/tensorflow/lite/experimental/examples/lstm/bidirectional_sequence_lstm_test.py	36	class
521	519	BidirectionalSequenceRnnTest	tensorflow/tensorflow/lite/experimental/examples/lstm/bidirectional_sequence_rnn_test.py	38	class
522	520	dynamic_rnn	tensorflow/tensorflow/lite/experimental/examples/lstm/rnn.py	42	function	Creates a recurrent neural network specified by RNNCell `cell`. Performs fully dynamic unrolling of `inputs`. Example: ```python # create a BasicRNNCell rnn_cell = tf.compat.v1.nn.rnn_cell.BasicRNNCell(hidden_size) # 'outputs' is a tensor of shape [batch_size, max_time, cell_state_size] # defining initial state initial_state = rnn_cell.zero_state(batch_size, dtype=tf.float32) # 'state' is a tensor of shape [batch_size, cell_state_size] outputs, state = tf.compat.v1.nn.dynamic_rnn(rnn_cell, input_data, initial_state=initial_state, dtype=tf.float32) ``` ```python # create 2 LSTMCells rnn_layers = [tf.compat.v1.nn.rnn_cell.LSTMCell(size) for size in [128, 256]] # create a RNN cell composed sequentially of a number of RNNCells multi_rnn_cell = tf.compat.v1.nn.rnn_cell.MultiRNNCell(rnn_layers) # 'outputs' is a tensor of shape [batch_size, max_time, 256] # 'state' is a N-tuple where N is the number of LSTMCells containing a # tf.nn.rnn_cell.LSTMStateTuple for each cell outputs, state = tf.compat.v1.nn.dynamic_rnn(cell=multi_rnn_cell, inputs=data, dtype=tf.float32) ``` Args: cell: An instance of RNNCell. inputs: The RNN inputs. If `time_major == False` (default), this must be a `Tensor` of shape: `[batch_size, max_time, ...]`, or a nested tuple of such elements. If `time_major == True`, this must be a `Tensor` of shape: `[max_time, batch_size, ...]`, or a nested tuple of such elements. This may also be a (possibly nested) tuple of Tensors satisfying this property. The first two dimensions must match across all the inputs, but otherwise the ranks and other shape components may differ. In this case, input to `cell` at each time-step will replicate the structure of these tuples, except for the time dimension (from which the time is taken). The input to `cell` at each time step will be a `Tensor` or (possibly nested) tuple of Tensors each with dimensions `[batch_size, ...]`. sequence_length: (optional) An int32/int64 vector sized `[batch_size]`. Used to copy-through state and zero-out outputs when past a batch element's sequence length. So it's more for performance than correctness. initial_state: (optional) An initial state for the RNN. If `cell.state_size` is an integer, this must be a `Tensor` of appropriate type and shape `[batch_size, cell.state_size]`. If `cell.state_size` is a tuple, this should be a tuple of tensors having shapes `[batch_size, s] for s in cell.state_size`. dtype: (optional) The data type for the initial state and expected output. Required if initial_state is not provided or RNN state has a heterogeneous dtype. parallel_iterations: (Default: 32). The number of iterations to run in parallel. Those operations which do not have any temporal dependency and can be run in parallel, will be. This parameter trades off time for space. Values >> 1 use more memory but take less time, while smaller values use less memory but computations take longer. swap_memory: Transparently swap the tensors produced in forward inference but needed for back prop from GPU to CPU. This allows training RNNs which would typically not fit on a single GPU, with very minimal (or no) performance penalty. time_major: The shape format of the `inputs` and `outputs` Tensors. If true, these `Tensors` must be shaped `[max_time, batch_size, depth]`. If false, these `Tensors` must be shaped `[batch_size, max_time, depth]`. Using `time_major = True` is a bit more efficient because it avoids transposes at the beginning and end of the RNN calculation. However, most TensorFlow data is batch-major, so by default this function accepts input and emits output in batch-major form. scope: VariableScope for the created subgraph; defaults to "rnn". Returns: A pair (outputs, state) where: outputs: The RNN output `Tensor`. If time_major == False (default), this will be a `Tensor` shaped: `[batch_size, max_time, cell.output_size]`. If time_major == True, this will be a `Tensor` shaped: `[max_time, batch_size, cell.output_size]`. Note, if `cell.output_size` is a (possibly nested) tuple of integers or `TensorShape` objects, then `outputs` will be a tuple having the same structure as `cell.output_size`, containing Tensors having shapes corresponding to the shape data in `cell.output_size`. state: The final state. If `cell.state_size` is an int, this will be shaped `[batch_size, cell.state_size]`. If it is a `TensorShape`, this will be shaped `[batch_size] + cell.state_size`. If it is a (possibly nested) tuple of ints or `TensorShape`, this will be a tuple having the corresponding shapes. If cells are `LSTMCells` `state` will be a tuple containing a `LSTMStateTuple` for each cell. Raises: TypeError: If `cell` is not an instance of RNNCell. ValueError: If inputs is None or an empty list. RuntimeError: If not using control flow v2.
523	521	bidirectional_dynamic_rnn	tensorflow/tensorflow/lite/experimental/examples/lstm/rnn.py	279	function	Creates a dynamic version of bidirectional recurrent neural network. Takes input and builds independent forward and backward RNNs. The input_size of forward and backward cell must match. The initial state for both directions is zero by default (but can be set optionally) and no intermediate states are ever returned -- the network is fully unrolled for the given (passed in) length(s) of the sequence(s) or completely unrolled if length(s) is not given. Args: cell_fw: An instance of RNNCell, to be used for forward direction. cell_bw: An instance of RNNCell, to be used for backward direction. inputs: The RNN inputs. If time_major == False (default), this must be a tensor of shape: `[batch_size, max_time, ...]`, or a nested tuple of such elements. If time_major == True, this must be a tensor of shape: `[max_time, batch_size, ...]`, or a nested tuple of such elements. sequence_length: (optional) An int32/int64 vector, size `[batch_size]`, containing the actual lengths for each of the sequences in the batch. If not provided, all batch entries are assumed to be full sequences; and time reversal is applied from time `0` to `max_time` for each sequence. initial_state_fw: (optional) An initial state for the forward RNN. This must be a tensor of appropriate type and shape `[batch_size, cell_fw.state_size]`. If `cell_fw.state_size` is a tuple, this should be a tuple of tensors having shapes `[batch_size, s] for s in cell_fw.state_size`. initial_state_bw: (optional) Same as for `initial_state_fw`, but using the corresponding properties of `cell_bw`. dtype: (optional) The data type for the initial states and expected output. Required if initial_states are not provided or RNN states have a heterogeneous dtype. parallel_iterations: (Default: 32). The number of iterations to run in parallel. Those operations which do not have any temporal dependency and can be run in parallel, will be. This parameter trades off time for space. Values >> 1 use more memory but take less time, while smaller values use less memory but computations take longer. swap_memory: Transparently swap the tensors produced in forward inference but needed for back prop from GPU to CPU. This allows training RNNs which would typically not fit on a single GPU, with very minimal (or no) performance penalty. time_major: The shape format of the `inputs` and `outputs` Tensors. If true, these `Tensors` must be shaped `[max_time, batch_size, depth]`. If false, these `Tensors` must be shaped `[batch_size, max_time, depth]`. Using `time_major = True` is a bit more efficient because it avoids transposes at the beginning and end of the RNN calculation. However, most TensorFlow data is batch-major, so by default this function accepts input and emits output in batch-major form. scope: VariableScope for the created subgraph; defaults to "bidirectional_rnn" Returns: A tuple (outputs, output_states) where: outputs: A tuple (output_fw, output_bw) containing the forward and the backward rnn output `Tensor`. If time_major == False (default), output_fw will be a `Tensor` shaped: `[batch_size, max_time, cell_fw.output_size]` and output_bw will be a `Tensor` shaped: `[batch_size, max_time, cell_bw.output_size]`. If time_major == True, output_fw will be a `Tensor` shaped: `[max_time, batch_size, cell_fw.output_size]` and output_bw will be a `Tensor` shaped: `[max_time, batch_size, cell_bw.output_size]`. It returns a tuple instead of a single concatenated `Tensor`, unlike in the `bidirectional_rnn`. If the concatenated one is preferred, the forward and backward outputs can be concatenated as `tf.concat(outputs, 2)`. output_states: A tuple (output_state_fw, output_state_bw) containing the forward and the backward final states of bidirectional rnn. Raises: TypeError: If `cell_fw` or `cell_bw` is not an instance of `RNNCell`.
524	522	TfLiteRNNCell	tensorflow/tensorflow/lite/experimental/examples/lstm/rnn_cell.py	39	class	The most basic RNN cell. This is used only for TfLite, it provides hints and it also makes the variables in the desired for the tflite ops.
525	523	TFLiteLSTMCell	tensorflow/tensorflow/lite/experimental/examples/lstm/rnn_cell.py	162	class	Long short-term memory unit (LSTM) recurrent network cell. This is used only for TfLite, it provides hints and it also makes the variables in the desired for the tflite ops (transposed and separated). The default non-peephole implementation is based on: https://pdfs.semanticscholar.org/1154/0131eae85b2e11d53df7f1360eeb6476e7f4.pdf Felix Gers, Jurgen Schmidhuber, and Fred Cummins. "Learning to forget: Continual prediction with LSTM." IET, 850-855, 1999. The peephole implementation is based on: https://research.google.com/pubs/archive/43905.pdf Hasim Sak, Andrew Senior, and Francoise Beaufays. "Long short-term memory recurrent neural network architectures for large scale acoustic modeling." INTERSPEECH, 2014. The class uses optional peep-hole connections, optional cell clipping, and an optional projection layer. Note that this cell is not optimized for performance. Please use `tf.contrib.cudnn_rnn.CudnnLSTM` for better performance on GPU, or `tf.contrib.rnn.LSTMBlockCell` and `tf.contrib.rnn.LSTMBlockFusedCell` for better performance on CPU.
526	524	UnidirectionalSequenceLstmTest	tensorflow/tensorflow/lite/experimental/examples/lstm/unidirectional_sequence_lstm_test.py	36	class
527	525	UnidirectionalSequenceRnnTest	tensorflow/tensorflow/lite/experimental/examples/lstm/unidirectional_sequence_rnn_test.py	37	class
528	526	AudioFeatureGenerationTest	tensorflow/tensorflow/lite/experimental/microfrontend/python/kernel_tests/audio_microfrontend_op_test.py	35	class
529	527	audio_microfrontend	tensorflow/tensorflow/lite/experimental/microfrontend/python/ops/audio_microfrontend_op.py	34	function	Audio Microfrontend Op. This Op converts a sequence of audio data into one or more feature vectors containing filterbanks of the input. The conversion process uses a lightweight library to perform: 1. A slicing window function 2. Short-time FFTs 3. Filterbank calculations 4. Noise reduction 5. PCAN Auto Gain Control 6. Logarithmic scaling Args: audio: 1D Tensor, int16 audio data in temporal ordering. sample_rate: Integer, the sample rate of the audio in Hz. window_size: Integer, length of desired time frames in ms. window_step: Integer, length of step size for the next frame in ms. num_channels: Integer, the number of filterbank channels to use. upper_band_limit: Float, the highest frequency included in the filterbanks. lower_band_limit: Float, the lowest frequency included in the filterbanks. smoothing_bits: Int, scale up signal by 2^(smoothing_bits) before reduction. even_smoothing: Float, smoothing coefficient for even-numbered channels. odd_smoothing: Float, smoothing coefficient for odd-numbered channels. min_signal_remaining: Float, fraction of signal to preserve in smoothing. enable_pcan: Bool, enable PCAN auto gain control. pcan_strength: Float, gain normalization exponent. pcan_offset: Float, positive value added in the normalization denominator. gain_bits: Int, number of fractional bits in the gain. enable_log: Bool, enable logarithmic scaling of filterbanks. scale_shift: Integer, scale filterbanks by 2^(scale_shift). left_context: Integer, number of preceding frames to attach to each frame. right_context: Integer, number of preceding frames to attach to each frame. frame_stride: Integer, M frames to skip over, where output[n] = frame[n*M]. zero_padding: Bool, if left/right context is out-of-bounds, attach frame of zeroes. Otherwise, frame[0] or frame[size-1] will be copied. out_scale: Integer, divide all filterbanks by this number. out_type: DType, type of the output Tensor, defaults to UINT16. Returns: filterbanks: 2D Tensor, each row is a time frame, each column is a channel. Raises: ValueError: If the audio tensor is not explicitly a vector.
530	528	SupportedOp	tensorflow/tensorflow/lite/experimental/tensorboard/ops_util.py	26	class	Spec of supported ops. Args: op: string of op name.
531	529	get_potentially_supported_ops	tensorflow/tensorflow/lite/experimental/tensorboard/ops_util.py	35	function	Returns operations potentially supported by TensorFlow Lite. The potentially support list contains a list of ops that are partially or fully supported, which is derived by simply scanning op names to check whether they can be handled without real conversion and specific parameters. Given that some ops may be partially supported, the optimal way to determine if a model's operations are supported is by converting using the TensorFlow Lite converter. Returns: A list of SupportedOp.
532	530	OpsUtilTest	tensorflow/tensorflow/lite/experimental/tensorboard/ops_util_test.py	24	class
533	531	main	tensorflow/tensorflow/lite/g3doc/tools/build_java_api_docs.py	53	function
534	532	main	tensorflow/tensorflow/lite/g3doc/tools/build_py_api_docs.py	55	function
535	533	time_wrapping	tensorflow/tensorflow/lite/micro/examples/magic_wand/train/data_augmentation.py	29	function	Generate (molecule/denominator)x speed data.
536	534	augment_data	tensorflow/tensorflow/lite/micro/examples/magic_wand/train/data_augmentation.py	43	function	Perform data augmentation.
537	535	TestAugmentation	tensorflow/tensorflow/lite/micro/examples/magic_wand/train/data_augmentation_test.py	32	class
538	536	DataLoader	tensorflow/tensorflow/lite/micro/examples/magic_wand/train/data_load.py	35	class	Loads data and prepares for training.
539	537	TestLoad	tensorflow/tensorflow/lite/micro/examples/magic_wand/train/data_load_test.py	30	class
540	538	prepare_original_data	tensorflow/tensorflow/lite/micro/examples/magic_wand/train/data_prepare.py	46	function	Read collected data from files.
541	539	generate_negative_data	tensorflow/tensorflow/lite/micro/examples/magic_wand/train/data_prepare.py	86	function	Generate negative data labeled as 'negative6~8'.
542	540	write_data	tensorflow/tensorflow/lite/micro/examples/magic_wand/train/data_prepare.py	143	function
543	541	TestPrepare	tensorflow/tensorflow/lite/micro/examples/magic_wand/train/data_prepare_test.py	32	class
544	542	read_data	tensorflow/tensorflow/lite/micro/examples/magic_wand/train/data_split.py	40	function
545	543	split_data	tensorflow/tensorflow/lite/micro/examples/magic_wand/train/data_split.py	51	function	Splits data into train, validation and test according to ratio.
546	544	person_split	tensorflow/tensorflow/lite/micro/examples/magic_wand/train/data_split_person.py	41	function	Split data by person.
547	545	TestSplitPerson	tensorflow/tensorflow/lite/micro/examples/magic_wand/train/data_split_person_test.py	28	class
548	546	TestSplit	tensorflow/tensorflow/lite/micro/examples/magic_wand/train/data_split_test.py	29	class
549	547	reshape_function	tensorflow/tensorflow/lite/micro/examples/magic_wand/train/train.py	37	function
550	548	calculate_model_size	tensorflow/tensorflow/lite/micro/examples/magic_wand/train/train.py	42	function
551	549	build_cnn	tensorflow/tensorflow/lite/micro/examples/magic_wand/train/train.py	51	function	Builds a convolutional neural network in Keras.
552	550	build_lstm	tensorflow/tensorflow/lite/micro/examples/magic_wand/train/train.py	78	function	Builds an LSTM in Keras.
553	551	load_data	tensorflow/tensorflow/lite/micro/examples/magic_wand/train/train.py	93	function
554	552	build_net	tensorflow/tensorflow/lite/micro/examples/magic_wand/train/train.py	101	function
555	553	train_net	tensorflow/tensorflow/lite/micro/examples/magic_wand/train/train.py	111	function	Trains the model.
556	554	TestTrain	tensorflow/tensorflow/lite/micro/examples/magic_wand/train/train_test.py	33	class
557	555	to_cc	tensorflow/tensorflow/lite/micro/examples/micro_speech/CMSIS/create_constants.py	26	function	Writes table values to a C++ source file.
558	556	to_h	tensorflow/tensorflow/lite/micro/examples/micro_speech/CMSIS/create_constants.py	44	function	Writes a header file for the table values.
559	557	new_data_to_array	tensorflow/tensorflow/lite/micro/examples/micro_speech/apollo3/captured_data_to_wav.py	28	function
560	558	new_data_to_array	tensorflow/tensorflow/lite/micro/examples/micro_speech/apollo3/compare_1k.py	29	function	Converts file information to an in-memory array.
561	559	to_float	tensorflow/tensorflow/lite/micro/examples/micro_speech/apollo3/compare_1k.py	63	function
562	560	check_file_existence	tensorflow/tensorflow/lite/micro/examples/person_detection/utils/raw_to_bitmap.py	52	function
563	561	show_and_save_bitmaps	tensorflow/tensorflow/lite/micro/examples/person_detection/utils/raw_to_bitmap.py	60	function	Display and save a list of bitmaps. Args: input_file: input file name bitmap_list: list of numpy arrays to represent bitmap images channels: color channel count
564	562	reshape_bitmaps	tensorflow/tensorflow/lite/micro/examples/person_detection/utils/raw_to_bitmap.py	87	function	Reshape flat integer arrays. Args: frame_list: list of 1-D arrays to represent raw image data width: image width in pixels height: image height in pixels channels: color channel count Returns: list of numpy arrays to represent bitmap images
565	563	parse_file	tensorflow/tensorflow/lite/micro/examples/person_detection/utils/raw_to_bitmap.py	109	function	Convert log file to array of pixels. Args: inputfile: log file to parse width: image width in pixels height: image height in pixels channels: color channel count Returns: list 1-D arrays to represent raw image data.
566	564	main	tensorflow/tensorflow/lite/micro/examples/person_detection/utils/raw_to_bitmap.py	159	function
567	565	RawToBitmapTest	tensorflow/tensorflow/lite/micro/examples/person_detection/utils/raw_to_bitmap_test.py	94	class
568	566	generate_conv_model	tensorflow/tensorflow/lite/micro/testing/generate_test_models.py	34	function	Creates a basic Keras model and converts to tflite. This model does not make any relevant classifications. It only exists to generate a model that is designed to run on embedded devices.
569	567	main	tensorflow/tensorflow/lite/micro/testing/generate_test_models.py	74	function
570	568	rename_example_subfolder_files	tensorflow/tensorflow/lite/micro/tools/make/fix_arduino_subfolders.py	29	function	Moves source files in example subfolders to equivalents at root.
571	569	move_person_data	tensorflow/tensorflow/lite/micro/tools/make/fix_arduino_subfolders.py	41	function	Moves the downloaded person model into the examples folder.
572	570	move_person_data_experimental	tensorflow/tensorflow/lite/micro/tools/make/fix_arduino_subfolders.py	61	function	Moves the downloaded person model into the examples folder.
573	571	move_image_data_experimental	tensorflow/tensorflow/lite/micro/tools/make/fix_arduino_subfolders.py	83	function	Moves the downloaded image detection model into the examples folder.
574	572	rename_example_main_inos	tensorflow/tensorflow/lite/micro/tools/make/fix_arduino_subfolders.py	104	function	Makes sure the .ino sketch files match the example name.
575	573	main	tensorflow/tensorflow/lite/micro/tools/make/fix_arduino_subfolders.py	114	function	Control the rewriting of source files.
576	574	parse_args	tensorflow/tensorflow/lite/micro/tools/make/fix_arduino_subfolders.py	124	function	Converts the raw arguments into accessible flags.
577	575	sanitize_xml	tensorflow/tensorflow/lite/micro/tools/make/generate_keil_project.py	29	function	Uses a allowlist to avoid generating bad XML.
578	576	main	tensorflow/tensorflow/lite/micro/tools/make/generate_keil_project.py	34	function	Generates a Keil project file from a template source.
579	577	parse_args	tensorflow/tensorflow/lite/micro/tools/make/generate_keil_project.py	82	function	Converts the raw arguments into accessible flags.
580	578	main	tensorflow/tensorflow/lite/micro/tools/make/merge_arduino_zips.py	27	function	Merges multiple Arduino zipfiles into a single result.
581	579	parse_args	tensorflow/tensorflow/lite/micro/tools/make/merge_arduino_zips.py	39	function	Converts the raw arguments into accessible flags.
582	580	replace_includes	tensorflow/tensorflow/lite/micro/tools/make/transform_arduino_source.py	29	function	Updates any includes to reference the new Arduino library paths.
583	581	replace_main	tensorflow/tensorflow/lite/micro/tools/make/transform_arduino_source.py	43	function	Updates any occurrences of a bare main definition to the Arduino equivalent.
584	582	check_ino_functions	tensorflow/tensorflow/lite/micro/tools/make/transform_arduino_source.py	51	function	Ensures the required functions exist.
585	583	add_example_ino_library_include	tensorflow/tensorflow/lite/micro/tools/make/transform_arduino_source.py	65	function	Makes sure the example includes the header that loads the library.
586	584	replace_example_includes	tensorflow/tensorflow/lite/micro/tools/make/transform_arduino_source.py	71	function	Updates any includes for local example files.
587	585	main	tensorflow/tensorflow/lite/micro/tools/make/transform_arduino_source.py	85	function	Transforms the input source file to work when exported to Arduino.
588	586	parse_args	tensorflow/tensorflow/lite/micro/tools/make/transform_arduino_source.py	108	function	Converts the raw arguments into accessible flags.
589	587	replace_arduino_includes	tensorflow/tensorflow/lite/micro/tools/make/transform_source.py	36	function	Updates any includes to reference the new Arduino library paths.
590	588	replace_arduino_main	tensorflow/tensorflow/lite/micro/tools/make/transform_source.py	50	function	Updates any occurrences of a bare main definition to the Arduino equivalent.
591	589	check_ino_functions	tensorflow/tensorflow/lite/micro/tools/make/transform_source.py	58	function	Ensures the required functions exist.
592	590	add_example_ino_library_include	tensorflow/tensorflow/lite/micro/tools/make/transform_source.py	72	function	Makes sure the example includes the header that loads the library.
593	591	replace_arduino_example_includes	tensorflow/tensorflow/lite/micro/tools/make/transform_source.py	78	function	Updates any includes for local example files.
594	592	replace_esp_example_includes	tensorflow/tensorflow/lite/micro/tools/make/transform_source.py	92	function	Updates any includes for local example files.
595	593	transform_arduino_sources	tensorflow/tensorflow/lite/micro/tools/make/transform_source.py	109	function	Transform sources for the Arduino platform. Args: input_lines: A sequence of lines from the input file to process. flags: Flags indicating which transformation(s) to apply. Returns: The transformed output as a string.
596	594	transform_esp_sources	tensorflow/tensorflow/lite/micro/tools/make/transform_source.py	138	function	Transform sources for the ESP-IDF platform. Args: input_lines: A sequence of lines from the input file to process. flags: Flags indicating which transformation(s) to apply. Returns: The transformed output as a string.
597	595	main	tensorflow/tensorflow/lite/micro/tools/make/transform_source.py	158	function	Transforms the input source file to work when exported as example.
598	596	parse_args	tensorflow/tensorflow/lite/micro/tools/make/transform_source.py	171	function	Converts the raw arguments into accessible flags.
599	597	_requires_input_stats	tensorflow/tensorflow/lite/python/convert.py	49	function
600	598	_try_convert_to_unicode	tensorflow/tensorflow/lite/python/convert.py	67	function
601	599	OpsSet	tensorflow/tensorflow/lite/python/convert.py	80	class	Enum class defining the sets of ops available to generate TFLite models. WARNING: Experimental interface, subject to change.
602	600	ConverterError	tensorflow/tensorflow/lite/python/convert.py	120	class	Raised when an error occurs during model conversion.
603	601	mlir_quantize	tensorflow/tensorflow/lite/python/convert.py	125	function	Quantize `input_data_str` with calibration results. Args: input_data_str: Input data in serialized form (e.g. a TFLITE model with calibration results). disable_per_channel: Bool indicating whether to do per-channel or per-tensor quantization fully_quantize: Bool indicating whether to fully quantize the model. Besides model body, the input/output will be quantized as well. inference_type: Data type for the activations. The default value is int8. Returns: Quantized model in serialized form (e.g. a TFLITE model) with floating-point inputs and outputs.
604	602	mlir_sparsify	tensorflow/tensorflow/lite/python/convert.py	150	function	Sparsify `input_data_str` to encode sparse tensor with proper format. Args: input_data_str: Input data in serialized form (e.g. a TFLITE model). Returns: Sparsified model in serialized form (e.g. a TFLITE model).
605	603	toco_convert_protos	tensorflow/tensorflow/lite/python/convert.py	162	function	Convert `input_data_str` according to model and toco parameters. Unless you know what you are doing consider using the more friendly `tf.compat.v1.lite.toco_convert`. Args: model_flags_str: Serialized proto describing model properties, see `toco/model_flags.proto`. toco_flags_str: Serialized proto describing conversion properties, see `toco/toco_flags.proto`. input_data_str: Input data in serialized form (e.g. a graphdef is common) debug_info_str: Serialized `GraphDebugInfo` proto describing logging information. (default None) enable_mlir_converter: Enables MLIR-based conversion instead of the default TOCO conversion. (default False) Returns: Converted model in serialized form (e.g. a TFLITE model is common). Raises: ConverterError: When conversion fails in TFLiteConverter, usually due to ops not being supported. RuntimeError: When conversion fails, an exception is raised with the error message embedded.
606	604	build_toco_convert_protos	tensorflow/tensorflow/lite/python/convert.py	291	function	Builds protocol buffers describing a conversion of a model using TOCO. Typically this is to convert from TensorFlow GraphDef to TFLite, in which case the default `input_format` and `output_format` are sufficient. Args: input_tensors: List of input tensors. Type and shape are computed using `foo.shape` and `foo.dtype`. output_tensors: List of output tensors (only .name is used from this). inference_type: Target data type of real-number arrays in the output file. Must be `{tf.float32, tf.uint8, tf.int8}`. (default tf.float32) inference_input_type: Target data type of real-number input arrays. Allows for a different type for input arrays in the case of quantization. Must be `{tf.float32, tf.uint8, tf.int8}`. (default `inference_type`) input_format: Type of data to read Currently must be `{TENSORFLOW_GRAPHDEF}`. (default TENSORFLOW_GRAPHDEF) input_shapes: Input array shape. It needs to be a list of the same length as `input_tensors`, or None. (default None) output_format: Output file format. Currently must be `{TFLITE, GRAPHVIZ_DOT}`. (default TFLITE) quantized_input_stats: List of tuples of floats representing the mean and standard deviation. Each tuple maps to the corresponding input tensor. Only need if `inference_input_type` is `QUANTIZED_UINT8` or `INT8`. real_input_value = (quantized_input_value - mean_value) / std_dev_value. (default None) default_ranges_stats: Tuple of integers representing (min, max) range values for all arrays without a specified range. Intended for experimenting with quantization via "dummy quantization". (default None) drop_control_dependency: Boolean indicating whether to drop control dependencies silently. This is due to TFLite not supporting control dependencies. (default True) reorder_across_fake_quant: Boolean indicating whether to reorder FakeQuant nodes in unexpected locations. Used when the location of the FakeQuant nodes is preventing graph transformations necessary to convert the graph. Results in a graph that differs from the quantized training graph, potentially causing differing arithmetic behavior. (default False) allow_custom_ops: Boolean indicating whether to allow custom operations. When false any unknown operation is an error. When true, custom ops are created for any op that is unknown. The developer will need to provide these to the TensorFlow Lite runtime with a custom resolver. (default False) custom_opdefs: List of strings representing custom ops OpDefs that are included in the GraphDef. Required when using custom operations with the MLIR-based converter. (default None) change_concat_input_ranges: Boolean to change behavior of min/max ranges for inputs and outputs of the concat operator for quantized models. Changes the ranges of concat operator overlap when true. (default False) post_training_quantize: Boolean indicating whether to quantize the weights of the converted float model. Model size will be reduced and there will be latency improvements (at the cost of accuracy). (default False) quantize_to_float16: Boolean indicating whether to convert float buffers to float16. (default False) dump_graphviz_dir: Full filepath of folder to dump the graphs at various stages of processing GraphViz .dot files. Preferred over --output_format=GRAPHVIZ_DOT in order to keep the requirements of the output file. (default None) dump_graphviz_video: Boolean indicating whether to dump the graph after every graph transformation. (default False) target_ops: Experimental flag, subject to change. Set of OpsSet options indicating which converter to use. (default set([OpsSet.TFLITE_BUILTINS])) allow_nonexistent_arrays: Allow specifying array names that don't exist or are unused in the final graph. (default False) debug_info: `GraphDebugInfo` proto containing the stack traces for the original nodes referred by the converted graph. conversion_summary_dir: A string, the path to the generated conversion logs. saved_model_dir: Filepath of the saved model to be converted. This value will be non-empty only when the saved model import path will be used. Otherwises, the graph def-based conversion will be processed. saved_model_version: SavedModel file format version of The saved model file to be converted. This value will be set only when the SavedModel import path will be used. saved_model_tags: Set of string saved model tags, formatted in the comma-separated value. This value will be set only when the SavedModel import path will be used. saved_model_exported_names: Names to be exported (default: export all) when the saved model import path is on. This value will be set only when the SavedModel import path will be used. Returns: model_flags, toco_flags, debug_info: three protocol buffers describing the conversion process and debug information. Raises: ValueError: If the input tensor type is unknown Missing mean_values or std_dev_values RuntimeError: If TOCO fails to convert (in which case the runtime error's error text will contain the TOCO error log)
607	605	toco_convert_graph_def	tensorflow/tensorflow/lite/python/convert.py	485	function	"Convert a model using TOCO. This function is used to convert GraphDefs that cannot be loaded into TensorFlow to TFLite. Conversion can be customized by providing arguments that are forwarded to `build_toco_convert_protos` (see documentation for details). Args: input_data: Input data (i.e. often `sess.graph_def`), input_arrays_with_shape: Tuple of strings representing input tensor names and list of integers representing input shapes (e.g., [("foo" : [1, 16, 16, 3])]). Use only when graph cannot be loaded into TensorFlow and when `input_tensors` is None. (default None) output_arrays: List of output tensors to freeze graph with. Use only when graph cannot be loaded into TensorFlow and when `output_tensors` is None. (default None) enable_mlir_converter: Enables MLIR-based conversion instead of TOCO conversion. *args: See `build_toco_convert_protos`, **kwargs: See `build_toco_convert_protos`. Returns: The converted data. For example if TFLite was the destination, then this will be a tflite flatbuffer in a bytes array. Raises: Defined in `build_toco_convert_protos`.
608	606	toco_convert_impl	tensorflow/tensorflow/lite/python/convert.py	541	function	"Convert a model using TOCO. Typically this function is used to convert from TensorFlow GraphDef to TFLite. Conversion can be customized by providing arguments that are forwarded to `build_toco_convert_protos` (see documentation for details). Args: input_data: Input data (i.e. often `sess.graph_def`), input_tensors: List of input tensors. Type and shape are computed using `foo.shape` and `foo.dtype`. output_tensors: List of output tensors (only .name is used from this). enable_mlir_converter: Enables MLIR-based conversion instead of TOCO conversion. *args: See `build_toco_convert_protos`, **kwargs: See `build_toco_convert_protos`. Returns: The converted data. For example if TFLite was the destination, then this will be a tflite flatbuffer in a bytes array. Raises: Defined in `build_toco_convert_protos`.
609	607	toco_convert	tensorflow/tensorflow/lite/python/convert.py	580	function	Convert a model using TOCO. Typically this function is used to convert from TensorFlow GraphDef to TFLite. Conversion can be customized by providing arguments that are forwarded to `build_toco_convert_protos` (see documentation for details). This function has been deprecated. Please use `lite.TFLiteConverter` instead. Args: input_data: Input data (i.e. often `sess.graph_def`), input_tensors: List of input tensors. Type and shape are computed using `foo.shape` and `foo.dtype`. output_tensors: List of output tensors (only .name is used from this). *args: See `build_toco_convert_protos`, **kwargs: See `build_toco_convert_protos`. Returns: The converted data. For example if TFLite was the destination, then this will be a tflite flatbuffer in a bytes array. Raises: Defined in `build_toco_convert_protos`.
610	608	run_main	tensorflow/tensorflow/lite/python/convert_file_to_c_source.py	29	function	Main in convert_file_to_c_source.py.
611	609	main	tensorflow/tensorflow/lite/python/convert_file_to_c_source.py	101	function
612	610	_log_tensor_details	tensorflow/tensorflow/lite/python/convert_saved_model.py	30	function	Log tensor details: name, shape, and type.
613	611	get_meta_graph_def	tensorflow/tensorflow/lite/python/convert_saved_model.py	46	function	Validate saved_model and extract MetaGraphDef. Args: saved_model_dir: saved_model path to convert. tag_set: Set of tag(s) of the MetaGraphDef to load. Returns: The meta_graph_def used for tflite conversion. Raises: ValueError: No valid MetaGraphDef for given tag_set.
614	612	get_signature_def	tensorflow/tensorflow/lite/python/convert_saved_model.py	63	function	Get the signature def from meta_graph with given signature_key. Args: meta_graph: meta_graph_def. signature_key: signature_def in the meta_graph_def. Returns: The signature_def used for tflite conversion. Raises: ValueError: Given signature_key is not valid for this meta_graph.
615	613	get_inputs_outputs	tensorflow/tensorflow/lite/python/convert_saved_model.py	88	function	Get inputs and outputs from SignatureDef. Args: signature_def: SignatureDef in the meta_graph_def for conversion. Returns: The inputs and outputs in the graph for conversion.
616	614	_get_tensors	tensorflow/tensorflow/lite/python/convert_saved_model.py	112	function	Gets the tensors associated with the tensor names. Either signature_def_tensor_names or user_tensor_names should be provided. If the user provides tensors, the tensors associated with the user provided tensor names are provided. Otherwise, the tensors associated with the names in the SignatureDef are provided. Args: graph: GraphDef representing graph. signature_def_tensor_names: Tensor names stored in either the inputs or outputs of a SignatureDef. (default None) user_tensor_names: Tensor names provided by the user. (default None) Returns: List of tensors. Raises: ValueError: signature_def_tensors and user_tensor_names are undefined or empty. user_tensor_names are not valid.
617	615	freeze_saved_model	tensorflow/tensorflow/lite/python/convert_saved_model.py	155	function	Converts a SavedModel to a frozen graph. Args: saved_model_dir: SavedModel directory to convert. input_arrays: List of input tensors to freeze graph with. Uses input arrays from SignatureDef when none are provided. input_shapes: Dict of strings representing input tensor names to list of integers representing input shapes (e.g., {"foo": : [1, 16, 16, 3]}). Automatically determined when input shapes is None (e.g., {"foo" : None}). output_arrays: List of output tensors to freeze graph with. Uses output arrays from SignatureDef when none are provided. tag_set: Set of tags identifying the MetaGraphDef within the SavedModel to analyze. All tags in the tag set must be present. signature_key: Key identifying SignatureDef containing inputs and outputs. Returns: frozen_graph_def: Frozen GraphDef. in_tensors: List of input tensors for the graph. out_tensors: List of output tensors for the graph. graph: `Graph` object. Raises: ValueError: SavedModel doesn't contain a MetaGraphDef identified by tag_set. signature_key is not in the MetaGraphDef. assets/ directory is in the MetaGraphDef. input_shapes does not match the length of input_arrays. input_arrays or output_arrays are not valid.
618	616	FreezeSavedModelTest	tensorflow/tensorflow/lite/python/convert_saved_model_test.py	40	class
619	617	ConvertTest	tensorflow/tensorflow/lite/python/convert_test.py	38	class
620	618	ConvertTestOpHint	tensorflow/tensorflow/lite/python/convert_test.py	168	class	Test the hint to stub functionality.
621	619	_tf_export	tensorflow/tensorflow/lite/python/interpreter.py	37	function
622	620	Delegate	tensorflow/tensorflow/lite/python/interpreter.py	42	class	Python wrapper class to manage TfLiteDelegate objects. The shared library is expected to have two functions: TfLiteDelegate* tflite_plugin_create_delegate( char**, char**, size_t, void (*report_error)(const char *)) void tflite_plugin_destroy_delegate(TfLiteDelegate*) The first one creates a delegate object. It may return NULL to indicate an error (with a suitable error message reported by calling report_error()). The second one destroys delegate object and must be called for every created delegate object. Passing NULL as argument value is allowed, i.e. tflite_plugin_destroy_delegate(tflite_plugin_create_delegate(...)) always works.
623	621	load_delegate	tensorflow/tensorflow/lite/python/interpreter.py	132	function	Returns loaded Delegate object. Args: library: Name of shared library containing the [TfLiteDelegate](https://www.tensorflow.org/lite/performance/delegates). options: Dictionary of options that are required to load the delegate. All keys and values in the dictionary should be convertible to str. Consult the documentation of the specific delegate for required and legal options. (default None) Returns: Delegate object. Raises: ValueError: Delegate failed to load. RuntimeError: If delegate loading is used on unsupported platform.
624	622	Interpreter	tensorflow/tensorflow/lite/python/interpreter.py	159	class	Interpreter interface for TensorFlow Lite Models. This makes the TensorFlow Lite interpreter accessible in Python. It is possible to use this interpreter in a multithreaded Python environment, but you must be sure to call functions of a particular instance from only one thread at a time. So if you want to have 4 threads running different inferences simultaneously, create an interpreter for each one as thread-local data. Similarly, if you are calling invoke() in one thread on a single interpreter but you want to use tensor() on another thread once it is done, you must use a synchronization primitive between the threads to ensure invoke has returned before calling tensor().
625	623	InterpreterWithCustomOps	tensorflow/tensorflow/lite/python/interpreter.py	552	class	Interpreter interface for TensorFlow Lite Models that accepts custom ops. The interface provided by this class is experimental and therefore not exposed as part of the public API. Wraps the tf.lite.Interpreter class and adds the ability to load custom ops by providing the names of functions that take a pointer to a BuiltinOpResolver and add a custom op.
626	624	InterpreterCustomOpsTest	tensorflow/tensorflow/lite/python/interpreter_test.py	43	class
627	625	InterpreterTest	tensorflow/tensorflow/lite/python/interpreter_test.py	63	class
628	626	InterpreterTestErrorPropagation	tensorflow/tensorflow/lite/python/interpreter_test.py	260	class
629	627	InterpreterTensorAccessorTest	tensorflow/tensorflow/lite/python/interpreter_test.py	298	class
630	628	InterpreterDelegateTest	tensorflow/tensorflow/lite/python/interpreter_test.py	353	class
631	629	Optimize	tensorflow/tensorflow/lite/python/lite.py	88	class	Enum defining the optimizations to apply when generating tflite graphs. Some optimizations may come at the cost of accuracy. DEFAULT Default optimization strategy. Converter will do its best to improve size and latency based on the information provided. Enhanced optimizations are gained by providing a representative_dataset. This is recommended, and is currently equivalent to the modes below. Currently, weights will be quantized and if representative_dataset is provided, activations for quantizable operations will also be quantized. OPTIMIZE_FOR_SIZE Deprecated. Does the same as DEFAULT. OPTIMIZE_FOR_LATENCY Deprecated. Does the same as DEFAULT.
632	630	RepresentativeDataset	tensorflow/tensorflow/lite/python/lite.py	131	class	Representative dataset to evaluate optimizations. A representative dataset that can be used to evaluate optimizations by the converter. E.g. converter can use these examples to estimate (min, max) ranges by calibrating the model on inputs. This can allow converter to quantize a converted floating point model.
633	631	TargetSpec	tensorflow/tensorflow/lite/python/lite.py	153	class	Specification of target device. Details about target device. Converter optimizes the generated model for specific device. Attributes: supported_ops: Experimental flag, subject to change. Set of OpsSet options supported by the device. (default set([OpsSet.TFLITE_BUILTINS])) supported_types: List of types for constant values on the target device. Supported values are types exported by lite.constants. Frequently, an optimization choice is driven by the most compact (i.e. smallest) type in this list (default [constants.FLOAT])
634	632	QuantizationMode	tensorflow/tensorflow/lite/python/lite.py	177	class	QuantizationMode determines the quantized conversion from user options.
635	633	TFLiteConverterBase	tensorflow/tensorflow/lite/python/lite.py	384	class	Converter subclass to share functionality between V1 and V2 converters.
636	634	TFLiteConverterBaseV2	tensorflow/tensorflow/lite/python/lite.py	522	class	Converter subclass to share functionality between V2 converters. Attributes: allow_custom_ops: Boolean indicating whether to allow custom operations. When False, any unknown operation is an error. When True, custom ops are created for any op that is unknown. The developer needs to provide these to the TensorFlow Lite runtime with a custom resolver. (default False) optimizations: Experimental flag, subject to change. A list of optimizations to apply when converting the model. E.g. `[Optimize.DEFAULT]` representative_dataset: A representative dataset that can be used to generate input and output samples for the model. The converter can use the dataset to evaluate different optimizations. Note that this is an optional attribute but it is necessary if INT8 is the only support builtin ops in target ops. target_spec: Experimental flag, subject to change. Specification of target device. inference_input_type: Data type of the input layer. Note that integer types (tf.int8 and tf.uint8) are currently only supported for post training integer quantization. (default tf.float32, must be in {tf.float32, tf.int8, tf.uint8}) inference_output_type: Data type of the output layer. Note that integer types (tf.int8 and tf.uint8) are currently only supported for post training integer quantization. (default tf.float32, must be in {tf.float32, tf.int8, tf.uint8}) experimental_new_converter: Experimental flag, subject to change. Enables MLIR-based conversion instead of TOCO conversion. (default True)
637	635	TFLiteSavedModelConverterV2	tensorflow/tensorflow/lite/python/lite.py	652	class	Converts the given SavedModel into TensorFlow Lite model. Attributes: saved_model_dir: Directory of the SavedModel.
638	636	TFLiteKerasModelConverterV2	tensorflow/tensorflow/lite/python/lite.py	719	class	Converts the given Keras model into TensorFlow Lite model.
639	637	TFLiteFrozenGraphConverterV2	tensorflow/tensorflow/lite/python/lite.py	840	class	Converts the given frozen graph into TensorFlow Lite model.
640	638	TFLiteConverterV2	tensorflow/tensorflow/lite/python/lite.py	910	class	Converts a TensorFlow model into TensorFlow Lite model. Attributes: allow_custom_ops: Boolean indicating whether to allow custom operations. When False, any unknown operation is an error. When True, custom ops are created for any op that is unknown. The developer needs to provide these to the TensorFlow Lite runtime with a custom resolver. (default False) optimizations: Experimental flag, subject to change. A list of optimizations to apply when converting the model. E.g. `[Optimize.DEFAULT]` representative_dataset: A representative dataset that can be used to generate input and output samples for the model. The converter can use the dataset to evaluate different optimizations. Note that this is an optional attribute but it is necessary if INT8 is the only support builtin ops in target ops. target_spec: Experimental flag, subject to change. Specification of target device. inference_input_type: Data type of the input layer. Note that integer types (tf.int8 and tf.uint8) are currently only supported for post training integer quantization. (default tf.float32, must be in {tf.float32, tf.int8, tf.uint8}) inference_output_type: Data type of the output layer. Note that integer types (tf.int8 and tf.uint8) are currently only supported for post training integer quantization. (default tf.float32, must be in {tf.float32, tf.int8, tf.uint8}) experimental_new_converter: Experimental flag, subject to change. Enables MLIR-based conversion instead of TOCO conversion. (default True) Example usage: ```python # Converting a SavedModel to a TensorFlow Lite model. converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_dir) tflite_model = converter.convert() # Converting a tf.Keras model to a TensorFlow Lite model. converter = tf.lite.TFLiteConverter.from_keras_model(model) tflite_model = converter.convert() # Converting ConcreteFunctions to a TensorFlow Lite model. converter = tf.lite.TFLiteConverter.from_concrete_functions([func]) tflite_model = converter.convert() ```
641	639	TFLiteConverterBaseV1	tensorflow/tensorflow/lite/python/lite.py	1085	class	Converter subclass to share functionality between V1 converters. Attributes: inference_type: Target data type of real-number arrays in the output file. Must be `{tf.float32, tf.uint8}`. If `optimzations` are provided, this parameter is ignored. (default tf.float32) inference_input_type: Target data type of real-number input arrays. Allows for a different type for input arrays. If an integer type is provided and `optimizations` are not used, `quantized_inputs_stats` must be provided. If `inference_type` is tf.uint8, signaling conversion to a fully quantized model from a quantization-aware trained input model, then `inference_input_type` defaults to tf.uint8. In all other cases, `inference_input_type` defaults to tf.float32. Must be `{tf.float32, tf.uint8, tf.int8}` inference_output_type: Target data type of real-number output arrays. Allows for a different type for output arrays. If `inference_type` is tf.uint8, signaling conversion to a fully quantized model from a quantization-aware trained output model, then `inference_output_type` defaults to tf.uint8. In all other cases, `inference_output_type` must be tf.float32, an error will be thrown otherwise. Must be `{tf.float32, tf.uint8, tf.int8}` output_format: Output file format. Currently must be `{TFLITE, GRAPHVIZ_DOT}`. (default TFLITE) quantized_input_stats: Dict of strings representing input tensor names mapped to tuple of floats representing the mean and standard deviation of the training data (e.g., {"foo" : (0., 1.)}). Only need if `inference_input_type` is `QUANTIZED_UINT8`. real_input_value = (quantized_input_value - mean_value) / std_dev_value. (default {}) default_ranges_stats: Tuple of integers representing (min, max) range values for all arrays without a specified range. Intended for experimenting with quantization via "dummy quantization". (default None) drop_control_dependency: Boolean indicating whether to drop control dependencies silently. This is due to TFLite not supporting control dependencies. (default True) reorder_across_fake_quant: Boolean indicating whether to reorder FakeQuant nodes in unexpected locations. Used when the location of the FakeQuant nodes is preventing graph transformations necessary to convert the graph. Results in a graph that differs from the quantized training graph, potentially causing differing arithmetic behavior. (default False) change_concat_input_ranges: Boolean to change behavior of min/max ranges for inputs and outputs of the concat operator for quantized models. Changes the ranges of concat operator overlap when true. (default False) allow_custom_ops: Boolean indicating whether to allow custom operations. When false any unknown operation is an error. When true, custom ops are created for any op that is unknown. The developer will need to provide these to the TensorFlow Lite runtime with a custom resolver. (default False) post_training_quantize: Deprecated. Please specify `[Optimize.DEFAULT]` for `optimizations` instead. Boolean indicating whether to quantize the weights of the converted float model. Model size will be reduced and there will be latency improvements (at the cost of accuracy). (default False) dump_graphviz_dir: Full filepath of folder to dump the graphs at various stages of processing GraphViz .dot files. Preferred over --output_format=GRAPHVIZ_DOT in order to keep the requirements of the output file. (default None) dump_graphviz_video: Boolean indicating whether to dump the graph after every graph transformation. (default False) conversion_summary_dir: A string indicating the path to the generated conversion logs. target_ops: Deprecated. Please specify `target_spec.supported_ops` instead. Set of OpsSet options indicating which converter to use. (default set([OpsSet.TFLITE_BUILTINS])) target_spec: Experimental flag, subject to change. Specification of target device. optimizations: Experimental flag, subject to change. A list of optimizations to apply when converting the model. E.g. `[Optimize.DEFAULT]` representative_dataset: A representative dataset that can be used to generate input and output samples for the model. The converter can use the dataset to evaluate different optimizations. experimental_new_converter: Experimental flag, subject to change. Enables MLIR-based conversion instead of TOCO conversion. (default True)
642	640	TFLiteSavedModelConverter	tensorflow/tensorflow/lite/python/lite.py	1410	class	Converts the given SavedModel into TensorFlow Lite model. Attributes: saved_model_dir: Directory of the SavedModel.
643	641	TFLiteKerasModelConverter	tensorflow/tensorflow/lite/python/lite.py	1458	class	Converts the given SavedModel into TensorFlow Lite model.
644	642	TFLiteFrozenGraphConverter	tensorflow/tensorflow/lite/python/lite.py	1586	class	Converts the given frozen graph def into TensorFlow Lite model.
645	643	TFLiteConverter	tensorflow/tensorflow/lite/python/lite.py	1634	class	Convert a TensorFlow model into `output_format`. This is used to convert from a TensorFlow GraphDef, SavedModel or tf.keras model into either a TFLite FlatBuffer or graph visualization. Attributes: inference_type: Target data type of real-number arrays in the output file. Must be `{tf.float32, tf.uint8}`. If `optimzations` are provided, this parameter is ignored. (default tf.float32) inference_input_type: Target data type of real-number input arrays. Allows for a different type for input arrays. If an integer type is provided and `optimizations` are not used, `quantized_inputs_stats` must be provided. If `inference_type` is tf.uint8, signaling conversion to a fully quantized model from a quantization-aware trained input model, then `inference_input_type` defaults to tf.uint8. In all other cases, `inference_input_type` defaults to tf.float32. Must be `{tf.float32, tf.uint8, tf.int8}` inference_output_type: Target data type of real-number output arrays. Allows for a different type for output arrays. If `inference_type` is tf.uint8, signaling conversion to a fully quantized model from a quantization-aware trained output model, then `inference_output_type` defaults to tf.uint8. In all other cases, `inference_output_type` must be tf.float32, an error will be thrown otherwise. Must be `{tf.float32, tf.uint8, tf.int8}` output_format: Output file format. Currently must be `{TFLITE, GRAPHVIZ_DOT}`. (default TFLITE) quantized_input_stats: Dict of strings representing input tensor names mapped to tuple of floats representing the mean and standard deviation of the training data (e.g., {"foo" : (0., 1.)}). Only need if `inference_input_type` is `QUANTIZED_UINT8`. real_input_value = (quantized_input_value - mean_value) / std_dev_value. (default {}) default_ranges_stats: Tuple of integers representing (min, max) range values for all arrays without a specified range. Intended for experimenting with quantization via "dummy quantization". (default None) drop_control_dependency: Boolean indicating whether to drop control dependencies silently. This is due to TFLite not supporting control dependencies. (default True) reorder_across_fake_quant: Boolean indicating whether to reorder FakeQuant nodes in unexpected locations. Used when the location of the FakeQuant nodes is preventing graph transformations necessary to convert the graph. Results in a graph that differs from the quantized training graph, potentially causing differing arithmetic behavior. (default False) change_concat_input_ranges: Boolean to change behavior of min/max ranges for inputs and outputs of the concat operator for quantized models. Changes the ranges of concat operator overlap when true. (default False) allow_custom_ops: Boolean indicating whether to allow custom operations. When false any unknown operation is an error. When true, custom ops are created for any op that is unknown. The developer will need to provide these to the TensorFlow Lite runtime with a custom resolver. (default False) post_training_quantize: Deprecated. Please specify `[Optimize.DEFAULT]` for `optimizations` instead. Boolean indicating whether to quantize the weights of the converted float model. Model size will be reduced and there will be latency improvements (at the cost of accuracy). (default False) dump_graphviz_dir: Full filepath of folder to dump the graphs at various stages of processing GraphViz .dot files. Preferred over --output_format=GRAPHVIZ_DOT in order to keep the requirements of the output file. (default None) dump_graphviz_video: Boolean indicating whether to dump the graph after every graph transformation. (default False) conversion_summary_dir: A string indicating the path to the generated conversion logs. target_ops: Deprecated. Please specify `target_spec.supported_ops` instead. Set of OpsSet options indicating which converter to use. (default set([OpsSet.TFLITE_BUILTINS])) target_spec: Experimental flag, subject to change. Specification of target device. optimizations: Experimental flag, subject to change. A list of optimizations to apply when converting the model. E.g. `[Optimize.DEFAULT]` representative_dataset: A representative dataset that can be used to generate input and output samples for the model. The converter can use the dataset to evaluate different optimizations. experimental_new_converter: Experimental flag, subject to change. Enables MLIR-based conversion instead of TOCO conversion. (default True) Example usage: ```python # Converting a GraphDef from session. converter = tf.compat.v1.TFLiteConverter.from_session( sess, in_tensors, out_tensors) tflite_model = converter.convert() open("converted_model.tflite", "wb").write(tflite_model) # Converting a GraphDef from file. converter = tf.compat.v1.TFLiteConverter.from_frozen_graph( graph_def_file, input_arrays, output_arrays) tflite_model = converter.convert() open("converted_model.tflite", "wb").write(tflite_model) # Converting a SavedModel. converter = tf.compat.v1.TFLiteConverter.from_saved_model(saved_model_dir) tflite_model = converter.convert() open("converted_model.tflite", "wb").write(tflite_model) # Converting a tf.keras model. converter = tf.compat.v1.TFLiteConverter.from_keras_model_file(keras_model) tflite_model = converter.convert() open("converted_model.tflite", "wb").write(tflite_model) ```
646	644	TocoConverter	tensorflow/tensorflow/lite/python/lite.py	1979	class	Convert a TensorFlow model into `output_format` using TOCO. This class has been deprecated. Please use `lite.TFLiteConverter` instead.
647	645	FromSessionTest	tensorflow/tensorflow/lite/python/lite_flex_test.py	38	class
648	646	FromConcreteFunctionTest	tensorflow/tensorflow/lite/python/lite_flex_test.py	103	class
649	647	LiteTest	tensorflow/tensorflow/lite/python/lite_test.py	59	class	Base class of all the tests in this module.
650	648	TestModels	tensorflow/tensorflow/lite/python/lite_test.py	63	class
651	649	FromConstructor	tensorflow/tensorflow/lite/python/lite_test.py	76	class
652	650	FromSessionTest	tensorflow/tensorflow/lite/python/lite_test.py	115	class
653	651	FromFrozenGraphFile	tensorflow/tensorflow/lite/python/lite_test.py	1464	class
654	652	FromFrozenGraphObjectDetection	tensorflow/tensorflow/lite/python/lite_test.py	1650	class
655	653	FromSavedModelTest	tensorflow/tensorflow/lite/python/lite_test.py	1713	class
656	654	MyAddLayer	tensorflow/tensorflow/lite/python/lite_test.py	1897	class
657	655	FromKerasFile	tensorflow/tensorflow/lite/python/lite_test.py	1912	class
658	656	GrapplerTest	tensorflow/tensorflow/lite/python/lite_test.py	2292	class
659	657	ImportOpsUtilTest	tensorflow/tensorflow/lite/python/lite_test.py	2384	class
660	658	DefaultConverterAttrsTest	tensorflow/tensorflow/lite/python/lite_test.py	2390	class
661	659	FromConcreteFunctionTest	tensorflow/tensorflow/lite/python/lite_v2_test.py	48	class
662	660	FromSavedModelTest	tensorflow/tensorflow/lite/python/lite_v2_test.py	498	class
663	661	FromKerasModelTest	tensorflow/tensorflow/lite/python/lite_v2_test.py	709	class
664	662	ControlFlowTest	tensorflow/tensorflow/lite/python/lite_v2_test.py	825	class
665	663	GrapplerTest	tensorflow/tensorflow/lite/python/lite_v2_test.py	1013	class
666	664	UnknownShapes	tensorflow/tensorflow/lite/python/lite_v2_test.py	1047	class
667	665	ModelTest	tensorflow/tensorflow/lite/python/lite_v2_test_util.py	34	class	Base test class for TensorFlow Lite 2.x model tests.
668	666	OpHint	tensorflow/tensorflow/lite/python/op_hint.py	97	class	A class that helps build tflite function invocations. It allows you to take a bunch of TensorFlow ops and annotate the construction such that toco knows how to convert it to tflite. This embeds a pseudo function in a TensorFlow graph. This allows embedding high-level API usage information in a lower level TensorFlow implementation so that an alternative implementation can be substituted later. Essentially, any "input" into this pseudo op is fed into an identity, and attributes are added to that input before being used by the constituent ops that make up the pseudo op. A similar process is done to any output that is to be exported from the current op.
669	667	_LiteOperand	tensorflow/tensorflow/lite/python/op_hint.py	471	class	Abstract operand for a tflite hint function._dynamic_rnn_loop. This is a base class that handles representing arguments to an OpHint. It also is able to serialize operands to the stubbed graph_def. Child classes are responsible for being able to store information about the hint identity operators. They are also responsible for knowing how to serialize to output graphdefs. Typically this will be implemented by holding one or more identity nodes that were previously discovered as hints.
670	668	_LiteSingleOperand	tensorflow/tensorflow/lite/python/op_hint.py	518	class	A simple operand that is non-aggregated (i.e. most hints).
671	669	_LiteAggregateOperand	tensorflow/tensorflow/lite/python/op_hint.py	544	class	An operand for a tflite hint function that is aggregated from many. For example, an LSTM is a grid of operators that are all related. Inputs going into them may need to be fused, so they should all be tracked as related arguments.
672	670	_LiteFuncCall	tensorflow/tensorflow/lite/python/op_hint.py	670	class	Represent a TensorFlow Lite custom function. This is uses to accumulate found hints in the graphdef into a single conceptual unit. Attributes: inputs: inputs to the op (hash from index # to argument) outputs: outputs to the op (hash from index # to argument) function_name: the tflite custom op name to use uuid: a unique call id for this particular call (i.e. multiple function calls would have the same function_name but different uuids. params: A param name to key value for op constant data. I.e. for axis on a reduction, strides on a convolution, etc. level: Level of the OpHint. children_inputs_mappings: If the Ophint has children, children inputs mappings indicate how their inputs & outputs are mapped.
673	671	_find_all_hints_in_nodes	tensorflow/tensorflow/lite/python/op_hint.py	730	function	Look at the all the input nodes and return a list of LiteFuncCall objs. Args: nodes: A TensorFlow graph_def to look for LiteFuncCalls. Returns: a list of `LifeFuncCall` objects in the form
674	672	_extract_topology_sequence_mapping	tensorflow/tensorflow/lite/python/op_hint.py	795	function
675	673	_find_children_hints_in_while_loop	tensorflow/tensorflow/lite/python/op_hint.py	800	function	Find children hints and all nodes inside the while loop. Args: function_def: Function def of the while loop. nodes_mapping: While loop input_arg : real node name. Returns: Ordered children hints and all re-mapped nodes inside the while loop.
676	674	_find_children_hints	tensorflow/tensorflow/lite/python/op_hint.py	833	function	Find all children hints. For a given OpHint, we find all children hints inside it, we also copy all the nodes inside function defs (if applicable) to the original graph_def, they are returned in a list as well. Args: call: Parent OpHint that contains children ophints. graph_def: Original graph def. Returns: Ordered children hints inside the parent ophint; new graph def that contains nodes inside function defs (if applicable); nodes inside function defs.
677	675	_tensor_name_base	tensorflow/tensorflow/lite/python/op_hint.py	887	function	Removes the device assignment code from a tensor. e.g. _tensor_name_base("foo:3") => "foo" Args: full_tensor_name: A tensor name that is annotated with a device placement (this is what tensor flow introspection gives). Returns: A name without any device assignment.
678	676	_tensorflow_output_name	tensorflow/tensorflow/lite/python/op_hint.py	904	function
679	677	_check_subgraph_closed	tensorflow/tensorflow/lite/python/op_hint.py	910	function	Checks to make sure node only connects to predecessor graph through inputs. Args: n: Node to check reachable_by_input: Nodes that are reachable by all inputs of subgraph input_nodes_set: The set of nodes that are "inputs". name_to_input_name: Maps from name to the list of inputs. Raises: TypeError: If the given node uses items past inputs directly.
680	678	_convert_single_op_hint_to_stub	tensorflow/tensorflow/lite/python/op_hint.py	940	function	Given a graph_def, converts `call` into a stub and returns a new graph_def. Args: call: A single function call to be converted. graph_def: A graph_def to use as input (that has call obviously). function_def_nodes: Nodes inside the function def those are not connected to the graph. is_last_run: Whether it is the last run for a given pass (for OpHint has children). Returns: A new transformed graph-def that has call as a stub (single op). Note: after this process, the graph_def can no longer be loaded into the tensorflow runtime, so all future manipulations are done in graph_def level.
681	679	_remove_one_redundant_stack_unstack	tensorflow/tensorflow/lite/python/op_hint.py	1070	function	Removes a stack->unstack pattern from in_graph_def in a returned graph. Args: in_graph_def: Graph def to use as input. Returns: Simplified tuple (graph_def, changed_something) where changed_something is true if anything was done.
682	680	_remove_redundant_stack_unstack	tensorflow/tensorflow/lite/python/op_hint.py	1161	function
683	681	_get_correct_mapping	tensorflow/tensorflow/lite/python/op_hint.py	1170	function
684	682	_convert_op_hints_to_stubs_helper	tensorflow/tensorflow/lite/python/op_hint.py	1180	function	Converts a graph_def to a new graph_def where all op hints are stubbed. Args: graph_def: A graph def that we should convert. write_callback: A function pointer that can be used to write intermediate steps of graph transformation (optional). Returns: A new stubbed graph_def.
685	683	find_all_hinted_output_nodes	tensorflow/tensorflow/lite/python/op_hint.py	1257	function	Find all Ophints output nodes in the graph. This is used to get all the output nodes those are ophinted, it is important for operation like convert_variables_to_constants keep all ophints structure. Note: only one of session or graph_def should be used, not both. Why this can be useful? Some TensorFlow ops (e.g. bidirectional rnn), can generate multiple outputs for unfused subgraph. If not all output nodes are consumed, graph optimization can potentially drop the unused nodes and cause ophints in an invalid states (due to missing ophinted output nodes). So it's important for us to find all those hinted output nodes and make sure they're not discarded away. Args: session: A TensorFlow session that contains the graph to convert. graph_def: A graph def that we should convert. Returns: A list of OpHints output nodes. Raises: ValueError: If both session and graph_def are provided.
686	684	is_ophint_converted	tensorflow/tensorflow/lite/python/op_hint.py	1292	function
687	685	convert_op_hints_to_stubs	tensorflow/tensorflow/lite/python/op_hint.py	1305	function	Converts a graphdef with LiteOp hints into stub operations. This is used to prepare for toco conversion of complex intrinsic usages. Note: only one of session or graph_def should be used, not both. Args: session: A TensorFlow session that contains the graph to convert. graph_def: A graph def that we should convert. write_callback: A function pointer that can be used to write intermediate steps of graph transformation (optional). Returns: A new graphdef with all ops contained in OpHints being replaced by a single op call with the right parameters. Raises: ValueError: If both session and graph_def are provided.
688	686	_parse_array	tensorflow/tensorflow/lite/python/tflite_convert.py	39	function
689	687	_parse_set	tensorflow/tensorflow/lite/python/tflite_convert.py	45	function
690	688	_parse_inference_type	tensorflow/tensorflow/lite/python/tflite_convert.py	51	function	Converts the inference type to the value of the constant. Args: value: str representing the inference type. flag: str representing the flag name. Returns: tf.dtype. Raises: ValueError: Unsupported value.
691	689	_get_tflite_converter	tensorflow/tensorflow/lite/python/tflite_convert.py	74	function	Makes a TFLiteConverter object based on the flags provided. Args: flags: argparse.Namespace object containing TFLite flags. Returns: TFLiteConverter object. Raises: ValueError: Invalid flags.
692	690	_convert_tf1_model	tensorflow/tensorflow/lite/python/tflite_convert.py	122	function	Calls function to convert the TensorFlow 1.X model into a TFLite model. Args: flags: argparse.Namespace object. Raises: ValueError: Invalid flags.
693	691	_convert_tf2_model	tensorflow/tensorflow/lite/python/tflite_convert.py	219	function	Calls function to convert the TensorFlow 2.0 model into a TFLite model. Args: flags: argparse.Namespace object. Raises: ValueError: Unsupported file format.
694	692	_check_tf1_flags	tensorflow/tensorflow/lite/python/tflite_convert.py	244	function	Checks the parsed and unparsed flags to ensure they are valid in 1.X. Raises an error if previously support unparsed flags are found. Raises an error for parsed flags that don't meet the required conditions. Args: flags: argparse.Namespace object containing TFLite flags. unparsed: List of unparsed flags. Raises: ValueError: Invalid flags.
695	693	_check_tf2_flags	tensorflow/tensorflow/lite/python/tflite_convert.py	313	function	Checks the parsed and unparsed flags to ensure they are valid in 2.X. Args: flags: argparse.Namespace object containing TFLite flags. Raises: ValueError: Invalid flags.
696	694	_get_tf1_flags	tensorflow/tensorflow/lite/python/tflite_convert.py	327	function	Returns ArgumentParser for tflite_convert for TensorFlow 1.X. Args: parser: ArgumentParser
697	695	_get_tf2_flags	tensorflow/tensorflow/lite/python/tflite_convert.py	511	function	Returns ArgumentParser for tflite_convert for TensorFlow 2.0. Args: parser: ArgumentParser
698	696	_ParseExperimentalNewConverter	tensorflow/tensorflow/lite/python/tflite_convert.py	535	class	Helper class to parse --experimental_new_converter argument.
699	697	_get_parser	tensorflow/tensorflow/lite/python/tflite_convert.py	565	function	Returns an ArgumentParser for tflite_convert. Args: use_v2_converter: Indicates which converter to return. Return: ArgumentParser.
700	698	run_main	tensorflow/tensorflow/lite/python/tflite_convert.py	596	function	Main in tflite_convert.py.
701	699	main	tensorflow/tensorflow/lite/python/tflite_convert.py	639	function
702	700	TestModels	tensorflow/tensorflow/lite/python/tflite_convert_test.py	45	class
703	701	TfLiteConvertV1Test	tensorflow/tensorflow/lite/python/tflite_convert_test.py	81	class
704	702	TfLiteConvertV2Test	tensorflow/tensorflow/lite/python/tflite_convert_test.py	298	class
705	703	ArgParserTest	tensorflow/tensorflow/lite/python/tflite_convert_test.py	339	class
706	704	convert_dtype_to_tflite_type	tensorflow/tensorflow/lite/python/util.py	59	function	Converts tf.dtype to TFLite proto type. Args: tf_dtype: tf.dtype Raises: ValueError: Unsupported tf.dtype. Returns: types_flag_pb2.
707	705	get_tensor_name	tensorflow/tensorflow/lite/python/util.py	77	function	Returns name of the input tensor. Args: tensor: tf.Tensor Returns: str
708	706	get_tensors_from_tensor_names	tensorflow/tensorflow/lite/python/util.py	98	function	Gets the Tensors associated with the `tensor_names` in the provided graph. Args: graph: TensorFlow Graph. tensor_names: List of strings that represent names of tensors in the graph. Returns: A list of Tensor objects in the same order the names are provided. Raises: ValueError: tensor_names contains an invalid tensor name.
709	707	set_tensor_shapes	tensorflow/tensorflow/lite/python/util.py	141	function	Sets Tensor shape for each tensor if the shape is defined. Args: tensors: TensorFlow ops.Tensor. shapes: Dict of strings representing input tensor names to list of integers representing input shapes (e.g., {"foo": : [1, 16, 16, 3]}). Raises: ValueError: `shapes` contains an invalid tensor. `shapes` contains an invalid shape for a valid tensor.
710	708	get_grappler_config	tensorflow/tensorflow/lite/python/util.py	172	function	Creates a tf.compat.v1.ConfigProto for configuring Grappler. Args: optimizers_list: List of strings that represents the list of optimizers. Returns: tf.ConfigProto.
711	709	run_graph_optimizations	tensorflow/tensorflow/lite/python/util.py	188	function	Apply standard TensorFlow optimizations to the graph_def. Args: graph_def: Frozen GraphDef to be optimized. input_arrays: List of arrays that are considered inputs of the graph. output_arrays: List of arrays that are considered outputs of the graph. config: tf.ConfigProto. graph: TensorFlow Graph. Required when Eager mode is enabled. (default None) Returns: A new, optimized GraphDef.
712	710	_convert_op_hints_if_present	tensorflow/tensorflow/lite/python/util.py	230	function
713	711	freeze_graph	tensorflow/tensorflow/lite/python/util.py	241	function	Returns a frozen GraphDef. Runs a Grappler pass and freezes a graph with Variables in it. Otherwise the existing GraphDef is returned. The Grappler pass is only run on models that are frozen in order to inline the functions in the graph. If OpHints is present, it will try to convert the OpHint graph. Args: sess: TensorFlow Session. input_tensors: List of input tensors. output_tensors: List of output tensors (only .name is used from this). Returns: Frozen GraphDef.
714	712	is_frozen_graph	tensorflow/tensorflow/lite/python/util.py	281	function	Determines if the graph is frozen. Determines if a graph has previously been frozen by checking for any operations of type Variable*. If variables are found, the graph is not frozen. Args: sess: TensorFlow Session. Returns: Bool.
715	713	build_debug_info_func	tensorflow/tensorflow/lite/python/util.py	300	function	Returns a method to retrieve the `GraphDebugInfo` from the original graph. Args: original_graph: The original `Graph` containing all the op stack traces. Returns: A function which retrieves the stack traces from the original graph and converts them to a `GraphDebugInfo` for a given set of nodes.
716	714	convert_debug_info_func	tensorflow/tensorflow/lite/python/util.py	339	function	Returns a method to retrieve the `GraphDebugInfo` from the original graph. Args: saved_debug_info: The `GraphDebugInfo` containing all the debug info. Returns: A function which retrieves the stack traces from the original graph and converts them to a `GraphDebugInfo` for a given set of nodes.
717	715	get_debug_info	tensorflow/tensorflow/lite/python/util.py	368	function	Returns the debug info for the original nodes in the `converted_graph`. Args: nodes_to_debug_info_func: The method to collect the op debug info for the nodes. converted_graph: A `GraphDef` after optimization and transformation. Returns: `GraphDebugInfo` for all the original nodes in `converted_graph`.
718	716	convert_bytes_to_c_source	tensorflow/tensorflow/lite/python/util.py	399	function	Returns strings representing a C constant array containing `data`. Args: data: Byte array that will be converted into a C constant. array_name: String to use as the variable name for the constant array. max_line_width: The longest line length, for formatting purposes. include_guard: Name to use for the include guard macro definition. include_path: Optional path to include in the source file. use_tensorflow_license: Whether to include the standard TensorFlow Apache2 license in the generated files. Returns: Text that can be compiled as a C source file to link in the data as a literal array of values. Text that can be used as a C header file to reference the literal array.
719	717	UtilTest	tensorflow/tensorflow/lite/python/util_test.py	39	class
720	718	TensorFunctionsTest	tensorflow/tensorflow/lite/python/util_test.py	124	class
721	719	wrapped_toco_convert	tensorflow/tensorflow/lite/python/wrap_toco.py	29	function	Wraps TocoConvert with lazy loader.
722	720	wrapped_get_potentially_supported_ops	tensorflow/tensorflow/lite/python/wrap_toco.py	41	function	Wraps TocoGetPotentiallySupportedOps with lazy loader.
723	721	wrapped_experimental_mlir_quantize	tensorflow/tensorflow/lite/python/wrap_toco.py	46	function	Wraps experimental mlir quantize model.
724	722	wrapped_experimental_mlir_sparsify	tensorflow/tensorflow/lite/python/wrap_toco.py	55	function	Wraps experimental mlir sparsify model.
725	723	Calibrator	tensorflow/tensorflow/lite/python/optimize/calibrator.py	33	class	Calibrates a floating point model and then quantizes it. This is an internal class, not a public interface.
726	724	CalibratorTest	tensorflow/tensorflow/lite/python/optimize/calibrator_test.py	33	class
727	725	TemporaryDirectoryResource	tensorflow/tensorflow/lite/schema/upgrade_schema.py	57	function
728	726	Converter	tensorflow/tensorflow/lite/schema/upgrade_schema.py	65	class	Converts TensorFlow flatbuffer models from old to new version of schema. This can convert between any version to the latest version. It uses an incremental upgrade strategy to go from version to version. Usage: converter = Converter() converter.Convert("a.tflite", "a.json") converter.Convert("b.json", "b.tflite")
729	727	main	tensorflow/tensorflow/lite/schema/upgrade_schema.py	344	function
730	728	JsonDumpAndFlush	tensorflow/tensorflow/lite/schema/upgrade_schema_test.py	242	function	Write the dictionary `data` to a JSON file `fp` (and flush). Args: data: in a dictionary that is JSON serializable. fp: File-like object
731	729	TestSchemaUpgrade	tensorflow/tensorflow/lite/schema/upgrade_schema_test.py	253	class
732	730	main	tensorflow/tensorflow/lite/testing/generate_examples.py	100	function
733	731	MultiGenState	tensorflow/tensorflow/lite/testing/generate_examples_lib.py	176	class	State of multiple set generation process. This state class stores the information needed when generating the examples for multiple test set. The stored informations are open archive object to be shared, information on test target for current iteration of generation, accumulated generation results.
734	732	Options	tensorflow/tensorflow/lite/testing/generate_examples_lib.py	203	class	All options for example generation.
735	733	_prepare_dir	tensorflow/tensorflow/lite/testing/generate_examples_lib.py	244	function
736	734	generate_examples	tensorflow/tensorflow/lite/testing/generate_examples_lib.py	256	function	Generate examples for a test set. Args: options: Options containing information to generate examples. Raises: RuntimeError: if the test function cannot be found.
737	735	generate_multi_set_examples	tensorflow/tensorflow/lite/testing/generate_examples_lib.py	294	function	Generate examples for test sets. Args: options: Options containing information to generate examples. test_sets: List of the name of test sets to generate examples.
738	736	make_report_table	tensorflow/tensorflow/lite/testing/generate_examples_report.py	32	function	Make an HTML report of the success/failure reports. Args: fp: File-like object in which to put the html. title: "Title of the zip file this pertains to." reports: a list of conversion attempts. (report_args, report_vals) i.e. ({"shape": [1,2,3], "type": "tf.float32"}, {"tf": "SUCCESS", "toco": "FAILURE", "toco_log": "Unsupported type.", "tf_log": ""})
739	737	toco_options	tensorflow/tensorflow/lite/testing/toco_convert.py	31	function	Create TOCO options to process a model. Args: data_types: input and inference types used by TOCO. input_arrays: names of the input tensors output_arrays: name of the output tensors shapes: shapes of the input tensors extra_toco_options: additional toco options Returns: the options in a string.
740	738	toco_convert	tensorflow/tensorflow/lite/testing/toco_convert.py	78	function	Convert a model's graph def into a tflite model. NOTE: this currently shells out to the toco binary, but we would like convert to Python API tooling in the future. Args: options: An Options instance. graph_def: A GraphDef object. input_tensors: List of input tensor tuples `(name, shape, type)`. output_tensors: List of output tensors (names). **kwargs: Extra options to be passed. Returns: output tflite model, log_txt from conversion or None, log_txt if it did not convert properly.
741	739	register_make_test_function	tensorflow/tensorflow/lite/testing/zip_test_utils.py	55	function
742	740	get_test_function	tensorflow/tensorflow/lite/testing/zip_test_utils.py	65	function	Get the test function according to the test function name.
743	741	ExtraTocoOptions	tensorflow/tensorflow/lite/testing/zip_test_utils.py	88	class	Additional toco options besides input, output, shape.
744	742	create_tensor_data	tensorflow/tensorflow/lite/testing/zip_test_utils.py	106	function	Build tensor data spreading the range [min_value, max_value).
745	743	create_scalar_data	tensorflow/tensorflow/lite/testing/zip_test_utils.py	126	function	Build scalar tensor data range from min_value to max_value exclusively.
746	744	freeze_graph	tensorflow/tensorflow/lite/testing/zip_test_utils.py	144	function	Freeze the current graph. Args: session: Tensorflow sessions containing the graph outputs: List of output tensors Returns: The frozen graph_def.
747	745	format_result	tensorflow/tensorflow/lite/testing/zip_test_utils.py	158	function	Convert a tensor to a format that can be used in test specs.
748	746	write_examples	tensorflow/tensorflow/lite/testing/zip_test_utils.py	168	function	Given a list `examples`, write a text format representation. The file format is csv like with a simple repeated pattern. We would ike to use proto here, but we can't yet due to interfacing with the Android team using this format. Args: fp: File-like object to write to. examples: Example dictionary consisting of keys "inputs" and "outputs"
749	747	write_test_cases	tensorflow/tensorflow/lite/testing/zip_test_utils.py	196	function	Given a dictionary of `examples`, write a text format representation. The file format is protocol-buffer-like, even though we don't use proto due to the needs of the Android team. Args: fp: File-like object to write to. model_name: Filename where the model was written to, relative to filename. examples: Example dictionary consisting of keys "inputs" and "outputs"
750	748	get_input_shapes_map	tensorflow/tensorflow/lite/testing/zip_test_utils.py	225	function	Gets a map of input names to shapes. Args: input_tensors: List of input tensor tuples `(name, shape, type)`. Returns: {string : list of integers}.
751	749	_normalize_output_name	tensorflow/tensorflow/lite/testing/zip_test_utils.py	251	function	Remove :0 suffix from tensor names.
752	750	make_zip_of_tests	tensorflow/tensorflow/lite/testing/zip_test_utils.py	262	function	Helper to make a zip file of a bunch of TensorFlow models. This does a cartesian product of the dictionary of test_parameters and calls make_graph() for each item in the cartesian product set. If the graph is built successfully, then make_test_inputs() is called to build expected input/output value pairs. The model is then converted to tflite with toco, and the examples are serialized with the tflite model into a zip file (2 files per item in the cartesian product set). Args: options: An Options instance. test_parameters: Dictionary mapping to lists for each parameter. e.g. `{"strides": [[1,3,3,1], [1,2,2,1]], "foo": [1.2, 1.3]}` make_graph: function that takes current parameters and returns tuple `[input1, input2, ...], [output1, output2, ...]` make_test_inputs: function taking `curr_params`, `session`, `input_tensors`, `output_tensors` and returns tuple `(input_values, output_values)`. extra_toco_options: Additional toco options. use_frozen_graph: Whether or not freeze graph before toco converter. expected_tf_failures: Number of times tensorflow is expected to fail in executing the input graphs. In some cases it is OK for TensorFlow to fail because the one or more combination of parameters is invalid. Raises: RuntimeError: if there are converter errors that can't be ignored.
753	751	get_filepath	tensorflow/tensorflow/lite/testing/model_coverage/model_coverage_lib.py	47	function	Returns the full path of the filename. Args: filename: Subdirectory and name of the model file. base_dir: Base directory containing model file. Returns: str.
754	752	get_image	tensorflow/tensorflow/lite/testing/model_coverage/model_coverage_lib.py	63	function	Returns an image loaded into an np.ndarray with dims [1, size, size, 3]. Args: size: Size of image. Returns: np.ndarray.
755	753	_convert	tensorflow/tensorflow/lite/testing/model_coverage/model_coverage_lib.py	80	function	Converts the model. Args: converter: TFLiteConverter object. **kwargs: Additional arguments to be passed into the converter. Supported flags are {"target_ops", "post_training_quantize", "quantize_to_float16"}. Returns: The converted TFLite model in serialized format. Raises: ValueError: Invalid version number.
756	754	_get_tflite_interpreter	tensorflow/tensorflow/lite/testing/model_coverage/model_coverage_lib.py	103	function	Creates a TFLite interpreter with resized input tensors. Args: tflite_model: Serialized TensorFlow Lite model. input_shapes_resize: A map where the key is the input tensor name and the value is the shape of the input tensor. This resize happens after model conversion, prior to calling allocate tensors. (default None) Returns: lite.Interpreter
757	755	_get_input_data_map	tensorflow/tensorflow/lite/testing/model_coverage/model_coverage_lib.py	127	function	Generates a map of input data based on the TFLite model. Args: tflite_model: Serialized TensorFlow Lite model. input_data: List of np.ndarray. Returns: {str: [np.ndarray]}.
758	756	_generate_random_input_data	tensorflow/tensorflow/lite/testing/model_coverage/model_coverage_lib.py	146	function	Generates input data based on the input tensors in the TFLite model. Args: tflite_model: Serialized TensorFlow Lite model. seed: Integer seed for the random generator. (default None) input_data_range: A map where the key is the input tensor name and the value is a tuple (min_val, max_val) which specifies the value range of the corresponding input tensor. For example, '{'input1': (1, 5)}' means to generate a random value for tensor `input1` within range [1.0, 5.0) (half-inclusive). (default None) input_shapes_resize: A map where the key is the input tensor name and the value is the shape of the input tensor. This resize happens after model conversion, prior to calling allocate tensors. (default None) Returns: ([np.ndarray], {str : [np.ndarray]}).
759	757	_evaluate_tflite_model	tensorflow/tensorflow/lite/testing/model_coverage/model_coverage_lib.py	191	function	Returns evaluation of input data on TFLite model. Args: tflite_model: Serialized TensorFlow Lite model. input_data: List of np.ndarray. input_shapes_resize: A map where the key is the input tensor name and the value is the shape of the input tensor. This resize happens after model conversion, prior to calling allocate tensors. (default None) Returns: List of np.ndarray.
760	758	evaluate_frozen_graph	tensorflow/tensorflow/lite/testing/model_coverage/model_coverage_lib.py	222	function	Returns a function that evaluates the frozen graph on input data. Args: filename: Full filepath of file containing frozen GraphDef. input_arrays: List of input tensors to freeze graph with. output_arrays: List of output tensors to freeze graph with. Returns: Lambda function ([np.ndarray data] : [np.ndarray result]).
761	759	evaluate_saved_model	tensorflow/tensorflow/lite/testing/model_coverage/model_coverage_lib.py	260	function	Returns a function that evaluates the SavedModel on input data. Args: directory: SavedModel directory to convert. tag_set: Set of tags identifying the MetaGraphDef within the SavedModel to analyze. All tags in the tag set must be present. signature_key: Key identifying SignatureDef containing inputs and outputs. Returns: Lambda function ([np.ndarray data] : [np.ndarray result]).
762	760	evaluate_keras_model	tensorflow/tensorflow/lite/testing/model_coverage/model_coverage_lib.py	286	function	Returns a function that evaluates the tf.keras model on input data. Args: filename: Full filepath of HDF5 file containing the tf.keras model. Returns: Lambda function ([np.ndarray data] : [np.ndarray result]).
763	761	compare_models	tensorflow/tensorflow/lite/testing/model_coverage/model_coverage_lib.py	299	function	Compares TensorFlow and TFLite models. Unless the input data is provided, the models are compared with random data. Args: tflite_model: Serialized TensorFlow Lite model. tf_eval_func: Lambda function that takes in input data and outputs the results of the TensorFlow model ([np.ndarray data] : [np.ndarray result]). input_shapes_resize: A map where the key is the input tensor name and the value is the shape of the input tensor. This resize happens after model conversion, prior to calling allocate tensors. (default None) input_data: np.ndarray to pass into models during inference. (default None) input_data_range: A map where the key is the input tensor name and the value is a tuple (min_val, max_val) which specifies the value range of the corresponding input tensor. For example, '{'input1': (1, 5)}' means to generate a random value for tensor `input1` within range [1.0, 5.0) (half-inclusive). (default None) tolerance: Decimal place to check accuracy to. (default 5).
764	762	compare_models_v2	tensorflow/tensorflow/lite/testing/model_coverage/model_coverage_lib.py	336	function	Compares TensorFlow and TFLite models for TensorFlow 2.0. Unless the input data is provided, the models are compared with random data. Currently only 1 input and 1 output are supported by this function. Args: tflite_model: Serialized TensorFlow Lite model. tf_eval_func: Function to evaluate TensorFlow model. Either a lambda function that takes in input data and outputs the results or a TensorFlow ConcreteFunction. input_data: np.ndarray to pass into models during inference. (default None). input_data_range: A map where the key is the input tensor name and the value is a tuple (min_val, max_val) which specifies the value range of the corresponding input tensor. For example, '{'input1': (1, 5)}' means to generate a random value for tensor `input1` within range [1.0, 5.0) (half-inclusive). (default None) tolerance: Decimal place to check accuracy to. (default 5)
765	763	test_frozen_graph_quant	tensorflow/tensorflow/lite/testing/model_coverage/model_coverage_lib.py	390	function	Sanity check to validate post quantize flag alters the graph. This test does not check correctness of the converted model. It converts the TensorFlow frozen graph to TFLite with and without the post_training_quantized flag. It ensures some tensors have different types between the float and quantized models in the case of an all TFLite model or mix-and-match model. It ensures tensor types do not change in the case of an all Flex model. Args: filename: Full filepath of file containing frozen GraphDef. input_arrays: List of input tensors to freeze graph with. output_arrays: List of output tensors to freeze graph with. input_shapes: Dict of strings representing input tensor names to list of integers representing input shapes (e.g., {"foo" : [1, 16, 16, 3]}). Automatically determined when input shapes is None (e.g., {"foo" : None}). (default None) **kwargs: Additional arguments to be passed into the converter. Raises: ValueError: post_training_quantize flag doesn't act as intended.
766	764	test_frozen_graph	tensorflow/tensorflow/lite/testing/model_coverage/model_coverage_lib.py	459	function	Validates the TensorFlow frozen graph converts to a TFLite model. Converts the TensorFlow frozen graph to TFLite and checks the accuracy of the model on random data. Args: filename: Full filepath of file containing frozen GraphDef. input_arrays: List of input tensors to freeze graph with. output_arrays: List of output tensors to freeze graph with. input_shapes: Dict of strings representing input tensor names to list of integers representing input shapes (e.g., {"foo" : [1, 16, 16, 3]}). Automatically determined when input shapes is None (e.g., {"foo" : None}). (default None) input_shapes_resize: A map where the key is the input tensor name and the value is the shape of the input tensor. This resize happens after model conversion, prior to calling allocate tensors. (default None) input_data: np.ndarray to pass into models during inference. (default None). input_data_range: A map where the key is the input tensor name and the value is a tuple (min_val, max_val) which specifies the value range of the corresponding input tensor. For example, '{'input1': (1, 5)}' means to generate a random value for tensor `input1` within range [1.0, 5.0) (half-inclusive). (default None) **kwargs: Additional arguments to be passed into the converter.
767	765	test_saved_model	tensorflow/tensorflow/lite/testing/model_coverage/model_coverage_lib.py	504	function	Validates the TensorFlow SavedModel converts to a TFLite model. Converts the TensorFlow SavedModel to TFLite and checks the accuracy of the model on random data. Args: directory: SavedModel directory to convert. input_shapes: Dict of strings representing input tensor names to list of integers representing input shapes (e.g., {"foo" : [1, 16, 16, 3]}). Automatically determined when input shapes is None (e.g., {"foo" : None}). (default None) tag_set: Set of tags identifying the MetaGraphDef within the SavedModel to analyze. All tags in the tag set must be present. signature_key: Key identifying SignatureDef containing inputs and outputs. input_data: np.ndarray to pass into models during inference. (default None). input_data_range: A map where the key is the input tensor name and the value is a tuple (min_val, max_val) which specifies the value range of the corresponding input tensor. For example, '{'input1': (1, 5)}' means to generate a random value for tensor `input1` within range [1.0, 5.0) (half-inclusive). (default None) **kwargs: Additional arguments to be passed into the converter.
768	766	test_saved_model_v2	tensorflow/tensorflow/lite/testing/model_coverage/model_coverage_lib.py	548	function	Validates the TensorFlow SavedModel converts to a TFLite model. Converts the TensorFlow SavedModel to TFLite and checks the accuracy of the model on random data. Args: directory: SavedModel directory to convert. tag_set: Set of tags identifying the MetaGraphDef within the SavedModel to analyze. All tags in the tag set must be present. signature_key: Key identifying SignatureDef containing inputs and outputs. input_data: np.ndarray to pass into models during inference. (default None). input_data_range: A map where the key is the input tensor name and the value is a tuple (min_val, max_val) which specifies the value range of the corresponding input tensor. For example, '{'input1': (1, 5)}' means to generate a random value for tensor `input1` within range [1.0, 5.0) (half-inclusive). (default None) **kwargs: Additional arguments to be passed into the converter.
769	767	test_saved_model_v2_quant_float16	tensorflow/tensorflow/lite/testing/model_coverage/model_coverage_lib.py	587	function	Validates the TensorFlow SavedModel converts to a TFLite model.
770	768	test_keras_model	tensorflow/tensorflow/lite/testing/model_coverage/model_coverage_lib.py	623	function	Validates the tf.keras model converts to a TFLite model. Converts the tf.keras model to TFLite and checks the accuracy of the model on random data. Args: filename: Full filepath of HDF5 file containing the tf.keras model. input_arrays: List of input tensors to freeze graph with. input_shapes: Dict of strings representing input tensor names to list of integers representing input shapes (e.g., {"foo" : [1, 16, 16, 3]}). Automatically determined when input shapes is None (e.g., {"foo" : None}). (default None) input_data: np.ndarray to pass into models during inference. (default None). input_data_range: A map where the key is the input tensor name and the value is a tuple (min_val, max_val) which specifies the value range of the corresponding input tensor. For example, '{'input1': (1, 5)}' means to generate a random value for tensor `input1` within range [1.0, 5.0) (half-inclusive). (default None) **kwargs: Additional arguments to be passed into the converter.
771	769	test_keras_model_v2	tensorflow/tensorflow/lite/testing/model_coverage/model_coverage_lib.py	661	function	Validates the tf.keras model converts to a TFLite model. Converts the tf.keras model to TFLite and checks the accuracy of the model on random data. Args: filename: Full filepath of HDF5 file containing the tf.keras model. input_shapes: List of list of integers representing input shapes in the order of the tf.keras model's .input attribute (e.g., [[1, 16, 16, 3]]). (default None) input_data: np.ndarray to pass into models during inference. (default None). input_data_range: A map where the key is the input tensor name and the value is a tuple (min_val, max_val) which specifies the value range of the corresponding input tensor. For example, '{'input1': (1, 5)}' means to generate a random value for tensor `input1` within range [1.0, 5.0) (half-inclusive). (default None) **kwargs: Additional arguments to be passed into the converter.
772	770	EvaluateFrozenGraph	tensorflow/tensorflow/lite/testing/model_coverage/model_coverage_lib_test.py	42	class
773	771	EvaluateSavedModel	tensorflow/tensorflow/lite/testing/model_coverage/model_coverage_lib_test.py	142	class
774	772	EvaluateKerasModel	tensorflow/tensorflow/lite/testing/model_coverage/model_coverage_lib_test.py	160	class
775	773	make_abs_tests	tensorflow/tensorflow/lite/testing/op_tests/abs.py	28	function	Make a set of tests to do abs.
776	774	make_add_n_tests	tensorflow/tensorflow/lite/testing/op_tests/add_n.py	27	function	Make a set of tests for AddN op.
777	775	make_arg_min_max_tests	tensorflow/tensorflow/lite/testing/op_tests/arg_min_max.py	29	function	Make a set of tests to do arg_max.
778	776	make_batch_to_space_nd_tests	tensorflow/tensorflow/lite/testing/op_tests/batch_to_space_nd.py	28	function	Make a set of tests to do batch_to_space_nd.
779	777	make_binary_op_tests	tensorflow/tensorflow/lite/testing/op_tests/binary_op.py	26	function	Make a set of tests to do binary ops with and without broadcast.
780	778	make_binary_op_tests_func	tensorflow/tensorflow/lite/testing/op_tests/binary_op.py	239	function	Return a function that does a test on a binary operator.
781	779	make_add_tests	tensorflow/tensorflow/lite/testing/op_tests/binary_op.py	245	function
782	780	make_div_tests	tensorflow/tensorflow/lite/testing/op_tests/binary_op.py	250	function	Make zip tests for div op with 5D case.
783	781	make_sub_tests	tensorflow/tensorflow/lite/testing/op_tests/binary_op.py	267	function	Make zip tests for sub op with additional cases.
784	782	make_mul_tests	tensorflow/tensorflow/lite/testing/op_tests/binary_op.py	287	function
785	783	make_pow_tests	tensorflow/tensorflow/lite/testing/op_tests/binary_op.py	292	function
786	784	make_floor_div_tests	tensorflow/tensorflow/lite/testing/op_tests/binary_op.py	297	function
787	785	make_floor_mod_tests	tensorflow/tensorflow/lite/testing/op_tests/binary_op.py	302	function
788	786	make_squared_difference_tests	tensorflow/tensorflow/lite/testing/op_tests/binary_op.py	307	function
789	787	make_cast_tests	tensorflow/tensorflow/lite/testing/op_tests/cast.py	27	function	Generate examples for cast.
790	788	make_ceil_tests	tensorflow/tensorflow/lite/testing/op_tests/ceil.py	27	function	Make a set of tests to do ceil.
791	789	make_concat_tests	tensorflow/tensorflow/lite/testing/op_tests/concat.py	27	function	Make a set of tests to do concatenation.
792	790	make_constant_tests	tensorflow/tensorflow/lite/testing/op_tests/constant.py	31	function	Make a set of tests to do constant ops.
793	791	make_conv_tests	tensorflow/tensorflow/lite/testing/op_tests/conv.py	28	function	Make a set of tests to do convolution.
794	792	make_conv2d_transpose_tests	tensorflow/tensorflow/lite/testing/op_tests/conv2d_transpose.py	28	function	Make a set of tests to do transpose_conv.
795	793	make_conv_activation_tests	tensorflow/tensorflow/lite/testing/op_tests/conv_activation.py	27	function	Make a set of tests to do convolution with activation.
796	794	make_conv_relu6_tests	tensorflow/tensorflow/lite/testing/op_tests/conv_activation.py	132	function	Make a set of tests to do conv_relu6.
797	795	make_conv_relu_tests	tensorflow/tensorflow/lite/testing/op_tests/conv_activation.py	138	function	Make a set of tests to do conv_relu.
798	796	relu1	tensorflow/tensorflow/lite/testing/op_tests/conv_activation.py	143	function
799	797	make_conv_relu1_tests	tensorflow/tensorflow/lite/testing/op_tests/conv_activation.py	151	function	Make a set of tests to do conv_relu1.
800	798	make_conv_to_depthwiseconv_with_shared_weights_tests	tensorflow/tensorflow/lite/testing/op_tests/conv_to_depthwiseconv_with_shared_weights.py	28	function	Make a test where 2 Conv ops shared the same constant weight tensor.
801	799	make_conv_with_shared_weights_tests	tensorflow/tensorflow/lite/testing/op_tests/conv_with_shared_weights.py	28	function	Make a test where 2 Conv ops shared the same constant weight tensor.
802	800	make_cos_tests	tensorflow/tensorflow/lite/testing/op_tests/cos.py	28	function	Make a set of tests to do cos.
803	801	make_depth_to_space_tests	tensorflow/tensorflow/lite/testing/op_tests/depth_to_space.py	27	function	Make a set of tests to do depth_to_space.
804	802	make_depthwiseconv_tests	tensorflow/tensorflow/lite/testing/op_tests/depthwiseconv.py	28	function	Make a set of tests to do convolution.
805	803	_make_elementwise_tests	tensorflow/tensorflow/lite/testing/op_tests/elementwise.py	26	function	Make a set of tests to do element-wise operations.
806	804	make_sin_tests	tensorflow/tensorflow/lite/testing/op_tests/elementwise.py	57	function	Make a set of tests to do sin.
807	805	make_log_tests	tensorflow/tensorflow/lite/testing/op_tests/elementwise.py	63	function	Make a set of tests to do log.
808	806	make_sqrt_tests	tensorflow/tensorflow/lite/testing/op_tests/elementwise.py	69	function	Make a set of tests to do sqrt.
809	807	make_rsqrt_tests	tensorflow/tensorflow/lite/testing/op_tests/elementwise.py	75	function	Make a set of tests to do 1/sqrt.
810	808	make_square_tests	tensorflow/tensorflow/lite/testing/op_tests/elementwise.py	81	function	Make a set of tests to do square.
811	809	make_elu_tests	tensorflow/tensorflow/lite/testing/op_tests/elu.py	28	function	Make a set of tests to do (float) tf.nn.elu.
812	810	make_embedding_lookup_tests	tensorflow/tensorflow/lite/testing/op_tests/embedding_lookup.py	27	function	Make a set of tests to do gather.
813	811	make_equal_tests	tensorflow/tensorflow/lite/testing/op_tests/equal.py	27	function	Make a set of tests to do equal.
814	812	make_exp_tests	tensorflow/tensorflow/lite/testing/op_tests/exp.py	27	function	Make a set of tests to do exp.
815	813	make_expand_dims_tests	tensorflow/tensorflow/lite/testing/op_tests/expand_dims.py	28	function	Make a set of tests to do expand_dims.
816	814	make_eye_tests	tensorflow/tensorflow/lite/testing/op_tests/eye.py	28	function	Make a set of tests for tf.eye op.
817	815	make_fill_tests	tensorflow/tensorflow/lite/testing/op_tests/fill.py	28	function	Make a set of tests to do fill.
818	816	make_floor_tests	tensorflow/tensorflow/lite/testing/op_tests/floor.py	27	function	Make a set of tests to do floor.
819	817	make_fully_connected_tests	tensorflow/tensorflow/lite/testing/op_tests/fully_connected.py	28	function	Make a set of tests to do fully_connected.
820	818	make_fused_batch_norm_tests	tensorflow/tensorflow/lite/testing/op_tests/fused_batch_norm.py	27	function	Make a set of tests to do fused_batch_norm.
821	819	make_gather_tests	tensorflow/tensorflow/lite/testing/op_tests/gather.py	27	function	Make a set of tests to do gather.
822	820	make_gather_nd_tests	tensorflow/tensorflow/lite/testing/op_tests/gather_nd.py	27	function	Make a set of tests to do gather_nd.
823	821	make_gather_with_constant_tests	tensorflow/tensorflow/lite/testing/op_tests/gather_with_constant.py	28	function	Make a set of test which feed a constant to gather toco.
824	822	make_global_batch_norm_tests	tensorflow/tensorflow/lite/testing/op_tests/global_batch_norm.py	27	function	Make a set of tests to do batch_norm_with_global_normalization.
825	823	make_greater_tests	tensorflow/tensorflow/lite/testing/op_tests/greater.py	27	function	Make a set of tests to do greater.
826	824	make_greater_equal_tests	tensorflow/tensorflow/lite/testing/op_tests/greater_equal.py	27	function	Make a set of tests to do greater_equal.
827	825	_tflite_convert_verify_num_ops	tensorflow/tensorflow/lite/testing/op_tests/hardswish.py	29	function	Verifies that the result of the conversion is a single op.
828	826	make_hardswish_tests	tensorflow/tensorflow/lite/testing/op_tests/hardswish.py	47	function	Make a set of tests to do hardswish.
829	827	make_identity_tests	tensorflow/tensorflow/lite/testing/op_tests/identity.py	29	function	Make a set of tests to do identity.
830	828	make_l2norm_tests	tensorflow/tensorflow/lite/testing/op_tests/l2norm.py	28	function	Make a set of tests to do l2norm.
831	829	make_l2norm_shared_epsilon_tests	tensorflow/tensorflow/lite/testing/op_tests/l2norm_shared_epsilon.py	28	function	Regression test for a bug (b/122651451).
832	830	make_leaky_relu_tests	tensorflow/tensorflow/lite/testing/op_tests/leaky_relu.py	28	function	Make a set of tests to do LeakyRelu.
833	831	make_less_tests	tensorflow/tensorflow/lite/testing/op_tests/less.py	27	function	Make a set of tests to do less.
834	832	make_less_equal_tests	tensorflow/tensorflow/lite/testing/op_tests/less_equal.py	27	function	Make a set of tests to do less_equal.
835	833	make_local_response_norm_tests	tensorflow/tensorflow/lite/testing/op_tests/local_response_norm.py	28	function	Make a set of tests to do local_response_norm.
836	834	make_log_softmax_tests	tensorflow/tensorflow/lite/testing/op_tests/log_softmax.py	27	function	Make a set of tests to do log_softmax.
837	835	_make_logical_tests	tensorflow/tensorflow/lite/testing/op_tests/logic.py	26	function	Make a set of tests to do logical operations.
838	836	make_logical_or_tests	tensorflow/tensorflow/lite/testing/op_tests/logic.py	65	function	Make a set of tests to do logical_or.
839	837	make_logical_and_tests	tensorflow/tensorflow/lite/testing/op_tests/logic.py	71	function	Make a set of tests to do logical_and.
840	838	make_logical_xor_tests	tensorflow/tensorflow/lite/testing/op_tests/logic.py	77	function	Make a set of tests to do logical_xor, test logical_not as well.
841	839	make_lstm_tests	tensorflow/tensorflow/lite/testing/op_tests/lstm.py	29	function	Make a set of tests to do basic Lstm cell.
842	840	make_matrix_diag_tests	tensorflow/tensorflow/lite/testing/op_tests/matrix_diag.py	27	function	Make a set of tests for tf.linalg.diag op.
843	841	make_matrix_set_diag_tests	tensorflow/tensorflow/lite/testing/op_tests/matrix_set_diag.py	27	function	Make a set of tests for tf.linalg.set_diag op.
844	842	make_maximum_tests	tensorflow/tensorflow/lite/testing/op_tests/maximum.py	27	function	Make a set of tests to do maximum.
845	843	make_minimum_tests	tensorflow/tensorflow/lite/testing/op_tests/minimum.py	27	function	Make a set of tests to do minimum.
846	844	make_mirror_pad_tests	tensorflow/tensorflow/lite/testing/op_tests/mirror_pad.py	28	function	Make a set of tests to do mirror_pad.
847	845	make_nearest_upsample_tests	tensorflow/tensorflow/lite/testing/op_tests/nearest_upsample.py	27	function	Make a set of tests to do nearest_upsample.
848	846	make_neg_tests	tensorflow/tensorflow/lite/testing/op_tests/neg.py	27	function	Make a set of tests to do neg.
849	847	make_not_equal_tests	tensorflow/tensorflow/lite/testing/op_tests/not_equal.py	27	function	Make a set of tests to do not equal.
850	848	make_one_hot_tests	tensorflow/tensorflow/lite/testing/op_tests/one_hot.py	27	function	Make a set of tests to do one_hot.
851	849	make_pack_tests	tensorflow/tensorflow/lite/testing/op_tests/pack.py	28	function	Make a set of tests to do stack.
852	850	make_pad_tests	tensorflow/tensorflow/lite/testing/op_tests/pad.py	28	function	Make a set of tests to do pad.
853	851	make_padv2_tests	tensorflow/tensorflow/lite/testing/op_tests/padv2.py	28	function	Make a set of tests to do padv2.
854	852	make_placeholder_with_default_tests	tensorflow/tensorflow/lite/testing/op_tests/placeholder_with_default.py	28	function	Make a set of tests to test placeholder_with_default.
855	853	make_pool_tests	tensorflow/tensorflow/lite/testing/op_tests/pool.py	26	function	Make a set of tests to do average pooling. Args: pool_op_in: TensorFlow pooling operation to test i.e. `tf.nn.avg_pool2d`. allow_fully_quantize: bool, whether fully_quantize is allowed. Returns: A function representing the true generator (after curried pool_op_in).
856	854	make_l2_pool	tensorflow/tensorflow/lite/testing/op_tests/pool.py	119	function	Given an input perform a sequence of TensorFlow ops to produce l2pool.
857	855	make_l2_pool_tests	tensorflow/tensorflow/lite/testing/op_tests/pool.py	131	function
858	856	make_avg_pool_tests	tensorflow/tensorflow/lite/testing/op_tests/pool.py	136	function
859	857	make_max_pool_tests	tensorflow/tensorflow/lite/testing/op_tests/pool.py	143	function
860	858	make_prelu_tests	tensorflow/tensorflow/lite/testing/op_tests/prelu.py	28	function	Make a set of tests to do PReLU.
861	859	make_range_tests	tensorflow/tensorflow/lite/testing/op_tests/range.py	27	function	Make a set of tests to do range.
862	860	make_rank_tests	tensorflow/tensorflow/lite/testing/op_tests/rank.py	27	function	Make a set of tests to do rank.
863	861	make_reduce_tests	tensorflow/tensorflow/lite/testing/op_tests/reduce.py	27	function	Make a set of tests to do reduce operation. Args: reduce_op: TensorFlow reduce operation to test, i.e. `tf.reduce_mean`. min_value: min value for created tensor data. max_value: max value for created tensor data. boolean_tensor_only: If true, will only generate tensor with boolean value. allow_fully_quantize: bool, whether fully_quantize is allowed. Returns: a function representing the true generator with `reduce_op_in` curried.
864	862	make_mean_tests	tensorflow/tensorflow/lite/testing/op_tests/reduce.py	219	function	Make a set of tests to do mean.
865	863	make_sum_tests	tensorflow/tensorflow/lite/testing/op_tests/reduce.py	231	function	Make a set of tests to do sum.
866	864	make_reduce_prod_tests	tensorflow/tensorflow/lite/testing/op_tests/reduce.py	243	function	Make a set of tests to do prod.
867	865	make_reduce_max_tests	tensorflow/tensorflow/lite/testing/op_tests/reduce.py	250	function	Make a set of tests to do max.
868	866	make_reduce_min_tests	tensorflow/tensorflow/lite/testing/op_tests/reduce.py	258	function	Make a set of tests to do min.
869	867	make_reduce_any_tests	tensorflow/tensorflow/lite/testing/op_tests/reduce.py	266	function	Make a set of tests to do any.
870	868	make_relu_tests	tensorflow/tensorflow/lite/testing/op_tests/relu.py	28	function	Make a set of tests to do relu.
871	869	make_relu1_tests	tensorflow/tensorflow/lite/testing/op_tests/relu1.py	28	function	Make a set of tests to do relu1.
872	870	make_relu6_tests	tensorflow/tensorflow/lite/testing/op_tests/relu6.py	28	function	Make a set of tests to do relu6.
873	871	make_reshape_tests	tensorflow/tensorflow/lite/testing/op_tests/reshape.py	28	function	Make a set of tests to do reshape.
874	872	make_resize_bilinear_tests	tensorflow/tensorflow/lite/testing/op_tests/resize_bilinear.py	27	function	Make a set of tests to do resize_bilinear.
875	873	make_resize_nearest_neighbor_tests	tensorflow/tensorflow/lite/testing/op_tests/resize_nearest_neighbor.py	27	function	Make a set of tests to do resize_nearest_neighbor.
876	874	make_resolve_constant_strided_slice_tests	tensorflow/tensorflow/lite/testing/op_tests/resolve_constant_strided_slice.py	29	function	Make a set of tests to show strided_slice yields incorrect results.
877	875	make_reverse_sequence_tests	tensorflow/tensorflow/lite/testing/op_tests/reverse_sequence.py	27	function	Make a set of tests to do reverse_sequence.
878	876	make_reverse_v2_tests	tensorflow/tensorflow/lite/testing/op_tests/reverse_v2.py	27	function	Make a set of tests to do reverse_v2.
879	877	make_rfft2d_tests	tensorflow/tensorflow/lite/testing/op_tests/rfft2d.py	28	function	Make a set of tests to do rfft2d.
880	878	make_round_tests	tensorflow/tensorflow/lite/testing/op_tests/round.py	27	function	Build the round op testing graph.
881	879	make_scatter_nd_tests	tensorflow/tensorflow/lite/testing/op_tests/scatter_nd.py	28	function	Make a set of tests to do scatter_nd.
882	880	make_shape_tests	tensorflow/tensorflow/lite/testing/op_tests/shape.py	28	function	Make a set of tests to do shape.
883	881	make_sigmoid_tests	tensorflow/tensorflow/lite/testing/op_tests/sigmoid.py	27	function	Make a set of tests to do sigmoid.
884	882	make_slice_tests	tensorflow/tensorflow/lite/testing/op_tests/slice.py	29	function	Make a set of tests to do slice.
885	883	make_softmax_tests	tensorflow/tensorflow/lite/testing/op_tests/softmax.py	27	function	Make a set of tests to do softmax.
886	884	make_space_to_batch_nd_tests	tensorflow/tensorflow/lite/testing/op_tests/space_to_batch_nd.py	28	function	Make a set of tests to do space_to_batch_nd.
887	885	make_space_to_depth_tests	tensorflow/tensorflow/lite/testing/op_tests/space_to_depth.py	27	function	Make a set of tests to do space_to_depth.
888	886	make_sparse_to_dense_tests	tensorflow/tensorflow/lite/testing/op_tests/sparse_to_dense.py	29	function	Make a set of tests to do sparse to dense.
889	887	make_split_tests	tensorflow/tensorflow/lite/testing/op_tests/split.py	28	function	Make a set of tests to do tf.split.
890	888	make_splitv_tests	tensorflow/tensorflow/lite/testing/op_tests/splitv.py	28	function	Make a set of tests to do tf.split_v.
891	889	make_squeeze_tests	tensorflow/tensorflow/lite/testing/op_tests/squeeze.py	27	function	Make a set of tests to do squeeze.
892	890	make_squeeze_transpose_tests	tensorflow/tensorflow/lite/testing/op_tests/squeeze_transpose.py	27	function	Make a set of tests to do squeeze followed by transpose.
893	891	_make_strided_slice_tests	tensorflow/tensorflow/lite/testing/op_tests/strided_slice.py	28	function	Utility function to make strided_slice_tests based on parameters.
894	892	make_strided_slice_tests	tensorflow/tensorflow/lite/testing/op_tests/strided_slice.py	100	function	Make a set of tests to do strided_slice.
895	893	make_strided_slice_1d_exhaustive_tests	tensorflow/tensorflow/lite/testing/op_tests/strided_slice.py	208	function	Make a set of exhaustive tests for 1D strided_slice.
896	894	make_strided_slice_np_style_tests	tensorflow/tensorflow/lite/testing/op_tests/strided_slice_np_style.py	29	function	Make a set of tests to test strided_slice in np style.
897	895	make_tanh_tests	tensorflow/tensorflow/lite/testing/op_tests/tanh.py	28	function	Make a set of tests to do tanh.
898	896	make_tile_tests	tensorflow/tensorflow/lite/testing/op_tests/tile.py	27	function	Make a set of tests to do tile.
899	897	make_topk_tests	tensorflow/tensorflow/lite/testing/op_tests/topk.py	28	function	Make a set of tests to do topk.
900	898	make_transpose_tests	tensorflow/tensorflow/lite/testing/op_tests/transpose.py	28	function	Make a set of tests to do transpose.
901	899	make_transpose_conv_tests	tensorflow/tensorflow/lite/testing/op_tests/transpose_conv.py	33	function	Make a set of tests to do transpose_conv.
902	900	make_unfused_gru_tests	tensorflow/tensorflow/lite/testing/op_tests/unfused_gru.py	27	function	Make a set of tests for unfused gru op.
903	901	make_unidirectional_sequence_lstm_tests	tensorflow/tensorflow/lite/testing/op_tests/unidirectional_sequence_lstm.py	29	function	Make a set of tests to do unidirectional_sequence_lstm.
904	902	make_unidirectional_sequence_rnn_tests	tensorflow/tensorflow/lite/testing/op_tests/unidirectional_sequence_rnn.py	29	function	Make a set of tests to do unidirectional_sequence_rnn.
905	903	make_unique_tests	tensorflow/tensorflow/lite/testing/op_tests/unique.py	27	function	Make a set of tests for Unique op.
906	904	make_unpack_tests	tensorflow/tensorflow/lite/testing/op_tests/unpack.py	27	function	Make a set of tests to do unpack.
907	905	make_unroll_batch_matmul_tests	tensorflow/tensorflow/lite/testing/op_tests/unroll_batch_matmul.py	27	function	Make a set of tests to test unroll_batch_matmul.
908	906	make_where_tests	tensorflow/tensorflow/lite/testing/op_tests/where.py	27	function	Make a set of tests to do where.
909	907	make_zeros_like_tests	tensorflow/tensorflow/lite/testing/op_tests/zeros_like.py	27	function	Make a set of tests to do zeros_like.
910	908	html_escape	tensorflow/tensorflow/lite/toco/logging/gen_html.py	37	function
911	909	get_input_type_from_signature	tensorflow/tensorflow/lite/toco/logging/gen_html.py	41	function	Parses op_signature and returns a string denoting the input tensor type. Args: op_signature: a string specifying the signature of a particular operator. The signature of an operator contains the input tensor's shape and type, output tensor's shape and type, operator's name and its version. It has the following schema: INPUT:input_1_shape::input_1_type::input_2_shape::input_2_type::.. ::OUTPUT:output_1_shape::output_1_type::output_2_shape::output_2_type:: ..::NAME:operator_name ::VERSION:operator_version An example of an operator signature is: INPUT:[1,73,73,160]::float::[64,1,1,160]::float::[64]::float:: OUTPUT:[1,73,73,64]::float::NAME:Conv::VERSION:1 Returns: A string denoting the input tensors' type. In the form of shape/type separated by comma. For example: shape:[1,73,73,160],type:float,shape:[64,1,1,160],type:float,shape:[64], type:float
912	910	get_operator_type	tensorflow/tensorflow/lite/toco/logging/gen_html.py	78	function
913	911	HTMLGenerator	tensorflow/tensorflow/lite/toco/logging/gen_html.py	87	class	Utility class to generate an HTML report.
914	912	gen_conversion_log_html	tensorflow/tensorflow/lite/toco/logging/gen_html.py	208	function	Generates an HTML report about the conversion process. Args: conversion_log_dir: A string specifying the file directory of the conversion logs. It's required that before calling this function, the `conversion_log_dir` already contains the following files: `toco_log_before.pb`, `toco_log_after.pb`, `toco_tf_graph.dot`, `toco_tflite_graph.dot`. quantization_enabled: A boolean, passed from the tflite converter to indicate whether post-training quantization is enabled during conversion. tflite_graph_path: A string, the filepath to the converted TFLite model. Raises: IOError: When any of the required files doesn't exist.
915	913	GenHtmlTest	tensorflow/tensorflow/lite/toco/logging/gen_html_test.py	32	class
916	914	execute	tensorflow/tensorflow/lite/toco/python/toco_from_protos.py	32	function	Runs the converter.
917	915	main	tensorflow/tensorflow/lite/toco/python/toco_from_protos.py	61	function
918	916	TensorName	tensorflow/tensorflow/lite/toco/python/toco_from_protos_test.py	30	function	Get the canonical (non foo:0 name).
919	917	TocoFromProtosTest	tensorflow/tensorflow/lite/toco/python/toco_from_protos_test.py	35	class
920	918	get_image	tensorflow/tensorflow/lite/tools/convert_image_to_csv.py	41	function	Returns an image loaded into an np.ndarray with dims [height, width, (3 or 1)]. Args: width: Width to rescale the image to. height: Height to rescale the image to. want_grayscale: Whether the result should be converted to grayscale. filepath: Path of the image file.. Returns: np.ndarray of shape (height, width, channels) where channels is 1 if want_grayscale is true, otherwise 3.
921	919	array_to_int_csv	tensorflow/tensorflow/lite/tools/convert_image_to_csv.py	65	function	Converts all elements in a numerical array to a comma-separated string. Args: array_data: Numerical array to convert. Returns: String containing array values as integers, separated by commas.
922	920	run_main	tensorflow/tensorflow/lite/tools/convert_image_to_csv.py	79	function	Application run loop.
923	921	main	tensorflow/tensorflow/lite/tools/convert_image_to_csv.py	110	function
924	922	ConvertImageToCsvTest	tensorflow/tensorflow/lite/tools/convert_image_to_csv_test.py	34	class
925	923	convert_bytearray_to_object	tensorflow/tensorflow/lite/tools/flatbuffer_utils.py	38	function	Converts a tflite model from a bytearray to an object for parsing.
926	924	read_model	tensorflow/tensorflow/lite/tools/flatbuffer_utils.py	44	function	Reads a tflite model as a python object. Args: input_tflite_file: Full path name to the input tflite file Raises: RuntimeError: If input_tflite_file path is invalid. IOError: If input_tflite_file cannot be opened. Returns: A python object corresponding to the input tflite file.
927	925	read_model_with_mutable_tensors	tensorflow/tensorflow/lite/tools/flatbuffer_utils.py	64	function	Reads a tflite model as a python object with mutable tensors. Similar to read_model() with the addition that the returned object has mutable tensors (read_model() returns an object with immutable tensors). Args: input_tflite_file: Full path name to the input tflite file Raises: RuntimeError: If input_tflite_file path is invalid. IOError: If input_tflite_file cannot be opened. Returns: A mutable python object corresponding to the input tflite file.
928	926	convert_object_to_bytearray	tensorflow/tensorflow/lite/tools/flatbuffer_utils.py	83	function	Converts a tflite model from an object to a bytearray.
929	927	write_model	tensorflow/tensorflow/lite/tools/flatbuffer_utils.py	93	function	Writes the tflite model, a python object, into the output file. Args: model_object: A tflite model as a python object output_tflite_file: Full path name to the output tflite file. Raises: IOError: If output_tflite_file path is invalid or cannot be opened.
930	928	strip_strings	tensorflow/tensorflow/lite/tools/flatbuffer_utils.py	108	function	Strips all nonessential strings from the model to reduce model size. We remove the following strings: (find strings by searching ":string" in the tensorflow lite flatbuffer schema) 1. Model description 2. SubGraph name 3. Tensor names We retain OperatorCode custom_code and Metadata name. Args: model: The model from which to remove nonessential strings.
931	929	randomize_weights	tensorflow/tensorflow/lite/tools/flatbuffer_utils.py	130	function	Randomize weights in a model. Args: model: The model in which to randomize weights. random_seed: The input to the random number generator (default value is 0).
932	930	WriteReadModelTest	tensorflow/tensorflow/lite/tools/flatbuffer_utils_test.py	29	class
933	931	StripStringsTest	tensorflow/tensorflow/lite/tools/flatbuffer_utils_test.py	74	class
934	932	RandomizeWeightsTest	tensorflow/tensorflow/lite/tools/flatbuffer_utils_test.py	119	class
935	933	main	tensorflow/tensorflow/lite/tools/randomize_weights.py	34	function
936	934	main	tensorflow/tensorflow/lite/tools/strip_strings.py	34	function	Application run loop.
937	935	build_mock_flatbuffer_model	tensorflow/tensorflow/lite/tools/test_utils.py	30	function	Creates a flatbuffer containing an example model.
938	936	load_model_from_flatbuffer	tensorflow/tensorflow/lite/tools/test_utils.py	211	function	Loads a model as a python object from a flatbuffer model.
939	937	build_mock_model	tensorflow/tensorflow/lite/tools/test_utils.py	218	function	Creates an object containing an example model.
940	938	TensorTypeToName	tensorflow/tensorflow/lite/tools/visualize.py	202	function	Converts a numerical enum to a readable tensor type.
941	939	BuiltinCodeToName	tensorflow/tensorflow/lite/tools/visualize.py	210	function	Converts a builtin op code enum to a readable name.
942	940	NameListToString	tensorflow/tensorflow/lite/tools/visualize.py	218	function	Converts a list of integers to the equivalent ASCII string.
943	941	OpCodeMapper	tensorflow/tensorflow/lite/tools/visualize.py	229	class	Maps an opcode index to an op name.
944	942	DataSizeMapper	tensorflow/tensorflow/lite/tools/visualize.py	245	class	For buffers, report the number of bytes.
945	943	TensorMapper	tensorflow/tensorflow/lite/tools/visualize.py	255	class	Maps a list of tensor indices to a tooltip hoverable indicator of more.
946	944	GenerateGraph	tensorflow/tensorflow/lite/tools/visualize.py	278	function	Produces the HTML required to have a d3 visualization of the dag.
947	945	GenerateTableHtml	tensorflow/tensorflow/lite/tools/visualize.py	337	function	Given a list of object values and keys to print, make an HTML table. Args: items: Items to print an array of dicts. keys_to_print: (key, display_fn). `key` is a key in the object. i.e. items[0][key] should exist. display_fn is the mapping function on display. i.e. the displayed html cell will have the string returned by `mapping_fn(items[0][key])`. display_index: add a column which is the index of each row in `items`. Returns: An html table.
948	946	CamelCaseToSnakeCase	tensorflow/tensorflow/lite/tools/visualize.py	375	function	Converts an identifier in CamelCase to snake_case.
949	947	FlatbufferToDict	tensorflow/tensorflow/lite/tools/visualize.py	381	function	Converts a hierarchy of FB objects into a nested dict. We avoid transforming big parts of the flat buffer into python arrays. This speeds conversion from ten minutes to a few seconds on big graphs. Args: fb: a flat buffer structure. (i.e. ModelT) preserve_as_numpy: true if all downstream np.arrays should be preserved. false if all downstream np.array should become python arrays Returns: A dictionary representing the flatbuffer rather than a flatbuffer object.
950	948	CreateDictFromFlatbuffer	tensorflow/tensorflow/lite/tools/visualize.py	413	function
951	949	CreateHtmlFile	tensorflow/tensorflow/lite/tools/visualize.py	419	function	Given a tflite model in `tflite_input` file, produce html description.
952	950	main	tensorflow/tensorflow/lite/tools/visualize.py	506	function
953	951	VisualizeTest	tensorflow/tensorflow/lite/tools/visualize_test.py	29	class
954	952	main	tensorflow/tensorflow/lite/tools/zip_files.py	32	function
955	953	_get_ground_truth_detections	tensorflow/tensorflow/lite/tools/evaluation/tasks/coco_object_detection/preprocess_coco_minival.py	44	function	Processes the annotations JSON file and returns ground truth data corresponding to allowlisted image IDs. Args: instances_file: COCO instances JSON file, usually named as instances_val20xx.json. allowlist_file: File containing COCO minival image IDs to allowlist for evaluation, one per line. num_images: Number of allowlisted images to pre-process. First num_images are chosen based on sorted list of filenames. If None, all allowlisted files are preprocessed. Returns: A dict mapping image id (int) to a per-image dict that contains: 'filename', 'image' & 'height' mapped to filename & image dimensions respectively AND 'detections' to a list of detection dicts, with each mapping: 'category_id' to COCO category id (starting with 1) & 'bbox' to a list of dimension-normalized [top, left, bottom, right] bounding-box values.
956	954	_dump_data	tensorflow/tensorflow/lite/tools/evaluation/tasks/coco_object_detection/preprocess_coco_minival.py	145	function	Dumps images & data from ground-truth objects into output_folder_path. The following are created in output_folder_path: images/: sub-folder for allowlisted validation images. ground_truth.pb: A binary proto file containing all ground-truth object-sets. Args: ground_truth_detections: A dict mapping image id to ground truth data. Output of _get_ground_truth_detections. images_folder_path: Validation images folder output_folder_path: folder to output files to.
957	955	_parse_args	tensorflow/tensorflow/lite/tools/evaluation/tasks/coco_object_detection/preprocess_coco_minival.py	190	function	Creates a parser that parse the command line arguments. Returns: A namespace parsed from command line arguments.
958	956	_synset_to_word	tensorflow/tensorflow/lite/tools/evaluation/tasks/imagenet_image_classification/generate_validation_labels.py	30	function	Returns synset to word dictionary by reading sysnset arrays.
959	957	_validation_file_path	tensorflow/tensorflow/lite/tools/evaluation/tasks/imagenet_image_classification/generate_validation_labels.py	50	function
960	958	_synset_array_path	tensorflow/tensorflow/lite/tools/evaluation/tasks/imagenet_image_classification/generate_validation_labels.py	54	function
961	959	_generate_validation_labels	tensorflow/tensorflow/lite/tools/evaluation/tasks/imagenet_image_classification/generate_validation_labels.py	58	function
962	960	_check_arguments	tensorflow/tensorflow/lite/tools/evaluation/tasks/imagenet_image_classification/generate_validation_labels.py	67	function
963	961	main	tensorflow/tensorflow/lite/tools/evaluation/tasks/imagenet_image_classification/generate_validation_labels.py	80	function
964	962	main	tensorflow/tensorflow/lite/tools/optimize/python/modify_model_interface.py	38	function	Application run loop.
965	963	_parse_type_to_int	tensorflow/tensorflow/lite/tools/optimize/python/modify_model_interface_lib.py	27	function	Converts a tflite type to it's integer representation. Args: dtype: tf.DType representing the inference type. flag: str representing the flag name. Returns: integer, a tflite TensorType enum value. Raises: ValueError: Unsupported tflite type.
966	964	modify_model_interface	tensorflow/tensorflow/lite/tools/optimize/python/modify_model_interface_lib.py	52	function	Modify a quantized model's interface (input/output) from float to integer. Args: input_file: Full path name to the input tflite file. output_file: Full path name to the output tflite file. input_type: Final input interface type. output_type: Final output interface type. Raises: RuntimeError: If the modification of the model interface was unsuccessful. ValueError: If the input_type or output_type is unsupported.
967	965	build_tflite_model_with_full_integer_quantization	tensorflow/tensorflow/lite/tools/optimize/python/modify_model_interface_lib_test.py	31	function
968	966	ModifyModelInterfaceTest	tensorflow/tensorflow/lite/tools/optimize/python/modify_model_interface_lib_test.py	56	class
969	967	FormatConverterTest	tensorflow/tensorflow/lite/tools/optimize/sparsity/python/format_converter_extension_test.py	28	class
970	968	get_build_cpus	tensorflow/tensorflow/lite/tools/pip_package/setup.py	72	function
971	969	make_args	tensorflow/tensorflow/lite/tools/pip_package/setup.py	80	function	Construct make command line.
972	970	make_output	tensorflow/tensorflow/lite/tools/pip_package/setup.py	94	function	Invoke make on the target and return output.
973	971	make	tensorflow/tensorflow/lite/tools/pip_package/setup.py	99	function	Invoke make to build tflite C++ sources. Build dependencies: apt-get install swig libjpeg-dev zlib1g-dev python3-dev python3-nump
974	972	download_dependencies	tensorflow/tensorflow/lite/tools/pip_package/setup.py	108	function	Download build dependencies if haven't done yet.
975	973	CustomBuildExt	tensorflow/tensorflow/lite/tools/pip_package/setup.py	114	class	Customized build extension.
976	974	CustomBuildPy	tensorflow/tensorflow/lite/tools/pip_package/setup.py	130	class
977	975	get_pybind_include	tensorflow/tensorflow/lite/tools/pip_package/setup.py	137	function	pybind11 include directory is not correctly resolved. This fixes include directory to /usr/local/pythonX.X Returns: include directories to find pybind11
978	976	set_signature_defs	tensorflow/tensorflow/lite/tools/signature/signature_def_utils.py	25	function	Sets SignatureDefs to the Metadata of a TfLite flatbuffer buffer. Args: tflite_model: Binary TFLite model (bytes or bytes-like object) to which to add signature_def. signature_def_map: dict containing SignatureDefs to store in metadata. Returns: buffer: A TFLite model binary identical to model buffer with metadata field containing SignatureDef. Raises: ValueError: tflite_model buffer does not contain a valid TFLite model. signature_def_map is empty or does not contain a SignatureDef.
979	977	get_signature_defs	tensorflow/tensorflow/lite/tools/signature/signature_def_utils.py	51	function	Get SignatureDef dict from the Metadata of a TfLite flatbuffer buffer. Args: tflite_model: TFLite model buffer to get the signature_def. Returns: dict containing serving names to SignatureDefs if exists, otherwise, empty dict. Raises: ValueError: tflite_model buffer does not contain a valid TFLite model. DecodeError: SignatureDef cannot be parsed from TfLite SignatureDef metadata.
980	978	clear_signature_defs	tensorflow/tensorflow/lite/tools/signature/signature_def_utils.py	78	function	Clears SignatureDefs from the Metadata of a TfLite flatbuffer buffer. Args: tflite_model: TFLite model buffer to remove signature_defs. Returns: buffer: A TFLite model binary identical to model buffer with no SignatureDef metadata. Raises: ValueError: tflite_model buffer does not contain a valid TFLite model.
981	979	SignatureDefUtilsTest	tensorflow/tensorflow/lite/tools/signature/signature_def_utils_test.py	30	class
982	980	read32	tensorflow/tensorflow/lite/tutorials/dataset.py	35	function	Read 4 bytes from bytestream as an unsigned 32-bit integer.
983	981	check_image_file_header	tensorflow/tensorflow/lite/tutorials/dataset.py	41	function	Validate that filename corresponds to images for the MNIST dataset.
984	982	check_labels_file_header	tensorflow/tensorflow/lite/tutorials/dataset.py	57	function	Validate that filename corresponds to labels for the MNIST dataset.
985	983	download	tensorflow/tensorflow/lite/tutorials/dataset.py	67	function	Download (and unzip) a file from the MNIST dataset if not already done.
986	984	dataset	tensorflow/tensorflow/lite/tutorials/dataset.py	86	function	Download and parse MNIST dataset.
987	985	train	tensorflow/tensorflow/lite/tutorials/dataset.py	114	function	tf.data.Dataset object for MNIST training data.
988	986	test	tensorflow/tensorflow/lite/tutorials/dataset.py	120	function	tf.data.Dataset object for MNIST test data.
989	987	test_image_generator	tensorflow/tensorflow/lite/tutorials/mnist_tflite.py	35	function
990	988	run_eval	tensorflow/tensorflow/lite/tutorials/mnist_tflite.py	47	function	Performs evaluation for input image over specified model. Args: interpreter: TFLite interpreter initialized with model to execute. input_image: Image input to the model. Returns: output: output tensor of model being executed.
991	989	main	tensorflow/tensorflow/lite/tutorials/mnist_tflite.py	72	function
992	990	set_dlopen_flags	tensorflow/tensorflow/python/pywrap_dlopen_global_flags.py	43	function
993	991	reset_dlopen_flags	tensorflow/tensorflow/python/pywrap_dlopen_global_flags.py	48	function
994	992	import_graphdef	tensorflow/tensorflow/python/pywrap_mlir.py	26	function
995	993	experimental_convert_saved_model_to_mlir	tensorflow/tensorflow/python/pywrap_mlir.py	32	function
996	994	experimental_convert_saved_model_v1_to_mlir	tensorflow/tensorflow/python/pywrap_mlir.py	39	function
997	995	experimental_run_pass_pipeline	tensorflow/tensorflow/python/pywrap_mlir.py	48	function
998	996	enable	tensorflow/tensorflow/python/tf2.py	30	function
999	997	disable	tensorflow/tensorflow/python/tf2.py	36	function
1000	998	enabled	tensorflow/tensorflow/python/tf2.py	42	function
1001	999	AssertTransformer	tensorflow/tensorflow/python/autograph/converters/asserts.py	27	class	Transforms Assert nodes to Call so they can be handled as functions.
1002	1000	transform	tensorflow/tensorflow/python/autograph/converters/asserts.py	50	function
1003	1001	AssertsTest	tensorflow/tensorflow/python/autograph/converters/asserts_test.py	30	class
1004	1002	_Break	tensorflow/tensorflow/python/autograph/converters/break_statements.py	29	class
1005	1003	BreakTransformer	tensorflow/tensorflow/python/autograph/converters/break_statements.py	39	class	Canonicalizes break statements into additional conditionals.
1006	1004	transform	tensorflow/tensorflow/python/autograph/converters/break_statements.py	183	function
1007	1005	BreakCanonicalizationTest	tensorflow/tensorflow/python/autograph/converters/break_statements_test.py	27	class
1008	1006	_Function	tensorflow/tensorflow/python/autograph/converters/call_trees.py	40	class
1009	1007	_ArgTemplateBuilder	tensorflow/tensorflow/python/autograph/converters/call_trees.py	51	class	Constructs a tuple representing the positional arguments in a call. Example (yes, it's legal Python 3): f(*args1, b, *args2, c, d) -> args1 + (b,) + args2 + (c, d)
1010	1008	CallTreeTransformer	tensorflow/tensorflow/python/autograph/converters/call_trees.py	96	class	Transforms the call tree by renaming transformed symbols.
1011	1009	transform	tensorflow/tensorflow/python/autograph/converters/call_trees.py	211	function	Transform function call to the compiled counterparts. Args: node: AST ctx: EntityContext Returns: A tuple (node, new_names): node: The transformed AST new_names: set(string), containing any newly-generated names
1012	1010	MockConvertedCall	tensorflow/tensorflow/python/autograph/converters/call_trees_test.py	30	class
1013	1011	CallTreesTest	tensorflow/tensorflow/python/autograph/converters/call_trees_test.py	42	class
1014	1012	ConditionalExpressionTransformer	tensorflow/tensorflow/python/autograph/converters/conditional_expressions.py	28	class	Converts conditional expressions to functional form.
1015	1013	transform	tensorflow/tensorflow/python/autograph/converters/conditional_expressions.py	48	function
1016	1014	ConditionalExpressionsTest	tensorflow/tensorflow/python/autograph/converters/conditional_expressions_test.py	26	class
1017	1015	_Continue	tensorflow/tensorflow/python/autograph/converters/continue_statements.py	29	class
1018	1016	_Block	tensorflow/tensorflow/python/autograph/converters/continue_statements.py	40	class	Tracks information about lexical blocks as they are visited in the AST. Mainly, this object tracks the creation of block guards that replace `continue` statements (e.g. `if not continue_:`). Attributes: create_guard_current: bool, whether to create a guard for the current statement. create_guard_next: bool, whether to create a guard for the next statement. is_loop_type: bool, whether this block is the body of a loop.
1019	1017	ContinueCanonicalizationTransformer	tensorflow/tensorflow/python/autograph/converters/continue_statements.py	60	class	Canonicalizes continue statements into additional conditionals.
1020	1018	transform	tensorflow/tensorflow/python/autograph/converters/continue_statements.py	163	function
1021	1019	ContinueCanonicalizationTest	tensorflow/tensorflow/python/autograph/converters/continue_statements_test.py	27	class
1022	1020	_Function	tensorflow/tensorflow/python/autograph/converters/control_flow.py	41	class
1023	1021	ControlFlowTransformer	tensorflow/tensorflow/python/autograph/converters/control_flow.py	46	class	Transforms control flow structures like loops an conditionals.
1024	1022	AnnotatedDef	tensorflow/tensorflow/python/autograph/converters/control_flow.py	395	class
1025	1023	transform	tensorflow/tensorflow/python/autograph/converters/control_flow.py	402	function
1026	1024	ControlFlowTransformer	tensorflow/tensorflow/python/autograph/converters/control_flow_deprecated_py2.py	44	class	Transforms control flow structures like loops an conditionals.
1027	1025	AnnotatedDef	tensorflow/tensorflow/python/autograph/converters/control_flow_deprecated_py2.py	623	class
1028	1026	transform	tensorflow/tensorflow/python/autograph/converters/control_flow_deprecated_py2.py	630	function
1029	1027	ControlFlowTestBase	tensorflow/tensorflow/python/autograph/converters/control_flow_test.py	43	class
1030	1028	NestedControlFlowTest	tensorflow/tensorflow/python/autograph/converters/control_flow_test.py	59	class
1031	1029	WhileStatementTest	tensorflow/tensorflow/python/autograph/converters/control_flow_test.py	106	class
1032	1030	IfStatementTest	tensorflow/tensorflow/python/autograph/converters/control_flow_test.py	352	class
1033	1031	ForStatementTest	tensorflow/tensorflow/python/autograph/converters/control_flow_test.py	598	class
1034	1032	AdvancedControlFlowTest	tensorflow/tensorflow/python/autograph/converters/control_flow_test.py	688	class
1035	1033	_LoopScope	tensorflow/tensorflow/python/autograph/converters/directives.py	48	class
1036	1034	_map_args	tensorflow/tensorflow/python/autograph/converters/directives.py	55	function	Maps AST call nodes to the actual function's arguments. Args: call_node: ast.Call function: Callable[..., Any], the actual function matching call_node Returns: Dict[Text, ast.AST], mapping each of the function's argument names to the respective AST node. Raises: ValueError: if the default arguments are not correctly set
1037	1035	DirectivesTransformer	tensorflow/tensorflow/python/autograph/converters/directives.py	90	class	Parses compiler directives and converts them into AST annotations.
1038	1036	transform	tensorflow/tensorflow/python/autograph/converters/directives.py	180	function
1039	1037	DirectivesTest	tensorflow/tensorflow/python/autograph/converters/directives_test.py	28	class
1040	1038	_Function	tensorflow/tensorflow/python/autograph/converters/functions.py	32	class
1041	1039	FunctionTransformer	tensorflow/tensorflow/python/autograph/converters/functions.py	38	class	Wraps function bodies around autograph-specific boilerplate.
1042	1040	transform	tensorflow/tensorflow/python/autograph/converters/functions.py	134	function
1043	1041	FunctionTransformer	tensorflow/tensorflow/python/autograph/converters/functions_test.py	31	class
1044	1042	ListCompTransformer	tensorflow/tensorflow/python/autograph/converters/list_comprehensions.py	42	class	Lowers list comprehensions into standard control flow.
1045	1043	transform	tensorflow/tensorflow/python/autograph/converters/list_comprehensions.py	81	function
1046	1044	ListCompTest	tensorflow/tensorflow/python/autograph/converters/list_comprehensions_test.py	26	class
1047	1045	_Statement	tensorflow/tensorflow/python/autograph/converters/lists.py	45	class
1048	1046	ListTransformer	tensorflow/tensorflow/python/autograph/converters/lists.py	51	class	Converts lists and related operations to their TF counterpart.
1049	1047	transform	tensorflow/tensorflow/python/autograph/converters/lists.py	239	function
1050	1048	ListTest	tensorflow/tensorflow/python/autograph/converters/lists_test.py	33	class
1051	1049	LogicalExpressionTransformer	tensorflow/tensorflow/python/autograph/converters/logical_expressions.py	49	class	Converts logical expressions to corresponding TF calls.
1052	1050	transform	tensorflow/tensorflow/python/autograph/converters/logical_expressions.py	135	function
1053	1051	LogicalExpressionTest	tensorflow/tensorflow/python/autograph/converters/logical_expressions_test.py	28	class
1054	1052	_RewriteBlock	tensorflow/tensorflow/python/autograph/converters/return_statements.py	37	class
1055	1053	ConditionalReturnRewriter	tensorflow/tensorflow/python/autograph/converters/return_statements.py	43	class	Rewrites a a pattern where it's unobvious that all paths return a value. This rewrite allows avoiding intermediate None return values. The following pattern: if cond: <block 1> return else: <block 2> <block 3> is converted to: if cond: <block 1> return else: <block 2> <block 3> and vice-versa (if the else returns, subsequent statements are moved under the if branch).
1056	1054	_Block	tensorflow/tensorflow/python/autograph/converters/return_statements.py	159	class
1057	1055	_Function	tensorflow/tensorflow/python/autograph/converters/return_statements.py	172	class
1058	1056	ReturnStatementsTransformer	tensorflow/tensorflow/python/autograph/converters/return_statements.py	183	class	Lowers return statements into variables and conditionals. Specifically, the following pattern: <block 1> return val <block 2> is converted to: do_return = False retval = None <block 1> do_return = True retval = val if not do_return: <block 2> return retval The conversion adjusts loops as well: <block 1> while cond: <block 2> return retval is converted to: <block 1> while not do_return and cond: <block 2> do_return = True retval = val
1059	1057	transform	tensorflow/tensorflow/python/autograph/converters/return_statements.py	392	function	Ensure a function has only a single return, at the end.
1060	1058	SingleReturnTest	tensorflow/tensorflow/python/autograph/converters/return_statements_test.py	28	class
1061	1059	SliceTransformer	tensorflow/tensorflow/python/autograph/converters/slices.py	28	class	Converts slicing operations to their TF counterpart. Currently, relying on the default slice operator that Tensor uses is insufficient, because TensorArray and tensor lists use dedicated index read and write functions.
1062	1060	transform	tensorflow/tensorflow/python/autograph/converters/slices.py	84	function
1063	1061	SliceTest	tensorflow/tensorflow/python/autograph/converters/slices_test.py	31	class
1064	1062	VariableAccessTransformer	tensorflow/tensorflow/python/autograph/converters/variables.py	28	class	Rewrites basic symbol reads. This transformer rewrites variable reads with a "read" operator which allows tracking activity. Example: For a basic statement: a = b + c This is translated to: a = ld(b) + ld(c) Augmented assignment operations also introduce a `ld` operator: a += b The assignment target also receives an operator to properly represent the read: a = ld(a) a += ld(b)
1065	1063	transform	tensorflow/tensorflow/python/autograph/converters/variables.py	100	function
1066	1064	VariablesTest	tensorflow/tensorflow/python/autograph/converters/variables_test.py	26	class
1067	1065	_control_ctx	tensorflow/tensorflow/python/autograph/core/ag_ctx.py	29	function
1068	1066	control_status_ctx	tensorflow/tensorflow/python/autograph/core/ag_ctx.py	35	function
1069	1067	Status	tensorflow/tensorflow/python/autograph/core/ag_ctx.py	40	class
1070	1068	ControlStatusCtx	tensorflow/tensorflow/python/autograph/core/ag_ctx.py	46	class	A context that tracks whether autograph is enabled by the user.
1071	1069	NullCtx	tensorflow/tensorflow/python/autograph/core/ag_ctx.py	66	class	Helper substitute for contextlib.nullcontext.
1072	1070	_default_control_status_ctx	tensorflow/tensorflow/python/autograph/core/ag_ctx.py	76	function
1073	1071	Rule	tensorflow/tensorflow/python/autograph/core/config_lib.py	27	class	Base class for conversion rules.
1074	1072	Action	tensorflow/tensorflow/python/autograph/core/config_lib.py	38	class
1075	1073	DoNotConvert	tensorflow/tensorflow/python/autograph/core/config_lib.py	44	class	Indicates that this module should be not converted.
1076	1074	Convert	tensorflow/tensorflow/python/autograph/core/config_lib.py	56	class	Indicates that this module should be converted.
1077	1075	Feature	tensorflow/tensorflow/python/autograph/core/converter.py	83	class	This enumeration represents optional conversion options. These conversion options are experimental. They are subject to change without notice and offer no guarantees. _Example Usage_ ```python optionals= tf.autograph.experimental.Feature.EQUALITY_OPERATORS @tf.function(experimental_autograph_options=optionals) def f(i): if i == 0: # EQUALITY_OPERATORS allows the use of == here. tf.print('i is zero') ``` Attributes: ALL: Enable all features. AUTO_CONTROL_DEPS: Insert of control dependencies in the generated code. ASSERT_STATEMENTS: Convert Tensor-dependent assert statements to tf.Assert. BUILTIN_FUNCTIONS: Convert builtin functions applied to Tensors to their TF counterparts. EQUALITY_OPERATORS: Whether to convert the comparison operators, like equality. This is soon to be deprecated as support is being added to the Tensor class. LISTS: Convert list idioms, like initializers, slices, append, etc. NAME_SCOPES: Insert name scopes that name ops according to context, like the function they were defined in.
1078	1076	ConversionOptions	tensorflow/tensorflow/python/autograph/core/converter.py	138	class	Immutable container for global conversion flags. Attributes: recursive: bool, whether to recursively convert any user functions or classes that the converted function may use. user_requested: bool, whether the conversion was explicitly requested by the user, as opposed to being performed as a result of other logic. This value always auto-resets resets to False in child conversions. optional_features: Union[Feature, Set[Feature]], controls the use of optional features in the conversion process. See Feature for available options.
1079	1077	ProgramContext	tensorflow/tensorflow/python/autograph/core/converter.py	236	class	ProgramContext keeps track of converting function hierarchies. Attributes: options: ConversionOptions autograph_module: Deprecated. Do not use.
1080	1078	Base	tensorflow/tensorflow/python/autograph/core/converter.py	249	class	All converters should inherit from this class. Attributes: ctx: EntityContext
1081	1079	TestConverter	tensorflow/tensorflow/python/autograph/core/converter_test.py	32	class
1082	1080	ConversionOptionsTest	tensorflow/tensorflow/python/autograph/core/converter_test.py	36	class
1083	1081	ConverterBaseTest	tensorflow/tensorflow/python/autograph/core/converter_test.py	64	class
1084	1082	allowlist	tensorflow/tensorflow/python/autograph/core/converter_testing.py	35	function	Helper that marks a callable as whtelitisted.
1085	1083	is_inside_generated_code	tensorflow/tensorflow/python/autograph/core/converter_testing.py	47	function	Tests whether the caller is generated code. Implementation-specific.
1086	1084	TestingTranspiler	tensorflow/tensorflow/python/autograph/core/converter_testing.py	66	class	Testing version that only applies given transformations.
1087	1085	TestCase	tensorflow/tensorflow/python/autograph/core/converter_testing.py	98	class	Base class for unit tests in this module. Contains relevant utilities.
1088	1086	FunctionScope	tensorflow/tensorflow/python/autograph/core/function_wrappers.py	33	class	Context manager that wraps the body of a converted function. This context manager handles various operations related to the scope of a function: * optional TF name scopes - these name scopes match the name of the function, for easy visualization in tensorBoard; * optional automatic control dependencies - this adds the same mechanism for control dependencies that is used by `@tf.function`; it can be optionally enabled when using `tf.autograph.to_graph`; * tracking of autograph conversion state (whether it's enabled by the user, conversion options;
1089	1087	with_function_scope	tensorflow/tensorflow/python/autograph/core/function_wrappers.py	114	function	Inline version of the FunctionScope context manager.
1090	1088	FunctionWrappersTest	tensorflow/tensorflow/python/autograph/core/function_wrappers_test.py	29	class
1091	1089	UnsupportedFeaturesChecker	tensorflow/tensorflow/python/autograph/core/unsupported_features_checker.py	26	class	Quick check for Python features we know we don't support. Any features detected will cause AutoGraph to not compile a function.
1092	1090	verify	tensorflow/tensorflow/python/autograph/core/unsupported_features_checker.py	60	function
1093	1091	is_autograph_strict_conversion_mode	tensorflow/tensorflow/python/autograph/impl/api.py	72	function
1094	1092	AutoGraphError	tensorflow/tensorflow/python/autograph/impl/api.py	82	class	Base class for all AutoGraph exceptions.
1095	1093	ConversionError	tensorflow/tensorflow/python/autograph/impl/api.py	87	class	Raised during the conversion process.
1096	1094	StagingError	tensorflow/tensorflow/python/autograph/impl/api.py	92	class	Raised during the staging (i.e. Python execution) of converted code.
1097	1095	_ErrorMetadata	tensorflow/tensorflow/python/autograph/impl/api.py	97	class	AutoGraph-specific error metadata. See base class.
1098	1096	_attach_error_metadata	tensorflow/tensorflow/python/autograph/impl/api.py	146	function	Augments an error with the metadata necessary for rewrite.
1099	1097	StackTraceMapper	tensorflow/tensorflow/python/autograph/impl/api.py	166	class	Remaps generated code to code it originated from.
1100	1098	PyToTF	tensorflow/tensorflow/python/autograph/impl/api.py	203	class	The TensorFlow AutoGraph transformer.
1101	1099	_convert_actual	tensorflow/tensorflow/python/autograph/impl/api.py	275	function	Applies AutoGraph to entity.
1102	1100	autograph_artifact	tensorflow/tensorflow/python/autograph/impl/api.py	298	function
1103	1101	is_autograph_artifact	tensorflow/tensorflow/python/autograph/impl/api.py	303	function
1104	1102	converted_call	tensorflow/tensorflow/python/autograph/impl/api.py	307	function	Converts a function call inline. For internal use only. Note: The argument list is optimized for readability of generated code, which may look like this: ag__.converted_call(f, (arg1, arg2), None, fscope) ag__.converted_call(f, (), dict(arg1=val1, **kwargs), fscope) ag__.converted_call(f, (arg1, arg2) + varargs, dict(**kwargs), lscope) Args: f: The function to convert. args: Tuple, the original positional arguments of f kwargs: Optional[Dict], the original keyword arguments of f caller_fn_scope: Optional[function_wrappers.FunctionScope], the function scope of the converted function in which this call was originally made. options: Optional[converter.ConversionOptions], conversion options. If not specified, the value of caller_fn_scope.callopts is used. Either options or caller_fn_scope must be present. Returns: Any, the result of executing a possibly-converted `f` with the given arguments.
1105	1103	_call_unconverted	tensorflow/tensorflow/python/autograph/impl/api.py	466	function	Calls the original function without converting with AutoGraph.
1106	1104	_fall_back_unconverted	tensorflow/tensorflow/python/autograph/impl/api.py	479	function	Falls back to calling the function unconverted, in case of error.
1107	1105	tf_convert	tensorflow/tensorflow/python/autograph/impl/api.py	506	function	Decorator that applies AutoGraph to a function. Use in internal APIs. This API is suitable for high order functions internal to the TensorFlow API, and more generally any function to which Autograph is not applied. Guidance: convert was a decorator meant for use directly by developers, and will be soon deprecated in favor of tf.function. tf_convert is to be called from high order functions internal to TF. Args: f: Callable. ctx: ag_ctx.ControlStatusCtx, the Autograph context in which `f` is used. convert_by_default: bool, whether to use AutoGraph when the context doesn't specify. user_requested: bool, whether to ignore the conversion allowlist. See ConversionOptions.user_requested. Returns: Either `f or the converted version of `f`.
1108	1106	call_with_unspecified_conversion_status	tensorflow/tensorflow/python/autograph/impl/api.py	565	function	Decorator that resets the conversion context to the unspecified status.
1109	1107	_log_callargs	tensorflow/tensorflow/python/autograph/impl/api.py	577	function	Logging helper.
1110	1108	do_not_convert	tensorflow/tensorflow/python/autograph/impl/api.py	599	function	Decorator that suppresses the conversion of a function. Args: func: function to decorate. Returns: If `func` is not None, returns a `Callable` which is equivalent to `func`, but is not converted by AutoGraph. If `func` is None, returns a decorator that, when invoked with a single `func` argument, returns a `Callable` equivalent to the above case.
1111	1109	convert	tensorflow/tensorflow/python/autograph/impl/api.py	626	function	Decorator that compiles a function to use TensorFlow ops. The decorator is dynamic - it recompiles the target whenever the decorated function is called. This means the parameter values are known at conversion. It also means that repeated calls with different types of parameters will be correctly processed. Args: recursive: bool, whether to recursively convert any functions or classes that the converted function may use. optional_features: converted.Feature, allows toggling optional or experimental features. When set to None, only the core features are enabled. user_requested: bool, whether this is a function that the user explicitly asked to be converted. See ConversionOptions.user_requested. conversion_ctx: Optional ag_ctx.ControlStatusCtx, the Autograph context in which `f` is used. Returns: Callable, a decorator that converts the given function into an equivalent function that uses TensorFlow ops.
1112	1110	to_graph	tensorflow/tensorflow/python/autograph/impl/api.py	682	function	Converts a Python entity into a TensorFlow graph. Also see: `tf.autograph.to_code`, `tf.function`. Unlike `tf.function`, `to_graph` is a low-level transpiler that converts Python code to TensorFlow graph code. It does not implement any caching, variable management or create any actual ops, and is best used where greater control over the generated TensorFlow graph is desired. Another difference from `tf.function` is that `to_graph` will not wrap the graph into a TensorFlow function or a Python callable. Internally, `tf.function` uses `to_graph`. Example usage: >>> def f(x): ... if x > 0: ... y = x * x ... else: ... y = -x ... return y ... >>> converted_f = to_graph(f) >>> x = tf.constant(2) >>> converted_f(x) # converted_foo is like a TensorFlow Op. <tf.Tensor: shape=(), dtype=int32, numpy=4> Supported Python entities include: * functions * classes * object methods Functions are converted into new functions with converted code. Classes are converted by generating a new class whose methods use converted code. Methods are converted into unbound function that have an additional first argument called `self`. For a tutorial, see the [tf.function and AutoGraph guide](https://www.tensorflow.org/guide/function). For more detailed information, see the [AutoGraph reference documentation](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/autograph/g3doc/reference/index.md). Args: entity: Python callable or class to convert. recursive: Whether to recursively convert any functions that the converted function may call. experimental_optional_features: `None`, a tuple of, or a single `tf.autograph.experimental.Feature` value. Returns: Same as `entity`, the converted Python function or class. Raises: ValueError: If the entity could not be converted.
1113	1111	to_graph_v1	tensorflow/tensorflow/python/autograph/impl/api.py	754	function	Converts a Python entity into a TensorFlow graph. Also see: `tf.autograph.to_code`, `tf.function`. Unlike `tf.function`, `to_graph` is a low-level transpiler that converts Python code to TensorFlow graph code. It does not implement any caching, variable management or create any actual ops, and is best used where greater control over the generated TensorFlow graph is desired. Another difference from `tf.function` is that `to_graph` will not wrap the graph into a TensorFlow function or a Python callable. Internally, `tf.function` uses `to_graph`. _Example Usage_ ```python def foo(x): if x > 0: y = x * x else: y = -x return y converted_foo = to_graph(foo) x = tf.constant(1) y = converted_foo(x) # converted_foo is a TensorFlow Op-like. assert is_tensor(y) ``` Supported Python entities include: * functions * classes * object methods Functions are converted into new functions with converted code. Classes are converted by generating a new class whose methods use converted code. Methods are converted into unbound function that have an additional first argument called `self`. Args: entity: Python callable or class to convert. recursive: Whether to recursively convert any functions that the converted function may call. arg_values: Deprecated. arg_types: Deprecated. experimental_optional_features: `None`, a tuple of, or a single `tf.autograph.experimental.Feature` value. Returns: Same as `entity`, the converted Python function or class. Raises: ValueError: If the entity could not be converted.
1114	1112	to_code_v1	tensorflow/tensorflow/python/autograph/impl/api.py	825	function	Returns the source code generated by AutoGraph, as a string. Example usage: >>> def f(x): ... if x < 0: ... x = -x ... return x >>> tf.autograph.to_code(f) "...def tf__f(x):..." Also see: `tf.autograph.to_graph`. Note: If a function has been decorated with `tf.function`, pass its underlying Python function, rather than the callable that `tf.function creates: >>> @tf.function ... def f(x): ... if x < 0: ... x = -x ... return x >>> tf.autograph.to_code(f.python_function) "...def tf__f(x):..." Args: entity: Python callable or class. recursive: Whether to recursively convert any functions that the converted function may call. arg_values: Deprecated. arg_types: Deprecated. indentation: Deprecated. experimental_optional_features: `None`, a tuple of, or a single `tf.autograph.experimental.Feature` value. Returns: The converted code as string.
1115	1113	to_code	tensorflow/tensorflow/python/autograph/impl/api.py	879	function	Returns the source code generated by AutoGraph, as a string. Example usage: >>> def f(x): ... if x < 0: ... x = -x ... return x >>> tf.autograph.to_code(f) "...def tf__f(x):..." Also see: `tf.autograph.to_graph`. Note: If a function has been decorated with `tf.function`, pass its underlying Python function, rather than the callable that `tf.function creates: >>> @tf.function ... def f(x): ... if x < 0: ... x = -x ... return x >>> tf.autograph.to_code(f.python_function) "...def tf__f(x):..." Args: entity: Python callable or class to convert. recursive: Whether to recursively convert any functions that the converted function may call. experimental_optional_features: `None`, a tuple of, or a single `tf.autograph.experimental.Feature` value. Returns: The converted code as string.
1116	1114	TestResource	tensorflow/tensorflow/python/autograph/impl/api_test.py	64	class
1117	1115	ApiTest	tensorflow/tensorflow/python/autograph/impl/api_test.py	70	class
1118	1116	_is_of_known_loaded_module	tensorflow/tensorflow/python/autograph/impl/conversion.py	37	function
1119	1117	_is_known_loaded_type	tensorflow/tensorflow/python/autograph/impl/conversion.py	46	function	Tests whether the function or method is an instance of a known type.
1120	1118	is_unsupported	tensorflow/tensorflow/python/autograph/impl/conversion.py	73	function	Checks whether an entity is supported by AutoGraph at all.
1121	1119	is_allowlisted	tensorflow/tensorflow/python/autograph/impl/conversion.py	116	function	Checks whether an entity is allowed for use in graph mode. Examples of allowed entities include all members of the tensorflow package. Args: o: A Python entity. check_call_override: Reserved for internal use. When set to `False`, it disables the rule according to which classes are allowed if their __call__ method is allowed. allow_namedtuple_subclass: Reserved for internal use. When `True`, namedtuple subclasses are not allowed. Returns: Boolean
1122	1120	is_in_allowlist_cache	tensorflow/tensorflow/python/autograph/impl/conversion.py	221	function
1123	1121	cache_allowlisted	tensorflow/tensorflow/python/autograph/impl/conversion.py	229	function
1124	1122	ConversionTest	tensorflow/tensorflow/python/autograph/impl/conversion_test.py	39	class
1125	1123	set_element_type	tensorflow/tensorflow/python/autograph/lang/directives.py	33	function	Indicates that the entity is expected hold items of specified type/shape. The staged TensorFlow ops will reflect and assert this data type. Ignored otherwise. Args: entity: The entity to annotate. dtype: TensorFlow dtype value to assert for entity. shape: Optional shape to assert for entity.
1126	1124	set_loop_options	tensorflow/tensorflow/python/autograph/lang/directives.py	50	function	Specifies additional arguments to be passed to the enclosing while_loop. The parameters apply to and only to the immediately enclosing loop. It only has effect if the loop is staged as a TF while_loop; otherwise the parameters have no effect. Usage: >>> @tf.function(autograph=True) ... def f(): ... n = 0 ... for i in tf.range(10): ... tf.autograph.experimental.set_loop_options(maximum_iterations=3) ... n += 1 ... return n >>> @tf.function(autograph=True) ... def f(): ... v = tf.constant((0,)) ... for i in tf.range(3): ... tf.autograph.experimental.set_loop_options( ... shape_invariants=[(v, tf.TensorShape([None]))] ... ) ... v = tf.concat((v, [i]), 0) ... return v Also see tf.while_loop. Args: parallel_iterations: The maximum number of iterations allowed to run in parallel at any given time. Note that this does not guarantee parallel execution. swap_memory: Whether to store intermediate values needed for gradients on the CPU instead of GPU. maximum_iterations: Allows limiting the total number of iterations executed by the loop. shape_invariants: Allows controlling the argument with the same name passed to tf.while_loop. Unlike tf.while_loop, this is a list of `(tensor, shape)` pairs.
1127	1125	_validate_list_constructor	tensorflow/tensorflow/python/autograph/lang/special_functions.py	31	function	Validates the inputs of tensor_list.
1128	1126	match_staging_level	tensorflow/tensorflow/python/autograph/lang/special_functions.py	50	function	Casts a value to be staged at the same level as another.
1129	1127	tensor_list	tensorflow/tensorflow/python/autograph/lang/special_functions.py	57	function	Creates an tensor list and populates it with the given elements. This function provides a more uniform access to tensor lists and tensor arrays, and allows optional initialization. Note: this function is a simplified wrapper. If you need greater control, it is recommended to use the underlying implementation directly. Args: elements: Iterable[tf.Tensor, ...], the elements to initially fill the list with element_dtype: Optional[tf.DType], data type for the elements in the list; required if the list is empty element_shape: Optional[tf.TensorShape], shape for the elements in the list; required if the list is empty use_tensor_array: bool, whether to use the more compatible but restrictive tf.TensorArray implementation Returns: Union[tf.Tensor, tf.TensorArray], the new list. Raises: ValueError: for invalid arguments
1130	1128	stack	tensorflow/tensorflow/python/autograph/lang/special_functions.py	92	function	Stacks the input, if it admits the notion of stacking. For example, a list of tensors can be stacked into a larger tensor. This function is similar to tf.stack, but it accepts non-lists and lists of non-tensors as arguments. In the latter case, the function does nothing. Args: list_or_tensor: Any element_dtype: tf.DType, optional dtypedtype for the elements in the list. Required if the input is stackable, and the list is untyped. strict: bool, if True an error is raised if the input is not stackable. Otherwise the function is a no-op. Returns: Any, if the input is stackable, the result will be a tf.Tensor. Otherwise, if strict=False, the result will be list_or_tensor. Raises: ValueError: if strict=True and the input is not stackable.
1131	1129	SpecialFunctionsTest	tensorflow/tensorflow/python/autograph/lang/special_functions_test.py	31	class
1132	1130	if_exp	tensorflow/tensorflow/python/autograph/operators/conditional_expressions.py	27	function
1133	1131	_tf_if_exp	tensorflow/tensorflow/python/autograph/operators/conditional_expressions.py	34	function	Overload of if_exp that stages a TF cond.
1134	1132	_py_if_exp	tensorflow/tensorflow/python/autograph/operators/conditional_expressions.py	55	function
1135	1133	_basic_expr	tensorflow/tensorflow/python/autograph/operators/conditional_expressions_test.py	29	function
1136	1134	IfExpTest	tensorflow/tensorflow/python/autograph/operators/conditional_expressions_test.py	38	class
1137	1135	_verify_loop_init_vars	tensorflow/tensorflow/python/autograph/operators/control_flow.py	102	function	Ensures that all values in the state are defined when entering a loop.
1138	1136	_is_subshape	tensorflow/tensorflow/python/autograph/operators/control_flow.py	117	function	Returns True if left shape is at least as specific as right shape.
1139	1137	_verify_single_loop_var	tensorflow/tensorflow/python/autograph/operators/control_flow.py	134	function	Verifies whether the initial, entry and exit values are consistent.
1140	1138	_verify_tf_loop_vars	tensorflow/tensorflow/python/autograph/operators/control_flow.py	191	function	Verifies loop variables for consistency.
1141	1139	verify_single_cond_var	tensorflow/tensorflow/python/autograph/operators/control_flow.py	233	function	Verifies whether body_var and orelse_var are consistent.
1142	1140	_verify_tf_cond_branch_vars	tensorflow/tensorflow/python/autograph/operators/control_flow.py	263	function	Verifies variables output by a conditional branch for consistency.
1143	1141	_verify_tf_cond_vars	tensorflow/tensorflow/python/autograph/operators/control_flow.py	276	function	Verifies variables manipulated by a conditional for consistency.
1144	1142	for_stmt	tensorflow/tensorflow/python/autograph/operators/control_flow.py	291	function	Functional form of a for statement. The loop operates on a state, which includes all symbols that are variant across loop iterations, excluding the variables local to the loop. For example, given the loop below that calculates the geometric and arithmetic means or some numbers: ``` geo_mean = 1 arith_mean = 0 for i in range(n): a = numbers[i] geo_mean *= a arith_mean += a ``` The state is represented by the variables geo_mean and arith_mean. The `extra_test`, `body`, `get_state` and `set_state` functions must bind to the original `geo_mean` and `arith_mean` symbols, using `nonlocal`. The inputs and outputs of the callables representing the loop blocks are not explicit - instead, these functions must use nonlocal/global for side effects. The inputs and outputs are instead controlled by the set_state/get_state functions. Args: iter_: The entity being iterated over. extra_test: Callable with boolean return type. An additional loop condition. body: Callable representing the actual loop body. get_state: Additional callable which can capture additional state (such as the values of composite symbols). This is only useful when staging the loop. set_state: Additional callable which save values captured by get_state back into the Python environment. This is only useful when staging the loop. symbol_names: Tuple containing names of the loop variables returned by get_state. opts: Optional dict of extra loop parameters. Returns: Tuple containing the final state.
1145	1143	_py_for_stmt	tensorflow/tensorflow/python/autograph/operators/control_flow.py	371	function	Overload of for_stmt that executes a Python for loop.
1146	1144	_known_len_tf_for_stmt	tensorflow/tensorflow/python/autograph/operators/control_flow.py	400	function	Overload of for_stmt that iterates over TF entities that admit a length.
1147	1145	_tf_ragged_for_stmt	tensorflow/tensorflow/python/autograph/operators/control_flow.py	447	function	Overload of for_stmt that iterates over TF ragged tensors.
1148	1146	_tf_range_for_stmt	tensorflow/tensorflow/python/autograph/operators/control_flow.py	494	function	Overload of for_stmt that iterates over a TF range (and elides it).
1149	1147	_tf_iterator_for_stmt	tensorflow/tensorflow/python/autograph/operators/control_flow.py	555	function	Overload of for_stmt that iterates over TF Iterators. See for_loop.
1150	1148	_general_purpose_scan	tensorflow/tensorflow/python/autograph/operators/control_flow.py	615	function	Variant of Dataset.scan with semantics of general-purpose computation.
1151	1149	_tf_dataset_for_stmt	tensorflow/tensorflow/python/autograph/operators/control_flow.py	629	function	Overload of _dataset_for_stmt with early stopping. See for_stmt.
1152	1150	_tf_distributed_iterable_for_stmt	tensorflow/tensorflow/python/autograph/operators/control_flow.py	700	function	Overload of for_stmt that iterates over TF distributed datasets.
1153	1151	while_stmt	tensorflow/tensorflow/python/autograph/operators/control_flow.py	727	function	Functional form of a while statement. The loop operates on a so-called state, which includes all symbols that are variant across loop iterations. In what follows we refer to state as either a tuple of entities that represent an actual state, or a list of arguments of the corresponding types. The inputs and outputs of the callables representing the loop blocks are not explicit - instead, these functions must use nonlocal/global for side effects. The inputs and outputs are instead controlled by the set_state/get_state functions. Args: test: Callable with boolean return type. The loop condition. body: Callable representing the actual loop body. get_state: Additional callable which can capture additional state (such as the values of composite symbols). This is only useful when staging the loop. set_state: Additional callable which save values captured by get_state back into the Python environment. This is only useful when staging the loop. symbol_names: Tuple containing the names of all loop variables. opts: Optional dict of extra loop parameters. Returns: Tuple containing the final state.
1154	1152	_PythonLoopChecker	tensorflow/tensorflow/python/autograph/operators/control_flow.py	777	class	Verifies Python loops for TF-specific limits.
1155	1153	_py_while_stmt	tensorflow/tensorflow/python/autograph/operators/control_flow.py	851	function	Overload of while_stmt that executes a Python while loop.
1156	1154	_shape_invariants_mapping_to_positional_list	tensorflow/tensorflow/python/autograph/operators/control_flow.py	872	function
1157	1155	_tf_while_stmt	tensorflow/tensorflow/python/autograph/operators/control_flow.py	882	function	Overload of while_stmt that stages a TF while_stmt.
1158	1156	if_stmt	tensorflow/tensorflow/python/autograph/operators/control_flow.py	915	function	Functional form of an if statement. The conditional operates on a state, which includes all symbols whose values are a function of the branch taken. For example, given the code below that calculates the abs function: ``` x = 1 if x > 0: x = -x ``` The state is represented by the variable `x`. The `body, `orelse` and `set_state` functions must bind to the original `x` symbol, using `nonlocal`. The inputs and outputs of the callables representing the loop blocks are not explicit - instead, these functions must use nonlocal/global for side effects. The inputs and outputs are instead controlled by the set_state/get_state functions. Args: cond: Boolean. body: Callable representing the main block of the conditional. orelse: Callable representing the else block of the conditional. get_state: Function that returns a tuple containing the values of all composite symbols modified within the conditional. This allows access to state that branches may mutate through side effects. This function is not needed and should not be called when dispatching to code matching Python's default semantics. This is useful for checkpointing to avoid unintended side-effects when staging requires evaluating all code-paths. set_state: Function to set the values of all composite symbols modified within the conditional. This is the complement to get_state, used to restore checkpointed values. The single argument a tuple containing values for each composite symbol that may be modified in a branch of the conditional. The is usually the result of a call to get_state. symbol_names: Tuple containing basic loop var names. nouts: Number of variables output by the statement. Vars which are not outputs will not be passed through staged control flow such as tf.cond. This includes variables that are defined before the conditional, but are not used after it.
1159	1157	_tf_if_stmt	tensorflow/tensorflow/python/autograph/operators/control_flow.py	965	function	Overload of if_stmt that stages a TF cond.
1160	1158	_py_if_stmt	tensorflow/tensorflow/python/autograph/operators/control_flow.py	1011	function	Overload of if_stmt that executes a Python if statement.
1161	1159	_disallow_undefs_into_loop	tensorflow/tensorflow/python/autograph/operators/control_flow_deprecated_py2.py	104	function	Ensures that all values in the state are defined when entering a loop.
1162	1160	_is_subshape	tensorflow/tensorflow/python/autograph/operators/control_flow_deprecated_py2.py	120	function	Returns True if left shape is at least as specific as right shape.
1163	1161	_verify_single_loop_var	tensorflow/tensorflow/python/autograph/operators/control_flow_deprecated_py2.py	137	function	Verifies whether the initial, entry and exit values are consistent.
1164	1162	_verify_tf_loop_vars	tensorflow/tensorflow/python/autograph/operators/control_flow_deprecated_py2.py	191	function	Verifies loop variables for consistency.
1165	1163	_verify_single_cond_var	tensorflow/tensorflow/python/autograph/operators/control_flow_deprecated_py2.py	223	function	Verifies whether body_var and orelse_var are consistent.
1166	1164	_verify_tf_cond_vars	tensorflow/tensorflow/python/autograph/operators/control_flow_deprecated_py2.py	248	function	Verifies variables manipulated by a conditional for consistency.
1167	1165	for_stmt	tensorflow/tensorflow/python/autograph/operators/control_flow_deprecated_py2.py	279	function	Functional form of a for statement. The loop operates on a state, which includes all symbols that are variant across loop iterations, excluding the iterate as well as the variables local to the loop. For example, given the loop below that calculates the geometric and arithmetic means or some numbers: geo_mean = 1 arith_mean = 0 for i in range(n): a = numbers[i] geo_mean *= a arith_mean += a The state is represented by the variables geo_mean and arith_mean. The argument for initial_state may contain the tuple (1, 0), the body will include the arguments geo_mean and arith_mean and will return a tuple representing the new values for geo_mean and respectively arith_mean. Args: iter_: The entity being iterated over. extra_test: Callable with the state as arguments, and boolean return type. An additional loop condition. body: Callable with the iterate and the state as arguments, and state as return type. The actual loop body. get_state: Additional callable which can capture additional state (such as the values of composite symbols). This is only useful when staging the loop. set_state: Additional callable which save values captured by get_state back into the Python environment. This is only useful when staging the loop. init_vars: Tuple containing the initial state. basic_symbol_names: Tuple containing basic loop var names. composite_symbol_names: Tuple containing composite loop var names. opts: Optional dict of extra loop parameters. Returns: Tuple containing the final state.
1168	1166	_py_for_stmt	tensorflow/tensorflow/python/autograph/operators/control_flow_deprecated_py2.py	364	function	Overload of for_stmt that executes a Python for loop.
1169	1167	_known_len_tf_for_stmt	tensorflow/tensorflow/python/autograph/operators/control_flow_deprecated_py2.py	383	function	Overload of for_stmt that iterates over TF entities that admit a length.
1170	1168	_tf_ragged_for_stmt	tensorflow/tensorflow/python/autograph/operators/control_flow_deprecated_py2.py	446	function	Overload of for_stmt that iterates over TF ragged tensors.
1171	1169	_tf_range_for_stmt	tensorflow/tensorflow/python/autograph/operators/control_flow_deprecated_py2.py	509	function	Overload of for_stmt that iterates over a TF range (and elides it).
1172	1170	_tf_iterator_for_stmt	tensorflow/tensorflow/python/autograph/operators/control_flow_deprecated_py2.py	572	function	Overload of for_stmt that iterates over TF Iterators. See for_loop.
1173	1171	_tf_dataset_for_stmt	tensorflow/tensorflow/python/autograph/operators/control_flow_deprecated_py2.py	636	function	Overload of for_stmt that iterates over TF Datasets.
1174	1172	_general_purpose_scan	tensorflow/tensorflow/python/autograph/operators/control_flow_deprecated_py2.py	653	function	Variant of Dataset.scan with semantics of general-purpose computation.
1175	1173	_dataset_for_stmt_with_extra_test	tensorflow/tensorflow/python/autograph/operators/control_flow_deprecated_py2.py	667	function	Overload of _dataset_for_stmt with early stopping. See for_stmt.
1176	1174	_dataset_for_stmt_no_extra_test	tensorflow/tensorflow/python/autograph/operators/control_flow_deprecated_py2.py	725	function	Overload of _dataset_for_stmt without early stopping. See for_stmt.
1177	1175	_tf_distributed_dataset_for_stmt	tensorflow/tensorflow/python/autograph/operators/control_flow_deprecated_py2.py	794	function	Overload of for..in statement that iterates over the input.
1178	1176	while_stmt	tensorflow/tensorflow/python/autograph/operators/control_flow_deprecated_py2.py	817	function	Functional form of a while statement. The loop operates on a so-called state, which includes all symbols that are variant across loop iterations. In what follows we refer to state as either a tuple of entities that represent an actual state, or a list of arguments of the corresponding types. Args: test: Callable with the state as arguments, and boolean return type. The loop condition. body: Callable with the state as arguments, and state as return type. The actual loop body. get_state: Additional callable which can capture additional state (such as the values of composite symbols). This is only useful when staging the loop. set_state: Additional callable which save values captured by get_state back into the Python environment. This is only useful when staging the loop. init_vars: Tuple containing the initial state. basic_symbol_names: Tuple containing basic loop var names. composite_symbol_names: Tuple containing composite loop var names. opts: Optional dict of extra loop parameters. Returns: Tuple containing the final state.
1179	1177	_shape_invariants_mapping_to_positional_list	tensorflow/tensorflow/python/autograph/operators/control_flow_deprecated_py2.py	873	function
1180	1178	_tf_while_stmt	tensorflow/tensorflow/python/autograph/operators/control_flow_deprecated_py2.py	883	function	Overload of while_stmt that stages a TF while_stmt.
1181	1179	_PythonLoopChecker	tensorflow/tensorflow/python/autograph/operators/control_flow_deprecated_py2.py	925	class	Verifies Python loops for TF-specific limits.
1182	1180	_py_while_stmt	tensorflow/tensorflow/python/autograph/operators/control_flow_deprecated_py2.py	987	function	Overload of while_stmt that executes a Python while loop.
1183	1181	if_stmt	tensorflow/tensorflow/python/autograph/operators/control_flow_deprecated_py2.py	1008	function	Functional form of an if statement. Args: cond: Boolean. body: Callable with no arguments, and outputs of the positive (if) branch as return type. orelse: Callable with no arguments, and outputs of the negative (else) branch as return type. get_state: Function that returns a tuple containing the values of all composite symbols modified within the conditional. This allows access to state that branches may mutate through side effects. This function is not needed and should not be called when dispatching to code matching Python's default semantics. This is useful for checkpointing to avoid unintended side-effects when staging requires evaluating all code-paths. set_state: Function to set the values of all composite symbols modified within the conditional. This is the complement to get_state, used to restore checkpointed values. The single argument a tuple containing values for each composite symbol that may be modified in a branch of the conditional. The is usually the result of a call to get_state. basic_symbol_names: Tuple containing basic loop var names. composite_symbol_names: Tuple containing composite loop var names. Returns: Tuple containing the statement outputs.
1184	1182	tf_if_stmt	tensorflow/tensorflow/python/autograph/operators/control_flow_deprecated_py2.py	1048	function	Overload of if_stmt that stages a TF cond.
1185	1183	_isolate_state	tensorflow/tensorflow/python/autograph/operators/control_flow_deprecated_py2.py	1088	function	Wraps func to (best-effort) isolate state mutations that func may do. The simplest example of state mutation is mutation of variables (via e.g. attributes), or modification of globals. This allows us to more safely execute this function without worrying about side effects when the function wasn't normally expected to execute. For example, staging requires that the function is executed ahead of time, and we need to ensure its effects are not observed during normal execution. Args: func: () -> Any get_state: () -> Any, returns the current state set_state: (Any) -> None, resets the state to the specified values. Typically the result of an earlier call to `get_state`. Returns: Tuple[Any, Any], where the first element is the return value of `func`, and the second is the final state values.
1186	1184	_wrap_disallow_undefs_from_cond	tensorflow/tensorflow/python/autograph/operators/control_flow_deprecated_py2.py	1121	function	Wraps conditional branch to disallow returning undefined symbols.
1187	1185	_py_if_stmt	tensorflow/tensorflow/python/autograph/operators/control_flow_deprecated_py2.py	1152	function	Overload of if_stmt that executes a Python if statement.
1188	1186	ForLoopTest	tensorflow/tensorflow/python/autograph/operators/control_flow_test.py	49	class
1189	1187	WhileLoopTest	tensorflow/tensorflow/python/autograph/operators/control_flow_test.py	540	class
1190	1188	IfStmtTest	tensorflow/tensorflow/python/autograph/operators/control_flow_test.py	775	class
1191	1189	new_list	tensorflow/tensorflow/python/autograph/operators/data_structures.py	36	function	The list constructor. Args: iterable: Optional elements to fill the list with. Returns: A list-like object. The exact return value depends on the initial elements.
1192	1190	tf_tensor_array_new	tensorflow/tensorflow/python/autograph/operators/data_structures.py	57	function	Overload of new_list that stages a Tensor list creation.
1193	1191	tf_tensor_list_new	tensorflow/tensorflow/python/autograph/operators/data_structures.py	107	function	Overload of new_list that stages a Tensor list creation.
1194	1192	_py_list_new	tensorflow/tensorflow/python/autograph/operators/data_structures.py	166	function	Overload of new_list that creates a Python list.
1195	1193	list_append	tensorflow/tensorflow/python/autograph/operators/data_structures.py	171	function	The list append function. Note: it is unspecified where list_ will be mutated or not. If list_ is a TensorFlow entity, it will not be typically mutated. If list_ is a plain list, it will be. In general, if the list is mutated then the return value should point to the original entity. Args: list_: An entity that supports append semantics. x: The element to append. Returns: Same as list_, after the append was performed. Raises: ValueError: if list_ is not of a known list-like type.
1196	1194	_tf_tensor_list_append	tensorflow/tensorflow/python/autograph/operators/data_structures.py	202	function	Overload of list_append that stages a Tensor list write.
1197	1195	_tf_tensorarray_append	tensorflow/tensorflow/python/autograph/operators/data_structures.py	218	function	Overload of list_append that stages a TensorArray write.
1198	1196	_py_list_append	tensorflow/tensorflow/python/autograph/operators/data_structures.py	223	function	Overload of list_append that executes a Python list append.
1199	1197	ListPopOpts	tensorflow/tensorflow/python/autograph/operators/data_structures.py	230	class
1200	1198	list_pop	tensorflow/tensorflow/python/autograph/operators/data_structures.py	235	function	The list pop function. Note: it is unspecified where list_ will be mutated or not. If list_ is a TensorFlow entity, it will not be typically mutated. If list_ is a plain list, it will be. In general, if the list is mutated then the return value should point to the original entity. Args: list_: An entity that supports pop semantics. i: Optional index to pop from. May be None. opts: A ListPopOpts. Returns: Tuple (x, out_list_): out_list_: same as list_, after the removal was performed. x: the removed element value. Raises: ValueError: if list_ is not of a known list-like type or the operation is not supported for that type.
1201	1199	_tf_tensor_list_pop	tensorflow/tensorflow/python/autograph/operators/data_structures.py	272	function	Overload of list_pop that stages a Tensor list pop.
1202	1200	_py_list_pop	tensorflow/tensorflow/python/autograph/operators/data_structures.py	289	function	Overload of list_pop that executes a Python list append.
1203	1201	ListStackOpts	tensorflow/tensorflow/python/autograph/operators/data_structures.py	299	class
1204	1202	list_stack	tensorflow/tensorflow/python/autograph/operators/data_structures.py	305	function	The list stack function. This does not have a direct correspondent in Python. The closest idiom to this is tf.append or np.stack. It's different from those in the sense that it accepts a Tensor list, rather than a list of tensors. It can also accept TensorArray. When the target is anything else, the dispatcher will rely on ctx.original_call for fallback. Args: list_: An entity that supports append semantics. opts: A ListStackOpts object. Returns: The output of the stack operation, typically a Tensor.
1205	1203	_tf_tensorarray_stack	tensorflow/tensorflow/python/autograph/operators/data_structures.py	335	function	Overload of list_stack that stages a TensorArray stack.
1206	1204	_tf_tensor_list_stack	tensorflow/tensorflow/python/autograph/operators/data_structures.py	340	function	Overload of list_stack that stages a Tensor list write.
1207	1205	_py_list_stack	tensorflow/tensorflow/python/autograph/operators/data_structures.py	348	function	Overload of list_stack that executes a Python list append.
1208	1206	ListTest	tensorflow/tensorflow/python/autograph/operators/data_structures_test.py	31	class
1209	1207	DispatchContext	tensorflow/tensorflow/python/autograph/operators/dispatch_context.py	27	class	Allows passing additional parameters to the specific implementations. Attributes: options: Optional dict of extra arguments that may be required by specific implementations.
1210	1208	assert_stmt	tensorflow/tensorflow/python/autograph/operators/exceptions.py	26	function	Functional form of an assert statement. This follows the semantics of the Python assert statement, however the concrete implementations may deviate from it. See the respective implementation for details. In general, the assert statement should not be used for control flow. Furthermore, it is encouraged that the assertion expressions should not have side effects. Args: expression1: Any expression2: Callable[[], Any], returns the expression to include in the error message when expression1 evaluates to False. When expression1 is True, the result of expression2 will not be evaluated, however, expression2 itself may be evaluated in some implementations. Returns: Any, implementation-dependent. Raises: ValueError: if any arguments are illegal.
1211	1209	_tf_assert_stmt	tensorflow/tensorflow/python/autograph/operators/exceptions.py	62	function	Overload of assert_stmt that stages a TF Assert. This implementation deviates from Python semantics as follows: (1) the assertion is verified regardless of the state of __debug__ (2) on assertion failure, the graph execution will fail with tensorflow.errors.ValueError, rather than AssertionError. Args: expression1: tensorflow.Tensor, must evaluate to a tf.bool scalar expression2: Callable[[], Union[tensorflow.Tensor, List[tensorflow.Tensor]]] Returns: tensorflow.Operation
1212	1210	_py_assert_stmt	tensorflow/tensorflow/python/autograph/operators/exceptions.py	83	function	Overload of assert_stmt that executes a Python assert statement.
1213	1211	ExceptionsTest	tensorflow/tensorflow/python/autograph/operators/exceptions_test.py	28	class
1214	1212	not_	tensorflow/tensorflow/python/autograph/operators/logical.py	26	function	Functional form of "not".
1215	1213	_tf_not	tensorflow/tensorflow/python/autograph/operators/logical.py	33	function	Implementation of the "not_" operator for TensorFlow.
1216	1214	_py_not	tensorflow/tensorflow/python/autograph/operators/logical.py	38	function	Default Python implementation of the "not_" operator.
1217	1215	and_	tensorflow/tensorflow/python/autograph/operators/logical.py	43	function	Functional form of "and". Uses lazy evaluation semantics.
1218	1216	_tf_lazy_and	tensorflow/tensorflow/python/autograph/operators/logical.py	51	function	Lazy-eval equivalent of "and" for Tensors.
1219	1217	_py_lazy_and	tensorflow/tensorflow/python/autograph/operators/logical.py	57	function	Lazy-eval equivalent of "and" in Python.
1220	1218	or_	tensorflow/tensorflow/python/autograph/operators/logical.py	62	function	Functional form of "or". Uses lazy evaluation semantics.
1221	1219	_tf_lazy_or	tensorflow/tensorflow/python/autograph/operators/logical.py	70	function	Lazy-eval equivalent of "or" for Tensors.
1222	1220	_py_lazy_or	tensorflow/tensorflow/python/autograph/operators/logical.py	76	function	Lazy-eval equivalent of "or" in Python.
1223	1221	eq	tensorflow/tensorflow/python/autograph/operators/logical.py	81	function	Functional form of "equal".
1224	1222	_tf_equal	tensorflow/tensorflow/python/autograph/operators/logical.py	88	function	Overload of "equal" for Tensors.
1225	1223	_py_equal	tensorflow/tensorflow/python/autograph/operators/logical.py	93	function	Overload of "equal" that falls back to Python's default implementation.
1226	1224	not_eq	tensorflow/tensorflow/python/autograph/operators/logical.py	98	function	Functional form of "not-equal".
1227	1225	LogicalOperatorsTest	tensorflow/tensorflow/python/autograph/operators/logical_test.py	27	class
1228	1226	overload_of	tensorflow/tensorflow/python/autograph/operators/py_builtins.py	65	function
1229	1227	_find_originating_frame	tensorflow/tensorflow/python/autograph/operators/py_builtins.py	71	function	Locates the frame in which `caller_fn_scope` was defined.
1230	1228	locals_in_original_context	tensorflow/tensorflow/python/autograph/operators/py_builtins.py	92	function	Executes the locals function in the context of a specified function.
1231	1229	globals_in_original_context	tensorflow/tensorflow/python/autograph/operators/py_builtins.py	97	function	Executes the locals function in the context of a specified function.
1232	1230	eval_in_original_context	tensorflow/tensorflow/python/autograph/operators/py_builtins.py	102	function	Executes the eval function in the context of a specified function.
1233	1231	super_in_original_context	tensorflow/tensorflow/python/autograph/operators/py_builtins.py	117	function	Executes the super function in the context of a specified function. See https://docs.python.org/3/library/functions.html#super for the exact details Args: f: Callable, typically the super builtin args: List[Any], the original call arguments caller_fn_scope: Optional[function_wrappers.FunctionScope], the function scope of the converted function in which this call was originally made Returns: The result of calling `f` as if it was called in the frame indicated by `caller_fn_scope`.
1234	1232	abs_	tensorflow/tensorflow/python/autograph/operators/py_builtins.py	179	function
1235	1233	_tf_abs	tensorflow/tensorflow/python/autograph/operators/py_builtins.py	187	function
1236	1234	_tf_dataset_abs	tensorflow/tensorflow/python/autograph/operators/py_builtins.py	191	function
1237	1235	_py_abs	tensorflow/tensorflow/python/autograph/operators/py_builtins.py	198	function
1238	1236	float_	tensorflow/tensorflow/python/autograph/operators/py_builtins.py	202	function
1239	1237	_tf_float	tensorflow/tensorflow/python/autograph/operators/py_builtins.py	208	function
1240	1238	_py_float	tensorflow/tensorflow/python/autograph/operators/py_builtins.py	215	function
1241	1239	int_	tensorflow/tensorflow/python/autograph/operators/py_builtins.py	219	function
1242	1240	_tf_int	tensorflow/tensorflow/python/autograph/operators/py_builtins.py	225	function
1243	1241	_py_int	tensorflow/tensorflow/python/autograph/operators/py_builtins.py	235	function
1244	1242	len_	tensorflow/tensorflow/python/autograph/operators/py_builtins.py	241	function
1245	1243	_tf_tensor_array_len	tensorflow/tensorflow/python/autograph/operators/py_builtins.py	253	function
1246	1244	_tf_tensor_list_len	tensorflow/tensorflow/python/autograph/operators/py_builtins.py	257	function
1247	1245	_tf_tensor_len	tensorflow/tensorflow/python/autograph/operators/py_builtins.py	261	function	Overload of len_ for Tensor arguments.
1248	1246	_tf_dataset_len	tensorflow/tensorflow/python/autograph/operators/py_builtins.py	294	function
1249	1247	_py_len	tensorflow/tensorflow/python/autograph/operators/py_builtins.py	314	function
1250	1248	print_	tensorflow/tensorflow/python/autograph/operators/py_builtins.py	318	function	Overload of the print builtin.
1251	1249	_py_print	tensorflow/tensorflow/python/autograph/operators/py_builtins.py	334	function
1252	1250	_tf_py_func_print	tensorflow/tensorflow/python/autograph/operators/py_builtins.py	338	function	Overload of print_ as a py_func implementation.
1253	1251	range_	tensorflow/tensorflow/python/autograph/operators/py_builtins.py	360	function
1254	1252	_tf_range	tensorflow/tensorflow/python/autograph/operators/py_builtins.py	366	function	Overload of range_ that generates a TF range tensor.
1255	1253	_py_range	tensorflow/tensorflow/python/autograph/operators/py_builtins.py	383	function
1256	1254	enumerate_	tensorflow/tensorflow/python/autograph/operators/py_builtins.py	391	function
1257	1255	_tf_dataset_enumerate	tensorflow/tensorflow/python/autograph/operators/py_builtins.py	401	function
1258	1256	_py_enumerate	tensorflow/tensorflow/python/autograph/operators/py_builtins.py	405	function
1259	1257	zip_	tensorflow/tensorflow/python/autograph/operators/py_builtins.py	409	function
1260	1258	_tf_dataset_zip	tensorflow/tensorflow/python/autograph/operators/py_builtins.py	415	function
1261	1259	_py_zip	tensorflow/tensorflow/python/autograph/operators/py_builtins.py	419	function
1262	1260	map_	tensorflow/tensorflow/python/autograph/operators/py_builtins.py	423	function
1263	1261	_tf_dataset_map	tensorflow/tensorflow/python/autograph/operators/py_builtins.py	429	function
1264	1262	_py_map	tensorflow/tensorflow/python/autograph/operators/py_builtins.py	433	function
1265	1263	next_	tensorflow/tensorflow/python/autograph/operators/py_builtins.py	437	function
1266	1264	_verify_spec_compatible	tensorflow/tensorflow/python/autograph/operators/py_builtins.py	444	function	Verifies that a symbol has a type compatible vith a given spec. Here, compatibility is viewed in the general TensorFlow sense: that the dtypes are the same after implicit conversion, if both are tensors. This verifier ensures consistent treatment of types across AutoGraph. Args: input_name: A name to use for `input_` in error messages. spec_name: A name to use for `spec` in error messages. input_: Any, value to verify. spec: TypeSpec that `input_` must be compatible with. Raises: ValueError if the two types have been determined not to be compatible.
1267	1265	_verify_structure_compatible	tensorflow/tensorflow/python/autograph/operators/py_builtins.py	480	function	Verifies that possibly-structured symbol has types compatible vith another. See _verify_spec_compatible for a more concrete meaning of "compatible". Unspec _verify_spec_compatible, which handles singular Tensor-spec objects, verify_structures_compatible can process structures recognized by tf.nest. Args: input_name: A name to use for `input_` in error messages. spec_name: A name to use for `spec` in error messages. input_: Any, value to verify. May, but doesn't need to, be a structure. spec: Any, value that `input_` must be compatible with. May, but doesn't need to, be a structure. Raises: ValueError if the two types have been determined not to be compatible.
1268	1266	next_tf_iterator	tensorflow/tensorflow/python/autograph/operators/py_builtins.py	509	function
1269	1267	next_py	tensorflow/tensorflow/python/autograph/operators/py_builtins.py	521	function
1270	1268	filter_	tensorflow/tensorflow/python/autograph/operators/py_builtins.py	527	function
1271	1269	_tf_dataset_filter	tensorflow/tensorflow/python/autograph/operators/py_builtins.py	533	function
1272	1270	_py_filter	tensorflow/tensorflow/python/autograph/operators/py_builtins.py	537	function
1273	1271	any_	tensorflow/tensorflow/python/autograph/operators/py_builtins.py	541	function
1274	1272	_tf_dataset_any	tensorflow/tensorflow/python/autograph/operators/py_builtins.py	552	function
1275	1273	_py_any	tensorflow/tensorflow/python/autograph/operators/py_builtins.py	566	function
1276	1274	all_	tensorflow/tensorflow/python/autograph/operators/py_builtins.py	570	function
1277	1275	_tf_dataset_all	tensorflow/tensorflow/python/autograph/operators/py_builtins.py	578	function
1278	1276	_py_all	tensorflow/tensorflow/python/autograph/operators/py_builtins.py	592	function
1279	1277	sorted_	tensorflow/tensorflow/python/autograph/operators/py_builtins.py	596	function
1280	1278	_tf_sorted	tensorflow/tensorflow/python/autograph/operators/py_builtins.py	602	function	Overload of sorted_ for Tensor iterable.
1281	1279	_py_sorted	tensorflow/tensorflow/python/autograph/operators/py_builtins.py	627	function
1282	1280	TestBase	tensorflow/tensorflow/python/autograph/operators/py_builtins_test.py	41	class
1283	1281	PyBuiltinsTest	tensorflow/tensorflow/python/autograph/operators/py_builtins_test.py	48	class
1284	1282	GetItemOpts	tensorflow/tensorflow/python/autograph/operators/slices.py	34	class
1285	1283	get_item	tensorflow/tensorflow/python/autograph/operators/slices.py	38	function	The slice read operator (i.e. __getitem__). Note: it is unspecified whether target will be mutated or not. In general, if target is mutable (like Python lists), it will be mutated. Args: target: An entity that supports getitem semantics. i: Index to read from. opts: A GetItemOpts object. Returns: The read element. Raises: ValueError: if target is not of a supported type.
1286	1284	_tf_tensorarray_get_item	tensorflow/tensorflow/python/autograph/operators/slices.py	70	function	Overload of get_item that stages a TensorArray read.
1287	1285	_tf_tensor_list_get_item	tensorflow/tensorflow/python/autograph/operators/slices.py	75	function	Overload of get_item that stages a Tensor list read.
1288	1286	_tf_tensor_get_item	tensorflow/tensorflow/python/autograph/operators/slices.py	84	function	Overload of get_item that stages a Tensor (not Tensor list) read.
1289	1287	_tf_tensor_string_get_item	tensorflow/tensorflow/python/autograph/operators/slices.py	89	function	Overload of get_item that stages a Tensor string read.
1290	1288	_py_get_item	tensorflow/tensorflow/python/autograph/operators/slices.py	95	function	Overload of get_item that executes a Python list modification.
1291	1289	set_item	tensorflow/tensorflow/python/autograph/operators/slices.py	100	function	The slice write operator (i.e. __setitem__). Note: it is unspecified whether target will be mutated or not. In general, if target is mutable (like Python lists), it will be mutated. Args: target: An entity that supports setitem semantics. i: Index to modify. x: The new element value. Returns: Same as target, after the update was performed. Raises: ValueError: if target is not of a supported type.
1292	1290	_tf_tensorarray_set_item	tensorflow/tensorflow/python/autograph/operators/slices.py	128	function	Overload of set_item that stages a TensorArray write.
1293	1291	_tf_tensor_list_set_item	tensorflow/tensorflow/python/autograph/operators/slices.py	133	function	Overload of set_item that stages a Tensor list update.
1294	1292	_tf_tensor_set_item	tensorflow/tensorflow/python/autograph/operators/slices.py	138	function	Overload of set_item that stages a Tensor scatter update.
1295	1293	_py_set_item	tensorflow/tensorflow/python/autograph/operators/slices.py	143	function	Overload of set_item that executes a Python list modification.
1296	1294	SlicesTest	tensorflow/tensorflow/python/autograph/operators/slices_test.py	27	class
1297	1295	ld	tensorflow/tensorflow/python/autograph/operators/variables.py	22	function	Load variable operator.
1298	1296	ldu	tensorflow/tensorflow/python/autograph/operators/variables.py	29	function	Load variable operator that returns Undefined when failing to evaluate. Note: the name ("load or return undefined") is abbreviated to minimize the amount of clutter in generated code. This variant of `ld` is useful when loading symbols that may be undefined at runtime, such as composite symbols, and whether they are defined or not cannot be determined statically. For example `d['a']` is undefined when `d` is an empty dict. Args: load_v: Lambda that executes the actual read. name: Human-readable name of the symbol being read. Returns: Either the value of the symbol, or Undefined, if the symbol is not fully defined.
1299	1297	Undefined	tensorflow/tensorflow/python/autograph/operators/variables.py	54	class	Represents an undefined symbol in Python. This is used to reify undefined symbols, which is required to use the functional form of loops. Example: while n > 0: n = n - 1 s = n return s # Runtime error if n == 0 This is valid Python code and will not result in an error as long as n is positive. The use of this class is to stay as close to Python semantics as possible for staged code of this nature. Converted version of the above showing the possible usage of this class: s = Undefined('s') init_state = (s,) s = while_loop(cond, body, init_state) return s # s is an instance of Undefined if the loop never runs Attributes: symbol_name: Text, identifier for the undefined symbol
1300	1298	UndefinedReturnValue	tensorflow/tensorflow/python/autograph/operators/variables.py	106	class	Represents a return value that is undefined.
1301	1299	SpecialValuesTest	tensorflow/tensorflow/python/autograph/operators/variables_test.py	25	class
1302	1300	NoValue	tensorflow/tensorflow/python/autograph/pyct/anno.py	37	class
1303	1301	Basic	tensorflow/tensorflow/python/autograph/pyct/anno.py	43	class	Container for basic annotation keys. The enum values are used strictly for documentation purposes.
1304	1302	Static	tensorflow/tensorflow/python/autograph/pyct/anno.py	67	class	Container for static analysis annotation keys. The enum values are used strictly for documentation purposes.
1305	1303	keys	tensorflow/tensorflow/python/autograph/pyct/anno.py	110	function
1306	1304	getanno	tensorflow/tensorflow/python/autograph/pyct/anno.py	116	function
1307	1305	hasanno	tensorflow/tensorflow/python/autograph/pyct/anno.py	123	function
1308	1306	setanno	tensorflow/tensorflow/python/autograph/pyct/anno.py	127	function
1309	1307	delanno	tensorflow/tensorflow/python/autograph/pyct/anno.py	137	function
1310	1308	copyanno	tensorflow/tensorflow/python/autograph/pyct/anno.py	145	function
1311	1309	dup	tensorflow/tensorflow/python/autograph/pyct/anno.py	154	function	Recursively copies annotations in an AST tree. Args: node: ast.AST copy_map: Dict[Hashable, Hashable], maps a source anno key to a destination key. All annotations with the source key will be copied to identical annotations with the destination key. field_name: str
1312	1310	AnnoTest	tensorflow/tensorflow/python/autograph/pyct/anno_test.py	30	class
1313	1311	CleanCopier	tensorflow/tensorflow/python/autograph/pyct/ast_util.py	30	class	NodeTransformer-like visitor that copies an AST.
1314	1312	copy_clean	tensorflow/tensorflow/python/autograph/pyct/ast_util.py	63	function	Creates a deep copy of an AST. The copy will not include fields that are prefixed by '__', with the exception of user-specified annotations. Args: node: ast.AST preserve_annos: Optional[Set[Hashable]], annotation keys to include in the copy Returns: ast.AST
1315	1313	SymbolRenamer	tensorflow/tensorflow/python/autograph/pyct/ast_util.py	79	class	Transformer that can rename symbols to a simple names.
1316	1314	rename_symbols	tensorflow/tensorflow/python/autograph/pyct/ast_util.py	130	function	Renames symbols in an AST. Requires qual_names annotations.
1317	1315	keywords_to_dict	tensorflow/tensorflow/python/autograph/pyct/ast_util.py	140	function	Converts a list of ast.keyword objects to a dict.
1318	1316	PatternMatcher	tensorflow/tensorflow/python/autograph/pyct/ast_util.py	150	class	Matches a node against a pattern represented by a node.
1319	1317	matches	tensorflow/tensorflow/python/autograph/pyct/ast_util.py	214	function	Basic pattern matcher for AST. The pattern may contain wildcards represented by the symbol '_'. A node matches a pattern if for every node in the tree, either there is a node of the same type in pattern, or a Name node with id='_'. Args: node: ast.AST pattern: ast.AST Returns: bool
1320	1318	apply_to_single_assignments	tensorflow/tensorflow/python/autograph/pyct/ast_util.py	236	function	Applies a function to each individual assignment. This function can process a possibly-unpacked (e.g. a, b = c, d) assignment. It tries to break down the unpacking if possible. In effect, it has the same effect as passing the assigned values in SSA form to apply_fn. Examples: The following will result in apply_fn(a, c), apply_fn(b, d): a, b = c, d The following will result in apply_fn(a, c[0]), apply_fn(b, c[1]): a, b = c The following will result in apply_fn(a, (b, c)): a = b, c It uses the visitor pattern to allow subclasses to process single assignments individually. Args: targets: Union[List[ast.AST, ...], Tuple[ast.AST, ...], ast.AST, should be used with the targets field of an ast.Assign node values: ast.AST apply_fn: Callable[[ast.AST, ast.AST], None], called with the respective nodes of each single assignment
1321	1319	parallel_walk	tensorflow/tensorflow/python/autograph/pyct/ast_util.py	283	function	Walks two ASTs in parallel. The two trees must have identical structure. Args: node: Union[ast.AST, Iterable[ast.AST]] other: Union[ast.AST, Iterable[ast.AST]] Yields: Tuple[ast.AST, ast.AST] Raises: ValueError: if the two trees don't have identical structure.
1322	1320	AstUtilTest	tensorflow/tensorflow/python/autograph/pyct/ast_util_test.py	35	class
1323	1321	_TransformedFnCache	tensorflow/tensorflow/python/autograph/pyct/cache.py	26	class	Generic hierarchical cache for transformed functions. The keys are soft references (i.e. they are discarded when the key is destroyed) created from the source function by `_get_key`. The subkeys are strong references and can be any value. Typically they identify different kinds of transformation.
1324	1322	CodeObjectCache	tensorflow/tensorflow/python/autograph/pyct/cache.py	63	class	A function cache based on code objects. Code objects are good proxies for the source code of a function. This cache efficiently handles functions that share code objects, such as functions defined in a loop, bound methods, etc. The cache falls back to the function object, if it doesn't have a code object.
1325	1323	UnboundInstanceCache	tensorflow/tensorflow/python/autograph/pyct/cache.py	81	class	A function cache based on unbound function objects. Using the function for the cache key allows efficient handling of object methods. Unlike the _CodeObjectCache, this discriminates between different functions even if they have the same code. This is needed for decorators that may masquerade as another function.
1326	1324	CacheTest	tensorflow/tensorflow/python/autograph/pyct/cache_test.py	25	class
1327	1325	Node	tensorflow/tensorflow/python/autograph/pyct/cfg.py	54	class	A node in the CFG. Although new instances of this class are mutable, the objects that a user finds in the CFG are typically not. The nodes represent edges in the CFG graph, and maintain pointers to allow efficient walking in both forward and reverse order. The following property holds for all nodes: "child in node.next" iff "node in child.prev". Attributes: next: FrozenSet[Node, ...], the nodes that follow this node, in control flow order prev: FrozenSet[Node, ...], the nodes that precede this node, in reverse control flow order ast_node: ast.AST, the AST node corresponding to this CFG node
1328	1326	Graph	tensorflow/tensorflow/python/autograph/pyct/cfg.py	95	class	A Control Flow Graph. The CFG maintains an index to allow looking up a CFG node by the AST node to which it is associated. The index can also be enumerated in top-down, depth first order. Walking the graph in forward or reverse order is supported by double parent-child links. Note: the error nodes are not wired to their corresponding finally guards, because these are shared, and wiring them would create a reverse path from normal control flow into the error nodes, which we want to avoid. The graph also maintains edges corresponding to higher level statements like for-else loops. A node is considered successor of a statement if there is an edge from a node that is lexically a child of that statement to a node that is not. Statement predecessors are analogously defined. Attributes: entry: Node, the entry node exit: FrozenSet[Node, ...], the exit nodes error: FrozenSet[Node, ...], nodes that exit due to an explicitly raised error (errors propagated from function calls are not accounted) index: Dict[ast.Node, Node], mapping AST nodes to the respective CFG node stmt_prev: Dict[ast.Node, FrozenSet[Node, ...]], mapping statement AST nodes to their predecessor CFG nodes stmt_next: Dict[ast.Node, FrozenSet[Node, ...]], mapping statement AST nodes to their successor CFG nodes
1329	1327	_WalkMode	tensorflow/tensorflow/python/autograph/pyct/cfg.py	145	class
1330	1328	GraphVisitor	tensorflow/tensorflow/python/autograph/pyct/cfg.py	152	class	Base class for a CFG visitors. This implementation is not thread safe. The visitor has some facilities to simplify dataflow analyses. In particular, it allows revisiting the nodes at the decision of the subclass. This can be used to visit the graph until the state reaches a fixed point. For more details on dataflow analysis, see https://www.seas.harvard.edu/courses/cs252/2011sp/slides/Lec02-Dataflow.pdf Note: the literature generally suggests visiting successor nodes only when the state of the current node changed, regardless of whether that successor has ever been visited. This implementation visits every successor at least once. Attributes: graph: Graph in_: Dict[Node, Any], stores node-keyed state during a visit out: Dict[Node, Any], stores node-keyed state during a visit
1331	1329	GraphBuilder	tensorflow/tensorflow/python/autograph/pyct/cfg.py	252	class	Builder that constructs a CFG from a given AST. This GraphBuilder facilitates constructing the DAG that forms the CFG when nodes are supplied in lexical order (i.e., top-down, depth first). Under these conditions, it supports building patterns found in typical structured programs. This builder ignores the flow generated by exceptions, which are assumed to always be catastrophic and present purely for diagnostic purposes (e.g. to print debug information). Statements like raise and try/catch sections are allowed and will generate control flow edges, but ordinary statements are assumed not to raise exceptions. Finally sections are also correctly interleaved between break/continue/return nodes and their subsequent statements. Important concepts: * nodes - nodes refer refer to CFG nodes; AST nodes are qualified explicitly * leaf set - since the graph is constructed gradually, a leaf set maintains the CFG nodes that will precede the node that the builder expects to receive next; when an ordinary node is added, it is connected to the existing leaves and it in turn becomes the new leaf * jump nodes - nodes that should generate edges other than what ordinary nodes would; these correspond to break, continue and return statements * sections - logical delimiters for subgraphs that require special edges; there are various types of nodes, each admitting various types of jump nodes; sections are identified by their corresponding AST node
1332	1330	AstToCfg	tensorflow/tensorflow/python/autograph/pyct/cfg.py	647	class	Converts an AST to CFGs. A separate CFG will be constructed for each function.
1333	1331	build	tensorflow/tensorflow/python/autograph/pyct/cfg.py	964	function
1334	1332	CountingVisitor	tensorflow/tensorflow/python/autograph/pyct/cfg_test.py	28	class
1335	1333	GraphVisitorTest	tensorflow/tensorflow/python/autograph/pyct/cfg_test.py	42	class
1336	1334	AstToCfgTest	tensorflow/tensorflow/python/autograph/pyct/cfg_test.py	93	class
1337	1335	FrameInfo	tensorflow/tensorflow/python/autograph/pyct/error_utils.py	26	class
1338	1336	_stack_trace_inside_mapped_code	tensorflow/tensorflow/python/autograph/pyct/error_utils.py	34	function	Summarizes inner traceback frames up to the call to a given function. This functions locates the innermost (i.e. most recent) frame that corresponds to code that can be mapped by source_map originated from, and returns a translated stack trace ending at that frame. If no such frame is found, the entire stack trace is summarized. For example, the following code: def f(): for i in tf.range(1): z = y + i # z only defined here Would generate this traceback: <converted code> ag__.for_stmt(...) <for_stmt> return _known_len_tf_for_stmt(iter_, extra_test, body, init_state) <_known_len_tf_for_stmt> _disallow_undefs_into_loop(*init_state) <_disallow_undefs_into_loop> raise ... Which is then processed into: <f> for i in tf.range(1): <for_stmt> return _known_len_tf_for_stmt(iter_, extra_test, body, init_state) <_known_len_tf_for_stmt> _disallow_undefs_into_loop(*init_state) <_disallow_undefs_into_loop> raise ... Args: tb: traceback.FrameSummary, The traceback corresponding to an error. Typically, the output of traceback.Summary.extract(capture_locals=True). source_map: Dict[LineLocation, OriginInfo], a source map as created by origin_info.create_source_map. converter_filename: str, the file path of the converted module. Call frames corresponding to this module are elided and their preceding frames are marked as allowlisted. Note that frames enclosing converted code are dropped using a different mechanism. Returns: List[FrameInfo]
1339	1337	MultilineMessageKeyError	tensorflow/tensorflow/python/autograph/pyct/error_utils.py	141	class
1340	1338	ErrorMetadataBase	tensorflow/tensorflow/python/autograph/pyct/error_utils.py	153	class	Container objects attached to exceptions raised in user code. This metadata allows re-raising exceptions that occur in generated code, with a custom error message that includes a stack trace relative to user-readable code from which the generated code originated.
1341	1339	ErrorMetadataBaseTest	tensorflow/tensorflow/python/autograph/pyct/error_utils_test.py	28	class
1342	1340	PyCTError	tensorflow/tensorflow/python/autograph/pyct/errors.py	22	class	Base class for all exceptions.
1343	1341	UnsupportedLanguageElementError	tensorflow/tensorflow/python/autograph/pyct/errors.py	27	class	Raised for code patterns that AutoGraph does not support.
1344	1342	_is_constant_gast_2	tensorflow/tensorflow/python/autograph/pyct/gast_util.py	31	function
1345	1343	_is_constant_gast_3	tensorflow/tensorflow/python/autograph/pyct/gast_util.py	36	function
1346	1344	is_literal	tensorflow/tensorflow/python/autograph/pyct/gast_util.py	40	function	Tests whether node represents a Python literal.
1347	1345	_is_ellipsis_gast_2	tensorflow/tensorflow/python/autograph/pyct/gast_util.py	53	function
1348	1346	_is_ellipsis_gast_3	tensorflow/tensorflow/python/autograph/pyct/gast_util.py	57	function
1349	1347	islambda	tensorflow/tensorflow/python/autograph/pyct/inspect_utils.py	60	function
1350	1348	isnamedtuple	tensorflow/tensorflow/python/autograph/pyct/inspect_utils.py	68	function	Returns True if the argument is a namedtuple-like.
1351	1349	isbuiltin	tensorflow/tensorflow/python/autograph/pyct/inspect_utils.py	82	function	Returns True if the argument is a built-in function.
1352	1350	isconstructor	tensorflow/tensorflow/python/autograph/pyct/inspect_utils.py	96	function	Returns True if the argument is an object constructor. In general, any object of type class is a constructor, with the exception of classes created using a callable metaclass. See below for why a callable metaclass is not a trivial combination: https://docs.python.org/2.7/reference/datamodel.html#customizing-class-creation Args: cls: Any Returns: Bool
1353	1351	_fix_linecache_record	tensorflow/tensorflow/python/autograph/pyct/inspect_utils.py	116	function	Fixes potential corruption of linecache in the presence of functools.wraps. functools.wraps modifies the target object's __module__ field, which seems to confuse linecache in special instances, for example when the source is loaded from a .par file (see https://google.github.io/subpar/subpar.html). This function simply triggers a call to linecache.updatecache when a mismatch was detected between the object's __module__ property and the object's source file. Args: obj: Any
1354	1352	getimmediatesource	tensorflow/tensorflow/python/autograph/pyct/inspect_utils.py	143	function	A variant of inspect.getsource that ignores the __wrapped__ property.
1355	1353	getnamespace	tensorflow/tensorflow/python/autograph/pyct/inspect_utils.py	151	function	Returns the complete namespace of a function. Namespace is defined here as the mapping of all non-local variables to values. This includes the globals and the closure variables. Note that this captures the entire globals collection of the function, and may contain extra symbols that it does not actually use. Args: f: User defined function. Returns: A dict mapping symbol names to values.
1356	1354	getqualifiedname	tensorflow/tensorflow/python/autograph/pyct/inspect_utils.py	177	function	Returns the name by which a value can be referred to in a given namespace. If the object defines a parent module, the function attempts to use it to locate the object. This function will recurse inside modules, but it will not search objects for attributes. The recursion depth is controlled by max_depth. Args: namespace: Dict[str, Any], the namespace to search into. object_: Any, the value to search. max_depth: Optional[int], a limit to the recursion depth when searching inside modules. visited: Optional[Set[int]], ID of modules to avoid visiting. Returns: Union[str, None], the fully-qualified name that resolves to the value o, or None if it couldn't be found.
1357	1355	_get_unbound_function	tensorflow/tensorflow/python/autograph/pyct/inspect_utils.py	240	function
1358	1356	getdefiningclass	tensorflow/tensorflow/python/autograph/pyct/inspect_utils.py	250	function	Resolves the class (e.g. one of the superclasses) that defined a method.
1359	1357	getmethodclass	tensorflow/tensorflow/python/autograph/pyct/inspect_utils.py	265	function	Resolves a function's owner, e.g. a method's class. Note that this returns the object that the function was retrieved from, not necessarily the class where it was defined. This function relies on Python stack frame support in the interpreter, and has the same limitations that inspect.currentframe. Limitations. This function will only work correctly if the owned class is visible in the caller's global or local variables. Args: m: A user defined function Returns: The class that this function was retrieved from, or None if the function is not an object or class method, or the class that owns the object or method is not visible to m. Raises: ValueError: if the class could not be resolved for any unexpected reason.
1360	1358	getfutureimports	tensorflow/tensorflow/python/autograph/pyct/inspect_utils.py	339	function	Detects what future imports are necessary to safely execute entity source. Args: entity: Any object Returns: A tuple of future strings
1361	1359	decorator	tensorflow/tensorflow/python/autograph/pyct/inspect_utils_test.py	37	function
1362	1360	function_decorator	tensorflow/tensorflow/python/autograph/pyct/inspect_utils_test.py	41	function
1363	1361	wrapping_decorator	tensorflow/tensorflow/python/autograph/pyct/inspect_utils_test.py	47	function
1364	1362	TestClass	tensorflow/tensorflow/python/autograph/pyct/inspect_utils_test.py	59	class
1365	1363	free_function	tensorflow/tensorflow/python/autograph/pyct/inspect_utils_test.py	85	function
1366	1364	factory	tensorflow/tensorflow/python/autograph/pyct/inspect_utils_test.py	89	function
1367	1365	free_factory	tensorflow/tensorflow/python/autograph/pyct/inspect_utils_test.py	93	function
1368	1366	InspectUtilsTest	tensorflow/tensorflow/python/autograph/pyct/inspect_utils_test.py	99	class
1369	1367	_remove_file	tensorflow/tensorflow/python/autograph/pyct/loader.py	37	function	Remove a file, if it exists.
1370	1368	load_source	tensorflow/tensorflow/python/autograph/pyct/loader.py	50	function	Loads the given source code as a Python module.
1371	1369	load_ast	tensorflow/tensorflow/python/autograph/pyct/loader.py	70	function	Loads the given AST as a Python module. Compiling the AST code this way ensures that the source code is readable by e.g. `pdb` or `inspect`. Args: nodes: Union[ast.AST, Iterable[ast.AST]], the code to compile, as an AST object. indentation: Text, the string to use for indentation. include_source_map: bool, whether return a source map. delete_on_exit: bool, whether to delete the temporary file used for compilation on exit. Returns: Tuple[module, Text, Dict[LineLocation, OriginInfo]], containing: the module containing the unparsed nodes, the source code corresponding to nodes, and the source map. Is include_source_map is False, the source map will be None.
1372	1370	load_source	tensorflow/tensorflow/python/autograph/pyct/loader_deprecated_py2.py	40	function	Loads the given source code as a Python module.
1373	1371	load_ast	tensorflow/tensorflow/python/autograph/pyct/loader_deprecated_py2.py	58	function	Loads the given AST as a Python module. Compiling the AST code this way ensures that the source code is readable by e.g. `pdb` or `inspect`. Args: nodes: Union[ast.AST, Iterable[ast.AST]], the code to compile, as an AST object. indentation: Text, the string to use for indentation. include_source_map: bool, whether return a source map. delete_on_exit: bool, whether to delete the temporary file used for compilation on exit. Returns: Tuple[module, Text, Dict[LineLocation, OriginInfo]], containing: the module containing the unparsed nodes, the source code corresponding to nodes, and the source map. Is include_source_map is False, the source map will be None.
1374	1372	LoaderTest	tensorflow/tensorflow/python/autograph/pyct/loader_test.py	33	class
1375	1373	Namer	tensorflow/tensorflow/python/autograph/pyct/naming.py	24	class	Symbol name generator.
1376	1374	NamerTest	tensorflow/tensorflow/python/autograph/pyct/naming_test.py	25	class
1377	1375	LineLocation	tensorflow/tensorflow/python/autograph/pyct/origin_info.py	35	class	Similar to Location, but without column information. Attributes: filename: Text lineno: int, 1-based
1378	1376	Location	tensorflow/tensorflow/python/autograph/pyct/origin_info.py	46	class	Encodes code location information. Attributes: filename: Text lineno: int, 1-based col_offset: int line_loc: LineLocation
1379	1377	OriginInfo	tensorflow/tensorflow/python/autograph/pyct/origin_info.py	62	class	Container for information about the source code before conversion. Attributes: loc: Location function_name: Optional[Text] source_code_line: Text comment: Optional[Text]
1380	1378	create_source_map	tensorflow/tensorflow/python/autograph/pyct/origin_info.py	89	function	Creates a source map between an annotated AST and the code it compiles to. Note: this function assumes nodes nodes, code and filepath correspond to the same code. Args: nodes: Iterable[ast.AST, ...], one or more AST modes. code: Text, the source code in which nodes are found. filepath: Text Returns: Dict[LineLocation, OriginInfo], mapping locations in code to locations indicated by origin annotations in node.
1381	1379	_Function	tensorflow/tensorflow/python/autograph/pyct/origin_info.py	160	class
1382	1380	OriginResolver	tensorflow/tensorflow/python/autograph/pyct/origin_info.py	166	class	Annotates an AST with additional source information like file name.
1383	1381	resolve	tensorflow/tensorflow/python/autograph/pyct/origin_info.py	226	function	Adds origin information to an AST, based on the source it was loaded from. This allows us to map the original source code line numbers to generated source code. Note: the AST may be a part of a larger context (e.g. a function is part of a module that may contain other things). However, this function does not assume the source argument contains the entire context, nor that it contains only code corresponding to node itself. However, it assumes that node was parsed from the given source code. For this reason, two extra arguments are required, and they indicate the location of the node in the original context. Args: node: gast.AST, the AST to annotate. source: Text, the source code representing node. context_filepath: Text context_lineno: int context_col_offset: int
1384	1382	resolve_entity	tensorflow/tensorflow/python/autograph/pyct/origin_info.py	271	function	Like resolve, but extracts the context information from an entity.
1385	1383	OriginInfoTest	tensorflow/tensorflow/python/autograph/pyct/origin_info_test.py	34	class
1386	1384	_unfold_continuations	tensorflow/tensorflow/python/autograph/pyct/parser.py	60	function	Removes any backslash line continuations from the code.
1387	1385	dedent_block	tensorflow/tensorflow/python/autograph/pyct/parser.py	65	function	Dedents a code so that its first line starts at row zero.
1388	1386	parse_entity	tensorflow/tensorflow/python/autograph/pyct/parser.py	133	function	Returns the AST and source code of given entity. Args: entity: Any, Python function/method/class future_features: Iterable[Text], future features to use (e.g. 'print_statement'). See https://docs.python.org/2/reference/simple_stmts.html#future Returns: gast.AST, Text: the parsed AST node; the source code that was parsed to generate the AST (including any prefixes that this function may have added).
1389	1387	_without_context	tensorflow/tensorflow/python/autograph/pyct/parser.py	169	function	Returns a clean node and source code without indenting and context.
1390	1388	_arg_name	tensorflow/tensorflow/python/autograph/pyct/parser.py	203	function
1391	1389	_node_matches_argspec	tensorflow/tensorflow/python/autograph/pyct/parser.py	212	function	Returns True is node fits the argspec of func.
1392	1390	_parse_lambda	tensorflow/tensorflow/python/autograph/pyct/parser.py	234	function	Returns the AST and source code of given lambda function. Args: lam: types.LambdaType, Python function/method/class Returns: gast.AST, Text: the parsed AST node; the source code that was parsed to generate the AST (including any prefixes that this function may have added).
1393	1391	parse	tensorflow/tensorflow/python/autograph/pyct/parser.py	323	function	Returns the AST of given piece of code. Args: src: Text preamble_len: Int, indicates leading nodes in the parsed AST which should be dropped. single_node: Bool, whether `src` is assumed to be represented by exactly one AST node. Returns: ast.AST
1394	1392	parse_expression	tensorflow/tensorflow/python/autograph/pyct/parser.py	347	function	Returns the AST of given identifier. Args: src: A piece of code that represents a single Python expression Returns: A gast.AST object. Raises: ValueError: if src does not consist of a single Expression.
1395	1393	unparse	tensorflow/tensorflow/python/autograph/pyct/parser.py	366	function	Returns the source code of given AST. Args: node: The code to compile, as an AST object. indentation: Unused, deprecated. The returning code will always be indented at 4 spaces. include_encoding_marker: Bool, thether to include a comment on the first line to explicitly specify UTF-8 encoding. Returns: code: The source code generated from the AST object source_mapping: A mapping between the user and AutoGraph generated code.
1396	1394	ParserTest	tensorflow/tensorflow/python/autograph/pyct/parser_test.py	31	class
1397	1395	PrettyPrinter	tensorflow/tensorflow/python/autograph/pyct/pretty_printer.py	26	class	Print AST nodes.
1398	1396	fmt	tensorflow/tensorflow/python/autograph/pyct/pretty_printer.py	128	function
1399	1397	PrettyPrinterTest	tensorflow/tensorflow/python/autograph/pyct/pretty_printer_test.py	28	class
1400	1398	CallerMustSetThis	tensorflow/tensorflow/python/autograph/pyct/qual_names.py	36	class
1401	1399	Symbol	tensorflow/tensorflow/python/autograph/pyct/qual_names.py	40	class	Represents a Python symbol.
1402	1400	Literal	tensorflow/tensorflow/python/autograph/pyct/qual_names.py	44	class	Represents a Python numeric literal.
1403	1401	QN	tensorflow/tensorflow/python/autograph/pyct/qual_names.py	57	class	Represents a qualified name.
1404	1402	QnResolver	tensorflow/tensorflow/python/autograph/pyct/qual_names.py	210	class	Annotates nodes with QN information. Note: Not using NodeAnnos to avoid circular dependencies.
1405	1403	resolve	tensorflow/tensorflow/python/autograph/pyct/qual_names.py	251	function
1406	1404	from_str	tensorflow/tensorflow/python/autograph/pyct/qual_names.py	255	function
1407	1405	QNTest	tensorflow/tensorflow/python/autograph/pyct/qual_names_test.py	31	class
1408	1406	QNResolverTest	tensorflow/tensorflow/python/autograph/pyct/qual_names_test.py	183	class
1409	1407	ContextAdjuster	tensorflow/tensorflow/python/autograph/pyct/templates.py	35	class	Adjusts the ctx field of nodes to ensure consistency. This transformer can change the ctx fields of a variable, tuple and other AST elements that allow one, based on whether the element is being read or written.
1410	1408	ReplaceTransformer	tensorflow/tensorflow/python/autograph/pyct/templates.py	108	class	Replace AST nodes.
1411	1409	_convert_to_ast	tensorflow/tensorflow/python/autograph/pyct/templates.py	218	function	Converts from a known data type to AST.
1412	1410	replace	tensorflow/tensorflow/python/autograph/pyct/templates.py	234	function	Replaces placeholders in a Python template. AST Name and Tuple nodes always receive the context that inferred from the template. However, when replacing more complex nodes (that can potentially contain Name children), then the caller is responsible for setting the appropriate context. Args: template: A string representing Python code. Any symbol name can be used that appears in the template code can be used as placeholder. **replacements: A mapping from placeholder names to (lists of) AST nodes that these placeholders will be replaced by. String values are also supported as a shorthand for AST Name nodes with the respective ID. Returns: An AST node or list of AST nodes with the replacements made. If the template was a function, a list will be returned. If the template was a node, the same node will be returned. If the template was a string, an AST node will be returned (a `Module` node in the case of a multi-line string, an `Expr` node otherwise). Raises: ValueError: if the arguments are incorrect.
1413	1411	replace_as_expression	tensorflow/tensorflow/python/autograph/pyct/templates.py	279	function	Variant of replace that generates expressions, instead of code blocks.
1414	1412	_CtxClearer	tensorflow/tensorflow/python/autograph/pyct/templates_test.py	33	class
1415	1413	_parse_with_unset_ctx	tensorflow/tensorflow/python/autograph/pyct/templates_test.py	42	function
1416	1414	_CtxChecker	tensorflow/tensorflow/python/autograph/pyct/templates_test.py	48	class
1417	1415	TemplatesTest	tensorflow/tensorflow/python/autograph/pyct/templates_test.py	64	class
1418	1416	AnalysisLevel	tensorflow/tensorflow/python/autograph/pyct/transformer.py	32	class
1419	1417	Context	tensorflow/tensorflow/python/autograph/pyct/transformer.py	41	class	Contains information about a source code transformation. This object is mutable, and is updated during conversion. Not thread safe. Attributes: info: EntityInfo, immutable. namer: naming.Namer. current_origin: origin_info.OriginInfo, holds the OriginInfo of the last AST node to be processed successfully. Useful for error handling. user: An user-supplied context object. The object is opaque to the infrastructure, but will pe passed through to all custom transformations.
1420	1418	EntityInfo	tensorflow/tensorflow/python/autograph/pyct/transformer.py	63	class	Contains information about a Python entity. Immutable. Examples of entities include functions and classes. Attributes: name: The name that identifies this entity. source_code: The entity's source code. source_file: The entity's source file. future_features: Tuple[Text], the future features that this entity was compiled with. See https://docs.python.org/2/reference/simple_stmts.html#future. namespace: Dict[str, ], containing symbols visible to the entity (excluding parameters).
1421	1419	_StateStack	tensorflow/tensorflow/python/autograph/pyct/transformer.py	87	class	Templated context manager. This class provides syntactic sugar for a stack of objects of known type. It allows accessing attributes of the object at the top of the stack directly against this object, which allows for very terse syntax. For example, this code: stack = _StateStack(Foo) stack.enter() stack.bar Is equivalent to: stack = [] stack.append(Foo()) foo = stack[-1] foo.bar See _State for more on how this is used. Attributes: type: Any, the type of objects that this stack holds level: int, the current stack depth stack: List[Any], the actual stack value: Any, the instance of the object at the top of the stack
1422	1420	_State	tensorflow/tensorflow/python/autograph/pyct/transformer.py	159	class	Syntactic sugar for accessing an instance of a StateStack context manager. This structure offers syntactic sugar over a dict of stacks of objects of known type. These structures are useful to keep state during AST walks. Multiple different scopes can be tracked in parallel. For example: s = _State() s[foo].enter() s[bar].enter() # this will not affect s[foo] Element access has special semantics: * keys are a data type * element values are _StateStack(type=key) objects * missing elements are automatically added, similarly to defaultdict For example, the following block : _State s s[Foo] Is equivalent to: s = {} if Foo not in s: s[Foo] = Foo() s[Foo] See Base for how it's used.
1423	1421	NodeStateTracker	tensorflow/tensorflow/python/autograph/pyct/transformer.py	200	class	Base class for general-purpose Python code transformation. This abstract class provides helpful functions, like state tracking within the scope of arbitrary node, helpers for processing code blocks, debugging, mapping of transformed code to original code, and others. Scope-local state tracking: to keep state across nodes, at the level of (possibly nested) scopes, use enter/exit_local_scope and set/get_local. You must call enter/exit_local_scope manually, but the transformer detects when they are not properly paired. The transformer allows keeping state across calls that is local to arbitrary nodes and their descendants, using the self.state attribute. Multiple independent scopes are allowed and automatically constructed. For example, to keep track of the `If` node that encloses any `Name` node, one can write: ``` class FooType(object): def __init__(self): self.foo_property = None class DummyTransformer(NodeStateTracker, ast.NodeTransformer): def visit_If(self, node): self.state[FooType].enter() self.state[FooType].foo_property = node node = self.veneric_visit(node) self.state[FooType].exit() return node def visit_Name(self, node): self.state[FooType].foo_property # will hold the innermost enclosing if ``` Alternatively, the `enter()`/`exit()` calls can be managed by a `with` statement: ``` def visit_If(self, node): with self.state[FooType] as foo: foo.foo_property = node return self.generic_visit(node) ```
1424	1422	Base	tensorflow/tensorflow/python/autograph/pyct/transformer.py	360	class	Base class for general-purpose Python-to-Python code transformation. This is an extension of ast.NodeTransformer that provides the additional functions offered by NodeStateTracker.
1425	1423	CodeGenerator	tensorflow/tensorflow/python/autograph/pyct/transformer.py	478	class	Base class for general-purpose Python-to-string code transformation. Similar to Base, but outputs arbitrary strings instead of a Python AST. This uses the same visitor mechanism that the standard NodeVisitor uses, meaning that subclasses write handlers for the different kinds of nodes. New code is generated using the emit method, which appends to a code buffer that can be afterwards obtained from code_buffer. Example: class SimpleCodeGen(CodeGenerator): def visitIf(self, node): self.emit('if ') self.visit(node.test) self.emit(' { ') self.visit(node.body) self.emit(' } else { ') self.visit(node.orelse) self.emit(' } ') node = ast.parse(...) gen = SimpleCodeGen() gen.visit(node) # gen.code_buffer contains the resulting code
1426	1424	TransformerTest	tensorflow/tensorflow/python/autograph/pyct/transformer_test.py	30	class
1427	1425	CodeGeneratorTest	tensorflow/tensorflow/python/autograph/pyct/transformer_test.py	302	class
1428	1426	_wrap_into_factory	tensorflow/tensorflow/python/autograph/pyct/transpiler.py	38	function	Wraps an AST into the body of a factory with consistent lexical context. The AST is expected to define some symbol with a name given by `entity_name`. This mechanism ensures that the resulting transformed entity has lexical scoping identical to that of the source entity, while allowing extra parametrization. Two nested factories achieve the following: 1. The inner factory dynamically creates the entity represented by `nodes`. 2. The inner factory is parametrized by a custom set of arguments. 3. The inner factory has a closure identical to that of the transformed entity. 4. The inner factory has local variables named like `args`, which `nodes` may use as additional parameters. 5. The inner factory returns the variables given by `entity_name`. 6. The outer factory is niladic. 7. The outer factory has no closure. 8. The outer factory creates the necessary lexical scope for the inner factory, so that the loaded code has the given configuration for closure/globals. 9. The outer factory returns the inner factory. Roughly speaking, the following code is generated: from __future__ import future_feature_1 from __future__ import future_feature_2 ... def outer_factory(): closure_var_1 = None closure_var_2 = None ... def inner_factory(arg_1, arg_2, ...): <<nodes>> return entity return inner_factory The lexical scoping is created using dummy symbol declarations which create local fariables in the body of the outer factory, so that the Python parser correctly marks them as free non-global variables upon load (that is, it creates cell slots for each symbol. Thes symbols are initialized with None, but their values are not expected to be used; instead, the caller is expected to replace them with the cells of the source entity. For more details, see: https://docs.python.org/3/reference/executionmodel.html#binding-of-names Args: nodes: Tuple[ast.AST], the source code to wrap. entity_name: Union[Text, ast.AST], the name of the principal entity that `nodes` define. inner_factory_name: Text, the name of the inner factory. outer_factory_name: Text, the name of the outer factory. closure_vars: Iterable[Text], names of the closure variables for the inner factory. factory_args: Iterable[Text], names of additional arguments for the inner factory. Useful to configure variables that the converted code can use. Typically, these are modules. future_features: Iterable[Text], names of future statements to associate the code with. Returns: ast.AST
1429	1427	_PythonFnFactory	tensorflow/tensorflow/python/autograph/pyct/transpiler.py	147	class	Helper object that wraps a Python function factory.
1430	1428	GenericTranspiler	tensorflow/tensorflow/python/autograph/pyct/transpiler.py	227	class	A generic transpiler for Python functions. Its interface is the `transform` API, which can process Python function objects. Internally, it handles parsing. Users typically subclass this, customizing the `transform_ast` method. The output of transformed_ast is returned directly by `transform`. Existing methods like `transform_function` may also be overloaded. Example: class MyTransformer(GenericTranspiler): def transform_ast(self, node, ctx): result = <<transform node>> return result transformer = MyTransfomer() result = transformer.transform(f, ...) # result is the output
1431	1429	PyToPy	tensorflow/tensorflow/python/autograph/pyct/transpiler.py	368	class	A generic Python-to-Python transpiler. Its `transform` method offers a function-in, function-out interface. Internally, it takes care of parsing, caching and loading of the translated code. Users typically subclass this, overriding `transform_ast`. Usually, instances of this class are singletons, since each instance manages its own cache. The caching can be controlled by overriding `get_caching_key`. Example: class MyTransformer(PyToPy): def transform_ast(self, node, ctx): node = <<transform node, usually using ast.NodeTransformer classes>> return node transformer = MyTransfomer() new_f, module, source_map = transformer.transform_function(f, ...) # new_f is a function with signature identical to f The transformed function has access to the same namespace as the original function. To allow access to internal APIs, users may inject additional symbols by overriding `get_extra_locals`.
1432	1430	FlipSignTransformer	tensorflow/tensorflow/python/autograph/pyct/transpiler_test.py	30	class
1433	1431	TestTranspiler	tensorflow/tensorflow/python/autograph/pyct/transpiler_test.py	38	class
1434	1432	PyToPyTest	tensorflow/tensorflow/python/autograph/pyct/transpiler_test.py	55	class
1435	1433	DummyGensym	tensorflow/tensorflow/python/autograph/pyct/common_transformers/anf.py	40	class	A dumb gensym that suffixes a stem by sequential numbers from 1000.
1436	1434	ASTEdgePattern	tensorflow/tensorflow/python/autograph/pyct/common_transformers/anf.py	60	class	A pattern defining a type of AST edge. This consists of three components: - The type of the parent node, checked with isinstance, - The name of the field, checked with string equality, and - The type of the child node, also checked with isinstance. If all three match, the whole pattern is considered to match. In all three slots, the special value `anf.ANY` is treated as "match anything". The internal nodes are produced from the `gast` library rather than the standard `ast` module, which may affect `isinstance` checks.
1437	1435	AnfTransformer	tensorflow/tensorflow/python/autograph/pyct/common_transformers/anf.py	89	class	Performs the conversion to A-normal form (ANF).
1438	1436	_is_py2_name_constant	tensorflow/tensorflow/python/autograph/pyct/common_transformers/anf.py	483	function
1439	1437	_is_trivial	tensorflow/tensorflow/python/autograph/pyct/common_transformers/anf.py	487	function	Returns whether to consider the given node 'trivial'. The definition of 'trivial' is a node that can't meaningfully be pulled out into its own assignment statement. This is surprisingly difficult to do robustly across versions of Python and gast, as the parsing of constants has changed, if I may, constantly. Args: node: An AST node to check for triviality Returns: trivial: A Python `bool` indicating whether the node is trivial.
1440	1438	transform	tensorflow/tensorflow/python/autograph/pyct/common_transformers/anf.py	527	function	Converts the given node to A-normal form (ANF). The general idea of A-normal form: https://en.wikipedia.org/wiki/A-normal_form The specific converters used here are based on Python AST semantics as documented at https://greentreesnakes.readthedocs.io/en/latest/. What exactly should be considered A-normal form for any given programming language is not completely obvious. The transformation defined here is therefore configurable as to which syntax to replace with a fresh variable and which to leave be. The configuration is intentionally flexible enough to define very precise variable insertion transformations, should that be desired. The configuration is a list of syntax rules, each of which is a 2-tuple: - An `ASTEdgePattern` (which see) defining a type of AST edge, and - Whether to transform children of such edges. The special object `anf.ANY` may be used as a pattern that matches all edges. Each replacement directive is one of three possible things: - The object `anf.REPLACE`, meaning "Replace this child node with a variable", - The object `anf.LEAVE`, meaning "Do not replace this child node with a variable", or - A Python callable. If a callable, it is called with the parent node, the field name, and the child node, and must compute a boolean indicating whether to transform the child node or not. The callable is free to use whatever context information it chooses. The callable may be invoked more than once on the same link, and must produce the same answer each time. The syntax rules are tested in order, and the first match governs. If no rule matches, the node is not transformed. The above rules notwithstanding, - Variable references are never replaced with (fresh) variables, as that would accomplish nothing. - The left-hand children of Assign and AugAssign nodes, and the children of Del nodes, are never replaced with variables, as that would break their semantics. - The right-hand children of Assign nodes are never replaced with variables, as the original assignment would still have to be present in the result to define the new variable. (That is, there's no point in transforming `x = sin(y)` into `tmp = sin(y); x = tmp`.) - The right-hand children of AugAssign nodes are never replaced with variables either, but only because the difference from Assign was considered a potential source of confusion (and it would have been slightly awkward in the code to treat the RHS differently than the LHS). - Various special-purpose AST nodes are not exposed to the configuration, lest the transform produce invalid syntax like, e.g., `tmp = +; x = 1 tmp 2`. For example, the configuration ```python [(anf.ASTEdgePattern(anf.ANY, anf.ANY, gast.expr), anf.REPLACE)] ``` gives explicit fresh names to all expressions regardless of context (except as outlined above), whereas ```python [(anf.ASTEdgePattern(gast.If, "test", anf.ANY), anf.REPLACE)] ``` only transforms the conditionals of `if` statements (but not, e.g., `while`). If no configuration is supplied, the default behavior is to transform all expressions except literal constants, which is defined as a configuration as ```python # For Python 3, and gast library versions before 0.3 literals = (gast.Num, gast.Str, gast.Bytes, gast.NameConstant) [(anf.ASTEdgePattern(anf.ANY, anf.ANY, literals), anf.LEAVE), (anf.ASTEdgePattern(anf.ANY, anf.ANY, gast.expr), anf.REPLACE)] ``` Args: node: The node to transform. ctx: transformer.EntityInfo. TODO(mdan): What information does this argument provide? config: Optional ANF configuration. If omitted, ANF replaces all expression expect literal constants.
1441	1439	exec_test_function	tensorflow/tensorflow/python/autograph/pyct/common_transformers/anf_test.py	34	function
1442	1440	exec_expected_result	tensorflow/tensorflow/python/autograph/pyct/common_transformers/anf_test.py	40	function
1443	1441	AnfTestBase	tensorflow/tensorflow/python/autograph/pyct/common_transformers/anf_test.py	49	class
1444	1442	AnfTransformerTest	tensorflow/tensorflow/python/autograph/pyct/common_transformers/anf_test.py	85	class
1445	1443	AnfNonTransformationTest	tensorflow/tensorflow/python/autograph/pyct/common_transformers/anf_test.py	433	class	Test that specifying "no transformation" does nothing. Reuses all the examples of AnfTransformerTest by overriding `assert_body_anfs_as_expected_`.
1446	1444	AnfConfiguredTest	tensorflow/tensorflow/python/autograph/pyct/common_transformers/anf_test.py	454	class
1447	1445	Scope	tensorflow/tensorflow/python/autograph/pyct/static_analysis/activity.py	36	class	Encloses local symbol definition and usage information. This can track for instance whether a symbol is modified in the current scope. Note that scopes do not necessarily align with Python's scopes. For example, the body of an if statement may be considered a separate scope. Caution - the AST references held by this object are weak. Scope objects are mutable during construction only, and must be frozen using `Scope.finalize()` before use. Furthermore, a scope is consistent only after all its children have been frozen. While analysing code blocks, scopes are being gradually built, from the innermost scope outward. Freezing indicates that the analysis of a code block is complete. Once frozen, mutation is no longer allowed. `is_final` tracks whether the scope is frozen or not. Certain properties, like `referenced`, are only accurate when called on frozen scopes. Attributes: parent: Optional[Scope], the parent scope, if any. isolated: bool, whether the scope is a true Python scope (e.g. the scope of a function), or just a surrogate tracking an ordinary code block. Using the terminology of the Python 3 reference documentation, True roughly represents an actual scope, whereas False represents an ordinary code block. function_name: Optional[str], name of the function owning this scope. isolated_names: Set[qual_names.QN], identifiers that are isolated to this scope (even if the scope is not isolated). annotations: Set[qual_names.QN], identifiers used as type annotations in this scope. read: Set[qual_names.QN], identifiers read in this scope. modified: Set[qual_names.QN], identifiers modified in this scope. deleted: Set[qual_names.QN], identifiers deleted in this scope. bound: Set[qual_names.QN], names that are bound to this scope. See https://docs.python.org/3/reference/executionmodel.html#binding-of-names for a precise definition. globals: Set[qual_names.QN], names that are explicitly marked as global in this scope. Note that this doesn't include free read-only vars bound to global symbols. nonlocals: Set[qual_names.QN], names that are explicitly marked as nonlocal in this scope. Note that this doesn't include free read-only vars bound to global symbols. free_vars: Set[qual_names.QN], the free variables in this scope. See https://docs.python.org/3/reference/executionmodel.html for a precise definition. params: WeakValueDictionary[qual_names.QN, ast.Node], function arguments visible in this scope, mapped to the function node that defines them. enclosing_scope: Scope, the innermost isolated scope that is a transitive parent of this scope. May be the scope itself. referenced: Set[qual_names.QN], the totality of the symbols used by this scope and its parents. is_final: bool, whether the scope is frozen or not. Note - simple statements may never delete and modify a symbol at the same time. However, compound ones like if statements can. In that latter case, it's undefined whether the symbol is actually modified or deleted upon statement exit. Certain analyses like reaching definitions need to be careful about this.
1448	1446	_Comprehension	tensorflow/tensorflow/python/autograph/pyct/static_analysis/activity.py	214	class
1449	1447	_FunctionOrClass	tensorflow/tensorflow/python/autograph/pyct/static_analysis/activity.py	224	class
1450	1448	ActivityAnalyzer	tensorflow/tensorflow/python/autograph/pyct/static_analysis/activity.py	230	class	Annotates nodes with local scope information. See Scope. The use of this class requires that qual_names.resolve() has been called on the node. This class will ignore nodes have not been annotated with their qualified names.
1451	1449	resolve	tensorflow/tensorflow/python/autograph/pyct/static_analysis/activity.py	707	function
1452	1450	ActivityAnalyzerTest	tensorflow/tensorflow/python/autograph/pyct/static_analysis/activity_py3_test.py	31	class	Tests which can only run in Python 3.
1453	1451	ScopeTest	tensorflow/tensorflow/python/autograph/pyct/static_analysis/activity_test.py	41	class
1454	1452	ActivityAnalyzerTestBase	tensorflow/tensorflow/python/autograph/pyct/static_analysis/activity_test.py	114	class
1455	1453	ActivityAnalyzerTest	tensorflow/tensorflow/python/autograph/pyct/static_analysis/activity_test.py	148	class
1456	1454	NoValue	tensorflow/tensorflow/python/autograph/pyct/static_analysis/annos.py	27	class
1457	1455	NodeAnno	tensorflow/tensorflow/python/autograph/pyct/static_analysis/annos.py	33	class	Additional annotations used by the static analyzer. These are in addition to the basic annotations declared in anno.py.
1458	1456	Analyzer	tensorflow/tensorflow/python/autograph/pyct/static_analysis/liveness.py	40	class	CFG visitor that performs liveness analysis at statement level.
1459	1457	TreeAnnotator	tensorflow/tensorflow/python/autograph/pyct/static_analysis/liveness.py	96	class	Runs liveness analysis on each of the functions defined in the AST. If a function defined other local functions, those will have separate CFGs. However, dataflow analysis needs to tie up these CFGs to properly emulate the effect of closures. In the case of liveness, the parent function's live variables must account for the variables that are live at the entry of each subfunction. For example: def foo(): # baz is live from here on def bar(): print(baz) This analyzer runs liveness analysis on each individual function, accounting for the effect above.
1460	1458	resolve	tensorflow/tensorflow/python/autograph/pyct/static_analysis/liveness.py	206	function	Resolves the live symbols at the exit of control flow statements. Args: node: ast.AST source_info: transformer.SourceInfo graphs: Dict[ast.FunctionDef, cfg.Graph] include_annotations: Bool, whether type annotations should be included in the analysis. Returns: ast.AST
1461	1459	LivenessAnalyzerTest	tensorflow/tensorflow/python/autograph/pyct/static_analysis/liveness_py3_test.py	30	class	Tests which can only run in Python 3.
1462	1460	LivenessAnalyzerTestBase	tensorflow/tensorflow/python/autograph/pyct/static_analysis/liveness_test.py	37	class
1463	1461	LivenessAnalyzerTest	tensorflow/tensorflow/python/autograph/pyct/static_analysis/liveness_test.py	76	class
1464	1462	Definition	tensorflow/tensorflow/python/autograph/pyct/static_analysis/reaching_definitions.py	40	class	Definition objects describe a unique definition of a variable. Subclasses of this may be used by passing an appropriate factory function to resolve. Attributes: param_of: Optional[ast.AST] directives: Dict, optional definition annotations
1465	1463	_NodeState	tensorflow/tensorflow/python/autograph/pyct/static_analysis/reaching_definitions.py	59	class	Abstraction for the state of the CFG walk for reaching definition analysis. This is a value type. Only implements the strictly necessary operators. Attributes: value: Dict[qual_names.QN, Set[Definition, ...]], the defined symbols and their possible definitions
1466	1464	Analyzer	tensorflow/tensorflow/python/autograph/pyct/static_analysis/reaching_definitions.py	112	class	CFG visitor that determines reaching definitions at statement level.
1467	1465	TreeAnnotator	tensorflow/tensorflow/python/autograph/pyct/static_analysis/reaching_definitions.py	169	class	AST visitor that annotates each symbol name with its reaching definitions. Simultaneously, the visitor runs the dataflow analysis on each function node, accounting for the effect of closures. For example: def foo(): bar = 1 def baz(): # bar = 1 reaches here
1468	1466	resolve	tensorflow/tensorflow/python/autograph/pyct/static_analysis/reaching_definitions.py	279	function	Resolves reaching definitions for each symbol. Args: node: ast.AST source_info: transformer.SourceInfo graphs: Dict[ast.FunctionDef, cfg.Graph] definition_factory: Callable[[], Definition] Returns: ast.AST
1469	1467	ReachingDefinitionsAnalyzerTest	tensorflow/tensorflow/python/autograph/pyct/static_analysis/reaching_definitions_py3_test.py	26	class	Tests which can only run in Python 3.
1470	1468	ReachingDefinitionsAnalyzerTestBase	tensorflow/tensorflow/python/autograph/pyct/static_analysis/reaching_definitions_test.py	38	class
1471	1469	ReachingDefinitionsAnalyzerTest	tensorflow/tensorflow/python/autograph/pyct/static_analysis/reaching_definitions_test.py	88	class
1472	1470	Definition	tensorflow/tensorflow/python/autograph/pyct/static_analysis/reaching_fndefs.py	32	class	Definition objects describe a unique definition of a function.
1473	1471	_NodeState	tensorflow/tensorflow/python/autograph/pyct/static_analysis/reaching_fndefs.py	39	class	Abstraction for the state of the CFG walk for reaching definition analysis. This is a value type. Only implements the strictly necessary operators. Attributes: value: Dict[qual_names.QN, Set[Definition, ...]], the defined symbols and their possible definitions
1474	1472	Analyzer	tensorflow/tensorflow/python/autograph/pyct/static_analysis/reaching_fndefs.py	76	class	CFG visitor that determines reaching definitions at statement level.
1475	1473	TreeAnnotator	tensorflow/tensorflow/python/autograph/pyct/static_analysis/reaching_fndefs.py	109	class	AST visitor that annotates each symbol name with its reaching definitions. Simultaneously, the visitor runs the dataflow analysis on each function node, accounting for the effect of closures. For example: def foo(): def f(): pass def g(): # `def f` reaches here
1476	1474	resolve	tensorflow/tensorflow/python/autograph/pyct/static_analysis/reaching_fndefs.py	170	function	Resolves reaching definitions for each symbol. Args: node: ast.AST source_info: transformer.SourceInfo graphs: Dict[ast.FunctionDef, cfg.Graph] Returns: ast.AST
1477	1475	ReachingFndefsAnalyzerTest	tensorflow/tensorflow/python/autograph/pyct/static_analysis/reaching_fndefs_test.py	33	class
1478	1476	Resolver	tensorflow/tensorflow/python/autograph/pyct/static_analysis/type_inference.py	41	class	Resolver objects handle the process of looking up actual names and types. All resolve_* methods: * have a first namespace argument, mapping string to actual values * specify names as QN objects * specify types as a Set of inferred types All resolve_* methods must return either: * a set of `type` objects * None
1479	1477	_SymbolTable	tensorflow/tensorflow/python/autograph/pyct/static_analysis/type_inference.py	83	class	Abstraction for the state of the CFG walk for type inference. This is a value type. Only implements the strictly necessary operators. Attributes: value: Dict[qual_names.QN, Set[Type]], mapping symbols to the set of possible types.
1480	1478	StmtInferrer	tensorflow/tensorflow/python/autograph/pyct/static_analysis/type_inference.py	162	class	Runs type inference on a single AST statement. This visitor annotates most nodes with type information. It also sets types for the symbols modified by this statement in its types_out property.
1481	1479	Analyzer	tensorflow/tensorflow/python/autograph/pyct/static_analysis/type_inference.py	329	class	CFG visitor that propagates type information across statements.
1482	1480	FunctionVisitor	tensorflow/tensorflow/python/autograph/pyct/static_analysis/type_inference.py	394	class	AST visitor that applies type inference to each function separately.
1483	1481	resolve	tensorflow/tensorflow/python/autograph/pyct/static_analysis/type_inference.py	417	function	Performs type inference. Args: node: ast.AST source_info: transformer.SourceInfo graphs: Dict[ast.FunctionDef, cfg.Graph] resolver: Resolver Returns: ast.AST
1484	1482	TestResolver	tensorflow/tensorflow/python/autograph/pyct/static_analysis/type_inference_test.py	32	class	A very basic resolver for testing.
1485	1483	TestTranspiler	tensorflow/tensorflow/python/autograph/pyct/static_analysis/type_inference_test.py	58	class
1486	1484	TypeInferenceAnalyzerTest	tensorflow/tensorflow/python/autograph/pyct/static_analysis/type_inference_test.py	77	class
1487	1485	simple_function	tensorflow/tensorflow/python/autograph/pyct/testing/basic_definitions.py	23	function	Docstring.
1488	1486	nested_functions	tensorflow/tensorflow/python/autograph/pyct/testing/basic_definitions.py	28	function	Docstring.
1489	1487	function_with_print	tensorflow/tensorflow/python/autograph/pyct/testing/basic_definitions.py	37	function
1490	1488	SimpleClass	tensorflow/tensorflow/python/autograph/pyct/testing/basic_definitions.py	44	class
1491	1489	function_with_multiline_call	tensorflow/tensorflow/python/autograph/pyct/testing/basic_definitions.py	53	function	Docstring.
1492	1490	basic_decorator	tensorflow/tensorflow/python/autograph/pyct/testing/basic_definitions.py	61	function
1493	1491	decorated_function	tensorflow/tensorflow/python/autograph/pyct/testing/basic_definitions.py	67	function
1494	1492	NodeSampler	tensorflow/tensorflow/python/autograph/pyct/testing/codegen.py	30	class
1495	1493	StatementSampler	tensorflow/tensorflow/python/autograph/pyct/testing/codegen.py	39	class
1496	1494	ExpressionSampler	tensorflow/tensorflow/python/autograph/pyct/testing/codegen.py	49	class
1497	1495	CompareSampler	tensorflow/tensorflow/python/autograph/pyct/testing/codegen.py	58	class
1498	1496	BinaryOpSampler	tensorflow/tensorflow/python/autograph/pyct/testing/codegen.py	71	class
1499	1497	UnaryOpSampler	tensorflow/tensorflow/python/autograph/pyct/testing/codegen.py	83	class
1500	1498	NameSampler	tensorflow/tensorflow/python/autograph/pyct/testing/codegen.py	87	class
1501	1499	CodeGenerator	tensorflow/tensorflow/python/autograph/pyct/testing/codegen.py	98	class	Generate random syntactically-valid Python ASTs.
1502	1500	generate_random_functiondef	tensorflow/tensorflow/python/autograph/pyct/testing/codegen.py	233	function
1503	1501	CodeGenTest	tensorflow/tensorflow/python/autograph/pyct/testing/codegen_test.py	28	class
1504	1502	wrapping_decorator	tensorflow/tensorflow/python/autograph/pyct/testing/decorators.py	24	function
1505	1503	standalone_decorator	tensorflow/tensorflow/python/autograph/pyct/testing/decorators.py	33	function
1506	1504	functional_decorator	tensorflow/tensorflow/python/autograph/pyct/testing/decorators.py	41	function
1507	1505	set_verbosity	tensorflow/tensorflow/python/autograph/utils/ag_logging.py	41	function	Sets the AutoGraph verbosity level. _Debug logging in AutoGraph_ More verbose logging is useful to enable when filing bug reports or doing more in-depth debugging. There are two means to control the logging verbosity: * The `set_verbosity` function * The `AUTOGRAPH_VERBOSITY` environment variable `set_verbosity` takes precedence over the environment variable. For example: ```python import os import tensorflow as tf os.environ['AUTOGRAPH_VERBOSITY'] = 5 # Verbosity is now 5 tf.autograph.set_verbosity(0) # Verbosity is now 0 os.environ['AUTOGRAPH_VERBOSITY'] = 1 # No effect, because set_verbosity was already called. ``` Logs entries are output to [absl](https://abseil.io)'s [default output](https://abseil.io/docs/python/guides/logging), with `INFO` level. Logs can be mirrored to stdout by using the `alsologtostdout` argument. Mirroring is enabled by default when Python runs in interactive mode. Args: level: int, the verbosity level; larger values specify increased verbosity; 0 means no logging. When reporting bugs, it is recommended to set this value to a larger number, like 10. alsologtostdout: bool, whether to also output log messages to `sys.stdout`.
1508	1506	trace	tensorflow/tensorflow/python/autograph/utils/ag_logging.py	92	function	Traces argument information at compilation time. `trace` is useful when debugging, and it always executes during the tracing phase, that is, when the TF graph is constructed. _Example usage_ ```python import tensorflow as tf for i in tf.range(10): tf.autograph.trace(i) # Output: <Tensor ...> ``` Args: *args: Arguments to print to `sys.stdout`.
1509	1507	get_verbosity	tensorflow/tensorflow/python/autograph/utils/ag_logging.py	114	function
1510	1508	has_verbosity	tensorflow/tensorflow/python/autograph/utils/ag_logging.py	121	function
1511	1509	_output_to_stdout	tensorflow/tensorflow/python/autograph/utils/ag_logging.py	125	function
1512	1510	error	tensorflow/tensorflow/python/autograph/utils/ag_logging.py	131	function
1513	1511	log	tensorflow/tensorflow/python/autograph/utils/ag_logging.py	138	function
1514	1512	warn	tensorflow/tensorflow/python/autograph/utils/ag_logging.py	145	function
1515	1513	BasicRef	tensorflow/tensorflow/python/autograph/utils/compat_util.py	27	class	This shim emulates the nonlocal keyword in Py2-compatible source.
1516	1514	deprecated_py2_support	tensorflow/tensorflow/python/autograph/utils/compat_util.py	34	function	Swaps calling module with a Py2-specific implementation. Noop in Py3.
1517	1515	control_dependency_on_returns	tensorflow/tensorflow/python/autograph/utils/context_managers.py	27	function	Create a TF control dependency on the return values of a function. If the function had no return value, a no-op context is returned. Args: return_value: The return value to set as control dependency. Returns: A context manager.
1518	1516	ContextManagersTest	tensorflow/tensorflow/python/autograph/utils/context_managers_test.py	28	class
1519	1517	alias_tensors	tensorflow/tensorflow/python/autograph/utils/misc.py	27	function	Wraps any Tensor arguments with an identity op. Any other argument, including Variables, is returned unchanged. Args: *args: Any arguments. Must contain at least one element. Returns: Same as *args, with Tensor instances replaced as described. Raises: ValueError: If args doesn't meet the requirements.
1520	1518	get_range_len	tensorflow/tensorflow/python/autograph/utils/misc.py	55	function
1521	1519	MiscTest	tensorflow/tensorflow/python/autograph/utils/misc_test.py	30	class
1522	1520	MatchDType	tensorflow/tensorflow/python/autograph/utils/py_func.py	28	class	Allows matching the dtype of an argument. Used in conjunction with function calls. For example, MatchDType(0) will match the DType of the first argument.
1523	1521	wrap_py_func	tensorflow/tensorflow/python/autograph/utils/py_func.py	38	function	Helper that wraps a callable to py_func. The helper passes tensor arguments through the py_func interface. Non-tensor arguments are allowed, and will be passed to f directly. Note that non-tensor arguments are captured by f will not update every time the wrapper is called (this is consistent with its argument list, which only includes the tensor arguments). In general, it's safest not to reuse this wrapper. Args: f: Callable return_dtypes: None, individual of tuple/list of DType or MatchDType, the data type for each of f's return value(s). Set to None if f has no return values or use_dummy_return is True. Use MatchDType to define a dtype identical to that of `i`th argument (argument 0 is the first); an argument must of Tensor type if it is to be used with MatchDType. args: Positional arguments for f, as list or tuple. kwargs: Keyword arguments for f, as dict with string keys. May be None. use_dummy_return: If True, the function will return a dummy value of 1 and discard its actual return value. Returns: The return values of f converted to tensor. Raises: ValueError: if any of the arguments are incorrect.
1524	1522	PyFuncTest	tensorflow/tensorflow/python/autograph/utils/py_func_test.py	27	class
1525	1523	dynamic_list_append	tensorflow/tensorflow/python/autograph/utils/tensor_list.py	26	function	Converts a list append call inline.
1526	1524	TensorList	tensorflow/tensorflow/python/autograph/utils/tensor_list.py	43	class	Tensor list wrapper API-compatible with Python built-in list.
1527	1525	TensorListTest	tensorflow/tensorflow/python/autograph/utils/tensor_list_test.py	32	class
1528	1526	is_dense_tensor	tensorflow/tensorflow/python/autograph/utils/tensors.py	32	function
1529	1527	is_tensor_array	tensorflow/tensorflow/python/autograph/utils/tensors.py	38	function
1530	1528	is_tensor_list	tensorflow/tensorflow/python/autograph/utils/tensors.py	42	function
1531	1529	is_range_tensor	tensorflow/tensorflow/python/autograph/utils/tensors.py	51	function	Returns True if a tensor is the result of a tf.range op. Best effort.
1532	1530	TensorsTest	tensorflow/tensorflow/python/autograph/utils/tensors_test.py	30	class
1533	1531	AutoGraphTestCase	tensorflow/tensorflow/python/autograph/utils/testing.py	30	class	Tests specialized for AutoGraph, which run as tf.functions. These tests use a staged programming-like approach: most of the test code runs as-is inside a tf.function, but the assertions are lifted outside the function, and run with the corresponding function values instead. For example, the test: def test_foo(self): baz = bar(); self.assertEqual(baz, value) is equivalent to writing: def test_foo(self): @tf.function def test_fn(): baz = bar(); return baz, value baz_actual, value_actual = test_fn() self.assertEqual(baz_actual, value_actual)
1534	1532	list_local_devices	tensorflow/tensorflow/python/client/device_lib.py	25	function	List the available devices available in the local process. Args: session_config: a session config proto or None to use the default config. Returns: A list of `DeviceAttribute` protocol buffers.
1535	1533	DeviceLibTest	tensorflow/tensorflow/python/client/device_lib_test.py	28	class
1536	1534	PywrapeventsWriterTest	tensorflow/tensorflow/python/client/events_writer_test.py	33	class
1537	1535	main	tensorflow/tensorflow/python/client/notebook.py	53	function
1538	1536	TF_NewSessionOptions	tensorflow/tensorflow/python/client/pywrap_tf_session.py	51	function
1539	1537	TF_Reset	tensorflow/tensorflow/python/client/pywrap_tf_session.py	65	function
1540	1538	SessionInterface	tensorflow/tensorflow/python/client/session.py	51	class	Base class for implementations of TensorFlow client sessions.
1541	1539	_get_indexed_slices_value_from_fetches	tensorflow/tensorflow/python/client/session.py	77	function
1542	1540	_get_feeds_for_indexed_slices	tensorflow/tensorflow/python/client/session.py	83	function
1543	1541	_convert_to_numpy_obj	tensorflow/tensorflow/python/client/session.py	139	function	Explicitly convert obj based on numpy type except for string type.
1544	1542	register_session_run_conversion_functions	tensorflow/tensorflow/python/client/session.py	144	function	Register fetch and feed conversion functions for `tf.Session.run()`. This function registers a triple of conversion functions for fetching and/or feeding values of user-defined types in a call to tf.Session.run(). An example ```python class SquaredTensor(object): def __init__(self, tensor): self.sq = tf.square(tensor) #you can define conversion functions as follows: fetch_function = lambda squared_tensor:([squared_tensor.sq], lambda val: val[0]) feed_function = lambda feed, feed_val: [(feed.sq, feed_val)] feed_function_for_partial_run = lambda feed: [feed.sq] #then after invoking this register function, you can use as follows: session.run(squared_tensor1, feed_dict = {squared_tensor2 : some_numpy_array}) ``` Args: tensor_type: The type for which you want to register a conversion function. fetch_function: A callable that takes an object of type `tensor_type` and returns a tuple, where the first element is a list of `tf.Tensor` objects, and the second element is a callable that takes a list of ndarrays and returns an object of some value type that corresponds to `tensor_type`. fetch_function describes how to expand fetch into its component Tensors and how to contract the fetched results back into a single return value. feed_function: A callable that takes feed_key and feed_value as input, and returns a list of tuples (feed_tensor, feed_val), feed_key must have type `tensor_type`, and feed_tensor must have type `tf.Tensor`. Each feed function describes how to unpack a single fed value and map it to feeds of one or more tensors and their corresponding values. feed_function_for_partial_run: A callable for specifying tensor values to feed when setting up a partial run, which takes a `tensor_type` type object as input, and returns a list of Tensors. Raises: ValueError: If `tensor_type` has already been registered.
1545	1543	_is_attrs_instance	tensorflow/tensorflow/python/client/session.py	199	function	Returns True if the given obj is an instance of attrs-decorated class.
1546	1544	_get_attrs_values	tensorflow/tensorflow/python/client/session.py	204	function	Returns the list of values from an attrs instance.
1547	1545	_FetchMapper	tensorflow/tensorflow/python/client/session.py	210	class	Definition of the interface provided by fetch mappers. Fetch mappers are utility classes used by the _FetchHandler to handle arbitrary structures for the `fetch` argument to `Session.run()`. The `fetch` argument can be of various shapes: single tensor or op, list of fetches, tuple of fetches, namedtuple of fetches, or dict of fetches. The structures can be arbitrarily nested. The low level run() API only wants a list of tensor or op names. The various `_FetchMapper` subclasses below take care of handling the different shapes: uniquifying the fetches, and constructing results with the original shape.
1548	1546	_ElementFetchMapper	tensorflow/tensorflow/python/client/session.py	282	class	Fetch mapper for singleton tensors and ops.
1549	1547	_uniquify_fetches	tensorflow/tensorflow/python/client/session.py	329	function	Uniquifies fetches from a list of fetch_mappers. This is a utility function used by _ListFetchMapper and _DictFetchMapper. It gathers all the unique fetches from a list of mappers and builds a list containing all of them but without duplicates (unique_fetches). It also returns a 2-D list of integers (values_indices) indicating at which index in unique_fetches the fetches of the mappers are located. This list is as follows: values_indices[mapper_index][mapper_fetch_index] = unique_fetches_index Args: fetch_mappers: list of fetch mappers. Returns: A list of fetches. A 2-D list of integers.
1550	1548	_ListFetchMapper	tensorflow/tensorflow/python/client/session.py	365	class	Fetch mapper for lists, tuples, and namedtuples.
1551	1549	_DictFetchMapper	tensorflow/tensorflow/python/client/session.py	399	class	Fetch mapper for dicts.
1552	1550	_AttrsFetchMapper	tensorflow/tensorflow/python/client/session.py	425	class	Fetch mapper for attrs decorated classes.
1553	1551	_FetchHandler	tensorflow/tensorflow/python/client/session.py	449	class	Handler for structured fetches. Given a graph, a user-provided structure for fetches, and a feed dict, this class takes care of generating a list of tensor names to fetch and op names to run for a low level `run()` call. Given the results of the low level run call, this class can also rebuild a result structure matching the user-provided structure for fetches, but containing the corresponding results.
1554	1552	_name_list	tensorflow/tensorflow/python/client/session.py	573	function	Utility function for transitioning to the new session API. Args: tensor_list: a list of `Tensor`s. Returns: A list of each `Tensor`s name (as byte arrays).
1555	1553	_DeviceAttributes	tensorflow/tensorflow/python/client/session.py	585	class	Struct-like object describing a device's attributes. Each device has 3 key properties: - name: the fully-qualified TensorFlow path to the device. For example: /job:worker/replica:0/task:3/device:CPU:0 - device_type: the type of the device (e.g. CPU, GPU, TPU, etc.) - memory_limit_bytes: the maximum amount of memory available on the device (in bytes).
1556	1554	BaseSession	tensorflow/tensorflow/python/client/session.py	627	class	A class for interacting with a TensorFlow computation. The BaseSession enables incremental graph building with inline execution of Operations and evaluation of Tensors.
1557	1555	Session	tensorflow/tensorflow/python/client/session.py	1509	class	A class for running TensorFlow operations. A `Session` object encapsulates the environment in which `Operation` objects are executed, and `Tensor` objects are evaluated. For example: ```python tf.compat.v1.disable_eager_execution() # need to disable eager in TF2.x # Build a graph. a = tf.constant(5.0) b = tf.constant(6.0) c = a * b # Launch the graph in a session. sess = tf.compat.v1.Session() # Evaluate the tensor `c`. print(sess.run(c)) # prints 30.0 ``` A session may own resources, such as `tf.Variable`, `tf.queue.QueueBase`, and `tf.compat.v1.ReaderBase`. It is important to release these resources when they are no longer required. To do this, either invoke the `tf.Session.close` method on the session, or use the session as a context manager. The following two examples are equivalent: ```python # Using the `close()` method. sess = tf.compat.v1.Session() sess.run(...) sess.close() # Using the context manager. with tf.compat.v1.Session() as sess: sess.run(...) ``` The [`ConfigProto`](https://www.tensorflow.org/code/tensorflow/core/protobuf/config.proto) protocol buffer exposes various configuration options for a session. For example, to create a session that uses soft constraints for device placement, and log the resulting placement decisions, create a session as follows: ```python # Launch the graph in a session that allows soft device placement and # logs the placement decisions. sess = tf.compat.v1.Session(config=tf.compat.v1.ConfigProto( allow_soft_placement=True, log_device_placement=True)) ```
1558	1556	InteractiveSession	tensorflow/tensorflow/python/client/session.py	1679	class	A TensorFlow `Session` for use in interactive contexts, such as a shell. The only difference with a regular `Session` is that an `InteractiveSession` installs itself as the default session on construction. The methods `tf.Tensor.eval` and `tf.Operation.run` will use that session to run ops. This is convenient in interactive shells and [IPython notebooks](http://ipython.org), as it avoids having to pass an explicit `Session` object to run ops. For example: ```python sess = tf.compat.v1.InteractiveSession() a = tf.constant(5.0) b = tf.constant(6.0) c = a * b # We can just use 'c.eval()' without passing 'sess' print(c.eval()) sess.close() ``` Note that a regular session installs itself as the default session when it is created in a `with` statement. The common usage in non-interactive programs is to follow that pattern: ```python a = tf.constant(5.0) b = tf.constant(6.0) c = a * b with tf.compat.v1.Session(): # We can also use 'c.eval()' here. print(c.eval()) ```
1559	1557	SessionBenchmark	tensorflow/tensorflow/python/client/session_benchmark.py	36	class	Tests and benchmarks for interacting with the `tf.compat.v1.Session`.
1560	1558	SessionClusterSpecPropagationTest	tensorflow/tensorflow/python/client/session_clusterspec_prop_test.py	45	class
1561	1559	SessionListDevicesTest	tensorflow/tensorflow/python/client/session_list_devices_test.py	33	class
1562	1560	PartialRunTest	tensorflow/tensorflow/python/client/session_partial_run_test.py	35	class
1563	1561	SessionTest	tensorflow/tensorflow/python/client/session_test.py	72	class
1564	1562	AllocationMaximum	tensorflow/tensorflow/python/client/timeline.py	32	class	Stores the maximum allocation for a given allocator within the timelne. Parameters: timestamp: `tensorflow::Env::NowMicros()` when this maximum was reached. num_bytes: the total memory used at this time. tensors: the set of tensors allocated at this time.
1565	1563	StepStatsAnalysis	tensorflow/tensorflow/python/client/timeline.py	44	class	Stores the step stats analysis output. Parameters: chrome_trace: A dict containing the chrome trace analysis. allocator_maximums: A dict mapping allocator names to AllocationMaximum.
1566	1564	_ChromeTraceFormatter	tensorflow/tensorflow/python/client/timeline.py	55	class	A helper class for generating traces in Chrome Trace Format.
1567	1565	_TensorTracker	tensorflow/tensorflow/python/client/timeline.py	265	class	An internal class to track the lifetime of a Tensor.
1568	1566	Timeline	tensorflow/tensorflow/python/client/timeline.py	346	class	A class for visualizing execution timelines of TensorFlow steps.
1569	1567	TimelineTest	tensorflow/tensorflow/python/client/timeline_test.py	34	class
1570	1568	VirtualGpuTestUtil	tensorflow/tensorflow/python/client/virtual_gpu_test.py	38	class
1571	1569	VirtualGpuTest	tensorflow/tensorflow/python/client/virtual_gpu_test.py	195	class
1572	1570	_date_to_date_number	tensorflow/tensorflow/python/compat/compat.py	41	function
1573	1571	_update_forward_compatibility_date_number	tensorflow/tensorflow/python/compat/compat.py	45	function	Update the base date to compare in forward_compatible function.
1574	1572	forward_compatible	tensorflow/tensorflow/python/compat/compat.py	70	function	Return true if the forward compatibility window has expired. See [Version compatibility](https://tensorflow.org/guide/version_compat#backward_forward). Forward-compatibility refers to scenarios where the producer of a TensorFlow model (a GraphDef or SavedModel) is compiled against a version of the TensorFlow library newer than what the consumer was compiled against. The "producer" is typically a Python program that constructs and trains a model while the "consumer" is typically another program that loads and serves the model. TensorFlow has been supporting a 3 week forward-compatibility window for programs compiled from source at HEAD. For example, consider the case where a new operation `MyNewAwesomeAdd` is created with the intent of replacing the implementation of an existing Python wrapper - `tf.add`. The Python wrapper implementation should change from something like: ```python def add(inputs, name=None): return gen_math_ops.add(inputs, name) ``` to: ```python from tensorflow.python.compat import compat def add(inputs, name=None): if compat.forward_compatible(year, month, day): # Can use the awesome new implementation. return gen_math_ops.my_new_awesome_add(inputs, name) # To maintain forward compatibility, use the old implementation. return gen_math_ops.add(inputs, name) ``` Where `year`, `month`, and `day` specify the date beyond which binaries that consume a model are expected to have been updated to include the new operations. This date is typically at least 3 weeks beyond the date the code that adds the new operation is committed. Args: year: A year (e.g., 2018). Must be an `int`. month: A month (1 <= month <= 12) in year. Must be an `int`. day: A day (1 <= day <= 31, or 30, or 29, or 28) in month. Must be an `int`. Returns: True if the caller can expect that serialized TensorFlow graphs produced can be consumed by programs that are compiled with the TensorFlow library source code after (year, month, day).
1575	1573	forward_compatibility_horizon	tensorflow/tensorflow/python/compat/compat.py	131	function	Context manager for testing forward compatibility of generated graphs. See [Version compatibility](https://tensorflow.org/guide/version_compat#backward_forward). To ensure forward compatibility of generated graphs (see `forward_compatible`) with older binaries, new features can be gated with: ```python if compat.forward_compatible(year=2018, month=08, date=01): generate_graph_with_new_features() else: generate_graph_so_older_binaries_can_consume_it() ``` However, when adding new features, one may want to unittest it before the forward compatibility window expires. This context manager enables such tests. For example: ```python from tensorflow.python.compat import compat def testMyNewFeature(self): with compat.forward_compatibility_horizon(2018, 08, 02): # Test that generate_graph_with_new_features() has an effect ``` Args: year: A year (e.g., 2018). Must be an `int`. month: A month (1 <= month <= 12) in year. Must be an `int`. day: A day (1 <= day <= 31, or 30, or 29, or 28) in month. Must be an `int`. Yields: Nothing.
1576	1574	CompatTest	tensorflow/tensorflow/python/compat/compat_test.py	27	class
1577	1575	DisableV2BehaviorTest	tensorflow/tensorflow/python/compat/disable_v2_behavior_test.py	27	class
1578	1576	enable_v2_behavior	tensorflow/tensorflow/python/compat/v2_compat.py	43	function	Enables TensorFlow 2.x behaviors. This function can be called at the beginning of the program (before `Tensors`, `Graphs` or other structures have been created, and before devices have been initialized. It switches all global behaviors that are different between TensorFlow 1.x and 2.x to behave as intended for 2.x. This function is called in the main TensorFlow `__init__.py` file, user should not need to call it, except during complex migrations.
1579	1577	disable_v2_behavior	tensorflow/tensorflow/python/compat/v2_compat.py	82	function	Disables TensorFlow 2.x behaviors. This function can be called at the beginning of the program (before `Tensors`, `Graphs` or other structures have been created, and before devices have been initialized. It switches all global behaviors that are different between TensorFlow 1.x and 2.x to behave as intended for 1.x. User can call this function to disable 2.x behavior during complex migrations.
1580	1578	convert_graph_def	tensorflow/tensorflow/python/compiler/mlir/mlir.py	26	function	Import a GraphDef and convert it to a textual MLIR module. Args: graph_def: An object of type graph_pb2.GraphDef or a textual proto representation of a valid GraphDef. pass_pipeline: A textual description of an MLIR Pass Pipeline to run on the module, see MLIR documentation for the [textual pass pipeline syntax](https://github.com/tensorflow/mlir/blob/master/g3doc/WritingAPass.md#textual-pass-pipeline-specification). Returns: A textual representation of the MLIR module corresponding to the graphdef. Raises a RuntimeError on error.
1581	1579	MLIRImportTest	tensorflow/tensorflow/python/compiler/mlir/mlir_test.py	26	class
1582	1580	_to_bytes	tensorflow/tensorflow/python/compiler/tensorrt/trt_convert.py	84	function	Encode s if it is a sequence of chars.
1583	1581	_to_string	tensorflow/tensorflow/python/compiler/tensorrt/trt_convert.py	91	function	Decode s if it is a sequence of bytes.
1584	1582	TrtPrecisionMode	tensorflow/tensorflow/python/compiler/tensorrt/trt_convert.py	98	class
1585	1583	TrtConversionParams	tensorflow/tensorflow/python/compiler/tensorrt/trt_convert.py	117	class	Parameters that are used for TF-TRT conversion. Fields: rewriter_config_template: a template RewriterConfig proto used to create a TRT-enabled RewriterConfig. If None, it will use a default one. max_workspace_size_bytes: the maximum GPU temporary memory which the TRT engine can use at execution time. This corresponds to the 'workspaceSize' parameter of nvinfer1::IBuilder::setMaxWorkspaceSize(). precision_mode: one the strings in TrtPrecisionMode.supported_precision_modes(). minimum_segment_size: the minimum number of nodes required for a subgraph to be replaced by TRTEngineOp. is_dynamic_op: whether to generate dynamic TRT ops which will build the TRT network and engine at run time. i.e. Since TensorRT version < 6.0 does not support dynamic dimensions other than the batch dimension, when the TensorFlow graph has a non-batch dimension of dynamic size, we would need to enable this option. This option should be set to True in TF 2.0. maximum_cached_engines: max number of cached TRT engines for dynamic TRT ops. Created TRT engines for a dynamic dimension are cached. This is the maximum number of engines that can be cached. If the number of cached engines is already at max but none of them supports the input shapes, the TRTEngineOp will fall back to run the original TF subgraph that corresponds to the TRTEngineOp. use_calibration: this argument is ignored if precision_mode is not INT8. If set to True, a calibration graph will be created to calibrate the missing ranges. The calibration graph must be converted to an inference graph by running calibration with calibrate(). If set to False, quantization nodes will be expected for every tensor in the graph (excluding those which will be fused). If a range is missing, an error will occur. Please note that accuracy may be negatively affected if there is a mismatch between which tensors TRT quantizes and which tensors were trained with fake quantization. max_batch_size: max size for the input batch. This parameter is only effective when is_dynamic_op=False which is not supported in TF 2.0. allow_build_at_runtime: whether to build TensorRT engines during runtime. If no TensorRT engine can be found in cache that can handle the given inputs during runtime, then a new TensorRT engine is built at runtime if allow_build_at_runtime=True, and otherwise native TF is used. This argument is only effective if is_dynamic_op=True.
1586	1584	_check_conversion_params	tensorflow/tensorflow/python/compiler/tensorrt/trt_convert.py	188	function	Validate the provided TrtConversionParams. Args: conversion_params: a TrtConversionParams instance. is_v2: whether we're getting a RewriterConfig for TF 2.0. Raises: TypeError: if any of the parameters are of unexpected type. ValueError: if any of the parameters are of unexpected value.
1587	1585	_check_trt_version_compatibility	tensorflow/tensorflow/python/compiler/tensorrt/trt_convert.py	252	function	Check compatibility of TensorRT version. Raises: RuntimeError: if the TensorRT library version is incompatible.
1588	1586	get_tensorrt_rewriter_config	tensorflow/tensorflow/python/compiler/tensorrt/trt_convert.py	292	function	Returns a RewriterConfig proto for TRT transformation. Args: conversion_params: a TrtConversionParams instance. is_v2: whether we're getting a RewriterConfig for TF 2.0. disable_non_trt_optimizers: Turn off all default Grappler optimizers. Returns: A RewriterConfig proto which sets a TensorRTOptimizer to run Grappler. Raises: TypeError: if any of the parameters are of unexpected type. ValueError: if any of the parameters are of unexpected value.
1589	1587	_get_canonical_engine_name	tensorflow/tensorflow/python/compiler/tensorrt/trt_convert.py	383	function
1590	1588	is_explicit_batch_mode_enabled	tensorflow/tensorflow/python/compiler/tensorrt/trt_convert.py	387	function	Checks whether explicit batch is enabled by the rewriter config.
1591	1589	TrtGraphConverter	tensorflow/tensorflow/python/compiler/tensorrt/trt_convert.py	398	class	A converter for TF-TRT transformation for TF 1.x GraphDef/SavedModels. To run the conversion without quantization calibration (e.g. for FP32/FP16 precision modes): ```python converter = TrtGraphConverter( input_saved_model_dir="my_dir", precision_mode=TrtPrecisionMode.FP16) converted_graph_def = converter.convert() converter.save(output_saved_model_dir) ``` To run the conversion with quantization calibration: ```python converter = TrtGraphConverter( input_saved_model_dir="my_dir", precision_mode=TrtPrecisionMode.INT8) converter.convert() # Run calibration 10 times. converted_graph_def = converter.calibrate( fetch_names=['output:0'], num_runs=10, feed_dict_fn=lambda: {'input:0': my_next_data()}) converter.save(output_saved_model_dir) ```
1592	1590	_get_resource_handle	tensorflow/tensorflow/python/compiler/tensorrt/trt_convert.py	833	function
1593	1591	_TRTEngineResourceDeleter	tensorflow/tensorflow/python/compiler/tensorrt/trt_convert.py	838	class	Resource deleter for destroying TRT engine cache resource.
1594	1592	_TRTEngineResource	tensorflow/tensorflow/python/compiler/tensorrt/trt_convert.py	853	class	Class to track the serialized engines resource.
1595	1593	TrtGraphConverterV2	tensorflow/tensorflow/python/compiler/tensorrt/trt_convert.py	880	class	An offline converter for TF-TRT transformation for TF 2.0 SavedModels. Currently this is not available on Windows platform. Note that in V2, is_dynamic_op=False is not supported, meaning TRT engines will be built only when the corresponding TRTEngineOp is executed. But we still provide a way to avoid the cost of building TRT engines during inference (see more below). There are several ways to run the conversion: 1. FP32/FP16 precision ```python params = tf.experimental.tensorrt.ConversionParams( precision_mode='FP16') converter = tf.experimental.tensorrt.Converter( input_saved_model_dir="my_dir", conversion_params=params) converter.convert() converter.save(output_saved_model_dir) ``` In this case, no TRT engines will be built or saved in the converted SavedModel. But if input data is available during conversion, we can still build and save the TRT engines to reduce the cost during inference (see option 2 below). 2. FP32/FP16 precision with pre-built engines ```python params = tf.experimental.tensorrt.ConversionParams( precision_mode='FP16', # Set this to a large enough number so it can cache all the engines. maximum_cached_engines=16) converter = tf.experimental.tensorrt.Converter( input_saved_model_dir="my_dir", conversion_params=params) converter.convert() # Define a generator function that yields input data, and use it to execute # the graph to build TRT engines. # With TensorRT 5.1, different engines will be built (and saved later) for # different input shapes to the TRTEngineOp. def my_input_fn(): for _ in range(num_runs): inp1, inp2 = ... yield inp1, inp2 converter.build(input_fn=my_input_fn) # Generate corresponding TRT engines converter.save(output_saved_model_dir) # Generated engines will be saved. ``` In this way, one engine will be built/saved for each unique input shapes of the TRTEngineOp. This is good for applications that cannot afford building engines during inference but have access to input data that is similar to the one used in production (for example, that has the same input shapes). Also, the generated TRT engines is platform dependent, so we need to run `build()` in an environment that is similar to production (e.g. with same type of GPU). 3. INT8 precision and calibration with pre-built engines ```python params = tf.experimental.tensorrt.ConversionParams( precision_mode='INT8', # Currently only one INT8 engine is supported in this mode. maximum_cached_engines=1, use_calibration=True) converter = tf.experimental.tensorrt.Converter( input_saved_model_dir="my_dir", conversion_params=params) # Define a generator function that yields input data, and run INT8 # calibration with the data. All input data should have the same shape. # At the end of convert(), the calibration stats (e.g. range information) # will be saved and can be used to generate more TRT engines with different # shapes. Also, one TRT engine will be generated (with the same shape as # the calibration data) for save later. def my_calibration_input_fn(): for _ in range(num_runs): inp1, inp2 = ... yield inp1, inp2 converter.convert(calibration_input_fn=my_calibration_input_fn) # (Optional) Generate more TRT engines offline (same as the previous # option), to avoid the cost of generating them during inference. def my_input_fn(): for _ in range(num_runs): inp1, inp2 = ... yield inp1, inp2 converter.build(input_fn=my_input_fn) # Save the TRT engine and the engines. converter.save(output_saved_model_dir) ```
1596	1594	create_inference_graph	tensorflow/tensorflow/python/compiler/tensorrt/trt_convert.py	1270	function	Python wrapper for the TRT transformation. Args: input_graph_def: a GraphDef object containing a model to be transformed. If set to None, the graph will be read from the SavedModel loaded from input_saved_model_dir. outputs: list of tensors or node names for the model outputs. Only used when input_graph_def is not None. max_batch_size: max size for the input batch. max_workspace_size_bytes: the maximum GPU temporary memory which the TRT engine can use at execution time. This corresponds to the 'workspaceSize' parameter of nvinfer1::IBuilder::setMaxWorkspaceSize(). precision_mode: one of TrtPrecisionMode.supported_precision_modes(). minimum_segment_size: the minimum number of nodes required for a subgraph to be replaced by TRTEngineOp. is_dynamic_op: whether to generate dynamic TRT ops which will build the TRT network and engine at run time. maximum_cached_engines: max number of cached TRT engines in dynamic TRT ops. If the number of cached engines is already at max but none of them can serve the input, the TRTEngineOp will fall back to run the TF function based on which the TRTEngineOp is created. input_saved_model_dir: the directory to load the SavedModel which contains the input graph to transforms. Used only when input_graph_def is None. input_saved_model_tags: list of tags to load the SavedModel. input_saved_model_signature_key: the key of the signature to optimize the graph for. output_saved_model_dir: if not None, construct a SavedModel using the returned GraphDef and save it to the specified directory. This option only works when the input graph is loaded from a SavedModel, i.e. when input_saved_model_dir is specified and input_graph_def is None. session_config: the ConfigProto used to create a Session. It's also used as a template to create a TRT-enabled ConfigProto for conversion. If not specified, a default ConfigProto will be used. Returns: A GraphDef transformed from input_graph_def (or the SavedModel graph def loaded from input_saved_model_dir, if input_graph_def is not present), where all TRT compatible subgraphs are replaced with TRTEngineOps, and a TF function is added for each of the subgraphs. If is_dynamic_op is True, each TRTEngineOp will contain a serialized subgraph GraphDef, which will be converted to a TRT engine at execution time and the TRT engine will be cached for future usage. A new TRT engine will be created each time when none of the cached engines match the input shapes. If it fails to execute the TRT engine or the number of cached engines reaches maximum_cached_engines, the op will fall back to call the corresponding TF function. If is_dynamic_op is False, each TRTEngineOp will contain a serialized TRT engine created from the corresponding subgraph. No more engines will be created on the fly, and the op will fall back to call the corresponding TF function when it fails to execute the engine. Raises: ValueError: if the combination of the parameters is invalid.
1597	1595	TrtConvertTest	tensorflow/tensorflow/python/compiler/tensorrt/trt_convert_test.py	67	class	Class to test Tensorflow-TensorRT integration python API.
1598	1596	TrtPrecisionMode	tensorflow/tensorflow/python/compiler/tensorrt/trt_convert_windows.py	31	class
1599	1597	TrtConversionParams	tensorflow/tensorflow/python/compiler/tensorrt/trt_convert_windows.py	43	class	Parameters that are used for TF-TRT conversion. Fields: rewriter_config_template: a template RewriterConfig proto used to create a TRT-enabled RewriterConfig. If None, it will use a default one. max_workspace_size_bytes: the maximum GPU temporary memory which the TRT engine can use at execution time. This corresponds to the 'workspaceSize' parameter of nvinfer1::IBuilder::setMaxWorkspaceSize(). precision_mode: one the strings in TrtPrecisionMode.supported_precision_modes(). minimum_segment_size: the minimum number of nodes required for a subgraph to be replaced by TRTEngineOp. is_dynamic_op: whether to generate dynamic TRT ops which will build the TRT network and engine at run time. i.e. Since TensorRT version < 6.0 does not support dynamic dimensions other than the batch dimension, when the TensorFlow graph has a non-batch dimension of dynamic size, we would need to enable this option. This option should be set to True in TF 2.0. maximum_cached_engines: max number of cached TRT engines for dynamic TRT ops. Created TRT engines for a dynamic dimension are cached. This is the maximum number of engines that can be cached. If the number of cached engines is already at max but none of them supports the input shapes, the TRTEngineOp will fall back to run the original TF subgraph that corresponds to the TRTEngineOp. use_calibration: this argument is ignored if precision_mode is not INT8. If set to True, a calibration graph will be created to calibrate the missing ranges. The calibration graph must be converted to an inference graph by running calibration with calibrate(). If set to False, quantization nodes will be expected for every tensor in the graph (exlcuding those which will be fused). If a range is missing, an error will occur. Please note that accuracy may be negatively affected if there is a mismatch between which tensors TRT quantizes and which tensors were trained with fake quantization. max_batch_size: max size for the input batch. This parameter is only effective when is_dynamic_op=False which is not supported in TF 2.0.
1600	1598	TrtConverterWindows	tensorflow/tensorflow/python/compiler/tensorrt/trt_convert_windows.py	97	class	An offline converter for TF-TRT transformation for TF 2.0 SavedModels. Currently this is not available on Windows platform.
1601	1599	SimpleSingleEngineTest	tensorflow/tensorflow/python/compiler/tensorrt/test/base_test.py	34	class
1602	1600	SimpleMultiEnginesTest	tensorflow/tensorflow/python/compiler/tensorrt/test/base_test.py	74	class
1603	1601	SimpleMultiEnginesTest2	tensorflow/tensorflow/python/compiler/tensorrt/test/base_test.py	134	class
1604	1602	ConstInputTest	tensorflow/tensorflow/python/compiler/tensorrt/test/base_test.py	175	class
1605	1603	ConstDataInputSingleEngineTest	tensorflow/tensorflow/python/compiler/tensorrt/test/base_test.py	211	class
1606	1604	ConstDataInputMultipleEnginesTest	tensorflow/tensorflow/python/compiler/tensorrt/test/base_test.py	232	class
1607	1605	ControlDependencyTest	tensorflow/tensorflow/python/compiler/tensorrt/test/base_test.py	264	class
1608	1606	BatchMatMulTwoTensorTest	tensorflow/tensorflow/python/compiler/tensorrt/test/batch_matmul_test.py	32	class	Testing conversion of BatchMatMul where both inputs are tensors.
1609	1607	BatchMatMulWeightBroadcastTest	tensorflow/tensorflow/python/compiler/tensorrt/test/batch_matmul_test.py	50	class	Testing BatchMatMulV2: one operand is weight and both have same rank.
1610	1608	BatchMatMulWeightBroadcastDims2Test	tensorflow/tensorflow/python/compiler/tensorrt/test/batch_matmul_test.py	69	class	Testing BatchMatMulV2: weight operand must be broadcasted.
1611	1609	BiasaddMatMulTest	tensorflow/tensorflow/python/compiler/tensorrt/test/biasadd_matmul_test.py	33	class	Testing conversion of BiasAdd MatMul in TF-TRT conversion.
1612	1610	BinaryTensorWeightBroadcastTest	tensorflow/tensorflow/python/compiler/tensorrt/test/binary_tensor_weight_broadcast_test.py	30	class	Tests for scale & elementwise layers in TF-TRT.
1613	1611	CastInt32ToFp32Test	tensorflow/tensorflow/python/compiler/tensorrt/test/cast_test.py	31	class	Tests cast to FP32 are splitted in FP16 mode.
1614	1612	CombinedNmsTest	tensorflow/tensorflow/python/compiler/tensorrt/test/combined_nms_test.py	30	class	Test for CombinedNMS op in TF-TRT.
1615	1613	ConcatenationTest	tensorflow/tensorflow/python/compiler/tensorrt/test/concatenation_test.py	32	class	Testing Concatenation in TF-TRT conversion.
1616	1614	ConstBroadcastTest	tensorflow/tensorflow/python/compiler/tensorrt/test/const_broadcast_test.py	28	class	Test for Constant broadcasting in TF-TRT.
1617	1615	conv2d_layer	tensorflow/tensorflow/python/compiler/tensorrt/test/conv2d_test.py	32	function
1618	1616	div_round_up	tensorflow/tensorflow/python/compiler/tensorrt/test/conv2d_test.py	62	function
1619	1617	build_graph	tensorflow/tensorflow/python/compiler/tensorrt/test/conv2d_test.py	66	function
1620	1618	Conv2DNCHWTest	tensorflow/tensorflow/python/compiler/tensorrt/test/conv2d_test.py	83	class	Testing conversion of Conv2D (data_format=NCHW) in TF-TRT conversion.
1621	1619	Conv2DNHWCTest	tensorflow/tensorflow/python/compiler/tensorrt/test/conv2d_test.py	118	class	Testing conversion of Conv2D (data_format=NCHW) in TF-TRT conversion.
1622	1620	Conv2DStridedNCHWTest	tensorflow/tensorflow/python/compiler/tensorrt/test/conv2d_test.py	141	class	Testing conversion of strided Conv2D (data_format=NCHW).
1623	1621	Conv2DTranposeTest	tensorflow/tensorflow/python/compiler/tensorrt/test/conv2d_test.py	172	class	Testing conversion of conv2d_transpose (AKA Conv2DBackpropInput)
1624	1622	DynamicInputShapesTest	tensorflow/tensorflow/python/compiler/tensorrt/test/dynamic_input_shapes_test.py	32	class
1625	1623	IdentityTest	tensorflow/tensorflow/python/compiler/tensorrt/test/identity_output_test.py	36	class	Testing engine with the same tensor repeated as output via identity.
1626	1624	ExcludeUnsupportedInt32Test	tensorflow/tensorflow/python/compiler/tensorrt/test/int32_test.py	32	class	Test exclusion of ops which are not supported in INT32 mode by TF-TRT
1627	1625	CalibrationInt32Support	tensorflow/tensorflow/python/compiler/tensorrt/test/int32_test.py	68	class	Test execution of calibration with int32 input
1628	1626	LRUCacheTest	tensorflow/tensorflow/python/compiler/tensorrt/test/lru_cache_test.py	33	class
1629	1627	MemoryAlignmentTest	tensorflow/tensorflow/python/compiler/tensorrt/test/memory_alignment_test.py	31	class	Testing conversion of BatchMatMul in TF-TRT conversion.
1630	1628	MultiConnectionNeighborEngineTest	tensorflow/tensorflow/python/compiler/tensorrt/test/multi_connection_neighbor_engine_test.py	31	class	Test for multi connection neighboring nodes wiring tests in TF-TRT.
1631	1629	NeighboringEngineTest	tensorflow/tensorflow/python/compiler/tensorrt/test/neighboring_engine_test.py	32	class	Neighboring node wiring tests in TF-TRT conversion.
1632	1630	QuantizationAwareTrainingMNISTTest	tensorflow/tensorflow/python/compiler/tensorrt/test/quantization_mnist_test.py	59	class	Testing usage of quantization ranges inserted in graph.
1633	1631	_GraphFn	tensorflow/tensorflow/python/compiler/tensorrt/test/quantization_test.py	33	function
1634	1632	_GetParams	tensorflow/tensorflow/python/compiler/tensorrt/test/quantization_test.py	53	function
1635	1633	QuantizationMissingAllRangesTest	tensorflow/tensorflow/python/compiler/tensorrt/test/quantization_test.py	57	class	Create a graph containing single segment with no quantization ranges.
1636	1634	QuantizationWithRangesTest	tensorflow/tensorflow/python/compiler/tensorrt/test/quantization_test.py	82	class	Create a graph containing single segment with no quantization ranges.
1637	1635	NonQuantizedPrecisionsWithRangesTest	tensorflow/tensorflow/python/compiler/tensorrt/test/quantization_test.py	110	class	Create a graph containing single segment with no quantization ranges.
1638	1636	RankTwoTest	tensorflow/tensorflow/python/compiler/tensorrt/test/rank_two_test.py	30	class	Test for rank 2 input in TF-TRT.
1639	1637	ReshapeTest	tensorflow/tensorflow/python/compiler/tensorrt/test/reshape_transpose_test.py	28	class
1640	1638	TransposeTest	tensorflow/tensorflow/python/compiler/tensorrt/test/reshape_transpose_test.py	79	class
1641	1639	IncompatibleTransposeTest	tensorflow/tensorflow/python/compiler/tensorrt/test/reshape_transpose_test.py	108	class
1642	1640	IsQuantizationMode	tensorflow/tensorflow/python/compiler/tensorrt/test/tf_trt_integration_test_base.py	95	function
1643	1641	IsQuantizationWithCalibration	tensorflow/tensorflow/python/compiler/tensorrt/test/tf_trt_integration_test_base.py	99	function
1644	1642	GraphState	tensorflow/tensorflow/python/compiler/tensorrt/test/tf_trt_integration_test_base.py	103	class
1645	1643	TfTrtIntegrationTestBase	tensorflow/tensorflow/python/compiler/tensorrt/test/tf_trt_integration_test_base.py	109	class	Class to test Tensorflow-TensorRT integration.
1646	1644	_GetTestConfigsV1	tensorflow/tensorflow/python/compiler/tensorrt/test/tf_trt_integration_test_base.py	883	function	Returns the config combinations to run the test.
1647	1645	_GetTestConfigsV2	tensorflow/tensorflow/python/compiler/tensorrt/test/tf_trt_integration_test_base.py	902	function	Returns the config combinations to run the test.
1648	1646	_GetTest	tensorflow/tensorflow/python/compiler/tensorrt/test/tf_trt_integration_test_base.py	928	function	Gets a single test method based on the parameters.
1649	1647	_AddTestsFor	tensorflow/tensorflow/python/compiler/tensorrt/test/tf_trt_integration_test_base.py	942	function	Adds test methods to TfTrtIntegrationTestBase for specific TF version.
1650	1648	_AddTests	tensorflow/tensorflow/python/compiler/tensorrt/test/tf_trt_integration_test_base.py	967	function	Adds test methods to TfTrtIntegrationTestBase.
1651	1649	TopKTest	tensorflow/tensorflow/python/compiler/tensorrt/test/topk_test.py	29	class	Testing Top-K in TF-TRT conversion.
1652	1650	TopKOutputTypeTest	tensorflow/tensorflow/python/compiler/tensorrt/test/topk_test.py	50	class	Testing that output type of engine using Top-K is set correctly.
1653	1651	TrtModeTestBase	tensorflow/tensorflow/python/compiler/tensorrt/test/trt_mode_test.py	31	class	Test squeeze on batch dim and some unary operations in TF-TRT.
1654	1652	ImplicitBatchTest	tensorflow/tensorflow/python/compiler/tensorrt/test/trt_mode_test.py	81	class
1655	1653	ExplicitBatchTest	tensorflow/tensorflow/python/compiler/tensorrt/test/trt_mode_test.py	104	class
1656	1654	DynamicShapesTest	tensorflow/tensorflow/python/compiler/tensorrt/test/trt_mode_test.py	140	class	Test with dynamic input shapes. DynamicShapesTest is different from ExplicitBatchTest in that it uses input and output masks to change the input and output shapes to unknown shapes.
1657	1655	UnaryTest	tensorflow/tensorflow/python/compiler/tensorrt/test/unary_test.py	33	class	Test for unary operations in TF-TRT.
1658	1656	VGGBlockNCHWTest	tensorflow/tensorflow/python/compiler/tensorrt/test/vgg_block_nchw_test.py	35	class	Single vgg layer in NCHW unit tests in TF-TRT.
1659	1657	VGGBlockTest	tensorflow/tensorflow/python/compiler/tensorrt/test/vgg_block_test.py	35	class	Single vgg layer test in TF-TRT conversion.
1660	1658	GetGraph	tensorflow/tensorflow/python/compiler/tensorrt/test/testdata/gen_tftrt_model.py	49	function	Define graph.
1661	1659	GenerateModelV2	tensorflow/tensorflow/python/compiler/tensorrt/test/testdata/gen_tftrt_model.py	59	function	Generate and convert a model using TFv2 API.
1662	1660	GenerateModelV1	tensorflow/tensorflow/python/compiler/tensorrt/test/testdata/gen_tftrt_model.py	90	function	Generate and convert a model using TFv1 API.
1663	1661	ExperimentalCompileTest	tensorflow/tensorflow/python/compiler/xla/experimental_compile_test.py	30	class
1664	1662	_XlaScope	tensorflow/tensorflow/python/compiler/xla/jit.py	32	class	Keeps track of previous XLA scope calls, and depth of current call.
1665	1663	experimental_jit_scope	tensorflow/tensorflow/python/compiler/xla/jit.py	42	function	Enable or disable JIT compilation of operators within the scope. NOTE: This is an experimental feature. The compilation is a hint and only supported on a best-effort basis. Example usage: ```python with tf.xla.experimental.jit_scope(): c = tf.matmul(a, b) # compiled with tf.xla.experimental.jit_scope(compile_ops=False): d = tf.matmul(a, c) # not compiled with tf.xla.experimental.jit_scope( compile_ops=lambda node_def: 'matmul' in node_def.op.lower()): e = tf.matmul(a, b) + d # matmul is compiled, the addition is not. ``` Example of `separate_compiled_gradients`: ```python # In the example below, the computations for f, g and h will all be compiled # in separate scopes. with tf.xla.experimental.jit_scope( separate_compiled_gradients=True): f = tf.matmul(a, b) g = tf.gradients([f], [a, b], name='mygrads1') h = tf.gradients([f], [a, b], name='mygrads2') ``` Args: compile_ops: Whether to enable or disable compilation in the scope. Either a Python bool, or a callable that accepts the parameter `node_def` and returns a python bool. separate_compiled_gradients: If true put each gradient subgraph into a separate compilation scope. This gives fine-grained control over which portions of the graph will be compiled as a single unit. Compiling gradients separately may yield better performance for some graphs. The scope is named based on the scope of the forward computation as well as the name of the gradients. As a result, the gradients will be compiled in a scope that is separate from both the forward computation, and from other gradients. Raises: RuntimeError: if called when eager execution is enabled. Yields: The current scope, enabling or disabling compilation.
1666	1664	enable_jit_nonstateful	tensorflow/tensorflow/python/compiler/xla/jit_test.py	39	function
1667	1665	JITTest	tensorflow/tensorflow/python/compiler/xla/jit_test.py	47	class
1668	1666	CompilationEnabledInGradientTest	tensorflow/tensorflow/python/compiler/xla/jit_test.py	187	class
1669	1667	compile	tensorflow/tensorflow/python/compiler/xla/xla.py	67	function	Builds an operator that compiles and runs `computation` with XLA. NOTE: In eager mode, `computation` will have `@tf.function` semantics. Args: computation: A Python function that builds a computation to apply to the input. If the function takes n inputs, 'inputs' should be a list of n tensors. `computation` may return a list of operations and tensors. Tensors must come before operations in the returned list. The return value of `compile` is a list of tensors corresponding to the tensors from the output of `computation`. All `Operation`s returned from `computation` will be executed when evaluating any of the returned output tensors. inputs: A list of inputs or `None` (equivalent to an empty list). Each input can be a nested structure containing values that are convertible to tensors. Note that passing an N-dimension list of compatible values will result in a N-dimension list of scalar tensors rather than a single Rank-N tensors. If you need different behavior, convert part of inputs to tensors with `tf.convert_to_tensor`. Returns: Same data structure as if computation(*inputs) is called directly with some exceptions for correctness. Exceptions include: 1) None output: a NoOp would be returned which control-depends on computation. 2) Single value output: A tuple containing the value would be returned. 3) Operation-only outputs: a NoOp would be returned which control-depends on computation. TODO(b/121383831): Investigate into removing these special cases. Raises: RuntimeError: if called when eager execution is enabled. Known issues: When a tf.random operation is built with XLA, the implementation doesn't pass the user provided seed to the XLA compiler. As such, the XLA compiler generates a random number and uses it as a seed when compiling the operation. This implementation causes a violation of the Tensorflow defined semantics in two aspects. First, changing the value of the user defined seed doesn't change the numbers generated by the operation. Second, when a seed is not specified, running the program multiple times will generate the same numbers.
1670	1668	XLACompileContext	tensorflow/tensorflow/python/compiler/xla/xla.py	125	class	A `ControlFlowContext` for nodes inside an XLA computation cluster. THIS IS ONLY FOR TENSORFLOW INTERNAL IMPLEMENTATION, DO NO USE DIRECTLY. The primary role of `XLACompileContext` is to mark operators inside a xla.compile() computation with attribute "_xla_compile_id=XYZ", where XYZ is a unique name. `ControlFlowContext` is used to perform the annotation since it integrates with Tensorflow constructs like ResourceVariables. For example, if a `ResourceVariable` is constructed inside a xla.compile() block, the `ResourceVariable` implementation can use `with ops.control_dependencies(None)` to build the variable's definition outside the compiled computation.
1671	1669	_compile_internal	tensorflow/tensorflow/python/compiler/xla/xla.py	306	function	Builds graph operators that compiles and symbolically executes computation. Args: computation: A Python function that builds the computation to compile and execute. inputs: A list of inputs or `None` (equivalent to an empty list). Each input can be a nested structure containing values that are convertible to tensors. Note that passing an N-dimension list of compatible values will result in a N-dimension list of scalar tensors rather than a single Rank-N tensors. If you need different behavior, convert part of inputs to tensors with `tf.convert_to_tensor`. Returns: Same data structure as if computation(*inputs) is called directly with some exceptions for correctness. Exceptions include: 1) None output 2) Single value output 3) Operation-only outputs Raises: ValueError: If any element in computation outputs is neither an operations or a value that can be converted to tensor. ValueError: If computation outputs is non-flat and contains any Operations. TypeError: If `inputs` is not a list or tuple.
1672	1670	is_flat	tensorflow/tensorflow/python/compiler/xla/xla.py	409	function	Checks if outputs is a flat structure. Following structures and values are considered flat: 1) None 2) A single object 3) A list or tuple of Tensors/Operations The only structures that this function understands are sequences, dictionaries and types defined using the attrs library. E.g. this means that if outputs contains a single user-defined Object, it is considered to be flat. Errors are raised later on if that Object cannot be converted to a Tensor. Args: outputs: Output from `computation` inside `xla.compile`. Returns: A boolean indicates whether outputs is flat.
1673	1671	_postprocess_flat_outputs	tensorflow/tensorflow/python/compiler/xla/xla.py	451	function	Validates flat outputs and adds back device assignments. Args: outputs: Output from `computation` inside `xla.compile`. Returns: Tensors and Operations extracted from outputs.
1674	1672	_postprocess_non_flat_outputs	tensorflow/tensorflow/python/compiler/xla/xla.py	503	function	Validates non-flat outputs and adds back device assignments. Args: outputs: Output from `computation` inside `xla.compile`. Returns: Tensors extracted from outputs and an empty list because Operations are not allowed in non-flat outputs..
1675	1673	_disable_summary_context	tensorflow/tensorflow/python/compiler/xla/xla.py	539	function	Enters a context where all summary ops are skipped. Summaries are not yet supported in xla.compile(). So we provide this context manager that can skip creating summary ops. This is a temporary workaround due to XLA not supporting summary ops. Yields: None.
1676	1674	_CapturedObject	tensorflow/tensorflow/python/compiler/xla/xla.py	558	class	A placeholder to capture an object.
1677	1675	_get_scaffold	tensorflow/tensorflow/python/compiler/xla/xla.py	576	function	Retrieves the Scaffold from `captured_scaffold_fn`.
1678	1676	check_function_argument_count	tensorflow/tensorflow/python/compiler/xla/xla.py	591	function	Validate the number of input arguments to an XLA function. Args: func: the Python function that will be called to generate the body of an XLA computation graph. input_arity: the number of explicit arguments supplied by the caller. infeed_queue: if not None, the infeed queue that will supply additional arguments to the function. Returns: None if function can be called with the supplied number of arguments, or an error string if it cannot.
1679	1677	XLACompileContextTest	tensorflow/tensorflow/python/compiler/xla/xla_test.py	47	class
1680	1678	XlaCompileTest	tensorflow/tensorflow/python/compiler/xla/xla_test.py	217	class
1681	1679	CheckFunctionArgumentCountTest	tensorflow/tensorflow/python/compiler/xla/xla_test.py	260	class
1682	1680	BatchBenchmark	tensorflow/tensorflow/python/data/benchmarks/batch_benchmark.py	27	class	Benchmarks for `tf.data.Dataset.batch()`.
1683	1681	DatasetBenchmarkBase	tensorflow/tensorflow/python/data/benchmarks/benchmark_base.py	31	class	Base class for dataset benchmarks.
1684	1682	FilterBenchmark	tensorflow/tensorflow/python/data/benchmarks/filter_benchmark.py	26	class	Benchmarks for `tf.data.Dataset.filter()`.
1685	1683	SingleThreadedFlatMapDataset	tensorflow/tensorflow/python/data/benchmarks/from_tensor_slices_benchmark.py	30	class	A `Dataset` that maps a function over its input and flattens the result.
1686	1684	FromTensorSlicesBenchmark	tensorflow/tensorflow/python/data/benchmarks/from_tensor_slices_benchmark.py	62	class	Benchmarks for `tf.data.Dataset.from_tensor_slices()`.
1687	1685	ListFilesBenchmark	tensorflow/tensorflow/python/data/benchmarks/list_files_benchmark.py	35	class	Benchmarks for `tf.data.Dataset.list_files()`.
1688	1686	MapBenchmark	tensorflow/tensorflow/python/data/benchmarks/map_benchmark.py	32	class	Benchmarks for `tf.data.Dataset.map()`.
1689	1687	MetaBenchmark	tensorflow/tensorflow/python/data/benchmarks/meta_benchmark.py	31	class	Benchmark that compares various ways of running tf.data benchmarks.
1690	1688	PrefetchBenchmark	tensorflow/tensorflow/python/data/benchmarks/prefetch_benchmark.py	24	class	Benchmarks for `tf.data.Dataset.prefetch()`.
1691	1689	RangeBenchmark	tensorflow/tensorflow/python/data/benchmarks/range_benchmark.py	24	class	Benchmarks for `tf.data.Dataset.range()`.
1692	1690	AutotuneBenchmark	tensorflow/tensorflow/python/data/experimental/benchmarks/autotune_benchmark.py	31	class	Benchmarks for autotuning performance knobs.
1693	1691	ChooseFastestBenchmark	tensorflow/tensorflow/python/data/experimental/benchmarks/choose_fastest_benchmark.py	31	class	Benchmarks for static optimizations.
1694	1692	ChooseFastestBranchBenchmark	tensorflow/tensorflow/python/data/experimental/benchmarks/choose_fastest_branch_benchmark.py	26	class	Benchmarks for ChooseFastestBranchDatast.
1695	1693	CsvDatasetBenchmark	tensorflow/tensorflow/python/data/experimental/benchmarks/csv_dataset_benchmark.py	38	class	Benchmarks for `tf.data.experimental.CsvDataset`.
1696	1694	MapAndBatchBenchmark	tensorflow/tensorflow/python/data/experimental/benchmarks/map_and_batch_benchmark.py	40	class	Benchmarks for `tf.data.experimental.map_and_batch()`.
1697	1695	MapDefunBenchmark	tensorflow/tensorflow/python/data/experimental/benchmarks/map_defun_benchmark.py	34	class	Benchmarks for MapDefunOp.
1698	1696	_generate_csv_test_case	tensorflow/tensorflow/python/data/experimental/benchmarks/map_vectorization_benchmark.py	37	function	Generates a `decode_csv()` test case.
1699	1697	_generate_parse_single_example_test_case	tensorflow/tensorflow/python/data/experimental/benchmarks/map_vectorization_benchmark.py	57	function	Generates a `parse_single_example()` test case.
1700	1698	MapVectorizationBenchmark	tensorflow/tensorflow/python/data/experimental/benchmarks/map_vectorization_benchmark.py	97	class	Benchmarks for the `MapVectorization` optimization.
1701	1699	MatchingFilesBenchmark	tensorflow/tensorflow/python/data/experimental/benchmarks/matching_files_benchmark.py	35	class	Benchmark for the experimental `MatchingFilesDataset`.
1702	1700	OptimizationBenchmark	tensorflow/tensorflow/python/data/experimental/benchmarks/optimize_benchmark.py	32	class	Benchmarks for static optimizations.
1703	1701	_make_fake_dataset_fn	tensorflow/tensorflow/python/data/experimental/benchmarks/parallel_interleave_benchmark.py	36	function	Returns a dataset that emulates a remote storage data source. Returns a dataset factory which creates a dataset with 100 elements that emulates the performance characteristic of a file-based dataset stored in a remote storage. In particular, the first element will take an order of magnitude longer to produce than the remaining elements (100ms vs. 1ms). Args: initial_delay_us: How long to wait before producing the first element. remainder_delay_us: How long to wait before producing subsequent elements.
1704	1702	ParallelInterleaveBenchmark	tensorflow/tensorflow/python/data/experimental/benchmarks/parallel_interleave_benchmark.py	68	class	Benchmarks for `tf.data.experimental.parallel_interleave()`.
1705	1703	_time_resampling	tensorflow/tensorflow/python/data/experimental/benchmarks/rejection_resample_benchmark.py	31	function
1706	1704	RejectionResampleBenchmark	tensorflow/tensorflow/python/data/experimental/benchmarks/rejection_resample_benchmark.py	56	class	Benchmarks for `tf.data.experimental.rejection_resample()`.
1707	1705	SnapshotDatasetBenchmark	tensorflow/tensorflow/python/data/experimental/benchmarks/snapshot_dataset_benchmark.py	34	class	Benchmarks for `tf.data.experimental.snapshot()`.
1708	1706	UnbatchBenchmark	tensorflow/tensorflow/python/data/experimental/benchmarks/unbatch_benchmark.py	32	class	Benchmarks for `tf.data.Dataset.unbatch()`.
1709	1707	AssertCardinalityTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/assert_cardinality_test.py	30	class	Tests for `tf.data.experimental.assert_cardinality()`.
1710	1708	AssertNextTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/assert_next_test.py	30	class
1711	1709	chunk	tensorflow/tensorflow/python/data/experimental/kernel_tests/auto_shard_dataset_test.py	46	function
1712	1710	AutoShardDatasetTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/auto_shard_dataset_test.py	51	class
1713	1711	AutoShardTextLineDatasetTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/auto_shard_dataset_test.py	509	class
1714	1712	_element_length_fn	tensorflow/tensorflow/python/data/experimental/kernel_tests/bucket_by_sequence_length_test.py	37	function
1715	1713	_to_sparse_tensor	tensorflow/tensorflow/python/data/experimental/kernel_tests/bucket_by_sequence_length_test.py	42	function
1716	1714	_format_record	tensorflow/tensorflow/python/data/experimental/kernel_tests/bucket_by_sequence_length_test.py	46	function
1717	1715	_get_record_type	tensorflow/tensorflow/python/data/experimental/kernel_tests/bucket_by_sequence_length_test.py	56	function
1718	1716	_get_record_shape	tensorflow/tensorflow/python/data/experimental/kernel_tests/bucket_by_sequence_length_test.py	66	function
1719	1717	BucketBySequenceLengthTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/bucket_by_sequence_length_test.py	76	class
1720	1718	_test_objects	tensorflow/tensorflow/python/data/experimental/kernel_tests/compression_ops_test.py	31	function
1721	1719	CompressionOpsTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/compression_ops_test.py	53	class
1722	1720	CopyToDeviceTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/copy_to_device_test.py	40	class
1723	1721	CounterTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/counter_test.py	30	class
1724	1722	CsvDatasetTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/csv_dataset_test.py	40	class
1725	1723	_make_scalar_ds	tensorflow/tensorflow/python/data/experimental/kernel_tests/dense_to_ragged_batch_test.py	38	function	Create a test dataset with scalar elements.
1726	1724	_make_vector_ds	tensorflow/tensorflow/python/data/experimental/kernel_tests/dense_to_ragged_batch_test.py	43	function	Create a test dataset with vector elements (of varying size).
1727	1725	_make_matrix_ds1	tensorflow/tensorflow/python/data/experimental/kernel_tests/dense_to_ragged_batch_test.py	48	function	Create a test dataset with matrix elements (of varying size).
1728	1726	_make_matrix_ds2	tensorflow/tensorflow/python/data/experimental/kernel_tests/dense_to_ragged_batch_test.py	53	function	Create a test dataset with matrix elements (of varying size).
1729	1727	_make_matrix_ds_fully_defined	tensorflow/tensorflow/python/data/experimental/kernel_tests/dense_to_ragged_batch_test.py	58	function	Create a test dataset with matrix elements (of varying size).
1730	1728	_make_5dtensor_ds	tensorflow/tensorflow/python/data/experimental/kernel_tests/dense_to_ragged_batch_test.py	63	function	Create a test dataset with matrix elements (of varying size).
1731	1729	_make_ragged_ds	tensorflow/tensorflow/python/data/experimental/kernel_tests/dense_to_ragged_batch_test.py	69	function	Create a test dataset with RaggedTensor elements (of varying size).
1732	1730	_make_dict_ds	tensorflow/tensorflow/python/data/experimental/kernel_tests/dense_to_ragged_batch_test.py	76	function	Create a test set with various element shapes.
1733	1731	_make_tuple_ds	tensorflow/tensorflow/python/data/experimental/kernel_tests/dense_to_ragged_batch_test.py	89	function	Create a test set with various element shapes.
1734	1732	_to_list	tensorflow/tensorflow/python/data/experimental/kernel_tests/dense_to_ragged_batch_test.py	98	function
1735	1733	RaggedBatchTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/dense_to_ragged_batch_test.py	102	class
1736	1734	DenseToSparseBatchTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/dense_to_sparse_batch_test.py	32	class
1737	1735	DirectedInterleaveDatasetTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/directed_interleave_dataset_test.py	34	class
1738	1736	GetSingleElementTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/get_single_element_test.py	34	class
1739	1737	GroupByReducerTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/group_by_reducer_test.py	37	class
1740	1738	GroupByWindowTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/group_by_window_test.py	41	class
1741	1739	IgnoreErrorsTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/ignore_errors_test.py	40	class
1742	1740	IOTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/io_test.py	32	class
1743	1741	MakeBatchedFeaturesDatasetTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/make_batched_features_dataset_test.py	38	class
1744	1742	MakeCsvDatasetTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/make_csv_dataset_test.py	38	class
1745	1743	MakeTFRecordDatasetTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/make_tf_record_dataset_test.py	33	class
1746	1744	MapAndBatchTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/map_and_batch_test.py	43	class
1747	1745	_test_combinations	tensorflow/tensorflow/python/data/experimental/kernel_tests/map_defun_op_test.py	44	function
1748	1746	MapDefunTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/map_defun_op_test.py	48	class
1749	1747	MatchingFilesDatasetTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/matching_files_test.py	34	class
1750	1748	ModelDatasetTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/model_dataset_test.py	30	class
1751	1749	NonSerializableTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/non_serializable_test.py	29	class
1752	1750	_captured_refvar_test_combinations	tensorflow/tensorflow/python/data/experimental/kernel_tests/optimize_dataset_test.py	44	function
1753	1751	OptimizeDatasetTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/optimize_dataset_test.py	106	class
1754	1752	OverrideThreadpoolTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/override_threadpool_test.py	38	class
1755	1753	ParallelInterleaveTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/parallel_interleave_test.py	42	class
1756	1754	ParseExampleDatasetTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/parse_example_dataset_test.py	54	class
1757	1755	PrefetchToDeviceTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/prefetch_to_device_test.py	37	class
1758	1756	PrefetchWithSlackTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/prefetch_with_slack_test.py	33	class
1759	1757	RandomDatasetTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/random_dataset_test.py	29	class
1760	1758	FixedLengthRecordDatasetTestBase	tensorflow/tensorflow/python/data/experimental/kernel_tests/reader_dataset_ops_test_base.py	36	class	Base class for setting up and testing FixedLengthRecordDataset.
1761	1759	MakeBatchedFeaturesDatasetTestBase	tensorflow/tensorflow/python/data/experimental/kernel_tests/reader_dataset_ops_test_base.py	63	class	Base class for setting up and testing `make_batched_features_dataset`.
1762	1760	TextLineDatasetTestBase	tensorflow/tensorflow/python/data/experimental/kernel_tests/reader_dataset_ops_test_base.py	271	class	Base class for setting up and testing TextLineDataset.
1763	1761	TFRecordDatasetTestBase	tensorflow/tensorflow/python/data/experimental/kernel_tests/reader_dataset_ops_test_base.py	311	class	Base class for setting up and testing TFRecordDataset.
1764	1762	BatchSizesForWorkerTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/rebatch_dataset_test.py	35	class
1765	1763	_flat_shapes	tensorflow/tensorflow/python/data/experimental/kernel_tests/rebatch_dataset_test.py	113	function
1766	1764	RebatchDatasetTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/rebatch_dataset_test.py	120	class
1767	1765	LegacyRebatchDatasetTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/rebatch_dataset_test.py	325	class
1768	1766	ComputeBatchSizeTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/rebatch_dataset_test.py	500	class
1769	1767	RejectionResampleTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/rejection_resample_test.py	36	class
1770	1768	LocalReplicateTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/replicate_test.py	44	class
1771	1769	_get_server_def	tensorflow/tensorflow/python/data/experimental/kernel_tests/replicate_test.py	225	function	Returns a server def with a single job + multiple tasks.
1772	1770	EagerClusterReplicateTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/replicate_test.py	245	class
1773	1771	GraphClusterReplicateTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/replicate_test.py	327	class
1774	1772	ScanTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/scan_test.py	46	class
1775	1773	ShuffleAndRepeatTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/shuffle_and_repeat_test.py	32	class
1776	1774	SleepTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/sleep_test.py	32	class
1777	1775	SnapshotDatasetTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/snapshot_test.py	40	class
1778	1776	LegacySnapshotDatasetTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/snapshot_test.py	318	class
1779	1777	SqlDatasetTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/sql_dataset_test.py	32	class
1780	1778	SqlDatasetTestBase	tensorflow/tensorflow/python/data/experimental/kernel_tests/sql_dataset_test_base.py	30	class	Base class for setting up and testing SqlDataset.
1781	1779	StatsDatasetTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/stats_dataset_ops_test.py	39	class
1782	1780	ThreadUtilizationStatsTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/stats_dataset_ops_test.py	334	class
1783	1781	FeatureStatsDatasetTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/stats_dataset_ops_test.py	399	class
1784	1782	StatsDatasetTestBase	tensorflow/tensorflow/python/data/experimental/kernel_tests/stats_dataset_test_base.py	37	class	Base class for testing statistics gathered in `StatsAggregator`.
1785	1783	_events_from_file	tensorflow/tensorflow/python/data/experimental/kernel_tests/stats_dataset_test_base.py	311	function	Returns all events in a single event file. Args: filepath: Path to the event file. Returns: A list of all tf.Event protos in the event file.
1786	1784	_events_from_logdir	tensorflow/tensorflow/python/data/experimental/kernel_tests/stats_dataset_test_base.py	329	function	Returns all events in the single eventfile in logdir. Args: logdir: The directory in which the single event file is sought. Returns: A list of all tf.Event protos from the single event file. Raises: AssertionError: If logdir does not contain exactly one file.
1787	1785	TakeWhileTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/take_while_test.py	34	class
1788	1786	TFRecordWriterTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/tf_record_writer_test.py	39	class
1789	1787	UniqueTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/unique_test.py	32	class
1790	1788	VariantTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/variant_test.py	28	class
1791	1789	WrapDatasetVariantTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/wrap_unwrap_test.py	31	class
1792	1790	ChooseFastestBranchDatasetTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/optimization/choose_fastest_branch_dataset_test.py	34	class
1793	1791	ChooseFastestDatasetTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/optimization/choose_fastest_dataset_test.py	31	class
1794	1792	_test_combinations	tensorflow/tensorflow/python/data/experimental/kernel_tests/optimization/filter_fusion_test.py	34	function
1795	1793	FilterFusionTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/optimization/filter_fusion_test.py	62	class
1796	1794	FilterWithRandomUniformFusionTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/optimization/filter_with_random_uniform_fusion_test.py	30	class
1797	1795	GrapplerTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/optimization/grappler_test.py	37	class
1798	1796	_test_combinations	tensorflow/tensorflow/python/data/experimental/kernel_tests/optimization/hoist_random_uniform_test.py	38	function
1799	1797	HoistRandomUniformTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/optimization/hoist_random_uniform_test.py	68	class
1800	1798	InjectPrefetchTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/optimization/inject_prefetch_test.py	29	class
1801	1799	LatencyAllEdgesTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/optimization/latency_all_edges_test.py	31	class
1802	1800	MapAndBatchFusionTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/optimization/map_and_batch_fusion_test.py	29	class
1803	1801	_test_combinations	tensorflow/tensorflow/python/data/experimental/kernel_tests/optimization/map_and_filter_fusion_test.py	34	function
1804	1802	MapAndFilterFusionTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/optimization/map_and_filter_fusion_test.py	77	class
1805	1803	_test_combinations	tensorflow/tensorflow/python/data/experimental/kernel_tests/optimization/map_fusion_test.py	34	function
1806	1804	MapFusionTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/optimization/map_fusion_test.py	66	class
1807	1805	_test_combinations	tensorflow/tensorflow/python/data/experimental/kernel_tests/optimization/map_parallelization_test.py	37	function
1808	1806	MapParallelizationTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/optimization/map_parallelization_test.py	58	class
1809	1807	_generate_test_combinations	tensorflow/tensorflow/python/data/experimental/kernel_tests/optimization/map_vectorization_test.py	51	function
1810	1808	_unary_bitwise_test_combinations	tensorflow/tensorflow/python/data/experimental/kernel_tests/optimization/map_vectorization_test.py	60	function
1811	1809	_unary_logical_test_combinations	tensorflow/tensorflow/python/data/experimental/kernel_tests/optimization/map_vectorization_test.py	65	function
1812	1810	_unary_complex_test_combinations	tensorflow/tensorflow/python/data/experimental/kernel_tests/optimization/map_vectorization_test.py	70	function
1813	1811	_unary_real_test_combinations	tensorflow/tensorflow/python/data/experimental/kernel_tests/optimization/map_vectorization_test.py	81	function
1814	1812	_binary_bitwise_test_combinations	tensorflow/tensorflow/python/data/experimental/kernel_tests/optimization/map_vectorization_test.py	135	function
1815	1813	_binary_logical_test_combinations	tensorflow/tensorflow/python/data/experimental/kernel_tests/optimization/map_vectorization_test.py	144	function
1816	1814	_binary_real_test_combinations	tensorflow/tensorflow/python/data/experimental/kernel_tests/optimization/map_vectorization_test.py	150	function
1817	1815	MapVectorizationTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/optimization/map_vectorization_test.py	192	class
1818	1816	_test_combinations	tensorflow/tensorflow/python/data/experimental/kernel_tests/optimization/noop_elimination_test.py	34	function
1819	1817	NoopEliminationTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/optimization/noop_elimination_test.py	90	class
1820	1818	ReorderDataDiscardingOpsTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/optimization/reorder_data_discarding_ops_test.py	29	class
1821	1819	ShuffleAndRepeatFusionTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/optimization/shuffle_and_repeat_fusion_test.py	30	class
1822	1820	AssertCardinalityDatasetSerializationTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/serialization/assert_cardinality_dataset_serialization_test.py	30	class
1823	1821	AutoShardDatasetSerializationTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/serialization/auto_shard_dataset_serialization_test.py	36	class
1824	1822	BatchDatasetSerializationTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/serialization/batch_dataset_serialization_test.py	33	class
1825	1823	CacheDatasetSerializationTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/serialization/cache_dataset_serialization_test.py	32	class
1826	1824	_test_combinations	tensorflow/tensorflow/python/data/experimental/kernel_tests/serialization/checkpoint_input_pipeline_hook_test.py	40	function
1827	1825	CheckpointInputPipelineHookTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/serialization/checkpoint_input_pipeline_hook_test.py	44	class
1828	1826	ChooseFastestBranchDatasetSerializationTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/serialization/choose_fastest_branch_dataset_serialization_test.py	33	class
1829	1827	ChooseFastestDatasetSerializationTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/serialization/choose_fastest_dataset_serialization_test.py	30	class
1830	1828	ConcatenateDatasetSerializationTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/serialization/concatenate_dataset_serialization_test.py	30	class
1831	1829	CsvDatasetSerializationTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/serialization/csv_dataset_serialization_test.py	32	class
1832	1830	FromTensorsSerializationTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/serialization/dataset_constructor_serialization_test.py	31	class
1833	1831	FromTensorSlicesSerializationTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/serialization/dataset_constructor_serialization_test.py	49	class
1834	1832	FromSparseTensorSlicesSerializationTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/serialization/dataset_constructor_serialization_test.py	71	class
1835	1833	remove_variants	tensorflow/tensorflow/python/data/experimental/kernel_tests/serialization/dataset_serialization_test_base.py	41	function	Remove variants from a nest structure, so sess.run will execute.
1836	1834	DatasetSerializationTestBase	tensorflow/tensorflow/python/data/experimental/kernel_tests/serialization/dataset_serialization_test_base.py	55	class	Base class for testing serializable datasets.
1837	1835	FilterDatasetSerializationTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/serialization/filter_dataset_serialization_test.py	31	class
1838	1836	FixedLengthRecordDatasetSerializationTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/serialization/fixed_length_record_dataset_serialization_test.py	30	class
1839	1837	FlatMapDatasetSerializationTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/serialization/flat_map_dataset_serialization_test.py	38	class
1840	1838	GroupByReducerSerializationTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/serialization/group_by_reducer_serialization_test.py	31	class
1841	1839	GroupByWindowSerializationTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/serialization/group_by_window_serialization_test.py	31	class
1842	1840	IgnoreErrorsSerializationTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/serialization/ignore_errors_serialization_test.py	31	class
1843	1841	InterleaveDatasetSerializationTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/serialization/interleave_dataset_serialization_test.py	32	class
1844	1842	MapAndBatchDatasetSerializationTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/serialization/map_and_batch_dataset_serialization_test.py	34	class
1845	1843	MapDatasetSerializationTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/serialization/map_dataset_serialization_test.py	38	class
1846	1844	MatchingFilesDatasetSerializationTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/serialization/matching_files_dataset_serialization_test.py	33	class
1847	1845	OptimizeDatasetSerializationTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/serialization/optimize_dataset_serialization_test.py	30	class
1848	1846	PaddedBatchDatasetSerializationTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/serialization/padded_batch_dataset_serialization_test.py	32	class
1849	1847	ParallelInterleaveDatasetSerializationTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/serialization/parallel_interleave_dataset_serialization_test.py	33	class
1850	1848	ParallelMapDatasetSerializationTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/serialization/parallel_map_dataset_serialization_test.py	37	class
1851	1849	ParseExampleDatasetSerializationTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/serialization/parse_example_dataset_serialization_test.py	29	class
1852	1850	PrefetchDatasetSerializationTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/serialization/prefetch_dataset_serialization_test.py	29	class
1853	1851	RangeDatasetSerializationTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/serialization/range_dataset_serialization_test.py	38	class
1854	1852	LegacyRebatchDatasetSerializationTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/serialization/rebatch_dataset_serialization_test.py	30	class
1855	1853	RebatchDatasetSerializationTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/serialization/rebatch_dataset_serialization_test.py	46	class
1856	1854	SampleFromDatasetsSerializationTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/serialization/sample_from_datasets_serialization_test.py	30	class
1857	1855	ScanDatasetSerializationTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/serialization/scan_dataset_serialization_test.py	30	class
1858	1856	SkipDatasetSerializationTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/serialization/sequence_dataset_serialization_test.py	30	class
1859	1857	TakeDatasetSerializationTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/serialization/sequence_dataset_serialization_test.py	61	class
1860	1858	RepeatDatasetSerializationTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/serialization/sequence_dataset_serialization_test.py	91	class
1861	1859	SerializationIntegrationTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/serialization/serialization_integration_test.py	33	class
1862	1860	ShardDatasetSerializationTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/serialization/shard_dataset_serialization_test.py	29	class
1863	1861	ShuffleAndRepeatSerializationTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/serialization/shuffle_and_repeat_dataset_serialization_test.py	30	class
1864	1862	ShuffleDatasetSerializationTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/serialization/shuffle_dataset_serialization_test.py	32	class
1865	1863	SnapshotDatasetSerializationTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/serialization/snapshot_dataset_serialization_test.py	33	class
1866	1864	LegacySnapshotDatasetSerializationTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/serialization/snapshot_dataset_serialization_test.py	124	class
1867	1865	SqlDatasetSerializationTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/serialization/sql_dataset_serialization_test.py	34	class
1868	1866	StatsDatasetSerializationTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/serialization/stats_dataset_serialization_test.py	36	class
1869	1867	TakeWhileDatasetSerializationTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/serialization/take_while_dataset_serialization_test.py	30	class
1870	1868	TextLineDatasetSerializationTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/serialization/textline_dataset_serialization_test.py	30	class
1871	1869	TFRecordDatasetSerializationTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/serialization/tf_record_dataset_serialization_test.py	34	class
1872	1870	UnbatchDatasetSerializationTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/serialization/unbatch_dataset_serialization_test.py	30	class
1873	1871	UniqueDatasetSerializationTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/serialization/unique_dataset_serialization_test.py	30	class
1874	1872	ZipDatasetSerializationTest	tensorflow/tensorflow/python/data/experimental/kernel_tests/serialization/zip_dataset_serialization_test.py	30	class
1875	1873	dense_to_ragged_batch	tensorflow/tensorflow/python/data/experimental/ops/batching.py	36	function	A transformation that batches ragged elements into `tf.RaggedTensor`s. This transformation combines multiple consecutive elements of the input dataset into a single element. Like `tf.data.Dataset.batch`, the components of the resulting element will have an additional outer dimension, which will be `batch_size` (or `N % batch_size` for the last element if `batch_size` does not divide the number of input elements `N` evenly and `drop_remainder` is `False`). If your program depends on the batches having the same outer dimension, you should set the `drop_remainder` argument to `True` to prevent the smaller batch from being produced. Unlike `tf.data.Dataset.batch`, the input elements to be batched may have different shapes: * If an input element is a `tf.Tensor` whose static `tf.TensorShape` is fully defined, then it is batched as normal. * If an input element is a `tf.Tensor` whose static `tf.TensorShape` contains one or more axes with unknown size (i.e., `shape[i]=None`), then the output will contain a `tf.RaggedTensor` that is ragged up to any of such dimensions. * If an input element is a `tf.RaggedTensor` or any other type, then it is batched as normal. Example: >>> dataset = tf.data.Dataset.from_tensor_slices(np.arange(6)) >>> dataset = dataset.map(lambda x: tf.range(x)) >>> dataset.element_spec.shape TensorShape([None]) >>> dataset = dataset.apply( ... tf.data.experimental.dense_to_ragged_batch(batch_size=2)) >>> for batch in dataset: ... print(batch) <tf.RaggedTensor [[], [0]]> <tf.RaggedTensor [[0, 1], [0, 1, 2]]> <tf.RaggedTensor [[0, 1, 2, 3], [0, 1, 2, 3, 4]]> Args: batch_size: A `tf.int64` scalar `tf.Tensor`, representing the number of consecutive elements of this dataset to combine in a single batch. drop_remainder: (Optional.) A `tf.bool` scalar `tf.Tensor`, representing whether the last batch should be dropped in the case it has fewer than `batch_size` elements; the default behavior is not to drop the smaller batch. row_splits_dtype: The dtype that should be used for the `row_splits` of any new ragged tensors. Existing `tf.RaggedTensor` elements do not have their row_splits dtype changed. Returns: Dataset: A `Dataset`.
1876	1874	dense_to_sparse_batch	tensorflow/tensorflow/python/data/experimental/ops/batching.py	102	function	A transformation that batches ragged elements into `tf.sparse.SparseTensor`s. Like `Dataset.padded_batch()`, this transformation combines multiple consecutive elements of the dataset, which might have different shapes, into a single element. The resulting element has three components (`indices`, `values`, and `dense_shape`), which comprise a `tf.sparse.SparseTensor` that represents the same data. The `row_shape` represents the dense shape of each row in the resulting `tf.sparse.SparseTensor`, to which the effective batch size is prepended. For example: ```python # NOTE: The following examples use `{ ... }` to represent the # contents of a dataset. a = { ['a', 'b', 'c'], ['a', 'b'], ['a', 'b', 'c', 'd'] } a.apply(tf.data.experimental.dense_to_sparse_batch( batch_size=2, row_shape=[6])) == { ([[0, 0], [0, 1], [0, 2], [1, 0], [1, 1]], # indices ['a', 'b', 'c', 'a', 'b'], # values [2, 6]), # dense_shape ([[0, 0], [0, 1], [0, 2], [0, 3]], ['a', 'b', 'c', 'd'], [1, 6]) } ``` Args: batch_size: A `tf.int64` scalar `tf.Tensor`, representing the number of consecutive elements of this dataset to combine in a single batch. row_shape: A `tf.TensorShape` or `tf.int64` vector tensor-like object representing the equivalent dense shape of a row in the resulting `tf.sparse.SparseTensor`. Each element of this dataset must have the same rank as `row_shape`, and must have size less than or equal to `row_shape` in each dimension. Returns: A `Dataset` transformation function, which can be passed to `tf.data.Dataset.apply`.
1877	1875	map_and_batch_with_legacy_function	tensorflow/tensorflow/python/data/experimental/ops/batching.py	153	function	Fused implementation of `map` and `batch`. NOTE: This is an escape hatch for existing uses of `map_and_batch` that do not work with V2 functions. New uses are strongly discouraged and existing uses should migrate to `map_and_batch` as this method will not be removed in V2. Args: map_func: A function mapping a nested structure of tensors to another nested structure of tensors. batch_size: A `tf.int64` scalar `tf.Tensor`, representing the number of consecutive elements of this dataset to combine in a single batch. num_parallel_batches: (Optional.) A `tf.int64` scalar `tf.Tensor`, representing the number of batches to create in parallel. On one hand, higher values can help mitigate the effect of stragglers. On the other hand, higher values can increase contention if CPU is scarce. drop_remainder: (Optional.) A `tf.bool` scalar `tf.Tensor`, representing whether the last batch should be dropped in case its size is smaller than desired; the default behavior is not to drop the smaller batch. num_parallel_calls: (Optional.) A `tf.int32` scalar `tf.Tensor`, representing the number of elements to process in parallel. If not specified, `batch_size * num_parallel_batches` elements will be processed in parallel. If the value `tf.data.experimental.AUTOTUNE` is used, then the number of parallel calls is set dynamically based on available CPU. Returns: A `Dataset` transformation function, which can be passed to `tf.data.Dataset.apply`. Raises: ValueError: If both `num_parallel_batches` and `num_parallel_calls` are specified.
1878	1876	map_and_batch	tensorflow/tensorflow/python/data/experimental/ops/batching.py	213	function	Fused implementation of `map` and `batch`. Maps `map_func` across `batch_size` consecutive elements of this dataset and then combines them into a batch. Functionally, it is equivalent to `map` followed by `batch`. This API is temporary and deprecated since input pipeline optimization now fuses consecutive `map` and `batch` operations automatically. Args: map_func: A function mapping a nested structure of tensors to another nested structure of tensors. batch_size: A `tf.int64` scalar `tf.Tensor`, representing the number of consecutive elements of this dataset to combine in a single batch. num_parallel_batches: (Optional.) A `tf.int64` scalar `tf.Tensor`, representing the number of batches to create in parallel. On one hand, higher values can help mitigate the effect of stragglers. On the other hand, higher values can increase contention if CPU is scarce. drop_remainder: (Optional.) A `tf.bool` scalar `tf.Tensor`, representing whether the last batch should be dropped in case its size is smaller than desired; the default behavior is not to drop the smaller batch. num_parallel_calls: (Optional.) A `tf.int32` scalar `tf.Tensor`, representing the number of elements to process in parallel. If not specified, `batch_size * num_parallel_batches` elements will be processed in parallel. If the value `tf.data.experimental.AUTOTUNE` is used, then the number of parallel calls is set dynamically based on available CPU. Returns: A `Dataset` transformation function, which can be passed to `tf.data.Dataset.apply`. Raises: ValueError: If both `num_parallel_batches` and `num_parallel_calls` are specified.
1879	1877	unbatch	tensorflow/tensorflow/python/data/experimental/ops/batching.py	269	function	Splits elements of a dataset into multiple elements on the batch dimension. For example, if elements of the dataset are shaped `[B, a0, a1, ...]`, where `B` may vary for each input element, then for each element in the dataset, the unbatched dataset will contain `B` consecutive elements of shape `[a0, a1, ...]`. ```python # NOTE: The following example uses `{ ... }` to represent the contents # of a dataset. a = { ['a', 'b', 'c'], ['a', 'b'], ['a', 'b', 'c', 'd'] } a.unbatch() == { 'a', 'b', 'c', 'a', 'b', 'a', 'b', 'c', 'd'} ``` Returns: A `Dataset` transformation function, which can be passed to `tf.data.Dataset.apply`.
1880	1878	_DenseToSparseBatchDataset	tensorflow/tensorflow/python/data/experimental/ops/batching.py	297	class	A `Dataset` that batches ragged dense elements into `tf.sparse.SparseTensor`s.
1881	1879	_MapAndBatchDataset	tensorflow/tensorflow/python/data/experimental/ops/batching.py	327	class	A `Dataset` that maps a function over a batch of elements.
1882	1880	_DenseToRaggedDataset	tensorflow/tensorflow/python/data/experimental/ops/batching.py	380	class	A `Dataset` that encodes dense inputs as ragged (w/ ragged_rank=0). In particular: * Any tf.Tensor elements with rank>0 are encoded as ragged tensors with ragged_rank=0. This allows tensors with varying shape to be batched together. * Any other elements are left as-is.
1883	1881	cardinality	tensorflow/tensorflow/python/data/experimental/ops/cardinality.py	38	function	Returns the cardinality of `dataset`, if known. The operation returns the cardinality of `dataset`. The operation may return `tf.data.experimental.INFINITE_CARDINALITY` if `dataset` contains an infinite number of elements or `tf.data.experimental.UNKNOWN_CARDINALITY` if the analysis fails to determine the number of elements in `dataset` (e.g. when the dataset source is a file). >>> dataset = tf.data.Dataset.range(42) >>> print(tf.data.experimental.cardinality(dataset).numpy()) 42 >>> dataset = dataset.repeat() >>> cardinality = tf.data.experimental.cardinality(dataset) >>> print((cardinality == tf.data.experimental.INFINITE_CARDINALITY).numpy()) True >>> dataset = dataset.filter(lambda x: True) >>> cardinality = tf.data.experimental.cardinality(dataset) >>> print((cardinality == tf.data.experimental.UNKNOWN_CARDINALITY).numpy()) True Args: dataset: A `tf.data.Dataset` for which to determine cardinality. Returns: A scalar `tf.int64` `Tensor` representing the cardinality of `dataset`. If the cardinality is infinite or unknown, the operation returns the named constant `INFINITE_CARDINALITY` and `UNKNOWN_CARDINALITY` respectively.
1884	1882	assert_cardinality	tensorflow/tensorflow/python/data/experimental/ops/cardinality.py	72	function	Asserts the cardinality of the input dataset. NOTE: The following assumes that "examples.tfrecord" contains 42 records. >>> dataset = tf.data.TFRecordDataset("examples.tfrecord") >>> cardinality = tf.data.experimental.cardinality(dataset) >>> print((cardinality == tf.data.experimental.UNKNOWN_CARDINALITY).numpy()) True >>> dataset = dataset.apply(tf.data.experimental.assert_cardinality(42)) >>> print(tf.data.experimental.cardinality(dataset).numpy()) 42 Args: expected_cardinality: The expected cardinality of the input dataset. Returns: A `Dataset` transformation function, which can be passed to `tf.data.Dataset.apply`. Raises: FailedPreconditionError: The assertion is checked at runtime (when iterating the dataset) and an error is raised if the actual and expected cardinality differ.
1885	1883	_AssertCardinalityDataset	tensorflow/tensorflow/python/data/experimental/ops/cardinality.py	103	class	A `Dataset` that assert the cardinality of its input.
1886	1884	compress	tensorflow/tensorflow/python/data/experimental/ops/compression_ops.py	24	function	Compress a dataset element. Args: element: A nested structure of types supported by Tensorflow. Returns: A variant tensor representing the compressed element. This variant can be passed to `uncompress` to get back the original element.
1887	1885	uncompress	tensorflow/tensorflow/python/data/experimental/ops/compression_ops.py	39	function	Uncompress a compressed dataset element. Args: element: A scalar variant tensor to uncompress. The element should have been created by calling `compress`. output_spec: A nested structure of `tf.TypeSpec` representing the type(s) of the uncompressed element. Returns: The uncompressed element.
1888	1886	CounterV2	tensorflow/tensorflow/python/data/experimental/ops/counter.py	29	function	Creates a `Dataset` that counts from `start` in steps of size `step`. For example: ```python Dataset.count() == [0, 1, 2, ...) Dataset.count(2) == [2, 3, ...) Dataset.count(2, 5) == [2, 7, 12, ...) Dataset.count(0, -1) == [0, -1, -2, ...) Dataset.count(10, -1) == [10, 9, ...) ``` Args: start: (Optional.) The starting value for the counter. Defaults to 0. step: (Optional.) The step size for the counter. Defaults to 1. dtype: (Optional.) The data type for counter elements. Defaults to `tf.int64`. Returns: A `Dataset` of scalar `dtype` elements.
1889	1887	CounterV1	tensorflow/tensorflow/python/data/experimental/ops/counter.py	59	function
1890	1888	ProcessingMode	tensorflow/tensorflow/python/data/experimental/ops/data_service_ops.py	36	class
1891	1889	_DataServiceDatasetV2	tensorflow/tensorflow/python/data/experimental/ops/data_service_ops.py	49	class	A `Dataset` that reads elements from the tf.data service.
1892	1890	_DataServiceDatasetV1	tensorflow/tensorflow/python/data/experimental/ops/data_service_ops.py	124	class	A `Dataset` that executes its input through the tf.data service.
1893	1891	_parse_service	tensorflow/tensorflow/python/data/experimental/ops/data_service_ops.py	148	function	Parses a tf.data service string into a (protocol, address) tuple. Args: service: A string in the format "protocol://address". Returns: The parsed (protocol, address) tuple
1894	1892	_from_dataset_id	tensorflow/tensorflow/python/data/experimental/ops/data_service_ops.py	175	function	Creates a dataset which reads data from the tf.data service. This transformation is similar to `from_dataset_id`, but supports additional parameters which we do not yet want to add to the public Python API. Args: processing_mode: A string specifying the policy for how data should be processed by tf.data workers. Currently, the only supported value is "parallel_epochs". service: A string indicating how to connect to the tf.data service. The string should be in the format "<protocol>://<address>", e.g. "grpc://localhost:5000". dataset_id: The id of the dataset to read from. This id is returned by `register_dataset` when the dataset is registered with the tf.data service. element_spec: A nested structure of `tf.TypeSpec`s representing the type of elements produced by the dataset. Use `tf.data.Dataset.element_spec` to see the element spec for a given dataset. job_name: (Optional.) The name of the job. This argument makes it possible for multiple datasets to share the same job. The default behavior is that the dataset creates anonymous, exclusively owned jobs. max_outstanding_requests: (Optional.) A limit on how many elements may be requested at the same time. You can use this option to control the amount of memory used, since `distribute` won't use more than `element_size` * `max_outstanding_requests` of memory. task_refresh_interval_hint_ms: (Optional.) A hint for how often to query the dispatcher for task changes. Returns: A `tf.data.Dataset` which reads from the tf.data service.
1895	1893	_distribute	tensorflow/tensorflow/python/data/experimental/ops/data_service_ops.py	248	function	A transformation that moves dataset processing to the tf.data service. This transformation is similar to `distribute`, but supports additional parameters which we do not yet want to add to the public Python API. Args: processing_mode: A string specifying the policy for how data should be processed by tf.data workers. Currently, the only supported value is "parallel_epochs". service: A string indicating how to connect to the tf.data service. The string should be in the format "<protocol>://<address>", e.g. "grpc://localhost:5000". job_name: (Optional.) The name of the job. This argument makes it possible for multiple datasets to share the same job. The default behavior is that the dataset creates anonymous, exclusively owned jobs. max_outstanding_requests: (Optional.) A limit on how many elements may be requested at the same time. You can use this option to control the amount of memory used, since `distribute` won't use more than `element_size` * `max_outstanding_requests` of memory. task_refresh_interval_hint_ms: (Optional.) A hint for how often to query the dispatcher for task changes. Returns: Dataset: A `Dataset` of the elements produced by the data service.
1896	1894	distribute	tensorflow/tensorflow/python/data/experimental/ops/data_service_ops.py	295	function	A transformation that moves dataset processing to the tf.data service. When you iterate over a dataset containing the `distribute` transformation, the tf.data service creates a "job" which produces data for the dataset iteration. The `processing_mode` argument controls what data is produced by a tf.data service job. Currently, the only supported mode is "parallel_epochs". processing_mode="parallel_epochs" means that multiple tf.data workers will iterate through the dataset in parallel, each producing all elements of the dataset. For example, if the dataset contains {0, 1, 2}, every tf.data worker used for execution will produce {0, 1, 2}. If there are 3 workers, the job will produce the elements {0, 0, 0, 1, 1, 1, 2, 2, 2} (though not necessarily in that order). To account for this, it is recommended to randomly shuffle your dataset, so that different tf.data workers will iterate through the dataset in different orders. In the future, there will be additional processing modes. For example, a "one_epoch" mode which partitions the dataset across the tf.data workers, so that the consumers see each element of the dataset only once. ``` dataset = tf.data.Dataset.range(5) dataset = dataset.map(lambda x: x*x) dataset = dataset.apply( tf.data.experimental.service.distribute("parallel_epochs", "grpc://dataservice:5000")) dataset = dataset.map(lambda x: x+1) for element in dataset: print(element) # prints { 1, 2, 5, 10, 17 } ``` In the above example, the first two lines (before the call to `distribute`) will be executed on tf.data workers, and the elements provided over RPC. The remaining transformations (after the call to `distribute`) will be executed locally. The `job_name` argument allows jobs to be shared across multiple datasets. Instead of each dataset creating its own job, all datasets with the same `job_name` will consume from the same job. A new job will be created for each iteration of the dataset (with each repetition of `Dataset.repeat` counting as a new iteration). Suppose two training workers (in either a single client or multi-client setup) iterate over the below dataset, and there is a single tf.data worker: ``` range5_dataset = tf.data.Dataset.range(5) dataset = range5_dataset.apply(tf.data.experimental.service.distribute( "parallel_epochs", "grpc://dataservice:5000", job_name="my_job_name")) for iteration in range(3): print(list(dataset)) ``` The elements of each job will be split between the two processes, with elements being consumed by the processes on a first-come first-served basis. One possible result is that process 1 prints ``` [0, 2, 4] [0, 1, 3] [1] ``` and process 2 prints ``` [1, 3] [2, 4] [0, 2, 3, 4] ``` Job names must not be re-used across different training jobs within the lifetime of the tf.data service. In general, the tf.data service is expected to live for the duration of a single training job. To use the tf.data service with multiple training jobs, make sure to use different job names to avoid conflicts. For example, suppose a training job calls `distribute` with `job_name="job"` and reads until end of input. If another independent job connects to the same tf.data service and tries to read from `job_name="job"`, it will immediately receive end of input, without getting any data. **Keras and Distribution Strategies** The dataset produced by the `distribute` transformation can be passed to Keras' `Model.fit` or Distribution Strategy's `tf.distribute.Strategy.experimental_distribute_dataset` like any other `tf.data.Dataset`. We recommend setting a `job_name` on the call to `distribute` so that if there are multiple workers, they read data from the same job. Note that the autosharding normally performed by `experimental_distribute_dataset` will be disabled when setting a `job_name`, since sharing the job already results in splitting data across the workers. When using a shared job, data will be dynamically balanced across workers, so that they reach end of input about the same time. This results in better worker utilization than with autosharding, where each worker processes an independent set of files, and some workers may run out of data earlier than others. Args: processing_mode: A string specifying the policy for how data should be processed by tf.data workers. Currently, the only supported value is "parallel_epochs". service: A string indicating how to connect to the tf.data service. The string should be in the format "protocol://address", e.g. "grpc://localhost:5000". job_name: (Optional.) The name of the job. This argument makes it possible for multiple datasets to share the same job. The default behavior is that the dataset creates anonymous, exclusively owned jobs. max_outstanding_requests: (Optional.) A limit on how many elements may be requested at the same time. You can use this option to control the amount of memory used, since `distribute` won't use more than `element_size` * `max_outstanding_requests` of memory. Returns: Dataset: A `Dataset` of the elements produced by the data service.
1897	1895	register_dataset	tensorflow/tensorflow/python/data/experimental/ops/data_service_ops.py	424	function	Registers a dataset with the tf.data service. `register_dataset` registers a dataset with the tf.data service so that datasets can be created later with `tf.data.experimental.service.from_dataset_id`. This is useful when the dataset is registered by one process, then used in another process. When the same process is both registering and reading from the dataset, it is simpler to use `tf.data.experimental.service.distribute` instead. If the dataset is already registered with the tf.data service, `register_dataset` returns the already-registered dataset's id. >>> dispatcher = tf.data.experimental.service.DispatchServer(port=0) >>> dispatcher_address = dispatcher.target.split("://")[1] >>> worker = tf.data.experimental.service.WorkerServer( ... port=0, dispatcher_address=dispatcher_address) >>> dataset = tf.data.Dataset.range(10) >>> dataset_id = tf.data.experimental.service.register_dataset( ... dispatcher.target, dataset) >>> dataset = tf.data.experimental.service.from_dataset_id( ... processing_mode="parallel_epochs", ... service=dispatcher.target, ... dataset_id=dataset_id, ... element_spec=dataset.element_spec) >>> print(list(dataset.as_numpy_iterator())) [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] Args: service: A string indicating how to connect to the tf.data service. The string should be in the format "protocol://address", e.g. "grpc://localhost:5000". dataset: A `tf.data.Dataset` to register with the tf.data service. Returns: A scalar int64 tensor of the registered dataset's id.
1898	1896	from_dataset_id	tensorflow/tensorflow/python/data/experimental/ops/data_service_ops.py	491	function	Creates a dataset which reads data from the tf.data service. This is useful when the dataset is registered by one process, then used in another process. When the same process is both registering and reading from the dataset, it is simpler to use `tf.data.experimental.service.distribute` instead. Before using `from_dataset_id`, the dataset must have been registered with the tf.data service using `tf.data.experimental.service.register_dataset`. `register_dataset` returns a dataset id for the registered dataset. That is the `dataset_id` which should be passed to `from_dataset_id`. The `element_spec` argument indicates the `tf.TypeSpec`s for the elements produced by the dataset. Currently `element_spec` must be explicitly specified, and match the dataset registered under `dataset_id`. `element_spec` defaults to `None` so that in the future we can support automatically discovering the `element_spec` by querying the tf.data service. `tf.data.experimental.service.distribute` is a convenience method which combines `register_dataset` and `from_dataset_id` into a dataset transformation. See the documentation for `tf.data.experimental.service.distribute` for more detail about how `from_dataset_id` works. >>> dispatcher = tf.data.experimental.service.DispatchServer(port=0) >>> dispatcher_address = dispatcher.target.split("://")[1] >>> worker = tf.data.experimental.service.WorkerServer( ... port=0, dispatcher_address=dispatcher_address) >>> dataset = tf.data.Dataset.range(10) >>> dataset_id = tf.data.experimental.service.register_dataset( ... dispatcher.target, dataset) >>> dataset = tf.data.experimental.service.from_dataset_id( ... processing_mode="parallel_epochs", ... service=dispatcher.target, ... dataset_id=dataset_id, ... element_spec=dataset.element_spec) >>> print(list(dataset.as_numpy_iterator())) [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] Args: processing_mode: A string specifying the policy for how data should be processed by tf.data workers. Currently, the only supported value is "parallel_epochs". service: A string indicating how to connect to the tf.data service. The string should be in the format "protocol://address", e.g. "grpc://localhost:5000". dataset_id: The id of the dataset to read from. This id is returned by `register_dataset` when the dataset is registered with the tf.data service. element_spec: A nested structure of `tf.TypeSpec`s representing the type of elements produced by the dataset. Use `tf.data.Dataset.element_spec` to see the element spec for a given dataset. job_name: (Optional.) The name of the job. This argument makes it possible for multiple datasets to share the same job. The default behavior is that the dataset creates anonymous, exclusively owned jobs. max_outstanding_requests: (Optional.) A limit on how many elements may be requested at the same time. You can use this option to control the amount of memory used, since `distribute` won't use more than `element_size` * `max_outstanding_requests` of memory. Returns: A `tf.data.Dataset` which reads from the tf.data service.
1899	1897	_AutoShardDataset	tensorflow/tensorflow/python/data/experimental/ops/distribute.py	34	class	A `Dataset` that shards the `Dataset` automatically. This dataset takes in an existing dataset and tries to automatically figure out how to shard the dataset in a multi-worker scenario. Currently, it uses Grappler to walk up the dataset graph until it finds a reader dataset (e.g. CSVDataset, TFRecordDataset), then inserts a ShardDataset op before that node so that each worker only sees some files. Args: num_workers: Total number of workers to shard this dataset across. index: The current worker index (out of the total number of workers) this dataset is for. Raises: NotFoundError: If we cannot find a suitable reader dataset to begin automatically sharding the dataset.
1900	1898	_AutoShardDatasetV1	tensorflow/tensorflow/python/data/experimental/ops/distribute.py	71	function
1901	1899	_RebatchDataset	tensorflow/tensorflow/python/data/experimental/ops/distribute.py	76	class	A `Dataset` that rebatches elements from its input into new batch sizes. `_RebatchDataset(input_dataset, batch_sizes)` is functionally equivalent to `input_dataset.unbatch().batch(N)`, where the value of N cycles through the `batch_sizes` input list. The elements produced by this dataset have the same rank as the elements of the input dataset. For example: ```python ds = tf.data.Dataset.range(8) ds = ds.batch(4) ds = _RebatchDataset(ds, batch_sizes=[2, 1, 1]) for elem in ds: print(elem) >> [0, 1], [2], [3], [4, 5], [6], [7] ds = tf.data.Dataset.range(16) ds = ds.batch(4) ds = _RebatchDataset(ds, batch_sizes=[6]) for elem in ds: print(elem) >> [0, 1, 2, 3, 4, 5], [6, 7, 8, 9, 10, 11], [12, 13, 14, 15] ```
1902	1900	_LegacyRebatchDataset	tensorflow/tensorflow/python/data/experimental/ops/distribute.py	209	class	A `Dataset` that divides its input batches into `num_replicas` sub-batches. For each batch in the input dataset, _LegacyRebatchDataset will produce `num_replicas` smaller batches whose sizes add up to the original batch size. For example: ```python ds = tf.data.Dataset.range(8) ds = ds.batch(4) ds = _LegacyRebatchDataset(ds, num_replicas=3) for elem in ds: print(elem) >> [0, 1], [2, 3], [], [4, 5], [6, 7], [] ```
1903	1901	_RemoteDataset	tensorflow/tensorflow/python/data/experimental/ops/distribute.py	280	class	Creates a dataset on a given `device` given a graph def.
1904	1902	replicate	tensorflow/tensorflow/python/data/experimental/ops/distribute.py	294	function	A transformation that replicates `dataset` onto a list of devices. Args: dataset: A `tf.data.Dataset` object. devices: A list of devices to replicate the dataset on. Returns: A dictionary mapping device name to a dataset on that device.
1905	1903	batch_sizes_for_worker	tensorflow/tensorflow/python/data/experimental/ops/distribute.py	328	function	Determines how to rebatch a dataset for the given worker. Given the global batch size, number of workers, number of replicas per worker, and worker index, returns the correct batch sizes for rebatching a dataset on worker `worker_index` of `num_workers`, such that each global step (across all workers and replicas) will consume global_batch_size elements. The returned value should be passed as the `batch_sizes` input parameter to `tf.data.experimental.rebatch()`. The returned batch sizes meet the following constraints: Let G = global_batch_size, W = num_workers, R = num_replicas_per_worker (A) for any worker, len(batch_sizes) = W * R (B) for any worker, sum(batch_sizes) == G (C) for any global step (i.e. R iterations on each worker), the sum of batches consumed by replicas across all workers is G. (D) any two batch sizes of any two replicas differs by at most one. For example, suppose we have G = 7, W = 2, R = 2, and suppose we have two files which each contain 7 elements: ```python # WORKER 0 batch_sizes_0 = batch_sizes_for_worker(global_batch_size=global_batch_size, num_workers=2, num_replicas_per_worker=2, worker_index=0) print(batch_sizes_0) >> [2, 2, 2, 1] dataset_0 = tf.data.Dataset.from_tensor_slices(["file_a", "file_b"]) dataset_0 = dataset_0.shard(num_shards, index=0) dataset_0 = dataset_0.batch(7) dataset_0 = dataset_0.apply(tf.data.experimental.rebatch(batch_sizes_0)) for elem in dataset_0: print(elem) >> [[A0, A1], [A2, A3], [A4, A5], [A6]] # WORKER 1 batch_sizes_1 = batch_sizes_for_worker(global_batch_size=global_batch_size, num_workers=2, num_replicas_per_worker=2, worker_index=1) print(batch_sizes_1) >> [2, 1, 2, 2] dataset_1 = tf.data.Dataset.from_tensor_slices(["file_a", "file_b"]) dataset_1 = dataset_1.shard(num_shards, index=1) dataset_1 = dataset_1.batch(7) dataset_1 = dataset_1.apply(tf.data.experimental.rebatch(batch_sizes_1)) for elem in dataset_1: print(elem) >> [[B0, B1], [B2], [B3, B4], [B5, B6]] ``` The above example will produce the following elements: Step 1: Worker 0 Replica 0: [A0, A1] Worker 0 Replica 1: [A2, A3] Worker 1 Replica 0: [B0, B1] Worker 1 Replica 1: [B2] Total batch size = 7 Step 2: Worker 0 Replica 0: [A4, A5] Worker 0 Replica 1: [A6] Worker 1 Replica 0: [B3, B4] Worker 1 Replica 1: [B5, B6] Total batch size = 7 Args: global_batch_size: A `tf.int64` scalar, representing the global batch size. num_workers: An integer representing the number of workers the dataset will be distributed across. num_replicas_per_worker: An integer representing the number of replicas per worker. All workers are assumed to have the same number of replicas. worker_index: An integer index of the worker to be rebatched. Returns: A `tf.int64` vector, representing the batch sizes to rebatch the dataset into.
1906	1904	compute_batch_size	tensorflow/tensorflow/python/data/experimental/ops/distribute.py	436	function	An operation that returns the batch size of the dataset. This op tries to infer the batch size statically by walking up the dataset tree from the final dataset node and returning the batch size of the first batching dataset (such as from .batch() and .padded_batch()) that it encounters. This differs from using the `element_spec` of a dataset in that it does not account for partial batches. This operation may fail if it encounters contradictory batch sizes (for example, if the dataset is created by zipping together two datasets with different batch sizes), if there are no explicit batching transformations, or if there are operations downstream from the batching transformation that may modify its batch size. In these cases, it returns a -1. Args: dataset: A `tf.data.Dataset` object. Returns: A `tf.int64` Tensor representing the batch size of the dataset sans partial batches. If this cannot be inferred statically, the value of this tensor will be -1.
1907	1905	AutoShardPolicy	tensorflow/tensorflow/python/data/experimental/ops/distribute_options.py	27	class	Represents the type of auto-sharding we enable. Please see the DistributeOptions.auto_shard_policy documentation for more information on each type of autosharding.
1908	1906	ExternalStatePolicy	tensorflow/tensorflow/python/data/experimental/ops/distribute_options.py	39	class
1909	1907	DistributeOptions	tensorflow/tensorflow/python/data/experimental/ops/distribute_options.py	46	class	Represents options for distributed data processing. You can set the distribution options of a dataset through the `experimental_distribute` property of `tf.data.Options`; the property is an instance of `tf.data.experimental.DistributeOptions`. ```python options = tf.data.Options() options.experimental_distribute.auto_shard_policy = AutoShardPolicy.OFF dataset = dataset.with_options(options) ```
1910	1908	enumerate_dataset	tensorflow/tensorflow/python/data/experimental/ops/enumerate_ops.py	26	function	A transformation that enumerates the elements of a dataset. It is similar to python's `enumerate`. For example: ```python # NOTE: The following examples use `{ ... }` to represent the # contents of a dataset. a = { 1, 2, 3 } b = { (7, 8), (9, 10) } # The nested structure of the `datasets` argument determines the # structure of elements in the resulting dataset. a.apply(tf.data.experimental.enumerate_dataset(start=5)) => { (5, 1), (6, 2), (7, 3) } b.apply(tf.data.experimental.enumerate_dataset()) => { (0, (7, 8)), (1, (9, 10)) } ``` Args: start: A `tf.int64` scalar `tf.Tensor`, representing the start value for enumeration. Returns: A `Dataset` transformation function, which can be passed to `tf.data.Dataset.apply`.
1911	1909	ignore_errors	tensorflow/tensorflow/python/data/experimental/ops/error_ops.py	26	function	Creates a `Dataset` from another `Dataset` and silently ignores any errors. Use this transformation to produce a dataset that contains the same elements as the input, but silently drops any elements that caused an error. For example: ```python dataset = tf.data.Dataset.from_tensor_slices([1., 2., 0., 4.]) # Computing `tf.debugging.check_numerics(1. / 0.)` will raise an InvalidArgumentError. dataset = dataset.map(lambda x: tf.debugging.check_numerics(1. / x, "error")) # Using `ignore_errors()` will drop the element that causes an error. dataset = dataset.apply(tf.data.experimental.ignore_errors()) # ==> {1., 0.5, 0.2} ``` Returns: A `Dataset` transformation function, which can be passed to `tf.data.Dataset.apply`.
1912	1910	_IgnoreErrorsDataset	tensorflow/tensorflow/python/data/experimental/ops/error_ops.py	56	class	A `Dataset` that silently ignores errors when computing its input.
1913	1911	get_single_element	tensorflow/tensorflow/python/data/experimental/ops/get_single_element.py	27	function	Returns the single element in `dataset` as a nested structure of tensors. This function enables you to use a `tf.data.Dataset` in a stateless "tensor-in tensor-out" expression, without creating an iterator. This can be useful when your preprocessing transformations are expressed as a `Dataset`, and you want to use the transformation at serving time. For example: ```python def preprocessing_fn(input_str): # ... return image, label input_batch = ... # input batch of BATCH_SIZE elements dataset = (tf.data.Dataset.from_tensor_slices(input_batch) .map(preprocessing_fn, num_parallel_calls=BATCH_SIZE) .batch(BATCH_SIZE)) image_batch, label_batch = tf.data.experimental.get_single_element(dataset) ``` Args: dataset: A `tf.data.Dataset` object containing a single element. Returns: A nested structure of `tf.Tensor` objects, corresponding to the single element of `dataset`. Raises: TypeError: if `dataset` is not a `tf.data.Dataset` object. InvalidArgumentError (at runtime): if `dataset` does not contain exactly one element.
1914	1912	group_by_reducer	tensorflow/tensorflow/python/data/experimental/ops/grouping.py	38	function	A transformation that groups elements and performs a reduction. This transformation maps element of a dataset to a key using `key_func` and groups the elements by key. The `reducer` is used to process each group; its `init_func` is used to initialize state for each group when it is created, the `reduce_func` is used to update the state every time an element is mapped to the matching group, and the `finalize_func` is used to map the final state to an output value. Args: key_func: A function mapping a nested structure of tensors (having shapes and types defined by `self.output_shapes` and `self.output_types`) to a scalar `tf.int64` tensor. reducer: An instance of `Reducer`, which captures the reduction logic using the `init_func`, `reduce_func`, and `finalize_func` functions. Returns: A `Dataset` transformation function, which can be passed to `tf.data.Dataset.apply`.
1915	1913	group_by_window	tensorflow/tensorflow/python/data/experimental/ops/grouping.py	68	function	A transformation that groups windows of elements by key and reduces them. This transformation maps each consecutive element in a dataset to a key using `key_func` and groups the elements by key. It then applies `reduce_func` to at most `window_size_func(key)` elements matching the same key. All except the final window for each key will contain `window_size_func(key)` elements; the final window may be smaller. You may provide either a constant `window_size` or a window size determined by the key through `window_size_func`. Args: key_func: A function mapping a nested structure of tensors (having shapes and types defined by `self.output_shapes` and `self.output_types`) to a scalar `tf.int64` tensor. reduce_func: A function mapping a key and a dataset of up to `window_size` consecutive elements matching that key to another dataset. window_size: A `tf.int64` scalar `tf.Tensor`, representing the number of consecutive elements matching the same key to combine in a single batch, which will be passed to `reduce_func`. Mutually exclusive with `window_size_func`. window_size_func: A function mapping a key to a `tf.int64` scalar `tf.Tensor`, representing the number of consecutive elements matching the same key to combine in a single batch, which will be passed to `reduce_func`. Mutually exclusive with `window_size`. Returns: A `Dataset` transformation function, which can be passed to `tf.data.Dataset.apply`. Raises: ValueError: if neither or both of {`window_size`, `window_size_func`} are passed.
1916	1914	bucket_by_sequence_length	tensorflow/tensorflow/python/data/experimental/ops/grouping.py	128	function	A transformation that buckets elements in a `Dataset` by length. Elements of the `Dataset` are grouped together by length and then are padded and batched. This is useful for sequence tasks in which the elements have variable length. Grouping together elements that have similar lengths reduces the total fraction of padding in a batch which increases training step efficiency. Args: element_length_func: function from element in `Dataset` to `tf.int32`, determines the length of the element, which will determine the bucket it goes into. bucket_boundaries: `list<int>`, upper length boundaries of the buckets. bucket_batch_sizes: `list<int>`, batch size per bucket. Length should be `len(bucket_boundaries) + 1`. padded_shapes: Nested structure of `tf.TensorShape` to pass to `tf.data.Dataset.padded_batch`. If not provided, will use `dataset.output_shapes`, which will result in variable length dimensions being padded out to the maximum length in each batch. padding_values: Values to pad with, passed to `tf.data.Dataset.padded_batch`. Defaults to padding with 0. pad_to_bucket_boundary: bool, if `False`, will pad dimensions with unknown size to maximum length in batch. If `True`, will pad dimensions with unknown size to bucket boundary minus 1 (i.e., the maximum length in each bucket), and caller must ensure that the source `Dataset` does not contain any elements with length longer than `max(bucket_boundaries)`. no_padding: `bool`, indicates whether to pad the batch features (features need to be either of type `tf.sparse.SparseTensor` or of same shape). drop_remainder: (Optional.) A `tf.bool` scalar `tf.Tensor`, representing whether the last batch should be dropped in the case it has fewer than `batch_size` elements; the default behavior is not to drop the smaller batch. Returns: A `Dataset` transformation function, which can be passed to `tf.data.Dataset.apply`. Raises: ValueError: if `len(bucket_batch_sizes) != len(bucket_boundaries) + 1`.
1917	1915	_GroupByReducerDataset	tensorflow/tensorflow/python/data/experimental/ops/grouping.py	247	class	A `Dataset` that groups its input and performs a reduction.
1918	1916	_GroupByWindowDataset	tensorflow/tensorflow/python/data/experimental/ops/grouping.py	370	class	A `Dataset` that groups its input and performs a windowed reduction.
1919	1917	Reducer	tensorflow/tensorflow/python/data/experimental/ops/grouping.py	443	class	A reducer is used for reducing a set of elements. A reducer is represented as a tuple of the three functions: 1) initialization function: key => initial state 2) reduce function: (old state, input) => new state 3) finalization function: state => result
1920	1918	parallel_interleave	tensorflow/tensorflow/python/data/experimental/ops/interleave_ops.py	43	function	A parallel version of the `Dataset.interleave()` transformation. `parallel_interleave()` maps `map_func` across its input to produce nested datasets, and outputs their elements interleaved. Unlike `tf.data.Dataset.interleave`, it gets elements from `cycle_length` nested datasets in parallel, which increases the throughput, especially in the presence of stragglers. Furthermore, the `sloppy` argument can be used to improve performance, by relaxing the requirement that the outputs are produced in a deterministic order, and allowing the implementation to skip over nested datasets whose elements are not readily available when requested. Example usage: ```python # Preprocess 4 files concurrently. filenames = tf.data.Dataset.list_files("/path/to/data/train*.tfrecords") dataset = filenames.apply( tf.data.experimental.parallel_interleave( lambda filename: tf.data.TFRecordDataset(filename), cycle_length=4)) ``` WARNING: If `sloppy` is `True`, the order of produced elements is not deterministic. Args: map_func: A function mapping a nested structure of tensors to a `Dataset`. cycle_length: The number of input `Dataset`s to interleave from in parallel. block_length: The number of consecutive elements to pull from an input `Dataset` before advancing to the next input `Dataset`. sloppy: A boolean controlling whether determinism should be traded for performance by allowing elements to be produced out of order. If `sloppy` is `None`, the `tf.data.Options.experimental_deterministic` dataset option (`True` by default) is used to decide whether to enforce a deterministic order. buffer_output_elements: The number of elements each iterator being interleaved should buffer (similar to the `.prefetch()` transformation for each interleaved iterator). prefetch_input_elements: The number of input elements to transform to iterators before they are needed for interleaving. Returns: A `Dataset` transformation function, which can be passed to `tf.data.Dataset.apply`.
1921	1919	_DirectedInterleaveDataset	tensorflow/tensorflow/python/data/experimental/ops/interleave_ops.py	104	class	A substitute for `Dataset.interleave()` on a fixed list of datasets.
1922	1920	sample_from_datasets_v2	tensorflow/tensorflow/python/data/experimental/ops/interleave_ops.py	146	function	Samples elements at random from the datasets in `datasets`. Args: datasets: A list of `tf.data.Dataset` objects with compatible structure. weights: (Optional.) A list of `len(datasets)` floating-point values where `weights[i]` represents the probability with which an element should be sampled from `datasets[i]`, or a `tf.data.Dataset` object where each element is such a list. Defaults to a uniform distribution across `datasets`. seed: (Optional.) A `tf.int64` scalar `tf.Tensor`, representing the random seed that will be used to create the distribution. See `tf.random.set_seed` for behavior. Returns: A dataset that interleaves elements from `datasets` at random, according to `weights` if provided, otherwise with uniform probability. Raises: TypeError: If the `datasets` or `weights` arguments have the wrong type. ValueError: If the `weights` argument is specified and does not match the length of the `datasets` element.
1923	1921	sample_from_datasets_v1	tensorflow/tensorflow/python/data/experimental/ops/interleave_ops.py	230	function
1924	1922	choose_from_datasets_v2	tensorflow/tensorflow/python/data/experimental/ops/interleave_ops.py	237	function	Creates a dataset that deterministically chooses elements from `datasets`. For example, given the following datasets: ```python datasets = [tf.data.Dataset.from_tensors("foo").repeat(), tf.data.Dataset.from_tensors("bar").repeat(), tf.data.Dataset.from_tensors("baz").repeat()] # Define a dataset containing `[0, 1, 2, 0, 1, 2, 0, 1, 2]`. choice_dataset = tf.data.Dataset.range(3).repeat(3) result = tf.data.experimental.choose_from_datasets(datasets, choice_dataset) ``` The elements of `result` will be: ``` "foo", "bar", "baz", "foo", "bar", "baz", "foo", "bar", "baz" ``` Args: datasets: A list of `tf.data.Dataset` objects with compatible structure. choice_dataset: A `tf.data.Dataset` of scalar `tf.int64` tensors between `0` and `len(datasets) - 1`. Returns: A dataset that interleaves elements from `datasets` according to the values of `choice_dataset`. Raises: TypeError: If the `datasets` or `choice_dataset` arguments have the wrong type.
1925	1923	choose_from_datasets_v1	tensorflow/tensorflow/python/data/experimental/ops/interleave_ops.py	280	function
1926	1924	save	tensorflow/tensorflow/python/data/experimental/ops/io.py	34	function	Saves the content of the given dataset. Example usage: >>> import tempfile >>> path = os.path.join(tempfile.gettempdir(), "saved_data") >>> # Save a dataset >>> dataset = tf.data.Dataset.range(2) >>> tf.data.experimental.save(dataset, path) >>> new_dataset = tf.data.experimental.load(path, ... tf.TensorSpec(shape=(), dtype=tf.int64)) >>> for elem in new_dataset: ... print(elem) tf.Tensor(0, shape=(), dtype=int64) tf.Tensor(1, shape=(), dtype=int64) The saved dataset is saved in multiple file "shards". By default, the dataset output is divided to shards in a round-robin fashion but custom sharding can be specified via the `shard_func` function. For example, you can save the dataset to using a single shard as follows: ```python dataset = make_dataset() def custom_shard_func(element): return 0 dataset = tf.data.experimental.save( path="/path/to/data", ..., shard_func=custom_shard_func) ``` NOTE: The directory layout and file format used for saving the dataset is considered an implementation detail and may change. For this reason, datasets saved through `tf.data.experimental.save` should only be consumed through `tf.data.experimental.load`, which is guaranteed to be backwards compatible. Args: dataset: The dataset to save. path: Required. A directory to use for saving the dataset. compression: Optional. The algorithm to use to compress data when writing it. Supported options are `GZIP` and `NONE`. Defaults to `NONE`. shard_func: Optional. A function to control the mapping of dataset elements to file shards. The function is expected to map elements of the input dataset to int64 shard IDs. If present, the function will be traced and executed as graph computation.
1927	1925	_LoadDataset	tensorflow/tensorflow/python/data/experimental/ops/io.py	107	class	A dataset that loads previously saved dataset.
1928	1926	load	tensorflow/tensorflow/python/data/experimental/ops/io.py	146	function	Loads a previously saved dataset. Example usage: >>> import tempfile >>> path = os.path.join(tempfile.gettempdir(), "saved_data") >>> # Save a dataset >>> dataset = tf.data.Dataset.range(2) >>> tf.data.experimental.save(dataset, path) >>> new_dataset = tf.data.experimental.load(path, ... tf.TensorSpec(shape=(), dtype=tf.int64)) >>> for elem in new_dataset: ... print(elem) tf.Tensor(0, shape=(), dtype=int64) tf.Tensor(1, shape=(), dtype=int64) Note that to load a previously saved dataset, you need to specify `element_spec` -- a type signature of the elements of the saved dataset, which can be obtained via `tf.data.Dataset.element_spec`. This requirement exists so that shape inference of the loaded dataset does not need to perform I/O. If the default option of sharding the saved dataset was used, the element order of the saved dataset will be preserved when loading it. The `reader_func` argument can be used to specify a custom order in which elements should be loaded from the individual shards. The `reader_func` is expected to take a single argument -- a dataset of datasets, each containing elements of one of the shards -- and return a dataset of elements. For example, the order of shards can be shuffled when loading them as follows: ```python def custom_reader_func(datasets): datasets = datasets.shuffle(NUM_SHARDS) return datasets.interleave(lambda x: x, num_parallel_calls=AUTOTUNE) dataset = tf.data.experimental.load( path="/path/to/data", ..., reader_func=custom_reader_func) ``` Args: path: Required. A path pointing to a previously saved dataset. element_spec: Required. A nested structure of `tf.TypeSpec` objects matching the structure of an element of the saved dataset and specifying the type of individual element components. compression: Optional. The algorithm to use to decompress the data when reading it. Supported options are `GZIP` and `NONE`. Defaults to `NONE`. reader_func: Optional. A function to control how to read data from shards. If present, the function will be traced and executed as graph computation. Returns: A `tf.data.Dataset` instance.
1929	1927	_convert_external_state_policy_to_enum	tensorflow/tensorflow/python/data/experimental/ops/iterator_ops.py	32	function
1930	1928	make_saveable_from_iterator	tensorflow/tensorflow/python/data/experimental/ops/iterator_ops.py	49	function	Returns a SaveableObject for saving/restoring iterator state using Saver. Args: iterator: Iterator. external_state_policy: A string that identifies how to handle input pipelines that depend on external state. Possible values are 'ignore': The external state is silently ignored. 'warn': The external state is ignored, logging a warning. 'fail': The operation fails upon encountering external state. By default we set it to 'fail'. Returns: A SaveableObject for saving/restoring iterator state using Saver. Raises: ValueError: If iterator does not support checkpointing. ValueError: If `external_state_policy` is not one of 'warn', 'ignore' or 'fail'. For example: ```python with tf.Graph().as_default(): ds = tf.data.Dataset.range(10) iterator = ds.make_initializable_iterator() # Build the iterator SaveableObject. saveable_obj = tf.data.experimental.make_saveable_from_iterator(iterator) # Add the SaveableObject to the SAVEABLE_OBJECTS collection so # it can be automatically saved using Saver. tf.compat.v1.add_to_collection(tf.GraphKeys.SAVEABLE_OBJECTS, saveable_obj) saver = tf.compat.v1.train.Saver() while continue_training: ... Perform training ... if should_save_checkpoint: saver.save() ``` Note: When restoring the iterator, the existing iterator state is completely discarded. This means that any changes you may have made to the Dataset graph will be discarded as well! This includes the new Dataset graph that you may have built during validation. So, while running validation, make sure to run the initializer for the validation input pipeline after restoring the checkpoint. Note: Not all iterators support checkpointing yet. Attempting to save the state of an unsupported iterator will throw an error.
1931	1929	CheckpointInputPipelineHook	tensorflow/tensorflow/python/data/experimental/ops/iterator_ops.py	106	class	Checkpoints input pipeline state every N steps or seconds. This hook saves the state of the iterators in the `Graph` so that when training is resumed the input pipeline continues from where it left off. This could potentially avoid overfitting in certain pipelines where the number of training steps per eval are small compared to the dataset size or if the training pipeline is pre-empted. Differences from `CheckpointSaverHook`: 1. Saves only the input pipelines in the "iterators" collection and not the global variables or other saveable objects. 2. Does not write the `GraphDef` and `MetaGraphDef` to the summary. Example of checkpointing the training pipeline: ```python est = tf.estimator.Estimator(model_fn) while True: est.train( train_input_fn, hooks=[tf.data.experimental.CheckpointInputPipelineHook(est)], steps=train_steps_per_eval) # Note: We do not pass the hook here. metrics = est.evaluate(eval_input_fn) if should_stop_the_training(metrics): break ``` This hook should be used if the input pipeline state needs to be saved separate from the model checkpoint. Doing so may be useful for a few reasons: 1. The input pipeline checkpoint may be large, if there are large shuffle or prefetch buffers for instance, and may bloat the checkpoint size. 2. If the input pipeline is shared between training and validation, restoring the checkpoint during validation may override the validation input pipeline. For saving the input pipeline checkpoint alongside the model weights use `tf.data.experimental.make_saveable_from_iterator` directly to create a `SaveableObject` and add to the `SAVEABLE_OBJECTS` collection. Note, however, that you will need to be careful not to restore the training iterator during eval. You can do that by not adding the iterator to the SAVEABLE_OBJECTS collector when building the eval graph.
1932	1930	_CustomSaver	tensorflow/tensorflow/python/data/experimental/ops/iterator_ops.py	297	class	`Saver` with a different default `latest_filename`. This is used in the `CheckpointInputPipelineHook` to avoid conflicts with the model ckpt saved by the `CheckpointSaverHook`.
1933	1931	map_defun	tensorflow/tensorflow/python/data/experimental/ops/map_defun.py	26	function	Map a function on the list of tensors unpacked from `elems` on dimension 0. Args: fn: A function (`function.defun`) that takes a list of tensors and returns another list of tensors. The output list has the same types as output_dtypes. The elements of the output list have the same dimension 0 as `elems`, and the remaining dimensions correspond to those of `fn_output_shapes`. elems: A list of tensors. output_dtypes: A list of dtypes corresponding to the output types of the function. output_shapes: A list of `TensorShape`s corresponding to the output shapes from each invocation of the function on slices of inputs. max_intra_op_parallelism: An integer. If positive, sets the max parallelism limit of each function call to this. Raises: ValueError: if any of the inputs are malformed. Returns: A list of `Tensor` objects with the same types as `output_dtypes`.
1934	1932	MatchingFilesDataset	tensorflow/tensorflow/python/data/experimental/ops/matching_files.py	28	class	A `Dataset` that list the files according to the input patterns.
1935	1933	model	tensorflow/tensorflow/python/data/experimental/ops/optimization.py	24	function	A transformation that models performance. Returns: A `Dataset` transformation function, which can be passed to `tf.data.Dataset.apply`.
1936	1934	optimize	tensorflow/tensorflow/python/data/experimental/ops/optimization.py	39	function	A transformation that applies optimizations. Args: optimizations: (Optional.) A `tf.string` vector `tf.Tensor` identifying optimizations to use. If not specified, the default set of optimizations is applied. Returns: A `Dataset` transformation function, which can be passed to `tf.data.Dataset.apply`.
1937	1935	_ChooseFastestDataset	tensorflow/tensorflow/python/data/experimental/ops/optimization.py	59	class	A `Dataset` that merges two input datasets.
1938	1936	_ChooseFastestBranchDataset	tensorflow/tensorflow/python/data/experimental/ops/optimization.py	106	class	A `Dataset` that merges two input datasets.
1939	1937	_AutotuneAlgorithm	tensorflow/tensorflow/python/data/experimental/ops/optimization_options.py	29	class	Controls what algorithm is used in the autotune implementation.
1940	1938	MapVectorizationOptions	tensorflow/tensorflow/python/data/experimental/ops/optimization_options.py	36	class	Represents options for the MapVectorization optimization.
1941	1939	OptimizationOptions	tensorflow/tensorflow/python/data/experimental/ops/optimization_options.py	70	class	Represents options for dataset optimizations. You can set the optimization options of a dataset through the `experimental_optimization` property of `tf.data.Options`; the property is an instance of `tf.data.experimental.OptimizationOptions`. ```python options = tf.data.Options() options.experimental_optimization.noop_elimination = True options.experimental_optimization.map_vectorization.enabled = True options.experimental_optimization.apply_default_optimizations = False dataset = dataset.with_options(options) ```
1942	1940	_ParseExampleDataset	tensorflow/tensorflow/python/data/experimental/ops/parsing_ops.py	31	class	A `Dataset` that parses `example` dataset into a `dict` dataset.
1943	1941	parse_example_dataset	tensorflow/tensorflow/python/data/experimental/ops/parsing_ops.py	110	function	A transformation that parses `Example` protos into a `dict` of tensors. Parses a number of serialized `Example` protos given in `serialized`. We refer to `serialized` as a batch with `batch_size` many entries of individual `Example` protos. This op parses serialized examples into a dictionary mapping keys to `Tensor`, `SparseTensor`, and `RaggedTensor` objects. `features` is a dict from keys to `VarLenFeature`, `RaggedFeature`, `SparseFeature`, and `FixedLenFeature` objects. Each `VarLenFeature` and `SparseFeature` is mapped to a `SparseTensor`; each `RaggedFeature` is mapped to a `RaggedTensor`; and each `FixedLenFeature` is mapped to a `Tensor`. See `tf.io.parse_example` for more details about feature dictionaries. Args: features: A `dict` mapping feature keys to `FixedLenFeature`, `VarLenFeature`, `RaggedFeature`, and `SparseFeature` values. num_parallel_calls: (Optional.) A `tf.int32` scalar `tf.Tensor`, representing the number of parsing processes to call in parallel. deterministic: (Optional.) A boolean controlling whether determinism should be traded for performance by allowing elements to be produced out of order if some parsing calls complete faster than others. If `deterministic` is `None`, the `tf.data.Options.experimental_deterministic` dataset option (`True` by default) is used to decide whether to produce elements deterministically. Returns: A dataset transformation function, which can be passed to `tf.data.Dataset.apply`. Raises: ValueError: if features argument is None.
1944	1942	prefetch_to_device	tensorflow/tensorflow/python/data/experimental/ops/prefetching_ops.py	37	function	A transformation that prefetches dataset values to the given `device`. NOTE: Although the transformation creates a `tf.data.Dataset`, the transformation must be the final `Dataset` in the input pipeline. Args: device: A string. The name of a device to which elements will be prefetched. buffer_size: (Optional.) The number of elements to buffer on `device`. Defaults to an automatically chosen value. Returns: A `Dataset` transformation function, which can be passed to `tf.data.Dataset.apply`.
1945	1943	copy_to_device	tensorflow/tensorflow/python/data/experimental/ops/prefetching_ops.py	60	function	A transformation that copies dataset elements to the given `target_device`. Args: target_device: The name of a device to which elements will be copied. source_device: The original device on which `input_dataset` will be placed. Returns: A `Dataset` transformation function, which can be passed to `tf.data.Dataset.apply`.
1946	1944	_CopyToDeviceDataset	tensorflow/tensorflow/python/data/experimental/ops/prefetching_ops.py	86	class	A `Dataset` that copies elements to another device.
1947	1945	_MapOnGpuDataset	tensorflow/tensorflow/python/data/experimental/ops/prefetching_ops.py	228	class	A `Dataset` that maps a function over elements in its using a GPU.
1948	1946	map_on_gpu	tensorflow/tensorflow/python/data/experimental/ops/prefetching_ops.py	260	function	Maps `map_func` across the elements of this dataset. NOTE: This is a highly experimental version of `tf.data.Dataset.map` that runs `map_func` on GPU. It must be used after applying the `tf.data.experimental.copy_to_device` transformation with a GPU device argument. Args: map_func: A function mapping a nested structure of tensors (having shapes and types defined by `self.output_shapes` and `self.output_types`) to another nested structure of tensors. Returns: A `Dataset` transformation function, which can be passed to `tf.data.Dataset.apply`.
1949	1947	RandomDatasetV2	tensorflow/tensorflow/python/data/experimental/ops/random_ops.py	32	class	A `Dataset` of pseudorandom values.
1950	1948	RandomDatasetV1	tensorflow/tensorflow/python/data/experimental/ops/random_ops.py	48	class	A `Dataset` of pseudorandom values.
1951	1949	_is_valid_int32	tensorflow/tensorflow/python/data/experimental/ops/readers.py	50	function
1952	1950	_is_valid_int64	tensorflow/tensorflow/python/data/experimental/ops/readers.py	59	function
1953	1951	_is_valid_float	tensorflow/tensorflow/python/data/experimental/ops/readers.py	67	function
1954	1952	_infer_type	tensorflow/tensorflow/python/data/experimental/ops/readers.py	74	function	Given a string, infers its tensor type. Infers the type of a value by picking the least 'permissive' type possible, while still allowing the previous type inference for this column to be valid. Args: str_val: String value to infer the type of. na_value: Additional string to recognize as a NA/NaN CSV value. prev_type: Type previously inferred based on values of this column that we've seen up till now. Returns: Inferred dtype.
1955	1953	_next_csv_row	tensorflow/tensorflow/python/data/experimental/ops/readers.py	111	function	Generator that yields rows of CSV file(s) in order.
1956	1954	_infer_column_defaults	tensorflow/tensorflow/python/data/experimental/ops/readers.py	131	function	Infers column types from the first N valid CSV records of files.
1957	1955	_infer_column_names	tensorflow/tensorflow/python/data/experimental/ops/readers.py	158	function	Infers column names from first rows of files.
1958	1956	_get_sorted_col_indices	tensorflow/tensorflow/python/data/experimental/ops/readers.py	183	function	Transforms select_columns argument into sorted column indices.
1959	1957	_maybe_shuffle_and_repeat	tensorflow/tensorflow/python/data/experimental/ops/readers.py	213	function	Optionally shuffle and repeat dataset, as requested.
1960	1958	make_tf_record_dataset	tensorflow/tensorflow/python/data/experimental/ops/readers.py	223	function	Reads and optionally parses TFRecord files into a dataset. Provides common functionality such as batching, optional parsing, shuffling, and performant defaults. Args: file_pattern: List of files or patterns of TFRecord file paths. See `tf.io.gfile.glob` for pattern rules. batch_size: An int representing the number of records to combine in a single batch. parser_fn: (Optional.) A function accepting string input to parse and process the record contents. This function must map records to components of a fixed shape, so they may be batched. By default, uses the record contents unmodified. num_epochs: (Optional.) An int specifying the number of times this dataset is repeated. If None (the default), cycles through the dataset forever. shuffle: (Optional.) A bool that indicates whether the input should be shuffled. Defaults to `True`. shuffle_buffer_size: (Optional.) Buffer size to use for shuffling. A large buffer size ensures better shuffling, but increases memory usage and startup time. shuffle_seed: (Optional.) Randomization seed to use for shuffling. prefetch_buffer_size: (Optional.) An int specifying the number of feature batches to prefetch for performance improvement. Defaults to auto-tune. Set to 0 to disable prefetching. num_parallel_reads: (Optional.) Number of threads used to read records from files. By default or if set to a value >1, the results will be interleaved. Defaults to `24`. num_parallel_parser_calls: (Optional.) Number of parallel records to parse in parallel. Defaults to `batch_size`. drop_final_batch: (Optional.) Whether the last batch should be dropped in case its size is smaller than `batch_size`; the default behavior is not to drop the smaller batch. Returns: A dataset, where each element matches the output of `parser_fn` except it will have an additional leading `batch-size` dimension, or a `batch_size`-length 1-D tensor of strings if `parser_fn` is unspecified.

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