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tensorflow/c/BUILD
":c_api_internal", ":c_api_macros_hdrs", ":checkpoint_reader", ":tf_buffer", ":tf_buffer_internal", "//tensorflow/c/eager:c_api", "//tensorflow/c/eager:c_api_internal", "//tensorflow/c/eager:tfe_context_internal", "//tensorflow/c/eager:tfe_op_internal", "//tensorflow/c/eager:tfe_tensorhandle_internal", "//tensorflow/compiler/jit:flags",
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tensorflow/c/eager/BUILD
"//tensorflow/core/common_runtime/eager:attr_builder", "//tensorflow/core/common_runtime/eager:context", "//tensorflow/core/common_runtime/eager:copy_to_device_node", "//tensorflow/core/common_runtime/eager:eager_executor", "//tensorflow/core/common_runtime/eager:eager_operation", "//tensorflow/core/common_runtime/eager:execute",
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tensorflow/BUILD
# cc_shared_library to work. "//tensorflow/c/eager:c_api_experimental", "//tensorflow/c/eager:c_api_internal", "//tensorflow/c/eager:dlpack", "//tensorflow/c/eager:tape", "//tensorflow/c/eager:tfe_context_internal", "//tensorflow/c/eager:tfe_op_internal", "//tensorflow/c/eager:tfe_tensorhandle_internal", "//tensorflow/c/experimental/gradients",
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RELEASE.md
* Added `DynamicEmbedding` Keras layer * Added 'UpdateEmbeddingCallback` * `DynamicEmbedding` layer allows for the continuous updating of the vocabulary and embeddings during the training process. This layer maintains a hash table to track the most up-to-date vocabulary based on the inputs received by the layer and the eviction policy. When this layer
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ci/official/containers/linux_arm64/devel.usertools/code_check_full.bats
//tensorflow/tools/pip_package:win_pip_package_marker //tensorflow/core:image_testdata //tensorflow/core/lib/lmdb:lmdb_testdata //tensorflow/core/lib/lmdb/testdata:lmdb_testdata //tensorflow/core/kernels/cloud:bigquery_reader_ops //tensorflow/python:extra_py_tests_deps //tensorflow/python:mixed_precision //tensorflow/python:tf_optimizer //tensorflow/python:compare_test_proto_py //tensorflow/python/framework:test_ops_2
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tensorflow/build_cleaner_spec.textproto
# proto-file: devtools/build_cleaner/proto/actions.proto # proto-message: ActionSpecs # Python rules should not have more than one source file. action_spec { action: CHECK_FILE_COUNT file_count_params { rule_selector { rule_kind_regex: "^.*py(type)?(_strict)?_(binary|library|test).*$" generator_function_regex: "^(?!boq_header)$" } max_source_count: 1 }
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ci/official/utilities/code_check_full.bats
//tensorflow/cc/saved_model:saved_model_half_plus_two //tensorflow:no_tensorflow_py_deps //tensorflow/tools/pip_package:win_pip_package_marker //tensorflow/core:image_testdata //tensorflow/core/kernels/cloud:bigquery_reader_ops //tensorflow/python:extra_py_tests_deps //tensorflow/python:mixed_precision //tensorflow/python:tf_optimizer //tensorflow/python:compare_test_proto_py //tensorflow/python/framework:test_file_system.so
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.bazelrc
# nothing in a bazelrc will default to a monolithic build. The following line # opts in to modular op registration support by default. build --define framework_shared_object=true build --define tsl_protobuf_header_only=true build --define=use_fast_cpp_protos=true build --define=allow_oversize_protos=true build --spawn_strategy=standalone build -c opt # Make Bazel print out all options from rc files.
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