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tensorflow/c/experimental/ops/BUILD
], visibility = [ "//tensorflow:internal", ], deps = [ "//tensorflow/c/eager:abstract_context", "//tensorflow/c/eager:abstract_operation", "//tensorflow/c/eager:abstract_tensor_handle", "//tensorflow/c/eager:c_api_unified_internal", "//tensorflow/c/eager:tracing_utils", "//tensorflow/core:framework", "//tensorflow/core/platform:errors",
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tensorflow/c/eager/parallel_device/BUILD
"//tensorflow/c:safe_ptr", "//tensorflow/c:tf_status_internal", "//tensorflow/c/eager:c_api", "//tensorflow/c/eager:c_api_experimental", "//tensorflow/c/eager:tfe_cancellation_manager_internal", "//tensorflow/c/eager:tfe_op_internal", "//tensorflow/c/eager:tfe_tensorhandle_internal", "//tensorflow/core:framework", "//tensorflow/core:lib",
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CODEOWNERS
# Where component owners are known, add them here. /tensorflow/c/eager @qqfish /tensorflow/core/common_runtime/eager @qqfish /tenosrflow/core/debug @caisq /tensorflow/core/kernels/mkl/ @penpornk /tensorflow/core/kernels/sparse/ @penpornk /tensorflow/core/nccl/ @azaks2 @chsigg /tensorflow/python/autograph/ @mdanatg /tensorflow/python/debug @caisq /tensorflow/python/eager @rohan100jain /tensorflow/tools/docs/ @markdaoust
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tensorflow/c/BUILD
":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", "//tensorflow/compiler/jit:get_compiler_ir",
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CONTRIBUTING.md
(by default) transferred to the TensorFlow team. This means that the benefit of the contribution must be compared against the cost of maintaining the feature. * Full new features (e.g., a new op implementing a cutting-edge algorithm) typically will live in [tensorflow/addons](https://github.com/tensorflow/addons) to get some airtime before a decision is made regarding whether they are to be migrated to the core.
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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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SECURITY.md
vulnerabilities. ## Security properties of execution modes TensorFlow has several execution modes, with Eager-mode being the default in v2. Eager mode lets users write imperative-style statements that can be easily inspected and debugged and it is intended to be used during the development phase. As part of the differences that make Eager mode easier to debug, the [shape inference
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tensorflow/c/experimental/gradients/tape/BUILD
":tape_operation", "//tensorflow/c/eager:abstract_context", ], ) cc_library( name = "tape_operation", srcs = ["tape_operation.cc"], hdrs = [ "tape_operation.h", ], visibility = [ "//tensorflow:internal", ], deps = [ "//tensorflow/c/eager:abstract_context", "//tensorflow/c/eager:abstract_operation",
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RELEASE.md
* Unified eager and `tf.function` execution: * Eager mode can now execute each op as a `tf.function`, allowing for more consistent feature support in future releases. * It is available for immediate use. * See the `TF_RUN_EAGER_OP_AS_FUNCTION` environment variable in
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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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