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tensorflow/api_template.__init__.py
# Do an eager load for Keras' code so that any function/method that needs to # happen at load time will trigger, eg registration of optimizers in the # SavedModel registry. # See b/196254385 for more details. try: if _tf_uses_legacy_keras: importlib.import_module("tf_keras.src.optimizers") else: importlib.import_module("keras.src.optimizers") except (ImportError, AttributeError): pass
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CONTRIBUTING.md
tensorflow/tools/ci_build/ci_build.sh CPU bazel test //tensorflow/... ``` See [TensorFlow Builds](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/tools/ci_build) for details. #### Running doctest for testable docstring There are two ways to test the code in the docstring locally: 1. If you are only changing the docstring of a class/function/method, then you
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RELEASE.md
which is a NumPy-compatible API for writing TF programs. See the [detailed guide](https://www.tensorflow.org/guide/tf_numpy) to learn more. Additional details below. * Adds Support for [TensorFloat-32](https://blogs.nvidia.com/blog/2020/05/14/tensorfloat-32-precision-format/) on Ampere based GPUs. TensorFloat-32, or TF32 for short, is a math mode for
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.bazelrc
# MACOS X86 PYCPP # These are defined as build configs so that we can run a build only job. See # the note under "ARM64 PYCPP" for more details. build:macos_x86_pycpp_test_filters --test_tag_filters=-no_oss,-oss_excluded,-oss_serial,-no_oss_py38,-no_oss_py39,-no_oss_py310,-nomac,-no_mac,-mac_excluded,-v1only,-gpu,-tpu,-benchmark-test
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configure.py
configure_ios(environ_cp) print('Preconfigured Bazel build configs. You can use any of the below by ' 'adding "--config=<>" to your build command. See .bazelrc for more ' 'details.') config_info_line('mkl', 'Build with MKL support.') config_info_line( 'mkl_aarch64', 'Build with oneDNN and Compute Library for the Arm Architecture (ACL).')
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tensorflow/c/eager/tape.h
gtl::ArraySlice<Gradient*> output_gradients, absl::Span<Gradient*> result, bool build_default_zeros_grads = true); // Whether the tape is persistent. See ctor for detailed description. bool IsPersistent() const { return persistent_; } private: TensorTape tensor_tape_; OpTape<BackwardFunction, TapeTensor> op_tape_; int64_t next_op_id_{0};
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