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  1. CONTRIBUTING.md

        and
        [tensorflow/python](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/python).
        TensorFlow has passed version 1.0 and hence cannot make
        non-backward-compatible API changes without a major release. Reviewers of
        your pull request will comment on any API compatibility issues
        [following API review practices](https://github.com/tensorflow/community/blob/master/governance/api-reviews.md).
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  2. RELEASE.md

            checkpoint with a root object. This allows users to create a
            `Checkpoint` object that is compatible with Keras `model.save_weights()`
            and `model.load_weights`. The checkpoint is also compatible with the
            checkpoint saved in the `variables/` folder in the SavedModel.
        *   When restoring, `save_path` can be a path to a SavedModel. The function
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  3. tensorflow/BUILD

        "@local_config_nccl//:__subpackages__",
        "@local_config_rocm//:__subpackages__",
        "@local_config_tensorrt//:__subpackages__",
        "@local_execution_config_platform//:__subpackages__",
        "@mkl_dnn_acl_compatible//:__subpackages__",
        "@ml_dtypes//:__subpackages__",
        "@nccl_archive//:__subpackages__",
        "@nvtx_archive//:__subpackages__",
        "@onednn//:__subpackages__",
        "@org_sqlite//:__subpackages__",
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  4. .bazelrc

    # This is the same as the official TensorFlow builds.
    # See https://developer.nvidia.com/cuda-gpus#compute
    # `compute_XY` enables PTX embedding in addition to SASS. PTX
    # is forward compatible beyond the current compute capability major
    # release while SASS is only forward compatible inside the current
    # major release. Example: sm_80 kernels can run on sm_89 GPUs but
    # not on sm_90 GPUs. compute_80 kernels though can also run on sm_90 GPUs.
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