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  1. CONTRIBUTING.md

        python tf_doctest.py --file=<file_path>
        ```
    
        This will run it using your installed version of TensorFlow. To be sure
        you're running the same code that you're testing:
    
        *   Use an up to date [tf-nightly](https://pypi.org/project/tf-nightly/)
            `pip install -U tf-nightly`
        *   Rebase your pull request onto a recent pull from
            [TensorFlow's](https://github.com/tensorflow/tensorflow) master branch.
    
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  2. CITATION.cff

      - family-names: Zheng
        given-names: Xiaoqiang
    identifiers:
      - type: doi
        value: 10.5281/zenodo.4724125
        description: The concept DOI for the collection containing all versions of the Citation File Format.
    date-released: "2015-11-09"
    license: "Apache-2.0"
    doi: 10.5281/zenodo.4724125
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  3. SECURITY.md

    potentially exploit a vulnerability in the TensorFlow code that handles videos,
    which could allow them to execute arbitrary code on the system running
    TensorFlow.
    
    It is important to keep TensorFlow up to date with the latest security patches
    and follow the sandboxing guideline above to protect against these types of
    vulnerabilities.
    
    ## Security properties of execution modes
    
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  4. LICENSE

          or a Contribution incorporated within the Work constitutes direct
          or contributory patent infringement, then any patent licenses
          granted to You under this License for that Work shall terminate
          as of the date such litigation is filed.
    
       4. Redistribution. You may reproduce and distribute copies of the
          Work or Derivative Works thereof in any medium, with or without
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  5. RELEASE.md

            optimizer.
        *   **Learning rate schedule access.** When using a `LearningRateSchedule`,
            The new optimizer's `learning_rate` property returns the
            current learning rate value instead of a `LearningRateSchedule` object
            as before. If you need to access the `LearningRateSchedule` object,
            please use `optimizer._learning_rate`.
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  6. README.md

    and [C++](https://www.tensorflow.org/api_docs/cc) APIs, as well as a
    non-guaranteed backward compatible API for
    [other languages](https://www.tensorflow.org/api_docs).
    
    Keep up-to-date with release announcements and security updates by subscribing
    to
    [******@****.***](https://groups.google.com/a/tensorflow.org/forum/#!forum/announce).
    See all the [mailing lists](https://www.tensorflow.org/community/forums).
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