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  1. ci/official/wheel_test/README.md

    [requirements updater readme](https://github.com/tensorflow/tensorflow/blob/master/ci/official/requirements_updater/README.md)
    
    ### Prerequisites for Local Testing
    
    To run tests locally, follow these steps:
    
    1.  Navigate to the relevant directory:
        ```
        cd ci/official/wheel_test
        ```
    2.  Run a script for creating requirements file:
        ```
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  2. CONTRIBUTING.md

      * If you work for a company that wants to allow you to contribute your work, then you'll need to sign a [corporate CLA](https://code.google.com/legal/corporate-cla-v1.0.html).
    
    Follow either of the two links above to access the appropriate CLA and instructions for how to sign and return it. Once we receive it, we'll be able to accept your pull requests.
    
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  3. SECURITY.md

    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
    
    TensorFlow has several execution modes, with Eager-mode being the default in v2.
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  4. RELEASE.md

        *   Fix device placement issues related to datasets with ragged tensors of
            strings (i.e. variant encoded data with types not supported on GPU).
        *   'experimental_follow_type_hints' for tf.function has been deprecated.
            Please use input_signature or reduce_retracing to minimize retracing.
    
    *   `tf.SparseTensor`:
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  5. README.md

    [Stack Overflow](https://stackoverflow.com/questions/tagged/tensorflow).**
    
    The TensorFlow project strives to abide by generally accepted best practices in
    open-source software development.
    
    ## Patching guidelines
    
    Follow these steps to patch a specific version of TensorFlow, for example, to
    apply fixes to bugs or security vulnerabilities:
    
    *   Clone the TensorFlow repo and switch to the corresponding branch for your
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