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  1. CONTRIBUTING.md

    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.
    
    ***NOTE***: Only original source code from you and other people that have signed the CLA can be accepted into the main repository.
    
    ### Contributing code
    
    If you have improvements to TensorFlow, send us your pull requests! For those
    just getting started, GitHub has a
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  2. LICENSE

          communication on electronic mailing lists, source code control systems,
          and issue tracking systems that are managed by, or on behalf of, the
          Licensor for the purpose of discussing and improving the Work, but
          excluding communication that is conspicuously marked or otherwise
          designated in writing by the copyright owner as "Not a Contribution."
    
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  3. RELEASE.md

            and `audio_dataset_from_directory`, to be used with the
            `validation_split` argument, for returning both dataset splits at once,
            as a tuple.
        *   Added `tf.keras.utils.split_dataset` utility to split a `Dataset` object
            or a list/tuple of arrays into two `Dataset` objects (e.g. train/test).
        *   Added step granularity to `BackupAndRestore` callback for handling
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  4. CODE_OF_CONDUCT.md

    However, for the vast majority of issues, we aim to empower individuals to first resolve conflicts themselves, asking for help when needed, and only after that fails to escalate further. This approach gives people more control over the outcome of their dispute. 
    
    If you are experiencing or witnessing conflict, we ask you to use the following escalation strategy to address the conflict:
    
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  5. CITATION.cff

    represent computation, shared state, and the operations that mutate that state. It maps the nodes of a dataflow graph across many machines in a cluster, and within a machine across multiple computational devices, including multicore CPUs, general purpose GPUs, and custom-designed ASICs known as Tensor Processing Units (TPUs). This architecture gives flexibility to the application developer, whereas in previous “parameter server” designs the management of shared state is built into the system, TensorFlow...
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