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  1. docs/en/docs/features.md

    ### Short
    
    It has sensible **defaults** for everything, with optional configurations everywhere. All the parameters can be fine-tuned to do what you need and to define the API you need.
    
    But by default, it all **"just works"**.
    
    ### Validation
    
    * Validation for most (or all?) Python **data types**, including:
        * JSON objects (`dict`).
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  2. architecture/ambient/ztunnel.md

    This offered performance benefits that were too large to leave on the table, as well as opportunities to tune to our specific needs.
    
    ## Configuration protocol
    
    Ztunnel, of course, needs to be dynamically configured in order to make decisions on how it should handle traffic.
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  3. docs/en/docs/deployment/docker.md

    Here are some examples of when that could make sense:
    
    #### A Simple App
    
    You could want a process manager in the container if your application is **simple enough** that you don't need (at least not yet) to fine-tune the number of processes too much, and you can just use an automated default (with the official Docker image), and you are running it on a **single server**, not a cluster.
    
    #### Docker Compose
    
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  4. .bazelrc

    build:linux --copt="-Wswitch"
    build:linux --copt="-Werror=switch"
    # Required for building with clang
    build:linux --copt="-Wno-error=unused-but-set-variable"
    
    # Linux ARM64 specific options
    build:linux_arm64 --copt="-mtune=generic" --copt="-march=armv8-a" --copt="-O3"
    
    
    # On Windows, `__cplusplus` is wrongly defined without this switch
    # See https://devblogs.microsoft.com/cppblog/msvc-now-correctly-reports-__cplusplus/
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  5. RELEASE.md

        under `tf.keras.dtensor` in this release (refer to the `tf.keras` entry).
        The tutoral and guide for DTensor will be published on
        https://www.tensorflow.org/. Please stay tuned.
    
    *   [oneDNN CPU performance optimizations](https://github.com/tensorflow/community/blob/master/rfcs/20210930-enable-onednn-ops.md)
        are available in Linux x86, Windows x86, and Linux aarch64 packages.
    
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  6. doc/go_spec.html

    and may explain why type inference fails in unusual code situations.
    But by and large these rules can be ignored when writing Go code:
    type inference is designed to mostly "work as expected",
    and the unification rules are fine-tuned accordingly.
    </p>
    
    <p>
    Type unification is controlled by a <i>matching mode</i>, which may
    be <i>exact</i> or <i>loose</i>.
    As unification recursively descends a composite type structure,
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