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  1. ci/official/README.md

    # Official CI Directory
    
    Maintainer: TensorFlow and TensorFlow DevInfra
    
    Issue Reporting: File an issue against this repo and tag
    [@devinfra](https://github.com/orgs/tensorflow/teams/devinfra)
    
    ********************************************************************************
    
    ## TensorFlow's Official CI and Build/Test Scripts
    
    TensorFlow's official CI jobs run the scripts in this folder. Our internal CI
    Plain Text
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  2. .github/ISSUE_TEMPLATE/tflite-converter-issue.md

    - Include code to invoke the TFLite Converter Python API and the errors.
    - Provide links to your TensorFlow model and (optionally) TensorFlow Lite Model.
    ```
    
    ### 3. Failure after conversion
    If the conversion is successful, but the generated model is wrong, then state what is wrong:
    
    - Model produces wrong results and/or has lesser accuracy.
    - Model produces correct results, but it is slower than expected.
    
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  3. ci/official/requirements_updater/README.md

    # Hermetic Python
    
    Hermetic Python allows not to rely on system-installed Python, and
    system-installed Python packages. \
    Instead, an independent Python toolchain is registered, ensuring the right
    dependencies are always used. \
    See https://github.com/bazelbuild/rules_python/ for more details.
    
    ### Specifying the Python version
    
    Note: Only a number of minor Python versions are supported at any given time.
    
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  4. WORKSPACE

    load("@pypi//:requirements.bzl", "install_deps")
    
    install_deps()
    
    # Initialize the TensorFlow repository and all dependencies.
    #
    # The cascade of load() statements and tf_workspace?() calls works around the
    # restriction that load() statements need to be at the top of .bzl files.
    # E.g. we can not retrieve a new repository with http_archive and then load()
    # a macro from that repository in the same file.
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  5. ci/official/envs/linux_arm64_onednn

    # distributed under the License is distributed on an "AS IS" BASIS,
    # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
    # See the License for the specific language governing permissions and
    # limitations under the License.
    # ==============================================================================
    source ci/official/envs/linux_arm64
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  6. .github/ISSUE_TEMPLATE/tflite-in-play-services.md

    the problem.
    
    **Any other info / logs**
    Include any logs or source code that would be helpful to diagnose the problem.
    If including tracebacks, please include the full traceback. Large logs and files
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  7. ci/official/envs/macos_x86_cross_compile

    # distributed under the License is distributed on an "AS IS" BASIS,
    # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
    # See the License for the specific language governing permissions and
    # limitations under the License.
    # ==============================================================================
    source ci/official/envs/macos_x86
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  8. tensorflow/c/experimental/filesystem/BUILD

            "//tensorflow/core/platform:env",
            "//tensorflow/core/platform:errors",
            "//tensorflow/core/platform:status",
            "@local_tsl//tsl/platform:errors",
        ],
    )
    
    # Compliance test for modules and for interface
    tf_cc_test(
        name = "modular_filesystem_test",
        size = "small",
        srcs = ["modular_filesystem_test.cc"],
        linkopts = ["-ldl"],
        tags = [
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  9. LICENSE

       list of conditions and the following disclaimer.
    
    2. Redistributions in binary form must reproduce the above copyright notice,
       this list of conditions and the following disclaimer in the documentation
       and/or other materials provided with the distribution.
    
       THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
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  10. SECURITY.md

    process images, audio, videos, and text. There are several modules specialized
    in taking those formats, modifying them, and/or converting them to intermediate
    formats that can be processed by TensorFlow.
    
    These modifications and conversions are handled by a variety of libraries that
    have different security properties and provide different levels of confidence
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