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  1. docs/en/docs/release-notes.md

    * 🔧 Update MkDocs for other languages. PR [#9734](https://github.com/tiangolo/fastapi/pull/9734) by [@tiangolo](https://github.com/tiangolo).
    * 👷 Refactor Docs CI, run in multiple workers with a dynamic matrix to optimize speed. PR [#9732](https://github.com/tiangolo/fastapi/pull/9732) by [@tiangolo](https://github.com/tiangolo).
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  2. okhttp/src/test/resources/okhttp3/internal/publicsuffix/public_suffix_list.dat

    jetzt
    
    // jewelry : 2015-03-05 Binky Moon, LLC
    jewelry
    
    // jio : 2015-04-02 Reliance Industries Limited
    jio
    
    // jll : 2015-04-02 Jones Lang LaSalle Incorporated
    jll
    
    // jmp : 2015-03-26 Matrix IP LLC
    jmp
    
    // jnj : 2015-06-18 Johnson & Johnson Services, Inc.
    jnj
    
    // joburg : 2014-03-24 ZA Central Registry NPC trading as ZA Central Registry
    joburg
    
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  3. ci/official/README.md

    -   Continuous jobs (Run on every GitHub commit)
        -   Uses `pycpp.sh`
    -   Presubmit jobs (Run on every GitHub PR)
        -   Uses `pycpp.sh`, `code_check_changed_files.sh`
    
    These "env" files match up with an environment matrix that roughly covers:
    
    -   Different Python versions
    -   Linux, MacOS, and Windows machines (these pool definitions are internal)
    -   x86 and arm64
    -   CPU-only, or with NVIDIA CUDA support (Linux only), or with TPUs
    
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  4. docs/en/docs/async.md

    * **Machine Learning**: it normally requires lots of "matrix" and "vector" multiplications. Think of a huge spreadsheet with numbers and multiplying all of them together at the same time.
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  5. RELEASE.md

        *   Update sklearn imports for deprecated packages.
        *   Deprecate `Variable.count_up_to` and `tf.count_up_to` in favor of
            `Dataset.range`.
        *   Export `confusion_matrix` op as `tf.math.confusion_matrix` instead of
            `tf.train.confusion_matrix`.
        *   Add `tf.dtypes.` endpoint for every constant in dtypes.py. Moving
            endpoints in versions.py to corresponding endpoints in `tf.sysconfig.`
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  6. CHANGELOG/CHANGELOG-1.22.md

    ### Workloads that saturate nodes with pods may see pods that fail due to node admission
    
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  7. docs/de/docs/async.md

    * **Maschinelles Lernen**: Normalerweise sind viele „Matrix“- und „Vektor“-Multiplikationen erforderlich. Stellen Sie sich eine riesige Tabelle mit Zahlen vor, in der Sie alle Zahlen gleichzeitig multiplizieren.
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