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  1. tensorflow/c/BUILD

            ":kernels",
        ],
    )
    
    tf_cuda_cc_test(
        name = "c_api_test",
        size = "medium",
        srcs = ["c_api_test.cc"],
        data = [
            ":test_op1.so",
            "//tensorflow/cc/saved_model:saved_model_half_plus_two",
        ],
        linkopts = select({
            "//tensorflow:macos": ["-headerpad_max_install_names"],
            "//conditions:default": [],
        }),
        tags = [
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  2. docs/ko/docs/tutorial/schema-extra-example.md

        ```
    
    추가 정보는 있는 그대로 해당 모델의 **JSON 스키마** 결과에 추가되고, API 문서에서 사용합니다.
    
    === "Pydantic v2"
    
        Pydantic 버전 2에서 <a href="https://docs.pydantic.dev/latest/usage/model_config/" class="external-link" target="_blank">Pydantic 공식 문서: Model Config</a>에 나와 있는 것처럼 `dict`를 받는 `model_config` 어트리뷰트를 사용할 것입니다.
    
        `"json_schema_extra"`를 생성된 JSON 스키마에서 보여주고 싶은 별도의 데이터와 `examples`를 포함하는 `dict`으로 설정할 수 있습니다.
    
    === "Pydantic v1"
    
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  3. ci/official/containers/linux_arm64/devel.usertools/code_check_full.bats

      cat > $BATS_TEST_TMPDIR/ignore_these_deps <<EOF
    benchmark
    _test$
    _test.py$
    _test_cpu$
    _test_cpu.py$
    _test_gpu$
    _test_gpu.py$
    _test_lib$
    //tensorflow/cc/saved_model:saved_model_test_files
    //tensorflow/cc/saved_model:saved_model_half_plus_two
    //tensorflow:no_tensorflow_py_deps
    //tensorflow/tools/pip_package:win_pip_package_marker
    //tensorflow/core:image_testdata
    //tensorflow/core/lib/lmdb:lmdb_testdata
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  4. docs/vi/docs/features.md

    * Tự động tài liệu hóa data model theo <a href="https://json-schema.org/" class="external-link" target="_blank"><strong>JSON Schema</strong></a> (OpenAPI bản thân nó được dựa trên JSON Schema).
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  5. docs/en/docs/tutorial/security/simple-oauth2.md

        ```
    
    ### Check the password
    
    At this point we have the user data from our database, but we haven't checked the password.
    
    Let's put that data in the Pydantic `UserInDB` model first.
    
    You should never save plaintext passwords, so, we'll use the (fake) password hashing system.
    
    If the passwords don't match, we return the same error.
    
    #### Password hashing
    
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  6. api/go1.6.txt

    pkg image, method (*NYCbCrA) At(int, int) color.Color
    pkg image, method (*NYCbCrA) Bounds() Rectangle
    pkg image, method (*NYCbCrA) COffset(int, int) int
    pkg image, method (*NYCbCrA) ColorModel() color.Model
    pkg image, method (*NYCbCrA) NYCbCrAAt(int, int) color.NYCbCrA
    pkg image, method (*NYCbCrA) Opaque() bool
    pkg image, method (*NYCbCrA) SubImage(Rectangle) Image
    pkg image, method (*NYCbCrA) YCbCrAt(int, int) color.YCbCr
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  7. CONTRIBUTING.md

        ```
    
        For a single/parameterized test e.g. `test_capture_variables` in
        `tensorflow/python/saved_model/load_test.py`:
    
        (Requires `python>=3.7`)
    
        ```bash
        bazel test ${flags} //tensorflow/python/saved_model:load_test --test_filter=*LoadTest.test_capture_variables*
        ```
    
        **Note:** You can add `--test_sharding_strategy=disabled` to the `flags` to
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  8. docs/ko/docs/tutorial/path-params.md

    *경로 매개변수*의 값은 *열거형 멤버*가 됩니다.
    
    #### *열거형 멤버* 비교
    
    열거형 `ModelName`의 *열거형 멤버*를 비교할 수 있습니다:
    
    ```Python hl_lines="17"
    {!../../../docs_src/path_params/tutorial005.py!}
    ```
    
    #### *열거형 값* 가져오기
    
    `model_name.value` 또는 일반적으로 `your_enum_member.value`를 이용하여 실제 값(위 예시의 경우 `str`)을 가져올 수 있습니다:
    
    ```Python hl_lines="20"
    {!../../../docs_src/path_params/tutorial005.py!}
    ```
    
    !!! tip "팁"
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  9. docs/ja/docs/tutorial/path-params.md

    !!! tip "豆知識"
        "AlexNet"、"ResNet"そして"LeNet"は機械学習<abbr title="Technically, Deep Learning model architectures">モデル</abbr>の名前です。
    
    ### *パスパラメータ*の宣言
    
    次に、作成したenumクラスである`ModelName`を使用した型アノテーションをもつ*パスパラメータ*を作成します:
    
    ```Python hl_lines="16"
    {!../../../docs_src/path_params/tutorial005.py!}
    ```
    
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  10. architecture/networking/pilot.md

    This is composed of two controllers - one driven from core Kubernetes types ("Kube Controller") and one by Istio types ("ServiceEntry controller").
    
    ```mermaid
    graph TD
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