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  1. tests/test_tutorial/test_schema_extra_example/test_tutorial005_an_py310.py

                                            "summary": "An example with converted data",
                                            "description": "FastAPI can convert price `strings` to actual `numbers` automatically",
                                            "value": {"name": "Bar", "price": "35.4"},
                                        },
                                        "invalid": {
    Python
    - Registered: Sun Apr 28 07:19:10 GMT 2024
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  2. docs/ko/docs/tutorial/path-params.md

    #### *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"
        `ModelName.lenet.value`로도 값 `"lenet"`에 접근할 수 있습니다.
    
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  3. docs/en/docs/features.md

    * Validation for most (or all?) Python **data types**, including:
        * JSON objects (`dict`).
        * JSON array (`list`) defining item types.
        * String (`str`) fields, defining min and max lengths.
        * Numbers (`int`, `float`) with min and max values, etc.
    
    * Validation for more exotic types, like:
        * URL.
        * Email.
        * UUID.
        * ...and others.
    
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  4. docs/pt/docs/features.md

        * objetos JSON (`dict`).
        * arrays JSON (`list`), definindo tipos dos itens.
        * campos String (`str`), definindo tamanho mínimo e máximo.
        * Numbers (`int`, `float`) com valores mínimos e máximos, etc.
    
    * Validação de tipos mais exóticos, como:
        * URL.
        * Email.
        * UUID.
        * ...e outros.
    
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  5. docs/en/docs/tutorial/query-params-str-validations.md

    * `min_length`
    * `max_length`
    * `pattern`
    
    In these examples you saw how to declare validations for `str` values.
    
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  6. docs_src/schema_extra_example/tutorial005.py

                    },
                },
                "converted": {
                    "summary": "An example with converted data",
                    "description": "FastAPI can convert price `strings` to actual `numbers` automatically",
                    "value": {
                        "name": "Bar",
                        "price": "35.4",
                    },
                },
                "invalid": {
    Python
    - Registered: Sun May 05 07:19:11 GMT 2024
    - Last Modified: Sat Aug 26 18:03:13 GMT 2023
    - 1.4K bytes
    - Viewed (0)
  7. tests/test_tutorial/test_schema_extra_example/test_tutorial005.py

                                            "summary": "An example with converted data",
                                            "description": "FastAPI can convert price `strings` to actual `numbers` automatically",
                                            "value": {"name": "Bar", "price": "35.4"},
                                        },
                                        "invalid": {
    Python
    - Registered: Sun Apr 28 07:19:10 GMT 2024
    - Last Modified: Sat Aug 26 18:03:13 GMT 2023
    - 6.7K bytes
    - Viewed (0)
  8. tests/test_tutorial/test_schema_extra_example/test_tutorial005_py310.py

                                            "summary": "An example with converted data",
                                            "description": "FastAPI can convert price `strings` to actual `numbers` automatically",
                                            "value": {"name": "Bar", "price": "35.4"},
                                        },
                                        "invalid": {
    Python
    - Registered: Sun Apr 28 07:19:10 GMT 2024
    - Last Modified: Sat Aug 26 18:03:13 GMT 2023
    - 6.8K bytes
    - Viewed (0)
  9. 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.
    * **Deep Learning**: this is a sub-field of Machine Learning, so, the same applies. It's just that there is not a single spreadsheet of numbers to multiply, but a huge set of them, and in many cases, you use a special processor to build and / or use those models.
    
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  10. docs/tr/docs/tutorial/path-params.md

    ```Python hl_lines="17"
    {!../../../docs_src/path_params/tutorial005.py!}
    ```
    
    #### *Enumeration Değerini* Edinelim
    
    `model_name.value` veya genel olarak `your_enum_member.value` tanımlarını kullanarak (bu durumda bir `str` olan) gerçek değere ulaşabilirsiniz:
    
    ```Python hl_lines="20"
    {!../../../docs_src/path_params/tutorial005.py!}
    ```
    
    !!! tip "İpucu"
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