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  1. docs/es/docs/python-types.md

    ## Modelos de Pydantic
    
    <a href="https://docs.pydantic.dev/" class="external-link" target="_blank">Pydantic</a> es una library de Python para llevar a cabo validación de datos.
    
    Tú declaras la "forma" de los datos mediante clases con atributos.
    
    Cada atributo tiene un tipo.
    
    Registered: Sun Nov 03 07:19:11 UTC 2024
    - Last Modified: Sun Oct 06 20:36:54 UTC 2024
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  2. tests/test_tutorial/test_header_param_models/test_tutorial002.py

                            "input": {"x_tag": [], "host": "testserver"},
                        }
                    )
                    | IsDict(
                        # TODO: remove when deprecating Pydantic v1
                        {
                            "type": "value_error.missing",
                            "loc": ["header", "save_data"],
                            "msg": "field required",
                        }
    Registered: Sun Nov 03 07:19:11 UTC 2024
    - Last Modified: Tue Sep 17 18:54:10 UTC 2024
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  3. tests/test_tutorial/test_schema_extra_example/test_tutorial004_an.py

                                            ],
                                        }
                                    )
                                    | IsDict(
                                        # TODO: remove when deprecating Pydantic v1
                                        {
                                            "allOf": [
                                                {"$ref": "#/components/schemas/Item"}
                                            ],
    Registered: Sun Nov 03 07:19:11 UTC 2024
    - Last Modified: Fri Jul 07 17:12:13 UTC 2023
    - 6.9K bytes
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  4. tests/utils.py

    needs_py310 = pytest.mark.skipif(
        sys.version_info < (3, 10), reason="requires python3.10+"
    )
    needs_pydanticv2 = pytest.mark.skipif(not PYDANTIC_V2, reason="requires Pydantic v2")
    Registered: Sun Nov 03 07:19:11 UTC 2024
    - Last Modified: Fri Jul 07 17:12:13 UTC 2023
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  5. tests/test_tutorial/test_response_model/test_tutorial003_01.py

                    "BaseUser": {
                        "title": "BaseUser",
                        "required": IsOneOf(
                            ["username", "email", "full_name"],
                            # TODO: remove when deprecating Pydantic v1
                            ["username", "email"],
                        ),
                        "type": "object",
                        "properties": {
                            "username": {"title": "Username", "type": "string"},
    Registered: Sun Nov 03 07:19:11 UTC 2024
    - Last Modified: Fri Aug 04 20:47:07 UTC 2023
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  6. docs/pt/docs/tutorial/extra-data-types.md

        * O `Decimal` padrão do Python.
        * Em requisições e respostas será representado como um `float`.
    * Você pode checar todos os tipos de dados válidos do Pydantic aqui: <a href="https://docs.pydantic.dev/latest/concepts/types/" class="external-link" target="_blank">Tipos de dados do Pydantic</a>.
    
    ## Exemplo
    
    Aqui está um exemplo de *operação de rota* com parâmetros utilizando-se de alguns dos tipos acima.
    
    ```Python hl_lines="1  3  12-16"
    Registered: Sun Nov 03 07:19:11 UTC 2024
    - Last Modified: Sun Oct 06 20:36:54 UTC 2024
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  7. tests/test_tutorial/test_body_multiple_params/test_tutorial001_an.py

                        "input": "foo",
                    }
                ]
            }
        ) | IsDict(
            # TODO: remove when deprecating Pydantic v1
            {
                "detail": [
                    {
                        "loc": ["path", "item_id"],
                        "msg": "value is not a valid integer",
                        "type": "type_error.integer",
    Registered: Sun Nov 03 07:19:11 UTC 2024
    - Last Modified: Thu Apr 18 19:40:57 UTC 2024
    - 7.5K bytes
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  8. docs/de/docs/tutorial/encoder.md

    Genauso würde die Datenbank kein Pydantic-Modell (ein Objekt mit Attributen) akzeptieren, sondern nur ein `dict`.
    
    Sie können für diese Fälle `jsonable_encoder` verwenden.
    
    Es nimmt ein Objekt entgegen, wie etwa ein Pydantic-Modell, und gibt eine JSON-kompatible Version zurück:
    
    //// tab | Python 3.10+
    
    ```Python hl_lines="4  21"
    Registered: Sun Nov 03 07:19:11 UTC 2024
    - Last Modified: Sun Oct 06 20:36:54 UTC 2024
    - 1.9K bytes
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  9. tests/test_tutorial/test_response_model/test_tutorial003_py310.py

                    "UserOut": {
                        "title": "UserOut",
                        "required": IsOneOf(
                            ["username", "email", "full_name"],
                            # TODO: remove when deprecating Pydantic v1
                            ["username", "email"],
                        ),
                        "type": "object",
                        "properties": {
                            "username": {"title": "Username", "type": "string"},
    Registered: Sun Nov 03 07:19:11 UTC 2024
    - Last Modified: Fri Aug 04 20:47:07 UTC 2023
    - 5.8K bytes
    - Viewed (0)
  10. tests/test_tutorial/test_extra_data_types/test_tutorial001.py

                                        }
                                    )
                                    | IsDict(
                                        # TODO: remove when deprecating Pydantic v1
                                        {
                                            "$ref": "#/components/schemas/Body_read_items_items__item_id__put"
                                        }
                                    )
    Registered: Sun Nov 03 07:19:11 UTC 2024
    - Last Modified: Fri Apr 19 00:11:40 UTC 2024
    - 6.8K bytes
    - Viewed (0)
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