Example:

    Source (English):

        «««
        ```mermaid
        flowchart LR

    # Extra Pydantic data types
    "pydantic-extra-types >=2.0.0",
]

[project.scripts]
fastapi = "fastapi.cli:main"

[tool.pdm]
version = { source = "file", path = "fastapi/__init__.py" }
distribution = true

[tool.pdm.build]
source-includes = [
    "tests/",
    "docs_src/",
    "requirements*.txt",
    "scripts/",
    # For a test
    "docs/en/docs/img/favicon.png",
    ]

    ) -> dict[str, Any]:
        return {"type": "string", "format": "binary"}

    @classmethod
    def __get_pydantic_core_schema__(
        cls, source: type[Any], handler: Callable[[Any], Mapping[str, Any]]
    ) -> Mapping[str, Any]:
        from ._compat.v2 import with_info_plain_validator_function

        return with_info_plain_validator_function(cls._validate)

* 🔥 Remove/clean extra imports from examples in docs for features. PR [#3709](https://github.com/tiangolo/fastapi/pull/3709) by [@tiangolo](https://github.com/tiangolo).
* ➕ Update docs library to include sources in Markdown. PR [#3648](https://github.com/tiangolo/fastapi/pull/3648) by [@tiangolo](https://github.com/tiangolo).

        ) -> dict[str, Any]:
            return {"type": "string", "format": "email"}

        @classmethod
        def __get_pydantic_core_schema__(
            cls, source: type[Any], handler: Callable[[Any], Mapping[str, Any]]
        ) -> Mapping[str, Any]:
            return with_info_plain_validator_function(cls._validate)


class BaseModelWithConfig(BaseModel):