{!> ../../../docs_src/body_fields/tutorial001_an_py39.py!}
    ```

=== "Python 3.8+"

    ```Python hl_lines="4"
    {!> ../../../docs_src/body_fields/tutorial001_an.py!}
    ```

=== "Python 3.10+ non-Annotated"

    !!! tip
        Prefer to use the `Annotated` version if possible.

    ```Python hl_lines="2"
    {!> ../../../docs_src/body_fields/tutorial001_py310.py!}
    ```

=== "Python 3.10+"

    ```Python hl_lines="4"
    {!> ../../../docs_src/body_fields/tutorial001_an_py310.py!}
    ```

=== "Python 3.9+"

    ```Python hl_lines="4"
    {!> ../../../docs_src/body_fields/tutorial001_an_py39.py!}
    ```

=== "Python 3.8+"

    ```Python hl_lines="4"
    {!> ../../../docs_src/body_fields/tutorial001_an.py!}
    ```

=== "Python 3.10+ non-Annotated"

    !!! tip

Sebastián Ramírez <******@****.***> 1691182027 +0200

    new_field.validate_always = field.validate_always  # type: ignore[attr-defined]
    if field.sub_fields:  # type: ignore[attr-defined]
        new_field.sub_fields = [  # type: ignore[attr-defined]
            create_cloned_field(sub_field, cloned_types=cloned_types)
            for sub_field in field.sub_fields  # type: ignore[attr-defined]
        ]
    if field.key_field:  # type: ignore[attr-defined]

        Union[str, None],
        Doc(
            """
            'Blacklist' validation step. The vanilla parameter field will be the
            single one of the alias' or set of aliases' fields and all the other
            fields will be ignored at serialization time.
            """
        ),
    ] = None,
    title: Annotated[
        Optional[str],
        Doc(
            """
            Human-readable title.

    for an OAuth2 password flow.

    The OAuth2 specification dictates that for a password flow the data should be
    collected using form data (instead of JSON) and that it should have the specific
    fields `username` and `password`.

    All the initialization parameters are extracted from the request.

    Read more about it in the

            "detail": [
                {
                    "type": "model_attributes_type",
                    "loc": ["body"],
                    "msg": "Input should be a valid dictionary or object to extract fields from",
                    "input": "name=Foo&price=50.5",
                }
            ]
        }
    ) | IsDict(
        # TODO: remove when deprecating Pydantic v1
        {
            "detail": [

            "detail": [
                {
                    "type": "model_attributes_type",
                    "loc": ["body"],
                    "msg": "Input should be a valid dictionary or object to extract fields from",
                    "input": "name=Foo&price=50.5",
                }
            ]
        }
    ) | IsDict(
        # TODO: remove when deprecating Pydantic v1
        {
            "detail": [

### Type Annotations and Tooling

First let's see how editors, mypy and other tools would see this.

`BaseUser` has the base fields. Then `UserIn` inherits from `BaseUser` and adds the `password` field, so, it will include all the fields from both models.

We annotate the function return type as `BaseUser`, but we are actually returning a `UserIn` instance.

    _get_model_config,
    is_bytes_sequence_annotation,
    is_uploadfile_sequence_annotation,
)
from fastapi.testclient import TestClient
from pydantic import BaseConfig, BaseModel, ConfigDict
from pydantic.fields import FieldInfo

from .utils import needs_pydanticv1, needs_pydanticv2


@needs_pydanticv2
def test_model_field_default_required():
    # For coverage
    field_info = FieldInfo(annotation=str)