Wir hatten diese Typannotation:

=== "Python 3.10+"

    ```Python
    q: str | None = None
    ```

=== "Python 3.8+"

    ```Python
    q: Union[str, None] = None
    ```

Wir wrappen das nun in `Annotated`, sodass daraus wird:

=== "Python 3.10+"

    ```Python
    q: Annotated[str | None] = None
    ```

=== "Python 3.8+"

    ```Python
    q: Annotated[Union[str, None]] = None

            continue
        param_schema = get_schema_from_model_field(
            field=param,
            schema_generator=schema_generator,
            model_name_map=model_name_map,
            field_mapping=field_mapping,
            separate_input_output_schemas=separate_input_output_schemas,
        )
        parameter = {
            "name": param.alias,
            "in": field_info.in_.value,

import inspect
from contextlib import AsyncExitStack, contextmanager
from copy import copy, deepcopy
from typing import (
    Any,
    Callable,
    Coroutine,
    Dict,
    ForwardRef,
    List,
    Mapping,
    Optional,
    Sequence,
    Tuple,
    Type,
    Union,
    cast,
)

import anyio
from fastapi import params
from fastapi._compat import (
    PYDANTIC_V2,
    ErrorWrapper,
    ModelField,

    The **FastAPI** specific code is as small as always.

## ORMs

**FastAPI** works with any database and any style of library to talk to the database.

A common pattern is to use an "ORM": an "object-relational mapping" library.

An ORM has tools to convert ("*map*") between *objects* in code and database tables ("*relations*").

    variables: Optional[Dict[str, ServerVariable]] = None


class Reference(BaseModel):
    ref: str = Field(alias="$ref")


class Discriminator(BaseModel):
    propertyName: str
    mapping: Optional[Dict[str, str]] = None


class XML(BaseModelWithConfig):
    name: Optional[str] = None
    namespace: Optional[str] = None
    prefix: Optional[str] = None
    attribute: Optional[bool] = None

        inputs = [
            (field, override_mode or field.mode, field._type_adapter.core_schema)
            for field in fields
        ]
        field_mapping, definitions = schema_generator.generate_definitions(
            inputs=inputs
        )
        return field_mapping, definitions  # type: ignore[return-value]

    def is_scalar_field(field: ModelField) -> bool:
        from fastapi import params

    This is useful when we use ConstrainedDecimal to represent Numeric(x,0)
    where a integer (but not int typed) is used. Encoding this as a float
    results in failed round-tripping between encode and parse.
    Our Id type is a prime example of this.

    >>> decimal_encoder(Decimal("1.0"))
    1.0

    >>> decimal_encoder(Decimal("1"))
    1
    """

Dazu können Sie die `WSGIMiddleware` verwenden und damit Ihre WSGI-Anwendung wrappen, zum Beispiel Flask, Django usw.

## `WSGIMiddleware` verwenden

Sie müssen `WSGIMiddleware` importieren.

Wrappen Sie dann die WSGI-Anwendung (z. B. Flask) mit der Middleware.

Und dann mounten Sie das auf einem Pfad.

```Python hl_lines="2-3  23"

  - &background_color >-
    {%- if layout.background_color -%}
      {{ layout.background_color }}
    {%- else -%}
      {%- set palette = config.theme.palette or {} -%}
      {%- if not palette is mapping -%}
        {%- set palette = palette | first -%}
      {%- endif -%}
      {%- set primary = palette.get("primary", "indigo") -%}
      {%- set primary = primary.replace(" ", "-") -%}
      {{ {

print(user_dict)
```

we would get a Python `dict` with:

```Python
{
    'username': 'john',
    'password': 'secret',
    'email': '******@****.***',
    'full_name': None,
}
```

#### Unwrapping a `dict`

If we take a `dict` like `user_dict` and pass it to a function (or class) with `**user_dict`, Python will "unwrap" it. It will pass the keys and values of the `user_dict` directly as key-value arguments.