```Python hl_lines="24"
    {!> ../../../docs_src/response_model/tutorial004.py!}
    ```

and those default values won't be included in the response, only the values actually set.

So, if you send a request to that *path operation* for the item with ID `foo`, the response (not including default values) will be:

```JSON
{
    "name": "Foo",
    "price": 50.2
}
```

!!! info

            """
        ),
    ] = _Unset,
    examples: Annotated[
        Optional[List[Any]],
        Doc(
            """
            Example values for this field.
            """
        ),
    ] = None,
    example: Annotated[
        Optional[Any],
        deprecated(
            "Deprecated in OpenAPI 3.1.0 that now uses JSON Schema 2020-12, "

        if use_type is None:
            use_type = create_model(original_type.__name__, __base__=original_type)
            cloned_types[original_type] = use_type
            for f in original_type.__fields__.values():
                use_type.__fields__[f.name] = create_cloned_field(
                    f, cloned_types=cloned_types
                )
    new_field = create_response_field(name=field.name, type_=use_type)

```Python hl_lines="1"
{!../../../docs_src/python_types/tutorial005.py!}
```

### Generic types with type parameters

There are some data structures that can contain other values, like `dict`, `list`, `set` and `tuple`. And the internal values can have their own type too.

These types that have internal types are called "**generic**" types. And it's possible to declare them, even with their internal types.

### Step 5: return the content

```Python hl_lines="8"
{!../../../docs_src/first_steps/tutorial001.py!}
```

You can return a `dict`, `list`, singular values as `str`, `int`, etc.

You can also return Pydantic models (you'll see more about that later).

    * Add support for enumerations in *path operation* parameters. New documentation: [Path Parameters: Predefined values](https://fastapi.tiangolo.com/tutorial/path-params/#predefined-values).

* Retrieve the stored data.
* Put that data in a Pydantic model.
* Generate a `dict` without default values from the input model (using `exclude_unset`).
    * This way you can update only the values actually set by the user, instead of overriding values already stored with default values in your model.
* Create a copy of the stored model, updating it's attributes with the received partial updates (using the `update` parameter).

    '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.

So, continuing with the `user_dict` from above, writing:

```Python
UserInDB(**user_dict)
```

would result in something equivalent to:

```Python

second_user_data = {
    "id": 4,
    "name": "Mary",
    "joined": "2018-11-30",
}

my_second_user: User = User(**second_user_data)
```

!!! info
    `**second_user_data` means:

    Pass the keys and values of the `second_user_data` dict directly as key-value arguments, equivalent to: `User(id=4, name="Mary", joined="2018-11-30")`

### Editor support

## Pydanticを使わない方法