```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

### 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).

    response = client.post("/tuple-form/", data={"values": ("1", "2")})
    assert response.status_code == 200, response.text
    assert response.json() == [1, 2]


def test_tuple_form_invalid():
    response = client.post("/tuple-form/", data={"values": ("1", "2", "3")})
    assert response.status_code == 422, response.text

    response = client.post("/tuple-form/", data={"values": ("1")})

```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.

## Без Pydantic

The `ContextVar` has to be created at the top of the module, like:

```Python
some_var = ContextVar("some_var", default="default value")
```

To set a value used in the current "context" (e.g. for the current request) use:

```Python
some_var.set("new value")
```

To get a value anywhere inside of the context (e.g. in any part handling the current request) use:

```Python
some_var.get()
```

    ```Python hl_lines="80-83"
    {!> ../../../docs_src/security/tutorial003.py!}
    ```

#### About `**user_dict`

`UserInDB(**user_dict)` means:

*Pass the keys and values of the `user_dict` directly as key-value arguments, equivalent to:*

```Python
UserInDB(
    username = user_dict["username"],
    email = user_dict["email"],
    full_name = user_dict["full_name"],

Common examples of CPU bound operations are things that require complex math processing.

For example:

* **Audio** or **image processing**.
* **Computer vision**: an image is composed of millions of pixels, each pixel has 3 values / colors, processing that normally requires computing something on those pixels, all at the same time.

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

    >>> decimal_encoder(Decimal("1"))
    1
    """
    if dec_value.as_tuple().exponent >= 0:  # type: ignore[operator]
        return int(dec_value)
    else:
        return float(dec_value)


ENCODERS_BY_TYPE: Dict[Type[Any], Callable[[Any], Any]] = {
    bytes: lambda o: o.decode(),
    Color: str,
    datetime.date: isoformat,

    return _annotation_is_sequence(annotation) or _annotation_is_sequence(
        get_origin(annotation)
    )


def value_is_sequence(value: Any) -> bool:
    return isinstance(value, sequence_types) and not isinstance(value, (str, bytes))  # type: ignore[arg-type]


def _annotation_is_complex(annotation: Union[Type[Any], None]) -> bool:
    return (