```JSON
{
    "name": "Foo",
    "price": 45.2
}
```

## Declare it as a parameter

To add it to your *path operation*, declare it the same way you declared path and query parameters:

{* ../../docs_src/body/tutorial001_py310.py hl[16] *}

...and declare its type as the model you created, `Item`.

## Results

With just that Python type declaration, **FastAPI** will:

* Read the body of the request as JSON.

It will always have the security scopes declared in the current `Security` dependencies and all the dependants for **that specific** *path operation* and **that specific** dependency tree.

Because the `SecurityScopes` will have all the scopes declared by dependants, you can use it to verify that a token has the required scopes in a central dependency function, and then declare different scope requirements in different *path operations*.

```

////

Let's focus on the parameters declared:

* Even though this function is a dependency ("dependable") itself, it also declares another dependency (it "depends" on something else).
    * It depends on the `query_extractor`, and assigns the value returned by it to the parameter `q`.
* It also declares an optional `last_query` cookie, as a `str`.

### `response_model` or Return Type

In this case, because the two models are different, if we annotated the function return type as `UserOut`, the editor and tools would complain that we are returning an invalid type, as those are different classes.

That's why in this example we have to declare it in the `response_model` parameter.

```JSON
{"item_id":"foo"}
```

## Path parameters with types

You can declare the type of a path parameter in the function, using standard Python type annotations:

```Python hl_lines="7"
{!../../docs_src/path_params/tutorial002.py!}
```

In this case, `item_id` is declared to be an `int`.

/// check

This will give you editor support inside of your function, with error checks, completion, etc.

Because each SQLModel model is also a Pydantic model, you can use it in the same **type annotations** that you could use Pydantic models.

For example, if you declare a parameter of type `Hero`, it will be read from the **JSON body**.

The same way, you can declare it as the function's **return type**, and then the shape of the data will show up in the automatic API docs UI.

And if you declared a `response_model`, it will still be used to filter and convert the object you returned.

**FastAPI** will use that *temporal* response to extract the status code (also cookies and headers), and will put them in the final response that contains the value you returned, filtered by any `response_model`.

## Use a `Response` parameter

You can declare a parameter of type `Response` in your *path operation function*.

And then you can set cookies in that *temporal* response object.

```Python hl_lines="1  8-9"
{!../../docs_src/response_cookies/tutorial002.py!}
```

And then you can return any object you need, as you normally would (a `dict`, a database model, etc).

You can also declare singular values to be received as part of the body.

/// note

This is supported since FastAPI version `0.113.0`. 🤓

///

## Pydantic Models for Forms

You just need to declare a **Pydantic model** with the fields you want to receive as **form fields**, and then declare the parameter as `Form`:

//// tab | Python 3.9+

```Python hl_lines="9-11  15"
{!> ../../docs_src/request_form_models/tutorial001_an_py39.py!}
```

////

//// tab | Python 3.8+