So, if we create a Pydantic object `user_in` like:

```Python
user_in = UserIn(username="john", password="secret", email="******@****.***")
```

and then we call:

```Python
user_dict = user_in.dict()
```

we now have a `dict` with the data in the variable `user_dict` (it's a `dict` instead of a Pydantic model object).

And if we call:

```Python
print(user_dict)
```

## Parameterized dependencies

All the dependencies we have seen are a fixed function or class.

But there could be cases where you want to be able to set parameters on the dependency, without having to declare many different functions or classes.

Let's imagine that we want to have a dependency that checks if the query parameter `q` contains some fixed content.

But we want to be able to parameterize that fixed content.

    ```Python hl_lines="14"
    {!> ../../../docs_src/body_nested_models/tutorial002.py!}
    ```

## Set types

But then we think about it, and realize that tags shouldn't repeat, they would probably be unique strings.

And Python has a special data type for sets of unique items, the `set`.

Then we can declare `tags` as a set of strings:

=== "Python 3.10+"

    ```Python hl_lines="12"

app = FastAPI()


class InternalError(Exception):
    pass


def get_username():
    try:
        yield "Rick"
    except InternalError:
        print("We don't swallow the internal error here, we raise again 😎")
        raise


@app.get("/items/{item_id}")
def get_item(item_id: str, username: str = Depends(get_username)):
    if item_id == "portal-gun":
        raise InternalError(

app = FastAPI()


class InternalError(Exception):
    pass


def get_username():
    try:
        yield "Rick"
    except InternalError:
        print("We don't swallow the internal error here, we raise again 😎")
        raise


@app.get("/items/{item_id}")
def get_item(item_id: str, username: Annotated[str, Depends(get_username)]):
    if item_id == "portal-gun":

app = FastAPI()


class InternalError(Exception):
    pass


def get_username():
    try:
        yield "Rick"
    except InternalError:
        print("We don't swallow the internal error here, we raise again 😎")
        raise


@app.get("/items/{item_id}")
def get_item(item_id: str, username: Annotated[str, Depends(get_username)]):
    if item_id == "portal-gun":

    ```

## Verify the `username` and data shape

We verify that we get a `username`, and extract the scopes.

And then we validate that data with the Pydantic model (catching the `ValidationError` exception), and if we get an error reading the JWT token or validating the data with Pydantic, we raise the `HTTPException` we created before.

For that, we update the Pydantic model `TokenData` with a new property `scopes`.

```

There are some differences with the code for the SQLAlchemy tutorial.

We don't pass a `db` attribute around. Instead we use the models directly. This is because the `db` object is a global object, that includes all the connection logic. That's why we had to do all the `contextvars` updates above.

Aso, when returning several objects, like in `get_users`, we directly call `list`, like in:

```Python
list(models.User.select())
```

But now, having `Query(max_length=50)` inside of `Annotated`, we are telling FastAPI that we want it to extract this value from the query parameters (this would have been the default anyway 🤷) and that we want to have **additional validation** for this value (that's why we do this, to get the additional validation). 😎

FastAPI will now:

* It also declares an optional `last_query` cookie, as a `str`.
    * If the user didn't provide any query `q`, we use the last query used, which we saved to a cookie before.

## Use the dependency

Then we can use the dependency with:

=== "Python 3.10+"

    ```Python hl_lines="23"
    {!> ../../../docs_src/dependencies/tutorial005_an_py310.py!}
    ```