You can import it directly from `fastapi`:

```python
from fastapi import BackgroundTasks
```

    ```

With this, we get tooling support, from editors and mypy as this code is correct in terms of types, but we also get the data filtering from FastAPI.

How does this work? Let's check that out. 🤓

### Type Annotations and Tooling

First let's see how editors, mypy and other tools would see this.

You can declare a parameter in a *path operation function* or dependency to be of type `Request` and then you can access the raw request object directly, without any validation, etc.

You can import it directly from `fastapi`:

```python
from fastapi import Request
```

!!! tip

One of the main features needed by API systems is data "<abbr title="also called marshalling, conversion">serialization</abbr>" which is taking data from the code (Python) and converting it into something that can be sent through the network. For example, converting an object containing data from a database into a JSON object. Converting `datetime` objects into strings, etc.

!!! note "Technical Details"
    You could also use `from starlette.testclient import TestClient`.

    **FastAPI** provides the same `starlette.testclient` as `fastapi.testclient` just as a convenience for you, the developer. But it comes directly from Starlette.

!!! tip

In the docs UI at <a href="http://127.0.0.1:9999/api/v1/docs" class="external-link" target="_blank">http://127.0.0.1:9999/api/v1/docs</a> it would look like:

<img src="/img/tutorial/behind-a-proxy/image03.png">

!!! tip
    The docs UI will interact with the server that you select.

### Disable automatic server from `root_path`

### Preprocess the OpenAPI Specification for the Client Generator

The generated code still has some **duplicated information**.

We already know that this method is related to the **items** because that word is in the `ItemsService` (taken from the tag), but we still have the tag name prefixed in the method name too. 😕

You can combine `dataclasses` with other type annotations in many different combinations to form complex data structures.

Check the in-code annotation tips above to see more specific details.

## Learn More

You can also combine `dataclasses` with other Pydantic models, inherit from them, include them in your own models, etc.

Dichiara il _body_ usando le annotazioni di tipo standard di Python, grazie a Pydantic.

```Python hl_lines="2  7-10  23-25"
from fastapi import FastAPI
from pydantic import BaseModel
from typing import Optional

app = FastAPI()


class Item(BaseModel):
    name: str
    price: float
    is_offer: bool = Optional[None]


@app.get("/")

```{ .dockerfile .annotate }
# (1)
FROM python:3.9 as requirements-stage

# (2)
WORKDIR /tmp

# (3)
RUN pip install poetry

# (4)
COPY ./pyproject.toml ./poetry.lock* /tmp/

# (5)
RUN poetry export -f requirements.txt --output requirements.txt --without-hashes

# (6)
FROM python:3.9

# (7)
WORKDIR /code

# (8)