## Импортируйте `File` и `Form`

=== "Python 3.9+"

    ```Python hl_lines="3"
    {!> ../../../docs_src/request_forms_and_files/tutorial001_an_py39.py!}
    ```

=== "Python 3.6+"

    ```Python hl_lines="1"
    {!> ../../../docs_src/request_forms_and_files/tutorial001_an.py!}
    ```

=== "Python 3.6+ без Annotated"

    !!! tip "Подсказка"

```Python hl_lines="13  15"
{!../../../docs_src/custom_request_and_route/tutorial002.py!}
```

发生异常时，`Request` 实例仍在作用域内，因此处理错误时可以读取和使用请求体：

```Python hl_lines="16-18"
{!../../../docs_src/custom_request_and_route/tutorial002.py!}
```

## 在路由中自定义 `APIRoute` 类

您还可以设置 `APIRoute` 的 `route_class` 参数：

```Python hl_lines="26"
{!../../../docs_src/custom_request_and_route/tutorial003.py!}
```

As the parameters are described with TypeScript types (similar to Python type hints), editor support is quite good.

But when checking benchmarks and comparisons you should keep the following in mind.

## Benchmarks and speed

When you check the benchmarks, it is common to see several tools of different types compared as equivalent.

Specifically, to see Uvicorn, Starlette and FastAPI compared together (among many other tools).

You would also have **inline errors** for everything.

And whenever you update the backend code, and **regenerate** the frontend, it would have any new *path operations* available as methods, the old ones removed, and any other change would be reflected on the generated code. 🤓

But in most of the cases where we need to do something like this, we want the model just to **filter/remove** some of the data as in this example.

And in those cases, we can use classes and inheritance to take advantage of function **type annotations** to get better support in the editor and tools, and still get the FastAPI **data filtering**.

    Remember that when you import `Query`, `Path`, and others from `fastapi`, those are actually functions that return special classes.

!!! tip
    Notice how each model's attribute with a type, default value and `Field` has the same structure as a *path operation function's* parameter, with `Field` instead of `Path`, `Query` and `Body`.

If you create a Python file that imports and uses FastAPI, and run it with the Python from your local environment, it will use your cloned local FastAPI source code.

And if you update that local FastAPI source code when you run that Python file again, it will use the fresh version of FastAPI you just edited.

That way, you don't have to "install" your local version to be able to test every change.

!!! note "Technical Details"

## Recap

With **FastAPI**, by using short, intuitive and standard Python type declarations, you get:

* Editor support: error checks, autocompletion, etc.
* Data "<abbr title="converting the string that comes from an HTTP request into Python data">parsing</abbr>"
* Data validation
* API annotation and automatic documentation

And you only have to declare them once.

# About FastAPI versions

**FastAPI** is already being used in production in many applications and systems. And the test coverage is kept at 100%. But its development is still moving quickly.

New features are added frequently, bugs are fixed regularly, and the code is still continuously improving.