包括许多与云存储，视频处理等交互的库。

```Python hl_lines="2  10-12  14"
{!../../docs_src/custom_response/tutorial008.py!}
```

/// tip | "小贴士"

注意在这里，因为我们使用的是不支持 `async` 和 `await` 的标准 `open()`，我们使用普通的 `def` 声明了路径操作。

///

### `FileResponse`

异步传输文件作为响应。

与其他响应类型相比，接受不同的参数集进行实例化：

* `path` - 要流式传输的文件的文件路径。
* `headers` - 任何自定义响应头，传入字典类型。
* `media_type` - 给出媒体类型的字符串。如果未设置，则文件名或路径将用于推断媒体类型。

Então esse objeto é um "chamável".

## Classes como dependências

Você deve ter percebido que para criar um instância de uma classe em Python, a mesma sintaxe é utilizada.

Por exemplo:

```Python
class Cat:
    def __init__(self, name: str):
        self.name = name


fluffy = Cat(name="Mr Fluffy")
```

Nesse caso,  `fluffy` é uma instância da classe `Cat`.

E para criar `fluffy`, você está "chamando" `Cat`.

```Python
contents = await myfile.read()
```

Se você estiver dentro de uma *função de operação de rota* definida normalmente com `def`, você pode acessar `UploadFile.file` diretamente, por exemplo:

```Python
contents = myfile.file.read()
```

/// note | Detalhes técnicos do `async`

```

then it is a "callable".

## Classes as dependencies

You might notice that to create an instance of a Python class, you use that same syntax.

For example:

```Python
class Cat:
    def __init__(self, name: str):
        self.name = name


fluffy = Cat(name="Mr Fluffy")
```

In this case, `fluffy` is an instance of the class `Cat`.

And to create `fluffy`, you are "calling" `Cat`.

```Python
from typing import List, Dict
from datetime import date

from pydantic import BaseModel

# Deklarieren Sie eine Variable als ein `str`
# und bekommen Sie Editor-Unterstütung innerhalb der Funktion
def main(user_id: str):
    return user_id


# Ein Pydantic-Modell
class User(BaseModel):
    id: int
    name: str
    joined: date
```

Das kann nun wie folgt verwendet werden:

```Python

You write standard Python with types:

```Python
from datetime import date

from pydantic import BaseModel

# Declare a variable as a str
# and get editor support inside the function
def main(user_id: str):
    return user_id


# A Pydantic model
class User(BaseModel):
    id: int
    name: str
    joined: date
```

That can then be used like:

```Python

Wystarczy, że napiszesz standardowe deklaracje typów Pythona:

```Python
from datetime import date

from pydantic import BaseModel

# Zadeklaruj parametr jako str
# i uzyskaj wsparcie edytora wewnątrz funkcji
def main(user_id: str):
    return user_id


# Model Pydantic
class User(BaseModel):
    id: int
    name: str
    joined: date
```

A one będą mogły zostać później użyte w następujący sposób:

```Python
from typing import List, Dict
from datetime import date

from pydantic import BaseModel

# Değişkeni str olarak belirt
# ve o fonksiyon için harika bir editör desteği al
def main(user_id: str):
    return user_id


# Pydantic modeli
class User(BaseModel):
    id: int
    name: str
    joined: date
```

Sonrasında bu şekilde kullanabilirsin

```Python

👆 ✍ 🐩 🐍 ⏮️ 🆎:

```Python
from datetime import date

from pydantic import BaseModel

# Declare a variable as a str
# and get editor support inside the function
def main(user_id: str):
    return user_id


# A Pydantic model
class User(BaseModel):
    id: int
    name: str
    joined: date
```

👈 💪 ⤴️ ⚙️ 💖:

```Python

    * 执行 `await myfile.read()` 后，需再次读取已读取内容时，这种方法特别好用；
* `close()`：关闭文件。

因为上述方法都是 `async` 方法，要搭配「await」使用。

例如，在 `async` *路径操作函数* 内，要用以下方式读取文件内容：

```Python
contents = await myfile.read()
```

在普通 `def` *路径操作函数*  内，则可以直接访问 `UploadFile.file`，例如：

```Python
contents = myfile.file.read()
```

/// note | "`async` 技术细节"

使用 `async` 方法时，**FastAPI** 在线程池中执行文件方法，并 `await` 操作完成。

///