///

## Use cases { #use-cases }

Some use cases include:

* Converting non-JSON request bodies to JSON (e.g. [`msgpack`](https://msgpack.org/index.html)).
* Decompressing gzip-compressed request bodies.
* Automatically logging all request bodies.

## Handling custom request body encodings { #handling-custom-request-body-encodings }

{* ../../docs_src/body_nested_models/tutorial007_py310.py hl[7,12,18,21,25] *}

/// info

Notice how `Offer` has a list of `Item`s, which in turn have an optional list of `Image`s

///

## Bodies of pure lists { #bodies-of-pure-lists }

If the top level value of the JSON body you expect is a JSON `array` (a Python `list`), you can declare the type in the parameter of the function, the same as in Pydantic models:

```Python

    than 63 characters between dots), and empty labels.
 *  New: Don't include the `Content-Length` header in multipart bodies. Servers must delimit
    OkHttp's request bodies using the boundary only. (This change makes OkHttp more consistent with
    browsers and other HTTP clients.)
 *  New: Drop the `tunnelProxy` argument in `MockWebServer.useHttps()`. This change only impacts

But that is still not that useful.

Let's make it give us the current user.

## Create a user model { #create-a-user-model }

First, let's create a Pydantic user model.

The same way we use Pydantic to declare bodies, we can use it anywhere else:

{* ../../docs_src/security/tutorial002_an_py310.py hl[5,12:6] *}

## Create a `get_current_user` dependency { #create-a-get-current-user-dependency }

- Support for query parameters, headers, body (String or stream), compression, SSL configuration, proxies, and timeouts
- Automatic in-memory or on-disk caching of request/response bodies
- Synchronous and asynchronous (callback) execution
- Minimal dependencies (only Apache Commons IO)

## Installation

### Maven

Add the dependency to your `pom.xml` (replace `x.y.z` with the latest version):

{* ../../docs_src/body_nested_models/tutorial007_py310.py hl[7,12,18,21,25] *}

/// info | 정보

`Offer`가 `Item`의 리스트를 가지고, 그 `Item`이 다시 선택 사항인 `Image` 리스트를 갖는지 주목하세요

///

## 순수 리스트의 본문 { #bodies-of-pure-lists }

예상되는 JSON 본문의 최상위 값이 JSON `array`(파이썬 `list`)라면, Pydantic 모델에서와 마찬가지로 함수의 매개변수에서 타입을 선언할 수 있습니다:

```Python
images: list[Image]
```

이를 아래처럼:

/// info

`Form` is a class that inherits directly from `Body`.

///

/// tip

To declare form bodies, you need to use `Form` explicitly, because without it the parameters would be interpreted as query parameters or body (JSON) parameters.

///

## About "Form Fields" { #about-form-fields }

你可以定義任意深度的巢狀模型：

{* ../../docs_src/body_nested_models/tutorial007_py310.py hl[7,12,18,21,25] *}

/// info

請注意，`Offer` 具有一個 `Item` 的列表，而每個 `Item` 又有一個可選的 `Image` 列表。

///

## 純列表的本文 { #bodies-of-pure-lists }

如果你期望的 JSON 本文頂層值是一個 JSON `array`（Python 的 `list`），可以像在 Pydantic 模型中那樣，直接在函式參數上宣告其型別：

```Python
images: list[Image]
```

如下所示：

If you want to secure your API, there are several better things you can do, for example:

* Make sure you have well defined Pydantic models for your request bodies and responses.
* Configure any required permissions and roles using dependencies.
* Never store plaintext passwords, only password hashes.
* Implement and use well-known cryptographic tools, like pwdlib and JWT tokens, etc.

```

**Warning**: The logs generated by this interceptor when using the `HEADERS` or `BODY` levels have
the potential to leak sensitive information such as "Authorization" or "Cookie" headers and the
contents of request and response bodies. This data should only be logged in a controlled way or in
a non-production environment.

You can redact headers that may contain sensitive information by calling `redactHeader()`.
```java