But Python has a specific way to declare lists with internal types, or "type parameters":

### Import typing's `List`

In Python 3.9 and above you can use the standard `list` to declare these type annotations as we'll see below. 💡

But in Python versions before 3.9 (3.6 and above), you first need to import `List` from standard Python's `typing` module:

```Python hl_lines="1"
{!> ../../../docs_src/body_nested_models/tutorial002.py!}
```

      val newSize = deflatedBytes.size - LAST_OCTETS_COUNT_TO_REMOVE_AFTER_DEFLATION
      deflatedBytes.readAndWriteUnsafe().use { cursor ->
        cursor.resizeBuffer(newSize)
      }
    } else {
      // Same as adding EMPTY_DEFLATE_BLOCK and then removing 4 bytes.
      deflatedBytes.writeByte(0x00)
    }

    buffer.write(deflatedBytes, deflatedBytes.size)
  }

  @Throws(IOException::class)

You will get completion in code you might even consider impossible before. As for example, the `price` key inside a JSON body (that could have been nested) that comes from a request.

No more typing the wrong key names, coming back and forth between docs, or scrolling up and down to find if you finally used `username` or `user_name`.

### Short

## The normal **FastAPI** app

Let's first see how the normal API app would look like before adding the callback.

It will have a *path operation* that will receive an `Invoice` body, and a query parameter `callback_url` that will contain the URL for the callback.

* 和你 **<abbr title="集成开发环境，和代码编辑器类似">IDE</abbr>/<abbr title="一个检查代码错误的程序">linter</abbr>/brain** 适配:
    * 因为 pydantic 数据结构仅仅是你定义的类的实例；自动补全，linting，mypy 以及你的直觉应该可以和你验证的数据一起正常工作。
* 验证**复杂结构**:
    * 使用分层的 Pydantic 模型, Python `typing`的 `List` 和 `Dict` 等等。
    * 验证器使我们能够简单清楚的将复杂的数据模式定义、检查并记录为 JSON Schema。
    * 你可以拥有深度**嵌套的 JSON** 对象并对它们进行验证和注释。
* **可扩展**:
    * Pydantic 允许定义自定义数据类型或者你可以用验证器装饰器对被装饰的模型上的方法扩展验证。

    * ↩️ Pydantic 📊 📊 👐 🎓 👆 🔬; 🚘-🛠️, 🧽, ✍ & 👆 🤔 🔜 🌐 👷 ☑ ⏮️ 👆 ✔ 💽.
* ✔ **🏗 📊**:
    * ⚙️ 🔗 Pydantic 🏷, 🐍 `typing`'Ⓜ `List` & `Dict`, ♒️.
    *  & 💳 ✔ 🏗 💽 🔗 🎯 & 💪 🔬, ✅ & 📄 🎻 🔗.
    * 👆 💪 ✔️ 🙇 **🐦 🎻** 🎚 & ✔️ 👫 🌐 ✔ & ✍.
* **🏧**:

```

!!! note
    Notice that as we communicate with the database using `await`, the *path operation function* is declared with `async`.

### Notice the `response_model=List[Note]`

It uses `typing.List`.

That documents (and validates, serializes, filters) the output data, as a `list` of `Note`s.

## Create notes

Create the *path operation function* to create notes:

```Python hl_lines="61-65"

    "Topic :: Software Development :: Libraries :: Python Modules",
    "Topic :: Software Development :: Libraries",
    "Topic :: Software Development",
    "Typing :: Typed",
    "Development Status :: 4 - Beta",
    "Environment :: Web Environment",
    "Framework :: AsyncIO",
    "Framework :: FastAPI",
    "Framework :: Pydantic",
    "Framework :: Pydantic :: 1",

    enum class Type {
      Handshake,
      Plaintext,
      Encrypted,
      Setup,
      Unknown,
    }

    val type: Type
      get() =
        when {
          message == "adding as trusted certificates" -> Type.Setup
          message == "Raw read" || message == "Raw write" -> Type.Encrypted
          message == "Plaintext before ENCRYPTION" || message == "Plaintext after DECRYPTION" -> Type.Plaintext

## Mounting a **FastAPI** application

"Mounting" means adding a completely "independent" application in a specific path, that then takes care of handling everything under that path, with the _path operations_ declared in that sub-application.

### Top-level application