*/
  @Throws(IOException::class)
  fun newStream(
    requestHeaders: List<Header>,
    out: Boolean,
  ): Http2Stream {
    return newStream(0, requestHeaders, out)
  }

  @Throws(IOException::class)
  private fun newStream(
    associatedStreamId: Int,
    requestHeaders: List<Header>,
    out: Boolean,
  ): Http2Stream {
    val outFinished = !out
    val inFinished = false

  optional string message = 5;
}

// DeploymentList is a list of Deployments.
message DeploymentList {
  // Standard list metadata.
  // +optional
  optional k8s.io.apimachinery.pkg.apis.meta.v1.ListMeta metadata = 1;

  // Items is the list of Deployments.
  repeated Deployment items = 2;
}

// DEPRECATED.

```

### 嵌套类型

有些容器数据结构可以包含其他的值，比如 `dict`、`list`、`set` 和 `tuple`。它们内部的值也会拥有自己的类型。

你可以使用 Python 的 `typing` 标准库来声明这些类型以及子类型。

它专门用来支持这些类型提示。

#### 列表

例如，让我们来定义一个由 `str` 组成的 `list` 变量。

从 `typing` 模块导入 `List`（注意是大写的 `L`）：

```Python hl_lines="1"
{!../../docs_src/python_types/tutorial006.py!}
```

同样以冒号（`:`）来声明这个变量。

输入 `List` 作为类型。

由于列表是带有"子类型"的类型，所以我们把子类型放在方括号中：

	}
	in.macros[name] = &Macro{
		name:   name,
		args:   args,
		tokens: tokens,
	}
}

// macroDefinition returns the list of formals and the tokens of the definition.
// The argument list is nil for no parens on the definition; otherwise a list of
// formal argument names.
func (in *Input) macroDefinition(name string) ([]string, []Token) {
	prevCol := in.Stack.Col()
	tok := in.Stack.Next()

    public ColumnInfo columnUpdatedTime() {
        return _columnUpdatedTime;
    }

    public ColumnInfo columnUsername() {
        return _columnUsername;
    }

    protected List<ColumnInfo> ccil() {
        List<ColumnInfo> ls = newArrayList();
        ls.add(columnCreatedBy());
        ls.add(columnCreatedTime());
        ls.add(columnFileConfigId());
        ls.add(columnHostname());

  // Token is the opaque bearer token.
  // +optional
  optional string token = 1;

  // Audiences is a list of the identifiers that the resource server presented
  // with the token identifies as. Audience-aware token authenticators will
  // verify that the token was intended for at least one of the audiences in
  // this list. If no audiences are provided, the audience will default to the
  // audience of the Kubernetes apiserver.

app = FastAPI()


class CommonHeaders(BaseModel):
    model_config = {"extra": "forbid"}

    host: str
    save_data: bool
    if_modified_since: str | None = None
    traceparent: str | None = None
    x_tag: list[str] = []


@app.get("/items/")
async def read_items(headers: CommonHeaders = Header()):

class CommonHeaders(BaseModel):
    class Config:
        extra = "forbid"

    host: str
    save_data: bool
    if_modified_since: str | None = None
    traceparent: str | None = None
    x_tag: list[str] = []


@app.get("/items/")
async def read_items(headers: CommonHeaders = Header()):

from pydantic import BaseModel

app = FastAPI()


class CommonHeaders(BaseModel):
    host: str
    save_data: bool
    if_modified_since: Union[str, None] = None
    traceparent: Union[str, None] = None
    x_tag: list[str] = []


@app.get("/items/")
async def read_items(headers: Annotated[CommonHeaders, Header()]):

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()


class Item(BaseModel):
    name: str
    description: str | None = None
    price: float
    tax: float | None = None
    tags: list[str] = []


@app.put("/items/{item_id}")
async def update_item(item_id: int, item: Item):
    results = {"item_id": item_id, "item": item}