return ["John Doe", "Jane Doe"]

    async def close(self) -> None:
        pass


async def get_client() -> AsyncGenerator[DummyClient, None]:
    client = DummyClient()
    yield client
    await client.close()


Client = Annotated[DummyClient, Depends(get_client)]


@pytest.fixture(name="client")
def client_fixture() -> TestClient:
    app = FastAPI()

    @app.get("/")

            t = (date.getTime() + SmbConstants.MILLISECONDS_BETWEEN_1970_AND_1601) * 10000L;
            yield enc_uint64be(t, dst, di);
        }
        case TIME_1601_NANOS_64LE -> {
            t = (date.getTime() + SmbConstants.MILLISECONDS_BETWEEN_1970_AND_1601) * 10000L;
            yield enc_uint64le(t, dst, di);
        }
        case TIME_1970_MILLIS_64BE -> enc_uint64be(date.getTime(), dst, di);

///

## Diffuser des JSON Lines avec FastAPI { #stream-json-lines-with-fastapi }

Pour diffuser des JSON Lines avec FastAPI, au lieu d'utiliser `return` dans votre fonction de chemin d'accès, utilisez `yield` pour produire chaque élément à tour de rôle.

{* ../../docs_src/stream_json_lines/tutorial001_py310.py ln[1:24] hl[24] *}

    equalsTester = new EqualsTester();
    equalObject1 = new ValidTestObject(1, 2);
    equalObject2 = new ValidTestObject(1, 2);
    notEqualObject1 = new ValidTestObject(0, 2);
  }

  /** Test null reference yields error */
  public void testAddNullReference() {
    assertThrows(NullPointerException.class, () -> equalsTester.addEqualityGroup((Object) null));
  }

engine = create_engine(sqlite_url, connect_args=connect_args)


def create_db_and_tables():
    SQLModel.metadata.create_all(engine)


def get_session():
    with Session(engine) as session:
        yield session


app = FastAPI()


@app.on_event("startup")
def on_startup():
    create_db_and_tables()


@app.post("/heroes/", response_model=HeroPublic)

engine = create_engine(sqlite_url, connect_args=connect_args)


def create_db_and_tables():
    SQLModel.metadata.create_all(engine)


def get_session():
    with Session(engine) as session:
        yield session


app = FastAPI()


@app.on_event("startup")
def on_startup():
    create_db_and_tables()


@app.post("/heroes/")

/// tip | 提示

如果你想流式传输二进制数据，例如视频或音频，请查看进阶指南：[流式传输数据](../advanced/stream-data.md)。

///

## 使用 FastAPI 流式传输 JSON Lines { #stream-json-lines-with-fastapi }

要在 FastAPI 中流式传输 JSON Lines，可以在路径操作函数中不用 `return`，而是用 `yield` 逐个产生每个数据项。

{* ../../docs_src/stream_json_lines/tutorial001_py310.py ln[1:24] hl[24] *}

如果你要返回的每个 JSON 项是类型 `Item`（一个 Pydantic 模型），并且这是一个异步函数，你可以将返回类型声明为 `AsyncIterable[Item]`：

  fmt.Printf("%v\n ", mv.Value)
}
```

output:
```
map[desc:RGB colors:[red green blue]]
map[desc:CMYK colors:[cyan magenta yellow black]]
```

likewise, increasing depth level to `3` yields:
```
red
green
blue
cyan
magenta
yellow
black
```

optionally, kev:value pairs can be emitted as an individual struct:
```go

              interruptee.interrupt();
              Thread.yield();
            }
          });
      this.shutdown = shutdown;
      start();
    }

    @SuppressWarnings("ThreadPriorityCheck") // TODO: b/175898629 - Consider onSpinWait.
    void shutdown() {
      shutdown.set(true);
      while (this.isAlive()) {
        Thread.yield();
      }
    }
  }

/// tip

如果你想串流二進位資料，例如影像或音訊，請參考進階指南：[串流資料](../advanced/stream-data.md)。

///

## 使用 FastAPI 串流 JSON Lines { #stream-json-lines-with-fastapi }

要用 FastAPI 串流 JSON Lines，你可以在你的*路徑操作函式*中改用 `yield` 逐一產生項目，而不是用 `return`。

{* ../../docs_src/stream_json_lines/tutorial001_py310.py ln[1:24] hl[24] *}

如果你要回傳的每個 JSON 項目型別都是 `Item`（一個 Pydantic 模型），而且該函式是 async，你可以將回傳型別宣告為 `AsyncIterable[Item]`：