import org.codelibs.fess.util.RenderDataUtil;
import org.codelibs.fess.util.ResourceUtil;
import org.codelibs.fess.util.SearchEngineUtil;
import org.dbflute.bhv.readable.EntityRowHandler;
import org.lastaflute.core.magic.async.AsyncManager;
import org.lastaflute.web.Execute;
import org.lastaflute.web.response.ActionResponse;
import org.lastaflute.web.response.HtmlResponse;
import org.lastaflute.web.response.StreamResponse;

```Python hl_lines="4"
{!../../docs_src/dependencies/tutorial007.py!}
```

`yield` 语句后面的代码会在创建响应后，发送响应前执行：

```Python hl_lines="5-6"
{!../../docs_src/dependencies/tutorial007.py!}
```

/// tip | 提示

你可以使用 `async` 或普通函数。

**FastAPI** 会像处理普通依赖一样，对每个依赖做正确的处理。

///

## 包含 `yield` 和 `try` 的依赖项

如果在包含 `yield` 的依赖中使用 `try` 代码块，你会捕获到使用依赖时抛出的任何异常。

例如，如果某段代码在另一个依赖中或在 *路由函数* 中使数据库事务"回滚"或产生任何其他错误，你将会在依赖中捕获到异常。

      : status_(TF_NewStatus()),
        // If the context's default exector is set to async, re-using that in
        // each thread would cause collectives to deadlock. For consistency we
        // create a new sync executor for every thread.
        //
        // TODO(allenl): We should have an async API that works with the
        // parallel device.
        device_(device),
        executor_(

```Python hl_lines="2  7  9"
{!../../docs_src/custom_response/tutorial006c.py!}
```

### `StreamingResponse`

Takes an async generator or a normal generator/iterator and streams the response body.

```Python hl_lines="2  14"
{!../../docs_src/custom_response/tutorial007.py!}
```

#### Using `StreamingResponse` with file-like objects

在高级使用场景中，如果不想使用「缓存」值，而是为需要在同一请求的每一步操作（多次）中都实际调用依赖项，可以把 `Depends` 的参数 `use_cache` 的值设置为 `False` :

```Python hl_lines="1"
async def needy_dependency(fresh_value: str = Depends(get_value, use_cache=False)):
    return {"fresh_value": fresh_value}
```

## 小结

千万别被本章里这些花里胡哨的词藻吓倒了，其实**依赖注入**系统非常简单。

依赖注入无非是与*路径操作函数*一样的函数罢了。

У даному випадку це асинхронна функція.

---

Ви також можете визначити її як звичайну функцію замість `async def`:

```Python hl_lines="7"
{!../../docs_src/first_steps/tutorial003.py!}
```

/// note | "Примітка"

Якщо не знаєте в чому різниця, подивіться [Конкурентність: *"Поспішаєш?"*](../async.md#in-a-hurry){.internal-link target=_blank}.

///

### Крок 5: поверніть результат

// executed in sequence. Assigning nodes to different executors allows executing
// nodes in parallel.
// in_flight_nodes_limit: when is_async is true, this value controls the
// maximum number of in flight async nodes. Enqueuing of additional async ops
// after the limit is reached blocks until some inflight nodes finishes.
// The effect is bounding the memory held by inflight TensorHandles that are
// referenced by the inflight nodes.

    from typing import Annotated

    from fastapi import Depends, FastAPI

    app = FastAPI()


    async def common_parameters(q: str | None = None, skip: int = 0, limit: int = 100):
        return {"q": q, "skip": skip, "limit": limit}


    @app.get("/items/")
    async def read_items(commons: Annotated[dict, Depends(common_parameters)]):
        return commons
    ```
    """

It was one of the first extremely fast Python frameworks based on `asyncio`. It was made to be very similar to Flask.

/// note | "Technical Details"

It used <a href="https://github.com/MagicStack/uvloop" class="external-link" target="_blank">`uvloop`</a> instead of the default Python `asyncio` loop. That's what made it so fast.

        public void process(Consumer<TransferEvent> consumer) {
            consumer.accept(event);
        }

        public void waitForProcessed() throws InterruptedException {
            // nothing, is async
        }
    }

    private static class BlockingExchange extends Exchange {
        private final CountDownLatch latch = new CountDownLatch(1);

        private BlockingExchange(TransferEvent event) {