return TensorHandlePtr(result_handle);
}

void TestCollective(bool async) {
  std::unique_ptr<TF_Status, decltype(&TF_DeleteStatus)> status(
      TF_NewStatus(), TF_DeleteStatus);
  std::unique_ptr<TFE_ContextOptions, decltype(&TFE_DeleteContextOptions)> opts(
      TFE_NewContextOptions(), TFE_DeleteContextOptions);
  TFE_ContextOptionsSetAsync(opts.get(), async);
  std::unique_ptr<TF_Buffer, decltype(&TF_DeleteBuffer)> config(

      : 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_(

    original_context: Lifespan[Any], nested_context: Lifespan[Any]
) -> Lifespan[Any]:
    @asynccontextmanager
    async def merged_lifespan(
        app: AppType,
    ) -> AsyncIterator[Optional[Mapping[str, Any]]]:
        async with original_context(app) as maybe_original_state:
            async with nested_context(app) as maybe_nested_state:
                if maybe_nested_state is None and maybe_original_state is None:

🏧 😐 🌐❔ 👆 💭 👆 💪 🔗 🤙 🔠 🔁 (🎲 💗 🕰) 🎏 📨 ↩️ ⚙️ "💾" 💲, 👆 💪 ⚒ 🔢 `use_cache=False` 🕐❔ ⚙️ `Depends`:

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

## 🌃

↖️ ⚪️➡️ 🌐 🎀 🔤 ⚙️ 📥, **🔗 💉** ⚙️ 🙅.

🔢 👈 👀 🎏 *➡ 🛠️ 🔢*.

// 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.

            endLatch.await();
        } catch (InterruptedException e) {
            e.printStackTrace();
        }

        // despite all are back, we need to make sure all the events are processed (are async)
        // this one should block until all processed
        listener.transferSucceeded(new TransferEvent.Builder(session, resource)
                .setType(TransferEvent.EventType.SUCCEEDED)
                .build());

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。

```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

		LastPing:           time.Now().Unix(),
		BaseFlags:          c.baseFlags,
		clientPingInterval: c.clientPingInterval,
	}
	// Acknowledge Mux created.
	// Send async.
	var wg sync.WaitGroup
	wg.Add(1)
	go func() {
		defer wg.Done()
		var ack message
		ack.Op = OpAckMux
		ack.Flags = m.BaseFlags
		ack.MuxID = m.ID
		m.send(ack)
		if debugPrint {