go func() {
		objAPI := newObjectLayerFn()
		for objAPI == nil {
			time.Sleep(time.Second)
			objAPI = newObjectLayerFn()
		}

		t := time.NewTicker(time.Minute)
		defer t.Stop()

		var exit bool
		for !exit {
			select {
			case <-t.C:
			case <-GlobalContext.Done():
				exit = true
			}
			m.mu.RLock()
			for _, v := range m.buckets {
				if !exit {
					v.cleanup()
				}
			}

					OutputBytes:     respRecorder.Size(),
					TimeToFirstByte: respRecorder.TTFB(),
				},
			},
		}

		globalTrace.Publish(t)
	})
}

func httpTrace(f http.HandlerFunc, logBody bool) http.HandlerFunc {
	return func(w http.ResponseWriter, r *http.Request) {
		tc, ok := r.Context().Value(mcontext.ContextTraceKey).(*mcontext.TraceCtxt)
		if !ok {
			// Tracing is not enabled for this request

      if (networkResponse == null && cacheCandidate != null) {
        cacheCandidate.body.closeQuietly()
      }
    }

    // If we have a cache response too, then we're doing a conditional get.
    if (cacheResponse != null) {
      if (networkResponse?.code == HTTP_NOT_MODIFIED) {
        val response =
          cacheResponse.newBuilder()

   *
   * @param value any {@code long} value
   * @return the same value cast to {@code int} if it is in the range of the {@code int} type,
   *     {@link Integer#MAX_VALUE} if it is too large, or {@link Integer#MIN_VALUE} if it is too
   *     small
   */
  public static int saturatedCast(long value) {
    if (value > Integer.MAX_VALUE) {
      return Integer.MAX_VALUE;
    }
    if (value < Integer.MIN_VALUE) {

Compiling models via the recommended entry points described in
[XLA](https://www.tensorflow.org/xla) and
[JAX](https://jax.readthedocs.io/en/latest/jax-101/02-jitting.html)
documentation should be safe, while some of the testing and debugging tools that
come with the compiler are not designed to be used with untrusted data and
should be used with caution when working with untrusted models.

### Saved graphs and checkpoints

		return
	}

	if opts.PreserveETag != "" ||
		r.Header.Get(xhttp.IfMatch) != "" ||
		r.Header.Get(xhttp.IfNoneMatch) != "" {
		opts.CheckPrecondFn = func(oi ObjectInfo) bool {
			if _, err := DecryptObjectInfo(&oi, r); err != nil {
				writeErrorResponse(ctx, w, toAPIError(ctx, err), r.URL)
				return true
			}
			return checkPreconditionsPUT(ctx, w, r, oi, opts)
		}
	}

        bool((must, should, mustNot, filter) -> notLambda.callback(mustNot), opLambda);
    }

    public void bool(BoolCall<FileAuthenticationCQ> boolLambda) {
        bool(boolLambda, null);
    }

    public void bool(BoolCall<FileAuthenticationCQ> boolLambda, ConditionOptionCall<BoolQueryBuilder> opLambda) {

class Settings(BaseSettings):
    github_repository: str
    input_token: SecretStr
    github_event_path: Path
    github_event_name: Union[str, None] = None
    httpx_timeout: int = 30
    input_debug: Union[bool, None] = False


class PartialGitHubEventIssue(BaseModel):
    number: int


class PartialGitHubEvent(BaseModel):
    pull_request: PartialGitHubEventIssue


def get_graphql_response(

Die Funktionsparameter werden wie folgt erkannt:

* Wenn der Parameter auch im **Pfad** deklariert wurde, wird er als Pfad-Parameter interpretiert.
* Wenn der Parameter ein **einfacher Typ** ist (wie `int`, `float`, `str`, `bool`, usw.), wird er als **Query**-Parameter interpretiert.
* Wenn der Parameter vom Typ eines **Pydantic-Modells** ist, wird er als Request**body** interpretiert.

/// note | "Hinweis"

////

//// tab | Python 3.8+

```Python hl_lines="18"
{!> ../../docs_src/body/tutorial004.py!}
```

////

函数参数按如下规则进行识别：

- **路径**中声明了相同参数的参数，是路径参数
- 类型是（`int`、`float`、`str`、`bool` 等）**单类型**的参数，是**查询**参数
- 类型是 **Pydantic 模型**的参数，是**请求体**

/// note | "笔记"

因为默认值是 `None`， FastAPI 会把 `q` 当作可选参数。

FastAPI 不使用 `Optional[str]` 中的 `Optional`， 但 `Optional` 可以让编辑器提供更好的支持，并检测错误。

///