Then wrap the WSGI (e.g. Flask) app with the middleware.

And then mount that under a path.

```Python hl_lines="2-3  23"
{!../../../docs_src/wsgi/tutorial001.py!}
```

## Check it

Now, every request under the path `/v1/` will be handled by the Flask application.

And the rest will be handled by **FastAPI**.

      while (iterator.hasNext()) {
        if (element.equals(iterator.next())) {
          return true;
        }
      }
    }
    return false;
  }

  /**
   * Traverses an iterator and removes every element that belongs to the provided collection. The
   * iterator will be left exhausted: its {@code hasNext()} method will return {@code false}.
   *
   * @param removeFrom the iterator to (potentially) remove elements from

)

const (
	dataScannerSleepPerFolder        = time.Millisecond                 // Time to wait between folders.
	dataUsageUpdateDirCycles         = 16                               // Visit all folders every n cycles.
	dataScannerCompactLeastObject    = 500                              // Compact when there is less than this many objects in a branch.

    url: https://github.com/sadikkuzu
  - login: Mehver
    avatarUrl: https://avatars.githubusercontent.com/u/75297777?u=dcd857f4278df055d98cd3486c2ce8bad368eb50&v=4
    url: https://github.com/Mehver
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    avatarUrl: https://avatars.githubusercontent.com/u/40308458?u=cd04a39e3655923be4f25c2ba8a5a07b3da3230a&v=4

   * underlying multimap.
   *
   * <p>The returned multimap isn't threadsafe or serializable, even if {@code unfiltered} is.
   *
   * <p>Many of the filtered multimap's methods, such as {@code size()}, iterate across every
   * key/value mapping in the underlying multimap and determine which satisfy the filter. When a
   * live view is <i>not</i> needed, it may be faster to copy the filtered multimap and use the
   * copy.
   *

Reading a file from disk is normally a costly (slow) operation, so you probably want to do it only once and then re-use the same settings object, instead of reading it for each request.

But every time we do:

```Python
Settings()
```

a new `Settings` object would be created, and at creation it would read the `.env` file again.

If the dependency function was just like:

```Python

   *
   * <p>The returned set isn't threadsafe or serializable, even if {@code unfiltered} is.
   *
   * <p>Many of the filtered set's methods, such as {@code size()}, iterate across every element in
   * the underlying set and determine which elements satisfy the filter. When a live view is
   * <i>not</i> needed, it may be faster to copy {@code Iterables.filter(unfiltered, predicate)} and
   * use the copy.

   * map.
   *
   * <p>The returned map isn't threadsafe or serializable, even if {@code unfiltered} is.
   *
   * <p>Many of the filtered map's methods, such as {@code size()}, iterate across every key/value
   * mapping in the underlying map and determine which satisfy the filter. When a live view is
   * <i>not</i> needed, it may be faster to copy the filtered map and use the copy.
   *

					success = false
				} else {
					stopFn(oi, nil)
				}
				ri.trackCurrentBucketObject(r.Target.Bucket, oi, success)
				globalBatchJobsMetrics.save(job.ID, ri)
				// persist in-memory state to disk after every 10secs.
				batchLogIf(ctx, ri.updateAfter(ctx, api, 10*time.Second, job))

				if wait := globalBatchConfig.ReplicationWait(); wait > 0 {
					time.Sleep(wait)
				}
			}()
		}
		wk.Wait()

	return t.staleUploadsExpiry
}

func (t *apiConfig) getDeleteCleanupInterval() time.Duration {
	t.mu.RLock()
	defer t.mu.RUnlock()

	if t.deleteCleanupInterval == 0 {
		return 5 * time.Minute // every 5 minutes
	}

	return t.deleteCleanupInterval
}

func (t *apiConfig) getClusterDeadline() time.Duration {
	t.mu.RLock()
	defer t.mu.RUnlock()

	if t.clusterDeadline == 0 {