```Python hl_lines="1  9  12"
{!../../docs_src/response_change_status_code/tutorial001.py!}
```

And then you can return any object you need, as you normally would (a `dict`, a database model, etc).

And if you declared a `response_model`, it will still be used to filter and convert the object you returned.

/**
 * A TimeLimiter implementation which actually does not attempt to limit time at all. This may be
 * desirable to use in some unit tests. More importantly, attempting to debug a call which is
 * time-limited would be extremely annoying, so this gives you a time-limiter you can easily swap in
 * for your real time-limiter while you're debugging.
 *
 * @author Kevin Bourrillion
 * @author Jens Nyman
 * @since 1.0
 */

// before issuing IO to underlying devices. ZFS users often enable features like
// compression and checksumming which currently necessitates mutating data in
// the kernel.
//
// DirectIO semantics for a filesystem like ZFS would be quite different than
// the semantics on filesystems like XFS, and these semantics are not
// implemented at this time.
// For more information on why typical DirectIO semantics do not apply to ZFS

     *  The implementation of this is a little slow, since it creates
     *  a number of objects each time it is called.  This could be optimized
     *  to keep the same objects around, but then thread-safety issues would arise.
     *
     *  @param contextNode The node to start searching from.
     *  @param expression A valid XPath string.

success.job_stopped=Stopped job {0}.
success.upload_synonym_file=Uploaded Synonym file.
success.upload_kuromoji_file=Uploaded Kuromoji file.
success.upload_elevate_word=Uploaded Additional Word file.
success.upload_bad_word=Uploaded Bad Word file.
success.upload_mapping_file=Uploaded Mapping file.
success.send_testmail=Sent the test mail.
success.job_log_delete_all=Deleted job logs.
success.changed_password=Changed your password.

  //    (hypothetical) unsafe read by our caller. Note: adding 'volatile' does not fix this issue,
  //    it would just add an edge such that if done() observed non-null, then it would also
  //    definitely observe all earlier writes, but we still have no guarantee that done() would see
  //    the initial write (just stronger guarantees if it does).
  //

    @Execute
    public HtmlResponse index() {
        throw new UnsupportedOperationException();
    }

    // POST /api/admin/scheduler/{id}/start
    @Execute(urlPattern = "{}/@word")
    public JsonResponse<ApiResult> post$start(final String id) {
        scheduledJobService.getScheduledJob(id).ifPresent(entity -> {
            if (!entity.isEnabled() || entity.isRunning()) {

	return err.Error() == "sql: statement is closed"
}

// TestPreparedStmtConcurrentReset test calling reset and executing SQL concurrently
// this test making sure that the gorm would not get a Segmentation Fault, and the only error cause by this is using a closed Stmt
func TestPreparedStmtConcurrentReset(t *testing.T) {
	name := "prepared_stmt_concurrent_reset"
	user := *GetUser(name, Config{})

number erasure sets to provide for uniform distribution across nodes*. This is to ensure that same number of drives are pariticipating in any erasure set. For example if you have 2 nodes with 180 drives then GCD is 15 but this would lead to uneven distribution, one of the nodes would participate more drives. To avoid this the affinity is given towards nodes which leads to next best GCD factor of 12 which provides uniform distribution.

- In this algorithm, we also make sure that we spread the drives...

          // caught and forwarded to the service the task would stop executing but the service would
          // have no idea.
          // TODO(lukes): consider building everything in terms of ListenableScheduledFuture then
          // the AbstractService could monitor the future directly. Rescheduling is still hard...
          // but it would help with some of these lock ordering issues.
          scheduleFailure = e;