z.rebalMeta.PoolStats[poolIdx].Info.Status = status
				z.rebalMeta.PoolStats[poolIdx].Info.EndTime = now
				z.rebalMu.Unlock()

			case <-timer.C:
				notify = false
				traceMsg = fmt.Sprintf("saved at %s", time.Now())
			}

			stopFn := globalRebalanceMetrics.log(rebalanceMetricSaveMetadata, poolIdx, traceMsg)
			err := z.saveRebalanceStats(GlobalContext, poolIdx, rebalSaveStats)
			stopFn(0, err)

// Endpoint set represents parsed ellipses values, also provides
// methods to get the sets of endpoints.
type endpointSet struct {
	argPatterns []ellipses.ArgPattern
	endpoints   []string   // Endpoints saved from previous GetEndpoints().
	setIndexes  [][]uint64 // All the sets.
}

// Supported set sizes this is used to find the optimal
// single set size.

	modTimes = bootModtimes(len(partsMetadata))
	for index, metadata := range partsMetadata {
		if errs[index] != nil {
			continue
		}
		// Once the file is found, save the uuid saved on disk.
		modTimes[index] = metadata.ModTime
	}
	return modTimes
}

func filterOnlineDisksInplace(fi FileInfo, partsMetadata []FileInfo, onlineDisks []StorageAPI) {
	for i, meta := range partsMetadata {

    *   Provided a new `experimental_skip_saver` argument which, if specified, will suppress the addition of `SavedModel`-native save and restore ops to the `SavedModel`, for cases where users already build custom save/restore ops and checkpoint formats for the model being saved, and the creation of the SavedModel-native save/restore ops simply cause longer model serialization times.

* Add ops to `tensorflow.raw_ops` that were missing.

		}
		p.Pools[idx].LastUpdate = now
		if err := p.save(ctx, pools); err != nil {
			return false, err
		}
		return true, nil
	}
	return false, nil
}

func (p poolMeta) save(ctx context.Context, pools []*erasureSets) error {
	if p.dontSave {
		return nil
	}
	data := make([]byte, 4, p.Msgsize()+4)

						updt = true
						break
					}
				}
				// Save the replication status if a new stats update is found and not saved in the backend yet
				if brs.LastUpdate.After(lastResyncStatusSave[bucket]) {
					updt = true
				}
				if updt {
					if err := saveResyncStatus(ctx, bucket, brs, objectAPI); err != nil {
						replLogIf(ctx, fmt.Errorf("could not save resync metadata to drive for %s - %w", bucket, err))
					} else {

		if lengthRange.Valid {
			hashReader.SetExpectedMin(lengthRange.Min)
			hashReader.SetExpectedMax(lengthRange.Max)
		}
	}

	// Extract metadata to be saved from received Form.
	metadata := make(map[string]string)
	err = extractMetadataFromMime(ctx, textproto.MIMEHeader(formValues), metadata)
	if err != nil {
		writeErrorResponse(ctx, w, toAPIError(ctx, err), r.URL)

}

TEST_F(CApiColocationTest, ClearViaProto) {
  TF_ColocateWith(desc_, feed1_);
  SetViaProto(desc_, {});
  FinishAndVerify(desc_, {});
}

TEST(CAPI, SavedModel) {
  // Load the saved model.
  const string saved_model_dir = tensorflow::GetDataDependencyFilepath(
      tensorflow::io::JoinPath("tensorflow", "cc", "saved_model", "testdata",
                               "half_plus_two", "00000123"));

	}
	diskHealthCheckOK(ctx, err)

	notInline := srcDataPath != "" && len(fi.Data) == 0 && fi.Size > 0
	if notInline {
		if healing {
			// renameAll only for objects that have xl.meta not saved inline.
			// this must be done in healing only, otherwise it is expected
			// that for fresh PutObject() call dstDataPath can never exist.
			// if its an overwrite then the caller deletes the DataDir

The method value <code>x.M</code> is a function value that is callable
with the same arguments as a method call of <code>x.M</code>.
The expression <code>x</code> is evaluated and saved during the evaluation of the
method value; the saved copy is then used as the receiver in any calls,
which may be executed later.
</p>

<pre>
type S struct { *T }
type T int
func (t T) M() { print(t) }

t := new(T)