storageStatVolRPC          = grid.NewSingleHandler[*grid.MSS, *VolInfo](grid.HandlerStatVol, grid.NewMSS, func() *VolInfo { return &VolInfo{} })
	storageUpdateMetadataRPC   = grid.NewSingleHandler[*MetadataHandlerParams, grid.NoPayload](grid.HandlerUpdateMetadata, func() *MetadataHandlerParams { return &MetadataHandlerParams{} }, grid.NewNoPayload)

	case errDiskOngoingReq.Error():
		return errDiskOngoingReq
	case grid.ErrUnknownHandler.Error():
		return errInconsistentDisk
	case grid.ErrDisconnected.Error():
		return errDiskNotFound
	}
	return err
}

// Abstracts a remote disk.
type storageRESTClient struct {
	endpoint   Endpoint
	restClient *rest.Client
	gridConn   *grid.Subroute
	diskID     atomic.Pointer[string]

					continue
				}
				partPath := pathJoin(tmpID, dstDataDir, fmt.Sprintf("part.%d", partNumber))
				if len(inlineBuffers) > 0 {
					buf := grid.GetByteBufferCap(int(erasure.ShardFileSize(latestMeta.Size)) + 64)
					inlineBuffers[i] = bytes.NewBuffer(buf[:0])
					defer grid.PutByteBuffer(buf)

					writers[i] = newStreamingBitrotWriterBuffer(inlineBuffers[i], DefaultBitrotAlgorithm, erasure.ShardSize())
				} else {

	// Copy inline data to a new buffer to function with deadlines.
	if len(fi.Data) > 0 {
		fi.Data = append(grid.GetByteBufferCap(len(fi.Data))[:0], fi.Data...)
	}
	return xioutil.WithDeadline[RenameDataResp](ctx, globalDriveConfig.GetMaxTimeout(), func(ctx context.Context) (res RenameDataResp, err error) {
		if len(fi.Data) > 0 {
			defer grid.PutByteBuffer(fi.Data)
		}

	for i, disk := range onlineDisks {
		if disk == nil {
			continue
		}
		if disk.IsOnline() {
			buf := grid.GetByteBufferCap(int(erasure.ShardFileSize(data.Size())) + 64)
			inlineBuffers[i] = bytes.NewBuffer(buf[:0])
			defer grid.PutByteBuffer(buf)
			writers[i] = newStreamingBitrotWriterBuffer(inlineBuffers[i], DefaultBitrotAlgorithm, erasure.ShardSize())
		}
	}

    *   Infer `tf.contrib.distributions.RelaxedOneHotCategorical` `dtype` from
        arguments.
    *   Make `tf.contrib.distributions` quadrature family parameterized by
        `quadrature_grid_and_prob` vs `quadrature_degree`.
    *   `auto_correlation` added to `tf.contrib.distributions`
    *   Add `tf.contrib.bayesflow.layers`, a collection of probabilistic
        (neural) layers.

        if (counts[i] >= 0) {
          sortedCounts[index] += counts[i];
        } else {
          sortedCounts[index] = ~counts[i];
        }
      }
      // Note that we're not getting rid, yet, of elements with count 0.  We'll do that in build().
      this.elements = sortedElements;
      this.counts = sortedCounts;
      this.length = uniques;
    }

    /**