*
   * <p>If the mode is {@link MapMode#READ_WRITE} and the file does not exist, it will be created
   * with the requested {@code size}. Thus this method is useful for creating memory mapped files
   * which do not yet exist.
   *
   * <p>This only works for files ≤ {@link Integer#MAX_VALUE} bytes.
   *
   * @param file the file to map
   * @param mode the mode to use when mapping {@code file}

        }

        //
        // TODO jvz Why isn't all this checking the job of the duty of the workspace resolver, we know that we
        // have a model that is suitable, yet more checks are done here and the one for the version is problematic
        // before because with parents as ranges it will never work in this scenario.
        //

        String groupId = candidateModel.getGroupId();

///

If in your use case there's no problem in running those previous steps **multiple times in parallel** (for example if you are not running database migrations, but just checking if the database is ready yet), then you could also just put them in each container right before starting the main process.

### Single Container { #single-container }

			rpt.SetStatus(file.Name, "", fmt.Errorf("malformed zip - expecting format bucket/<config.json>"))
			continue
		}
		bucket, fileName := slc[0], slc[1]

		// create bucket if it does not exist yet.
		if _, ok := bucketMap[bucket]; !ok {
			err = objectAPI.MakeBucket(ctx, bucket, MakeBucketOptions{
				ForceCreate: true, // ignore if it already exists
			})
			if err != nil {
				rpt.SetStatus(bucket, "", err)

// in-case you wish to clear existing metadata.
func (x *xlMetaV2) UpdateObjectVersion(fi FileInfo) error {
	if fi.VersionID == "" {
		// this means versioning is not yet
		// enabled or suspend i.e all versions
		// are basically default value i.e "null"
		fi.VersionID = nullVersionID
	}

	var uv uuid.UUID
	var err error

    visibility = ["//visibility:public"],
)

# This flag disables generating tensorflow.lite.python under LiteRT repo.
# Avoid using flag for creating tflite wheels as tensorflow/lite is not yet fully split from tf.
config_setting(
    name = "disable_tf_lite_py",
    define_values = {"disable_tf_lite_py": "true"},
    visibility = ["//visibility:public"],
)

		}
	}
}

// findFirstPart will find the part with 0 being the first that corresponds to the marker in the options.
// io.ErrUnexpectedEOF is returned if the place containing the marker hasn't been scanned yet.
// io.EOF indicates the marker is beyond the end of the stream and does not exist.
func (o *listPathOptions) findFirstPart(fi FileInfo) (int, error) {
	search := o.Marker
	if search == "" {
		search = o.Prefix
	}

			// write error response only if no data or headers has been written to client yet
			writeErrorResponse(ctx, w, toAPIError(ctx, err), r.URL)
			return
		}
		return
	}

	if err = httpWriter.Close(); err != nil {
		if !httpWriter.HasWritten() && !statusCodeWritten { // write error response only if no data or headers has been written to client yet
			writeErrorResponse(ctx, w, toAPIError(ctx, err), r.URL)
			return
		}

     * consist strictly of loading values, though during refresh a value may be both active and
     * loading.
     */
    boolean isActive();
  }

  /** Placeholder. Indicates that the value hasn't been set yet. */
  static final ValueReference<Object, Object> UNSET =
      new ValueReference<Object, Object>() {
        @Override
        public @Nullable Object get() {
          return null;
        }

Otherwise, each read of a single-word-sized or sub-word-sized memory location
must observe a value actually written to that location (perhaps by a concurrent executing goroutine)
and not yet overwritten.
These implementation constraints make Go more like Java or JavaScript,
in that most races have a limited number of outcomes,
and less like C and C++, where the meaning of any program with a race