code: def save_ground_truth_part(name, tuple_path, mean, sem, std, sestd):
      """Saves a ground truth part to strings.
  
      This is meant to be called with outputs of
      `nest.flatten_with_tuple_paths(ground_truth_mean)`.
  
      Args:
        name: Python `str`. Name of the sample transformation.
        tuple_path: Tuple path of the part of the ground truth we're saving. See
          `nest.flatten_with_tuple_paths`.
        mean: Ground truth mean, or `None` if it is absent.
        sem: Ground truth stadard error of the mean, or `None` if it is absent.
        std: Ground truth standard deviation, or `None` if it is absent.
        sestd: Ground truth mean, or `None` if it is absent.
  
      Returns:
        array_strs: Python list of strings, representing the encoded arrays (that
          were present). Typically these would be joined with a newline and written
          out to a module, which can then be passed to `load_ground_truth_part`.
      """
  
      array_strs = []
      (
          mean_name,
          sem_name,
          std_name,
          sestd_name,
      ) = _get_global_variable_names(name, tuple_path)
  
      if mean is not None:
          array_strs.append(
              array_to_source.array_to_source(mean_name, mean)
          )
  
      if sem is not None:
          array_strs.append(
              array_to_source.array_to_source(sem_name, sem)
          )
  
      if std is not None:
          array_strs.append(
              array_to_source.array_to_source(std_name, std)
          )
  
      if sestd is not None:
          array_strs.append(
              array_to_source.array_to_source(sestd_name, sestd)
          )
  
      return array_strs
  
imports: from inference_gym.internal import array_to_source
label_desc: Use image node_id 3 for calling a global function with example usage: save_ground_truth_part(name, tuple_path, mean, sem, std, sestd) and returns: array_strs