code: def load_ground_truth_part(module, name, tuple_path):
      """Loads a ground truth part from a module.
  
      We assume the module was created by saving the output of
      `save_ground_truth_part` to disk.
  
      This is meant to be called with the outputs of
      `nest.flatten_with_tuple_paths(sample_transformation.dtype)`.
  
      Args:
        module: Python module to load from.
        name: Python `str`. Name of the sample transformation.
        tuple_path: Tuple path of the part of the ground truth we're loading. See
          `nest.flatten_with_tuple_paths`.
  
      Returns:
        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.
      """
  
      (
          mean_name,
          sem_name,
          std_name,
          sestd_name,
      ) = _get_global_variable_names(name, tuple_path)
  
      return (
          getattr(module, mean_name, None),
          getattr(module, sem_name, None),
          getattr(module, std_name, None),
          getattr(module, sestd_name, None),
      )
  
imports: from inference_gym.internal import array_to_source
label_desc: Use image node_id 2 for calling a global function with example usage: load_ground_truth_part(module, name, tuple_path) and returns: getattr,  getattr,  getattr,  getattr