code: def get_batch_size():
      from mmf.utils.configuration import get_global_config
  
      batch_size = get_global_config("training.batch_size")
      world_size = get_world_size()
  
      batch_size_per_device = get_global_config(
          "training.batch_size_per_device"
      )
  
      if batch_size_per_device is not None:
          logger.info(
              f"training.batch_size_per_device has been used as {batch_size_per_device} "
              + "This will override training.batch_size and set the global batch size to "
              + f"{batch_size_per_device} x {world_size} = "
              + f"{batch_size_per_device * world_size}"
          )
          batch_size = batch_size_per_device * world_size
  
      if batch_size % world_size != 0:
          raise RuntimeError(
              "Batch size {} must be divisible by number "
              "of GPUs {} used.".format(batch_size, world_size)
          )
  
      return batch_size // world_size
  
imports: import collections
  import gc
  import logging
  import math
  import os
  import sys
  import time
  import warnings
  from bisect import bisect
  from typing import Any, Callable, Dict
  import torch
  from mmf.utils.distributed import get_rank, get_world_size, is_xla
  from mmf.utils.file_io import PathManager
  from packaging import version
  from torch import nn, Tensor
label_desc: Use image node_id 17 for calling a global function with example usage: get_batch_size() and returns: unknown