code: def load_fileclient_dist(filename, backend, map_location):
      """In distributed setting, this function only download checkpoint at local
      rank 0."""
      rank, world_size = get_dist_info()
      rank = int(os.environ.get("LOCAL_RANK", rank))
      allowed_backends = ["ceph"]
      if backend not in allowed_backends:
          raise ValueError(
              f"Load from Backend {backend} is not supported."
          )
      if rank == 0:
          fileclient = FileClient(backend=backend)
          buffer = io.BytesIO(fileclient.get(filename))
          checkpoint = torch.load(buffer, map_location=map_location)
      if world_size > 1:
          torch.distributed.barrier()
          if rank > 0:
              fileclient = FileClient(backend=backend)
              buffer = io.BytesIO(fileclient.get(filename))
              checkpoint = torch.load(buffer, map_location=map_location)
      return checkpoint
  
imports: import io
  import os
  import os.path
  import pkgutil
  import time
  import warnings
  from collections import OrderedDict
  from importlib import import_module
  from tempfile import TemporaryDirectory
  import torch
  import torchvision
  from torch.optim import Optimizer
  from torch.utils import model_zoo
  from torch.nn import functional
  import annotator.uniformer.mmcv
  from annotator.uniformer.mmcv.fileio import FileClient
  from annotator.uniformer.mmcv.fileio import load
  from annotator.uniformer.mmcv.parallel import is_module_wrapper
  from annotator.uniformer.mmcv.utils import mkdir_or_exist
  from annotator.uniformer.mmcv.runner import get_dist_info
label_desc: Use image node_id 5 for calling a global function with example usage: load_fileclient_dist(filename, backend, map_location) and returns: checkpoint