code: def _dist_init(rank, world_size, tempfile_name, backend):
      url = "file://" + tempfile_name
      dist.init_process_group(
          init_method=url,
          backend=backend,
          rank=rank,
          world_size=world_size,
      )
      torch.cuda.set_device(rank)
  
imports: import tempfile
  import numpy
  import pytest
  import torch
  from torch import Tensor
  import torch.distributed
  import torch.multiprocessing
  from torch.nn import Linear
  from torch.nn.parallel import DistributedDataParallel
  from torch.optim import SGD
  from fairscale.fair_dev.testing.golden_testing_data import adascale_test_data
  from fairscale.fair_dev.testing.testing import skip_if_single_gpu
  from fairscale.internal import torch_version
  from fairscale.nn.data_parallel import FullyShardedDataParallel
  from fairscale.nn.data_parallel import ShardedDataParallel
  from fairscale.optim import OSS, AdaScale
label_desc: Use image node_id 1 for calling a global function with example usage: _dist_init(rank, world_size, tempfile_name, backend) without return types