code: def dist_init(rank, world_size, backend):
      logging.info(f"Using backend: {backend}")
      dist.init_process_group(
          backend=backend,
          init_method="tcp://localhost:29501",
          rank=rank,
          world_size=world_size,
      )
  
imports: import argparse
  from enum import Enum
  import importlib
  import logging
  import tempfile
  import time
  from typing import Any, List, Optional, cast
  from golden_configs import oss_mnist
  import numpy
  import torch
  import torch.autograd.profiler
  from torch.cuda.amp import GradScaler
  import torch.distributed
  import torch.multiprocessing
  import torch.nn
  from torch.nn.parallel import DistributedDataParallel
  from torch.utils.data import BatchSampler, DataLoader, Sampler
  from torch.utils.data.distributed import DistributedSampler
  from torchvision.datasets import MNIST
  from torchvision.transforms import Compose, Resize, ToTensor
  from benchmarks.datasets.mnist import setup_cached_mnist
  from fairscale.nn.data_parallel import ShardedDataParallel
  from fairscale.optim import OSS
  from fairscale.optim.grad_scaler import ShardedGradScaler
label_desc: Use image node_id 1 for calling a global function with example usage: dist_init(rank, world_size, backend) without return types