code: def init_process_group(
      local_rank,
      local_world_size,
      shard_id,
      num_shards,
      init_method,
      dist_backend="nccl",
  ):
      """
      Initializes the default process group.
      Args:
          local_rank (int): the rank on the current local machine.
          local_world_size (int): the world size (number of processes running) on
          the current local machine.
          shard_id (int): the shard index (machine rank) of the current machine.
          num_shards (int): number of shards for distributed training.
          init_method (string): supporting three different methods for
              initializing process groups:
              "file": use shared file system to initialize the groups across
              different processes.
              "tcp": use tcp address to initialize the groups across different
          dist_backend (string): backend to use for distributed training. Options
              includes gloo, mpi and nccl, the details can be found here:
              https://pytorch.org/docs/stable/distributed.html
      """
      # Sets the GPU to use.
      torch.cuda.set_device(local_rank)
      # Initialize the process group.
      proc_rank = local_rank + shard_id * local_world_size
      world_size = local_world_size * num_shards
      dist.init_process_group(
          backend=dist_backend,
          init_method=init_method,
          world_size=world_size,
          rank=proc_rank,
      )
  
imports: import functools
  import logging
  import pickle
  import torch
  import torch.distributed
  from pytorchvideo.layers.distributed import cat_all_gather, get_local_process_group, get_local_rank, get_local_size, get_world_size, init_distributed_training
label_desc: Use image node_id 4 for calling a global function with example usage: init_process_group(local_rank, local_world_size, shard_id, num_shards, init_method, dist_backend) without return types