code: def init_distributed(
      rank, num_gpus, group_name, dist_backend, dist_url
  ):
      assert (
          torch.cuda.is_available()
      ), "Distributed mode requires CUDA."
  
      # Set cuda device so everything is done on the right GPU.
      torch.cuda.set_device(rank % torch.cuda.device_count())
  
      # Initialize distributed communication
      dist.init_process_group(
          dist_backend,
          init_method=dist_url,
          world_size=num_gpus,
          rank=rank,
          group_name=group_name,
      )
  
file_path: TTS/TTS/utils/distribute.py
imports: import math
  import torch
  import torch.distributed
  from torch._utils import _flatten_dense_tensors, _unflatten_dense_tensors
  from torch.autograd import Variable
  from torch.utils.data.sampler import Sampler
label_desc: Use image node_id 2 for calling a global function with example usage: init_distributed(rank, num_gpus, group_name, dist_backend, dist_url) without return types