code: def init_distributed_data_parallel_model(
      model: torch.nn.Module,
      broadcast_buffers: bool = False,
      find_unused_parameters: bool = True,
      bucket_cap_mb: int = 25,
  ) -> torch.nn.parallel.DistributedDataParallel:
      global _cuda_device_index
  
      if _cuda_device_index == _CPU_DEVICE_INDEX:
          # CPU-only model, don't specify device
          return torch.nn.parallel.DistributedDataParallel(
              model,
              broadcast_buffers=broadcast_buffers,
              find_unused_parameters=find_unused_parameters,
              bucket_cap_mb=bucket_cap_mb,
          )
      else:
          # GPU model
          return torch.nn.parallel.DistributedDataParallel(
              model,
              device_ids=[_cuda_device_index],
              output_device=_cuda_device_index,
              broadcast_buffers=broadcast_buffers,
              find_unused_parameters=find_unused_parameters,
              bucket_cap_mb=bucket_cap_mb,
          )
  
imports: import io
  import tempfile
  from typing import Any, Callable, List, Tuple
  import torch
label_desc: Use image node_id 20 for calling a global function with example usage: init_distributed_data_parallel_model(model, broadcast_buffers, find_unused_parameters, bucket_cap_mb) and returns: torch, torch