code: def replicate(
      network: T,
      devices: Sequence[Union[int, torch.device]],
      detach: bool = False,
  ) -> List[T]:
      if not _replicatable_module(network):
          raise RuntimeError(
              "Cannot replicate network where python modules are "
              "childrens of ScriptModule"
          )
  
      if not devices:
          return []
  
      devices = [_get_device_index(x, True) for x in devices]
      num_replicas = len(devices)
  
      params = list(network.parameters())
      param_indices = {param: idx for idx, param in enumerate(params)}
      param_copies = _broadcast_coalesced_reshape(
          params, devices, detach
      )
  
      buffers = list(network.buffers())
      buffers_rg: List[torch.Tensor] = []
      buffers_not_rg: List[torch.Tensor] = []
      for buf in buffers:
          if buf.requires_grad and not detach:
              buffers_rg.append(buf)
          else:
              buffers_not_rg.append(buf)
  
      buffer_indices_rg = {
          buf: idx for idx, buf in enumerate(buffers_rg)
      }
      buffer_indices_not_rg = {
          buf: idx for idx, buf in enumerate(buffers_not_rg)
      }
  
      buffer_copies_rg = _broadcast_coalesced_reshape(
          buffers_rg, devices, detach=detach
      )
      buffer_copies_not_rg = _broadcast_coalesced_reshape(
          buffers_not_rg, devices, detach=True
      )
  
      modules = list(network.modules())
      module_copies: List[List[Module]] = [[] for _ in devices]
      module_indices: Dict[Module, int] = {}
  
      for i, module in enumerate(modules):
          module_indices[module] = i
          for j in range(num_replicas):
              replica = module._replicate_for_data_parallel()
              # This is a temporary fix for DDP. DDP needs to access the
              # replicated model parameters. It used to do so through
              # `mode.parameters()`. The fix added in #33907 for DP stops the
              # `parameters()` API from exposing the replicated parameters.
              # Hence, we add a `_former_parameters` dict here to support DDP.
              replica._former_parameters = OrderedDict()
  
              module_copies[j].append(replica)
  
      for i, module in enumerate(modules):
          for key, child in module._modules.items():
              if child is None:
                  for j in range(num_replicas):
                      replica = module_copies[j][i]
                      replica._modules[key] = None
              else:
                  module_idx = module_indices[child]
                  for j in range(num_replicas):
                      replica = module_copies[j][i]
                      setattr(
                          replica, key, module_copies[j][module_idx]
                      )
          for key, param in module._parameters.items():
              if param is None:
                  for j in range(num_replicas):
                      replica = module_copies[j][i]
                      replica._parameters[key] = None
              else:
                  param_idx = param_indices[param]
                  for j in range(num_replicas):
                      replica = module_copies[j][i]
                      param_copy = param_copies[j][param_idx]
                      # parameters in replicas are no longer leaves,
                      # so setattr them as non-parameter attributes
                      setattr(replica, key, param_copy)
                      # expose the parameter for DDP
                      replica._former_parameters[key] = param_copy
          for key, buf in module._buffers.items():  # type: ignore[assignment]
              if buf is None:
                  for j in range(num_replicas):
                      replica = module_copies[j][i]
                      replica._buffers[key] = None
              else:
                  if buf.requires_grad and not detach:
                      buffer_copies = buffer_copies_rg
                      buffer_idx = buffer_indices_rg[buf]
                  else:
                      buffer_copies = buffer_copies_not_rg
                      buffer_idx = buffer_indices_not_rg[buf]
                  for j in range(num_replicas):
                      replica = module_copies[j][i]
                      setattr(
                          replica, key, buffer_copies[j][buffer_idx]
                      )
  
      return [cast(T, module_copies[j][0]) for j in range(num_replicas)]
  
file_path: pytorch/torch/nn/parallel/replicate.py
imports: import torch
  from ..modules import Module
  from .None import comm
  from typing import TYPE_CHECKING, Dict, Iterator, List, Optional, Sequence, Set, TypeVar, Union, cast
  from torch._utils import _get_device_index
  from collections import OrderedDict
label_desc: Use image node_id 7 for calling a global function with example usage: replicate(network, devices, detach) and returns: unknown, list