code: def _replicate_params(self, params, inputs, device_ids, detach=False):
      if params is None:
          module_params = OrderedDict(self.module.named_parameters())
      else:
          # Temporarily disable the warning if no parameter with key prefix
          # `module` was found. In that case, the original params dictionary
          # is used.
          with warnings.catch_warnings():
              warnings.simplefilter("ignore")
              module_params = self.get_subdict(params, key="module")
          if module_params is None:
              module_params = params
  
      replicas = _broadcast_coalesced_reshape(
          list(module_params.values()),
          device_ids[: len(inputs)],
          detach,
      )
      replicas = tuple(
          OrderedDict(zip(module_params.keys(), replica))
          for replica in replicas
      )
      return replicas
  
file_path: pytorch-meta/torchmeta/modules/parallel.py
imports: import torch
  import warnings
  from torch.nn import DataParallel
  from torchmeta.modules.module import MetaModule
  from collections import OrderedDict
  from torch.nn.parallel import parallel_apply
  from torch.nn.parallel.scatter_gather import scatter_kwargs
  from torch.nn.parallel.replicate import _broadcast_coalesced_reshape
label_desc: Use image node_id 2 for calling the DataParallel obj's underlying member method code with example usage: obj._replicate_params(params, inputs, device_ids, detach) and returns: replicas