code: def scatter(self, inputs, kwargs, device_ids):
      if not isinstance(self.module, MetaModule):
          return super(DataParallel, self).scatter(
              inputs, kwargs, device_ids
          )
  
      params = kwargs.pop("params", None)
      inputs_, kwargs_ = scatter_kwargs(
          inputs, kwargs, device_ids, dim=self.dim
      )
      # Add params argument unchanged back in kwargs
      replicas = self._replicate_params(
          params,
          inputs_,
          device_ids,
          detach=not torch.is_grad_enabled(),
      )
      kwargs_ = tuple(
          dict(params=replica, **kwarg)
          for (kwarg, replica) in zip(kwargs_, replicas)
      )
      return inputs_, kwargs_
  
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 1 for calling the DataParallel obj's underlying member method code with example usage: obj.scatter(inputs, kwargs, device_ids) and returns: inputs_,  kwargs_, super