code: def test_dataparallel_params_maml(model):
      device = torch.device("cuda:0")
      model = DataParallel(model)
      model.to(device=device)
  
      train_inputs = torch.rand(5, 2).to(device=device)
      train_outputs = model(train_inputs)
  
      inner_loss = train_outputs.sum()  # Dummy loss
      params = gradient_update_parameters(model, inner_loss)
  
      test_inputs = torch.rand(5, 2).to(device=device)
      test_outputs = model(test_inputs, params=params)
  
      assert test_outputs.shape == (5, 1)
      assert test_outputs.device == device
  
      outer_loss = test_outputs.sum()  # Dummy loss
      outer_loss.backward()
  
file_path: pytorch-meta/torchmeta/tests/modules/test_parallel.py
imports: import pytest
  import torch
  import torch.nn
  from torchmeta.modules import MetaLinear, MetaSequential
  from torchmeta.modules import DataParallel
  from torchmeta.utils.gradient_based import gradient_update_parameters
label_desc: Use image node_id 9 for calling a global function with example usage: test_dataparallel_params_maml(model) without return types