code: def model():
      model = MetaSequential(
          MetaLinear(2, 3, bias=True),
          nn.ReLU(),
          MetaLinear(3, 1, bias=False),
      )
  
      return model
  
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 1 for calling a global function with example usage: model() and returns: model