code: def test_metasequential_params():
      meta_model = MetaSequential(
          nn.Linear(2, 3, bias=True),
          nn.ReLU(),
          MetaLinear(3, 5, bias=True),
      )
      model = nn.Sequential(
          nn.Linear(2, 3, bias=True),
          nn.ReLU(),
          nn.Linear(3, 5, bias=True),
      )
  
      # Set same weights for both models (first layer)
      weight0 = torch.randn(3, 2)
      meta_model[0].weight.data.copy_(weight0)
      model[0].weight.data.copy_(weight0)
  
      bias0 = torch.randn(3)
      meta_model[0].bias.data.copy_(bias0)
      model[0].bias.data.copy_(bias0)
  
      params = OrderedDict()
      params["2.weight"] = torch.randn(5, 3)
      model[2].weight.data.copy_(params["2.weight"])
  
      params["2.bias"] = torch.randn(5)
      model[2].bias.data.copy_(params["2.bias"])
  
      inputs = torch.randn(5, 2)
  
      outputs_torchmeta = meta_model(inputs, params=params)
      outputs_nn = model(inputs)
  
      np.testing.assert_equal(
          outputs_torchmeta.detach().numpy(),
          outputs_nn.detach().numpy(),
      )
  
file_path: pytorch-meta/torchmeta/tests/modules/test_container.py
imports: import pytest
  import numpy
  import torch
  import torch.nn
  from collections import OrderedDict
  from torchmeta.modules import MetaSequential, MetaModule, MetaLinear
label_desc: Use image node_id 2 for calling a global function with example usage: test_metasequential_params() without return types