code: def test_metabilinear(bias):
      meta_model = MetaBilinear(2, 3, 5, bias=bias)
      model = nn.Bilinear(2, 3, 5, bias=bias)
  
      assert isinstance(meta_model, MetaModule)
      assert isinstance(meta_model, nn.Bilinear)
  
      # Set same weights for both models
      weight = torch.randn(5, 2, 3)
      meta_model.weight.data.copy_(weight)
      model.weight.data.copy_(weight)
  
      if bias:
          bias = torch.randn(5)
          meta_model.bias.data.copy_(bias)
          model.bias.data.copy_(bias)
  
      inputs1 = torch.randn(7, 2)
      inputs2 = torch.randn(7, 3)
  
      outputs_torchmeta = meta_model(inputs1, inputs2, params=None)
      outputs_nn = model(inputs1, inputs2)
  
      np.testing.assert_equal(
          outputs_torchmeta.detach().numpy(),
          outputs_nn.detach().numpy(),
      )
  
file_path: pytorch-meta/torchmeta/tests/modules/test_linear.py
imports: import pytest
  import numpy
  import torch
  import torch.nn
  from collections import OrderedDict
  from torchmeta.modules import MetaModule
  from torchmeta.modules.linear import MetaLinear, MetaBilinear
label_desc: Use image node_id 3 for calling a global function with example usage: test_metabilinear(bias) without return types