code: def bimodal_interaction_fusion():
      mi2m_vector = BimodalInteractionFusion(
          input_dims=(10, 20), output_dim=30, output="vector"
      )
      m1 = torch.randn(4, 10)
      m2 = torch.randn(4, 20)
      output = mi2m_vector([m1, m2])
      assert output.shape == (4, 20)
  
      mi2m_matrix = BimodalInteractionFusion(
          input_dims=(10, 20), output_dim=30, output="matrix"
      )
      m1 = torch.randn(4, 10)
      m2 = torch.randn(4, 20)
      output = mi2m_matrix([m1, m2])
      assert output.shape == (4, 30)
  
      mi2m_scalar = BimodalInteractionFusion(
          input_dims=(10, 20), output_dim=30, output="scalar"
      )
      m1 = torch.randn(4, 10)
      m2 = torch.randn(4, 20)
      output = mi2m_scalar([m1, m2])
      assert output.shape == (4, 20)
  
imports: import torch
  from kale.embed.feature_fusion import BimodalInteractionFusion, Concat, LowRankTensorFusion
label_desc: Use image node_id 2 for calling a global function with example usage: bimodal_interaction_fusion() without return types