code: def test_mlp_decoder():
      # Test with additional layers
      in_dim, hidden_dim, out_dim = 8, 16, 32
      include_decoder_layers = True
      dropout_rate = 0.1
      mlp_decoder = MLPDecoder(
          in_dim=in_dim,
          hidden_dim=hidden_dim,
          out_dim=out_dim,
          dropout_rate=dropout_rate,
          include_decoder_layers=include_decoder_layers,
      )
      assert mlp_decoder.fc1.weight.size() == (hidden_dim, in_dim)
      assert mlp_decoder.fc2.weight.size() == (hidden_dim, hidden_dim)
      assert mlp_decoder.fc3.weight.size() == (out_dim, hidden_dim)
      assert mlp_decoder.fc4.weight.size() == (1, out_dim)
      input_batch = torch.randn((16, in_dim))
      output = mlp_decoder(input_batch)
      assert output.size() == (16, 1)
  
      # Test without additional layers
      in_dim, hidden_dim, out_dim = 8, 16, 2
      include_decoder_layers = False
      mlp_decoder = MLPDecoder(
          in_dim=in_dim,
          hidden_dim=hidden_dim,
          out_dim=out_dim,
          dropout_rate=dropout_rate,
          include_decoder_layers=include_decoder_layers,
      )
      assert mlp_decoder.fc1.weight.size() == (hidden_dim, in_dim)
      assert mlp_decoder.fc2.weight.size() == (out_dim, hidden_dim)
      assert not hasattr(
          mlp_decoder, "fc3"
      )  # There should be no fc3 layer
      assert not hasattr(
          mlp_decoder, "fc4"
      )  # There should be no fc4 layer
      input_batch = torch.randn((16, in_dim))
      output = mlp_decoder(input_batch)
      assert output.size() == (16, out_dim)
  
file_path: pykale/tests/predict/test_decode.py
imports: import pytest
  import torch
  from kale.predict.decode import LinearClassifier, MLPDecoder, VCDN
  from kale.utils.seed import set_seed
label_desc: Use image node_id 1 for calling a global function with example usage: test_mlp_decoder() without return types