code: def test_gated_cnn_encoder_layers(self):
      cnn_encoder = GatedCnnEncoder(
          input_dim=32,
          layers=[
              [[4, 32]],
              [[1, 16], [5, 16], [1, 32]],
              [[1, 64], [5, 64], [1, 32]],
          ],
          return_all_layers=True,
      )
  
      token_embeddings = torch.rand(5, 10, 32)
      mask = torch.ones(5, 10).bool()
      mask[0, 7:] = False
      mask[1, 5:] = False
  
      output = cnn_encoder(token_embeddings, mask)
      assert len(output) == 3
      concat_layers = torch.cat(
          [layer.unsqueeze(1) for layer in output], dim=1
      )
      assert list(concat_layers.size()) == [5, 3, 10, 64]
  
file_path: allennlp/tests/modules/seq2seq_encoders/gated_cnn_encoder_test.py
imports: import torch
  from allennlp.common.testing import AllenNlpTestCase
  from allennlp.modules.seq2seq_encoders.gated_cnn_encoder import GatedCnnEncoder
label_desc: Use image node_id 3 for calling the TestGatedCnnEncoder obj's underlying member method code with example usage: obj.test_gated_cnn_encoder_layers() without return types