code: def test_elmo_lstm(self):
      input_tensor = torch.rand(4, 5, 3)
      input_tensor[1, 4:, :] = 0.0
      input_tensor[2, 2:, :] = 0.0
      input_tensor[3, 1:, :] = 0.0
      mask = torch.ones([4, 5])
      mask[1, 4:] = 0.0
      mask[2, 2:] = 0.0
      mask[3, 1:] = 0.0
  
      lstm = ElmoLstm(
          num_layers=2,
          input_size=3,
          hidden_size=5,
          cell_size=7,
          memory_cell_clip_value=2,
          state_projection_clip_value=1,
      )
      output_sequence = lstm(input_tensor, mask)
  
      # Check all the layer outputs are masked properly.
      numpy.testing.assert_array_equal(
          output_sequence.data[:, 1, 4:, :].numpy(), 0.0
      )
      numpy.testing.assert_array_equal(
          output_sequence.data[:, 2, 2:, :].numpy(), 0.0
      )
      numpy.testing.assert_array_equal(
          output_sequence.data[:, 3, 1:, :].numpy(), 0.0
      )
  
      # LSTM state should be (num_layers, batch_size, hidden_size)
      assert list(lstm._states[0].size()) == [2, 4, 10]
      # LSTM memory cell should be (num_layers, batch_size, cell_size)
      assert list((lstm._states[1].size())) == [2, 4, 14]
  
file_path: magnitude/pymagnitude/third_party/allennlp/tests/modules/stacked_elmo_lstm_test.py
imports: from __future__ import absolute_import
  import numpy
  import torch
  from allennlp.modules.elmo_lstm import ElmoLstm
  from allennlp.common.testing import AllenNlpTestCase
label_desc: Use image node_id 1 for calling the TestElmoLstmCell obj's underlying member method code with example usage: obj.test_elmo_lstm() without return types