code: def test_elmo_lstm_cell_completes_forward_pass(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
  
      initial_hidden_state = torch.ones([1, 4, 5])
      initial_memory_state = torch.ones([1, 4, 7])
  
      lstm = LstmCellWithProjection(
          input_size=3,
          hidden_size=5,
          cell_size=7,
          memory_cell_clip_value=2,
          state_projection_clip_value=1,
      )
      output_sequence, lstm_state = lstm(
          input_tensor,
          [5, 4, 2, 1],
          (initial_hidden_state, initial_memory_state),
      )
      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
      )
  
      # Test the state clipping.
      numpy.testing.assert_array_less(output_sequence.data.numpy(), 1.0)
      numpy.testing.assert_array_less(
          -output_sequence.data.numpy(), 1.0
      )
  
      # LSTM state should be (num_layers, batch_size, hidden_size)
      assert list(lstm_state[0].size()) == [1, 4, 5]
      # LSTM memory cell should be (num_layers, batch_size, cell_size)
      assert list((lstm_state[1].size())) == [1, 4, 7]
  
      # Test the cell clipping.
      numpy.testing.assert_array_less(lstm_state[0].data.numpy(), 2.0)
      numpy.testing.assert_array_less(-lstm_state[0].data.numpy(), 2.0)
  
imports: import numpy
  import torch
  from allennlp.modules.lstm_cell_with_projection import LstmCellWithProjection
  from allennlp.common.testing import AllenNlpTestCase
label_desc: Use image node_id 1 for calling the TestLstmCellWithProjection obj's underlying member method code with example usage: obj.test_elmo_lstm_cell_completes_forward_pass() without return types