code: def test_SequentialRNNLM_backward(rnn_type, tie_weights):
      model = SequentialRNNLM(
          10, rnn_type=rnn_type, tie_weights=tie_weights
      )
      input = torch.randint(0, 9, [2, 10])
  
      out, h = model(input, None)
      out, h = model(input, h)
      out.sum().backward()
  
file_path: espnet/test/espnet2/lm/test_seq_rnn_lm.py
imports: import pytest
  import torch
  from espnet2.lm.seq_rnn_lm import SequentialRNNLM
  from espnet.nets.batch_beam_search import BatchBeamSearch
  from espnet.nets.beam_search import BeamSearch
label_desc: Use image node_id 1 for calling a global function with example usage: test_SequentialRNNLM_backward(rnn_type, tie_weights) without return types