code: def test_assert_jit_vs_nonjit_(self):
      task, parser = get_dummy_task_and_parser()
      LSTMModel.add_args(parser)
      args = parser.parse_args([])
      args.criterion = ""
      model = LSTMModel.build_model(args, task)
      model.eval()
      scripted_model = torch.jit.script(model)
      scripted_model.eval()
      idx = len(task.source_dictionary)
      iter = 100
      # Inject random input and check output
      seq_len_tensor = torch.randint(1, 10, (iter,))
      num_samples_tensor = torch.randint(1, 10, (iter,))
      for i in range(iter):
          seq_len = seq_len_tensor[i]
          num_samples = num_samples_tensor[i]
          src_token = (torch.randint(0, idx, (num_samples, seq_len)),)
          src_lengths = torch.randint(1, seq_len + 1, (num_samples,))
          src_lengths, _ = torch.sort(src_lengths, descending=True)
          # Force the first sample to have seq_len
          src_lengths[0] = seq_len
          prev_output_token = (torch.randint(0, idx, (num_samples, 1)),)
          result = model(
              src_token[0], src_lengths, prev_output_token[0], None
          )
          scripted_result = scripted_model(
              src_token[0], src_lengths, prev_output_token[0], None
          )
          self.assertTensorEqual(result[0], scripted_result[0])
          self.assertTensorEqual(result[1], scripted_result[1])
  
file_path: fairseq/tests/test_lstm_jitable.py
imports: import argparse
  import tempfile
  import unittest
  import torch
  from fairseq.data.dictionary import Dictionary
  from fairseq.models.lstm import LSTMModel
  from fairseq.tasks.fairseq_task import LegacyFairseqTask
label_desc: Use image node_id 4 for calling the TestJitLSTMModel obj's underlying member method code with example usage: obj.test_assert_jit_vs_nonjit_() without return types