code: def test_run(self):
      # 测试能否跑通
      (
          embed,
          src_words_idx,
          tgt_words_idx,
          src_seq_len,
          tgt_seq_len,
      ) = prepare_env()
  
      for bind_encoder_decoder_embed in [True, False]:
          tgt_embed = None
          for bind_decoder_input_output_embed in [True, False]:
              if bind_encoder_decoder_embed == False:
                  tgt_embed = embed
              model = LSTMSeq2SeqModel.build_model(
                  src_embed=embed,
                  tgt_embed=tgt_embed,
                  num_layers=2,
                  hidden_size=20,
                  dropout=0.1,
                  bind_encoder_decoder_embed=bind_encoder_decoder_embed,
                  bind_decoder_input_output_embed=bind_decoder_input_output_embed,
              )
              output = model(src_words_idx, tgt_words_idx, src_seq_len)
              assert output["pred"].size() == (2, 4, len(embed))
  
file_path: fastNLP/tests/models/torch/test_seq2seq_model.py
imports: import pytest
  from fastNLP.envs.imports import _NEED_IMPORT_TORCH
  from fastNLP import seq_len_to_mask
label_desc: Use image node_id 1 for calling the TestLSTMSeq2SeqModel obj's underlying member method code with example usage: obj.test_run() without return types