code: def prepare_env():
      vocab = Vocabulary().add_word_lst("This is a test .".split())
      vocab.add_word_lst("Another test !".split())
      embed = StaticEmbedding(
          vocab, model_dir_or_name=None, embedding_dim=5
      )
  
      src_words_idx = torch.LongTensor([[3, 1, 2], [1, 2, 0]])
      tgt_words_idx = torch.LongTensor([[1, 2, 3, 4], [2, 3, 0, 0]])
      src_seq_len = torch.LongTensor([3, 2])
      tgt_seq_len = torch.LongTensor([4, 2])
  
      return (
          embed,
          src_words_idx,
          tgt_words_idx,
          src_seq_len,
          tgt_seq_len,
      )
  
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 a global function with example usage: prepare_env() and returns: embed,  src_words_idx,  tgt_words_idx,  src_seq_len,  tgt_seq_len