code: def sequential_files(deeprec_resource_path):
      data_path = os.path.join(deeprec_resource_path, "slirec")
      train_file = os.path.join(data_path, "train_data")
      valid_file = os.path.join(data_path, "valid_data")
      test_file = os.path.join(data_path, "test_data")
      user_vocab = os.path.join(data_path, "user_vocab.pkl")
      item_vocab = os.path.join(data_path, "item_vocab.pkl")
      cate_vocab = os.path.join(data_path, "category_vocab.pkl")
  
      reviews_name = "reviews_Movies_and_TV_5.json"
      meta_name = "meta_Movies_and_TV.json"
      reviews_file = os.path.join(data_path, reviews_name)
      meta_file = os.path.join(data_path, meta_name)
  
      # number of negative instances with a positive instance for validation
      valid_num_ngs = 4
      # number of negative instances with a positive instance for testing
      test_num_ngs = 9
      # sample a small item set for training and testing here for example
      sample_rate = 0.01
  
      input_files = [
          reviews_file,
          meta_file,
          train_file,
          valid_file,
          test_file,
          user_vocab,
          item_vocab,
          cate_vocab,
      ]
      download_and_extract(reviews_name, reviews_file)
      download_and_extract(meta_name, meta_file)
      data_preprocessing(
          *input_files,
          sample_rate=sample_rate,
          valid_num_ngs=valid_num_ngs,
          test_num_ngs=test_num_ngs,
      )
  
      return (
          data_path,
          user_vocab,
          item_vocab,
          cate_vocab,
      )
  
file_path: recommenders/tests/unit/recommenders/models/test_deeprec_model.py
imports: import os
  import pytest
  from recommenders.datasets import movielens
  from recommenders.datasets.amazon_reviews import download_and_extract, data_preprocessing
  from recommenders.datasets.python_splitters import python_stratified_split
label_desc: Use image node_id 2 for calling a global function with example usage: sequential_files(deeprec_resource_path) and returns: data_path,  user_vocab,  item_vocab,  cate_vocab