code: def load_amazon_book(dataset_folder="dataset/"):
      raw_data = dict()
      raw_data["total_users"] = 99473
      raw_data["total_items"] = 450166
  
      raw_data["train_data"] = np.load(
          dataset_folder + "amazon/user_data_train.npy"
      )
      raw_data["val_data"] = np.load(
          dataset_folder + "amazon/user_data_val.npy"
      )
      raw_data["test_data"] = np.load(
          dataset_folder + "amazon/user_data_test.npy"
      )
  
      raw_data["item_features"] = np.array(
          np.memmap(
              dataset_folder + "amazon/book_features_update.mem",
              dtype=np.float32,
              mode="r",
              shape=(raw_data["max_item"], 4096),
          )
      )
      raw_data["user_features"] = np.load(
          dataset_folder + "amazon/user_features_categories.npy"
      )
      return raw_data
  
imports: import tensorflow
  import numpy
label_desc: Use image node_id 1 for calling a global function with example usage: load_amazon_book(dataset_folder) and returns: raw_data