code: def setUpClass(cls):
      env_config_path = os.path.abspath(
          os.path.join(
              os.path.dirname(__file__), "..", "..", "..", ".env"
          )
      )
      load_dotenv(env_config_path)
  
      cls.model_paths = [
          "facebook/bart-large-cnn",
          "t5-small",
          "t5-base",
      ]
  
      cls.text = """
              The history of natural language processing (NLP) generally started in the 1950s, although work can be 
              found from earlier periods. In 1950, Alan Turing published an article titled "Computing Machinery and 
              Intelligence" which proposed what is now called the Turing test as a criterion of intelligence. 
              The Georgetown experiment in 1954 involved fully automatic translation of more than sixty Russian 
              sentences into English. The authors claimed that within three or five years, machine translation would
              be a solved problem. However, real progress was much slower, and after the ALPAC report in 1966, 
              which found that ten-year-long research had failed to fulfill the expectations, funding for machine 
              translation was dramatically reduced. Little further research in machine translation was conducted 
              until the late 1980s when the first statistical machine translation systems were developed.
          """
      cls.texts = [
          cls.text,
          # https://www.sciencedirect.com/science/article/abs/pii/S0957417418307735
          """
                  Summarization, is to reduce the size of the document while preserving the meaning, is one of the 
                  most researched areas among the Natural Language Processing (NLP) community. Summarization 
                  techniques, on the basis of whether the exact sentences are considered as they appear in the 
                  original text or new sentences are generated using natural language processing techniques, are 
                  categorized into extractive and abstractive techniques. Extractive summarization has been a very 
                  extensively researched topic and has reached to its maturity stage. Now the research has shifted 
                  towards the abstractive summarization. The complexities underlying with the natural language text 
                  makes abstractive summarization a difficult and a challenging task.
              """,
      ]
  
file_path: nlpaug/test/augmenter/sentence/test_abst_summ.py
imports: import unittest
  import os
  import torch
  from dotenv import load_dotenv
  import nlpaug.augmenter.sentence
  import nlpaug.util.text.tokenizer
label_desc: Use image node_id 1 for calling the TestAbstSummAug obj's underlying member method code with example usage: obj.setUpClass(cls) without return types