code: def __init__(
      self,
      model_path="t5-base",
      min_length=10,
      max_length=20,
      num_beam=3,
      no_repeat_ngram_size=3,
      device="cuda",
      silence=True,
  ):
      super().__init__(
          device, temperature=None, top_k=None, top_p=None, silence=True
      )
      try:
          from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
      except ModuleNotFoundError:
          raise ModuleNotFoundError(
              "Missed transformers library. Install transfomers by `pip install transformers`"
          )
  
      self.model_path = model_path
      self.min_length = min_length
      self.max_length = max_length
      self.num_beam = num_beam
      self.no_repeat_ngram_size = no_repeat_ngram_size
  
      self.tokenizer = AutoTokenizer.from_pretrained(model_path)
      if silence:
          # Transformers thrown an warning regrading to weight initialization. It is expected
          orig_log_level = logging.getLogger(
              "transformers." + "modeling_utils"
          ).getEffectiveLevel()
          logging.getLogger(
              "transformers." + "modeling_utils"
          ).setLevel(logging.ERROR)
          self.model = AutoModelForSeq2SeqLM.from_pretrained(model_path)
          logging.getLogger(
              "transformers." + "modeling_utils"
          ).setLevel(orig_log_level)
      else:
          self.model = AutoModelForSeq2SeqLM.from_pretrained(model_path)
  
      self.model.to(self.device)
      self.model.eval()
  
      self.text_prefix = "summarize: "
      self.return_tensor = "pt"  # PyTorch
      self.early_stopping = True
      self.skip_special_token = True
      self.default_max_length_ratio = 0.5
  
file_path: nlpaug/nlpaug/model/lang_models/t5.py
imports: import logging
  from nlpaug.model.lang_models import LanguageModels
  import nlpaug.util.text.tokenizer
label_desc: Use image node_id 1 to create a new T5 object from inherited base classes: LanguageModels with example: obj = T5(model_path, min_length, max_length, num_beam, no_repeat_ngram_size, device, silence)