code: def __init__(
      self,
      cls_model_dir,
      gen_model_dir,
      threshold=0.7,
      min_length=100,
      max_length=300,
      batch_size=32,
      temperature=1.0,
      top_k=50,
      top_p=0.9,
      repetition_penalty=1.0,
      device="cuda",
  ):
      super().__init__(device, model_type=None)
      try:
          from transformers import GPT2LMHeadModel
      except ModuleNotFoundError:
          raise ModuleNotFoundError(
              "Missed transformers library. Install transfomers by `pip install transformers`"
          )
      try:
          from simpletransformers.classification import (
              ClassificationModel,
          )
      except ModuleNotFoundError:
          raise ModuleNotFoundError(
              "Missed simpletransformers library. Install transfomers by `pip install simpletransformers`"
          )
  
      self.cls_model_dir = cls_model_dir
      self.gen_model_dir = gen_model_dir
      self.threshold = threshold
      self.min_length = min_length
      self.max_length = max_length
      self.batch_size = batch_size
      self.temperature = temperature
      self.top_k = top_k
      self.top_p = top_p
      self.repetition_penalty = repetition_penalty
      self.sep_token = "[SEP]"
      self.stop_token = "<|endoftext|>"
  
      with open(os.path.join(cls_model_dir, "label_encoder.json")) as f:
          self.label2id = json.load(f)
          self.id2label = {v: k for k, v in self.label2id.items()}
  
      with open(os.path.join(cls_model_dir, "cls_config.json")) as f:
          cls_config = json.load(f)
      self.cls_model = ClassificationModel(
          cls_config["model_type"],
          cls_model_dir,
          use_cuda=device != "cpu",
          args={"silent": True},
      )
      self.gen_model = GPT2LMHeadModel.from_pretrained(gen_model_dir)
      self.gen_model.eval()
      self.gen_tokenizer = GPT2Tokenizer.from_pretrained(gen_model_dir)
      self.to_device()
  
file_path: nlpaug/nlpaug/model/lang_models/lambada.py
imports: import pandas
  import json
  import os
  from nlpaug.model.lang_models import LanguageModels
label_desc: Use image node_id 1 to create a new Lambada object from inherited base classes: LanguageModels with example: obj = Lambada(cls_model_dir, gen_model_dir, threshold, min_length, max_length, batch_size, temperature, top_k, top_p, repetition_penalty, device)