code: def __init__(
      self,
      src_model_name="facebook/wmt19-en-de",
      tgt_model_name="facebook/wmt19-de-en",
      device="cuda",
      silence=True,
      batch_size=32,
      max_length=None,
  ):
      super().__init__(device, model_type=None, silence=silence)
      try:
          from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
      except ModuleNotFoundError:
          raise ModuleNotFoundError(
              "Missed transformers library. Install transfomers by `pip install transformers`"
          )
  
      self.src_model_name = src_model_name
      self.tgt_model_name = tgt_model_name
      self.src_model = AutoModelForSeq2SeqLM.from_pretrained(
          self.src_model_name
      )
      self.src_model.eval()
      self.src_model.to(device)
      self.tgt_model = AutoModelForSeq2SeqLM.from_pretrained(
          self.tgt_model_name
      )
      self.tgt_model.eval()
      self.tgt_model.to(device)
      self.src_tokenizer = AutoTokenizer.from_pretrained(
          self.src_model_name
      )
      self.tgt_tokenizer = AutoTokenizer.from_pretrained(
          self.tgt_model_name
      )
  
      self.batch_size = batch_size
      self.max_length = max_length
  
imports: from nlpaug.model.lang_models import LanguageModels
label_desc: Use image node_id 1 to create a new MtTransformers object from inherited base classes: LanguageModels with example: obj = MtTransformers(src_model_name, tgt_model_name, device, silence, batch_size, max_length)