code: def __init__(
      self,
      from_model_name,
      from_model_checkpt,
      to_model_name,
      to_model_checkpt,
      is_load_from_github=True,
      tokenzier_name="moses",
      bpe_name="fastbpe",
      device="cuda",
  ):
      super().__init__(device, temperature=None, top_k=None, top_p=None)
  
      try:
          import fairseq
          from fairseq.models.transformer import TransformerModel
      except ModuleNotFoundError:
          raise ModuleNotFoundError(
              "Missed fairseq library. Install fairseq by https://github.com/pytorch/fairseq"
          )
  
      self.from_model_name = from_model_name
      self.from_model_checkpt = from_model_checkpt
      self.to_model_name = to_model_name
      self.to_model_checkpt = to_model_checkpt
      self.is_load_from_github = is_load_from_github
      self.tokenzier_name = tokenzier_name
      self.bpe_name = bpe_name
  
      if is_load_from_github:
          self.from_model = torch.hub.load(
              "pytorch/fairseq",
              model=from_model_name,
              checkpoint_file=from_model_checkpt,
              tokenizer=tokenzier_name,
              bpe=bpe_name,
          )
          self.to_model = torch.hub.load(
              "pytorch/fairseq",
              model=to_model_name,
              checkpoint_file=to_model_checkpt,
              tokenizer=tokenzier_name,
              bpe=bpe_name,
          )
      else:
          try:
              self.from_model = TransformerModel.from_pretrained(
                  model_name_or_path=os.path.join(from_model_name, ""),
                  checkpoint_file=from_model_checkpt,
                  tokenizer=tokenzier_name,
                  bpe=bpe_name,
              )
          except TypeError:
              err_msg = "Cannot load model from local path. You may check the following parameters are correct or not."
              err_msg += " Model Directory: " + from_model_name
              err_msg += ", Checkpoint File Name: " + from_model_checkpt
              err_msg += ", Tokenizer Name: " + tokenzier_name
              err_msg += ", BPE Name: " + bpe_name
              raise ValueError(err_msg)
  
          try:
              self.to_model = TransformerModel.from_pretrained(
                  model_name_or_path=os.path.join(to_model_name, ""),
                  checkpoint_file=to_model_checkpt,
                  tokenizer=tokenzier_name,
                  bpe=bpe_name,
              )
          except TypeError:
              err_msg = "Cannot load model from local path. You may check the following parameters are correct or not."
              err_msg += " Model Directory: " + to_model_name
              err_msg += ", Checkpoint File Name: " + to_model_checkpt
              err_msg += ", Tokenizer Name: " + tokenzier_name
              err_msg += ", BPE Name: " + bpe_name
              raise ValueError(err_msg)
  
      self.from_model.eval()
      self.to_model.eval()
      if self.device == "cuda":
          self.from_model.cuda()
          self.to_model.cuda()
  
imports: import os
  from nlpaug.model.lang_models import LanguageModels
  from nlpaug.util.selection.filtering import *
label_desc: Use image node_id 1 to create a new Fairseq object from inherited base classes: LanguageModels with example: obj = Fairseq(from_model_name, from_model_checkpt, to_model_name, to_model_checkpt, is_load_from_github, tokenzier_name, bpe_name, device)