code: def translate_one_step_batched(self, data, tokenizer, model):
      tokenized_texts = tokenizer(
          data, padding=True, truncation=True, return_tensors="pt"
      )
      tokenized_dataset = t_data.TensorDataset(
          *(tokenized_texts.values())
      )
      tokenized_dataloader = t_data.DataLoader(
          tokenized_dataset,
          batch_size=self.batch_size,
          shuffle=False,
          num_workers=1,
      )
  
      all_translated_ids = []
      with torch.no_grad():
          for batch in tokenized_dataloader:
              batch = tuple(t.to(self.device) for t in batch)
              input_ids, attention_mask = batch
  
              translated_ids_batch = model.generate(
                  input_ids=input_ids,
                  attention_mask=attention_mask,
                  max_length=self.max_length,
              )
  
              all_translated_ids.append(
                  translated_ids_batch.detach().cpu().numpy()
              )
  
      all_translated_texts = []
      for translated_ids_batch in all_translated_ids:
          translated_texts = tokenizer.batch_decode(
              translated_ids_batch, skip_special_tokens=True
          )
          all_translated_texts.extend(translated_texts)
  
      return all_translated_texts
  
imports: from nlpaug.model.lang_models import LanguageModels
label_desc: Use image node_id 4 for calling the MtTransformers obj's underlying member method code with example usage: obj.translate_one_step_batched(data, tokenizer, model) and returns: all_translated_texts