code: def _prep_inference_labels(
      self, input_ids, input_attn_mask, training_mask_end, device
  ):
      # generate answers by decoding inputs and format into ipnut template
      decoded_inputs = self.tokenizer.tokenizer.batch_decode(
          input_ids, skip_special_tokens=True
      )
      generated_texts = self._generate_candidates(
          input_ids, input_attn_mask, training_mask_end
      )
      inputs_with_answer = [
          f"{inp}{ans}{self.tokenizer.tokenizer.eos_token}"
          if ans
          else f"{inp}{self.tokenizer.tokenizer.eos_token}"
          for inp, ans in zip(decoded_inputs, generated_texts)
      ]
  
      # encode template with generated answers
      encoded_dict = self.tokenizer.tokenizer(
          inputs_with_answer,
          truncation=True,
          max_length=self._cfg.dataset.max_seq_length,
          padding="max_length",
          return_tensors="pt",
      )
      input_ids, input_attn_mask = (
          tensor.to(device)
          for tensor in [
              encoded_dict["input_ids"],
              encoded_dict["attention_mask"],
          ]
      )
      labels = GPTQADataset.update_labels_for_no_pad_loss(
          input_ids, training_mask_end, input_attn_mask
      )
      if len(labels.shape) == 1:
          labels = torch.unsqueeze(labels, 0)
      labels = labels.to(device)
  
      return input_ids, input_attn_mask, labels, generated_texts
  
imports: import collections
  from typing import List, Optional
  import torch
  from omegaconf import DictConfig
  from pytorch_lightning import Trainer
  from transformers import AutoModelForCausalLM
  from nemo.collections.nlp.data.question_answering.data_processor.qa_processing import QAProcessor
  from nemo.collections.nlp.data.question_answering.dataset.qa_gpt_dataset import GPTQADataset
  from nemo.collections.nlp.metrics.qa_metrics import QAMetrics
  from nemo.collections.nlp.models.language_modeling.megatron_gpt_model import MegatronGPTModel
  from nemo.collections.nlp.models.question_answering.qa_base_model import BaseQAModel
  from nemo.core.classes.common import PretrainedModelInfo, typecheck
  from nemo.utils import logging
label_desc: Use image node_id 13 for calling the GPTQAModel obj's underlying member method code with example usage: obj._prep_inference_labels(input_ids, input_attn_mask, training_mask_end, device) and returns: input_ids,  input_attn_mask,  labels,  generated_texts