code: def _generate_candidates(
      self, input_ids, input_attn_mask, training_mask_end
  ):
      num_tokens_to_generate = self.cfg.tokens_to_generate
      if self.cfg.library == "huggingface":
          generated_token_ids = []
          max_length = 0
          for i in range(input_ids.size(0)):
              param_dict = {
                  "input_ids": input_ids[
                      i : i + 1, : training_mask_end[i]
                  ],
                  "max_length": training_mask_end[i]
                  + num_tokens_to_generate,
                  "pad_token_id": self.tokenizer.tokenizer.pad_token_id,
              }
              generated_token_ids.append(
                  self.language_model.generate(**param_dict)
              )
              max_length = max(
                  max_length, generated_token_ids[-1].size(1)
              )
  
          # pad each generated to ensure they are of same length in dim 1, therefore stack-able
          generated_token_ids = [
              torch.cat(
                  [
                      i,
                      torch.ones((1, max_length - i.size(1))).to(
                          i.device
                      )
                      * self.tokenizer.tokenizer.pad_token_id,
                  ],
                  axis=-1,
              )
              for i in generated_token_ids
          ]
          generated_token_ids = torch.cat(generated_token_ids, axis=0)
          generated_answers = self._get_answers_from_generated_tokens(
              generated_token_ids, training_mask_end=training_mask_end
          )
  
      elif self.cfg.library == "megatron":
          raise ValueError(
              "Megatron Generation is not supported by GPTQAModel"
          )
  
      return generated_answers
  
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 14 for calling the GPTQAModel obj's underlying member method code with example usage: obj._generate_candidates(input_ids, input_attn_mask, training_mask_end) and returns: generated_answers