code: def _generate_candidates(self, input_ids, input_attn_mask):
      num_tokens_to_generate = self.cfg.tokens_to_generate
  
      if self.cfg.library == "huggingface":
          param_dict = {
              "input_ids": input_ids,
              "attention_mask": input_attn_mask,
              "max_length": num_tokens_to_generate,
          }
          generated_tokens = self.language_model.generate(**param_dict)
          generated_answers = self.tokenizer.tokenizer.batch_decode(
              generated_tokens,
              skip_special_tokens=True,
          )
          generated_answers = [ans.strip() for ans in generated_answers]
  
      elif self.cfg.library == "megatron":
          raise ValueError(
              "Megatron Generation is not supported by S2SQAModel"
          )
  
      return generated_answers
  
imports: import collections
  from typing import List, Optional
  import torch
  from omegaconf import DictConfig, OmegaConf, open_dict
  from pytorch_lightning import Trainer
  from torch.cuda.amp import autocast
  from transformers import AutoModelForSeq2SeqLM
  from nemo.collections.nlp.data.question_answering.data_processor.qa_processing import QAProcessor
  from nemo.collections.nlp.data.question_answering.dataset.qa_s2s_dataset import S2SQADataset
  from nemo.collections.nlp.metrics.qa_metrics import QAMetrics
  from nemo.collections.nlp.models.language_modeling.megatron_t5_model import MegatronT5Model
  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 S2SQAModel obj's underlying member method code with example usage: obj._generate_candidates(input_ids, input_attn_mask) and returns: generated_answers