code: def inference(
      self,
      file: str,
      batch_size: int = 1,
      num_samples: int = -1,
      output_prediction_file: Optional[str] = None,
      output_nbest_file: Optional[str] = None,
  ):
      all_predictions = []
      mode = self.training
      device = (
          "cuda" if isinstance(self.trainer.device_ids, list) else "cpu"
      )
      if self.cfg.library == "huggingface":
          try:
              # switch model to evaluation mode
              self.eval()
              self.to(device)
              logging_level = logging.get_verbosity()
              logging.set_verbosity(logging.WARNING)
  
              inference_dl = self.setup_inference_data(
                  file, batch_size=batch_size, num_samples=num_samples
              )
  
              outputs = self._inference(inference_dl, device)
              (
                  generated_answers,
                  unique_ids,
                  per_sample_perplexity,
              ) = QAMetrics.convert_dict_outputs_to_lists(
                  outputs,
                  [
                      "generated_answers",
                      "unique_ids",
                      "per_sample_perplexity",
                  ],
              )
              (
                  all_predictions,
                  all_nbest_predictions,
              ) = self._get_predictions(
                  inference_dl.dataset.features,
                  inference_dl.dataset.examples,
                  unique_ids,
                  per_sample_perplexity,
                  generated_answers,
              )
  
              if output_prediction_file:
                  QAMetrics.dump_predicted_answers_to_file(
                      output_prediction_file,
                      inference_dl.dataset.examples,
                      all_predictions,
                  )
  
              if output_nbest_file:
                  QAMetrics.dump_nbest_predictions_to_file(
                      output_nbest_file,
                      inference_dl.dataset.examples,
                      all_nbest_predictions,
                      keys_to_dump=["generated_text", "perplexity"],
                  )
  
          finally:
              # set mode back to its original value
              self.train(mode=mode)
              logging.set_verbosity(logging_level)
  
      elif self.cfg.library == "megatron":
          raise ValueError(
              "Megatron Inference is not supported by S2SQAModel"
          )
  
      return all_predictions, all_nbest_predictions
  
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 8 for calling the S2SQAModel obj's underlying member method code with example usage: obj.inference(file, batch_size, num_samples, output_prediction_file, output_nbest_file) and returns: all_predictions,  all_nbest_predictions