code: def _get_predictions(
      self,
      features,
      examples: List,
      unique_ids: List[int],
      per_sample_perplexity: List,
      generated_texts: List,
  ):
      unique_id_to_pos = {}
      for index, unique_id in enumerate(unique_ids):
          unique_id_to_pos[unique_id] = index
  
      example_index_to_features = collections.defaultdict(list)
      for feature in features:
          example_index_to_features[feature.example_index].append(
              feature
          )
  
      _PrelimPrediction = collections.namedtuple(
          "PrelimPrediction",
          ["feature_index", "perplexity", "generated_text"],
      )
  
      all_predictions = collections.OrderedDict()
      all_nbest_json = collections.OrderedDict()
      for example_index, example in enumerate(examples):
          # finish this loop if we went through all batch examples
          if example_index >= len(unique_ids):
              break
  
          curr_features = example_index_to_features[example_index]
          prelim_predictions = []
          for feature_index, feature in enumerate(curr_features):
              pos = unique_id_to_pos[feature.unique_id]
              curr_perplexity = per_sample_perplexity[pos]
              curr_generated_text = generated_texts[pos]
              prelim_prediction = _PrelimPrediction(
                  feature_index, curr_perplexity, curr_generated_text
              )
              prelim_predictions.append(prelim_prediction)
  
          prelim_predictions = sorted(
              prelim_predictions, key=lambda x: x.perplexity
          )
          all_predictions[example.qas_id] = prelim_predictions[
              0
          ].generated_text
          all_nbest_json[example.qas_id] = [
              pred._asdict() for pred in prelim_predictions
          ]
  
      return all_predictions, all_nbest_json
  
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 11 for calling the S2SQAModel obj's underlying member method code with example usage: obj._get_predictions(features, examples, unique_ids, per_sample_perplexity, generated_texts) and returns: all_predictions,  all_nbest_json