code: def evaluate(
      self,
      features,
      examples,
      unique_ids,
      per_sample_perplexity,
      generated_texts,
  ):
      all_predictions, all_nbest_json = self._get_predictions(
          features,
          examples,
          unique_ids,
          per_sample_perplexity,
          generated_texts,
      )
  
      eval_results = QAMetrics.evaluate_predictions(
          examples, all_predictions
      )
  
      return eval_results, 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 9 for calling the S2SQAModel obj's underlying member method code with example usage: obj.evaluate(features, examples, unique_ids, per_sample_perplexity, generated_texts) and returns: eval_results,  all_predictions,  all_nbest_json