code: def evaluate(
      self,
      eval_dataset=None,
      eval_examples=None,
      ignore_keys=None,
      metric_key_prefix: str = "eval",
  ):
      eval_dataset = (
          self.eval_dataset if eval_dataset is None else eval_dataset
      )
      eval_dataloader = self.get_eval_dataloader(eval_dataset)
      eval_examples = (
          self.eval_examples if eval_examples is None else eval_examples
      )
  
      # Temporarily disable metric computation, we will do it in the loop here.
      compute_metrics = self.compute_metrics
      self.compute_metrics = None
      eval_loop = (
          self.prediction_loop
          if self.args.use_legacy_prediction_loop
          else self.evaluation_loop
      )
      try:
          output = eval_loop(
              eval_dataloader,
              description="Evaluation",
              # No point gathering the predictions if there are no metrics, otherwise we defer to
              # self.args.prediction_loss_only
              prediction_loss_only=True
              if compute_metrics is None
              else None,
              ignore_keys=ignore_keys,
          )
      finally:
          self.compute_metrics = compute_metrics
  
      if (
          self.post_process_function is not None
          and self.compute_metrics is not None
      ):
          eval_preds = self.post_process_function(
              eval_examples, eval_dataset, output.predictions
          )
          metrics = self.compute_metrics(eval_preds)
  
          # Prefix all keys with metric_key_prefix + '_'
          for key in list(metrics.keys()):
              if not key.startswith(f"{metric_key_prefix}_"):
                  metrics[f"{metric_key_prefix}_{key}"] = metrics.pop(
                      key
                  )
  
          self.log(metrics)
      else:
          metrics = {}
  
      if self.args.tpu_metrics_debug or self.args.debug:
          # tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
          xm.master_print(met.metrics_report())
  
      self.control = self.callback_handler.on_evaluate(
          self.args, self.state, self.control, metrics
      )
      return metrics
  
file_path: transformers/examples/research_projects/quantization-qdqbert/trainer_quant_qa.py
imports: import logging
  import os
  import quant_trainer
  import torch
  from torch.utils.data import DataLoader
  from transformers import Trainer, is_torch_tpu_available
  from transformers.trainer_utils import PredictionOutput
label_desc: Use image node_id 4 for calling the QuestionAnsweringTrainer obj's underlying member method code with example usage: obj.evaluate(eval_dataset, eval_examples, ignore_keys, metric_key_prefix) and returns: metrics