code: def evaluate(
      self,
      eval_dataset: Optional[Dataset] = None,
      eval_examples=None,
      ignore_keys: Optional[List[str]] = None,
      metric_key_prefix: str = "eval",
      **gen_kwargs,
  ) -> Dict[str, float]:
      gen_kwargs = gen_kwargs.copy()
      gen_kwargs["max_length"] = (
          gen_kwargs["max_length"]
          if gen_kwargs.get("max_length") is not None
          else self.args.generation_max_length
      )
      gen_kwargs["num_beams"] = (
          gen_kwargs["num_beams"]
          if gen_kwargs.get("num_beams") is not None
          else self.args.generation_num_beams
      )
      self._gen_kwargs = gen_kwargs
  
      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
      start_time = time.time()
      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
      total_batch_size = (
          self.args.eval_batch_size * self.args.world_size
      )
      output.metrics.update(
          speed_metrics(
              metric_key_prefix,
              start_time,
              num_samples=output.num_samples,
              num_steps=math.ceil(
                  output.num_samples / total_batch_size
              ),
          )
      )
  
      if (
          self.post_process_function is not None
          and self.compute_metrics is not None
          and self.args.should_save
      ):
          # Only the main node write the results by default
          eval_preds = self.post_process_function(
              eval_examples, eval_dataset, output
          )
          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
                  )
  
          output.metrics.update(metrics)
      else:
          metrics = {}
  
      if self.args.should_log:
          # Only the main node log the results by default
          self.log(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, output.metrics
      )
      return metrics
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/examples/pytorch/question-answering/trainer_seq2seq_qa.py
imports: import math
  import time
  from typing import Dict, List, Optional
  from torch.utils.data import Dataset
  from transformers import Seq2SeqTrainer, is_torch_tpu_available
  from transformers.trainer_utils import PredictionOutput, speed_metrics
label_desc: Use image node_id 2 for calling the QuestionAnsweringSeq2SeqTrainer obj's underlying member method code with example usage: obj.evaluate(eval_dataset, eval_examples, ignore_keys, metric_key_prefix) and returns: metrics