def METHOD_NAME(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