def METHOD_NAME(self, eval_dataset=None, eval_examples=None, ignore_keys=None):
    """
    Obtain the predictions using either eval data passed to method (if given).
    Otherwise use data given to constructor at instantiation.
    Args:
        eval_examples: Eval examples `Dataset` from `BasePreprocessor.process_eval`.
        eval_dataset: Eval features `Dataset` from `BasePreprocessor.process_eval`.
        ignore_keys: Keys to ignore in evaluation loop.
    Returns:
        Answer predictions if post-processing function was provided to constructor 
        at instantiation, otherwise an empty dict.
    """
    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
    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
            # gather predictions if running in eval mode
            prediction_loss_only=self.args.prediction_loss_only, #True if compute_metrics is None else None,
            ignore_keys=ignore_keys,
        )
    finally:
        pass
    if self.post_process_function is not None:
        eval_preds = self.post_process_function(eval_examples, eval_dataset, output.predictions)
    else:
        eval_preds = {}
    return eval_preds