def configure_evaluator() -> Evaluator:
        """Configure the default evaluator.

        The default evaluator includes metrics for both image-level and
        pixel-level evaluation. Override this method to provide custom metrics for the model.

        Returns:
            Evaluator: Configured evaluator with default metrics

        Example:
            >>> evaluator = AnomalibModule.configure_evaluator()
            >>> isinstance(evaluator, Evaluator)
            True
        """
        image_auroc = AUROC(fields=["pred_score", "gt_label"], prefix="image_")
        image_f1score = F1Score(fields=["pred_label", "gt_label"], prefix="image_")
        pixel_auroc = AUROC(fields=["anomaly_map", "gt_mask"], prefix="pixel_", strict=False)
        pixel_f1score = F1Score(fields=["pred_mask", "gt_mask"], prefix="pixel_", strict=False)
        test_metrics = [image_auroc, image_f1score, pixel_auroc, pixel_f1score]
        return Evaluator(test_metrics=test_metrics)
Configure the default evaluator.

        The default evaluator includes metrics for both image-level and
        pixel-level evaluation. Override this method to provide custom metrics for the model.

        Returns:
            Evaluator: Configured evaluator with default metrics

        Example:
            >>> evaluator = AnomalibModule.configure_evaluator()
            >>> isinstance(evaluator, Evaluator)
            True
        
src/anomalib/models/components/base/anomalib_module.py