def METHOD_NAME(self, subset_indices: List[int]):
    if subset_indices is None:
        subset_indices = list(range(len(self.data_loader)))
    if self.use_original_metric:
        compiled_model = self._ie.compile_model(model=self._model, device_name=self._device)
        per_sample_metrics = calc_per_sample_metrics(
            compiled_model, self._validation_fn, self._validation_dataset, subset_indices
        )
    else:
        dataset_wrapper = DatasetWrapper(self._validation_dataset.get_inference_data(subset_indices))
        ans = super().predict(None, dataset_wrapper, None, True, None)
        (per_sample_metrics, _), *_ = ans
    return per_sample_metrics