def METHOD_NAME(ground_truths: List[Union[ObjectAnnotation,
                                          ClassificationAnnotation]],
                predictions: List[Union[ObjectAnnotation,
                                        ClassificationAnnotation]],
                include_subclasses=False) -> List[ScalarMetric]:
    """
    Computes miou between two sets of annotations.
    These annotations should relate to the same data (image/video).
    Each class in the annotation list is weighted equally in the iou score.
    Args:
        ground_truth : Label containing human annotations or annotations known to be correct
        prediction: Label representing model predictions
        include_subclasses (bool): Whether or not to include subclasses in the iou calculation.
            If set to True, the iou between two overlapping objects of the same type is 0 if the subclasses are not the same.
    Returns:
        Returns a list of ScalarMetrics. Will be empty if there were no predictions and labels. Otherwise a single metric will be returned.
    """
    iou = miou(ground_truths, predictions, include_subclasses)
    # If both gt and preds are empty there is no metric
    if iou is None:
        return []
    return [ScalarMetric(metric_name="custom_iou", value=iou)]