def METHOD_NAME(
    y_pred: npt.NDArray[np.float32], y_true: npt.NDArray[np.float32]
) -> npt.NDArray[np.float32]:
    """
    Computes the mean reciprocal rank of the predictions.
    Args:
        y_pred: Array of shape [N, D] with the predictions (N: number of elements, D: number of
            metrics).
        y_true: Array of shape [N, D] with the true metrics.
    Returns:
        Array of shape [D] with the average MRR for each metric.
    """
    minimum_indices = y_pred.argmin(0)  # [D]
    true_ranks = st.rankdata(y_true, method="min", axis=0)  # [N, D]
    ranks = np.take_along_axis(
        true_ranks, minimum_indices[None, :], axis=0
    )  # [N, D]
    result = 1 / ranks
    return result.mean(0)