def METHOD_NAME(X: np.ndarray, k: int = 7, p_threshold: float = 0.05, mask: Optional[np.ndarray] = None) -> np.ndarray:
    """Compute scores using Moran's I method.
    Args:
        X: Numpy array containing (possibly smoothed) UMI counts or binarized
            values.
        k: Kernel size
        p_threshold: P-value threshold. Test. Test Test
        mask: If provided, only consider pixels within the mask
    Returns:
        A 2D Numpy array indicating pixel scores
    """
    # Create Gaussian kernel
    kx = cv2.getGaussianKernel(k, 0)
    ky = cv2.getGaussianKernel(k, 0)
    kernel = (ky * kx.T) * utils.circle(k)
    kernel[(k - 1) // 2, (k - 1) // 2] = 0
    z, c, i, pvalue = moranI(X, kernel, mask=mask)
    # Set pixels whose p values are < p_threshold to zero, which indicate
    # no spatial correlation.
    c[pvalue >= p_threshold] = 0
    return c