How to use the fancyimpute.normalized_distance.all_pairs_normalized_distances function in fancyimpute

def knn_initialize(X, missing_mask, verbose=False):
    """
    Fill X with NaN values if necessary, construct the n_samples x n_samples
    distance matrix and set the self-distance of each row to infinity.
    """
    X_row_major = X.copy("C")
    if missing_mask.sum() != np.isnan(X_row_major).sum():
        # if the missing values have already been zero-filled need
        # to put NaN's back in the data matrix for the distances function
        X_row_major[missing_mask] = np.nan
    D = all_pairs_normalized_distances(X_row_major, verbose=verbose)
    # set diagonal of distance matrix to infinity since we don't want
    # points considering themselves as neighbors
    for i in range(X.shape[0]):
        D[i, i] = np.inf
    return X_row_major, D