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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def spearman_correlation(X, rowvar=False):
 """ Computes the spearman correlation estimate. This is effectively a bias corrected pearson correlation between rank transformed columns of X. Parameters X: array-like, shape = [n_samples, n_features] Data matrix using which we compute the empirical correlation Returns ------- rank_correlation References Xue, Lingzhou; Zou, Hui. "Regularized rank-based estimation of high-dimensional nonparanormal graphical models." Ann. Statist. 40 (2012), no. 5, 2541--2571. doi:10.1214/12-AOS1041. Liu, Han, Fang; Yuan, Ming; Lafferty, John; Wasserman, Larry. "High-dimensional semiparametric Gaussian copula graphical models." Ann. Statist. 40.4 (2012):
 2293-2326. doi:10.1214/12-AOS1037 """


    Xrank = _compute_ranks(X)
    rank_correlation = np.corrcoef(Xrank, rowvar=rowvar)

    return 2 * np.sin(rank_correlation * np.pi / 6)