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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 rand_bivar(X, rho):
 """Transform two unrelated random variables into correlated bivariate data X : ndarray two univariate random variables with N observations as <N x 2> matrix rho : float The Pearson correlations coefficient as number between [-1, +1] """

    import numpy as np
    Y = np.empty(X.shape)
    Y[:, 0] = X[:, 0]
    Y[:, 1] = rho * X[:, 0] + np.sqrt(1.0 - rho**2) * X[:, 1]
    return Y