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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_chol(X, rho):
 """Transform C uncorrelated random variables into correlated data X : ndarray C univariate correlated random variables with N observations as <N x C> matrix rho : ndarray Correlation Matrix (Pearson method) with coefficients between [-1, +1] """

    import numpy as np
    return np.dot(X, np.linalg.cholesky(rho).T)