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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 wquantiles(W, x, alphas=(0.25, 0.50, 0.75)):
 """Quantiles for weighted data. Parameters W: (N,) ndarray normalised weights (weights are >=0 and sum to one) x: (N,) or (N,d) ndarray data alphas: list-like of size k (default: (0.25, 0.50, 0.75)) probabilities (between 0. and 1.) Returns ------- a (k,) or (d, k) ndarray containing the alpha-quantiles """

    if len(x.shape) == 1:
        return _wquantiles(W, x, alphas=alphas)
    elif len(x.shape) == 2: 
        return np.array([_wquantiles(W, x[:, i], alphas=alphas) 
                         for i in range(x.shape[1])])