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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 mahalanobis(x, mean, cov):
 """ Computes the Mahalanobis distance between the state vector x from the Gaussian `mean` with covariance `cov`. This can be thought as the number of standard deviations x is from the mean, i.e. a return value of 3 means x is 3 std from mean. Parameters x : (N,) array_like, or float Input state vector mean : (N,) array_like, or float mean of multivariate Gaussian cov : (N, N) array_like or float covariance of the multivariate Gaussian Returns ------- mahalanobis : double The Mahalanobis distance between vectors `x` and `mean` Examples -------- 0.125 3.0 0.42533327058913922 """


    x = _validate_vector(x)
    mean = _validate_vector(mean)

    if x.shape != mean.shape:
        raise ValueError("length of input vectors must be the same")

    y = x - mean
    S = np.atleast_2d(cov)

    dist = float(np.dot(np.dot(y.T, inv(S)), y))
    return math.sqrt(dist)