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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 _to_cov(x, n):
 """ If x is a scalar, returns a covariance matrix generated from it as the identity matrix multiplied by x. The dimension will be nxn. If x is already a 2D numpy array then it is returned unchanged. Raises ValueError if not positive definite """


    if np.isscalar(x):
        if x < 0:
            raise ValueError('covariance must be > 0')
        return np.eye(n) * x

    x = np.atleast_2d(x)
    try:
        # quickly find out if we are positive definite
        np.linalg.cholesky(x)
    except:
        raise ValueError('covariance must be positive definit')

    return x