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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 predict_withGradients(self, X):
 """ Returns the mean, standard deviation, mean gradient and standard deviation gradient at X. """

        if X.ndim==1: X = X[None,:]
        m, v = self.model.predict(X)
        v = np.clip(v, 1e-10, np.inf)
        dmdx, dvdx = self.model.predictive_gradients(X)
        dmdx = dmdx[:,:,0]
        dsdx = dvdx / (2*np.sqrt(v))

        return m, np.sqrt(v), dmdx, dsdx