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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 parameters_changed(self):
 """Update the gradients of parameters for warping function This method is called when having new values of parameters for warping function, kernels and other parameters in a normal GP """

        # using the warped X to update
        self.X = self.transform_data(self.X_untransformed)
        super(InputWarpedGP, self).parameters_changed()
        # the gradient of log likelihood w.r.t. input AFTER warping is a product of dL_dK and dK_dX
        dL_dX = self.kern.gradients_X(self.grad_dict['dL_dK'], self.X)
        self.warping_function.update_grads(self.X_untransformed, dL_dX)