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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 update(self, max_norm=None):
 """Updates parameters according to the installed optimizer and the gradients computed in the previous forward-backward batch. Gradients are clipped by their global norm if `max_norm` is set. Parameters max_norm: float, optional If set, clip values of all gradients the ratio of the sum of their norms. """

        if max_norm is not None:
            self._clip_by_global_norm(max_norm)
        self._module.update()