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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 hook(self, name, state):
 r"""Registers a backward hook. The hook will be called every time a gradient with respect to the Tensor is computed. The hook should have the following signature:: hook (grad) -> Tensor or None The hook should not modify its argument, but it can optionally return a new gradient which will be used in place of :attr:`grad`. This function returns a handle with a method ``handle.remove()`` that removes the hook from the module. Example: 2 4 6 [torch.FloatTensor of size (3,)] """

        if name in self.hooks:
            self.hooks[name](state)