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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 on_backward_end(self, **kwargs):
        "accumulated step and reset samples, True will result in no stepping"
        if (self.acc_batches % self.n_step) == 0:
            for p in (self.learn.model.parameters()):
                if p.requires_grad: p.grad.div_(self.acc_samples)
            self.acc_samples = 0
        else: return {'skip_step':True, 'skip_zero':True}