0, T_mult)
            >>> iters = len(dataloader)
            >>> for epoch in range(20):
            >>>     for i, sample in enumerate(dataloader):
            >>>         inputs, labels = sample['inputs'], sample['labels']
            >>>         optimizer.zero_grad()
            >>>         outputs = net(inputs)
            >>>         loss = criterion(outputs, labels)
            >>>         loss.backward()
            >>>         optimizer.step()
            >>>         scheduler.step(epoch + i / iters)

        This function can be called in an interleaved way.

        Example:
            >>> # xdoctest: +SKIP("Undefined vars")
            >>> scheduler = CosineAnnealingWarmRestarts(optimizer, T_0, T_mult)
            >>> for epoch in range(20):
            >>>     scheduler.step()
            >>> scheduler.step(26)
            >>> scheduler.step() # scheduler.step(27), instead of scheduler(20)
        Nr