src/run.py [203:211]:
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    if 'optimizer' in load_data:
        optimizer.load_state_dict(load_data['optimizer'])
        for state in optimizer.state.values():
            for k, v in state.items():
                if torch.is_tensor(v):
                    state[k] = v.to(opts.device)

    # Initialize learning rate scheduler, decay by lr_decay once per epoch!
    lr_scheduler = optim.lr_scheduler.LambdaLR(optimizer, lambda epoch: opts.lr_decay ** epoch)
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src/run.py [352:360]:
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    if 'optimizer' in load_data:
        optimizer.load_state_dict(load_data['optimizer'])
        for state in optimizer.state.values():
            for k, v in state.items():
                if torch.is_tensor(v):
                    state[k] = v.to(opts.device)

    # Initialize learning rate scheduler, decay by lr_decay once per epoch!
    lr_scheduler = optim.lr_scheduler.LambdaLR(optimizer, lambda epoch: opts.lr_decay ** epoch)
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