Target optimizer not set properly when loading from state dict

[stefan-baumann]

When loading the GradualWarmupScheduler from a state dict to resume a training, the optimizer attribute of the nested after_scheduler is loaded from the state_dict. This causes a static learning rate after resuming a training, as the after_scheduler tries to update the learning rate of an optimizer that doesn't match the one used by the resumed training. Setting self.after_scheduler.optimizer = self.optimizer as a part of the load_state_dict() method should probably suffice to fix this.