def METHOD_NAME(self, iters, student):
    if isinstance(student, DDP):
        student = student.module.state_dict()
    else:
        student = student.state_dict()
    with torch.no_grad():
        momentum = ema_scheduler(iters, self.ema_momentum)
        for name, param in self.model.state_dict().items():
            if param.dtype.is_floating_point:
                param *= momentum
                param += (1.0 - momentum) * student[name].detach()