def step(self, closure=None):
        """Perform a single optimization step.

        Arguments:
            closure (`Callable`, *optional*, defaults to `None`):
                A closure that reevaluates the model and returns the loss.
        """
        loss = None
        if closure is not None:
            with torch.enable_grad():
                loss = closure()

        overflows = []

        if not self.initialized:
            self.check_overrides()
            self.to_gpu()  # needed for fairseq pure fp16 training
            self.initialized = True

        # if self.is_paged: self.page_mng.prefetch_all()
        for gindex, group in enumerate(self.param_groups):
            for pindex, p in enumerate(group["params"]):
                if p.grad is None:
                    continue
                state = self.state[p]
                if len(state) == 0:
                    self.init_state(group, p, gindex, pindex)

                self.prefetch_state(p)
                self.update_step(group, p, gindex, pindex)
                torch.cuda.synchronize()
        if self.is_paged:
            # all paged operation are asynchronous, we need
            # to sync to make sure all tensors are in the right state
            torch.cuda.synchronize()

        return loss
Perform a single optimization step.

        Arguments:
            closure (`Callable`, *optional*, defaults to `None`):
                A closure that reevaluates the model and returns the loss.
        
bitsandbytes/optim/optimizer.py