docstr: Performs a single optimization step.
  
  Arguments:
      closure (callable, optional): A closure that reevaluates the model
          and returns the loss.
code: def step(self, closure=None):
      """Performs a single optimization step.
  
      Arguments:
          closure (callable, optional): 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
  
imports: from collections import abc
  from collections import defaultdict
  from copy import deepcopy
  from itertools import chain
  import torch
  import bitsandbytes.functional
label_desc: Use image node_id 7 for calling the Optimizer8bit obj's underlying member method code with example usage: obj.step(closure) and returns: loss