docstr: Performs a single optimization step.
  
  Args:
      closure (callable, optional): A closure that reevaluates the model
          and returns the loss.
docstr_tok: ["Performs","a","single","optimization","step",".","Args",":","closure","(","callable",",","optional",")",":","A","closure","that","reevaluates","the","model","and","returns","the","loss","."]
code: def step(self, closure=None):
      """Performs a single optimization step.
  
      Args:
          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()
  
      for group in self.param_groups:
          params_with_grad = []
          d_p_list = []
          momentum_buffer_list = []
          weight_decay = group["weight_decay"]
          momentum = group["momentum"]
          dampening = group["dampening"]
          nesterov = group["nesterov"]
          max_unorm = group["max_unorm"]
          lr = group["lr"]
  
          for p in group["params"]:
              if p.grad is None:
                  continue
  
              state = self.state[p]
              d_p = p.grad
              if weight_decay != 0:
                  d_p = d_p.add(p, alpha=weight_decay)
  
              if momentum != 0:
                  buf = state.get("momentum_buffer", None)
  
                  if buf is None:
                      buf = torch.clone(d_p).detach()
                      state["momentum_buffer"] = buf
                  else:
                      buf.mul_(momentum).add_(d_p, alpha=1 - dampening)
  
                  if nesterov:
                      update = d_p + buf * momentum
                  else:
                      update = buf
  
              update_scale = 1.0
              if max_unorm > 0.0:
                  assert p.dtype == torch.float32
                  pnorm = torch.norm(p.detach())
                  unorm = torch.norm(update)
                  if unorm > max_unorm * pnorm:
                      update_scale = max_unorm * pnorm / unorm
  
              p.add_(update, alpha=-lr * update_scale)
  
      return loss
  
file_path: bitsandbytes/bitsandbytes/optim/lars.py
imports: import torch
  from torch.optim import Optimizer
  from bitsandbytes.optim.optimizer import Optimizer1State
label_desc: Use image node_id 3 for calling the PytorchLARS obj's underlying member method code with example usage: obj.step(closure) and returns: loss