docstr: Performs a single optimization step.
  
  Arguments:
      closure: A closure that reevaluates the model and returns the loss.
docstr_tok: ["Performs","a","single","optimization","step",".","Arguments",":","closure",":","A","closure","that","reevaluates","the","model","and","returns","the","loss","."]
code: def step(self, closure: OptLossClosure = None) -> OptFloat:
      """Performs a single optimization step.
  
      Arguments:
          closure: A closure that reevaluates the model and returns the loss.
      """
      loss = None
      if closure is not None:
          loss = closure()
  
      for group in self.param_groups:
          lr, nu, momentum = group["lr"], group["nu"], group["momentum"]
          weight_decay, weight_decay_type = (
              group["weight_decay"],
              group["weight_decay_type"],
          )
  
          for p in group["params"]:
              if p.grad is None:
                  continue
              d_p = p.grad.data
              param_state = self.state[p]
  
              if weight_decay != 0:
                  if weight_decay_type == self.GRAD:
                      d_p.add_(p.data, alpha=weight_decay)
                  else:
                      p.data.mul_(1.0 - lr * weight_decay)
  
              if len(param_state) == 0:
                  param_state["momentum_buffer"] = torch.zeros_like(
                      p.data, memory_format=torch.preserve_format
                  )
  
              momentum_buffer = param_state["momentum_buffer"]
              momentum_buffer.mul_(momentum).add_(
                  d_p, alpha=1.0 - momentum
              )
  
              p.data.add_(momentum_buffer, alpha=-lr * nu)
              p.data.add_(d_p, alpha=-lr * (1.0 - nu))
  
      return loss
  
imports: import torch
  from torch.optim.optimizer import Optimizer
  from .types import OptFloat, OptLossClosure, Params
label_desc: Use image node_id 2 for calling the QHM obj's underlying member method code with example usage: obj.step(closure) and returns: loss