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 = []
          grads = []
          prevs = []
          step_sizes = []
          etaminus, etaplus = group["etas"]
          step_size_min, step_size_max = group["step_sizes"]
          foreach = group["foreach"]
          maximize = group["maximize"]
  
          has_complex = self._init_group(
              group, params, grads, prevs, step_sizes
          )
  
          rprop(
              params,
              grads,
              prevs,
              step_sizes,
              step_size_min=step_size_min,
              step_size_max=step_size_max,
              etaminus=etaminus,
              etaplus=etaplus,
              foreach=foreach,
              maximize=maximize,
              differentiable=group["differentiable"],
              has_complex=has_complex,
          )
  
      return loss
  
file_path: pytorch/torch/optim/rprop.py
imports: import torch
  from torch import Tensor
  from .optimizer import Optimizer, _use_grad_for_differentiable, _default_to_fused_or_foreach, _differentiable_doc, _foreach_doc, _maximize_doc, _view_as_real
  from typing import List, Optional
label_desc: Use image node_id 4 for calling the Rprop obj's underlying member method code with example usage: obj.step(closure) and returns: loss