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 = []
          grads = []
          square_avgs = []
          grad_avgs = []
          momentum_buffer_list = []
  
          has_complex = self._init_group(
              group,
              params_with_grad,
              grads,
              square_avgs,
              momentum_buffer_list,
              grad_avgs,
          )
  
          rmsprop(
              params_with_grad,
              grads,
              square_avgs,
              grad_avgs,
              momentum_buffer_list,
              lr=group["lr"],
              alpha=group["alpha"],
              eps=group["eps"],
              weight_decay=group["weight_decay"],
              momentum=group["momentum"],
              centered=group["centered"],
              foreach=group["foreach"],
              maximize=group["maximize"],
              differentiable=group["differentiable"],
              has_complex=has_complex,
          )
  
      return loss
  
file_path: pytorch/torch/optim/rmsprop.py
imports: import torch
  from torch import Tensor
  from .optimizer import Optimizer, _default_to_fused_or_foreach, _use_grad_for_differentiable, _differentiable_doc, _foreach_doc, _maximize_doc, _view_as_real
  from typing import List, Optional
label_desc: Use image node_id 4 for calling the RMSprop obj's underlying member method code with example usage: obj.step(closure) and returns: loss