docstr: Perform a single optimization step.
  
  Args:
      closure (Callable, optional): A closure that reevaluates the model
          and returns the loss.
docstr_tok: ["Perform","a","single","optimization","step",".","Args",":","closure","(","Callable",",","optional",")",":","A","closure","that","reevaluates","the","model","and","returns","the","loss","."]
code: def step(self, closure=None):
      """Perform 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 = []
          acc_deltas = []
          (
              lr,
              rho,
              eps,
              weight_decay,
              foreach,
              maximize,
              differentiable,
          ) = (
              group["lr"],
              group["rho"],
              group["eps"],
              group["weight_decay"],
              group["foreach"],
              group["maximize"],
              group["differentiable"],
          )
  
          has_complex = self._init_group(
              group, params_with_grad, grads, square_avgs, acc_deltas
          )
  
          adadelta(
              params_with_grad,
              grads,
              square_avgs,
              acc_deltas,
              lr=lr,
              rho=rho,
              eps=eps,
              weight_decay=weight_decay,
              foreach=foreach,
              maximize=maximize,
              differentiable=differentiable,
              has_complex=has_complex,
          )
  
      return loss
  
file_path: pytorch/torch/optim/adadelta.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 Adadelta obj's underlying member method code with example usage: obj.step(closure) and returns: loss