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 = []
  
          has_sparse_grad = self._init_group(
              group, params_with_grad, d_p_list, momentum_buffer_list
          )
  
          sgdw(
              params_with_grad,
              d_p_list,
              momentum_buffer_list,
              weight_decay=group["weight_decay"],
              momentum=group["momentum"],
              lr=group["lr"],
              dampening=group["dampening"],
              nesterov=group["nesterov"],
              maximize=group["maximize"],
              has_sparse_grad=has_sparse_grad,
              foreach=group["foreach"],
          )
  
          # update momentum_buffers in state
          for p, momentum_buffer in zip(
              params_with_grad, momentum_buffer_list
          ):
              state = self.state[p]
              state["momentum_buffer"] = momentum_buffer
  
      return loss
  
file_path: pytorch-image-models/timm/optim/sgdw.py
imports: from functools import update_wrapper, wraps
  import torch
  from torch import Tensor
  from torch.optim.optimizer import Optimizer
  from typing import List, Optional
label_desc: Use image node_id 4 for calling the SGDW obj's underlying member method code with example usage: obj.step(closure) and returns: loss