docstr: Performs a single optimization step.
  
  Arguments:
      closure (callable, optional): A closure that reevaluates the model
          and returns the loss.
docstr_tok: ["Performs","a","single","optimization","step",".","Arguments",":","closure","(","callable",",","optional",")",":","A","closure","that","reevaluates","the","model","and","returns","the","loss","."]
code: def step(self, closure=None):
      """Performs a single optimization step.
  
      Arguments:
          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 = group["params"]
          n = len(params)
          tau = group["tau"]
          for target_param, source_param in zip(
              params[: n // 2], params[n // 2 :]
          ):
              if target_param is source_param:
                  # skip soft-updating when the target network share s the parameter with
                  # the network being train.
                  continue
              new_param = (
                  tau * source_param.data
                  + (1.0 - tau) * target_param.data
              )
              target_param.data.copy_(new_param)
      return loss
  
imports: import torch
label_desc: Use image node_id 3 for calling the SoftUpdate obj's underlying member method code with example usage: obj.step(closure) and returns: loss