class_docstr: torch.optim.RMSprop with eps inside sqrt.
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:
          loss = closure()
  
      for group in self.param_groups:
          for p in group["params"]:
              if p.grad is None:
                  continue
              grad = p.grad.data
              if grad.is_sparse:
                  raise RuntimeError(
                      "RMSprop does not support sparse gradients"
                  )
              state = self.state[p]
  
              # State initialization
              if len(state) == 0:
                  state["step"] = 0
                  state["square_avg"] = torch.zeros_like(p.data)
                  if group["momentum"] > 0:
                      state["momentum_buffer"] = torch.zeros_like(
                          p.data
                      )
                  if group["centered"]:
                      state["grad_avg"] = torch.zeros_like(p.data)
  
              square_avg = state["square_avg"]
              alpha = group["alpha"]
  
              state["step"] += 1
  
              if group["weight_decay"] != 0:
                  grad = grad.add(group["weight_decay"], p.data)
  
              square_avg.mul_(alpha).addcmul_(1 - alpha, grad, grad)
  
              if group["centered"]:
                  grad_avg = state["grad_avg"]
                  grad_avg.mul_(alpha).add_(1 - alpha, grad)
                  avg = (
                      square_avg.addcmul(-1, grad_avg, grad_avg)
                      .add_(group["eps"])
                      .sqrt_()
                  )
              else:
                  avg = square_avg.add(group["eps"]).sqrt_()
  
              if group["momentum"] > 0:
                  buf = state["momentum_buffer"]
                  buf.mul_(group["momentum"]).addcdiv_(grad, avg)
                  p.data.add_(-group["lr"], buf)
              else:
                  p.data.addcdiv_(-group["lr"], grad, avg)
  
      return loss
  
imports: import torch
label_desc: Use image node_id 1 for calling the RMSpropEpsInsideSqrt obj's underlying member method code with example usage: obj.step(closure) and returns: loss