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.
      """
  
      if closure is not None:
          with torch.enable_grad():
              # to fix linter, we do not keep the returned loss for use atm.
              closure()
  
      for group in self.param_groups:
          beta1, beta2 = group["betas"]
          lr = group["lr"]
          weight_decay = group["weight_decay"]
          eps = group["eps"]
          use_kahan_summation = group["use_kahan_summation"]
  
          momentum_dtype = group["momentum_dtype"]
          variance_dtype = group["variance_dtype"]
          compensation_buffer_dtype = group["compensation_buffer_dtype"]
  
          for p in group["params"]:
              if p.grad is None:
                  continue
  
              if p.grad.is_sparse:
                  raise RuntimeError(
                      "AnyPrecisionAdamW does not support sparse gradients"
                  )
  
              state = self.state[p]
  
              # State initialization
              if len(state) == 0:
                  state["step"] = torch.tensor(0.0)
  
                  # momentum - EMA of gradient values
                  state["exp_avg"] = torch.zeros_like(
                      p,
                      dtype=momentum_dtype,
                  )
  
                  # variance uncentered - EMA of squared gradient values
                  state["exp_avg_sq"] = torch.zeros_like(
                      p,
                      dtype=variance_dtype,
                  )
  
                  # optional Kahan summation - accumulated error tracker
                  if use_kahan_summation:
                      state["compensation"] = torch.zeros_like(
                          p,
                          dtype=compensation_buffer_dtype,
                      )
  
              # main processing -------------------------
  
              # update the steps for each param group update
              state["step"] += 1
              step = state["step"]
  
              exp_avg = state["exp_avg"]
              exp_avg_sq = state["exp_avg_sq"]
  
              grad = p.grad
  
              # weight decay, AdamW style
              if weight_decay:
                  p.data.mul_(1 - lr * weight_decay)
  
              # update momentum
              exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)
  
              # update uncentered variance
              exp_avg_sq.mul_(beta2).addcmul_(
                  grad, grad, value=1 - beta2
              )
  
              # adjust using bias1
              bias_correction1 = 1 - beta1**step
  
              step_size = lr / bias_correction1
  
              # adjust using bias2
              denom_correction = (
                  1 - beta2**step
              ) ** 0.5  # avoids math import
  
              centered_variance = (
                  exp_avg_sq.sqrt() / denom_correction
              ).add_(eps, alpha=1)
  
              # lr update to compensation
              if use_kahan_summation:
                  compensation = state["compensation"]
  
                  compensation.addcdiv_(
                      exp_avg, centered_variance, value=-step_size
                  )
  
                  # update weights with compensation (Kahan summation)
                  # save error back to compensation for next iteration
                  temp_buffer = p.detach().clone()
                  p.data.add_(compensation)
                  compensation.add_(temp_buffer.sub_(p.data))
  
              else:
                  # usual AdamW updates
                  p.data.addcdiv_(
                      exp_avg, centered_variance, value=-step_size
                  )
  
imports: import torch
  from torch.optim.optimizer import Optimizer
label_desc: Use image node_id 2 for calling the AnyPrecisionAdamW obj's underlying member method code with example usage: obj.step(closure) without return types