docstr: Performs a single optimization step.
  Arguments:
      closure (:obj:`Callable`, `optional`): A closure that reevaluates the model
      and returns the loss.
  
  modified from
  https://github.com/huggingface/transformers/blob/d2f9cb838ec1ed7f62ddfb850dccd223e19441ad/src/transformers/optimization.py#L259-L318  # NoQA
docstr_tok: ["Performs","a","single","optimization","step",".","Arguments",":","closure","(",":","obj",":","`","Callable","`",",","`","optional","`",")",":","A","closure","that","reevaluates","the","model","and","returns","the","loss",".","modified","from","https",":","\/\/github.com\/huggingface\/transformers\/blob\/d2f9cb838ec1ed7f62ddfb850dccd223e19441ad\/src\/transformers\/optimization.py","#","L259-L318","#","NoQA"]
code: def step(self, closure: Callable = None):
      """
      Performs a single optimization step.
      Arguments:
          closure (:obj:`Callable`, `optional`): A closure that reevaluates the model
          and returns the loss.
  
      modified from
      https://github.com/huggingface/transformers/blob/d2f9cb838ec1ed7f62ddfb850dccd223e19441ad/src/transformers/optimization.py#L259-L318  # NoQA
      """
      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
              if p.grad.abs().sum().item() == 0:
                  continue
              grad = p.grad.data
              if grad.is_sparse:
                  raise RuntimeError(
                      "Adam does not support sparse gradients, please consider "
                      "SparseAdam instead"
                  )
  
              state = self.state[p]
  
              # State initialization
              if len(state) == 0:
                  state["step"] = 0
                  # Exponential moving average of gradient values
                  state["exp_avg"] = torch.zeros_like(p.data)
                  # Exponential moving average of squared gradient values
                  state["exp_avg_sq"] = torch.zeros_like(p.data)
  
              exp_avg, exp_avg_sq = (
                  state["exp_avg"],
                  state["exp_avg_sq"],
              )
              beta1, beta2 = group["betas"]
  
              state["step"] += 1
  
              # Decay the first and second moment running average coefficient
              # In-place operations to update the averages at the same time
              exp_avg.mul_(beta1).add_(grad, alpha=1.0 - beta1)
              exp_avg_sq.mul_(beta2).addcmul_(
                  grad, grad, value=1.0 - beta2
              )
              denom = exp_avg_sq.sqrt().add_(group["eps"])
  
              step_size = group["lr"]
              if group["correct_bias"]:  # No bias correction for Bert
                  bias_correction1 = 1.0 - beta1 ** state["step"]
                  bias_correction2 = 1.0 - beta2 ** state["step"]
                  step_size = (
                      step_size
                      * math.sqrt(bias_correction2)
                      / bias_correction1
                  )
  
              p.data.addcdiv_(exp_avg, denom, value=-step_size)
  
              # Just adding the square of the weights to the loss function is *not*
              # the correct way of using L2 regularization/weight decay with Adam,
              # since that will interact with the m and v parameters in strange ways.
              #
              # Instead we want to decay the weights in a manner that doesn't interact
              # with the m/v parameters. This is equivalent to adding the square
              # of the weights to the loss with plain (non-momentum) SGD.
              # Add weight decay at the end (fixed version)
              if group["weight_decay"] > 0.0:
                  p.data.add_(
                      p.data, alpha=-group["lr"] * group["weight_decay"]
                  )
  
      return loss
  
imports: import math
  from typing import Callable
  import torch
  from mmf.common.registry import registry
label_desc: Use image node_id 1 for calling the AdamWSkipParamsWithZeroGrad obj's underlying member method code with example usage: obj.step(closure) and returns: loss