class_docstr: Implements Novograd algorithm.
  
  Args:
      params (iterable): iterable of parameters to optimize or dicts defining
          parameter groups
      lr (float, optional): learning rate (default: 1e-3)
      betas (Tuple[float, float], optional): coefficients used for computing
          running averages of gradient and its square (default: (0.95, 0.98))
      eps (float, optional): term added to the denominator to improve
          numerical stability (default: 1e-8)
      weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
      grad_averaging: gradient averaging
      amsgrad (boolean, optional): whether to use the AMSGrad variant of this
          algorithm from the paper `On the Convergence of Adam and Beyond`_
          (default: False)
code: def __setstate__(self, state):
      super(NvNovoGrad, self).__setstate__(state)
      for group in self.param_groups:
          group.setdefault("amsgrad", False)
  
file_path: pytorch-image-models/timm/optim/nvnovograd.py
imports: import torch
  from torch.optim.optimizer import Optimizer
  import math
label_desc: Use image node_id 2 for calling the NvNovoGrad obj's underlying member method code with example usage: obj.__setstate__(state) without return types