class_docstr: Implements Novograd optimization algorithm.
  
  It has been proposed in `Stochastic Gradient Methods with Layer-wise
  Adaptive Moments for Training of Deep Networks`__.
  
  Arguments:
      params: iterable of parameters to optimize or dicts defining
          parameter groups
      lr: learning rate (default: 1e-3)
      betas: coefficients used for computing
          running averages of gradient and its square (default: (0.95, 0))
      eps: term added to the denominator to improve
          numerical stability (default: 1e-8)
      weight_decay: weight decay (L2 penalty) (default: 0)
      grad_averaging: gradient averaging (default: False)
      amsgrad: whether to use the AMSGrad variant of this
          algorithm from the paper `On the Convergence of Adam and Beyond`
          (default: False)
  
  Example:
      >>> import torch_optimizer as optim
      >>> optimizer = optim.Yogi(model.parameters(), lr=0.1)
      >>> optimizer.zero_grad()
      >>> loss_fn(model(input), target).backward()
      >>> scheduler = StepLR(optimizer, step_size=1, gamma=0.7)
      >>> optimizer.step()
      >>> scheduler.step()
  
  __ https://arxiv.org/abs/1905.11286
  
  Note:
      Reference code: https://github.com/NVIDIA/DeepLearningExamples
code: def __setstate__(self, state: dict) -> None:
      super(NovoGrad, self).__setstate__(state)
      for group in self.param_groups:
          group.setdefault("amsgrad", False)
  
imports: import torch
  from torch.optim.optimizer import Optimizer
  from .types import Betas2, OptFloat, OptLossClosure, Params
label_desc: Use image node_id 2 for calling the NovoGrad obj's underlying member method code with example usage: obj.__setstate__(state) without return types