class_docstr: Implements Novograd algorithm.
  It has been proposed  in "Stochastic Gradient Methods with Layer-wise
  Adaptive Moments for Training of Deep Networks"
  (https://arxiv.org/abs/1905.11286)
  Arguments:
      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: (0.9, 0.999))
      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)
      amsgrad (boolean, optional): whether to use the AMSGrad variant of this
          algorithm from the paper "On the Convergence of Adam and Beyond"
code: def __setstate__(self, state):
      super(Novograd, self).__setstate__(state)
      for group in self.param_groups:
          group.setdefault("amsgrad", False)
  
file_path: NeMo/nemo/core/optim/novograd.py
imports: import torch
  from torch.optim.optimizer import Optimizer
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