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 __init__(
      self,
      params,
      lr=1e-3,
      betas=(0.95, 0.98),
      eps=1e-8,
      weight_decay=0,
      grad_averaging=False,
      amsgrad=False,
      luc=False,
      luc_trust=1e-3,
      luc_eps=1e-8,
  ):
      _check_valid_opt_params(lr, eps, betas)
      defaults = dict(
          lr=lr,
          betas=betas,
          eps=eps,
          weight_decay=weight_decay,
          grad_averaging=grad_averaging,
          amsgrad=amsgrad,
      )
      self.luc = luc
      self.luc_trust = luc_trust
      self.luc_eps = luc_eps
      super(Novograd, self).__init__(params, defaults)
  
file_path: NeMo/nemo/core/optim/novograd.py
imports: import torch
  from torch.optim.optimizer import Optimizer
label_desc: Use image node_id 1 to create a new Novograd object from inherited base classes: Optimizer with example: obj = Novograd(params, lr, betas, eps, weight_decay, grad_averaging, amsgrad, luc, luc_trust, luc_eps)