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 __init__(
      self,
      params: Params,
      lr: float = 1e-3,
      betas: Betas2 = (0.95, 0),
      eps: float = 1e-8,
      weight_decay: float = 0,
      grad_averaging: bool = False,
      amsgrad: bool = False,
  ):
      if lr <= 0.0:
          raise ValueError("Invalid learning rate: {}".format(lr))
      if eps < 0.0:
          raise ValueError("Invalid epsilon value: {}".format(eps))
      if not 0.0 <= betas[0] < 1.0:
          raise ValueError(
              "Invalid beta parameter at index 0: {}".format(betas[0])
          )
      if not 0.0 <= betas[1] < 1.0:
          raise ValueError(
              "Invalid beta parameter at index 1: {}".format(betas[1])
          )
      if weight_decay < 0:
          raise ValueError(
              "Invalid weight_decay value: {}".format(weight_decay)
          )
      defaults = dict(
          lr=lr,
          betas=betas,
          eps=eps,
          weight_decay=weight_decay,
          grad_averaging=grad_averaging,
          amsgrad=amsgrad,
      )
  
      super(NovoGrad, self).__init__(params, defaults)
  
imports: import torch
  from torch.optim.optimizer import Optimizer
  from .types import Betas2, OptFloat, OptLossClosure, Params
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)