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 __init__(
      self,
      params,
      lr=1e-3,
      betas=(0.95, 0.98),
      eps=1e-8,
      weight_decay=0,
      grad_averaging=False,
      amsgrad=False,
  ):
      if not 0.0 <= lr:
          raise ValueError("Invalid learning rate: {}".format(lr))
      if not 0.0 <= eps:
          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])
          )
      defaults = dict(
          lr=lr,
          betas=betas,
          eps=eps,
          weight_decay=weight_decay,
          grad_averaging=grad_averaging,
          amsgrad=amsgrad,
      )
  
      super(NvNovoGrad, self).__init__(params, defaults)
  
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 1 to create a new NvNovoGrad object from inherited base classes: Optimizer with example: obj = NvNovoGrad(params, lr, betas, eps, weight_decay, grad_averaging, amsgrad)