class_docstr: Implements Nadam algorithm (a variant of Adam based on Nesterov momentum).
  
  It has been proposed in `Incorporating Nesterov Momentum into Adam`__.
  
  Arguments:
      params (iterable): iterable of parameters to optimize or dicts defining
          parameter groups
      lr (float, optional): learning rate (default: 2e-3)
      betas (Tuple[float, float], optional): coefficients used for computing
          running averages of gradient and its square
      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)
      schedule_decay (float, optional): momentum schedule decay (default: 4e-3)
  
  __ http://cs229.stanford.edu/proj2015/054_report.pdf
  __ http://www.cs.toronto.edu/~fritz/absps/momentum.pdf
  
      Originally taken from: https://github.com/pytorch/pytorch/pull/1408
      NOTE: Has potential issues but does work well on some problems.
code: def __init__(
      self,
      params,
      lr=2e-3,
      betas=(0.9, 0.999),
      eps=1e-8,
      weight_decay=0,
      schedule_decay=4e-3,
  ):
      if not 0.0 <= lr:
          raise ValueError("Invalid learning rate: {}".format(lr))
      defaults = dict(
          lr=lr,
          betas=betas,
          eps=eps,
          weight_decay=weight_decay,
          schedule_decay=schedule_decay,
      )
      super(Nadam, self).__init__(params, defaults)
  
imports: import math
  import torch
  from torch.optim.optimizer import Optimizer
label_desc: Use image node_id 1 to create a new Nadam object from inherited base classes: Optimizer with example: obj = Nadam(params, lr, betas, eps, weight_decay, schedule_decay)