class_docstr: Implements Lamb algorithm.
  It has been proposed in `Large Batch Optimization for Deep Learning: Training BERT in 76 minutes`_.
  
  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-6)
      weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
      adam (bool, optional): always use trust ratio = 1, which turns this into
          Adam. Useful for comparison purposes.
  
  .. _Large Batch Optimization for Deep Learning\: Training BERT in 76 minutes:
      https://arxiv.org/abs/1904.00962
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code: def __init__(
      self,
      params,
      lr=1e-3,
      betas=(0.9, 0.999),
      eps=1e-6,
      weight_decay=0,
      adam=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
      )
      self.adam = adam
      super(Lamb, self).__init__(params, defaults)
  
imports: import torch
  from torch.optim import Optimizer
label_desc: Use image node_id 1 to create a new Lamb object from inherited base classes: Optimizer with example: obj = Lamb(params, lr, betas, eps, weight_decay, adam)