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: 0.01)
  
  .. _Large Batch Optimization for Deep Learning: Training BERT in 76 minutes:
      https://arxiv.org/abs/1904.00962
code: def __init__(
      self,
      params,
      lr=1e-3,
      betas=(0.9, 0.999),
      eps=1e-6,
      weight_decay=0.01,
  ):
      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
      )
      super(RefLAMB, self).__init__(params, defaults)
      if multi_tensor_applier.available:
          import amp_C
  
          self.multi_tensor_l2norm = amp_C.multi_tensor_l2norm
          # Skip buffer
          self._dummy_overflow_buf = torch.tensor(
              [0],
              dtype=torch.int,
              device=self.param_groups[0]["params"][0].device,
          )
          self.multi_tensor_lamb = amp_C.multi_tensor_lamb
      else:
          raise RuntimeError(
              "apex.optimizers.FusedLAMB requires cuda extensions"
          )
  
imports: import unittest
  import os
  import torch
  from torch.optim import Optimizer
  import apex
  from apex.multi_tensor_apply import multi_tensor_applier
  from itertools import product
label_desc: Use image node_id 1 to create a new RefLAMB object from inherited base classes: Optimizer with example: obj = RefLAMB(params, lr, betas, eps, weight_decay)