class_docstr: Implements Lamb algorithm.
  
  It has been proposed in `Large Batch Optimization for Deep Learning:
  Training BERT in 76 minutes`__.
  
  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: (0.9, 0.999))
      eps: term added to the denominator to improve
          numerical stability (default: 1e-8)
      weight_decay: weight decay (L2 penalty) (default: 0)
      clamp_value: clamp weight_norm in (0,clamp_value) (default: 10)
          set to a high value to avoid it (e.g 10e3)
      adam: always use trust ratio = 1, which turns this
          into Adam. Useful for comparison purposes. (default: False)
      debias: debias adam by (1 - beta**step) (default: False)
  
  Example:
      >>> import torch_optimizer as optim
      >>> optimizer = optim.Lamb(model.parameters(), lr=0.1)
      >>> optimizer.zero_grad()
      >>> loss_fn(model(input), target).backward()
      >>> optimizer.step()
  
  __ https://arxiv.org/abs/1904.00962
  
  Note:
      Reference code: https://github.com/cybertronai/pytorch-lamb
code: def __init__(
      self,
      params: Params,
      lr: float = 1e-3,
      betas: Betas2 = (0.9, 0.999),
      eps: float = 1e-6,
      weight_decay: float = 0,
      clamp_value: float = 10,
      adam: bool = False,
      debias: bool = False,
  ) -> None:
      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)
          )
      if clamp_value < 0.0:
          raise ValueError(
              "Invalid clamp value: {}".format(clamp_value)
          )
  
      defaults = dict(
          lr=lr, betas=betas, eps=eps, weight_decay=weight_decay
      )
      self.clamp_value = clamp_value
      self.adam = adam
      self.debias = debias
  
      super(Lamb, self).__init__(params, defaults)
  
imports: import math
  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 Lamb object from inherited base classes: Optimizer with example: obj = Lamb(params, lr, betas, eps, weight_decay, clamp_value, adam, debias)