class_docstr: Implements Lamb algorithm.
  THIS WAS DIRECTLY COPIED OVER FROM pytorch/contrib:
  https://github.com/cybertronai/pytorch-lamb
  It has been proposed in `Large Batch Optimization for Deep Learning: Training BERT in 76 minutes`.
  https://arxiv.org/abs/1904.00962
  
  Has the option for minimum trust LAMB as described in "Single Headed
  Attention RNN: Stop Thinking With Your Head" section 6.3
  https://arxiv.org/abs/1911.11423
code: def __init__(
      self,
      params,
      lr=1e-3,
      betas=(0.9, 0.999),
      eps=1e-6,
      weight_decay=0,
      min_trust=None,
  ):
      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])
          )
      PT_Optimizer.__init__(
          self,
          params,
          {
              "lr": lr,
              "betas": betas,
              "eps": eps,
              "weight_decay": weight_decay,
          },
      )
  
      self.min_trust = min_trust
  
file_path: pytext/pytext/optimizer/lamb.py
imports: from typing import Optional
  import torch
  from pytext.optimizer.optimizers import Optimizer
  from torch.optim import Optimizer
label_desc: Use image node_id 2 to create a new Lamb object from inherited base classes: Optimizer, PT_Optimizer with example: obj = Lamb(params, lr, betas, eps, weight_decay, min_trust)