class_docstr: Implements the LAMB algorithm. Currently GPU-only.
  
  LAMB was proposed in `Large Batch Optimization for Deep Learning: Training BERT in 76 minutes.
  https://arxiv.org/abs/1904.00962
  
  Arguments:
      params (iterable): iterable of parameters to optimize or dicts defining
          parameter groups.
      lr (float, optional): learning rate. (default: 1e-3)
      bias_correction (bool, optional): bias correction (default: True)
      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-8)
      eps_inside_sqrt (boolean, optional): in the 'update parameters' step,
          adds eps to the bias-corrected second moment estimate before
          evaluating square root instead of adding it to the square root of
          second moment estimate as in the original paper. (default: False)
      weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
      max_grad_norm (float, optional): value used to clip global grad norm
          (default: 0.0)
      max_coeff(float, optional): maximum value of the lamb coefficient (default: 10.0)
      min_coeff(float, optional): minimum value of the lamb coefficient (default: 0.01)
      amsgrad (boolean, optional): NOT SUPPORTED in FusedLamb!
code: def get_lamb_coeffs(self):
      lamb_coeffs = [
          lamb_coeff.item() for lamb_coeff in self.lamb_coeffs
      ]
      return lamb_coeffs
  
file_path: DeepSpeed/deepspeed/ops/lamb/fused_lamb.py
imports: import types
  import torch
  from deepspeed.ops.op_builder import FusedLambBuilder
label_desc: Use image node_id 3 for calling the FusedLamb obj's underlying member method code with example usage: obj.get_lamb_coeffs() and returns: lamb_coeffs