class_docstr: Modified from Fairseq and Use LightSeq adam kernel.
  
  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-8)
      weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
      amsgrad (boolean, optional): whether to use the AMSGrad variant of this
          algorithm from the paper `On the Convergence of Adam and Beyond`_
          (default: False) NOT SUPPORTED in LSAdam!
      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)
  .. _Adam: A Method for Stochastic Optimization:
      https://arxiv.org/abs/1412.6980
  .. _On the Convergence of Adam and Beyond:
      https://openreview.net/forum?id=ryQu7f-RZ
code: def supports_flat_params(self):
      return True
  
file_path: lightseq/lightseq/training/ops/pytorch/adam.py
imports: import torch
  from lightseq.training.ops.pytorch.builder import AdamBuilder
label_desc: Use image node_id 3 for calling the LSAdam obj's underlying member method code with example usage: obj.supports_flat_params() and returns: True