class_docstr: Implements AdamW algorithm.
  
  The original Adam algorithm was proposed in `Adam: A Method for Stochastic Optimization`_.
  The AdamW variant was proposed in `Decoupled Weight Decay Regularization`_.
  
  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 coefficient (default: 1e-2)
      amsgrad (boolean, optional): whether to use the AMSGrad variant of this
          algorithm from the paper `On the Convergence of Adam and Beyond`_
          (default: False)
  
  .. _Adam\: A Method for Stochastic Optimization:
      https://arxiv.org/abs/1412.6980
  .. _Decoupled Weight Decay Regularization:
      https://arxiv.org/abs/1711.05101
  .. _On the Convergence of Adam and Beyond:
      https://openreview.net/forum?id=ryQu7f-RZ
code: def __setstate__(self, state):
      super(AdamW, self).__setstate__(state)
      for group in self.param_groups:
          group.setdefault("amsgrad", False)
  
file_path: pytorch-image-models/timm/optim/adamw.py
imports: import math
  import torch
  from torch.optim.optimizer import Optimizer
label_desc: Use image node_id 2 for calling the AdamW obj's underlying member method code with example usage: obj.__setstate__(state) without return types