class_docstr: Implements AdaBound algorithm with Decoupled Weight Decay (arxiv.org/abs/1711.05101)
  It has been proposed in `Adaptive Gradient Methods with Dynamic Bound of Learning Rate`_.
  Arguments:
      params (iterable): iterable of parameters to optimize or dicts defining
          parameter groups
      lr (float, optional): Adam 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))
      final_lr (float, optional): final (SGD) learning rate (default: 0.1)
      gamma (float, optional): convergence speed of the bound functions (default: 1e-3)
      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)
      amsbound (boolean, optional): whether to use the AMSBound variant of this algorithm
  .. Adaptive Gradient Methods with Dynamic Bound of Learning Rate:
      https://openreview.net/forum?id=Bkg3g2R9FX
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code: def __setstate__(self, state):
      super(AdaBoundW, self).__setstate__(state)
      for group in self.param_groups:
          group.setdefault("amsbound", False)
  
file_path: AdaBound/adabound/adabound.py
imports: import math
  import torch
  from torch.optim import Optimizer
label_desc: Use image node_id 2 for calling the AdaBoundW obj's underlying member method code with example usage: obj.__setstate__(state) without return types