class_docstr: Implements AdaBound algorithm.
  
  It has been proposed in `Adaptive Gradient Methods with Dynamic Bound of
  Learning Rate`__.
  
  Arguments:
      params: iterable of parameters to optimize or dicts defining
          parameter groups
      lr: learning rate (default: 1e-3)
      betas: coefficients used for computing running averages of gradient
          and its square (default: (0.9, 0.999))
      final_lr: final (SGD) learning rate (default: 0.1)
      gamma: convergence speed of the bound functions
          (default: 1e-3)
      eps: term added to the denominator to improve numerical stability
          (default: 1e-8)
      weight_decay: weight decay (L2 penalty) (default: 0)
      amsbound: whether to use the AMSBound variant of this algorithm
  
  Example:
      >>> import torch_optimizer as optim
      >>> optimizer = optim.AdaBound(model.parameters(), lr=0.1)
      >>> optimizer.zero_grad()
      >>> loss_fn(model(input), target).backward()
      >>> optimizer.step()
  
  __ https://arxiv.org/abs/1902.09843
  
  Note:
      Reference code: https://github.com/Luolc/AdaBound
code: def __setstate__(self, state: State) -> None:
      super(AdaBound, self).__setstate__(state)
      for group in self.param_groups:
          group.setdefault("amsbound", False)
  
file_path: pytorch-optimizer/torch_optimizer/adabound.py
imports: import math
  import torch
  from torch.optim.optimizer import Optimizer
  from .types import Betas2, OptFloat, OptLossClosure, Params, State
label_desc: Use image node_id 2 for calling the AdaBound obj's underlying member method code with example usage: obj.__setstate__(state) without return types