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 __init__(
      self,
      params: Params,
      lr: float = 1e-3,
      betas: Betas2 = (0.9, 0.999),
      final_lr: float = 0.1,
      gamma: float = 1e-3,
      eps: float = 1e-8,
      weight_decay: float = 0,
      amsbound: bool = False,
  ) -> None:
      if lr <= 0.0:
          raise ValueError("Invalid learning rate: {}".format(lr))
      if eps < 0.0:
          raise ValueError("Invalid epsilon value: {}".format(eps))
      if not 0.0 <= betas[0] < 1.0:
          raise ValueError(
              "Invalid beta parameter at index 0: {}".format(betas[0])
          )
      if not 0.0 <= betas[1] < 1.0:
          raise ValueError(
              "Invalid beta parameter at index 1: {}".format(betas[1])
          )
      if final_lr < 0.0:
          raise ValueError(
              "Invalid final learning rate: {}".format(final_lr)
          )
      if not 0.0 <= gamma < 1.0:
          raise ValueError("Invalid gamma parameter: {}".format(gamma))
      if weight_decay < 0:
          raise ValueError(
              "Invalid weight_decay value: {}".format(weight_decay)
          )
      defaults = dict(
          lr=lr,
          betas=betas,
          final_lr=final_lr,
          gamma=gamma,
          eps=eps,
          weight_decay=weight_decay,
          amsbound=amsbound,
      )
      super(AdaBound, self).__init__(params, defaults)
      self.base_lrs = [group["lr"] for group in self.param_groups]
  
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 1 to create a new AdaBound object from inherited base classes: Optimizer with example: obj = AdaBound(params, lr, betas, final_lr, gamma, eps, weight_decay, amsbound)