class_docstr: Implements the QHAdam optimization algorithm.
  
  It has been proposed in `Adaptive methods for Nonconvex Optimization`__.
  
  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))
      nus: immediate discount factors used to estimate the gradient and its
          square (default: (1.0, 1.0))
      eps: term added to the denominator to improve
          numerical stability (default: 1e-8)
      weight_decay: weight decay (L2 penalty) (default: 0)
      decouple_weight_decay: whether to decouple the weight
          decay from the gradient-based optimization step (default: False)
  
  Example:
      >>> import torch_optimizer as optim
      >>> optimizer = optim.QHAdam(model.parameters(), lr=0.1)
      >>> optimizer.zero_grad()
      >>> loss_fn(model(input), target).backward()
      >>> optimizer.step()
  
  __ https://arxiv.org/abs/1810.06801
  
  Note:
      Reference code: https://github.com/facebookresearch/qhoptim
code: def __init__(
      self,
      params: Params,
      lr: float = 1e-3,
      betas: Betas2 = (0.9, 0.999),
      nus: Nus2 = (1.0, 1.0),
      weight_decay: float = 0.0,
      decouple_weight_decay: bool = False,
      eps: float = 1e-8,
  ):
      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 weight_decay < 0:
          raise ValueError(
              "Invalid weight_decay value: {}".format(weight_decay)
          )
  
      defaults = {
          "lr": lr,
          "betas": betas,
          "nus": nus,
          "weight_decay": weight_decay,
          "decouple_weight_decay": decouple_weight_decay,
          "eps": eps,
      }
      super(QHAdam, self).__init__(params, defaults)
  
imports: import torch
  from torch.optim.optimizer import Optimizer
  from .types import Betas2, Nus2, OptFloat, OptLossClosure, Params
label_desc: Use image node_id 1 to create a new QHAdam object from inherited base classes: Optimizer with example: obj = QHAdam(params, lr, betas, nus, weight_decay, decouple_weight_decay, eps)