class_docstr: AdaBound optimizer.
  
  This class is a special case of :class:`~chainer.optimizers.Adam`.
  
  See: `Adaptive Gradient Methods with Dynamic Bound of Learning Rate
  <https://openreview.net/forum?id=Bkg3g2R9FX>`_
  
  Args:
      alpha (float): Coefficient of learning rate.
      beta1 (float): Exponential decay rate of the first order moment.
      beta2 (float): Exponential decay rate of the second order moment.
      final_lr (float): Final (SGD) learning rate in AdaBound.
      gamma (float): Convergence speed of the bound functions in AdaBound.
      eps (float): Small value for the numerical stability.
      eta (float): Schedule multiplier, can be used for warm restarts.
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code: def __init__(
      self,
      alpha=_default_hyperparam.alpha,
      beta1=_default_hyperparam.beta1,
      beta2=_default_hyperparam.beta2,
      final_lr=_default_hyperparam.final_lr,
      gamma=_default_hyperparam.gamma,
      eps=_default_hyperparam.eps,
      eta=_default_hyperparam.eta,
  ):
      super(AdaBound, self).__init__(
          alpha=alpha,
          beta1=beta1,
          beta2=beta2,
          eps=eps,
          eta=eta,
          amsgrad=False,
          adabound=True,
          final_lr=final_lr,
          gamma=gamma,
      )
  
imports: from __future__ import division
  import math
  import warnings
  import numpy
  import chainer
  from chainer.backends import cuda
  from chainer.backends import intel64
  from chainer import optimizer
  from chainer import types
label_desc: Use image node_id 1 to create a new AdaBound object from inherited base classes: Adam with example: obj = AdaBound(alpha, beta1, beta2, final_lr, gamma, eps, eta)