class_docstr: Adam optimizer.
  
  See: `Adam: A Method for Stochastic Optimization
  <https://arxiv.org/abs/1412.6980v8>`_
  
  Modified for proper weight decay (also called
  :class:`~chainer.optimizers.AdamW`).
  AdamW introduces the additional parameters ``eta``
  and ``weight_decay_rate``, which can be used to properly scale the
  learning rate, and decouple the weight decay rate from ``alpha``,
  as shown in the below paper.
  
  Note that with the default values ``eta = 1`` and
  ``weight_decay_rate = 0``, this implementation is identical to
  the standard Adam method.
  
  See: `Fixing Weight Decay Regularization in Adam
  <https://openreview.net/forum?id=rk6qdGgCZ>`_
  
  A flag ``amsgrad`` to use the :class:`~chainer.optimizers.AMSGrad`
  variant of Adam from the paper:
  `On the Convergence of Adam and Beyond
  <https://openreview.net/forum?id=ryQu7f-RZ>`_
  
  A flag ``adabound`` to use the :class:`~chainer.optimizers.AdaBound`
  variant of Adam from the paper:
  `Adaptive Gradient Methods with Dynamic Bound of Learning Rate
  <https://openreview.net/forum?id=Bkg3g2R9FX>`_
  
  If both ``amsgrad`` and ``adabound`` are ``True``, the optimizer is
  equivalent to :class:`~chainer.optimizers.AMSBound` proposed in the
  AdaBound paper.
  
  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.
      eps (float): Small value for the numerical stability.
      eta (float): Schedule multiplier, can be used for warm restarts.
      weight_decay_rate (float): Weight decay rate.
      amsgrad (bool): Whether to use AMSGrad variant of Adam.
      adabound (bool): Whether to use the AdaBound variant of Adam.
      final_lr (float): Final (SGD) learning rate in AdaBound.
      gamma (float): Convergence speed of the bound functions in AdaBound.
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code: def alpha_t(self):
      return _learning_rate(self.hyperparam, self.t)
  
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 3 for calling the Adam obj's underlying member method code with example usage: obj.alpha_t() and returns: _learning_rate