class_docstr: AdamW optimizer.
  
  This class is a special case of :class:`~chainer.optimizers.Adam`.
  
  See: `Fixing Weight Decay Regularization in Adam
  <https://openreview.net/forum?id=rk6qdGgCZ>`_
  
  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.
          The default value is 1.0.
      weight_decay_rate (float): Weight decay rate.
          The default value is 0.
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code: def __init__(
      self,
      alpha=_default_hyperparam.alpha,
      beta1=_default_hyperparam.beta1,
      beta2=_default_hyperparam.beta2,
      eps=_default_hyperparam.eps,
      eta=_default_hyperparam.eta,
      weight_decay_rate=_default_hyperparam.weight_decay_rate,
  ):
      super(AdamW, self).__init__(
          alpha=alpha,
          beta1=beta1,
          beta2=beta2,
          eps=eps,
          eta=eta,
          weight_decay_rate=weight_decay_rate,
      )
  
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 AdamW object from inherited base classes: Adam with example: obj = AdamW(alpha, beta1, beta2, eps, eta, weight_decay_rate)