class_docstr: The AdaMax optimizer.
  
  It is a variant of Adam based on the infinity norm
  available at http://arxiv.org/abs/1412.6980 Section 7.
  
  The optimizer updates the weight by::
  
      grad = clip(grad * rescale_grad, clip_gradient) + wd * weight
      m = beta1 * m_t + (1 - beta1) * grad
      u = maximum(beta2 * u, abs(grad))
      weight -= lr / (1 - beta1**t) * m / (u + epsilon)
  
  This optimizer accepts the following parameters in addition to those accepted
  by :class:`.Optimizer`.
  
  Parameters
  ----------
  learning_rate : float, default 0.002
      The initial learning rate. If None, the optimization will use the
      learning rate from ``lr_scheduler``. If not None, it will overwrite
      the learning rate in ``lr_scheduler``. If None and ``lr_scheduler``
      is also None, then it will be set to 0.01 by default.
  beta1 : float, default 0.9
      Exponential decay rate for the first moment estimates.
  beta2 : float, default 0.999
      Exponential decay rate for the second moment estimates.
  use_fused_step : bool, default False
      Whether or not to use fused kernels for optimizer.
      When use_fused_step=False, step is called,
      otherwise, fused_step is called.
code: def create_state(self, index, weight):
      return (
          zeros(
              weight.shape, weight.context, dtype=weight.dtype
          ),  # mean
          zeros(weight.shape, weight.context, dtype=weight.dtype),
      )
  
imports: from __future__ import absolute_import
  from ..ndarray import zeros, clip, maximum, abs
  from .optimizer import Optimizer, register
label_desc: Use image node_id 2 for calling the Adamax obj's underlying member method code with example usage: obj.create_state(index, weight) and returns: zeros,  zeros