class_docstr: The Nesterov Adam optimizer.
  
  Much like Adam is essentially RMSprop with momentum,
  Nadam is Adam RMSprop with Nesterov momentum available
  at http://cs229.stanford.edu/proj2015/054_report.pdf.
  
  This optimizer accepts the following parameters in addition to those accepted
  by :class:`.Optimizer`.
  
  Parameters
  ----------
  learning_rate : float, default 0.001
      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.
  epsilon : float, default 1e-8
      Small value to avoid division by 0.
  schedule_decay : float, default 0.004
      Exponential decay rate for the momentum schedule
  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, sqrt, square
  from .optimizer import Optimizer, register
label_desc: Use image node_id 2 for calling the Nadam obj's underlying member method code with example usage: obj.create_state(index, weight) and returns: zeros,  zeros