class_docstr: Nesterov accelerated gradient.
  
  This optimizer updates each weight by::
  
      grad = clip(grad * rescale_grad, clip_gradient) + wd * weight
      state = momentum * state + lr * grad
      weight = weight - (momentum * state + lr * grad)
  
  Parameters
  ----------
  learning_rate : float, default 0.1
      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.
  momentum : float, default 0.9
     The momentum value.
  multi_precision: bool, default False
      Flag to control the internal precision of the optimizer.
      False: results in using the same precision as the weights (default),
      True: makes internal 32-bit copy of the weights and applies gradients
      in 32-bit precision even if actual weights used in the model have lower precision.
      Turning this on can improve convergence and accuracy when training with float16.
  use_fused_step : bool, default True
      Whether or not to use fused kernels for optimizer.
      When use_fused_step=False, step is called,
      otherwise, fused_step is called.
code: def __init__(
      self,
      learning_rate=0.1,
      momentum=0.9,
      multi_precision=False,
      use_fused_step=True,
      **kwargs,
  ):
      super(NAG, self).__init__(
          learning_rate=learning_rate,
          multi_precision=multi_precision,
          use_fused_step=use_fused_step,
          **kwargs,
      )
      self.momentum = momentum
  
file_path: mxnet/python/mxnet/optimizer/nag.py
imports: from __future__ import absolute_import
  import numpy
  from ..ndarray import zeros, clip
  from ..ndarray import sgd_update, mp_sgd_update, nag_mom_update, mp_nag_mom_update
  from .optimizer import Optimizer, register
label_desc: Use image node_id 1 to create a new NAG object from inherited base classes: Optimizer with example: obj = NAG(learning_rate, momentum, multi_precision, use_fused_step)