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.
docstr: Override update_multi_precision.
          
code: def update_multi_precision(self, indices, weights, grads, states):
      """Override update_multi_precision."""
      if self.use_fused_step:
          self.update(indices, weights, grads, states)
      else:
          super(NAG, self).update_multi_precision(
              indices, weights, grads, states
          )
  
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 5 for calling the NAG obj's underlying member method code with example usage: obj.update_multi_precision(indices, weights, grads, states) without return types