class_docstr: Update rule for Nesterov's Accelerated Gradient.
  
  See :class:`~chainer.optimizers.NesterovAG` for the default values of the
  hyperparameters.
  
  Args:
      parent_hyperparam (~chainer.optimizer.Hyperparameter): Hyperparameter
          that provides the default values.
      lr (float): Learning rate.
      momentum (float): Exponential decay rate of the first order moment.
class_docstr_tok: ["Update","rule","for","Nesterov","'s","Accelerated","Gradient",".","See",":","class",":","`","~chainer.optimizers.NesterovAG","`","for","the","default","values","of","the","hyperparameters",".","Args",":","parent_hyperparam","(","~chainer.optimizer.Hyperparameter",")",":","Hyperparameter","that","provides","the","default","values",".","lr","(","float",")",":","Learning","rate",".","momentum","(","float",")",":","Exponential","decay","rate","of","the","first","order","moment","."]
code: def __init__(self, parent_hyperparam=None, lr=None, momentum=None):
      super(NesterovAGRule, self).__init__(
          parent_hyperparam or _default_hyperparam
      )
      if lr is not None:
          self.hyperparam.lr = lr
      if momentum is not None:
          self.hyperparam.momentum = momentum
  
file_path: chainer/chainer/optimizers/nesterov_ag.py
imports: import chainer
  from chainer.backends import cuda
  from chainer import optimizer
  from chainer import types
label_desc: Use image node_id 1 to create a new NesterovAGRule object from inherited base classes: optimizer with example: obj = NesterovAGRule(parent_hyperparam, lr, momentum)