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 update_core_cpu(self, param):
      grad = param.grad
      if grad is None:
          return
      v = self.state["v"]
      lr, momentum = self.hyperparam.lr, self.hyperparam.momentum
  
      v *= momentum
      v -= lr * grad
      param.data += momentum * momentum * v
      param.data -= (1 + momentum) * lr * grad
  
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 3 for calling the NesterovAGRule obj's underlying member method code with example usage: obj.update_core_cpu(param) without return types