class_docstr: Update rule for the corrected momentum SGD.
  
  See :class:`~chainer.optimizers.CorrectedMomentumSGD` 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","the","corrected","momentum","SGD",".","See",":","class",":","`","~chainer.optimizers.CorrectedMomentumSGD","`","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_gpu(self, param):
      grad = param.grad
      if grad is None:
          return
      cuda.elementwise(
          "T grad, T lr, T momentum",
          "T param, T v",
          """v = momentum * v - grad;
                 param += lr * v;""",
          "momentum_sgd",
      )(
          grad,
          self.hyperparam.lr,
          self.hyperparam.momentum,
          param.data,
          self.state["v"],
      )
  
file_path: chainer/chainer/optimizers/corrected_momentum_sgd.py
imports: import chainer
  from chainer.backends import cuda
  from chainer.backends import intel64
  from chainer import optimizer
  from chainer import types
label_desc: Use image node_id 4 for calling the CorrectedMomentumSGDRule obj's underlying member method code with example usage: obj.update_core_gpu(param) without return types