class_docstr: Update rule for the classical momentum SGD.
  
  See :class:`~chainer.optimizers.MomentumSGD` 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","classical","momentum","SGD",".","See",":","class",":","`","~chainer.optimizers.MomentumSGD","`","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
      if MomentumSGDRule._kernel is None:
          MomentumSGDRule._kernel = cuda.elementwise(
              "T grad, T lr, T momentum",
              "T param, T v",
              """v = momentum * v - lr * grad;
                     param += v;""",
              "momentum_sgd",
          )
      MomentumSGDRule._kernel(
          grad,
          self.hyperparam.lr,
          self.hyperparam.momentum,
          param.data,
          self.state["v"],
      )
  
file_path: chainer/chainer/optimizers/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 MomentumSGDRule obj's underlying member method code with example usage: obj.update_core_gpu(param) without return types