class_docstr: Update rule of vanilla stochastic gradient descent.
  
  See :class:`~chainer.optimizers.SGD` for the default values of the
  hyperparameters.
  
  Args:
      parent_hyperparam (~chainer.optimizer.Hyperparameter): Hyperparameter
          that provides the default values.
      lr (float): Learning rate.
class_docstr_tok: ["Update","rule","of","vanilla","stochastic","gradient","descent",".","See",":","class",":","`","~chainer.optimizers.SGD","`","for","the","default","values","of","the","hyperparameters",".","Args",":","parent_hyperparam","(","~chainer.optimizer.Hyperparameter",")",":","Hyperparameter","that","provides","the","default","values",".","lr","(","float",")",":","Learning","rate","."]
code: def update_core_cpu(self, param):
      grad = param.grad
      if grad is None:
          return
      if isinstance(param.data, intel64.mdarray):
          param.data.inplace_axpby(1.0, -self.hyperparam.lr, grad)
      else:
          param.data -= self.hyperparam.lr * grad
  
file_path: chainer/chainer/optimizers/sgd.py
imports: from chainer.backends import cuda
  from chainer.backends import intel64
  from chainer import optimizer
  from chainer import types
label_desc: Use image node_id 2 for calling the SGDRule obj's underlying member method code with example usage: obj.update_core_cpu(param) without return types