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 __init__(self, parent_hyperparam=None, lr=None):
      super(SGDRule, self).__init__(
          parent_hyperparam or _default_hyperparam
      )
      if lr is not None:
          self.hyperparam.lr = lr
  
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 1 to create a new SGDRule object from inherited base classes: optimizer with example: obj = SGDRule(parent_hyperparam, lr)