class_docstr: Update rule of AdaGrad.
  
  See :class:`~chainer.optimizers.AdaGrad` for the default values of the
  hyperparameters.
  
  Args:
      parent_hyperparam (~chainer.optimizer.Hyperparameter): Hyperparameter
          that provides the default values.
      lr (float): Learning rate.
      eps (float): Small value for the numerical stability.
class_docstr_tok: ["Update","rule","of","AdaGrad",".","See",":","class",":","`","~chainer.optimizers.AdaGrad","`","for","the","default","values","of","the","hyperparameters",".","Args",":","parent_hyperparam","(","~chainer.optimizer.Hyperparameter",")",":","Hyperparameter","that","provides","the","default","values",".","lr","(","float",")",":","Learning","rate",".","eps","(","float",")",":","Small","value","for","the","numerical","stability","."]
code: def update_core_gpu(self, param):
      grad = param.grad
      if grad is None:
          return
      if AdaGradRule._kernel is None:
          AdaGradRule._kernel = cuda.elementwise(
              "T grad, T lr, T eps",
              "T param, T h",
              """h += grad * grad;
                     param -= lr * grad / (sqrt(h) + eps);""",
              "adagrad",
          )
      AdaGradRule._kernel(
          grad,
          self.hyperparam.lr,
          self.hyperparam.eps,
          param.data,
          self.state["h"],
      )
  
file_path: chainer/chainer/optimizers/ada_grad.py
imports: import numpy
  import chainer
  from chainer.backends import cuda
  from chainer import optimizer
  from chainer import types
label_desc: Use image node_id 4 for calling the AdaGradRule obj's underlying member method code with example usage: obj.update_core_gpu(param) without return types