class_docstr: Computes the gradient of the Dropout function.
class_docstr_tok: ["Computes","the","gradient","of","the","Dropout","function","."]
code: def forward(self, inputs):
      if intel64.should_use_ideep(
          ">=auto"
      ) and intel64.inputs_all_ready(inputs):
          return self._forward_ideep(inputs)
  
      y = inputs[0] * self.mask
      return (y,)
  
imports: import numpy
  import chainer
  from chainer.backends import cuda
  from chainer.backends import intel64
  from chainer import configuration
  from chainer import function_node
  from chainer.utils import argument
  from chainer.utils import type_check
label_desc: Use image node_id 2 for calling the DropoutGrad obj's underlying member method code with example usage: obj.forward(inputs) and returns: y, self