class_docstr: Exponential Linear Unit gradient function.
class_docstr_tok: ["Exponential","Linear","Unit","gradient","function","."]
code: def forward_cpu(self, inputs):
      (y,) = inputs
      gx = utils.force_array(y + y.dtype.type(self.alpha))
      gx[self.cond > 0] = 1
      return (gx,)
  
file_path: chainer/chainer/functions/activation/elu.py
imports: import numpy
  from chainer.backends import cuda
  from chainer import function_node
  from chainer import utils
  from chainer.utils import type_check
label_desc: Use image node_id 2 for calling the ELUGrad obj's underlying member method code with example usage: obj.forward_cpu(inputs) and returns: gx