class_docstr: Exponential Linear Unit gradient function.
class_docstr_tok: ["Exponential","Linear","Unit","gradient","function","."]
code: def forward_gpu(self, inputs):
      (y,) = inputs
      gx = cuda.elementwise(
          "T y, T alpha, T cond",
          "T gx",
          "gx = cond > 0 ? (T)1 : (T)(y + alpha)",
          "elu_bwd",
      )(y, self.alpha, self.cond)
      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 3 for calling the ELUGrad obj's underlying member method code with example usage: obj.forward_gpu(inputs) and returns: gx