}

TEST_F(NNGradTest, EluGrad) {
  TensorShape shape({5, 2});
  auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(shape));
  auto y = Elu(scope_, x);
  Tensor x_init_value = test::AsTensor<float>(
      {-0.9f, -0.7f, -0.5f, -0.3f, -0.1f, 0.1f, 0.3f, 0.5f, 0.7f, 0.9f},
      {5, 2});
  RunTest(x, x_init_value, y, shape);
}

TEST_F(NNGradTest, SeluGrad) {
  TensorShape shape({5, 2});

                       std::vector<Output>* grad_outputs) {
  auto dx = internal::Relu6Grad(scope, grad_inputs[0], op.input(0));
  grad_outputs->push_back(dx);
  return scope.status();
}
REGISTER_GRADIENT_OP("Relu6", Relu6GradHelper);

Status LeakyReluGradHelper(const Scope& scope, const Operation& op,
                           const std::vector<Output>& grad_inputs,

          (MHLO_ClampOp (MHLO_ConstantOp (GetScalarOfType<0> $input)), $input,
                       (MHLO_ConstantOp (GetScalarOfType<6> $input))),
          [(TF_IntOrFpTensor $input)]>;

// ReluGrad(gradients, features) = gradients * (features > 0)
// The condition that $gradients and $features need to have the same shape is
// implicitly enforced: $zero is created to have the same shape as $features,