double max_error;
  TF_ASSERT_OK((ComputeGradientError<double, double, double>(
      scope, {x}, {x_shape}, {z}, {z_shape}, &max_error)));
  EXPECT_LT(max_error, 1e-10);
}

TEST(GradientCheckerTest, SplitGrad) {
  // Split is an op with single inputs and multiple outputs.
  Scope scope = Scope::NewRootScope();
  TensorShape x_shape({5, 2});
  auto x = Placeholder(scope, DT_DOUBLE, Placeholder::Shape(x_shape));

Status SplitGrad(const Scope& scope, const Operation& op,
                 const std::vector<Output>& grad_inputs,
                 std::vector<Output>* grad_outputs) {
  grad_outputs->push_back(NoGradient());
  grad_outputs->push_back(Concat(scope, grad_inputs, op.input(0)));
  return scope.status();
}
REGISTER_GRADIENT_OP("Split", SplitGrad);

TEST_F(ArrayGradTest, IdentityGrad) {
  TensorShape shape({5, 2});
  auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(shape));
  auto y = Identity(scope_, x);
  RunTest(x, shape, y, shape);
}

TEST_F(ArrayGradTest, SplitGrad) {
  TensorShape x_shape({5, 2});
  auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(x_shape));
  // Split along the second dimension.
  auto split_dim = Const(scope_, 1, {});