const std::vector<Output>& outputs,
                            const std::vector<Output>& inputs,
                            std::vector<Output>* grad_outputs) {
  std::vector<Output> grad_inputs;
  grad_inputs.reserve(outputs.size());
  for (const Output& output : outputs) {
    grad_inputs.emplace_back(ops::OnesLike(scope, output));
  }

  params_->body_outputs[1] = params_->body_inputs[1];

  // Finalize while loop
  ExpectOK();

  // Validate while loop outputs returned by TF_FinishWhile()
  EXPECT_TRUE(outputs_[0].oper != nullptr);
  EXPECT_GE(outputs_[0].index, 0);
  EXPECT_TRUE(outputs_[1].oper != nullptr);
  EXPECT_GE(outputs_[1].index, 0);

  // Check that cond and body inputs are not present
  for (int i = 0; i < params_->ninputs; ++i) {

      %outputs_4, %control_5 = tf_executor.island wraps "tf.Range"(%outputs_2, %outputs_0, %outputs) {device = ""} : (tensor<i32>, tensor<i32>, tensor<i32>) -> tensor<?xi32>
      %outputs_6, %control_7 = tf_executor.island wraps "tf.Sub"(%outputs_0, %arg0) {device = ""} : (tensor<i32>, tensor<i32>) -> tensor<i32>
      %outputs_8, %control_9 = tf_executor.island wraps "tf.FloorDiv"(%outputs_6, %outputs) {device = ""} : (tensor<i32>, tensor<i32>) -> tensor<i32>

    %outputs_4, %control_4 = tf_executor.island wraps "tf.Identity"(%outputs_3) {T = "tfdtype$DT_FLOAT", device = "", name = "output_0_shard_0"} : (tensor<0xf32>) -> tensor<0xf32>
    %control_5 = tf_executor.island(%control, %control_4) wraps "tf.NoOp"() : () -> ()

      %outputs_5, %control_6 = tf_executor.island wraps "tf.PreventGradient"(%outputs_2) {device = "", message = "The jax2tf-converted function does not support gradients. Use `with_gradient` parameter to enable gradients"} : (tensor<2x3xi32>) -> tensor<2x3xi32>
      %outputs_7, %control_8 = tf_executor.island wraps "tf.Identity"(%outputs_5) {device = ""} : (tensor<2x3xi32>) -> tensor<2x3xi32>

      %outputs_2, %control_3 = tf_executor.island wraps "tf.Const"() {device = "", value = dense<-1> : tensor<i64>} : () -> tensor<i64>
      %outputs_4, %control_5 = tf_executor.island wraps "tf.Const"() {device = "", value = dense<""> : tensor<1x!tf_type.string>} : () -> tensor<1x!tf_type.string>

      %outputs_17, %control_18 = tf_executor.island wraps "tf.Mul"(%outputs_15, %outputs_0) {T = "tfdtype$DT_FLOAT", _tpu_replicate = "cluster", device = "", name = "mul_1"} : (tensor<0xf32>, tensor<f32>) -> tensor<0xf32>
      %outputs_19, %control_20 = tf_executor.island wraps "tf.AddV2"(%outputs_9, %outputs_17) {T = "tfdtype$DT_FLOAT", _tpu_replicate = "cluster", device = "", name = "add"} : (tensor<0xf32>, tensor<0xf32>) -> tensor<0xf32>

      %outputs_26, %control_27 = tf_executor.island wraps "tf.All"(%outputs_24, %outputs_20) {device = "/device:CPU:0", keep_dims = false} : (tensor<*xi1>, tensor<*xi32>) -> tensor<*xi1>

          %outputs_2, %control_3 = tf_executor.island wraps "tf.XlaSpmdFullToShardShape"(%outputs_0) {dim = -1 : i64, manual_sharding = "\08\03\1A\02\02\01\22\02\00\01", unspecified_dims = []} : (tensor<2x2xf32>) -> tensor<1x2xf32>

      %outputs_8, %control_9 = tf_executor.island wraps "tf.Const"() {device = "", dtype = !tf_type.string, value = dense<""> : tensor<2x!tf_type.string>} : () -> tensor<2x!tf_type.string>