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_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_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_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_52 =  "tf.Reshape"(%outputs_50, %outputs_0) {device = ""} : (tensor<*xf32>, tensor<2xi32>) -> tensor<*xf32>
  %outputs_54 =  "tf.MatMul"(%outputs_40, %outputs_52) {device = "", transpose_a = false, transpose_b = false} : (tensor<*xf32>, tensor<*xf32>) -> tensor<*xf32>
  %outputs_56 =  "tf.MatrixDeterminant"(%outputs_54) {T = f32, device = ""} : (tensor<*xf32>) -> tensor<*xf32>

      %outputs_18, %control_19 = tf_executor.island wraps "tf.MatMul"(%outputs_16, %outputs_4) {device = "", transpose_a = false, transpose_b = false} : (tensor<?x16384xf32>, tensor<*xf32>) -> tensor<?x?xf32>

  %outputs_2 = "tf.add"(%outputs_0, %outputs_1): (tensor<1x1xf32>, tensor<1x1xf32>) -> tensor<1x1xf32>
  return %outputs_2 : tensor<1x1xf32>
}

// CHECK-LABEL: @_func
func.func private @_func(%arg0: tensor<1x3xf32>, %arg1: tensor<3x1xf32>) -> (tensor<1x1xf32>) {

    %outputs_11, %control_12 = tf_executor.island wraps "tf.Mul"(%outputs, %outputs_9) {_replication_info = "cluster", _xla_compile_device_type = "TPU", device = ""} : (tensor<f32>, tensor<*xf32>) -> tensor<*xf32>