%0 = "arith.constant"() {value = 42 : i32} : () -> i32
  %1 = "arith.constant"() {value = 41 : i32} : () -> i32
  %2 = "tfr.build_list"(%0, %1) : (i32, i32) -> !tfr.attr
  func.return %2 : !tfr.attr
}

// -----

// CHECK-LABEL: build_high_dim_const_list
func.func @build_high_dim_const_list() -> !tfr.attr {
  %0 = "arith.constant"() {value = 42 : i32} : () -> i32

      }, {
        %2 = "tf.opC"() : () -> tensor<i32>
        %3 = "tf.opD"(%2) : (tensor<i32>) -> tensor<i32>
        tf_device.return %3 : tensor<i32>
      }) : () -> (tensor<i1>, tensor<i32>)
      tf_executor.yield %1#0, %1#1 : tensor<i1>, tensor<i32>
    }
    tf_executor.fetch %outputs#0, %outputs#1 : tensor<i1>, tensor<i32>
  }
  return
}

module attributes {tf.versions = {bad_consumers = [], min_consumer = 12 : i32, producer = 486 : i32}} {
  func.func @main() {
    tf_executor.graph {
      %outputs, %control = tf_executor.island wraps "tf.Const"() {device = "", value = dense<1> : tensor<i32>} : () -> tensor<i32>
      %outputs_0, %control_1 = tf_executor.island wraps "tf.Const"() {device = "", value = dense<0> : tensor<i32>} : () -> tensor<i32>

  %max_iter = "tf.Const"() {__op_key = 1, value = dense<2> : tensor<i32>} : () -> tensor<i32>
  // CHECK: tf_mlrt.map_fn
  %result = "tf_mlrt.tf_map_fn"(%max_iter, %input2, %2) { operandSegmentSizes = array<i32: 1, 1, 1>, body_fn = @NopMapFnBody, num_tensor_list_or_flow_in = 1 : i32} : (tensor<i32>, tensor<!tf_type.variant<tensor<*xf32>>>, tensor<i32>) -> tensor<i32>
  return %result : tensor<i32>
}

// CHECK-LABEL: @NopMapFnBody

      %outputs_22, %control_23 = tf_executor.island wraps "tf.Const"() {device = "/device:CPU:0", value = dense<3> : tensor<i32>} : () -> tensor<i32>
      %outputs_24, %control_25 = tf_executor.island wraps "tf.LessEqual"(%outputs_0, %outputs_22) {device = "/device:CPU:0"} : (tensor<i32>, tensor<i32>) -> tensor<*xi1>

      func.return %0, %1, %2, %3 : tensor<i32>, tensor<i32>, tensor<i32>, tensor<i32>
    }
    func.func @while_body_with_cluster_attr(%arg0: tensor<i32>) -> tensor<i32> {
      %0 = "tf.some_op"(%arg0) {_xla_compile_device_type = "TPU", _replication_info = "cluster"} : (tensor<i32>) -> tensor<i32>
      func.return %0 : tensor<i32>
    }

func.func @then(%x: tensor<i32>, %y: tensor<i32>) -> (tensor<i32>) {
  %0 = "tf.AddV2"(%x, %y) : (tensor<i32>, tensor<i32>) -> tensor<i32>
  func.return %0 : tensor<i32>
}

func.func @else(%x: tensor<i32>, %y: tensor<i32>) -> (tensor<i32>) {
  %0 = "tf.Const"() {value = dense<1> : tensor<i32> } : () -> tensor<i32>
  %1 = "tf.AddV2"(%x, %0) : (tensor<i32>, tensor<i32>) -> tensor<i32>

      %3 = "tf.opB"(%2, %arg0) : (tensor<i32>, tensor<i32>) -> tensor<i32>
      tf_executor.yield %3 : tensor<i32>
    }
    %4:2 = tf_executor.island {
      %5 = "tf.opC"(%1#0, %arg1) : (tensor<i32>, tensor<i32>) -> tensor<i32>
      %6 = "tf.opD"(%5, %arg0) : (tensor<i32>, tensor<i32>) -> tensor<i32>
      tf_executor.yield %6 : tensor<i32>
    }

    // core_tpu ops:
    %res_t = "tf.Identity"(%res_f) {_replication_info = "repl_info"} : (tensor<i32>) -> tensor<i32>

    // non_tpu_ops
    %res_n = "tf.Const"() {value = dense<2> : tensor<i32>} : () -> tensor<i32>

    return %res_n : tensor<i32>
  }
  func.func private @while_cond(%arg0: tensor<i32>) -> tensor<i1> {

  "tf.AssignVariableOp"(%arg0, %device_output) : (!tf_res_i32, tensor<i32>) -> ()
  "tf.SomeOp"(%device_output) : (tensor<i32>) -> ()
  func.return
}

// CHECK-LABEL: func @device_func_1
// CHECK-NOT: tf.aliasing_output
func.func @device_func_1(%arg0: tensor<i32>) -> tensor<i32> {
  func.return %arg0 : tensor<i32>
}