if (auto global = tf_saved_model::LookupBoundInputOfType<
              tf_saved_model::GlobalTensorOp>(func, i, syms)) {
        OpBuilder builder(func.getBody());
        auto dummy = builder.create<TF::VarHandleOp>(
            global.getLoc(), func.getArgument(i).getType(), "dummy", "dummy");
        func.getArgument(i).replaceAllUsesWith(dummy.getResult());
        argsToErase.set(i);
      }
    }

  TFE_DeleteContextOptions(opts);

  TFE_Op* var_op = TFE_NewOp(ctx, "VarHandleOp", status);
  TFE_OpSetAttrType(var_op, "dtype", TF_INT64);
  TFE_OpSetAttrShape(var_op, "shape", {}, 0, status);
  const TFE_OpAttrs* attributes = TFE_OpGetAttrs(var_op);

  TFE_Op* copy_op = TFE_NewOp(ctx, "VarHandleOp", status);
  TFE_OpSetAttrType(copy_op, "dtype", TF_FLOAT);
  TFE_OpAddAttrs(copy_op, attributes);

TFE_TensorHandle* TestVariable(TFE_Context* ctx, float value,
                               const tensorflow::string& device_name) {
  TF_Status* status = TF_NewStatus();
  // Create the variable handle.
  TFE_Op* op = TFE_NewOp(ctx, "VarHandleOp", status);
  if (TF_GetCode(status) != TF_OK) return nullptr;
  TFE_OpSetAttrType(op, "dtype", TF_FLOAT);
  TFE_OpSetAttrShape(op, "shape", {}, 0, status);
  TFE_OpSetAttrString(op, "container", "localhost", 0);

// Tests loop with two resource types, one of them being unique per iteration.
//
// Similar to above test but with one additional resource that is not unique per
// iteration (created by `tf.VarHandleOp`).

func.func @mixed_unique_resource_chain(%arg0: tensor<i32>, %arg1: tensor<f32>) {
  tf_executor.graph {

  "tf_device.cluster"() ({
    // CHECK:      "tf.VarHandleOp"
    // CHECK-SAME: _xla_outside_compilation
    "tf.VarHandleOp"() {allowed_devices = [], container = "", device = "", shared_name = ""} : () -> tensor<!tf_type.resource<tensor<!tf_type.string>>>
    tf_device.return

                           const int64_t* dims, const int num_dims,
                           const char* device, TF_Status* status) {
  std::unique_ptr<TFE_Op, decltype(&TFE_DeleteOp)> op(
      TFE_NewOp(context, "VarHandleOp", status), TFE_DeleteOp);
  if (TF_GetCode(status) != TF_OK) return nullptr;
  TFE_OpSetAttrType(op.get(), "dtype", type);
  TFE_OpSetAttrShape(op.get(), "shape", dims, num_dims, status);

  // these regions, so use a pre-order walk.
  WalkResult walk_res = func_op.walk<WalkOrder::PreOrder>([&](Operation* op) {
    if (auto var_handle = dyn_cast<VarHandleOp>(op)) {
      // Record VarHandleOp's device attribute.
      StringRef device_attr = GetDeviceAttr(op);
      if (device_attr.empty()) return WalkResult::advance();

}

// CHECK-LABEL: test_expensive_ops
func.func @test_expensive_ops(%arg: tensor<?x512xf32>) -> tensor<?x512xf32> {
    // expected-remark@+1 {{Cost: 1}}
    %0 = "tf.VarHandleOp"() {allowed_devices = [], container = "", device = "/job:localhost/replica:0/task:0/device:CPU:0", shared_name = "var"} : () -> tensor<!tf_type.resource<tensor<512x512xf32>>>
    // expected-remark@+1 {{Cost: 2}}

  func.func @infer_var_handle_op_from_assigns() -> tensor<1xi8> {
    %cst = arith.constant dense<1> : tensor<1xi8>
    %0 = "tf.VarHandleOp"() {container = "", shared_name = "bar"} : () -> tensor<!tf_type.resource<tensor<*xi8>>>
    // CHECK: "tf.VarHandleOp"() <{container = "", shared_name = "bar"}> : () -> tensor<!tf_type.resource<tensor<1xi8>>>

  func.return %arg : tensor<!tf_type.resource<tensor<i32>>>
}

// CHECK-LABEL: func @side_effect_while_test
func.func @side_effect_while_test() -> (tensor<i32>) {
  %0 = "tf.VarHandleOp"() {device = "/device:CPU:0", container = "c", shared_name = "v"} : () -> tensor<!tf_type.resource<tensor<i32>>>
  // CHECK: [[while_res:%.]]:2 = tfrt.while {{%.*}} @"side_effect_while_body_add2/tfrt_body_1"