// CHECK: }

func.func @unknown_side_effecting_op(%arg0: tensor<32xf32>) -> () {
  tf_executor.graph {
    %island = tf_executor.island {
      %vh0 = "tf.VarHandleOp"() {container = "c", shared_name = "v0"} : () -> tensor<*x!tf_type.resource<tensor<32xf32>>>
      %vh1 = "tf.VarHandleOp"() {container = "c", shared_name = "v1"} : () -> tensor<*x!tf_type.resource<tensor<32xf32>>>

    resource variables directly..

    Here is a simple example in TensorFlow where a device doubles the value of a
    TensorFlow resource variable and returns new value:

    ```mlir
    %resource_handle = "tf.VarHandleOp"()
    %1 = "tf_device.cluster"() ( {
      %init_value = "tf.ReadVariableOp"(%resource_handle)
      "tf.AssignAddVariableOp"(%resource_handle, %init_value)
      %new_value = "tf.ReadVariableOp"(%resource_handle)

// CHECK: }

func.func @unknown_side_effecting_op(%arg0: tensor<32xf32>) -> () {
  tf_executor.graph {
    %island = tf_executor.island {
      %vh0 = "tf.VarHandleOp"() {container = "c", shared_name = "v0"} : () -> tensor<*x!tf_type.resource<tensor<32xf32>>>
      %vh1 = "tf.VarHandleOp"() {container = "c", shared_name = "v1"} : () -> tensor<*x!tf_type.resource<tensor<32xf32>>>

func.func @unknown_side_effecting_op(%arg0: tensor<32xf32>) {
  tf_executor.graph {
    %outputs, %control = tf_executor.island wraps "tf.VarHandleOp"() {container = "c", shared_name = "v0"} : () -> tensor<*x!tf_type.resource<tensor<32xf32>>>
    %outputs_0, %control_1 = tf_executor.island wraps "tf.VarHandleOp"() {container = "c", shared_name = "v1"} : () -> tensor<*x!tf_type.resource<tensor<32xf32>>>

// Creates the per-device variables that represent the formatting state of each
// device.
llvm::SmallVector<TF::VarHandleOp, 4> CreateStateVars(
    const llvm::SmallDenseMap<llvm::StringRef, llvm::SmallVector<StringRef, 4>>&
        devices,
    Location loc, RankedTensorType key_type, OpBuilder* builder) {
  llvm::SmallVector<TF::VarHandleOp, 4> state_vars;

  // TODO(b/148913020): Remove this constraint once model parallelism is
  // supported.

                               result);
}

Node* MakeWrite(const Scope& scope, Output value_to_write, const string& id) {
  Output var_handle = ops::VarHandleOp(scope.WithOpName("Var_" + id), DT_FLOAT,
                                       TensorShape({}));
  ops::AssignVariableOp assign_op(scope.WithOpName("Assignee_" + id),

}

// CHECK-LABEL: func @no_set_stateless
func.func @no_set_stateless(%arg: tensor<i32>, %cond: tensor<i1>) -> (tensor<i32>, tensor<i32>) {
  %handle = "tf.VarHandleOp"() { container = "", shared_name = "var" } : () -> tensor<!tf_type.resource<tensor<i32>>>
  "tf.AssignVariableOp"(%handle, %arg) : (tensor<!tf_type.resource<tensor<i32>>>, tensor<i32>) -> ()
  // CHECK: tf.While

// Test lowering of IfrtLoadVariableOp

// CHECK-LABEL: func @ifrt_load_variable_test
func.func @ifrt_load_variable_test() -> () {
  // CHECK: [[HANDLE:%.*]] = tf_mlrt.executeop()
  // CHECK-SAME:  VarHandleOp
  %0 = "tf.VarHandleOp"() {__op_key = 1: i32, device = "/device:CPU:0", container = "", shared_name = "variable"} : () -> tensor<!tf_type.resource<tensor<1x3xf32>>>
  // CHECK-NEXT: "tf_mlrt.ifrt_load_variable"([[HANDLE]])

// CHECK-LABEL: func private @main_stream_{{[0-9]*}}
// CHECK-SAME: ([[CONTROL_FUTURE:%.*]]: !mlrt.future, [[PROMISE:%.*]]: !mlrt.promise, [[PROMISE_HANDLE:%.*]]: !mlrt.promise)
// CHECK: [[HANDLE:%.*]] = "tf.VarHandleOp"
// CHECK-NEXT: "tf_mlrt.tf_promise"([[PROMISE_HANDLE]], [[HANDLE]])
// CHECK: mlrt.await_control [[CONTROL_FUTURE]]
// CHECK-NEXT: [[V:%.*]] = "tf.ReadVariableOp"([[HANDLE]])

      %out_0, %ctl_0 = tf_executor.island wraps "tf.Const"() {value = dense<"var_0"> : tensor<1x!tf_type.string>} : () -> tensor<1x!tf_type.string>
      %out_1, %ctl_1 = tf_executor.island wraps "tf.VarHandleOp"() {container = "", shared_name = "var_0", device = "/device:CPU:0"} : () -> tensor<!tf_type.resource<tensor<2xf32>>>