// CHECK-NOT: "tf.VarHandleOp"
  // CHECK-NOT: "tf.ReadVariableOp"
  // CHECK: %[[CONST:.*]] = "tf.Const"()
  // CHECK: %[[ADD:[0-9]*]] = "tf.AddV2"(%arg1, %[[CONST]])
  // CHECK: %[[PACK:[0-9]*]] = "tf.Pack"(%[[CONST]], %[[ADD]])
  // CHECK: return %[[PACK]]
  %0 = "tf.Const"() {value = dense<4.200000e+01> : tensor<f32>} : () -> tensor<f32>

    // CHECK: tf_executor.island
    %island = tf_executor.island {
      // expected-remark@below {{Result #0, ID 0 : 0, 1, 2}}
      %vh0 = "tf.VarHandleOp"() {container = "c", shared_name = "v0"} : () -> !tf_res
      // expected-remark@below {{Result #0, ID 1 : 0, 1, 2}}
      %vh1 = "tf.VarHandleOp"() {container = "c", shared_name = "v0"} : () -> !tf_res
      // expected-remark@below {{Result #0, ID 2 : 0, 1, 2}}

  // CHECK-NOT: tf.VarHandleOp
  // CHECK-NOT: tf.ReadVariableOp
  // CHECK: [[v:%.*]] = "tf._TfrtGetResource"() <{container = [""], indices = [0], shared_name = [""]}> {device = "/CPU:0"} : () -> tensor<i32>
  // CHECK: [[r:%.*]] = "tf.AddV2"({{.*}}, [[v]]) {device = "/CPU:0"} : (tensor<i32>, tensor<i32>) -> tensor<i32>
  // CHECK: return [[r]]

// readonly.

// CHECK-LABEL: readvariableop_is_hoisted_if_readonly2
// CHECK:       [[CST_0:%.*]] = "tf.Const"
// CHECK:       [[VAR_1:%.*]] = "tf.VarHandleOp"
// CHECK-SAME:  "shared_name_var1"
// CHECK:       [[VAR_2:%.*]] = "tf.VarHandleOp"
// CHECK-SAME:  "shared_name_var2"
// CHECK:       [[CST_1:%.*]] = "tf.Const"
// CHECK:       [[VAR_VAL:%.*]] = "tf.ReadVariableOp"([[VAR_1]])

  func.return %3 : tensor<*xi32>
}

}

// -----

module attributes {tf_saved_model.semantics} {

// CHECK-LABEL: func private @reorder_execute_arg_defining_ops
// CHECK: tf.VarHandleOp
// CHECK-NEXT: tf.ReadVariableOp
// CHECK-NEXT: tf.TPUCompileMlirAndExecute
func.func private @reorder_execute_arg_defining_ops(%arg0: tensor<1x3xf32> {tf.device = "/CPU:0"}) -> (tensor<1x1xf32> {tf.device = "/TPU:0"}) {

        %0 = "tf.VarHandleOp"() {container = "", device = "/device:CPU:0", shared_name = "var_0"} : () -> tensor<!tf_type.resource<tensor<2xf32>>>
        "tf.AssignVariableOp"(%0, %cst) : (tensor<!tf_type.resource<tensor<2xf32>>>, tensor<2xf32>) -> ()

        %cst_0 = "tf.Const"() {device = "", value = dense<[3, 4, 5, 6]> : tensor<4xi32>} : () -> tensor<4xi32>

// GraphDef.
std::unique_ptr<OperationPass<ModuleOp>> CreateMergeSaveFunctionOpsToMainPass();

// Creates a pass that "unfreezes" ConstOps into variables. Each ConstOp's use
// will be replaced by a VarHandleOp -> ReadVariableOp pattern. The newly
// created variables will be initialized in the session initializer function via
// AssignVariableOps.
std::unique_ptr<OperationPass<ModuleOp>> CreateUnfreezeConstantsPass();

             %arg3: !tf_res_md_f32 {tf.device = "/device:TPU:1"}) {

    %0 = "tf.Const"() {value = dense<100> : tensor<i32>} : () -> tensor<i32>
    // CHECK: %[[STATE0:.*]] = "tf.VarHandleOp"()
    // CHECK-SAME: device = "/device:TPU:0"
    // CHECK: %[[STATE1:.*]] = "tf.VarHandleOp"()
    // CHECK-SAME: device = "/device:TPU:1"
    // CHECK: %[[WHILE:.*]] = "tf.WhileRegion"(
    %1 = "tf.WhileRegion"(%0) ({
       // Condition region

// 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)