// CHECK-LABEL: func @cluster_with_nested_loop
func.func @cluster_with_nested_loop() -> () {
  // CHECK: %[[VH:.*]] = "tf.VarHandleOp"()
  %0 = "tf.VarHandleOp"() {container = "c", shared_name = "v"} : () -> tensor<*x!tf_type.resource<tensor<f32>>>
  // CHECK: %[[VH_UNUSED:.*]] = "tf.VarHandleOp"()
  %1 = "tf.VarHandleOp"() {container = "c", shared_name = "v2"} : () -> tensor<*x!tf_type.resource<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]]

    // CHECK: tf_executor.island wraps "tf.VarHandleOp"() <{container = "a", shared_name = "x"}> {_mlir_name = "x", _output_shapes = [#tf_type.shape<>], allowed_devices = [], device = "/device:CPU:0", dtype = i64, shape = #tf_type.shape<>} : () -> tensor<!tf_type.resource<tensor<i64>>>

    %outputs_7, %control_8 = tf_executor.island wraps "tf.Const"() {value = dense<"MatMul/b_0"> : tensor<1x!tf_type.string>} : () -> tensor<1x!tf_type.string>
    %outputs_9, %control_10 = tf_executor.island wraps "tf.VarHandleOp"() {container = "", shared_name = "MatMul/b_0"} : () -> tensor<!tf_type.resource<tensor<20x4096xf32>>>

    // 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>

// RUN: tf-opt -verify-diagnostics -tf-readonly-references-to-resources -split-input-file %s | FileCheck %s

// Test case: Basic converting.

func.func @f() {
  // CHECK: "tf.VarHandleOp"
  // CHECK: "tf.ReadVariableOp"
  %val0 = "tf.VariableV2"() {_class = ["loc:@v"], container = "", device = "", shape = #tf_type.shape<96>, shared_name = ""} : () -> tensor<96x!tf_type.f32ref>
  %val1 = "tf.Identity"(%val0) : (tensor<96x!tf_type.f32ref>) -> tensor<96xf32>

    %handle_0 = "tf.VarHandleOp"() {container="c", shared_name="a"} : () -> tensor<!tf_type.resource<tensor<1x10xf32>>>
    %handle_1 = "tf.VarHandleOp"() {container="c", shared_name="b"} : () -> tensor<!tf_type.resource<tensor<1x10xi64>>>

  %arg0: !tf_res {tf.device = "/TPU:0"},
  %arg1: !tf_res {tf.device = "/TPU:1"}) {
  tf_executor.graph {
    // CHECK: tf_executor.island
    %island = tf_executor.island {
      // CHECK-NEXT: "tf.VarHandleOp"
      %var_handle = "tf.VarHandleOp"() {container = "c", shared_name = "v0", device = "/CPU:0"}
        : () -> !tf_res
      // CHECK-NEXT: "tf.Identity"
      // CHECK-SAME: {device = "/TPU:0"}
      %id0 = "tf.Identity"(%arg0) : (!tf_res)

    %0 = "tf.VarHandleOp"() {_class = ["loc:@dense/kernel"], allowed_devices = [], container = "", device = "", shared_name = "dense/kernel"} : () -> tensor<!tf_type.resource<tensor<100x50xf32>>>
    %1 = "tf.ReadVariableOp"(%0) {device = ""} : (tensor<!tf_type.resource<tensor<100x50xf32>>>) -> tensor<100x50xf32>