%cst_6 = "tf.Const"() {value = dense<0.00156862743> : tensor<f32>} : () -> tensor<f32>
    %0 = "tf.HashTableV2"() {container = "", device = "", key_dtype = !tf_type.string, shared_name = "hash_table_1", use_node_name_sharing = false, value_dtype = i64} : () -> tensor<!tf_type.resource>

      %out_0, %ctl_1 = tf_executor.island wraps "tf.Const"() {device = "", value = dense<[1]> : tensor<1xi64>} : () -> tensor<1xi64>
      %out_1, %ctl_2 = tf_executor.island wraps "tf.HashTableV2"() {container = "", device = "", key_dtype = !tf_type.string, shared_name = "1", use_node_name_sharing = false, value_dtype = i64} : () -> tensor<!tf_type.resource>

}

// CHECK-LABEL: @merge_hashtable_ops
// CHECK: %[[OUT_0:.*]], %[[CTL_0:.*]] = tf_executor.island wraps "tf.HashTableV2"()
// CHECK: %[[OUT_1:.*]], %[[CTL_1:.*]] = tf_executor.island wraps "tf.LookupTableSizeV2"(%[[OUT_0]])

// Check that LookupTableImportV2 is using the same HashTableV2 with LookupTableSizeV2.

              StrEq("arg_0:0"));
}

TEST_F(ConvertMlirModuleToExportedModelTest,
       InitNodeNameSetToLocOfControlOutput) {
  // Define a module that initializes a tf.HashTableV2 whose control output node
  // for the initialization is named "init_op_init_all_tables".
  mlir::OwningOpRef<mlir::ModuleOp> module_op = ParseModuleOpString(R"mlir(
    module attributes {tf_saved_model.semantics} {

}

// CHECK-LABEL: func @set_readonly_stateless
func.func @set_readonly_stateless(%arg: tensor<i32>, %cond: tensor<i1>) -> tensor<i32> {
  %handle = "tf.HashTableV2"() {container = "", key_dtype = i32, shared_name = "hash_table", use_node_name_sharing = false, value_dtype = i32} : () -> tensor<!tf_type.resource>
  // CHECK: tf.While
  // CHECK-SAME: is_stateless = true

  // CHECK-NOT: tf.HashTableV2
  // CHECK-NOT: tf.LookupTableSizeV2
  // CHECK: [[v:%.*]]:2 = "tf._TfrtGetResource"() <{container = ["", ""], indices = [0, 1], shared_name = [{{.*}}, {{.*}}]}> {device = "/job:localhost/replica:0/task:0/device:CPU:0"}
  // CHECK: [[r:%.*]] = "tf.LookupTableFindV2"([[v]]#[[handle_idx]]
  // CHECK: return [[v]]#[[size_idx]], [[r]]

  func.func @NoOp(%arg0: tensor<!tf_type.string> {tf_saved_model.bound_input = @__tf_saved_model_asset0_mydata.txt}) attributes {tf_saved_model.exported_names = ["__tf_saved_model_session_initializer_NoOp"]} {
    %0 = "tf.HashTableV2"() {container = "", device = "", key_dtype = !tf_type.string, shared_name = "", use_node_name_sharing = false, value_dtype = i64} : () -> tensor<!tf_type.resource>

  func.return %1 : tensor<i32>
}

}

// -----

module attributes {tf_saved_model.semantics} {

// Test sinks in HashTableV2 op to batch function.

// CHECK-LABEL: func private @batched_function
func.func private @batched_function(%arg0: tensor<1xi32>, %arg1: tensor<*x!tf_type.resource>) -> tensor<1xi32>

    // expected-remark@+1 {{Cost: 1}}
    %default = "tf.Const"() {device = "/job:localhost/replica:0/task:0/device:CPU:0", value = dense<-1> : tensor<i64>} : () -> tensor<i64>
    // expected-remark@+1 {{Cost: 1}}
    %0 = "tf.HashTableV2"() {container = "", device = "/job:localhost/replica:0/task:0/device:CPU:0", key_dtype = !tf_type.string, shared_name = "hash_table", use_node_name_sharing = false, value_dtype = i64} : () -> tensor<!tf_type.resource>

  %error_message = "tf.Const"() {value = dense<"error"> : tensor<!tf_type.string>} : () -> tensor<!tf_type.string>
  %default = "tf.Const"() {value = dense<-1> : tensor<i64>} : () -> tensor<i64>
  %handle = "tf.HashTableV2"() {container = "", device = "/job:localhost/replica:0/task:0/device:CPU:0", key_dtype = !tf_type.string, shared_name = "hash_table", use_node_name_sharing = false, value_dtype = i64} : () -> tensor<!tf_type.resource>