// CHECK-NEXT:  [[HASH_TABLE:%.*]] = "tfl.hashtable"() <{key_dtype = !tf_type.string, table_id = -1323619995 : i32, value_dtype = i64}> : () -> tensor<1x!tf_type.resource>
  // CHECK-NEXT:   "tfl.hashtable_import"([[HASH_TABLE]], [[CST]], [[CST_1]]) : (tensor<1x!tf_type.resource>, tensor<3x!tf_type.string>, tensor<3xi64>) -> ()

  %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>

  // CHECK: tf.LookupTableFindV2
  // CHECK-NOT: tf.Assert
  // CHECK: tf.LookupTableFindV2
  // CHECK: tf.Assert

    return
  }
  // CHECK: @init_func_restore_op
  // CHECK-SAME: tf_saved_model.initializer_type = "restore_op"
  // CHECK: %[[CST:.*]] = "tf.Const"()
  // CHECK-NEXT: %[[HASH_TABLE:.*]] = "tf.HashTableV2"()
  // CHECK-NEXT: "tf.AssignVariableOp"(%[[HASH_TABLE]], %[[CST]])
}

// -----


// Nothing happens when there are no `tf_saved_model.session_initializer`.

// CHECK-LABEL: module

# CHECK-NEXT: [[R6:%.*]] = "tf.Const"()
# CHECK-NEXT: [[R5:%.*]] = "tf.Const"() <{value = dense<[1, 2,
# CHECK-NEXT: [[R7:%.*]] = "tf.HashTableV2"()
# CHECK-SAME: shared_name = "[[hash_table:.*]]"
# CHECK-NEXT: "tf.LookupTableImportV2"([[R7]], [[R5]], [[R6]])

# CHECK:      func {{@[a-zA-Z_0-9]+}}(
# CHECK-SAME: [[ARG0:%.*]]: tensor<i32>
# CHECK-SAME: [[ARG1:%.*]]: tensor<!tf_type.resource

}

// Checks if the HashTable is initialized. This function assumes that the
// HashTable is initialized if it appears in the initializer since it can't
// check the actual value.
bool IsResourceInitialized(ModuleOp module_op, Operation* hash_table) {
  StringRef shared_name = GetSharedName(hash_table);
  if (shared_name.empty()) return false;

  for (func::FuncOp init_func_op :

}

// 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-SAME: [[ARG1:%.*]]: tensor<!tf_type.string> {tf_saved_model.bound_input = @[[asset1]]}
# CHECK-SAME: tf_saved_model.initializer_type = "init_op"
# CHECK-NEXT: [[R0:%.*]] = "tf.HashTableV2"()
# CHECK-SAME: shared_name = "[[hash_table:.*]]"
# CHECK-NEXT: "tf.InitializeTableFromTextFileV2"([[R0]], [[ARG0]])


def write_vocabulary_file(vocabulary):
  """Write temporary vocab file for module construction."""
  tmpdir = tempfile.mkdtemp()

    // 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>
    // expected-remark@+1 {{Cost: 1024}}

  %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>


  %keys = "tf.Const"() {value = dense<["a", "b", "c", "d"]> : tensor<4x!tf_type.string>} : () -> tensor<4x!tf_type.string>

    return %6 : tensor<*xi64>
  }
// Check that HashTable op is lifted.
// CHECK: func.func private @serving_default
// CHECK-SAME: (%arg0: tensor<?x!tf_type.string>, %arg1: tensor<!tf_type.resource>, %arg2: tensor<!tf_type.resource>) -> tensor<*xi64>
// CHECK-SAME: tf.entry_function = {control_outputs = "", inputs = "input_vocabs:0,hash_table_1:0,hash_table_2:0", outputs = "FakeQuantWithMinMaxArgs_2:0"}