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

  %value0 = "tf.LookupTableFindV2"(%handle, %key0, %default) {device = "/job:localhost/replica:0/task:0/device:CPU:0"} : (tensor<!tf_type.resource>, tensor<!tf_type.string>, tensor<i64>) -> tensor<i64>

    %3 = "tf.StringToHashBucketFast"(%arg0) {device = "", num_buckets = 5 : i64} : (tensor<?x!tf_type.string>) -> tensor<?xi64>
    %4 = "tf.AddV2"(%3, %1) {device = ""} : (tensor<?xi64>, tensor<i64>) -> tensor<?xi64>
    %5 = "tf.LookupTableFindV2"(%0, %arg0, %cst) {device = ""} : (tensor<!tf_type.resource>, tensor<?x!tf_type.string>, tensor<i64>) -> tensor<*xi64>
    return %5 : tensor<*xi64>
  }

// Check that HashTable op is lifted.

  // CHECK-DAG: [[r:%.*]] = "tf.LookupTableFindV2"([[v0]]
  // CHECK-DAG: return [[v1]], [[r]]
  %0 = "tf.HashTableV2"() {container = "", device = "", key_dtype = !tf_type.string, shared_name = "x", use_node_name_sharing = false, value_dtype = i64} : () -> tensor<!tf_type.resource>
  %1 = "tf.LookupTableSizeV2"(%0) {device = ""} : (tensor<!tf_type.resource>) -> tensor<i64>

  attributes {tf.entry_function = {control_outputs = "", inputs = "input:0", outputs = "hash_table_Lookup/LookupTableFindV2:0"}, tf_saved_model.exported_names = ["serving_default"]} {
    %cst = arith.constant dense<"f"> : tensor<!tf_type.string>

  %1 = "tf.LookupTableFindV2"(%0, %cst, %arg0) {device = ""} : (tensor<!tf_type.resource>, tensor<!tf_type.string>, tensor<i64>) -> tensor<*xi64>
  // CHECK-LABEL: hashtable_string_to_int64

  if (type.hasRank()) return GetRankedTensorSize(type);
  return context.default_unranked_tensor_size;
}

// The cost function for tf.LookupTableFindV2.
int64_t InferLookupTableFindV2Cost(const CostContext& context,
                                   mlir::TF::LookupTableFindV2Op op) {
  // tf.LookupTableFindV2 ops are usually more costly than tf.AddV2 with the
  // same input size, as it involves more operations like hashing, map lookup,
  // etc.

}

func.func private @body(%arg: tensor<i32>, %handle: tensor<!tf_type.resource>) -> (tensor<i32>, tensor<!tf_type.resource>) {
  %default = "tf.Const"() {value = dense<16> : tensor<i32> } : () -> tensor<i32>
  %0 = "tf.LookupTableFindV2"(%handle, %arg, %default) : (tensor<!tf_type.resource>, tensor<i32>, tensor<i32>) -> tensor<i32>
  func.return %0, %handle : tensor<i32>, tensor<!tf_type.resource>
}

// CHECK-LABEL: func @set_readonly_stateless

# CHECK-NEXT: [[R0:%.*]] = "tf.Const"()
# CHECK-NEXT: [[R1:%.*]] = "tf.HashTableV2"()
# CHECK-SAME: shared_name = "[[hash_table]]"
# CHECK-NEXT: [[R2:%.*]] = "tf.LookupTableFindV2"([[R1]], [[ARG0]], [[R0]])
# CHECK-NEXT: [[R3:%.*]] = "tf.ReadVariableOp"([[ARG1]])
# CHECK-NEXT: [[R4:%.*]] = "tf.AddV2"([[R2]], [[R3]])
# CHECK-NEXT: return [[R4]]


def Test():

      %out_1, %ctl_2 = tf_executor.island wraps "tf.LookupTableFindV2"(%out_0, %arg0, %out) {device = ""} : (tensor<!tf_type.resource>, tensor<?x!tf_type.string>, tensor<i64>) -> tensor<*xi64>
      tf_executor.fetch %out_1 : tensor<*xi64>
    }
    return %0 : tensor<*xi64>
  }

  // 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]]