%1 = "tf.LookupTableSizeV2"(%0) {device = ""} : (tensor<!tf_type.resource>) -> tensor<i64>
  // CHECK-LABEL: hashtable_size
  // CHECK-NEXT:  [[HASH_TABLE:%.*]] = "tfl.hashtable"() <{key_dtype = !tf_type.string, table_id = -1323619995 : i32, value_dtype = i64}> : () -> tensor<1x!tf_type.resource>

    auto value_dtype = hashtable.getValueDtype();

    // Only allow string -> int64 and int64 -> string mappings due to kernel
    // capability.
    if (!((mlir::isa<TF::StringType>(key_dtype) &&
           mlir::isa<IntegerType>(value_dtype) &&
           mlir::cast<IntegerType>(value_dtype).getWidth() == 64) ||
          (mlir::isa<TF::StringType>(value_dtype) &&

    %0 = "tf.HashTableV2"() {container = "", device = "", key_dtype = i64, shared_name = "hash_table_dba2ccaa-f1b1-46d6-b276-98008f69da71", use_node_name_sharing = false, value_dtype = !tf_type.string} : () -> tensor<!tf_type.resource>
    "tf.LookupTableImportV2"(%0, %cst, %cst_0) {device = ""} : (tensor<!tf_type.resource>, tensor<4xi64>, tensor<4x!tf_type.string>) -> ()
    func.return

    %0 = "tf.HashTableV2"() {container = "", device = "", key_dtype = i64, shared_name = "hash_table_dba2ccaa-f1b1-46d6-b276-98008f69da71", use_node_name_sharing = false, value_dtype = !tf_type.string} : () -> tensor<!tf_type.resource>
    "tf.LookupTableImportV2"(%0, %cst, %cst_0) {device = ""} : (tensor<!tf_type.resource>, tensor<4xi64>, tensor<4x!tf_type.string>) -> ()

  // CHECK-SAME: key_dtype = !tf_type.string
  // CHECK-SAME: shared_name = "table"
  // CHECK-SAME: value_dtype = i32
  // CHECK-SAME: device = ""
  // CHECK-SAME: () -> tensor<!tf_type.resource>
  %handle = "tf.HashTable"() {container = "", device = "", shared_name = "table", key_dtype = !tf_type.string, value_dtype = i32} : () -> tensor<*x!tf_type.stringref>

  // CHECK: "tf.InitializeTableFromTextFileV2"([[handle]]

  auto _table_id = _o->table_id;
  auto _key_dtype = _o->key_dtype;
  auto _value_dtype = _o->value_dtype;
  return tflite::CreateHashtableOptions(
      _fbb,
      _table_id,
      _key_dtype,
      _value_dtype);
}

inline HashtableFindOptionsT *HashtableFindOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {

           << "Expect " << kValidNumOfOutput[input_type.getRank()]
           << "output(s) when input has rank " << input_type.getRank();
  }

  auto value_type = GetResultType(func, 0);
  if (!RankEquals(value_type, 1) ||
      !mlir::isa<StringType>(value_type.getElementType())) {
    return func.emitError() << "1st output should be string tensor";
  }
  if (func.getNumResults() > 1) {

                      llvm::ArrayRef<int32_t> redux_axes,
                      PatternRewriter* rewriter) {
  Value redux_op = createI32ConstantOp(redux_axes, loc, rewriter);

  auto value_type = mlir::cast<RankedTensorType>(value.getType());
  auto shape = value_type.getShape();
  llvm::SmallVector<int64_t> sum_shape;
  for (int i = 0; i < shape.size(); ++i) {
    if (std::find(redux_axes.begin(), redux_axes.end(), i) ==
        redux_axes.end()) {

    } finally {
        leaveScope(scope)
    }
}


internal
fun checkIsAssignable(valueType: DataType, isAssignableTo: DataType): Boolean = when (isAssignableTo) {
    is DataClass -> valueType is DataClass && (sameType(valueType, isAssignableTo) || isAssignableTo.name in valueType.supertypes)
    else -> sameType(valueType, isAssignableTo)
}


/**

        Class<?> valueType = propertyMetadata.getDeclaredType().getRawType();
        validateNotDirectoryType(propertyMetadata, validationContext, valueType);
        validateNotFileType(propertyMetadata, validationContext, valueType);
        validateNotOptionalPrimitiveType(propertyMetadata, validationContext, valueType);
        validateNotUrlType(propertyMetadata, validationContext);
    }