.create<TF::ReadVariableOp>(
          loc,
          ArrayRef<Type>{mlir::cast<TF::ResourceType>(
                             getElementTypeOrSelf(local_var.getType()))
                             .getSubtypes()[0]},
          ArrayRef<Value>{local_var})
      .getValue();
}

// Creates an AssignVariableOp on a local variable.
TF::AssignVariableOp WriteLocalVariable(Value local_var, Value value,

  auto variant_type =
      getElementTypeOrSelf(getOperand().getType()).cast<TF::VariantType>();
  if (variant_type.getSubtypes().empty()) return {};
  return ConvertShapeToAttr(variant_type.getSubtypes()[0], width);
}

//===----------------------------------------------------------------------===//
// TensorListStackOp

    Type type, llvm::SmallVector<int64_t, 8>* shape) {
  auto variant_type =
      mlir::dyn_cast<TF::VariantType>(getElementTypeOrSelf(type));
  if (!variant_type || variant_type.getSubtypes().size() != 1) return failure();
  TensorType tensor_type = variant_type.getSubtypes().front();
  if (!tensor_type.hasStaticShape()) return failure();
  for (auto d : tensor_type.getShape()) shape->push_back(d);
  return success();
}

    return size.emitOpError("unknown tensor array");
  }
  auto buffer_type = getElementTypeOrSelf(local_var.getType())
                         .cast<TF::ResourceType>()
                         .getSubtypes()[0]
                         .cast<RankedTensorType>();
  OpBuilder builder(size);
  auto result = cutil::CreateScalarConst(buffer_type.getDimSize(0), builder,
                                         size.getLoc());

    // Get the data type of the resource.
    auto subtypes = mlir::cast<TF::ResourceType>(
                        getElementTypeOrSelf(execute.getOperand(it->second)))
                        .getSubtypes();
    if (subtypes.size() != 1) continue;
    auto data_type = getElementTypeOrSelf(subtypes[0]);
    // The XLA backend does not yet support formatting 64-bit data types.

  if (mlir::isa<mlir::TF::StringType>(type)) return true;

  auto sub_type = mlir::dyn_cast<mlir::TF::TensorFlowTypeWithSubtype>(type);
  if (!sub_type) return false;

  bool has_string = llvm::any_of(sub_type.GetSubtypes(), [](TensorType type) {
    return mlir::isa<mlir::TF::StringType>(type.getElementType());
  });
  return has_string;
}

bool HasStringOperand(Operation& op) {
  for (auto operand : op.getOperands()) {

  if (auto resource_type =
          mlir::dyn_cast<mlir::TF::ResourceType>(arg_type.getElementType())) {
    llvm::ArrayRef<mlir::TensorType> subtypes = resource_type.getSubtypes();
    if (!subtypes.empty()) {
      AttrValue handle_dtypes_attr;
      AttrValue handle_shapes_attr;
      for (mlir::TensorType subtype : subtypes) {
        DataType dtype;

  if (auto resource_type =
          mlir::dyn_cast<mlir::TF::ResourceType>(arg_type.getElementType())) {
    llvm::ArrayRef<mlir::TensorType> subtypes = resource_type.getSubtypes();
    if (!subtypes.empty()) {
      AttrValue handle_dtypes_attr;
      AttrValue handle_shapes_attr;
      for (mlir::TensorType subtype : subtypes) {
        DataType dtype;

      "return getElementTypeOrSelf(element_type());">;

  let extraClassDeclaration = [{
    // Returns type of the TensorList element produced by this op.
    TensorType element_type() { return handle_dtype().getSubtypes()[0]; }

    // Returns data type of the result handle. Returned type contains type of
    // the TensorList element as a subtype.
    VariantType handle_dtype() {

  }

  // We only support up to one nested type in tf_type.variant_type.
  if (variant_type.getSubtypes().size() > 1) {
    return std::nullopt;
  }
  if (variant_type.getSubtypes().empty()) {
    return variant_params;
  }
  mlir::TensorType tensor_type = variant_type.getSubtypes().front();
  tflite::TensorType tflite_element_type =
      GetTFLiteType(tensor_type.getElementType()).value();