// 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();

// no single type in the resource.
Type GetResourceSubtype(Value value) {
  auto resource_type =
      mlir::dyn_cast<TF::ResourceType>(getElementTypeOrSelf(value.getType()));
  auto subtypes = resource_type.getSubtypes();
  if (subtypes.size() == 1) return subtypes[0];
  return nullptr;
}

// Replaces all `tf.VarIsInitializedOp` in a block with a constant true.
// TODO(b/171039585): Replace this with proper analysis of

  // This is required to populate derived attributes during export in a
  // meaningful way. Else during export to GraphDef element_type() query
  // will result in out of bounds access/assert.
  if (handle_dtype().getSubtypes().size() != 1) {
    return emitOpError(
        "must have exactly one subtype in the result variant type");
  }

  if (!IsOfRankOrUnranked(op.getElementShape(), 0) &&