}
  return mlir::success();
}

static mlir::LogicalResult ReformatOpAttributes(
    mlir::MLIRContext *context, llvm::ArrayRef<mlir::NamedAttribute> attrs,
    llvm::SmallVectorImpl<mlir::NamedAttribute> &output) {
  for (mlir::NamedAttribute attr : attrs) {
    if (attr.getName().strref().contains(
            mlir::tfg::TFGraphDialect::getDeviceAttrKey())) {
      tensorflow::DeviceNameUtils::ParsedName parsed_name;

                              const ArrayRef<NamedAttribute> attributes,
                              const ArrayRef<Operation*> ops) {
  // A map to find which operation an attribute belongs to.
  // The key for this map uses the entire NamedAttribute object, i.e. the
  // {attribute_name, attribute_value} pair.
  llvm::SmallDenseMap<NamedAttribute, Operation*> attr_to_op_map;
  for (Operation* op : ops) {

    // NOLINTNEXTLINE
    Location loc, llvm::SmallVectorImpl<mlir::NamedAttribute> *attributes);

// TODO(zichuanwei@): Populate Builtin_options_2 manual for now, should automate
// these in the future
void BuiltinOptions2ToAttributes(
    tflite::BuiltinOptions2Union op_union, mlir::Builder builder,
    llvm::SmallVectorImpl<mlir::NamedAttribute> &attributes);

  if (auto device_attr =
          func.getArgAttrOfType<mlir::StringAttr>(index, kDeviceAttr))
    *node_def->mutable_device() = device_attr.getValue().str();

  llvm::ArrayRef<mlir::NamedAttribute> func_arg_i_attrs =
      mlir::function_interface_impl::getArgAttrs(func, index);
  absl::flat_hash_set<absl::string_view> attrs_to_ignore = {kDeviceAttr,

  return node_def;
}

Status ConvertAttributes(
    const llvm::ArrayRef<mlir::NamedAttribute> attrs,
    const absl::flat_hash_set<absl::string_view>& attrs_to_ignore,
    bool remove_ref_type, AttrValueMap* values) {
  AttrValueMap func_call_attrs;
  for (const mlir::NamedAttribute& named_attr : attrs) {
    auto name_strref = named_attr.getName().str();
    auto attr = named_attr.getValue();

              << " but enclosing XlaCallModule has a function list of size "
              << function_list.size();
          return WalkResult::interrupt();
        }

        llvm::SmallVector<NamedAttribute> new_config;
        // Copy the attributes in the current config except `called_index`.
        for (auto attr : backend_config) {
          if (attr.getName() != kCalledIndexAttrName) {

                            rewriter.getIntegerType(64)),
      padding_nums);
  return padding_attr;
}

template <typename UniformQuantizedConvolutionOp>
FailureOr<SmallVector<NamedAttribute>> ConvertToMhloConvolutionOpAttrs(
    UniformQuantizedConvolutionOp op, PatternRewriter &rewriter) {
  // TODO(b/261005147): Update the lowering logic after migration to mhlo
  // ConvolutionDimensionNumbers.

    for (auto [value, aggregator_id] :
         llvm::zip_equal(quantizable_values, aggregator_ids)) {
      // ID attribute will have empty value for now.
      SmallVector<NamedAttribute, 5> attributes{
          rewriter.getNamedAttr("id", rewriter.getStringAttr(aggregator_id)),
          rewriter.getNamedAttr(
              "calibration_method",

}

Status mlir::CustomOptionsToAttributes(
    const std::string& custom_code, const std::vector<uint8_t>& custom_options,
    mlir::Builder builder, mlir::Location loc,
    llvm::SmallVectorImpl<mlir::NamedAttribute>* attributes) {
  attributes->emplace_back(
      builder.getNamedAttr("custom_code", builder.getStringAttr(custom_code)));
  std::string content;
  content.assign(reinterpret_cast<const char*>(custom_options.data()),

    // original ophint converted node's 3rd output.
    SmallVector<Type, 4> result_types;
    result_types.push_back(op->getOpResult(2).getType());

    // Populate attributes.
    SmallVector<NamedAttribute, 4> attributes;
    // Activation will always be tanh.
    attributes.push_back(rewriter.getNamedAttr("fused_activation_function",
                                               rewriter.getStringAttr("TANH")));