// of the index path attribute.
SmallVector<NamedAttribute> ReplaceBoundInputAttrWithIndexPathAttr(
    const ArrayRef<NamedAttribute> arg_attrs, const StringRef index_path,
    Builder& builder) {
  // Keep all other attributes except the tf_saved_model.bound_input attribute,
  // as we are replacing it with tf_saved_model.index_path.
  SmallVector<NamedAttribute> new_arg_attrs;
  for (auto arg_attr : arg_attrs) {

// (NamedAttributeList (NamedAttr<"transpose_a"> $transpose_a))
def NamedAttributeList : NativeCodeCall<"llvm::SmallVector<NamedAttribute>{$0...}">;

// Creates a NamedAttribute given its name and value. Essentially creates
// a pair: {attribute_name, attribute_value}.
class NamedAttr<string attr_name> :
    NativeCodeCall<"NamedAttribute{$_builder.getStringAttr(\"" # attr_name # "\"), $0}">;

        NamedAttribute num_layers_attr(
            num_layers_str,
            IntegerAttr::get(IntegerType::get(fn.getContext(), /*width=*/32),
                             num_layers));

        // current_layer_index Composite Attribute.
        mlir::StringAttr current_layer_str =
            builder.getStringAttr("layer_index");
        NamedAttribute current_layer_attr(

                                         NamedAttribute named_attr) override;
  LogicalResult verifyRegionResultAttribute(Operation *op,
                                            unsigned region_index,
                                            unsigned result_index,
                                            NamedAttribute named_attr) override;
  LogicalResult verifyOperationAttribute(Operation *op,

void CopyAttributes(Operation *from, Operation *to, Predicate P) {
  for (const NamedAttribute &attr : from->getAttrs())
    if (P(attr)) to->setAttr(attr.getName(), attr.getValue());
}

// Copies attributes whose name begins with an _ from `from` to `to`.
inline void CopyUnderscoredAttributes(Operation *from, Operation *to) {
  CopyAttributes(from, to, [](const NamedAttribute &attr) {
    return attr.getName().strref().front() == '_';
  });
}

LogicalResult SmuggleOp(Operation* op, PatternRewriter& rewriter) {
  auto call_target =
      rewriter.getNamedAttr("call_target_name", op->getName().getIdentifier());
  SmallVector<NamedAttribute> attrs{op->getAttrs()};
  attrs.push_back(call_target);
  auto custom_call = rewriter.create<mlir::stablehlo::CustomCallOp>(
      op->getLoc(), op->getResultTypes(), op->getOperands(), attrs);

          versions.bad_consumers().data(),
          versions.bad_consumers().data() + versions.bad_consumers().size())));
  module->setAttr("tf.versions",
                  b.getDictionaryAttr(llvm::ArrayRef<mlir::NamedAttribute>(
                      {producer, min_consumer, bad_consumers})));
}

mlir::LogicalResult ExtractTfVersions(mlir::ModuleOp module,
                                      VersionDef* versions) {
  versions->Clear();

// "name" and "device" attributes are ignored by default. Use attrs_to_ignore to
// specify any other attributes that should be ignored.
Status ConvertAttributes(
    llvm::ArrayRef<mlir::NamedAttribute> attrs,
    const absl::flat_hash_set<absl::string_view>& attrs_to_ignore,
    bool remove_ref_type, AttrValueMap* values);

// Fill in the contents of TensorShapeProto for the given shape.

      return true;
    }
  }

  SymbolTable symbol_table(op->getParentOfType<ModuleOp>());
  // Check the functions associated to CaseOp, IfOp and WhileOp.
  for (const NamedAttribute& attr : op->getAttrs()) {
    FlatSymbolRefAttr symbol_attr =
        dyn_cast_or_null<FlatSymbolRefAttr>(attr.getValue());
    if (!symbol_attr) continue;

            {}, TF::ResourceType::get(ArrayRef<TensorType>{tensor_type},
                                      builder.getContext()))},
        ArrayRef<Value>{},
        ArrayRef<NamedAttribute>{
            builder.getNamedAttr("device", device_attr),
            builder.getNamedAttr("container",
                                 variable_v2_op.getContainerAttr()),
            builder.getNamedAttr("shared_name",