if (tensorflow::IsRefType(dtype)) dtype = tensorflow::RemoveRefType(dtype);
  value->set_type(dtype);
  return absl::OkStatus();
}

Status ConvertAttribute(const mlir::TypeAttr& type, bool remove_ref_type,
                        AttrValue* value) {
  return ConvertAttribute(type.getValue(), remove_ref_type, value);
}

Status ConvertAttribute(const mlir::UnitAttr& attr, AttrValue* value) {

        "effective_hidden_scale_intermediate"};
    for (auto type_and_name :
         llvm::zip(intermediate_types, kIntermediateNames)) {
      mlir::TypeAttr type_attr =
          mlir::TypeAttr::get(std::get<0>(type_and_name));
      auto named_attr =
          builder.getNamedAttr(std::get<1>(type_and_name), type_attr);
      op_state.addAttribute(named_attr.getName(), named_attr.getValue());

    builder.create<tf_saved_model::GlobalTensorOp>(
        NameLoc::get(builder.getStringAttr(name.str())),
        builder.getStringAttr(name), tensor_attr,
        TypeAttr::get(tensor_attr.getType()), builder.getUnitAttr());
  }

  return success();
}

}  // namespace

LogicalResult LiftVariables(ModuleOp module, Session* session) {

    // determined while going through quantization passes.
    OptionalAttr<TypeAttr>:$input_to_input_intermediate,
    OptionalAttr<TypeAttr>:$input_to_forget_intermediate,
    OptionalAttr<TypeAttr>:$input_to_cell_intermediate,
    OptionalAttr<TypeAttr>:$input_to_output_intermediate,
    OptionalAttr<TypeAttr>:$effective_hidden_scale_intermediate
  );

  let results = (outs AnyTensor:$output);

            res_types, res_attrs)))
      return failure();

    // Update the function type which has excluded the control args.
    func->setAttr("function_type", TypeAttr::get(rewriter.getFunctionType(
                                       arg_types, res_types)));

    // Update arg/result attributes.
    func.setAllArgAttrs(arg_attrs);
    func.setAllResultAttrs(res_attrs);

  return builder.getI32IntegerAttr(value);
}

static mlir::Attribute BuildTypeAttr(tflite::TensorType value,
                                     mlir::Builder builder) {
  return mlir::TypeAttr::get(ConvertElementType(value, builder));
}

static mlir::Attribute BuildTFL_AFAttr(tflite::ActivationFunctionType value,
                                       mlir::Builder builder) {

    TypeAttr:$key_dtype,
    TypeAttr:$value_dtype
  );

  let results = (outs
    Res<TF_ResourceTensor, [{Handle to a table.}], [TF_LookupTableAlloc]>:$table_handle
  );

  let builders = [
    OpBuilder<(ins "StringAttr":$container, "StringAttr":$shared_name,
      "BoolAttr":$use_node_name_sharing, "TypeAttr":$key_dtype, "TypeAttr":$value_dtype),
    [{

      for (const std::string& intermediate : intermediate_names) {
        auto intermediate_attr = inst.getAttr(intermediate);
        if (auto attr =
                mlir::dyn_cast_or_null<mlir::TypeAttr>(intermediate_attr)) {
          Type qtype = attr.getValue();
          auto tensor_or = BuildTensorFromType(
              qtype, name_mapper_.GetUniqueName(intermediate).str());
          if (!tensor_or.has_value()) {

// attributes.
Type GetType(Attribute shape_attr, Attribute type_attr) {
  auto shape = mlir::cast<tf_type::ShapeAttr>(shape_attr);
  auto type = mlir::cast<TypeAttr>(type_attr);
  if (shape.hasRank())
    return tensorflow::GetTypeFromTFTensorShape(shape.getShape(),
                                                type.getValue());
  else