static std::string getTypeName() { return "TFRTensorType"; }
};

class TFRTensorListType : public detail::TFRTypeImpl<TFRTensorListType> {
 public:
  using TFRBase::TFRBase;
  static constexpr StringLiteral name = "tfr.tensor_list";
  static std::string getTypeName() { return "TFRTensorListType"; }
};

class TFRAttrType : public Type::TypeBase<TFRAttrType, TFRType, TypeStorage> {
 public:

    : Dialect(/*name=*/"tfr", context, TypeID::get<TFRDialect>()) {
  // TFR depends on TensorFlow for its canonicalization
  context->getOrLoadDialect<TF::TensorFlowDialect>();

  addTypes<TFRTensorType, TFRTensorListType, TFRAttrType>();
  addOperations<
#define GET_OP_LIST
#include "tensorflow/compiler/mlir/tfr/ir/tfr_ops.cc.inc"
      >();

  addInterfaces<TFRInlinerInterface>();
}

      return success();
    }
  }

  // If there is an attribute associated to the input in the signature,
  // we store it as an derived attribute.
  if (auto list_type = mlir::dyn_cast<TFRTensorListType>(input_tfr_type)) {
    auto attr_names = list_type.getAttrKeys();
    if (attr_names.empty()) return success();

    // N*T case
    if (attr_names.size() == 2) {

                                             op->getOperand(arg.index()));
        new_operands.push_back(casted);
      } else if (auto list_type =
                     mlir::dyn_cast<TFRTensorListType>(arg.value())) {
        llvm::SmallVector<Value, 4> variadic_operands;
        for (int i = arg.index(); i < op->getNumOperands(); i++) {
          auto casted = builder.create<CastOp>(