ArrayRef<StringAttr> getAttrKeys() { return Base::getImpl()->GetAttrs(); }
};
}  // namespace detail

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

class TFRTensorListType : public detail::TFRTypeImpl<TFRTensorListType> {
 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>();
}

  }
  // Create the tfr.cast ops on the results and replace the uses of the
  // original call op.
  TFRTensorType unconstrainted_type = rewriter.getType<TFRTensorType>();
  SmallVector<Value, 4> new_results;
  for (auto res : llvm::enumerate(call_op.getResultTypes())) {
    Type res_type = res.value();
    if (mlir::dyn_cast<TFRTensorType>(res_type)) {
      Value new_res = new_op->getResult(res.index());

        op->getName().getStringRef().str());

    auto compose_func_type = compose_func.getFunctionType();
    builder.setInsertionPoint(op);
    TFRTensorType unconstrainted_tensor_type = builder.getType<TFRTensorType>();

    // Create the new operands. This is mapping the operands from the target
    // TF ops to the TFR function arguments. If the TFR function argument is