CHECK:       }

      CHECK-LABEL: tfr.func @tf__test_identity_n_op(%x: !tfr.tensor_list) -> (!tfr.tensor_list) {
      CHECK-NEXT:   %[[raw_data:.*]] = tfr.quant_raw_data(%x) : (!tfr.tensor_list) -> (!tfr.tensor_list)
      CHECK:        tfr.return %[[raw_data:.*]] : !tfr.tensor_list
      CHECK:       }
    """
    self._check_code(mlir_code, mlir_code_exp)


if __name__ == '__main__':

}

// -----

// expected-error@+1 {{More than one tfr.tensor_list result isn't allowed}}
tfr.func @Foo_multiple_tensor_list_results(%arg0: !tfr.tensor<C>,
              %arg1: !tfr.tensor_list<A>,
              %arg2: i32 {tfr.name = "A"},
              %arg3: vector<1xi32> {tfr.name = "C"}) ->
    (!tfr.tensor_list<A>, !tfr.tensor_list<A>) {

    Example:

    ```mlir
    %2 = tfr.call @my_add(%0, %1) : (tfr.tensor, f32) -> tfr.tensor_list
    ```

    Note that the operands of the `call` operation can only be with tfr.tensor,
    tfr.tensor_list, tfr.attr and mlir float and integer types. The results of
    the `call` operation can only be with tfr.tensor and tfr.tensor_list types.
  }];

  let arguments = (ins
    FlatSymbolRefAttr:$callee,

}

// CHECK-LABEL: @tf__fused_n
tfr.func @tf__fused_n(
    %arg0: !tfr.tensor,
    %arg1: !tfr.tensor_list,
    %arg2: index {tfr.name="A",tfr.default=1:index})
  -> !tfr.tensor_list {
  %0 = tfr.call @tf__intermediate(%arg0) : (!tfr.tensor) -> !tfr.tensor
  %1 = tfr.get_element %arg1[%arg2] : (!tfr.tensor_list, index) -> !tfr.tensor
  %2 = tfr.call @tf__intermediate(%1) : (!tfr.tensor) -> !tfr.tensor

  %5 = "tfr.build_list"(%3, %4) : (!tfr.tensor, !tfr.tensor) -> !tfr.tensor_list
  %6 = tfr.quant_raw_data(%5) : (!tfr.tensor_list) -> !tfr.tensor_list
  %7 = "tfr.cast"(%cst_1) : (tensor<i64>) -> !tfr.tensor
  %8 = tfr.call @tf__concat(%7, %6) : (!tfr.tensor, !tfr.tensor_list) -> !tfr.tensor
  func.return %8 : !tfr.tensor

tfr.func @tf__risc_same_(!tfr.tensor<T>) -> !tfr.tensor<T> attributes {T}
tfr.func @tf__risc_concat_(!tfr.tensor_list<N, T>) -> !tfr.tensor<T> attributes {T, N}
tfr.func @tf__risc_split_(!tfr.tensor<T>, i32 {tfr.name="N"}) -> !tfr.tensor_list<N, T> attributes {T, N}
tfr.func @tf__risc_cast_(!tfr.tensor, !tfr.attr {tfr.name="K"}) -> !tfr.tensor<K> attributes {T, K}

// Replicates the TensorList initialization ops for all the uses.
// No need to delete the original TensorList as it might be used elsewhere.
template <typename T>
void ReplicateTensorListForUses(T tensor_list_op) {
  Value tensor_list = tensor_list_op.getResult();
  std::vector<OpOperand*> uses;
  for (auto& use : tensor_list.getUses()) {
    uses.emplace_back(&use);
  }

// OpDefs.

tfr.func @tf__my_add_n(%values: !tfr.tensor_list,
                       %n: i64 {tfr.name="N"}) -> !tfr.tensor {
  %index = arith.constant 0 : index
  %cst = arith.constant 1 : i64
  %eq = arith.cmpi eq, %n, %cst : i64
  %v1 = tfr.get_element %values[%index] : (!tfr.tensor_list, index) -> !tfr.tensor
  %res = scf.if %eq -> !tfr.tensor {
    scf.yield %v1 : !tfr.tensor
  } else {

  tfr.return %0 : !tfr.tensor
}

tfr.func @tf__my_pack(%values: !tfr.tensor_list,
                      %n: i32 {tfr.name="N"},
                      %axis: i32 {tfr.name="axis"}) -> !tfr.tensor {
  %index = arith.constant 0 : index
  %cst = arith.constant 1 : i32
  %eq = arith.cmpi eq, %n, %cst : i32
  %v1 = tfr.get_element %values[%index] : (!tfr.tensor_list, index) -> !tfr.tensor

constexpr char tfr_raw_text[] = R"(

tfr.func @tf__my_add_n(%values: !tfr.tensor_list,
                       %n: i64 {tfr.name="N"}) -> !tfr.tensor {
  %index = arith.constant 0 : index
  %cst = arith.constant 1 : i64
  %eq = arith.cmpi "eq", %n, %cst : i64
  %v1 = tfr.get_element %values[%index] : (!tfr.tensor_list, index) -> !tfr.tensor
  %res = scf.if %eq -> !tfr.tensor {
    scf.yield %v1 : !tfr.tensor
  } else {