*tensor_proto = TensorProtoHelpers::GenerateTensorProto();

  CreateAndCallKernelWithAttr(my_create_func, "TestKernelAttrTensor", v);
}

TEST_F(TestKernelAttr, TensorList) {
  struct TensorProtoHelpers {
   public:
    static ::tensorflow::TensorProto GenerateTensorProto1() {
      ::tensorflow::TensorProto tensor_proto;
      tensor_proto.mutable_tensor_shape()->add_dim()->set_size(2);

      "return getElementTypeOrSelf(element_type());">;

  let extraClassDeclaration = [{
    // Returns type of the TensorList element produced by this op.
    TensorType element_type() { return handle_dtype().getSubtypes()[0]; }

    // Returns data type of the result handle. Returned type contains type of
    // the TensorList element as a subtype.
    VariantType handle_dtype() {

  LogicalResult matchAndRewrite(Operation *op,
                                PatternRewriter &rewriter) const override {
    auto addn_op = cast<AddNOp>(op);

    // TODO(hinsu): Support variant with TensorList type. tf.AddV2 doesn't
    // support variant type so variant types require special handling.
    if (mlir::isa<VariantType>(getElementTypeOrSelf(addn_op.getType())))
      return failure();

  }];

  let arguments = (ins
    Arg<Variadic<TF_Float32Tensor>, [{A TensorList of gradients with which to update embedding tables.}]>:$gradients,
    Arg<Variadic<TF_Float32Tensor>, [{A TensorList of learning rates used for updating the embedding
tables via the optimizer. The length of the TensorList must be equal to the
number of dynamic learning rate tags specified in the

                              TF_TString_GetSize(t_out_tstr)));
  TF_DeleteTensor(t_out);
  TF_TString_Dealloc(&tstr[0]);
}

TEST_F(CApiAttributesTest, TensorList) {
  const char tensor1[] = {5, 7};
  const int64_t dims1[] = {1, 2};
  const size_t ndims1 = TF_ARRAYSIZE(dims1);

  const char tensor2[] = {2, 4, 6, 8};
  const int64_t dims2[] = {2, 2};

}

// -----

// 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>) {

}

// 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

    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,

  %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.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