// CHECK: [[tensor_list:%.*]] = "tf.TensorListReserve"([[shape:%.*]], [[reserve_size:%.*]]) {
  %0 = "tf.TensorListReserve"(%cst_1, %cst_2) {device = "/job:localhost/replica:0/task:0/device:CPU:0"} : (tensor<i32>, tensor<i32>) -> tensor<!tf_type.variant<tensor<*xf32>>>
  // CHECK: [[map_fn_result:%.*]] = tf_mlrt.tf_map_fn([[reserve_size]], [[tensor_list]], %arg0)

      output << " input_index=" << xla_output_description.input_index;
    if (xla_output_description.is_constant) output << " constant";
    if (xla_output_description.is_tensor_list) output << " tensor_list";
    output << '\n';
  }

  for (const auto& resource_update : compilation_result.resource_updates) {
    output << "// ResourceUpdate input_index=" << resource_update.input_index

  if (a.TypeId() == tensorflow::TypeIndex::Make<::tensorflow::TensorList>()) {
    TF_RETURN_IF_ERROR(ValidateVariantType<::tensorflow::TensorList>(a));
    *out = ::tensorflow::TensorList();

    return ::tensorflow::TensorListBinaryAdd(
        cc_ctx, *a.get<::tensorflow::TensorList>(),
        *b.get<::tensorflow::TensorList>(),
        out->get<::tensorflow::TensorList>(), cc_binary_add);

// Returns a `llvm::DenseMap` which maps from the index of tensorlist in the
// result, to the index of the same tensorlist in the arguments. For `If` op's
// branch functions, the results and arguments are not usually matched 1-1. This
// will let us konw which tensorlist result maps to which tensorlist in the
// arguments. Once we know this info it will help us decide the types of the
// result tensorlist based on the operand's of the `If` op.

// RUN: tf-opt -tfl-legalize-tensorlist -canonicalize -split-input-file %s | FileCheck %s

// CHECK-LABEL: listReserveScalarShapeI32
func.func @listReserveScalarShapeI32(%arg0: tensor<i32>, %arg1: tensor<i32>) -> tensor<!tf_type.variant<tensor<*xi32>>> {
  %0 = "tf.TensorListReserve"(%arg0, %arg1) : (tensor<i32>, tensor<i32>) -> tensor<!tf_type.variant<tensor<*xi32>>>

A. Unique TensorFlower <******@****.***> 1712999357 -0700

      });
  return has_variant_input || has_variant_output;
}

// There are 2 standard tf ops which are not TensorList ops that may take as
// input a tensorlist. These are tf.AddN and tf.ZeroesLike. Since the runtime
// implementation of a tensorlist are not compatible between tf and tflite
// we cannot use tflite tensorlist kernels until these cases are handled.
bool IsNonTensorListVariantOp(Operation* op) {

    // meaningless value -1 for non resource outputs.
    out_desc.input_index =
        it != output_to_input_alias.end() ? it->getSecond() : -1;
    // MLIR-based TF-Compiler bridge doesn't support tensorlist output yet.
    // TODO(ycao): Support tensorlist-type output.
    out_desc.is_tensor_list = false;
  }

  // XLA computation always uses Tuple shape.
  *xla_output_shape = xla::ShapeUtil::MakeTupleShape(shapes);

    name = "tensor_types_test_util",
    testonly = True,
    hdrs = ["tensor_types_test_util.h"],
    deps = [
        "//tensorflow/c:tf_datatype",
    ],
)

tf_cc_test(
    name = "tensor_test",
    srcs = [
        "tensor_test.cc",
    ],
    deps = [
        ":tensor_types_test_util",
        "//tensorflow/c:tf_datatype",
        "//tensorflow/cc/experimental/base/public:status",

    }
  };

  while (!worklist.empty()) {
    tensorlist = worklist.top();
    worklist.pop();

    // TensorLists are semantically immutable. For example, TensorListSetItem
    // takes a TensorList as input and produces a TensorList as output. So to
    // traverse modifications to TensorList and verify that all elements written