typ := &b.Func.Config.Types
	// match: (Mul64 x y)
	// result: (Int64Make (Add32 <typ.UInt32> (Mul32 <typ.UInt32> (Int64Lo x) (Int64Hi y)) (Add32 <typ.UInt32> (Mul32 <typ.UInt32> (Int64Hi x) (Int64Lo y)) (Select0 <typ.UInt32> (Mul32uhilo (Int64Lo x) (Int64Lo y))))) (Select1 <typ.UInt32> (Mul32uhilo (Int64Lo x) (Int64Lo y))))
	for {
		x := v_0
		y := v_1
		v.reset(OpInt64Make)
		v0 := b.NewValue0(v.Pos, OpAdd32, typ.UInt32)

    SmallVector<int64_t> start_indices(input_rank, 0);
    SmallVector<int64_t> slice_sizes = reshaped_permuted_shape;
    SmallVector<int64_t> strides(input_rank, 1);
    auto crop_values = llvm::to_vector<4>(crops.getValues<APInt>());
    for (int i = 0; i < block_rank; ++i) {
      int64_t crop_start = crop_values[i * 2].getSExtValue();
      int64_t crop_end = crop_values[i * 2 + 1].getSExtValue();

// body and condition. The sets of keys of the two maps must be the same.
llvm::SmallDenseMap<int64_t, ResourceArgUseInfo> MergeArgResourceUseInfo(
    const llvm::SmallDenseMap<int64_t, ResourceArgUseInfo>& infos0,
    const llvm::SmallDenseMap<int64_t, ResourceArgUseInfo>& infos1) {
  llvm::SmallDenseMap<int64_t, ResourceArgUseInfo> result;
  for (const auto& entry : infos0) {
    auto info1_it = infos1.find(entry.getFirst());

        input_size - begin_shape[0] + 1 + absl::popcount(new_axis_mask);

    int64_t begin_mask = strided_slice_op.getBeginMask();
    int64_t end_mask = strided_slice_op.getEndMask();
    int64_t revised_begin_mask = 0;
    int64_t revised_end_mask = 0;
    int64_t revised_shrink_axis_mask = 0;
    int64_t revised_new_axis_mask = 0;

    SmallVector<int32_t, 4> padded_begin;

TF_CAPI_EXPORT extern void TF_SetAttrInt(TF_OperationDescription* desc,
                                         const char* attr_name, int64_t value);
TF_CAPI_EXPORT extern void TF_SetAttrIntList(TF_OperationDescription* desc,
                                             const char* attr_name,
                                             const int64_t* values,
                                             int num_values);

  ///     been read due to EOF.
  ///   * Must return -1 for any other error and must set `status` to any
  ///     other value to provide more information about the error.
  int64_t (*read)(const TF_RandomAccessFile* file, uint64_t offset, size_t n,
                  char* buffer, TF_Status* status);
} TF_RandomAccessFileOps;
// LINT.ThenChange(:random_access_file_ops_version)

// LINT.IfChange

	// Adding 1 ensures the encoded int64 sequence never
	// starts or ends with 0x00, so that present int64s can be
	// distinguished from zero-padding in the top bits,
	// so the length need not be separately encoded.
	// Inverting the int64s increases the chance that a
	// 4-int64 encoding will still be ≥ len(text).
	// In particular, if the first int64 is ASCII (<= 0x7E, so +1 <= 0x7F)

  }
}

void TestEncodeDecode(int line, const std::vector<string>& data) {
  const int64_t n = data.size();
  Status status;
  for (const std::vector<int64_t>& dims :
       std::vector<std::vector<int64_t>>{{n}, {1, n}, {n, 1}, {n / 2, 2}}) {
    // Create C++ Tensor
    Tensor src(tensorflow::DT_STRING, TensorShape(dims));
    for (int64_t i = 0; i < src.NumElements(); ++i) {
      src.flat<tstring>()(i) = data[i];
    }

    struct MyCustomKernel* s = new struct MyCustomKernel;
    s->created = true;
    s->compute_called = false;

    const int64_t list[] = {1, 2, 3, 4};
    const size_t list_size = TF_ARRAYSIZE(list);
    int64_t values[list_size];

    TF_Status* status = TF_NewStatus();
    EXPECT_TF_SIZE(/*attr_name*/ "Attr", /*expected_list_size*/ list_size,

  a.insert(a.end(), b.begin(), b.end());
}

template <typename Vector>
void Append(Vector& a, const Vector& b) {
  a.insert(a.end(), b.begin(), b.end());
}

int64_t GetNumOps(func::FuncOp func) {
  int64_t num_ops = 0;
  for (auto it = func.begin(); it != func.end(); ++it) ++num_ops;
  return num_ops;
}

std::vector<Value> ResultsAsVector(Operation* op) {
  std::vector<Value> vec;