const char* needle_expr, const char* haystack_expr,
    const wchar_t* needle, const wchar_t* haystack);
GTEST_API_ AssertionResult IsSubstring(
    const char* needle_expr, const char* haystack_expr,
    const ::std::string& needle, const ::std::string& haystack);
GTEST_API_ AssertionResult IsNotSubstring(
    const char* needle_expr, const char* haystack_expr,
    const ::std::string& needle, const ::std::string& haystack);

  ASSERT_THAT(status, Eq(kTfLiteOk));
  const auto& subgraph = model_.subgraphs[0];
  auto conv_op = subgraph->operators[0].get();
  const int input_tensor_idx = 0;
  const int weights_tensor_idx = 1;
  const int bias_tensor_index = 2;
  const int output_tensor_idx = 0;
  const auto bias_tensor =
      subgraph->tensors[conv_op->inputs[bias_tensor_index]].get();
  const auto input_tensor =

// TF-DAG: %[[cst_6:.*]] = "tf.Const"() <{value = dense<7.84313714E-4> : tensor<f32>}
// TF-DAG: %[[cst_7:.*]] = "tf.Const"() <{value = dense<0.00117647066> : tensor<f32>}
// TF-DAG: %[[cst_8:.*]] = "tf.Const"() <{value = dense<0.00602002116> : tensor<f32>}
// TF-DAG: %[[cst_9:.*]] = "tf.Const"() <{value = dense<0> : tensor<i32>}
// TF-DAG: %[[cst_10:.*]] = "tf.Const"() <{value = dense<0.0075123054> : tensor<f32>}

  Predicate* start() const { return operands_[0]; }
  Predicate* step() const { return operands_[1]; }
  absl::Span<const string> frame() const { return frame_; }

  string ToString() const override {
    return absl::StrCat("{", start()->ToString(), ",&,", step()->ToString(),
                        "}<", absl::StrJoin(frame(), ";"), ">");
  }

  Kind kind() const override { return Kind::kAndRecurrence; }

  // CHECK-NEXT: [[loop_counter:%.*]] = "tf.Const"
  %outputs_16 =  "tf.Const"() {device = "", value = dense<0> : tensor<i32>} : () -> tensor<i32>
  // CHECK-NEXT: [[max_iterations:%.*]] = "tf.Const"
  %outputs_18 =  "tf.Const"() {device = "", value = dense<16> : tensor<i32>} : () -> tensor<i32>
  %outputs_20 =  "tf.Const"() {device = "", value = dense<1> : tensor<i32>} : () -> tensor<i32>

// TODO(krzysd) Handle function calls
StatusOr<Operation*> ConvertOp(
    const tflite::OperatorT& op, const std::vector<Value>& vals_map,
    const std::vector<mlir::TensorType>& intermediate_types,
    Value optional_arg_marker,
    const std::vector<std::unique_ptr<tflite::OperatorCodeT>>& op_codes,
    const std::vector<std::string>& func_names,
    const std::vector<std::unique_ptr<tflite::TensorT>>& tensors, Location loc,

  static bool FullMatch(const char* str, const RE& re);
  static bool PartialMatch(const char* str, const RE& re);

 private:
  void Init(const char* regex);

  // We use a const char* instead of an std::string, as Google Test used to be
  // used where std::string is not available.  TODO(******@****.***): change to
  // std::string.
  const char* pattern_;
  bool is_valid_;

#if GTEST_USES_POSIX_RE

  delete_called = true;
  delete s;
}

namespace tensorflow {
Status TF_TensorToTensor(const TF_Tensor* src, Tensor* dst);

static std::unique_ptr<OpKernel> GetFakeKernel(const char* device_name,
                                               const char* op_name,
                                               const char* node_name,
                                               Status* status) {
  NodeDef def;

  static bool FullMatch(const char* str, const RE& re);
  static bool PartialMatch(const char* str, const RE& re);

 private:
  void Init(const char* regex);

  // We use a const char* instead of an std::string, as Google Test used to be
  // used where std::string is not available.  TODO(******@****.***): change to
  // std::string.
  const char* pattern_;
  bool is_valid_;

#if GTEST_USES_POSIX_RE

  explicit QuantizeCompositeFunctionsPass() = default;

  explicit QuantizeCompositeFunctionsPass(
      const QuantMethod quantization_method, const OpSet target_opset,
      const bool enable_per_channel_quantization,
      const int min_num_elements_for_weights,
      const bool enable_legacy_weight_only,
      std::optional<const std::string> mlir_dump_file_name)
      : enable_legacy_weight_only_(enable_legacy_weight_only),