//        |         |
    //      Const_0    Const_1
    //
    const float const0_val[] = {1.0, 2.0, 3.0, 4.0};
    const float const1_val[] = {1.0, 0.0, 0.0, 1.0};
    TF_Operation* const0 = FloatConst2x2(graph_, s_, const0_val, "Const_0");
    TF_Operation* const1 = FloatConst2x2(graph_, s_, const1_val, "Const_1");
    TF_Operation* matmul = MatMul(graph_, s_, const0, const1, "MatMul");
    inputs[0] = TF_Output{const0, 0};

// CHECK-DAG: %[[CONST_2:.+]] = stablehlo.constant dense<3> : tensor<1xi32>
// CHECK-DAG: %[[CONST_3:.+]] = stablehlo.constant dense<4> : tensor<1xi32>
// CHECK-DAG: %[[CONST_4:.+]] = stablehlo.constant dense<2> : tensor<1xi32>

  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 =

  // 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,

  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),

        *UniformQuantizeFunctionCallPattern::Match(uniform_quantize_call_op);
    const double input_inverse_scales_value =
        uniform_quantize_call_pattern_for_input.GetInverseScalesValueAttr()
            .getSplatValue<APFloat>()
            .convertToFloat();
    const double input_scale_value = 1.0 / input_inverse_scales_value;
    const int64_t input_zero_point_value =

template <typename R>
void UpdateTensorListResultTypesForIf(
    const llvm::SmallSet<int, 4> &tensor_list_index,
    const llvm::SmallSet<int, 4> &resized_tensor_list_index,
    const llvm::DenseMap<int, int> &tensor_list_map, ArrayRef<Type> types,
    R &&range, ValueRange operands,
    llvm::SmallVectorImpl<Type> *updated_types) {
  int i = 0;
  for (const auto it : llvm::zip(types, range)) {
    if (!tensor_list_index.count(i)) {

  %3 = stablehlo.add %2, %1 : tensor<1x3xf32>
  func.return %3: tensor<1x3xf32>
}
// CHECK: %[[CONST_0:.*]] = stablehlo.constant dense<2.000000e+00>
// CHECK: %[[CONST_1:.*]] = stablehlo.constant dense<2.000000e+00>
// CHECK: %[[XLA_CALL_MODULE:.*]] = "tf.XlaCallModule"(%arg0, %[[CONST_0]], %[[CONST_1]])
// CHECK: return %[[XLA_CALL_MODULE:.*]] : tensor<1x3xf32>
// CHECK: }

    if (!input_type) {
      return failure();
    }
    const ArrayRef<int64_t> input_shape = input_type.getShape();

    const int input_size = input_shape.size();

    RankedTensorType begin_type = mlir::cast<RankedTensorType>(begin.getType());
    const ArrayRef<int64_t> begin_shape = begin_type.getShape();
    const int begin_dim = begin_shape.size();

    if (begin_dim != 1) return failure();