public:
    static int GetCoefficientOperandIndex() { return OperandIndex; }
    static int GetQuantizationDim() { return QuantDim; }
  };
};

// This class provides the API for ops that can be quantized.
// This is as a trait like this:
//
//   class LessOp : public Op<LessOp, OpTrait::quant::QuantizableResult> {
//
template <typename ConcreteType>
class QuantizableResult

// RUN: stablehlo-quant-opt %s -split-input-file -stablehlo-prepare-quantize=enable-per-channel-quantized-weight=true -verify-diagnostics | FileCheck %s

// -----

module {
  // CHECK-LABEL: conv_with_bias_and_relu
  func.func private @conv_with_bias_and_relu(%arg0: tensor<1x3x2x3xf32>) -> tensor<1x2x2x2xf32> {
    %cst = "tf.Const"() {device = "", value = dense<[7.11401462, 7.05456924]> : tensor<2xf32>} : () -> tensor<2xf32>

  if (auto typeAttr = mlir::dyn_cast<TypeAttr>(quantSpec)) {
    Type spec = typeAttr.getValue();
    if (mlir::isa<TensorType, VectorType>(spec)) return false;

    // The spec should be either a quantized type which is compatible to the
    // expressed type, or a primitive type which is as same as the
    // (element type of) the expressed type.
    if (auto quantizedType = mlir::dyn_cast<QuantizedType>(spec))

  if (auto typeAttr = mlir::dyn_cast<TypeAttr>(quantSpec)) {
    Type spec = typeAttr.getValue();
    if (mlir::isa<TensorType, VectorType>(spec)) return false;

    // The spec should be either a quantized type which is compatible to the
    // expressed type, or a primitive type which is as same as the
    // (element type of) the expressed type.
    if (auto quantizedType = mlir::dyn_cast<QuantizedType>(spec))

  bool failableRewrite(FakeQuantOp op, PatternRewriter &rewriter) const {
    auto converter = ExpressedToQuantizedConverter::forInputType(op.getType());
    if (!converter) {
      return (op.emitError("unsupported quantized type conversion"), true);
    }

    quant::QuantizedType elementType =
        static_cast<const ConcreteRewriteClass *>(this)
            ->convertFakeQuantAttrsToType(op, converter.expressed_type);

    return %1 : tensor<1x3xf32>
  }
}

// -----

// Do not merge when function is not quantized function.

module attributes {tf_saved_model.semantics} {
  // CHECK-LABEL: func.func private @merge_relu_fusion
  func.func private @merge_relu_fusion(%arg0: tensor<1x4xf32>) -> tensor<1x3xf32> {

// This file test the selective quantiation feature in TF Quantizer. In the test
// config, the op named "test_opt_out" will not be quantized.

module attributes {tf.versions = {bad_consumers = [], min_consumer = 12 : i32, producer = 1269 : i32}} {

      auto input_type =
          mlir::dyn_cast_or_null<RankedTensorType>(input.getType());
      if (input_type == nullptr || !input_type.hasStaticShape()) continue;
      // Quantized type does not support getSizeInBits.
      if (IsQUI8Type(input_type) || IsQI8Type(input_type)) {
        total_size_transferred += input_type.getNumElements() * 8;
      } else {

// Quantized tfl.add
// CHECK: %[[add:.*]] = tfl.add(%[[q1]], %[[q0]]) <{fused_activation_function = "NONE"}> : (tensor<2x2x!quant.uniform<u8:f32, 0.0078431372549019607:128>>
// CHECK: %[[dq:.*]] = "tfl.dequantize"(%[[add]]) : (tensor<2x2x!quant.uniform<u8:f32, 0.0078431372549019607:128>>)
// CHECK: return %[[dq]]
}

// CHECK-LABEL: hardcode_input

          clEnumValN(OpSet::XLA, "XLA", "Uses TF XLA ops"),
          clEnumValN(OpSet::UNIFORM_QUANTIZED, "UNIFORM_QUANTIZED",
                     "Uses TF Uniform Quantized ops"))};

  // Initialize for tests.
  void initializeForTest() {
    if (!test_mode_) return;

    op_set_.setCallback([this](const OpSet& new_op_set) {
      quant_options_.set_op_set(new_op_set);