LogicalResult matchAndRewrite(mlir::stablehlo::CustomCallOp op,
                                PatternRewriter &rewriter) const override {
    auto backendConfig =
        mlir::dyn_cast<DictionaryAttr>(op->getAttr("composite.backend_config"));
    if (!backendConfig)
      return op->emitError(
          "custom_call has no 'composite.backend_config' attribute or the "
          "attribute is not a dictionary");

  auto spec = std::make_unique<OpQuantSpec>();
  if (auto call_op = dyn_cast<TF::PartitionedCallOp>(op)) {
    StringRef function_name =
        mlir::cast<FlatSymbolRefAttr>(call_op.getFAttr()).getValue();
    if (!function_name.starts_with("composite_")) {
      return spec;
    }
    if (function_name.contains("depthwise_conv2d")) {
      spec->coeff_op_quant_dim[1] = 3;

                                PatternRewriter& rewriter) const override {
    auto input_op = op.getArg().getDefiningOp();
    if (auto q = llvm::dyn_cast_or_null<quantfork::QuantizeCastOp>(input_op)) {
      if (!q->getAttr(kVolatileOpAttrName)) return failure();

      if (remove_volatile_ops_type == kPreserveInputsAndOutputs) {
        // Don't remove leading and trailing QDQ for PTQ workflow, so the io
        // modifying lib can work correctly.

    if (op->getDialect() != tf_dialect) return WalkResult::advance();

    if (parallel_group_attr) {
      op->setAttr(TF::kParallelExecAnnotation, parallel_group_attr);
    }
    auto device_attr = op->getAttr(kDeviceAttr);
    if (!device_attr) {
      op->setAttr(kDeviceAttr, launch.getDeviceAttr());
      return WalkResult::advance();
    }

    if (auto device_str_attr = mlir::dyn_cast<StringAttr>(device_attr)) {

    if operation_id in operation_ids:
        message = (
            f"Duplicate Operation ID {operation_id} for function "
            + f"{route.endpoint.__name__}"
        )
        file_name = getattr(route.endpoint, "__globals__", {}).get("__file__")
        if file_name:
            message += f" at {file_name}"
        warnings.warn(message, stacklevel=1)
    operation_ids.add(operation_id)

    auto cond = symbol_table.lookup<mlir::func::FuncOp>(
        while_op->getAttr("cond").cast<mlir::FlatSymbolRefAttr>().getValue());
    AddCallOpInWhileOpRegion(while_op.getCond(), cond);
    while_op->removeAttr("cond");
    auto body = symbol_table.lookup<mlir::func::FuncOp>(
        while_op->getAttr("body").cast<mlir::FlatSymbolRefAttr>().getValue());
    AddCallOpInWhileOpRegion(while_op.getBody(), body);

                                PatternRewriter& rewriter) const override {
    auto input_op = op.getArg().getDefiningOp();
    if (auto q = llvm::dyn_cast_or_null<quantfork::QuantizeCastOp>(input_op)) {
      if (!q->getAttr(kVolatileOpAttrName)) return failure();

      // If the quantize op is a requantize op, it is being used in other scale
      // adjustments and should be kept. Instead, move dequantize op before the

  EXPECT_TRUE(isa<TF::XlaCallModuleOp>(lifted_op));
  EXPECT_EQ(
      mlir::cast<StringAttr>(lifted_op->getAttr("_original_entry_function")),
      "composite_dot_general_fn_1");
  EXPECT_EQ(
      mlir::cast<ArrayAttr>(lifted_dot_general_op->getAttr("precision_config")),
      builder_.getArrayAttr(SmallVector<Attribute>(
          1, mlir::stablehlo::PrecisionAttr::get(

            << min_num_elements_for_weights_ << " elements).";
        call_op->removeAttr(kQuantTraitAttrName);
      }
    }

    StringRef function_name =
        mlir::cast<FlatSymbolRefAttr>(call_op.getFAttr()).getValue();
    if ((quantization_method_ == tensorflow::quantization::QuantizationMethod::
                                     METHOD_DYNAMIC_RANGE_INT8) &&
        (function_name.contains("batch_matmul") ||

  }

  LogicalResult matchAndRewrite(TF::PartitionedCallOp op,
                                PatternRewriter& rewriter) const override {
    const auto f_attr = mlir::dyn_cast<FlatSymbolRefAttr>(op.getFAttr());
    StringRef function_name = f_attr.getValue();
    if (!function_name.starts_with(kDequantizeFunctionName)) return failure();

    llvm::SmallVector<Operation*> users(op->getUsers().begin(),