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;

		TableConvertor: printerstorage.TableConvertor{TableGenerator: printers.NewTableGenerator().With(printersinternal.AddHandlers)},
	}
	options := &generic.StoreOptions{RESTOptions: optsGetter, AttrFunc: podschedulingcontext.GetAttrs}
	if err := store.CompleteWithOptions(options); err != nil {
		return nil, nil, err
	}

	statusStore := *store
	statusStore.UpdateStrategy = podschedulingcontext.StatusStrategy

}

impl::NodeMatcherProperties impl::Attr(std::pair<string, AttrValue> attr) {
  impl::NodeMatcherProperties props;
  props.set_attr(std::move(attr));
  return props;
}

impl::NodeMatcherProperties impl::Attr(string name) {
  impl::NodeMatcherProperties props;
  props.set_attr({std::move(name), std::nullopt});
  return props;
}

NodeMatcherProperties ConstantValue(

  }

  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(),

                                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

  SymbolTableCollection symbol_table;
  OpBuilder builder(module.getContext());

  OwningOpRef<ModuleOp> pruned =
      builder.create<ModuleOp>(module->getLoc());
  (*pruned)->setAttrs(module->getAttrs());
  builder.setInsertionPointToEnd(pruned->getBody());

  llvm::SmallDenseSet<func::FuncOp> added;
  for (const llvm::StringRef function_name : function_names) {
    auto func =

    auto *new_ewise_op =
        rewriter.create(op->getLoc(), op->getName().getIdentifier(),
                        {tpose_arg1.getOperand(0), tpose_arg2.getOperand(0)},
                        new_out_type, op->getAttrs());

    // Apply original tranpose to output of ewise op.
    auto out_tpose_op = rewriter.create<TFL::TransposeOp>(
        new_ewise_op->getLoc(), op->getResult(0).getType(),

    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)

    }
    OpBuilder builder(call);
    auto new_call = builder.create<CallOp>(
        call.getLoc(), info.decomposed_callee.getFunctionType().getResults(),
        new_operands, call->getAttrs());
    new_call->setAttr(
        "f", SymbolRefAttr::get(
                 builder.getContext(),
                 const_cast<func::FuncOp&>(info.decomposed_callee).getName()));

  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(