return success();
  }

  auto stride_h = op.getStrideHAttr().getInt();
  auto stride_w = op.getStrideWAttr().getInt();
  auto dilation_h = op.getDilationHFactorAttr().getInt();
  auto dilation_w = op.getDilationWFactorAttr().getInt();

  // We don't have EXPLICIT PADDING in TfLite.
  auto paddings = op.getPadding();
  tensorflow::Padding padding;

  MLIRContext context;
  auto id =
      StringAttr::get(&context, "//tensorflow/python/keras/keras_file.py");
  auto loc = FileLineColLoc::get(&context, id, 0, 0);
  auto id2 =
      StringAttr::get(&context, "//tensorflow/python/something/my_test.py");
  auto loc2 = FileLineColLoc::get(&context, id2, 0, 0);
  auto id3 = StringAttr::get(&context, "python/tensorflow/show_file.py");
  auto loc3 = FileLineColLoc::get(&context, id3, 0, 0);

    // Construct forward graph.
    auto a = Const(scope, 1, {4, 2});
    auto b = Const(scope, 2, {4, 2});
    auto c = Const(scope, 3, {4, 2});

    auto pack = Stack(scope, {a, b, c});
    auto unpack = Unstack(scope, pack.output, 3);
    TF_ASSERT_OK(scope.status());

    // Construct grad inputs.
    auto dx = Const(scope, 4, {4, 2});
    auto dy = Const(scope, 5, {4, 2});

  TensorShape scale_shape({shape.dim_size(channel_dim)});
  auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(shape));
  auto scale = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(scale_shape));
  auto offset = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(scale_shape));
  auto mean = ops::ZerosLike(scope_, scale);
  auto var = ops::OnesLike(scope_, scale);

  if (!channel_first) {

  // a = [3 2; -1 0]
  auto a = Const(root, {{3.f, 2.f}, {-1.f, 0.f}});
  // x = [1.0; 1.0]
  auto x = Const(root.WithOpName("x"), {{1.f}, {1.f}});
  // y = a * x
  auto y = MatMul(root.WithOpName("y"), a, x);
  // y2 = y.^2
  auto y2 = Square(root, y);
  // y2_sum = sum(y2)
  auto y2_sum = Sum(root, y2, 0);
  // y_norm = sqrt(y2_sum)
  auto y_norm = Sqrt(root, y2_sum);

    return rewriter.notifyMatchFailure(op, "zero_points must be constant");
  }

  auto original_element_type = getElementTypeOrSelf(original_type);
  if (!mlir::isa<TF::Qint8Type, TF::Qint32Type>(original_element_type)) {
    return rewriter.notifyMatchFailure(
        op, "Quantized type must be qint8 or qint32.");
  }
  auto storage_type = GetIntTypeFromTFQint(original_element_type);

  llvm::SmallPtrSet<Operation*, 4> defining_ops;
  llvm::SmallVector<Type, 4> types;
  for (auto& item :
       llvm::make_early_inc_range(llvm::reverse(graph_op.GetBody()))) {
    auto it = new_control_inputs.find(&item);
    if (it == new_control_inputs.end()) continue;
    auto& new_control_inputs_for_item = it->second;
    builder.setInsertionPoint(&item);

                manifest {
                    attributes 'Automatic-Module-Name': 'auto'
                }
            }

        """
        file('auto-module/src/main/java/auto/AutoClass.java')  << """
            package auto;

            public class AutoClass {
                public void m() {
                    // can access, because a auto module sees the unnamed module
                    new moda.ModaClass();

  // has the property that if there is an edge from SCC1->SCC2, SCC1 is visited
  // after SCC2, i.e., the graph is traversed bottom up just the way we want.
  auto scc_begin = llvm::scc_begin(&call_graph);
  auto scc_end = llvm::scc_end(&call_graph);
  for (auto& scc : make_range(scc_begin, scc_end)) {
    // Each SCC node is a collection of callgraph nodes that form a cycle. We

    TFL::QConstOp rhs_qconst_op;

    auto GetBroadcastedConstOp = [&](Value operand) -> TFL::QConstOp {
      if (auto broadcast_op = dyn_cast_or_null<stablehlo::BroadcastInDimOp>(
              operand.getDefiningOp())) {
        auto stablehlo_const_op = dyn_cast_or_null<stablehlo::ConstantOp>(
            broadcast_op.getOperand().getDefiningOp());
        auto const_uniform_quantized_type =