CPred<"IsLastDimensionEqualOne($0) || "
        "(!$0.getType().cast<ShapedType>().hasStaticShape() && "
        "  $0.getType().cast<ShapedType>().hasRank() && "
        "  $0.getType().cast<ShapedType>().getRank() == 2 && "
        "  !$0.getType().cast<ShapedType>().getShape().empty() && "
        "  $0.getType().cast<ShapedType>().getShape()[1] == 1)">>;

// Replace
//   Equal(X, indices)
// With

      return failure();
    }

    ShapedType tensor_qtype =
        mlir::cast<ShapedType>(q_op.getResult().getType());
    Attribute tensor_proto_attr = Quantize(attr, tensor_qtype);
    if (!tensor_proto_attr) {
      return failure();
    }

    Type storage_type = mlir::cast<QuantizedType>(tensor_qtype.getElementType())
                            .getStorageType();
    ShapedType new_type = tensor_qtype.clone(storage_type);

    auto loc = op.getLoc();
    auto result_ty = mlir::cast<ShapedType>(op.getType());

    auto input = op.getImages();
    auto input_ty = mlir::cast<ShapedType>(input.getType());
    auto input_element_ty = input_ty.getElementType();
    auto out_size = op.getSize();
    auto out_size_ty = mlir::cast<ShapedType>(out_size.getType());
    auto out_size_element_ty = out_size_ty.getElementType();

// destination type is not inferred and must be given explicitly.
//
// Preconditions: The given value must have a ShapedType.
static Value CreateTFCastOpI32(OpBuilder *builder, Location loc, Value x,
                               BoolAttr truncate) {
  auto x_type = mlir::dyn_cast_or_null<ShapedType>(x.getType());
  if (!x_type) llvm_unreachable("unsupported type");
  Type type = x_type.clone(builder->getI32Type());

    // Here we assume that the element_shape won't be changed before calling
    // the first `TensorListSetItemOp`.
    if (auto shaped_type = element_shape.getType().dyn_cast<ShapedType>()) {
      if (shaped_type.hasRank() && shaped_type.getRank() == 0) {
        bool element_shape_acquired = false;
        auto uses = op.getResult().getUses();
        for (auto &use : llvm::make_early_inc_range(uses)) {

  if (!tensor.quantization || tfl::IsQuantized(tensor)) return nullptr;
  // If the result isn't float and unquantizable, the min/max is ignored.
  if (!res.getType()
           .cast<mlir::ShapedType>()
           .getElementType()
           .isa<mlir::FloatType>()) {
    return nullptr;
  }
  auto mins = tensor.quantization->min;
  auto maxs = tensor.quantization->max;

  );

  DerivedTypeAttr dtype = DerivedTypeAttr<
      "return getElementTypeOrSelf(resource_subtype());">;
  DerivedAttr shape = DerivedAttr<
      "ShapedType",
      "return resource_subtype().cast<ShapedType>();",
      [{ mlir::TF::ShapeAttr::get($_ctxt, $_self) }]>;

  let extraClassDeclaration = [{
    TensorType resource_subtype() { return resource_type().getSubtypes()[0]; }