polymorphic := field.TagSettings["POLYMORPHIC"]

	relation.Polymorphic = &Polymorphic{
		Value: schema.Table,
	}

	var (
		typeName = polymorphic + "Type"
		typeId   = polymorphic + "ID"
	)

	if value, ok := field.TagSettings["POLYMORPHICTYPE"]; ok {
		typeName = strings.TrimSpace(value)
	}

	if value, ok := field.TagSettings["POLYMORPHICID"]; ok {
		typeId = strings.TrimSpace(value)
	}

// The reduce_element_type is guaranteed to be a float, int, or complex type
// suitable for use with GetScalarConstOfType or GetScalarLimitConstOfType.
template <typename Derived, typename OpTy, typename ReductionOp,
          bool is_accumulation = true>
class GenericConvertReductionOp : public OpRewritePattern<OpTy> {
  using OpRewritePattern<OpTy>::OpRewritePattern;

				Type: r,
			})
		}
		return &ast.FuncType{
			Params: &ast.FieldList{
				List: params,
			},
			Results: &ast.FieldList{
				List: returns,
			},
		}
	case interface{ Obj() *types.TypeName }: // *types.{Alias,Named,TypeParam}
		if t.Obj().Pkg() == nil {
			return ast.NewIdent(t.Obj().Name())
		}
		if t.Obj().Pkg() == pkg {
			return ast.NewIdent(t.Obj().Name())
		}

func WrapClient[I controllers.ComparableObject](c kclient.Informer[I], opts ...CollectionOption) Collection[I] {
	o := buildCollectionOptions(opts...)
	if o.name == "" {
		o.name = fmt.Sprintf("NewInformer[%v]", ptr.TypeName[I]())
	}
	h := &informer[I]{
		inf:            c,
		log:            log.WithLabels("owner", o.name),
		collectionName: o.name,
		id:             nextUID(),
		eventHandlers:  &handlers[I]{},

      ArrayRef<Value>({size_leading_dim, partial_size}));

  return rewriter->create<TF::SliceOp>(loc, result_type, input_list,
                                       start_position, slice_size);
}

template <typename OpT>
class TensorListOpConverterBase : public OpConversionPattern<OpT> {
 public:
  explicit TensorListOpConverterBase<OpT>(MLIRContext *context,
                                          bool allow_tensorlist_pass_through,

	//
	// Invariant: Defs[id] == nil || Defs[id].Pos() == id.Pos()
	Defs map[*syntax.Name]Object

	// Uses maps identifiers to the objects they denote.
	//
	// For an embedded field, Uses returns the *TypeName it denotes.
	//
	// Invariant: Uses[id].Pos() != id.Pos()
	Uses map[*syntax.Name]Object

	// Implicits maps nodes to their implicitly declared objects, if any.
	// The following node and object types may appear:

    if (DeviceNameUtils::IsCompleteSpecification(spec, device))
      matching_devices.push_back(device);
  return matching_devices;
}

// Create error message for a conflicting attribute of a device.
template <typename T>
absl::Status MismatchedTPUSystemAttributeErr(absl::string_view attribute, T a,
                                             T b) {
  return absl::InvalidArgumentError(

std::unique_ptr<FlatBufferModel> ReadCustomOpTestModel() {
  auto model_path =
      tensorflow::io::JoinPath(*g_test_model_dir, internal::kModelWithCustomOp);
  return FlatBufferModel::BuildFromFile(model_path.c_str());
}

template <typename T>
std::vector<T> GetAsVector(const flatbuffers::Vector<T>* vec) {
  return std::vector<T>(vec->begin(), vec->end());
}

class QuantizeWeightsTest : public testing::Test {
 protected:
  QuantizeWeightsTest() {}

using tensorflow::OpOrArgLocNameMapper;
using tensorflow::OpOrArgNameMapper;
using tensorflow::Status;
using tflite::flex::IsAllowlistedFlexOp;

template <typename T>
using BufferOffset = flatbuffers::Offset<T>;

template <typename T>
using VectorBufferOffset = flatbuffers::Offset<flatbuffers::Vector<T>>;

using CustomOptionsOffset = VectorBufferOffset<uint8_t>;

namespace tfl = mlir::TFL;

	switch x := x.(type) {
	case *Basic:
		// Basic types are singletons except for the rune and byte
		// aliases, thus we cannot solely rely on the x == y check
		// above. See also comment in TypeName.IsAlias.
		if y, ok := y.(*Basic); ok {
			return x.kind == y.kind
		}

	case *Array:
		// Two array types unify if they have the same array length
		// and their element types unify.