}
	if os.Stdout == nil {
		panic("os.Stdout is nil")
	}

	Initialized = true
}

var Initialized bool

var SlicePtr interface{} = &[]int{}

var V int = 1

var HasMask []string = []string{"hi"}

type HasProg struct {
	array [1024]*byte
}

type Dep struct {
	X int
}

func (d *Dep) Method() int {
	// This code below causes various go.itab.* symbols to be generated in

    size *= std::max(kDefaultCheapCost, dim);
  }

  return size;
}

int64_t InferTensorSize(const CostContext& context, mlir::TensorType type) {
  if (type.hasRank()) return GetRankedTensorSize(type);
  return context.default_unranked_tensor_size;
}

// The cost function for tf.LookupTableFindV2.
int64_t InferLookupTableFindV2Cost(const CostContext& context,

def AreTheSameValue : Constraint<
  CPred<"$0 == $1">>;

// Checks if the value has rank.
def HasRank : Constraint<
  CPred<"$0.getType().cast<ShapedType>().hasRank()">>;

// Checks if the value has rank of `n`.
class HasRankOf<int n> : Constraint<
  CPred<"$0.getType().cast<ShapedType>().hasRank() && "
        "$0.getType().cast<ShapedType>().getRank() == " # n>,
  "Checks if the value has rank of 'n'.">;

// ShapeContainerT is any type with the following methods:
//   bool hasRank()
//   ArrayRef<int64_t> getShape()
// This includes mlir::TF::ShapeAttr and mlir::ShapedType.
template <typename ShapeContainerT>
void SetTensorShapeProto(ShapeContainerT shape, TensorShapeProto* proto) {
  if (shape.hasRank()) {
    for (int64_t dim : shape.getShape()) {

def ReshapeTo1DTensor : NativeCodeCall<
  "quant::ReshapeTo1DTensor($_builder, $_loc, $0)">;

def HasEqualShape : Constraint<CPred<
  "$0.getType().cast<ShapedType>().hasRank() && "
  "$1.getType().cast<ShapedType>().hasRank() && "
  "$0.getType().cast<ShapedType>().getShape() == $1.getType().cast<ShapedType>().getShape()">,
  "Checks if the shapes of tensors are same.">;

    auto input_type = mlir::cast<ShapedType>(op.getInput().getType());
    auto output_type = mlir::cast<ShapedType>(op.getOutput().getType());

    if (!input_type.hasRank() || !output_type.hasRank()) return failure();

    // The pattern attempts to reduce the rank of the input to BroadcastTo.
    // Thus, we fail to match if the consuming reshape rank is larger.

    // Scalar identity is broadcastable to any operand shape, we only need to
    // check that operand has the same shape as a result.
    bool scalar_identity = identity_ty.hasRank() && identity_ty.getRank() == 0;
    if (scalar_identity) return operand_ty == result_ty;

    // If identity is not a scalar, we must verify that identity shape is

    first_operand_type =
        mlir::cast<TF::ResourceType>(element_type).getSubtypes().front();
  }

  auto tensor_type = mlir::dyn_cast_or_null<TensorType>(first_operand_type);
  if (!(tensor_type && tensor_type.hasRank())) {
    return op->emitError()
           << "cannot convert op with unranked or non-tensor input type "
           << tensor_type << ".";
  }

  int rank = tensor_type.getRank();
  if (rank <= partition_dim) {

  // If the updated type doesn't have a rank, then it can't be a more refined
  // type.
  if (!updated.hasRank()) return false;

  // If the original type doesn't have a rank, then refine as the updated type
  // has a rank.
  if (!original.hasRank()) return true;

  // Both types must have the same rank.
  if (original.getRank() != updated.getRank()) return false;

def ZeroIntAttr
  : AttrConstraint<CPred<"$_self.cast<::mlir::IntegerAttr>().getInt() == 0">>;

// Checks if the value has rank at most 'n'.
class HasRankAtLeast<int n> : Constraint<
    CPred<"$0.getType().cast<ShapedType>().hasRank() && "
          "$0.getType().cast<ShapedType>().getRank() >= " # n>>;

// Checks value is not produced by a TFL_Quant or
// from TFL_Quant Op with same quant type.
def NotFromQuantOpOrSameQuantType : Constraint<