(CreateXlaDotV2OpFromTfMatMulOp
    $input, $weight, $input_zp, $weight_zp, $matmul, $transpose_a, $transpose_b),
  [(IsInt8ElementType $input),
   (IsInt8ElementType $weight),
   (HasRank $input),
   (HasRank $weight),
   (HasRankOf<0> $input_zp),
   (HasRankOf<0> $weight_zp),
   (IsInt32ElementType $matmul)],
  [], (addBenefit 10)>;

    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,

        !mlir::isa<StringType>(output_values.getElementType())) {
      return func.emitError()
             << "Output " << kValues << " should be a string tensor";
    }

    if (input_values.hasRank() && output_values.hasRank() &&
        input_values.getRank() != output_values.getRank()) {
      return func.emitError() << "Input " << kValues << " and output "
                              << kValues << " should have the same rank";

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_shape = mlir::cast<ShapedType>(input.getType());
  auto filter_shape = mlir::cast<ShapedType>(filter.getType());
  if (!input_shape.hasRank() || input_shape.getRank() != 4 ||
      !filter_shape.hasRank() || filter_shape.getRank() != 4) {
    emitError(loc, "input and filter are expected to be 4D tensors");
    return {};
  }

  const int feature_group_cnt =

    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.

                         ArrayRef<int> val2_indices) {
  ShapedType val1_shape = mlir::cast<ShapedType>(val1.getType());
  ShapedType val2_shape = mlir::cast<ShapedType>(val2.getType());
  if (!val1_shape.hasRank() || !val2_shape.hasRank()) return false;

  int val1_result = 1;
  int val2_result = 1;
  for (auto idx : val1_indices) {
    if (idx < 0) idx = idx + val1_shape.getRank();

  // If data is unranked or data_rank is 0, this will remain -2. Otherwise
  // refers to first dimension of then and/or else.
  int64_t data_first_dim = -2;
  bool then_has_rank = then_tensor.hasRank();
  bool else_has_rank = else_tensor.hasRank();
  if (then_has_rank && else_has_rank) {
    data_rank = then_tensor.getRank();
    if (then_tensor.getRank() > 0)
      data_first_dim = then_tensor.getShape().front();