"TFL::AreLastTwoDimsTransposed($0)">>;

// Checks if the param passed is of NoneType.
def IsNoneType : Constraint<CPred<"$0.getType().isa<NoneType>()">>;

def ConstantLikePred : CPred<"::mlir::matchPattern($0, ::mlir::m_Constant())">;
def IsConstantLike : Constraint<ConstantLikePred>;

    auto qconst_op = llvm::dyn_cast_or_null<QConstOp>(def_op);
    if (qconst_op == nullptr) return failure();

    DenseIntElementsAttr perm_tensor;
    if (!matchPattern(op.getPerm(), m_Constant(&perm_tensor))) return failure();

    if (!mlir::isa<quant::UniformQuantizedType>(
            (getElementTypeOrSelf(op.getOutput().getType()))))
      return failure();

namespace mlir {
namespace TFL {
namespace common {

bool IsConstantOrNone(Operation* op) {
  return (op->getNumResults() == 1 &&
          mlir::isa<NoneType>(op->getResult(0).getType())) ||
         matchPattern(op, m_Constant()) || isa<QConstOp>(op);
}

// Pre-order traverse, adding results and BlockArgs to `been_defined` and
// collecting operands not contained within `been_defined`. If we encounter an

      rewriter.eraseOp(op.getOperation());
      return success();
    }

    // Extract the constant cond value.
    DenseElementsAttr cond;
    if (!matchPattern(op.getCond(), m_Constant(&cond))) return failure();

    // TODO(hinsu): Handle constants that are not scalar booleans.
    auto cond_type = mlir::dyn_cast<RankedTensorType>(cond.getType());
    if (!cond_type || !cond_type.getShape().equals({}) ||

    Operation *op = result.getOwner();
    if (!isa<IdentityOp>(op) && !isa<IdentityNOp>(op)) break;
    val = op->getOperand(result.getResultNumber());
  }
  return matchPattern(val, m_Constant(&attr));
}

// Checks if both compilation and replication attributes are present in the
// operation, and if their values are valid.
LogicalResult HasValidCompilationAndReplicationAttributes(Operation &op);

    if (!type || !mlir::isa<FloatType>(type.getElementType())) return success();

    DenseFPElementsAttr attr;
    if (!matchPattern(const_op->getResult(0), m_Constant(&attr))) {
      const_op->emitError("Not a constant op.");
      return failure();
    }

    UniformQuantizedType quant_type = nullptr;

    const bool is_per_channel_quantization =
        enable_per_channel_quantization_ && quant_dim != -1;

    QuantizedType quant_type;
    DenseFPElementsAttr attr;
    if (!matchPattern(op->getResult(0), m_Constant(&attr))) return false;

    if (attr.size() < quant_specs_.minimum_elements_for_weights) {
      op->emitRemark("Quantization is skipped for ")
          << quantized_op->getName().getStringRef().str() << " because it has "

                     : op.emitOpError(error_info);
        }
      }
    }

    DenseIntElementsAttr dense_elem_attr;
    if (matchPattern(element_shape, m_Constant(&dense_elem_attr))) {
      // Note: It's technically unsafe to rewrite
      //     TensorListReserve(num_element, element_shape)
      // to
      //     Fill(Concat(num_element, element_shape), 0)

  }

  void rewrite(Operation* op, PatternRewriter& rewriter) const override {
    Operation* quantizable_op = *op->getUsers().begin();
    DenseFPElementsAttr attr;
    matchPattern(op->getResult(0), m_Constant(&attr));

    Method method = GetQuantizationMethodOrDefault(quantizable_op);
    const WeightOnlyPtq& weight_only_ptq = method.weight_only_ptq();

    Type weight_type;
    if (IsPerTensor(weight_only_ptq)) {

  LogicalResult matchAndRewrite(QuantizeOp op,
                                PatternRewriter& rewriter) const override {
    DenseFPElementsAttr attr;
    if (matchPattern(op.getInput(), m_Constant(&attr))) {
      auto qtype = op.getQtypeAttr();
      Attribute quantized_attr;
      if (legacy_float_scale_) {
        quantized_attr = quant::QuantizeLegacy(attr, qtype.getValue());
      } else {