func.func @convolution_add_add(
    %lhs: tensor<?x3x2x1xi8>, %rhs: tensor<2x1x1x1xi8>,
    %zp_offset: tensor<?x2x2x1xi32>, %bias: tensor<1xi32>
  ) -> tensor<?x2x2x1xi32> {
  // CHECK-DAG: %[[conv:.*]] = mhlo.convolution
  // CHECK-DAG: %[[combined:.*]] = chlo.broadcast_add %[[zp_offset:.*]], %[[bias:.*]]
  // CHECK-DAG: %[[result:.*]] = chlo.broadcast_add %[[conv]], %[[combined]]
  // CHECK: return %[[result]]

          mlir::cast<quant::QuantizedType>(non_bias_type.getElementType());
      non_bias_types.push_back(non_bias_ele_type);
    } else {
      // The non-bias hasn't been quantized, let's skip this bias.
      break;
    }
  }
  // The non-bias hasn't been quantized, let's skip this bias.
  if (non_bias_types.size() != non_biases.size()) return {};

  return func(/*op_types=*/non_bias_types, /*adjusted_quant_dim=*/-1,

  [(IsNotInLiftedFunc $res), (IsStableHLOConstantOp $bias)], [], (addBenefit 5)>;

def LiftDotGeneralWithBias : Pat<
  (StableHLO_AddOp:$res
    (StableHLO_DotGeneralOp
        $lhs, $rhs, $dot_dimension_numbers, $precision_config),
    (StableHLO_BroadcastInDimOp $bias, $dims)),
  (LiftAsTFXlaCallModule<"composite_dot_general_with_bias_fn">
    (ArgumentList $lhs, $rhs, $bias),
    (ResultList $res),
    (NamedAttributeList

        auto attr = rewriter.getZeroAttr(type);
        bias = rewriter.create<arith::ConstantOp>(add_op.getLoc(), type, attr);
        auto none_af = rewriter.getStringAttr("NONE");

        bias =
            rewriter.create<AddOp>(add_op.getLoc(), bias, constant_val, none_af)
                .getOutput();
      } else {
        // If there no pre-existing bias and the `constant_val` is 1D, simply

              when {
                k <= bias -> TMIN
                k >= bias + TMAX -> TMAX
                else -> k - bias
              }
            if (q < t) break
            result.writeByte((t + ((q - t) % (BASE - t))).punycodeDigit)
            q = (q - t) / (BASE - t)
          }

          result.writeByte(q.punycodeDigit)
          bias = adapt(delta, h + 1, h == b)
          delta = 0

    (TF_BiasAddOp:$bias_add
      $conv_out,
      (TF_ConstOp:$bias IsFloatElementsAttr:$bias_value), $data_format),
    (TF_ConstOp:$add_rhs IsFloatElementsAttr:$add_rhs_value)),
  (TF_BiasAddOp
    $conv_out, (TF_AddV2Op $bias, (ReshapeTo1DTensor $add_rhs)), $data_format),
  [(HasOneUse $bias_add),
   (ReshapableTo1DTensor $add_rhs),
   (HasEqualElementSize<[-1], [-1]> $bias, $add_rhs)]>;

// Fuse AffineOp followed by an MulOp patterns.

      : public QuantizationSpecTraitBase<
            ConcreteType, AccumulatorUniformScale<Bias, Operands...>::Impl> {
   public:
    // Whether the index-th operand is a bias.
    static bool IsBias(int index) { return index == Bias; }

    // Returns the indexes of all the non-bias operands.
    static std::vector<int> GetAllNonBiasOperands() {
      return std::vector<int>({Operands...});
    }
  };
};

def UndoBroadcastFullyConnectedBiasAdd : Pat<
  (TFL_AddOp $lhs, (Arith_ConstantOp:$const_value $bias), TFL_AF_None),
  (TFL_AddOp $lhs, (Arith_ConstantOp (FlattenTo1D $bias)), TFL_AF_None),
  [(AnyStaticShapeTensor $lhs),
   (IsLastDimEqualToNumElements $bias, $bias),
   (HasRankAtMost<4> $bias),
   (HasRankAtLeast<2> $bias),
   (IsDefinedByFullyConnectedOp $lhs),

  }
}

// Creates a new `tfl.qconst` op for the bias. The bias values are 0s, because
// this bias a dummy bias (note that bias fusion is not considered for this
// transformation). The quantization scale for the bias is input scale *
// filter scale. `filter_const_op` is used to retrieve the filter scales and
// the size of the bias constant.
TFL::QConstOp CreateTflConstOpForDummyBias(

    }
    const int64_t cost_per_col = 2 * weight_type.getNumElements();

    *count = cost_per_col * cols;

    auto bias = op->getOperand(2);
    if (bias) {
      auto bias_type =
          mlir::dyn_cast_or_null<mlir::RankedTensorType>(bias.getType());
      if (bias_type && bias_type.hasStaticShape()) {
        *count += output_type.getNumElements();
      }
    }

    return true;
  }