diff --git a/src/Builder/FrontendDialectTransformer.cpp b/src/Builder/FrontendDialectTransformer.cpp
index 6396f9c..5f382fe 100644
--- a/src/Builder/FrontendDialectTransformer.cpp
+++ b/src/Builder/FrontendDialectTransformer.cpp
@@ -25,7 +25,7 @@ namespace bstd = mpark;
 #include "FrontendDialectTransformer.hpp"
 
 namespace onnx_mlir {
-namespace {
+namespace detail {
 
 /*!
  *  The list of tensors initialized by the ONNX model.
@@ -37,6 +37,7 @@ public:
   FrontendGenImpl(mlir::MLIRContext &context)
       : context_(context), builder_(&context) {
     module_ = mlir::ModuleOp::create(mlir::UnknownLoc::get(&context));
+    InitHandlerMap();
   }
 
   mlir::ModuleOp ImportONNXModel(onnx::ModelProto model) {
@@ -52,6 +53,11 @@ private:
   // mapping between string name and symbol
   OnnxMlirSymbolMapping frontend_symbols_;
 
+  typedef void (onnx_mlir::detail::FrontendGenImpl::*ImportHandlerType)(
+      const onnx::NodeProto &);
+
+  std::map<std::string, ImportHandlerType> import_handler_map_;
+
   mlir::Location UnknownLoc() { return mlir::UnknownLoc::get(&context_); }
 
   /*!
@@ -329,7 +335,7 @@ private:
   /*!
    * Special handle for MaxPool operations.
    */
-  void ImportNodeMaxPool(onnx::NodeProto node) {
+  void ImportNodeMaxPool(const onnx::NodeProto &node) {
     int nOuts = node.output().size();
     if (nOuts == 1) {
       buildOperation<mlir::ONNXMaxPoolSingleOutOp>(node);
@@ -341,7 +347,7 @@ private:
   /*!
    * Special handle for BatchNormalization operations.
    */
-  void ImportNodeBatchNormalization(onnx::NodeProto node) {
+  void ImportNodeBatchNormalization(const onnx::NodeProto &node) {
     int nOuts = node.output().size();
     if (nOuts == 1) {
       // Test mode with one output.
@@ -355,7 +361,7 @@ private:
   /*!
    * Special handle for Pad operations.
    */
-  void ImportNodePad(onnx::NodeProto node) {
+  void ImportNodePad(const onnx::NodeProto &node) {
 
     int nOps = node.input().size();
     if (nOps == 2) {
@@ -400,12 +406,16 @@ private:
     // the generic operator is used
     // one known reeason is the optional input
 
+    (this->*(import_handler_map_[opName.str()]))(node);
+  }
+
+  void InitHandlerMap() {
 #include "src/Builder/OpBuildTable.inc"
   }
 
   /*!
    * Import output tensor, by doing the following:
-   * - Add the type of this output tensor to a list of tensor
+   * - Add the t/yp this output tensor to a list of tensor
    *   types representing return types of this graph function.
    * - Add this output tensor to the list of mlir::Value
    *   to be returned by the function representing computation graph.
@@ -499,7 +509,7 @@ private:
     mainFunc.setType(funcType);
   }
 }; // FrontendGenImpl class
-} // namespace
+} // namespace detail
 } // namespace onnx_mlir
 
 namespace onnx_mlir {
@@ -512,7 +522,7 @@ void ImportFrontendModelFile(std::string model_fname,
   auto parse_success = model.ParseFromIstream(&input);
   assert(parse_success && "Onnx Model Parsing Failed.");
 
-  FrontendGenImpl myONNXGen(context);
+  detail::FrontendGenImpl myONNXGen(context);
   module = myONNXGen.ImportONNXModel(model);
 }
 } // namespace onnx_mlir
diff --git a/src/Builder/OpBuildTable.inc b/src/Builder/OpBuildTable.inc
index b13c619..dcd78af 100644
--- a/src/Builder/OpBuildTable.inc
+++ b/src/Builder/OpBuildTable.inc
@@ -4,351 +4,351 @@
 //   Details can be found in docs/ImportONNXDefs.md .
 //********************************************************
 
-if (opName == "Abs")
-   buildOperation<mlir::ONNXAbsOp>(node);
-if (opName == "Acos")
-   buildOperation<mlir::ONNXAcosOp>(node);
-if (opName == "Acosh")
-   buildOperation<mlir::ONNXAcoshOp>(node);
-if (opName == "Add")
-   buildOperation<mlir::ONNXAddOp>(node);
-if (opName == "And")
-   buildOperation<mlir::ONNXAndOp>(node);
-if (opName == "ArgMax")
-   buildOperation<mlir::ONNXArgMaxOp>(node);
-if (opName == "ArgMin")
-   buildOperation<mlir::ONNXArgMinOp>(node);
-if (opName == "Asin")
-   buildOperation<mlir::ONNXAsinOp>(node);
-if (opName == "Asinh")
-   buildOperation<mlir::ONNXAsinhOp>(node);
-if (opName == "Atan")
-   buildOperation<mlir::ONNXAtanOp>(node);
-if (opName == "Atanh")
-   buildOperation<mlir::ONNXAtanhOp>(node);
-if (opName == "AveragePool")
-   buildOperation<mlir::ONNXAveragePoolOp>(node);
-if (opName == "BatchNormalization")
-   ImportNodeBatchNormalization(node);
-if (opName == "BitShift")
-   buildOperation<mlir::ONNXBitShiftOp>(node);
-if (opName == "Cast")
-   buildOperation<mlir::ONNXCastOp>(node);
-if (opName == "Ceil")
-   buildOperation<mlir::ONNXCeilOp>(node);
-if (opName == "Clip")
-   buildOperation<mlir::ONNXClipOp>(node);
-if (opName == "Compress")
-   buildOperation<mlir::ONNXCompressOp>(node);
-if (opName == "Concat")
-   buildOperation<mlir::ONNXConcatOp>(node);
-if (opName == "ConcatFromSequence")
-   buildOperation<mlir::ONNXConcatFromSequenceOp>(node);
-if (opName == "Constant")
-   buildOperation<mlir::ONNXConstantOp>(node);
-if (opName == "ConstantOfShape")
-   buildOperation<mlir::ONNXConstantOfShapeOp>(node);
-if (opName == "Conv")
-   buildOperation<mlir::ONNXConvOp>(node);
-if (opName == "ConvInteger")
-   buildOperation<mlir::ONNXConvIntegerOp>(node);
-if (opName == "ConvTranspose")
-   buildOperation<mlir::ONNXConvTransposeOp>(node);
-if (opName == "Cos")
-   buildOperation<mlir::ONNXCosOp>(node);
-if (opName == "Cosh")
-   buildOperation<mlir::ONNXCoshOp>(node);
-if (opName == "CumSum")
-   buildOperation<mlir::ONNXCumSumOp>(node);
-if (opName == "DepthToSpace")
-   buildOperation<mlir::ONNXDepthToSpaceOp>(node);
-if (opName == "DequantizeLinear")
-   buildOperation<mlir::ONNXDequantizeLinearOp>(node);
-if (opName == "Det")
-   buildOperation<mlir::ONNXDetOp>(node);
-if (opName == "Div")
-   buildOperation<mlir::ONNXDivOp>(node);
-if (opName == "Dropout")
-   buildOperation<mlir::ONNXDropoutOp>(node);
-if (opName == "DynamicQuantizeLinear")
-   buildOperation<mlir::ONNXDynamicQuantizeLinearOp>(node);
-if (opName == "Elu")
-   buildOperation<mlir::ONNXEluOp>(node);
-if (opName == "Equal")
-   buildOperation<mlir::ONNXEqualOp>(node);
-if (opName == "Erf")
-   buildOperation<mlir::ONNXErfOp>(node);
-if (opName == "Exp")
-   buildOperation<mlir::ONNXExpOp>(node);
-if (opName == "Expand")
-   buildOperation<mlir::ONNXExpandOp>(node);
-if (opName == "EyeLike")
-   buildOperation<mlir::ONNXEyeLikeOp>(node);
-if (opName == "Flatten")
-   buildOperation<mlir::ONNXFlattenOp>(node);
-if (opName == "Floor")
-   buildOperation<mlir::ONNXFloorOp>(node);
-if (opName == "GRU")
-   buildOperation<mlir::ONNXGRUOp>(node);
-if (opName == "Gather")
-   buildOperation<mlir::ONNXGatherOp>(node);
-if (opName == "GatherElements")
-   buildOperation<mlir::ONNXGatherElementsOp>(node);
-if (opName == "GatherND")
-   buildOperation<mlir::ONNXGatherNDOp>(node);
-if (opName == "Gemm")
-   buildOperation<mlir::ONNXGemmOp>(node);
-if (opName == "GlobalAveragePool")
-   buildOperation<mlir::ONNXGlobalAveragePoolOp>(node);
-if (opName == "GlobalLpPool")
-   buildOperation<mlir::ONNXGlobalLpPoolOp>(node);
-if (opName == "GlobalMaxPool")
-   buildOperation<mlir::ONNXGlobalMaxPoolOp>(node);
-if (opName == "Greater")
-   buildOperation<mlir::ONNXGreaterOp>(node);
-if (opName == "HardSigmoid")
-   buildOperation<mlir::ONNXHardSigmoidOp>(node);
-if (opName == "Hardmax")
-   buildOperation<mlir::ONNXHardmaxOp>(node);
-if (opName == "Identity")
-   buildOperation<mlir::ONNXIdentityOp>(node);
-if (opName == "If")
-   buildOperation<mlir::ONNXIfOp>(node);
-if (opName == "InstanceNormalization")
-   buildOperation<mlir::ONNXInstanceNormalizationOp>(node);
-if (opName == "IsInf")
-   buildOperation<mlir::ONNXIsInfOp>(node);
-if (opName == "IsNaN")
-   buildOperation<mlir::ONNXIsNaNOp>(node);
-if (opName == "LRN")
-   buildOperation<mlir::ONNXLRNOp>(node);
-if (opName == "LSTM")
-   buildOperation<mlir::ONNXLSTMOp>(node);
-if (opName == "LeakyRelu")
-   buildOperation<mlir::ONNXLeakyReluOp>(node);
-if (opName == "Less")
-   buildOperation<mlir::ONNXLessOp>(node);
-if (opName == "Log")
-   buildOperation<mlir::ONNXLogOp>(node);
-if (opName == "LogSoftmax")
-   buildOperation<mlir::ONNXLogSoftmaxOp>(node);
-if (opName == "Loop")
-   buildOperation<mlir::ONNXLoopOp>(node);
-if (opName == "LpNormalization")
-   buildOperation<mlir::ONNXLpNormalizationOp>(node);
-if (opName == "LpPool")
-   buildOperation<mlir::ONNXLpPoolOp>(node);
-if (opName == "MatMul")
-   buildOperation<mlir::ONNXMatMulOp>(node);
-if (opName == "MatMulInteger")
-   buildOperation<mlir::ONNXMatMulIntegerOp>(node);
-if (opName == "Max")
-   buildOperation<mlir::ONNXMaxOp>(node);
-if (opName == "MaxPool")
-   ImportNodeMaxPool(node);
-if (opName == "MaxRoiPool")
-   buildOperation<mlir::ONNXMaxRoiPoolOp>(node);
-if (opName == "MaxUnpool")
-   buildOperation<mlir::ONNXMaxUnpoolOp>(node);
-if (opName == "Mean")
-   buildOperation<mlir::ONNXMeanOp>(node);
-if (opName == "MeanVarianceNormalization")
-   buildOperation<mlir::ONNXMeanVarianceNormalizationOp>(node);
-if (opName == "Min")
-   buildOperation<mlir::ONNXMinOp>(node);
-if (opName == "Mod")
-   buildOperation<mlir::ONNXModOp>(node);
-if (opName == "Mul")
-   buildOperation<mlir::ONNXMulOp>(node);
-if (opName == "Multinomial")
-   buildOperation<mlir::ONNXMultinomialOp>(node);
-if (opName == "Neg")
-   buildOperation<mlir::ONNXNegOp>(node);
-if (opName == "NonMaxSuppression")
-   buildOperation<mlir::ONNXNonMaxSuppressionOp>(node);
-if (opName == "NonZero")
-   buildOperation<mlir::ONNXNonZeroOp>(node);
-if (opName == "Not")
-   buildOperation<mlir::ONNXNotOp>(node);
-if (opName == "OneHot")
-   buildOperation<mlir::ONNXOneHotOp>(node);
-if (opName == "Or")
-   buildOperation<mlir::ONNXOrOp>(node);
-if (opName == "PRelu")
-   buildOperation<mlir::ONNXPReluOp>(node);
-if (opName == "Pad")
-   ImportNodePad(node);
-if (opName == "Pow")
-   buildOperation<mlir::ONNXPowOp>(node);
-if (opName == "QLinearConv")
-   buildOperation<mlir::ONNXQLinearConvOp>(node);
-if (opName == "QLinearMatMul")
-   buildOperation<mlir::ONNXQLinearMatMulOp>(node);
-if (opName == "QuantizeLinear")
-   buildOperation<mlir::ONNXQuantizeLinearOp>(node);
-if (opName == "RNN")
-   buildOperation<mlir::ONNXRNNOp>(node);
-if (opName == "RandomNormal")
-   buildOperation<mlir::ONNXRandomNormalOp>(node);
-if (opName == "RandomNormalLike")
-   buildOperation<mlir::ONNXRandomNormalLikeOp>(node);
-if (opName == "RandomUniform")
-   buildOperation<mlir::ONNXRandomUniformOp>(node);
-if (opName == "RandomUniformLike")
-   buildOperation<mlir::ONNXRandomUniformLikeOp>(node);
-if (opName == "Range")
-   buildOperation<mlir::ONNXRangeOp>(node);
-if (opName == "Reciprocal")
-   buildOperation<mlir::ONNXReciprocalOp>(node);
-if (opName == "ReduceL1")
-   buildOperation<mlir::ONNXReduceL1Op>(node);
-if (opName == "ReduceL2")
-   buildOperation<mlir::ONNXReduceL2Op>(node);
-if (opName == "ReduceLogSum")
-   buildOperation<mlir::ONNXReduceLogSumOp>(node);
-if (opName == "ReduceLogSumExp")
-   buildOperation<mlir::ONNXReduceLogSumExpOp>(node);
-if (opName == "ReduceMax")
-   buildOperation<mlir::ONNXReduceMaxOp>(node);
-if (opName == "ReduceMean")
-   buildOperation<mlir::ONNXReduceMeanOp>(node);
-if (opName == "ReduceMin")
-   buildOperation<mlir::ONNXReduceMinOp>(node);
-if (opName == "ReduceProd")
-   buildOperation<mlir::ONNXReduceProdOp>(node);
-if (opName == "ReduceSum")
-   buildOperation<mlir::ONNXReduceSumOp>(node);
-if (opName == "ReduceSumSquare")
-   buildOperation<mlir::ONNXReduceSumSquareOp>(node);
-if (opName == "Relu")
-   buildOperation<mlir::ONNXReluOp>(node);
-if (opName == "Reshape")
-   buildOperation<mlir::ONNXReshapeOp>(node);
-if (opName == "Resize")
-   buildOperation<mlir::ONNXResizeOp>(node);
-if (opName == "ReverseSequence")
-   buildOperation<mlir::ONNXReverseSequenceOp>(node);
-if (opName == "RoiAlign")
-   buildOperation<mlir::ONNXRoiAlignOp>(node);
-if (opName == "Round")
-   buildOperation<mlir::ONNXRoundOp>(node);
-if (opName == "Scan")
-   buildOperation<mlir::ONNXScanOp>(node);
-if (opName == "Scatter")
-   buildOperation<mlir::ONNXScatterOp>(node);
-if (opName == "ScatterElements")
-   buildOperation<mlir::ONNXScatterElementsOp>(node);
-if (opName == "ScatterND")
-   buildOperation<mlir::ONNXScatterNDOp>(node);
-if (opName == "Selu")
-   buildOperation<mlir::ONNXSeluOp>(node);
-if (opName == "SequenceAt")
-   buildOperation<mlir::ONNXSequenceAtOp>(node);
-if (opName == "SequenceConstruct")
-   buildOperation<mlir::ONNXSequenceConstructOp>(node);
-if (opName == "SequenceEmpty")
-   buildOperation<mlir::ONNXSequenceEmptyOp>(node);
-if (opName == "SequenceErase")
-   buildOperation<mlir::ONNXSequenceEraseOp>(node);
-if (opName == "SequenceInsert")
-   buildOperation<mlir::ONNXSequenceInsertOp>(node);
-if (opName == "SequenceLength")
-   buildOperation<mlir::ONNXSequenceLengthOp>(node);
-if (opName == "Shape")
-   buildOperation<mlir::ONNXShapeOp>(node);
-if (opName == "Shrink")
-   buildOperation<mlir::ONNXShrinkOp>(node);
-if (opName == "Sigmoid")
-   buildOperation<mlir::ONNXSigmoidOp>(node);
-if (opName == "Sign")
-   buildOperation<mlir::ONNXSignOp>(node);
-if (opName == "Sin")
-   buildOperation<mlir::ONNXSinOp>(node);
-if (opName == "Sinh")
-   buildOperation<mlir::ONNXSinhOp>(node);
-if (opName == "Size")
-   buildOperation<mlir::ONNXSizeOp>(node);
-if (opName == "Slice")
-   buildOperation<mlir::ONNXSliceOp>(node);
-if (opName == "Softmax")
-   buildOperation<mlir::ONNXSoftmaxOp>(node);
-if (opName == "Softplus")
-   buildOperation<mlir::ONNXSoftplusOp>(node);
-if (opName == "Softsign")
-   buildOperation<mlir::ONNXSoftsignOp>(node);
-if (opName == "SpaceToDepth")
-   buildOperation<mlir::ONNXSpaceToDepthOp>(node);
-if (opName == "Split")
-   buildOperation<mlir::ONNXSplitOp>(node);
-if (opName == "SplitToSequence")
-   buildOperation<mlir::ONNXSplitToSequenceOp>(node);
-if (opName == "Sqrt")
-   buildOperation<mlir::ONNXSqrtOp>(node);
-if (opName == "Squeeze")
-   buildOperation<mlir::ONNXSqueezeOp>(node);
-if (opName == "StringNormalizer")
-   buildOperation<mlir::ONNXStringNormalizerOp>(node);
-if (opName == "Sub")
-   buildOperation<mlir::ONNXSubOp>(node);
-if (opName == "Sum")
-   buildOperation<mlir::ONNXSumOp>(node);
-if (opName == "Tan")
-   buildOperation<mlir::ONNXTanOp>(node);
-if (opName == "Tanh")
-   buildOperation<mlir::ONNXTanhOp>(node);
-if (opName == "TfIdfVectorizer")
-   buildOperation<mlir::ONNXTfIdfVectorizerOp>(node);
-if (opName == "ThresholdedRelu")
-   buildOperation<mlir::ONNXThresholdedReluOp>(node);
-if (opName == "Tile")
-   buildOperation<mlir::ONNXTileOp>(node);
-if (opName == "TopK")
-   buildOperation<mlir::ONNXTopKOp>(node);
-if (opName == "Transpose")
-   buildOperation<mlir::ONNXTransposeOp>(node);
-if (opName == "Unique")
-   buildOperation<mlir::ONNXUniqueOp>(node);
-if (opName == "Unsqueeze")
-   buildOperation<mlir::ONNXUnsqueezeOp>(node);
-if (opName == "Upsample")
-   buildOperation<mlir::ONNXUpsampleOp>(node);
-if (opName == "Where")
-   buildOperation<mlir::ONNXWhereOp>(node);
-if (opName == "Xor")
-   buildOperation<mlir::ONNXXorOp>(node);
-if (opName == "ArrayFeatureExtractor")
-   buildOperation<mlir::ONNXArrayFeatureExtractorOp>(node);
-if (opName == "Binarizer")
-   buildOperation<mlir::ONNXBinarizerOp>(node);
-if (opName == "CastMap")
-   buildOperation<mlir::ONNXCastMapOp>(node);
-if (opName == "CategoryMapper")
-   buildOperation<mlir::ONNXCategoryMapperOp>(node);
-if (opName == "DictVectorizer")
-   buildOperation<mlir::ONNXDictVectorizerOp>(node);
-if (opName == "FeatureVectorizer")
-   buildOperation<mlir::ONNXFeatureVectorizerOp>(node);
-if (opName == "Imputer")
-   buildOperation<mlir::ONNXImputerOp>(node);
-if (opName == "LabelEncoder")
-   buildOperation<mlir::ONNXLabelEncoderOp>(node);
-if (opName == "LinearClassifier")
-   buildOperation<mlir::ONNXLinearClassifierOp>(node);
-if (opName == "LinearRegressor")
-   buildOperation<mlir::ONNXLinearRegressorOp>(node);
-if (opName == "Normalizer")
-   buildOperation<mlir::ONNXNormalizerOp>(node);
-if (opName == "OneHotEncoder")
-   buildOperation<mlir::ONNXOneHotEncoderOp>(node);
-if (opName == "SVMClassifier")
-   buildOperation<mlir::ONNXSVMClassifierOp>(node);
-if (opName == "SVMRegressor")
-   buildOperation<mlir::ONNXSVMRegressorOp>(node);
-if (opName == "Scaler")
-   buildOperation<mlir::ONNXScalerOp>(node);
-if (opName == "TreeEnsembleClassifier")
-   buildOperation<mlir::ONNXTreeEnsembleClassifierOp>(node);
-if (opName == "TreeEnsembleRegressor")
-   buildOperation<mlir::ONNXTreeEnsembleRegressorOp>(node);
-if (opName == "ZipMap")
-   buildOperation<mlir::ONNXZipMapOp>(node);
+import_handler_map_["Abs"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXAbsOp>;
+import_handler_map_["Acos"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXAcosOp>;
+import_handler_map_["Acosh"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXAcoshOp>;
+import_handler_map_["Add"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXAddOp>;
+import_handler_map_["And"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXAndOp>;
+import_handler_map_["ArgMax"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXArgMaxOp>;
+import_handler_map_["ArgMin"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXArgMinOp>;
+import_handler_map_["Asin"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXAsinOp>;
+import_handler_map_["Asinh"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXAsinhOp>;
+import_handler_map_["Atan"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXAtanOp>;
+import_handler_map_["Atanh"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXAtanhOp>;
+import_handler_map_["AveragePool"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXAveragePoolOp>;
+import_handler_map_["BatchNormalization"] = 
+   &onnx_mlir::detail::FrontendGenImpl::ImportNodeBatchNormalization;
+import_handler_map_["BitShift"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXBitShiftOp>;
+import_handler_map_["Cast"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXCastOp>;
+import_handler_map_["Ceil"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXCeilOp>;
+import_handler_map_["Clip"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXClipOp>;
+import_handler_map_["Compress"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXCompressOp>;
+import_handler_map_["Concat"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXConcatOp>;
+import_handler_map_["ConcatFromSequence"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXConcatFromSequenceOp>;
+import_handler_map_["Constant"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXConstantOp>;
+import_handler_map_["ConstantOfShape"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXConstantOfShapeOp>;
+import_handler_map_["Conv"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXConvOp>;
+import_handler_map_["ConvInteger"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXConvIntegerOp>;
+import_handler_map_["ConvTranspose"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXConvTransposeOp>;
+import_handler_map_["Cos"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXCosOp>;
+import_handler_map_["Cosh"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXCoshOp>;
+import_handler_map_["CumSum"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXCumSumOp>;
+import_handler_map_["DepthToSpace"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXDepthToSpaceOp>;
+import_handler_map_["DequantizeLinear"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXDequantizeLinearOp>;
+import_handler_map_["Det"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXDetOp>;
+import_handler_map_["Div"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXDivOp>;
+import_handler_map_["Dropout"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXDropoutOp>;
+import_handler_map_["DynamicQuantizeLinear"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXDynamicQuantizeLinearOp>;
+import_handler_map_["Elu"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXEluOp>;
+import_handler_map_["Equal"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXEqualOp>;
+import_handler_map_["Erf"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXErfOp>;
+import_handler_map_["Exp"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXExpOp>;
+import_handler_map_["Expand"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXExpandOp>;
+import_handler_map_["EyeLike"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXEyeLikeOp>;
+import_handler_map_["Flatten"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXFlattenOp>;
+import_handler_map_["Floor"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXFloorOp>;
+import_handler_map_["GRU"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXGRUOp>;
+import_handler_map_["Gather"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXGatherOp>;
+import_handler_map_["GatherElements"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXGatherElementsOp>;
+import_handler_map_["GatherND"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXGatherNDOp>;
+import_handler_map_["Gemm"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXGemmOp>;
+import_handler_map_["GlobalAveragePool"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXGlobalAveragePoolOp>;
+import_handler_map_["GlobalLpPool"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXGlobalLpPoolOp>;
+import_handler_map_["GlobalMaxPool"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXGlobalMaxPoolOp>;
+import_handler_map_["Greater"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXGreaterOp>;
+import_handler_map_["HardSigmoid"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXHardSigmoidOp>;
+import_handler_map_["Hardmax"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXHardmaxOp>;
+import_handler_map_["Identity"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXIdentityOp>;
+import_handler_map_["If"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXIfOp>;
+import_handler_map_["InstanceNormalization"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXInstanceNormalizationOp>;
+import_handler_map_["IsInf"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXIsInfOp>;
+import_handler_map_["IsNaN"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXIsNaNOp>;
+import_handler_map_["LRN"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXLRNOp>;
+import_handler_map_["LSTM"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXLSTMOp>;
+import_handler_map_["LeakyRelu"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXLeakyReluOp>;
+import_handler_map_["Less"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXLessOp>;
+import_handler_map_["Log"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXLogOp>;
+import_handler_map_["LogSoftmax"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXLogSoftmaxOp>;
+import_handler_map_["Loop"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXLoopOp>;
+import_handler_map_["LpNormalization"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXLpNormalizationOp>;
+import_handler_map_["LpPool"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXLpPoolOp>;
+import_handler_map_["MatMul"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXMatMulOp>;
+import_handler_map_["MatMulInteger"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXMatMulIntegerOp>;
+import_handler_map_["Max"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXMaxOp>;
+import_handler_map_["MaxPool"] = 
+   &onnx_mlir::detail::FrontendGenImpl::ImportNodeMaxPool;
+import_handler_map_["MaxRoiPool"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXMaxRoiPoolOp>;
+import_handler_map_["MaxUnpool"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXMaxUnpoolOp>;
+import_handler_map_["Mean"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXMeanOp>;
+import_handler_map_["MeanVarianceNormalization"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXMeanVarianceNormalizationOp>;
+import_handler_map_["Min"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXMinOp>;
+import_handler_map_["Mod"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXModOp>;
+import_handler_map_["Mul"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXMulOp>;
+import_handler_map_["Multinomial"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXMultinomialOp>;
+import_handler_map_["Neg"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXNegOp>;
+import_handler_map_["NonMaxSuppression"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXNonMaxSuppressionOp>;
+import_handler_map_["NonZero"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXNonZeroOp>;
+import_handler_map_["Not"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXNotOp>;
+import_handler_map_["OneHot"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXOneHotOp>;
+import_handler_map_["Or"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXOrOp>;
+import_handler_map_["PRelu"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXPReluOp>;
+import_handler_map_["Pad"] = 
+   &onnx_mlir::detail::FrontendGenImpl::ImportNodePad;
+import_handler_map_["Pow"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXPowOp>;
+import_handler_map_["QLinearConv"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXQLinearConvOp>;
+import_handler_map_["QLinearMatMul"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXQLinearMatMulOp>;
+import_handler_map_["QuantizeLinear"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXQuantizeLinearOp>;
+import_handler_map_["RNN"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXRNNOp>;
+import_handler_map_["RandomNormal"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXRandomNormalOp>;
+import_handler_map_["RandomNormalLike"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXRandomNormalLikeOp>;
+import_handler_map_["RandomUniform"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXRandomUniformOp>;
+import_handler_map_["RandomUniformLike"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXRandomUniformLikeOp>;
+import_handler_map_["Range"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXRangeOp>;
+import_handler_map_["Reciprocal"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXReciprocalOp>;
+import_handler_map_["ReduceL1"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXReduceL1Op>;
+import_handler_map_["ReduceL2"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXReduceL2Op>;
+import_handler_map_["ReduceLogSum"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXReduceLogSumOp>;
+import_handler_map_["ReduceLogSumExp"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXReduceLogSumExpOp>;
+import_handler_map_["ReduceMax"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXReduceMaxOp>;
+import_handler_map_["ReduceMean"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXReduceMeanOp>;
+import_handler_map_["ReduceMin"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXReduceMinOp>;
+import_handler_map_["ReduceProd"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXReduceProdOp>;
+import_handler_map_["ReduceSum"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXReduceSumOp>;
+import_handler_map_["ReduceSumSquare"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXReduceSumSquareOp>;
+import_handler_map_["Relu"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXReluOp>;
+import_handler_map_["Reshape"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXReshapeOp>;
+import_handler_map_["Resize"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXResizeOp>;
+import_handler_map_["ReverseSequence"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXReverseSequenceOp>;
+import_handler_map_["RoiAlign"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXRoiAlignOp>;
+import_handler_map_["Round"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXRoundOp>;
+import_handler_map_["Scan"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXScanOp>;
+import_handler_map_["Scatter"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXScatterOp>;
+import_handler_map_["ScatterElements"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXScatterElementsOp>;
+import_handler_map_["ScatterND"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXScatterNDOp>;
+import_handler_map_["Selu"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXSeluOp>;
+import_handler_map_["SequenceAt"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXSequenceAtOp>;
+import_handler_map_["SequenceConstruct"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXSequenceConstructOp>;
+import_handler_map_["SequenceEmpty"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXSequenceEmptyOp>;
+import_handler_map_["SequenceErase"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXSequenceEraseOp>;
+import_handler_map_["SequenceInsert"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXSequenceInsertOp>;
+import_handler_map_["SequenceLength"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXSequenceLengthOp>;
+import_handler_map_["Shape"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXShapeOp>;
+import_handler_map_["Shrink"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXShrinkOp>;
+import_handler_map_["Sigmoid"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXSigmoidOp>;
+import_handler_map_["Sign"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXSignOp>;
+import_handler_map_["Sin"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXSinOp>;
+import_handler_map_["Sinh"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXSinhOp>;
+import_handler_map_["Size"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXSizeOp>;
+import_handler_map_["Slice"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXSliceOp>;
+import_handler_map_["Softmax"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXSoftmaxOp>;
+import_handler_map_["Softplus"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXSoftplusOp>;
+import_handler_map_["Softsign"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXSoftsignOp>;
+import_handler_map_["SpaceToDepth"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXSpaceToDepthOp>;
+import_handler_map_["Split"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXSplitOp>;
+import_handler_map_["SplitToSequence"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXSplitToSequenceOp>;
+import_handler_map_["Sqrt"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXSqrtOp>;
+import_handler_map_["Squeeze"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXSqueezeOp>;
+import_handler_map_["StringNormalizer"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXStringNormalizerOp>;
+import_handler_map_["Sub"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXSubOp>;
+import_handler_map_["Sum"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXSumOp>;
+import_handler_map_["Tan"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXTanOp>;
+import_handler_map_["Tanh"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXTanhOp>;
+import_handler_map_["TfIdfVectorizer"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXTfIdfVectorizerOp>;
+import_handler_map_["ThresholdedRelu"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXThresholdedReluOp>;
+import_handler_map_["Tile"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXTileOp>;
+import_handler_map_["TopK"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXTopKOp>;
+import_handler_map_["Transpose"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXTransposeOp>;
+import_handler_map_["Unique"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXUniqueOp>;
+import_handler_map_["Unsqueeze"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXUnsqueezeOp>;
+import_handler_map_["Upsample"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXUpsampleOp>;
+import_handler_map_["Where"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXWhereOp>;
+import_handler_map_["Xor"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXXorOp>;
+import_handler_map_["ArrayFeatureExtractor"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXArrayFeatureExtractorOp>;
+import_handler_map_["Binarizer"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXBinarizerOp>;
+import_handler_map_["CastMap"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXCastMapOp>;
+import_handler_map_["CategoryMapper"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXCategoryMapperOp>;
+import_handler_map_["DictVectorizer"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXDictVectorizerOp>;
+import_handler_map_["FeatureVectorizer"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXFeatureVectorizerOp>;
+import_handler_map_["Imputer"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXImputerOp>;
+import_handler_map_["LabelEncoder"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXLabelEncoderOp>;
+import_handler_map_["LinearClassifier"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXLinearClassifierOp>;
+import_handler_map_["LinearRegressor"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXLinearRegressorOp>;
+import_handler_map_["Normalizer"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXNormalizerOp>;
+import_handler_map_["OneHotEncoder"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXOneHotEncoderOp>;
+import_handler_map_["SVMClassifier"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXSVMClassifierOp>;
+import_handler_map_["SVMRegressor"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXSVMRegressorOp>;
+import_handler_map_["Scaler"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXScalerOp>;
+import_handler_map_["TreeEnsembleClassifier"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXTreeEnsembleClassifierOp>;
+import_handler_map_["TreeEnsembleRegressor"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXTreeEnsembleRegressorOp>;
+import_handler_map_["ZipMap"] = 
+   &onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXZipMapOp>;
diff --git a/utils/gen_onnx_mlir.py b/utils/gen_onnx_mlir.py
index 80369e9..d2d4360 100644
--- a/utils/gen_onnx_mlir.py
+++ b/utils/gen_onnx_mlir.py
@@ -954,7 +954,7 @@ special cases:
 
 def gen_op_importer(schema, file):
     indent = inc_indent()
-    s = indent + 'if (opName == "' + schema.name + '")\n'
+    s = indent + 'import_handler_map_["' + schema.name +'"] = \n '
 
     expected_num_operands = len(schema.inputs)
     expected_num_results = len(schema.outputs)
@@ -978,8 +978,8 @@ def gen_op_importer(schema, file):
         args.append(
             '/* expected_num_results = */ {}'.format(expected_num_results))
     """
-    s += inc_indent(indent) + " {}({});\n".format(
-        handler_func, ", ".join(args))
+    s += inc_indent(indent) + '&onnx_mlir::detail::FrontendGenImpl::'
+    s += handler_func+';\n'
 
     file.write(s)