diff --git a/src/Transform/ONNX/ShapeInferencePass.cpp b/src/Transform/ONNX/ShapeInferencePass.cpp
index 1ea68bb..b0322ff 100644
--- a/src/Transform/ONNX/ShapeInferencePass.cpp
+++ b/src/Transform/ONNX/ShapeInferencePass.cpp
@@ -59,7 +59,7 @@ public:
     if (dynamicOperations != 0) {
       f.emitError("Shape inference failed, ")
           << dynamicOperations << " operations couldn't be inferred\n";
-      signalPassFailure();
+      return signalPassFailure();
     }
 
     if (auto terminator_op = f.getBody().back().getTerminator()) {
@@ -73,75 +73,6 @@ public:
    *  Check if the given operation has a dynamically shaped result.
    */
   static bool returnsDynamicShape(Operation *op) {
-    // TODO: remove this check.
-    // Temporary fix until more ops are supported.
-    // All operations which do not return a ranked tensor type have dynamic
-    // shaped outputs. All those operation need to implement the inferShape()
-    // method.
-    if (op->getName().getStringRef() != "onnx.Exp" &&
-        op->getName().getStringRef() != "onnx.Atan" &&
-        op->getName().getStringRef() != "onnx.Tan" &&
-        op->getName().getStringRef() != "onnx.Tanh" &&
-        op->getName().getStringRef() != "onnx.Sin" &&
-        op->getName().getStringRef() != "onnx.Sinh" &&
-        op->getName().getStringRef() != "onnx.Cosh" &&
-        op->getName().getStringRef() != "onnx.Cos" &&
-        op->getName().getStringRef() != "onnx.Log" &&
-        op->getName().getStringRef() != "onnx.Sigmoid" &&
-        op->getName().getStringRef() != "onnx.HardSigmoid" &&
-        op->getName().getStringRef() != "onnx.Elu" &&
-        op->getName().getStringRef() != "onnx.Relu" &&
-        op->getName().getStringRef() != "onnx.LeakyRelu" &&
-        op->getName().getStringRef() != "onnx.Selu" &&
-        op->getName().getStringRef() != "onnx.Reciprocal" &&
-        op->getName().getStringRef() != "onnx.Softplus" &&
-        op->getName().getStringRef() != "onnx.Softsign" &&
-        op->getName().getStringRef() != "onnx.Sign" &&
-        op->getName().getStringRef() != "onnx.Mul" &&
-        op->getName().getStringRef() != "onnx.Add" &&
-        op->getName().getStringRef() != "onnx.Div" &&
-        op->getName().getStringRef() != "onnx.Sub" &&
-        op->getName().getStringRef() != "onnx.And" &&
-        op->getName().getStringRef() != "onnx.Or" &&
-        op->getName().getStringRef() != "onnx.Xor" &&
-        op->getName().getStringRef() != "onnx.Sum" &&
-        op->getName().getStringRef() != "onnx.Max" &&
-        op->getName().getStringRef() != "onnx.AveragePool" &&
-        op->getName().getStringRef() != "onnx.MaxPoolSingleOut" &&
-        op->getName().getStringRef() != "onnx.Min" &&
-        op->getName().getStringRef() != "onnx.Identity" &&
-        op->getName().getStringRef() != "onnx.MatMul" &&
-        op->getName().getStringRef() != "onnx.Gemm" &&
-        op->getName().getStringRef() != "onnx.Reshape" &&
-        op->getName().getStringRef() != "onnx.Transpose" &&
-        op->getName().getStringRef() != "onnx.ReduceMax" &&
-        op->getName().getStringRef() != "onnx.ReduceMin" &&
-        op->getName().getStringRef() != "onnx.ReduceProd" &&
-        op->getName().getStringRef() != "onnx.ReduceSum" &&
-        op->getName().getStringRef() != "onnx.Softmax" &&
-        op->getName().getStringRef() != "onnx.Sqrt" &&
-        op->getName().getStringRef() != "onnx.Conv" &&
-        op->getName().getStringRef() != "onnx.Pad" &&
-        op->getName().getStringRef() != "onnx.PadConstantPad" &&
-        op->getName().getStringRef() != "onnx.PadConstantValuePad" &&
-        op->getName().getStringRef() != "onnx.BatchNormalizationTestMode" &&
-        op->getName().getStringRef() != "onnx.Abs" &&
-        op->getName().getStringRef() != "onnx.Constant" &&
-        op->getName().getStringRef() != "onnx.Concat" &&
-        op->getName().getStringRef() != "onnx.Split" &&
-        op->getName().getStringRef() != "onnx.Neg" &&
-        op->getName().getStringRef() != "onnx.RNN" &&
-        op->getName().getStringRef() != "onnx.LSTM" &&
-        op->getName().getStringRef() != "onnx.GRU" &&
-        op->getName().getStringRef() != "onnx.Unsqueeze" &&
-        op->getName().getStringRef() != "onnx.Cast" &&
-        op->getName().getStringRef() != "onnx.ConvTranspose" &&
-        op->getName().getStringRef() != "onnx.Flatten" &&
-        op->getName().getStringRef() != "onnx.DynamicQuantizeLinear" &&
-        op->getName().getStringRef() != "onnx.QuantizeLinear" &&
-        op->getName().getStringRef() != "onnx.DequantizeLinear" &&
-        op->getName().getStringRef() != "onnx.ConvInteger")
-      return false;
     return llvm::any_of(op->getResultTypes(), [](Type result_type) {
       return !result_type.isa<NoneType>() &&
              !result_type.isa<RankedTensorType>();