[tmva][sofie] fix: correct Mod/FMod Func() constant-folding, add regression test

tmva/sofie/inc/TMVA/ROperator_BasicBinary.hxx

@@ -54,13 +54,13 @@ template <typename T>
 struct BinaryOperatorTrait<T, Mod> {
    static const std::string Name() { return "Mod"; }
    static std::string Op(const std::string & t1, const std::string t2) { return "(" + t1 + " % " + t2 + ")"; }
-   static T Func (T t1, T t2) { return std::pow(t1,t2);}
+   static T Func(T t1, T t2) { return t1 % t2; }
 };
 template <typename T>
 struct BinaryOperatorTrait<T, FMod> {
    static const std::string Name() { return "FMod"; }
    static std::string Op(const std::string & t1, const std::string t2) { return "std::fmod(" + t1 + "," + t2 + ")"; }
-   static T Func (T t1, T t2) { return std::pow(t1,t2);}
+   static T Func(T t1, T t2) { return std::fmod(t1, t2); }
 };
 
 template <typename T, EBasicBinaryOperator Op>

tmva/sofie/test/TestCustomModelsFromONNX.cxx

@@ -896,6 +896,17 @@ TEST(ONNX, Pow_broadcast){
 
 }
 
+TEST(ONNX, FMod_ConstantFolding)
+{
+   // Both inputs are Constant nodes, so SOFIE constant-folds via Func().
+   // fmod([10, 7, 5], [3, 3, 3]) = [1, 1, 2]
+   std::vector<float> correct_output = {1, 1, 2};
+   ASSERT_INCLUDE_AND_RUN_0(std::vector<float>, "FMod_ConstantFolding");
+   EXPECT_EQ(output.size(), correct_output.size());
+   for (size_t i = 0; i < output.size(); ++i)
+      EXPECT_LE(std::abs(output[i] - correct_output[i]), DEFAULT_TOLERANCE);
+}
+
    TEST(ONNX, ReduceMean){
    constexpr float TOLERANCE = DEFAULT_TOLERANCE;