Fix format

src/Dialect/ONNX/Transforms/Decompose.cpp

@@ -872,24 +872,22 @@ LogicalResult ONNXGroupNormalizationCommon(
   //"numgroups" and "C" should have the same dimension index
   llvm::SmallVector<int64_t, 4> axesList, biasScaleVal;
 
-  if constexpr
-    scaleAndBiasWithNumGroupShape(<OP_TYPE>) {
-      // Opset18 Uses "numgroups" the number of groups of channels for the scale
-      // and bias
-      // Unsqueeze scale/bias from [NG] to [1 x NG x 1 x ... x 1] with numInNorm
-      // 1s.
-      biasScaleVal.emplace_back(numGroups);
-      for (int64_t i = 1; i <= numInNorm; ++i) {
-        biasScaleVal.emplace_back(1);
-        axesList.emplace_back(i);
-      }
-
-      axes = create.onnx.constantInt64(axesList);
-      biasScaleType = RankedTensorType::get(biasScaleVal, elementType);
-      newScale = create.onnx.unsqueeze(biasScaleType, scale, axes);
-      newBias = create.onnx.unsqueeze(biasScaleType, bias, axes);
+  if constexpr (scaleAndBiasWithNumGroupShape<OP_TYPE>) {
+    // Opset18 Uses "numgroups" the number of groups of channels for the scale
+    // and bias
+    // Unsqueeze scale/bias from [NG] to [1 x NG x 1 x ... x 1] with numInNorm
+    // 1s.
+    biasScaleVal.emplace_back(numGroups);
+    for (int64_t i = 1; i <= numInNorm; ++i) {
+      biasScaleVal.emplace_back(1);
+      axesList.emplace_back(i);
     }
-  else {
+
+    axes = create.onnx.constantInt64(axesList);
+    biasScaleType = RankedTensorType::get(biasScaleVal, elementType);
+    newScale = create.onnx.unsqueeze(biasScaleType, scale, axes);
+    newBias = create.onnx.unsqueeze(biasScaleType, bias, axes);
+  } else {
     // Opset21 Uses "C" the number of channels for the scale and bias
     // The equivalent of "C" when split is "NG x C/NG"
     // Reshape scale/bias from [C] to [NG x C/NG x 1 x ... x 1] with numInNorm