run lint

docs/source/docs/objectDetection/about-object-detection.md

@@ -15,7 +15,7 @@ Before you get started with object detection, ensure that you have followed the
 
 PhotonVision currently ships with a NOTE detector based on a [YOLOv5 model](https://docs.ultralytics.com/yolov5/). This model is trained to detect one or more object "classes" (such as cars, stoplights, or in our case, NOTES) in an input image. For each detected object, the model outputs a bounding box around where in the image the object is located, what class the object belongs to, and a unitless confidence between 0 and 1.
 
-:::{note} 
+:::{note}
 This model output means that while its fairly easy to say that "this rectangle probably contains a NOTE", we don't have any information about the NOTE's orientation or location. Further math in user code would be required to make estimates about where an object is physically located relative to the camera.
 :::
 

photon-targeting/src/main/native/include/photon/dataflow/structures/Packet.h

@@ -170,7 +170,7 @@ struct SerdeType<std::vector<T>> {
   static constexpr std::string_view GetSchema() {
     // TODO: this gets us the plain type name of T, but this is not schema JSON
     // compliant!
-    std::string name = wpi::Demangle(typeid(T).name());
+    static std::string name = wpi::Demangle(typeid(T).name());
     return name;
   }
 };
@@ -200,7 +200,7 @@ struct SerdeType<std::optional<T>> {
   static constexpr std::string_view GetSchema() {
     // TODO: this gets us the plain type name of T, but this is not schema JSON
     // compliant!
-    std::string name = wpi::Demangle(typeid(T).name());
+    static std::string name = wpi::Demangle(typeid(T).name());
     return name;
   }
 };