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Don't need to specify the starting line number which is prone to errors

source/docs/contributing/frc-docs/style-guide.rst

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 ```
 
 Make sure to link to the raw version of the file on GitHub. There is a handy ``Raw`` button in the top right corner of the page.
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    ```c++
    // Start the timer.

source/docs/romi-robot/programming-romi.rst

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 Now to start your Romi robot code, open the WPILib Command Palette (type :kbd:`Ctrl+Shift+P`) and select "Simulate Robot Code", or press :kbd:`F5`.

source/docs/software/advanced-controls/controllers/profiled-pidcontroller.rst

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source/docs/software/advanced-controls/controllers/trapezoidal-profiles.rst

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source/docs/software/advanced-controls/state-space/state-space-flywheel-walkthrough.rst

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 ### Modeling Using Flywheel Moment of Inertia and Gearing
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 ## Kalman Filters: Observing Flywheel State
 
@@ -174,32 +164,28 @@ Because the feedback controller computes error using the :term:`x-hat` estimated
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 Because Kalman filters use our state-space model in the :ref:`docs/software/advanced-controls/state-space/state-space-observers:Predict step`, it is important that our model is as accurate as possible. One way to verify this is to record a flywheel's input voltage and velocity over time, and replay this data by calling only ``predict`` on the Kalman filter. Then, the kV and kA gains (or moment of inertia and other constants) can be adjusted until the model closely matches the recorded data.
 
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 ## Bringing it All Together: LinearSystemLoop
 
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 Once we have our ``LinearSystemLoop``, the only thing left to do is actually run it. To do that, we'll periodically update our Kalman filter with our new encoder velocity measurements and apply new voltage commands to it. To do that, we first set the :term:`reference`, then ``correct`` with the current flywheel speed, ``predict`` the Kalman filter into the next timestep, and apply the inputs generated using ``getU``.
 
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 ## Angle Wrap with LQR
 

source/docs/software/advanced-controls/state-space/state-space-observers.rst

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 ## Footnotes