fix linting

neural_lam/weather_dataset.py

@@ -121,8 +121,8 @@ def _sample_time(self, da, idx, n_steps: int, n_timesteps_offset: int = 0):
             # this implies that the data will have both `analysis_time` and
             # `elapsed_forecast_duration` dimensions for forecasts. We for now
             # simply select a analysis time and the first `n_steps` forecast
-            # times (given no offset). Note that this means that we get one sample 
-            # per forecast, always starting at forecast time 2.
+            # times (given no offset). Note that this means that we get one
+            # sample per forecast, always starting at forecast time 2.
             da = da.isel(
                 analysis_time=idx,
                 elapsed_forecast_duration=slice(