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pvnet/models/multimodal/multimodal.py

@@ -58,22 +58,22 @@ def __init__(
         """Neural network which combines information from different sources.
 
         Notes:
-            In the args, where it says a module `m` is partially instantiated, it means that a 
+            In the args, where it says a module `m` is partially instantiated, it means that a
             normal pytorch module will be returned by running `mod = m(**kwargs)`. In this library,
-            this partial instantiation is generally achieved using partial instantiation via hydra. 
-            However, the arg is still valid as long as `m(**kwargs)` returns a valid pytorch module 
+            this partial instantiation is generally achieved using partial instantiation via hydra.
+            However, the arg is still valid as long as `m(**kwargs)` returns a valid pytorch module
             - for example if `m` is a regular function.
 
         Args:
-            output_network: A partially instatiated pytorch Module class used to combine the 1D 
+            output_network: A partially instatiated pytorch Module class used to combine the 1D
                 features to produce the forecast.
             output_quantiles: A list of float (0.0, 1.0) quantiles to predict values for. If set to
                 None the output is a single value.
-            nwp_encoder: A partially instatiated pytorch Module class used to encode the NWP data 
+            nwp_encoder: A partially instatiated pytorch Module class used to encode the NWP data
                 from 4D into an 1D feature vector.
-            sat_encoder: A partially instatiated pytorch Module class used to encode the satellite 
+            sat_encoder: A partially instatiated pytorch Module class used to encode the satellite
                 data from 4D into an 1D feature vector.
-            pv_encoder: A partially instatiated pytorch Module class used to encode the site-level 
+            pv_encoder: A partially instatiated pytorch Module class used to encode the site-level
                 PV data from 2D into an 1D feature vector.
             add_image_embedding_channel: Add a channel to the NWP and satellite data with the
                 embedding of the GSP ID.
@@ -83,9 +83,9 @@ def __init__(
                 `None`.
             forecast_minutes: The amount of minutes that should be forecasted.
             history_minutes: The default amount of historical minutes that are used.
-            sat_history_minutes: Length of recent observations used for satellite inputs. Defaults 
+            sat_history_minutes: Length of recent observations used for satellite inputs. Defaults
                 to `history_minutes` if not provided.
-            min_sat_delay_minutes: Minimum delay with respect to t0 of the latest available 
+            min_sat_delay_minutes: Minimum delay with respect to t0 of the latest available
                 satellite image.
             nwp_forecast_minutes: Period of future NWP forecast data used as input. Defaults to
                 `forecast_minutes` if not provided.