diff --git a/pvnet/models/multimodal/site_encoders/encoders.py b/pvnet/models/multimodal/site_encoders/encoders.py
index 22139941..126086d9 100644
--- a/pvnet/models/multimodal/site_encoders/encoders.py
+++ b/pvnet/models/multimodal/site_encoders/encoders.py
@@ -339,7 +339,9 @@ def _encode_key(self, x):
         sensor_id_embed = torch.tile(self.pv_id_embedding(self._sensor_ids), (batch_size, 1, 1))
 
         # Each concated (Sensor sequence, Sensor ID embedding) is processed with encoder
-        x_seq_in = torch.cat((sensor_site_seqs.swapaxes(1, 2), sensor_id_embed), dim=2).flatten(0, 1)
+        x_seq_in = torch.cat((sensor_site_seqs.swapaxes(1, 2), sensor_id_embed), dim=2).flatten(
+            0, 1
+        )
         key = self._key_encoder(x_seq_in)
 
         # Reshape to [batch size, PV site, kdim]
@@ -353,9 +355,13 @@ def _encode_value(self, x):
 
         if self.use_sensor_id_in_value:
             # Sensor ID embeddings are the same for each sample
-            sensor_id_embed = torch.tile(self.value_sensor_id_embedding(self._sensor_ids), (batch_size, 1, 1))
+            sensor_id_embed = torch.tile(
+                self.value_sensor_id_embedding(self._sensor_ids), (batch_size, 1, 1)
+            )
             # Each concated (Sensor sequence, Sensor ID embedding) is processed with encoder
-            x_seq_in = torch.cat((sensor_site_seqs.swapaxes(1, 2), sensor_id_embed), dim=2).flatten(0, 1)
+            x_seq_in = torch.cat((sensor_site_seqs.swapaxes(1, 2), sensor_id_embed), dim=2).flatten(
+                0, 1
+            )
         else:
             # Encode each PV sequence independently
             x_seq_in = sensor_site_seqs.swapaxes(1, 2).flatten(0, 1)