formatting

pts/model/transformer/transformer_estimator.py

@@ -34,7 +34,10 @@ from pts.feature import (
     get_lags_for_frequency,
 )
 
-from .transformer_network import TransformerTrainingNetwork, TransformerPredictionNetwork
+from .transformer_network import (
+    TransformerTrainingNetwork,
+    TransformerPredictionNetwork,
+)
 
 
 class TransformerEstimator(PTSEstimator):
@@ -80,8 +83,7 @@ class TransformerEstimator(PTSEstimator):
         self.embedding_dimension = embedding_dimension
         self.num_parallel_samples = num_parallel_samples
         self.lags_seq = (
-            lags_seq if lags_seq is not None else get_lags_for_frequency(
-                freq_str=freq)
+            lags_seq if lags_seq is not None else get_lags_for_frequency(freq_str=freq)
         )
         self.time_features = (
             time_features
@@ -112,14 +114,16 @@ class TransformerEstimator(PTSEstimator):
                 [SetField(output_field=FieldName.FEAT_STATIC_CAT, value=[0])]
                 if not self.use_feat_static_cat
                 else []
-            ) + (
+            )
+            + (
                 [SetField(output_field=FieldName.FEAT_STATIC_REAL, value=[0.0])]
                 if not self.use_feat_static_real
                 else []
             )
             + [
-                AsNumpyArray(field=FieldName.FEAT_STATIC_CAT,
-                             expected_ndim=1, dtype=np.long),
+                AsNumpyArray(
+                    field=FieldName.FEAT_STATIC_CAT, expected_ndim=1, dtype=np.long
+                ),
                 AsNumpyArray(
                     field=FieldName.FEAT_STATIC_REAL, expected_ndim=1, dtype=self.dtype,
                 ),
@@ -170,7 +174,9 @@ class TransformerEstimator(PTSEstimator):
             ]
         )
 
-    def create_training_network(self, device: torch.device) -> TransformerTrainingNetwork:
+    def create_training_network(
+        self, device: torch.device
+    ) -> TransformerTrainingNetwork:
 
         training_network = TransformerTrainingNetwork(
             input_size=self.input_size,
@@ -194,7 +200,10 @@ class TransformerEstimator(PTSEstimator):
         return training_network
 
     def create_predictor(
-        self, transformation: Transformation, trained_network: nn.Module,  device: torch.device,
+        self,
+        transformation: Transformation,
+        trained_network: nn.Module,
+        device: torch.device,
     ) -> Predictor:
 
         prediction_network = TransformerPredictionNetwork(