Fixed formatting

flair/nn/decoder.py

@@ -163,7 +163,6 @@ def __init__(
                 decay - after every batch,
             multi_label: Whether to predict multiple labels per sentence (default is False, and performs multi-class clsasification).
         """
-
         super().__init__()
 
         self.label_dictionary = label_dictionary

flair/nn/model.py

@@ -5,7 +5,7 @@
 from abc import ABC, abstractmethod
 from collections import Counter
 from pathlib import Path
-from typing import Any, List, Optional, Tuple, Union
+from typing import Any, Optional, Union
 
 import torch.nn
 from torch import Tensor
@@ -780,7 +780,7 @@ def forward_loss(self, sentences: list[DT]) -> tuple[torch.Tensor, int]:
 
         # decode, passing label tensor if needed, such as for prototype updates
         if "label_tensor" in inspect.signature(self.decoder.forward).parameters:
-            scores = self.decoder(data_point_tensor, label_tensor)
+            scores = self.decoder(data_point_tensor, label_tensor=label_tensor)
         else:
             scores = self.decoder(data_point_tensor)
 
@@ -817,7 +817,7 @@ def predict(
         label_name: Optional[str] = None,
         return_loss: bool = False,
         embedding_storage_mode: EmbeddingStorageMode = "none",
-    ) -> Optional[Union[List[DT], Tuple[float, int]]]:
+    ) -> Optional[Union[list[DT], tuple[float, int]]]:
         """Predicts the class labels for the given sentences. The labels are directly added to the sentences.
 
         Args: