style(pre-commit.ci): auto fixes [...]

src/careamics/careamist.py

@@ -623,7 +623,6 @@ def predict(
                 # record datamodule
                 self.pred_datamodule = datamodule
 
-
             elif isinstance(source, np.ndarray):
                 # create datamodule
                 datamodule = CAREamicsPredictData(
@@ -635,7 +634,6 @@ def predict(
                 # record datamodule
                 self.pred_datamodule = datamodule
 
-
             else:
                 raise ValueError(
                     f"Invalid input. Expected a CAREamicsWood instance, paths or "
@@ -653,8 +651,8 @@ def predict(
         if is_tiled:
             # "de-collate"
             tile_infos = [
-                tile_info 
-                for _, tile_info_list in predictions 
+                tile_info
+                for _, tile_info_list in predictions
                 for tile_info in tile_info_list
             ]
             predictions = torch.concatenate([preds for preds, _ in predictions])
@@ -671,8 +669,6 @@ def predict(
         else:
             return predictions
 
-
-
     def _create_data_for_bmz(
         self,
         input_array: Optional[np.ndarray] = None,

src/careamics/dataset/tiling/stitch_prediction.py

@@ -22,7 +22,7 @@ def stitch_prediction(
     Parameters
     ----------
     tiles : list of numpy.ndarray
-        Cropped tiles and their respective stitching coordinates. Can contain tiles 
+        Cropped tiles and their respective stitching coordinates. Can contain tiles
         from multiple images.
     tile_infos : list of TileInformation
         List of information and coordinates obtained from

tests/dataset/prediction/test_stitch_prediction.py

@@ -1,7 +1,11 @@
 import numpy as np
 import pytest
 
-from careamics.dataset.tiling import extract_tiles, stitch_prediction_single, stitch_prediction
+from careamics.dataset.tiling import (
+    extract_tiles,
+    stitch_prediction,
+    stitch_prediction_single,
+)
 
 
 @pytest.mark.parametrize(