diff --git a/sleap/nn/data/providers.py b/sleap/nn/data/providers.py
index eff107287..4aa05638f 100644
--- a/sleap/nn/data/providers.py
+++ b/sleap/nn/data/providers.py
@@ -94,7 +94,9 @@ def videos(self) -> List[sleap.Video]:
         return self.labels.videos
 
     def max_height_and_width(self) -> Tuple[int, int]:
-        return max(video.shape[1] for video in self.videos), max(video.shape[2] for video in self.videos)
+        return max(video.shape[1] for video in self.videos), max(
+            video.shape[2] for video in self.videos
+        )
 
     def make_dataset(
         self, ds_index: Optional[tf.data.Dataset] = None
diff --git a/sleap/nn/data/resizing.py b/sleap/nn/data/resizing.py
index ec6efcd34..cc6217b68 100644
--- a/sleap/nn/data/resizing.py
+++ b/sleap/nn/data/resizing.py
@@ -275,7 +275,6 @@ class SizeMatcher:
     max_image_height: int = None
     max_image_width: int = None
 
-
     @classmethod
     def from_config(
         cls,
@@ -286,7 +285,7 @@ def from_config(
         scale_key: Text = "scale",
         keep_full_image: bool = False,
         full_image_key: Text = "full_image",
-        points_key: Optional[Text] = "instances"
+        points_key: Optional[Text] = "instances",
     ) -> "SizeMatcher":
         """Build an instance of this class from configuration.
 
@@ -320,7 +319,9 @@ def from_config(
                 try:
                     max_height, max_width = provider.max_height_and_width()
                 except:
-                    raise ValueError("target_height / target_width could not be determined")
+                    raise ValueError(
+                        "target_height / target_width could not be determined"
+                    )
                 if update_config:
                     config.target_height = max_height
                     config.target_width = max_width
@@ -378,25 +379,30 @@ def resize_and_pad(example):
             current_shape = tf.shape(image)
 
             # Only apply this transform if image shape differs from target
-            if current_shape[-3] != self.max_image_height or current_shape[-2] != self.max_image_width:
+            if (
+                current_shape[-3] != self.max_image_height
+                or current_shape[-2] != self.max_image_width
+            ):
                 # Calculate target height and width for resizing the image (no padding yet)
                 hratio = self.max_image_height / tf.cast(current_shape[-3], tf.float32)
                 wratio = self.max_image_width / tf.cast(current_shape[-2], tf.float32)
                 if hratio > wratio:
                     # The bottleneck is width, scale to fit width first then pad to height
-                    target_height=tf.cast(tf.cast(current_shape[-3], tf.float32) * wratio, tf.int32)
-                    target_width=self.max_image_width
+                    target_height = tf.cast(
+                        tf.cast(current_shape[-3], tf.float32) * wratio, tf.int32
+                    )
+                    target_width = self.max_image_width
                     example[self.scale_key] = example[self.scale_key] * wratio
                 else:
                     # The bottleneck is height, scale to fit height first then pad to width
-                    target_height=self.max_image_height
-                    target_width=tf.cast(tf.cast(current_shape[-2], tf.float32) * hratio, tf.int32)
+                    target_height = self.max_image_height
+                    target_width = tf.cast(
+                        tf.cast(current_shape[-2], tf.float32) * hratio, tf.int32
+                    )
                     example[self.scale_key] = example[self.scale_key] * hratio
                 # Resize the image to fill one of the dimensions by preserving aspect ratio
                 image = tf.image.resize_with_pad(
-                    image,
-                    target_height=target_height,
-                    target_width=target_width
+                    image, target_height=target_height, target_width=target_width
                 )
                 # Pad the image on bottom/right with zeroes to match specified dimensions
                 image = tf.image.pad_to_bounding_box(
@@ -404,16 +410,17 @@ def resize_and_pad(example):
                     offset_height=0,
                     offset_width=0,
                     target_height=self.max_image_height,
-                    target_width=self.max_image_width
+                    target_width=self.max_image_width,
                 )
                 example[self.image_key] = tf.cast(image, example[self.image_key].dtype)
                 # Scale the instance points accordingly
                 if self.points_key:
-                    example[self.points_key] = example[self.points_key] * example[self.scale_key]
+                    example[self.points_key] = (
+                        example[self.points_key] * example[self.scale_key]
+                    )
 
             return example
 
-
         ds_output = ds_input.map(
             resize_and_pad, num_parallel_calls=tf.data.experimental.AUTOTUNE
         )
diff --git a/tests/nn/data/test_resizing.py b/tests/nn/data/test_resizing.py
index 5f5b01d20..66c31ab61 100644
--- a/tests/nn/data/test_resizing.py
+++ b/tests/nn/data/test_resizing.py
@@ -123,6 +123,7 @@ def test_resizer_from_config():
             config=resizing.PreprocessingConfig(input_scaling=0.5, pad_to_stride=None)
         )
 
+
 def test_size_matcher():
     # Create some fake data using two different size videos.
     skeleton = sleap.Skeleton.from_names_and_edge_inds(["A"])