add some minimal typing

Makes user code (in explainable_embedding repo specifically) complain less in VSCode.

src/distance_explainer.py

@@ -6,6 +6,7 @@
 from dianna.utils.maskers import generate_masks_for_images
 from sklearn.metrics import pairwise_distances
 from tqdm import tqdm
+import numpy.typing
 
 logging.getLogger(__name__).addHandler(logging.NullHandler())
 
@@ -50,7 +51,7 @@ def __init__(self, n_masks=1000, feature_res=8, p_keep=.5,  # pylint: disable=to
         self.mask_selection_negative_range_min = mask_selection_negative_range_min
         self.batch_size = batch_size
 
-    def explain_image_distance(self, model_or_function, input_data, embedded_reference, masks=None):
+    def explain_image_distance(self, model_or_function, input_data, embedded_reference, masks=None) -> tuple[numpy.typing.NDArray, float]:
         """Explain an image with respect to a reference point in an embedded space.
 
         Args:
@@ -104,6 +105,7 @@ def describe(x, name):
             statistics.append(describe(highest_mask_weights, 'highest_mask_weights'))
             unnormalized_sal_highest = np.mean(highest_distances_masks, axis=0)
         else:
+            print("HIGHEST IS NUL")
             unnormalized_sal_highest = 0
 
         lowest_distances_masks, lowest_mask_weights = self._get_lowest_distance_masks_and_weights(
@@ -116,6 +118,7 @@ def describe(x, name):
             statistics.append(describe(lowest_mask_weights, 'lowest_mask_weights'))
             unnormalized_sal_lowest = np.mean(lowest_distances_masks, axis=0)
         else:
+            print("LOWEST IS NUL")
             unnormalized_sal_lowest = 0
 
         self.statistics = '\n'.join(statistics)