diff --git a/aydin/nn/util/data_util.py b/aydin/nn/util/data_util.py
index 261c2986..27bf3903 100644
--- a/aydin/nn/util/data_util.py
+++ b/aydin/nn/util/data_util.py
@@ -11,6 +11,11 @@ def random_sample_patches(
     which we call patch. Also sorts the patches, and makes sure only patches with
     higher entropy in the intensity histogram are selected.
 
+    To be able to work with any adoption_rate between 0 and 1, we accordingly
+    generate more patches per image during patch generation. After sorting, we
+    are able to apply the adoption rate to the total number of patches we generated
+    for each image.
+
     Parameters
     ----------
     image : numpy.ArrayLike
@@ -21,6 +26,7 @@ def random_sample_patches(
 
     Returns
     -------
+    List of Tuples of Slicing Objects
 
     """
     list_of_slice_objects = []
diff --git a/aydin/restoration/denoise/noise2selfcnn.py b/aydin/restoration/denoise/noise2selfcnn.py
index d00aeb72..3ba0b68e 100644
--- a/aydin/restoration/denoise/noise2selfcnn.py
+++ b/aydin/restoration/denoise/noise2selfcnn.py
@@ -184,7 +184,7 @@ def train(self, noisy_image, *, batch_axes=None, chan_axes=None, **kwargs):
 
         """
         with lsection("Noise2Self train is starting..."):
-            if any(chan_axes):
+            if chan_axes:
                 return
 
             self.it = self.get_translator()