diff --git a/examples/gmlvq.py b/examples/gmlvq.py
index 23b8924..17d2aad 100644
--- a/examples/gmlvq.py
+++ b/examples/gmlvq.py
@@ -1,4 +1,4 @@
-"""ProtoTorch CBC example using 2D Iris data."""
+"""ProtoTorch GMLVQ example using Iris data."""
 
 import torch
 
diff --git a/prototorch/core/distances.py b/prototorch/core/distances.py
index 5de7c37..b3278fd 100644
--- a/prototorch/core/distances.py
+++ b/prototorch/core/distances.py
@@ -11,7 +11,7 @@ def squared_euclidean_distance(x, y):
     **Alias:**
     ``prototorch.functions.distances.sed``
     """
-    x, y = [arr.view(arr.size(0), -1) for arr in (x, y)]
+    x, y = (arr.view(arr.size(0), -1) for arr in (x, y))
     expanded_x = x.unsqueeze(dim=1)
     batchwise_difference = y - expanded_x
     differences_raised = torch.pow(batchwise_difference, 2)
@@ -27,14 +27,14 @@ def euclidean_distance(x, y):
     :returns: Distance Tensor of shape :math:`X \times Y`
     :rtype: `torch.tensor`
     """
-    x, y = [arr.view(arr.size(0), -1) for arr in (x, y)]
+    x, y = (arr.view(arr.size(0), -1) for arr in (x, y))
     distances_raised = squared_euclidean_distance(x, y)
     distances = torch.sqrt(distances_raised)
     return distances
 
 
 def euclidean_distance_v2(x, y):
-    x, y = [arr.view(arr.size(0), -1) for arr in (x, y)]
+    x, y = (arr.view(arr.size(0), -1) for arr in (x, y))
     diff = y - x.unsqueeze(1)
     pairwise_distances = (diff @ diff.permute((0, 2, 1))).sqrt()
     # Passing `dim1=-2` and `dim2=-1` to `diagonal()` takes the
@@ -54,7 +54,7 @@ def lpnorm_distance(x, y, p):
 
     :param p: p parameter of the lp norm
     """
-    x, y = [arr.view(arr.size(0), -1) for arr in (x, y)]
+    x, y = (arr.view(arr.size(0), -1) for arr in (x, y))
     distances = torch.cdist(x, y, p=p)
     return distances
 
@@ -66,7 +66,7 @@ def omega_distance(x, y, omega):
 
     :param `torch.tensor` omega: Two dimensional matrix
     """
-    x, y = [arr.view(arr.size(0), -1) for arr in (x, y)]
+    x, y = (arr.view(arr.size(0), -1) for arr in (x, y))
     projected_x = x @ omega
     projected_y = y @ omega
     distances = squared_euclidean_distance(projected_x, projected_y)
@@ -80,7 +80,7 @@ def lomega_distance(x, y, omegas):
 
     :param `torch.tensor` omegas: Three dimensional matrix
     """
-    x, y = [arr.view(arr.size(0), -1) for arr in (x, y)]
+    x, y = (arr.view(arr.size(0), -1) for arr in (x, y))
     projected_x = x @ omegas
     projected_y = torch.diagonal(y @ omegas).T
     expanded_y = torch.unsqueeze(projected_y, dim=1)
diff --git a/prototorch/core/similarities.py b/prototorch/core/similarities.py
index 9929610..0e21e5d 100644
--- a/prototorch/core/similarities.py
+++ b/prototorch/core/similarities.py
@@ -21,7 +21,7 @@ def cosine_similarity(x, y):
     Expected dimension of x is 2.
     Expected dimension of y is 2.
     """
-    x, y = [arr.view(arr.size(0), -1) for arr in (x, y)]
+    x, y = (arr.view(arr.size(0), -1) for arr in (x, y))
     norm_x = x.pow(2).sum(1).sqrt()
     norm_y = y.pow(2).sum(1).sqrt()
     norm_mat = norm_x.unsqueeze(-1) @ norm_y.unsqueeze(-1).T
diff --git a/prototorch/utils/utils.py b/prototorch/utils/utils.py
index 6e4d6c8..d366866 100644
--- a/prototorch/utils/utils.py
+++ b/prototorch/utils/utils.py
@@ -5,6 +5,7 @@ from typing import (
     Dict,
     Iterable,
     List,
+    Optional,
     Union,
 )
 
@@ -18,7 +19,7 @@ def generate_mesh(
     maxima: torch.TensorType,
     border: float = 1.0,
     resolution: int = 100,
-    device: torch.device = None,
+    device: Optional[torch.device] = None,
 ):
     # Apply Border
     ptp = maxima - minima
@@ -55,14 +56,15 @@ def mesh2d(x=None, border: float = 1.0, resolution: int = 100):
 
 
 def distribution_from_list(list_dist: List[int],
-                           clabels: Iterable[int] = None):
+                           clabels: Optional[Iterable[int]] = None):
     clabels = clabels or list(range(len(list_dist)))
     distribution = dict(zip(clabels, list_dist))
     return distribution
 
 
-def parse_distribution(user_distribution,
-                       clabels: Iterable[int] = None) -> Dict[int, int]:
+def parse_distribution(
+        user_distribution,
+        clabels: Optional[Iterable[int]] = None) -> Dict[int, int]:
     """Parse user-provided distribution.
 
     Return a dictionary with integer keys that represent the class labels and
diff --git a/setup.py b/setup.py
index a120aed..4d29c40 100644
--- a/setup.py
+++ b/setup.py
@@ -15,7 +15,7 @@ from setuptools import find_packages, setup
 PROJECT_URL = "https://github.com/si-cim/prototorch"
 DOWNLOAD_URL = "https://github.com/si-cim/prototorch.git"
 
-with open("README.md", "r") as fh:
+with open("README.md") as fh:
     long_description = fh.read()
 
 INSTALL_REQUIRES = [
diff --git a/tests/test_datasets.py b/tests/test_datasets.py
index acde01e..007385a 100644
--- a/tests/test_datasets.py
+++ b/tests/test_datasets.py
@@ -1,7 +1,6 @@
 """ProtoTorch datasets test suite"""
 
 import os
-import shutil
 import unittest
 
 import numpy as np