diff --git a/src/dope/utils.py b/src/dope/utils.py
index 7aa75179..a3ccf5da 100644
--- a/src/dope/utils.py
+++ b/src/dope/utils.py
@@ -2,14 +2,10 @@
 
 import torch
 
-irange = range
-
 
 def make_grid(tensor, nrow=8, padding=2,
-              normalize=False, range=None, scale_each=False, pad_value=0):
-    """
-    Make a grid of images.
-
+              normalize=False, range_=None, scale_each=False, pad_value=0):
+    """Make a grid of images.
     Args:
         tensor (Tensor or list): 4D mini-batch Tensor of shape (B x C x H x W)
             or a list of images all of the same size.
@@ -24,6 +20,8 @@ def make_grid(tensor, nrow=8, padding=2,
         scale_each (bool, optional): If True, scale each image in the batch of
             images separately rather than the (min, max) over all images.
         pad_value (float, optional): Value for the padded pixels.
+    Example:
+        See this notebook `here <https://gist.github.com/anonymous/bf16430f7750c023141c562f3e9f2a91>`_
     """
     if not (torch.is_tensor(tensor) or
             (isinstance(tensor, list) and all(torch.is_tensor(t) for t in tensor))):
@@ -43,23 +41,23 @@ def make_grid(tensor, nrow=8, padding=2,
     if tensor.dim() == 4 and tensor.size(1) == 1:  # single-channel images
         tensor = torch.cat((tensor, tensor, tensor), 1)
 
-    if normalize == True:
+    if normalize is True:
         tensor = tensor.clone()  # avoid modifying tensor in-place
-        if range is not None:
-            assert isinstance(range, tuple), \
+        if range_ is not None:
+            assert isinstance(range_, tuple), \
                 "range has to be a tuple (min, max) if specified. min and max are numbers"
 
         def norm_ip(img, min, max):
             img.clamp_(min=min, max=max)
             img.add_(-min).div_(max - min + 1e-5)
 
-        def norm_range(t, range):
-            if range is not None:
-                norm_ip(t, range[0], range[1])
+        def norm_range(t, range_):
+            if range_ is not None:
+                norm_ip(t, range_[0], range_[1])
             else:
                 norm_ip(t, float(t.min()), float(t.max()))
 
-        if scale_each == True:
+        if scale_each is True:
             for t in tensor:  # loop over mini-batch dimension
                 norm_range(t, range)
         else:
@@ -75,8 +73,8 @@ def norm_range(t, range):
     height, width = int(tensor.size(2) + padding), int(tensor.size(3) + padding)
     grid = tensor.new(3, height * ymaps + padding, width * xmaps + padding).fill_(pad_value)
     k = 0
-    for y in irange(ymaps):
-        for x in irange(xmaps):
+    for y in range(ymaps):
+        for x in range(xmaps):
             if k >= nmaps:
                 break
             grid.narrow(1, y * height + padding, height - padding) \