diff --git a/datasets/vggface2.py b/datasets/vggface2.py
index a8b0ae8ef..cbbf87898 100644
--- a/datasets/vggface2.py
+++ b/datasets/vggface2.py
@@ -26,11 +26,11 @@
 from torchvision import transforms
 import torchvision.transforms.functional as FT
 
-from utils import augmentation_utils
 from skimage import transform as trans
 from hawk_eyes.face import RetinaFace
 import kornia.geometry.transform as GT
 
+from utils import augmentation_utils
 import ai8x
 
 
@@ -157,6 +157,8 @@ def __getitem__(self, index):
         if self.mode == 'identification_dr':
             return self.__getitem_identification_dr(index)
 
+        #Will never reached
+        return None
 
     def __getitem_detection(self, index):
 
diff --git a/models/ai85net-faceid_112.py b/models/ai85net-faceid_112.py
index c1d86731b..be8a3e19d 100644
--- a/models/ai85net-faceid_112.py
+++ b/models/ai85net-faceid_112.py
@@ -11,11 +11,12 @@
 
 """
 
-import ai8x
-import ai8x_blocks
 from torch import nn
 import torch.nn.functional as F
 
+import ai8x
+import ai8x_blocks
+
 
 class AI85FaceIDNet_112(nn.Module):
     """
@@ -28,7 +29,7 @@ def __init__(  # pylint: disable=too-many-arguments
             bottleneck_settings,
             last_layer_width,
             emb_dimensionality,
-            num_classes,
+            num_classes=None, # pylint: disable=unused-argument
             avg_pool_size=(7,7),
             num_channels=3,
             dimensions=(112, 112),  # pylint: disable=unused-argument
@@ -38,25 +39,31 @@ def __init__(  # pylint: disable=too-many-arguments
             **kwargs
     ):
         super().__init__()
+        # bias = False due to streaming
         self.pre_stage = ai8x.FusedConv2dReLU(num_channels, bottleneck_settings[0][1], 3,
                                                 padding=1, stride=pre_layer_stride,
-                                                bias=False, **kwargs) # bias = False due to streaming
-        self.pre_stage_2 = ai8x.FusedMaxPoolConv2dReLU(bottleneck_settings[0][1], bottleneck_settings[0][1], 3, padding=1, stride=1,
-                                                            pool_size=2, pool_stride=2,
-                                                             bias=False, **kwargs) # bias = False due to streaming
+                                                bias=False, **kwargs)
+        # bias = False due to streaming
+        self.pre_stage_2 = ai8x.FusedMaxPoolConv2dReLU(bottleneck_settings[0][1],
+                                                       bottleneck_settings[0][1], 3, padding=1,
+                                                       stride=1, pool_size=2, pool_stride=2,
+                                                       bias=False, **kwargs)
         self.feature_stage = nn.ModuleList([])
         for setting in bottleneck_settings:
-            self._create_bottleneck_stage(setting, bias, depthwise_bias, reduced_depthwise_bias, **kwargs)
+            self._create_bottleneck_stage(setting, bias, depthwise_bias,
+                                          reduced_depthwise_bias, **kwargs)
 
         self.post_stage = ai8x.FusedConv2dReLU(bottleneck_settings[-1][2], last_layer_width, 1,
                                                padding=0, stride=1, bias=False, **kwargs)
 
-        self.pre_avg = ai8x.Conv2d(last_layer_width, last_layer_width, 3, padding=1, stride=1, bias = False, **kwargs)
+        self.pre_avg = ai8x.Conv2d(last_layer_width, last_layer_width, 3, padding=1, stride=1,
+                                   bias = False, **kwargs)
         self.avg_pool = ai8x.AvgPool2d(avg_pool_size, stride=1)
         self.linear = ai8x.Linear(last_layer_width, emb_dimensionality, bias=bias, **kwargs)
 
 
-    def _create_bottleneck_stage(self, setting, bias, depthwise_bias, reduced_depthwise_bias, **kwargs):
+    def _create_bottleneck_stage(self, setting, bias, depthwise_bias,
+                                 reduced_depthwise_bias, **kwargs):
         """Function to create bottlencek stage. Setting format is:
            [num_repeat, in_channels, out_channels, stride, expansion_factor]
         """
@@ -77,8 +84,8 @@ def _create_bottleneck_stage(self, setting, bias, depthwise_bias, reduced_depthw
                                                                 stride=1,
                                                                 expansion_factor=setting[4],
                                                                 bias=bias,
-                                                                depthwise_bias=(i%2==0) and depthwise_bias, #Reduce depthwise bias
-                                                                **kwargs))
+                                                                depthwise_bias=(i%2==0) and
+                                                                depthwise_bias, **kwargs))
                 else:
                     stage.append(ai8x_blocks.ConvResidualBottleneck(in_channels=setting[2],
                                                                 out_channels=setting[2],
@@ -120,7 +127,8 @@ def ai85faceidnet_112(pretrained=False, **kwargs):
     ]
 
     return AI85FaceIDNet_112(pre_layer_stride=1, bottleneck_settings=bottleneck_settings,
-                           last_layer_width=128, emb_dimensionality=64, avg_pool_size=(7,7),  depthwise_bias=True, reduced_depthwise_bias = True, **kwargs)
+                             last_layer_width=128, emb_dimensionality=64, avg_pool_size=(7,7),
+                             depthwise_bias=True, reduced_depthwise_bias = True, **kwargs)
 
 models = [
     {