diff --git a/pl_examples/domain_templates/reinforce_learn_ppo.py b/pl_examples/domain_templates/reinforce_learn_ppo.py
index a7cbc654830ad..026784f900622 100644
--- a/pl_examples/domain_templates/reinforce_learn_ppo.py
+++ b/pl_examples/domain_templates/reinforce_learn_ppo.py
@@ -289,7 +289,7 @@ def calc_advantage(self, rewards: List[float], values: List[float], last_value:
 
         return adv
 
-    def generate_trajectory_samples(self, ) -> Tuple[List[torch.Tensor], List[torch.Tensor], List[torch.Tensor]]:
+    def generate_trajectory_samples(self) -> Tuple[List[torch.Tensor], List[torch.Tensor], List[torch.Tensor]]:
         """
         Contains the logic for generating trajectory data to train policy and value network
         Yield:
diff --git a/pl_examples/domain_templates/unet.py b/pl_examples/domain_templates/unet.py
index 3b4ea5cc72ad2..f083ae434bd33 100644
--- a/pl_examples/domain_templates/unet.py
+++ b/pl_examples/domain_templates/unet.py
@@ -92,8 +92,12 @@ class DoubleConv(nn.Module):
     def __init__(self, in_ch: int, out_ch: int):
         super().__init__()
         self.net = nn.Sequential(
-            nn.Conv2d(in_ch, out_ch, kernel_size=3, padding=1), nn.BatchNorm2d(out_ch), nn.ReLU(inplace=True),
-            nn.Conv2d(out_ch, out_ch, kernel_size=3, padding=1), nn.BatchNorm2d(out_ch), nn.ReLU(inplace=True)
+            nn.Conv2d(in_ch, out_ch, kernel_size=3, padding=1),
+            nn.BatchNorm2d(out_ch),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(out_ch, out_ch, kernel_size=3, padding=1),
+            nn.BatchNorm2d(out_ch),
+            nn.ReLU(inplace=True),
         )
 
     def forward(self, x):