diff --git a/nowcasting_gan/common.py b/nowcasting_gan/common.py
index 26c0ef2..1c856c4 100644
--- a/nowcasting_gan/common.py
+++ b/nowcasting_gan/common.py
@@ -10,9 +10,13 @@
 
 class GBlock(torch.nn.Module):
     """Residual generator block without upsampling"""
+
     def __init__(
-        self, input_channels: int = 12, output_channels: int = 12, conv_type: str = "standard",
-            spectral_normalized_eps = 0.0001
+        self,
+        input_channels: int = 12,
+        output_channels: int = 12,
+        conv_type: str = "standard",
+        spectral_normalized_eps=0.0001,
     ):
         """
         G Block from Skillful Nowcasting, see https://arxiv.org/pdf/2104.00954.pdf
@@ -71,10 +75,14 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
 
 class UpsampleGBlock(torch.nn.Module):
     """Residual generator block with upsampling"""
+
     def __init__(
-            self, input_channels: int = 12, output_channels: int = 12, conv_type: str = "standard",
-            spectral_normalized_eps = 0.0001
-            ):
+        self,
+        input_channels: int = 12,
+        output_channels: int = 12,
+        conv_type: str = "standard",
+        spectral_normalized_eps=0.0001,
+    ):
         """
         G Block from Skillful Nowcasting, see https://arxiv.org/pdf/2104.00954.pdf
         Args:
@@ -95,7 +103,7 @@ def __init__(
             in_channels=input_channels,
             out_channels=output_channels,
             kernel_size=1,
-            )
+        )
         self.upsample = torch.nn.Upsample(scale_factor=2, mode="nearest")
         # Upsample 2D conv
         self.first_conv_3x3 = torch.nn.ConvTranspose2d(
@@ -104,10 +112,10 @@ def __init__(
             kernel_size=3,
             stride=2,
             padding=1,
-            )
+        )
         self.last_conv_3x3 = conv2d(
             in_channels=input_channels, out_channels=output_channels, kernel_size=3, padding=1
-            )
+        )
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         # Spectrally normalized 1x1 convolution