diff --git a/lanfactory/trainers/jax_mlp.py b/lanfactory/trainers/jax_mlp.py
index b16069d..bb72dfc 100755
--- a/lanfactory/trainers/jax_mlp.py
+++ b/lanfactory/trainers/jax_mlp.py
@@ -185,11 +185,7 @@ def load_state_from_file(self, seed=42, input_dim=6, file_path=None):
         return loaded_state
 
     def make_forward_partial(
-        self,
-        seed=42,
-        input_dim=6,
-        state=None,
-        add_jitted=False,
+        self, seed=42, input_dim=6, state=None, add_jitted=False,
     ):
         """Creates a partial function for the forward pass of the network.
 
diff --git a/lanfactory/trainers/torch_mlp.py b/lanfactory/trainers/torch_mlp.py
index c5a9e17..0023130 100755
--- a/lanfactory/trainers/torch_mlp.py
+++ b/lanfactory/trainers/torch_mlp.py
@@ -169,11 +169,7 @@ class TorchMLP(nn.Module):
     # In the end I want 'eval', but differentiable
     # w.r.t to input ...., might be a problem
     def __init__(
-        self,
-        network_config=None,
-        input_shape=10,
-        network_type=None,
-        **kwargs,
+        self, network_config=None, input_shape=10, network_type=None, **kwargs,
     ):
         super(TorchMLP, self).__init__()
 
@@ -614,8 +610,7 @@ def train_and_evaluate(
             print("Saving model state dict")
             train_state_path = full_path + "_train_state_dict_torch.pt"
             torch.save(
-                self.model.state_dict(),
-                train_state_path,
+                self.model.state_dict(), train_state_path,
             )
             print("Saving model parameters to: " + train_state_path)
             self.file_path_model = train_state_path
@@ -706,7 +701,9 @@ def __init__(self, model_file_path=None, network_config=None, input_dim=None):
         )
         # self.net.load_state_dict(torch.load(self.model_file_path))
         if torch.cuda.is_available() == False:
-            self.net.load_state_dict(torch.load(self.model_file_path, map_location=torch.device('cpu')))
+            self.net.load_state_dict(
+                torch.load(self.model_file_path, map_location=torch.device("cpu"))
+            )
         else:
             self.net.load_state_dict(torch.load(self.model_file_path))
         self.net.to(self.dev)
@@ -774,7 +771,9 @@ def __init__(self, model_file_path=None, network_config=None, input_dim=None):
         )
         # self.net.load_state_dict(torch.load(self.model_file_path))
         if torch.cuda.is_available() == False:
-            self.net.load_state_dict(torch.load(self.model_file_path, map_location=torch.device('cpu')))
+            self.net.load_state_dict(
+                torch.load(self.model_file_path, map_location=torch.device("cpu"))
+            )
         else:
             self.net.load_state_dict(torch.load(self.model_file_path))
         self.net.to(self.dev)