diff --git a/examples/FedSimSiam/client/train.py b/examples/FedSimSiam/client/train.py
index 26c498955..0e7c565f6 100644
--- a/examples/FedSimSiam/client/train.py
+++ b/examples/FedSimSiam/client/train.py
@@ -89,7 +89,7 @@ def train(in_model_path, out_model_path, data_path=None, batch_size=32, epochs=1
         trainset, batch_size=batch_size, shuffle=True)
 
     device = torch.device(
-        'cuda') if torch.cuda.is_available() else torch.device('cpu')
+        "cuda") if torch.cuda.is_available() else torch.device("cpu")
     model = model.to(device)
 
     model.train()
@@ -103,7 +103,7 @@ def train(in_model_path, out_model_path, data_path=None, batch_size=32, epochs=1
             optimizer.zero_grad()
             data_dict = model.forward(images[0].to(
                 device, non_blocking=True), images[1].to(device, non_blocking=True))
-            loss = data_dict['loss'].mean()
+            loss = data_dict["loss"].mean()
             print(loss)
             loss.backward()
             optimizer.step()
@@ -112,10 +112,10 @@ def train(in_model_path, out_model_path, data_path=None, batch_size=32, epochs=1
     # Metadata needed for aggregation server side
     metadata = {
         # num_examples are mandatory
-        'num_examples': len(x_train),
-        'batch_size': batch_size,
-        'epochs': epochs,
-        'lr': lr
+        "num_examples": len(x_train),
+        "batch_size": batch_size,
+        "epochs": epochs,
+        "lr": lr
     }
 
     # Save JSON metadata file (mandatory)