clean code

freqtrade/freqai/prediction_models/PyTorchMLPModel.py

@@ -36,7 +36,7 @@ class PyTorchMLPModel(nn.Module):
     """
 
     def __init__(self, input_dim: int, output_dim: int, **kwargs):
-        super(PyTorchMLPModel, self).__init__()
+        super().__init__()
         hidden_dim: int = kwargs.get("hidden_dim", 256)
         dropout_percent: int = kwargs.get("dropout_percent", 0.2)
         n_layer: int = kwargs.get("n_layer", 1)
@@ -65,7 +65,7 @@ class Block(nn.Module):
     """
 
     def __init__(self, hidden_dim: int, dropout_percent: int):
-        super(Block, self).__init__()
+        super().__init__()
         self.ff = FeedForward(hidden_dim)
         self.dropout = nn.Dropout(p=dropout_percent)
         self.ln = nn.LayerNorm(hidden_dim)
@@ -85,7 +85,7 @@ class FeedForward(nn.Module):
     """
 
     def __init__(self, hidden_dim: int):
-        super(FeedForward, self).__init__()
+        super().__init__()
         self.net = nn.Sequential(
             nn.Linear(hidden_dim, hidden_dim),
             nn.ReLU(),

freqtrade/freqai/prediction_models/PyTorchRegressor.py

@@ -47,4 +47,4 @@ class PyTorchRegressor(BasePyTorchModel):
 
         y = self.model.model(x)
         pred_df = DataFrame(y.detach().numpy(), columns=[dk.label_list[0]])
-        return (pred_df, dk.do_predict)
+        return (pred_df, dk.do_predict)