diff --git a/qlib/contrib/model/pytorch_gats_ts.py b/qlib/contrib/model/pytorch_gats_ts.py
index 6fb455b76..b20e57d45 100644
--- a/qlib/contrib/model/pytorch_gats_ts.py
+++ b/qlib/contrib/model/pytorch_gats_ts.py
@@ -32,7 +32,6 @@ from ...contrib.model.pytorch_gru import GRUModel
 
 
 class DailyBatchSampler(Sampler):
-
     def __init__(self, data_source):
         self.data_source = data_source
         self.data = self.data_source.data.loc[self.data_source.get_index()]
@@ -41,7 +40,7 @@ class DailyBatchSampler(Sampler):
 
     def __iter__(self):
         for idx, count in zip(self.daily_index, self.daily_count):
-            yield slice(idx, idx+count)
+            yield slice(idx, idx + count)
 
     def __len__(self):
         return len(self.data_source)
@@ -272,10 +271,14 @@ class GATs(Model):
                 raise ValueError("the path of the pretrained model should be given first!")
             self.logger.info("Loading pretrained model...")
             if self.base_model == "LSTM":
-                pretrained_model = LSTMModel(d_feat=self.d_feat, hidden_size=self.hidden_size, num_layers=self.num_layers)
+                pretrained_model = LSTMModel(
+                    d_feat=self.d_feat, hidden_size=self.hidden_size, num_layers=self.num_layers
+                )
                 pretrained_model.load_state_dict(torch.load(self.model_path))
             elif self.base_model == "GRU":
-                pretrained_model = GRUModel(d_feat=self.d_feat, hidden_size=self.hidden_size, num_layers=self.num_layers)
+                pretrained_model = GRUModel(
+                    d_feat=self.d_feat, hidden_size=self.hidden_size, num_layers=self.num_layers
+                )
                 pretrained_model.load_state_dict(torch.load(self.model_path))
             else:
                 raise ValueError("unknown base model name `%s`" % self.base_model)
diff --git a/qlib/data/dataset/__init__.py b/qlib/data/dataset/__init__.py
index a07f7ab8f..96e4a6e41 100644
--- a/qlib/data/dataset/__init__.py
+++ b/qlib/data/dataset/__init__.py
@@ -238,7 +238,7 @@ class TSDataSampler:
         self.start_idx, self.end_idx = self.data.index.slice_locs(start=pd.Timestamp(start), end=pd.Timestamp(end))
         # self.index_link = self.build_link(self.data)
         self.idx_df, self.idx_map = self.build_index(self.data)
-        self.idx_arr = np.array(self.idx_df.values, dtype=np.float64) # for better performance
+        self.idx_arr = np.array(self.idx_df.values, dtype=np.float64)  # for better performance
 
     def get_index(self):
         """
@@ -368,7 +368,6 @@ class TSDataSampler:
         else:
             indices = self._get_indices(*self._get_row_col(idx))
 
-
         # 1) for better performance, use the last nan line for padding the lost date
         # 2) In case of precision problems. We use np.float64. # TODO: I'm not sure if whether np.float64 will result in
         # precision problems. It will not cause any problems in my tests at least