diff --git a/examples/workflow_by_code_gats.py b/examples/workflow_by_code_gats.py
index 3bb4edf08..b5bad31ec 100644
--- a/examples/workflow_by_code_gats.py
+++ b/examples/workflow_by_code_gats.py
@@ -7,19 +7,16 @@ from pathlib import Path
 import qlib
 import pandas as pd
 from qlib.config import REG_CN
-from qlib.contrib.model.pytorch_gats import GAT
-from qlib.contrib.data.handler import ALPHA360_Denoise
+
 from qlib.contrib.strategy.strategy import TopkDropoutStrategy
 from qlib.contrib.evaluate import (
     backtest as normal_backtest,
     risk_analysis,
 )
 from qlib.utils import exists_qlib_data
-
-# from qlib.model.learner import train_model
 from qlib.utils import init_instance_by_config
 
-import pickle
+
 
 if __name__ == "__main__":
 
diff --git a/qlib/contrib/model/pytorch_gats.py b/qlib/contrib/model/pytorch_gats.py
index 07af4eda4..fad52e834 100755
--- a/qlib/contrib/model/pytorch_gats.py
+++ b/qlib/contrib/model/pytorch_gats.py
@@ -28,14 +28,12 @@ class GAT(Model):
 
     Parameters
     ----------
-    input_dim : int
-        input dimension
-    output_dim : int
-        output dimension
-    layers : tuple
-        layer sizes
     lr : float
         learning rate
+    d_feat : int
+        input dimensions for each time step
+    metric : str
+        the evaluate metric used in early stop
     optimizer : str
         optimizer name
     GPU : str
@@ -398,10 +396,6 @@ class GATModel(nn.Module):
         hidden = self.bn1(hidden)
 
         gamma = self.cal_convariance(hidden, hidden)
-        # gamma = hidden.mm(torch.t(hidden))
-        # gamma = self.leaky_relu(gamma)
-        # gamma = self.softmax(gamma)
-        # gamma = gamma * (torch.ones(x.shape[0], x.shape[0]).to(device) - torch.diag(torch.ones(x.shape[0])).to(device))
         output = gamma.mm(hidden)
         output = self.fc(output)
         output = self.bn2(output)