From 181a4bb0fa5b77aeda8504c711a5ad3aad8166d1 Mon Sep 17 00:00:00 2001
From: jiajingbin <jiajaybin@126.com>
Date: Tue, 18 Mar 2025 17:59:43 +0800
Subject: [PATCH] fix: fix flake8 check

---
 .../tdgpt/dockerfile/tdgpt/taos_ts_server.py  | 20 +++++++++++++++----
 1 file changed, 16 insertions(+), 4 deletions(-)

diff --git a/tools/tdgpt/dockerfile/tdgpt/taos_ts_server.py b/tools/tdgpt/dockerfile/tdgpt/taos_ts_server.py
index 1836475f81..68dbdc14e1 100644
--- a/tools/tdgpt/dockerfile/tdgpt/taos_ts_server.py
+++ b/tools/tdgpt/dockerfile/tdgpt/taos_ts_server.py
@@ -97,6 +97,9 @@ class TaosConfig(PretrainedConfig):
         self.Taos_max_position_embeddings = Taos_max_position_embeddings
         super().__init__(**kwargs)
 
+class BaseStreamer:
+    pass
+
 class TaosTSGenerationMixin(GenerationMixin):
     @torch.no_grad()
     def generate(
@@ -593,7 +596,7 @@ class TaosForPrediction(TaosPreTrainedModel, TaosTSGenerationMixin):
         output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
         output_hidden_states = output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-        
+
         if revin:
             mean, std = input_ids.mean(dim=-1, keepdim=True), input_ids.std(dim=-1, keepdim=True)
             input_ids = (input_ids - mean) / std
@@ -636,7 +639,7 @@ class TaosForPrediction(TaosPreTrainedModel, TaosTSGenerationMixin):
             if output_token_len > max_output_length:
                 predictions = predictions[:, :max_output_length]
             if revin:
-                predictions = predictions * std + mean 
+                predictions = predictions * std + mean
         if not return_dict:
             output = (predictions,) + outputs[1:]
             return (loss) + output if loss is not None else output
@@ -718,18 +721,27 @@ def init_model():
         Taos_hidden_act="gelu",
         Taos_output_token_lens=[96],
     )
-    
+
     Taos_model = TaosForPrediction(config)
     model_path = "taos.pth"
     Taos_model.load_state_dict(torch.load(model_path, map_location=torch.device('cpu')), strict=True)
     Taos_model = Taos_model.to(device)
-    
+
     #src_data = load_data('src_data/holt-winters_1.txt')
     #print(f"src_data:{src_data}")
     #seqs = torch.tensor(src_data).unsqueeze(0).float()
     #seqs = seqs.to(device)
     print(Taos_model)
 
+def train():
+    pass
+
+def infer():
+    pass
+
+def data_view():
+    pass
+
 @app.route('/get_train_data', methods=['POST'])
 def get_train_data():
     try: