diff --git a/expo/experimenter/autogluon.py b/expo/experimenter/autogluon.py
index 478ecfc01..93dfdb4bc 100644
--- a/expo/experimenter/autogluon.py
+++ b/expo/experimenter/autogluon.py
@@ -1,17 +1,19 @@
 from datetime import datetime
-from autogluon.tabular import TabularDataset, TabularPredictor
+
 from expo.experimenter.custom import CustomExperimenter
 
 
 class AGRunner:
     preset = "best_quality"
-    time_limit = 1000 # 1000s
+    time_limit = 1000  # 1000s
 
     def __init__(self, state=None):
         self.state = state
         self.datasets = self.state["datasets_dir"]
 
     def run(self):
+        from autogluon.tabular import TabularDataset, TabularPredictor
+
         train_path = self.datasets["train"]
         dev_wo_target_path = self.datasets["dev_wo_target"]
         test_wo_target_path = self.datasets["test_wo_target"]
@@ -21,7 +23,11 @@ class AGRunner:
         test_data = TabularDataset(test_wo_target_path)
         eval_metric = self.state["dataset_config"]["metric"].replace(" ", "_")
         # predictor = TabularPredictor(label=target_col, eval_metric=eval_metric, path="AutogluonModels/ag-{}-{}".format(self.state['task'], datetime.now().strftime("%y%m%d_%H%M"))).fit(train_data, presets=self.preset, time_limit=self.time_limit, fit_weighted_ensemble=False, num_gpus=1)
-        predictor = TabularPredictor(label=target_col, eval_metric=eval_metric, path="AutogluonModels/ag-{}-{}".format(self.state['task'], datetime.now().strftime("%y%m%d_%H%M"))).fit(train_data, num_gpus=1)
+        predictor = TabularPredictor(
+            label=target_col,
+            eval_metric=eval_metric,
+            path="AutogluonModels/ag-{}-{}".format(self.state["task"], datetime.now().strftime("%y%m%d_%H%M")),
+        ).fit(train_data, num_gpus=1)
         dev_preds = predictor.predict(dev_data)
         test_preds = predictor.predict(test_data)
         return {"test_preds": test_preds, "dev_preds": dev_preds}
@@ -44,4 +50,4 @@ class GluonExperimenter(CustomExperimenter):
             "test_score": self.evaluate_predictions(test_preds, "test"),
         }
         results = [0, {"score_dict": score_dict, "user_requirement": user_requirement, "args": vars(self.args)}]
-        self.save_result(results)
\ No newline at end of file
+        self.save_result(results)