code: def predict(
      self, df: pd.DataFrame, args: Optional[Dict] = None
  ) -> pd.DataFrame:
      model = dill.loads(self.model_storage.file_get("model"))
      le_dict = dill.loads(self.model_storage.file_get("le_dict"))
      target = self.model_storage.json_get("args").get("target")
  
      # Encode the categorical columns in the input DataFrame using the saved LabelEncoders
      for col, le in le_dict.items():
          if col in df.columns:
              df[col] = le.transform(df[col])
  
      # Make predictions using the trained TPOT model
      results = pd.DataFrame(model.predict(df), columns=[target])
  
      # Decode the predicted categorical values back into their original values
      for col, le in le_dict.items():
          if col in results.columns:
              results[col] = le.inverse_transform(results[col])
  
      return results
  
file_path: mindsdb/mindsdb/integrations/handlers/tpot_handler/tpot_handler.py
imports: from typing import Optional
  import dill
  import pandas
  from mindsdb.integrations.libs.base import BaseMLEngine
  from typing import Dict, Optional
  from type_infer.api import infer_types
  from tpot import TPOTClassifier, TPOTRegressor
  from sklearn.preprocessing import LabelEncoder
label_desc: Use image node_id 2 for calling the TPOTHandler obj's underlying member method code with example usage: obj.predict(df, args) and returns: results