text: 
  class MyModel(object):
      """
      Model template. You can load your model parameters in __init__ from a location accessible at runtime
      """
      
      def __init__(self):
          """
          Add any initialization parameters. These will be passed at runtime from the graph definition parameters defined in your seldondeployment kubernetes resource manifest.
          """
          print("Initializing")
  
      def predict(self,X,features_names):
          """
          Return a prediction.
  
          Parameters
          ----------
          X : array-like
          feature_names : array of feature names (optional)
          """
          print("Predict called - will run idenity function")
          return X
  
      def send_feedback(self,features,feature_names,reward,truth,routing):
          """
          Handle feedback
  
          Parameters
          ----------
          features : array - the features sent in the original predict request
          feature_names : array of feature names. May be None if not available.
          reward : float - the reward
          truth : array with correct value (optional)
          """
          print("Send feedback called")
          return []
  