code: def predict(self, feed_dict):
      with self.graph.as_default():
          prediction = []
          for output in self.net_outputs:
              o_pred = self.session.run(
                  output, feed_dict=feed_dict
              ).tolist()
              for i, val in enumerate(
                  o_pred
              ):  # Reshape pred per sample
                  if len(self.net_outputs) > 1:
                      if not len(prediction) > i:
                          prediction.append([])
                      prediction[i].append(val)
                  else:
                      prediction.append(val)
          return prediction
  
file_path: tflearn/tflearn/helpers/generator.py
imports: from __future__ import division, print_function, absolute_import
  import tensorflow.compat.v1
  from ..utils import to_list
label_desc: Use image node_id 2 for calling the SequenceGenerator obj's underlying member method code with example usage: obj.predict(feed_dict) and returns: prediction