code: def build_prediction_graph(self, inputs, training=None):
      # Prediction Graph: standard voting
      complete_outputs = [
          self.fold_graphs[i].predict(inputs)
          for i in range(len(self.fold_graphs))
      ]
  
      lstm_seq_len = complete_outputs[0][
          "out_len"
      ]  # is the same for all children
      softmax_outputs = tf.stack(
          [out["blank_last_softmax"] for out in complete_outputs],
          axis=0,
      )
  
      blank_last_softmax = tf.reduce_mean(softmax_outputs, axis=0)
      return self.make_outputs(
          blank_last_softmax, lstm_seq_len, complete_outputs
      )
  
file_path: calamari/calamari_ocr/ocr/model/ensemblegraph.py
imports: import logging
  from tfaip.model.graphbase import GenericGraphBase
  import tensorflow
  import tensorflow.keras.backend
  from tensorflow.python.ops import ctc_ops
  from calamari_ocr.ocr.model.graph import CalamariGraph
  from calamari_ocr.ocr.model.params import ModelParams
label_desc: Use image node_id 3 for calling the EnsembleGraph obj's underlying member method code with example usage: obj.build_prediction_graph(inputs, training) and returns: self