code: def create_voter(self, data_params: "DataParams") -> MultiModelVoter:
      # Cut non text processors (first two)
      # force run_parallel = False because the voter itself already runs in separate threads
      post_proc_params = [
          SequentialProcessorPipelineParams(
              run_parallel=False,
              processors=data.params.post_proc.processors[2:],
          )
          for data in self.datas
      ]
      post_proc = [
          p.create(self.params.pipeline, self.data.params)
          for p in post_proc_params
      ]
      pre_proc = self.data.params.pre_proc.create(
          self.params.pipeline, self.data.params
      )
      out_to_in_transformer = OutputToInputTransformer(pre_proc)
      return CalamariMultiModelVoter(
          self.voter_params,
          self.datas,
          post_proc,
          out_to_in_transformer,
      )
  
file_path: calamari/calamari_ocr/ocr/predict/predictor.py
imports: from typing import List
  from tensorflow import keras
  from tfaip.data.pipeline.processor.params import SequentialProcessorPipelineParams
  from tfaip.device.device_config import DeviceConfig
  import tfaip.imports
  from tfaip.predict.multimodelpredictor import MultiModelVoter
  from calamari_ocr.ocr.predict.params import PredictorParams
  from calamari_ocr.ocr.scenario import CalamariScenario
  from calamari_ocr.ocr.voting import VoterParams
  from calamari_ocr.ocr import SavedCalamariModel, DataParams
  from calamari_ocr.ocr.voting.adapter import CalamariMultiModelVoter
  from calamari_ocr.utils.output_to_input_transformer import OutputToInputTransformer
label_desc: Use image node_id 3 for calling the MultiPredictor obj's underlying member method code with example usage: obj.create_voter(data_params) and returns: CalamariMultiModelVoter