code: def from_paths(
      cls,
      checkpoints: List[str],
      auto_update_checkpoints=True,
      predictor_params: PredictorParams = None,
      voter_params: VoterParams = None,
      **kwargs,
  ) -> "tfaip_cls.MultiModelPredictor":
      if not checkpoints:
          raise Exception("No checkpoints provided.")
  
      if predictor_params is None:
          predictor_params = PredictorParams(
              silent=True, progress_bar=True
          )
  
      DeviceConfig(predictor_params.device)
      checkpoints = [
          SavedCalamariModel(ckpt, auto_update=auto_update_checkpoints)
          for ckpt in checkpoints
      ]
  
      multi_predictor = super(MultiPredictor, cls).from_paths(
          [ckpt.json_path for ckpt in checkpoints],
          predictor_params,
          CalamariScenario,
          model_paths=[ckpt.ckpt_path for ckpt in checkpoints],
          predictor_args={"voter_params": voter_params},
      )
  
      return multi_predictor
  
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 1 for calling the MultiPredictor obj's underlying member method code with example usage: obj.from_paths(cls, checkpoints, auto_update_checkpoints, predictor_params, voter_params) and returns: multi_predictor