code: def _convert_sample_to_prediction_results(
      self, sample: Sample
  ) -> List[PredictionResult]:
      inputs, outputs, meta = sample.inputs, sample.outputs, sample.meta
      prediction_results = []
  
      for i, (prediction, m, codec, post_) in enumerate(
          zip(outputs, meta, self.codecs, self.post_proc)
      ):
          prediction.id = f"fold_{i}"
          prediction_results.append(
              PredictionResult(
                  prediction,
                  codec=codec,
                  text_postproc=post_,
                  out_to_in_trans=make_out_to_in(
                      meta=m,
                      out_to_in_transformer=self.out_to_in_transformer,
                      model_factor=sample.inputs["img_len"]
                      / prediction.logits.shape[0],
                  ),
              )
          )
  
      return prediction_results
  
file_path: calamari/calamari_ocr/ocr/voting/adapter.py
imports: from typing import List
  from tfaip.data.pipeline.definitions import Sample
  from tfaip.predict.multimodelvoter import MultiModelVoter
  from calamari_ocr.ocr.predict.params import PredictionResult
  from calamari_ocr.ocr.voting import voter_from_params
  from calamari_ocr.utils.output_to_input_transformer import OutputToInputTransformer
label_desc: Use image node_id 2 for calling the CalamariMultiModelVoter obj's underlying member method code with example usage: obj._convert_sample_to_prediction_results(sample) and returns: prediction_results