code: def prepare_ctc_decoder_params(ctc_decoder: CTCDecoderParams):
      if ctc_decoder.dictionary:
          dictionary = set()
          logger.info("Creating dictionary")
          for path in glob_all(ctc_decoder.dictionary):
              with open(path, "r") as f:
                  dictionary = dictionary.union(
                      {word for word in f.read().split()}
                  )
  
          ctc_decoder.dictionary = dictionary
          logger.info(
              "Dictionary with {} unique words successfully created.".format(
                  len(dictionary)
              )
          )
  
      if ctc_decoder.dictionary:
          logger.warning(
              "USING A LANGUAGE MODEL IS CURRENTLY EXPERIMENTAL ONLY. NOTE: THE PREDICTION IS VERY SLOW!"
          )
          ctc_decoder.type = CTCDecoderType.WordBeamSearch
  
file_path: calamari/calamari_ocr/scripts/predict.py
imports: import os
  import zlib
  from dataclasses import dataclass, field
  from typing import TYPE_CHECKING, List, Optional
  import tfaip.util.logging
  from bidi.algorithm import get_base_level
  from paiargparse import PAIArgumentParser, pai_meta, pai_dataclass
  from calamari_ocr import __version__
  from calamari_ocr.ocr.dataset.datareader.base import CalamariDataGeneratorParams
  from calamari_ocr.ocr.dataset.datareader.file import FileDataParams
  from calamari_ocr.ocr.dataset.params import DATA_GENERATOR_CHOICES
  from calamari_ocr.ocr.model.ctcdecoder.ctc_decoder import CTCDecoderParams, CTCDecoderType
  from calamari_ocr.ocr.predict.params import Predictions, PredictorParams
  from calamari_ocr.ocr.voting import VoterParams
  from calamari_ocr.utils.glob import glob_all
label_desc: Use image node_id 1 for calling a global function with example usage: prepare_ctc_decoder_params(ctc_decoder) without return types