code: def predict_args(
      n_models=1, data: CalamariDataGeneratorParams = file_dataset()
  ) -> PredictArgs:
      p = PredictArgs(
          checkpoint=[os.path.join(this_dir, "models", "best.ckpt")]
          * n_models,
          data=data,
      )
      return p
  
imports: import os
  import unittest
  import numpy
  from calamari_ocr.ocr import SavedCalamariModel
  from calamari_ocr.ocr.predict.params import PredictionResult, Predictions
  from tensorflow import keras
  from calamari_ocr.ocr.dataset.datareader.abbyy.reader import Abbyy
  from calamari_ocr.ocr.dataset.datareader.base import CalamariDataGeneratorParams
  from calamari_ocr.ocr.dataset.datareader.extended_prediction import ExtendedPredictionDataParams
  from calamari_ocr.ocr.dataset.datareader.file import FileDataParams
  from calamari_ocr.ocr.dataset.datareader.hdf5.reader import Hdf5
  from calamari_ocr.ocr.dataset.datareader.pagexml.reader import PageXML
  from calamari_ocr.ocr.predict.predictor import Predictor, PredictorParams, MultiPredictor
  from calamari_ocr.scripts.compute_average_prediction_confidence import run
  from calamari_ocr.scripts.predict import run, PredictArgs
  from calamari_ocr.utils import glob_all
  from calamari_ocr.utils.image import ImageLoaderParams
label_desc: Use image node_id 6 for calling a global function with example usage: predict_args(n_models, data) and returns: p