code: def default_cross_fold_params(
      trainer_params, pretrained="none", with_augmentation=False
  ):
      cfp = CrossFoldTrainerParams(
          trainer=trainer_params,
          n_folds=3,
          max_parallel_models=1,
          single_fold=[0],  # Only one fold for tests
      )
      checkpoint = os.path.join(this_dir, "models", "best.ckpt")
      if pretrained == "one":
          cfp.weights = [checkpoint]
      elif pretrained == "all":
          cfp.weights = [checkpoint] * cfp.n_folds
      elif pretrained == "none":
          pass
      else:
          raise NotImplementedError
  
      if with_augmentation:
          for (
              dp
          ) in cfp.trainer.scenario.data.pre_proc.processors_of_type(
              AugmentationProcessorParams
          ):
              dp.n_augmentations = 1
      return cfp
  
imports: import os
  import tempfile
  import unittest
  from tensorflow import keras
  from calamari_ocr.ocr.dataset.imageprocessors import AugmentationProcessorParams
  from calamari_ocr.ocr.training.cross_fold_trainer import CrossFoldTrainerParams
  from calamari_ocr.scripts.cross_fold_train import main
  from calamari_ocr.test.test_train_file import uw3_trainer_params
  from calamari_ocr.test.test_train_pagexml import default_trainer_params
label_desc: Use image node_id 1 for calling a global function with example usage: default_cross_fold_params(trainer_params, pretrained, with_augmentation) and returns: cfp