code: def run_for_single_line(args):
      # lines/network/pretraining as base dir
      args.base_dir = os.path.join(
          args.base_dir,
          "all" if args.n_lines < 0 else str(args.n_lines),
      )
      pretrain_prefix = "scratch"
      if args.weights and len(args.weights) > 0:
          pretrain_prefix = ",".join(
              [
                  split_all_ext(os.path.basename(path))[0]
                  for path in args.weights
              ]
          )
  
      args.base_dir = os.path.join(
          args.base_dir, args.network, pretrain_prefix
      )
  
      if not os.path.exists(args.base_dir):
          os.makedirs(args.base_dir)
  
      tmp_dir = os.path.join(args.base_dir, "tmp")
      if not os.path.exists(tmp_dir):
          os.makedirs(tmp_dir)
  
      best_models_dir = os.path.join(args.base_dir, "models")
      if not os.path.exists(best_models_dir):
          os.makedirs(best_models_dir)
  
      prediction_dir = os.path.join(args.base_dir, "predictions")
      if not os.path.exists(prediction_dir):
          os.makedirs(prediction_dir)
  
      # select number of files
      files = args.train_files
      if args.n_lines > 0:
          all_files = glob_all(args.train_files)
          files = random.sample(all_files, args.n_lines)
  
      # run the cross-fold-training
      setattr(args, "max_parallel_models", args.max_parallel_models)
      setattr(args, "best_models_dir", best_models_dir)
      setattr(args, "temporary_dir", tmp_dir)
      setattr(args, "keep_temporary_files", False)
      setattr(args, "files", files)
      setattr(args, "text_files", None)
      setattr(args, "gt_extension", None)
      setattr(args, "dataset", DataSetType.FILE)
      setattr(args, "best_model_label", "{id}")
      if not args.skip_train:
          cross_fold_train.main(args)
  
      dump_file = os.path.join(tmp_dir, "prediction.pkl")
  
      # run the prediction
      if not args.skip_eval:
          # locate the eval script (must be in the same dir as "this")
          predict_script_path = os.path.join(
              this_absdir, "experiment_eval.py"
          )
  
          if len(args.single_fold) > 0:
              models = [
                  os.path.join(
                      best_models_dir, "{}.ckpt.json".format(sf)
                  )
                  for sf in args.single_fold
              ]
              for m in models:
                  if not os.path.exists(m):
                      raise Exception(
                          "Expected model at '{}', but file does not exist".format(
                              m
                          )
                      )
          else:
              models = [
                  os.path.join(best_models_dir, d)
                  for d in sorted(os.listdir(best_models_dir))
                  if d.endswith("json")
              ]
              if len(models) != args.n_folds:
                  raise Exception(
                      "Expected {} models, one for each fold respectively, but only {} models were found".format(
                          args.n_folds, len(models)
                      )
                  )
  
          for line in run(
              prefix_run_command(
                  [
                      "python3",
                      "-u",
                      predict_script_path,
                      "-j",
                      str(args.num_threads),
                      "--batch_size",
                      str(args.batch_size),
                      "--dump",
                      dump_file,
                      "--eval_imgs",
                  ]
                  + args.eval_files
                  + []
                  + (["--verbose"] if args.verbose else [])
                  + ["--checkpoint"]
                  + models
                  + [],
                  args.run,
                  {"threads": args.num_threads},
              ),
              verbose=args.verbose,
          ):
              # Print the output of the thread
              if args.verbose:
                  print(line)
  
      import pickle
  
      with open(dump_file, "rb") as f:
          prediction = pickle.load(f)
  
      return prediction
  
file_path: calamari/calamari_ocr/scripts/experiment_cross_fold.py
imports: import argparse
  import os
  import copy
  import numpy
  import random
  import inspect
  from calamari_ocr.ocr import DataSetType
  from calamari_ocr.scripts.train import setup_train_args
  import calamari_ocr.scripts.cross_fold_train
  from calamari_ocr.scripts.eval import print_confusions, write_xlsx
  from calamari_ocr.utils import glob_all, split_all_ext
  from calamari_ocr.utils.multiprocessing import parallel_map, run, prefix_run_command
label_desc: Use image node_id 1 for calling a global function with example usage: run_for_single_line(args) and returns: prediction