old_contents: """
  Use orca_train with a parser.
  
  Usage:
      parser_orcatrain.py FOLDER LIST CONFIG MODEL
      parser_orcatrain.py (-h | --help)
  
  Arguments:
      FOLDER  Path to the folder where everything gets saved to, e.g. the summary.txt, the plots, the trained models, etc.
      LIST    A .toml file which contains the pathes of the training and validation files.
              An example can be found in config/lists/example_list.toml
      CONFIG  A .toml file which sets up the training.
              An example can be found in config/models/example_config.toml. The possible parameters are listed in
              utilities/input_output_utilities.py in the class Configuration.
      MODEL   Path to a .toml file with infos about a model.
  
  Options:
      -h --help                       Show this screen.
  
  """
  from docopt import docopt
  from orcanet.core import orca_train, Configuration
  from orcanet.model_archs.model_setup import build_nn_model
  
  
  def run_train(main_folder, list_file, config_file, model_file):
      """
      Use orca_train with a parser.
  
      Parameters
      ----------
      main_folder : str
          Path to the folder where everything gets saved to, e.g. the summary log file, the plots, the trained models, etc.
      list_file : str
          Path to a list file which contains pathes to all the h5 files that should be used for training and validation.
      config_file : str
          Path to a .toml file which overwrite some of the default settings for training and validating a model.
      model_file : str
          Path to a file with parameters to build a model of a predefined architecture with OrcaNet.
  
      """
      # Set up the cfg object with the input data
      cfg = Configuration(main_folder, list_file, config_file)
      # If this is the start of the training, a compiled model needs to be handed to the orca_train function
      if cfg.get_latest_epoch() == (0, 1):
          # Add Info for building a model with OrcaNet to the cfg object
          cfg.set_from_model_file(model_file)
          # Build it
          initial_model = build_nn_model(cfg)
      else:
          # No model is required if the training is continued, as it will be loaded automatically
          initial_model = None
      orca_train(cfg, initial_model)
  
  
  def parse_input():
      """ Run the orca_train function with a parser. """
      args = docopt(__doc__)
      main_folder = args['FOLDER']
      list_file = args['LIST']
      config_file = args['CONFIG']
      model_file = args['MODEL']
      run_train(main_folder, list_file, config_file, model_file)
  
  
  if __name__ == '__main__':
      parse_input()
  
new_contents: """
  Use orca_train with a parser.
  
  Usage:
      parser_orcatrain.py FOLDER LIST CONFIG MODEL
      parser_orcatrain.py (-h | --help)
  
  Arguments:
      FOLDER  Path to the folder where everything gets saved to, e.g. the summary.txt, the plots, the trained models, etc.
      LIST    A .toml file which contains the pathes of the training and validation files.
              An example can be found in config/lists/example_list.toml
      CONFIG  A .toml file which sets up the training.
              An example can be found in config/models/example_config.toml. The possible parameters are listed in
              utilities/input_output_utilities.py in the class Configuration.
      MODEL   Path to a .toml file with infos about a model.
  
  Options:
      -h --help                       Show this screen.
  
  """
  from docopt import docopt
  from orcanet.core import orca_train, Configuration
  from orcanet.model_archs.model_setup import build_nn_model
  
  
  def run_train(main_folder, list_file, config_file, model_file):
      """
      Use orca_train with a parser.
  
      Parameters
      ----------
      main_folder : str
          Path to the folder where everything gets saved to, e.g. the summary log file, the plots, the trained models, etc.
      list_file : str
          Path to a list file which contains pathes to all the h5 files that should be used for training and validation.
      config_file : str
          Path to a .toml file which overwrite some of the default settings for training and validating a model.
      model_file : str
          Path to a file with parameters to build a model of a predefined architecture with OrcaNet.
  
      """
      # Set up the cfg object with the input data
      cfg = Configuration(main_folder, list_file, config_file)
      # If this is the start of the training, a compiled model needs to be handed to the orca_train function
      if cfg.get_latest_epoch() == (0, 0):
          # Add Info for building a model with OrcaNet to the cfg object
          cfg.set_from_model_file(model_file)
          # Build it
          initial_model = build_nn_model(cfg)
      else:
          # No model is required if the training is continued, as it will be loaded automatically
          initial_model = None
      orca_train(cfg, initial_model)
  
  
  def parse_input():
      """ Run the orca_train function with a parser. """
      args = docopt(__doc__)
      main_folder = args['FOLDER']
      list_file = args['LIST']
      config_file = args['CONFIG']
      model_file = args['MODEL']
      run_train(main_folder, list_file, config_file, model_file)
  
  
  if __name__ == '__main__':
      parse_input()
  
subject: Fix again.