

def train(config, level_name=None, timestamp=None, time_budget=None, verbose_logging=None, debug=None):
    '\n    Trains a given YAML file.\n\n    Parameters\n    ----------\n    config : str\n        A YAML configuration file specifying the\n        training procedure.\n    level_name : bool, optional\n        Display the log level (e.g. DEBUG, INFO)\n        for each logged message.\n    timestamp : bool, optional\n        Display human-readable timestamps for\n        each logged message.\n    time_budget : int, optional\n        Time budget in seconds. Stop training at\n        the end of an epoch if more than this\n        number of seconds has elapsed.\n    verbose_logging : bool, optional\n        Display timestamp, log level and source\n        logger for every logged message\n        (implies timestamp and level_name are True).\n    debug : bool, optional\n        Display any DEBUG-level log messages,\n        False by default.\n    '
    train_obj = serial.load_train_file(config)
    try:
        iter(train_obj)
        iterable = True
    except TypeError:
        iterable = False
    restore_defaults()
    root_logger = logging.getLogger()
    if verbose_logging:
        formatter = logging.Formatter(fmt='%(asctime)s %(name)s %(levelname)s %(message)s')
        handler = CustomStreamHandler(formatter=formatter)
    else:
        if timestamp:
            prefix = '%(asctime)s '
        else:
            prefix = ''
        formatter = CustomFormatter(prefix=prefix, only_from='pylearn2')
        handler = CustomStreamHandler(formatter=formatter)
    root_logger.addHandler(handler)
    if debug:
        root_logger.setLevel(logging.DEBUG)
    else:
        root_logger.setLevel(logging.INFO)
    if iterable:
        for (number, subobj) in enumerate(iter(train_obj)):
            phase_variable = 'PYLEARN2_TRAIN_PHASE'
            phase_value = ('phase%d' % (number + 1))
            os.environ[phase_variable] = phase_value
            subobj.main_loop(time_budget=time_budget)
            del subobj
            gc.collect()
    else:
        train_obj.main_loop(time_budget=time_budget)

dataset/ETHPy150Open lisa-lab/pylearn2/pylearn2/scripts/train.py train/original