def train(config, level_name=None, timestamp=None, time_budget=None,
          verbose_logging=None, debug=None):
    """
    Trains a given YAML file.

    Parameters
    ----------
    config : str
        A YAML configuration file specifying the
        training procedure.
    level_name : bool, optional
        Display the log level (e.g. DEBUG, INFO)
        for each logged message.
    timestamp : bool, optional
        Display human-readable timestamps for
        each logged message.
    time_budget : int, optional
        Time budget in seconds. Stop training at
        the end of an epoch if more than this
        number of seconds has elapsed.
    verbose_logging : bool, optional
        Display timestamp, log level and source
        logger for every logged message
        (implies timestamp and level_name are True).
    debug : bool, optional
        Display any DEBUG-level log messages,
        False by default.
    """
    train_obj = serial.load_train_file(config)
    try:
        iter(train_obj)
        iterable = True
    except TypeError:
        iterable = False

    # Undo our custom logging setup.
    restore_defaults()
    # Set up the root logger with a custom handler that logs stdout for INFO
    # and DEBUG and stderr for WARNING, ERROR, CRITICAL.
    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)
    # Set the root logger level.
    if debug:
        root_logger.setLevel(logging.DEBUG)
    else:
        root_logger.setLevel(logging.INFO)

    if iterable:
        for number, subobj in enumerate(iter(train_obj)):
            # Publish a variable indicating the training phase.
            phase_variable = 'PYLEARN2_TRAIN_PHASE'
            phase_value = 'phase%d' % (number + 1)
            os.environ[phase_variable] = phase_value

            # Execute this training phase.
            subobj.main_loop(time_budget=time_budget)

            # Clean up, in case there's a lot of memory used that's
            # necessary for the next phase.
            del subobj
            gc.collect()
    else:
        train_obj.main_loop(time_budget=time_budget)
dataset/ETHPy150Open lisa-lab/pylearn2/pylearn2/scripts/train.py/train/original