<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def run_datafind_instance(cp, outputDir, connection, observatory, frameType, startTime, endTime, ifo, tags=None):
 """ This function will query the datafind server once to find frames between the specified times for the specified frame type and observatory. Parameters cp : ConfigParser instance Source for any kwargs that should be sent to the datafind module outputDir : Output cache files will be written here. We also write the commands for reproducing what is done in this function to this directory. connection : datafind connection object Initialized through the glue.datafind module, this is the open connection to the datafind server. observatory : string The observatory to query frames for. Ex. 'H', 'L' or 'V'. NB: not 'H1', 'L1', 'V1' which denote interferometers. frameType : string The frame type to query for. startTime : int Integer start time to query the datafind server for frames. endTime : int Integer end time to query the datafind server for frames. ifo : string The interferometer to use for naming output. Ex. 'H1', 'L1', 'V1'. Maybe this could be merged with the observatory string, but this could cause issues if running on old 'H2' and 'H1' data. tags : list of string, optional (default=None) Use this to specify tags. This can be used if this module is being called more than once to give call specific configuration (by setting options in [workflow-datafind-${TAG}] rather than [workflow-datafind]). This is also used to tag the Files returned by the class to uniqueify the Files and uniquify the actual filename. FIXME: Filenames may not be unique with current codes! Returns -------- dfCache : glue.lal.Cache instance The glue.lal.Cache representation of the call to the datafind server and the returned frame files. cacheFile : pycbc.workflow.core.File Cache file listing all of the datafind output files for use later in the pipeline. """

    if tags is None:
        tags = []

    seg = segments.segment([startTime, endTime])
    # Take the datafind kwargs from config (usually urltype=file is
    # given).
    dfKwargs = {}
    # By default ignore missing frames, this case is dealt with outside of here
    dfKwargs['on_gaps'] = 'ignore'
    if cp.has_section("datafind"):
        for item, value in cp.items("datafind"):
            dfKwargs[item] = value
    for tag in tags:
        if cp.has_section('datafind-%s' %(tag)):
            for item, value in cp.items("datafind-%s" %(tag)):
                dfKwargs[item] = value

    # It is useful to print the corresponding command to the logs
    # directory to check if this was expected.
    log_datafind_command(observatory, frameType, startTime, endTime,
                         os.path.join(outputDir,'logs'), **dfKwargs)
    logging.debug("Asking datafind server for frames.")
    dfCache = connection.find_frame_urls(observatory, frameType,
                                        startTime, endTime, **dfKwargs)
    logging.debug("Frames returned")
    # workflow format output file
    cache_file = File(ifo, 'DATAFIND', seg, extension='lcf',
                      directory=outputDir, tags=tags)
    cache_file.PFN(cache_file.cache_entry.path, site='local')

    dfCache.ifo = ifo
    # Dump output to file
    fP = open(cache_file.storage_path, "w")
    # FIXME: CANNOT use dfCache.tofile because it will print 815901601.00000
    #        as a gps time which is incompatible with the lal cache format
    #        (and the C codes) which demand an integer.
    #dfCache.tofile(fP)
    for entry in dfCache:
        start = str(int(entry.segment[0]))
        duration = str(int(abs(entry.segment)))
        print("%s %s %s %s %s" \
              % (entry.observatory, entry.description, start, duration, entry.url), file=fP)
        entry.segment = segments.segment(int(entry.segment[0]), int(entry.segment[1]))

    fP.close()
    return dfCache, cache_file