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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def load(fnames, tag=None, sat_id=None, **kwargs):
 """Loads data using pysat.utils.load_netcdf4 . This routine is called as needed by pysat. It is not intended for direct user interaction. Parameters fnames : array-like iterable of filename strings, full path, to data files to be loaded. This input is nominally provided by pysat itself. tag : string tag name used to identify particular data set to be loaded. This input is nominally provided by pysat itself. sat_id : string Satellite ID used to identify particular data set to be loaded. This input is nominally provided by pysat itself. **kwargs : extra keywords Passthrough for additional keyword arguments specified when instantiating an Instrument object. These additional keywords are passed through to this routine by pysat. Returns ------- data, metadata Data and Metadata are formatted for pysat. Data is a pandas DataFrame while metadata is a pysat.Meta instance. Note ---- Any additional keyword arguments passed to pysat.Instrument upon instantiation are passed along to this routine and through to the load_netcdf4 call. Examples -------- :: inst = pysat.Instrument('sport', 'ivm') inst.load(2019,1) # create quick Instrument object for a new, random netCDF4 file # define filename template string to identify files # this is normally done by instrument code, but in this case # there is no built in pysat instrument support # presumes files are named default_2019-01-01.NC format_str = 'default_{year:04d}-{month:02d}-{day:02d}.NC' inst = pysat.Instrument('netcdf', 'pandas', custom_kwarg='test' data_path='./', format_str=format_str) inst.load(2019,1) """

    
    return pysat.utils.load_netcdf4(fnames, **kwargs)