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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 open_mfbpchdataset(paths, concat_dim='time', compat='no_conflicts', preprocess=None, lock=None, **kwargs):
 """ Open multiple bpch files as a single dataset. You must have dask installed for this to work, as this greatly simplifies issues relating to multi-file I/O. Also, please note that this is not a very performant routine. I/O is still limited by the fact that we need to manually scan/read through each bpch file so that we can figure out what its contents are, since that metadata isn't saved anywhere. So this routine will actually sequentially load Datasets for each bpch file, then concatenate them along the "time" axis. You may wish to simply process each file individually, coerce to NetCDF, and then ingest through xarray as normal. Parameters paths : list of strs Filenames to load; order doesn't matter as they will be lexicographically sorted before we read in the data concat_dim : str, default='time' Dimension to concatenate Datasets over. We default to "time" since this is how GEOS-Chem splits output files compat : str (optional) String indicating how to compare variables of the same name for potential conflicts when merging: - 'broadcast_equals': all values must be equal when variables are broadcast against each other to ensure common dimensions. - 'equals': all values and dimensions must be the same. - 'identical': all values, dimensions and attributes must be the same. - 'no_conflicts': only values which are not null in both datasets must be equal. The returned dataset then contains the combination of all non-null values. preprocess : callable (optional) A pre-processing function to apply to each Dataset prior to concatenation lock : False, True, or threading.Lock (optional) Passed to :py:func:`dask.array.from_array`. By default, xarray employs a per-variable lock when reading data from NetCDF files, but this model has not yet been extended or implemented for bpch files and so this is not actually used. However, it is likely necessary before dask's multi-threaded backend can be used **kwargs : optional Additional arguments to pass to :py:func:`xbpch.open_bpchdataset`. """


    from xarray.backends.api import _MultiFileCloser

    # TODO: Include file locks?

    # Check for dask
    dask = kwargs.pop('dask', False)
    if not dask:
        raise ValueError("Reading multiple files without dask is not supported")
    kwargs['dask'] = True

    # Add th

    if isinstance(paths, basestring):
        paths = sorted(glob(paths))
    if not paths:
        raise IOError("No paths to files were passed into open_mfbpchdataset")

    datasets = [open_bpchdataset(filename, **kwargs)
                for filename in paths]
    bpch_objs = [ds._file_obj for ds in datasets]

    if preprocess is not None:
        datasets = [preprocess(ds) for ds in datasets]

    # Concatenate over time
    combined = xr.auto_combine(datasets, compat=compat, concat_dim=concat_dim)

    combined._file_obj = _MultiFileCloser(bpch_objs)
    combined.attrs = datasets[0].attrs
    ts = get_timestamp()
    fns_str = " ".join(paths)
    combined.attrs['history'] = (
        "{}: Processed/loaded by xbpch-{} from {}"
        .format(ts, ver, fns_str)
    )


    return combined