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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_bpchdataset(filename, fields=[], categories=[], tracerinfo_file='tracerinfo.dat', diaginfo_file='diaginfo.dat', endian=">", decode_cf=True, memmap=True, dask=True, return_store=False):
 """ Open a GEOS-Chem BPCH file output as an xarray Dataset. Parameters filename : string Path to the output file to read in. {tracerinfo,diaginfo}_file : string, optional Path to the metadata "info" .dat files which are used to decipher the metadata corresponding to each variable in the output dataset. If not provided, will look for them in the current directory or fall back on a generic set. fields : list, optional List of a subset of variable names to return. This can substantially improve read performance. Note that the field here is just the tracer name - not the category, e.g. 'O3' instead of 'IJ-AVG-$_O3'. categories : list, optional List a subset of variable categories to look through. This can substantially improve read performance. endian : {'=', '>', '<'}, optional Endianness of file on disk. By default, "big endian" (">") is assumed. decode_cf : bool Enforce CF conventions for variable names, units, and other metadata default_dtype : numpy.dtype, optional Default datatype for variables encoded in file on disk (single-precision float by default). memmap : bool Flag indicating that data should be memory-mapped from disk instead of eagerly loaded into memory dask : bool Flag indicating that data reading should be deferred (delayed) to construct a task-graph for later execution return_store : bool Also return the underlying DataStore to the user Returns ------- ds : xarray.Dataset Dataset containing the requested fields (or the entire file), with data contained in proxy containers for access later. store : xarray.AbstractDataStore Underlying DataStore which handles the loading and processing of bpch files on disk """


    store = BPCHDataStore(
        filename, fields=fields, categories=categories,
        tracerinfo_file=tracerinfo_file,
        diaginfo_file=diaginfo_file, endian=endian,
        use_mmap=memmap, dask_delayed=dask
    )
    ds = xr.Dataset.load_store(store)
    # Record what the file object underlying the store which we culled this
    # Dataset from is so that we can clean it up later
    ds._file_obj = store._bpch

    # Handle CF corrections
    if decode_cf:
        decoded_vars = OrderedDict()
        rename_dict = {}
        for v in ds.variables:
            cf_name = cf.get_valid_varname(v)
            rename_dict[v] = cf_name
            new_var = cf.enforce_cf_variable(ds[v])
            decoded_vars[cf_name] = new_var
        ds = xr.Dataset(decoded_vars, attrs=ds.attrs.copy())

        # ds.rename(rename_dict, inplace=True)

        # TODO: There's a bug with xr.decode_cf which eagerly loads data.
        #       Re-enable this once that bug is fixed
        # Note that we do not need to decode the times because we explicitly
        # kept track of them as we parsed the data.
        # ds = xr.decode_cf(ds, decode_times=False)

    # Set attributes for CF conventions
    ts = get_timestamp()
    ds.attrs.update(dict(
        Conventions='CF1.6',
        source=filename,
        tracerinfo=tracerinfo_file,
        diaginfo=diaginfo_file,
        filetype=store._bpch.filetype,
        filetitle=store._bpch.filetitle,
        history=(
            "{}: Processed/loaded by xbpch-{} from {}"
            .format(ts, ver, filename)
        ),
    ))

    # To immediately load the data from the BPCHDataProxy paylods, need
    # to execute ds.data_vars for some reason...
    if return_store:
        return ds, store
    else:
        return ds