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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 annotate(self, fname, tables, feature_strand=False, in_memory=False, header=None, out=sys.stdout, parallel=False):
 """ annotate a file with a number of tables Parameters fname : str or file file name or file-handle tables : list list of tables with which to annotate `fname` feature_strand : bool if this is True, then the up/downstream designations are based on the features in `tables` rather than the features in `fname` in_memoory : bool if True, then tables are read into memory. This usually makes the annotation much faster if there are more than 500 features in `fname` and the number of features in the table is less than 100K. header : str header to print out (if True, use existing header) out : file where to print output parallel : bool if True, use multiprocessing library to execute the annotation of each chromosome in parallel. Uses more memory. """

        from .annotate import annotate
        return annotate(self, fname, tables, feature_strand, in_memory, header=header,
                out=out, parallel=parallel)