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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 summary(args):
 """ %prog summary gffile fastafile Print summary stats, including: - Gene/Exon/Intron - Number - Average size (bp) - Median size (bp) - Total length (Mb) - % of genome - % GC """

    p = OptionParser(summary.__doc__)
    opts, args = p.parse_args(args)

    if len(args) != 2:
        sys.exit(not p.print_help())

    gff_file, ref = args
    s = Fasta(ref)
    g = make_index(gff_file)
    geneseqs, exonseqs, intronseqs = [], [], []  # Calc % GC
    for f in g.features_of_type("gene"):
        fid = f.id
        fseq = s.sequence({'chr': f.chrom, 'start': f.start, 'stop': f.stop})
        geneseqs.append(fseq)
        exons = set((c.chrom, c.start, c.stop) for c in g.children(fid, 2) \
                    if c.featuretype == "exon")
        exons = list(exons)
        for chrom, start, stop in exons:
            fseq = s.sequence({'chr': chrom, 'start': start, 'stop': stop})
            exonseqs.append(fseq)
        introns = range_interleave(exons)
        for chrom, start, stop in introns:
            fseq = s.sequence({'chr': chrom, 'start': start, 'stop': stop})
            intronseqs.append(fseq)

    r = {}  # Report
    for t, tseqs in zip(("Gene", "Exon", "Intron"), (geneseqs, exonseqs, intronseqs)):
        tsizes = [len(x) for x in tseqs]
        tsummary = SummaryStats(tsizes, dtype="int")
        r[t, "Number"] = tsummary.size
        r[t, "Average size (bp)"] = tsummary.mean
        r[t, "Median size (bp)"] = tsummary.median
        r[t, "Total length (Mb)"] = human_size(tsummary.sum, precision=0, target="Mb")
        r[t, "% of genome"] = percentage(tsummary.sum, s.totalsize, precision=0, mode=-1)
        r[t, "% GC"] = gc(tseqs)

    print(tabulate(r), file=sys.stderr)