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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 gff3_to_recarray(path, attributes=None, region=None, score_fill=-1, phase_fill=-1, attributes_fill='.', tabix='tabix', dtype=None):
 """Load data from a GFF3 into a NumPy recarray. Parameters path : string Path to input file. attributes : list of strings, optional List of columns to extract from the "attributes" field. region : string, optional Genome region to extract. If given, file must be position sorted, bgzipped and tabix indexed. Tabix must also be installed and on the system path. score_fill : int, optional Value to use where score field has a missing value. phase_fill : int, optional Value to use where phase field has a missing value. attributes_fill : object or list of objects, optional Value(s) to use where attribute field(s) have a missing value. tabix : string, optional Tabix command. dtype : dtype, optional Override dtype. Returns ------- np.recarray """


    # read records
    recs = list(iter_gff3(path, attributes=attributes, region=region,
                          score_fill=score_fill, phase_fill=phase_fill,
                          attributes_fill=attributes_fill, tabix=tabix))

    if not recs:
        return None

    # determine dtype
    if dtype is None:
        dtype = [('seqid', object),
                 ('source', object),
                 ('type', object),
                 ('start', int),
                 ('end', int),
                 ('score', float),
                 ('strand', object),
                 ('phase', int)]
        if attributes:
            for n in attributes:
                dtype.append((n, object))

    a = np.rec.fromrecords(recs, dtype=dtype)
    return a