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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 _read( filename, schema, seq_label='sequence', alphabet=None, use_uids=True, **kwargs):
 """Use BioPython's sequence parsing module to convert any file format to a Pandas DataFrame. The resulting DataFrame has the following columns: - name - id - description - sequence """

    # Check Alphabet if given
    if alphabet is None:
        alphabet = Bio.Alphabet.Alphabet()

    elif alphabet in ['dna', 'rna', 'protein', 'nucleotide']:
        alphabet = getattr(Bio.Alphabet, 'generic_{}'.format(alphabet))

    else:
        raise Exception(
            "The alphabet is not recognized. Must be 'dna', 'rna', "
            "'nucleotide', or 'protein'.")

    kwargs.update(alphabet=alphabet)

    # Prepare DataFrame fields.
    data = {
        'id': [],
        seq_label: [],
        'description': [],
        'label': []
    }
    if use_uids:
        data['uid'] = []

    # Parse Fasta file.
    for i, s in enumerate(SeqIO.parse(filename, format=schema, **kwargs)):
        data['id'].append(s.id)
        data[seq_label].append(str(s.seq))
        data['description'].append(s.description)
        data['label'].append(s.name)

        if use_uids:
            data['uid'].append(get_random_id(10))

    # Port to DataFrame.
    return pd.DataFrame(data)