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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 _generate_sequences_for_texts(self, l1, t1, l2, t2, ngrams):
 """Generates and outputs aligned sequences for the texts `t1` and `t2` from `ngrams`. :param l1: label of first witness :type l1: `str` :param t1: text content of first witness :type t1: `str` :param l2: label of second witness :type l2: `str` :param t2: text content of second witness :type t2: `str` :param ngrams: n-grams to base sequences on :type ngrams: `list` of `str` """

        # self._subsitutes is gradually populated as each text is
        # processed, so this needs to be regenerated each time.
        self._reverse_substitutes = dict((v, k) for k, v in
                                         self._substitutes.items())
        sequences = []
        # Keep track of spans within each text that have been covered
        # by an aligned sequence, to ensure that they aren't reported
        # more than once. The first sub-list contains span indices for
        # text t1, the second for t2.
        covered_spans = [[], []]
        for ngram in ngrams:
            sequences.extend(self._generate_sequences_for_ngram(
                t1, t2, ngram, covered_spans))
        if sequences:
            sequences.sort(key=lambda x: x.start_index)
            context = {'l1': l1, 'l2': l2, 'sequences': sequences}
            report_name = '{}-{}.html'.format(l1, l2)
            os.makedirs(self._output_dir, exist_ok=True)
            self._write(context, self._output_dir, report_name)