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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 transform(self, sequences, mode='clip'):
 """Transform a list of sequences to internal indexing Recall that `sequences` can be arbitrary labels, whereas ``transmat_`` and ``countsmat_`` are indexed with integers between 0 and ``n_states - 1``. This methods maps a set of sequences from the labels onto this internal indexing. Parameters sequences : list of array-like List of sequences, or a single sequence. Each sequence should be a 1D iterable of state labels. Labels can be integers, strings, or other orderable objects. mode : {'clip', 'fill'} Method by which to treat labels in `sequences` which do not have a corresponding index. This can be due, for example, to the ergodic trimming step. ``clip`` Unmapped labels are removed during transform. If they occur at the beginning or end of a sequence, the resulting transformed sequence will be shorted. If they occur in the middle of a sequence, that sequence will be broken into two (or more) sequences. (Default) ``fill`` Unmapped labels will be replaced with NaN, to signal missing data. [The use of NaN to signal missing data is not fantastic, but it's consistent with current behavior of the ``pandas`` library.] Returns ------- mapped_sequences : list List of sequences in internal indexing """

        if mode not in ['clip', 'fill']:
            raise ValueError('mode must be one of ["clip", "fill"]: %s' % mode)
        sequences = list_of_1d(sequences)

        result = []
        for y in sequences:
            if mode == 'fill':
                result.append(self.partial_transform(y, mode))
            elif mode == 'clip':
                result.extend(self.partial_transform(y, mode))
            else:
                raise RuntimeError()

        return result