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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 distribution(self, start=None, end=None, normalized=True, mask=None):
 """Calculate the distribution of values over the given time range from `start` to `end`. Args: start (orderable, optional):
 The lower time bound of when to calculate the distribution. By default, the first time point will be used. end (orderable, optional):
 The upper time bound of when to calculate the distribution. By default, the last time point will be used. normalized (bool):
 If True, distribution will sum to one. If False and the time values of the TimeSeries are datetimes, the units will be seconds. mask (:obj:`TimeSeries`, optional):
 A domain on which to calculate the distribution. Returns: :obj:`Histogram` with the results. """


        start, end, mask = self._check_boundaries(start, end, mask=mask)

        counter = histogram.Histogram()
        for start, end, _ in mask.iterperiods(value=True):
            for t0, t1, value in self.iterperiods(start, end):
                duration = utils.duration_to_number(
                    t1 - t0,
                    units='seconds',
                )
                try:
                    counter[value] += duration
                except histogram.UnorderableElements as e:

                    counter = histogram.Histogram.from_dict(
                        dict(counter), key=hash)
                    counter[value] += duration

        # divide by total duration if result needs to be normalized
        if normalized:
            return counter.normalized()
        else:
            return counter