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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 apply(self, func, axis=0, broadcast=None, reduce=None, result_type=None):
 """ Analogous to DataFrame.apply, for SparseDataFrame Parameters func : function Function to apply to each column axis : {0, 1, 'index', 'columns'} broadcast : bool, default False For aggregation functions, return object of same size with values propagated .. deprecated:: 0.23.0 This argument will be removed in a future version, replaced by result_type='broadcast'. reduce : boolean or None, default None Try to apply reduction procedures. If the DataFrame is empty, apply will use reduce to determine whether the result should be a Series or a DataFrame. If reduce is None (the default), apply's return value will be guessed by calling func an empty Series (note: while guessing, exceptions raised by func will be ignored). If reduce is True a Series will always be returned, and if False a DataFrame will always be returned. .. deprecated:: 0.23.0 This argument will be removed in a future version, replaced by result_type='reduce'. result_type : {'expand', 'reduce', 'broadcast, None} These only act when axis=1 {columns}: * 'expand' : list-like results will be turned into columns. * 'reduce' : return a Series if possible rather than expanding list-like results. This is the opposite to 'expand'. * 'broadcast' : results will be broadcast to the original shape of the frame, the original index & columns will be retained. The default behaviour (None) depends on the return value of the applied function: list-like results will be returned as a Series of those. However if the apply function returns a Series these are expanded to columns. .. versionadded:: 0.23.0 Returns ------- applied : Series or SparseDataFrame """

        if not len(self.columns):
            return self
        axis = self._get_axis_number(axis)

        if isinstance(func, np.ufunc):
            new_series = {}
            for k, v in self.items():
                applied = func(v)
                applied.fill_value = func(v.fill_value)
                new_series[k] = applied
            return self._constructor(
                new_series, index=self.index, columns=self.columns,
                default_fill_value=self._default_fill_value,
                default_kind=self._default_kind).__finalize__(self)

        from pandas.core.apply import frame_apply
        op = frame_apply(self,
                         func=func,
                         axis=axis,
                         reduce=reduce,
                         broadcast=broadcast,
                         result_type=result_type)
        return op.get_result()