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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 clip_upper(self, threshold, axis=None, inplace=False):
 """ Trim values above a given threshold. .. deprecated:: 0.24.0 Use clip(upper=threshold) instead. Elements above the `threshold` will be changed to match the `threshold` value(s). Threshold can be a single value or an array, in the latter case it performs the truncation element-wise. Parameters threshold : numeric or array-like Maximum value allowed. All values above threshold will be set to this value. * float : every value is compared to `threshold`. * array-like : The shape of `threshold` should match the object it's compared to. When `self` is a Series, `threshold` should be the length. When `self` is a DataFrame, `threshold` should 2-D and the same shape as `self` for ``axis=None``, or 1-D and the same length as the axis being compared. axis : {0 or 'index', 1 or 'columns'}, default 0 Align object with `threshold` along the given axis. inplace : bool, default False Whether to perform the operation in place on the data. .. versionadded:: 0.21.0 Returns ------- Series or DataFrame Original data with values trimmed. See Also -------- Series.clip : General purpose method to trim Series values to given threshold(s). DataFrame.clip : General purpose method to trim DataFrame values to given threshold(s). Examples -------- 0 1 1 2 2 3 3 4 4 5 dtype: int64 0 1 1 2 2 3 3 3 4 3 dtype: int64 [5, 4, 3, 2, 1] 0 1 1 2 2 3 3 2 4 1 dtype: int64 """

        warnings.warn('clip_upper(threshold) is deprecated, '
                      'use clip(upper=threshold) instead',
                      FutureWarning, stacklevel=2)
        return self._clip_with_one_bound(threshold, method=self.le,
                                         axis=axis, inplace=inplace)