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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 reindex_like(self, other, method=None, copy=True, limit=None, tolerance=None):
 """ Return an object with matching indices as other object. Conform the object to the same index on all axes. Optional filling logic, placing NaN in locations having no value in the previous index. A new object is produced unless the new index is equivalent to the current one and copy=False. Parameters other : Object of the same data type Its row and column indices are used to define the new indices of this object. method : {None, 'backfill'/'bfill', 'pad'/'ffill', 'nearest'} Method to use for filling holes in reindexed DataFrame. Please note: this is only applicable to DataFrames/Series with a monotonically increasing/decreasing index. * None (default):
 don't fill gaps * pad / ffill: propagate last valid observation forward to next valid * backfill / bfill: use next valid observation to fill gap * nearest: use nearest valid observations to fill gap copy : bool, default True Return a new object, even if the passed indexes are the same. limit : int, default None Maximum number of consecutive labels to fill for inexact matches. tolerance : optional Maximum distance between original and new labels for inexact matches. The values of the index at the matching locations most satisfy the equation ``abs(index[indexer] - target) <= tolerance``. Tolerance may be a scalar value, which applies the same tolerance to all values, or list-like, which applies variable tolerance per element. List-like includes list, tuple, array, Series, and must be the same size as the index and its dtype must exactly match the index's type. .. versionadded:: 0.21.0 (list-like tolerance) Returns ------- Series or DataFrame Same type as caller, but with changed indices on each axis. See Also -------- DataFrame.set_index : Set row labels. DataFrame.reset_index : Remove row labels or move them to new columns. DataFrame.reindex : Change to new indices or expand indices. Notes ----- Same as calling Examples -------- temp_celsius temp_fahrenheit windspeed 2014-02-12 24.3 75.7 high 2014-02-13 31.0 87.8 high 2014-02-14 22.0 71.6 medium 2014-02-15 35.0 95.0 medium temp_celsius windspeed 2014-02-12 28.0 low 2014-02-13 30.0 low 2014-02-15 35.1 medium temp_celsius temp_fahrenheit windspeed 2014-02-12 28.0 NaN low 2014-02-13 30.0 NaN low 2014-02-14 NaN NaN NaN 2014-02-15 35.1 NaN medium """

        d = other._construct_axes_dict(axes=self._AXIS_ORDERS, method=method,
                                       copy=copy, limit=limit,
                                       tolerance=tolerance)

        return self.reindex(**d)