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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, tolerance=None, copy=True):
 """Conform this object onto the indexes of another object, filling in missing values with NaN. Parameters other : Dataset or DataArray Object with an 'indexes' attribute giving a mapping from dimension names to pandas.Index objects, which provides coordinates upon which to index the variables in this dataset. The indexes on this other object need not be the same as the indexes on this dataset. Any mis-matched index values will be filled in with NaN, and any mis-matched dimension names will simply be ignored. method : {None, 'nearest', 'pad'/'ffill', 'backfill'/'bfill'}, optional Method to use for filling index values from other not found in this dataset: * None (default):
 don't fill gaps * pad / ffill: propagate last valid index value forward * backfill / bfill: propagate next valid index value backward * nearest: use nearest valid index value (requires pandas>=0.16) tolerance : optional Maximum distance between original and new labels for inexact matches. The values of the index at the matching locations must satisfy the equation ``abs(index[indexer] - target) <= tolerance``. Requires pandas>=0.17. copy : bool, optional If ``copy=True``, data in the return value is always copied. If ``copy=False`` and reindexing is unnecessary, or can be performed with only slice operations, then the output may share memory with the input. In either case, a new xarray object is always returned. Returns ------- reindexed : Dataset Another dataset, with this dataset's data but coordinates from the other object. See Also -------- Dataset.reindex align """

        indexers = alignment.reindex_like_indexers(self, other)
        return self.reindex(indexers=indexers, method=method, copy=copy,
                            tolerance=tolerance)