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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, args=(), **kwargs):
 """Apply a function over each Dataset in the group and concatenate them together into a new Dataset. `func` is called like `func(ds, *args, **kwargs)` for each dataset `ds` in this group. Apply uses heuristics (like `pandas.GroupBy.apply`) to figure out how to stack together the datasets. The rule is: 1. If the dimension along which the group coordinate is defined is still in the first grouped item after applying `func`, then stack over this dimension. 2. Otherwise, stack over the new dimension given by name of this grouping (the argument to the `groupby` function). Parameters func : function Callable to apply to each sub-dataset. args : tuple, optional Positional arguments to pass to `func`. **kwargs Used to call `func(ds, **kwargs)` for each sub-dataset `ar`. Returns ------- applied : Dataset or DataArray The result of splitting, applying and combining this dataset. """

        kwargs.pop('shortcut', None)  # ignore shortcut if set (for now)
        applied = (func(ds, *args, **kwargs) for ds in self._iter_grouped())
        return self._combine(applied)