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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 rolling(self, dim: Optional[Mapping[Hashable, int]] = None, min_periods: Optional[int] = None, center: bool = False, **dim_kwargs: int):
 """ Rolling window object. Parameters dim: dict, optional Mapping from the dimension name to create the rolling iterator along (e.g. `time`) to its moving window size. min_periods : int, default None Minimum number of observations in window required to have a value (otherwise result is NA). The default, None, is equivalent to setting min_periods equal to the size of the window. center : boolean, default False Set the labels at the center of the window. **dim_kwargs : optional The keyword arguments form of ``dim``. One of dim or dim_kwargs must be provided. Returns ------- Rolling object (core.rolling.DataArrayRolling for DataArray, core.rolling.DatasetRolling for Dataset.) Examples -------- <xarray.DataArray (time: 12)> array([ 0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11.]) Coordinates: <xarray.DataArray (time: 12)> array([nan, 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., nan]) Coordinates: Remove the NaNs using ``dropna()``: <xarray.DataArray (time: 10)> array([ 1., 2., 3., 4., 5., 6., 7., 8., 9., 10.]) Coordinates: See Also -------- core.rolling.DataArrayRolling core.rolling.DatasetRolling """
  # noqa
        dim = either_dict_or_kwargs(dim, dim_kwargs, 'rolling')
        return self._rolling_cls(self, dim, min_periods=min_periods,
                                 center=center)