class_docstr: Provides rolling window calculations.
class_docstr_tok: ["Provides","rolling","window","calculations","."]
code: def __repr__(self):
      def order(item):
          k, v = item
          _order = {
              "window": 0,
              "min_periods": 1,
              "center": 2,
              "win_type": 3,
              "axis": 4,
          }
          return _order[k]
  
      rolling_kwargs = self._rolling_kwargs()
      rolling_kwargs["window"] = self.window
      rolling_kwargs["win_type"] = self._win_type
      return "Rolling [{}]".format(
          ",".join(
              f"{k}={v}"
              for k, v in sorted(rolling_kwargs.items(), key=order)
              if v is not None
          )
      )
  
imports: from __future__ import annotations
  import datetime
  import warnings
  from numbers import Integral
  import pandas
  from pandas.api.types import is_datetime64_any_dtype
  from pandas.core.window import Rolling
  from dask.array.core import normalize_arg
  from dask.base import tokenize
  from dask.blockwise import BlockwiseDepDict
  from dask.dataframe import methods
  from dask.dataframe._compat import check_axis_keyword_deprecation
  from dask.dataframe.core import Scalar, _Frame, _get_divisions_map_partitions, _get_meta_map_partitions, _maybe_from_pandas, apply_and_enforce, new_dd_object, partitionwise_graph
  from dask.dataframe.io import from_pandas
  from dask.dataframe.multi import _maybe_align_partitions
  from dask.dataframe.utils import insert_meta_param_description, is_dask_collection, is_dataframe_like, is_series_like
  from dask.delayed import unpack_collections
  from dask.highlevelgraph import HighLevelGraph
  from dask.typing import no_default
  from dask.utils import M, apply, derived_from, funcname, has_keyword
label_desc: Use image node_id 20 for calling the Rolling obj's underlying member method code with example usage: obj.__repr__() and returns: str, _order