code: def _prepare_index_for_partitioning(
      df: pd.DataFrame, index: IndexLabel
  ):
      import pandas as pd
  
      from dask.dataframe.core import _Frame
  
      list_like = pd.api.types.is_list_like(
          index
      ) and not is_dask_collection(index)
  
      if not isinstance(index, _Frame):
          if list_like:
              # Make sure we don't try to select with pd.Series/pd.Index
              index = list(index)
          index = df._select_columns_or_index(index)
      elif hasattr(index, "to_frame"):
          # If this is an index, we should still convert to a
          # DataFrame. Otherwise, the hashed values of a column
          # selection will not match (important when merging).
          index = index.to_frame()
      return index
  
imports: from __future__ import annotations
  from collections.abc import Iterable, Sequence
  from typing import TYPE_CHECKING, Any
  import dask
  from dask.base import is_dask_collection, tokenize
  from dask.highlevelgraph import HighLevelGraph
  from dask.layers import Layer
  from distributed.shuffle._arrow import check_minimal_arrow_version
  from distributed.shuffle._core import ShuffleId, barrier_key, get_worker_plugin
  from distributed.shuffle._shuffle import shuffle_barrier, shuffle_transfer
label_desc: Use image node_id 1 for calling a global function with example usage: _prepare_index_for_partitioning(df, index) and returns: index