code: def shuffle(
      df,
      index,
      shuffle_method=None,
      npartitions=None,
      max_branch=32,
      ignore_index=False,
      compute=None,
  ):
      """Group DataFrame by index
  
      Hash grouping of elements. After this operation all elements that have
      the same index will be in the same partition. Note that this requires
      full dataset read, serialization and shuffle. This is expensive. If
      possible you should avoid shuffles.
  
      This does not preserve a meaningful index/partitioning scheme. This is not
      deterministic if done in parallel.
  
      See Also
      --------
      set_index
      set_partition
      shuffle_disk
      """
      list_like = pd.api.types.is_list_like(
          index
      ) and not is_dask_collection(index)
      shuffle_method = shuffle_method or get_default_shuffle_method()
  
      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()
  
      dtypes = {}
      for col, dtype in index.dtypes.items():
          if pd.api.types.is_numeric_dtype(dtype):
              dtypes[col] = np.float64
      if not dtypes:
          dtypes = None
  
      meta = df._meta._constructor_sliced([0])
      # Ensure that we have the same index as before to avoid alignment
      # when calculating meta dtypes later on
      meta.index = df._meta_nonempty.index[:1]
      partitions = index.map_partitions(
          partitioning_index,
          npartitions=npartitions or df.npartitions,
          meta=meta,
          transform_divisions=False,
          cast_dtype=dtypes,
      )
      df2 = df.assign(_partitions=partitions)
      df2._meta.index.name = df._meta.index.name
      df3 = rearrange_by_column(
          df2,
          "_partitions",
          npartitions=npartitions,
          max_branch=max_branch,
          shuffle_method=shuffle_method,
          compute=compute,
          ignore_index=ignore_index,
      )
      del df3["_partitions"]
      return df3
  
imports: from __future__ import annotations
  import contextlib
  import logging
  import math
  import shutil
  import tempfile
  import uuid
  import warnings
  from collections.abc import Callable, Mapping, Sequence
  from typing import Any, Literal
  import numpy
  import pandas
  import tlz
  from pandas.api.types import is_numeric_dtype
  from dask import config
  from dask.base import compute, compute_as_if_collection, is_dask_collection, tokenize
  from dask.dataframe import methods
  from dask.dataframe.core import DataFrame, Series, _deprecated_kwarg, _Frame, map_partitions, new_dd_object
  from dask.dataframe.dispatch import group_split_dispatch, hash_object_dispatch, partd_encode_dispatch
  from dask.dataframe.utils import UNKNOWN_CATEGORIES
  from dask.highlevelgraph import HighLevelGraph
  from dask.layers import ShuffleLayer, SimpleShuffleLayer
  from dask.sizeof import sizeof
  from dask.utils import M, digit, get_default_shuffle_method
label_desc: Use image node_id 5 for calling a global function with example usage: shuffle(df, index, shuffle_method, npartitions, max_branch, ignore_index, compute) and returns: df3