code: def set_partition(
      df: DataFrame,
      index: str | Series,
      divisions: Sequence,
      max_branch: int = 32,
      drop: bool = True,
      shuffle_method: str | None = None,
      compute: bool | None = None,
  ) -> DataFrame:
      """Group DataFrame by index
  
      Sets a new index and partitions data along that index according to
      divisions.  Divisions are often found by computing approximate quantiles.
      The function ``set_index`` will do both of these steps.
  
      Parameters
      ----------
      df: DataFrame/Series
          Data that we want to re-partition
      index: string or Series
          Column to become the new index
      divisions: list
          Values to form new divisions between partitions
      drop: bool, default True
          Whether to delete columns to be used as the new index
      shuffle_method: str (optional)
          Either 'disk' for an on-disk shuffle or 'tasks' to use the task
          scheduling framework.  Use 'disk' if you are on a single machine
          and 'tasks' if you are on a distributed cluster.
      max_branch: int (optional)
          If using the task-based shuffle, the amount of splitting each
          partition undergoes.  Increase this for fewer copies but more
          scheduler overhead.
  
      See Also
      --------
      set_index
      shuffle
      partd
      """
      if isinstance(divisions, tuple):
          # pd.isna considers tuples to be scalars. Convert to a list.
          divisions = list(divisions)
  
      if not isinstance(index, Series):
          dtype = df[index].dtype
      else:
          dtype = index.dtype
  
      if pd.isna(divisions).any() and pd.api.types.is_integer_dtype(
          dtype
      ):
          # Can't construct a Series[int64] when any / all of the divisions are NaN.
          divisions = df._meta._constructor_sliced(divisions)
      elif (
          isinstance(dtype, pd.CategoricalDtype)
          and UNKNOWN_CATEGORIES in dtype.categories
      ):
          # If categories are unknown, leave as a string dtype instead.
          divisions = df._meta._constructor_sliced(divisions)
      else:
          divisions = df._meta._constructor_sliced(
              divisions, dtype=dtype
          )
  
      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]
  
      if not isinstance(index, Series):
          partitions = df[index].map_partitions(
              set_partitions_pre, divisions=divisions, meta=meta
          )
          df2 = df.assign(_partitions=partitions)
      else:
          partitions = index.map_partitions(
              set_partitions_pre, divisions=divisions, meta=meta
          )
          df2 = df.assign(_partitions=partitions, _index=index)
  
      df3 = rearrange_by_column(
          df2,
          "_partitions",
          max_branch=max_branch,
          npartitions=len(divisions) - 1,
          shuffle_method=shuffle_method,
          compute=compute,
          ignore_index=True,
      )
  
      if not isinstance(index, Series):
          df4 = df3.map_partitions(
              set_index_post_scalar,
              index_name=index,
              drop=drop,
              column_dtype=df.columns.dtype,
          )
      else:
          df4 = df3.map_partitions(
              set_index_post_series,
              index_name=index.name,
              drop=drop,
              column_dtype=df.columns.dtype,
          )
  
      divisions = methods.tolist(divisions)
      # None and pd.NA values are not sortable
      df4.divisions = tuple(
          i if not pd.isna(i) else np.nan for i in divisions
      )
  
      return df4.map_partitions(M.sort_index)
  
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 4 for calling a global function with example usage: set_partition(df, index, divisions, max_branch, drop, shuffle_method, compute) and returns: df4