code: def fix_overlap(ddf, mins, maxes, lens):
      """Ensures that the upper bound on each partition of ddf (except the last) is exclusive
  
      This is accomplished by first removing empty partitions, then altering existing
      partitions as needed to include all the values for a particular index value in
      one partition.
      """
      name = "fix-overlap-" + tokenize(ddf, mins, maxes, lens)
  
      non_empties = [i for i, length in enumerate(lens) if length != 0]
      # If all empty, collapse into one partition
      if len(non_empties) == 0:
          divisions = (None, None)
          dsk = {(name, 0): (ddf._name, 0)}
          graph = HighLevelGraph.from_collections(
              name, dsk, dependencies=[ddf]
          )
          return new_dd_object(graph, name, ddf._meta, divisions)
  
      # drop empty partitions by mapping each partition in a new graph to a particular
      # partition on the old graph.
      dsk = {
          (name, i): (ddf._name, div)
          for i, div in enumerate(non_empties)
      }
      ddf_keys = list(dsk.values())
      divisions = tuple(mins) + (maxes[-1],)
  
      overlap = [
          i for i in range(1, len(mins)) if mins[i] >= maxes[i - 1]
      ]
  
      frames = []
      for i in overlap:
          # `frames` is a list of data from previous partitions that we may want to
          # move to partition i.  Here, we add "overlap" from the previous partition
          # (i-1) to this list.
          frames.append((get_overlap, ddf_keys[i - 1], divisions[i]))
  
          # Make sure that any data added from partition i-1 to `frames` is removed
          # from partition i-1.
          dsk[(name, i - 1)] = (
              drop_overlap,
              dsk[(name, i - 1)],
              divisions[i],
          )
  
          # We do not want to move "overlap" from the previous partition (i-1) into
          # this partition (i) if the data from this partition will need to be moved
          # to the next partition (i+1) anyway.  If we concatenate data too early,
          # we may lose rows (https://github.com/dask/dask/issues/6972).
          if divisions[i] == divisions[i + 1] and i + 1 in overlap:
              continue
  
          frames.append(ddf_keys[i])
          dsk[(name, i)] = (methods.concat, frames)
          frames = []
  
      graph = HighLevelGraph.from_collections(
          name, dsk, dependencies=[ddf]
      )
      return new_dd_object(graph, name, ddf._meta, divisions)
  
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 25 for calling a global function with example usage: fix_overlap(ddf, mins, maxes, lens) and returns: new_dd_object, new_dd_object