code: def normalize_slice(idx, dim):
      """Normalize slices to canonical form
  
      Parameters
      ----------
      idx: slice or other index
      dim: dimension length
  
      Examples
      --------
      >>> normalize_slice(slice(0, 10, 1), 10)
      slice(None, None, None)
      """
  
      if isinstance(idx, slice):
          if math.isnan(dim):
              return idx
          start, stop, step = idx.indices(dim)
          if step > 0:
              if start == 0:
                  start = None
              if stop >= dim:
                  stop = None
              if step == 1:
                  step = None
              if (
                  stop is not None
                  and start is not None
                  and stop < start
              ):
                  stop = start
          elif step < 0:
              if start >= dim - 1:
                  start = None
              if stop < 0:
                  stop = None
          return slice(start, stop, step)
      return idx
  
imports: from __future__ import annotations
  import bisect
  import functools
  import math
  import warnings
  from itertools import product
  from numbers import Integral, Number
  from operator import itemgetter
  import numpy
  from tlz import concat, memoize, merge, pluck
  from dask import config, core, utils
  from dask.array.chunk import getitem
  from dask.base import is_dask_collection, tokenize
  from dask.highlevelgraph import HighLevelGraph
  from dask.utils import cached_cumsum, is_arraylike
label_desc: Use image node_id 17 for calling a global function with example usage: normalize_slice(idx, dim) and returns: idx, slice, idx