code: def optimize_slicer(
      slicer,
      dim_len,
      all_full,
      is_slowest,
      stride,
      heuristic=threshold_heuristic,
  ):
      """Return maybe modified slice and post-slice slicing for `slicer`
  
      Parameters
      ----------
      slicer : slice object or int
      dim_len : int
          length of axis along which to slice
      all_full : bool
          Whether dimensions up until now have been full (all elements)
      is_slowest : bool
          Whether this dimension is the slowest changing in memory / on disk
      stride : int
          size of one step along this axis
      heuristic : callable, optional
          function taking slice object, dim_len, stride length as arguments,
          returning one of 'full', 'contiguous', None. See
          :func:`threshold_heuristic` for an example.
  
      Returns
      -------
      to_read : slice object or int
          maybe modified slice based on `slicer` expressing what data should be
          read from an underlying file or buffer. `to_read` must always have
          positive ``step`` (because we don't want to go backwards in the buffer
          / file)
      post_slice : slice object
          slice to be applied after array has been read.  Applies any
          transformations in `slicer` that have not been applied in `to_read`. If
          axis will be dropped by `to_read` slicing, so no slicing would make
          sense, return string ``dropped``
  
      Notes
      -----
      This is the heart of the algorithm for making segments from slice objects.
  
      A contiguous slice is a slice with ``slice.step in (1, -1)``
  
      A full slice is a continuous slice returning all elements.
  
      The main question we have to ask is whether we should transform `to_read`,
      `post_slice` to prefer a full read and partial slice.  We only do this in
      the case of all_full==True.  In this case we might benefit from reading a
      continuous chunk of data even if the slice is not continuous, or reading
      all the data even if the slice is not full. Apply a heuristic `heuristic`
      to decide whether to do this, and adapt `to_read` and `post_slice` slice
      accordingly.
  
      Otherwise (apart from constraint to be positive) return `to_read` unaltered
      and `post_slice` as ``slice(None)``
      """
      # int or slice as input?
      try:  # if int - we drop a dim (no append)
          slicer = int(slicer)  # casts float to int as well
      except TypeError:  # slice
          # Deal with full cases first
          if slicer == slice(None):
              return slicer, slicer
          slicer = fill_slicer(slicer, dim_len)
          # actually equivalent to slice(None)
          if slicer == slice(0, dim_len, 1):
              return slice(None), slice(None)
          # full, but reversed
          if slicer == slice(dim_len - 1, None, -1):
              return slice(None), slice(None, None, -1)
          # Not full, maybe continuous
          is_int = False
      else:  # int
          if slicer < 0:  # make negative offsets positive
              slicer = dim_len + slicer
          is_int = True
      if all_full:
          action = heuristic(slicer, dim_len, stride)
          # Check return values (we may be using a custom function)
          if action not in ("full", "contiguous", None):
              raise ValueError(
                  f"Unexpected return {action} from heuristic"
              )
          if is_int and action == "contiguous":
              raise ValueError("int index cannot be contiguous")
          # If this is the slowest changing dimension, never upgrade None or
          # contiguous beyond contiguous (we've already covered the already-full
          # case)
          if is_slowest and action == "full":
              action = None if is_int else "contiguous"
          if action == "full":
              return slice(None), slicer
          elif action == "contiguous":  # Cannot be int
              # If this is already contiguous, default None behavior handles it
              step = slicer.step
              if step not in (-1, 1):
                  if step < 0:
                      slicer = _positive_slice(slicer)
                  return (
                      slice(slicer.start, slicer.stop, 1),
                      slice(None, None, step),
                  )
      # We only need to be positive
      if is_int:
          return slicer, "dropped"
      if slicer.step > 0:
          return slicer, slice(None)
      return _positive_slice(slicer), slice(None, None, -1)
  
imports: import operator
  from functools import reduce
  from mmap import mmap
  from numbers import Integral
  import numpy
label_desc: Use image node_id 10 for calling a global function with example usage: optimize_slicer(slicer, dim_len, all_full, is_slowest, stride, heuristic) and returns: _positive_slice,  slice, slicer,  str, slicer,  slice, slice,  slicer, slicer,  slicer, slice,  slice, slice,  slice, slice,  slice