code: def _compute_rechunk(x, chunks):
      """Compute the rechunk of *x* to the given *chunks*."""
      if x.size == 0:
          # Special case for empty array, as the algorithm below does not behave correctly
          return empty(x.shape, chunks=chunks, dtype=x.dtype)
  
      ndim = x.ndim
      crossed = intersect_chunks(x.chunks, chunks)
      x2 = dict()
      intermediates = dict()
      token = tokenize(x, chunks)
      merge_name = "rechunk-merge-" + token
      split_name = "rechunk-split-" + token
      split_name_suffixes = count()
  
      # Pre-allocate old block references, to allow re-use and reduce the
      # graph's memory footprint a bit.
      old_blocks = np.empty([len(c) for c in x.chunks], dtype="O")
      for index in np.ndindex(old_blocks.shape):
          old_blocks[index] = (x.name,) + index
  
      # Iterate over all new blocks
      new_index = product(*(range(len(c)) for c in chunks))
  
      for new_idx, cross1 in zip(new_index, crossed):
          key = (merge_name,) + new_idx
          old_block_indices = [
              [cr[i][0] for cr in cross1] for i in range(ndim)
          ]
          subdims1 = [
              len(set(old_block_indices[i])) for i in range(ndim)
          ]
  
          rec_cat_arg = np.empty(subdims1, dtype="O")
          rec_cat_arg_flat = rec_cat_arg.flat
  
          # Iterate over the old blocks required to build the new block
          for rec_cat_index, ind_slices in enumerate(cross1):
              old_block_index, slices = zip(*ind_slices)
              name = (split_name, next(split_name_suffixes))
              old_index = old_blocks[old_block_index][1:]
              if all(
                  slc.start == 0 and slc.stop == x.chunks[i][ind]
                  for i, (slc, ind) in enumerate(zip(slices, old_index))
              ):
                  rec_cat_arg_flat[rec_cat_index] = old_blocks[
                      old_block_index
                  ]
              else:
                  intermediates[name] = (
                      getitem,
                      old_blocks[old_block_index],
                      slices,
                  )
                  rec_cat_arg_flat[rec_cat_index] = name
  
          assert rec_cat_index == rec_cat_arg.size - 1
  
          # New block is formed by concatenation of sliced old blocks
          if all(d == 1 for d in rec_cat_arg.shape):
              x2[key] = rec_cat_arg.flat[0]
          else:
              x2[key] = (concatenate3, rec_cat_arg.tolist())
  
      del old_blocks, new_index
  
      layer = toolz.merge(x2, intermediates)
      graph = HighLevelGraph.from_collections(
          merge_name, layer, dependencies=[x]
      )
      return Array(graph, merge_name, chunks, meta=x)
  
imports: from __future__ import annotations
  import heapq
  import math
  from functools import reduce
  from itertools import chain, count, product
  from operator import add, itemgetter, mul
  from warnings import warn
  import numpy
  import tlz
  from tlz import accumulate
  from dask import config
  from dask.array.chunk import getitem
  from dask.array.core import Array, concatenate3, normalize_chunks
  from dask.array.utils import validate_axis
  from dask.array.wrap import empty
  from dask.base import tokenize
  from dask.highlevelgraph import HighLevelGraph
  from dask.utils import parse_bytes
label_desc: Use image node_id 16 for calling a global function with example usage: _compute_rechunk(x, chunks) and returns: Array, empty