code: def find_merge_rechunk(old_chunks, new_chunks, block_size_limit):
      """
      Find an intermediate rechunk that would merge some adjacent blocks
      together in order to get us nearer the *new_chunks* target, without
      violating the *block_size_limit* (in number of elements).
      """
      ndim = len(old_chunks)
  
      old_largest_width = [max(c) for c in old_chunks]
      new_largest_width = [max(c) for c in new_chunks]
  
      graph_size_effect = {
          dim: len(nc) / len(oc)
          for dim, (oc, nc) in enumerate(zip(old_chunks, new_chunks))
      }
  
      block_size_effect = {
          dim: new_largest_width[dim] / (old_largest_width[dim] or 1)
          for dim in range(ndim)
      }
  
      # Our goal is to reduce the number of nodes in the rechunk graph
      # by merging some adjacent chunks, so consider dimensions where we can
      # reduce the # of chunks
      merge_candidates = [
          dim for dim in range(ndim) if graph_size_effect[dim] <= 1.0
      ]
  
      # Merging along each dimension reduces the graph size by a certain factor
      # and increases memory largest block size by a certain factor.
      # We want to optimize the graph size while staying below the given
      # block_size_limit.  This is in effect a knapsack problem, except with
      # multiplicative values and weights.  Just use a greedy algorithm
      # by trying dimensions in decreasing value / weight order.
      def key(k):
          gse = graph_size_effect[k]
          bse = block_size_effect[k]
          if bse == 1:
              bse = 1 + 1e-9
          return (np.log(gse) / np.log(bse)) if bse > 0 else 0
  
      sorted_candidates = sorted(merge_candidates, key=key)
  
      largest_block_size = reduce(mul, old_largest_width)
  
      chunks = list(old_chunks)
      memory_limit_hit = False
  
      for dim in sorted_candidates:
          # Examine this dimension for possible graph reduction
          new_largest_block_size = (
              largest_block_size
              * new_largest_width[dim]
              // (old_largest_width[dim] or 1)
          )
          if new_largest_block_size <= block_size_limit:
              # Full replacement by new chunks is possible
              chunks[dim] = new_chunks[dim]
              largest_block_size = new_largest_block_size
          else:
              # Try a partial rechunk, dividing the new chunks into
              # smaller pieces
              largest_width = old_largest_width[dim]
              chunk_limit = int(
                  block_size_limit * largest_width / largest_block_size
              )
              c = divide_to_width(new_chunks[dim], chunk_limit)
              if len(c) <= len(old_chunks[dim]):
                  # We manage to reduce the number of blocks, so do it
                  chunks[dim] = c
                  largest_block_size = (
                      largest_block_size * max(c) // largest_width
                  )
  
              memory_limit_hit = True
  
      assert largest_block_size == _largest_block_size(chunks)
      assert largest_block_size <= block_size_limit
      return tuple(chunks), memory_limit_hit
  
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 13 for calling a global function with example usage: find_merge_rechunk(old_chunks, new_chunks, block_size_limit) and returns: tuple,  memory_limit_hit, unknown