code: def accelerated_search_for_good_permutation(
      matrix_group, options=None, verbosity=0
  ):
      """This function is used to call the permutation search CUDA kernels.
      users can provide prefer search strategy by providing a valid 'options' as a dictionary,
      or users can implement their customized 'accelerated_search_for_good_permutation' function.
      """
      input_matrix = matrix_group.cpu().detach().numpy()
      if verbosity > 1:
          print(
              "\n[accelerated_search_for_good_permutation] input matrix shape: '{:}'.".format(
                  input_matrix.shape
              )
          )
  
      result = np.copy(input_matrix)
      # init a sequential permutation search sequence
      input_channel_num = matrix_group.size(1)
      permutation_sequence = [n for n in range(input_channel_num)]
      duration = 0.0
  
      if options == None:
          options = {}
      if (
          "strategy" not in options
      ):  # right now, the default permutation search strategy is: 'exhaustive' search
          options["strategy"] = "exhaustive"
  
      if verbosity > 1:
          print(
              "[accelerated_search_for_good_permutation] the permutation strategy is: '{:} search'.".format(
                  options["strategy"]
              )
          )
  
      # define sub options for each search strategy
      if options["strategy"] == "exhaustive":
          # right now, the default options for 'exhaustive' search is: 'exhaustive,8,100'
          if "stripe_group_size" not in options:
              options["stripe_group_size"] = 8
          if "escape_attempts" not in options:
              options["escape_attempts"] = 100
      elif options["strategy"] == "progressive channel swap":
          # just swaps meaningful channels, keeping the good swaps, until the search time limit expires.
          if "progressive_search_time_limit" not in options:
              options["progressive_search_time_limit"] = 60
          if "improvement_threshold" not in options:
              options["improvement_threshold"] = 1e-9
  
      # execute the requested strategy
      if options["strategy"] == "exhaustive":
          result, duration, permutation_sequence = Exhaustive_Search(
              result,
              stripe_group_size=options["stripe_group_size"],
              escape_attempts=options["escape_attempts"],
          )
      elif options["strategy"] == "progressive channel swap":
          real_swap_num = 0
          start_time = time.perf_counter()
          while (
              time.perf_counter() - start_time
              < options["progressive_search_time_limit"]
          ):
              src = np.random.randint(result.shape[1])
              dst = np.random.randint(result.shape[1])
              src_group = int(src / 4)
              dst_group = int(dst / 4)
              if (
                  src_group == dst_group
              ):  # channel swapping within a stripe does nothing
                  continue
              new_sum, improvement = try_swap(result, dst, src)
              if improvement > options["improvement_threshold"]:
                  result[..., [src, dst]] = result[..., [dst, src]]
                  (
                      permutation_sequence[src],
                      permutation_sequence[dst],
                  ) = (
                      permutation_sequence[dst],
                      permutation_sequence[src],
                  )
                  real_swap_num += 1
          duration = time.perf_counter() - start_time
          if verbosity > 1:
              print(
                  "\tFinally swap {} channel pairs until the search time limit expires.".format(
                      real_swap_num
                  )
              )
      elif (
          options["strategy"] == "user defined"
      ):  # need to get the permutated matrix (result) by applying customized permutation search function
          if verbosity > 1:
              print(
                  "[accelerated_search_for_good_permutation] Use the user customized permutation search function!"
              )
      else:
          if verbosity >= 0:
              print(
                  "[accelerated_search_for_good_permutation] Cannot find the implementation of the required strategy!"
              )
  
      if verbosity > 1:
          print(
              "[accelerated_search_for_good_permutation] Take {:.4f} seconds to search the permutation sequence.".format(
                  duration
              )
          )
  
      return permutation_sequence
  
imports: import numpy
  from .permutation_utilities import *
  from .exhaustive_search import Exhaustive_Search
label_desc: Use image node_id 1 for calling a global function with example usage: accelerated_search_for_good_permutation(matrix_group, options, verbosity) and returns: permutation_sequence