class_docstr: YOLO anchor optimizer using differential evolution algorithm.
  
  Args:
      num_anchors (int) : Number of anchors.
      iters (int): Maximum iterations for k-means.
      strategy (str): The differential evolution strategy to use.
          Should be one of:
  
              - 'best1bin'
              - 'best1exp'
              - 'rand1exp'
              - 'randtobest1exp'
              - 'currenttobest1exp'
              - 'best2exp'
              - 'rand2exp'
              - 'randtobest1bin'
              - 'currenttobest1bin'
              - 'best2bin'
              - 'rand2bin'
              - 'rand1bin'
  
          Default: 'best1bin'.
      population_size (int): Total population size of evolution algorithm.
          Default: 15.
      convergence_thr (float): Tolerance for convergence, the
          optimizing stops when ``np.std(pop) <= abs(convergence_thr)
          + convergence_thr * np.abs(np.mean(population_energies))``,
          respectively. Default: 0.0001.
      mutation (tuple[float]): Range of dithering randomly changes the
          mutation constant. Default: (0.5, 1).
      recombination (float): Recombination constant of crossover probability.
          Default: 0.7.
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code: def optimize(self):
      anchors = self.differential_evolution()
      self.save_result(anchors, self.out_dir)
  
imports: import argparse
  import os.path
  import numpy
  import torch
  from mmengine.config import Config
  from mmengine.fileio import dump
  from mmengine.logging import MMLogger
  from mmengine.registry import init_default_scope
  from mmengine.utils import ProgressBar
  from scipy.optimize import differential_evolution
  from mmdet.registry import DATASETS
  from mmdet.structures.bbox import bbox_cxcywh_to_xyxy, bbox_overlaps, bbox_xyxy_to_cxcywh
  from mmdet.utils import replace_cfg_vals, update_data_root
label_desc: Use image node_id 2 for calling the YOLODEAnchorOptimizer obj's underlying member method code with example usage: obj.optimize() without return types