code: def remove_all_but_the_largest_connected_component(
      image: np.ndarray,
      for_which_classes: list,
      volume_per_voxel: float,
      minimum_valid_object_size: dict = None,
  ):
      if for_which_classes is None:
          for_which_classes = np.unique(image)
          for_which_classes = for_which_classes[for_which_classes > 0]
  
      assert 0 not in for_which_classes, "cannot remove background"
      largest_removed = {}
      kept_size = {}
      for c in for_which_classes:
          if isinstance(c, (list, tuple)):
              c = tuple(c)
              mask = np.zeros_like(image, dtype=bool)
              for cl in c:
                  mask[image == cl] = True
          else:
              mask = image == c
          lmap, num_objects = label(mask.astype(int))
          object_sizes = {}
          for object_id in range(1, num_objects + 1):
              object_sizes[object_id] = (
                  lmap == object_id
              ).sum() * volume_per_voxel
  
          largest_removed[c] = None
          kept_size[c] = None
          if num_objects > 0:
              maximum_size = max(object_sizes.values())
              kept_size[c] = maximum_size
  
              for object_id in range(1, num_objects + 1):
                  if object_sizes[object_id] != maximum_size:
                      remove = True
                      if minimum_valid_object_size is not None:
                          remove = (
                              object_sizes[object_id]
                              < minimum_valid_object_size[c]
                          )
                      if remove:
                          image[(lmap == object_id) & mask] = 0
                          if largest_removed[c] is None:
                              largest_removed[c] = object_sizes[
                                  object_id
                              ]
                          else:
                              largest_removed[c] = max(
                                  largest_removed[c],
                                  object_sizes[object_id],
                              )
      return image, largest_removed, kept_size
  
file_path: PaddleSeg/contrib/MedicalSeg/nnunet/utils/postprocessing.py
imports: import os
  import shutil
  import json
  import numpy
  import SimpleITK
  from multiprocessing.pool import Pool
  from scipy.ndimage import label
  from copy import deepcopy
  from .evaluator import aggregate_scores
label_desc: Use image node_id 2 for calling a global function with example usage: remove_all_but_the_largest_connected_component(image, for_which_classes, volume_per_voxel, minimum_valid_object_size) and returns: image,  largest_removed,  kept_size