class_docstr: This one finds a mask of nonzero elements (must be nonzero in all modalities) and crops the image to that mask.
  In the case of BRaTS and ISLES data this results in a significant reduction in image size.
  Args:
      num_threads (int): Number of threads. Default: None.
      output_folder (str, None): path to store the cropped data. Default: None.
code: def __init__(self, output_folder=None, num_threads=8):
      self.output_folder = output_folder
      self.num_threads = num_threads
  
      if self.output_folder is not None:
          print(self.output_folder)
          os.makedirs(self.output_folder, exist_ok=True)
  
imports: import os
  import json
  import shutil
  import pickle
  import numpy
  import SimpleITK
  from scipy.ndimage import binary_fill_holes
  from multiprocessing import Pool
  from collections import OrderedDict
  from .path_utils import join_paths
label_desc: Use image node_id 1 to create a new ImageCropper object  with example: obj = ImageCropper(output_folder, num_threads)