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 crop_from_list_of_files(data_files, seg_file=None):
      data, seg, properties = load_case_from_list_of_files(
          data_files, seg_file
      )
      return ImageCropper.crop(data, properties, seg)
  
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 3 for calling the ImageCropper obj's underlying member method code with example usage: obj.crop_from_list_of_files(data_files, seg_file) and returns: ImageCropper