code: def __call__(self, data):
      d: Dict = dict(data)
      first_key: Union[Hashable, List] = self.first_key(d)
      if not first_key:
          return d
  
      guidance = d[self.guidance]
      original_spatial_shape = d[first_key].shape[1:]
      box_start, box_end = self.bounding_box(
          np.array(guidance[0] + guidance[1]), original_spatial_shape
      )
      center = list(
          np.mean([box_start, box_end], axis=0).astype(int, copy=False)
      )
      spatial_size = self.spatial_size
  
      box_size = list(
          np.subtract(box_end, box_start).astype(int, copy=False)
      )
      spatial_size = spatial_size[-len(box_size) :]
  
      if np.all(np.less(box_size, spatial_size)):
          cropper = SpatialCrop(
              roi_center=center, roi_size=spatial_size
          )
      else:
          cropper = SpatialCrop(roi_start=box_start, roi_end=box_end)
  
      # update bounding box in case it was corrected by the SpatialCrop constructor
      box_start = np.array([s.start for s in cropper.slices])
      box_end = np.array([s.stop for s in cropper.slices])
  
      for key in self.keys:
          image = d[key]
          meta = image.meta
          meta[self.start_coord_key] = box_start
          meta[self.end_coord_key] = box_end
          meta[self.original_shape_key] = d[key].shape
  
          result = cropper(image)
          result.meta[self.cropped_shape_key] = result.shape
          d[key] = result
  
      pos_clicks, neg_clicks = guidance[0], guidance[1]
      pos = (
          np.subtract(pos_clicks, box_start).tolist()
          if len(pos_clicks)
          else []
      )
      neg = (
          np.subtract(neg_clicks, box_start).tolist()
          if len(neg_clicks)
          else []
      )
  
      d[self.guidance] = [pos, neg]
      return d
  
imports: import json
  import logging
  from typing import Callable, Dict, Hashable, List, Optional, Sequence, Union
  import numpy
  import torch
  from monai.config import IndexSelection, KeysCollection
  from monai.data import MetaTensor
  from monai.transforms import MapTransform, Randomizable, Resize, SpatialCrop, generate_spatial_bounding_box, is_positive
  from monai.utils import InterpolateMode, PostFix, ensure_tuple_rep
  from scipy.ndimage import distance_transform_cdt, gaussian_filter
  from skimage import measure
label_desc: Use image node_id 3 for calling the SpatialCropGuidanced obj's underlying member method code with example usage: obj.__call__(data) and returns: d, d