code: def __call__(self, data):
      d: Dict = dict(data)
      for key in self.key_iterator(d):
          # Logic:
  
          # 1/ randomly select vertebra
          # 2/ binarise volume
          # 3/ apply cropForeground transform
  
          # Alternative Logic:
  
          # 1/ randomly select vertebra
          # 2/ spatial crop based on the centroid
          # 3/ binarise cropped volume
  
          d["original_size"] = (
              d[key].shape[-3],
              d[key].shape[-2],
              d[key].shape[-1],
          )
          tmp_label = copy.deepcopy(d[key])
  
          # TO DO: WHAT'S THE BEST WAY TO SELECT A DIFFERENT SEGMENT EACH ITERATION - Randomizable should work?
          # PERHAPS DOING BATCHES AS IN TRANSFORM RandCropByLabelClassesd??
  
          current_idx = self.R.randint(0, len(d["centroids"]))
  
          d["current_idx"] = current_idx
  
          d["current_label"] = list(
              d["centroids"][current_idx].values()
          )[0][0]
  
          logger.info(f'Processing vertebra: {d["current_label"]}')
  
          # Make binary the label
          tmp_label[tmp_label != d["current_label"]] = 0
          tmp_label[tmp_label > 0] = 1
  
          ##########
          # Cropping
          ##########
          def condition(x):
              # threshold at 1
              return x > 0
  
          cropper = CropForeground(select_fn=condition, margin=4)
  
          start, stop = cropper.compute_bounding_box(tmp_label)
  
          slices_cropped = [
              [start[-3], stop[-3]],
              [start[-2], stop[-2]],
              [start[-1], stop[-1]],
          ]
  
          d["slices_cropped"] = slices_cropped
  
          # Cropping label
          d["label"] = tmp_label[
              :,
              start[-3] : stop[-3],
              start[-2] : stop[-2],
              start[-1] : stop[-1],
          ]
  
          # Cropping image
          d["image"] = d["image"][
              :,
              start[-3] : stop[-3],
              start[-2] : stop[-2],
              start[-1] : stop[-1],
          ]
  
      return d
  
imports: import copy
  import logging
  from typing import Dict, Hashable, Mapping
  import numpy
  import torch
  from monai.config import KeysCollection, NdarrayOrTensor
  from monai.transforms import CropForeground, GaussianSmooth, Randomizable, Resize, ScaleIntensity, SpatialCrop
  from monai.transforms.transform import MapTransform, Transform
label_desc: Use image node_id 2 for calling the SelectVertebraAndCroppingd obj's underlying member method code with example usage: obj.__call__(data) and returns: d, unknown