code: def __call__(
      self, data: Mapping[Hashable, NdarrayOrTensor]
  ) -> Dict[Hashable, NdarrayOrTensor]:
      d: Dict = dict(data)
      for key in self.key_iterator(d):
          # Dictionary containing new label numbers
          new_label_names = {}
          label = torch.zeros_like(d[key])
          # Making sure the range values and number of labels are the same
          idx = 1
          for key_label, val_label in self.label_names.items():
              if key_label != "background":
                  new_label_names[key_label] = idx
                  label[d[key] == val_label] = idx
                  idx += 1
              if key_label == "background":
                  new_label_names["background"] = 0
  
          d["label_names"] = new_label_names
          d[key].array = label
      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 NormalizeLabelsInDatasetd obj's underlying member method code with example usage: obj.__call__(data) and returns: d