code: def __call__(self, data):
      d = dict(data)
      meta_dict = (
          d[self.ref_image].meta
          if d.get(self.ref_image) is not None
          and isinstance(d[self.ref_image], MetaTensor)
          else d.get(f"{self.ref_image}_{self.meta_key_postfix}", {})
      )
  
      for idx, key in enumerate(self.keys):
          result = d[key]
          current_size = (
              result.shape[1:] if self.has_channel else result.shape
          )
          spatial_shape = meta_dict.get("spatial_shape", current_size)
          spatial_size = spatial_shape[-len(current_size) :]
  
          # Undo Spacing
          if np.any(np.not_equal(current_size, spatial_size)):
              resizer = Resize(
                  spatial_size=spatial_size, mode=self.mode[idx]
              )
              result = resizer(
                  result,
                  mode=self.mode[idx],
                  align_corners=self.align_corners[idx],
              )
  
          if self.invert_orient:
              # Undo Orientation
              orig_affine = meta_dict.get("original_affine", None)
              if orig_affine is not None:
                  orig_axcodes = nib.orientations.aff2axcodes(
                      orig_affine
                  )
                  inverse_transform = Orientation(axcodes=orig_axcodes)
                  # Apply inverse
                  with inverse_transform.trace_transform(False):
                      result = inverse_transform(result)
              else:
                  logging.info(
                      "Failed invert orientation - original_affine is not on the image header"
                  )
  
          # Converting label indexes to the ones originally defined in the config file
          if self.config_labels is not None:
              new_pred = result * 0.0
              for j, (label_name, idx) in enumerate(
                  self.config_labels.items(), 1
              ):
                  # Consider only labels different than background
                  if label_name != "background":
                      new_pred[result == j] = idx
              result = new_pred
  
          d[key] = (
              result
              if len(result.shape) <= 3
              else result[0]
              if result.shape[0] == 1
              else result
          )
  
          meta = d.get(f"{key}_{self.meta_key_postfix}")
          if meta is None:
              meta = dict()
              d[f"{key}_{self.meta_key_postfix}"] = meta
          meta["affine"] = meta_dict.get("original_affine")
  
      return d
  
imports: import logging
  from typing import Dict, Hashable, Mapping, Optional, Sequence, Union
  import cv2
  import nibabel
  import numpy
  import skimage.measure
  import torch
  from monai.config import KeysCollection, NdarrayOrTensor
  from monai.data import MetaTensor
  from monai.transforms import MapTransform, Orientation, Resize, Transform, generate_spatial_bounding_box, get_extreme_points
  from monai.utils import InterpolateMode, convert_to_numpy, ensure_tuple_rep
  from shapely.geometry import Point, Polygon
  from torchvision.utils import make_grid, save_image
  from monailabel.utils.others.label_colors import get_color
label_desc: Use image node_id 2 for calling the Restored obj's underlying member method code with example usage: obj.__call__(data) and returns: d