code: def multi_inverse(self, input_shape, init_params):
      input_data = (
          np.arange(np.prod(input_shape)).reshape(*input_shape) + 1
      )
      xform = self.Cropper(**init_params)
      xform.set_random_state(1234)
      out = xform(input_data)
      if "num_samples" in init_params:
          self.assertEqual(len(out), init_params["num_samples"])
      inv = xform.inverse(out)
      self.assertIsInstance(inv, MetaTensor)
      self.assertEqual(inv.applied_operations, [])
      self.assertTrue("patch_index" not in inv.meta)
      self.assertTupleEqual(inv.shape, input_shape)
      inv_np = inv.numpy()
  
      # get list of all numbers that exist inside the crops
      uniques = set()
      for o in out:
          uniques.update(set(o.flatten().tolist()))
  
      # make sure that
      for i in uniques:
          a = np.where(input_data == i)
          b = np.where(inv_np == i)
          self.assertTupleEqual(a, b)
      # there should be as many zeros as elements missing from uniques
      missing = input_data.size - len(uniques)
      self.assertEqual((inv_np == 0).sum(), missing)
  
file_path: MONAI/tests/croppers.py
imports: from __future__ import annotations
  import unittest
  from copy import deepcopy
  import numpy
  from monai.data.meta_tensor import MetaTensor
  from monai.transforms import Randomizable
  from monai.transforms.lazy.functional import apply_pending
  from monai.transforms.transform import MapTransform
  from tests.utils import TEST_NDARRAYS_ALL, assert_allclose
label_desc: Use image node_id 4 for calling the CropTest obj's underlying member method code with example usage: obj.multi_inverse(input_shape, init_params) without return types