code: def test_dict_matches(self, im_shape, im_type):
      data = self.get_data(im_shape, im_type)
      # use same image for both dictionary entries to check same trans is applied to them
      data = {
          KEYS[0]: deepcopy(data[KEYS[0]]),
          KEYS[1]: deepcopy(data[KEYS[0]]),
      }
      loc = [0] + [int(im_shape[i] / 2) for i in range(len(im_shape))]
      k_intensity = 10
  
      t = KSpaceSpikeNoised(KEYS, loc, k_intensity)
      out = t(deepcopy(data))
      assert_allclose(out[KEYS[0]], out[KEYS[1]], type_test=False)
  
file_path: MONAI/tests/test_k_space_spike_noised.py
imports: from __future__ import annotations
  import unittest
  from copy import deepcopy
  import numpy
  import torch
  from numpy.fft import fftn, fftshift
  from parameterized import parameterized
  from monai.data.synthetic import create_test_image_2d, create_test_image_3d
  from monai.transforms import KSpaceSpikeNoised
  from monai.utils.misc import set_determinism
  from tests.utils import TEST_NDARRAYS, assert_allclose
label_desc: Use image node_id 6 for calling the TestKSpaceSpikeNoised obj's underlying member method code with example usage: obj.test_dict_matches(im_shape, im_type) without return types