code: def augment_mirroring(sample_data, sample_seg=None, axes=(0, 1, 2)):
      if (len(sample_data.shape) != 3) and (
          len(sample_data.shape) != 4
      ):
          raise ValueError(
              "Invalid dimension for sample_data and sample_seg. sample_data and sample_seg should be either "
              "[channels, x, y] or [channels, x, y, z]"
          )
      if 0 in axes and np.random.uniform() < 0.5:
          sample_data[:, :] = sample_data[:, ::-1]
          if sample_seg is not None:
              sample_seg[:, :] = sample_seg[:, ::-1]
      if 1 in axes and np.random.uniform() < 0.5:
          sample_data[:, :, :] = sample_data[:, :, ::-1]
          if sample_seg is not None:
              sample_seg[:, :, :] = sample_seg[:, :, ::-1]
      if 2 in axes and len(sample_data.shape) == 4:
          if np.random.uniform() < 0.5:
              sample_data[:, :, :, :] = sample_data[:, :, :, ::-1]
              if sample_seg is not None:
                  sample_seg[:, :, :, :] = sample_seg[:, :, :, ::-1]
      return sample_data, sample_seg
  
imports: from typing import List, Tuple, Union, Callable
  import collections
  import numbers
  import random
  import numpy
  import scipy
  import scipy.ndimage
  import SimpleITK
  from scipy.ndimage.filters import gaussian_filter, gaussian_gradient_magnitude
  from skimage.transform import resize
label_desc: Use image node_id 16 for calling a global function with example usage: augment_mirroring(sample_data, sample_seg, axes) and returns: sample_data,  sample_seg