code: def gaussian_baseline(
      image, sigma, window_size, axes=2, skip_axes=0, dtype=np.uint8
  ):
      sigma_xyz = to_cv_sigma(sigma, axes)
      win_xyz = to_cv_win_size(window_size, axes, sigma)
      filters = [
          cv2.getGaussianKernel(win_xyz[i], sigma_xyz[i])
          for i in range(axes)
      ]
      filters = [np.float32(f).squeeze() for f in filters]
      filters.reverse()
      for i in reversed(range(axes)):
          axis = i + skip_axes
          if image.shape[axis] == 1:
              mode = "nearest"
          else:
              mode = "mirror"
          image = convolve1d(
              np.float32(image), filters[i], axis, mode=mode
          )
      if dtype == np.float32:
          return image
      else:
          return dtype(image + 0.5)
  
imports: from nvidia.dali.pipeline import Pipeline, pipeline_def
  import nvidia.dali.types
  import nvidia.dali.fn
  import numpy
  import cv2
  from scipy.ndimage import convolve1d
  import os
  from nose_utils import assert_raises, raises
  from nose.plugins.attrib import attr
  from sequences_test_utils import video_suite_helper, ArgCb
  from test_utils import get_dali_extra_path, check_batch, compare_pipelines, RandomlyShapedDataIterator, dali_type
label_desc: Use image node_id 5 for calling a global function with example usage: gaussian_baseline(image, sigma, window_size, axes, skip_axes, dtype) and returns: image, dtype