code: def laplacian_baseline(
      img,
      out_type,
      window_size,
      smoothing_size,
      scale,
      axes,
      skip_axes=0,
  ):
      scales = spread_values(scale, axes)
      all_sizes = get_window_sizes(window_size, smoothing_size, axes)
      acc = np.zeros(img.shape, dtype=np.float32)
      img = np.float32(img)
      all_windows = get_windows(all_sizes)
      for windows, scale in zip(all_windows, scales):
          partial = img
          for i in reversed(range(axes)):
              axis = i + skip_axes
              if img.shape[axis] == 1:
                  mode = "nearest"
              else:
                  mode = "mirror"
              partial = convolve1d(partial, windows[i], axis, mode=mode)
          acc += scale * partial
      if out_type == np.float32:
          return acc
      else:
          return convert_sat(acc, out_type)
  
imports: from nvidia.dali.pipeline import pipeline_def
  import nvidia.dali.types
  import nvidia.dali.fn
  import numpy
  import cv2
  from scipy.ndimage import convolve1d, filters
  import os
  from nose_utils import assert_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, RandomlyShapedDataIterator
label_desc: Use image node_id 17 for calling a global function with example usage: laplacian_baseline(img, out_type, window_size, smoothing_size, scale, axes, skip_axes) and returns: acc, convert_sat