code: def gaussian_filter(data, sigma):
      """
      Drop-in replacement for scipy.ndimage.gaussian_filter.
  
      (note: results are only approximately equal to the output of
       gaussian_filter)
      """
      if np.isscalar(sigma):
          sigma = (sigma,) * data.ndim
  
      baseline = data.mean()
      filtered = data - baseline
      for ax in range(data.ndim):
          s = float(sigma[ax])
          if s == 0:
              continue
  
          # generate 1D gaussian kernel
          ksize = int(s * 6)
          x = np.arange(-ksize, ksize)
          kernel = np.exp(-(x**2) / (2 * s**2))
          kshape = [
              1,
          ] * data.ndim
          kshape[ax] = len(kernel)
          kernel = kernel.reshape(kshape)
  
          # convolve as product of FFTs
          shape = data.shape[ax] + ksize
          scale = 1.0 / (abs(s) * (2 * np.pi) ** 0.5)
          filtered = scale * np.fft.irfft(
              np.fft.rfft(filtered, shape, axis=ax)
              * np.fft.rfft(kernel, shape, axis=ax),
              axis=ax,
          )
  
          # clip off extra data
          sl = [slice(None)] * data.ndim
          sl[ax] = slice(
              filtered.shape[ax] - data.shape[ax], None, None
          )
          filtered = filtered[tuple(sl)]
      return filtered + baseline
  
label_desc: Use image node_id 1 for calling a global function with example usage: gaussian_filter(data, sigma) and returns: unknown