code: def silverman_transform(bw, M, RANGE):
      """
      FFT of Gaussian kernel following to Silverman AS 176.
  
      Notes
      -----
      Underflow is intentional as a dampener.
      """
      J = np.arange(M / 2 + 1)
      FAC1 = 2 * (np.pi * bw / RANGE) ** 2
      JFAC = J**2 * FAC1
      BC = 1 - 1.0 / 3 * (J * 1.0 / M * np.pi) ** 2
      FAC = np.exp(-JFAC) / BC
      kern_est = np.r_[FAC, FAC[1:-1]]
      return kern_est
  
imports: import numpy
label_desc: Use image node_id 3 for calling a global function with example usage: silverman_transform(bw, M, RANGE) and returns: kern_est