code: def bw_silverman(x, kernel=None):
      """
      Silverman's Rule of Thumb
  
      Parameters
      ----------
      x : array_like
          Array for which to get the bandwidth
      kernel : CustomKernel object
          Unused
  
      Returns
      -------
      bw : float
          The estimate of the bandwidth
  
      Notes
      -----
      Returns .9 * A * n ** (-1/5.) where ::
  
         A = min(std(x, ddof=1), IQR/1.349)
         IQR = np.subtract.reduce(np.percentile(x, [75,25]))
  
      References
      ----------
  
      Silverman, B.W. (1986) `Density Estimation.`
      """
      A = _select_sigma(x)
      n = len(x)
      return 0.9 * A * n ** (-0.2)
  
file_path: statsmodels/statsmodels/nonparametric/bandwidths.py
imports: import numpy
  from scipy.stats import scoreatpercentile
  from statsmodels.compat.pandas import Substitution
  from statsmodels.sandbox.nonparametric import kernels
label_desc: Use image node_id 3 for calling a global function with example usage: bw_silverman(x, kernel) and returns: unknown