code: def _bw_taylor(x):
      """Taylor's rule for circular bandwidth estimation.
  
      This function implements a rule-of-thumb for choosing the bandwidth of a von Mises kernel
      density estimator that assumes the underlying distribution is von Mises as introduced in [1]_.
      It is analogous to Scott's rule for the Gaussian KDE.
  
      Circular bandwidth has a different scale from linear bandwidth. Unlike linear scale, low
      bandwidths are associated with oversmoothing and high values with undersmoothing.
  
      References
      ----------
      .. [1] C.C Taylor (2008). Automatic bandwidth selection for circular
             density estimation.
             Computational Statistics and Data Analysis, 52, 7, 3493–3500.
      """
      x_len = len(x)
      kappa = _kappa_mle(x)
      num = 3 * x_len * kappa**2 * ive(2, 2 * kappa)
      den = 4 * np.pi**0.5 * ive(0, kappa) ** 2
      return (num / den) ** 0.4
  
imports: import warnings
  import numpy
  from scipy.fftpack import fft
  from scipy.optimize import brentq
  from scipy.signal import convolve, convolve2d, gaussian
  from scipy.sparse import coo_matrix
  from scipy.special import ive
  from ..utils import _cov, _dot, _stack, conditional_jit
label_desc: Use image node_id 5 for calling a global function with example usage: _bw_taylor(x) and returns: unknown