code: def _CO_Embed2_Dist_tau_d_expfit_meandiff(X, acfz):
      # Exponential fit to successive distances in 2-d embedding space.
      tau = acfz
      if tau > len(X) / 10:
          tau = int(len(X) / 10)
  
      d = np.zeros(len(X) - tau - 1)
      d_mean = 0
      for i in range(len(d)):
          n = np.sqrt(
              np.power(X[i + 1] - X[i], 2)
              + np.power(X[i + tau + 1] - X[i + tau], 2)
          )
          d[i] = n
          d_mean += n
      d_mean /= len(X) - tau - 1
  
      smin = np.min(d)
      smax = np.max(d)
      srange = smax - smin
      std = np.std(d)
  
      if std == 0:
          return np.nan
  
      num_bins = int(
          np.ceil(
              srange
              / (3.5 * np.std(d) / np.power(len(d), 0.3333333333333333))
          )
      )
  
      if num_bins == 0:
          return np.nan
      bin_width = srange / num_bins
  
      histogram = np.zeros(num_bins)
      for val in d:
          idx = int((val - smin) / bin_width)
          if idx >= num_bins:
              idx = num_bins - 1
          histogram[idx] += 1
  
      sum = 0
      for i in range(num_bins):
          center = ((smin + bin_width * i) * 2 + bin_width) / 2
          n = np.exp(-center / d_mean) / d_mean
          if n < 0:
              n = 0
  
          sum += np.abs(histogram[i] / len(d) - n)
  
      return sum / num_bins
  
imports: import math
  import numpy
  from sktime.utils.numba.njit import njit
label_desc: Use image node_id 32 for calling a global function with example usage: _CO_Embed2_Dist_tau_d_expfit_meandiff(X, acfz) and returns: unknown, np, np