code: def zero_crossing_derivative(epochs, param=zero_param):
      diff = np.diff(epochs)
      norm = diff - diff.mean()
      return [
          (
              "e_{}".format(e),
              np.apply_along_axis(
                  lambda epoch: np.sum(
                      ((epoch[:-5] <= e) & (epoch[5:] > e))
                  ),
                  0,
                  norm,
              ).ravel()[0],
          )
          for e in param
      ]
  
imports: import pandas
  import numpy
  from statsmodels.tsa.stattools import acf, adfuller, pacf
  import itertools
  from statsmodels.tsa.ar_model import AR
  from scipy.signal import cwt, find_peaks_cwt, ricker, welch
  from scipy.stats import linregress
  import scipy.stats
  from scipy.stats import kurtosis
  from scipy.stats import skew
  import numpy
  from scipy import signal, integrate
  from scipy.interpolate import interp1d
  import math
  from statsmodels.tools.sm_exceptions import MissingDataError
  from numpy.linalg import LinAlgError
label_desc: Use image node_id 38 for calling a global function with example usage: zero_crossing_derivative(epochs, param) and returns: unknown