code: def index_mass_quantile(x, param=[{"q": 0.3}]):
      x = np.asarray(x)
      abs_x = np.abs(x)
      s = sum(abs_x)
  
      if s == 0:
          # all values in x are zero or it has length 0
          return [
              ("q_{}".format(config["q"]), np.NaN) for config in param
          ]
      else:
          # at least one value is not zero
          mass_centralized = np.cumsum(abs_x) / s
          return [
              (
                  "q_{}".format(config["q"]),
                  (np.argmax(mass_centralized >= config["q"]) + 1)
                  / len(x),
              )
              for config 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 23 for calling a global function with example usage: index_mass_quantile(x, param) and returns: unknown, unknown