code: def smooth_moving_avg(signal, window=11):
      window = int(window)
      if signal.ndim != 1:
          raise ValueError("")
      if signal.size < window:
          raise ValueError(
              "Input vector needs to be bigger than window size."
          )
      if window < 3:
          return signal
      s = np.r_[
          2 * signal[0] - signal[window - 1 :: -1],
          signal,
          2 * signal[-1] - signal[-1:-window:-1],
      ]
      w = np.ones(window, "d")
      y = np.convolve(w / w.sum(), s, mode="same")
      return y[window : -window + 1]
  
imports: from __future__ import print_function
  import os
  import csv
  import glob
  import scipy
  import sklearn
  import numpy
  import hmmlearn.hmm
  import sklearn.cluster
  import pickle
  import matplotlib.pyplot
  from scipy.spatial import distance
  import sklearn.discriminant_analysis
  from sklearn.preprocessing import StandardScaler
  import sys
  import pyAudioAnalysis.audioBasicIO
  import pyAudioAnalysis.audioTrainTest
  import pyAudioAnalysis.MidTermFeatures
  import pyAudioAnalysis.ShortTermFeatures
label_desc: Use image node_id 1 for calling a global function with example usage: smooth_moving_avg(signal, window) and returns: y, signal