code: def neighbourhood_peaks(signal, n=10):
      """Computes the number of peaks from a defined neighbourhood of the signal.
  
      Reference: Christ, M., Braun, N., Neuffer, J. and Kempa-Liehr A.W. (2018). Time Series FeatuRe Extraction on basis
       of Scalable Hypothesis tests (tsfresh -- A Python package). Neurocomputing 307 (2018) 72-77
  
      Parameters
      ----------
      signal : nd-array
           Input from which the number of neighbourhood peaks is computed
      n :  int
          Number of peak's neighbours to the left and to the right
  
      Returns
      -------
      int
          The number of peaks from a defined neighbourhood of the signal
      """
      signal = np.array(signal)
      subsequence = signal[n:-n]
      # initial iteration
      peaks = (subsequence > np.roll(signal, 1)[n:-n]) & (
          subsequence > np.roll(signal, -1)[n:-n]
      )
      for i in range(2, n + 1):
          peaks &= subsequence > np.roll(signal, i)[n:-n]
          peaks &= subsequence > np.roll(signal, -i)[n:-n]
      return np.sum(peaks)
  
imports: import scipy.signal
  from tsfel.feature_extraction.features_utils import *
label_desc: Use image node_id 14 for calling a global function with example usage: neighbourhood_peaks(signal, n) and returns: np