code: def fundamental_frequency(signal, fs):
      """Computes fundamental frequency of the signal.
  
      The fundamental frequency integer multiple best explain
      the content of the signal spectrum.
  
      Feature computational cost: 1
  
      Parameters
      ----------
      signal : nd-array
          Input from which fundamental frequency is computed
      fs : float
          Sampling frequency
  
      Returns
      -------
      f0: float
         Predominant frequency of the signal
  
      """
      signal = signal - np.mean(signal)
      f, fmag = calc_fft(signal, fs)
  
      # Finding big peaks, not considering noise peaks with low amplitude
  
      bp = scipy.signal.find_peaks(fmag, height=max(fmag) * 0.3)[0]
  
      # # Condition for offset removal, since the offset generates a peak at frequency zero
      bp = bp[bp != 0]
      if not list(bp):
          f0 = 0
      else:
          # f0 is the minimum big peak frequency
          f0 = f[min(bp)]
  
      return f0
  
imports: import scipy.signal
  from tsfel.feature_extraction.features_utils import *
label_desc: Use image node_id 37 for calling a global function with example usage: fundamental_frequency(signal, fs) and returns: f0