code: def max_power_spectrum(signal, fs):
      """Computes maximum power spectrum density of the signal.
  
      Feature computational cost: 1
  
      Parameters
      ----------
      signal : nd-array
          Input from which maximum power spectrum is computed
      fs : float
          Sampling frequency
  
      Returns
      -------
      nd-array
          Max value of the power spectrum density
  
      """
      if np.std(signal) == 0:
          return float(
              max(
                  scipy.signal.welch(signal, fs, nperseg=len(signal))[1]
              )
          )
      else:
          return float(
              max(
                  scipy.signal.welch(
                      signal / np.std(signal), fs, nperseg=len(signal)
                  )[1]
              )
          )
  
imports: import scipy.signal
  from tsfel.feature_extraction.features_utils import *
label_desc: Use image node_id 38 for calling a global function with example usage: max_power_spectrum(signal, fs) and returns: float, float