code: def validate_frequencies(frequencies, max_freq, min_freq,
                           allow_negatives=False):
      
      # If flag is true, map frequencies to their absolute value.
      if allow_negatives:
          frequencies = np.abs(frequencies)
      # Make sure no frequency values are huge
      if (np.abs(frequencies) > max_freq).any():
          raise ValueError('A frequency of {} was found which is greater than '
                           'the maximum allowable value of max_freq = {} (did '
                           'you supply frequency values in '
                           'Hz?)'.format(frequencies.max(), max_freq))
      # Make sure no frequency values are tiny
      if (np.abs(frequencies) < min_freq).any():
          raise ValueError('A frequency of {} was found which is less than the '
                           'minimum allowable value of min_freq = {} (did you '
                           'supply frequency values in '
                           'Hz?)'.format(frequencies.min(), min_freq))
      # Make sure frequency values are 1-d np ndarrays
      if frequencies.ndim != 1:
          raise ValueError('Frequencies should be 1-d numpy ndarray, '
                           'but shape={}'.format(frequencies.shape))
docs: Checks that a 1-d frequency ndarray is well-formed, and raises
      errors if not.
  
      Parameters
      ----------
      frequencies : np.ndarray, shape=(n,)
          Array of frequency values
      max_freq : float
          If a frequency is found above this pitch, a ValueError will be raised.
          (Default value = 5000.)
      min_freq : float
          If a frequency is found below this pitch, a ValueError will be raised.
          (Default value = 20.)
      allow_negatives : bool
          Whether or not to allow negative frequency values.