code: def absolute_spike_times_from_labels(self,time_dimension=0,*args,**kwargs):
          
          return self._absolute_spike_times_from_labels(time_dimension,*args,**kwargs)[0]
docs: Creates a list of spike times where each value of the supplied dimensions
              is added as a trial-time offset. This can be used as the inverse of 
              `label_by_time` or `label_peri_signals` (while the later can lead to spike duplicates).
  
              Eg. for three stimuli, in 5 trials each, for one second of recording
              the spike time for the arguments absolute_spike_times_from_labels(
              't','trial','stimulus') would be (in ms):
                  t + 1000.0 * trial + 5 * 1000.0 * stimulus
  
              For absolute_spike_times_from_labels('t','stimulus','trial') it would be:
                  t + 1000.0 * stimulus + 3 * 1000.0 * trial
  
              A custom multiplier can be supplied when the dimension is given as a keyword argument:
              absolute_spike_times_from_labels('t',stimulus=10,trial=None)
                  t + 1000.0 * stimulus + 10 * 1000.0 * trial
  
              If the keyword has the value `None`, the length of the dimension is used.