How to use the essentia.standard.EasyLoader function in essentia

def __init__(self, audio_file, mono=True, sample_rate=44100, normalize_gain=False):
        """"""
        self.fs = sample_rate
        if normalize_gain:
            self.audio_vector = estd.EasyLoader(filename=audio_file, sampleRate=self.fs, replayGain=-9)()
        else:
            self.audio_vector = estd.MonoLoader(filename=audio_file, sampleRate=self.fs)()
        print "== Audio vector of %s loaded with shape %s and sample rate %s ==" % (audio_file, self.audio_vector.shape, self.fs)
        return

ascending_candidate, ascending_candidate_note, non_candidate_ascending_note = CS.candidate_selection(expression_style_note[:,0:3], SB.short_ascending_pattern)
        # select descending candidate
        descending_candidate, descending_candidate_note, non_candidate_descending_note = CS.candidate_selection(expression_style_note[:,0:3], SB.short_descending_pattern)

        # save result: candidate
        np.savetxt(args.output_dir+os.sep+name+'.ascending.candidate',ascending_candidate, fmt='%s')
        np.savetxt(args.output_dir+os.sep+name+'.descending.candidate',descending_candidate, fmt='%s')
        
        """
        -----------------------------------------------------
        S.4.2 Extract features of selected candidate regions.
        -----------------------------------------------------
        """
        print '    Extracting features...'
        # load audio
        audio = EasyLoader(filename = f)()
        # extract features of ascending candidate
        feature_vec_all = extract_feature_of_audio_clip(audio, ascending_candidate, sr=contour_sr) 
        # write to text file
        np.savetxt(args.output_dir+os.sep+name+'.ascending'+'.candidate'+'.raw.feature', feature_vec_all, fmt='%s')
        # extract features of descending candidate
        feature_vec_all = extract_feature_of_audio_clip(audio, descending_candidate, sr=contour_sr) 
        # write to text file
        np.savetxt(args.output_dir+os.sep+name+'.descending'+'.candidate'+'.raw.feature', feature_vec_all, fmt='%s')

        """
        -----------------------------------------------
        S.4.3 Classfication using pre-train classifier.
        -----------------------------------------------
        """        

        if len(args.input_model)==1: