How to use the sidekit.StatServer.read function in SIDEKIT

Args:
            explain (boolean): If True, write another text file that contain
            the same information as the one within ubm_scores file but in a 
            readible way.
        """
        ############################# READING ############################
        # Create Feature server
        server = self.createFeatureServer()
        # Read the index for the test datas
        test_ndx = sidekit.Ndx.read(os.path.join(self.BASE_DIR, "task", "test_ndx.h5"))
        # Read the UBM model
        ubm = sidekit.Mixture()
        model_name = "ubm_{}.h5".format(self.NUM_GAUSSIANS)
        ubm.read(os.path.join(self.BASE_DIR, "ubm", model_name))
        filename = "enroll_stat_{}.h5".format(self.NUM_GAUSSIANS)
        enroll_stat = sidekit.StatServer.read(os.path.join(self.BASE_DIR, "stat", filename))
        # MAP adaptation of enrollment speaker models
        enroll_sv = enroll_stat.adapt_mean_map_multisession(ubm=ubm,
                                                            r=3 # MAP regulation factor
                                                           )

        ############################ Evaluating ###########################
        # Compute scores
        scores_gmm_ubm = sidekit.gmm_scoring(ubm=ubm,
                                             enroll=enroll_sv,
                                             ndx=test_ndx,
                                             feature_server=server,
                                             num_thread=self.NUM_THREADS
                                            )
        # Save the model's Score object
        filename = "ubm_scores_{}.h5".format(self.NUM_GAUSSIANS)
        scores_gmm_ubm.write(os.path.join(self.BASE_DIR, "result", filename))

outputPath = os.path.join(self.BASE_DIR, "ivector", filename)
        fa = sidekit.FactorAnalyser(outputPath+".h5")

        # Extract i-vectors from enrollment data
        logging.info("Extracting i-vectors from enrollment data")
        filename = 'enroll_stat_{}.h5'.format(self.NUM_GAUSSIANS)
        enroll_stat = sidekit.StatServer.read(os.path.join(self.BASE_DIR, 'stat', filename))
        enroll_iv = fa.extract_ivectors_single( ubm=ubm,
                                                stat_server=enroll_stat,
                                                uncertainty=False
                                              )
    
        # Extract i-vectors from test data
        logging.info("Extracting i-vectors from test data")
        filename = 'test_stat_{}.h5'.format(self.NUM_GAUSSIANS)
        test_stat = sidekit.StatServer.read(os.path.join(self.BASE_DIR, 'stat', filename))
        test_iv = fa.extract_ivectors_single(ubm=ubm,
                                             stat_server=test_stat,
                                             uncertainty=False
                                            )

        # Do cosine distance scoring and write results
        logging.info("Calculating cosine score")
        test_ndx = sidekit.Ndx.read(os.path.join(self.BASE_DIR, "task", "test_ndx.h5"))
        scores_cos = sidekit.iv_scoring.cosine_scoring( enroll_iv,
                                                        test_iv,
                                                        test_ndx,
                                                        wccn=None
                                                      )
        # Write scores
        filename = "ivector_scores_cos_{}.h5".format(self.NUM_GAUSSIANS)
        scores_cos.write(os.path.join(self.BASE_DIR, "result", filename))

This method is used to score our trained model. 
        """
        # Load UBM model
        model_name = "ubm_{}.h5".format(self.NUM_GAUSSIANS)
        ubm = sidekit.Mixture()
        ubm.read(os.path.join(self.BASE_DIR, "ubm", model_name))

        # Load TV matrix
        filename = "tv_matrix_{}".format(self.NUM_GAUSSIANS)
        outputPath = os.path.join(self.BASE_DIR, "ivector", filename)
        fa = sidekit.FactorAnalyser(outputPath+".h5")

        # Extract i-vectors from enrollment data
        logging.info("Extracting i-vectors from enrollment data")
        filename = 'enroll_stat_{}.h5'.format(self.NUM_GAUSSIANS)
        enroll_stat = sidekit.StatServer.read(os.path.join(self.BASE_DIR, 'stat', filename))
        enroll_iv = fa.extract_ivectors_single( ubm=ubm,
                                                stat_server=enroll_stat,
                                                uncertainty=False
                                              )
    
        # Extract i-vectors from test data
        logging.info("Extracting i-vectors from test data")
        filename = 'test_stat_{}.h5'.format(self.NUM_GAUSSIANS)
        test_stat = sidekit.StatServer.read(os.path.join(self.BASE_DIR, 'stat', filename))
        test_iv = fa.extract_ivectors_single(ubm=ubm,
                                             stat_server=test_stat,
                                             uncertainty=False
                                            )

        # Do cosine distance scoring and write results
        logging.info("Calculating cosine score")