How to use the sidekit.FactorAnalyser function in SIDEKIT

def evaluate(self, explain=True):
        """
        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,

This method is used to train the Total Variability (TV) matrix
        and save it into 'ivector' directory !! 
        """
        # Create status servers
        self.__create_stats()

        # 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))

        # Train TV matrix using FactorAnalyser
        filename = "tv_matrix_{}".format(self.NUM_GAUSSIANS)
        outputPath = os.path.join(self.BASE_DIR, "ivector", filename)
        tv_filename = 'tv_stat_{}.h5'.format(self.NUM_GAUSSIANS)
        fa = sidekit.FactorAnalyser()
        fa.total_variability_single(os.path.join(self.BASE_DIR, "stat", tv_filename),
                                    ubm,
                                    tv_rank=self.TV_RANK,
                                    nb_iter=self.TV_ITERATIONS,
                                    min_div=True,
                                    tv_init=None,
                                    batch_size=self.BATCH_SIZE,
                                    save_init=False,
                                    output_file_name=outputPath
                                   )
        # tv = fa.F # TV matrix
        # tv_mean = fa.mean # Mean vector
        # tv_sigma = fa.Sigma # Residual covariance matrix

        # Clear files produced at each iteration
        filename_regex = "tv_matrix_{}_it-*.h5".format(self.NUM_GAUSSIANS)