How to use the delve.writers.STATMAP function in delve

return
        for key in self.logs:
            train_sats = []
            val_sats = []
            for i, layer_name in enumerate(self.logs[key]):
                if layer_name in self.ignore_layer_names:
                    continue
                if self.logs[key][layer_name]._cov_mtx is None:
                    raise ValueError("Attempting to compute intrinsic"
                                     "dimensionality when covariance"
                                     "is not initialized")
                cov_mat = self.logs[key][layer_name].fix()
                log_values = {}
                for stat in self.stats:
                    if stat == 'lsat':
                        log_values[key.replace(STATMAP['cov'], STATMAP['lsat'])+'_'+layer_name] = compute_saturation(cov_mat, thresh=self.threshold)
                    elif stat == 'idim':
                        log_values[key.replace(STATMAP['cov'], STATMAP['idim'])+'_'+layer_name] = compute_intrinsic_dimensionality(cov_mat, thresh=self.threshold)
                    elif stat == 'cov':
                        log_values[key+'_'+layer_name] = cov_mat.cpu().numpy()
                    elif stat == 'det':
                        log_values[key.replace(STATMAP['cov'], STATMAP['det'])+'_'+layer_name] = compute_cov_determinant(cov_mat)
                    elif stat == 'trc':
                        log_values[key.replace(STATMAP['cov'], STATMAP['trc'])+'_'+layer_name] = compute_cov_trace(cov_mat)
                    elif stat == 'dtrc':
                        log_values[key.replace(STATMAP['cov'], STATMAP['dtrc'])+'_'+layer_name] = compute_diag_trace(cov_mat)
                self.seen_samples[key.split('-')[0]][layer_name] = 0
                if self.reset_covariance:
                    self.logs[key][layer_name]._cov_mtx = None
                if self.layerwise_sat:
                    self.writer.add_scalars(prefix='', value_dict=log_values)

def _filter_by_stat_shortcuts(paths: List[str], stats: List[str], neg: bool = False) -> List[str]:
    result = []
    for stat in stats:
        result += _filter_by_stat(paths, STATMAP[stat], neg)
    return list(set(result))

if not isinstance(stats, list):
            stats = list(stats)
        supported_stats = [
            'lsat',
            'idim',
            'cov',
            'det',
            'trc',
            'dtrc',
        ]
        compatible = [stat in supported_stats for stat in stats]
        incompatible = [i for i, x in enumerate(compatible) if not x]
        assert all(compatible), "Stat {} is not supported".format(
            stats[incompatible[0]])

        name_mapper = STATMAP

        logs = {
            f'{mode}-{name_mapper[stat]}': OrderedDict()
            for mode, stat in product(['train', 'eval'], ['cov'])
        }

        return logs, stats