code: def _total_loss(self, metric, vab, vcd, prior_inv):
      # Regularization loss
      sign, logdet = np.linalg.slogdet(metric)
      reg_loss = np.sum(metric * prior_inv) - sign * logdet
      return self._comparison_loss(metric, vab, vcd) + reg_loss
  
imports: import numpy
  import scipy.linalg
  from sklearn.base import TransformerMixin
  from .base_metric import _QuadrupletsClassifierMixin, MahalanobisMixin
  from .constraints import Constraints
  from ._util import components_from_metric, _initialize_metric_mahalanobis
  import warnings
label_desc: Use image node_id 4 for calling the _BaseLSML obj's underlying member method code with example usage: obj._total_loss(metric, vab, vcd, prior_inv) and returns: unknown