code: def _comparison_loss(self, metric, vab, vcd):
      dab = np.sum(vab.dot(metric) * vab, axis=1)
      dcd = np.sum(vcd.dot(metric) * vcd, axis=1)
      violations = dab > dcd
      return self.w_[violations].dot(
          (np.sqrt(dab[violations]) - np.sqrt(dcd[violations])) ** 2
      )
  
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 3 for calling the _BaseLSML obj's underlying member method code with example usage: obj._comparison_loss(metric, vab, vcd) and returns: self