code: def _fit(self, pairs, y):
      pairs, y = self._prepare_inputs(pairs, y, type_of_inputs="tuples")
  
      self.A_ = _initialize_metric_mahalanobis(
          pairs,
          self.init,
          random_state=self.random_state,
          matrix_name="init",
      )
  
      if self.diagonal:
          return self._fit_diag(pairs, y)
      else:
          return self._fit_full(pairs, y)
  
imports: import numpy
  from sklearn.base import TransformerMixin
  from sklearn.utils.validation import assert_all_finite
  from .base_metric import _PairsClassifierMixin, MahalanobisMixin
  from .constraints import Constraints, wrap_pairs
  from ._util import components_from_metric, _initialize_metric_mahalanobis
  import warnings
label_desc: Use image node_id 2 for calling the _BaseMMC obj's underlying member method code with example usage: obj._fit(pairs, y) and returns: self, self