code: def _fit_ovo_binary(estimator, X, y, i, j):
      """Fit a single binary estimator (one-vs-one)."""
      cond = np.logical_or(y == i, y == j)
      y = y[cond]
      y_binary = np.empty(y.shape, np.int)
      y_binary[y == i] = 0
      y_binary[y == j] = 1
      indcond = np.arange(X.shape[0])[cond]
      return (
          _fit_binary(
              estimator,
              _safe_split(estimator, X, None, indices=indcond)[0],
              y_binary,
              classes=[i, j],
          ),
          indcond,
      )
  
imports: import array
  import numpy
  import warnings
  import scipy.sparse
  import itertools
  from .base import BaseEstimator, ClassifierMixin, clone, is_classifier
  from .base import MetaEstimatorMixin, is_regressor
  from .preprocessing import LabelBinarizer
  from .metrics.pairwise import euclidean_distances
  from .utils import check_random_state
  from .utils.validation import _num_samples
  from .utils.validation import check_is_fitted
  from .utils.validation import check_X_y
  from .utils.multiclass import _check_partial_fit_first_call, check_classification_targets, _ovr_decision_function
  from .utils.metaestimators import _safe_split
  from .externals.joblib import Parallel
  from .externals.joblib import delayed
  from .externals.six.moves import zip
label_desc: Use image node_id 5 for calling a global function with example usage: _fit_ovo_binary(estimator, X, y, i, j) and returns: _fit_binary,  indcond