code: def _predict_binary(estimator, X):
      """Make predictions using a single binary estimator."""
      if is_regressor(estimator):
          return estimator.predict(X)
      try:
          score = np.ravel(estimator.decision_function(X))
      except (AttributeError, NotImplementedError):
          # probabilities of the positive class
          score = estimator.predict_proba(X)[:, 1]
      return score
  
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 3 for calling a global function with example usage: _predict_binary(estimator, X) and returns: score, estimator