code: def predict_proba(self, X):
      check_is_fitted(self, "y_")
  
      return np.repeat(
          [np.hstack([1 - self.y_, self.y_])], X.shape[0], axis=0
      )
  
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 4 for calling the _ConstantPredictor obj's underlying member method code with example usage: obj.predict_proba(X) and returns: np