class_docstr: Helper predictor to be used when only one class is present.
class_docstr_tok: ["Helper","predictor","to","be","used","when","only","one","class","is","present","."]
code: def decision_function(self, X):
      check_is_fitted(self)
      self._validate_data(
          X,
          force_all_finite=False,
          dtype=None,
          accept_sparse=True,
          ensure_2d=False,
          reset=False,
      )
  
      return np.repeat(self.y_, _num_samples(X))
  
imports: import array
  import itertools
  import warnings
  from numbers import Integral, Real
  import numpy
  import scipy.sparse
  from .base import BaseEstimator, ClassifierMixin, MetaEstimatorMixin, MultiOutputMixin, _fit_context, clone, is_classifier, is_regressor
  from .metrics.pairwise import pairwise_distances_argmin
  from .preprocessing import LabelBinarizer
  from .utils import check_random_state
  from .utils._param_validation import HasMethods, Interval
  from .utils._tags import _safe_tags
  from .utils.metadata_routing import MetadataRouter, MethodMapping, _raise_for_params, process_routing
  from .utils.metaestimators import _safe_split, available_if
  from .utils.multiclass import _check_partial_fit_first_call, _ovr_decision_function, check_classification_targets
  from .utils.parallel import Parallel, delayed
  from .utils.validation import _check_method_params, _num_samples, check_is_fitted
label_desc: Use image node_id 3 for calling the _ConstantPredictor obj's underlying member method code with example usage: obj.decision_function(X) and returns: np