code: def _partial_fit_binary(estimator, X, y):
      """Partially fit a single binary estimator."""
      estimator.partial_fit(X, y, np.array((0, 1)))
      return estimator
  
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.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 _num_samples, check_is_fitted
label_desc: Use image node_id 2 for calling a global function with example usage: _partial_fit_binary(estimator, X, y) and returns: estimator