code: def _set_label_transformers(self, y, reencode=False, neg_label=-1):
      if reencode:
          self.label_encoder_ = LabelEncoder()
          y = self.label_encoder_.fit_transform(y).astype(np.int32)
      else:
          y = y.astype(np.int32)
  
      self.label_binarizer_ = LabelBinarizer(
          neg_label=neg_label, pos_label=1
      )
      self.label_binarizer_.fit(y)
      self.classes_ = self.label_binarizer_.classes_.astype(np.int32)
      n_classes = len(self.label_binarizer_.classes_)
      n_vectors = 1 if n_classes <= 2 else n_classes
  
      return y, n_classes, n_vectors
  
imports: import numpy
  import scipy.sparse
  import scipy.special
  from sklearn.base import BaseEstimator
  from sklearn.base import ClassifierMixin, RegressorMixin
  from sklearn.utils.extmath import safe_sparse_dot
  from sklearn.preprocessing import LabelBinarizer
  from sklearn.preprocessing import LabelEncoder
  from .randomkit import RandomState
label_desc: Use image node_id 3 for calling the BaseClassifier obj's underlying member method code with example usage: obj._set_label_transformers(y, reencode, neg_label) and returns: y,  n_classes,  n_vectors