code: def _init_errors(self, Y):
      n_samples, n_vectors = Y.shape
      if self.loss == "squared":
          self.errors_ = -Y.T
      else:
          self.errors_ = np.ones(
              (n_vectors, n_samples), dtype=np.float64
          )
  
imports: import numpy
  from joblib import Parallel, delayed
  from .base import BaseClassifier
  from .base import BaseRegressor
  from .dataset_fast import get_dataset
  from .primal_cd_fast import _primal_cd
  from .primal_cd_fast import Squared
  from .primal_cd_fast import SmoothHinge
  from .primal_cd_fast import SquaredHinge
  from .primal_cd_fast import ModifiedHuber
  from .primal_cd_fast import Log
label_desc: Use image node_id 4 for calling the _BaseCD obj's underlying member method code with example usage: obj._init_errors(Y) without return types