code: def get_default_target_names(estimator, num_targets=None):
      """
      Return a vector of target names: "y" if there is only one target,
      and "y0", "y1", ... if there are multiple targets.
      """
      if num_targets is None:
          if len(estimator.coef_.shape) <= 1:
              num_targets = 1
          else:
              num_targets, _ = estimator.coef_.shape
      if num_targets == 1:
          target_names = ["y"]
      else:
          target_names = ["y%d" % i for i in range(num_targets)]
      return np.array(target_names)
  
imports: from __future__ import absolute_import
  from distutils.version import LooseVersion
  from typing import Any, Optional, List, Tuple
  import numpy
  import scipy.sparse
  from sklearn.multiclass import OneVsRestClassifier
  from eli5.sklearn.unhashing import invert_hashing_and_fit, handle_hashing_vec
  from eli5._feature_names import FeatureNames
label_desc: Use image node_id 8 for calling a global function with example usage: get_default_target_names(estimator, num_targets) and returns: np