code: def test_xgb_n_targets():
      clf = XGBClassifier()
      clf.fit(np.array([[0], [1]]), np.array([0, 1]))
      assert _xgb_n_targets(clf) == 1
  
      clf = XGBClassifier()
      clf.fit(np.array([[0], [1], [2]]), np.array([0, 1, 2]))
      assert _xgb_n_targets(clf) == 3
  
      reg = XGBRegressor()
      reg.fit(np.array([[0], [1], [2]]), np.array([0, 1, 2]))
      assert _xgb_n_targets(reg) == 1
  
      with pytest.raises(TypeError):
          _xgb_n_targets(object())
  
imports: from __future__ import absolute_import
  import pytest
  import numpy
  import scipy.sparse
  from sklearn.feature_extraction.text import CountVectorizer
  import xgboost
  from xgboost import XGBClassifier, XGBRegressor
  from sklearn.pipeline import FeatureUnion
  from sklearn.preprocessing import FunctionTransformer
  from eli5.xgboost import _parse_tree_dump, _xgb_n_targets, _missing_values_set_to_nan, _parent_value, _parse_dump_line, _check_booster_args
  from eli5.explain import explain_prediction, explain_weights
  from eli5.formatters.text import format_as_text
  from eli5.formatters import fields
  from .utils import format_as_all, get_all_features, check_targets_scores
  from .test_sklearn_explain_weights import test_explain_tree_classifier, test_explain_random_forest_and_tree_feature_filter, test_feature_importances_no_remaining, assert_tree_classifier_explained
  from .test_sklearn_explain_prediction import assert_linear_regression_explained, assert_trained_linear_regression_explained, assert_explain_prediction_single_target, test_explain_clf_binary_iris, test_explain_prediction_pandas
label_desc: Use image node_id 23 for calling a global function with example usage: test_xgb_n_targets() without return types