code: def test_set_missing_values_to_nan_sparse(
      matrix_type, value, sparse_missing
  ):
      ms = matrix_type((1, 100))
      ms[0, 54] = 12
      ms[0, 42] = 0
      ms[0, 7] = -13
      m = _missing_values_set_to_nan(ms, value, sparse_missing)
      assert ms[0, 54] == 12
      assert ms[0, 42] == 0
      assert ms[0, 7] == -13
      assert not sp.issparse(m)
      assert m.shape == (100,)
      if sparse_missing:
          assert np.isnan(m[8])
          if value == 0:
              assert np.isnan(m[42])
          else:
              assert m[42] == 0
      else:
          if value == 0:
              assert np.isnan(m[8])
          else:
              assert m[8] == 0
      if value == 0:
          assert np.isnan(m[42])
      elif value == 12:
          assert np.isnan(m[54])
      assert m[7] == -13
  
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 24 for calling a global function with example usage: test_set_missing_values_to_nan_sparse(matrix_type, value, sparse_missing) without return types