code: def test_transform_accepts_nan_inf():
      # Test that transform doesn't check for np.inf and np.nan values.
      clf = NaNTagRandomForest(n_estimators=100, random_state=0)
      nan_data = data.copy()
  
      model = SelectFromModel(estimator=clf)
      model.fit(nan_data, y)
  
      nan_data[0] = np.nan
      nan_data[1] = np.inf
  
      model.transform(nan_data)
  
imports: import re
  import warnings
  from unittest.mock import Mock
  import numpy
  import pytest
  from sklearn import datasets
  from sklearn.base import BaseEstimator
  from sklearn.cross_decomposition import CCA, PLSCanonical, PLSRegression
  from sklearn.datasets import make_friedman1
  from sklearn.decomposition import PCA
  from sklearn.ensemble import HistGradientBoostingClassifier, RandomForestClassifier
  from sklearn.exceptions import NotFittedError
  from sklearn.feature_selection import SelectFromModel
  from sklearn.linear_model import ElasticNet, ElasticNetCV, Lasso, LassoCV, LogisticRegression, PassiveAggressiveClassifier, SGDClassifier
  from sklearn.pipeline import make_pipeline
  from sklearn.svm import LinearSVC
  from sklearn.utils._testing import MinimalClassifier, assert_allclose, assert_array_almost_equal, assert_array_equal, skip_if_32bit
label_desc: Use image node_id 23 for calling a global function with example usage: test_transform_accepts_nan_inf() without return types