code: def test_max_features():
      # Test max_features parameter using various values
      X, y = datasets.make_classification(
          n_samples=1000,
          n_features=10,
          n_informative=3,
          n_redundant=0,
          n_repeated=0,
          shuffle=False,
          random_state=0,
      )
      max_features = X.shape[1]
      est = RandomForestClassifier(n_estimators=50, random_state=0)
  
      transformer1 = SelectFromModel(estimator=est, threshold=-np.inf)
      transformer2 = SelectFromModel(
          estimator=est, max_features=max_features, threshold=-np.inf
      )
      X_new1 = transformer1.fit_transform(X, y)
      X_new2 = transformer2.fit_transform(X, y)
      assert_allclose(X_new1, X_new2)
  
      # Test max_features against actual model.
      transformer1 = SelectFromModel(
          estimator=Lasso(alpha=0.025, random_state=42)
      )
      X_new1 = transformer1.fit_transform(X, y)
      scores1 = np.abs(transformer1.estimator_.coef_)
      candidate_indices1 = np.argsort(-scores1, kind="mergesort")
  
      for n_features in range(1, X_new1.shape[1] + 1):
          transformer2 = SelectFromModel(
              estimator=Lasso(alpha=0.025, random_state=42),
              max_features=n_features,
              threshold=-np.inf,
          )
          X_new2 = transformer2.fit_transform(X, y)
          scores2 = np.abs(transformer2.estimator_.coef_)
          candidate_indices2 = np.argsort(-scores2, kind="mergesort")
          assert_allclose(
              X[:, candidate_indices1[:n_features]],
              X[:, candidate_indices2[:n_features]],
          )
      assert_allclose(
          transformer1.estimator_.coef_, transformer2.estimator_.coef_
      )
  
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 8 for calling a global function with example usage: test_max_features() without return types