code: def test_partial_fit_validate_max_features(
      error, err_msg, max_features
  ):
      """Test that partial_fit from SelectFromModel validates `max_features`."""
      X, y = datasets.make_classification(
          n_samples=100,
          n_features=4,
          random_state=0,
      )
  
      with pytest.raises(error, match=err_msg):
          SelectFromModel(
              estimator=SGDClassifier(), max_features=max_features
          ).partial_fit(X, y, classes=[0, 1])
  
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 29 for calling a global function with example usage: test_partial_fit_validate_max_features(error, err_msg, max_features) without return types