code: def test_select_from_model_pls(PLSEstimator):
      """Check the behaviour of SelectFromModel with PLS estimators.
  
      Non-regression test for:
      https://github.com/scikit-learn/scikit-learn/issues/12410
      """
      X, y = make_friedman1(n_samples=50, n_features=10, random_state=0)
      estimator = PLSEstimator(n_components=1)
      model = make_pipeline(SelectFromModel(estimator), estimator).fit(
          X, y
      )
      assert model.score(X, y) > 0.5
  
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 27 for calling a global function with example usage: test_select_from_model_pls(PLSEstimator) without return types