code: def test_threshold_string():
      est = RandomForestClassifier(n_estimators=50, random_state=0)
      model = SelectFromModel(est, threshold="0.5*mean")
      model.fit(data, y)
      X_transform = model.transform(data)
  
      # Calculate the threshold from the estimator directly.
      est.fit(data, y)
      threshold = 0.5 * np.mean(est.feature_importances_)
      mask = est.feature_importances_ > threshold
      assert_array_almost_equal(X_transform, data[:, mask])
  
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 20 for calling a global function with example usage: test_threshold_string() without return types