code: def check_l1_min_c(
      X, y, loss, fit_intercept=True, intercept_scaling=1.0
  ):
      min_c = l1_min_c(
          X,
          y,
          loss=loss,
          fit_intercept=fit_intercept,
          intercept_scaling=intercept_scaling,
      )
  
      clf = {
          "log": LogisticRegression(penalty="l1", solver="liblinear"),
          "squared_hinge": LinearSVC(
              loss="squared_hinge", penalty="l1", dual=False
          ),
      }[loss]
  
      clf.fit_intercept = fit_intercept
      clf.intercept_scaling = intercept_scaling
  
      clf.C = min_c
      clf.fit(X, y)
      assert (np.asarray(clf.coef_) == 0).all()
      assert (np.asarray(clf.intercept_) == 0).all()
  
      clf.C = min_c * 1.01
      clf.fit(X, y)
      assert (np.asarray(clf.coef_) != 0).any() or (
          np.asarray(clf.intercept_) != 0
      ).any()
  
file_path: scikit-learn/sklearn/svm/tests/test_bounds.py
imports: import numpy
  import pytest
  from scipy import stats
  from sklearn.linear_model import LogisticRegression
  from sklearn.svm import LinearSVC
  from sklearn.svm._bounds import l1_min_c
  from sklearn.svm._newrand import bounded_rand_int_wrap, set_seed_wrap
  from sklearn.utils.fixes import CSR_CONTAINERS
label_desc: Use image node_id 2 for calling a global function with example usage: check_l1_min_c(X, y, loss, fit_intercept, intercept_scaling) without return types