code: def test_multiclass(pyplot, response_method):
      """Check multiclass gives expected results."""
      grid_resolution = 10
      eps = 1.0
      X, y = make_classification(
          n_classes=3, n_informative=3, random_state=0
      )
      X = X[:, [0, 1]]
      lr = LogisticRegression(random_state=0).fit(X, y)
  
      disp = DecisionBoundaryDisplay.from_estimator(
          lr,
          X,
          response_method=response_method,
          grid_resolution=grid_resolution,
          eps=1.0,
      )
  
      x0_min, x0_max = X[:, 0].min() - eps, X[:, 0].max() + eps
      x1_min, x1_max = X[:, 1].min() - eps, X[:, 1].max() + eps
      xx0, xx1 = np.meshgrid(
          np.linspace(x0_min, x0_max, grid_resolution),
          np.linspace(x1_min, x1_max, grid_resolution),
      )
      response = lr.predict(np.c_[xx0.ravel(), xx1.ravel()])
      assert_allclose(disp.response, response.reshape(xx0.shape))
      assert_allclose(disp.xx0, xx0)
      assert_allclose(disp.xx1, xx1)
  
imports: import warnings
  import numpy
  import pytest
  from sklearn.base import BaseEstimator, ClassifierMixin
  from sklearn.datasets import load_diabetes, load_iris, make_classification, make_multilabel_classification
  from sklearn.ensemble import IsolationForest
  from sklearn.inspection import DecisionBoundaryDisplay
  from sklearn.inspection._plot.decision_boundary import _check_boundary_response_method
  from sklearn.linear_model import LogisticRegression
  from sklearn.preprocessing import scale
  from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
  from sklearn.utils._testing import assert_allclose, assert_array_equal
label_desc: Use image node_id 7 for calling a global function with example usage: test_multiclass(pyplot, response_method) without return types