class_docstr: Test FeatureImportances visualizer
docstr: Test fit with multidimensional coefficients and stack warning
docstr_tok: ["Test","fit","with","multidimensional","coefficients","and","stack","warning"]
code: def test_multi_coefs(self):
      """
      Test fit with multidimensional coefficients and stack warning
      """
      coefs = np.array(
          [
              [0.4, 0.2, -0.08, 0.07, 0.16, 0.23, -0.38, 0.1, -0.05],
              [0.41, 0.12, -0.1, 0.1, 0.14, 0.21, 0.01, 0.31, -0.15],
              [0.31, 0.2, -0.01, 0.1, 0.22, 0.23, 0.01, 0.12, -0.15],
          ]
      )
  
      model = MockEstimator()
      model.make_importance_param(value=coefs)
  
      visualizer = FeatureImportances(model, stack=False)
  
      with pytest.warns(YellowbrickWarning):
          visualizer.fit(
              np.random.rand(100, len(np.mean(coefs, axis=0))),
              np.random.rand(100),
          )
  
      npt.assert_equal(visualizer.feature_importances_.ndim, 1)
  
imports: import pytest
  import numpy
  import numpy.testing
  import matplotlib.pyplot
  from yellowbrick.exceptions import NotFitted
  from yellowbrick.model_selection.importances import *
  from yellowbrick.datasets import load_occupancy, load_concrete
  from sklearn.datasets import load_iris
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.ensemble import GradientBoostingClassifier
  from sklearn.base import BaseEstimator, ClassifierMixin
  from sklearn.linear_model import LogisticRegression, Lasso
  from sklearn.impute import SimpleImputer
  from sklearn.pipeline import Pipeline
  from unittest import mock
  from tests.base import VisualTestCase
label_desc: Use image node_id 9 for calling the TestFeatureImportancesVisualizer obj's underlying member method code with example usage: obj.test_multi_coefs() without return types