code: def test_plot_pd_variance_warning(exp_importance, mocker, caplog):
      """Tests if a warning is raise when the ``summarise=False`` and the length of `targets` is > 1."""
      mocker.patch(
          "alibi.explainers.pd_variance._plot_feature_importance",
          return_value=None,
      )
      plot_pd_variance(
          exp=exp_importance, targets=[0, 1], summarise=False
      )
      assert (
          "`targets` should be a list containing a single element"
          in caplog.records[0].message
      )
  
imports: import re
  from copy import deepcopy
  import matplotlib.pyplot
  import numpy
  import pandas
  import pytest
  from pytest_lazyfixture import lazy_fixture
  from sklearn.ensemble import GradientBoostingRegressor
  from sklearn.linear_model import LinearRegression
  from alibi.api.defaults import DEFAULT_DATA_PDVARIANCE, DEFAULT_META_PDVARIANCE
  from alibi.api.interfaces import Explanation
  from alibi.explainers import PartialDependence, PartialDependenceVariance, TreePartialDependence
  from alibi.explainers.pd_variance import _plot_feature_importance, _plot_feature_interaction, _plot_hbar, plot_pd_variance
label_desc: Use image node_id 28 for calling a global function with example usage: test_plot_pd_variance_warning(exp_importance, mocker, caplog) without return types