code: def test_zero_interaction(predictor):
      """Test whether the explainer attributes zero feature interaction."""
      X = np.random.uniform(low=-1, high=1, size=(100, 2))
      points = np.linspace(-0.9, 0.9, 10)
      grid_points = {i: points for i in range(X.shape[1])}
  
      explainer = PartialDependenceVariance(predictor=predictor)
      exp = explainer.explain(
          X=X, method="interaction", grid_points=grid_points
      )
      assert np.allclose(exp.data["feature_interaction"], 0)
  
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 7 for calling a global function with example usage: test_zero_interaction(predictor) without return types