code: def exp_importance():
      """Creates importance explanation object."""
      meta = deepcopy(DEFAULT_META_PDVARIANCE)
      data = deepcopy(DEFAULT_DATA_PDVARIANCE)
      meta["params"] = {
          "percentiles": (0.0, 1.0),
          "grid_resolution": 4,
          "feature_names": ["f_0", "f_1", "f_2", "f_3"],
          "categorical_names": {
              2: [0, 1, 2, 3, 4],
              3: [0, 1, 2, 3, 4, 5, 6],
          },
          "target_names": ["c_0", "c_1"],
          "method": "importance",
      }
      data["feature_deciles"] = [
          np.array(
              [
                  -2.319,
                  -2.032,
                  -1.744,
                  -1.456,
                  -1.169,
                  -0.881,
                  -0.593,
                  -0.305,
                  -0.018,
                  0.269,
                  0.557,
              ]
          ),
          np.array(
              [
                  0.080,
                  0.217,
                  0.354,
                  0.491,
                  0.628,
                  0.765,
                  0.902,
                  1.039,
                  1.175,
                  1.312,
                  1.449,
              ]
          ),
          None,
          None,
      ]
      data["pd_values"] = [
          np.array(
              [
                  [-98.144, 105.232],
                  [-181.330, 75.769],
                  [-81.942, 20.868],
              ]
          ),
          np.array(
              [
                  [-55.346, 62.434],
                  [-76.017, -29.543],
                  [-31.394, -29.679],
              ]
          ),
          np.array(
              [[90.638, 3.543], [-52.780, 12.725], [-30.537, 32.840]]
          ),
          np.array(
              [[13.115, 54.495], [-28.445, 41.850], [-11.403, -6.029]]
          ),
      ]
      data["feature_values"] = [
          np.array([-2.319, 0.557]),
          np.array([0.080, 1.449]),
          np.array([1.0, 4.0]),
          np.array([3.0, 4.0]),
      ]
      data["feature_names"] = ["f_0", "f_1", "f_2", "f_3"]
      data["feature_importance"] = np.array(
          [
              [143.809, 83.283, 77.481, 17.177],
              [181.973, 32.615, 32.840, 11.403],
              [72.698, 1.212, 41.693, 10.782],
          ]
      )
      return Explanation(meta=meta, data=data)
  
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 15 for calling a global function with example usage: exp_importance() and returns: Explanation