code: def exp_estimate():
      """Creates an estimate explanation object."""
      meta = deepcopy(DEFAULT_META_PERMUTATION_IMPORTANCE)
      data = deepcopy(DEFAULT_DATA_PERMUTATION_IMPORTANCE)
  
      meta["params"] = {
          "feature_names": ["f_0", "f_2", "f_4"],
          "method": "estimate",
          "kind": "ratio",
          "n_repeats": 3,
          "sample_weight": None,
      }
  
      data["feature_names"] = ["f_0", "f_2", "f_4"]
      data["metric_names"] = [
          "mean_squared_error",
          "mean_absolute_error",
          "r2",
      ]
      data["feature_importance"] = [
          [
              {
                  "mean": 2627.702,
                  "std": 189.985,
                  "samples": np.array([2399.303, 2864.445, 2619.357]),
              },
              {
                  "mean": 4661.244,
                  "std": 161.543,
                  "samples": np.array([4530.869, 4563.963, 4888.901]),
              },
              {
                  "mean": 2012.644,
                  "std": 214.766,
                  "samples": np.array([2312.335, 1905.535, 1820.063]),
              },
          ],
          [
              {
                  "mean": 37.519,
                  "std": 2.382,
                  "samples": np.array([39.548, 34.174, 38.834]),
              },
              {
                  "mean": 61.574,
                  "std": 5.984,
                  "samples": np.array([61.956, 68.705, 54.061]),
              },
              {
                  "mean": 47.815,
                  "std": 2.525,
                  "samples": np.array([50.765, 48.082, 44.597]),
              },
          ],
          [
              {
                  "mean": 0.219,
                  "std": 0.015,
                  "samples": np.array([0.200, 0.238, 0.218]),
              },
              {
                  "mean": 0.403,
                  "std": 0.013,
                  "samples": np.array([0.392, 0.395, 0.423]),
              },
              {
                  "mean": 0.177,
                  "std": 0.018,
                  "samples": np.array([0.203, 0.167, 0.160]),
              },
          ],
      ]
      return Explanation(meta=meta, data=data)
  
imports: import re
  from copy import deepcopy
  import matplotlib.pyplot
  import numpy
  import pytest
  from pytest_lazyfixture import lazy_fixture
  from sklearn.metrics import accuracy_score, f1_score, max_error
  from alibi.api.defaults import DEFAULT_DATA_PERMUTATION_IMPORTANCE, DEFAULT_META_PERMUTATION_IMPORTANCE
  from alibi.api.interfaces import Explanation
  from alibi.explainers import PermutationImportance, plot_permutation_importance
label_desc: Use image node_id 16 for calling a global function with example usage: exp_estimate() and returns: Explanation