code: def dataset():
      return {
          "classification": {
              "y_true": np.random.randint(0, 2, size=(100,)),
              "y_pred": np.random.randint(0, 2, size=(100,)),
          },
          "regression": {
              "y_true": np.random.randn(100),
              "y_pred": np.random.randn(100),
          },
          "sample_weight": np.random.rand(100),
      }
  
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 1 for calling a global function with example usage: dataset() and returns: dict