code: def test_save_ALE(ale_explainer, lr_classifier, iris_data):
      X = iris_data["X_test"]
      exp0 = ale_explainer.explain(X)
      with tempfile.TemporaryDirectory() as temp_dir:
          ale_explainer.save(temp_dir)
          ale_explainer1 = load_explainer(
              temp_dir, predictor=lr_classifier.predict_proba
          )
  
          assert isinstance(ale_explainer1, ALE)
          # TODO: cannot pass as meta updated after explain
          # assert ale_explainer.meta == ale_explainer1.meta
          exp1 = ale_explainer1.explain(X)
  
          # ALE explanations are deterministic
          assert exp0.meta == exp1.meta
          # assert exp0.data == exp1.data # cannot compare as many types instide TODO: define equality for explanations?
          # or compare pydantic schemas?
          assert np.all(exp0.ale_values[0] == exp1.ale_values[0])
  
imports: import numbers
  import sys
  import numpy
  from numpy.testing import assert_allclose
  import pytest
  from pytest_lazyfixture import lazy_fixture
  from sklearn.linear_model import LogisticRegression
  from sklearn.ensemble import RandomForestClassifier
  import tempfile
  import tensorflow
  from typing import List, Union
  from alibi.explainers import ALE, AnchorImage, AnchorTabular, AnchorText, IntegratedGradients, KernelShap, TreeShap, CounterfactualRLTabular, GradientSimilarity
  from alibi.explainers.integrated_gradients import LayerState
  from alibi.saving import load_explainer
  from alibi_testing.data import get_adult_data, get_iris_data, get_movie_sentiment_data
  import alibi_testing
  from alibi.utils.download import spacy_model
  from alibi.explainers.tests.utils import predict_fcn
label_desc: Use image node_id 22 for calling a global function with example usage: test_save_ALE(ale_explainer, lr_classifier, iris_data) without return types