code: def test_explain_tree(
      caplog,
      monkeypatch,
      mock_tree_shap_explainer,
      data_type,
      summarise_result,
      interactions,
  ):
      """
      Test explain method.
      """
  
      caplog.set_level(logging.WARNING)
      # create fake data and records to explain
      seed = 0
      n_feats, n_samples, n_instances = 15, 20, 2
      background_data = get_data(
          data_type, n_rows=n_samples, n_cols=n_feats, seed=seed
      )
      if data_type != "none":
          instances = get_data(
              data_type,
              n_rows=n_instances,
              n_cols=n_feats,
              seed=seed + 1,
          )
      else:
          instances = get_data(
              "array", n_rows=n_instances, n_cols=n_feats
          )
  
      cat_vars_start_idx, cat_vars_enc_dim = None, None
      if summarise_result:
          cat_vars_start_idx, cat_vars_enc_dim = [0, n_feats - 3], [
              2,
              3,
          ]
  
      # initialise and fit explainer
      explainer = mock_tree_shap_explainer
      monkeypatch.setattr(shap, "kmeans", KMeansMock())
      explainer.use_groups = use_groups
      explainer.fit(background_data)
  
      # patch _check_* methods to make testing easier: the point is to test explain
      # TODO: @janis: let's do path.multiple or something like that
      with unittest.mock.patch.object(explainer, "_check_interactions"):
          with unittest.mock.patch.object(
              explainer, "_check_explainer_setup"
          ):
              with unittest.mock.patch.object(
                  explainer, "_build_explanation"
              ):
                  # explain some instances
                  explainer.explain(
                      instances,
                      interactions=interactions,
                      summarise_result=summarise_result,
                      cat_vars_enc_dim=cat_vars_enc_dim,
                      cat_vars_start_idx=cat_vars_start_idx,
                  )
  
                  if interactions:
                      explainer._check_explainer_setup.assert_not_called()
                      explainer._check_interactions.assert_called_with(
                          False, background_data, None
                      )
                  else:
                      explainer._check_interactions.assert_not_called()
                      explainer._check_explainer_setup.assert_called_with(
                          background_data, explainer.model_output, None
                      )
  
                  explainer._build_explanation.assert_called_once()
                  build_args = explainer._build_explanation.call_args
                  # check shap values and expected value are of the correct data type for all dimensions
                  assert isinstance(build_args[0][1], list)
                  assert isinstance(build_args[0][2], list)
                  assert isinstance(build_args[0][1][0], np.ndarray)
                  assert isinstance(build_args[0][2][0], float)
  
                  assert (
                      explainer.meta["params"]["interactions"]
                      == interactions
                  )
                  assert not explainer.meta["params"]["approximate"]
                  assert not explainer.meta["params"]["explain_loss"]
  
imports: import itertools
  import logging
  import unittest
  from copy import copy
  from itertools import chain
  from unittest.mock import MagicMock
  from typing import Any, List
  import catboost
  import pandas
  import pytest
  import scipy.sparse
  import sklearn
  import shap
  import numpy
  import pandas
  import shap.utils._legacy
  from numpy.testing import assert_allclose, assert_almost_equal
  from scipy.special import expit
  from alibi.api.defaults import DEFAULT_META_KERNEL_SHAP, DEFAULT_DATA_KERNEL_SHAP, DEFAULT_META_TREE_SHAP, DEFAULT_DATA_TREE_SHAP, KERNEL_SHAP_PARAMS, TREE_SHAP_PARAMS
  from alibi.explainers.shap_wrappers import sum_categories, rank_by_importance, KernelExplainerWrapper
  from alibi.explainers.shap_wrappers import KERNEL_SHAP_BACKGROUND_THRESHOLD, TREE_SHAP_BACKGROUND_WARNING_THRESHOLD
  from alibi.explainers.tests.utils import get_random_matrix
  from alibi.tests.utils import assert_message_in_logs, not_raises
  from alibi.utils import DistributedExplainer
label_desc: Use image node_id 26 for calling a global function with example usage: test_explain_tree(caplog, monkeypatch, mock_tree_shap_explainer, data_type, summarise_result, interactions) without return types