code: def generate_test_data(
      dimensions,
      b_group_names,
      b_groups,
      b_weights,
      correct=True,
      error_type=None,
      data_type="",
      dim_mismatch=3,
      seed=None,
  ):
      """
      Generates:
          - a random dataset `data` with dim `dimensions` of type `data_type`
          - depending on the values of the `b_*` inputs groups of indices (`groups`),
          `group_names` and `weights` for each data point are also generated
          - if `correct=True`, then the inputs should not causes errors. Otherwise,
          depending on the value of `error_type`  the `data` array or the groups and
          group_names lists are specified so that specific errors or warnings are generated.
          `dim_mismatch` controls dimension mismatches
      """
  
      np.random.seed(seed)
      # create dimension mismatches by removing `dim_mismatch` rows/columns from the data
      if all([dim <= dim_mismatch for dim in dimensions]):
          raise ValueError(
              "dim_mismatch has to be greater than at least one dimension in order to "
              "correctly generate erroneous data!",
          )
  
      # generate groups setup and data according to booleans for groups and weights
      n_samples, n_features = dimensions
  
      group_names, groups, weights = setup_groups_and_weights(
          (n_features, n_samples),
          b_group_names,
          b_groups,
          b_weights,
      )
  
      # we modify the data or the groups so that the input is
      # no longer correct to check warnings
      if not correct:
          # switch type of group without affecting dimensions
          if error_type == "groups_type":
              if b_groups:
                  dummy_group = [str(x) for x in range(len(groups[0]))]
                  groups[0] = "".join(dummy_group)
          # name_dim_mismatch: only specified group names (not groups)
          # but the nb. of name doesn't match any data dimension
          # groups_dim_mismatch: expected dim according to groups does
          # not match any data dimension
          elif (
              error_type == "name_dim_mismatch"
              or error_type == "groups_dim_mismatch"
          ):
              n_features -= dim_mismatch
              if n_samples not in (0, 1):
                  n_samples -= dim_mismatch
              # we don't test weights warnings
              if weights is not None:
                  weights = weights[:-dim_mismatch]
          # expected dim according to grouping matches first not second dim
          # so we transpose the data
          elif error_type == "check_transpose":
              n_features, n_samples = n_samples, n_features
          # less weights compared to data points
          elif error_type == "weights_dim_mismatch":
              if n_samples == 1:
                  n_samples += dim_mismatch
              else:
                  n_samples -= dim_mismatch
          # number of groups is different to number of group names
          elif error_type == "groups_group_names_mismatch":
              if b_groups and b_group_names:
                  group_names = group_names[:-1]
  
      data = get_data(data_type, n_rows=n_samples, n_cols=n_features)
  
      return group_names, groups, weights, data
  
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 8 for calling a global function with example usage: generate_test_data(dimensions, b_group_names, b_groups, b_weights, correct, error_type, data_type, dim_mismatch, seed) and returns: group_names,  groups,  weights,  data