code: def _array_indexing(array, key, key_dtype, axis):
      """Index an array or scipy.sparse consistently across NumPy version."""
      if np_version < parse_version("1.12") or issparse(array):
          # FIXME: Remove the check for NumPy when using >= 1.12
          # check if we have an boolean array-likes to make the proper indexing
          if key_dtype == "bool":
              key = np.asarray(key)
      if isinstance(key, tuple):
          key = list(key)
      return array[key] if axis == 0 else array[:, key]
  
imports: import functools
  import logging
  import numbers
  import time
  import warnings
  from collections import Counter
  from contextlib import suppress
  from itertools import compress
  from traceback import format_exc
  import numpy
  import scipy.sparse
  from catboost import CatBoostClassifier, CatBoostRegressor
  from joblib import Parallel, logger
  from scipy.sparse import issparse
  from sklearn import config_context, get_config
  from sklearn.base import clone, is_classifier
  from sklearn.exceptions import FitFailedWarning, NotFittedError
  from sklearn.metrics import check_scoring
  from sklearn.metrics._scorer import _MultimetricScorer
  from sklearn.model_selection import check_cv
  from sklearn.utils.fixes import np_version, parse_version
  from sklearn.utils.validation import indexable
label_desc: Use image node_id 13 for calling a global function with example usage: _array_indexing(array, key, key_dtype, axis) and returns: unknown