code: def _make_indexable(iterable):
      """Ensure iterable supports indexing or convert to an indexable variant.
  
      Convert sparse matrices to csr and other non-indexable iterable to arrays.
      Let `None` and indexable objects (e.g. pandas dataframes) pass unchanged.
  
      Parameters
      ----------
      iterable : {list, dataframe, ndarray, sparse matrix} or None
          Object to be converted to an indexable iterable.
      """
      if sp.issparse(iterable):
          return iterable.tocsr()
      elif hasattr(iterable, "__getitem__") or hasattr(
          iterable, "iloc"
      ):
          return iterable
      elif iterable is None:
          return iterable
      return np.array(iterable)
  
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 20 for calling a global function with example usage: _make_indexable(iterable) and returns: np, iterable, iterable, iterable