code: def check_groups(arr, prot_attr, ensure_binary=False):
      """Get groups from the index of arr.
  
      If there are multiple protected attributes provided, the index is flattened
      to be a 1-D Index of tuples. If ensure_binary is ``True``, raises a
      ValueError if there are not exactly two unique groups. Also checks that all
      provided protected attributes are in the index.
  
      Args:
          arr (array-like): Either a Pandas object containing protected attribute
              information in the index or array-like with explicit protected
              attribute array(s) for `prot_attr`.
          prot_attr (label or array-like or list of labels/arrays): Protected
              attribute(s). If contains labels, arr must include these in its
              index. If ``None``, all protected attributes in ``arr.index`` are
              used. Can also be 1D array-like of the same length as arr or a
              list of a combination of such arrays and labels in which case, arr
              may not necessarily be a Pandas type.
          ensure_binary (bool): Raise an error if the resultant groups are not
              binary.
  
      Returns:
          tuple:
  
              * **groups** (:class:`pandas.Index`) -- Label (or tuple of labels)
                of protected attribute for each sample in arr.
              * **prot_attr** (`FrozenList`) -- Modified input. If input is a
                single label, returns single-item list. If input is ``None``
                returns list of all protected attributes.
      """
      arr_is_pandas = isinstance(arr, (pd.DataFrame, pd.Series))
      if (
          prot_attr is None
      ):  # use all protected attributes provided in arr
          if not arr_is_pandas:
              raise TypeError(
                  "Expected `Series` or `DataFrame` for arr, got "
                  f"{type(arr).__name__} instead. Otherwise, pass "
                  "explicit prot_attr array(s)."
              )
          groups = arr.index
      elif arr_is_pandas:
          df = arr.index.to_frame()
          groups = df.set_index(
              prot_attr
          ).index  # let pandas handle errors
      else:  # arr isn't pandas. might be okay if prot_attr is array-like
          df = pd.DataFrame(
              index=[None] * len(arr)
          )  # dummy to check lengths match
          try:
              groups = df.set_index(prot_attr).index
          except KeyError as e:
              raise TypeError(
                  "arr does not include protected attributes in the "
                  "index. Check if this got dropped or prot_attr is "
                  "formatted incorrectly."
              ) from e
      prot_attr = groups.names
      groups = groups.to_flat_index()
  
      n_unique = groups.nunique()
      if ensure_binary and n_unique != 2:
          raise ValueError(
              "Expected 2 protected attribute groups, got "
              f"{groups.unique() if n_unique > 5 else n_unique}"
          )
  
      return groups, prot_attr
  
imports: import numpy
  import pandas
  from pandas.core.dtypes.common import is_list_like
  from sklearn.utils import check_consistent_length
  from sklearn.utils.validation import column_or_1d
label_desc: Use image node_id 2 for calling a global function with example usage: check_groups(arr, prot_attr, ensure_binary) and returns: groups,  prot_attr