code: def check_target_type(y, indicate_one_vs_all=False):
      """Check the target types to be conform to the current samplers.
  
      The current samplers should be compatible with ``'binary'``,
      ``'multilabel-indicator'`` and ``'multiclass'`` targets only.
  
      Parameters
      ----------
      y : ndarray
          The array containing the target.
  
      indicate_one_vs_all : bool, default=False
          Either to indicate if the targets are encoded in a one-vs-all fashion.
  
      Returns
      -------
      y : ndarray
          The returned target.
  
      is_one_vs_all : bool, optional
          Indicate if the target was originally encoded in a one-vs-all fashion.
          Only returned if ``indicate_multilabel=True``.
      """
      type_y = type_of_target(y)
      if type_y == "multilabel-indicator":
          if np.any(y.sum(axis=1) > 1):
              raise ValueError(
                  "Imbalanced-learn currently supports binary, multiclass and "
                  "binarized encoded multiclasss targets. Multilabel and "
                  "multioutput targets are not supported."
              )
          y = y.argmax(axis=1)
      else:
          y = column_or_1d(y)
  
      return (
          (y, type_y == "multilabel-indicator")
          if indicate_one_vs_all
          else y
      )
  
imports: import warnings
  from collections import OrderedDict
  from functools import wraps
  from inspect import Parameter, signature
  from numbers import Real
  from typing import Any, Dict
  import numpy
  from sklearn.utils import column_or_1d
  from sklearn.utils.multiclass import type_of_target
label_desc: Use image node_id 12 for calling a global function with example usage: check_target_type(y, indicate_one_vs_all) and returns: unknown