code: def _check_multi_class(multi_class, solver, n_classes):
      if multi_class == "auto":
          return "multinomial"
      if multi_class == "ovr":
          if n_classes == 2:
              return "multinomial"
          else:
              raise ValueError(
                  "Solver %s does not support "
                  "an ovr backend with number of classes "
                  "larger than 2." % solver
              )
      if multi_class == "multinomial":
          return "multinomial"
  
      raise ValueError(
          "multi_class should be 'multinomial', 'ovr' or 'auto'. Got %s."
          % multi_class
      )
  
file_path: mars/mars/learn/glm/_logistic.py
imports: import numbers
  from sklearn.utils.validation import _deprecate_positional_args
  from ...None import tensor
  from ..linear_model._base import LinearClassifierMixin
  from ..base import BaseEstimator
  from ..utils.validation import check_is_fitted
  from ..utils.multiclass import check_classification_targets
  from ..utils.extmath import softmax
  from ._optimizers import gradient_descent
label_desc: Use image node_id 2 for calling a global function with example usage: _check_multi_class(multi_class, solver, n_classes) and returns: str, str, str