code: def check_model(model):
      """
      Check if model has a predict_proba method is a one column dataframe of integer or float
      and if y_pred index matches x_init index
  
      Parameters
      ----------
      model: model object
          model used to check the different values of target estimate predict or predict_proba
  
      Returns
      -------
      string:
          'regression' or 'classification' according to the attributes of the model
      """
      _classes = None
      if hasattr(model, "predict"):
          if hasattr(model, "predict_proba") or any(
              hasattr(model, attrib)
              for attrib in ["classes_", "_classes"]
          ):
              if hasattr(model, "_classes"):
                  _classes = model._classes
              if hasattr(model, "classes_"):
                  _classes = model.classes_
              if isinstance(_classes, np.ndarray):
                  _classes = _classes.tolist()
              if hasattr(model, "predict_proba") and _classes == []:
                  _classes = [0, 1]  # catboost binary
              if hasattr(model, "predict_proba") and _classes is None:
                  raise ValueError(
                      "No attribute _classes, classification model not supported"
                  )
          if _classes not in (None, []):
              return "classification", _classes
          else:
              return "regression", None
      else:
          raise ValueError(
              "No method predict in the specified model. Please, check model parameter"
          )
  
imports: import copy
  import numpy
  import pandas
  from shapash.utils.category_encoder_backend import no_dummies_category_encoder, supported_category_encoder, dummies_category_encoder
  from shapash.utils.columntransformer_backend import no_dummies_sklearn, supported_sklearn
  from shapash.utils.model import extract_features_model
  from shapash.utils.model_synoptic import dict_model_feature
  from shapash.utils.transform import preprocessing_tolist, check_transformers
  from shapash.utils.columntransformer_backend import columntransformer, get_feature_names, get_list_features_names
label_desc: Use image node_id 2 for calling a global function with example usage: check_model(model) and returns: str,  _classes, str,  None