class_docstr: Base Class for column ensemble.
class_docstr_tok: ["Base","Class","for","column","ensemble","."]
code: def _validate_estimators(self):
      if not self.estimators:
          return
  
      names, estimators, _ = zip(*self.estimators)
  
      self._check_names(names)
  
      # validate estimators
      for t in estimators:
          if t == "drop":
              continue
          if not (hasattr(t, "fit") or hasattr(t, "predict_proba")):
              raise TypeError(
                  "All estimators should implement fit and predict proba"
                  "or can be 'drop' "
                  "specifiers. '%s' (type %s) doesn't." % (t, type(t))
              )
  
imports: from itertools import chain
  import numpy
  import pandas
  from sklearn.preprocessing import LabelEncoder
  from sktime.base import _HeterogenousMetaEstimator
  from sktime.classification.base import BaseClassifier
label_desc: Use image node_id 4 for calling the BaseColumnEnsembleClassifier obj's underlying member method code with example usage: obj._validate_estimators() without return types