class_docstr: Handles parameter management for estimators composed of named estimators.
  
  Partly adapted from sklearn utils.metaestimator.py.
docstr: Return list of estimators, from a list or tuple.
  
  Parameters
  ----------
  estimators : list of estimators, or list of (str, estimator tuples)
  
  Returns
  -------
  list of estimators - identical with estimators if list of estimators
      if list of (str, estimator) tuples, the str get removed
docstr_tok: ["Return","list","of","estimators",",","from","a","list","or","tuple",".","Parameters","--","--","--","--","--","estimators",":","list","of","estimators",",","or","list","of","(","str",",","estimator","tuples",")","Returns","--","--","--","-","list","of","estimators","-","identical","with","estimators","if","list","of","estimators","if","list","of","(","str",",","estimator",")","tuples",",","the","str","get","removed"]
code: def _get_estimator_list(self, estimators):
      """Return list of estimators, from a list or tuple.
  
      Parameters
      ----------
      estimators : list of estimators, or list of (str, estimator tuples)
  
      Returns
      -------
      list of estimators - identical with estimators if list of estimators
          if list of (str, estimator) tuples, the str get removed
      """
      return [self._coerce_estimator_tuple(x)[1] for x in estimators]
  
imports: from inspect import isclass
  import numpy
  import pandas
  from sktime.base import BaseEstimator
label_desc: Use image node_id 13 for calling the _HeterogenousMetaEstimator obj's underlying member method code with example usage: obj._get_estimator_list(estimators) and returns: unknown