class_docstr: Handles parameter management for estimators composed of named estimators.
  
  Partly adapted from sklearn utils.metaestimator.py.
docstr: Coerce estimator or (str, estimator) tuple to (str, estimator) tuple.
  
  Parameters
  ----------
  obj : estimator or (str, estimator) tuple
      assumes that this has been checked, no checks are performed
  clone_est : boolean, optional. Default = False.
      Whether to return clone of estimator in obj (True) or a reference (False).
  
  Returns
  -------
  est_tuple : (str, stimator tuple)
      obj if obj was (str, estimator) tuple
      (obj class name, obj) if obj was estimator
docstr_tok: ["Coerce","estimator","or","(","str",",","estimator",")","tuple","to","(","str",",","estimator",")","tuple",".","Parameters","--","--","--","--","--","obj",":","estimator","or","(","str",",","estimator",")","tuple","assumes","that","this","has","been","checked",",","no","checks","are","performed","clone_est",":","boolean",",","optional",".","Default","=","False",".","Whether","to","return","clone","of","estimator","in","obj","(","True",")","or","a","reference","(","False",")",".","Returns","--","--","--","-","est_tuple",":","(","str",",","stimator","tuple",")","obj","if","obj","was","(","str",",","estimator",")","tuple","(","obj","class","name",",","obj",")","if","obj","was","estimator"]
code: def _coerce_estimator_tuple(self, obj, clone_est=False):
      """Coerce estimator or (str, estimator) tuple to (str, estimator) tuple.
  
      Parameters
      ----------
      obj : estimator or (str, estimator) tuple
          assumes that this has been checked, no checks are performed
      clone_est : boolean, optional. Default = False.
          Whether to return clone of estimator in obj (True) or a reference (False).
  
      Returns
      -------
      est_tuple : (str, stimator tuple)
          obj if obj was (str, estimator) tuple
          (obj class name, obj) if obj was estimator
      """
      if isinstance(obj, tuple):
          est = obj[1]
          name = obj[0]
      else:
          est = obj
          name = type(obj).__name__
  
      if clone_est:
          return (name, est.clone())
      else:
          return (name, est)
  
imports: from inspect import isclass
  import numpy
  import pandas
  from sktime.base import BaseEstimator
label_desc: Use image node_id 12 for calling the _HeterogenousMetaEstimator obj's underlying member method code with example usage: obj._coerce_estimator_tuple(obj, clone_est) and returns: name,  est, name,  est