class_docstr: Handles parameter management for estimators composed of named estimators.
  
  Partly adapted from sklearn utils.metaestimator.py.
docstr: Check whether obj is a tuple of type (str, cls_type).
  
  Parameters
  ----------
  cls_type : class or tuple of class, optional. Default = BaseEstimator.
      class(es) that all estimators are checked to be an instance of
  
  Returns
  -------
  bool : True if obj is (str, cls_type) tuple, False otherwise
docstr_tok: ["Check","whether","obj","is","a","tuple","of","type","(","str",",","cls_type",")",".","Parameters","--","--","--","--","--","cls_type",":","class","or","tuple","of","class",",","optional",".","Default","=","BaseEstimator",".","class","(","es",")","that","all","estimators","are","checked","to","be","an","instance","of","Returns","--","--","--","-","bool",":","True","if","obj","is","(","str",",","cls_type",")","tuple",",","False","otherwise"]
code: def _is_name_and_est(obj, cls_type=None):
      """Check whether obj is a tuple of type (str, cls_type).
  
      Parameters
      ----------
      cls_type : class or tuple of class, optional. Default = BaseEstimator.
          class(es) that all estimators are checked to be an instance of
  
      Returns
      -------
      bool : True if obj is (str, cls_type) tuple, False otherwise
      """
      if cls_type is None:
          cls_type = BaseEstimator
      if not isinstance(obj, tuple) or len(obj) != 2:
          return False
      if not isinstance(obj[0], str) or not isinstance(
          obj[1], cls_type
      ):
          return False
      return True
  
imports: from inspect import isclass
  import numpy
  import pandas
  from sktime.base import BaseEstimator
label_desc: Use image node_id 10 for calling the _HeterogenousMetaEstimator obj's underlying member method code with example usage: obj._is_name_and_est(obj, cls_type) and returns: True, False, False