class_docstr: MultiplexClassifier for selecting among different models.
  
  MultiplexClassifier facilitates a framework for performing
  model selection process over different model classes.
  It should be used in conjunction with GridSearchCV to get full utilization.
  It can be used with univariate and multivariate classifiers,
  single-output and multi-output classifiers.
  
  MultiplexClassifier is specified with a (named) list of classifiers
  and a selected_classifier hyper-parameter, which is one of the classifier names.
  The MultiplexClassifier then behaves precisely as the classifier with
  name selected_classifier, ignoring functionality in the other classifiers.
  
  When used with GridSearchCV, MultiplexClassifier
  provides an ability to tune across multiple estimators, i.e., to perform AutoML,
  by tuning the selected_classifier hyper-parameter. This combination will then
  select one of the passed classifiers via the tuning algorithm.
  
  Parameters
  ----------
  classifiers : list of sktime classifiers, or
      list of tuples (str, estimator) of sktime classifiers
      MultiplexClassifier can switch ("multiplex") between these classifiers.
      These are "blueprint" classifiers, states do not change when `fit` is called.
  selected_classifier: str or None, optional, Default=None.
      If str, must be one of the classifier names.
          If no names are provided, must coincide with auto-generated name strings.
          To inspect auto-generated name strings, call get_params.
      If None, behaves as if the first classifier in the list is selected.
      Selects the classifier as which MultiplexClassifier behaves.
  
  Attributes
  ----------
  classifier_ : sktime classifier
      clone of the selected classifier used for fitting and classification.
  _classifiers : list of (str, classifier) tuples
      str are identical to those passed, if passed strings are unique
      otherwise unique strings are generated from class name; if not unique,
      the string `_[i]` is appended where `[i]` is count of occurrence up until then
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code: def _classifiers(self, value):
      self.classifiers = value
  
file_path: sktime/sktime/classification/compose/_multiplexer.py
imports: from sktime.base import _HeterogenousMetaEstimator
  from sktime.classification._delegate import _DelegatedClassifier
  from sktime.classification.base import BaseClassifier
  from sktime.datatypes import MTYPE_LIST_PANEL, MTYPE_LIST_TABLE
label_desc: Use image node_id 3 for calling the MultiplexClassifier obj's underlying member method code with example usage: obj._classifiers(value) without return types