docstr: Returns the list of all models involved in the current model.
  For single model, it returns the list with the name of the model.
  For ensemble model, it returns the list with the name of the ensemble and all internal models
  (used to build ensemble).
  For single model but trained on stacked data, it returns the list with the name of the model
  (names of models used in stacking are not included).
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code: def involved_model_names(self):
      """Returns the list of all models involved in the current model.
      For single model, it returns the list with the name of the model.
      For ensemble model, it returns the list with the name of the ensemble and all internal models
      (used to build ensemble).
      For single model but trained on stacked data, it returns the list with the name of the model
      (names of models used in stacking are not included)."""
      if self.selected_models is None or not self.selected_models:
          return [self._name]
      l = []
      for m in self.selected_models:
          l += m["model"].involved_model_names()
      return [self._name] + l
  
imports: import copy
  import json
  import logging
  import operator
  import os
  import time
  import uuid
  import numpy
  import pandas
  from supervised.algorithms.algorithm import BaseAlgorithm
  from supervised.algorithms.registry import BINARY_CLASSIFICATION, MULTICLASS_CLASSIFICATION, REGRESSION
  from supervised.exceptions import NotTrainedException
  from supervised.model_framework import ModelFramework
  from supervised.utils.additional_metrics import AdditionalMetrics
  from supervised.utils.config import LOG_LEVEL
  from supervised.utils.metric import Metric
  from supervised.utils.jsonencoder import MLJSONEncoder
  import matplotlib.pyplot
  from tabulate import tabulate
  from supervised.utils.learning_curves import LearningCurves
label_desc: Use image node_id 8 for calling the Ensemble obj's underlying member method code with example usage: obj.involved_model_names() and returns: unknown, list