code: def test_init_tars_and_switch(tasks_base_path):
      # test corpus
      corpus = ClassificationCorpus(tasks_base_path / "imdb")
  
      # create a TARS classifier
      tars = TARSClassifier(
          task_name="2_CLASS",
          label_dictionary=corpus.make_label_dictionary(
              label_type="class"
          ),
          label_type="class",
      )
  
      # check if right number of classes
      assert len(tars.get_current_label_dictionary()) == 2
  
      # switch to task with only one label
      tars.add_and_switch_to_new_task(
          "1_CLASS", "one class", "testlabel"
      )
  
      # check if right number of classes
      assert len(tars.get_current_label_dictionary()) == 1
  
      # switch to task with three labels provided as list
      tars.add_and_switch_to_new_task(
          "3_CLASS", ["list 1", "list 2", "list 3"], "testlabel"
      )
  
      # check if right number of classes
      assert len(tars.get_current_label_dictionary()) == 3
  
      # switch to task with four labels provided as set
      tars.add_and_switch_to_new_task(
          "4_CLASS", {"set 1", "set 2", "set 3", "set 4"}, "testlabel"
      )
  
      # check if right number of classes
      assert len(tars.get_current_label_dictionary()) == 4
  
      # switch to task with two labels provided as Dictionary
      tars.add_and_switch_to_new_task(
          "2_CLASS_AGAIN",
          corpus.make_label_dictionary(label_type="class"),
          "testlabel",
      )
  
      # check if right number of classes
      assert len(tars.get_current_label_dictionary()) == 2
  
imports: from flair.data import Sentence
  from flair.datasets import ClassificationCorpus
  from flair.models import TARSClassifier
  from flair.trainers import ModelTrainer
label_desc: Use image node_id 1 for calling a global function with example usage: test_init_tars_and_switch(tasks_base_path) without return types