code: def test_train_tars(tasks_base_path, results_base_path):
      # test corpus
      corpus = ClassificationCorpus(tasks_base_path / "imdb_underscore")
  
      # create a TARS classifier
      tars = TARSClassifier(
          embeddings="sshleifer/tiny-distilroberta-base"
      )
  
      # switch to a new task (TARS can do multiple tasks so you must define one)
      tars.add_and_switch_to_new_task(
          task_name="question 2_CLASS",
          label_dictionary=corpus.make_label_dictionary(
              label_type="class"
          ),
          label_type="class",
      )
  
      # initialize the text classifier trainer
      trainer = ModelTrainer(tars, corpus)
  
      # start the training
      trainer.train(
          base_path=results_base_path,
          learning_rate=0.02,
          mini_batch_size=1,
          max_epochs=1,
      )
  
      sentence = Sentence("This is great!")
      tars.predict(sentence)
  
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 2 for calling a global function with example usage: test_train_tars(tasks_base_path, results_base_path) without return types