code: def test_train_load_use_classifier(
      results_base_path, tasks_base_path
  ):
      # --- Embeddings that are shared by both models --- #
      shared_embedding = TransformerDocumentEmbeddings(
          "distilbert-base-uncased", fine_tune=True
      )
  
      # --- Task 1: Sentiment Analysis (5-class) --- #
      flair.set_seed(123)
  
      # Define corpus and model
      corpus_1 = SENTEVAL_SST_GRANULAR().downsample(0.01)
  
      model_1 = TextClassifier(
          shared_embedding,
          label_dictionary=corpus_1.make_label_dictionary(
              "class", add_unk=False
          ),
          label_type="class",
      )
  
      # -- Task 2: Binary Sentiment Analysis on Customer Reviews -- #
      flair.set_seed(123)
  
      # Define corpus and model
      corpus_2 = SENTEVAL_CR().downsample(0.01)
  
      model_2 = TextClassifier(
          shared_embedding,
          label_dictionary=corpus_2.make_label_dictionary(
              "sentiment", add_unk=False
          ),
          label_type="sentiment",
          inverse_model=True,
      )
  
      # -- Define mapping (which tagger should train on which model) -- #
      multitask_model, multicorpus = make_multitask_model_and_corpus(
          [
              (model_1, corpus_1),
              (model_2, corpus_2),
          ]
      )
  
      # -- Create model trainer and train -- #
      trainer = ModelTrainer(multitask_model, multicorpus)
  
      trainer.fine_tune(results_base_path, max_epochs=1)
  
      del trainer, multitask_model, corpus_1, corpus_2
      loaded_model = MultitaskModel.load(
          results_base_path / "final-model.pt"
      )
  
      sentence = Sentence("I love Berlin")
      sentence_empty = Sentence("       ")
  
      loaded_model.predict(sentence)
      loaded_model.predict([sentence, sentence_empty])
      loaded_model.predict([sentence_empty])
  
      for label in sentence.labels:
          assert label.value is not None
          assert 0.0 <= label.score <= 1.0
          assert isinstance(label.score, float)
      del loaded_model
  
file_path: flair/tests/test_multitask.py
imports: import pytest
  import flair
  from flair.data import Sentence
  from flair.datasets import SENTEVAL_CR, SENTEVAL_SST_GRANULAR
  from flair.embeddings import TransformerDocumentEmbeddings
  from flair.models import MultitaskModel, TextClassifier
  from flair.nn.multitask import make_multitask_model_and_corpus
  from flair.trainers import ModelTrainer
label_desc: Use image node_id 1 for calling a global function with example usage: test_train_load_use_classifier(results_base_path, tasks_base_path) without return types