code: def test_train_load_use_model_multi_corpus(
      self,
      results_base_path,
      multi_corpus,
      embeddings,
      example_sentence,
      train_test_sentence,
  ):
      flair.set_seed(123)
      label_dict = multi_corpus.make_label_dictionary(
          label_type=self.train_label_type
      )
  
      model = self.build_model(embeddings, label_dict)
      corpus = self.transform_corpus(model, multi_corpus)
  
      trainer = ModelTrainer(model, corpus)
  
      if self.finetune_instead_of_train:
          trainer.fine_tune(
              results_base_path, shuffle=False, **self.training_args
          )
      else:
          trainer.train(
              results_base_path, shuffle=False, **self.training_args
          )
  
      model.predict(train_test_sentence)
      self.assert_training_example(train_test_sentence)
  
      for label in train_test_sentence.get_labels(
          self.train_label_type
      ):
          assert label.value is not None
          assert 0.0 <= label.score <= 1.0
          assert isinstance(label.score, float)
  
      del trainer, model, corpus
  
      loaded_model = self.model_cls.load(
          results_base_path / "final-model.pt"
      )
  
      loaded_model.predict(example_sentence)
      loaded_model.predict([example_sentence, self.empty_sentence])
      loaded_model.predict([self.empty_sentence])
      del loaded_model
  
imports: from typing import Any, Dict, List, Optional, Type
  import pytest
  import flair
  from flair.data import Dictionary, Sentence
  from flair.embeddings import TransformerEmbeddings
  from flair.models import FewshotClassifier
  from flair.nn import Model
  from flair.trainers import ModelTrainer
label_desc: Use image node_id 16 for calling the BaseModelTest obj's underlying member method code with example usage: obj.test_train_load_use_model_multi_corpus(results_base_path, multi_corpus, embeddings, example_sentence, train_test_sentence) without return types