code: def test_train_language_model(results_base_path, resources_path):
      # get default dictionary
      dictionary: Dictionary = Dictionary.load("chars")
  
      # init forward LM with 128 hidden states and 1 layer
      language_model: LanguageModel = LanguageModel(
          dictionary, is_forward_lm=True, hidden_size=128, nlayers=1
      )
  
      # get the example corpus and process at character level in forward direction
      corpus: TextCorpus = TextCorpus(
          resources_path / "corpora/lorem_ipsum",
          dictionary,
          language_model.is_forward_lm,
          character_level=True,
      )
  
      # train the language model
      trainer: LanguageModelTrainer = LanguageModelTrainer(
          language_model, corpus, test_mode=True
      )
  
      trainer.train(
          results_base_path,
          sequence_length=10,
          mini_batch_size=10,
          max_epochs=2,
      )
  
      # use the character LM as embeddings to embed the example sentence 'I love Berlin'
      char_lm_embeddings: TokenEmbeddings = FlairEmbeddings(
          str(results_base_path / "best-lm.pt")
      )
      sentence = Sentence("I love Berlin")
      char_lm_embeddings.embed(sentence)
  
      text, likelihood = language_model.generate_text(
          number_of_characters=100
      )
      assert text is not None
      assert len(text) >= 100
  
      # clean up results directory
      del trainer, language_model, corpus, char_lm_embeddings
  
file_path: flair/tests/test_language_model.py
imports: import pytest
  from flair.data import Dictionary, Sentence
  from flair.embeddings import FlairEmbeddings, TokenEmbeddings
  from flair.models import LanguageModel
  from flair.trainers.language_model_trainer import LanguageModelTrainer, TextCorpus
label_desc: Use image node_id 1 for calling a global function with example usage: test_train_language_model(results_base_path, resources_path) without return types