code: def test_if_loaded_embeddings_have_all_attributes(tasks_base_path):
      # dummy model with embeddings
      embeddings = TransformerDocumentEmbeddings(
          "distilbert-base-uncased",
          use_context=True,
          use_context_separator=False,
      )
  
      model = TextClassifier(
          label_type="ner",
          label_dictionary=Dictionary(),
          embeddings=embeddings,
      )
  
      # save the dummy and load it again
      model.save(tasks_base_path / "single.pt")
      loaded_single_task = Classifier.load(
          tasks_base_path / "single.pt"
      )
  
      # check that context_length and use_context_separator is the same for both
      assert (
          model.embeddings.context_length
          == loaded_single_task.embeddings.context_length
      )
      assert (
          model.embeddings.use_context_separator
          == loaded_single_task.embeddings.use_context_separator
      )
  
file_path: flair/tests/embeddings/test_transformer_document_embeddings.py
imports: from flair.data import Dictionary
  from flair.embeddings import TransformerDocumentEmbeddings
  from flair.models import TextClassifier
  from flair.nn import Classifier
  from tests.embedding_test_utils import BaseEmbeddingsTest
label_desc: Use image node_id 1 for calling a global function with example usage: test_if_loaded_embeddings_have_all_attributes(tasks_base_path) without return types