code: def test_scierc_ner_tokens():
      train_dict, test_dict = scierc_ner.load_data(data_format="Dict")
      train_ds, test_ds = scierc_ner.load_data(data_format="TextData")
      (
          train_preds,
          test_preds,
      ) = scierc_ner.load_precalculated_predictions()
      train_props, test_props = scierc_ner.load_properties()
      train_embeddings, test_embeddings = scierc_ner.load_embeddings()
  
      assert_that(
          len(train_dict["text"]), equal_to(len(train_dict["label"]))
      )
      assert_that(
          len(test_dict["text"]), equal_to(len(test_dict["label"]))
      )
  
      assert_that(len(train_ds.text), equal_to(len(train_preds)))
      assert_that(len(test_ds.text), equal_to(len(test_preds)))
  
      assert_that(
          train_props.columns, contains_exactly(*test_props.columns)
      )
  
      assert_that(train_embeddings.shape, contains_exactly(350, 1536))
      assert_that(test_embeddings.shape, contains_exactly(100, 1536))
  
imports: import numpy
  from hamcrest import assert_that, contains_exactly, equal_to
  from deepchecks.nlp.datasets.classification import just_dance_comment_analysis, tweet_emotion
  from deepchecks.nlp.datasets.token_classification import scierc_ner
label_desc: Use image node_id 3 for calling a global function with example usage: test_scierc_ner_tokens() without return types