code: def test_tweet_emotion():
      # Arrange
      train, test = tweet_emotion.load_data(
          data_format="Dataframe", as_train_test=True
      )
      full = tweet_emotion.load_data(
          data_format="Dataframe", as_train_test=False
      )
      full_ds = tweet_emotion.load_data(
          data_format="TextData", as_train_test=False
      )
      preds = tweet_emotion.load_precalculated_predictions(
          pred_format="predictions", as_train_test=False
      )
      probas = tweet_emotion.load_precalculated_predictions(
          pred_format="probabilities", as_train_test=False
      )
      properties = tweet_emotion.load_properties(as_train_test=False)
      train_props, test_props = tweet_emotion.load_properties(
          as_train_test=True
      )
      embeddings = tweet_emotion.load_embeddings(as_train_test=False)
      train_embeddings, test_embeddings = tweet_emotion.load_embeddings(
          as_train_test=True
      )
  
      # Act & Assert
      assert_that(len(train) + len(test), equal_to(len(full)))
      assert_that(train.columns, contains_exactly(*test.columns))
      assert_that(train.columns, contains_exactly(*full.columns))
  
      assert_that(len(full_ds.text), equal_to(len(full)))
      assert_that(len(full.text), equal_to(len(preds)))
      assert_that(
          [
              tweet_emotion._LABEL_MAP[x]
              for x in np.argmax(probas, axis=1)
          ],  # pylint: disable=protected-access
          contains_exactly(*preds),
      )
  
      assert_that(len(properties), equal_to(len(full)))
      assert_that(
          len(train_props) + len(test_props), equal_to(len(full))
      )
      assert_that(len(train_props), equal_to(len(train)))
  
      assert_that(len(embeddings), equal_to(len(full)))
      assert_that(
          len(train_embeddings) + len(test_embeddings),
          equal_to(len(full)),
      )
      assert_that(len(train_embeddings), equal_to(len(train)))
      assert_that(embeddings.shape, contains_exactly(4653, 1536))
      assert_that(train_embeddings.shape, contains_exactly(2675, 1536))
      assert_that(test_embeddings.shape, contains_exactly(1978, 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 1 for calling a global function with example usage: test_tweet_emotion() without return types