code: def test_multilabel_classification_predict_on_Predictor_instance():
      np.random.seed(0)
  
      (
          df_twitter_train,
          df_twitter_test,
      ) = (
          utils.get_twitter_sentiment_multilabel_classification_dataset()
      )
      # Note that this does not take 'text' into account, intentionally (as that takes a while longer to train)
      ml_predictor = utils.train_basic_multilabel_classifier(
          df_twitter_train
      )
  
      predictions = ml_predictor.predict(df_twitter_test)
      test_score = accuracy_score(
          predictions, df_twitter_test.airline_sentiment
      )
      # Make sure our score is good, but not unreasonably good
      print("test_score")
      print(test_score)
      assert 0.72 < test_score < 0.77
  
imports: import datetime
  import os
  import random
  import sys
  from auto_ml import Predictor
  from auto_ml.utils_models import load_ml_model
  import dill
  import numpy
  from nose.tools import assert_equal, assert_not_equal, with_setup
  from sklearn.metrics import accuracy_score
  from sklearn.model_selection import train_test_split
  import utils_testing
label_desc: Use image node_id 7 for calling a global function with example usage: test_multilabel_classification_predict_on_Predictor_instance() without return types