code: def get_twitter_sentiment_multilabel_classification_dataset():
      file_name = os.path.join("tests", "twitter_sentiment.csv")
  
      try:
          df_twitter = pd.read_csv(
              open(file_name, "rU"), encoding="latin-1", engine="python"
          )
      except Exception as e:
          print("Error")
          print(e)
          dataset_url = "https://raw.githubusercontent.com/ClimbsRocks/sample_datasets/master/twitter_airline_sentiment.csv"
          df_twitter = pd.read_csv(dataset_url, encoding="latin-1")
          # Do not write the index that pandas automatically creates
  
          df_twitter.to_csv(file_name, index=False, encoding="latin-1")
  
      # Grab only 10% of the dataset- runs much faster this way
      df_twitter = df_twitter.sample(frac=0.1)
  
      df_twitter["tweet_created"] = pd.to_datetime(
          df_twitter.tweet_created
      )
  
      df_twitter_train, df_twitter_test = train_test_split(
          df_twitter, test_size=0.33, random_state=42
      )
      return df_twitter_train, df_twitter_test
  
file_path: auto_ml/tests/advanced_tests/utils_testing.py
imports: import sys, os
  import pandas
  from sklearn.datasets import load_boston
  from sklearn.metrics import brier_score_loss, mean_squared_error
  from sklearn.model_selection import train_test_split
  from auto_ml import Predictor
label_desc: Use image node_id 7 for calling a global function with example usage: get_twitter_sentiment_multilabel_classification_dataset() and returns: df_twitter_train,  df_twitter_test