code: def setup_mnli_test_train_split():
      train_df = get_mnli_test_dataset("train")
      X_str = train_df["sentence1"]
      ylabels = train_df["genre"]
      X_train, X_test, y_train, y_test = train_test_split(
          X_str, ylabels, train_size=0.8, test_size=0.2
      )
      return X_train, X_test, y_train, y_test
  
file_path: interpret-text/test/unit/utils_test.py
imports: from interpret_text.experimental.common.preprocessor.bert_preprocessor import BertPreprocessor
  from interpret_text.experimental.common.preprocessor.glove_preprocessor import GlovePreprocessor
  from interpret_text.experimental.common.utils_bert import BERTSequenceClassifier
  from interpret_text.experimental.introspective_rationale import IntrospectiveRationaleExplainer
  from notebooks.test_utils.utils_mnli import load_mnli_pandas_df
  from notebooks.test_utils.utils_sst2 import load_sst2_pandas_df
  from sklearn.model_selection import train_test_split
label_desc: Use image node_id 6 for calling a global function with example usage: setup_mnli_test_train_split() and returns: X_train,  X_test,  y_train,  y_test