code: def get_mnli_test_dataset(file_split):
      DATA_FOLDER = "./temp"
      df = load_mnli_pandas_df(DATA_FOLDER, file_split)
      df = df[df["gold_label"] == "neutral"]  # get unique sentences
      return df[:50]
  
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 1 for calling a global function with example usage: get_mnli_test_dataset(file_split) and returns: df