code: def test_bert_explain_local(self):
      train_data = get_ssts_dataset("train")
      test_data = get_ssts_dataset("test")
      X_train = train_data[TEXT_COL]
      X_test = test_data[TEXT_COL]
      preprocessor = BertPreprocessor()
  
      df_train = pd.concat(
          [train_data[LABEL_COL], preprocessor.preprocess(X_train)],
          axis=1,
      )
      df_test = pd.concat(
          [test_data[LABEL_COL], preprocessor.preprocess(X_test)],
          axis=1,
      )
      model_config = BERT_MODEL_CONFIG
      explainer = IntrospectiveRationaleExplainer(
          classifier_type=CLASSIFIER_TYPE_BERT, cuda=CUDA
      )
      explainer.build_model_config(model_config)
      explainer.set_preprocessor(preprocessor)
      explainer.load()
      explainer.fit(df_train, df_test)
  
      local_explanation = explainer.explain_local(SENTENCE)
      # BERT adds [CLS] at the beginning of a sentence and [SEP] at the end of each sentence  but we remove them.
      assert len(local_explanation.local_importance_values) == len(
          SENTENCE.split()
      )
  
imports: import pandas
  import pytest
  from interpret_text.experimental.common.preprocessor.bert_preprocessor import BertPreprocessor
  from interpret_text.experimental.common.preprocessor.glove_preprocessor import GlovePreprocessor
  from interpret_text.experimental.introspective_rationale.components import ClassifierModule
  from interpret_text.experimental.introspective_rationale import IntrospectiveRationaleExplainer
  from model_config_constants import RNN_MODEL_CONFIG, BERT_MODEL_CONFIG
  from utils_test import get_ssts_dataset, setup_mock_rnn_introspective_rationale_explainer, setup_mock_bert_introspective_rationale_explainer
label_desc: Use image node_id 11 for calling the TestIntrospectiveRationaleExplainer obj's underlying member method code with example usage: obj.test_bert_explain_local() without return types