code: def get_bert_model():
      NUM_LABELS = 2
      cache_dir = "./temp"
      classifier = BERTSequenceClassifier(
          language="bert-base-uncased",
          num_labels=NUM_LABELS,
          cache_dir=cache_dir,
      )
      return classifier.model
  
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 3 for calling a global function with example usage: get_bert_model() and returns: classifier