code: def interpret_data(X, y, func, class_names):
      explainer = LimeTextExplainer(class_names=class_names)
      times, scores = [], []
      for r_idx in range(10):
          start_time = time.time()
          exp = explainer.explain_instance(
              newsgroups_test.data[r_idx], func, num_features=6
          )
          times.append(time.time() - start_time)
          scores.append(exp.score)
          print("...")
  
      return times, scores
  
file_path: lime/benchmark/text_perf.py
imports: import time
  import sklearn
  import sklearn.ensemble
  import sklearn.metrics
  from sklearn.datasets import fetch_20newsgroups
  from sklearn.pipeline import make_pipeline
  from lime.lime_text import LimeTextExplainer
label_desc: Use image node_id 1 for calling a global function with example usage: interpret_data(X, y, func, class_names) and returns: times,  scores