code: def test_text_explainer_rbf_sigma():
      text = "foo bar baz egg spam"
      predict_proba = substring_presence_predict_proba("bar")
  
      te1 = TextExplainer().fit(text, predict_proba)
      te2 = TextExplainer(rbf_sigma=0.1).fit(text, predict_proba)
      te3 = TextExplainer(rbf_sigma=1.0).fit(text, predict_proba)
  
      assert te1.similarity_.sum() < te3.similarity_.sum()
      assert te1.similarity_.sum() > te2.similarity_.sum()
  
file_path: eli5/tests/test_lime.py
imports: from __future__ import absolute_import
  import numpy
  from sklearn.feature_extraction.text import HashingVectorizer, CountVectorizer
  from sklearn.naive_bayes import MultinomialNB
  from sklearn.tree import DecisionTreeClassifier
  from sklearn.pipeline import make_pipeline
  import pytest
  from eli5.lime import TextExplainer
  from eli5.formatters import format_as_text
  from .utils import format_as_all, check_targets_scores
label_desc: Use image node_id 8 for calling a global function with example usage: test_text_explainer_rbf_sigma() without return types