code: def test_no_weighted_spans():
      doc = "I see: a leaning lemon tree"
      vec = CountVectorizer(analyzer="char", ngram_range=(3, 4))
      vec.fit([doc])
      w_spans = get_weighted_spans(
          doc, vec, FeatureWeights(pos=[], neg=[])
      )
      assert w_spans == WeightedSpans(
          [
              DocWeightedSpans(
                  document="i see: a leaning lemon tree",
                  spans=[],
                  preserve_density=True,
              )
          ],
          other=FeatureWeights(pos=[], neg=[]),
      )
  
file_path: eli5/tests/test_sklearn_text.py
imports: from sklearn.feature_extraction.text import CountVectorizer
  from sklearn.pipeline import FeatureUnion
  from eli5.base import DocWeightedSpans, WeightedSpans, FeatureWeights, FeatureWeight
  from eli5.formatters import FormattedFeatureName
  from eli5.sklearn.text import get_weighted_spans
label_desc: Use image node_id 5 for calling a global function with example usage: test_no_weighted_spans() without return types