code: def test_np_scorer():
      doc1 = (
          "It is based on representing any term of a training corpus using word embeddings in "
          "order to estimate the similarity between the seed terms and any candidate term."
          " Noun phrases provide good approximation for candidate terms and are extracted in"
          "our system using a noun phrase chunker. At expansion time, given a seed of terms, "
          "the most similar terms are returned."
      )
      doc2 = (
          "The expand server gets requests containing seed terms, and expands them based on"
          " the given word embedding model. You can use the model you trained yourself in the "
          "previous step, or to provide a pre-trained model you own. Important note: "
          "default server will listen on localhost:1234. If you set the host/port you "
          "should also set it in the ui/settings.py file."
      )
      docs = [doc1, doc2]
      np = NPScorer()
      phrase_scores = np.score_documents(
          docs, limit=3, return_all=True, min_tf=1
      )
      assert len(phrase_scores) == 3
      phrase_scores = np.score_documents(
          docs, return_all=True, min_tf=100
      )
      assert len(phrase_scores) == 0
  
imports: from nlp_architect.utils.io import load_files_from_path
  from .np_scorer import NPScorer
label_desc: Use image node_id 1 for calling a global function with example usage: test_np_scorer() without return types