code: def build_graph(path, term_depth=1000, skim_depth=10,
                  d_weights=False, **kwargs):
  
      
  
      # Tokenize text.
      click.echo('\nTokenizing text...')
      t = Text.from_file(path)
      click.echo('Extracted %d tokens' % len(t.tokens))
  
      m = Matrix()
  
      # Index the term matrix.
      click.echo('\nIndexing terms:')
      m.index(t, t.most_frequent_terms(term_depth), **kwargs)
  
      g = Skimmer()
  
      # Construct the network.
      click.echo('\nGenerating graph:')
      g.build(t, m, skim_depth, d_weights)
  
      return g
docs: Tokenize a text, index a term matrix, and build out a graph.
  
      Args:
          path (str): The file path.
          term_depth (int): Consider the N most frequent terms.
          skim_depth (int): Connect each word to the N closest siblings.
          d_weights (bool): If true, give "close" nodes low weights.
  
      Returns:
          Skimmer: The indexed graph.