code: def visibility_graph(series):
      """
      Return a Visibility Graph of an input Time Series.
  
      A visibility graph converts a time series into a graph. The constructed graph
      uses integer nodes to indicate which event in the series the node represents.
      Edges are formed as follows: consider a bar plot of the series and view that
      as a side view of a landscape with a node at the top of each bar. An edge
      means that the nodes can be connected by a straight "line-of-sight" without
      being obscured by any bars between the nodes.
  
      The resulting graph inherits several properties of the series in its structure.
      Thereby, periodic series convert into regular graphs, random series convert
      into random graphs, and fractal series convert into scale-free networks [1]_.
  
      Parameters
      ----------
      series : Sequence[Number]
         A Time Series sequence (iterable and sliceable) of numeric values
         representing times.
  
      Returns
      -------
      NetworkX Graph
          The Visibility Graph of the input series
  
      Examples
      --------
      >>> series_list = [range(10), [2, 1, 3, 2, 1, 3, 2, 1, 3, 2, 1, 3]]
      >>> for s in series_list:
      ...     g = nx.visibility_graph(s)
      ...     print(g)
      Graph with 10 nodes and 9 edges
      Graph with 12 nodes and 18 edges
  
      References
      ----------
      .. [1] Lacasa, Lucas, Bartolo Luque, Fernando Ballesteros, Jordi Luque, and Juan Carlos Nuno.
             "From time series to complex networks: The visibility graph." Proceedings of the
             National Academy of Sciences 105, no. 13 (2008): 4972-4975.
             https://www.pnas.org/doi/10.1073/pnas.0709247105
      """
  
      # Sequential values are always connected
      G = nx.path_graph(len(series))
      nx.set_node_attributes(G, dict(enumerate(series)), "value")
  
      # Check all combinations of nodes n series
      for (n1, t1), (n2, t2) in itertools.combinations(
          enumerate(series), 2
      ):
          # check if any value between obstructs line of sight
          slope = (t2 - t1) / (n2 - n1)
          offset = t2 - slope * n2
  
          obstructed = any(
              t >= slope * n + offset
              for n, t in enumerate(series[n1 + 1 : n2], start=n1 + 1)
          )
  
          if not obstructed:
              G.add_edge(n1, n2)
  
      return G
  
label_desc: Use image node_id 1 for calling a global function with example usage: visibility_graph(series) and returns: G