code: def spiral_layout(
      G, scale=1, center=None, dim=2, resolution=0.35, equidistant=False
  ):
      """Position nodes in a spiral layout.
  
      Parameters
      ----------
      G : NetworkX graph or list of nodes
          A position will be assigned to every node in G.
      scale : number (default: 1)
          Scale factor for positions.
      center : array-like or None
          Coordinate pair around which to center the layout.
      dim : int, default=2
          Dimension of layout, currently only dim=2 is supported.
          Other dimension values result in a ValueError.
      resolution : float, default=0.35
          The compactness of the spiral layout returned.
          Lower values result in more compressed spiral layouts.
      equidistant : bool, default=False
          If True, nodes will be positioned equidistant from each other
          by decreasing angle further from center.
          If False, nodes will be positioned at equal angles
          from each other by increasing separation further from center.
  
      Returns
      -------
      pos : dict
          A dictionary of positions keyed by node
  
      Raises
      ------
      ValueError
          If dim != 2
  
      Examples
      --------
      >>> G = nx.path_graph(4)
      >>> pos = nx.spiral_layout(G)
      >>> nx.draw(G, pos=pos)
  
      Notes
      -----
      This algorithm currently only works in two dimensions.
  
      """
      import numpy as np
  
      if dim != 2:
          raise ValueError("can only handle 2 dimensions")
  
      G, center = _process_params(G, center, dim)
  
      if len(G) == 0:
          return {}
      if len(G) == 1:
          return {nx.utils.arbitrary_element(G): center}
  
      pos = []
      if equidistant:
          chord = 1
          step = 0.5
          theta = resolution
          theta += chord / (step * theta)
          for _ in range(len(G)):
              r = step * theta
              theta += chord / r
              pos.append([np.cos(theta) * r, np.sin(theta) * r])
  
      else:
          dist = np.arange(len(G), dtype=float)
          angle = resolution * dist
          pos = np.transpose(
              dist * np.array([np.cos(angle), np.sin(angle)])
          )
  
      pos = rescale_layout(np.array(pos), scale=scale) + center
  
      pos = dict(zip(G, pos))
  
      return pos
  
imports: import networkx
  from networkx.utils import np_random_state
label_desc: Use image node_id 16 for calling a global function with example usage: spiral_layout(G, scale, center, dim, resolution, equidistant) and returns: pos, dict, dict