code: def arf_layout(
      G,
      pos=None,
      scaling=1,
      a=1.1,
      etol=1e-6,
      dt=1e-3,
      max_iter=1000,
  ):
      """Arf layout for networkx
  
      The attractive and repulsive forces (arf) layout [1]
      improves the spring layout in three ways. First, it
      prevents congestion of highly connected nodes due to
      strong forcing between nodes. Second, it utilizes the
      layout space more effectively by preventing large gaps
      that spring layout tends to create. Lastly, the arf
      layout represents symmetries in the layout better than
      the default spring layout.
  
      Parameters
      ----------
      G : nx.Graph or nx.DiGraph
          Networkx graph.
      pos : dict
          Initial  position of  the nodes.  If set  to None  a
          random layout will be used.
      scaling : float
          Scales the radius of the circular layout space.
      a : float
          Strength of springs between connected nodes. Should be larger than 1. The greater a, the clearer the separation ofunconnected sub clusters.
      etol : float
          Gradient sum of spring forces must be larger than `etol` before successful termination.
      dt : float
          Time step for force differential equation simulations.
      max_iter : int
          Max iterations before termination of the algorithm.
  
      References
      .. [1] "Self-Organization Applied to Dynamic Network Layout", M. Geipel,
              International Journal of Modern Physics C, 2007, Vol 18, No 10, pp. 1537-1549.
              https://doi.org/10.1142/S0129183107011558 https://arxiv.org/abs/0704.1748
  
      Returns
      -------
      pos : dict
          A dictionary of positions keyed by node.
  
      Examples
      --------
      >>> G = nx.grid_graph((5, 5))
      >>> pos = nx.arf_layout(G)
  
      """
      import warnings
  
      import numpy as np
  
      if a <= 1:
          msg = "The parameter a should be larger than 1"
          raise ValueError(msg)
  
      pos_tmp = nx.random_layout(G)
      if pos is None:
          pos = pos_tmp
      else:
          for node in G.nodes():
              if node not in pos:
                  pos[node] = pos_tmp[node].copy()
  
      # Initialize spring constant matrix
      N = len(G)
      # No nodes no computation
      if N == 0:
          return pos
  
      # init force of springs
      K = np.ones((N, N)) - np.eye(N)
      node_order = {node: i for i, node in enumerate(G)}
      for x, y in G.edges():
          if x != y:
              idx, jdx = (node_order[i] for i in (x, y))
              K[idx, jdx] = a
  
      # vectorize values
      p = np.asarray(list(pos.values()))
  
      # equation 10 in [1]
      rho = scaling * np.sqrt(N)
  
      # looping variables
      error = etol + 1
      n_iter = 0
      while error > etol:
          diff = p[:, np.newaxis] - p[np.newaxis]
          A = np.linalg.norm(diff, axis=-1)[..., np.newaxis]
          # attraction_force - repulsions force
          # suppress nans due to division; caused by diagonal set to zero.
          # Does not affect the computation due to nansum
          with warnings.catch_warnings():
              warnings.simplefilter("ignore")
              change = K[..., np.newaxis] * diff - rho / A * diff
          change = np.nansum(change, axis=0)
          p += change * dt
  
          error = np.linalg.norm(change, axis=-1).sum()
          if n_iter > max_iter:
              break
          n_iter += 1
      return dict(zip(G.nodes(), p))
  
imports: import networkx
  from networkx.utils import np_random_state
label_desc: Use image node_id 18 for calling a global function with example usage: arf_layout(G, pos, scaling, a, etol, dt, max_iter) and returns: dict, pos