code: def spectral_layout(G, weight="weight", scale=1, center=None, dim=2):
      """Position nodes using the eigenvectors of the graph Laplacian.
  
      Using the unnormalized Laplacian, the layout shows possible clusters of
      nodes which are an approximation of the ratio cut. If dim is the number of
      dimensions then the positions are the entries of the dim eigenvectors
      corresponding to the ascending eigenvalues starting from the second one.
  
      Parameters
      ----------
      G : NetworkX graph or list of nodes
          A position will be assigned to every node in G.
  
      weight : string or None   optional (default='weight')
          The edge attribute that holds the numerical value used for
          the edge weight.  If None, then all edge weights are 1.
  
      scale : number (default: 1)
          Scale factor for positions.
  
      center : array-like or None
          Coordinate pair around which to center the layout.
  
      dim : int
          Dimension of layout.
  
      Returns
      -------
      pos : dict
          A dictionary of positions keyed by node
  
      Examples
      --------
      >>> G = nx.path_graph(4)
      >>> pos = nx.spectral_layout(G)
  
      Notes
      -----
      Directed graphs will be considered as undirected graphs when
      positioning the nodes.
  
      For larger graphs (>500 nodes) this will use the SciPy sparse
      eigenvalue solver (ARPACK).
      """
      # handle some special cases that break the eigensolvers
      import numpy as np
  
      G, center = _process_params(G, center, dim)
  
      if len(G) <= 2:
          if len(G) == 0:
              pos = np.array([])
          elif len(G) == 1:
              pos = np.array([center])
          else:
              pos = np.array([np.zeros(dim), np.array(center) * 2.0])
          return dict(zip(G, pos))
      try:
          # Sparse matrix
          if len(G) < 500:  # dense solver is faster for small graphs
              raise ValueError
          A = nx.to_scipy_sparse_array(G, weight=weight, dtype="d")
          # Symmetrize directed graphs
          if G.is_directed():
              A = A + np.transpose(A)
          pos = _sparse_spectral(A, dim)
      except (ImportError, ValueError):
          # Dense matrix
          A = nx.to_numpy_array(G, weight=weight)
          # Symmetrize directed graphs
          if G.is_directed():
              A += A.T
          pos = _spectral(A, dim)
  
      pos = rescale_layout(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 12 for calling a global function with example usage: spectral_layout(G, weight, scale, center, dim) and returns: pos, dict