code: def laplacian_spectrum(G, weight="weight"):
      """Returns eigenvalues of the Laplacian of G
  
      Parameters
      ----------
      G : graph
         A NetworkX graph
  
      weight : string or None, optional (default='weight')
         The edge data key used to compute each value in the matrix.
         If None, then each edge has weight 1.
  
      Returns
      -------
      evals : NumPy array
        Eigenvalues
  
      Notes
      -----
      For MultiGraph/MultiDiGraph, the edges weights are summed.
      See :func:`~networkx.convert_matrix.to_numpy_array` for other options.
  
      See Also
      --------
      laplacian_matrix
  
      Examples
      --------
      The multiplicity of 0 as an eigenvalue of the laplacian matrix is equal
      to the number of connected components of G.
  
      >>> G = nx.Graph()  # Create a graph with 5 nodes and 3 connected components
      >>> G.add_nodes_from(range(5))
      >>> G.add_edges_from([(0, 2), (3, 4)])
      >>> nx.laplacian_spectrum(G)
      array([0., 0., 0., 2., 2.])
  
      """
      import scipy as sp
  
      return sp.linalg.eigvalsh(
          nx.laplacian_matrix(G, weight=weight).todense()
      )
  
imports: import networkx
label_desc: Use image node_id 1 for calling a global function with example usage: laplacian_spectrum(G, weight) and returns: sp