code: def _laplacian_dense(graph, normed=False, axis=0):
      m = np.array(graph)
      np.fill_diagonal(m, 0)
      w = m.sum(axis=axis)
      if normed:
          isolated_node_mask = w == 0
          w = np.where(isolated_node_mask, 1, np.sqrt(w))
          m /= w
          m /= w[:, np.newaxis]
          m *= -1
          _setdiag_dense(m, 1 - isolated_node_mask)
      else:
          m *= -1
          _setdiag_dense(m, w)
      return m, w
  
imports: from __future__ import division, print_function, absolute_import
  import numpy
  from scipy.sparse import isspmatrix
label_desc: Use image node_id 4 for calling a global function with example usage: _laplacian_dense(graph, normed, axis) and returns: m,  w