code: def _laplacian_dense(
      graph, normed, axis, copy, form, dtype, symmetrized
  ):
      if form != "array":
          raise ValueError(f'{form!r} must be "array"')
  
      if dtype is None:
          dtype = graph.dtype
  
      if copy:
          m = np.array(graph)
      else:
          m = np.asarray(graph)
  
      if dtype is None:
          dtype = m.dtype
  
      if symmetrized:
          m += m.T.conj()
      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.astype(dtype, copy=False), w.astype(dtype, copy=False)
  
file_path: scipy/scipy/sparse/csgraph/_laplacian.py
imports: import numpy
  from scipy.sparse import issparse
  from scipy.sparse.linalg import LinearOperator
label_desc: Use image node_id 11 for calling a global function with example usage: _laplacian_dense(graph, normed, axis, copy, form, dtype, symmetrized) and returns: m,  w