code: def test_laplacian_symmetrized(arr_type, form):
      # adjacency matrix
      n = 3
      mat = arr_type(np.arange(n * n).reshape(n, n))
      L_in, d_in = csgraph.laplacian(
          mat,
          return_diag=True,
          form=form,
      )
      L_out, d_out = csgraph.laplacian(
          mat,
          return_diag=True,
          use_out_degree=True,
          form=form,
      )
      Ls, ds = csgraph.laplacian(
          mat,
          return_diag=True,
          symmetrized=True,
          form=form,
      )
      Ls_normed, ds_normed = csgraph.laplacian(
          mat,
          return_diag=True,
          symmetrized=True,
          normed=True,
          form=form,
      )
      mat += mat.T
      Lss, dss = csgraph.laplacian(mat, return_diag=True, form=form)
      Lss_normed, dss_normed = csgraph.laplacian(
          mat,
          return_diag=True,
          normed=True,
          form=form,
      )
  
      assert_allclose(ds, d_in + d_out)
      assert_allclose(ds, dss)
      assert_allclose(ds_normed, dss_normed)
  
      d = {}
      for L in [
          "L_in",
          "L_out",
          "Ls",
          "Ls_normed",
          "Lss",
          "Lss_normed",
      ]:
          if form == "array":
              d[L] = eval(L)
          else:
              d[L] = eval(L)(np.eye(n, dtype=mat.dtype))
  
      _assert_allclose_sparse(d["Ls"], d["L_in"] + d["L_out"].T)
      _assert_allclose_sparse(d["Ls"], d["Lss"])
      _assert_allclose_sparse(d["Ls_normed"], d["Lss_normed"])
  
file_path: scipy/scipy/sparse/csgraph/tests/test_graph_laplacian.py
imports: import pytest
  import numpy
  from numpy.testing import assert_allclose
  from pytest import raises
  from scipy import sparse
  from scipy.sparse import csgraph
  from scipy._lib._util import np_long, np_ulong
label_desc: Use image node_id 11 for calling a global function with example usage: test_laplacian_symmetrized(arr_type, form) without return types