code: def test_asymmetric_laplacian(
      use_out_degree, normed, copy, dtype, arr_type
  ):
      # adjacency matrix
      A = [[0, 1, 0], [4, 2, 0], [0, 0, 0]]
      A = arr_type(np.array(A), dtype=dtype)
      A_copy = A.copy()
  
      if not normed and use_out_degree:
          # Laplacian matrix using out-degree
          L = [[1, -1, 0], [-4, 4, 0], [0, 0, 0]]
          d = [1, 4, 0]
  
      if normed and use_out_degree:
          # normalized Laplacian matrix using out-degree
          L = [[1, -0.5, 0], [-2, 1, 0], [0, 0, 0]]
          d = [1, 2, 1]
  
      if not normed and not use_out_degree:
          # Laplacian matrix using in-degree
          L = [[4, -1, 0], [-4, 1, 0], [0, 0, 0]]
          d = [4, 1, 0]
  
      if normed and not use_out_degree:
          # normalized Laplacian matrix using in-degree
          L = [[1, -0.5, 0], [-2, 1, 0], [0, 0, 0]]
          d = [2, 1, 1]
  
      _check_laplacian_dtype_none(
          A,
          L,
          d,
          normed=normed,
          use_out_degree=use_out_degree,
          copy=copy,
          dtype=dtype,
          arr_type=arr_type,
      )
  
      _check_laplacian_dtype(
          A_copy,
          L,
          d,
          normed=normed,
          use_out_degree=use_out_degree,
          copy=copy,
          dtype=dtype,
          arr_type=arr_type,
      )
  
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 9 for calling a global function with example usage: test_asymmetric_laplacian(use_out_degree, normed, copy, dtype, arr_type) without return types