text: from scipy import sparse as spsp
  import unittest
  import networkx as nx
  import numpy as np
  import dgl
  import dgl.function as fn
  import backend as F
  from dgl.graph_index import from_scipy_sparse_matrix
  import unittest
  
  D = 5
  
  # line graph related
  
  def test_line_graph():
      N = 5
      G = dgl.DGLGraph(nx.star_graph(N))
      G.edata['h'] = F.randn((2 * N, D))
      n_edges = G.number_of_edges()
      L = G.line_graph(shared=True)
      assert L.number_of_nodes() == 2 * N
      L.ndata['h'] = F.randn((2 * N, D))
      # update node features on line graph should reflect to edge features on
      # original graph.
      u = [0, 0, 2, 3]
      v = [1, 2, 0, 0]
      eid = G.edge_ids(u, v)
      L.nodes[eid].data['h'] = F.zeros((4, D))
      assert F.allclose(G.edges[u, v].data['h'], F.zeros((4, D)))
  
      # adding a new node feature on line graph should also reflect to a new
      # edge feature on original graph
      data = F.randn((n_edges, D))
      L.ndata['w'] = data
      assert F.allclose(G.edata['w'], data)
  
  
  def test_no_backtracking():
      N = 5
      G = dgl.DGLGraph(nx.star_graph(N))
      L = G.line_graph(backtracking=False)
      assert L.number_of_nodes() == 2 * N
      for i in range(1, N):
          e1 = G.edge_id(0, i)
          e2 = G.edge_id(i, 0)
          assert not L.has_edge_between(e1, e2)
          assert not L.has_edge_between(e2, e1)
  
  # reverse graph related
  
  
  def test_reverse():
      g = dgl.DGLGraph()
      g.add_nodes(5)
      # The graph need not to be completely connected.
      g.add_edges([0, 1, 2], [1, 2, 1])
      g.ndata['h'] = F.tensor([[0.], [1.], [2.], [3.], [4.]])
      g.edata['h'] = F.tensor([[5.], [6.], [7.]])
      rg = g.reverse()
  
      assert g.is_multigraph == rg.is_multigraph
  
      assert g.number_of_nodes() == rg.number_of_nodes()
      assert g.number_of_edges() == rg.number_of_edges()
      assert F.allclose(F.astype(rg.has_edges_between(
          [1, 2, 1], [0, 1, 2]), F.float32), F.ones((3,)))
      assert g.edge_id(0, 1) == rg.edge_id(1, 0)
      assert g.edge_id(1, 2) == rg.edge_id(2, 1)
      assert g.edge_id(2, 1) == rg.edge_id(1, 2)
  
  
  def test_reverse_shared_frames():
      g = dgl.DGLGraph()
      g.add_nodes(3)
      g.add_edges([0, 1, 2], [1, 2, 1])
      g.ndata['h'] = F.tensor([[0.], [1.], [2.]])
      g.edata['h'] = F.tensor([[3.], [4.], [5.]])
  
      rg = g.reverse(share_ndata=True, share_edata=True)
      assert F.allclose(g.ndata['h'], rg.ndata['h'])
      assert F.allclose(g.edata['h'], rg.edata['h'])
      assert F.allclose(g.edges[[0, 2], [1, 1]].data['h'],
                        rg.edges[[1, 1], [0, 2]].data['h'])
  
      rg.ndata['h'] = rg.ndata['h'] + 1
      assert F.allclose(rg.ndata['h'], g.ndata['h'])
  
      g.edata['h'] = g.edata['h'] - 1
      assert F.allclose(rg.edata['h'], g.edata['h'])
  
      src_msg = fn.copy_src(src='h', out='m')
      sum_reduce = fn.sum(msg='m', out='h')
  
      rg.update_all(src_msg, sum_reduce)
      assert F.allclose(g.ndata['h'], rg.ndata['h'])
  
  
  def test_simple_graph():
      elist = [(0, 1), (0, 2), (1, 2), (0, 1)]
      g = dgl.DGLGraph(elist, readonly=True)
      assert g.is_multigraph
      sg = dgl.to_simple_graph(g)
      assert not sg.is_multigraph
      assert sg.number_of_edges() == 3
      src, dst = sg.edges()
      eset = set(zip(list(F.asnumpy(src)), list(F.asnumpy(dst))))
      assert eset == set(elist)
  
  
  def test_bidirected_graph():
      def _test(in_readonly, out_readonly):
          elist = [(0, 0), (0, 1), (1, 0),
                  (1, 1), (2, 1), (2, 2)]
          num_edges = 7
          g = dgl.DGLGraph(elist, readonly=in_readonly)
          elist.append((1, 2))
          elist = set(elist)
          big = dgl.to_bidirected(g, out_readonly)
          assert big.number_of_edges() == num_edges
          src, dst = big.edges()
          eset = set(zip(list(F.asnumpy(src)), list(F.asnumpy(dst))))
          assert eset == set(elist)
  
      _test(True, True)
      _test(True, False)
      _test(False, True)
      _test(False, False)
  
  
  def test_khop_graph():
      N = 20
      feat = F.randn((N, 5))
      g = dgl.DGLGraph(nx.erdos_renyi_graph(N, 0.3))
      for k in range(4):
          g_k = dgl.khop_graph(g, k)
          # use original graph to do message passing for k times.
          g.ndata['h'] = feat
          for _ in range(k):
              g.update_all(fn.copy_u('h', 'm'), fn.sum('m', 'h'))
          h_0 = g.ndata.pop('h')
          # use k-hop graph to do message passing for one time.
          g_k.ndata['h'] = feat
          g_k.update_all(fn.copy_u('h', 'm'), fn.sum('m', 'h'))
          h_1 = g_k.ndata.pop('h')
          assert F.allclose(h_0, h_1, rtol=1e-3, atol=1e-3)
  
  
  def test_khop_adj():
      N = 20
      feat = F.randn((N, 5))
      g = dgl.DGLGraph(nx.erdos_renyi_graph(N, 0.3))
      for k in range(3):
          adj = F.tensor(dgl.khop_adj(g, k))
          # use original graph to do message passing for k times.
          g.ndata['h'] = feat
          for _ in range(k):
              g.update_all(fn.copy_u('h', 'm'), fn.sum('m', 'h'))
          h_0 = g.ndata.pop('h')
          # use k-hop adj to do message passing for one time.
          h_1 = F.matmul(adj, feat)
          assert F.allclose(h_0, h_1, rtol=1e-3, atol=1e-3)
  
  
  def test_laplacian_lambda_max():
      N = 20
      eps = 1e-6
      # test DGLGraph
      g = dgl.DGLGraph(nx.erdos_renyi_graph(N, 0.3))
      l_max = dgl.laplacian_lambda_max(g)
      assert (l_max[0] < 2 + eps)
      # test batched DGLGraph
      N_arr = [20, 30, 10, 12]
      bg = dgl.batch([
          dgl.DGLGraph(nx.erdos_renyi_graph(N, 0.3))
          for N in N_arr
      ])
      l_max_arr = dgl.laplacian_lambda_max(bg)
      assert len(l_max_arr) == len(N_arr)
      for l_max in l_max_arr:
          assert l_max < 2 + eps
  
  
  def test_add_self_loop():
      g = dgl.DGLGraph()
      g.add_nodes(5)
      g.add_edges([0, 1, 2], [1, 1, 2])
      # Nodes 0, 3, 4 don't have self-loop
      new_g = dgl.transform.add_self_loop(g)
      assert F.allclose(new_g.edges()[0], F.tensor([0, 0, 1, 2, 3, 4]))
      assert F.allclose(new_g.edges()[1], F.tensor([1, 0, 1, 2, 3, 4]))
  
  
  def test_remove_self_loop():
      g = dgl.DGLGraph()
      g.add_nodes(5)
      g.add_edges([0, 1, 2], [1, 1, 2])
      new_g = dgl.transform.remove_self_loop(g)
      assert F.allclose(new_g.edges()[0], F.tensor([0]))
      assert F.allclose(new_g.edges()[1], F.tensor([1]))
  
  def create_large_graph_index(num_nodes):
      row = np.random.choice(num_nodes, num_nodes * 10)
      col = np.random.choice(num_nodes, num_nodes * 10)
      spm = spsp.coo_matrix((np.ones(len(row)), (row, col)))
      return from_scipy_sparse_matrix(spm, True)
  
  def get_nodeflow(g, node_ids, num_layers):
      batch_size = len(node_ids)
      expand_factor = g.number_of_nodes()
      sampler = dgl.contrib.sampling.NeighborSampler(g, batch_size,
              expand_factor=expand_factor, num_hops=num_layers,
              seed_nodes=node_ids)
      return next(iter(sampler))
  
  def test_partition_with_halo():
      g = dgl.DGLGraph(create_large_graph_index(1000), readonly=True)
      node_part = np.random.choice(4, g.number_of_nodes())
      subgs = dgl.transform.partition_graph_with_halo(g, node_part, 2)
      for part_id, subg in subgs.items():
          node_ids = np.nonzero(node_part == part_id)[0]
          lnode_ids = np.nonzero(F.asnumpy(subg.ndata['inner_node']))[0]
          nf = get_nodeflow(g, node_ids, 2)
          lnf = get_nodeflow(subg, lnode_ids, 2)
          for i in range(nf.num_layers):
              layer_nids1 = F.asnumpy(nf.layer_parent_nid(i))
              layer_nids2 = lnf.layer_parent_nid(i)
              layer_nids2 = F.asnumpy(F.gather_row(subg.parent_nid, layer_nids2))
              assert np.all(np.sort(layer_nids1) == np.sort(layer_nids2))
  
          for i in range(nf.num_blocks):
              block_eids1 = F.asnumpy(nf.block_parent_eid(i))
              block_eids2 = lnf.block_parent_eid(i)
              block_eids2 = F.asnumpy(F.gather_row(subg.parent_eid, block_eids2))
              assert np.all(np.sort(block_eids1) == np.sort(block_eids2))
  
  @unittest.skipIf(F._default_context_str == 'gpu', reason="METIS doesn't support GPU")
  def test_metis_partition():
      g = dgl.DGLGraph(create_large_graph_index(1000), readonly=True)
      subgs = dgl.transform.metis_partition(g, 4, 0)
      num_inner_nodes = 0
      num_inner_edges = 0
      if subgs is not None:
          for part_id, subg in subgs.items():
              assert np.all(F.asnumpy(subg.ndata['inner_node']) == 1)
              assert np.all(F.asnumpy(subg.edata['inner_edge']) == 1)
              assert np.all(F.asnumpy(subg.ndata['part_id']) == part_id)
              num_inner_nodes += subg.number_of_nodes()
              num_inner_edges += subg.number_of_edges()
          assert num_inner_nodes == g.number_of_nodes()
          print(g.number_of_edges() - num_inner_edges)
  
      subgs = dgl.transform.metis_partition(g, 4, 1)
      num_inner_nodes = 0
      num_inner_edges = 0
      if subgs is not None:
          for part_id, subg in subgs.items():
              lnode_ids = np.nonzero(F.asnumpy(subg.ndata['inner_node']))[0]
              ledge_ids = np.nonzero(F.asnumpy(subg.edata['inner_edge']))[0]
              num_inner_nodes += len(lnode_ids)
              num_inner_edges += len(ledge_ids)
              assert np.sum(F.asnumpy(subg.ndata['part_id']) == part_id) == len(lnode_ids)
          assert num_inner_nodes == g.number_of_nodes()
          print(g.number_of_edges() - num_inner_edges)
  
  @unittest.skipIf(F._default_context_str == 'gpu', reason="GPU not implemented")
  def test_in_subgraph():
      g1 = dgl.graph([(1,0),(2,0),(3,0),(0,1),(2,1),(3,1),(0,2)], 'user', 'follow')
      g2 = dgl.bipartite([(0,0),(0,1),(1,2),(3,2)], 'user', 'play', 'game')
      g3 = dgl.bipartite([(2,0),(2,1),(2,2),(1,0),(1,3),(0,0)], 'game', 'liked-by', 'user')
      g4 = dgl.bipartite([(0,0),(1,0),(2,0),(3,0)], 'user', 'flips', 'coin')
      hg = dgl.hetero_from_relations([g1, g2, g3, g4])
      subg = dgl.in_subgraph(hg, {'user' : [0,1], 'game' : 0})
      assert len(subg.ntypes) == 3
      assert len(subg.etypes) == 4
      u, v = subg['follow'].edges()
      edge_set = set(zip(list(F.asnumpy(u)), list(F.asnumpy(v))))
      assert F.array_equal(hg['follow'].edge_ids(u, v), subg['follow'].edata[dgl.EID])
      assert edge_set == {(1,0),(2,0),(3,0),(0,1),(2,1),(3,1)}
      u, v = subg['play'].edges()
      edge_set = set(zip(list(F.asnumpy(u)), list(F.asnumpy(v))))
      assert F.array_equal(hg['play'].edge_ids(u, v), subg['play'].edata[dgl.EID])
      assert edge_set == {(0,0)}
      u, v = subg['liked-by'].edges()
      edge_set = set(zip(list(F.asnumpy(u)), list(F.asnumpy(v))))
      assert F.array_equal(hg['liked-by'].edge_ids(u, v), subg['liked-by'].edata[dgl.EID])
      assert edge_set == {(2,0),(2,1),(1,0),(0,0)}
      assert subg['flips'].number_of_edges() == 0
  
  @unittest.skipIf(F._default_context_str == 'gpu', reason="GPU not implemented")
  def test_out_subgraph():
      g1 = dgl.graph([(1,0),(2,0),(3,0),(0,1),(2,1),(3,1),(0,2)], 'user', 'follow')
      g2 = dgl.bipartite([(0,0),(0,1),(1,2),(3,2)], 'user', 'play', 'game')
      g3 = dgl.bipartite([(2,0),(2,1),(2,2),(1,0),(1,3),(0,0)], 'game', 'liked-by', 'user')
      g4 = dgl.bipartite([(0,0),(1,0),(2,0),(3,0)], 'user', 'flips', 'coin')
      hg = dgl.hetero_from_relations([g1, g2, g3, g4])
      subg = dgl.out_subgraph(hg, {'user' : [0,1], 'game' : 0})
      assert len(subg.ntypes) == 3
      assert len(subg.etypes) == 4
      u, v = subg['follow'].edges()
      edge_set = set(zip(list(F.asnumpy(u)), list(F.asnumpy(v))))
      assert edge_set == {(1,0),(0,1),(0,2)}
      assert F.array_equal(hg['follow'].edge_ids(u, v), subg['follow'].edata[dgl.EID])
      u, v = subg['play'].edges()
      edge_set = set(zip(list(F.asnumpy(u)), list(F.asnumpy(v))))
      assert edge_set == {(0,0),(0,1),(1,2)}
      assert F.array_equal(hg['play'].edge_ids(u, v), subg['play'].edata[dgl.EID])
      u, v = subg['liked-by'].edges()
      edge_set = set(zip(list(F.asnumpy(u)), list(F.asnumpy(v))))
      assert edge_set == {(0,0)}
      assert F.array_equal(hg['liked-by'].edge_ids(u, v), subg['liked-by'].edata[dgl.EID])
      u, v = subg['flips'].edges()
      edge_set = set(zip(list(F.asnumpy(u)), list(F.asnumpy(v))))
      assert edge_set == {(0,0),(1,0)}
      assert F.array_equal(hg['flips'].edge_ids(u, v), subg['flips'].edata[dgl.EID])
  
  @unittest.skipIf(F._default_context_str == 'gpu', reason="GPU compaction not implemented")
  def test_compact():
      g1 = dgl.heterograph({
          ('user', 'follow', 'user'): [(1, 3), (3, 5)],
          ('user', 'plays', 'game'): [(2, 4), (3, 4), (2, 5)],
          ('game', 'wished-by', 'user'): [(6, 7), (5, 7)]},
          {'user': 20, 'game': 10})
  
      g2 = dgl.heterograph({
          ('game', 'clicked-by', 'user'): [(3, 1)],
          ('user', 'likes', 'user'): [(1, 8), (8, 9)]},
          {'user': 20, 'game': 10})
  
      g3 = dgl.graph([(0, 1), (1, 2)], card=10, ntype='user')
      g4 = dgl.graph([(1, 3), (3, 5)], card=10, ntype='user')
  
      def _check(g, new_g, induced_nodes):
          assert g.ntypes == new_g.ntypes
          assert g.canonical_etypes == new_g.canonical_etypes
  
          for ntype in g.ntypes:
              assert -1 not in induced_nodes[ntype]
  
          for etype in g.canonical_etypes:
              g_src, g_dst = g.all_edges(order='eid', etype=etype)
              g_src = F.asnumpy(g_src)
              g_dst = F.asnumpy(g_dst)
              new_g_src, new_g_dst = new_g.all_edges(order='eid', etype=etype)
              new_g_src_mapped = induced_nodes[etype[0]][F.asnumpy(new_g_src)]
              new_g_dst_mapped = induced_nodes[etype[2]][F.asnumpy(new_g_dst)]
              assert (g_src == new_g_src_mapped).all()
              assert (g_dst == new_g_dst_mapped).all()
  
      # Test default
      new_g1 = dgl.compact_graphs(g1)
      induced_nodes = {ntype: new_g1.nodes[ntype].data[dgl.NID] for ntype in new_g1.ntypes}
      induced_nodes = {k: F.asnumpy(v) for k, v in induced_nodes.items()}
      assert set(induced_nodes['user']) == set([1, 3, 5, 2, 7])
      assert set(induced_nodes['game']) == set([4, 5, 6])
      _check(g1, new_g1, induced_nodes)
  
      # Test with always_preserve given a dict
      new_g1 = dgl.compact_graphs(
          g1, always_preserve={'game': F.tensor([4, 7], dtype=F.int64)})
      induced_nodes = {ntype: new_g1.nodes[ntype].data[dgl.NID] for ntype in new_g1.ntypes}
      induced_nodes = {k: F.asnumpy(v) for k, v in induced_nodes.items()}
      assert set(induced_nodes['user']) == set([1, 3, 5, 2, 7])
      assert set(induced_nodes['game']) == set([4, 5, 6, 7])
      _check(g1, new_g1, induced_nodes)
  
      # Test with always_preserve given a tensor
      new_g3 = dgl.compact_graphs(
          g3, always_preserve=F.tensor([1, 7], dtype=F.int64))
      induced_nodes = {ntype: new_g3.nodes[ntype].data[dgl.NID] for ntype in new_g3.ntypes}
      induced_nodes = {k: F.asnumpy(v) for k, v in induced_nodes.items()}
      assert set(induced_nodes['user']) == set([0, 1, 2, 7])
      _check(g3, new_g3, induced_nodes)
  
      # Test multiple graphs
      new_g1, new_g2 = dgl.compact_graphs([g1, g2])
      induced_nodes = {ntype: new_g1.nodes[ntype].data[dgl.NID] for ntype in new_g1.ntypes}
      induced_nodes = {k: F.asnumpy(v) for k, v in induced_nodes.items()}
      assert set(induced_nodes['user']) == set([1, 3, 5, 2, 7, 8, 9])
      assert set(induced_nodes['game']) == set([3, 4, 5, 6])
      _check(g1, new_g1, induced_nodes)
      _check(g2, new_g2, induced_nodes)
  
      # Test multiple graphs with always_preserve given a dict
      new_g1, new_g2 = dgl.compact_graphs(
          [g1, g2], always_preserve={'game': F.tensor([4, 7], dtype=F.int64)})
      induced_nodes = {ntype: new_g1.nodes[ntype].data[dgl.NID] for ntype in new_g1.ntypes}
      induced_nodes = {k: F.asnumpy(v) for k, v in induced_nodes.items()}
      assert set(induced_nodes['user']) == set([1, 3, 5, 2, 7, 8, 9])
      assert set(induced_nodes['game']) == set([3, 4, 5, 6, 7])
      _check(g1, new_g1, induced_nodes)
      _check(g2, new_g2, induced_nodes)
  
      # Test multiple graphs with always_preserve given a tensor
      new_g3, new_g4 = dgl.compact_graphs(
          [g3, g4], always_preserve=F.tensor([1, 7], dtype=F.int64))
      induced_nodes = {ntype: new_g3.nodes[ntype].data[dgl.NID] for ntype in new_g3.ntypes}
      induced_nodes = {k: F.asnumpy(v) for k, v in induced_nodes.items()}
      assert set(induced_nodes['user']) == set([0, 1, 2, 3, 5, 7])
      _check(g3, new_g3, induced_nodes)
      _check(g4, new_g4, induced_nodes)
  
  
  def test_to_simple():
      g = dgl.heterograph({
          ('user', 'follow', 'user'): [(0, 1), (1, 3), (2, 2), (1, 3), (1, 4), (1, 4)],
          ('user', 'plays', 'game'): [(3, 5), (2, 3), (1, 4), (1, 4), (3, 5), (2, 3), (2, 3)]})
      sg = dgl.to_simple(g, return_counts='weights', writeback_mapping='new_eid')
  
      for etype in g.canonical_etypes:
          u, v = g.all_edges(form='uv', order='eid', etype=etype)
          u = F.asnumpy(u).tolist()
          v = F.asnumpy(v).tolist()
          uv = list(zip(u, v))
          eid_map = F.asnumpy(g.edges[etype].data['new_eid'])
  
          su, sv = sg.all_edges(form='uv', order='eid', etype=etype)
          su = F.asnumpy(su).tolist()
          sv = F.asnumpy(sv).tolist()
          suv = list(zip(su, sv))
          sw = F.asnumpy(sg.edges[etype].data['weights'])
  
          assert set(uv) == set(suv)
          for i, e in enumerate(suv):
              assert sw[i] == sum(e == _e for _e in uv)
          for i, e in enumerate(uv):
              assert eid_map[i] == suv.index(e)
  
  @unittest.skipIf(F._default_context_str == 'gpu', reason="GPU compaction not implemented")
  def test_to_block():
      def check(g, bg, ntype, etype, dst_nodes):
          if dst_nodes is not None:
              assert F.array_equal(bg.dstnodes[ntype].data[dgl.NID], dst_nodes)
          n_dst_nodes = bg.number_of_nodes('DST/' + ntype)
          assert F.array_equal(
              bg.srcnodes[ntype].data[dgl.NID][:n_dst_nodes],
              bg.dstnodes[ntype].data[dgl.NID])
  
          g = g[etype]
          bg = bg[etype]
          induced_src = bg.srcdata[dgl.NID]
          induced_dst = bg.dstdata[dgl.NID]
          induced_eid = bg.edata[dgl.EID]
          bg_src, bg_dst = bg.all_edges(order='eid')
          src_ans, dst_ans = g.all_edges(order='eid')
  
          induced_src_bg = F.gather_row(induced_src, bg_src)
          induced_dst_bg = F.gather_row(induced_dst, bg_dst)
          induced_src_ans = F.gather_row(src_ans, induced_eid)
          induced_dst_ans = F.gather_row(dst_ans, induced_eid)
  
          assert F.array_equal(induced_src_bg, induced_src_ans)
          assert F.array_equal(induced_dst_bg, induced_dst_ans)
  
      def checkall(g, bg, dst_nodes):
          for etype in g.etypes:
              ntype = g.to_canonical_etype(etype)[2]
              if dst_nodes is not None and ntype in dst_nodes:
                  check(g, bg, ntype, etype, dst_nodes[ntype])
              else:
                  check(g, bg, ntype, etype, None)
  
      g = dgl.heterograph({
          ('A', 'AA', 'A'): [(0, 1), (2, 3), (1, 2), (3, 4)],
          ('A', 'AB', 'B'): [(0, 1), (1, 3), (3, 5), (1, 6)],
          ('B', 'BA', 'A'): [(2, 3), (3, 2)]})
      g_a = g['AA']
  
      bg = dgl.to_block(g_a)
      check(g_a, bg, 'A', 'AA', None)
  
      dst_nodes = F.tensor([3, 4], dtype=F.int64)
      bg = dgl.to_block(g_a, dst_nodes)
      check(g_a, bg, 'A', 'AA', dst_nodes)
  
      dst_nodes = F.tensor([4, 3, 2, 1], dtype=F.int64)
      bg = dgl.to_block(g_a, dst_nodes)
      check(g_a, bg, 'A', 'AA', dst_nodes)
  
      g_ab = g['AB']
  
      bg = dgl.to_block(g_ab)
      assert bg.number_of_nodes('SRC/B') == 4
      assert F.array_equal(bg.srcnodes['B'].data[dgl.NID], bg.dstnodes['B'].data[dgl.NID])
      assert bg.number_of_nodes('DST/A') == 0
      checkall(g_ab, bg, None)
  
      dst_nodes = {'B': F.tensor([5, 6], dtype=F.int64)}
      bg = dgl.to_block(g, dst_nodes)
      assert bg.number_of_nodes('SRC/B') == 2
      assert F.array_equal(bg.srcnodes['B'].data[dgl.NID], bg.dstnodes['B'].data[dgl.NID])
      assert bg.number_of_nodes('DST/A') == 0
      checkall(g, bg, dst_nodes)
  
      dst_nodes = {'A': F.tensor([3, 4], dtype=F.int64), 'B': F.tensor([5, 6], dtype=F.int64)}
      bg = dgl.to_block(g, dst_nodes)
      checkall(g, bg, dst_nodes)
  
      dst_nodes = {'A': F.tensor([4, 3, 2, 1], dtype=F.int64), 'B': F.tensor([3, 5, 6, 1], dtype=F.int64)}
      bg = dgl.to_block(g, dst_nodes=dst_nodes)
      checkall(g, bg, dst_nodes)
  
  @unittest.skipIf(F._default_context_str == 'gpu', reason="GPU not implemented")
  def test_remove_edges():
      def check(g1, etype, g, edges_removed):
          src, dst, eid = g.edges(etype=etype, form='all')
          src1, dst1 = g1.edges(etype=etype, order='eid')
          if etype is not None:
              eid1 = g1.edges[etype].data[dgl.EID]
          else:
              eid1 = g1.edata[dgl.EID]
          src1 = F.asnumpy(src1)
          dst1 = F.asnumpy(dst1)
          eid1 = F.asnumpy(eid1)
          src = F.asnumpy(src)
          dst = F.asnumpy(dst)
          eid = F.asnumpy(eid)
          sde_set = set(zip(src, dst, eid))
  
          for s, d, e in zip(src1, dst1, eid1):
              assert (s, d, e) in sde_set
          assert not np.isin(edges_removed, eid1).any()
  
      for fmt in ['coo', 'csr', 'csc']:
          for edges_to_remove in [[2], [2, 2], [3, 2], [1, 3, 1, 2]]:
              g = dgl.graph([(0, 1), (2, 3), (1, 2), (3, 4)], restrict_format=fmt)
              g1 = dgl.remove_edges(g, F.tensor(edges_to_remove))
              check(g1, None, g, edges_to_remove)
  
              g = dgl.graph(
                  spsp.csr_matrix(([1, 1, 1, 1], ([0, 2, 1, 3], [1, 3, 2, 4])), shape=(5, 5)),
                  restrict_format=fmt)
              g1 = dgl.remove_edges(g, F.tensor(edges_to_remove))
              check(g1, None, g, edges_to_remove)
  
      g = dgl.heterograph({
          ('A', 'AA', 'A'): [(0, 1), (2, 3), (1, 2), (3, 4)],
          ('A', 'AB', 'B'): [(0, 1), (1, 3), (3, 5), (1, 6)],
          ('B', 'BA', 'A'): [(2, 3), (3, 2)]})
      g2 = dgl.remove_edges(g, {'AA': F.tensor([2]), 'AB': F.tensor([3]), 'BA': F.tensor([1])})
      check(g2, 'AA', g, [2])
      check(g2, 'AB', g, [3])
      check(g2, 'BA', g, [1])
  
  if __name__ == '__main__':
      test_line_graph()
      test_no_backtracking()
      test_reverse()
      test_reverse_shared_frames()
      test_simple_graph()
      test_bidirected_graph()
      test_khop_adj()
      test_khop_graph()
      test_laplacian_lambda_max()
      test_remove_self_loop()
      test_add_self_loop()
      test_partition_with_halo()
      test_metis_partition()
      test_compact()
      test_to_simple()
      test_in_subgraph()
      test_out_subgraph()
      test_to_block()
      test_remove_edges()
  