text: import tensorflow as tf
  from tensorflow.keras import layers
  import networkx as nx
  import pytest
  import dgl
  import dgl.nn.tensorflow as nn
  import dgl.function as fn
  import backend as F
  from test_utils.graph_cases import get_cases, random_graph, random_bipartite, random_dglgraph
  from copy import deepcopy
  
  import numpy as np
  import scipy as sp
  
  def _AXWb(A, X, W, b):
      X = tf.matmul(X, W)
      Y = tf.reshape(tf.matmul(A, tf.reshape(X, (X.shape[0], -1))), X.shape)
      return Y + b
  
  def test_graph_conv():
      g = dgl.DGLGraph(nx.path_graph(3))
      ctx = F.ctx()
      adj = tf.sparse.to_dense(tf.sparse.reorder(g.adjacency_matrix(ctx=ctx)))
  
      conv = nn.GraphConv(5, 2, norm='none', bias=True)
      # conv = conv
      print(conv)
      # test#1: basic
      h0 = F.ones((3, 5))
      h1 = conv(g, h0)
      assert len(g.ndata) == 0
      assert len(g.edata) == 0
      assert F.allclose(h1, _AXWb(adj, h0, conv.weight, conv.bias))
      # test#2: more-dim
      h0 = F.ones((3, 5, 5))
      h1 = conv(g, h0)
      assert len(g.ndata) == 0
      assert len(g.edata) == 0
      assert F.allclose(h1, _AXWb(adj, h0, conv.weight, conv.bias))
  
      conv = nn.GraphConv(5, 2)
      # conv = conv
      # test#3: basic
      h0 = F.ones((3, 5))
      h1 = conv(g, h0)
      assert len(g.ndata) == 0
      assert len(g.edata) == 0
      # test#4: basic
      h0 = F.ones((3, 5, 5))
      h1 = conv(g, h0)
      assert len(g.ndata) == 0
      assert len(g.edata) == 0
  
      conv = nn.GraphConv(5, 2)
      # conv = conv
      # test#3: basic
      h0 = F.ones((3, 5))
      h1 = conv(g, h0)
      assert len(g.ndata) == 0
      assert len(g.edata) == 0
      # test#4: basic
      h0 = F.ones((3, 5, 5))
      h1 = conv(g, h0)
      assert len(g.ndata) == 0
      assert len(g.edata) == 0
  
      # test rest_parameters
      # old_weight = deepcopy(conv.weight.data)
      # conv.reset_parameters()
      # new_weight = conv.weight.data
      # assert not F.allclose(old_weight, new_weight)
  
  @pytest.mark.parametrize('g', get_cases(['path', 'bipartite', 'small'], exclude=['zero-degree']))
  @pytest.mark.parametrize('norm', ['none', 'both', 'right'])
  @pytest.mark.parametrize('weight', [True, False])
  @pytest.mark.parametrize('bias', [True, False])
  def test_graph_conv2(g, norm, weight, bias):
      conv = nn.GraphConv(5, 2, norm=norm, weight=weight, bias=bias)
      ext_w = F.randn((5, 2))
      nsrc = g.number_of_nodes() if isinstance(g, dgl.DGLGraph) else g.number_of_src_nodes()
      ndst = g.number_of_nodes() if isinstance(g, dgl.DGLGraph) else g.number_of_dst_nodes()
      h = F.randn((nsrc, 5))
      h_dst = F.randn((ndst, 2))
      if weight:
          h_out = conv(g, h)
      else:
          h_out = conv(g, h, weight=ext_w)
      assert h_out.shape == (ndst, 2)
  
      if not isinstance(g, dgl.DGLGraph) and len(g.ntypes) == 2:
          # bipartite, should also accept pair of tensors
          if weight:
              h_out2 = conv(g, (h, h_dst))
          else:
              h_out2 = conv(g, (h, h_dst), weight=ext_w)
          assert h_out2.shape == (ndst, 2)
          assert F.array_equal(h_out, h_out2)
  
  def test_simple_pool():
      ctx = F.ctx()
      g = dgl.DGLGraph(nx.path_graph(15))
  
      sum_pool = nn.SumPooling()
      avg_pool = nn.AvgPooling()
      max_pool = nn.MaxPooling()
      sort_pool = nn.SortPooling(10) # k = 10
      print(sum_pool, avg_pool, max_pool, sort_pool)
  
      # test#1: basic
      h0 = F.randn((g.number_of_nodes(), 5))
      h1 = sum_pool(g, h0)
      assert F.allclose(F.squeeze(h1, 0), F.sum(h0, 0))
      h1 = avg_pool(g, h0)
      assert F.allclose(F.squeeze(h1, 0), F.mean(h0, 0))
      h1 = max_pool(g, h0)
      assert F.allclose(F.squeeze(h1, 0), F.max(h0, 0))
      h1 = sort_pool(g, h0)
      assert h1.shape[0] == 1 and h1.shape[1] == 10 * 5 and h1.ndim == 2
  
      # test#2: batched graph
      g_ = dgl.DGLGraph(nx.path_graph(5))
      bg = dgl.batch([g, g_, g, g_, g])
      h0 = F.randn((bg.number_of_nodes(), 5))
      h1 = sum_pool(bg, h0)
      truth = tf.stack([F.sum(h0[:15], 0),
                        F.sum(h0[15:20], 0),
                        F.sum(h0[20:35], 0),
                        F.sum(h0[35:40], 0),
                        F.sum(h0[40:55], 0)], 0)
      assert F.allclose(h1, truth)
  
      h1 = avg_pool(bg, h0)
      truth = tf.stack([F.mean(h0[:15], 0),
                        F.mean(h0[15:20], 0),
                        F.mean(h0[20:35], 0),
                        F.mean(h0[35:40], 0),
                        F.mean(h0[40:55], 0)], 0)
      assert F.allclose(h1, truth)
  
      h1 = max_pool(bg, h0)
      truth = tf.stack([F.max(h0[:15], 0),
                        F.max(h0[15:20], 0),
                        F.max(h0[20:35], 0),
                        F.max(h0[35:40], 0),
                        F.max(h0[40:55], 0)], 0)
      assert F.allclose(h1, truth)
  
      h1 = sort_pool(bg, h0)
      assert h1.shape[0] == 5 and h1.shape[1] == 10 * 5 and h1.ndim == 2
  
  def uniform_attention(g, shape):
      a = F.ones(shape)
      target_shape = (g.number_of_edges(),) + (1,) * (len(shape) - 1)
      return a / tf.cast(tf.reshape(g.in_degrees(g.edges()[1]), target_shape), tf.float32)
  
  def test_edge_softmax():
      # Basic
      g = dgl.DGLGraph(nx.path_graph(3))
      edata = F.ones((g.number_of_edges(), 1))
      a = nn.edge_softmax(g, edata)
      assert len(g.ndata) == 0
      assert len(g.edata) == 0
      assert F.allclose(a, uniform_attention(g, a.shape))
  
      # Test higher dimension case
      edata = F.ones((g.number_of_edges(), 3, 1))
      a = nn.edge_softmax(g, edata)
      assert len(g.ndata) == 0
      assert len(g.edata) == 0
      assert F.allclose(a, uniform_attention(g, a.shape))
  
      # Test both forward and backward with Tensorflow built-in softmax.
      g = dgl.DGLGraph()
      g.add_nodes(30)
      # build a complete graph
      for i in range(30):
          for j in range(30):
              g.add_edge(i, j)
      
      score = F.randn((900, 1))
      with tf.GradientTape() as tape:
          tape.watch(score)
          grad = F.randn((900, 1))
          y = tf.reshape(F.softmax(tf.reshape(score,(30, 30)), dim=0), (-1, 1))
          grads = tape.gradient(y, [score])
          grad_score = grads[0]
  
      with tf.GradientTape() as tape:
          tape.watch(score)
          y_dgl = nn.edge_softmax(g, score)
          assert len(g.ndata) == 0
          assert len(g.edata) == 0
          # check forward
          assert F.allclose(y_dgl, y)
          grads = tape.gradient(y_dgl, [score])
      # checkout gradient
      assert F.allclose(grads[0], grad_score)
      print(grads[0][:10], grad_score[:10])
      
      # Test 2
      def generate_rand_graph(n):
        arr = (sp.sparse.random(n, n, density=0.1, format='coo') != 0).astype(np.int64)
        return dgl.DGLGraph(arr, readonly=True)
      
      g = generate_rand_graph(50)
      a1 = F.randn((g.number_of_edges(), 1))
      a2 = tf.identity(a1)
      with tf.GradientTape() as tape:
          tape.watch(a1)
          g.edata['s'] = a1
          g.group_apply_edges('dst', lambda edges: {'ss':F.softmax(edges.data['s'], 1)})
          loss = tf.reduce_sum(g.edata['ss'])
          a1_grad = tape.gradient(loss, [a1])[0]
      
      with tf.GradientTape() as tape:
          tape.watch(a2)
          builtin_sm = nn.edge_softmax(g, a2)
          loss = tf.reduce_sum(builtin_sm)
          a2_grad = tape.gradient(loss, [a2])[0]
      print(a1_grad - a2_grad)
      assert len(g.ndata) == 0
      assert len(g.edata) == 2
      assert F.allclose(a1_grad, a2_grad, rtol=1e-4, atol=1e-4) # Follow tolerance in unittest backend
  
  def test_partial_edge_softmax():
      g = dgl.DGLGraph()
      g.add_nodes(30)
      # build a complete graph
      for i in range(30):
          for j in range(30):
              g.add_edge(i, j)
  
      score = F.randn((300, 1))
      grad = F.randn((300, 1))
      import numpy as np
      eids = np.random.choice(900, 300, replace=False).astype('int64')
      eids = F.zerocopy_from_numpy(eids)
      # compute partial edge softmax
      with tf.GradientTape() as tape:
          tape.watch(score)
          y_1 = nn.edge_softmax(g, score, eids)
          grads = tape.gradient(y_1, [score])
      grad_1 = grads[0]
      # compute edge softmax on edge subgraph
      subg = g.edge_subgraph(eids)
      with tf.GradientTape() as tape:
          tape.watch(score)
          y_2 = nn.edge_softmax(subg, score)
          grads = tape.gradient(y_2, [score])
      grad_2 = grads[0]
  
      assert F.allclose(y_1, y_2)
      assert F.allclose(grad_1, grad_2)
  
  def test_glob_att_pool():
      g = dgl.DGLGraph(nx.path_graph(10))
  
      gap = nn.GlobalAttentionPooling(layers.Dense(1), layers.Dense(10))
      print(gap)
  
      # test#1: basic
      h0 = F.randn((g.number_of_nodes(), 5))
      h1 = gap(g, h0)
      assert h1.shape[0] == 1 and h1.shape[1] == 10 and h1.ndim == 2
  
      # test#2: batched graph
      bg = dgl.batch([g, g, g, g])
      h0 = F.randn((bg.number_of_nodes(), 5))
      h1 = gap(bg, h0)
      assert h1.shape[0] == 4 and h1.shape[1] == 10 and h1.ndim == 2
  
  
  def test_rgcn():
      etype = []
      g = dgl.DGLGraph(sp.sparse.random(100, 100, density=0.1), readonly=True)
      # 5 etypes
      R = 5
      for i in range(g.number_of_edges()):
          etype.append(i % 5)
      B = 2
      I = 10
      O = 8
  
      rgc_basis = nn.RelGraphConv(I, O, R, "basis", B)
      rgc_basis_low = nn.RelGraphConv(I, O, R, "basis", B, low_mem=True)
      rgc_basis_low.weight = rgc_basis.weight
      rgc_basis_low.w_comp = rgc_basis.w_comp
      h = tf.random.normal((100, I))
      r = tf.constant(etype)
      h_new = rgc_basis(g, h, r)
      h_new_low = rgc_basis_low(g, h, r)
      assert list(h_new.shape) == [100, O]
      assert list(h_new_low.shape) == [100, O]
      assert F.allclose(h_new, h_new_low)
  
      rgc_bdd = nn.RelGraphConv(I, O, R, "bdd", B)
      rgc_bdd_low = nn.RelGraphConv(I, O, R, "bdd", B, low_mem=True)
      rgc_bdd_low.weight = rgc_bdd.weight
      h = tf.random.normal((100, I))
      r = tf.constant(etype)
      h_new = rgc_bdd(g, h, r)
      h_new_low = rgc_bdd_low(g, h, r)
      assert list(h_new.shape) == [100, O]
      assert list(h_new_low.shape) == [100, O]
      assert F.allclose(h_new, h_new_low)
  
      # with norm
      norm = tf.zeros((g.number_of_edges(), 1))
  
      rgc_basis = nn.RelGraphConv(I, O, R, "basis", B)
      rgc_basis_low = nn.RelGraphConv(I, O, R, "basis", B, low_mem=True)
      rgc_basis_low.weight = rgc_basis.weight
      rgc_basis_low.w_comp = rgc_basis.w_comp
      h = tf.random.normal((100, I))
      r = tf.constant(etype)
      h_new = rgc_basis(g, h, r, norm)
      h_new_low = rgc_basis_low(g, h, r, norm)
      assert list(h_new.shape) == [100, O]
      assert list(h_new_low.shape) == [100, O]
      assert F.allclose(h_new, h_new_low)
  
      rgc_bdd = nn.RelGraphConv(I, O, R, "bdd", B)
      rgc_bdd_low = nn.RelGraphConv(I, O, R, "bdd", B, low_mem=True)
      rgc_bdd_low.weight = rgc_bdd.weight
      h = tf.random.normal((100, I))
      r = tf.constant(etype)
      h_new = rgc_bdd(g, h, r, norm)
      h_new_low = rgc_bdd_low(g, h, r, norm)
      assert list(h_new.shape) == [100, O]
      assert list(h_new_low.shape) == [100, O]
      assert F.allclose(h_new, h_new_low)
  
      # id input
      rgc_basis = nn.RelGraphConv(I, O, R, "basis", B)
      rgc_basis_low = nn.RelGraphConv(I, O, R, "basis", B, low_mem=True)
      rgc_basis_low.weight = rgc_basis.weight
      rgc_basis_low.w_comp = rgc_basis.w_comp
      h = tf.constant(np.random.randint(0, I, (100,)))
      r = tf.constant(etype)
      h_new = rgc_basis(g, h, r)
      h_new_low = rgc_basis_low(g, h, r)
      assert list(h_new.shape) == [100, O]
      assert list(h_new_low.shape) == [100, O]
      assert F.allclose(h_new, h_new_low)
  
  def test_gat_conv():
      g = dgl.DGLGraph(sp.sparse.random(100, 100, density=0.1), readonly=True)
      gat = nn.GATConv(5, 2, 4)
      feat = F.randn((100, 5))
      h = gat(g, feat)
      assert h.shape == (100, 4, 2)
  
      g = dgl.bipartite(sp.sparse.random(100, 200, density=0.1))
      gat = nn.GATConv((5, 10), 2, 4)
      feat = (F.randn((100, 5)), F.randn((200, 10)))
      h = gat(g, feat)
  
  @pytest.mark.parametrize('aggre_type', ['mean', 'pool', 'gcn', 'lstm'])
  def test_sage_conv(aggre_type):
      ctx = F.ctx()
      g = dgl.DGLGraph(sp.sparse.random(100, 100, density=0.1), readonly=True)
      sage = nn.SAGEConv(5, 10, aggre_type)
      feat = F.randn((100, 5))
      h = sage(g, feat)
      assert h.shape[-1] == 10
  
      g = dgl.graph(sp.sparse.random(100, 100, density=0.1))
      sage = nn.SAGEConv(5, 10, aggre_type)
      feat = F.randn((100, 5))
      h = sage(g, feat)
      assert h.shape[-1] == 10
  
      g = dgl.bipartite(sp.sparse.random(100, 200, density=0.1))
      dst_dim = 5 if aggre_type != 'gcn' else 10
      sage = nn.SAGEConv((10, dst_dim), 2, aggre_type)
      feat = (F.randn((100, 10)), F.randn((200, dst_dim)))
      h = sage(g, feat)
      assert h.shape[-1] == 2
      assert h.shape[0] == 200
  
      # Test the case for graphs without edges
      g = dgl.bipartite([], num_nodes=(5, 3))
      sage = nn.SAGEConv((3, 3), 2, 'gcn')
      feat = (F.randn((5, 3)), F.randn((3, 3)))
      h = sage(g, feat)
      assert h.shape[-1] == 2
      assert h.shape[0] == 3
      for aggre_type in ['mean', 'pool', 'lstm']:
          sage = nn.SAGEConv((3, 1), 2, aggre_type)
          feat = (F.randn((5, 3)), F.randn((3, 1)))
          h = sage(g, feat)
          assert h.shape[-1] == 2
          assert h.shape[0] == 3
  
  def test_sgc_conv():
      ctx = F.ctx()
      g = dgl.DGLGraph(sp.sparse.random(100, 100, density=0.1), readonly=True)
      # not cached
      sgc = nn.SGConv(5, 10, 3)
      feat = F.randn((100, 5))
  
      h = sgc(g, feat)
      assert h.shape[-1] == 10
  
      # cached
      sgc = nn.SGConv(5, 10, 3, True)
      h_0 = sgc(g, feat)
      h_1 = sgc(g, feat + 1)
      assert F.allclose(h_0, h_1)
      assert h_0.shape[-1] == 10
  
  def test_appnp_conv():
      g = dgl.DGLGraph(sp.sparse.random(100, 100, density=0.1), readonly=True)
      appnp = nn.APPNPConv(10, 0.1)
      feat = F.randn((100, 5))
  
      h = appnp(g, feat)
      assert h.shape[-1] == 5
  
  @pytest.mark.parametrize('aggregator_type', ['mean', 'max', 'sum'])
  def test_gin_conv(aggregator_type):
      g = dgl.DGLGraph(sp.sparse.random(100, 100, density=0.1), readonly=True)
      gin = nn.GINConv(
          tf.keras.layers.Dense(12),
          aggregator_type
      )
      feat = F.randn((100, 5))
      gin = gin
      h = gin(g, feat)
      assert h.shape == (100, 12)
  
      g = dgl.bipartite(sp.sparse.random(100, 200, density=0.1))
      gin = nn.GINConv(
          tf.keras.layers.Dense(12),
          aggregator_type
      )
      feat = (F.randn((100, 5)), F.randn((200, 5)))
      h = gin(g, feat)
      assert h.shape == (200, 12)
  
  def myagg(alist, dsttype):
      rst = alist[0]
      for i in range(1, len(alist)):
          rst = rst + (i + 1) * alist[i]
      return rst
  
  @pytest.mark.parametrize('agg', ['sum', 'max', 'min', 'mean', 'stack', myagg])
  def test_hetero_conv(agg):
      g = dgl.heterograph({
          ('user', 'follows', 'user'): [(0, 1), (0, 2), (2, 1), (1, 3)],
          ('user', 'plays', 'game'): [(0, 0), (0, 2), (0, 3), (1, 0), (2, 2)],
          ('store', 'sells', 'game'): [(0, 0), (0, 3), (1, 1), (1, 2)]})
      conv = nn.HeteroGraphConv({
          'follows': nn.GraphConv(2, 3),
          'plays': nn.GraphConv(2, 4),
          'sells': nn.GraphConv(3, 4)},
          agg)
      uf = F.randn((4, 2))
      gf = F.randn((4, 4))
      sf = F.randn((2, 3))
      uf_dst = F.randn((4, 3))
      gf_dst = F.randn((4, 4))
  
      h = conv(g, {'user': uf})
      assert set(h.keys()) == {'user', 'game'}
      if agg != 'stack':
          assert h['user'].shape == (4, 3)
          assert h['game'].shape == (4, 4)
      else:
          assert h['user'].shape == (4, 1, 3)
          assert h['game'].shape == (4, 1, 4)
  
      h = conv(g, {'user': uf, 'store': sf})
      assert set(h.keys()) == {'user', 'game'}
      if agg != 'stack':
          assert h['user'].shape == (4, 3)
          assert h['game'].shape == (4, 4)
      else:
          assert h['user'].shape == (4, 1, 3)
          assert h['game'].shape == (4, 2, 4)
  
      h = conv(g, {'store': sf})
      assert set(h.keys()) == {'game'}
      if agg != 'stack':
          assert h['game'].shape == (4, 4)
      else:
          assert h['game'].shape == (4, 1, 4)
  
      # test with pair input
      conv = nn.HeteroGraphConv({
          'follows': nn.SAGEConv(2, 3, 'mean'),
          'plays': nn.SAGEConv((2, 4), 4, 'mean'),
          'sells': nn.SAGEConv(3, 4, 'mean')},
          agg)
  
      h = conv(g, ({'user': uf}, {'user' : uf, 'game' : gf}))
      assert set(h.keys()) == {'user', 'game'}
      if agg != 'stack':
          assert h['user'].shape == (4, 3)
          assert h['game'].shape == (4, 4)
      else:
          assert h['user'].shape == (4, 1, 3)
          assert h['game'].shape == (4, 1, 4)
  
      # pair input requires both src and dst type features to be provided
      h = conv(g, ({'user': uf}, {'game' : gf}))
      assert set(h.keys()) == {'game'}
      if agg != 'stack':
          assert h['game'].shape == (4, 4)
      else:
          assert h['game'].shape == (4, 1, 4)
  
      # test with mod args
      class MyMod(tf.keras.layers.Layer):
          def __init__(self, s1, s2):
              super(MyMod, self).__init__()
              self.carg1 = 0
              self.carg2 = 0
              self.s1 = s1
              self.s2 = s2
          def call(self, g, h, arg1=None, *, arg2=None):
              if arg1 is not None:
                  self.carg1 += 1
              if arg2 is not None:
                  self.carg2 += 1
              return tf.zeros((g.number_of_dst_nodes(), self.s2))
      mod1 = MyMod(2, 3)
      mod2 = MyMod(2, 4)
      mod3 = MyMod(3, 4)
      conv = nn.HeteroGraphConv({
          'follows': mod1,
          'plays': mod2,
          'sells': mod3},
          agg)
      mod_args = {'follows' : (1,), 'plays' : (1,)}
      mod_kwargs = {'sells' : {'arg2' : 'abc'}}
      h = conv(g, {'user' : uf, 'store' : sf}, mod_args=mod_args, mod_kwargs=mod_kwargs)
      assert mod1.carg1 == 1
      assert mod1.carg2 == 0
      assert mod2.carg1 == 1
      assert mod2.carg2 == 0
      assert mod3.carg1 == 0
      assert mod3.carg2 == 1
  
  if __name__ == '__main__':
      test_graph_conv()
      test_edge_softmax()
      test_partial_edge_softmax()
      # test_set2set()
      test_glob_att_pool()
      test_simple_pool()
      # test_set_trans()
      test_rgcn()
      # test_tagconv()
      test_gat_conv()
      test_sage_conv()
      test_sgc_conv()
      test_appnp_conv()
      test_gin_conv()
      # test_agnn_conv()
      # test_gated_graph_conv()
      # test_nn_conv()
      # test_gmm_conv()
      # test_dense_graph_conv()
      # test_dense_sage_conv()
      # test_dense_cheb_conv()
      # test_sequential()
  