chore: import upstream snapshot with attribution

examples/pytorch/rect/model.py

@@ -0,0 +1,44 @@
+import torch.nn as nn
+import torch.nn.functional as F
+
+from dgl.nn.pytorch import GraphConv
+
+
+class GCN(nn.Module):
+    def __init__(self, g, in_feats, n_hidden, n_classes, activation, dropout):
+        super(GCN, self).__init__()
+        self.g = g
+        self.gcn_1 = GraphConv(in_feats, n_hidden, activation=activation)
+        self.gcn_2 = GraphConv(n_hidden, n_classes)
+        self.dropout = nn.Dropout(p=dropout)
+
+    def forward(self, features):
+        h = self.gcn_1(self.g, features)
+        h = self.dropout(h)
+        preds = self.gcn_2(self.g, h)
+        return preds
+
+    def embed(self, inputs):
+        h_1 = self.gcn_1(self.g, inputs)
+        return h_1.detach()
+
+
+class RECT_L(nn.Module):
+    def __init__(self, g, in_feats, n_hidden, activation, dropout=0.0):
+        super(RECT_L, self).__init__()
+        self.g = g
+        self.gcn_1 = GraphConv(in_feats, n_hidden, activation=activation)
+        self.fc = nn.Linear(n_hidden, in_feats)
+        self.dropout = dropout
+        nn.init.xavier_uniform_(self.fc.weight.data)
+
+    def forward(self, inputs):
+        h_1 = self.gcn_1(self.g, inputs)
+        h_1 = F.dropout(h_1, p=self.dropout, training=self.training)
+        preds = self.fc(h_1)
+        return preds
+
+    # Detach the return variables
+    def embed(self, inputs):
+        h_1 = self.gcn_1(self.g, inputs)
+        return h_1.detach()