chore: import upstream snapshot with attribution

examples/pytorch/diffpool/model/dgl_layers/bundler.py

@@ -0,0 +1,33 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class Bundler(nn.Module):
+    """
+    Bundler, which will be the node_apply function in DGL paradigm
+    """
+
+    def __init__(self, in_feats, out_feats, activation, dropout, bias=True):
+        super(Bundler, self).__init__()
+        self.dropout = nn.Dropout(p=dropout)
+        self.linear = nn.Linear(in_feats * 2, out_feats, bias)
+        self.activation = activation
+
+        nn.init.xavier_uniform_(
+            self.linear.weight, gain=nn.init.calculate_gain("relu")
+        )
+
+    def concat(self, h, aggre_result):
+        bundle = torch.cat((h, aggre_result), 1)
+        bundle = self.linear(bundle)
+        return bundle
+
+    def forward(self, node):
+        h = node.data["h"]
+        c = node.data["c"]
+        bundle = self.concat(h, c)
+        bundle = F.normalize(bundle, p=2, dim=1)
+        if self.activation:
+            bundle = self.activation(bundle)
+        return {"h": bundle}