class_docstr: Bundler, which will be the node_apply function in DGL paradigm
code: 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}
  
file_path: dgl/examples/pytorch/diffpool/model/dgl_layers/bundler.py
imports: import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 3 for calling the Bundler obj's underlying member method code with example usage: obj.forward(node) and returns: dict