class_docstr: Bundler, which will be the node_apply function in DGL paradigm
code: 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")
      )
  
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 1 to create a new Bundler object from inherited base classes: nn with example: obj = Bundler(in_feats, out_feats, activation, dropout, bias)