code: def forward(self, graph, feat):
      # For full graph training, directly use the graph
      # formula 4
      sim = th.tanh(self.layers[0].MLP(feat))
  
      # Forward of n layers of CARE-GNN
      for layer in self.layers:
          feat = layer(graph, feat)
  
      return feat, sim
  
file_path: dgl/examples/pytorch/caregnn/model.py
imports: import dgl.function
  import numpy
  import torch
  import torch.nn
label_desc: Use image node_id 2 for calling the CAREGNN obj's underlying member method code with example usage: obj.forward(graph, feat) and returns: feat,  sim