code: def forward(self, blocks, feat):
      # formula 4
      sim = th.tanh(
          self.layers[0].MLP(blocks[-1].dstdata["feature"].float())
      )
  
      # Forward of n layers of CARE-GNN
      for block, layer in zip(blocks, self.layers):
          feat = layer(block, feat)
      return feat, sim
  
imports: import dgl
  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(blocks, feat) and returns: feat,  sim