code: def forward(self, features):
      # prediction step
      h = features
      h = self.feat_drop(h)
      h = self.activation(self.layers[0](h))
      for layer in self.layers[1:-1]:
          h = self.activation(layer(h))
      h = self.layers[-1](self.feat_drop(h))
      # propagation step
      h = self.propagate(self.g, h)
      return h
  
imports: import torch
  import torch.nn
  from dgl.nn.pytorch import AGNNConv, APPNPConv, ChebConv, GATConv, GINConv, GraphConv, SAGEConv, SGConv, TAGConv
label_desc: Use image node_id 3 for calling the APPNP obj's underlying member method code with example usage: obj.forward(features) and returns: h