code: def forward(self, graph, labels=None):
      etypes = graph.edata.pop("type")
      annotation = graph.ndata.pop("annotation").float()
  
      assert annotation.size()[-1] == self.annotation_size
  
      node_num = graph.num_nodes()
  
      zero_pad = torch.zeros(
          [node_num, self.out_feats - self.annotation_size],
          dtype=torch.float,
          device=annotation.device,
      )
  
      h1 = torch.cat([annotation, zero_pad], -1)
      out = self.ggnn(graph, h1, etypes)
  
      out = torch.cat([out, annotation], -1)
  
      out = self.pooling(graph, out)
  
      logits = self.output_layer(out)
      preds = torch.argmax(logits, -1)
  
      if labels is not None:
          loss = self.loss_fn(logits, labels)
          return loss, preds
      return preds
  
imports: import torch
  from dgl.nn.pytorch import GatedGraphConv, GlobalAttentionPooling
  from torch import nn
label_desc: Use image node_id 2 for calling the GraphClsGGNN obj's underlying member method code with example usage: obj.forward(graph, labels) and returns: preds, loss,  preds