code: def forward(self, graph, node_feat, edge_feat=None):
      if self.embed_size > 0:
          dgl_warning(
              "The embedding for node feature is used, and input node_feat is ignored, due to the provided embed_size."
          )
          h = self.embed.weight
      else:
          h = node_feat
      for i in range(self.num_layers):
          h = self.conv_list[i](graph, h)
      h = self.out_mlp(h)
      return h
  
imports: import torch.nn
  from dgl.base import dgl_warning
  from dgl.nn import GINConv
label_desc: Use image node_id 2 for calling the GIN obj's underlying member method code with example usage: obj.forward(graph, node_feat, edge_feat) and returns: h