code: def forward(self, bipartite, features):
      out = self.gcn_layer(bipartite, features)
  
      if self.use_GAT:
          out = torch.mean(out + self.bias, dim=1)
  
      out = out.reshape(out.shape[0], -1)
      out = F.relu(out)
      if self.dropout > 0:
          out = F.dropout(out, self.dropout, training=self.training)
  
      return out
  
file_path: dgl/examples/pytorch/hilander/models/graphconv.py
imports: import dgl.function
  import torch
  import torch.nn
  import torch.nn.functional
  from dgl.nn.pytorch import GATConv
  from torch.nn import init
label_desc: Use image node_id 2 for calling the GraphConv obj's underlying member method code with example usage: obj.forward(bipartite, features) and returns: out