code: def forward(self, data, mode="batch"):
      if mode == "batch":
          edge_indices, x = data
          for i in range(len(self.convs) - 1):
              edge_index = edge_indices[i]
              x = self.convs[i](x, edge_index)
              x = self.bns[i](x)
              x = F.relu(x)
              x = F.dropout(x, p=self.dropout, training=self.training)
          x = self.convs[-1](x, edge_indices[len(self.convs) - 1])
      else:
          x, edge_index = data.node_feature, data.edge_index
          for i in range(len(self.convs) - 1):
              x = self.convs[i](x, edge_index)
              x = self.bns[i](x)
              x = F.relu(x)
              x = F.dropout(x, p=self.dropout, training=self.training)
          x = self.convs[-1](x, edge_index)
      x = self.post_mp(x)
      x = F.log_softmax(x, dim=1)
      return x
  
file_path: deepsnap/examples/sampling/model.py
imports: import torch
  import torch.nn
  import torch.nn.functional
  from torch_geometric.nn import SAGEConv
label_desc: Use image node_id 2 for calling the GNN obj's underlying member method code with example usage: obj.forward(data, mode) and returns: x