code: def forward(self, data):
      x, edge_index = data.x, data.edge_index
      x = F.dropout(x, p=0.6, training=self.training)
      x = F.elu(self.conv1(x, edge_index))
      x = F.dropout(x, p=0.6, training=self.training)
      x = self.conv2(x, edge_index)
      return F.log_softmax(x, dim=1)
  
imports: import torch
  import torch.nn.functional
  from torch_geometric.nn import GATConv
label_desc: Use image node_id 2 for calling the GAT obj's underlying member method code with example usage: obj.forward(data) and returns: F