code: def forward(self, batch):
      x, edge_index, edge_feature = (
          batch.node_feature,
          batch.edge_index,
          batch.edge_feature,
      )
      x = F.dropout(x, training=self.training)
      x = F.elu(self.conv1(x, edge_index, edge_feature))
      x = F.dropout(x, training=self.training)
      x = self.conv2(x, edge_index, edge_feature)
      return F.log_softmax(x, dim=1)
  
imports: import math
  import copy
  import torch
  import torch.nn.functional
  from torch_geometric.datasets import Planetoid
  import torch_geometric.transforms
  from torch_geometric.nn import SplineConv
  from torch_geometric.nn import GCNConv
  import sys
  import networkx
  import pdb
  import deepsnap
  from converter import *
  from deepsnap.dataset import GraphDataset
  from deepsnap.batch import Batch
  from torch.utils.data import DataLoader
label_desc: Use image node_id 2 for calling the Net obj's underlying member method code with example usage: obj.forward(batch) and returns: F