class_docstr: Description
  -----------
  Graph Classifier for graph classification.
  GXN + MLP
class_docstr_tok: ["Description","--","--","--","--","--","-","Graph","Classifier","for","graph","classification",".","GXN","+","MLP"]
code: def forward(
      self,
      graph: DGLGraph,
      node_feat: Tensor,
      edge_feat: Optional[Tensor] = None,
  ):
      embed, logits1, logits2 = self.gxn(graph, node_feat, edge_feat)
      logits = F.relu(self.lin1(embed))
      if self.dropout > 0:
          logits = F.dropout(
              logits, p=self.dropout, training=self.training
          )
      logits = self.lin2(logits)
      return F.log_softmax(logits, dim=1), logits1, logits2
  
file_path: dgl/examples/pytorch/gxn/networks.py
imports: from typing import List, Tuple, Union
  from layers import *
  import dgl.function
  import torch
  import torch.nn
  import torch.nn.functional
  from dgl.nn.pytorch.glob import SortPooling
label_desc: Use image node_id 2 for calling the GraphClassifier obj's underlying member method code with example usage: obj.forward(graph, node_feat, edge_feat) and returns: F,  logits1,  logits2