class_docstr: Description
  -----------
  Graph Classifier for graph classification.
  GXN + MLP
class_docstr_tok: ["Description","--","--","--","--","--","-","Graph","Classifier","for","graph","classification",".","GXN","+","MLP"]
code: def __init__(self, args):
      super(GraphClassifier, self).__init__()
      self.gxn = GraphCrossNet(
          in_dim=args.in_dim,
          out_dim=args.embed_dim,
          edge_feat_dim=args.edge_feat_dim,
          hidden_dim=args.hidden_dim,
          pool_ratios=args.pool_ratios,
          readout_nodes=args.readout_nodes,
          conv1d_dims=args.conv1d_dims,
          conv1d_kws=args.conv1d_kws,
          cross_weight=args.cross_weight,
          fuse_weight=args.fuse_weight,
      )
      self.lin1 = torch.nn.Linear(
          args.embed_dim, args.final_dense_hidden_dim
      )
      self.lin2 = torch.nn.Linear(
          args.final_dense_hidden_dim, args.out_dim
      )
      self.dropout = args.dropout
  
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 1 to create a new GraphClassifier object from inherited base classes: torch.nn with example: obj = GraphClassifier(args)