code: def forward(self, g, z, x=None, edge_weight=None):
      z_emb = self.z_embedding(z)
      if z_emb.ndim == 3:  # in case z has multiple integer labels
          z_emb = z_emb.sum(dim=1)
      if x is not None:
          x = torch.cat([z_emb, x.to(torch.float)], 1)
      else:
          x = z_emb
      xs = [x]
  
      for conv in self.convs:
          xs += [torch.tanh(conv(g, xs[-1], edge_weight=edge_weight))]
      x = torch.cat(xs[1:], dim=-1)
  
      # global pooling
      x = self.sort_pool(g, x)
      x = x.unsqueeze(1)  # [num_graphs, 1, k * hidden]
      x = F.relu(self.conv1(x))
      x = self.maxpool1d(x)
      x = F.relu(self.conv2(x))
      x = x.view(x.size(0), -1)  # [num_graphs, dense_dim]
  
      # MLP.
      x = F.relu(self.lin1(x))
      x = F.dropout(x, p=0.5, training=self.training)
      x = self.lin2(x)
      return x
  
imports: import argparse
  import math
  import os
  import random
  import sys
  import time
  import dgl
  import numpy
  import torch
  import torch.nn.functional
  from dgl.dataloading import DataLoader, Sampler
  from dgl.nn import GraphConv, SortPooling
  from dgl.sampling import global_uniform_negative_sampling
  from ogb.linkproppred import DglLinkPropPredDataset, Evaluator
  from scipy.sparse.csgraph import shortest_path
  from torch.nn import BCEWithLogitsLoss, Conv1d, Embedding, Linear, MaxPool1d, ModuleList
  from tqdm import tqdm
label_desc: Use image node_id 2 for calling the DGCNN obj's underlying member method code with example usage: obj.forward(g, z, x, edge_weight) and returns: x