code: def forward(self, x, edge_indices):
      pos_hs, neg_hs, summaries = [], [], []
      for conv, edge_index in zip(self.convs, edge_indices):
          pos_h = F.dropout(x, p=0.5, training=self.training)
          pos_h = conv(pos_h, edge_index).relu()
          pos_hs.append(pos_h)
  
          neg_h = F.dropout(x, p=0.5, training=self.training)
          neg_h = neg_h[
              torch.randperm(neg_h.size(0), device=neg_h.device)
          ]
          neg_h = conv(neg_h, edge_index).relu()
          neg_hs.append(neg_h)
  
          summaries.append(pos_h.mean(dim=0, keepdim=True))
  
      return pos_hs, neg_hs, summaries
  
file_path: pytorch_geometric/examples/hetero/dmgi_unsup.py
imports: import os.path
  import torch
  import torch.nn.functional
  from sklearn.linear_model import LogisticRegression
  from torch.optim import Adam
  import torch_geometric.transforms
  from torch_geometric.datasets import IMDB
  from torch_geometric.nn import GCNConv
label_desc: Use image node_id 3 for calling the DMGI obj's underlying member method code with example usage: obj.forward(x, edge_indices) and returns: pos_hs,  neg_hs,  summaries