code: def forward(self, x, adj_t, batch):
      x = self.atom_encoder(x)
      edge_attr = adj_t.coo()[2]
      adj_t = adj_t.set_value(
          self.bond_encoder(edge_attr), layout="coo"
      )
  
      for conv in self.convs:
          x = conv(x, adj_t)
          x = F.dropout(x, p=self.dropout, training=self.training)
  
      x = global_mean_pool(x, batch)
      x = self.lin(x)
      return x
  
imports: import os
  import torch
  import torch.distributed
  import torch.multiprocessing
  import torch.nn.functional
  from ogb.graphproppred import Evaluator
  from ogb.graphproppred import PygGraphPropPredDataset
  from ogb.graphproppred.mol_encoder import AtomEncoder, BondEncoder
  from torch.nn import BatchNorm1d
  from torch.nn import Linear, ReLU, Sequential
  from torch.nn.parallel import DistributedDataParallel
  from torch.utils.data.distributed import DistributedSampler
  import torch_geometric.transforms
  from torch_geometric.loader import DataLoader
  from torch_geometric.nn import GINEConv, global_mean_pool
  from torch_geometric.typing import WITH_TORCH_SPARSE
label_desc: Use image node_id 2 for calling the GIN obj's underlying member method code with example usage: obj.forward(x, adj_t, batch) and returns: x