code: def forward(self, x, adjs, n_id):
      # `train_loader` computes the k-hop neighborhood of a batch of nodes,
      # and returns, for each layer, a bipartite graph object, holding the
      # bipartite edges `edge_index`, the index `e_id` of the original edges,
      # and the size/shape `size` of the bipartite graph.
      # Target nodes are also included in the source nodes so that one can
      # easily apply skip-connections or add self-loops.
      for i, (edge_index, _, size) in enumerate(adjs):
          x_target = x[
              : size[1]
          ]  # Target nodes are always placed first.
          x = self.convs[i](
              (x, x_target), edge_index
          )  # results before MLPs
          ####################################################################################
          ### moving average
          ####################################################################################
          if args.alpha > 0:
              x_target_history = node_emb_dict[i][n_id][: size[1]]
              x = (1 - args.alpha) * x + args.alpha * x_target_history
              node_emb_dict[i][n_id[: size[1]]] = x.detach().clone()
          if self.normalize:
              x = F.normalize(x, p=2.0, dim=-1)
          x = self.lins_l[i](x) + self.lins_r[i](x_target)
          if i != self.num_layers - 1:
              x = F.relu(x)
              x = F.dropout(x, p=self.dropout, training=self.training)
      return x.log_softmax(dim=-1)
  
imports: import argparse
  import os.path
  import time
  import torch
  import torch.nn.functional
  from tqdm import tqdm
  from torch_geometric.datasets import Reddit, Reddit2
  from torch_geometric.nn.conv import MessagePassing
  from torch.nn import Linear
  from torch import Tensor
  from logger import Logger
  from torch_geometric.data import NeighborSampler
label_desc: Use image node_id 3 for calling the GNN obj's underlying member method code with example usage: obj.forward(x, adjs, n_id) and returns: x