code: def forward(self, x, adjs):
      # `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)
          if i != self.num_layers - 1:
              x = F.relu(x)
              x = F.dropout(x, p=0.5, training=self.training)
      return x.log_softmax(dim=-1)
  
file_path: pytorch_geometric/examples/quiver/single_gpu_quiver.py
imports: import os.path
  import quiver
  import torch
  import torch.nn.functional
  from tqdm import tqdm
  from torch_geometric.datasets import Reddit
  from torch_geometric.loader import NeighborSampler
  from torch_geometric.nn import SAGEConv
label_desc: Use image node_id 2 for calling the SAGE obj's underlying member method code with example usage: obj.forward(x, adjs) and returns: x