code: def forward(self, x: Tensor, edge_index: Tensor) -> Tensor:
      for i, conv in enumerate(self.convs):
          x = conv(x, edge_index)
          if i < len(self.convs) - 1:
              x = x.relu_()
              x = F.dropout(x, p=0.5, training=self.training)
      return x
  
file_path: pytorch_geometric/examples/multi_gpu/distributed_sampling.py
imports: import copy
  import os
  import torch
  import torch.distributed
  import torch.multiprocessing
  import torch.nn.functional
  from torch import Tensor
  from torch.nn.parallel import DistributedDataParallel
  from tqdm import tqdm
  from torch_geometric.datasets import Reddit
  from torch_geometric.loader import NeighborLoader
  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, edge_index) and returns: x