code: def forward(self, x, edge, num_sampled_nodes, num_sampled_edges):
      edge = edge.cuda()
      x = x.cuda().to(torch.float32)
  
      for i, conv in enumerate(self.convs):
          x, edge, _ = self._trim(
              i, num_sampled_nodes, num_sampled_edges, x, edge, None
          )
  
          s = x.shape[0]
          x = conv(x, edge, size=(s, s))
          x = F.relu(x)
          x = F.dropout(x, p=0.5)
  
      x = x.narrow(
          dim=0, start=0, length=x.shape[0] - num_sampled_nodes[1]
      )
  
      # assert x.shape[0] == num_sampled_nodes[0]
      return x
  
imports: import torch
  from torch_geometric.nn import SAGEConv
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 2 for calling the GraphSAGE obj's underlying member method code with example usage: obj.forward(x, edge, num_sampled_nodes, num_sampled_edges) and returns: x