code: def train():
      model.train()
  
      total_loss = 0
      for batch in train_loader:
          batch = batch.to(device)
          optimizer.zero_grad()
          h = model(batch.x, batch.edge_index)
          h_src = h[batch.edge_label_index[0]]
          h_dst = h[batch.edge_label_index[1]]
          pred = (h_src * h_dst).sum(dim=-1)
          loss = F.binary_cross_entropy_with_logits(
              pred, batch.edge_label
          )
          loss.backward()
          optimizer.step()
          total_loss += float(loss) * pred.size(0)
  
      return total_loss / data.num_nodes
  
file_path: pytorch_geometric/examples/graph_sage_unsup.py
imports: import os.path
  import time
  import torch
  import torch.nn.functional
  from sklearn.linear_model import LogisticRegression
  import torch_geometric.transforms
  from torch_geometric.datasets import Planetoid
  from torch_geometric.loader import LinkNeighborLoader
  from torch_geometric.nn import GraphSAGE
label_desc: Use image node_id 1 for calling a global function with example usage: train() and returns: unknown