code: def train():
      model.train()
      total_loss = total_examples = 0
      for batch in train_loader:
          batch = batch.to(device)
          optimizer.zero_grad()
          pred = model(
              batch.x_dict,
              batch.edge_index_dict,
              batch["user", "movie"].edge_label_index,
          )
          target = batch["user", "movie"].edge_label.float()
          loss = F.mse_loss(pred, target)
          loss.backward()
          optimizer.step()
  
          total_loss += float(loss * pred.size(0))
          total_examples += pred.size(0)
  
      return total_loss / total_examples
  
imports: import os.path
  import torch
  import torch.nn.functional
  from torch.nn import Linear
  import torch_geometric.transforms
  from torch_geometric.datasets import MovieLens
  from torch_geometric.loader import LinkNeighborLoader
  from torch_geometric.nn import SAGEConv, to_hetero
label_desc: Use image node_id 1 for calling a global function with example usage: train() and returns: unknown