code: def train():
      model.train()
      optimizer.zero_grad()
      out = model(
          train_data.x_dict,
          train_data.edge_index_dict,
          train_data["user", "movie"].edge_label_index,
      )
      loss = F.mse_loss(out, train_data["user", "movie"].edge_label)
      loss.backward()
      optimizer.step()
      return float(loss)
  
imports: import os.path
  import torch
  import torch.nn.functional
  from torch.nn import Embedding, Linear
  import torch_geometric.transforms
  from torch_geometric.datasets import MovieLens
  from torch_geometric.nn import SAGEConv
  from torch_geometric.nn.conv.gcn_conv import gcn_norm
label_desc: Use image node_id 1 for calling a global function with example usage: train() and returns: float