code: def train():
      model.train()
      optimizer.zero_grad()
      out = model(data.x, data.edge_index)
      mask = data.train_mask[
          :, 0
      ]  # Use the first set of the five masks.
      loss = F.cross_entropy(out[mask], data.y[mask])
      loss.backward()
      optimizer.step()
      return float(loss)
  
imports: import os.path
  import torch
  import torch.nn.functional
  from torch_geometric.datasets import LINKXDataset
  from torch_geometric.nn import LINKX
label_desc: Use image node_id 1 for calling a global function with example usage: train() and returns: float