code: def train():
      model.train()
      optimizer.zero_grad()
  
      out = model(
          train_data.x,
          train_data.edge_index,
          train_data.edge_label_index,
      )
      loss = F.binary_cross_entropy_with_logits(
          out, train_data.edge_label
      )
      loss.backward()
      optimizer.step()
      return float(loss)
  
file_path: pytorch_geometric/examples/explain/gnn_explainer_link_pred.py
imports: import os.path
  import torch
  import torch.nn.functional
  from sklearn.metrics import roc_auc_score
  import torch_geometric.transforms
  from torch_geometric.datasets import Planetoid
  from torch_geometric.explain import Explainer, GNNExplainer, ModelConfig
  from torch_geometric.nn import GCNConv
label_desc: Use image node_id 1 for calling a global function with example usage: train() and returns: float