code: def train():
      model.train()
      optimizer.zero_grad()
      out = model(data.x, data.edge_index)
      loss = F.cross_entropy(out[train_idx], data.y[train_idx])
      torch.nn.utils.clip_grad_norm_(model.parameters(), 2.0)
      loss.backward()
      optimizer.step()
      return float(loss)
  
file_path: pytorch_geometric/examples/explain/gnn_explainer_ba_shapes.py
imports: import torch
  import torch.nn.functional
  from sklearn.metrics import roc_auc_score
  from sklearn.model_selection import train_test_split
  from tqdm import tqdm
  import torch_geometric.transforms
  from torch_geometric.datasets import ExplainerDataset
  from torch_geometric.datasets.graph_generator import BAGraph
  from torch_geometric.explain import Explainer, GNNExplainer
  from torch_geometric.nn import GCN
  from torch_geometric.utils import k_hop_subgraph
label_desc: Use image node_id 1 for calling a global function with example usage: train() and returns: float