code: def train():
      total_loss = total_examples = 0
      for batch in train_loader:
          batch = batch.to(device)
          optimizer.zero_grad()
          out = model(batch.x, batch.batch)
          loss = F.binary_cross_entropy_with_logits(out, batch.y)
          loss.backward()
          optimizer.step()
          total_loss += batch.num_graphs * float(loss)
          total_examples += batch.num_graphs
      return total_loss / total_examples
  
imports: import argparse
  import torch
  import torch.nn.functional
  from torch import Tensor
  from torch_geometric.data import Data, InMemoryDataset
  from torch_geometric.loader import DataLoader
  from torch_geometric.nn import LCMAggregation
  from torch_geometric.transforms import BaseTransform
label_desc: Use image node_id 1 for calling a global function with example usage: train() and returns: unknown