code: def train():
      model.train()
      optimizer.zero_grad()
      out = model(data.x, data.edge_index)
      loss = F.cross_entropy(
          out[data.train_mask], data.y[data.train_mask]
      )
      loss.backward()
      optimizer.step()
      scheduler.step()
      return float(loss)
  
file_path: pytorch_geometric/examples/mixhop.py
imports: import os.path
  import torch
  import torch.nn.functional
  from torch_geometric.datasets import Planetoid
  from torch_geometric.nn import BatchNorm, Linear, MixHopConv
label_desc: Use image node_id 1 for calling a global function with example usage: train() and returns: float