code: def mini_train(
      model,
      loader,
      criterion,
      optimizer,
      max_steps,
      grad_norm=None,
      edge_dropout=0.0,
  ):
      model.train()
  
      total_loss = total_examples = 0
      for i, (batch, batch_size, *args) in enumerate(loader):
          x = batch.x.to(model.device)
          adj_t = batch.adj_t.to(model.device)
          y = batch.y[:batch_size].to(model.device)
          train_mask = batch.train_mask[:batch_size].to(model.device)
  
          if train_mask.sum() == 0:
              continue
  
          # We make use of edge dropout on ogbn-products to avoid overfitting.
          adj_t = dropout(adj_t, p=edge_dropout)
  
          optimizer.zero_grad()
          # cut the args with only n_id, which makes the call to be _async=False
          args = args[:1]
          out = model(x, adj_t, batch_size, *args)
          loss = criterion(out[train_mask], y[train_mask])
          loss.backward()
          if grad_norm is not None:
              torch.nn.utils.clip_grad_norm_(
                  model.parameters(), grad_norm
              )
          optimizer.step()
  
          total_loss += float(loss) * int(train_mask.sum())
          total_examples += int(train_mask.sum())
  
          # We may abort after a fixed number of steps to refresh histories...
          if (i + 1) >= max_steps and (i + 1) < len(loader):
              break
  
      return total_loss / total_examples
  
imports: import time
  import hydra
  from omegaconf import OmegaConf
  import torch
  from torch_geometric.nn.conv.gcn_conv import gcn_norm
  from dig.lsgraph.dataset import get_data, SubgraphLoader, EvalSubgraphLoader
  from dig.lsgraph.method.GraphFMOB.metis import metis, permute
  import dig.lsgraph.method.GraphFMOB.models
  from dig.lsgraph.method.GraphFMOB.utils import compute_micro_f1, dropout
  from torch import Tensor
  from typing import Optional
  from torch_geometric.typing import Adj
  from torch_geometric.utils.num_nodes import maybe_num_nodes
  from torch_scatter import scatter_add
  from torch_sparse import SparseTensor
  import logging
label_desc: Use image node_id 2 for calling a global function with example usage: mini_train(model, loader, criterion, optimizer, max_steps, grad_norm, edge_dropout) and returns: unknown