code: def train(state, train_loader):
      epoch = state.epoch
      batch_offset = state.batch
  
      state.model.train()
      state.train_sampler.set_epoch(epoch)
      train_loss = Metric("train_loss")
      train_accuracy = Metric("train_accuracy")
  
      for batch_idx, (data, target) in enumerate(train_loader):
          # Elastic Horovod: update the current batch index this epoch
          # and commit / check for host updates. Do not check hosts when
          # we commit as it would be redundant.
          state.batch = batch_offset + batch_idx
          if (
              args.batches_per_commit > 0
              and state.batch % args.batches_per_commit == 0
          ):
              state.commit()
          elif (
              args.batches_per_host_check > 0
              and state.batch % args.batches_per_host_check == 0
          ):
              state.check_host_updates()
  
          if args.cuda:
              data, target = data.cuda(), target.cuda()
          state.optimizer.zero_grad()
  
          output = state.model(data)
          train_accuracy.update(accuracy(output, target))
  
          loss = F.cross_entropy(output, target)
          train_loss.update(loss)
          loss.backward()
          state.optimizer.step()
          # Only log from the 0th rank worker.
          if hvd.rank() == 0:
              ray_logger.log(
                  {
                      "tqdm_mode": "train",
                      "train/loss": train_loss.avg.item(),
                      "train/accuracy": 100.0
                      * train_accuracy.avg.item(),
                      "total": len(train_loader),
                      "epoch": epoch,
                      "world_size": hvd.size(),
                  }
              )
  
imports: import argparse
  import os
  import numpy
  from tqdm import tqdm
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.optim
  from torchvision import datasets, transforms
  import horovod.torch
  from horovod.torch.elastic.sampler import ElasticSampler
  from horovod.ray import ray_logger
  from horovod.ray.elastic import TestDiscovery
label_desc: Use image node_id 2 for calling a global function with example usage: train(state, train_loader) without return types