code: def train(state):
      model.train()
      epoch = state.epoch
      train_loss = Metric("train_loss")
      train_accuracy = Metric("train_accuracy")
  
      batch_offset = state.batch
      with tqdm(
          total=len(train_loader),
          desc="Train Epoch     #{}".format(epoch + 1),
          disable=not verbose,
      ) as t:
          for 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_idx = batch_offset + 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()
  
              adjust_learning_rate(epoch, batch_idx)
  
              if args.cuda:
                  data, target = data.cuda(), target.cuda()
              optimizer.zero_grad()
              # Split data into sub-batches of size batch_size
              for i in range(0, len(data), args.batch_size):
                  data_batch = data[i : i + args.batch_size]
                  target_batch = target[i : i + args.batch_size]
                  output = model(data_batch)
                  train_accuracy.update(accuracy(output, target_batch))
                  loss = F.cross_entropy(output, target_batch)
                  train_loss.update(loss)
                  # Average gradients among sub-batches
                  loss.div_(
                      math.ceil(float(len(data)) / args.batch_size)
                  )
                  loss.backward()
  
              # Elastic Horovod: record which samples were processed this batch
              # so we do not reprocess them if a reset event occurs
              state.train_sampler.record_batch(
                  idx, allreduce_batch_size
              )
  
              # Gradient is applied across all ranks
              optimizer.step()
              t.set_postfix(
                  {
                      "loss": train_loss.avg.item(),
                      "accuracy": 100.0 * train_accuracy.avg.item(),
                  }
              )
  
              t.update(1)
  
      if log_writer:
          log_writer.add_scalar("train/loss", train_loss.avg, epoch)
          log_writer.add_scalar(
              "train/accuracy", train_accuracy.avg, epoch
          )
  
      state.commit()
  
imports: import torch
  import argparse
  import torch.backends.cudnn
  import torch.multiprocessing
  import torch.nn.functional
  import torch.optim
  import torch.utils.data.distributed
  from torch.utils.tensorboard import SummaryWriter
  from torchvision import datasets, transforms, models
  import horovod.torch
  import os
  import math
  from tqdm import tqdm
label_desc: Use image node_id 1 for calling a global function with example usage: train(state) without return types