code: def train(state):
      state.rendezvous += 1
      while state.epoch < args.epochs:
          print("epoch {} batch {}".format(state.epoch, state.batch))
  
          while state.batch < args.batches_per_epoch:
              check_exit(state.epoch, state.batch)
  
              optimizer.zero_grad()
              output = model(data)
              loss = F.cross_entropy(output, target)
              loss.backward()
              optimizer.step()
  
              state.batch += 1
              if state.batch % args.batches_per_commit == 0:
                  state.commits += 1
                  state.commit()
  
          if hvd.rank() == 0:
              log_state(state)
  
              current_hosts = epoch_to_hosts.get(
                  state.epoch, default_hosts
              )
              next_hosts = epoch_to_hosts.get(
                  state.epoch + 1, default_hosts
              )
              if (
                  args.discovery_wait > 0
                  and current_hosts != next_hosts
              ):
                  print(
                      "host changes: {} -> {}".format(
                          current_hosts, next_hosts
                      )
                  )
                  start = int(time.time())
  
                  # Reset the last updated timestamp to ensure we still raise HostsUpdatedInterrupt even
                  # if the timestamp from the driver isn't greater than the last (due to time loops).
                  state._last_updated_timestamp = 0
                  while state._host_messages.empty():
                      if int(time.time()) - start > args.discovery_wait:
                          raise TimeoutError(
                              "Timed out waiting for notifications from driver."
                          )
                      time.sleep(0.1)
  
          if args.epoch_wait > 0:
              time.sleep(args.epoch_wait)
  
          state.epoch += 1
          state.batch = 0
          state.commits += 1
          state.commit()
  
file_path: horovod/test/integration/data/elastic_torch_main.py
imports: import argparse
  import json
  import os
  import psutil
  import time
  import torch
  import torch.nn.functional
  import horovod.torch
label_desc: Use image node_id 3 for calling a global function with example usage: train(state) without return types