code: def _train(
      model,
      optimizer,
      opt_param_scheduler,
      forward_step,
      train_dataloader,
      valid_dataloader,
      end_of_epoch_callback,
  ):
      """Train the model."""
      args = get_args()
      timers = get_timers()
  
      assert (
          get_num_microbatches() == 1
      ), "finetuning with gradient accumulation doesn't currently work"
  
      # Turn on training mode which enables dropout.
      for m in model:
          m.train()
  
      # Tracking loss.
      losses_dict_sum = {}
  
      # Starting epoch and iteration
      start_epoch = args.iteration // args.train_iters_per_epoch
      start_iteration = args.iteration % args.train_iters_per_epoch
      iteration = args.iteration
  
      # Memory reporting flag.
      report_memory_flag = True
  
      # For each remaining epoch
      timers("interval-time", log_level=0).start(barrier=True)
      for epoch in range(start_epoch, args.epochs):
          print_rank_0("working on epoch {} ...".format(epoch + 1))
  
          # Set the data loader epoch to shuffle the index iterator.
          train_dataloader.sampler.set_epoch(args.seed + epoch)
  
          # For all the batches in the dataset.
          for iteration_, batch in enumerate(train_dataloader):
              # Ignore the iterations before starting value
              if iteration_ < start_iteration:
                  continue
              # Set to zero so the next epoch does not skip any batches.
              start_iteration = 0
  
              # Train for one step.
              out = train_step(
                  forward_step,
                  batch,
                  model,
                  optimizer,
                  opt_param_scheduler,
              )
  
              (
                  losses_dict,
                  skipped_iter,
                  grad_norm,
                  num_zeros_in_grad,
              ) = out
              iteration += 1
  
              # Logging.
              params_norm = None
              if args.log_params_norm:
                  params_norm = calc_params_l2_norm(model)
              report_memory_flag = training_log(
                  losses_dict,
                  losses_dict_sum,
                  optimizer.param_groups[0]["lr"],
                  iteration,
                  optimizer.get_loss_scale().item(),
                  report_memory_flag,
                  skipped_iter,
                  grad_norm,
                  params_norm,
                  num_zeros_in_grad,
              )
  
              # Autoresume
              if args.adlr_autoresume and (
                  iteration % args.adlr_autoresume_interval == 0
              ):
                  check_adlr_autoresume_termination(
                      iteration, model, optimizer, opt_param_scheduler
                  )
  
              # Checkpointing
              saved_checkpoint = False
              if (
                  args.save
                  and args.save_interval
                  and iteration % args.save_interval == 0
              ):
                  save_checkpoint(
                      iteration, model, optimizer, opt_param_scheduler
                  )
                  saved_checkpoint = True
  
              # Evaluation
              if (
                  args.eval_interval
                  and iteration % args.eval_interval == 0
              ):
                  prefix = "iteration {}".format(iteration)
                  evaluate_and_print_results(
                      prefix,
                      forward_step,
                      valid_dataloader,
                      model,
                      iteration,
                      None,
                      False,
                  )
  
              # Exiting based on iterations
              if (
                  args.exit_interval
                  and iteration % args.exit_interval == 0
              ):
                  if not saved_checkpoint:
                      save_checkpoint(
                          iteration,
                          model,
                          optimizer,
                          opt_param_scheduler,
                      )
                  torch.distributed.barrier()
                  print_rank_0(
                      "exiting program at iteration {}".format(
                          iteration
                      )
                  )
                  sys.exit()
  
          # Checkpointing at the end of each epoch.
          if args.save:
              save_checkpoint(
                  iteration, model, optimizer, opt_param_scheduler
              )
  
          # Callback at the end of each epoch.
          if end_of_epoch_callback is not None:
              end_of_epoch_callback(model, epoch)
  
imports: from functools import partial
  import sys
  import torch
  from megatron import get_args, get_num_microbatches
  from megatron import print_rank_0
  from megatron import get_timers
  from megatron.core import mpu
  from megatron.core.enums import ModelType
  from megatron.checkpointing import load_checkpoint
  from megatron.checkpointing import save_checkpoint
  from megatron.training import evaluate_and_print_results
  from megatron.training import setup_model_and_optimizer
  from megatron.training import train_step
  from megatron.training import training_log
  from megatron.utils import average_losses_across_data_parallel_group
  from megatron.utils import calc_params_l2_norm
  from megatron.utils import check_adlr_autoresume_termination
label_desc: Use image node_id 7 for calling a global function with example usage: _train(model, optimizer, opt_param_scheduler, forward_step, train_dataloader, valid_dataloader, end_of_epoch_callback) without return types