code: def train(
      rank: int,
      args: argparse.Namespace,
      backend: str = "gloo",
      optim_type: OptimType = OptimType.vanilla,
      check_regression: bool = True,
  ):
      logging.basicConfig(
          level=logging.INFO if not args.debug else logging.DEBUG
      )
  
      use_multi_tensor = args.multi_tensor_optim and hasattr(
          torch.optim, "_multi_tensor"
      )
      OPTIM = torch.optim._multi_tensor.RMSprop if use_multi_tensor else torch.optim.RMSprop  # type: ignore  # attr is  checked but mypy misses that
      logging.info(
          "Multi tensor optimizer: {}".format(use_multi_tensor)
      )
  
      # DDP
      dist_init(rank=rank, world_size=args.world_size, backend=backend)
  
      # Setup
      if not args.cpu:
          torch.cuda.set_device(rank)
          torch.cuda.manual_seed(0)
      torch.manual_seed(0)  # also sets the cuda seed
      np.random.seed(0)
  
      if backend == "nccl":
          torch.backends.cudnn.deterministic = True
          torch.backends.cudnn.benchmark = False
  
      device = torch.device("cpu") if args.cpu else torch.device(rank)
      model, dataloader, loss_fn = get_problem(
          rank, args.world_size, args.batch_size, device, args.model
      )
  
      # Shard the optimizer
      optimizer: Optional[torch.optim.Optimizer] = None
      model = cast(nn.Module, model)
      scaler = (
          (
              TorchGradScaler()
              if args.optim_type == OptimType.vanilla
              else ShardedGradScaler()
          )
          if args.amp
          else None
      )
  
      if optim_type == OptimType.oss_sharded_ddp:
          optimizer = OSS(
              params=model.parameters(),
              optim=OPTIM,
              lr=1e-4,
              momentum=0.9,
          )
          # Single node run typically, no need for reduce buckets
          model = ShardedDDP(model, optimizer, reduce_buffer_size=0)
      else:
          device_ids = None if args.cpu else [rank]
          model = DDP(model, device_ids=device_ids, find_unused_parameters=False)  # type: ignore
          optimizer = (
              OSS(
                  params=model.parameters(),
                  optim=OPTIM,
                  lr=1e-4,
                  momentum=0.9,
              )
              if optim_type == OptimType.oss_ddp
              else OPTIM(model.parameters(), lr=1e-4, momentum=0.9)
          )
      optimizer = cast(torch.optim.Optimizer, optimizer)
  
      # Reset the memory use counter
      if not args.cpu:
          torch.cuda.empty_cache()
          torch.cuda.reset_peak_memory_stats(rank)
          torch.cuda.synchronize(rank)
  
      # Standard training loop
      training_start = time.monotonic()
      model.train()
  
      measurements = []
      final_loss: Optional[float] = -1.0
      min_loss = 100.0
      need_profiling = args.profile
  
      for epoch in range(args.epochs):
          n_items = 0
          epoch_runtime = 0.0
  
          for batch in dataloader:
              if not args.cpu:
                  torch.cuda.synchronize(rank)
              batch_start = time.monotonic()
  
              def closure(data=batch, grad_scaler=None):
                  model.zero_grad()
                  if (
                      args.debug
                      and rank == 0
                      and next(model.parameters()).grad is not None
                  ):
                      logging.debug(
                          "\nbefore:  param {} -- grad {}".format(
                              next(model.parameters()).norm().item(),
                              next(model.parameters())
                              .grad.norm()
                              .item(),
                          )
                      )
                  if grad_scaler is not None:
                      # Automatically computes the FW pass in half precision
                      with torch.cuda.amp.autocast():
                          outputs = model(data["inputs"])
                          loss = loss_fn(outputs, data["label"])
  
                          # Accumulates scaled gradients.
                          grad_scaler.scale(loss).backward()
                  else:
                      outputs = model(data["inputs"])
                      loss = loss_fn(outputs, data["label"])
                      loss.backward()
  
                  if (
                      args.debug
                      and rank == 0
                      and next(model.parameters()).grad is not None
                  ):
                      logging.debug(
                          "after BW: param {} -- grad {}".format(
                              next(model.parameters()).norm().item(),
                              next(model.parameters())
                              .grad.norm()
                              .item(),
                          )
                      )
                  return loss
  
              def run_closure(closure, scaler, optimizer):
                  if scaler is not None:
                      final_loss = closure(
                          grad_scaler=scaler
                      )  # AMP scaler.step does not support closures
                      scaler.step(optimizer)
                      scaler.update()
                      return final_loss
                  else:
                      return optimizer.step(closure)
  
              if need_profiling and not args.cpu:
                  logging.info("Profiling the run")
                  with profiler.profile(use_cuda=True, record_shapes=True, profile_memory=True) as prof:  # type: ignore
                      with profiler.record_function("batch"):
                          final_loss = run_closure(
                              closure, scaler, optimizer
                          )
  
                  prof.export_chrome_trace(
                      f"{optim_type}_trace_rank_{rank}.json"
                  )
                  need_profiling = False  # only profile once
  
              else:
                  final_loss = run_closure(closure, scaler, optimizer)
  
              if args.debug and rank == 0:
                  logging.debug(
                      "buffer: {}".format(
                          next(model.buffers()).norm().item()
                      )
                  )
                  logging.debug(
                      "after update: param {} -- grad {}".format(
                          next(model.parameters()).norm().item(),
                          next(model.parameters()).grad.norm().item(),
                      )
                  )
  
              n_items += args.batch_size
  
              if not args.cpu:
                  # make sure that the cuda kernels are finished before taking a timestamp
                  torch.cuda.synchronize(rank)
  
              batch_end = time.monotonic()
              epoch_runtime += batch_end - batch_start
  
          if (
              optim_type == OptimType.oss_ddp
              or optim_type == OptimType.oss_sharded_ddp
          ):
              # Check the checkpointing in the case of the OSS optimizer
              # Memory usage could spill over from there
              optimizer = cast(OSS, optimizer)
              optimizer.consolidate_state_dict()
              if dist.get_rank() == 0:
                  _ = optimizer.state_dict()
                  logging.info("... State dict collected")
  
          measurements.append(n_items / epoch_runtime)
          min_loss = min(min_loss, final_loss)
          if dist.get_rank() == 0:
              logging.info(
                  f"Epoch {epoch} - processed {measurements[-1]:.2f} img per sec. "
                  f"Loss {final_loss:.3f} min loss {min_loss:.3f}"
              )
  
      training_stop = time.monotonic()
      img_per_sec = (
          n_items / (training_stop - training_start) * args.epochs
      )
      logging.info(
          f"[{dist.get_rank()}] : Training done. {img_per_sec:.2f} img per sec inc. checkpoint"
      )
  
      # Use min_loss to check instead of final_loss since the final_loss is a bit random.
      # If the training min_loss reaches certain number, we can be reasonably certain the
      # training process was correct.
      validate_benchmark(measurements, min_loss, args, check_regression)
  
      dist.destroy_process_group()
  
imports: import argparse
  from enum import Enum
  import importlib
  import logging
  import tempfile
  import time
  from typing import Any, List, Optional, cast
  from golden_configs import oss_mnist
  import numpy
  import torch
  import torch.autograd.profiler
  from torch.cuda.amp import GradScaler
  import torch.distributed
  import torch.multiprocessing
  import torch.nn
  from torch.nn.parallel import DistributedDataParallel
  from torch.utils.data import BatchSampler, DataLoader, Sampler
  from torch.utils.data.distributed import DistributedSampler
  from torchvision.datasets import MNIST
  from torchvision.transforms import Compose, Resize, ToTensor
  from benchmarks.datasets.mnist import setup_cached_mnist
  from fairscale.nn.data_parallel import ShardedDataParallel
  from fairscale.optim import OSS
  from fairscale.optim.grad_scaler import ShardedGradScaler
label_desc: Use image node_id 4 for calling a global function with example usage: train(rank, args, backend, optim_type, check_regression) and returns: loss, final_loss, optimizer