code: def single_process_function(
      rank,
      world_size,
      lr,
      iterations,
      model,
      inputs,
      labels,
      ref_inputs,
      ref_labels,
      loss_fn,
      miner_fn,
      original_model,
      efficient,
      pass_labels_to_loss_fn,
      use_xbm_enqueue_mask,
      enqueue_mask,
  ):
      setup(rank, world_size)
      if TEST_DEVICE == torch.device("cpu"):
          device = TEST_DEVICE
          device_ids = None
          output_device = None
      else:
          device = torch.device("cuda:{}".format(rank))
          device_ids = [rank]
          output_device = rank
  
      ddp_mp_model = DDP(
          model.to(device),
          device_ids=device_ids,
          output_device=output_device,
      )
  
      loss_fn = distributed.DistributedLossWrapper(
          loss=loss_fn, efficient=efficient
      )
  
      if miner_fn:
          miner_fn = distributed.DistributedMinerWrapper(
              miner=miner_fn, efficient=efficient
          )
  
      optimizer = optim.SGD(ddp_mp_model.parameters(), lr=lr)
  
      original_model = original_model.to(device)
      assert not parameters_are_equal(
          original_model, ddp_mp_model.module
      )
  
      for i in range(iterations):
          optimizer.zero_grad()
          outputs = ddp_mp_model(inputs[i][rank].to(device))
          curr_labels = labels[i][rank]
          ref_outputs, curr_ref_labels = None, None
          if ref_inputs:
              ref_outputs = ddp_mp_model(ref_inputs[i][rank].to(device))
              curr_ref_labels = ref_labels[i][rank]
          indices_tuple = None
          if miner_fn:
              indices_tuple = miner_fn(
                  outputs, curr_labels, ref_outputs, curr_ref_labels
              )
          if miner_fn and not pass_labels_to_loss_fn:
              loss = loss_fn(
                  outputs,
                  indices_tuple=indices_tuple,
                  ref_emb=ref_outputs,
              )
          elif use_xbm_enqueue_mask and isinstance(
              loss_fn.loss, CrossBatchMemory
          ):
              loss = loss_fn(
                  outputs,
                  curr_labels,
                  indices_tuple,
                  enqueue_mask=enqueue_mask[rank],
              )
          else:
              loss = loss_fn(
                  outputs,
                  curr_labels,
                  indices_tuple,
                  ref_outputs,
                  curr_ref_labels,
              )
  
          dist.barrier()
          loss.backward()
          dist.barrier()
          optimizer.step()
  
      dist.barrier()
      assert parameters_are_equal(original_model, ddp_mp_model.module)
      dist.barrier()
      cleanup()
  
imports: import logging
  import os
  import unittest
  import torch
  import torch.distributed
  import torch.multiprocessing
  import torch.optim
  from torch.nn.parallel import DistributedDataParallel
  from pytorch_metric_learning.losses import ContrastiveLoss, CrossBatchMemory
  from pytorch_metric_learning.miners import PairMarginMiner
  from pytorch_metric_learning.utils import distributed
  from ..None import TEST_DEVICE, TEST_DTYPES
label_desc: Use image node_id 4 for calling a global function with example usage: single_process_function(rank, world_size, lr, iterations, model, inputs, labels, ref_inputs, ref_labels, loss_fn, miner_fn, original_model, efficient, pass_labels_to_loss_fn, use_xbm_enqueue_mask, enqueue_mask) without return types