code: def loss_and_miner_tester(
      self,
      loss_class,
      miner_class,
      efficient,
      xbm,
      use_ref,
      loss_kwargs=None,
      miner_kwargs=None,
      pass_labels_to_loss_fn=True,
      use_xbm_enqueue_mask=False,
  ):
      torch.manual_seed(75210)
      loss_kwargs = {} if loss_kwargs is None else loss_kwargs
      miner_kwargs = {} if miner_kwargs is None else miner_kwargs
      if TEST_DEVICE == torch.device("cpu"):
          return
      max_world_size = min(4, torch.cuda.device_count())
      if max_world_size < 1:
          logging.warning(
              "In GPU mode but no GPUs available. Skipping distributed test"
          )
          return
      for dtype in TEST_DTYPES:
          if dtype == torch.float16:
              continue
          for world_size in range(2, max_world_size + 1):
              batch_size = 20
              lr = 0.1
              iterations = 10
              original_model = ToyMpModel().type(dtype)
              model = ToyMpModel().type(dtype)
              model.load_state_dict(original_model.state_dict())
              self.assertTrue(
                  parameters_are_equal(original_model, model)
              )
  
              original_model = original_model.to(TEST_DEVICE)
              original_loss_fn = loss_class(**loss_kwargs)
              loss_fn = loss_class(**loss_kwargs)
              if xbm:
                  original_loss_fn = CrossBatchMemory(
                      original_loss_fn, embedding_size=5
                  )
                  loss_fn = CrossBatchMemory(loss_fn, embedding_size=5)
  
              if miner_class:
                  original_miner_fn = miner_class(**miner_kwargs)
                  miner_fn = miner_class(**miner_kwargs)
              else:
                  original_miner_fn = None
                  miner_fn = None
  
              optimizer = optim.SGD(original_model.parameters(), lr=lr)
              inputs = create_inputs(
                  batch_size, world_size, iterations, dtype
              )
              labels = create_labels(batch_size, world_size, iterations)
              (
                  ref_inputs,
                  ref_labels,
                  all_ref_outputs,
                  all_ref_labels,
              ) = (
                  None,
                  None,
                  None,
                  None,
              )
              if use_ref:
                  ref_inputs = create_inputs(
                      batch_size, world_size, iterations, dtype
                  )
                  ref_labels = create_labels(
                      batch_size, world_size, iterations
                  )
  
              (
                  local_enqueue_mask,
                  global_enqueue_mask,
              ) = create_enqueue_mask(batch_size, world_size)
  
              for aaa in range(iterations):
                  optimizer.zero_grad()
                  all_outputs, all_labels = get_all_outputs_and_labels(
                      inputs, labels, original_model, aaa
                  )
                  if use_ref:
                      (
                          all_ref_outputs,
                          all_ref_labels,
                      ) = get_all_outputs_and_labels(
                          ref_inputs, ref_labels, original_model, aaa
                      )
  
                  indices_tuple = None
                  if efficient:
                      losses = []
                      for i in range(len(inputs[aaa])):
                          curr_emb, other_emb = create_efficient_batch(
                              all_outputs,
                              all_ref_outputs,
                              i,
                              batch_size,
                          )
                          (
                              curr_labels,
                              other_labels,
                          ) = create_efficient_batch(
                              all_labels, all_ref_labels, i, batch_size
                          )
                          if original_miner_fn:
                              indices_tuple = (
                                  distributed.get_indices_tuple(
                                      curr_labels,
                                      other_labels,
                                      embeddings=curr_emb,
                                      ref_emb=other_emb,
                                      miner=original_miner_fn,
                                  )
                              )
                          else:
                              indices_tuple = (
                                  distributed.get_indices_tuple(
                                      curr_labels, other_labels
                                  )
                              )
                          loss = original_loss_fn(
                              curr_emb,
                              curr_labels,
                              indices_tuple,
                              other_emb,
                              other_labels,
                          )
                          losses.append(loss)
                      loss = sum(losses)
                  else:
                      if original_miner_fn:
                          indices_tuple = original_miner_fn(
                              all_outputs,
                              all_labels,
                              all_ref_outputs,
                              all_ref_labels,
                          )
                      if xbm:
                          enqueue_mask = (
                              global_enqueue_mask
                              if use_xbm_enqueue_mask
                              else None
                          )
                          loss = original_loss_fn(
                              all_outputs,
                              all_labels,
                              indices_tuple,
                              enqueue_mask,
                          )
                      else:
                          loss = original_loss_fn(
                              all_outputs,
                              all_labels,
                              indices_tuple,
                              all_ref_outputs,
                              all_ref_labels,
                          )
  
                  loss.backward()
                  optimizer.step()
  
              mp.spawn(
                  single_process_function,
                  args=(
                      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,
                      local_enqueue_mask,
                  ),
                  nprocs=world_size,
                  join=True,
              )
  
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 1 for calling the TestDistributedLossWrapper obj's underlying member method code with example usage: obj.loss_and_miner_tester(loss_class, miner_class, efficient, xbm, use_ref, loss_kwargs, miner_kwargs, pass_labels_to_loss_fn, use_xbm_enqueue_mask) without return types