docstr: Verify that we can use the queue of the SwAV distillation
  criterion to increase the stability of the assignments
docstr_tok: ["Verify","that","we","can","use","the","queue","of","the","SwAV","distillation","criterion","to","increase","the","stability","of","the","assignments"]
code: def _test_swav_distillation_criterion_queue_worker(gpu_id: int):
      """
      Verify that we can use the queue of the SwAV distillation
      criterion to increase the stability of the assignments
      """
      torch.random.manual_seed(gpu_id)
  
      num_crops = 6
      batch_size = 5
      embedding_dim = 16
      num_prototypes = 32
      criterion = SwAVDistillationCriterion(
          temperature=0.1,
          num_crops=num_crops,
          num_iters=3,
          epsilon=0.05,
          use_double_prec=False,
          num_prototypes=[num_prototypes],
          output_dir=".",
          temp_hard_assignment_iters=0,
          local_queue_length=30,
          swapped_assignment=True,
      ).cuda(gpu_id)
  
      logits = torch.randn(
          size=(batch_size * num_crops, embedding_dim)
      ).cuda(gpu_id)
      teacher_prototypes = torch.randn(
          size=(num_prototypes, embedding_dim)
      ).cuda(gpu_id)
      teacher_scores_1 = torch.rand(
          size=(batch_size, num_prototypes)
      ).cuda(gpu_id)
      teacher_scores_2 = torch.rand(
          size=(batch_size, num_prototypes)
      ).cuda(gpu_id)
      for i in range(batch_size):
          teacher_scores_1[i, i + 2 * batch_size * gpu_id] += 10
          teacher_scores_2[
              i, i + 2 * batch_size * gpu_id + batch_size
          ] += 10
  
      # Compute loss with no previous scores in the queue
      loss_1 = criterion(logits, teacher_scores_1, teacher_prototypes)
      asssignments_1 = criterion.compute_teacher_assignements(
          teacher_scores_1
      )
      print(loss_1.item(), "-", asssignments_1.max().item())
  
      # Add some previous teacher scores in the queue
      criterion.add_to_teacher_scores_queue(teacher_scores_2)
      criterion.use_queue = True
  
      # Compute loss with this queue
      loss_2 = criterion(logits, teacher_scores_1, teacher_prototypes)
      asssignments_2 = criterion.compute_teacher_assignements(
          teacher_scores_1
      )
      print(loss_2.item(), "-", asssignments_2.max().item())
  
      # Check that the assignments are less spread (more certain)
      assert asssignments_2.max().item() > asssignments_1.max().item()
      assert loss_1.item() != loss_2.item(), "Loss should be different"
  
imports: import os
  import unittest
  import torch
  import torch.nn.functional
  from vissl.losses.swav_distillation_loss import SwAVDistillationCriterion
  from vissl.losses.swav_loss import SwAVCriterion
  from vissl.utils.hydra_config import compose_hydra_configuration, convert_to_attrdict
  from vissl.utils.test_utils import gpu_test, in_temporary_directory, run_integration_test, spawn_distributed_test
label_desc: Use image node_id 7 for calling the TestDistillationSwAV obj's underlying member method code with example usage: obj._test_swav_distillation_criterion_queue_worker(gpu_id) without return types