class_docstr: Minimal testing of the losses: ensure that a forward pass with believable
  dimensions succeeds. This does not make them correct per say.
code: def test_swav_loss(self) -> None:
      loss_layer = SwAVCriterion(
          temperature=0.1,
          crops_for_assign=[0, 1],
          num_crops=2,
          num_iters=3,
          epsilon=0.05,
          use_double_prec=False,
          num_prototypes=[3000],
          local_queue_length=0,
          embedding_dim=EMBEDDING_DIM,
          temp_hard_assignment_iters=0,
          output_dir="",
      )
      _ = loss_layer(scores=self._get_embedding(), head_id=0)
  
imports: import logging
  import unittest
  from collections import namedtuple
  import torch
  import torch.nn
  import torch.optim
  from classy_vision.generic.distributed_util import set_cpu_device
  from parameterized import param, parameterized
  from vissl.config.attr_dict import AttrDict
  from vissl.losses.barlow_twins_loss import BarlowTwinsCriterion
  from vissl.losses.cross_entropy_multiple_output_single_target import CrossEntropyMultipleOutputSingleTargetCriterion, CrossEntropyMultipleOutputSingleTargetLoss
  from vissl.losses.distillation_loss import DistillationCriterion, DistillationLoss
  from vissl.losses.multicrop_simclr_info_nce_loss import MultiCropSimclrInfoNCECriterion
  from vissl.losses.simclr_info_nce_loss import SimclrInfoNCECriterion
  from vissl.losses.swav_loss import SwAVCriterion
label_desc: Use image node_id 4 for calling the TestLossesForward obj's underlying member method code with example usage: obj.test_swav_loss() without return types