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_barlow_twins_loss(self) -> None:
      loss_layer = BarlowTwinsCriterion(
          lambda_=0.0051, scale_loss=0.024, embedding_dim=EMBEDDING_DIM
      )
      _ = loss_layer(self._get_embedding())
  
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 5 for calling the TestLossesForward obj's underlying member method code with example usage: obj.test_barlow_twins_loss() without return types