class_docstr: Specific tests on Barlow Twins going further than just doing a forward pass
code: def test_barlow_twins_backward(self) -> None:
      EMBEDDING_DIM = 3
      criterion = BarlowTwinsCriterion(
          lambda_=0.0051, scale_loss=0.024, embedding_dim=EMBEDDING_DIM
      )
      embeddings = torch.randn((4, EMBEDDING_DIM), requires_grad=True)
  
      self.assertTrue(embeddings.grad is None)
      criterion(embeddings).backward()
      self.assertTrue(embeddings.grad is not None)
      with torch.no_grad():
          next_embeddings = (
              embeddings - embeddings.grad
          )  # gradient descent
          self.assertTrue(
              criterion(next_embeddings) < criterion(embeddings)
          )
  
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 1 for calling the TestBarlowTwinsCriterion obj's underlying member method code with example usage: obj.test_barlow_twins_backward() without return types