code: def test_single_target_label_smoothing(
      self, batch_size: int, target_count: int
  ):
      torch.random.manual_seed(0)
      logits = torch.randn(size=(batch_size, target_count))
      target = torch.randint(0, target_count, size=(batch_size,))
  
      # Verify that label smoothing is supported in forward pass
      criterion = CrossEntropyMultipleOutputSingleTargetCriterion(
          label_smoothing=0.1
      )
      loss = criterion(logits, target)
      self.assertTrue(loss.item() > 0.0)
  
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 5 for calling the TestCrossEntropyMultipleOutputSingleTargetLoss obj's underlying member method code with example usage: obj.test_single_target_label_smoothing(batch_size, target_count) without return types