code: def test_padding(self):
      self.args.label_smoothing = 0.1
      crit = LabelSmoothedCrossEntropyCriterion.build_criterion(
          self.args, self.task
      )
      loss, _, logging_output = crit(self.model, self.sample)
  
      def get_one_no_padding(idx):
          # create a new sample with just a single batch item so that there's
          # no padding
          sample1 = next(test_utils.dummy_dataloader([self.data[idx]]))
          args1 = copy.copy(self.args)
          args1.probs = args1.probs[idx, :, :].unsqueeze(0)
          model1 = self.task.build_model(args1)
          loss1, _, _ = crit(model1, sample1)
          return loss1
  
      loss1 = get_one_no_padding(0)
      loss2 = get_one_no_padding(1)
      self.assertAlmostEqual(loss, loss1 + loss2)
  
file_path: fairseq/tests/test_label_smoothing.py
imports: import argparse
  import copy
  import unittest
  import tests.utils
  import torch
  from fairseq.criterions.cross_entropy import CrossEntropyCriterion
  from fairseq.criterions.label_smoothed_cross_entropy import LabelSmoothedCrossEntropyCriterion
label_desc: Use image node_id 3 for calling the TestLabelSmoothing obj's underlying member method code with example usage: obj.test_padding() and returns: loss1