code: def forward(self, inputs, targets):
      log_probs = self.logsoftmax(inputs)
      targets = torch.zeros_like(log_probs).scatter_(
          1, targets.unsqueeze(1), 1
      )
      targets = (
          1 - self.epsilon
      ) * targets + self.epsilon / self.num_classes
      loss = (-targets * log_probs).mean(0).sum()
      return loss
  
imports: import math
  import os
  import random
  import sys
  import time
  import warnings
  import numpy
  import numpy
  import shutil
  import logging
  import torch
  import torchvision.transforms
  import torchvision.datasets
  import torch.nn
label_desc: Use image node_id 2 for calling the CrossEntropyLabelSmooth obj's underlying member method code with example usage: obj.forward(inputs, targets) and returns: loss