new_contents: import torch
  import torch.nn as nn
  import torch.nn.functional as F
  
  
  class DenseBinaryCrossEntropy(nn.Module):
      """ BCE using one-hot from dense targets w/ label smoothing
      NOTE for experiments comparing CE to BCE /w label smoothing, may remove
      """
      def __init__(self, smoothing=0.1):
          super(DenseBinaryCrossEntropy, self).__init__()
          assert 0. <= smoothing < 1.0
          self.smoothing = smoothing
          self.bce = nn.BCEWithLogitsLoss()
  
      def forward(self, x, target):
          num_classes = x.shape[-1]
          off_value = self.smoothing / num_classes
          on_value = 1. - self.smoothing + off_value
          target = target.long().view(-1, 1)
          target = torch.full(
              (target.size()[0], num_classes), off_value, device=x.device, dtype=x.dtype).scatter_(1, target, on_value)
          return self.bce(x, target)
  
subject: Add a BCE loss impl that converts dense targets to sparse /w smoothing as an alternate to CE w/ smoothing. For training experiments.