new_contents: import torch
  from torch import nn
  from torch.autograd import Variable
  
  
  class TripletLoss(nn.Module):
      def __init__(self, margin=0):
          super(TripletLoss, self).__init__()
          self.margin = margin
          self.ranking_loss = nn.MarginRankingLoss(margin=margin)
  
      def forward(self, inputs, targets):
          n = inputs.size(0)
          # Compute pairwise distance, replace by the official when merged
          dist = torch.pow(inputs, 2).sum(1).expand(n, n)
          dist = dist + dist.t()
          dist.addmm_(1, -2, inputs, inputs.t())
          dist = dist.clamp(min=0).sqrt()
          # Enumerate triplets (a, n, p), and save (an, ap) distance indices
          d, y = dist.data, targets.data
          indices = []
          for i in range(n):
              for j in range(n):
                  if i == j: continue
                  if y[i] != y[j]: continue
                  for k in range(n):
                      if y[k] == y[i]: continue
                      if d[i][k] - d[i][j] >= self.margin: continue
                      indices.append((i * n + k, i * n + j))
          indices = torch.Tensor(indices).long()
          if dist.is_cuda:
              indices = indices.cuda()
          # Since torch does not support 2d indexing yet, we need to flatten it
          dist = dist.view(-1)
          dist_an, dist_ap = dist[indices[:, 0]], dist[indices[:, 1]]
          # Compute ranking hinge loss
          y = torch.ones(dist_an.size(0))
          if dist.is_cuda:
              y = y.cuda()
          y = Variable(y)
          return self.ranking_loss(dist_an, dist_ap, y)
  
subject: Add a simple implementation of triplet loss (not efficient)