code: def forward(self, batch):
      dtype, device = batch.dtype, batch.device
      self.mask = self.mask.type(dtype).to(device)
      # compute the relevance scores via cosine similarity of the CNN-produced embedding vectors
  
      sim_all = self.compute_aff(batch)
  
      sim_all_repeat = sim_all.unsqueeze(dim=1).repeat(
          1, self.batch_size, 1
      )
      # compute the difference matrix
      sim_diff = sim_all_repeat - sim_all_repeat.permute(0, 2, 1)
      # pass through the sigmoid and ignores the relevance score of the query to itself
      sim_sg = self.sigmoid(sim_diff, temp=self.anneal) * self.mask
      # compute the rankings,all batch
      sim_all_rk = torch.sum(sim_sg, dim=-1)
      if self.variant == "PNP-D_s":
          sim_all_rk = torch.log(1 + sim_all_rk)
      elif self.variant == "PNP-D_q":
          sim_all_rk = 1 / (1 + sim_all_rk) ** (self.alpha)
  
      elif self.variant == "PNP-I_u":
          sim_all_rk = (1 + sim_all_rk) * torch.log(1 + sim_all_rk)
  
      elif self.variant == "PNP-I_b":
          b = self.b
          sim_all_rk = (
              1
              / b**2
              * (b * sim_all_rk - torch.log(1 + b * sim_all_rk))
          )
      elif self.variant == "PNP-O":
          pass
      else:
          raise Exception(
              "variantation <{}> not available!".format(self.variant)
          )
  
      # sum the values of the Smooth-AP for all instances in the mini-batch
      loss = torch.zeros(1).type(dtype).to(device)
      group = int(self.batch_size / self.num_id)
  
      for ind in range(self.num_id):
          neg_divide = torch.sum(
              sim_all_rk[
                  (ind * group) : ((ind + 1) * group),
                  (ind * group) : ((ind + 1) * group),
              ]
              / group
          )
          loss = loss + (neg_divide / self.batch_size)
      if self.variant == "PNP-D_q":
          return 1 - loss
      else:
          return loss
  
file_path: pytorch-metric-learning/tests/losses/test_pnp_loss.py
imports: import unittest
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.nn.functional
  from pytorch_metric_learning.losses import PNPLoss
  from ..None import TEST_DEVICE, TEST_DTYPES
label_desc: Use image node_id 2 for calling the OriginalImplementationPNP obj's underlying member method code with example usage: obj.forward(batch) and returns: unknown, loss