code: def bpr_max_loss_with_elu(self, O, Y, M):
      if self.elu_param > 0:
          O = nn.functional.elu(O, self.elu_param)
      softmax_scores = self.softmax_neg(O)
      target_scores = torch.diag(O)
      target_scores = target_scores.reshape(target_scores.shape[0], -1)
      return torch.sum(
          (
              -torch.log(
                  torch.sum(
                      torch.sigmoid(target_scores - O) * softmax_scores,
                      dim=1,
                  )
                  + 1e-24
              )
              + self.bpreg * torch.sum((O**2) * softmax_scores, dim=1)
          )
      )
  
imports: from collections import Counter
  import numpy
  import torch
  from torch import nn
  from torch.autograd import Variable
  from torch.optim import Optimizer
  from cornac.utils.common import get_rng
label_desc: Use image node_id 6 for calling the GRU4RecModel obj's underlying member method code with example usage: obj.bpr_max_loss_with_elu(O, Y, M) and returns: torch