code: def xe_loss_with_softmax(self, O, Y, M):
      if self.logq > 0:
          O = O - self.logq * torch.log(
              torch.cat(
                  [self.P0[Y[:M]], self.P0[Y[M:]] ** self.sample_alpha]
              )
          )
      X = torch.exp(O - O.max(dim=1, keepdim=True)[0])
      X = X / X.sum(dim=1, keepdim=True)
      return -torch.sum(torch.log(torch.diag(X) + 1e-24))
  
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 4 for calling the GRU4RecModel obj's underlying member method code with example usage: obj.xe_loss_with_softmax(O, Y, M) and returns: unknown