code: def forward(self, data, target, *mems):
      # nn.DataParallel does not allow size(0) tensors to be broadcasted.
      # So, have to initialize size(0) mems inside the model forward.
      # Moreover, have to return new_mems to allow nn.DataParallel to piece
      # them together.
      if not mems:
          mems = self.init_mems(data)
  
      tgt_len = target.size(0)
      hidden, new_mems = self._forward(data, mems=mems)
  
      pred_hid = hidden[-tgt_len:]
      if self.sample_softmax > 0 and self.training:
          assert self.tie_weight
          logit = sample_logits(
              self.word_emb,
              self.out_layer.bias,
              target,
              pred_hid,
              self.sampler,
          )
          loss = -F.log_softmax(logit, -1)[:, :, 0]
      else:
          loss = self.crit(
              pred_hid.view(-1, pred_hid.size(-1)),
              target.contiguous().view(-1),
          )
          loss = loss.view(tgt_len, -1)
  
      if new_mems is None:
          return [loss]
      else:
          return [loss] + new_mems
  
imports: import sys
  import math
  import functools
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from proj_adaptive_softmax import ProjectedAdaptiveLogSoftmax, Projection
  from log_uniform_sampler import LogUniformSampler, sample_logits
  from fmoe import FMoETransformerMLP
label_desc: Use image node_id 8 for calling the MemTransformerLM obj's underlying member method code with example usage: obj.forward(data, target) and returns: list, unknown