code: def create_transfo_xl_lm_head_trainer_incompatible_tuple(
      self, config, input_ids_1, input_ids_2, lm_labels
  ):
      config.trainer_compatible = False
      model = TransfoXLLMHeadModel(config)
      model.to(torch_device)
      model.eval()
  
      lm_logits_1 = model(input_ids_1, return_dict=False)[0]
      outputs1 = model(input_ids_1, labels=lm_labels, return_dict=False)
      losses_1, _, mems_1 = outputs1[:3]
      loss_1 = outputs1[-1]
      lm_logits_2 = model(input_ids_2, mems=mems_1, return_dict=False)[
          0
      ]
      outputs2 = model(input_ids_2, labels=lm_labels, mems=mems_1)
      losses_2, _, mems_2 = outputs2[:3]
      loss_2 = outputs2[-1]
  
      outputs = {
          "losses_1": losses_1,
          "mems_1": mems_1,
          "lm_logits_1": lm_logits_1,
          "loss_1": loss_1,
          "losses_2": losses_2,
          "mems_2": mems_2,
          "lm_logits_2": lm_logits_2,
          "loss_2": loss_2,
      }
  
      config.trainer_compatible = None
      return outputs
  
imports: import copy
  import random
  import unittest
  from transformers import TransfoXLConfig, is_torch_available
  from transformers.testing_utils import require_torch, require_torch_multi_gpu, slow, torch_device
  from ...generation.test_generation_utils import GenerationTesterMixin
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, ids_tensor
label_desc: Use image node_id 10 for calling the TransfoXLModelTester obj's underlying member method code with example usage: obj.create_transfo_xl_lm_head_trainer_incompatible_tuple(config, input_ids_1, input_ids_2, lm_labels) and returns: outputs