code: def create_and_check_use_mems_train(
      self,
      config,
      input_ids_1,
      input_ids_2,
      input_ids_q,
      perm_mask,
      input_mask,
      target_mapping,
      segment_ids,
      lm_labels,
      sequence_labels,
      is_impossible_labels,
      token_labels,
  ):
      model = XLNetForSequenceClassification(config)
      model.to(torch_device)
      model.train()
  
      train_size = input_ids_1.shape[0]
  
      batch_size = 4
      for i in range(train_size // batch_size + 1):
          input_ids = input_ids_1[i : (i + 1) * batch_size]
          labels = sequence_labels[i : (i + 1) * batch_size]
          outputs = model(
              input_ids=input_ids, labels=labels, return_dict=True
          )
          self.parent.assertIsNone(outputs.mems)
          self.parent.assertIsNotNone(outputs.loss)
  
imports: import random
  import unittest
  from transformers import XLNetConfig, is_torch_available
  from transformers.testing_utils import require_torch, slow, torch_device
  from ...generation.test_utils import GenerationTesterMixin
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
  from ...test_pipeline_mixin import PipelineTesterMixin
label_desc: Use image node_id 6 for calling the XLNetModelTester obj's underlying member method code with example usage: obj.create_and_check_use_mems_train(config, input_ids_1, input_ids_2, input_ids_q, perm_mask, input_mask, target_mapping, segment_ids, lm_labels, sequence_labels, is_impossible_labels, token_labels) without return types