code: def create_and_check_xlnet_for_token_classification(
      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,
  ):
      config.num_labels = input_ids_1.shape[1]
      model = TFXLNetForTokenClassification(config)
      inputs = {
          "input_ids": input_ids_1,
          "attention_mask": input_mask,
          # 'token_type_ids': token_type_ids
      }
      result = model(inputs)
      self.parent.assertEqual(
          result.logits.shape,
          (self.batch_size, self.seq_length, config.num_labels),
      )
      self.parent.assertListEqual(
          [mem.shape for mem in result.mems],
          [(self.seq_length, self.batch_size, self.hidden_size)]
          * self.num_hidden_layers,
      )
  
imports: from __future__ import annotations
  import inspect
  import random
  import unittest
  from transformers import XLNetConfig, is_tf_available
  from transformers.testing_utils import require_tf, slow
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
  from ...test_pipeline_mixin import PipelineTesterMixin
label_desc: Use image node_id 8 for calling the TFXLNetModelTester obj's underlying member method code with example usage: obj.create_and_check_xlnet_for_token_classification(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) without return types