code: def prepare_config_and_inputs(self):
      input_ids_1 = ids_tensor(
          [self.batch_size, self.seq_length], self.vocab_size
      )
      input_ids_2 = ids_tensor(
          [self.batch_size, self.seq_length], self.vocab_size
      )
      segment_ids = ids_tensor(
          [self.batch_size, self.seq_length], self.type_vocab_size
      )
      input_mask = random_attention_mask(
          [self.batch_size, self.seq_length]
      )
  
      input_ids_q = ids_tensor(
          [self.batch_size, self.seq_length + 1], self.vocab_size
      )
      perm_mask = torch.zeros(
          self.batch_size,
          self.seq_length + 1,
          self.seq_length + 1,
          dtype=torch.float,
          device=torch_device,
      )
      perm_mask[:, :, -1] = 1.0  # Previous tokens don't see last token
      target_mapping = torch.zeros(
          self.batch_size,
          1,
          self.seq_length + 1,
          dtype=torch.float,
          device=torch_device,
      )
      target_mapping[:, 0, -1] = 1.0  # predict last token
  
      sequence_labels = None
      lm_labels = None
      is_impossible_labels = None
      token_labels = None
      if self.use_labels:
          lm_labels = ids_tensor(
              [self.batch_size, self.seq_length], self.vocab_size
          )
          sequence_labels = ids_tensor(
              [self.batch_size], self.type_sequence_label_size
          )
          is_impossible_labels = ids_tensor(
              [self.batch_size], 2
          ).float()
          token_labels = ids_tensor(
              [self.batch_size, self.seq_length], self.type_vocab_size
          )
  
      config = self.get_config()
  
      return (
          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,
      )
  
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 2 for calling the XLNetModelTester obj's underlying member method code with example usage: obj.prepare_config_and_inputs() and returns: 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