code: def __init__(
      self,
      parent,
      batch_size=13,
      seq_length=7,
      is_training=True,
      entity_length=3,
      mention_length=5,
      use_attention_mask=True,
      use_token_type_ids=True,
      use_entity_ids=True,
      use_entity_attention_mask=True,
      use_entity_token_type_ids=True,
      use_entity_position_ids=True,
      use_labels=True,
      vocab_size=99,
      entity_vocab_size=10,
      entity_emb_size=6,
      hidden_size=32,
      num_hidden_layers=2,
      num_attention_heads=4,
      intermediate_size=37,
      hidden_act="gelu",
      hidden_dropout_prob=0.1,
      attention_probs_dropout_prob=0.1,
      max_position_embeddings=512,
      type_vocab_size=16,
      type_sequence_label_size=2,
      initializer_range=0.02,
      num_labels=3,
      num_choices=4,
      num_entity_classification_labels=9,
      num_entity_pair_classification_labels=6,
      num_entity_span_classification_labels=4,
      use_entity_aware_attention=True,
      scope=None,
  ):
      self.parent = parent
      self.batch_size = batch_size
      self.seq_length = seq_length
      self.is_training = is_training
      self.entity_length = entity_length
      self.mention_length = mention_length
      self.use_attention_mask = use_attention_mask
      self.use_token_type_ids = use_token_type_ids
      self.use_entity_ids = use_entity_ids
      self.use_entity_attention_mask = use_entity_attention_mask
      self.use_entity_token_type_ids = use_entity_token_type_ids
      self.use_entity_position_ids = use_entity_position_ids
      self.use_labels = use_labels
      self.vocab_size = vocab_size
      self.entity_vocab_size = entity_vocab_size
      self.entity_emb_size = entity_emb_size
      self.hidden_size = hidden_size
      self.num_hidden_layers = num_hidden_layers
      self.num_attention_heads = num_attention_heads
      self.intermediate_size = intermediate_size
      self.hidden_act = hidden_act
      self.hidden_dropout_prob = hidden_dropout_prob
      self.attention_probs_dropout_prob = attention_probs_dropout_prob
      self.max_position_embeddings = max_position_embeddings
      self.type_vocab_size = type_vocab_size
      self.type_sequence_label_size = type_sequence_label_size
      self.initializer_range = initializer_range
      self.num_labels = num_labels
      self.num_choices = num_choices
      self.num_entity_classification_labels = (
          num_entity_classification_labels
      )
      self.num_entity_pair_classification_labels = (
          num_entity_pair_classification_labels
      )
      self.num_entity_span_classification_labels = (
          num_entity_span_classification_labels
      )
      self.scope = scope
      self.use_entity_aware_attention = use_entity_aware_attention
  
      self.encoder_seq_length = seq_length
      self.key_length = seq_length
      self.num_hidden_states_types = 2
  
imports: import unittest
  from transformers import LukeConfig, is_torch_available
  from transformers.testing_utils import require_torch, slow, torch_device
  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 1 to create a new LukeModelTester object  with example: obj = LukeModelTester(parent, batch_size, seq_length, is_training, entity_length, mention_length, use_attention_mask, use_token_type_ids, use_entity_ids, use_entity_attention_mask, use_entity_token_type_ids, use_entity_position_ids, use_labels, vocab_size, entity_vocab_size, entity_emb_size, hidden_size, num_hidden_layers, num_attention_heads, intermediate_size, hidden_act, hidden_dropout_prob, attention_probs_dropout_prob, max_position_embeddings, type_vocab_size, type_sequence_label_size, initializer_range, num_labels, num_choices, num_entity_classification_labels, num_entity_pair_classification_labels, num_entity_span_classification_labels, use_entity_aware_attention, scope)