code: def __init__(
      self,
      parent,
      vocab_size=300,
      hidden_size=28,
      num_attention_heads=2,
      num_labels=2,
      intermediate_size=64,
      hidden_act="gelu",
      hidden_dropout_prob=0.1,
      attention_probs_dropout_prob=0.1,
      max_position_embeddings=512,
      type_vocab_size=2,
      initializer_range=0.02,
      layer_norm_eps=1e-12,
      pad_token_id=0,
      num_qa_labels=30,
      num_object_labels=16,
      num_attr_labels=4,
      num_visual_features=10,
      l_layers=2,
      x_layers=1,
      r_layers=1,
      visual_feat_dim=128,
      visual_pos_dim=4,
      visual_loss_normalizer=6.67,
      seq_length=20,
      batch_size=4,
      is_training=True,
      task_matched=True,
      task_mask_lm=True,
      task_obj_predict=True,
      task_qa=True,
      visual_obj_loss=True,
      visual_attr_loss=True,
      visual_feat_loss=True,
      use_token_type_ids=True,
      use_lang_mask=True,
      output_attentions=False,
      output_hidden_states=False,
      scope=None,
  ):
      self.parent = parent
      self.vocab_size = vocab_size
      self.hidden_size = hidden_size
      self.num_attention_heads = num_attention_heads
      self.num_labels = num_labels
      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.initializer_range = initializer_range
      self.layer_norm_eps = layer_norm_eps
      self.pad_token_id = pad_token_id
      self.num_qa_labels = num_qa_labels
      self.num_object_labels = num_object_labels
      self.num_attr_labels = num_attr_labels
      self.l_layers = l_layers
      self.x_layers = x_layers
      self.r_layers = r_layers
      self.visual_feat_dim = visual_feat_dim
      self.visual_pos_dim = visual_pos_dim
      self.visual_loss_normalizer = visual_loss_normalizer
      self.seq_length = seq_length
      self.batch_size = batch_size
      self.is_training = is_training
      self.use_lang_mask = use_lang_mask
      self.task_matched = task_matched
      self.task_mask_lm = task_mask_lm
      self.task_obj_predict = task_obj_predict
      self.task_qa = task_qa
      self.visual_obj_loss = visual_obj_loss
      self.visual_attr_loss = visual_attr_loss
      self.visual_feat_loss = visual_feat_loss
      self.num_visual_features = num_visual_features
      self.use_token_type_ids = use_token_type_ids
      self.output_attentions = output_attentions
      self.output_hidden_states = output_hidden_states
      self.scope = scope
      self.num_hidden_layers = {
          "vision": r_layers,
          "cross_encoder": x_layers,
          "language": l_layers,
      }
  
imports: import copy
  import unittest
  import numpy
  from transformers import LxmertConfig, is_tf_available, is_torch_available
  from transformers.models.auto import get_values
  from transformers.testing_utils import require_torch, slow, torch_device
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, ids_tensor
label_desc: Use image node_id 1 to create a new LxmertModelTester object  with example: obj = LxmertModelTester(parent, vocab_size, hidden_size, num_attention_heads, num_labels, intermediate_size, hidden_act, hidden_dropout_prob, attention_probs_dropout_prob, max_position_embeddings, type_vocab_size, initializer_range, layer_norm_eps, pad_token_id, num_qa_labels, num_object_labels, num_attr_labels, num_visual_features, l_layers, x_layers, r_layers, visual_feat_dim, visual_pos_dim, visual_loss_normalizer, seq_length, batch_size, is_training, task_matched, task_mask_lm, task_obj_predict, task_qa, visual_obj_loss, visual_attr_loss, visual_feat_loss, use_token_type_ids, use_lang_mask, output_attentions, output_hidden_states, scope)