code: def prepare_config_and_inputs(self):
      output_attentions = self.output_attentions
      input_ids = ids_tensor(
          [self.batch_size, self.seq_length], vocab_size=self.vocab_size
      )
      visual_feats = torch.rand(
          self.batch_size,
          self.num_visual_features,
          self.visual_feat_dim,
          device=torch_device,
      )
      bounding_boxes = torch.rand(
          self.batch_size,
          self.num_visual_features,
          4,
          device=torch_device,
      )
  
      input_mask = None
      if self.use_lang_mask:
          input_mask = ids_tensor(
              [self.batch_size, self.seq_length], vocab_size=2
          )
      token_type_ids = None
      if self.use_token_type_ids:
          token_type_ids = ids_tensor(
              [self.batch_size, self.seq_length], self.type_vocab_size
          )
      obj_labels = None
      if self.task_obj_predict:
          obj_labels = {}
      if self.visual_attr_loss and self.task_obj_predict:
          obj_labels["attr"] = (
              ids_tensor(
                  [self.batch_size, self.num_visual_features],
                  self.num_attr_labels,
              ),
              ids_tensor(
                  [self.batch_size, self.num_visual_features],
                  self.num_attr_labels,
              ),
          )
      if self.visual_feat_loss and self.task_obj_predict:
          obj_labels["feat"] = (
              ids_tensor(
                  [
                      self.batch_size,
                      self.num_visual_features,
                      self.visual_feat_dim,
                  ],
                  self.num_visual_features,
              ),
              ids_tensor(
                  [self.batch_size, self.num_visual_features],
                  self.num_visual_features,
              ),
          )
      if self.visual_obj_loss and self.task_obj_predict:
          obj_labels["obj"] = (
              ids_tensor(
                  [self.batch_size, self.num_visual_features],
                  self.num_object_labels,
              ),
              ids_tensor(
                  [self.batch_size, self.num_visual_features],
                  self.num_object_labels,
              ),
          )
      ans = None
      if self.task_qa:
          ans = ids_tensor([self.batch_size], self.num_qa_labels)
      masked_lm_labels = None
      if self.task_mask_lm:
          masked_lm_labels = ids_tensor(
              [self.batch_size, self.seq_length], self.vocab_size
          )
      matched_label = None
      if self.task_matched:
          matched_label = ids_tensor([self.batch_size], self.num_labels)
  
      config = self.get_config()
  
      return (
          config,
          input_ids,
          visual_feats,
          bounding_boxes,
          token_type_ids,
          input_mask,
          obj_labels,
          masked_lm_labels,
          matched_label,
          ans,
          output_attentions,
      )
  
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 2 for calling the LxmertModelTester obj's underlying member method code with example usage: obj.prepare_config_and_inputs() and returns: config,  input_ids,  visual_feats,  bounding_boxes,  token_type_ids,  input_mask,  obj_labels,  masked_lm_labels,  matched_label,  ans,  output_attentions