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 = tf.random.uniform(
          (
              self.batch_size,
              self.num_visual_features,
              self.visual_feat_dim,
          )
      )
      bounding_boxes = tf.random.uniform(
          (self.batch_size, self.num_visual_features, 4)
      )
  
      input_mask = None
      if self.use_lang_mask:
          input_mask = random_attention_mask(
              [self.batch_size, self.seq_length]
          )
      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 = LxmertConfig(
          vocab_size=self.vocab_size,
          hidden_size=self.hidden_size,
          num_attention_heads=self.num_attention_heads,
          num_labels=self.num_labels,
          intermediate_size=self.intermediate_size,
          hidden_act=self.hidden_act,
          hidden_dropout_prob=self.hidden_dropout_prob,
          attention_probs_dropout_prob=self.attention_probs_dropout_prob,
          max_position_embeddings=self.max_position_embeddings,
          type_vocab_size=self.type_vocab_size,
          initializer_range=self.initializer_range,
          layer_norm_eps=self.layer_norm_eps,
          pad_token_id=self.pad_token_id,
          num_qa_labels=self.num_qa_labels,
          num_object_labels=self.num_object_labels,
          num_attr_labels=self.num_attr_labels,
          l_layers=self.l_layers,
          x_layers=self.x_layers,
          r_layers=self.r_layers,
          visual_feat_dim=self.visual_feat_dim,
          visual_pos_dim=self.visual_pos_dim,
          visual_loss_normalizer=self.visual_loss_normalizer,
          task_matched=self.task_matched,
          task_mask_lm=self.task_mask_lm,
          task_obj_predict=self.task_obj_predict,
          task_qa=self.task_qa,
          visual_obj_loss=self.visual_obj_loss,
          visual_attr_loss=self.visual_attr_loss,
          visual_feat_loss=self.visual_feat_loss,
          output_attentions=self.output_attentions,
          output_hidden_states=self.output_hidden_states,
      )
  
      return (
          config,
          input_ids,
          visual_feats,
          bounding_boxes,
          token_type_ids,
          input_mask,
          obj_labels,
          masked_lm_labels,
          matched_label,
          ans,
          output_attentions,
      )
  
imports: from __future__ import annotations
  import tempfile
  import unittest
  import numpy
  from transformers import LxmertConfig, 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 2 for calling the TFLxmertModelTester 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