code: def prepare_config_and_inputs(self):
      input_ids = ids_tensor(
          [self.batch_size, self.seq_length], self.vocab_size
      )
  
      bbox = ids_tensor(
          [self.batch_size, self.seq_length, 4], self.range_bbox
      )
      # Ensure that bbox is legal
      for i in range(bbox.shape[0]):
          for j in range(bbox.shape[1]):
              if bbox[i, j, 3] < bbox[i, j, 1]:
                  t = bbox[i, j, 3]
                  bbox[i, j, 3] = bbox[i, j, 1]
                  bbox[i, j, 1] = t
              if bbox[i, j, 2] < bbox[i, j, 0]:
                  t = bbox[i, j, 2]
                  bbox[i, j, 2] = bbox[i, j, 0]
                  bbox[i, j, 0] = t
  
      image = ImageList(
          torch.zeros(
              self.batch_size,
              self.num_channels,
              self.image_size,
              self.image_size,
              device=torch_device,
          ),
          self.image_size,
      )
  
      input_mask = None
      if self.use_input_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
          )
  
      sequence_labels = None
      token_labels = None
      if self.use_labels:
          sequence_labels = ids_tensor(
              [self.batch_size], self.type_sequence_label_size
          )
          token_labels = ids_tensor(
              [self.batch_size, self.seq_length], self.num_labels
          )
  
      config = LayoutLMv2Config(
          vocab_size=self.vocab_size,
          hidden_size=self.hidden_size,
          num_hidden_layers=self.num_hidden_layers,
          num_attention_heads=self.num_attention_heads,
          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,
          is_decoder=False,
          initializer_range=self.initializer_range,
          image_feature_pool_shape=self.image_feature_pool_shape,
          coordinate_size=self.coordinate_size,
          shape_size=self.shape_size,
      )
  
      # use smaller resnet backbone to make tests faster
      config.detectron2_config_args["MODEL.RESNETS.DEPTH"] = 18
      config.detectron2_config_args[
          "MODEL.RESNETS.RES2_OUT_CHANNELS"
      ] = 64
      config.detectron2_config_args["MODEL.RESNETS.NUM_GROUPS"] = 1
  
      return (
          config,
          input_ids,
          bbox,
          image,
          token_type_ids,
          input_mask,
          sequence_labels,
          token_labels,
      )
  
imports: import os
  import random
  import tempfile
  import unittest
  from transformers.testing_utils import require_detectron2, require_torch, require_torch_multi_gpu, slow, torch_device
  from transformers.utils import is_detectron2_available, is_torch_available
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, _config_zero_init, ids_tensor, random_attention_mask
label_desc: Use image node_id 2 for calling the LayoutLMv2ModelTester obj's underlying member method code with example usage: obj.prepare_config_and_inputs() and returns: config,  input_ids,  bbox,  image,  token_type_ids,  input_mask,  sequence_labels,  token_labels