code: def prepare_config_and_inputs(self):
      pixel_values = floats_tensor(
          [
              self.batch_size,
              self.num_channels,
              self.min_size,
              self.max_size,
          ]
      )
  
      pixel_mask = torch.ones(
          [self.batch_size, self.min_size, self.max_size],
          device=torch_device,
      )
  
      labels = None
      if self.use_labels:
          # labels is a list of Dict (each Dict being the labels for a given example in the batch)
          labels = []
          for i in range(self.batch_size):
              target = {}
              target["class_labels"] = torch.randint(
                  high=self.num_labels,
                  size=(self.n_targets,),
                  device=torch_device,
              )
              target["boxes"] = torch.rand(
                  self.n_targets, 4, device=torch_device
              )
              target["masks"] = torch.rand(
                  self.n_targets,
                  self.min_size,
                  self.max_size,
                  device=torch_device,
              )
              labels.append(target)
  
      config = self.get_config()
      return config, pixel_values, pixel_mask, labels
  
imports: import inspect
  import math
  import unittest
  from huggingface_hub import hf_hub_download
  from transformers import TableTransformerConfig, is_timm_available, is_vision_available
  from transformers.testing_utils import require_timm, require_vision, slow, torch_device
  from ...generation.test_generation_utils import GenerationTesterMixin
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor
label_desc: Use image node_id 2 for calling the TableTransformerModelTester obj's underlying member method code with example usage: obj.prepare_config_and_inputs() and returns: config,  pixel_values,  pixel_mask,  labels