code: def prepare_config_and_inputs(self):
      pixel_values = floats_tensor(
          [
              self.batch_size,
              self.num_channels,
              self.min_size,
              self.max_size,
          ]
      ).to(torch_device)
  
      pixel_mask = torch.ones(
          [self.batch_size, self.min_size, self.max_size],
          device=torch_device,
      )
  
      mask_labels = (
          torch.rand(
              [
                  self.batch_size,
                  self.num_labels,
                  self.min_size,
                  self.max_size,
              ],
              device=torch_device,
          )
          > 0.5
      ).float()
      class_labels = (
          torch.rand(
              (self.batch_size, self.num_labels), device=torch_device
          )
          > 0.5
      ).long()
  
      config = self.get_config()
      return config, pixel_values, pixel_mask, mask_labels, class_labels
  
imports: import inspect
  import unittest
  import numpy
  from tests.test_modeling_common import floats_tensor
  from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
  from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
  from transformers.utils import cached_property
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin
label_desc: Use image node_id 2 for calling the MaskFormerModelTester obj's underlying member method code with example usage: obj.prepare_config_and_inputs() and returns: config,  pixel_values,  pixel_mask,  mask_labels,  class_labels