code: def __init__(
      self,
      parent,
      batch_size=7,
      num_channels=3,
      min_resolution=30,
      max_resolution=400,
      size=None,
      do_resize=True,
      do_normalize=True,
      image_mean=[0.5, 0.5, 0.5],
      image_std=[0.5, 0.5, 0.5],
      num_labels=10,
      do_reduce_labels=True,
      ignore_index=255,
  ):
      self.parent = parent
      self.batch_size = batch_size
      self.num_channels = num_channels
      self.min_resolution = min_resolution
      self.max_resolution = max_resolution
      self.do_resize = do_resize
      self.size = (
          {"shortest_edge": 32, "longest_edge": 1333}
          if size is None
          else size
      )
      self.do_normalize = do_normalize
      self.image_mean = image_mean
      self.image_std = image_std
      self.size_divisor = 0
      # for the post_process_functions
      self.batch_size = 2
      self.num_queries = 3
      self.num_classes = 2
      self.height = 3
      self.width = 4
      self.num_labels = num_labels
      self.do_reduce_labels = do_reduce_labels
      self.ignore_index = ignore_index
  
imports: import unittest
  import numpy
  from datasets import load_dataset
  from huggingface_hub import hf_hub_download
  from transformers.testing_utils import require_torch, require_vision
  from transformers.utils import is_torch_available, is_vision_available
  from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
label_desc: Use image node_id 1 to create a new MaskFormerImageProcessingTester object from inherited base classes: unittest with example: obj = MaskFormerImageProcessingTester(parent, batch_size, num_channels, min_resolution, max_resolution, size, do_resize, do_normalize, image_mean, image_std, num_labels, do_reduce_labels, ignore_index)