code: def __init__(
      self,
      parent,
      batch_size=7,
      num_channels=3,
      min_resolution=30,
      max_resolution=400,
      do_resize=True,
      size=18,
      max_size=1333,  # by setting max_size > max_resolution we're effectively not testing this :p
      do_normalize=True,
      image_mean=[0.5, 0.5, 0.5],
      image_std=[0.5, 0.5, 0.5],
  ):
      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 = size
      self.max_size = max_size
      self.do_normalize = do_normalize
      self.image_mean = image_mean
      self.image_std = image_std
  
imports: import json
  import pathlib
  import unittest
  import numpy
  from transformers.testing_utils import require_torch, require_vision, slow
  from transformers.utils import is_torch_available, is_vision_available
  from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin, prepare_image_inputs
label_desc: Use image node_id 1 to create a new ConditionalDetrFeatureExtractionTester object from inherited base classes: unittest with example: obj = ConditionalDetrFeatureExtractionTester(parent, batch_size, num_channels, min_resolution, max_resolution, do_resize, size, max_size, do_normalize, image_mean, image_std)