code: def __init__(
      self,
      parent,
      do_resize: bool = True,
      size: Dict[str, int] = {"longest_edge": 40},
      do_center_crop: bool = False,
      crop_size: Dict[str, int] = None,
      do_rescale: bool = False,
      rescale_factor: Union[int, float] = 1 / 255,
      do_pad: bool = True,
      pad_size: Dict[str, int] = {"height": 80, "width": 80},
      fill: int = None,
      pad_mode: PaddingMode = None,
      do_normalize: bool = True,
      image_mean: Optional[Union[float, List[float]]] = [
          0.48145466,
          0.4578275,
          0.40821073,
      ],
      image_std: Optional[Union[float, List[float]]] = [
          0.26862954,
          0.26130258,
          0.27577711,
      ],
      batch_size=2,
      min_resolution=40,
      max_resolution=80,
      num_channels=3,
      num_frames=2,
  ):
      self.do_resize = do_resize
      self.size = size
      self.do_center_crop = do_center_crop
      self.crop_size = crop_size
      self.do_rescale = do_rescale
      self.rescale_factor = rescale_factor
      self.do_pad = do_pad
      self.pad_size = pad_size
      self.fill = fill
      self.pad_mode = pad_mode
      self.do_normalize = do_normalize
      self.image_mean = image_mean
      self.image_std = image_std
      self.batch_size = batch_size
      self.num_channels = num_channels
      self.min_resolution = min_resolution
      self.max_resolution = max_resolution
      self.num_frames = num_frames
  
imports: import unittest
  from typing import Dict, List, Optional, Union
  import numpy
  from transformers.image_transforms import PaddingMode
  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_video_inputs
label_desc: Use image node_id 1 to create a new TvpImageProcessingTester object from inherited base classes: unittest with example: obj = TvpImageProcessingTester(parent, do_resize, size, do_center_crop, crop_size, do_rescale, rescale_factor, do_pad, pad_size, fill, pad_mode, do_normalize, image_mean, image_std, batch_size, min_resolution, max_resolution, num_channels, num_frames)