code: def __init__(self, mean, crop_size, scale_range=[0.5, 2.0]):
      self.mean = mean
      self.scale_range = scale_range
      self.crop_size = crop_size
  
imports: import argparse
  import copy
  import multiprocessing
  import numpy
  import chainer
  import chainer.functions
  import chainer.links
  from chainer import training
  from chainer.training import extensions
  from chainer.training.extensions import PolynomialShift
  from chainercv.datasets import ade20k_semantic_segmentation_label_names
  from chainercv.datasets import ADE20KSemanticSegmentationDataset
  from chainercv.datasets import cityscapes_semantic_segmentation_label_names
  from chainercv.datasets import CityscapesSemanticSegmentationDataset
  from chainercv.experimental.links import PSPNetResNet101
  from chainercv.experimental.links import PSPNetResNet50
  from chainercv.chainer_experimental.datasets.sliceable import TransformDataset
  from chainercv.extensions import SemanticSegmentationEvaluator
  from chainercv.links import Conv2DBNActiv
  from chainercv import transforms
  from chainercv.links.model.ssd import GradientScaling
  import PIL
  import chainermn
label_desc: Use image node_id 1 to create a new Transform object from inherited base classes: object with example: obj = Transform(mean, crop_size, scale_range)