class_docstr: The UNet implementation based on PaddlePaddle.
  
  The original article refers to
  Olaf Ronneberger, et, al. "U-Net: Convolutional Networks for Biomedical Image Segmentation"
  (https://arxiv.org/abs/1505.04597).
  
  Args:
      num_classes (int): The unique number of target classes.
      align_corners (bool): An argument of F.interpolate. It should be set to False when the output size of feature
          is even, e.g. 1024x512, otherwise it is True, e.g. 769x769.  Default: False.
      use_deconv (bool, optional): A bool value indicates whether using deconvolution in upsampling.
          If False, use resize_bilinear. Default: False.
      pretrained (str, optional): The path or url of pretrained model for fine tuning. Default: None.
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code: def transform(
      self, img: Union[np.ndarray, str]
  ) -> Union[np.ndarray, str]:
      return self.transforms(img)
  
imports: import os
  from typing import Union, List, Tuple
  import paddle
  from paddle import nn
  import paddle.nn.functional
  import numpy
  from paddlehub.module.module import moduleinfo
  import paddlehub.vision.segmentation_transforms
  from paddlehub.module.cv_module import ImageSegmentationModule
  import unet_cityscapes.layers
label_desc: Use image node_id 2 for calling the UNet obj's underlying member method code with example usage: obj.transform(img) and returns: self