class_docstr: Input image is pre-processed before passing it to the network. This class applies the pre-processing to input image. It also
  applies post-processing to the output of the network and saves the output as an image.
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docstr: Before passing the input image to the network, it is resized to target shape, maintaining the aspect ratio.
  This is done in order to obtain better results if the input image shape is very small or very large than the
  the image shape on which the network was trained. The default shape for target image is (513,513).
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code: def pre_process(self):
      """
      Before passing the input image to the network, it is resized to target shape, maintaining the aspect ratio.
      This is done in order to obtain better results if the input image shape is very small or very large than the
      the image shape on which the network was trained. The default shape for target image is (513,513).
      """
  
      return pre_process_image(self.image, self.target_h, self.target_w)
  
file_path: nnabla/python/src/nnabla/models/semantic_segmentation/utils.py
imports: import numpy
  from nnabla.models.semantic_segmentation import image_preprocess
  from nnabla.utils.image_utils import imsave, imresize
label_desc: Use image node_id 2 for calling the ProcessImage obj's underlying member method code with example usage: obj.pre_process() and returns: pre_process_image