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.
class_docstr_tok: ["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","."]
code: def __init__(self, image, target_h, target_w):
      self.image = image
      self.target_size = (target_h, target_w)
      self.target_h, self.target_w = target_h, 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 1 to create a new ProcessImage object from inherited base classes: object with example: obj = ProcessImage(image, target_h, target_w)