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: The pre processed image is finally visulalized and saved. Each category of the label has been
  assigned a particular color and same color mapping is used during visualization. When the color_map
  is None, the number of categories by default is 20 and the color mapping is selected from a pre defined
  color mapping array. In case you want to save (say) 40 class output labels, color map may be defined as 
  `color_map=np.random.rand(40, 3)`. This is an example of color sampling from RGB space.
  
  Args:
          label(numpy.ndarray):
               Post processed image.
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code: def save_segmentation_image(self, path, color_map=None):
      """
      The pre processed image is finally visulalized and saved. Each category of the label has been
      assigned a particular color and same color mapping is used during visualization. When the color_map
      is None, the number of categories by default is 20 and the color mapping is selected from a pre defined
      color mapping array. In case you want to save (say) 40 class output labels, color map may be defined as
      `color_map=np.random.rand(40, 3)`. This is an example of color sampling from RGB space.
  
      Args:
              label(numpy.ndarray):
                   Post processed image.
  
      """
  
      return save_image(path, self.post_proc, color_map)
  
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 4 for calling the ProcessImage obj's underlying member method code with example usage: obj.save_segmentation_image(path, color_map) and returns: save_image