class_docstr: Diffusers handler class for text to image generation.
class_docstr_tok: ["Diffusers","handler","class","for","text","to","image","generation","."]
docstr: Post Process Function converts the generated image into Torchserve readable format.
  Args:
      inference_output (list): It contains the generated image of the input text.
  Returns:
      (list): Returns a list of the images.
docstr_tok: ["Post","Process","Function","converts","the","generated","image","into","Torchserve","readable","format",".","Args",":","inference_output","(","list",")",":","It","contains","the","generated","image","of","the","input","text",".","Returns",":","(","list",")",":","Returns","a","list","of","the","images","."]
code: def postprocess(self, inference_output):
      """Post Process Function converts the generated image into Torchserve readable format.
      Args:
          inference_output (list): It contains the generated image of the input text.
      Returns:
          (list): Returns a list of the images.
      """
      images = []
      for image in inference_output:
          images.append(np.array(image).tolist())
      return images
  
file_path: serve/examples/large_models/deepspeed_mii/DeepSpeed_mii_handler.py
imports: import json
  import logging
  import os
  import zipfile
  from abc import ABC
  import mii
  import numpy
  import torch
  from ts.torch_handler.base_handler import BaseHandler
label_desc: Use image node_id 5 for calling the DeepSpeedMIIHandler obj's underlying member method code with example usage: obj.postprocess(inference_output) and returns: images