class_docstr: Diffusers handler class for text to image generation.
class_docstr_tok: ["Diffusers","handler","class","for","text","to","image","generation","."]
docstr: Basic text preprocessing, of the user's prompt.
  Args:
      requests (str): The Input data in the form of text is passed on to the preprocess
      function.
  Returns:
      list : The preprocess function returns a list of prompts.
docstr_tok: ["Basic","text","preprocessing",",","of","the","user","'s","prompt",".","Args",":","requests","(","str",")",":","The","Input","data","in","the","form","of","text","is","passed","on","to","the","preprocess","function",".","Returns",":","list",":","The","preprocess","function","returns","a","list","of","prompts","."]
code: def preprocess(self, requests):
      """Basic text preprocessing, of the user's prompt.
      Args:
          requests (str): The Input data in the form of text is passed on to the preprocess
          function.
      Returns:
          list : The preprocess function returns a list of prompts.
      """
      inputs = []
      for _, data in enumerate(requests):
          input_text = data.get("data")
          if input_text is None:
              input_text = data.get("body")
          if isinstance(input_text, (bytes, bytearray)):
              input_text = input_text.decode("utf-8")
          logger.info("Received text: '%s'", input_text)
          inputs.append(input_text)
      return inputs
  
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 3 for calling the DeepSpeedMIIHandler obj's underlying member method code with example usage: obj.preprocess(requests) and returns: inputs