class_docstr: Diffusers handler class for text to image generation.
class_docstr_tok: ["Diffusers","handler","class","for","text","to","image","generation","."]
docstr: Generates the image relevant to the received text.
  Args:
      input_batch (list): List of Text from the pre-process function is passed here
  Returns:
      list : It returns a list of the generate images for the input text
docstr_tok: ["Generates","the","image","relevant","to","the","received","text",".","Args",":","input_batch","(","list",")",":","List","of","Text","from","the","pre-process","function","is","passed","here","Returns",":","list",":","It","returns","a","list","of","the","generate","images","for","the","input","text"]
code: def inference(self, inputs):
      """Generates the image relevant to the received text.
      Args:
          input_batch (list): List of Text from the pre-process function is passed here
      Returns:
          list : It returns a list of the generate images for the input text
      """
      # Handling inference for sequence_classification.
      generator = torch.Generator(self.device).manual_seed(1024)
      inferences = self.pipe(
          inputs,
          guidance_scale=self.setup_config["model_config"][
              "guidance_scale"
          ],
          num_inference_steps=self.setup_config["model_config"][
              "num_inference_steps"
          ],
          generator=generator,
      ).images
  
      logger.info("Generated image: '%s'", inferences)
      return inferences
  
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 4 for calling the DeepSpeedMIIHandler obj's underlying member method code with example usage: obj.inference(inputs) and returns: inferences