class_docstr: Diffusers handler class for text to image generation.
class_docstr_tok: ["Diffusers","handler","class","for","text","to","image","generation","."]
docstr: In this initialize function, the Stable Diffusion model is loaded and
  initialized here.
  Args:
      ctx (context): It is a JSON Object containing information
      pertaining to the model artifacts parameters.
docstr_tok: ["In","this","initialize","function",",","the","Stable","Diffusion","model","is","loaded","and","initialized","here",".","Args",":","ctx","(","context",")",":","It","is","a","JSON","Object","containing","information","pertaining","to","the","model","artifacts","parameters","."]
code: def initialize(self, ctx):
      """In this initialize function, the Stable Diffusion model is loaded and
      initialized here.
      Args:
          ctx (context): It is a JSON Object containing information
          pertaining to the model artifacts parameters.
      """
      self.manifest = ctx.manifest
      properties = ctx.system_properties
      model_dir = properties.get("model_dir")
  
      self.device = torch.device(
          "cuda:" + str(properties.get("gpu_id"))
          if torch.cuda.is_available()
          and properties.get("gpu_id") is not None
          else "cpu"
      )
      with zipfile.ZipFile(model_dir + "/model.zip", "r") as zip_ref:
          zip_ref.extractall(model_dir + "/model")
  
      # read configs for the mode, model_name, etc. from setup_config.json
      setup_config_path = os.path.join(model_dir, "setup_config.json")
      if os.path.isfile(setup_config_path):
          with open(setup_config_path) as setup_config_file:
              self.setup_config = json.load(setup_config_file)
      else:
          logger.warning("Missing the setup_config.json file.")
  
      provider = mii.constants.ModelProvider[
          self.setup_config["provider"].upper()
      ]
      mii_configs = mii.MIIConfig(**self.setup_config["mii_configs"])
      self.pipe = mii.models.load_models(
          task_name=self.setup_config["task_name"],
          model_name=model_dir + "/model",
          model_path=model_dir + "/model",
          ds_optimize=self.setup_config["ds_optimize"],
          ds_zero=self.setup_config["ds_zero"],
          provider=provider,
          mii_config=mii_configs,
      )
      self.pipe.to(self.device)
      logger.info("Model from path %s loaded successfully", model_dir)
  
      self.initialized = True
  
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 2 for calling the DeepSpeedMIIHandler obj's underlying member method code with example usage: obj.initialize(ctx) without return types