class_docstr: Transformers handler class for sequence, token classification and question answering.
class_docstr_tok: ["Transformers","handler","class","for","sequence",",","token","classification","and","question","answering","."]
docstr: In this initialize function, the llama model is loaded using Fairscale and
  partitioned into multiple stages each on one device using PiPPy.
  Args:
      ctx (context): It is a JSON Object containing information
      pertaining to the model artefacts parameters.
docstr_tok: ["In","this","initialize","function",",","the","llama","model","is","loaded","using","Fairscale","and","partitioned","into","multiple","stages","each","on","one","device","using","PiPPy",".","Args",":","ctx","(","context",")",":","It","is","a","JSON","Object","containing","information","pertaining","to","the","model","artefacts","parameters","."]
code: def initialize(self, ctx):
      """
      In this initialize function, the llama model is loaded using Fairscale and
      partitioned into multiple stages each on one device using PiPPy.
      Args:
          ctx (context): It is a JSON Object containing information
          pertaining to the model artefacts parameters.
      """
      # super().initialize(ctx)
      if not torch.distributed.is_initialized():
          torch.distributed.init_process_group("nccl")
  
      self.context = ctx
  
      self.context.cache = {}
  
      self.manifest = ctx.manifest
      properties = ctx.system_properties
      model_dir = properties.get("model_dir")
  
      seed = ctx.model_yaml_config["handler"]["manual_seed"]
      self.max_new_tokens = ctx.model_yaml_config["handler"][
          "max_new_tokens"
      ]
      self.temperature = ctx.model_yaml_config["handler"]["temperature"]
      self.top_p = ctx.model_yaml_config["handler"]["top_p"]
  
      torch.manual_seed(seed)
      logger.info("Instantiating Llama model")
      model_load_start = time.perf_counter()
      llama_model_and_tok = Llama.build(
          model_args=f'{model_dir}/{ctx.model_yaml_config["handler"]["model_args_path"]}',
          converted_ckpt_dir=f'{model_dir}/{ctx.model_yaml_config["handler"]["converted_ckpt_dir"]}',
          tokenizer_path=f'{model_dir}/{ctx.model_yaml_config["handler"]["tokenizer_path"]}',
      )
      load_time = time.perf_counter() - model_load_start
      self.model = llama_model_and_tok.model
      self.model.eval()
  
      self.tokenizer = llama_model_and_tok.tokenizer
  
      logger.info(
          f"Llama model from path {model_dir} loaded successfully in {load_time} seconds"
      )
  
      self.max_bsz = self.model.layers[0].attention.cache_k.size(0)
      self.batch_idx_to_req_ids = [
          None,
      ] * self.max_bsz
  
      self.device = next(iter(self.model.parameters())).device
  
      self.initialized = True
  
imports: import json
  import logging
  import os
  import sys
  import time
  from abc import ABC
  from typing import List
  import packaging.version
  import requests
  import torch
  import transformers
  from ts.torch_handler.base_handler import BaseHandler
  from generate import sample_top_p
  from llama2 import Llama
label_desc: Use image node_id 2 for calling the LlamaHandler obj's underlying member method code with example usage: obj.initialize(ctx) without return types