class_docstr: Transformers handler class for text completion streaming on Inferentia2
class_docstr_tok: ["Transformers","handler","class","for","text","completion","streaming","on","Inferentia2"]
code: def get_micro_batch_req_id_map(self, micro_batch_idx: int):
      start_idx = micro_batch_idx * self.handle.micro_batch_size
      micro_batch_req_id_map = {
          index: self.context.request_ids[batch_index]
          for index, batch_index in enumerate(
              range(start_idx, start_idx + self.handle.micro_batch_size)
          )
          if batch_index in self.context.request_ids
      }
  
      return micro_batch_req_id_map
  
imports: import logging
  import os
  from abc import ABC
  from threading import Thread
  import torch_neuronx
  from transformers import AutoConfig, LlamaTokenizer
  from transformers_neuronx.generation_utils import HuggingFaceGenerationModelAdapter
  from transformers_neuronx.llama.model import LlamaForSampling
  from ts.handler_utils.hf_batch_streamer import TextIteratorStreamerBatch
  from ts.handler_utils.micro_batching import MicroBatching
  from ts.protocol.otf_message_handler import send_intermediate_predict_response
  from ts.torch_handler.base_handler import BaseHandler
label_desc: Use image node_id 6 for calling the LLMHandler obj's underlying member method code with example usage: obj.get_micro_batch_req_id_map(micro_batch_idx) and returns: micro_batch_req_id_map