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 __init__(self):
      super().__init__()
      self.initialized = False
      self.max_length = None
      self.tokenizer = None
      self.output_streamer = None
      # enable micro batching
      self.handle = MicroBatching(self)
  
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 1 to create a new LLMHandler object from inherited base classes: BaseHandler, ABC with example: obj = LLMHandler()