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 preprocess(self, requests):
      input_text = []
      for req in requests:
          data = req.get("data") or req.get("body")
          if isinstance(data, (bytes, bytearray)):
              data = data.decode("utf-8")
          logger.info(f"received req={data}")
          input_text.append(data.strip())
  
      # Ensure the compiled model can handle the input received
      if len(input_text) > self.handle.micro_batch_size:
          raise ValueError(
              f"Model is compiled for batch size {self.handle.micro_batch_size} but received input of size {len(input_text)}"
          )
  
      # Pad input to match compiled model batch size
      input_text.extend(
          [""] * (self.handle.micro_batch_size - len(input_text))
      )
  
      return self.tokenizer(
          input_text, return_tensors="pt", padding=True
      )
  
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 3 for calling the LLMHandler obj's underlying member method code with example usage: obj.preprocess(requests) and returns: self