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: Post Process Function converts the predicted response into Torchserve readable format.
  Args:
      inference_output (list): It contains the predicted response of the input text.
  Returns:
      (list): Returns a list of the Predictions and Explanations.
docstr_tok: ["Post","Process","Function","converts","the","predicted","response","into","Torchserve","readable","format",".","Args",":","inference_output","(","list",")",":","It","contains","the","predicted","response","of","the","input","text",".","Returns",":","(","list",")",":","Returns","a","list","of","the","Predictions","and","Explanations","."]
code: def postprocess(self, inference_output):
      """Post Process Function converts the predicted response into Torchserve readable format.
      Args:
          inference_output (list): It contains the predicted response of the input text.
      Returns:
          (list): Returns a list of the Predictions and Explanations.
      """
      return inference_output
  
file_path: serve/examples/large_models/Huggingface_pippy/pippy_handler.py
imports: import logging
  import time
  from abc import ABC
  import packaging.version
  import requests
  import torch
  import transformers
  from transformers import AutoModelForCausalLM, AutoTokenizer
  from ts.handler_utils.distributed.pt_pippy import get_pipeline_driver
  from ts.torch_handler.distributed.base_pippy_handler import BasePippyHandler
label_desc: Use image node_id 6 for calling the TransformersSeqClassifierHandler obj's underlying member method code with example usage: obj.postprocess(inference_output) and returns: inference_output