class_docstr: Transformers handler class for testing
class_docstr_tok: ["Transformers","handler","class","for","testing"]
docstr: Predict the class (or classes) of the received text using the
  serialized transformers checkpoint.
  Args:
      input_batch (list): List of Text Tensors from the pre-process function is passed here
  Returns:
      list : It returns a list of the predicted value for the input text
docstr_tok: ["Predict","the","class","(","or","classes",")","of","the","received","text","using","the","serialized","transformers","checkpoint",".","Args",":","input_batch","(","list",")",":","List","of","Text","Tensors","from","the","pre-process","function","is","passed","here","Returns",":","list",":","It","returns","a","list","of","the","predicted","value","for","the","input","text"]
code: def inference(self, input_batch):
      """Predict the class (or classes) of the received text using the
      serialized transformers checkpoint.
      Args:
          input_batch (list): List of Text Tensors from the pre-process function is passed here
      Returns:
          list : It returns a list of the predicted value for the input text
      """
      input_ids_batch, attention_mask_batch = input_batch
      inferences = []
  
      do_sample = self.inference_config.get("do_sample", True)
      top_p = self.inference_config.get(
          "top_p", 0.95 if do_sample else 1.0
      )
      top_k = self.inference_config.get(
          "top_k", 60 if do_sample else 50
      )
      input_ids_batch = input_ids_batch.to(self.device)
      outputs = self.infer_engine.generate(
          dict(
              input_ids=input_ids_batch,
              attention_mask=attention_mask_batch,
          ),
          do_sample=do_sample,
          top_p=top_p,
          top_k=top_k,
      )
  
      for i, _ in enumerate(outputs):
          inferences.append(
              self.tokenizer.decode(
                  outputs[i], skip_special_tokens=True
              )
          )
  
      # For testing only
      logger.info(
          f"Generated text: {inferences}",
      )
  
      return inferences
  
file_path: colossalai/colossalai/legacy/inference/serving/torch_serve/Colossal_Inference_Handler.py
imports: import logging
  import os
  import zipfile
  from abc import ABC
  import torch
  import transformers
  from transformers import AutoTokenizer, BloomForCausalLM, BloomTokenizerFast, LlamaForCausalLM
  from ts.torch_handler.base_handler import BaseHandler
  import colossalai
  from colossalai.inference.tensor_parallel.engine import TPInferEngine
  from colossalai.shardformer import ShardConfig
  from colossalai.testing import free_port
label_desc: Use image node_id 4 for calling the ColossalInferenceHandler obj's underlying member method code with example usage: obj.inference(input_batch) and returns: inferences