code: def get_word_token(input_ids, tokenizer):
      """constructs word tokens from token id using the BERT's
      Auto Tokenizer
      Args:
          input_ids (list): Input IDs from construct_input_ref method
          tokenizer (class): The Auto Tokenizer Pre-Trained model object
      Returns:
          (list): Returns the word tokens
      """
      indices = input_ids[0].detach().tolist()
      tokens = tokenizer.convert_ids_to_tokens(indices)
      # Remove unicode space character from BPE Tokeniser
      tokens = [token.replace("Ġ", "") for token in tokens]
      return tokens
  
imports: import ast
  import json
  import logging
  import os
  from abc import ABC
  import torch
  import transformers
  from captum.attr import LayerIntegratedGradients
  from transformers import AutoModelForCausalLM, AutoModelForQuestionAnswering, AutoModelForSequenceClassification, AutoModelForTokenClassification, AutoTokenizer, GPT2TokenizerFast
  from ts.torch_handler.base_handler import BaseHandler
label_desc: Use image node_id 4 for calling a global function with example usage: get_word_token(input_ids, tokenizer) and returns: tokens