class_docstr: tokenizing and encoding/decoding text using SentencePiece.
docstr: Decodes a list of token IDs into a string.
  
  Args:
      t (List[int]): The list of token IDs to be decoded.
  
  Returns:
      str: The decoded string.
docstr_tok: ["Decodes","a","list","of","token","IDs","into","a","string",".","Args",":","t","(","List","[","int","]",")",":","The","list","of","token","IDs","to","be","decoded",".","Returns",":","str",":","The","decoded","string","."]
code: def decode(self, t: List[int]) -> str:
      """
      Decodes a list of token IDs into a string.
  
      Args:
          t (List[int]): The list of token IDs to be decoded.
  
      Returns:
          str: The decoded string.
      """
      return self.sp_model.decode(t)
  
file_path: llama/llama/tokenizer.py
imports: import os
  from logging import getLogger
  from typing import List
  from sentencepiece import SentencePieceProcessor
label_desc: Use image node_id 3 for calling the Tokenizer obj's underlying member method code with example usage: obj.decode(t) and returns: self