code: def decode_tuple(tuple_to_decode: tuple):
      """Reconstructs a potentially nested tuple of type `torch.Tensor` as a nested list.
      Args:
          tuple_to_decode (tuple): The tuple to decode.
      Returns:
          list: A nested list containing the same values as the input tuple.
      Raises:
          TypeError: If the tuple holds values of different type than `tuple` or `torch.Tensor`.
      """
      inner_model_output = []
      if isinstance(tuple_to_decode, torch.Tensor):
          return (
              tuple_to_decode.cpu().numpy().astype(np.float32).tolist()
          )
      elif isinstance(tuple_to_decode, tuple):
          for value in tuple_to_decode:
              inner_model_output.append(decode_tuple(value))
          return inner_model_output
      else:
          raise TypeError(
              "ERROR occured during decoding of output tensors! The tuple holds values of different type "
              "than `tuple` or `torch.Tensor`."
          )
  
imports: import os
  import random
  from typing import Any, Union
  import numpy
  import torch
  from PIL import Image
  from torch import squeeze
  import jsonlines
  import requests
  import transformers
  from adapters import AutoAdapterModel, init
  from transformers import AlbertConfig, BartConfig, BatchEncoding, BeitConfig, BeitImageProcessor, BertConfig, CLIPProcessor, CLIPVisionConfig, CLIPVisionModelWithProjection, DebertaConfig, DebertaV2Config, DistilBertConfig, EncoderDecoderConfig, EncoderDecoderModel, GPT2Config, GPTJConfig, MBartConfig, RobertaConfig, T5Config, ViTConfig, ViTImageProcessor, XLMRobertaConfig
label_desc: Use image node_id 9 for calling a global function with example usage: decode_tuple(tuple_to_decode) and returns: tuple_to_decode, inner_model_output