code: def tensor_decoder(dct):
      for t_name in ["boxes", "mask", "scores", "keypoints", "labels"]:
          if t_name in dct:
              if t_name == "labels":
                  dct[t_name] = torch.as_tensor(
                      dct[t_name], dtype=torch.int64
                  )
              else:
                  dct[t_name] = torch.as_tensor(dct[t_name])
  
              if t_name == "boxes":
                  dct[t_name] = torch.reshape(
                      dct[t_name], shape=(-1, 4)
                  )
              # TODO: implement mask shape
  
      return dct
  
imports: import os
  from pathlib import Path
  import torch
  import json
  from typing import Any, Generic, List, Union, TypeVar, Tuple, Dict, TYPE_CHECKING, Type, Callable, Sequence, Optional
  from avalanche.benchmarks.utils.data import AvalancheDataset
  from torch import Tensor
  from json import JSONEncoder
  from torch.utils.data import Subset, ConcatDataset
  from typing_extensions import Protocol
  from avalanche.evaluation import PluginMetric
  from avalanche.evaluation.metric_results import MetricValue
  from avalanche.evaluation.metric_utils import get_metric_name
label_desc: Use image node_id 1 for calling a global function with example usage: tensor_decoder(dct) and returns: dct