code: def copy(self) -> "TorchDataLoader":
      dataset = self.dataset
      self.__dict__.pop("dataset")
      copied = super().copy()
      assert isinstance(copied, TorchDataLoader)
      self.dataset = copied.dataset = dataset
      return copied
  
imports: import torch
  import numpy
  from typing import Any
  from typing import Dict
  from typing import List
  from typing import Type
  from typing import Tuple
  from typing import Union
  from typing import Optional
  from cftool.array import to_numpy
  from cftool.types import np_dict_type
  from torch.utils.data import Sampler
  from torch.utils.data import DataLoader
  from torch.utils.data import SequentialSampler
  from torch.utils.data.dataloader import _BaseDataLoaderIter
  from torch.utils.data.distributed import DistributedSampler
  from ...schema import IData
  from ...schema import IDataset
  from ...schema import DataConfig
  from ...schema import IDataLoader
  from ...schema import DataProcessor
  from ...schema import DataProcessorConfig
  from ...misc.toolkit import get_ddp_info
  from ...misc.toolkit import get_world_size
label_desc: Use image node_id 6 for calling the TorchDataLoader obj's underlying member method code with example usage: obj.copy() and returns: copied