code: def train_shuffle(self) -> bool:
      shuffle = self.loader_kwargs.get("sampler", None) is None
      shuffle &= self.loader_kwargs.get("batch_sampler", None) is None
      return shuffle
  
imports: import copy
  import inspect
  import warnings
  from typing import Any, Dict, Optional, Tuple, Type, Union
  import torch
  from torch_geometric.data import Data, Dataset, HeteroData
  from torch_geometric.loader import DataLoader, LinkLoader, NodeLoader
  from torch_geometric.sampler import BaseSampler, NeighborSampler
  from torch_geometric.typing import InputEdges, InputNodes, OptTensor
label_desc: Use image node_id 2 for calling the LightningData obj's underlying member method code with example usage: obj.train_shuffle() and returns: shuffle