code: def test_label_matches_path(self, dataset_mock, config):
      # We read the labels from the csv files instead. But for the trainset, the labels are also part of the path.
      # This test makes sure that they're both the same
      if config["split"] != "train":
          return
  
      dataset, _ = dataset_mock.load(config)
  
      for sample in dataset:
          label_from_path = int(Path(sample["path"]).parent.name)
          assert sample["label"] == label_from_path
  
imports: import io
  import pickle
  from collections import deque
  from pathlib import Path
  import pytest
  import torch
  import torchvision.transforms.v2
  from builtin_dataset_mocks import DATASET_MOCKS, parametrize_dataset_mocks
  from torch.testing._comparison import not_close_error_metas, ObjectPair, TensorLikePair
  from torch.utils.data import DataLoader
  from torch.utils.data.graph_settings import get_all_graph_pipes
  from torchdata.dataloader2.graph.utils import traverse_dps
  from torchdata.datapipes.iter import ShardingFilter, Shuffler
  from torchdata.datapipes.utils import StreamWrapper
  from torchvision import tv_tensors
  from torchvision._utils import sequence_to_str
  from torchvision.prototype import datasets
  from torchvision.prototype.datasets.utils import EncodedImage
  from torchvision.prototype.datasets.utils._internal import INFINITE_BUFFER_SIZE
  from torchvision.prototype.tv_tensors import Label
  from torchvision.transforms.v2._utils import is_pure_tensor
label_desc: Use image node_id 1 for calling the TestGTSRB obj's underlying member method code with example usage: obj.test_label_matches_path(dataset_mock, config) without return types