code: def test_repr_with_imagenet_with_mean_std_norm(
      mean: Union[float, list], std: Union[float, list]
  ) -> None:
      opts = get_config()
      setattr(
          opts, "image_augmentation.random_horizontal_flip.enable", True
      )
      setattr(
          opts, "image_augmentation.random_resized_crop.enable", True
      )
      setattr(
          opts,
          "image_augmentation.to_tensor.mean_std_normalization.enable",
          True,
      )
      setattr(
          opts,
          "image_augmentation.to_tensor.mean_std_normalization.mean",
          mean,
      )
      setattr(
          opts,
          "image_augmentation.to_tensor.mean_std_normalization.std",
          std,
      )
  
      mock_imagenet_dataset = MockImageNetDataset(
          opts, is_training=True, is_evaluation=False
      )
      mock_imagenet_dataset.n_classes = 10
      actual_repr = repr(mock_imagenet_dataset)
      # ignore some extra spaces
      actual_repr = actual_repr.replace("\t ", "\t").replace(
          " \n", "\n"
      )
  
      expected_repr = f"""\
  MockImageNetDataset(
      root=None
      is_training=True
      num_samples=100
      transforms=Compose(
              RandomResizedCrop(scale=(0.08, 1.0), ratio=(0.75, 1.3333333333333333), size=(256, 256), interpolation=bilinear),
              RandomHorizontalFlip(p=0.5),
              ToTensor(dtype=torch.float32, norm_factor=255, mean_std_normalization_enable=True, normalization_mean={mean}, normalization_std={std})
          )
      num_classes=10
  )""".replace(
          " " * 4, "\t"
      )
  
      assert actual_repr == expected_repr
  
imports: from typing import Any, Union
  import pytest
  from data.datasets.dataset_base import BaseDataset
  from tests.configs import get_config
  from tests.data.datasets.classification.mock_imagenet import MockImageNetDataset
label_desc: Use image node_id 2 for calling a global function with example usage: test_repr_with_imagenet_with_mean_std_norm(mean, std) without return types