code: def numpy_shape_dataloader(
      shape: tuple = None,
      value: Union[float, np.ndarray] = 255,
      collate_fn=None,
  ):
      if collate_fn is None:
          collate_fn = np.stack
  
      class TwoTupleDataset(Dataset):
          def __getitem__(self, index):
              if isinstance(value, (float, int)):
                  return np.ones(shape) * value
              else:
                  return value
  
          def __len__(self) -> int:
              return 8
  
      return DataLoader(
          TwoTupleDataset(), batch_size=4, collate_fn=collate_fn
      )
  
imports: from itertools import permutations
  from typing import Union
  import numpy
  import torch
  from hamcrest import assert_that, calling, close_to, equal_to, raises
  from torch.utils.data import DataLoader, Dataset
  from deepchecks.vision.datasets.detection import coco_torch
  from deepchecks.vision.utils import image_properties
  from deepchecks.vision.utils.detection_formatters import convert_batch_of_bboxes, convert_bbox, verify_bbox_format_notation
  from deepchecks.vision.vision_data import VisionData
label_desc: Use image node_id 1 for calling a global function with example usage: numpy_shape_dataloader(shape, value, collate_fn) and returns: DataLoader, int, unknown, value