code: def run_update_loop(dataset: VisionData):
      context: Context = Context(dataset, random_state=0)
      dataset.init_cache()
      for batch in context.train:
          batch = BatchWrapper(
              batch, dataset.task_type, dataset.number_of_images_cached
          )
          dataset.update_cache(
              len(batch), batch.numpy_labels, batch.numpy_predictions
          )
  
imports: import pathlib
  import numpy
  import pytest
  import torch
  from torch.utils.data import DataLoader, Dataset
  from deepchecks.vision.context import Context
  from deepchecks.vision.datasets.classification.mnist_torch import collate_without_model
  from deepchecks.vision.datasets.classification.mnist_torch import deepchecks_collate
  from deepchecks.vision.datasets.classification.mnist_torch import load_dataset
  from deepchecks.vision.datasets.classification.mnist_torch import load_model
  from deepchecks.vision.datasets.detection import coco_tensorflow
  from deepchecks.vision.datasets.detection.coco_torch import collate_without_model
  from deepchecks.vision.datasets.detection.coco_torch import load_dataset
  from deepchecks.vision.datasets.segmentation.segmentation_coco import load_dataset
  from deepchecks.vision.utils.test_utils import replace_collate_fn_dataloader, replace_collate_fn_visiondata, un_normalize_batch
  from deepchecks.vision.vision_data import TaskType, VisionData
  from deepchecks.vision.vision_data.batch_wrapper import BatchWrapper
  from deepchecks.vision.vision_data.utils import set_seeds
label_desc: Use image node_id 29 for calling a global function with example usage: run_update_loop(dataset) without return types