class_docstr: Class for loading the COCO dataset meant for the DETR ResNet50 model`.
  
  Implement the necessary methods to load the images, labels and generate model predictions in a format comprehensible
   by deepchecks.
class_docstr_tok: ["Class","for","loading","the","COCO","dataset","meant","for","the","DETR","ResNet50","model","`",".","Implement","the","necessary","methods","to","load","the","images",",","labels","and","generate","model","predictions","in","a","format","comprehensible","by","deepchecks","."]
docstr: Convert the batch to a list of images. Copied from deepchecks.vision.datasets.detection.coco
docstr_tok: ["Convert","the","batch","to","a","list","of","images",".","Copied","from","deepchecks.vision.datasets.detection.coco"]
code: def batch_to_images(batch) -> Iterable[np.ndarray]:
      """Convert the batch to a list of images. Copied from deepchecks.vision.datasets.detection.coco"""
      return [np.array(x) for x in batch[0]]
  
file_path: deepchecks/examples/integrations/hugging_face/deepchecks_hugging_face_tutorial.py
imports: import torch
  from transformers import DetrForObjectDetection
  from typing import Union, List, Iterable
  import numpy
  from deepchecks.vision import VisionData
  import torchvision.transforms
  from deepchecks.vision.datasets.detection import coco_torch
  from deepchecks.vision.datasets.detection import coco_utils
  from deepchecks.vision.vision_data import BatchOutputFormat
  from deepchecks.vision.checks import MeanAveragePrecisionReport
label_desc: Use image node_id 3 for calling the COCODETRData obj's underlying member method code with example usage: obj.batch_to_images(batch) and returns: unknown