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 labels. Copied from deepchecks.vision.datasets.detection.coco
docstr_tok: ["Convert","the","batch","to","a","list","of","labels",".","Copied","from","deepchecks.vision.datasets.detection.coco"]
code: def batch_to_labels(batch) -> Union[List[torch.Tensor], torch.Tensor]:
      """Convert the batch to a list of labels. Copied from deepchecks.vision.datasets.detection.coco"""
  
      def move_class(tensor):
          return (
              torch.index_select(
                  tensor,
                  1,
                  torch.LongTensor([4, 0, 1, 2, 3]).to(tensor.device),
              )
              if len(tensor) > 0
              else tensor
          )
  
      return [move_class(tensor) for tensor in batch[1]]
  
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 2 for calling the COCODETRData obj's underlying member method code with example usage: obj.batch_to_labels(batch) and returns: unknown, unknown