code: def deepchecks_collate(model) -> t.Callable:
      """Process batch to deepchecks format.
  
      Parameters
      ----------
      model
          model to predict with
      Returns
      -------
      BatchOutputFormat
          batch of data in deepchecks format
      """
  
      def _process_batch_to_deepchecks_format(
          data,
      ) -> BatchOutputFormat:
          raw_images = [x[0] for x in data]
          images = [np.array(x) for x in raw_images]
  
          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
              )
  
          labels = [move_class(x[1]) for x in data]
  
          predictions = []
          with warnings.catch_warnings():
              warnings.simplefilter(
                  action="ignore", category=UserWarning
              )
              raw_predictions: "yolov5.models.common.Detections" = (
                  model(raw_images)
              )  # noqa: F821
  
              # yolo Detections objects have List[torch.Tensor] xyxy output in .pred
              for single_image_tensor in raw_predictions.pred:
                  pred_modified = torch.clone(single_image_tensor)
                  pred_modified[:, 2] = (
                      pred_modified[:, 2] - pred_modified[:, 0]
                  )  # w = x_right - x_left
                  pred_modified[:, 3] = (
                      pred_modified[:, 3] - pred_modified[:, 1]
                  )  # h = y_bottom - y_top
                  predictions.append(pred_modified)
          return BatchOutputFormat(
              images=images, labels=labels, predictions=predictions
          )
  
      return _process_batch_to_deepchecks_format
  
imports: import logging
  import typing
  import warnings
  import zipfile
  from io import BytesIO
  from pathlib import Path
  from urllib.request import urlopen
  import albumentations
  import numpy
  import torch
  from PIL import Image
  from torch.utils.data import DataLoader
  from typing_extensions import Literal
  from deepchecks.vision.datasets.assets.coco_detection.static_predictions_yolo import coco_detections_static_predictions_dict
  from deepchecks.vision.datasets.detection.coco_utils import COCO_DIR, LABEL_MAP, download_coco128, get_image_and_label
  from deepchecks.vision.utils.test_utils import get_data_loader_sequential, hash_image
  from deepchecks.vision.vision_data import BatchOutputFormat, VisionData
label_desc: Use image node_id 4 for calling a global function with example usage: deepchecks_collate(model) and returns: _process_batch_to_deepchecks_format, BatchOutputFormat, unknown