code: def coco_collate_with_bias_one_class(data):
      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]
      for i, bboxes_per_image in enumerate(labels):
          for bbox in bboxes_per_image:
              if bbox[0] == 74:
                  x, y, w, h = [round(float(n)) for n in bbox[1:]]
                  images[i][y : y + h, x : x + w] = images[i][
                      y : y + h, x : x + w
                  ].clip(min=200)
      return {"images": images, "labels": labels}
  
imports: import numpy
  import torch
  from hamcrest import assert_that, close_to, equal_to, greater_than, has_entries, has_length
  from deepchecks.vision.checks import PropertyLabelCorrelationChange
  from deepchecks.vision.utils.test_utils import replace_collate_fn_visiondata
  from tests.base.utils import equal_condition_result
label_desc: Use image node_id 1 for calling a global function with example usage: coco_collate_with_bias_one_class(data) and returns: dict, unknown