code: def generate_collate_fn_with_label_drift(collate_fn):
      def collate_fn_with_label_drift(batch):
          batch_dict = collate_fn(batch)
          images = batch_dict["images"]
          labels = batch_dict["labels"]
          for i in range(len(images)):
              image, label = images[i], labels[i]
              if label == 0:
                  if np.random.randint(5) != 0:
                      batch_dict["labels"][i] = 1
                      # In 9/10 cases, the prediction vector will change to match the label
                      if np.random.randint(10) != 0:
                          batch_dict["predictions"][i] = torch.tensor(
                              [0, 1, 0, 0, 0, 0, 0, 0, 0, 0]
                          )
  
          return batch_dict
  
      return collate_fn_with_label_drift
  
imports: from deepchecks.vision.checks import PredictionDrift
  from deepchecks.vision.datasets.classification.mnist_torch import load_dataset
  from deepchecks.vision.checks import ClassPerformance
  import numpy
  import torch
  from deepchecks.vision.datasets.detection.coco_torch import load_dataset
label_desc: Use image node_id 1 for calling a global function with example usage: generate_collate_fn_with_label_drift(collate_fn) and returns: collate_fn_with_label_drift, batch_dict