chunk_content: tch["annotation"]]
      inputs = image_processor(images, labels)
      return inputs
  
  
  def val_transforms(example_batch):
      images = [handle_grayscale_image(x) for x in example_batch["image"]]
      labels = [x for x in example_batch["annotation"]]
      inputs = image_processor(images, labels)
      return inputs
  To apply the preprocessing functions over the entire dataset, use the 🤗 Datasets set_transform function:
  
  
  	Copied
  train_ds.set_transform(t