code: def preprocess(features, label):
      """Preprocessing code specific to ViT models."""
      label_tensor = tf.cast(tf.squeeze(label, [-1]), tf.int32)
      return {
          "inputs": tf.image.resize(
              features["image"], [256, 256]
          ),  # Resize pileups to make side length divisible by 4.
          "label": tf.one_hot(label_tensor, 3),
      }
  
file_path: scenic/scenic/projects/svvit/datasets/pileup_coverage_dataset.py
imports: import functools
  from typing import Optional, Tuple
  from absl import logging
  from flax import jax_utils
  import jax
  import jax.numpy
  from scenic.dataset_lib import dataset_utils
  from scenic.dataset_lib import datasets
  import tensorflow
label_desc: Use image node_id 4 for calling a global function with example usage: preprocess(features, label) and returns: dict