code: def preprocess(features, label):
      """Preprocessing code specific to ViT models."""
      padded_key = tf.strings.join(
          [
              get_padding(tf.strings.length(features["key"])),
              features["key"],
          ]
      )
  
      return {
          "inputs": tf.image.resize(
              features["pileup"], [256, 256]
          ),  # Resize pileups to make side length divisible by 4.
          "label": label,
          "event_len": features["event_len"],
          "key": tf.strings.unicode_decode(
              padded_key, "UTF-8"
          ).to_tensor(),
          "key_length": tf.strings.length(features["key"]),
      }
  
file_path: scenic/scenic/projects/svvit/datasets/pileup_window_dataset.py
imports: import functools
  from typing import Optional, Dict, Tuple
  from absl import logging
  from flax import jax_utils
  import jax
  import jax.numpy
  import 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