code: def pileup_parser(
      serialized: str,
      mode: str,
      pileup_height: int,
      pileup_width: int,
  ) -> Tuple[Dict[str, tf.Tensor], Optional[tf.Tensor]]:
      """Parses an EventFeatures serialized example into feature and label tensors."""
      example = tf.io.parse_single_example(
          serialized,
          {
              "event_name": tf.io.FixedLenFeature([1], dtype=tf.string),
              "pileup_1d": tf.io.FixedLenFeature(
                  [pileup_height * pileup_width * PILEUP_NUM_CHANNELS],
                  dtype=tf.float32,
              ),
              "zygosity": tf.io.FixedLenFeature([1], dtype=tf.int64),
              "event_len": tf.io.FixedLenFeature([1], dtype=tf.int64),
          },
      )
  
      features = {
          "pileup": tf.reshape(
              example["pileup_1d"],
              [
                  pileup_height,
                  pileup_width,
                  PILEUP_NUM_CHANNELS,
              ],
          ),
          "key": example["event_name"],
          "event_len": tf.squeeze(example["event_len"], [-1]),
      }
  
      # Define the label.
      if mode == "train":
          label_tensor = tf.squeeze(example["zygosity"], [-1])
          labels = tf.one_hot(label_tensor, 3)
      else:
          labels = None
  
      return features, labels
  
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 1 for calling a global function with example usage: pileup_parser(serialized, mode, pileup_height, pileup_width) and returns: features,  labels