code: def tile_label_key(batch: Batch) -> Batch:
      """Tile labels and keys to match input videos when num_test_clips > 1.
  
      When multiple test crops are used (ie num_test_clips > 1), the batch dimension
      of batch['inputs'] = test_batch_size * num_test_clips.
      However, labels and keys remain of size [test_batch_size].
      This function repeats label and key to match the inputs.
  
      Args:
        batch: Batch from iterator
  
      Returns:
        Batch with 'label' and 'key' tiled to match 'inputs'. The input batch is
        mutated by the function.
      """
      n_repeats = batch["inputs"].shape[0] // batch["label"].shape[0]
      batch["label"] = np.repeat(batch["label"], n_repeats, axis=0)
      if "key" in batch:
          batch["key"] = np.repeat(batch["key"], n_repeats, axis=0)
      return batch
  
imports: import functools
  import os
  from typing import Dict, Iterator, List, Optional, Text, Tuple, Union
  from absl import logging
  from dmvr import builders
  from dmvr import modalities
  from dmvr import video_dataset
  from flax import jax_utils
  import jax
  import jax.numpy
  import ml_collections
  import numpy
  from scenic.dataset_lib import dataset_utils
  from scenic.dataset_lib import datasets
  from scenic.dataset_lib import video_ops
  from scenic.projects.vivit.data import file_utils
  import tensorflow
label_desc: Use image node_id 4 for calling a global function with example usage: tile_label_key(batch) and returns: batch