code: def tile_label_key(batch, return_as_dict=False):
      """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
        return_as_dict: Whether to return multimodal inputs as a dictionary.
  
      Returns:
        batch: Batch with 'label' and 'key' tiled to match 'inputs'.
      """
      if not return_as_dict:
          n_repeats = (
              batch["inputs"].shape[0] // batch["label"].shape[0]
          )
      elif "rgb" in batch["inputs"]:
          n_repeats = (
              batch["inputs"]["rgb"].shape[0] // batch["label"].shape[0]
          )
      elif "spectrogram" in batch["inputs"]:
          n_repeats = (
              batch["inputs"]["spectrogram"].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
  from typing import Dict, Iterator, List, Optional, Text, Tuple, Union
  from absl import logging
  from dmvr import modalities
  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.mbt.datasets.dataset_utils import add_spectrogram
  from scenic.projects.vivit.data import video_tfrecord_dataset
  import tensorflow
label_desc: Use image node_id 4 for calling a global function with example usage: tile_label_key(batch, return_as_dict) and returns: batch