code: def maybe_pad_batch(batch, train, batch_size, return_as_dict):
      """Zero pad the batch on the right to the batch_size."""
      if not return_as_dict:
          return dataset_utils.maybe_pad_batch(batch, train, batch_size)
  
      assert "batch_mask" not in batch
      if "rgb" in batch["inputs"]:
          unpadded_mask_shape = batch["inputs"]["rgb"].shape[0]
          batch_pad = batch_size - unpadded_mask_shape
      elif "spectrogram" in batch["inputs"]:
          unpadded_mask_shape = batch["inputs"]["spectrogram"].shape[0]
          batch_pad = batch_size - unpadded_mask_shape
      else:
          raise ValueError("invalid input batch")
  
      if train and batch_pad != 0:
          raise ValueError(
              "In this codebase, we assumed that we always drop the "
              "last partial batch of the train set. Please use "
              "` drop_remainder=True` for the training set."
          )
  
      # Most batches will not need padding so we quickly return to avoid slowdown.
      if train or batch_pad == 0:
          if "batch_mask" not in batch:
              batch["batch_mask"] = np.ones(
                  unpadded_mask_shape, dtype=np.float32
              )
          return batch
  
      def zero_pad(array):
          pad_with = [(0, batch_pad)] + [(0, 0)] * (array.ndim - 1)
          return np.pad(array, pad_with, mode="constant")
  
      padded_batch = jax.tree_util.tree_map(zero_pad, batch)
      padded_batch_mask = zero_pad(
          np.ones(unpadded_mask_shape, dtype=np.float32)
      )
      padded_batch["batch_mask"] = padded_batch_mask
      return padded_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 1 for calling a global function with example usage: maybe_pad_batch(batch, train, batch_size, return_as_dict) and returns: padded_batch, dataset_utils, batch, np