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
      elif "waveform" in batch["inputs"]:
          unpadded_mask_shape = batch["inputs"]["waveform"].shape[0]
          batch_pad = batch_size - unpadded_mask_shape
      elif "text" in batch["inputs"]:
          unpadded_mask_shape = batch["inputs"]["text"].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 Any, Dict, Iterator, Optional, Sequence, Text, Tuple
  from absl import logging
  from dmvr import builders
  from dmvr import modalities
  from dmvr import tokenizers
  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
  from scenic.dataset_lib import video_ops
  from scenic.projects.avatar.datasets.dataset_utils import add_int64
  from scenic.projects.avatar.datasets.dataset_utils import add_spectrogram
  from scenic.projects.avatar.datasets.dataset_utils import add_spectrogram_from_audio
  from scenic.projects.avatar.datasets.dataset_utils import add_text
  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