code: def maybe_pad_batch(
      batch, train, batch_size, num_clips, num_captions
  ):
      """Zero pad the batch on the right to the 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
      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, batch_pad=batch_pad):
          pad_with = [(0, batch_pad)] + [(0, 0)] * (array.ndim - 1)
          return np.pad(array, pad_with, mode="constant")
  
      # For the test set, we only keep one text input and one key for each video
      text_indices = (
          text_mask
      ) = keys = labels = verbs = verb_indices = None
      pad_text_mask = False
      pad_keys = False
      pad_labels = False
      pad_verbs = False
      pad_verb_indices = False
      if not train and batch_pad != 0:
          text_indices = batch["text_indices"]
          batch_pad_text = (batch_pad * num_captions) // num_clips
          logging.info(
              "Batch pad text in the test loop is %s", batch_pad_text
          )
          text_indices = zero_pad(text_indices, batch_pad_text)
          del batch["text_indices"]
          if "text_mask" in batch:
              text_mask = batch.pop("text_mask")
              text_mask = zero_pad(text_mask, batch_pad_text)
              pad_text_mask = True
          if "key" in batch:
              keys = batch["key"]
              pad_keys = True
              del batch["key"]
              batch_pad_key = batch_pad // num_clips
              keys = zero_pad(keys, batch_pad_key)
          if "label" in batch:
              labels = batch["label"]
              pad_labels = True
              del batch["label"]
              batch_pad_key = batch_pad // num_clips
              labels = zero_pad(labels, batch_pad_key)
          if "verb" in batch:
              verbs = batch["verb"]
              pad_verbs = True
              del batch["verb"]
              batch_pad_verb = batch_pad // num_clips
              verbs = zero_pad(verbs, batch_pad_verb)
          if "verb_indices" in batch:
              verb_indices = batch["verb_indices"]
              pad_verb_indices = True
              del batch["verb_indices"]
              batch_pad_verb_indices = batch_pad // num_clips
              verb_indices = zero_pad(
                  verb_indices, batch_pad_verb_indices
              )
  
      # Note here, batch_pad is repeated num_clips times if there are multiple clips
      # sampled per video
      padded_batch = jax.tree_util.tree_map(zero_pad, batch)
      padded_batch_mask = zero_pad(
          np.ones(unpadded_mask_shape, dtype=np.float32), batch_pad
      )
      padded_batch["batch_mask"] = padded_batch_mask
      if not train and batch_pad != 0:
          padded_batch["text_indices"] = text_indices
          if pad_text_mask:
              padded_batch["text_mask"] = text_mask
          if pad_keys:
              padded_batch["key"] = keys
          if pad_labels:
              padded_batch["label"] = labels
          if pad_verbs:
              padded_batch["verb"] = verbs
          if pad_verb_indices:
              padded_batch["verb_indices"] = verb_indices
      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 numpy
  from scenic.dataset_lib import dataset_utils
  from scenic.dataset_lib import datasets
  from scenic.dataset_lib import video_ops
  from scenic.projects.baselines.clip import tokenizer
  from scenic.projects.vivit.data import video_tfrecord_dataset
  import tensorflow
  from dmvr import tokenizers
label_desc: Use image node_id 1 for calling a global function with example usage: maybe_pad_batch(batch, train, batch_size, num_clips, num_captions) and returns: padded_batch, batch, np