code: def pad_and_batch(
      tree: T, batch_size: int, axis: int = 0
  ) -> tuple[T, int]:
      """Pads and batch the pytree."""
      # We peek the first padding size, to then check that the same one applies to
      # all arguments. We can't support multiple padding sizes because we don't
      # know which padding sizes apply to each output argument. So we just use the
      # same one for everything.
      first = jax.tree_util.tree_flatten(tree)[0][0]
      padding_size = pad_array_to_be_divisible(
          first, batch_size, axis=axis
      )[1]
  
      def _pad_and_batch(a: jnp.ndarray) -> jnp.ndarray:
          a, padding_size_ = pad_array_to_be_divisible(
              a, batch_size, axis=axis
          )
          assert padding_size_ == padding_size, (
              f"Multiple padding sizes aren't "
              f"supported because it's hard to know which "
              f"padding sizes will apply to each output "
              f"argument when this inputs are used. "
              f"Actual padding sizes: {(padding_size, padding_size_)}"
          )
          return a.reshape(
              *a.shape[:axis], -1, batch_size, *a.shape[axis + 1 :]
          )
  
      batched_tree = jax.tree_util.tree_map(_pad_and_batch, tree)
  
      return batched_tree, padding_size
  
imports: import collections
  from collections.abc import Callable
  from collections.abc import Iterable
  from collections.abc import Mapping
  import functools
  from typing import Any
  from typing import Hashable
  from typing import Literal
  from typing import Optional
  from typing import Sequence
  from typing import TypeVar
  from typing import Union
  from absl import logging
  from clu import metric_writers
  import flax
  from flax import jax_utils
  from flax import traverse_util
  from flax.core.frozen_dict import FrozenDict
  import jax
  import jax.numpy
  import ml_collections
  import numpy
  from scenic.dataset_lib import dataset_utils
  from scenic.projects.lang4video import util
  from scenic.projects.lang4video.model.base_encoders import ImageTextEncoder
  from scenic.train_lib import train_utils
label_desc: Use image node_id 16 for calling a global function with example usage: pad_and_batch(tree, batch_size, axis) and returns: batched_tree,  padding_size, a