code: def postprocess_eval_batch(batch, num_frames=30):
      """Postprocesses the given batch for evaluation.
  
      Reshapes the batch from [bs, num_clips * num_frames, ...] into
        [bs * num_clips, num_frames, ...].
  
      Args:
        batch: dict; Batch that has an 'inputs'.
        num_frames: Number of frames per subclip.
      Returns:
        dict; Example that has an 'inputs'.
      """
      batch_clips = batch["inputs"]
      batch_clips = tf.reshape(
          batch_clips, (-1, num_frames, *batch_clips.shape[2:])
      )
      return {"inputs": batch_clips}
  
file_path: scenic/scenic/dataset_lib/bair_dataset.py
imports: import functools
  from typing import Optional
  from absl import logging
  from dmvr import processors
  from flax import jax_utils
  import jax.numpy
  from scenic.dataset_lib import dataset_utils
  from scenic.dataset_lib import datasets
  import tensorflow
label_desc: Use image node_id 4 for calling a global function with example usage: postprocess_eval_batch(batch, num_frames) and returns: dict