code: def preprocess_eval_example(
      example,
      camera_name="image_main",
      dtype=tf.float32,
      num_frames=30,
      stride=1,
      num_clips=1,
      zero_centering=True,
  ):
      """Preprocesses the given video for evaluation.
  
      Args:
        example: dict; Example that has an 'inputs'.
        camera_name: Name of the image sequence to use.
        dtype: Tensorflow data type; Data type of the image.
        num_frames: Number of frames per subclip.
        stride: Temporal stride to sample frames.
        num_clips: Linearly spaced clips to sample from each example.
        zero_centering: If True, frames are normalized to values in [-1, 1].
          If False, values in [0, 1].
  
      Returns:
        dict; Example that has an 'inputs'.
      """
      frames = example[camera_name]
      frames = processors.normalize_image(frames, zero_centering, dtype)
      clips = processors.sample_linspace_sequence(
          frames, num_clips, num_frames, stride
      )
      return {"inputs": 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 3 for calling a global function with example usage: preprocess_eval_example(example, camera_name, dtype, num_frames, stride, num_clips, zero_centering) and returns: dict