code: def get_representation(
      train_state: OptaxTrainState,
      video_inputs,
      text_indices,
      flax_model: nn.Module,
  ) -> Tuple[jnp.ndarray, jnp.ndarray]:
      """Feeds the inputs to the model and returns their representations."""
      variables = {
          "params": train_state["weights"],
          **train_state["model_state"],
      }
      (encoded_video, encoded_text) = flax_model.apply(
          variables,
          video_inputs[0],
          text_indices,
          mutable=False,
          train=False,
          debug=False,
      )
      return encoded_video, encoded_text
  
imports: import os
  import re
  from typing import Any, Callable, Dict, List, Mapping, Optional, Tuple, Union
  from absl import logging
  import flax
  from flax import struct
  from flax.core import frozen_dict
  import flax.linen
  from flax.training import checkpoints
  import jax
  import jax.numpy
  import jax.profiler
  import ml_collections
  import numpy
  import optax
  from scenic.dataset_lib import dataset_utils
  from scenic.model_lib.base_models import base_model
  from scenic.projects.baselines.clip import model
  from scenic.train_lib import lr_schedules
  from scenic.train_lib import optimizers
  from scenic.train_lib import train_utils
  from tensorflow.io import gfile
label_desc: Use image node_id 16 for calling a global function with example usage: get_representation(train_state, video_inputs, text_indices, flax_model) and returns: encoded_video,  encoded_text