code: def representation_fn_video(
      train_state: train_utils.TrainState,
      batch: Batch,
      *,
      flax_model: nn.Module,
      config: ml_collections.ConfigDict,
      gather_to_host: bool = True,
  ) -> Tuple[jnp.ndarray, jnp.ndarray, jnp.ndarray]:
      """Feeds the video inputs to the model and returns their representations.
  
      Video representations are obtained by temporally average-pooling per-frame
      representations from the input video clip.
  
      Args:
        train_state: TrainState, the state of training including the current
          global_step, model_state, rng, params, and optimizer. The buffer of this
          argument can be donated to the computation.
        batch: A single batch of data from the video dataset.
        flax_model: A Flax model.
        config: Configurations of the experiment.
        gather_to_host: Whether to gather results from all devices to the host,
          rather than leaving them distributed.
  
      Returns:
        Representation learned by the model for the given inputs and the labels and
        masks. If `gather_to_host` is True, these are collected from all hosts.
        The shape of the returned tensors when  `gather_to_host` is False are:
        representation: `[num_devices, global_batch, features]`.
        labels: `[num_devices, global_batch]`.
        mask: `[num_devices, global_batch]`.
        If `gather_to_host` is True then each shape is prepended with
        `[num_hosts,]`
      """
      variables = {
          "params": train_state.params,
          **train_state.model_state,
      }
  
      representation_layer = (
          config.video_fewshot.representation_layer.split("/")
      )
      filter_rep = lambda mdl, _: mdl.name == representation_layer[-1]
  
      def get_representation(
          inputs,
          variables,
          training,
          capture_intermediates,
          mutable,
          debug,
      ):
          _, model_state = flax_model.apply(
              variables,
              inputs,
              train=training,
              capture_intermediates=capture_intermediates,
              mutable=mutable,
              debug=debug,
          )
          if "intermediates" not in model_state:
              raise ValueError(
                  f'Layer with name "{config.video_fewshot.representation_layer}"'
                  " does not exist in your model."
              )
  
          representation = model_state["intermediates"]
          for rep_layer in representation_layer:
              if rep_layer:
                  representation = representation[rep_layer]
          representation = representation["__call__"][0]
          return representation
  
      # Get representations for each frame in the video sample.
      if config.video_fewshot.get("n_sampled_frames"):
          inputs = video_utils.sample_frames_uniformly(
              batch["inputs"], config.video_fewshot.n_sampled_frames
          )
      else:
          inputs = batch["inputs"]
      representation = jax.vmap(
          functools.partial(
              get_representation,
              variables=variables,
              training=False,
              capture_intermediates=filter_rep,
              mutable=["intermediates"],
              debug=False,
          ),
          in_axes=1,
          out_axes=1,
          axis_name="time",
      )(inputs)
      # Average pooling of representations over time axis.
      representation = jnp.mean(representation, axis=1)
      if gather_to_host:
          representation = jax.lax.all_gather(representation, "batch")
          batch = jax.lax.all_gather(batch, "batch")
      return representation, batch["label"], batch["batch_mask"]
  
imports: import functools
  from typing import Any, Callable, Dict, Iterator, Tuple, Optional, Type
  from absl import logging
  from clu import metric_writers
  from clu import periodic_actions
  from clu import platform
  from flax import jax_utils
  import flax.linen
  import jax
  from jax.example_libraries.optimizers import clip_grads
  import jax.numpy
  import jax.profiler
  import ml_collections
  import numpy
  import optax
  from scenic.common_lib import video_utils
  from scenic.dataset_lib import dataset_utils
  from scenic.model_lib.base_models import base_model
  from scenic.train_lib import lr_schedules
  from scenic.train_lib import optimizers
  from scenic.train_lib import pretrain_utils
  from scenic.train_lib import train_utils
  from scenic.train_lib.transfer import fewshot_utils
  from scenic.train_lib.transfer import linear_probe_utils
label_desc: Use image node_id 4 for calling a global function with example usage: representation_fn_video(train_state, batch) and returns: representation,  batch,  batch, representation