code: def representation_fn(
      train_state: train_utils.TrainState,
      batch: Batch,
      *,
      flax_model: nn.Module,
      representation_layer: str,
      gather_to_host: bool = True,
  ) -> Tuple[jnp.ndarray, jnp.ndarray, jnp.ndarray]:
      """Feeds the inputs to the model and returns their representations.
  
      Args:
        train_state: TrainState, the state of training including the current
          global_step, model_state, rng, and optimizer. The buffer of this argument
          can be donated to the computation.
        batch: A single batch of data from the dataset.
        flax_model: A Flax model.
        representation_layer: The name of the layer to use as the representation.
        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.
      """
      modality = "image"
      task = polyvit_base_model.Task.FEWSHOT
  
      variables = {
          "params": train_state.params,
          **train_state.model_state,
      }
  
      representation_layer_parts = representation_layer.split("/")
      filter_rep = (
          lambda mdl, _: mdl.name == representation_layer_parts[-1]
      )
      _, model_state = flax_model.apply(
          variables,
          x=batch["inputs"],
          targets=batch["label"],
          task=task,
          modality=modality,
          train=False,
          capture_intermediates=filter_rep,
          mutable=["intermediates"],
          debug=False,
      )
      if "intermediates" not in model_state:
          raise ValueError(
              f'Layer with name "{representation_layer}"'
              " does not exist in your model."
          )
  
      representation = model_state["intermediates"]
      for rep_layer in representation_layer_parts:
          if rep_layer:
              representation = representation[rep_layer]
      representation = representation["__call__"][0]
      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 collections
  import copy
  import functools
  from typing import Any, Callable, Dict, Optional, Tuple
  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 import optimizers
  import jax.numpy
  import jax.profiler
  import ml_collections
  import numpy
  import optax
  from scenic.dataset_lib import dataset_utils
  from scenic.projects.mbt import trainer
  from scenic.projects.polyvit import polyvit_base_model
  from scenic.projects.polyvit import train_utils
  from scenic.projects.vivit import evaluation_lib
  from scenic.train_lib import optax
  from scenic.train_lib import pretrain_utils
  from scenic.train_lib import train_utils
  from scenic.train_lib.transfer import fewshot_utils
label_desc: Use image node_id 3 for calling a global function with example usage: representation_fn(train_state, batch) and returns: representation,  batch,  batch