code: def eval_step_autoregressive_decoding(
      train_state: train_utils.TrainState,
      batch: constants.Batch,
      *,
      model: Any,
      metrics_fn: Any,
      model_config: ml_collections.ConfigDict,
      vocab_size: int,
      num_decodes: int,
      beam_search: bool = True,
      debug: Optional[bool] = False,
  ) -> Tuple[Dict[str, Tuple[float, int]], constants.JTensor, Any]:
      """Evaluate autoregressive generation.
  
      Args:
        train_state: The state of training including the current global_step,
          model_state, rng, and optimizer. The buffer of this argument are donated
          to the computation.
        batch: A single batch of data. The buffer of this argument can be donated to
          the computation.
        model: The scenic model.
        metrics_fn: A metrics function that given logits and batch of data,
          calculates the metrics as well as the loss.
        model_config: Config for model architecture.
        vocab_size: size of the vocabulary.
        num_decodes: number of decoding attempts. A larger number means longer
          inference time but better performance.
        beam_search: if True, perform beam search. If False, perform temperature
          sampling.
        debug: Whether the debug mode is enabled during training. `debug=True`
          enables model specific logging/storing some values using
          jax.host_callback.
  
      Returns:
        metrics: dictionary mapping metrics to values.
        logits: predicted logits of the model.
      """
      variables = {
          "params": train_state.params,
          **train_state.model_state,
      }
  
      # Loop per example since decoding only works for single examples.
      (
          predicted_tokens,
          _,
          retr_top_image,
      ) = model.apply_with_autoregressive_decoding(
          variables,
          **batch,
          num_decodes=num_decodes,
          beam_search=beam_search,
          debug=debug,
          use_memory=True,
          retrieve_local=model_config.retrieve_local,
      )
  
      # The autoregressive decoder yields tokens. However, the metrics want
      # logits. So make the predictions into one-hot predictions.
      logits = common_utils.onehot(predicted_tokens, vocab_size)
      metrics = metrics_fn(logits, batch)
  
      return metrics, retr_top_image, logits
  
imports: import functools
  import gc
  from typing import Any, 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
  from flax.training import common_utils
  import jax
  import jax.example_libraries.optimizers
  import jax.numpy
  import jax.profiler
  import ml_collections
  import numpy
  import optax
  from scenic.dataset_lib import dataset_utils
  from scenic.xm import xm_utils
  from scenic.model_lib.base_models import base_model
  from scenic.projects.knowledge_visual_language import trainer_utils
  from scenic.projects.knowledge_visual_language.models import constants
  from scenic.projects.knowledge_visual_language.models import local_memory
  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
label_desc: Use image node_id 4 for calling a global function with example usage: eval_step_autoregressive_decoding(train_state, batch) and returns: metrics,  retr_top_image,  logits