code: def translate_and_calculate_bleu(
      *,
      p_pred_step,
      p_init_cache,
      params,
      predict_ds: tf.data.Dataset,
      decode_tokens,
      max_predict_length: int,
  ):
      """Translates the `predict_ds` and calculates the BLEU score."""
      n_devices = jax.local_device_count()
      logging.info("Translating evaluation dataset.")
      sources, references, predictions = [], [], []
      for pred_batch in predict_ds:
          pred_batch = jax.tree_util.tree_map(
              lambda x: x._numpy(), pred_batch
          )  # pylint: disable=protected-access
          # Handle final odd-sized batch by padding instead of dropping it.
          cur_pred_batch_size = pred_batch["inputs"].shape[0]
          if cur_pred_batch_size % n_devices:
              padded_size = int(
                  np.ceil(cur_pred_batch_size / n_devices) * n_devices
              )
              pred_batch = jax.tree_util.tree_map(
                  lambda x: pad_examples(
                      x, padded_size
                  ),  # pylint: disable=cell-var-from-loop
                  pred_batch,
              )
          pred_batch = common_utils.shard(pred_batch)
          cache = p_init_cache(pred_batch["inputs"])
          predicted = p_pred_step(
              pred_batch["inputs"],
              params,
              cache,
              decode.EOS_ID,
              max_predict_length,
          )
          predicted = tohost(predicted)
          inputs = tohost(pred_batch["inputs"])
          targets = tohost(pred_batch["targets"])
          # Iterate through non-padding examples of batch.
          for i, s in enumerate(predicted[:cur_pred_batch_size]):
              sources.append(decode_tokens(inputs[i]))
              references.append(decode_tokens(targets[i]))
              predictions.append(decode_tokens(s))
      logging.info(
          "Translation: %d predictions %d references %d sources.",
          len(predictions),
          len(references),
          len(sources),
      )
  
      # Calculate BLEU score for translated eval corpus against reference.
      bleu_matches = bleu.bleu_partial(references, predictions)
      all_bleu_matches = per_host_sum_pmap(bleu_matches)
      bleu_score = bleu.complete_bleu(*all_bleu_matches)
      # Save translation samples for tensorboard.
      exemplars = ""
      for n in np.random.choice(np.arange(len(predictions)), 8):
          exemplars += (
              f"{sources[n]}\n\n{references[n]}\n\n{predictions[n]}\n\n"
          )
      return exemplars, bleu_score
  
imports: import collections
  import functools
  import os
  from absl import logging
  from clu import metric_writers
  from clu import periodic_actions
  from flax import jax_utils
  from flax import linen
  from flax.training import checkpoints
  from flax.training import common_utils
  from flax.training import dynamic_scale
  from flax.training import train_state
  import jax
  import jax.numpy
  import ml_collections
  import numpy
  import optax
  import tensorflow
  import bleu
  import decode
  import input_pipeline
  import models
label_desc: Use image node_id 14 for calling a global function with example usage: translate_and_calculate_bleu() and returns: exemplars,  bleu_score