code: def predict_step(
      inputs, params, cache, eos_id, max_decode_len, config, beam_size=4
  ):
      """Predict translation with fast decoding beam search on a batch."""
      # Prepare transformer fast-decoder call for beam search: for beam search, we
      # need to set up our decoder model to handle a batch size equal to
      # batch_size * beam_size, where each batch item"s data is expanded in-place
      # rather than tiled.
      # i.e. if we denote each batch element subtensor as el[n]:
      # [el0, el1, el2] --> beamsize=2 --> [el0,el0,el1,el1,el2,el2]
      encoded_inputs = decode.flat_batch_beam_expand(
          models.Transformer(config).apply(
              {"params": params},
              inputs,
              method=models.Transformer.encode,
          ),
          beam_size,
      )
      raw_inputs = decode.flat_batch_beam_expand(inputs, beam_size)
  
      def tokens_ids_to_logits(flat_ids, flat_cache):
          """Token slice to logits from decoder model."""
          # --> [batch * beam, 1, vocab]
          flat_logits, new_vars = models.Transformer(config).apply(
              {"params": params, "cache": flat_cache},
              encoded_inputs,
              raw_inputs,  # only needed for input padding mask
              flat_ids,
              mutable=["cache"],
              method=models.Transformer.decode,
          )
          new_flat_cache = new_vars["cache"]
          # Remove singleton sequence-length dimension:
          # [batch * beam, 1, vocab] --> [batch * beam, vocab]
          flat_logits = flat_logits.squeeze(axis=1)
          return flat_logits, new_flat_cache
  
      # Using the above-defined single-step decoder function, run a
      # beam search over possible sequences given input encoding.
      beam_seqs, _ = decode.beam_search(
          inputs,
          cache,
          tokens_ids_to_logits,
          beam_size=beam_size,
          alpha=0.6,
          eos_id=eos_id,
          max_decode_len=max_decode_len,
      )
  
      # Beam search returns [n_batch, n_beam, n_length + 1] with beam dimension
      # sorted in increasing order of log-probability.
      # Return the highest scoring beam sequence, drop first dummy 0 token.
      return beam_seqs[:, -1, 1:]
  
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 9 for calling a global function with example usage: predict_step(inputs, params, cache, eos_id, max_decode_len, config, beam_size) and returns: beam_seqs, flat_logits,  new_flat_cache