code: def predict_step(
      inputs,
      params,
      rngkey,
      eos_id,
      max_decode_len,
      config,
      temperature,
      top_k,
  ):
      """Predict language model on a batch."""
      target_shape = (inputs.shape[0], max_decode_len) + inputs.shape[
          2:
      ]
      initial_variables = models.TransformerLM(config).init(
          jax.random.PRNGKey(0), jnp.ones(target_shape, config.dtype)
      )
      cache = initial_variables["cache"]
  
      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.TransformerLM(config).apply(
              {"params": params, "cache": flat_cache},
              flat_ids,
              mutable=["cache"],
          )
          new_flat_cache = new_vars["cache"]
          # Remove singleton sequence-length dimension:
          # [batch, 1, vocab] --> [batch, 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.
      seqs = temperature_sampler.temperature_sample(
          inputs,
          cache,
          tokens_ids_to_logits,
          rngkey,
          temperature=temperature,
          topk=top_k,
          eos_token=eos_id,
      )
  
      return seqs
  
imports: import collections
  import os
  from absl import logging
  from clu import metric_writers
  from clu import periodic_actions
  from flax import linen
  from flax.training import checkpoints
  from flax.training import common_utils
  import jax
  from jax import random
  import jax.numpy
  from jax.sharding import PartitionSpec, Mesh, NamedSharding
  import ml_collections
  import numpy
  import optax
  import tensorflow
  import input_pipeline
  import models
  import temperature_sampler
  import utils
label_desc: Use image node_id 8 for calling a global function with example usage: predict_step(inputs, params, rngkey, eos_id, max_decode_len, config, temperature, top_k) and returns: seqs, flat_logits,  new_flat_cache