code: def generate_prediction(
      *,
      jit_pred_step,
      params,
      tokenized_prompts,
      eos_id,
      inference_rng,
      decode_tokens,
      config,
      predict_config,
  ):
      """Generate text from the prompt."""
      n_devices = jax.local_device_count()
  
      logging.info("Generating text.")
      predictions = []
      # Use batch of prompts provided by user.
      for pred_batch in jnp.array_split(
          tokenized_prompts,
          int(np.ceil(len(tokenized_prompts) / n_devices)),
      ):
          cur_pred_batch_size = pred_batch.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), pred_batch
              )  # pylint: disable=cell-var-from-loop
          pred_batch = common_utils.shard(pred_batch)
          inference_rng, sub_rng = random.split(inference_rng)
          inference_rngs = random.split(sub_rng, n_devices)
  
          predicted = jit_pred_step(
              pred_batch,
              params,
              inference_rngs,
              eos_id,
              config.max_predict_length,
              predict_config,
              config.sampling_temperature,
              config.sampling_top_k,
          )
          predicted = tohost(predicted)
          # Iterate through non-padding examples of batch.
          for s in predicted[:cur_pred_batch_size]:
              prediction = decode_tokens(s)
              logging.info("Sample: %s", str(prediction))
              predictions.append(prediction)
  
          # Save generated texts for tensorboard.
          exemplars = ""
          for prediction in predictions:
              exemplars += f"{prediction}\n\n"
      return exemplars
  
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 13 for calling a global function with example usage: generate_prediction() and returns: exemplars