code: def decode_batch(
      state: train_state.TrainState,
      batch: Dict[str, Array],
      decode_rng: PRNGKey,
      ctable: CTable,
  ):
      """Decodes and log results for a batch."""
      inputs, outputs = batch["query"], batch["answer"][:, 1:]
      decode_rng = jax.random.fold_in(decode_rng, state.step)
      inferred = decode(state.params, inputs, decode_rng, ctable)
      questions = ctable.decode_onehot(inputs)
      infers = ctable.decode_onehot(inferred)
      goldens = ctable.decode_onehot(outputs)
  
      for question, inferred, golden in zip(questions, infers, goldens):
          log_decode(question, inferred, golden)
  
imports: import functools
  from typing import Any, Dict, Tuple
  from absl import app
  from absl import flags
  from absl import logging
  from clu import metric_writers
  from flax import linen
  from flax.training import train_state
  import jax
  import jax.numpy
  import optax
  import models
  from input_pipeline import CharacterTable
  from input_pipeline import get_sequence_lengths
  from input_pipeline import mask_sequences
label_desc: Use image node_id 9 for calling a global function with example usage: decode_batch(state, batch, decode_rng, ctable) without return types