code: def _decode_batch_input_fn(
      num_decode_batches,
      sorted_inputs,
      vocabulary,
      batch_size,
      max_input_size,
      task_id=-1,
      has_input=True,
  ):
      """Generator to produce batches of inputs."""
      tf.logging.info(" batch %d" % num_decode_batches)
      for b in range(num_decode_batches):
          tf.logging.info("Decoding batch %d" % b)
          batch_length = 0
          batch_inputs = []
          for inputs in sorted_inputs[
              b * batch_size : (b + 1) * batch_size
          ]:
              input_ids = vocabulary.encode(inputs)
              if max_input_size > 0:
                  # Subtract 1 for the EOS_ID.
                  input_ids = input_ids[: max_input_size - 1]
              if (
                  has_input or task_id > -1
              ):  # Do not append EOS for pure LM tasks.
                  final_id = (
                      text_encoder.EOS_ID if task_id < 0 else task_id
                  )
                  input_ids.append(final_id)
              batch_inputs.append(input_ids)
              if len(input_ids) > batch_length:
                  batch_length = len(input_ids)
          final_batch_inputs = []
          for input_ids in batch_inputs:
              assert len(input_ids) <= batch_length
              x = input_ids + [0] * (batch_length - len(input_ids))
              final_batch_inputs.append(x)
  
          yield {
              "inputs": np.array(final_batch_inputs).astype(np.int32),
          }
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import collections
  import operator
  import os
  import re
  import string
  import time
  import numpy
  import six
  from six.moves import input
  from tensor2tensor.data_generators import problem
  from tensor2tensor.data_generators import text_encoder
  from tensor2tensor.data_generators import text_problems
  from tensor2tensor.utils import contrib
  from tensor2tensor.utils import hparam
  from tensor2tensor.utils import mlperf_log
  from tensor2tensor.utils import registry
  import tensorflow.compat.v1
  from tensorflow.compat.v1 import estimator
label_desc: Use image node_id 10 for calling a global function with example usage: _decode_batch_input_fn(num_decode_batches, sorted_inputs, vocabulary, batch_size, max_input_size, task_id, has_input) without return types