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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 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),
    }