class_docstr: Transformer that predicts blocks of the output in parallel.
code: def _slow_greedy_infer_guess_and_check(self, features, decode_length):
      assert self._hparams.block_size > 0
      assert self._hparams.force_full_predict
      assert self._hparams.sampling_method == "argmax"
      assert self._decode_hparams.batch_size == 1
      assert self._decode_hparams.block_size > 0
      assert self._decode_hparams.block_size <= self._hparams.block_size
      assert self._decode_hparams.guess_and_check_top_k > 0
  
      inputs_old = features["inputs"]
      assert "targets" not in features
  
      assert len(features["inputs"].shape) in [3, 4]
      if len(features["inputs"].shape) < 4:
          features["inputs"] = tf.expand_dims(features["inputs"], 2)
  
      block_size = self._decode_hparams.block_size
      decode_length += tf.shape(features["inputs"])[1]
  
      def while_exit_cond(
          result, length
      ):  # pylint: disable=unused-argument
          return tf.logical_and(
              length < decode_length,
              tf.reduce_all(
                  tf.not_equal(
                      result[:, :length, :, :], text_encoder.EOS_ID
                  )
              ),
          )
  
      def infer_step(result, length):
          """Inference step."""
  
          def print_info(result, length, new_length):
              vocab = self.problem_hparams.vocabulary["targets"]
              tf.logging.info(
                  "length=%s new_length=%s length_diff=%s new_suffix=%s",
                  length,
                  new_length,
                  new_length - length,
                  str(
                      [
                          vocab._subtoken_id_to_subtoken_string(
                              index
                          )  # pylint: disable=protected-access
                          for index in result[0, -block_size:, 0, 0][
                              : new_length - length
                          ]
                      ]
                  ).decode("unicode-escape"),
              )
  
          features["targets"] = tf.pad(
              result, [[0, 0], [0, 1], [0, 0], [0, 0]]
          )
          samples, logits, losses = self.sample(
              features
          )  # pylint: disable=unused-variable
  
          _, top_k_indices = tf.nn.top_k(
              logits[:, :-1, :1, :, :],
              k=self._decode_hparams.guess_and_check_top_k,
          )
          in_top_k = tf.reduce_any(
              tf.equal(
                  tf.to_int64(top_k_indices), tf.expand_dims(result, 4)
              ),
              axis=4,
          )
  
          eos_cumsum = tf.cumsum(
              tf.to_int32(tf.equal(result, text_encoder.EOS_ID)), axis=1
          )
          after_eos = tf.greater(
              common_layers.shift_right(eos_cumsum), 0
          )
  
          correct = tf.logical_and(in_top_k, tf.logical_not(after_eos))
          correct_cumsum = tf.cumsum(tf.to_int32(correct), axis=1)
          perfect_cumsum = 1 + tf.range(tf.shape(correct)[1])
          for axis in [0, 2, 3]:
              perfect_cumsum = tf.expand_dims(perfect_cumsum, axis=axis)
  
          new_length = tf.reduce_sum(
              tf.to_int32(tf.equal(correct_cumsum, perfect_cumsum)),
              axis=1,
          )
          new_length = tf.squeeze(new_length, axis=[0, 1, 2])
          new_length = tf.minimum(new_length, decode_length)
  
          new_result = tf.concat(
              [
                  result[:, :new_length, :, :],
                  tf.reshape(
                      samples[:, new_length, :block_size, :],
                      [1, block_size, 1, 1],
                  ),
              ],
              axis=1,
          )
  
          with tf.control_dependencies(
              [tf.py_func(print_info, [result, length, new_length], [])]
          ):
              new_result = tf.identity(new_result)
  
          return new_result, new_length
  
      result = tf.zeros((1, 0, 1, 1), dtype=tf.int64)
      length = tf.squeeze(tf.zeros(1, dtype=tf.int32))
  
      result, length = tf.while_loop(
          while_exit_cond,
          infer_step,
          [result, length],
          shape_invariants=[
              tf.TensorShape([1, None, 1, 1]),
              tf.TensorShape([]),
          ],
          back_prop=False,
          parallel_iterations=1,
      )
  
      result = result[:, :length, :, :]
  
      features["inputs"] = inputs_old
  
      return {
          "outputs": result,
          "scores": None,
      }
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from tensor2tensor.data_generators import text_encoder
  from tensor2tensor.layers import common_layers
  from tensor2tensor.models import transformer
  from tensor2tensor.utils import registry
  import tensorflow.compat.v1
  from tensorflow.compat.v1 import estimator
label_desc: Use image node_id 6 for calling the TransformerBlockParallel obj's underlying member method code with example usage: obj._slow_greedy_infer_guess_and_check(features, decode_length) and returns: dict, tf, new_result,  new_length