class_docstr: Image-to-image transformer predicting blocks of the output in parallel.
class_docstr_tok: ["Image-to-image","transformer","predicting","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) + (
          self._decode_hparams.guess_and_check_epsilon >= 0
      ) == 1
  
      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 length < decode_length
  
      def infer_step(result, length):
          """Inference step."""
  
          def print_info(samples, result, length, new_length):
              tf.logging.info(
                  "length=%s new_length=%s length_diff=%s samples-result=%s",
                  length,
                  new_length,
                  new_length - length,
                  np.array_str(
                      samples[0, -block_size - 1 : -1, 0, 0]
                      - result[0, -block_size:, 0, 0]
                  ).replace("\n", ""),
              )
  
          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,
          )
  
          within_epsilon = tf.less_equal(
              tf.abs(result - samples[:, :-1, :1, :]),
              self._decode_hparams.guess_and_check_epsilon,
          )
  
          if self._decode_hparams.guess_and_check_top_k:
              tf.logging.info(
                  "Using guess_and_check_top_k=%s",
                  self._decode_hparams.guess_and_check_top_k,
              )
              correct = in_top_k
          else:
              tf.logging.info(
                  "Using guess_and_check_epsilon=%s",
                  self._decode_hparams.guess_and_check_epsilon,
              )
              correct = within_epsilon
  
          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,
                      [samples, 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
  import copy
  import numpy
  from tensor2tensor.layers import common_hparams
  from tensor2tensor.layers import common_image_attention
  from tensor2tensor.layers import common_layers
  from tensor2tensor.layers import modalities
  from tensor2tensor.utils import registry
  from tensor2tensor.utils import t2t_model
  import tensorflow.compat.v1
  from tensorflow.compat.v1 import estimator
label_desc: Use image node_id 6 for calling the Img2imgTransformerBlockParallel obj's underlying member method code with example usage: obj._slow_greedy_infer_guess_and_check(features, decode_length) and returns: dict, unknown, new_result,  new_length