def _slow_greedy_infer_tpu(self, features, decode_length):
    """A slow greedy inference method on TPU.

    Quadratic time in decode_length.

    Args:
      features: An map of string to `Tensor`.
      decode_length: An integer, how many additional timesteps to decode.

    Returns:
      A dict of decoding results {
          "outputs": integer `Tensor` of decoded ids of shape
              [batch_size, <= decode_length] if beam_size == 1 or
              [batch_size, top_beams, <= decode_length]
          "scores": None
          "logits": `Tensor` of shape [batch_size, time, 1, 1, vocab_size].
          "losses": a dictionary: {loss-name (string): floating point `Scalar`}
      }
    """
    if not features:
      features = {}
    inputs_old = None
    if "inputs" in features and len(features["inputs"].shape) < 4:
      inputs_old = features["inputs"]
      features["inputs"] = tf.expand_dims(features["inputs"], 2)
    if not self.has_input:
      # Prepare partial targets.
      # In either features["inputs"] or features["targets"].
      # We force the outputs to begin with these sequences.
      partial_targets = features.get("inputs")
      if partial_targets is None:
        partial_targets = features["targets"]
      features["partial_targets"] = tf.to_int64(partial_targets)
    # Save the targets in a var and reassign it after the tf.while loop to avoid
    # having targets being in a 'while' frame. This ensures targets when used
    # in metric functions stays in the same frame as other vars.
    targets_old = features.get("targets", None)

    target_modality = self._problem_hparams.modality["targets"]

    def infer_step(i, recent_output, recent_logits, unused_loss):
      """Inference step."""
      if not tf.executing_eagerly():
        recent_output.set_shape([None, None, None, 1])
      padded = tf.pad(recent_output, [[0, 0], [0, 1], [0, 0], [0, 0]])
      features["targets"] = padded
      # This is inefficient in that it generates samples at all timesteps,
      # not just the last one, except if target_modality is pointwise.
      features["decode_loop_step"] = i
      samples, logits, losses = self.sample(features)
      # Concatenate the already-generated recent_output with last timestep
      # of the newly-generated samples.z
      top = self._hparams.top.get("targets",
                                  modalities.get_top(target_modality))
      if getattr(top, "pointwise", False):
        cur_sample = samples[:, -1, :, :]
      else:
        cur_sample = samples[:, i, :, :]
      samples = tf.transpose(recent_output, perm=[1, 0, 2, 3])
      samples = inplace_ops.alias_inplace_update(samples, i,
                                                 tf.to_int64(cur_sample))
      samples = tf.transpose(samples, perm=[1, 0, 2, 3])
      if not tf.executing_eagerly():
        samples.set_shape([None, None, None, 1])

      # Assuming we have one shard for logits.
      recent_logits = tf.transpose(recent_logits, perm=[1, 0, 2, 3, 4])
      recent_logits = inplace_ops.alias_inplace_update(
          recent_logits, i, tf.squeeze(logits[:, -1:], axis=1))
      logits = tf.transpose(recent_logits, perm=[1, 0, 2, 3, 4])
      loss = sum([l for l in losses.values() if l is not None])
      return i + 1, samples, logits, loss

    # Create an initial output tensor. This will be passed
    # to the infer_step, which adds one timestep at every iteration.
    if "partial_targets" in features:
      initial_output = tf.to_int64(features["partial_targets"])
      while len(initial_output.get_shape().as_list()) < 4:
        initial_output = tf.expand_dims(initial_output, 2)
      batch_size = common_layers.shape_list(initial_output)[0]
    else:
      batch_size = common_layers.shape_list(features["inputs"])[0]
      initial_output = tf.zeros((batch_size, 0, 1, 1), dtype=tf.int64)
    # Hack: foldl complains when the output shape is less specified than the
    # input shape, so we confuse it about the input shape.
    initial_output = tf.slice(initial_output, [0, 0, 0, 0],
                              common_layers.shape_list(initial_output))
    target_modality = self._problem_hparams.modality["targets"]
    if target_modality == modalities.ModalityType.CLASS_LABEL:
      decode_length = 1
    else:
      if "partial_targets" in features:
        prefix_length = common_layers.shape_list(features["partial_targets"])[1]
      else:
        prefix_length = common_layers.shape_list(features["inputs"])[1]
      decode_length = prefix_length + decode_length

    # Initial values of result, logits and loss.
    result = tf.concat(
        [initial_output,
         tf.zeros([batch_size, decode_length, 1, 1], tf.int64)],
        axis=1)
    # tensor padded to [batch_size, decode_length, 1, 1, vocab_size]
    vocab_size = self._problem_hparams.vocab_size["targets"]
    if vocab_size is not None and hasattr(self._hparams, "vocab_divisor"):
      vocab_size += (-vocab_size) % self._hparams.vocab_divisor
    logits = tf.zeros((batch_size, decode_length, 1, 1, vocab_size))
    if not tf.executing_eagerly():
      logits.set_shape([None, None, None, None, None])
    loss = 0.0

    def while_exit_cond(i, result, logits, loss):  # pylint: disable=unused-argument
      """Exit the loop either if reach decode_length or EOS."""
      not_overflow = i < decode_length

      if self._problem_hparams.stop_at_eos:

        def fn_not_eos():
          # Check if the last predicted element is a EOS
          return tf.reduce_any(
              tf.not_equal(
                  tf.squeeze(result[:, -1, :, :]), text_encoder.EOS_ID))

        not_eos = tf.cond(
            # We only check for early stopping if there is at least 1 element (
            # otherwise not_eos will crash).
            tf.not_equal(i, 0),
            fn_not_eos,
            lambda: True,
        )

        return tf.cond(
            tf.equal(batch_size, 1),
            # If batch_size == 1, we check EOS for early stopping.
            lambda: tf.logical_and(not_overflow, not_eos),
            # Else, just wait for max length
            lambda: not_overflow)
      return not_overflow

    _, result, logits, loss = tf.while_loop(
        while_exit_cond,
        infer_step, [tf.constant(0), result, logits, loss],
        shape_invariants=[
            tf.TensorShape([]),
            tf.TensorShape([batch_size, decode_length, 1, 1]),
            tf.TensorShape([batch_size, decode_length, 1, 1, vocab_size]),
            tf.TensorShape([]),
        ],
        back_prop=False,
        parallel_iterations=1)
    if inputs_old is not None:  # Restore to not confuse Estimator.
      features["inputs"] = inputs_old
    # Reassign targets back to the previous value.
    if targets_old is not None:
      features["targets"] = targets_old
    losses = {"training": loss}
    if "partial_targets" in features:
      partial_target_length = common_layers.shape_list(
          features["partial_targets"])[1]
      result = tf.slice(result, [0, partial_target_length, 0, 0],
                        [-1, -1, -1, -1])
    return {
        "outputs": result,
        "scores": None,
        "logits": logits,
        "losses": losses,
    }
A slow greedy inference method on TPU.

    Quadratic time in decode_length.

    Args:
      features: An map of string to `Tensor`.
      decode_length: An integer, how many additional timesteps to decode.

    Returns:
      A dict of decoding results {
          "outputs": integer `Tensor` of decoded ids of shape
              [batch_size, <= decode_length] if beam_size == 1 or
              [batch_size, top_beams, <= decode_length]
          "scores": None
          "logits": `Tensor` of shape [batch_size, time, 1, 1, vocab_size].
          "losses": a dictionary: {loss-name (string): floating point `Scalar`}
      }