//  [python]
def body(self,
           features,
           decode_step=None,
           cache=None,
           decoding_stats=None,
           add_summary=True):
    encoder_output = None
    extra_losses = []
    padding_bias = None
    if not self.hparams.fast_decode:
      decode_step = None
    if "inputs" in features:
      inputs = features["inputs"]
      # remove the last two dimensions that are always 1.
      inputs = tf.reshape(inputs,
                          utils.shape_list(inputs)[:2] + [self.hidden_size])
      # Padding bias only used for seq2seq models.
      padding_bias = utils.embedding_to_padding(inputs)
      # Mask random positions
      shape = utils.shape_list(inputs)
      if self.hparams.input_dropout:
        inputs = tf.where(
            tf.random.uniform(shape) < self.hparams.input_dropout,
            tf.zeros_like(inputs), inputs)
      if self.hparams.add_timing_signal:
        inputs += utils.get_timing_signal_1d(self.hparams.max_length,
                                             self.hidden_size)
      if cache is not None and -1 in cache:
        encoder_output = cache[-1]
      else:
        encoder_output = utils.transformer_encoder_layers(
            inputs=inputs,
            num_layers=self.num_encoder_layers,
            hparams=self.hparams,
            losses=extra_losses,
            name="encoder",
            token_bias=features.get("token_bias_inputs"),
            padding_bias=padding_bias)
      if cache is not None and -1 not in cache:
        cache[-1] = encoder_output
    targets = tf.to_int32(features["targets"])
    # remove the last two dimensions that are always 1.
    targets = tf.reshape(targets, utils.shape_list(targets)[:2])
    # Clamp targets to max_target_length
    targets = targets[:, :self.hparams.max_target_length]
    if self.is_decode:
      targets = self.process_partial_targets_decoding(targets)
    decoder_input = self.prepare_decoder(targets)

    decoder_output = utils.transformer_decoder_layers(
        inputs=decoder_input,
        num_layers=self.num_decoder_layers,
        hparams=self.hparams,
        encoder_output=encoder_output,
        decode_step=decode_step,
        losses=extra_losses,
        cache=cache,
        name="decoder",
        decoding_stats=decoding_stats,
        token_bias_inputs=features.get("token_bias_inputs"),
        token_bias_targets=features.get("token_bias_targets"),
        padding_bias=padding_bias)
    logits = self.produce_output(decoder_output)

    # Return logits as-is in decoding mode
    if self.is_decode:
      return logits

    # Add cross entropy loss
    one_hot_targets = tf.one_hot(
        tf.cast(targets, dtype=tf.int32), self.vocab_size)
    x_entropy = tf.nn.softmax_cross_entropy_with_logits_v2(
        labels=one_hot_targets, logits=logits)
    weights = tf.to_float(tf.not_equal(targets, 0))
    loss = tf.reduce_sum(x_entropy * weights) / tf.reduce_sum(weights)
    if add_summary:
      tf.summary.scalar("losses/weight", tf.reduce_sum(weights))
      tf.summary.scalar("losses/x_entropy", tf.reduce_sum(x_entropy * weights))

    loss_dict = {"training": loss}
    if extra_losses:
      loss_dict["extra_loss"] = tf.add_n(extra_losses)
    # hack for T2T metrics
    logits = tf.reshape(
        logits,
        utils.shape_list(logits)[:2] + [1, 1] + utils.shape_list(logits)[-1:])
    return logits, loss_dict