//  [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.cast(features['targets'], dtype=tf.int32)
        # 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