//  [python]
def _mtf_model_fn(self, features, mesh):
    self._original_features = features
    features = copy.copy(features)
    hparams = self._hparams
    extra_losses = []
    targets = tf.cast(features["targets"], tf.int32)
    mode = getattr(hparams, "mode", tf.estimator.ModeKeys.TRAIN)
    is_training = mode == tf.estimator.ModeKeys.TRAIN
    if len(targets.get_shape()) > 2:
      tf.logging.info("targets = %s" % targets)
      targets = tf.squeeze(targets, [2, 3])
    # pad targets to max_length
    def pad_to_max_length(x):
      extra_length = hparams.max_length - tf.shape(x)[1]
      x = tf.pad(x, [[0, 0], [0, extra_length]])
      x = tf.reshape(x, [hparams.batch_size, hparams.max_length])
      return x
    targets = pad_to_max_length(targets)
    targets = self._import_to_batch_by_length(targets, "targets", mesh, hparams)
    for key in ["targets_segmentation", "targets_position",
                "inputs_segmentation", "inputs_position"]:
      if key in features:
        features[key] = pad_to_max_length(features[key])
    if hparams.decoder_type == "autoregressive":
      shifted_targets = mtf.shift(
          targets, offset=1, dim=self.length_dim, wrap=False)
    elif hparams.decoder_type == "denoising":
      shifted_targets = self._noisy_targets(targets, extra_losses)
    else:
      raise ValueError(
          "unknown hparams.decoder_type = %s" % hparams.decoder_type)

    if "targets_segmentation" in features:
      # "Packed" dataset - keep the examples from seeing each other.
      targets_segmentation = self._import_to_batch_by_length(
          features["targets_segmentation"], "targets_segmentation",
          mesh, hparams)
      targets_position = self._import_to_batch_by_length(
          features["targets_position"], "targets_position",
          mesh, hparams)
      decoder_self_attention_mask = mtf.layers.attention_mask_same_segment(
          targets_segmentation, dtype=self.activation_dtype)
      if hparams.decoder_type == "autoregressive":
        decoder_self_attention_mask += mtf.layers.attention_mask_autoregressive(
            targets_position, dtype=self.activation_dtype)
    else:
      targets_position = mtf.range(mesh, self.length_dim, dtype=tf.int32)
      if hparams.decoder_type == "autoregressive":
        decoder_self_attention_mask = mtf.layers.attention_mask_autoregressive(
            targets_position, dtype=self.activation_dtype)
      else:
        decoder_self_attention_mask = None

    def layer_prepostprocess_dropout(x):
      return mtf.dropout(
          x, is_training, keep_prob=1.0 - hparams.layer_prepostprocess_dropout,
          noise_shape=mtf.Shape(self.batch_dims + [self.model_dim]))

    (inputs_embedding_var,
     targets_embedding_var,
     softmax_var,
     positional_embedding_var) = self._embedding_and_softmax_vars(mesh)
    if hparams.transformer_type == "decoder":
      encoder_output = None
      encoder_decoder_attention_mask = None
    else:
      inputs = tf.squeeze(tf.cast(features["inputs"], tf.int32), [2, 3])
      inputs = pad_to_max_length(inputs)
      inputs = self._import_to_batch_by_length(inputs, "inputs", mesh, hparams)
      if "inputs_segmentation" in features:
        # "Packed" dataset - keep the examples from seeing each other.
        inputs_segmentation = self._import_to_batch_by_length(
            features["inputs_segmentation"], "inputs_segmentation",
            mesh, hparams)
        inputs_position = self._import_to_batch_by_length(
            features["inputs_position"], "inputs_position",
            mesh, hparams)
        encoder_self_attention_mask = (
            mtf.layers.attention_mask_same_segment(
                inputs_segmentation, dtype=self.activation_dtype))
      else:
        inputs_position = mtf.range(mesh, self.length_dim, dtype=tf.int32)
        encoder_self_attention_mask = (
            mtf.layers.attention_mask_ignore_padding(
                inputs, dtype=self.activation_dtype))

      x = (mtf.gather(inputs_embedding_var, inputs, self.inputs_vocab_dim) +
           mtf.gather(positional_embedding_var, inputs_position,
                      self.max_length_dim))
      x = layer_prepostprocess_dropout(x)
      with tf.variable_scope("encoder"):
        x = self._layer_stack(x,
                              hparams.encoder_layers,
                              self_attention_mask=encoder_self_attention_mask,
                              losses=extra_losses)

    if hparams.transformer_type == "encdec":
      if "inputs_segmentation" in features:
        encoder_decoder_attention_mask = (
            mtf.layers.attention_mask_same_segment(
                targets_segmentation, inputs_segmentation,
                dtype=self.activation_dtype))
      else:
        encoder_decoder_attention_mask = encoder_self_attention_mask
      encoder_output = mtf.rename_dimension(
          x, self.length_dim.name, self.memory_length_dim.name)

    if hparams.transformer_type != "encoder":
      # DECODER
      x = (mtf.gather(
          targets_embedding_var, shifted_targets, self.targets_vocab_dim) +
           mtf.gather(
               positional_embedding_var, targets_position, self.max_length_dim))
      x = layer_prepostprocess_dropout(x)
      with tf.variable_scope("decoder"):
        x = self._layer_stack(
            x,
            hparams.decoder_layers,
            encoder_output=encoder_output,
            self_attention_mask=decoder_self_attention_mask,
            encdec_attention_mask=encoder_decoder_attention_mask,
            losses=extra_losses)
    if (hparams.reshape_logits_hack and
        hparams.mode == tf.estimator.ModeKeys.TRAIN):
      # For some reason, the logits computation is extremely slow on TPU
      # in some cases where the batch size per core is 1.  Reshape the logits
      # and the targets to double the batch size and halve the length.
      # TODO(noam): file a bug.
      old_dims = self.batch_dims + [self.length_dim]
      new_dims = self.batch_dims[:-1] + [
          mtf.Dimension(self.batch_dims[-1].name,
                        self.batch_dims[-1].size * 2),
          mtf.Dimension(self.length_dim.name, self.length_dim.size // 2)]
      x = mtf.reshape(x, new_dims + [self.model_dim])
      targets = mtf.reshape(targets, new_dims)

    logits = mtf.matmul(x, softmax_var)
    if hparams.mode == tf.estimator.ModeKeys.TRAIN:
      logits = mtf.layers.multiplicative_jitter(logits, epsilon=1e-2)
    off_value = hparams.label_smoothing / self._targets_vocab_size
    on_value = 1.0 - hparams.label_smoothing + off_value
    soft_targets = mtf.one_hot(
        targets, self.targets_vocab_dim, on_value=on_value, off_value=off_value,
        dtype=self.activation_dtype)
    loss = mtf.layers.softmax_cross_entropy_with_logits(
        logits, soft_targets, self.targets_vocab_dim)
    weights = mtf.layers.weights_nonzero(targets, dtype=self.activation_dtype)
    loss = mtf.reduce_mean(loss * weights)
    for l in extra_losses:
      loss += l
    if (hparams.reshape_logits_hack and
        hparams.mode == tf.estimator.ModeKeys.TRAIN):
      logits = mtf.reshape(logits, old_dims + [self.targets_vocab_dim])
    logits = mtf.to_float(logits)
    return logits, loss