Run the model and extract samples.

    Args:
      features: an map of string to `Tensor`.

    Returns:
       samples: an integer `Tensor`.
       logits: a list of `Tensor`s, one per datashard.
       losses: a dictionary: {loss-name (string): floating point `Scalar`}.

def sample(self, features):
    """Run the model and extract samples.

    Args:
      features: an map of string to `Tensor`.

    Returns:
       samples: an integer `Tensor`.
       logits: a list of `Tensor`s, one per datashard.
       losses: a dictionary: {loss-name (string): floating point `Scalar`}.
    """
    logits, losses = self(features)  # pylint: disable=not-callable
    if self._target_modality_is_real:
      return logits, logits, losses  # Raw numbers returned from real modality.
    if self.hparams.sampling_method == "argmax":
      samples = tf.argmax(logits, axis=-1)
    else:
      assert self.hparams.sampling_method == "random"

      def multinomial_squeeze(logits, temperature=1.0):
        logits_shape = common_layers.shape_list(logits)
        reshaped_logits = (
            tf.reshape(logits, [-1, logits_shape[-1]]) / temperature)
        choices = tf.multinomial(reshaped_logits, 1)
        choices = tf.reshape(choices, logits_shape[:-1])
        return choices

      samples = multinomial_squeeze(logits, self.hparams.sampling_temp)

    return samples, logits, losses