Point-wise feed forward net.

    Args:
      inputs: A 3d tensor with shape of [N, T, C].
      num_units: A list of two integers.
      scope: Optional scope for `variable_scope`.
      reuse: Boolean, whether to reuse the weights of a previous layer
        by the same name.

    Returns:
      A 3d tensor with the same shape and dtype as inputs

def feedforward(inputs,
                num_units,
                scope="multihead_attention"):
    '''Point-wise feed forward net.

    Args:
      inputs: A 3d tensor with shape of [N, T, C].
      num_units: A list of two integers.
      scope: Optional scope for `variable_scope`.
      reuse: Boolean, whether to reuse the weights of a previous layer
        by the same name.

    Returns:
      A 3d tensor with the same shape and dtype as inputs
    '''
    with tf.variable_scope(scope):
        # Inner layer
        params = {"inputs": inputs, "filters": num_units[0], "kernel_size": 1,
                  "activation": tf.nn.relu, "use_bias": True}
        outputs = tf.layers.conv1d(**params)

        # Readout layer
        params = {"inputs": outputs, "filters": num_units[1], "kernel_size": 1,
                  "activation": None, "use_bias": True}
        outputs = tf.layers.conv1d(**params)

        # Residual connection
        outputs += inputs

        # Normalize
        outputs = normalize(outputs)

    return outputs