SRU cell as in https://arxiv.org/abs/1709.02755.

  This implementation uses tf.scan and can incur overhead, see the full SRU
  function doc for details and an implementation that is sometimes faster.

  Args:
    x: A tensor of shape [batch, ..., channels] ; ... is treated as time.
    num_layers: How many SRU layers; default is 2 as results for 1 disappoint.
    activation: Optional activation function, try tf.nn.tanh or tf.nn.relu.
    initial_state: Optional initial c-state, set to zeros if None.
    name: Optional name, "sru" by default.
    reuse: Optional reuse.

  Returns:
    A tensor of the same shape as x.

  Raises:
    ValueError: if num_layers is not positive.

def sru_with_scan(x,
                  num_layers=2,
                  activation=None,
                  initial_state=None,
                  name=None,
                  reuse=None):
  """SRU cell as in https://arxiv.org/abs/1709.02755.

  This implementation uses tf.scan and can incur overhead, see the full SRU
  function doc for details and an implementation that is sometimes faster.

  Args:
    x: A tensor of shape [batch, ..., channels] ; ... is treated as time.
    num_layers: How many SRU layers; default is 2 as results for 1 disappoint.
    activation: Optional activation function, try tf.nn.tanh or tf.nn.relu.
    initial_state: Optional initial c-state, set to zeros if None.
    name: Optional name, "sru" by default.
    reuse: Optional reuse.

  Returns:
    A tensor of the same shape as x.

  Raises:
    ValueError: if num_layers is not positive.
  """
  if num_layers < 1:
    raise ValueError("Number of layers must be positive: %d" % num_layers)
  with tf.variable_scope(name, default_name="sru", values=[x], reuse=reuse):
    # We assume x is [batch, ..., channels] and treat all ... as time.
    x_shape = shape_list(x)
    x = tf.reshape(x, [x_shape[0], -1, x_shape[-1]])
    x = tf.transpose(x, [1, 0, 2])  # Scan assumes time on axis 0.
    initial_state = initial_state or tf.zeros([x_shape[0], x_shape[-1]])

    # SRU state manipulation function.
    def next_state(cur_state, args_tup):
      cur_x_times_one_minus_f, cur_f = args_tup
      return cur_f * cur_state + cur_x_times_one_minus_f

    # Calculate SRU on each layer.
    for i in range(num_layers):
      # The parallel part of the SRU.
      x_orig = x
      x, f, r = tf.split(
          layers().Dense(3 * x_shape[-1], name="kernel_%d" % i)(x), 3, axis=-1)
      f, r = tf.sigmoid(f), tf.sigmoid(r)
      x_times_one_minus_f = x * (1.0 - f)  # Compute in parallel for speed.
      # Calculate states.
      c_states = tf.scan(
          next_state, (x_times_one_minus_f, f),
          initializer=initial_state,
          parallel_iterations=2,
          name="scan_%d" % i)
      # Final output.
      if activation is not None:
        c_states = activation(c_states)
      h = c_states * r + (1.0 - r) * x_orig
      x = h  # Next layer.
    # Transpose back to batch-major.
    x = tf.transpose(x, [1, 0, 2])
    return tf.reshape(x, x_shape)