message: Update docstring for keras.layers.ReLU.
diff: @@ -276,22 +276,42 @@ class ReLU(Layer):
  With default values, it returns element-wise `max(x, 0)`.
  Otherwise, it follows:
  - `f(x) = max_value` for `x >= max_value`,
  - `f(x) = x` for `threshold <= x < max_value`,
  - `f(x) = negative_slope * (x - threshold)` otherwise.
  + $$f(x) = max_value if x >= max_value$$
  + $$f(x) = x if threshold <= x < max_value$$
  + $$f(x) = negative_slope * (x - threshold) otherwise$$
  +
  + Usage:
  +
  + >>> layer = tf.keras.layers.ReLU()
  + >>> output = layer([-3.0, -1.0, 0.0, 2.0])
  + >>> list(output.numpy())
  + [0.0, 0.0, 0.0, 2.0]
  + >>> layer = tf.keras.layers.ReLU(max_value=1.0)
  + >>> output = layer([-3.0, -1.0, 0.0, 2.0])
  + >>> list(output.numpy())
  + [0.0, 0.0, 0.0, 1.0]
  + >>> layer = tf.keras.layers.ReLU(negative_slope=1.0)
  + >>> output = layer([-3.0, -1.0, 0.0, 2.0])
  + >>> list(output.numpy())
  + [-3.0, -1.0, 0.0, 2.0]
  + >>> layer = tf.keras.layers.ReLU(threshold=1.5)
  + >>> output = layer([-3.0, -1.0, 1.0, 2.0])
  + >>> list(output.numpy())
  + [0.0, 0.0, 0.0, 2.0]
  Input shape:
  Arbitrary. Use the keyword argument `input_shape`
  - (tuple of integers, does not include the samples axis)
  + (tuple of integers, does not include the batch axis)
  when using this layer as the first layer in a model.
  Output shape:
  Same shape as the input.
  Arguments:
  - max_value: Float >= 0. Maximum activation value.
  - negative_slope: Float >= 0. Negative slope coefficient.
  - threshold: Float. Threshold value for thresholded activation.
  + max_value: Float >= 0. Maximum activation value. Default to None, which
  + means unlimited.
  + negative_slope: Float >= 0. Negative slope coefficient. Default to 0.
  + threshold: Float. Threshold value for thresholded activation. Default to 0.
  """
  def __init__(self, max_value=None, negative_slope=0, threshold=0, **kwargs):
  