message: Add docstrings for get/set layer weights.
diff: @@ -124,7 +124,7 @@ class Layer(module.Module):
  using Python control flow. If `False`, we assume that the layer can
  safely be used to generate a static computation graph.
  - Read-only properties:
  + Attributes (read-only properties):
  name: The name of the layer (string).
  dtype: The dtype of the layer's computations and weights. If mixed
  precision is used with a `tf.keras.mixed_precision.experimental.Policy`,
  @@ -1264,6 +1264,36 @@ class Layer(module.Module):
  def set_weights(self, weights):
  """Sets the weights of the layer, from Numpy arrays.
  + The weights of a layer represent the state of the layer. This function
  + sets the weight values from numpy arrays. The weight values should be
  + passed in the order they are created by the layer. Note that the layer's
  + weights must be instantiated before calling this function by calling
  + the layer.
  +
  + For example, a Dense layer returns a list of two values-- per-output
  + weights and the bias value. These can be used to set the weights of another
  + Dense layer:
  +
  + >>> a = tf.keras.layers.Dense(1,
  + ... kernel_initializer=tf.constant_initializer(1.))
  + >>> a_out = a(tf.convert_to_tensor([[1., 2., 3.]]))
  + >>> a.get_weights()
  + [array([[1.],
  + [1.],
  + [1.]], dtype=float32), array([0.], dtype=float32)]
  + >>> b = tf.keras.layers.Dense(1,
  + ... kernel_initializer=tf.constant_initializer(2.))
  + >>> b_out = b(tf.convert_to_tensor([[10., 20., 30.]]))
  + >>> b.get_weights()
  + [array([[2.],
  + [2.],
  + [2.]], dtype=float32), array([0.], dtype=float32)]
  + >>> b.set_weights(a.get_weights())
  + >>> b.get_weights()
  + [array([[1.],
  + [1.],
  + [1.]], dtype=float32), array([0.], dtype=float32)]
  +
  Arguments:
  weights: a list of Numpy arrays. The number
  of arrays and their shape must match
  @@ -1314,6 +1344,35 @@ class Layer(module.Module):
  def get_weights(self):
  """Returns the current weights of the layer.
  + The weights of a layer represent the state of the layer. This function
  + returns both trainable and non-trainable weight values associated with this
  + layer as a list of Numpy arrays, which can in turn be used to load state
  + into similarly parameterized layers.
  +
  + For example, a Dense layer returns a list of two values-- per-output
  + weights and the bias value. These can be used to set the weights of another
  + Dense layer:
  +
  + >>> a = tf.keras.layers.Dense(1,
  + ... kernel_initializer=tf.constant_initializer(1.))
  + >>> a_out = a(tf.convert_to_tensor([[1., 2., 3.]]))
  + >>> a.get_weights()
  + [array([[1.],
  + [1.],
  + [1.]], dtype=float32), array([0.], dtype=float32)]
  + >>> b = tf.keras.layers.Dense(1,
  + ... kernel_initializer=tf.constant_initializer(2.))
  + >>> b_out = b(tf.convert_to_tensor([[10., 20., 30.]]))
  + >>> b.get_weights()
  + [array([[2.],
  + [2.],
  + [2.]], dtype=float32), array([0.], dtype=float32)]
  + >>> b.set_weights(a.get_weights())
  + >>> b.get_weights()
  + [array([[1.],
  + [1.],
  + [1.]], dtype=float32), array([0.], dtype=float32)]
  +
  Returns:
  Weights values as a list of numpy arrays.
  """
  