message: Fixed all 'too long line' errors
diff: @@ -43,16 +43,18 @@ _TF_ACTIVATIONS_V2 = {
  @keras_export('keras.activations.softmax')
  def softmax(x, axis=-1):
  """The softmax activation function transforms the outputs so that all values are in
  - range (0, 1) and sum to 1. It is often used as the activation for the last layer of a
  - classification network because the result could be interpreted as a probability
  - distribution. The softmax of x is calculated by exp(x)/tf.reduce_sum(exp(x)).
  + range (0, 1) and sum to 1. It is often used as the activation for the last
  + layer of a classification network because the result could be interpreted as
  + a probability distribution. The softmax of x is calculated by
  + exp(x)/tf.reduce_sum(exp(x)).
  Arguments:
  x : Input tensor.
  axis: Integer, axis along which the softmax normalization is applied.
  Returns:
  - Tensor, output of softmax transformation (all values are non-negative and sum to 1).
  + Tensor, output of softmax transformation (all values are non-negative
  + and sum to 1).
  Raises:
  ValueError: In case `dim(x) == 1`.
  @@ -176,9 +178,9 @@ def sigmoid(x):
  """Sigmoid.
  Applies the sigmoid activation function. The sigmoid function is defined as
  1 divided by (1 + exp(-x)). It's curve is like an "S" and is like a smoothed
  - version of the Heaviside (Unit Step Function) function. For small values (<-5)
  - the sigmoid returns a value close to zero and for larger values (>5) the result
  - of the function gets close to 1.
  + version of the Heaviside (Unit Step Function) function. For small values
  + (<-5) the sigmoid returns a value close to zero and for larger values (>5)
  + the result of the function gets close to 1.
  Arguments:
  x: A tensor or variable.
  Returns:
  