(axis=-1)
return Fn('Softmax',
            lambda x: jnp.exp(log_softmax(x, axis=axis)))
Returns a layer that applies softmax along one tensor axis.

  `Softmax` acts on a group of values and normalizes them to look like a set
  of probability values. (Probability values must be non-negative, and as a
  set must sum to 1.)

  Args:
    axis: Axis along which values are grouped for computing softmax.
Returns a layer that applies softmax along one tensor axis.
Returns a layer that applies softmax along one tensor axis .
def Softmax(axis=-1):
  """Returns a layer that applies softmax along one tensor axis.

  `Softmax` acts on a group of values and normalizes them to look like a set
  of probability values. (Probability values must be non-negative, and as a
  set must sum to 1.)

  Args:
    axis: Axis along which values are grouped for computing softmax.
  """
  return Fn('Softmax',
            lambda x: jnp.exp(log_softmax(x, axis=axis)))
def Softmax ( axis = - 1 ) : return Fn ( 'Softmax' , lambda x : jnp . exp ( log_softmax ( x , axis = axis ) ) )