code: 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)))
  
imports: from absl import logging
  import numpy
  import tensorflow
  from trax import fastmath
  from trax.fastmath import numpy
  from trax.layers import base
  from trax.layers import initializers
  from trax.layers.assert_shape import assert_shape
  from trax.layers.base import Fn
label_desc: Use image node_id 5 for calling a global function with example usage: Softmax(axis) and returns: Fn