code: def _get_gradient_at_activation(self, activation_vars):
      if self.verbose == True:
          print(
              "Heads-up: I assume softmax is the output layer, "
              "not an intermediate one; if it's an intermediate layer, "
              "please let me know and I will prioritise that use-case"
          )
      return 0.0
  
imports: from __future__ import division
  from __future__ import print_function
  from __future__ import absolute_import
  from .core import SingleInputMixin, OneDimOutputMixin, Node, ScoringMode, NonlinearMxtsMode
  import tensorflow
  from deeplift.util import NEAR_ZERO_THRESHOLD
  from deeplift.layers import helper_functions
label_desc: Use image node_id 2 for calling the Softmax obj's underlying member method code with example usage: obj._get_gradient_at_activation(activation_vars) and returns: int