code: def _get_gradient_at_activation(self, activation_vars):
      # derivative: https://towardsdatascience.com/derivative-of-the-sigmoid-function-536880cf918e
      out_act = self._build_activation_vars(activation_vars)
      return out_act * (1 - out_act)
  
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 Sigmoid obj's underlying member method code with example usage: obj._get_gradient_at_activation(activation_vars) and returns: unknown