code: def _get_gradient_at_activation(self, activation_vars):
      to_return = (
          hf.lte_mask(activation_vars, 0.0) * self.alpha
      ) + hf.gt_mask(activation_vars, 0.0) * 1.0
      return to_return
  
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 3 for calling the PReLU obj's underlying member method code with example usage: obj._get_gradient_at_activation(activation_vars) and returns: to_return