code: def _build_fwd_pass_vars(self):
      # can't just inherit from parent due to gradient building
      self._build_fwd_pass_vars_core()
      self._activation_vars = self._build_activation_vars(
          self._get_input_activation_vars()
      )
      self._reference_vars = self._build_reference_vars()
      self._diff_from_reference_vars = (
          self._build_diff_from_reference_vars()
      )
      self._gradient_at_default_activation = (
          self._get_gradient_at_activation(self.get_reference_vars())
      )
      (
          self._pos_contribs,
          self._neg_contribs,
      ) = self._build_pos_and_neg_contribs()
      self._initialize_mxts()
  
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 4 for calling the Activation obj's underlying member method code with example usage: obj._build_fwd_pass_vars() without return types