code: def test_relu_after_dense(self):
      input_layer = layers.Input(batch_shape=(None, 4))
      dense_layer = layers.Dense(
          kernel=np.random.random((4, 2)),
          bias=np.random.random((2,)),
          dense_mxts_mode=DenseMxtsMode.Linear,
      )
      dense_layer.set_inputs(input_layer)
      relu_after_dense = layers.ReLU(
          nonlinear_mxts_mode=NonlinearMxtsMode.DeepLIFT_GenomicsDefault
      )
      relu_after_dense.set_inputs(dense_layer)
      relu_after_dense.build_fwd_pass_vars()
      self.assertEqual(
          relu_after_dense.nonlinear_mxts_mode,
          NonlinearMxtsMode.RevealCancel,
      )
  
imports: from __future__ import division
  from __future__ import print_function
  from __future__ import absolute_import
  import unittest
  from unittest import skip
  from nose.tools import raises
  import sys
  import os
  import numpy
  import deeplift.layers
  from deeplift.util import compile_func
  from deeplift.layers import ConvMxtsMode, DenseMxtsMode, NonlinearMxtsMode
  from deeplift.layers.convolutional import PaddingMode, PoolMode
label_desc: Use image node_id 1 for calling the TestDense obj's underlying member method code with example usage: obj.test_relu_after_dense() without return types