code: def test_relu_after_conv1d_batchnorm(self):
      input_layer = layers.Input(batch_shape=(None, 2, 2))
      conv_layer = layers.Conv1D(
          kernel=np.random.random((2, 2, 2)).astype("float32"),
          bias=np.random.random((2,)).astype("float32"),
          conv_mxts_mode=ConvMxtsMode.Linear,
          stride=1,
          padding=PaddingMode.valid,
      )
      conv_layer.set_inputs(input_layer)
      batch_norm = layers.BatchNormalization(
          gamma=np.array([1.0, 1.0]).astype("float32"),
          beta=np.array([-0.5, 0.5]).astype("float32"),
          axis=-1,
          mean=np.array([-0.5, 0.5]).astype("float32"),
          var=np.array([1.0, 1.0]).astype("float32"),
          epsilon=0.001,
      )
      batch_norm.set_inputs(conv_layer)
      relu_after_bn = layers.ReLU(
          nonlinear_mxts_mode=NonlinearMxtsMode.DeepLIFT_GenomicsDefault
      )
      relu_after_bn.set_inputs(batch_norm)
      relu_after_bn.build_fwd_pass_vars()
      self.assertEqual(
          relu_after_bn.nonlinear_mxts_mode, NonlinearMxtsMode.Rescale
      )
  
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 4 for calling the TestDense obj's underlying member method code with example usage: obj.test_relu_after_conv1d_batchnorm() without return types