code: def create_small_net_with_conv_layer(
      self, conv_layer, outputs_per_channel
  ):
      self.conv_layer = conv_layer
      self.conv_layer.set_inputs(self.input_layer)
  
      self.flatten_layer = layers.Flatten()
      self.flatten_layer.set_inputs(self.conv_layer)
  
      self.dense_layer = layers.Dense(
          kernel=(
              np.array(
                  [
                      list(
                          itertools.chain(
                              *[
                                  [1.0, -1.0]
                                  for i in range(outputs_per_channel)
                              ]
                          )
                      )
                  ]
              ).T
          ).astype("float32"),
          bias=np.array([1]).astype("float32"),
          dense_mxts_mode=DenseMxtsMode.Linear,
      )
      print(outputs_per_channel)
      print(self.dense_layer.kernel)
      self.dense_layer.set_inputs(self.flatten_layer)
  
      self.dense_layer.build_fwd_pass_vars()
      self.input_layer.reset_mxts_updated()
      self.dense_layer.set_scoring_mode(layers.ScoringMode.OneAndZeros)
      self.dense_layer.set_active()
      self.input_layer.update_mxts()
  
      self.inp = (
          (np.arange(16).reshape((2, 2, 4)).astype("float32")) - 8.0
      ).transpose((0, 2, 1))
  
imports: from __future__ import division
  from __future__ import print_function
  from __future__ import absolute_import
  import unittest
  from unittest import skip
  import sys
  import os
  import numpy
  import deeplift.layers
  from deeplift.layers import DenseMxtsMode
  from deeplift.layers.convolutional import PoolMode, PaddingMode
  from deeplift.util import compile_func
  import itertools
label_desc: Use image node_id 2 for calling the TestConv obj's underlying member method code with example usage: obj.create_small_net_with_conv_layer(conv_layer, outputs_per_channel) without return types