code: def test_convert_conv1d_model_compute_scores(self):
      deeplift_model = kc.convert_model_from_saved_files(
          self.saved_file_path,
          nonlinear_mxts_mode=NonlinearMxtsMode.Rescale,
      )
      # print(deeplift_model.get_name_to_layer()['inp1_0'].get_shape())
      # print(deeplift_model.get_name_to_layer()['convolution1d_1_0'].get_shape())
      # print(deeplift_model.get_name_to_layer()['maxpooling1d_1_0'].get_shape())
      # print(deeplift_model.get_name_to_layer()['merge_1'].get_shape())
      # print(deeplift_model.get_name_to_layer()['flatten_1'].get_shape())
      deeplift_contribs_func = deeplift_model.get_target_contribs_func(
          find_scores_layer_name=["inp1_0", "inp2_0"],
          pre_activation_target_layer_name="output_preact_0",
      )
  
      grads_inp1, grads_inp2 = self.grad_func(self.inp1, self.inp2)
      np.testing.assert_almost_equal(
          np.array(
              deeplift_contribs_func(
                  task_idx=0,
                  input_data_list={
                      "inp1_0": self.inp1,
                      "inp2_0": self.inp2,
                  },
                  input_references_list={
                      "inp1_0": np.zeros_like(self.inp1),
                      "inp2_0": np.zeros_like(self.inp2),
                  },
                  batch_size=10,
                  progress_update=None,
              )
          ),
          # when biases are 0 and ref is 0, deeplift
          # with the rescale rule is the same as grad*inp
          np.array([grads_inp1 * self.inp1, grads_inp2 * self.inp2]),
          decimal=6,
      )
  
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
  from deeplift.conversion import kerasapi_conversion
  import deeplift.layers
  from deeplift.layers import NonlinearMxtsMode
  from deeplift.util import compile_func
  import tensorflow
  import keras
  from keras import models
  from keras import backend
label_desc: Use image node_id 3 for calling the TestFunctionalConcatModel obj's underlying member method code with example usage: obj.test_convert_conv1d_model_compute_scores() without return types