code: def setUp(self):
      self.inp = (
          np.random.randn(10 * 10 * 51).reshape(10, 10, 51)
      ).transpose(0, 2, 1)
      self.keras_model = keras.models.Sequential()
      self.keras_model.add(keras.layers.InputLayer((51, 10)))
      conv_layer = keras.layers.convolutional.Convolution1D(
          nb_filter=2,
          filter_length=4,
          subsample_length=2,
          activation="sigmoid",
          input_shape=(51, 10),
      )
      self.keras_model.add(conv_layer)
      conv_layer2 = keras.layers.convolutional.Convolution1D(
          nb_filter=2,
          filter_length=4,
          activation="tanh",
          padding="same",
      )
      self.keras_model.add(conv_layer2)
      self.keras_model.add(
          keras.layers.pooling.MaxPooling1D(pool_length=4, stride=2)
      )
      self.keras_model.add(
          keras.layers.pooling.AveragePooling1D(pool_length=4, stride=2)
      )
      self.keras_model.add(keras.layers.Flatten())
      self.keras_model.add(keras.layers.Dense(output_dim=1))
      self.keras_model.add(keras.layers.core.Activation("sigmoid"))
      self.keras_model.compile(loss="mse", optimizer="sgd")
      self.keras_output_fprop_func = compile_func(
          [self.keras_model.layers[0].input, K.learning_phase()],
          self.keras_model.layers[-1].output,
      )
  
      grad = tf.gradients(
          tf.reduce_sum(self.keras_model.layers[-2].output[:, 0]),
          [self.keras_model.layers[0].input],
      )[0]
      self.grad_func = compile_func(
          [self.keras_model.layers[0].input, K.learning_phase()], grad
      )
  
      self.saved_file_path = "conv1model_validpadding.h5"
      if os.path.isfile(self.saved_file_path):
          os.remove(self.saved_file_path)
      self.keras_model.save(self.saved_file_path)
  
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 1 for calling the TestConvolutionalModel obj's underlying member method code with example usage: obj.setUp() without return types