def METHOD_NAME(self):
    """
    Tests spatial svd factory creates the right algo.
    Bokeh session is not used.
    :return: None
    """
    sess = tf.compat.v1.Session(graph=tf.Graph())
    with sess.graph.as_default():
        model = tf.keras.Sequential([
            tf.keras.layers.Reshape(target_shape=(28, 28, 1), input_shape=(28 * 28,)),
            tf.keras.layers.Conv2D(32, 5, name='conv1', padding='same'),
            tf.keras.layers.Conv2D(64, 32, name='conv2', padding='same'),
            tf.keras.layers.SeparableConvolution2D(64, 64, name='conv3', padding='same'),
            tf.keras.layers.Flatten(),
            tf.keras.layers.Dense(10, name='linear')
        ])
        init = tf.compat.v1.global_variables_initializer()
    sess.run(init)
    eval_callback = MagicMock()
    greedy_params = GreedySelectionParameters(0.5)
    auto_params = SpatialSvdParameters.AutoModeParams(greedy_params)
    params = SpatialSvdParameters(input_op_names=['reshape_input'], output_op_names=['linear/BiasAdd'],
                                  mode=SpatialSvdParameters.Mode.auto, params=auto_params)
    spatial_svd_algo = CompressionFactory.create_spatial_svd_algo(sess, None, eval_callback, 100, (1, 28, 28),
                                                                  CostMetric.mac, params)
    self.assertTrue(0 == 0)
    # delete temp directory
    shutil.rmtree(str('./temp_meta/'))
    sess.close()