def METHOD_NAME(self, model_type):
    model = _simple_conv_model(model_type)
    transformed_model, _ = replace_relu6_with_relu(model, True)
    conv1, activation1, conv2, activation2, _, dense1, dense2 = _get_layers(
        transformed_model, model_type
    )
    # conv1 doesn't change because it's not a pattern that fits (Conv2D + ReLU6)
    assert conv1.activation == tf.keras.activations.linear
    # activation1.max_value is transformed from 6 to None
    assert activation1.max_value is None
    # fused Conv2D(filters=..., activation=tf.nn.relu6) is transformed to
    #   Conv2D(filters=..., activation=tf.nn.relu')
    assert conv2.activation == tf.keras.activations.relu
    # activation2.max_value is transformed from 6 to None
    assert activation2.max_value is None
    # dense1.activation is transformed from relu6 to linear
    assert dense1.activation == tf.keras.activations.relu
    # dense2.activation shouldn't be transformed
    assert dense2.activation == tf.keras.activations.softmax
    assert dense2.activation != tf.keras.activations.linear
    # Even If the layer of the model changes, the weights should not change
    for original_layer, transformed_layer in zip(model.layers, transformed_model.layers):
        if isinstance(original_layer, (layers.Conv2D, layers.Dense)):
            original_weight, original_bias = original_layer.get_weights()
            transformed_weight, transformed_bias = transformed_layer.get_weights()
            assert np.array_equal(original_weight, transformed_weight)
            assert np.array_equal(original_bias, transformed_bias)