@combinations.generate(strategy_combinations())
def test_flatten_predict_outputs(self, distribution):
    with self.cached_session():
        model = multi_input_output_model()
        optimizer = gradient_descent.GradientDescentOptimizer(learning_rate=0.001)
        loss = 'mse'
        model.compile(optimizer, loss, distribute=distribution)
        input_a_np = np.asarray(np.random.random((6, 3)), dtype=np.float32)
        input_b_np = np.asarray(np.random.random((6, 5)), dtype=np.float32)
        inputs = [input_a_np, input_b_np]
        outs = model.predict(inputs, steps=1)
        self.assertEqual(2, len(outs))
        self.assertAllEqual([6, 7], outs[0].shape)
        self.assertAllEqual([6, 7], outs[1].shape)
@combinations.generate(strategy_combinations())
def test_flatten_predict_outputs(self, distribution):
    with self.cached_session():
        model = multi_input_output_model()
        optimizer = gradient_descent.GradientDescentOptimizer(learning_rate=0.001)
        loss = 'mse'
        model.compile(optimizer, loss, distribute=distribution)
        input_a_np = np.asarray(np.random.random((6, 3)), dtype=np.float32)
        input_b_np = np.asarray(np.random.random((6, 5)), dtype=np.float32)
        inputs = [input_a_np, input_b_np]
        outs = model.predict(inputs, steps=1)
        self.assertEqual(2, len(outs))
        self.assertAllEqual([6, 7], outs[0].shape)
        self.assertAllEqual([6, 7], outs[1].shape)