@combinations.generate(strategy_minus_tpu_combinations())
def test_numpy_with_sample_weights(self, distribution):
    model = get_model()
    optimizer = rmsprop.RMSPropOptimizer(learning_rate=0.001)
    loss = 'mse'
    model.compile(optimizer, loss, distribute=distribution)
    inputs = np.zeros((10, 3), np.float32)
    targets = np.zeros((10, 4), np.float32)
    sample_weights = np.ones(10, np.float32)
    model.fit(inputs, targets, sample_weight=sample_weights, epochs=1, steps_per_epoch=2, verbose=1)
@combinations.generate(strategy_minus_tpu_combinations())
def test_numpy_with_sample_weights(self, distribution):
    model = get_model()
    optimizer = rmsprop.RMSPropOptimizer(learning_rate=0.001)
    loss = 'mse'
    model.compile(optimizer, loss, distribute=distribution)
    inputs = np.zeros((10, 3), np.float32)
    targets = np.zeros((10, 4), np.float32)
    sample_weights = np.ones(10, np.float32)
    model.fit(inputs, targets, sample_weight=sample_weights, epochs=1, steps_per_epoch=2, verbose=1)