message: Add test case for using dataset with class_weight
  Pylint fix
diff: @@ -225,6 +225,24 @@ class StandardizeWeightsTest(keras_parameterized.TestCase):
  expected = sample_weights * np.array([0.5, 1., 0.5, 0.5, 1.5])
  self.assertAllClose(weights, expected)
  + @tf_test_util.run_in_graph_and_eager_modes
  + def test_dataset_with_class_weight(self):
  + model = testing_utils.get_small_functional_mlp(1, 4, input_dim=3)
  + optimizer = RMSPropOptimizer(learning_rate=0.001)
  + loss = 'mse'
  + metrics = ['mae', metrics_module.CategoricalAccuracy()]
  + model.compile(optimizer, loss, metrics=metrics)
  +
  + inputs = np.zeros((10, 3), np.float32)
  + targets = np.zeros((10, 4), np.float32)
  + dataset = dataset_ops.Dataset.from_tensor_slices((inputs, targets))
  + dataset = dataset.repeat(100)
  + dataset = dataset.batch(10)
  + class_weight_np = np.array([0.25, 0.25, 0.25, 0.25])
  + class_weight = dict(enumerate(class_weight_np))
  +
  + model.fit(dataset, epochs=1, steps_per_epoch=2, verbose=1,
  + class_weight=class_weight)
  if __name__ == '__main__':
  test.main()
  