message: Test dictionary input to Keras models with more distribution strategies.
diff: @@ -805,15 +805,7 @@ class TestDistributionStrategyWithDatasets(test.TestCase,
  self.assertEqual(interleaved_output.history['val_categorical_accuracy'],
  [x[2] for x in user_controlled_output])
  - # TODO(priyag): Enable this test for TPU. Currently tuples/dict don't work
  - # as clone_model's input_tensors argument only seems to accept list and not
  - # tuples or dict.
  - @combinations.generate(
  - combinations.combine(
  - distribution=[
  - strategy_combinations.mirrored_strategy_with_gpu_and_cpu
  - ],
  - mode=['graph', 'eager'], cloning=[True, False]))
  + @combinations.generate(all_strategy_combinations_plus_cloning())
  def test_fit_with_tuple_and_dict_dataset_inputs(self, distribution, cloning):
  with self.cached_session():
  with distribution.scope():
  @@ -828,10 +820,10 @@ class TestDistributionStrategyWithDatasets(test.TestCase,
  metrics = ['mae', keras.metrics.CategoricalAccuracy()]
  model.compile(optimizer, loss, metrics=metrics, cloning=cloning)
  - input_a_np = np.random.random((10, 3))
  - input_b_np = np.random.random((10, 5))
  - output_d_np = np.random.random((10, 7))
  - output_e_np = np.random.random((10, 7))
  + input_a_np = np.random.random((10, 3)).astype('float32')
  + input_b_np = np.random.random((10, 5)).astype('float32')
  + output_d_np = np.random.random((10, 7)).astype('float32')
  + output_e_np = np.random.random((10, 7)).astype('float32')
  # Test with tuples
  dataset_tuple = dataset_ops.Dataset.from_tensor_slices((
  