message: chore: simplify test based on feedback
  - verify it passes running locally with bazel
  - remove call methods since we're just measuring the output_shape and making sure the wrapper in TimeDistributed doesn't crash like it did before.
diff: @@ -296,25 +296,16 @@ class TimeDistributedTest(test.TestCase):
  class TupleOutputShapeLayer(keras.layers.Layer):
  - def call(self, inputs):
  - return inputs
  -
  def compute_output_shape(self, input_shape):
  return (input_shape[0], input_shape[1])
  - class ListOutputShape(keras.layers.Layer):
  -
  - def call(self, inputs):
  - return inputs
  + class ListOutputShapeLayer(keras.layers.Layer):
  def compute_output_shape(self, input_shape):
  return [input_shape[0], input_shape[1]]
  class TensorShapeOutputShapeLayer(keras.layers.Layer):
  - def call(self, inputs):
  - return inputs
  -
  def compute_output_shape(self, input_shape):
  return tensor_shape.TensorShape([input_shape[0], input_shape[1]])
  @@ -323,10 +314,12 @@ class TimeDistributedTest(test.TestCase):
  # Custom layers can specify output shape as a list, tuple, or TensorShape
  for layer in test_layers:
  time_distributed = keras.layers.TimeDistributed(layer())
  - placeholder = keras.backend.placeholder(shape=(None, 1, 13))
  - out = time_distributed(placeholder)
  + input_shape = (None, 1, 13)
  + output_shape = time_distributed.compute_output_shape(input_shape)
  # all output shapes are exposed in the end as TensorShape instances
  - self.assertEqual(isinstance(out, tensor_shape.TensorShape), True)
  + self.assertIsInstance(output_shape, tensor_shape.TensorShape)
  + # verify the expected shapes from our passthrough layers
  + self.assertListEqual(list(input_shape), output_shape.as_list())
  class BidirectionalTest(test.TestCase):
  