Function run_in_graph_and_eager_modes

tensorflow/python/framework/test_util.py:1007–1116

Execute the decorated test with and without enabling eager execution. This function returns a decorator intended to be applied to test methods in a `tf.test.TestCase` class. Doing so will cause the contents of the test method to be executed twice - once normally, and once with eager execution

(func=None,
                                 config=None,
                                 use_gpu=True,
                                 reset_test=True,
                                 assert_no_eager_garbage=False)

def run_in_graph_and_eager_modes(func=None,
                                 config=None,
                                 use_gpu=True,
                                 reset_test=True,
                                 assert_no_eager_garbage=False):
  """Execute the decorated test with and without enabling eager execution.

  This function returns a decorator intended to be applied to test methods in
  a `tf.test.TestCase` class. Doing so will cause the contents of the test
  method to be executed twice - once normally, and once with eager execution
  enabled. This allows unittests to confirm the equivalence between eager
  and graph execution (see `tf.compat.v1.enable_eager_execution`).

  For example, consider the following unittest:

  ```python
  class MyTests(tf.test.TestCase):

    @run_in_graph_and_eager_modes
    def test_foo(self):
      x = tf.constant([1, 2])
      y = tf.constant([3, 4])
      z = tf.add(x, y)
      self.assertAllEqual([4, 6], self.evaluate(z))

  if __name__ == "__main__":
    tf.test.main()
  ```

  This test validates that `tf.add()` has the same behavior when computed with
  eager execution enabled as it does when constructing a TensorFlow graph and
  executing the `z` tensor in a session.

  `deprecated_graph_mode_only`, `run_v1_only`, `run_v2_only`, and
  `run_in_graph_and_eager_modes` are available decorators for different
  v1/v2/eager/graph combinations.


  Args:
    func: function to be annotated. If `func` is None, this method returns a
      decorator the can be applied to a function. If `func` is not None this
      returns the decorator applied to `func`.
    config: An optional config_pb2.ConfigProto to use to configure the session
      when executing graphs.
    use_gpu: If True, attempt to run as many operations as possible on GPU.
    reset_test: If True, tearDown and SetUp the test case between the two
      executions of the test (once with and once without eager execution).
    assert_no_eager_garbage: If True, sets DEBUG_SAVEALL on the garbage
      collector and asserts that no extra garbage has been created when running
      the test with eager execution enabled. This will fail if there are
      reference cycles (e.g. a = []; a.append(a)). Off by default because some
      tests may create garbage for legitimate reasons (e.g. they define a class
      which inherits from `object`), and because DEBUG_SAVEALL is sticky in some
      Python interpreters (meaning that tests which rely on objects being
      collected elsewhere in the unit test file will not work). Additionally,
      checks that nothing still has a reference to Tensors that the test
      allocated.