message: Repro for bug where taking gradient of restored tf.function with captured float64 variable fails.
diff: @@ -723,6 +723,41 @@ class LoadTest(test.TestCase, parameterized.TestCase):
  self.assertIsNotNone(imported_gradient)
  self.assertAllClose(imported_gradient, 8.)
  + def _test_restored_func_with_captured_var_backprop(self, cycles, dtype):
  + weight = variables.Variable(2., trainable=True, dtype=dtype)
  +
  + @def_function.function(input_signature=[
  + tensor_spec.TensorSpec(dtype=dtype, shape=())])
  + def g(x):
  + return x * weight
  +
  + root = tracking.AutoTrackable()
  + root.weight = weight
  + root.g = g
  + imported = cycle(root, cycles)
  +
  + def get_gradient(obj):
  + with backprop.GradientTape() as t:
  + x = constant_op.constant(2.)
  + y = obj.g(x)
  + self.assertAllClose(y, obj.weight * 2.)
  + self.assertAllEqual(t.watched_variables(), [obj.weight])
  + return t.gradient(y, obj.weight)
  +
  + imported_gradient = get_gradient(imported)
  + original_gradient = get_gradient(root)
  + self.assertIsNotNone(original_gradient)
  + self.assertAllClose(original_gradient, 2.)
  + self.assertIsNotNone(imported_gradient)
  + self.assertAllClose(imported_gradient, 2.)
  +
  + def test_restored_func_with_captured_var_backprop_float32(self, cycles):
  + self._test_restored_func_with_captured_var_backprop(cycles, dtypes.float32)
  +
  + def test_restored_func_with_captured_var_backprop_float64(self, cycles):
  + self.skipTest("b/144573917")
  + self._test_restored_func_with_captured_var_backprop(cycles, dtypes.float64)
  +
  def test_callable(self, cycles):
  class M1(tracking.AutoTrackable):
  