Add test to verify that one can use Sequential with a defun on call.

PiperOrigin-RevId: 207977760

tensorflow/python/keras/engine/sequential.py

@@ -233,6 +233,9 @@ class Sequential(Model):
     return outputs
 
   def _call_and_compute_mask(self, inputs, training=None, mask=None):
+    if not self.built:
+      self.build(inputs.shape)
+
     x = inputs
     for layer in self.layers:
       kwargs = {}

tensorflow/python/keras/engine/sequential_test.py

@@ -23,6 +23,7 @@ import numpy as np
 
 from tensorflow.python import keras
 from tensorflow.python.data.ops import dataset_ops
+from tensorflow.python.eager import function
 from tensorflow.python.framework import test_util as tf_test_util
 from tensorflow.python.ops import array_ops
 from tensorflow.python.platform import test
@@ -318,5 +319,28 @@ class TestSequential(test.TestCase, parameterized.TestCase):
         [v.name for v in model.variables])
 
 
+class TestSequentialEagerIntegration(test.TestCase):
+
+  @tf_test_util.run_in_graph_and_eager_modes
+  def test_defun_on_call(self):
+    # Check that one can subclass Sequential and place the `call` in a `defun`.
+
+    class MySequential(keras.Sequential):
+
+      def __init__(self, name=None):
+        super(MySequential, self).__init__(name=name)
+        self.call = function.defun(self.call)
+
+    model = MySequential()
+    model.add(keras.layers.Dense(4, activation='relu'))
+    model.add(keras.layers.Dense(5, activation='softmax'))
+
+    model.compile(loss='mse', optimizer=rmsprop.RMSPropOptimizer(1e-3))
+
+    x = np.random.random((2, 6))
+    y = np.random.random((2, 5))
+    model.fit(x, y, epochs=1)
+
+
 if __name__ == '__main__':
   test.main()