diff --git a/tensorflow/python/keras/engine/base_layer.py b/tensorflow/python/keras/engine/base_layer.py
index 6e3a6814608..2d5aab83665 100644
--- a/tensorflow/python/keras/engine/base_layer.py
+++ b/tensorflow/python/keras/engine/base_layer.py
@@ -921,7 +921,7 @@ class Layer(module.Module, version_utils.LayerVersionSelector):
     # >> inputs = tf.keras.Input(10)
     # >> outputs = MyLayer()(inputs)  # Functional construction mode.
     # >> model = tf.keras.Model(inputs, outputs)
-    if _in_functional_construction_mode(inputs, args, kwargs, input_list):
+    if _in_functional_construction_mode(self, inputs, args, kwargs, input_list):
       return self._functional_construction_call(inputs, args, kwargs,
                                                 input_list)
 
@@ -3205,7 +3205,7 @@ class AddMetric(Layer):
     return config
 
 
-def _in_functional_construction_mode(inputs, args, kwargs, input_list):  # pylint: disable=unused-argument
+def _in_functional_construction_mode(layer, inputs, args, kwargs, input_list):  # pylint: disable=unused-argument
   """Check the arguments to see if we are constructing a functional model."""
   if keras_tensor.keras_tensors_enabled():
     # We are constructing a functional model if any of the inputs
@@ -3217,15 +3217,16 @@ def _in_functional_construction_mode(inputs, args, kwargs, input_list):  # pylin
     if context.executing_eagerly():
       all_inputs_symbolic = all(
           tf_utils.is_symbolic_tensor(t) for t in input_list)
-      if (any(tf_utils.is_symbolic_tensor(t) for t in nest.flatten(
-          [inputs, args, kwargs])) and not all_inputs_symbolic):
+      if (base_layer_utils.is_subclassed(layer) and
+          any(tf_utils.is_symbolic_tensor(t) for t in nest.flatten(
+              [inputs, args, kwargs])) and not all_inputs_symbolic):
         raise ValueError('It appears you are trying to construct a '
                          'functional model, but not all of the inputs in '
                          'the first positional argument of your layer call '
                          'are symbolic tensors. '
                          '(Input objects, or the output of another layer) '
-                         'Functional models cannot correctly track layers '
-                         'unless all values in the first call argument '
+                         'Functional models cannot correctly track custom '
+                         'layers unless all values in the first call argument '
                          'are symbolic.')
       return all_inputs_symbolic
     else: