Allow training in any layer call instead of erroring out. (#291)

* Added testing

* Readd traceback

* Format

* Format

* Update layer.py

keras_core/layers/layer.py

@@ -1139,7 +1139,17 @@ def is_backend_tensor_or_symbolic(x):
 class CallSpec:
     def __init__(self, call_fn, args, kwargs):
         sig = inspect.signature(call_fn)
-        bound_args = sig.bind(*args, **kwargs)
+
+        # `training` and `mask` are special kwargs that are always available in
+        # a layer, if user specifies them in their call without adding to spec,
+        # we remove them to be able to bind variables. User is not using
+        # `training` anyway so we can ignore.
+        # TODO: If necessary use workaround for `mask`
+        if "training" in kwargs and "training" not in sig.parameters:
+            kwargs.pop("training")
+            bound_args = sig.bind(*args, **kwargs)
+        else:
+            bound_args = sig.bind(*args, **kwargs)
         self.user_arguments_dict = {
             k: v for k, v in bound_args.arguments.items()
         }

keras_core/layers/layer_test.py

@@ -627,6 +627,21 @@ class LayerTest(testing.TestCase):
 
         MatchingArguments()(foo, bar)
 
+    def test_training_arg_not_specified(self):
+        class NoTrainingSpecified(layers.Layer):
+            def __init__(self):
+                super().__init__()
+
+            def build(self, input_shape):
+                self.activation = layers.Activation("linear")
+
+            def call(self, inputs):
+                return self.activation(inputs)
+
+        layer = NoTrainingSpecified()
+        inputs = ops.random.uniform(shape=(1, 100, 100, 3))
+        layer(inputs, training=True)
+
     def test_tracker_locking(self):
         class BadLayer(layers.Layer):
             def call(self, x):