diff --git a/tensorflow/python/eager/function.py b/tensorflow/python/eager/function.py
index dbc9461e146..91e00c42b6e 100644
--- a/tensorflow/python/eager/function.py
+++ b/tensorflow/python/eager/function.py
@@ -1000,6 +1000,8 @@ class FunctionSpec(object):
 
     if self._is_method:
       # Remove `self`: default arguments shouldn't be matched to it.
+      # TODO(b/127938157): Should this error out if there is no arg to
+      # be removed?
       args = fullargspec.args[1:]
     else:
       args = fullargspec.args
diff --git a/tensorflow/python/saved_model/function_deserialization.py b/tensorflow/python/saved_model/function_deserialization.py
index 5924b2e0e16..04136048a31 100644
--- a/tensorflow/python/saved_model/function_deserialization.py
+++ b/tensorflow/python/saved_model/function_deserialization.py
@@ -89,6 +89,13 @@ def _concrete_function_callable_with(function, inputs, allow_conversion):
     flatten_inputs = nest.flatten_up_to(expected_structure, inputs)
   except (TypeError, ValueError):
     return False
+  try:
+    # Verify that no input elements were dropped during flattening.
+    repacked = nest.pack_sequence_as(expected_structure, flatten_inputs)
+    nest.assert_same_structure(inputs, repacked)
+  except (TypeError, ValueError):
+    return False
+
   for arg, expected in zip(flatten_inputs, nest.flatten(expected_structure)):
     if isinstance(expected, tensor_spec.TensorSpec):
       if allow_conversion:
@@ -105,23 +112,36 @@ def _concrete_function_callable_with(function, inputs, allow_conversion):
   return True
 
 
-def _deserialize_function_spec(function_spec_proto, coder):
+def _deserialize_function_spec_as_nonmethod(function_spec_proto, coder):
   """Deserialize a FunctionSpec object from its proto representation."""
   typeless_fullargspec = coder.decode_proto(function_spec_proto.fullargspec)
+
+  # Convert a method function into a non method.
+  if function_spec_proto.is_method:
+    if not typeless_fullargspec.args:
+      raise NotImplementedError(
+          "Missing support to deserialize a method function without a named "
+          "'self' argument.")
+    args = typeless_fullargspec.args[1:]
+  else:
+    args = typeless_fullargspec.args
+
   fullargspec = tf_inspect.FullArgSpec(
-      args=typeless_fullargspec.args,
+      args=args,
       varargs=typeless_fullargspec.varargs,
       varkw=typeless_fullargspec.varkw,
       defaults=typeless_fullargspec.defaults,
       kwonlyargs=typeless_fullargspec.kwonlyargs,
       kwonlydefaults=typeless_fullargspec.kwonlydefaults,
       annotations=typeless_fullargspec.annotations)
-  is_method = function_spec_proto.is_method
   args_to_prepend = coder.decode_proto(function_spec_proto.args_to_prepend)
   kwargs_to_include = coder.decode_proto(function_spec_proto.kwargs_to_include)
   input_signature = coder.decode_proto(function_spec_proto.input_signature)
-  return function_lib.FunctionSpec(fullargspec, is_method, args_to_prepend,
-                                   kwargs_to_include, input_signature)
+  return function_lib.FunctionSpec(fullargspec=fullargspec,
+                                   is_method=False,
+                                   args_to_prepend=args_to_prepend,
+                                   kwargs_to_include=kwargs_to_include,
+                                   input_signature=input_signature)
 
 
 # TODO(allenl): The fact that we can't derive ConcreteFunction calling
@@ -178,8 +198,19 @@ def recreate_function(saved_function, concrete_functions):
   # serialization cycle.
 
   coder = nested_structure_coder.StructureCoder()
-  function_spec = _deserialize_function_spec(saved_function.function_spec,
-                                             coder)
+
+  # Note: handling method functions is tricky since make_decorator does not
+  # allows control of "ismethod". Additionally since restored functions do
+  # not behave as methods i.e. they always use the same captured tensors
+  # independent of the object they are bound to, there is little value on
+  # propagating that correctly.
+  #
+  # Ideally this conversion should happen at serialization time. But since
+  # there are SavedModels which have "ismethod" populated and have an extra
+  # argument that they expect to be ignored, we do it at deserialization.
+  function_spec = _deserialize_function_spec_as_nonmethod(
+      saved_function.function_spec,
+      coder)
 
   def restored_function_body(*args, **kwargs):
     """Calls a restored function."""
diff --git a/tensorflow/python/saved_model/function_serialization.py b/tensorflow/python/saved_model/function_serialization.py
index e876eef8b34..9915a7e4842 100644
--- a/tensorflow/python/saved_model/function_serialization.py
+++ b/tensorflow/python/saved_model/function_serialization.py
@@ -25,6 +25,10 @@ from tensorflow.python.saved_model import nested_structure_coder
 
 def _serialize_function_spec(function_spec, coder):
   """Serialize a FunctionSpec object into its proto representation."""
+  if function_spec.is_method and not function_spec.fullargspec.args:
+    raise NotImplementedError(
+        "Missing support to serialize a method function without a named "
+        "'self' argument.")
   proto = saved_object_graph_pb2.FunctionSpec()
   proto.fullargspec.CopyFrom(coder.encode_structure(function_spec.fullargspec))
   proto.is_method = function_spec.is_method
diff --git a/tensorflow/python/saved_model/load_test.py b/tensorflow/python/saved_model/load_test.py
index bd7bceceb84..c772cb066f2 100644
--- a/tensorflow/python/saved_model/load_test.py
+++ b/tensorflow/python/saved_model/load_test.py
@@ -1218,6 +1218,24 @@ class LoadTest(test.TestCase, parameterized.TestCase):
 
     self.assertEqual([2], root.f([2]).numpy())
 
+  def test_extra_args(self, cycles):
+
+    @def_function.function
+    def f(x):
+      return math_ops.add(x["a"], 1.)
+    # Trigger a trace.
+    f({"a": constant_op.constant(2.0)})
+
+    obj = tracking.AutoTrackable()
+    obj.__call__ = f
+    imported = self.cycle(obj)
+
+    self.assertEqual(4.0, imported({"a": 3.0}).numpy())
+
+    with self.assertRaisesRegexp(ValueError,
+                                 "Could not find matching function to call"):
+      imported({"a": 2.0, "b": 3.0})
+
   def test_shapes_available(self, cycles):
 
     @def_function.function(input_signature=[