diff --git a/tensorflow/python/eager/function.py b/tensorflow/python/eager/function.py
index 3f8dac0bd46..e04595f5ed8 100644
--- a/tensorflow/python/eager/function.py
+++ b/tensorflow/python/eager/function.py
@@ -101,7 +101,7 @@ class CapturingGraph(ops.Graph):
       The entries are in the order they were captured.
   """
 
-  def __init__(self, graph=None):
+  def __init__(self):
     super(CapturingGraph, self).__init__()
 
     self.captures = collections.OrderedDict()
@@ -197,15 +197,16 @@ class FuncGraph(CapturingGraph):
     seed: The graph-level random seed.
   """
 
-  def __init__(self, name, graph=None):
+  def __init__(self, name):
     """Construct a new FuncGraph.
 
+    The graph will inherit its graph key, collections, seed, and distribution
+    strategy stack from the current context or graph.
+
     Args:
       name: the name of the function.
-      graph: if specified, this FuncGraph will inherit its graph key,
-        collections, and seed from `graph`.
     """
-    super(FuncGraph, self).__init__(graph=graph)
+    super(FuncGraph, self).__init__()
 
     self.name = name
     self.inputs = []
@@ -213,30 +214,29 @@ class FuncGraph(CapturingGraph):
     self.structured_outputs = None
     self.variables = []
 
-    if graph is not None:
+    if context.executing_eagerly():
+      self.seed = context.global_seed()
+      self._xla_compile = (context.context().device_spec.device_type == "TPU")
+    else:
+      graph = ops.get_default_graph()
       # Inherit the graph key, since this is used for matching variables in
       # optimizers.
       self._graph_key = graph._graph_key  # pylint: disable=protected-access
+      self.seed = graph.seed
+      self._xla_compile = getattr(graph, "_xla_compile", False)
 
-      # Copy the graph collections to ensure summaries and other things work.
-      # This lets the function access (but not mutate) collections of the
-      # containing graph, such as the global step and the summary writer
-      # collections.
-      for collection in graph.collections:
-        self.get_collection_ref(collection)[:] = graph.get_collection(
-            collection)
-
-      # Copy distribution strategy scope from the containing graph as well.
-      self._distribution_strategy_stack = graph._distribution_strategy_stack  # pylint: disable=protected-access
-
-      if context.executing_eagerly():
-        self.seed = context.global_seed()
-        self._xla_compile = (context.context().device_spec.device_type == "TPU")
-      else:
-        self.seed = graph.seed
-        self._xla_compile = getattr(graph, "_xla_compile", False)
-    else:
-      self._xla_compile = False
+    graph = ops.get_default_graph()
+    # TODO(b/112165328, b/112906995): summaries depend on inheriting collections
+    # from the default graph even in eager mode. It'd be nice to not have a
+    # default graph with eager execution, so hopefully this will go away when we
+    # remove collections.
+    # pylint: disable=protected-access
+    self._collections = graph._collections
+    # TODO(b/112906995): distribution strategy depends on inheriting this stack
+    # from the default graph even in eager mode. Maybe it should be part of the
+    # eager context?
+    self._distribution_strategy_stack = graph._distribution_strategy_stack
+    # pylint: enable=protected-access
 
   def capture(self, tensor, name=None):
     """Calls CapturingGraph.capture and updates self.inputs if necessary."""
@@ -476,8 +476,7 @@ class GraphCallable(object):
 
   def _construct_backprop_function(self):
     """Constructs the backprop function object for this function."""
-    backwards_graph = FuncGraph(
-        _backward_name(self._func_graph.name), self._func_graph)
+    backwards_graph = FuncGraph(_backward_name(self._func_graph.name))
     with backwards_graph.as_default():
       gradients_wrt_outputs = [
           graph_placeholder(x.dtype, x.shape) for x in self._func_graph.outputs
@@ -697,7 +696,7 @@ def _func_graph_from_py_func(name, python_func, args, kwds, signature=None):
     TypeError: If any of `python_func`'s return values is neither `None` nor a
       `Tensor`.
   """
-  func_graph = FuncGraph(name, graph=ops.get_default_graph())
+  func_graph = FuncGraph(name)
   with func_graph.as_default(), AutomaticControlDependencies() as a:
     variable_scope.get_variable_scope().set_use_resource(True)
 
diff --git a/tensorflow/python/framework/ops_test.py b/tensorflow/python/framework/ops_test.py
index 318387c61b2..9144a38dae6 100644
--- a/tensorflow/python/framework/ops_test.py
+++ b/tensorflow/python/framework/ops_test.py
@@ -1614,6 +1614,33 @@ class CollectionTest(test_util.TensorFlowTestCase):
       # Collections are ordered.
       self.assertEqual([90, 100], ops.get_collection("key"))
 
+  def test_defun(self):
+    with context.eager_mode():
+
+      @eager_function.defun
+      def defun():
+        ops.add_to_collection("int", 1)
+        ops.add_to_collection("tensor", constant_op.constant(2))
+
+        @eager_function.defun
+        def inner_defun():
+          self.assertEqual(ops.get_collection("int"), [1])
+          three = ops.get_collection("tensor")[0] + ops.get_collection("int")[0]
+          ops.add_to_collection("int", 2)
+          self.assertEqual(ops.get_collection("int"), [1, 2])
+          ops.add_to_collection("foo", "bar")
+          self.assertEqual(ops.get_collection("foo"), ["bar"])
+          return three
+
+        self.assertEqual(ops.get_collection("int"), [1])
+        three = inner_defun()
+        self.assertEqual(ops.get_collection("int"), [1, 2])
+        self.assertEqual(ops.get_collection("foo"), ["bar"])
+        return three
+
+      three = defun()
+      self.assertEqual(three.numpy(), 3)
+
 
 ops.NotDifferentiable("FloatOutput")